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+{"metadata":{"id":"0034a7f69ae68d4539fca6f5939e404c","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/94bbbc5f-7008-4bb6-8abb-6bb9b60e9bfd/retrieve"},"pageCount":14,"title":"Temporal rainfall trend analysis in different agro-ecological regions of southern Africa","keywords":[],"chapters":[{"head":"INTRODUCTION","index":1,"paragraphs":[{"index":1,"size":214,"text":"Smallholder farming systems in sub-Saharan Africa (SSA) are threatened by climate change and variability and face a huge challenge of producing enough food for close to a billion people in the region (Sonwa et al., 2017). The situation is critical in southern Africa because the region is one of the climate-change hotspots as indicated by recent projections (Lobell et al., 2008;Maure et al., 2018). Rainfall projections indicate mixed trends where some parts of the region will experience no significant changes while rainfall decrease is expected in others (Shongwe et al., 2009;Nicholson et al., 2014;Conway et al., 2015). Approximately 41 million people are already food insecure in southern Africa, the majority of whom are in rural communities that depend on rainfed agriculture (SADC, 2016). The food availability situation is further exacerbated by the continued decline in yields of major cereals and pulses due to a plethora of reasons, including high variability in the start and end of growing seasons, intra-seasonal dry spells, deteriorating soil health and limited use of mineral fertilizers, among others (Cooper et al., 2008;Sileshi et al., 2009;Van Ittersum et al., 2013). Production of major food crops has also been constrained by inappropriate policy environments that do not promote conducive inputoutput markets and producer prices for the smallholder farmers (Smale et al., 2011)."},{"index":2,"size":245,"text":"Rainfall is a major driver of crop and livestock production in SSA, with the majority of smallholder agriculture relying on its seasonal amount and distribution (Zinyengere et al., 2011;Mamombe et al., 2017). Smallholder agriculture is dependent on seasonal rainfall because irrigation is generally limited due to poor infrastructure and dwindling water sources (Fanadzo and Ncube, 2018). Major droughts have intensified over time and the current trends show increased frequency in southern Africa (Manatsa et al., 2008;Masih et al., 2014). Compared to seasonal totals, rainfall distribution during the growing season currently has a greater impact on crop and livestock productivity on smallholder farms of southern Africa (Twomlow et al., 2006). The start and the end of the growing season is highly variable (Usman and Reason, 2004;Tadross et al., 2005), making selection of crop types and varieties, and crop establishment methods in the field, difficult for smallholder farmers (Mupangwa et al., 2011;Nyagumbo et al., 2017). Intra-seasonal dry spells are a common feature in the region and often coincide with flowering and early reproductive growth stages of major cereal crops (Usman and Reason, 2004;Cooper et al., 2008). These dry spells significantly reduce yield of the major food security crops and limit biomass production for livestock feed (Ogenga et al., 2018). The high variability in the start and end of rains not only affects food and forage crop productivity on the smallholder farms, but also for communal grazing lands which are critical for livestock production (Manyawu et al., 2016)."},{"index":3,"size":248,"text":"Studies from SSA have reported daily rainfall dominated by amounts of less than 10 mm•day -1 and these have limited impact on crop growth and development (Dixit et al., 2011;Goenster et al., 2015). In Sudan, Goenster et al. (2015) observed that daily rainfall amounts of less than 3 mm have increased, while 10-20 mm events have declined, since 1970. In southern Africa where daily pan evaporation averages 5-10 mm•day -1 (Woltering, 2005) and atmospheric evaporative demand is 1.5-10 times the annual total rainfall (Barron, 2004), rainfall amounts of less than 5 mm•day -1 have limited impact on crop productivity on smallholder farms where soil water conservation techniques are rarely part of the cropping systems. Clay and sandy soils often require 20-25 mm and 30-50 mm of rainfall, respectively, to fully wet the top 30 cm of the soil profile (Twomlow, 1994;Twomlow and Bruneau, 2000). During the growing season, high intensity storms (>40 mm•day -1 ) with high erosivity occur frequently in some parts of southern Africa (Love et al., 2010), leading to reduced water infiltration due to capping and surface sealing in certain soil types and widespread soil erosion on farmlands (Elwell and Stocking, 1988;Twomlow et al., 2006). Additionally, rainfall conditions in southern Africa are already conducive for emerging pests such as the fall armyworm (Spodoptera frugiperda (Smith)) and further variability might worsen pest and disease pressure on smallholder farms (Prasanna et al., 2018). Consequently, household food security in southern Africa remains under threat in the coming decades."},{"index":4,"size":159,"text":"Information on the trends in elements of the weather that drive rainfed farming is critical for decision making by smallholder farmers, agricultural extension agents and research practitioners, rural development agents, the private sector involved in agriculture insurance, and national policy makers. This study was undertaken to assess the trends in daily, monthly and seasonal rainfall over the past decades in selected locations of southern Africa. It was hypothesized that (i) daily rainfall amounts are dominated by light showers (<5 mm•day -1 ), (ii) there are linear trends in daily rainfall amounts, and monthly and seasonal rainfall totals, and (iii) seasonal rainfall variability leading to droughts exists in most parts of southern Africa. The specific objectives were to determine: (i) the frequency and assess linear trends in different rainfall classes (<5, 5-10, 11-20,  21-40, >40 mm•day -1 ), (ii) trends in monthly and seasonal total rainfall, and (iii) drought occurrences in selected locations under different agro-ecological conditions of southern Africa."}]},{"head":"MATERIALS AND METHODS","index":2,"paragraphs":[]},{"head":"Data source and quality","index":3,"paragraphs":[{"index":1,"size":181,"text":"Initial daily rainfall data were collected from 23 meteorological stations spread to cover different agro-ecological conditions of the selected southern African countries (Malawi, Mozambique, South Africa and Zimbabwe). The final daily rainfall data used in the analyses were derived from 18 meteorological stations located in the 4 countries (Appendix, Table A1; Fig. 1). The choice of these stations was based on availability of complete long-term measured/ observed daily rainfall data. Any meteorological station that had missing measured daily rainfall data and needed data infilling at daily, monthly or seasonal timesteps considered in this study, was not included in the analyses, hence reducing the number from 23 to 18 locations. For the final 18 stations, only periods with at least 30 years of data without any missing data were considered, and are summarized in Table A1. The choice of these criteria was based on the availability of data which is considered to be longterm enough for valid trend results in climate change research (Burn and Elnur, 2002). The length of available rainfall datasets varied from country to country and station to station."},{"index":2,"size":89,"text":"Analyses were conducted at daily, monthly and seasonal timesteps in order to answer the research questions selected for the study. Acquired data were converted to the standard June-July calendar (agriculture year in southern Africa) and underwent data quality control routines to identify missing data, errors and suspect data, as well as to ensure that data were consistent and met the data quality objectives. The quality checks were performed using the computer program RClimDex 1.1 and its software package RHtestV3 (Wang and Feng, 2013), that can be accessed at: http://etccdi.pacificclimate.org."}]},{"head":"Data analyses","index":4,"paragraphs":[{"index":1,"size":57,"text":"Time series of daily, monthly and seasonal rainfall data were used to identify trends at different temporal resolutions. Seasonal rainfall totals were used to assess drought occurrences at locations in different agro-ecological regions of Malawi, Mozambique, South Africa and Zimbabwe. Daily rainfall amounts were divided into classes of <5, 5-10, 11-20, 21-40 and >40 mm•day -1 ."}]},{"head":"Mann-Kendall (MK) test","index":5,"paragraphs":[{"index":1,"size":145,"text":"Non-parametric statistical methods were used to detect temporal linear trends in the daily, monthly and seasonal rainfall data. The main advantages of non-parametric methods are that datasets with missing values are allowed and the data need not conform to any particular distribution (Da Silva at al., 2015). The Mann-Kendall test analysis was performed in R version 3.5.2 (R Core Team, 2018) using the Kendall package to detect the existence of monotonic trends for daily, monthly and seasonal rainfall during the summer season (November-March). The test compares a data value (x i and y i ) to its subsequent values (x j and y j ) and adds an increment or decrement of 1 to the MK test statistic (S) when the subsequent data values were higher or lower, respectively (Karmeshu, 2012), as illustrated in Eq: 1 below. Any missing values were automatically removed during analysis."}]},{"head":"S x x x s y y","index":6,"paragraphs":[{"index":1,"size":44,"text":"When there were no ties between the x and y variables, the strength of monotonic association was given by Kendall`s rank correlation, tau (τ) (Eq. 2) and subsequent p-value of tau for the null hypothesis of no association was calculated (Best and Gipps, 1974)."},{"index":2,"size":35,"text":"In the presence of less extensive ties, a normal approximation of S with subsequent continuity correction was made with mean zero, and variance var(S) where var(S) was given by Kendall (1975, Eq. 4.4, p. 55)."}]},{"head":"The Theil-Sen slope estimator","index":7,"paragraphs":[{"index":1,"size":77,"text":"Since a monotonic trend was demonstrated from the Mann-Kendall test which appeared linear in some stations, the Theil-Sen slope test was further performed to examine the magnitude of the slope for daily, monthly and seasonal rainfall. The test was performed in R version 3.5.2 (R Core Team, 2018) using the Trend package (Pohlert, 2018). The test computes slope using Sen's method, which calculates a set of linear slopes, followed by a median of the slopes, as follows:"},{"index":2,"size":39,"text":"where d is the slope, X denotes the variable, n is the number of data points, and i, j are indices. As a result, the Sen slope (b Sen ) is given by b Sen = median d k"}]},{"head":"Rainfall variability index","index":8,"paragraphs":[{"index":1,"size":7,"text":"Rainfall variability index (δ) is calculated as:"},{"index":2,"size":34,"text":"where δ i = rainfall variability index for year i, P i = seasonal rainfall for year i, µ and σ are the mean seasonal rainfall and standard deviation for the period under consideration."},{"index":3,"size":50,"text":"In this study P i represented the November-March seasonal rainfall; consequently, µ and σ were the mean and standard deviation of the total seasonal rainfall. A drought year occurs if the δ is negative and, according to WMO (1975), rainfall timeseries can be classified into different climatic regimes (Table 1)."},{"index":4,"size":12,"text":"All calculations for the different rainfall ranges were performed in Microsoft Excel."}]},{"head":"RESULTS","index":9,"paragraphs":[]},{"head":"Frequency and trends of different daily rainfall amounts","index":10,"paragraphs":[{"index":1,"size":250,"text":"Daily rainfall events occurring in southern Africa were dominated by amounts of <5 mm at all locations (Table 2). Generally, the chances of getting higher daily rainfall amounts decreased consistently across the rainfall classes, regardless of agro-ecological conditions. In Zimbabwe, 5-10 mm•day -1 , and 11-20 mm•day -1 amounts occurred more frequently in sub-humid locations than semi-arid sites. Beitbridge had the least chance (1.4%) of getting more than 40 mm•day -1 during the growing season. In Malawi, 21-40 mm•day -1 amounts occurred more frequently along Lake Malawi than at further inland locations. Chitedze and Dedza had better chances of getting 11-20 mm•day -1 than 5-10 mm•day -1 during the growing season. In Mozambique, rainfall amounts of <5 mm•day -1 occurred more frequently at Chimoio compared to the other locations. In South Africa 5-10 mm•day -1 events occurred more frequently than <5 mm•day -1 at Harmony.   Trends of the different rainfall classes varied with location (Tables 3 and 4). Significant increases in the >40 mm•day -1 class were detected at one of the 6 locations in Zimbabwe. Beitbridge experienced an increasing trajectory in <5 and 5-10 mm•day -1 amounts. A significantly decreasing trajectory of <5 day -1 amounts was detected at one of the 6 Zimbabwean locations. Chitala and Chitedze experienced decreases in <5 and 5-10 mm•day -1 , and increases in <5 mm•day -1 rainfall amounts, respectively. Significant decreases in <5 and 11-20 mm•day -1 , and 5-10 mm•day -1 amounts were detected at Chimoio and Chokwe, respectively (Table 5)."},{"index":2,"size":17,"text":"The >40 and 11-20 mm•day -1 amounts decreased over time at Harmony and Mertz, respectively (Table 6).  "}]},{"head":"Trends of monthly and seasonal rainfall","index":11,"paragraphs":[{"index":1,"size":107,"text":"The presence of linear trends in monthly and seasonal (November-March) rainfall varied between locations. The November-March period had significant increasing (0.09 mm•season -1 ) and decreasing rainfall trajectories at Matopos and Beitbridge, respectively (  8). February rainfall significantly (P = 0.0132) decreased by 2.5 mm•season -1 at Chitala. Rainfall decreased by 0.15-3.7 mm•season -1 during the November-March period at 3 of the 5 locations in Mozambique (Table 9). February rainfall decreased (P = 0.0111) by 3.3 mm•season -1 at Xai Xai. In South Africa, February rainfall decreased at three locations (Table 10). During the November-March period, rainfall decreased (P = 0.0421) by 1.24 mm•season -1 at Harmony. "}]},{"head":"Seasonal rainfall variability","index":12,"paragraphs":[{"index":1,"size":100,"text":"The number of drought years varied with location and the period considered for each station. Harare experienced 20 droughts, with 12 being very dry, and 3 wet years during the 38-year period (Fig. 2 At Chitala, 26 droughts were experienced, evenly distributed over the 52 years, with the majority occurring during the 1980s and 1990s (Fig. 3); 6 wet years were recorded between 1976 and 1986. At Chitedze, 18 droughts were experienced and 6 of them were very dry; 7 dry and 4 wet seasons were experienced at Dedza where a total of 21 droughts were recorded in 33 years."},{"index":2,"size":136,"text":"Chimoio experienced an extremely dry 1972 and the extreme drought of 1972 was immediately followed by 3 consecutive wet years (Fig. 4). A total of 34 droughts occurred at Chimoio in 62 years. At Chokwe, 18 droughts occurred and most of the droughts occurred in the 1980s; 5 wet years, evenly distributed over the 35 years, occurred at Chokwe; and 29 droughts with 1 extreme and 5 wet years were experienced at Pemba in 54 years. The droughts were concentrated in the 1960s and 1970s. Quelimane had 28 droughts, one of them being extreme and 5 wet years were experienced in the 1960s and 1980s. A very dry year followed by a relatively wet one occurred once at Quelimane during the 1960s. At Xai Xai, 24 droughts and 7 wet years were recorded in 38 years."},{"index":3,"size":89,"text":"The Mertz location experienced 58 droughts and 15 wet years over a 96-year period. Most of the droughts occurred in the 1940s, 1980s and 1990s (Fig. 5). At Harmony, 56 droughts, with 8 being very dry, and 9 wet years occurred in 96 years. Most of the droughts occurred between the 1920s and 1940s. Over a 39-year period, 22 droughts and 5 wet years occurred at Levubu. Most of the droughts occurred between 1982 and 2004. In 45 years, 26 droughts and 5 wet years were experienced at Polokwane.   "}]},{"head":"DISCUSSION","index":13,"paragraphs":[]},{"head":"Frequency and trends of different daily rainfall amounts","index":14,"paragraphs":[{"index":1,"size":37,"text":"Daily rainfall was dominated by <5 mm events under semiarid and sub-humid conditions of the selected locations of southern Africa. These rainfall quantities have a negligible effect on recharging soil moisture in soil types of southern Africa."},{"index":2,"size":110,"text":"Previous studies have shown that 20-25 and 30-50 mm of rainfall are required to fully charge the top 30 cm of sandy and clay soils, respectively (Twomlow 1994;Twomlow and Bruneau, 2000). Most of the rainwater from such light showers can be lost through evaporation because of the high atmospheric evaporative demand (Barron, 2004). Some semi-arid areas experience 5-8 mm•day -1 evaporative water losses (Woltering, 2005) and less than 5 mm•day -1 showers are therefore insignificant for crop production. Under sub-humid conditions of southern Africa, atmospheric evaporative demand can reach 5 mm•day -1 during summer months (Trambauer et al. 2014), thereby making the light showers insignificant for cropping under such conditions."},{"index":3,"size":122,"text":"Decreasing trajectories of 5-10 and, 11-20 mm•day -1 were detected at some locations in southern Africa. Rainfall amounts of 5-10 and, 11-20 mm•day -1 can have significant influence on crop growth, depending on soil and crop type, atmospheric evaporative demand and water management practices implemented in the cropping systems. When these rainfall amounts are received for 2 or 3 days, the soil profiles can be recharged with moisture, thereby facilitating crop growth. With the current trend of poor seasonal rainfall distribution and frequent in-crop dry spells (Ngetich et al., 2014), it is critical to capture this rainwater in order to prolong soil moisture availability in cropping systems. There were no linear trends in >40 mm•day -1 at most of the selected locations."},{"index":4,"size":36,"text":"It is critical that rainwater from the few 40 mm•day -1 events be conserved through in-situ water capture practices (Mupangwa et al., 2007) or ex-situ storage for later use as supplementary irrigation (Rockström et al., 2003)."},{"index":5,"size":31,"text":"Despite the increased moisture stress associated with low rainfall of less than 5 mm•day -1 , such amounts have sustained crop production across the region, though the yield gap is high."},{"index":6,"size":88,"text":"Farmers have adapted to this through use of alternate cropping systems such as intercrops as well as shifting planting time. There is, however, still greater value from low rainfall events compared to the high rainfall events. Most farmers do not use in-situ moisture conservation; hence such amounts would not be very useful to the farmer given the fact that they occur at low frequencies. In addition, heavy rains are associated with challenges such as nutrient leaching, which increases fertilizer costs and ultimately reduces productivity (Geneti et al., 2019)."}]},{"head":"Trends of monthly and seasonal rainfall","index":15,"paragraphs":[{"index":1,"size":119,"text":"Linear trends in monthly and seasonal rainfall varied with location, as some increasing and decreasing rainfall trajectories were indicated in the analyses conducted. This result is consistent with previous findings from studies conducted in southern Africa (Bellpart et al., 2015;Muthoni et al., 2019). Mitigation and adaptive measures to climate variability need to be informed by these local trends as blanket recommendations will not be effective. Significant seasonal rainfall increase occurred at semiarid Matopos station, and this is consistent with results from Muthoni et al. ( 2019), which revealed a 3-15 mm•year -1 increase in rainfall at some locations in south-western Zambia. Future rainfall projections have also indicated increases in rainfall in some parts of SSA (Shongwe et al., 2011)."},{"index":2,"size":325,"text":"Decreasing rainfall trends in some parts of southern Africa have been reported previously (Mason, 2001;Shi et al., 2007;Bellpart et al., 2015). These decreases in rainfall have been attributed to the influence of El Niño and shifts in atmospheric circulation processes (Nicholson et al., 2014;Gaughan et al., 2016). Smallholder farmers in parts of the region have generally observed declining rainfall over the years and acknowledge the importance of increasing adaptive measures in their farming systems (Zuma-Netshiukwi et al., 2013;Mkuhlani et al., 2019). Significant rainfall decrease (up to 3.3 mm•year -1 ) in February rainfall occurred at a few of the locations. Differences in atmospheric drivers of rainfall patterns exist over short distances in southern Africa (Hachigonta and Reason, 2006;Manatsa and Matarira, 2009), and this could explain the variability between locations within the same agro-  2014) reported that most of the inter-annual rainfall variability is generated during the March-April period of the growing season. The second half of the peak rainfall period is therefore at risk in southern Africa and cropping systems will continue experiencing soil moisture deficits at critical crop growth stages. The major food security cereals in southern Africa are sensitive to soil moisture deficits at reproductive growth stage which often occurs around December to February (Zaman-Allah, 2016), and this leads to significant yield reduction. Smallholder farming families would therefore be exposed to food deficits which are already prevalent in some parts of the region (FAO and ECA, 2018). Another related study, analysing optimum planting dates at Chitala and Chitedze in Malawi, confirmed a significant delay (P < 0.05) of 0.28 and 0.39 days•yr -1 in optimum planting dates at Chitala and Chitedze in Malawi, respectively, within the last 30 years, thereby making the length of the growing season increasingly shorter at these locations (Nyagumbo et al., 2017). Such changes in rainfall patterns over time corroborate findings from this study that the southern Africa region increasingly faces more difficult weather patterns for rainfed crop production."},{"index":3,"size":145,"text":"Linear trends in monthly rainfall were location specific, a result that has been reported elsewhere in the region and for other SSA countries (Gummadi et al., 2017;Muthoni et al., 2019). Local factors such as topography or the presence of an inland water body can have a significant influence on spatial and temporal rainfall patterns (Goenster et al., 2015;Muthoni et al., 2019). The proximity of Chitala to Lake Malawi influenced the rainfall pattern and the location had greater chances of getting more rainfall than Chitedze and Dedza, which are located further inland in a relatively wetter agro-ecology. Muthoni et al. ( 2019) also reported the effects of local physical features such as mountains on spatiotemporal rainfall patterns in Tanzania. Adaptation strategies on smallholder farms in such areas with natural drivers of local rainfall patterns need to be tailor-made accordingly and cannot be generalized for the region."}]},{"head":"Seasonal rainfall variability","index":16,"paragraphs":[{"index":1,"size":252,"text":"The rainfall variability index (WMO, 1975) indicated the occurrence of drought conditions at the selected locations under different agro-ecological conditions. Droughts of varying degrees of severity occurred in 50% or more of the time periods considered in this study. Such drought frequency has been reported and is now a common phenomenon in southern Africa (Cooper et al., 2008;Nicholson et al., 2014;Bellprat et al., 2015). A drought frequency of every 3 to 4 years has been reported in some parts of southern Africa (World Bank, 2017). With such high frequencies, drought mitigation measures adapted to different biophysical and socio-economic smallholder farmer circumstances ought to be implemented to buffer cropping systems. Various adaptation and mitigation options have been developed and tested for smallholder conditions, and these include crop diversification (Twomlow et al., 2006), adapted crop types and varieties (Setimela et al., 2018), improving soil fertility (Zougmore et al., 2014), conservation agriculture-based practices (Thierfelder et al., 2017;Steward et al., 2018), and in-situ or ex-situ rainwater harvesting (Motsi et al., 2004;Mupangwa et al., 2007). Traditionally, droughts have been more severe in semi-arid areas (Graef and Haigis, 2001) and this is consistent with results from the low rainfall agro-ecologies of the current study, particularly Beitbridge in southern Zimbabwe. The importance of designing and implementing drought mitigation strategies cannot be over-emphasized in order to buffer rainfed farming systems. Climate-smart crop and livestock production practices are core for semi-arid areas and some adapted options are available for southern Africa (Chakoma et al., 2016;Thierfelder et al., 2017;Setimela et al., 2018)."},{"index":2,"size":176,"text":"Chitala location illustrated the influence of existing water bodies on local rainfall patterns in some parts of southern Africa. Despite this localized influence on rainfall, the threat of severe droughts was evident at Chitala and this highlights the importance of developing adaptation and mitigation interventions suited to local climatic conditions. All locations experienced incidences of either wet seasons followed immediately by mild to strong drought or the reverse trend. This has been occurring in southern Africa and is one of the major causes of chronic food shortages (Bell et al., 2003). Generally, the frequency of dry years increased between 1980 and 2007 compared to past years of 1950-1975(Gaughan et al., 2016)). This shift in southern Africa rainfall patterns and other climatic forcings has been reported previously (Manatsa and Behera, 2013;Nicholson et al., 2014;Bellprat et al., 2015), and emphasizes the need for climate-smart agricultural practices to buffer smallholder farming systems. The inter-seasonal rainfall variability has made planning and decision making on selection of crop species and cropping systems, and investments in agricultural inputs, difficult on smallholder farms."}]},{"head":"CONCLUSION","index":17,"paragraphs":[{"index":1,"size":149,"text":"Rainfall in southern Africa was dominated by <5 mm•day -1 events in both semi-arid and sub-humid agro-ecological conditions. The frequency of 5-10 and 11-20 mm•day -1 varied with location, even where a large water body influenced the rainfall pattern. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 selected locations from southern Africa. Where trends were significant, a decreasing trajectory in February rainfall was detected at two locations. Increasing March and seasonal rainfall trajectories were apparent at a semi-arid location in southwestern Zimbabwe. Moderate and strong drought conditions were detected, and these also varied with location. Drought frequency was higher than 50%, and a location close to a large water body also experienced strong drought conditions during the November-March growing season. All locations experienced incidences of a wet season followed immediately by very dry conditions, or vice versa, regardless of agro-ecological conditions."},{"index":2,"size":101,"text":"Results of this study emphasize the need for policy to take due consideration of the prevailing climatic patterns in programming appropriate climate-smart adaptation measures that can help farmers to cope with the increasing frequency of droughts and ineffective rainfall events that make rainfed cropping riskier to farmers than it has been in the past 3 to 4 decades. It is also clear that policy makers need to invest more in reliable weather monitoring instruments so as to provide a higher density of measured weather patterns that help in informing the need for appropriate technological investments to cope with emerging weather patterns."}]}],"figures":[{"text":"Figure 1 . Figure 1. Location of selected meteorological stations in Malawi, Mozambique, South Africa and Zimbabwe used in the study. Different agro-ecological regions of southern Africa are indicated by different colour codes. "},{"text":" ). The worst droughts occurredduring 1964during  , 1968during   and  1995during  . Marondera experienced one extreme drought (1992) )  and 7 wet years over a 49-year period; 41 droughts were experienced between 1940 and 2015, and most wet years occurred before 1975 at Matopos. The number of wet years decreased between 1980 to 2015. At Bulawayo, 39 droughts and 13 wet years were experienced. West Nicholson experienced 21 droughts and just 6 wet years in 39 years; 29 droughts in 50 years were experienced at Beitbridge and the 1960 and 1980s were the driest decades with 5 severe droughts occurring. Only 4 wet years were experienced between 1952 and 2001 and this included the El Niño year. "},{"text":"Figure 2 . Figure 2. Seasonal rainfall variation at different locations in Zimbabwe. Dotted line represents 5-year moving average. "},{"text":"Figure 3 . Figure 3. Seasonal rainfall variation at different locations in Malawi. Dotted line represents 5-year moving average. "},{"text":"Figure 4 . Figure 4. Seasonal rainfall variation at different locations in Mozambique. Dotted line represents 5-year moving average. "},{"text":"Figure 5 . Figure 5. Seasonal rainfall variation at different locations in South Africa. Dotted line represents 5-year moving average. "},{"text":"Table 1 . Rainfall  "},{"text":"Table 2 . The Country Station   Rainfall amount (mm•day -1 )   CountryStationRainfall amount (mm•day -1 )   <5 5-10 11-20 21-40 >40 <55-1011-2021-40>40     Probability (%)   Probability (%) Zimbabwe Harare 22.5 11.8 12.7 8.0 2.9 ZimbabweHarare22.511.812.78.02.9  Marondera 20.5 12.0 11.9 8.5 3.3 Marondera20.512.011.98.53.3  Matopos 13.2 7.5 8.0 6.3 1.9 Matopos13.27.58.06.31.9  Bulawayo 16.1 8.8 8.6 6.1 2.0 Bulawayo16.18.88.66.12.0  W. Nich. 16.5 6.5 5.8 4.4 1.8 W. Nich.16.56.55.84.41.8  Beitbridge 13.1 5.3 4.7 3.1 1.3 Beitbridge13.15.34.73.11.3 Malawi Chitala 17.4 12.5 12.3 10.4 4.0 MalawiChitala17.412.512.310.44.0  Chitedze 23.1 13.3 14.6 8.9 3.8 Chitedze23.113.314.68.93.8  Dedza 26.1 14.8 15.5 9.7 3.3 Dedza26.114.815.59.73.3 Mozambique Chimoio 21.3 11.2 11.2 8.6 5.4 MozambiqueChimoio21.311.211.28.65.4  Chokwe 14.7 6.5 5.0 3.9 2.1 Chokwe14.76.55.03.92.1  Pemba 17.8 9.1 9.3 6.7 3.6 Pemba17.89.19.36.73.6  Quelimane 17.9 9.2 9.8 8.6 6.6 Quelimane17.99.29.88.66.6  Xai Xai 19.4 7.2 6.3 5.0 3.4 Xai Xai19.47.26.35.03.4 South Africa Harmony 10.8 12.5 6.8 4.0 1.8 South AfricaHarmony10.812.56.84.01.8  Levubu 22.0 8.3 7.7 6.3 5.0 Levubu22.08.37.76.35.0  Mertz 10.5 10.3 7.1 3.8 1.8 Mertz10.510.37.13.81.8  Polokwane 15.1 7.3 6.2 4.1 1.4 Polokwane15.17.36.24.11.4  "},{"text":"Table 3 . Mann-Kendall trend and Sen slope for different rainfall classes at weather stations in Zimbabwe Station Rainfall class Kendall's tau P-value Sen slope P-value StationRainfall classKendall's tauP-valueSen slopeP-value Harare <5 0.0247 0.8367 0.0000 0.8367 Harare<50.02470.83670.00000.8367  5-10 0.0125 0.9226 0.0000 0.9226 5-100.01250.92260.00000.9226  11-20 0.0961 0.4084 0.0625 0.4084 11-200.09610.40840.06250.4084  21-40 −0.0310 0.7979 0.0000 0.7979 21-40−0.03100.79790.00000.7979  >40 0.2287 0.0577 0.0400 0.0557 >400.22870.05770.04000.0557 Marondera <5 −0.2937 0.0036 −0.200 0.0036 Marondera<5−0.29370.0036−0.2000.0036  5-10 −0.1770 0.0807 −0.1026 0.0807 5-10−0.17700.0807−0.10260.0807  11-20 −0.1302 0.2004 −0.0659 0.2004 11-20−0.13020.2004−0.06590.2004  21-40 0.1053 0.3061 0.0345 0.3061 21-400.10530.30610.03450.3061  >40 −0.0792 0.4557 0.0000 0.4557 >40−0.07920.45570.00000.4557 Matopos <5 −0.0956 0.2335 −0.0396 0.2335 Matopos<5−0.09560.2335−0.03960.2335  5-10 −0.1720 0.0344 −0.0367 0.0344 5-10−0.17200.0344−0.03670.0344  11-20 −0.1717 0.0335 −0.0440 0.0335 11-20−0.17170.0335−0.04400.0335  21-40 −0.0978 0.2327 0.0000 0.2327 21-40−0.09780.23270.00000.2327  >40 −0.0111 0.9007 0.0000 0.9007 >40−0.01110.90070.00000.9007 Bulawayo <5 0.0698 0.4030 0.0222 0.4030 Bulawayo<50.06980.40300.02220.4030  5-10 −0.1417 0.0941 −0.0263 0.0941 5-10−0.14170.0941−0.02630.0941  11-20 0.0462 0.5834 0.0000 0.5834 11-200.04620.58340.00000.5834  21-40 −0.0183 0.8333 0.0000 0.8333 21-40−0.01830.83330.00000.8333  >40 0.0169 0.8509 0.0000 0.8509 >400.01690.85090.00000.8509 West Nich <5 0.1022 0.3756 0.0800 0.3756 West Nich<50.10220.37560.08000.3756  5-10 −0.0700 0.9612 0.0000 0.9612 5-10−0.07000.96120.00000.9612  11-20 −0.0958 0.4140 −0.0345 0.4140 11-20−0.09580.4140−0.03450.4140  21-40 0.0904 0.4473 0.0000 0.4473 21-400.09040.44730.00000.4473  >40 0.0699 0.5684 0.0000 0.5684 >400.06990.56840.00000.5684 Beitbridge <5 0.2396 0.0181 0.1429 0.0181 Beitbridge<50.23960.01810.14290.0181  5-10 0.0238 0.0212 0.0667 0.0212 5-100.02380.02120.06670.0212  11-20 0.1902 0.0643 0.0606 0.0643 11-200.19020.06430.06060.0643  21-40 0.1770 0.0954 0.0000 0.0954 21-400.17700.09540.00000.0954  >40 −0.0558 0.6175 0.0000 0.6175 >40−0.05580.61750.00000.6175  "},{"text":"Table 4 . Mann-Kendall trend and Sen slope for different rainfall classes at weather stations in Malawi Station Rainfall class Kendall's tau P-value Sen slope P-value StationRainfall classKendall's tauP-valueSen slopeP-value Chitala <5 −0.0882 0.3745 −0.0278 0.3745 Chitala<5−0.08820.3745−0.02780.3745  5-10 −0.3657 0.0002 −0.1567 0.0002 5-10−0.36570.0002−0.15670.0002  11-20 −0.0976 0.3303 0.0000 0.3303 11-20−0.09760.33030.00000.3303  21-40 0.0755 0.4506 0.0000 0.4506 21-400.07550.45060.00000.4506  >40 0.0489 0.6353 0.0000 0.6353 >400.04890.63530.00000.6353 Chitedze <5 0.2864 0.0230 0.0262 0.0230 Chitedze<50.28640.02300.02620.0230  5-10 −0.0176 0.9008 0.0000 0.9008 5-10−0.01760.90080.00000.9008  11-20 0.0938 0.4638 0.0625 0.4638 11-200.09380.46380.06250.4638  21-40 0.0885 0.5194 0.0000 0.5194 21-400.08850.51940.00000.5194  >40 −0.1816 0.1655 −0.0465 0.1655 >40−0.18160.1655−0.04650.1655 Dedza <5 −0.1556 0.1575 −0.1053 0.1575 Dedza<5−0.15560.1575−0.10530.1575  5-10 −0.1325 0.2313 −0.0690 0.2313 5-10−0.13250.2313−0.06900.2313  11-20 0.0598 0.9653 0.0000 0.9653 11-200.05980.96530.00000.9653  21-40 0.1649 0.1406 0.0625 0.1406 21-400.16490.14060.06250.1406  >40 −0.1742 0.1389 0.0000 0.1389 >40−0.17420.13890.00000.1389  "},{"text":"Table 5 . Mann-Kendall trend test of different rainfall classes at weather stations in Mozambique Country Station Rainfall class Kendall's tau P-value Sen slope P-value CountryStationRainfall classKendall's tauP-valueSen slopeP-value Mozambique Chimoio <5 −0.1609 0.0722 −0.0714 0.0722 MozambiqueChimoio<5−0.16090.0722−0.07140.0722   5-10 −0.1062 0.2388 −0.0357 0.2388 5-10−0.10620.2388−0.03570.2388   11-20 −0.1950 0.0295 −0.0769 0.0295 11-20−0.19500.0295−0.07690.0295   21-40 −0.0055 0.9562 0.0000 0.9562 21-40−0.00550.95620.00000.9562   >40 −0.0269 0.7732 0.0000 0.7732 >40−0.02690.77320.00000.7732  Chokwe <5 −0.0104 0.9431 0.0000 0.9431 Chokwe<5−0.01040.94310.00000.9431   5-10 −0.2707 0.0292 −0.0952 0.0292 5-10−0.27070.0292−0.09520.0292   11-20 0.2403 0.0567 0.0606 0.0567 11-200.24030.05670.06060.0567   21-40 0.0432 0.7401 0.0000 0.7401 21-400.04320.74010.00000.7401   >40 −0.2177 0.0889 −0.0370 0.0889 >40−0.21770.0889−0.03700.0889  Quelimane <5 −0.0671 0.5155 0.0000 0.5155 Quelimane<5−0.06710.51550.00000.5155   5-10 −0.0798 0.4401 0.0000 0.4401 5-10−0.07980.44010.00000.4401   11-20 0.0505 0.6270 0.0000 0.6270 11-200.05050.62700.00000.6270   21-40 −0.0142 0.8965 0.0000 0.8965 21-40−0.01420.89650.00000.8965   >40 −0.0876 0.3990 0.0000 0.3990 >40−0.08760.39900.00000.3990  Pemba <5 −0.0700 0.4661 −0.0256 0.4661 Pemba<5−0.07000.4661−0.02560.4661   5-10 −0.0237 0.8098 0.0000 0.8098 5-10−0.02370.80980.00000.8098   11-20 0.1865 0.0529 0.0513 0.0529 11-200.18650.05290.05130.0529   21-40 0.0204 0.8378 0.0000 0.8378 21-400.02040.83780.00000.8378   >40 0.0836 0.4052 0.0000 0.4052 >400.08360.40520.00000.4052  Xai Xai <5 −0.0570 0.6315 −0.0370 0.6315 Xai Xai<5−0.05700.6315−0.03700.6315   5-10 −0.0707 0.5586 0.0000 0.5586 5-10−0.07070.55860.00000.5586   11-20 −0.0431 0.7227 0.0000 0.7227 11-20−0.04310.72270.00000.7227   21-40 0.0597 0.6261 0.0000 0.6261 21-400.05970.62610.00000.6261   >40 −0.1438 0.2315 −0.0333 0.2315 >40−0.14380.2315−0.03330.2315  "},{"text":"Table 6 . Mann-Kendall trend test of different rainfall classes at weather stations in South Africa Country Station Rainfall class Kendall's tau P-value Sen slope P-value CountryStationRainfall classKendall's tauP-valueSen slopeP-value SA Harmony <5 0.2667 0.0002 0.1053 0.0002 SAHarmony<50.26670.00020.10530.0002   5-10 0.0351 0.6210 0.0000 0.6210 5-100.03510.62100.00000.6210   11-20 −0.0887 0.2220 0.0000 0.2220 11-20−0.08870.22200.00000.2220   21-40 −0.0720 0.3237 0.0000 0.3237 21-40−0.07200.32370.00000.3237   >40 −0.2698 0.0004 −0.0141 0.0004 >40−0.26980.0004−0.01410.0004  Levubu <5 −0.0497 0.6709 −0.0313 0.6709 Levubu<5−0.04970.6709−0.03130.6709   5-10 0.0535 0.6521 0.0000 0.6521 5-100.05350.65210.00000.6521   11-20 −0.0324 0.7884 0.0000 0.7884 11-20−0.03240.78840.00000.7884   21-40 0.0423 0.7234 0.0000 0.7234 21-400.04230.72340.00000.7234   >40 −0.0084 0.9515 0.0000 0.9515 >40−0.00840.95150.00000.9515  Mertz <5 −0.1591 0.0244 −0.0606 0.0244 Mertz<5−0.15910.0244−0.06060.0244   5-10 −0.3836 6.65e -8 −0.1092 6.65e -8 5-10−0.38366.65e -8−0.10926.65e -8   11-20 −0.1756 0.0146 −0.0323 0.0146 11-20−0.17560.0146−0.03230.0146   21-40 −0.0401 0.5864 0.0000 0.5864 21-40−0.04010.58640.00000.5864   >40 0.2840 0.0002 0.0156 0.0002 >400.28400.00020.01560.0002  Polokwane <5 −0.1454 0.1681 −0.0588 0.1681 Polokwane<5−0.14540.1681−0.05880.1681   5-10 0.0440 0.6876 0.0000 0.6876 5-100.04400.68760.00000.6876   11-20 −0.0446 0.6815 0.0000 0.6815 11-20−0.04460.68150.00000.6815   21-40 0.0186 0.8704 0.0000 0.8704 21-400.01860.87040.00000.8704   >40 0.2001 0.0803 0.0000 0.0803 >400.20010.08030.00000.0803  "},{"text":"Table 7  2.1 mm•season -1 , respectively, at Bulawayo. There was a general 2.1 mm•season -1 , respectively, at Bulawayo. There was a general decrease in February and March rainfall at Malawian locations decrease in February and March rainfall at Malawian locations (Table (Table 6 and 6 and  "},{"text":"Table 7 . Mann-Kendall trend and Sen slope tests of monthly and seasonal (November-March) rainfall at weather stations in Zimbabwe Station Month(s) N Kendall's tau P-value Sen slope P-value StationMonth(s)NKendall's tauP-valueSen slopeP-value Harare Nov 38 −0.053 0.651 −0.5429 0.6418 HarareNov38−0.0530.651−0.54290.6418  Dec 38 −0.027 0.821 −0.2462 0.8210 Dec38−0.0270.821−0.24620.8210  Jan 38 0.260 0.022 3.2550 0.0221 Jan380.2600.0223.25500.0221  Feb 38 −0.018 0.022 −0.2320 0.8801 Feb38−0.0180.022−0.23200.8801  Mar 38 0.240 0.035 1.7889 0.0347 Mar380.2400.0351.78890.0347  Nov-Mar 38 0.073 0.530 2.2273 0.5296 Nov-Mar380.0730.5302.22730.5296 Marondera Nov 50 −0.034 0.737 0.2292 0.3234 MaronderaNov50−0.0340.7370.22920.3234  Dec 50 −0.160 0.107 0.2727 0.2767 Dec50−0.1600.1070.27270.2767  Jan 50 −0.075 0.453 −0.0370 0.9067 Jan50−0.0750.453−0.03700.9067  Feb 50 −0.095 0.339 0.2286 0.4413 Feb50−0.0950.3390.22860.4413  Mar 50 0.153 0.123 0.1600 0.5347 Mar500.1530.1230.16000.5347  Nov-Mar 50 −0.095 0.339 0.8929 0.3194 Nov-Mar50−0.0950.3390.89290.3194 Matopos Nov 76 −0.105 0.184 0.000 0.2884 MatoposNov76−0.1050.1840.0000.2884  Dec 76 −0.056 0.476 −0.0910 0.8365 Dec76−0.0560.476−0.09100.8365  Jan 76 −0.094 0.231 0.0498 0.8894 Jan76−0.0940.2310.04980.8894  Feb 76 −0.110 0.163 0.2388 0.5068 Feb76−0.1100.1630.23880.5068  Mar 76 −0.168 0.033 0.4493 0.0817 Mar76−0.1680.0330.44930.0817  Nov-Mar 76 −0.176 0.025 0.0941 0.0517 Nov-Mar76−0.1760.0250.09410.0517 Bulawayo Nov 71 0.084 0.302 0.5887 0.0685 BulawayoNov710.0840.3020.58870.0685  Dec 71 −0.046 0.571 −0.4550 0.2770 Dec71−0.0460.571−0.45500.2770  Jan 71 −0.038 0.641 0.2866 0.5715 Jan71−0.0380.6410.28660.5715  Feb 71 0.002 0.984 0.7154 0.1056 Feb710.0020.9840.71540.1056  Mar 71 0.008 0.925 0.6000 0.0110 Mar710.0080.9250.60000.0110  Nov-Mar 71 0.012 0.889 2.0817 0.0418 Nov-Mar710.0120.8892.08170.0418 West Nich Nov 39 −0.004 0.981 −0.0333 0.9807 West NichNov39−0.0040.981−0.03330.9807  Dec 39 −0.112 0.321 −0.7214 0.3212 Dec39−0.1120.321−0.72140.3212  Jan 39 0.093 0.411 0.9182 0.4107 Jan390.0930.4110.91820.4107  Feb 39 −0.007 0.961 −0.0250 0.9614 Feb39−0.0070.961−0.02500.9614  Mar 39 0.119 0.293 0.6000 0.2926 Mar390.1190.2930.60000.2926  Nov-Mar 39 0.026 0.828 0.4677 0.8276 Nov-Mar390.0260.8280.46770.8276 Beitbridge Nov 50 0.138 0.160 −0.1875 0.5582 BeitbridgeNov500.1380.160−0.18750.5582  Dec 50 −0.026 0.795 −0.0681 0.8474 Dec50−0.0260.795−0.06810.8474  Jan 50 0.156 0.112 −1.7727 0.0052 Jan500.1560.112−1.77270.0052  Feb 50 0.105 0.284 −0.6429 0.2553 Feb500.1050.284−0.64290.2553  Mar 50 0.180 0.035 −0.2826 0.2880 Mar500.1800.035−0.28260.2880  Nov-Mar 50 0.176 0.043 −0.3000 0.0139 Nov-Mar500.1760.043−0.30000.0139  "},{"text":"Table 8 . Mann-Kendall trend test of monthly and seasonal rainfall at weather stations in Malawi Station Month N Kendall's tau P-value Sen slope P-value StationMonthNKendall's tauP-valueSen slopeP-value Chitala Nov 52 −0.0630 0.5173 −0.1467 0.5173 ChitalaNov52−0.06300.5173−0.14670.5173  Dec 52 0.1028 0.2866 0.8475 0.2866 Dec520.10280.28660.84750.2866  Jan 52 0.1086 0.2590 0.8967 0.2591 Jan520.10860.25900.89670.2591  Feb 52 −0.2382 0.0132 −2.5286 0.0132 Feb52−0.23820.0132−2.52860.0132  Mar 52 −0.0045 0.9685 −0.0063 0.9685 Mar52−0.00450.9685−0.00630.9685  Nov−Mar 52 −0.0166 0.8684 −0.3896 0.8684 Nov−Mar52−0.01660.8684−0.38960.8684 Chitedze Nov 33 0.0000 1.0000 0.0110 1.0000 ChitedzeNov330.00001.00000.01101.0000  Dec 33 −0.0057 0.9753 −0.0577 0.9753 Dec33−0.00570.9753−0.05770.9753  Jan 33 0.16330 0.1930 1.9912 0.1930 Jan330.163300.19301.99120.1930  Feb 33 −0.0909 0.4665 −1.1479 0.4665 Feb33−0.09090.4665−1.14790.4665  Mar 33 −0.0719 0.5664 −0.9393 0.5664 Mar33−0.07190.5664−0.93930.5664  Nov−Mar 33 0.0000 1.0000 0.0110 1.0000 Nov−Mar330.00001.00000.01101.0000 Dedza Nov 41 0.1049 0.3397 0.5476 0.3397 DedzaNov410.10490.33970.54760.3397  Dec 41 0.0317 0.7789 0.2730 0.7797 Dec410.03170.77890.27300.7797  Jan 41 −0.0952 0.3871 −1.2443 0.3871 Jan41−0.09520.3871−1.24430.3871  Feb 41 −0.0647 0.5592 −0.6125 0.5592 Feb41−0.06470.5592−0.61250.5592  Mar 41 −0.0354 0.7531 −0.3711 0.7531 Mar41−0.03540.7531−0.37110.7531  Nov−Mar 41 −0.0195 0.8662 −0.5900 0.8662 Nov−Mar41−0.01950.8662−0.59000.8662  "},{"text":"Table 9 . Mann-Kendall trend test of monthly and seasonal rainfall at weather stations in Mozambique Station Month N Kendall's tau P-value Sen slope P-value StationMonthNKendall's tauP-valueSen slopeP-value Chimoio Nov 62 0.0952 0.2769 0.5000 0.2769 ChimoioNov620.09520.27690.50000.2769  Dec 62 −0.0407 0.6443 −0.4357 0.6443 Dec62−0.04070.6443−0.43570.6443  Jan 62 0.0085 0.9274 0.1333 0.9274 Jan620.00850.92740.13330.9274  Feb 62 0.0619 0.4811 0.7103 0.4811 Feb620.06190.48110.71030.4811  Mar 62 0.0709 0.4192 0.4864 0.4192 Mar620.07090.41920.48640.4192  Nov-Mar 62 0.0423 0.6313 1.1143 0.6313 Nov-Mar620.04230.63131.11430.6313 Chokwe Nov 35 0.0151 0.9095 0.0944 0.9095 ChokweNov350.01510.90950.09440.9095  Dec 35 0.0723 0.5509 0.5125 0.5509 Dec350.07230.55090.51250.5509  Jan 35 −0.1899 0.1117 −2.1200 0.1117 Jan35−0.18990.1117−2.12000.1117  Feb 35 0.0151 0.9095 0.1000 0.9095 Feb350.01510.90950.10000.9095  Mar 35 −0.0538 0.6597 −0.2286 0.6597 Mar35−0.05380.6597−0.22860.6597  Nov-Mar 35 −0.1261 0.2933 −3.2917 0.2933 Nov-Mar35−0.12610.2933−3.29170.2933 Quelimane Nov 49 0.1192 0.2308 0.5489 0.2308 QuelimaneNov490.11920.23080.54890.2308  Dec 49 −0.0459 0.6478 −0.4681 0.6478 Dec49−0.04590.6478−0.46810.6478  Jan 49 −0.0799 0.4228 −1.1542 0.4228 Jan49−0.07990.4228−1.15420.4228  Feb 49 0.0204 0.8428 0.2133 0.8428 Feb490.02040.84280.21330.8428  Mar 49 0.1225 0.2177 1.9106 0.2177 Mar490.12250.21771.91060.2177  Nov-Mar 49 0.0136 0.8971 −0.5482 0.8971 Nov-Mar490.01360.8971−0.54820.8971 Pemba Nov 55 −0.0866 0.3563 −0.1469 0.3563 PembaNov55−0.08660.3563−0.14690.3563  Dec 55 0.1447 0.1203 0.9424 0.1203 Dec550.14470.12030.94240.1203  Jan 55 −0.0842 0.3680 −0.5927 0.3680 Jan55−0.08420.3680−0.59270.3680  Feb 55 −0.0074 0.9421 −0.0500 0.9421 Feb55−0.00740.9421−0.05000.9421  Mar 55 0.0303 0.7494 0.2910 0.7494 Mar550.03030.74940.29100.7494  Nov-Mar 55 0.0222 0.8163 0.3333 0.8163 Nov-Mar550.02220.81630.33330.8163 Xai Xai Nov 38 −0.1480 0.1953 −0.7895 0.1953 Xai XaiNov38−0.14800.1953−0.78950.1953  Dec 38 0.0655 0.5715 0.5000 0.5715 Dec380.06550.57150.50000.5715  Jan 38 0.0370 0.7533 0.3054 0.7533 Jan380.03700.75330.30540.7533  Feb 38 −0.2888 0.0111 −3.3167 0.0111 Feb38−0.28880.0111−3.31670.0111  Mar 38 0.0213 0.8603 0.1333 0.8603 Mar380.02130.86030.13330.8603  Nov-Mar 38 −0.1607 0.1591 −3.7250 0.1591 Nov-Mar38−0.16070.1591−3.72500.1591  "},{"text":"Table 10 . Mann-Kendall trend test of monthly and seasonal rainfall at weather stations in South Africa Station Month N Kendall's tau P-value Sen slope P-value StationMonthNKendall's tauP-valueSen slopeP-value Harmony Nov 96 −0.0715 0.3036 −0.1562 0.3036 HarmonyNov96−0.07150.3036−0.15620.3036  Dec 96 −0.1562 0.0244 −0.4119 0.0244 Dec96−0.15620.0244−0.41190.0244  Jan 96 −0.0959 0.1676 −0.2490 0.1676 Jan96−0.09590.1676−0.24900.1676  Feb 96 −0.0748 0.2818 −0.2846 0.2818 Feb96−0.07480.2818−0.28460.2818  Mar 96 −0.0242 0.7301 −0.0552 0.7301 Mar96−0.02420.7301−0.05520.7301  Nov-Mar 96 −0.1410 0.0421 −1.2397 0.0421 Nov-Mar96−0.14100.0421−1.23970.0421 Levubu Nov 39 0.0202 0.8655 0.1629 0.8655 LevubuNov390.02020.86550.16290.8655  Dec 39 0.0418 0.7167 0.3044 0.7167 Dec390.04180.71670.30440.7167  Jan 39 −0.0065 0.9614 −0.1106 0.9614 Jan39−0.00650.9614−0.11060.9614  Feb 39 −0.0958 0.3971 −1.1417 0.3971 Feb39−0.09580.3971−1.14170.3971  Mar 39 −0.0445 0.6987 −0.3950 0.6987 Mar39−0.04450.6987−0.39500.6987  Nov-Mar 39 0.0122 0.9229 0.6000 0.9229 Nov-Mar390.01220.92290.60000.9229 Mertz Nov 96 0.0466 0.5041 0.1043 0.5041 MertzNov960.04660.50410.10430.5041  Dec 96 −0.0147 0.8345 −0.0417 0.8345 Dec96−0.01470.8345−0.04170.8345  Jan 96 0.0029 0.9697 0.0072 0.9697 Jan960.00290.96970.00720.9697  Feb 96 −0.0411 0.5560 −0.1321 0.5560 Feb96−0.04110.5560−0.13210.5560  Mar 96 0.0176 0.8025 0.0427 0.8025 Mar960.01760.80250.04270.8025  Nov-Mar 96 −0.0075 0.9167 0.0000 0.9167 Nov-Mar96−0.00750.91670.00000.9167 Polokwane Nov 45 0.0657 0.5313 0.3114 0.5313 PolokwaneNov450.06570.53130.31140.5313  Dec 45 0.0172 0.8756 0.0599 0.8756 Dec450.01720.87560.05990.8756  Jan 45 0.0000 1.0000 −0.0063 1.0000 Jan450.00001.0000−0.00631.0000  Feb 45 0.0424 0.6884 0.1289 0.6884 Feb450.04240.68840.12890.6884  Mar 45 0.0788 0.4513 0.3285 0.4513 Mar450.07880.45130.32850.4513  Nov-Mar 45 0.1051 0.3137 1.2479 0.3137 Nov-Mar450.10510.31371.24790.3137  "}],"sieverID":"b5aa8390-f02e-46dd-b8f1-2ac57e332535","abstract":"Rainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (<5, 5-10, 11-20, 21-40 and >40 mm•day -1 ), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by <5 mm•day -1 followed by 5-10 mm•day -1 . Three locations experienced increasing linear trends of <5 mm•day -1 amounts and two others in sub-humid region had increases in the >40 mm day -1 category. Semi-arid location experienced increasing trends in <5 and 5-10 mm•day -1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm•season -1 ). A 3 mm•season -1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November-March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s."}

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+{"metadata":{"id":"00723e0154facbc46b11b591b2695a37","source":"gardian_index","url":"https://digitalarchive.worldfishcenter.org/bitstream/handle/20.500.12348/4496/ffc3c18f31fbdbb2f2cc4e2e354f6dff.pdf"},"pageCount":23,"title":"Demand for Imported versus Domestic Fish in Nigeria","keywords":["Africa","demand systems","fish","imports","Nigeria","EASI"],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":59,"text":"Developing countries' share of consumption of global fish output rose from 61% in 1990 to 78% in 2017, according to FAOSTAT. Despite the rising dominance of developing regions in world fish consumption, Bush et al. (2019) note that demand for fish in developing countries is under-researched. However, the gap in the literature is distinctly different between Asia and Africa."},{"index":2,"size":123,"text":"Asia has been the focus of the great majority of fish demand literature for developing countries. The rise of Asian fish demand and supply occurred earlier than Africa. Asia also dominates developing country fish output: in 2014 Asia's share of fish output from developing countries was 84%, with Africa only 10% and Latin America and the Caribbean, 4%. A wave of household survey studies in the past two decades showed that the rapid increase in Asian fish consumption was driven by rising incomes, falling fish prices, and shifting preferences associated with urbanisation, lifestyle changes and employment: Huang and Bouis (2001) for Taiwan; Hovhannisyan and Gould (2014) for China; Dey et al. (2011), Toufique and Belton (2014) and Toufique et al. (2018) for Bangladesh."},{"index":3,"size":174,"text":"Africa has received far less attention in the developing country fish demand literature. This may be because of a perception of African fish production being stagnant and small, which was largely the case in the 1970s and 1980s when its share of world fish production was about 5%. Yet FAOSTAT data in Table 1 show that from 1970 to 1990 African fish production rose 1.25 fold; from 1990 to 2010 it rose 1.5 fold; and if the growth rate from 2010 to 2017 holds, production will rise 2.0 fold from 2010 to 2030. From 1970 to 2017, fish output in Africa rose 2.5 fold. But as African population rose 3.5 fold, per capita consumption of fish should have fallen over time if it had been only sourced from domestic production. Instead, consumption hovered around 10 kg per capita for those five decades. The gap between demand and supply was met by rapidly rising imports, which rose from 16% to 39% of African fish consumption over those decades. We discuss imports in more detail below."},{"index":4,"size":298,"text":"Fish is crucial to nutrition in Africa (Chan et al., 2019). Ensuring adequate levels of animal-sourced food consumption is considered to be one key to combatting malnutrition (Headey et al., 2018). Africa experiences high levels of food insecurity and malnutrition (Akombi et al., 2017), and fish is among the most important animal-sourced foods across most of the continent (Desiere et al., 2018). This is also the scenario in Nigeria, where rates of malnutrition are high and fish is one of the main animalsourced foods (Kuku-Shittu et al., 2016;Ogundari, 2017). We analyse nationally representative data for Nigeriathe most populous country with the largest economy in Africa. We find that fish consumption accounted for about 35% of consumption expenditure for animal proteins in 2015 and constituted about 10% of food consumption expenditure by the average Nigerianas much as any of the individual main staples (rice or maize or tubers or pulses). Changes in fish consumption therefore have important implications for food and nutrition security in Nigeria (Bradley et al., 2020).   Despite the importance of fish consumption in Africa, particularly for addressing malnutrition, examination of fish demand has been limited. There are few surveybased analyses of fish demand in Africa, though exceptions include: Abdulai and Aubert (2004) for Tanzania; Tambi (2001) for Cameroon; and local area studies such as Amao et al. (2006) for Lagos State in Nigeria. Zhou and Staatz (2016) used Living Standards Measurement Study (LSMS) data from around 2012 to estimate income elasticities for fish as a general category compared with other food categories for West Africa. Desiere et al. (2018) also used LSMS and FAO data to assess current and future meat and fish consumption in a group of countries in sub-Saharan Africa. Genschick et al. (2018) analysed urban Zambian fish consumption patterns of the poor strata."},{"index":5,"size":177,"text":"Moreover, there has been little research globally on the determinants of the form in which fish is purchased. 'Traditional forms' include dried/salted, smoked, and fresh, all of which were common prior to the advent of refrigeration and freezing. The main non-traditional product form is frozen fish, which is thawed after purchase for use at home or in restaurants. Fish consumption analyses have often treated fish (and 'seafood') as a homogeneous group of products and few studies differentiate either species or form. There are some exceptions: Toufique et al. (2018) distinguish fish originating from capture or aquaculture. Dey et al. (2008) distinguish dried fish from other fish in Asia. In Europe and the US, Trondsen et al. (2004) distinguish processed from fresh, and Verbeke et al. (2007) distinguish traditional preservation styles versus fresh. In the United States, Muhammad and Hanson (2009) distinguish fresh and frozen catfish. In Africa, studies of demand for different fish forms are either of a locality, or of one species, or limited product forms (Kumar et al., 2005;Jimoh et al., 2013;Dauda et al., 2016)."},{"index":6,"size":60,"text":"In sum, the African literature has not had a systematic analysis of: (i) consumption of domestically produced versus imported fish; (ii) consumption of different forms of fish, such as frozen, fresh, dried and smoked; (iii) consumption of fish over spatial categories such as agroecological zones, and regions with different levels of development. These gaps are important for the following reasons."},{"index":7,"size":147,"text":"First, unlike Asia, food imports are among the top policy concerns in Africa (African Development Bank, 2016) due to their viewed foreign exchange burden and their competition with the domestic fish sector. In Africa, the share of imports in total apparent consumption of fish more than doubled over the four decades 1970s-2000s, to a high of 39% by 2017 (Table 1). This compares to the import share (derived from FAOSTAT) in all food for 2017 of 13% (Liverpool-Tasie et al., 2020). Despite the importance of fish imports, no survey-based analysis of the patterns and determinants of imported versus domestic fish consumption has been done for Africa. As discussed below, imports are mainly in the form of frozen fish, and the latter are nearly all imported, so there is a correlation between a lack of analysis of demand for different forms of fish and demand for imported fish."},{"index":8,"size":167,"text":"Second, food demand analyses using nationally representative surveys in developing regions are often focused only on the national level. However, in African countries such as Nigeria, there are particularly sharp inter-regional differences in development levels as well as consumption habits. We posit that this holds for Northern versus Southern Nigeria. Northern Nigeria is often in the international news because of the Boko Haram insurgency, but it has also long had severe development constraints and lagged growth because of its semi-arid agroecology and lower education compared with the much richer South, which benefits from more oil revenue and has higher education. Nigeria thus presents a pertinent case concerning how differentor similar food consumption transformation is in the two regions, and whether the 'imported fish' phenomenon is driven more by the larger middle class of the South or is occurring in both regions. This is more broadly interesting than just Nigeria: the dichotomy of poorer interior and more developed coastal regions is found over a large part of Africa."},{"index":9,"size":142,"text":"To address these three gaps, we analyse consumption patterns, food expenditure, and price elasticities of imported versus domestic fish consumption using data from a nationally representative panel survey, the Nigeria Living Standard Measurement Study-Integrated Survey on Agriculture (LSMS-ISA). It has data on the same households for 5 years over 2010 to 2015. The LSMS data allow stratification by urban and rural as well as by North and South Nigeria. We also explore the heterogeneity of demand across rural and urban areas as proxies for employment, lifestyle and preferences, found to be so important in the Asian studies noted above. Following the same households over multiple years is our fourth contribution to the literature. The great majority of fish demand studies in developing countries (and, to our knowledge, all those in Africa) use cross-section analysis. We are able to track changes over time,"},{"index":10,"size":20,"text":"something not yet done in Africa for fish consumption, to see whether changes in the two regions diverge or converge."},{"index":11,"size":182,"text":"However, the Nigeria LSMS data do not directly indicate whether the fish consumed is from imports or domestic sources. While fish indicated as fresh are, with near certainty, from domestic capture or aquaculture (although these two sources are not indicated in the data), processed fish can be either domestic or imported. We have assigned dried and smoked fish to the domestic category because the great majority of dried and almost all smoked fish are domestically produced except for a small amount received in informal cross-border trade, and for Norwegian dried cod (stock fish) imports. 1 By contrast, we assign all frozen fish to the imported category because Nigeria lacks a significant fish freezing industry using domestic fish as inputs, and nearly all the frozen fish purchased in the country are imported. 2  The paper proceeds as follows. In Section 2 we discuss the data used. Section 3 presents a description of fish consumption patterns across Nigeria. Section 4 presents the econometric approach featuring an Exact Affine Stone Index (EASI) demand model and section 5 presents the associated regression results. Section 6 concludes."}]},{"head":"Data","index":2,"paragraphs":[{"index":1,"size":167,"text":"We use data from three rounds of the Nigeria World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA); 2010/11, 2011/12, 1 Given the unconditional dried fish consumption by households derived from the nationally representative LSMS data and extrapolated to annual national levels (assuming 5-7 people per household) we compare the expected total dried fish demand to the amount of dried fish recorded to have been imported by COMTRADE and find this to be between 4% and 6% depending on the assumed household size. and 2015/16 . It includes about 5,000 households in each survey year surveyed twice a year during the agricultural season and post-harvest season. This generates a panel as the same individuals were interviewed during each period of data collection. The survey was nationally representative, covering rural and urban areas in the two geographical regions that also capture agroecological variation; the North and the South, as discussed above. The data cover household demography, assets, production and food consumption from own production, purchases and gifts."},{"index":2,"size":148,"text":"We computed household food consumption expenditure as the total value of consumption (from purchases, own production and gifts) for 10 categories of food used to categorise all the food items available in the data: cereals and tubers, pulses, dairy, beef and other meats, poultry and eggs, dairy products, fresh fish, frozen fish, smoked fish, dried fish, and other foods. The choice of the categories was informed by other consumption studies in developing countries (Fashogbon and Oni, 2013;Dolislager, 2017). We disaggregated fish consumption into the four categories reported in the survey: fresh, frozen, dried and smoked. Price indices were computed for each of the food categories as a weighted average of transaction-derived prices of items included in the specific group. Nominal prices and values were converted into real values using the consumer price index (CPI) at the national level for each survey year with 2010 being the base year."}]},{"head":"Fish Consumption Patterns","index":3,"paragraphs":[{"index":1,"size":54,"text":"Table 2 shows overall fish consumption in the country and the two regions. Several points stand out. First, fish consumption is widespread and increasing: 59% of Nigerians ate fish in 2010 versus 72% in 2015. The North stayed steady at about 50%, while the South leapt from 71% to 90% in the 5 years."},{"index":2,"size":58,"text":"Second, per capita consumption of fish products (unconstrained) in the South in 2015 was more than double that of the North. Annual fish consumption per capita is 13 kg. This is slightly higher than the average apparent consumption per capita for Africa as a whole (Table 1), but about half the global average of 20.3 kg (FAO, 2018)."},{"index":3,"size":127,"text":"Table 3 compares the North and South over 5 years, and disaggregates fish consumption into frozen fish (largely imported), and fresh, dried and smoked fish (primarily domestic origin). 3 There are several striking points. First, the share of people consuming frozen fish is far higher in the South compared to the North. Only 14% of Northern fish consumers ate frozen fish in 2015, versus 62% in the South. This gap grew over the 5 years. Frozen fish might be more accessible in the South because the ports are close by and more households own refrigerators. Only 10% of households in the North own refrigerators, all of them in urban areas, as compared to 30% of households in the South (20% rural and 40% urban) (Table 4, below)."},{"index":4,"size":73,"text":"Second, there is surprisingly little difference between the North and South in terms of the share of people eating fresh fish (14% and 12%, respectively). The share of fresh fish in total food consumption expenditure is even closer, around 1.5% in both regions. This similarity may be because capture fisheries in rivers and lakes in the North balance more abundant aquaculture and marine fisheries in the South in providing access to fresh fish."},{"index":5,"size":50,"text":"Third, while dried and smoked fish are preserved product forms, often thought to be eaten in arid areas far from supplies of fresh fish, Southern consumers eat more of both (each consumed by around 25% of households) than those in the North (consumed by 17% and 11% of households, respectively)."},{"index":6,"size":204,"text":"In the North, the share of consumers eating fish was much higher in urban areas than in rural: 61% versus 45% in 2015. In the South, the shares of people eating fish were similar in urban and rural areas, at around 90%. Accordingly, the share of fish in overall food consumption is lower in the North (3%) than the South (12%) in 2015, likely reflecting higher incomes in the South as well as access to a wide variety of fish (including the more expensive fresh fish) and different food culture traditions (see Table S1). Comparing urban to rural per region shows that there was an increase in the share of households consuming fish in the South in both rural and urban areas from 2010 to 2012, but little change in the South from 2012 to 2015, or in the North in any year. Given that relative incomes of urban and rural areas did not change much over the 5 years, this appears to imply that access to fish has grown in the South, leading to increasing equality of fish market access between rural and urban areas. Food supply chains are critical for fish consumption; approximately 95% of fish consumed in both regions is purchased."},{"index":7,"size":132,"text":"Fish expenditure by fish type, as a share of total fish budget (by region, and rural versus urban) is presented in Table S1. We find that in the North, frozen fish were 34% of all fish consumption in urban areas, versus 23% in rural areas where about half of fish consumption is dried or smoked fish. This could be because of differences  in income and shares of families with iceboxes or refrigerators. Rural areas are often poorly served by electricity grids or generators. Even when an area has electricity it is often shut off for significant periods, making it hard for consumers as well as retailers and wholesalers to store frozen fish. By contrast, in the South, frozen fish dominates in both urban areas (67% of fish consumption) and rural areas (54%)."},{"index":8,"size":90,"text":"In both the North and South, the share of dried fish is slightly higher in rural areas than urban. This appears to be due in part to the association of dried fish with lower incomes and transportability and ambient storability that favour consumption of it in less accessible rural locations, particularly in the North. Fresh fish consumption is similar in urban and rural areas (around 25% in the North, and 10% in the South). Smoked fish also has similar shares in urban and rural areas in both regions (roughly 18%)."},{"index":9,"size":152,"text":"Table 4 shows real prices. Frozen fish tends to be more expensive than the traditional processed forms of fish in both North and South. However, the price of fresh fish is higher than the price of frozen fish (up until 2015). In 2015, the price of frozen fish increased significantly (by about 50% in the South and 30% in the North) compared to the previous round. The price increase is largely due to the higher exchange rate from the currency devaluation following the oil price collapse in 2013/2014. 4  Interestingly, despite the large hike in imported frozen fish prices, consumption per capita dipped only slightly in the North but increased in the South. This shows that frozen fish is not viewed as a luxury to be dropped when its price rises, but is likely a price-inelastic product, having penetrated basic consumption habits at least of certain consumer strata, particularly in the South."},{"index":10,"size":86,"text":"Contrary to expectations, frozen fish is more expensive in the South than the North. This is surprising because the North is further away from ports through which frozen fish is imported. The result might reflect heterogeneity in the dominant kinds of fish consumed per region. A rapid reconnaissance of fish markets conducted to add information on frozen fish indicated that the North tends to consume cheaper frozen fish such as herrings, whereas relatively more expensive frozen fish such as croaker are consumed more in the South."},{"index":11,"size":81,"text":"Controlling for inflation, the average price of fish (irrespective of form) more than doubled between 2010 and 2015, from ₦319 to ₦666 per kg (Table 4). The price of dried fish increased by over 170% in the North and over 230% in the South. This appears to be partly driven by the Boko Haram insurgency in North-Eastern Nigeria, home to the Baga market, one of the largest fish trading centres supplying dried fish to the entire country (Mukhtar and Gazali, 2016)."},{"index":12,"size":92,"text":"The price of fish rose most sharply from 2010 to 2012 (70%). This price rise was linked to a drop in fish consumption per capita of 10%. Fish prices increased, but less sharply (by 20%), from 2012 to 2015 (Table 5). During that period, fish consumption per capita rose, likely due to fish remaining the cheapest animal sourced food. The price of fresh fish declined by 20% between 2012 and 2015, perhaps in response to rapidly growing aquaculture production. This change was accompanied by an 80% increase in consumption of fresh fish.   "}]},{"head":"Econometric Analysis","index":4,"paragraphs":[]},{"head":"Empirical approach","index":5,"paragraphs":[{"index":1,"size":218,"text":"Our demand analysis focuses on four forms of fish (fresh, frozen, smoked and dried) with other foods (beef, cereals and tubers, pulses, dairy, poultry, and eggs and 'other foods'). We assume, as is usual, that household food consumption is determined in a two-stage budgeting process (Deaton and Muellbauer, 1980). In the first stage, households allocate their total household consumption expenditures to food versus nonfood items conditional on prices, income and household characteristics. In the second stage, the household allocates food consumption to different food types including the various forms of fish. We allow substitution between different fish products and other protein sources or other food groups, and estimate a full food demand system. Preliminary local polynomial regressions (Figure 1) of fish consumption shares and total food consumption (in logs) for the North and the South 5 show that the consumption shares for most fish forms are non-linearbut also show that they are not quadratic in total food consumption (as a proxy for income). Thus, neither the Linear Approximate nor the Quadratic Almost Ideal Demand System model is appropriate for the analysis of fish demand in Nigeria. We address this by using the more flexible Exact Affine Stone Index (EASI) demand system of Lewbel and Pendakur (2009). The preliminary analysis also shows clear regional differences in the Engel curves."}]},{"head":"The EASI demand system","index":6,"paragraphs":[{"index":1,"size":79,"text":"We apply the EASI demand system of Lewbel and Pendakur (2009) with a pooled cross-section data. 6 The EASI demand system does not impose any particular functional form on the relationship between income and food consumption but allows for arbitrarily complex Engel curves. In addition, it allows us to control for individual preference heterogeneity across households and time-specific factors rather than leave them as part of the error term as is done in other models (Lewbel and Pendakur, 2009)."},{"index":2,"size":135,"text":"We assume that: households have demographic and other characteristics that affect food preferences, including fish products, in vector z; households have log nominal total food consumption x; they face a vector of log prices p. Households then choose a vector of consumption shares w, to maximize utility subject to the household budget constraint. Lewbel and Pendakur (2009) and Pendakur (2008) show how the Hicksian budget shares associated with the households utility function expressed as a function of p and z and utility level u can be expressed as a function of log real consumption with an implicit Marshallian consumption shares function. We follow Lewbel and Pendakur to define an implicit utility function y which only depends on observed variables. The implicit utility function is used to derive the implicit Marshallian consumption (budget) shares as follows:"},{"index":3,"size":1,"text":"5"},{"index":4,"size":48,"text":"The advantage of this approach is that the relationship is modelled as linear in the neighbourhood but may vary across values of the log of total fish consumption. The degree of polynomial smoothing used here is 1, meaning that the graphs are a locally weighted least squares model. "}]},{"head":"North","index":7,"paragraphs":[{"index":1,"size":60,"text":"where p and z are the vector of J prices and L demographic variables and ϵ is a vector of error terms which include unobservable preference heterogeneity. y is a measure of real total food consumption and is specified as the equal affine transform of the Stone index-deflated log nominal consumption levels. That transform, by Lewbel and Pendakur (2009), is:"},{"index":2,"size":1,"text":"(2)"},{"index":3,"size":124,"text":"The budget share expressed in equation ( 1) has all the desirable properties of traditional demand models with some added advantages. Similar to the Almost Ideal Demand System (AIDS) model, these implicit Marshallian budget shares are linear in parameters (thus easy to estimate) and have additive error terms including unobserved preferences due to taste and time. However, while the AIDS budget shares are linear in p, z, and y, the EASI budget shares are linear in p but are polynomials of any order in zand y. Thus, as noted above, the EASI Engel curves can take any shape through the addition of polynomials of any order in real consumption. 7 The budget shares can also include interaction terms such as py, zy, and pz'."},{"index":4,"size":52,"text":"Subsequently, income and price elasticities of different fish forms across regions in Nigeria can be estimated using the semi-elasticities in equations ( 3) and ( 4) below (i.e., the derivatives of the budget shares with respect to total consumption expenditures (as a proxy for income) and prices) following Lewbel and Pendakur (2009)."},{"index":5,"size":31,"text":"These semi-elasticities in equations ( 3) and ( 4) are easier to present algebraically and they can be converted into the relevant elasticities by dividing these expressions by the budget share."}]},{"head":"Resolving the zero consumption and endogeneity problems","index":8,"paragraphs":[{"index":1,"size":129,"text":"Estimating demand systems for subgroups of food often faces the 'zero consumption' problem. Three main reasons for this problem are discussed in the literature. First, households may never consume that product. Second, a limited survey period may not find the household consuming a product that it might consume in a period outside the survey recall period. Third, a household might not report consuming the product because it feels it would reveal its making a bad decision (e.g. because the product was too expensive or they could not really afford it but still bought it) (Meyerhoefer et al., 2005;Tafere et al., 2011). In the LSMS-ISA data, there is zero consumption for one or more fish forms among 41%, 30% and 29% of the households in 2010, 2012 and 2015, respectively."},{"index":2,"size":176,"text":"To address this, we employ a two-step procedure to estimate a system of equations with limited dependent variables to obtain a synthetic dataset with imputed consumption for households with zero consumption (e.g. Magrini et al., 2017;Tefera et al., 2018). In the first step, we estimate the determinants of consuming different forms of fish (and other food groups) with a Correlated Random Effect (CRE) multivariate probit model, which accounts for correlation among the food groups (Wooldridge, 2010). 8 The explanatory variables (z is ) used in the estimation include a vector of log of total household consumption expenditure on food, log of prices of the 10 food groups, and demographic variables (education, gender, asset index, living in an urban area, living in the north, household adult equivalent, round of data collection and the mean of all the time-varying household characteristics. In the second step, we calculate the cumulative distribution (φ(.)) and normal probability density functions (φ(.)) for each food group. This is then used to generate new consumption shares for all food groups w * it as:"},{"index":3,"size":167,"text":"where w it is the budget share of food group i at time t and the estimated partameter δ i is the covariance between the first and second stage error terms. As mentioned above, z 0 is refers to the explanatory variables explaining purchasing behaviour and θ is are the associated parameters for the i food groups from the multivariate probit regressions. 9 One challenge with this transformation (equation 5) is that the new consumption shares w * it no longer satisfy the additivity condition as required by demand theory. We address this issue by reweighting the transformed shares (Steele and Weatherspoon, 2016) to obtain w * * it . This approach has two advantages. First, we do not have to choose arbitrarily any of the fish groups as the residual category with no specific demand. Second, it avoids obtaining negative consumption shares for the good since it is possible that the sum of the other goods is greater than one when one imposes the following condition:"},{"index":4,"size":40,"text":"Not accounting for the endogeneity of the allocation of consumption across fish forms with respect to the demand for fish as a product category relative to demand for other food products, can lead to biased and inconsistent demand parameter 8"},{"index":5,"size":236,"text":"The correlated random effects (CRE) estimator allows for correlation between the time invariant unobserved household omitted variable and included explanatory variables. One class of CRE models allows for modelling the distribution of the unobserved household characteristic conditional on the means of time-varying exogenous variables (Mundlak, 1978;Chamberlain, 1980). estimates. To address this, we follow Lewbel and Pendakur (2009) and use an instrumental variables approach. Our instruments are logged prices and logged assets and powers of both to the third order. 10 P represents prices for each of the food groups. 11 4.4. Accounting for time and regional effects on fish demand As noted above, culture and income vary significantly between the two regions. The importance of this variation on food choices is well acknowledged (e.g. Ma, 2015). The regions differ in other characteristics as well. The major ports through which imported fish enter the country are in the South. Also, while there are many lakes and rivers dispersed across the country, the South is closer to the Atlantic Ocean, which is a major source of fish from capture fisheries. The South has also experienced a rapid growth in fish farming over the past decade. All these are likely to influence fish demand. We account for regional differences by estimating the EASI demand model separately for the North and South. Thus, we derive consumption and price elasticities by region. We do the same for rural and urban areas."},{"index":6,"size":43,"text":"Furthermore, we use a Linear EASI demand model that controls for time-specific factors (such as season) that influence food choices and preferences for particular fish forms. Thus, we estimate equation ( 7) for North and South Nigeria distinguishing between rural and urban areas."},{"index":7,"size":53,"text":"To account for the possible heteroscedasticity of error terms and the simultaneous determination of budget shares and total consumption, the estimation of the EASI demand system in the software R uses an iterative linear three-stage least squares (3SLS) estimator as in Hoareau et al. (2012) that is similar to Blundell and Robin (1999)."}]},{"head":"Regression Results","index":9,"paragraphs":[{"index":1,"size":23,"text":"The estimated expenditure elasticities from the EASI for different food items and fish forms are reported in Table 6. Several points stand out."},{"index":2,"size":91,"text":"First, as incomes rise, Nigerians consume more of all forms of fish: frozen, fresh, smoked, or dried; imported or domestic. However, in Southern Nigeria imported frozen fish has the lowest expenditure elasticity (compared to all other fish forms). This shows it is likely a necessity in the South. By contrast, in the North, frozen fish has an expenditure elasticity above 1, and hence is a 'luxury'. This shows how deeply imports have penetrated the basic fish consumption habits of the South, but in the North are limited to the middle class."},{"index":3,"size":61,"text":"Second, fresh fish have the highest consumption elasticities (among all fish forms) and remain a luxury. A 1% increase in income is associated with an increase of 1.1% and 1.2% in expenditure on fresh fish in the North and the South respectively. This is a strong indicator of the potential for domestic aquaculture to grow further as incomes increase in Nigeria."},{"index":4,"size":156,"text":"Third, the expenditure elasticities for smoked and dried fish indicate they are luxuries in the South (Table 6) on average. This is surprising given that these are the traditional forms in which fish are consumed. By contrast, the lowest elasticity in the North (among fish forms) is for the traditional largely domestic dry fish, indicating it as a necessity rather than luxury. A closer look shows that rural consumers drive the inelasticity of dried fish in the North, as their consumption elasticity is lower than urban consumers for dried fish. Higher responsiveness among urban consumers in the North might reflect some sort of quality trade-off between types of dried fish. Our data cannot disaggregate between different types of dried fish such as stock fish, which is much more expensive than other traditional dried fish. Similar explanations could also explain the higher expenditure elasticities for dried fish in urban areas (compared to rural areas) in the South."},{"index":5,"size":60,"text":"Table 7 shows the own-and cross-price elasticities of different foods and fish forms, by region, and by urban versus rural. As predicted by demand theory, compensated own-price elasticities are negative for all the food groups and fish forms. The ownprice elasticities of all food groups (except dairy in the North and cereals, poultry and dairy in the South) are inelastic."},{"index":6,"size":90,"text":"When own price increases, households in the North tend to reduce the quantities of fresh fish purchased the most while households in the South tend to reduce the quantities of frozen fish consumed the most, for both urban and rural areas in both regions. For the North, this finding is consistent with our earlier finding that fresh fish is a luxury with high consumption elasticities. Overall, southerners reduce the quantity of smoked fish the least while Northerners reduce that of frozen fish the least because of changes in own prices."},{"index":7,"size":138,"text":"Finally, we compare the cross-price elasticities to see the substitutability and complementarity among fish types as well as food groups. Though statistically significant,   the cross-price elasticities of different fish forms are extremely small. This implies they occupy specific niches in local cuisines. Several fish forms are complements to poultry products. In the South, frozen fish is the exception as it is substitute to poultry products. Households appear to consider the different fish forms and poultry products as distinct food items, consistent with their use in different dishes in a given multi-dish meal for a family, or over different meals in the day, or even in joint use in various traditional dishes. Surprisingly, imported frozen fish is the only substitute for beef and other meats. Thus, as beef and other meat prices increase, more imported frozen fish is consumed."}]},{"head":"Conclusions","index":10,"paragraphs":[{"index":1,"size":102,"text":"Our analysis of nationally representative food expenditure data in Nigeria yield several key findings. First, fish is among the most important sources of animal protein in Nigeria. It accounts for 10% of the total food budget and 35% of the budget allocated to animal source foods of the average Nigerian, rising to 45% in the South. Second, fish is the cheapest animal protein consumed, with a price significantly lower than that of poultry and eggs as well as other meats, and less than half that of dairy products. This status underlines the importance of fish in Nigeria for food and nutrition security."},{"index":2,"size":75,"text":"Third, there are substantial differences in fish consumption between the poorer North and the richer South. In the South, 90% of households consume fish (accounting for 11% of total food consumption expenditures), versus 50% in the North (with 3% of food outlay). Conditional on consuming some fish, the per capita fish consumption is about 1.2 times higher in the South than the North but not too different between urban and rural areas in each region."},{"index":3,"size":49,"text":"Fourth, frozen (imported) fish makes up 30% of total fish consumption in Nigeria. This national figure masks a large difference between the more developed South (closer to ports for imports, with more refrigeration and higher incomes), with 40% of fish consumption as frozen, compared with 13% in the North."},{"index":4,"size":134,"text":"Fifth, the share of imported frozen fish in rural fish consumption is 20%, versus 35% in urban areas. Urbanization is associated with more consumption of imported frozen fish. Rural fish consumption is much more skewed toward traditional forms (dried, smoked) than frozen/imported, because of differences in access to and costs of the different product types and refrigeration facilities. Urban consumers appear more likely to shift to frozen and fresh fish (partly from rapidly growing aquaculture) and pay more for it. Yet despite these general differences, there is still a non-trivial share of frozen/imported fish consumed in rural areas, at levels similar to smoked and fresh fish. However, smoked and dried fish together account for half of total fish consumption in Nigeria, underlining the continuing importance of these product forms for food and nutrition security."},{"index":5,"size":60,"text":"Sixth, our EASI elasticity estimates show that while frozen imported fish is largely a necessity in the South (but still a luxury in the North), domestically produced fish (particularly fresh fish) remains a luxury with much higher elasticities. These results indicate that if incomes increase in Nigeria, spending on most forms of domestically produced fish will increase more than proportionately."},{"index":6,"size":218,"text":"Together, these findings suggest that fish plays an important role in food and nutrition security in Nigeria. This can be further supported with investment and interventions to increase supplies of fish and reduce the cost of fish to the consumer. This is of particular concern in the North where food security is low and still only about 50% of households consume fish. The higher cost of imported products since 2015 and the 2019 devaluation of the naira have created a greater opportunity for domestic fish production to compete with imported fish. 12  The highly differentiated nature of demand for fish by product type and geographical region revealed here suggests that multiple policy responses may be required. These could include: (i) Ensuring that trade restrictions are not imposed on imported frozen fish, which are shown to make up a significant part of the food basket even in rural areas of the North (where ~95% is purchased); (ii) Supporting the expansion and increasing the productivity and efficiency of the domestic aquaculture sector to increase supplies of fresh fish and produce raw material for fish smokers and driers; (iii) Instituting governance arrangements and regulations that maintain the long-term productivity of inland and marine capture fisheries at sustainable levels, to ensure continued provision of fresh, dried and smoked fish from these sources."}]}],"figures":[{"text":"Ó 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. "},{"text":" from FAOSTAT data. Ó 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. "},{"text":"4 Crude oil prices declined from about $80 a barrel in 2010 to about $40 a barrel in 2015 (US Energy Information Administration, 2017). Ó 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. "},{"text":"Figure 1 . Figure 1. Engel curves.Note: Conditional on the household having consumed fish.Source: Authors' estimations from the LSMS-ISA 2010, 2012 and 2015 data. "},{"text":"9 Standard errors are clustered at the household level. Ó 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. "},{"text":"ÓÓ 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. "},{"text":"Table 1 Sub-Saharan African fish production (capture + farmed), imports, exports and apparent consumption percapita, 1970-2017      "},{"text":"Table 2 Annual fish consumption in Nigeria by rural and urban locations and region, 2010-2015 Year Total number of households North South All YearTotal number of householdsNorthSouthAll  Households consuming fish (%)   Households consuming fish (%) 2010 9,246 46 71 59 20109,246467159 2012 9,284 48 90 72 20129,284489072 2015 9,124 49 90 72 20159,124499072  National (kg/per capita)   National (kg/per capita) 2010 9,246 6.4 17.0 12.1 20109,2466.417.012.1 2012 9,284 5.7 15.0 11.0 20129,2845.715.011.0 2015 9,124 6.3 18.7 13.2 20159,1246.318.713.2  Urban (kg/per capita)   Urban (kg/per capita) 2010 2,994 8.7 16.1 14.1 20102,9948.716.114.1 2012 2,853 7.3 15.2 13.4 20122,8537.315.213.4 2015 2,904 8.6 18.6 16.0 20152,9048.618.616.0  Rural (kg/per capita)   Rural (kg/per capita) 2010 6,252 5.7 18.1 10.8 20106,2525.718.110.8 2012 6,431 5.2 14.8 9.4 20126,4315.214.89.4 2015 6,220 5.5 18.8 11.2 20156,2205.518.811.2  "},{"text":"Table 3 Share of households consuming fish, share of fish in the food budget, and fish consumption per capita (by fishtype and region, 2010-2015)    Fresh fish Frozen fish Smoked fish Dried fish All fish North South All North South All North South All North South All North South All Households consuming fish (%) 2010 14.3 11.4 12.7 12.9 49.9 33 10.8 18.2 14.8 15.9 19.5 17.9 46 71 59 2012 13.8 9.4 11.3 15.8 66.5 44.7 9.3 19.6 15.1 15.9 23.9 20.5 48 90 72 2015 13.9 12.3 13 13.5 61.5 40.2 11 24.6 18.6 17.2 26.4 22.3 49 90 72 Fish as share of food budget (%) 2010 1.6 1.7 1.6 1.1 6.3 3.9 0.5 0.9 0.7 0.5 0.7 0.6 3.7 9.7 6.9 2012 2.1 1.8 1.9 1.5 7.6 5.0 0.5 0.8 0.7 0.7 0.8 0.7 4.7 11.0 8.3 2015 1.4 1.6 1.5 1.2 8.0 5.0 0.4 1.3 0.9 0.4 0.5 0.5 3.4 11.5 7.9 Per capita fish consumption (kg/capita) 2010 12.4 10.1 11.2 7.9 39.6 25.0 6.6 7.5 7.1 8.2 9.8 9.1 6.4 17.0 12.1 2012 9.5 7.9 8.6 8.6 33.1 22.6 4.8 6.9 6.0 6.9 7.3 7.1 5.7 15.0 11.0 2015 16.7 14.7 15.6 7.8 34.6 22.7 4.7 11.9 8.7 5.0 5.2 5.1 6.3 18.7 13.2 Source: Authors' estimations from the LSMS-ISA 2010, 2012 and 2015 data. Note that these values are unconstrained and households can consume more than one fish form. Fresh fish Frozen fish Smoked fish Dried fish All fishNorth South All North South All North South All North South All North South AllHouseholds consuming fish (%)2010 14.3 11.4 12.7 12.9 49.9 33 10.8 18.2 14.8 15.9 19.5 17.9 46 71 592012 13.8 9.4 11.3 15.8 66.5 44.7 9.3 19.6 15.1 15.9 23.9 20.5 48 90 722015 13.9 12.3 13 13.5 61.5 40.2 11 24.6 18.6 17.2 26.4 22.3 49 90 72Fish as share of food budget (%)2010 1.6 1.7 1.6 1.1 6.3 3.9 0.5 0.9 0.7 0.5 0.7 0.6 3.7 9.7 6.92012 2.1 1.8 1.9 1.5 7.6 5.0 0.5 0.8 0.7 0.7 0.8 0.7 4.7 11.0 8.32015 1.4 1.6 1.5 1.2 8.0 5.0 0.4 1.3 0.9 0.4 0.5 0.5 3.4 11.5 7.9Per capita fish consumption (kg/capita)2010 12.4 10.1 11.2 7.9 39.6 25.0 6.6 7.5 7.1 8.2 9.8 9.1 6.4 17.0 12.12012 9.5 7.9 8.6 8.6 33.1 22.6 4.8 6.9 6.0 6.9 7.3 7.1 5.7 15.0 11.02015 16.7 14.7 15.6 7.8 34.6 22.7 4.7 11.9 8.7 5.0 5.2 5.1 6.3 18.7 13.2Source: Authors' estimations from the LSMS-ISA 2010, 2012 and 2015 data. Note that these values are unconstrained and households can consumemore than one fish form.  "},{"text":"Table 4 Authors' estimations from theLSMS-ISA 2010, 2012 and 2015  data. The prices are real prices created using CPI from the World Bank: 2010 is the base year, CPI for 2012 and 2015 are 124.382 and 158.943 respectively. Expenditure values are per capita. Due to significant variation in prices and components of categories such as other foods and cereals and tubers, median prices were also computed and show similar trends but less dramatic changes.  Descriptive statistics for key regression variables    Descriptive statistics for key regression variables  2010   2012   2015   201020122015  North South All North South All North South All North South AllNorth South AllNorth South All Mean prices (Naira/kg)          Mean prices (Naira/kg) Price of other food 394 553 480 361 478 427 1215 1313 1270 Price of other food 394553480361478427121513131270 Price of cereals and 230 392 318 221 323 279 1621 1138 1352 Price of cereals and230392318221323279162111381352 tubers          tubers Price of pulses 210 251 232 342 440 398 435 466 452 Price of pulses210251232342440398435466452 Price of beef and 610 710 664 926 979 956 1289 1379 1339 Price of beef and610710664926979956128913791339 other meats          other meats Price of poultry 492 566 532 1070 843 940 852 788 816 Price of poultry4925665321070843940852788816 and eggs          and eggs Price of dairy 414 949 704 659 1495 1136 1347 2142 1789 Price of dairy41494970465914951136 134721421789 products          products Price of fresh fish 360 578 478 1025 820 909 763 699 727 Price of fresh fish3605784781025820909763699727 Price of frozen fish 324 452 393 561 614 591 723 901 822 Price of frozen fish 324452393561614591723901822 Price of smoked 189 260 228 302 313 308 712 435 558 Price of smoked189260228302313308712435558 fish          fish Price of dried fish 156 194 176 383 364 372 436 655 558 Price of dried fish156194176383364372436655558 Price of fish 257 371 319 568 528 545 659 673 666 Price of fish257371319568528545659673666 Other demographic characteristics        Other demographic characteristics Education (0/1) 0.6 0.7 0.7 0.6 0.7 0.7 0.6 0.7 0.7 Education (0/1)0.60.70.70.60.70.70.60.70.7 Female head of 0.1 0.2 0.1 0.1 0.2 0.2 0.1 0.3 0.2 Female head of0.10.20.10.10.20.20.10.30.2 household (0/1)          household (0/1) Own refrigerator/ 0.1 0.3 0.2 0.1 0.3 0.2 0.1 0.3 0.2 Own refrigerator/0.10.30.20.10.30.20.10.30.2 freezer          freezer Household adult 5.1 3.7 4.3 5.3 3.6 4.3 5.6 3.7 4.5 Household adult5.13.74.35.33.64.35.63.74.5 equivalent          equivalent Total expenditure 26 141 88 56 145 106 53 213 142 Total expenditure26141885614510653213142 on fish (Naira/week)          on fish (Naira/week) Total expenditure 683 902 802 1,032 1,333 1,204 1,270 1,861 1,599 Total expenditure6839028021,032 1,333 1,204 1,270 1,861 1,599 on food (Naira/          on food (Naira/ week)          week) Total expenditure 412 909 677 614 1514 1116 327 741 557 Total expenditure41290967761415141116 327741557 on non-food items          on non-food items (Naira/week)          (Naira/week) Total expenditure 1,085 1,761 1,451 1,637 2,760 2,277 1,597 2,602 2,156 Total expenditure1,085 1,761 1,451 1,637 2,760 2,277 1,597 2,602 2,156 on food and non-          on food and non- food items (Naira/          food items (Naira/ week)          week) Number of 4,395 4,851 9,246 4,517 4,767 9,284 4,527 4,597 9,124 Number of4,395 4,851 9,246 4,517 4,767 9,284 4,527 4,597 9,124 observations          observations Source:          Source:  "},{"text":"Table 5 Changes in fish consumption per capita and real prices, by fish type, time period, rural and urban locations and region Authors' estimations from the LSMS-ISA 2010, 2012 and 2015 data. The prices are real prices created using CPI from the World Bank: 2010 is the base year, CPI for 2012 and 2015 are 124.382 and 158.943 respectively.  National  39.5 -9.2 22.9 -44.1 8.4  52.09 109.16 144.74 217.05 108.78  National39.5-9.222.9-44.18.452.09109.16144.74217.05108.78  South  44.9 -12.5 58.4 -47.4 9.8  20.93 99.34 67.31 237.63 81.40  South44.9-12.558.4-47.49.820.9399.3467.31237.6381.40 2010-2015 North  34.8 -0.4 -28.6 -39.6 -2.2  111.94 123.15 276.72 179.49 156.42  2010-2015North34.8-0.4-28.6-39.6-2.2111.94123.15276.72179.49156.42  National  81.5 0.6 45.2 -28.9 19.6  −20.02 39.09 81.17 50.00 22.20  National81.50.645.2-28.919.6−20.0239.0981.1750.0022.20  South  85.2 4.6 73.4 -29.5 24.2  −14.76 46.74 38.98 79.95 27.46  South85.24.673.4-29.524.2−14.7646.7438.9879.9527.46 2012-2015 North  76.7 -9.4 -3.0 -28.0 10.8  −25.56 28.88 135.76 13.84 16.02  2012-2015North76.7-9.4-3.0-28.010.8−25.5628.88135.7613.8416.02  National  -23.2 -9.8 -15.4 -21.4 -9.4  90.17 50.38 35.09 111.36 70.85  National-23.2-9.8-15.4-21.4-9.490.1750.3835.09111.3670.85 2010-2012 North South Change in consumption per capita (%) -21.8 Fresh fish -23.7 -16.4 Frozen fish 10.0 -8.7 Smoked fish -26.4 -25.3 Dried fish -16.1 -11.6 All fish -11.7 Change in real prices (%) 41.87 184.72 Fresh fish 35.84 73.15 Frozen fish 20.38 59.79 Smoked fish 87.63 145.51 Dried fish 42.32 121.01 All fish Source: 2010-2012North SouthChange in consumption per capita (%)-21.8 Fresh fish -23.7-16.4 Frozen fish 10.0-8.7 Smoked fish -26.4-25.3 Dried fish -16.1-11.6 All fish -11.7Change in real prices (%)41.87 184.72 Fresh fish35.84 73.15 Frozen fish20.38 59.79 Smoked fish87.63 145.51 Dried fish42.32 121.01 All fishSource:  "},{"text":"Table 6 Expenditure elasticities by fish type, rural and urban locations and region  North   South   NorthSouth  Rural Urban All Rural Urban All RuralUrbanAllRuralUrbanAll Fresh fish (domestic) 1.10*** 1.09*** 1.09*** 1.17*** 1.19*** 1.18*** Fresh fish (domestic)1.10***1.09***1.09***1.17***1.19***1.18*** Frozen fish (imported) 1.07*** 1.05*** 1.05*** 0.91*** 0.91*** 0.91*** Frozen fish (imported)1.07***1.05***1.05***0.91***0.91***0.91*** Smoked fish (domestic) 1.07*** 1.09*** 1.08*** 1.04*** 1.09*** 1.06*** Smoked fish (domestic)1.07***1.09***1.08***1.04***1.09***1.06*** Dried fish (domestic) 0.95*** 0.97*** 0.97*** 0.99*** 1.05*** 1.02*** Dried fish (domestic)0.95***0.97***0.97***0.99***1.05***1.02*** Cereals and tubers 0.97*** 1.10*** 0.99*** 0.84*** 0.83*** 0.83*** Cereals and tubers0.97***1.10***0.99***0.84***0.83***0.83*** Pulses 0.81*** 0.75*** 0.81*** 0.90*** 0.84*** 0.88*** Pulses0.81***0.75***0.81***0.90***0.84***0.88*** Beef and other meats 0.99*** 0.91*** 0.97*** 1.05*** 0.99*** 1.03*** Beef and other meats0.99***0.91***0.97***1.05***0.99***1.03*** Poultry and eggs 1.42*** 1.25*** 1.35*** 1.37*** 1.24*** 1.30*** Poultry and eggs1.42***1.25***1.35***1.37***1.24***1.30*** Dairy products 1.19*** 1.08*** 1.15*** 1.15*** 1.08*** 1.12*** Dairy products1.19***1.08***1.15***1.15***1.08***1.12*** Other food 0.74*** 0.81*** 0.75*** 0.77*** 0.89*** 0.81*** Other food0.74***0.81***0.75***0.77***0.89***0.81*** Source: Authors' calculation from the EASI model estimation. ***P < 0.01, ** P < 0.05, * Source: Authors' calculation from the EASI model estimation. ***P < 0.01, ** P < 0.05, * P < 0.1.       P < 0.1.  "},{"text":"Table 7 Compensated own-and cross-price elasticities by rural and urban locations and region  "}],"sieverID":"38d9c5a8-5759-4d96-b29d-3ba99e8f2d5e","abstract":"Fish is among the most important animal-sourced foods in Africa and is crucial in combatting malnutrition. Fish demand in Africa has far outpaced supply as the import share rose from 16% in 1970 to 39% by 2017. Little is known about who is consuming the imports: rural versus urban, rich versus poor. This is the first fish consumption analysis in Africa distinguishing imported and domestic fish, and within domestic fish, fresh versus traditional-processed. We analyse three rounds of nationally representative data from Nigeria, disaggregating the richer South from the poorer North, and urban and rural. Frozen (imported) fish accounted for 34% of urban fish consumption in the North (23% for rural), compared with 67% in urban areas in the South (54% for rural). The large difference in frozen fish consumption between regions is due mainly to differences in income and refrigerator ownership. For other fish forms (fresh, dried, smoked), regional differences are far less pronounced. Income and price elasticities confirm that imported fish have become deeply incorporated into fish consumption habits. From a policy perspective, this intensifies concerns about import bills as fish demand grows. However, our elasticity results show that Nigerian consumers are keen to consume fresh fish as incomes increase, and that demand for smoked and dried fish also remains strong at high levels of income. Promoting aquaculture is a promising policy path to reduce import dependence. Domestic capture fisheries remain a major source of"}

data/part_5/009aab1d3c7765ec4f0b6a89ffaa4fc3.json

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+{"metadata":{"id":"009aab1d3c7765ec4f0b6a89ffaa4fc3","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/f9fd3c3e-ccd0-4847-b2d5-261ab16b4133/retrieve"},"pageCount":4,"title":"Highlights: Vietnam launches report on better managing risks to food safety","keywords":[],"chapters":[{"head":"","index":1,"paragraphs":[{"index":1,"size":93,"text":"The report includes an urgent call for better management of food safety issues in Vietnam and more effective communications to raise public awareness of food safety issues. The report includes an urgent call for Nguyen Viet Hung, regional representative of ILRI East and Southeast Asia presented key research findings at the meeting. The report found that the primary cause of food-borne illness in Vietnam comes from bacterial rather than chemical contamination and that both kinds of contamination can be prevented by effecting higher levels of food hygiene throughout the country's food value chains."},{"index":2,"size":50,"text":"Among the biggest challenges to ensuring safe food are the changing practices of millions of small food producers throughout the country. The authors stress that although Vietnam has a modern food safety regulatory framework in place, more results-focused and risk-based approaches are required for further improving the country's food safety."},{"index":3,"size":17,"text":"Deputy Prime Minister recommended taking up the report evidences and recommendations for food safety management in Vietnam."},{"index":4,"size":23,"text":"Vietnam Deputy Prime Minister Vu Duc Dam (standing) speaks at the launch of a World Bank food safety report (photo credit: ILRI/Chi Nguyen). "}]},{"head":"ILRI East and Southeast Asia newsletter","index":2,"paragraphs":[]},{"head":"PigRISK project team shares findings on improving food safety in pig value chain","index":3,"paragraphs":[{"index":1,"size":58,"text":"The project team reported key results of PigRISK, an ACIAR funded project, with local authorities and partners of Nghe An and Hung Yen provinces including microbiological risk assessment of Salmonella, assessment of heavy metals and antibiotics residue in carcasses and feed, risk factors of contaminations along the value chains; and overall assessment of animal (pig) production and health."},{"index":2,"size":21,"text":"Interactive session in the feedback workshop in Nghe An province, Vietnam (28 April 2017) (photo credit: Vietnam National University of Agriculture)."}]},{"head":"ILRI East and Southeast Asia staff attend Global Agenda on Sustainable Livestock (GASL) meeting","index":4,"paragraphs":[{"index":1,"size":47,"text":"Held in Addis Ababa, Ethiopia, 8-12 May 2017 the 7th Multi-Stakeholder Partnership meeting of the Global Agenda for Sustainable Livestock brought together more than 250 livestock experts from over 50 countries. ILRI staff from East and Southeast Asia showcased sustainable and innovative livestock solutions at a sharefair."}]},{"head":"ILRI Nairobi colleagues visit China","index":5,"paragraphs":[{"index":1,"size":55,"text":"In May, ILRI colleagues from Nairobi met with representatives from the China Agriculture University (Beijing), Huazhong Agriculture University (Wuhan) and Jiangxi Agriculture University (Nanchang) to discuss ongoing projects on mycoplasma and animal source food genetic resistance. They also visited the China-ILRI joint genetics and ruminant disease lab and the China Academy of Agriculture Science (CAAS)."}]},{"head":"Project on health surveillance of novel respiratory viruses kicks off in Vietnam","index":6,"paragraphs":[{"index":1,"size":64,"text":"A workshop on emerging respiratory virus threats was held 10-13 April in Hanoi marking the start of a strategic partnership between ILRI, Duke University and the National Institute of Veterinary Research of Vietnam. This project will use a new bioaerosol and pig oral secretion sampling method and molecular analyses to address the transmission of novel respiratory viruses at the human-animal interface in the country."},{"index":2,"size":27,"text":"Gregory Gray (left) from Duke University and other participants spends a half day to visit a pig farm in Bac Giang province, Vietnam (photo credit: Duke University)."}]},{"head":"Vietnam Institute of Animal Science and ILRI enhance collaboration in livestock research","index":7,"paragraphs":[{"index":1,"size":63,"text":"A meeting between the Vietnam National Institute of Animal Science (NIAS) and the ILRI on 27 June sought to enhance mutual understanding of the livestock research programs and to assess areas where the two organizations can further join up their efforts. Among other areas, the two partners will focus on enhancing smallholder indigenous pig production, animal genetics resources and animal feed and forages."}]},{"head":"Exploring ways of uplifting pig farmers' livelihoods","index":8,"paragraphs":[{"index":1,"size":47,"text":"A stakeholder workshop that is part of a nine-month scoping study (October 2016 -June 2017) for the 'Assessing the competitiveness of smallholder pig farming in northwest Vietnam' project, was held on 30 May. The meeting was cohosted by ILRI and the Vietnam National University of Agriculture (VNUA)."},{"index":2,"size":11,"text":"Maize farmers in the northwest of Vietnam (photo credit: ILRI/Jo Cadilhon)."}]}],"figures":[{"text":" We hope to share a newsletter with you every six months to give you, our donors, partners and colleagues timely updates on ILRI's ongoing research activities, news and events in the region. I hope you will enjoy reading it and thank you for supporting ILRI East and Southeast Asia over the past years and in future.Learn more about our work at www.asia.ilri.org. If you have any feedback please contact [email protected]. Hung Nguyen-Viet ILRI regional representative for East and Southeast Asia Project news Phase 2 of CGIAR Research Programs (CRP) launched ILRI leads the Livestock Agri-Food systems CGIAR Research Program (the Livestock CRP) 2017-2022 that focuses on increasing the productivity and resilience of small-scale livestock systems in sustainable ways, making meat, milk and egg more available and affordable to poor consumers across the developing world. ILRI participates in the CGIAR Research Program on Agriculture for Nutrition and Health (A4NH CRP) with a focus on maximizing health and nutrition benefits through agricultural practices, interventions and policies while reducing health risks. "},{"text":"  "}],"sieverID":"e3275fae-1b17-44bc-bb7e-fa8f00c46238","abstract":""}

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+{"metadata":{"id":"00c18d844bd44622bcb6baecf0d06465","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/db278473-e6d8-4e1c-bdce-5154d40ffa0a/retrieve"},"pageCount":12,"title":"Biomass production and nutrient use efficiency in white Guinea yam (Dioscorea rotundata Poir.) genotypes grown under contrasting soil mineral nutrient availability","keywords":["nutrient recovery efficiency","nutrient uptake","fertilizer response","low soil fertility","West Africa","genotypic variation"],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":158,"text":"Yam (Dioscorea spp.) is a multispecies tuberous crop with immense potential for improving food security, especially with respect to the food and cultural systems of West Africa (Asiedu and Sartie, 2010); about 93% (66.8 million tons) of the global yam production occurs in this region (FAOSTAT, 2021). Among the species of Dioscorea, which vary in origin and distribution depending on tropical, subtropical, and temperate regions (Darkwa et al., 2020), white Guinea yam (Dioscorea rotundata) is predominantly cultivated and consumed in West Africa (Asfaw et al., 2020). The cultivation of white Guinea yam has steadily increased over the past few decades in West Africa, from 14.5 million tons in 1988 to 66.8 million tons in 2018 (FAOSTAT, 2021). The substantial increase in yam production has mainly been attributed to the expansion of the cultivation area rather than the productivity increase per unit area (FAOSTAT, 2021). The increase in yam productivity was marginal compared to that of potatoes (FAOSTAT, 2021)."},{"index":2,"size":159,"text":"In West Africa, yams are usually cultivated without chemical or organic fertilizers, often using landraces (Degras, 1993;Scott et al., 2000;Maliki et al., 2012). Traditionally, yam is the first crop after a long-term fallow because it requires fertile soils for optimum growth and yielding potential (Carsky et al., 2010). Diby et al. (2011a) reported that tuber yield was higher in fertile forest soils than in low-fertile savannah sites. It was suggested that soil fertility is crucial in yam cultivation. Similarly, Kassi et al. (2017) reported that soil organic carbon stocks contributed to the increased tuber yield, as D. rotundata crops harvested after Chromoleana odorata (green fertilizer) fallows produced the maximum yield. Consequently, yam producers perceive the decline in soil fertility as a critical constraint for yam production in areas under intensive cultivation (Lebot, 2019). Despite this, fertilizer use in Sub-Saharan Africa is generally low, partly because farmers do not recognize adequate profit opportunities with acceptable risks (Kaizzi et al., 2017)."},{"index":3,"size":444,"text":"The impact of fertilizer application on yam productivity remains unresolved due to several conflicting reports. While some studies have reported positive effects (Irving, 1956;Kpeglo et al., 1981;Lyonga, 1981;Diby et al., 2009;Diby et al., 2011c), others have reported no changes in that productivity (Kang and Wilson, 1981;Carsky et al., 2010). These discrepancies on the impact of fertilizer application on yam growth and yield could be attributed to the nutrient status of the experimental plots, as response to fertilization is affected by the soil fertility of the cultivation area. In the nutrient-poor savanna soils of Africa, the impact of fertilizer input on yam crops was positive, while it was significantly lower in the relatively fertile forest soils (Lugo et al., 1993;Diby et al., 2009;Diby et al., 2011c). Nevertheless, considering soil nutrient status, suitable fertilizer input can benefit crop yield. For example, several studies have reported that appropriate fertilizer application positively affected yam productivity in Sub-Saharan Africa (Lugo et al., 1993;Diby et al., 2009;Diby et al., 2011c;Cornet et al., 2022). Therefore, various soil management techniques are currently being developed, tested, and implemented to improve crop productivity in lowinput farming systems in Africa (Kihara et al., 2020). Matsumoto et al. (2021a) reported differential responses of white Guinea yam genotypes to available soil nutrients and identified genotypes with low soil nutrient tolerance and a high response to applied fertilizer. One of the factors for the difference in fertilizer response and tolerance to low-fertility soil among varieties is the difference in nutrient use efficiency (El-Sharkawy et al., 1998;Martı́and Mills, 2002;Tamele et al., 2020). A better understanding of the physiological mechanism of fertilizer response and tolerance to low soil fertility is important for selecting and developing varieties suitable for cultivation under low fertilizer input and improving fertilizer utilization. However, there is little research on this aspect. Interspecific variation in nutrient uptake and nutrient use efficiency has been reported in D. alata and D. rotundata (Diby et al., 2011b;Hgaza et al., 2019); however, whether varietal differences exist in terms of nutrient uptake and nutrient use efficiency remains unknown. Although tuber dry matter content is a crucial characteristics highly valued by traders and consumers (Chukwu et al., 2007;Asiedu and Sartie, 2010), limited information is available regarding the effect of fertilizer on the percent dry matter content and its relationship with a fertilizer response of genotype. This study aimed to determine the biomass production and tuber dry matter content of different genotypes of white Guinea yam and their response to fertilizer input in terms of nutrient uptake and nutrient use efficiency. Our results would contribute to the development of cultivation techniques and varieties of white Guinea yam for improved biomass production and fertilizer response."}]},{"head":"Materials and methods","index":2,"paragraphs":[]},{"head":"Site and soil properties","index":3,"paragraphs":[{"index":1,"size":269,"text":"Field experiments were conducted during the 2017 and 2018 cropping seasons (April to December) in the experimental field with low soil fertility at the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria (7°29′ N, 3°54′ E). The low soil fertility field was induced artificially by successive planting of cassava, maize, and sorghum, without fertilizer input in IITA, Ibadan (Matsumoto et al., 2021a). To assess the soil properties in the experimental field, soil samples were collected before conducting the experiment at depths of 0-20 cm from 30 randomly selected plots. Soil pH was determined by initially suspending the soil in water (1:2.5 soil:water ratio). Exchangeable Ca 2+ , Mg 2+ , K + , and available P were extracted according to the Mehlich-3 procedure (Mehlich, 1984). Cations were determined using an atomic absorption spectrophotometer (Accusys 211 Atomic Spectrophotometer, Buck Scientific, Connecticut, USA). P was assayed by colorimetric determination using a Genesys 10S UV-Vis spectrophotometer (Thermo Scientific, Waltham, MA, USA). Organic carbon was determined by chromic acid digestion with a spectrophotometric procedure using Genesys 10S UV-Vis spectrophotometer (Heanes, 1984). Total N was determined using the Kjeldahl method for digestion and colorimetric determination using a Technicon AAII Autoanalyzer (Seal Analytical, Wisconsin, USA) (Bremner and Mulvaney, 1982). Weather data for the experimental period were assessed using the data obtained from the Geographical Information System (GIS) unit of the IITA. Figure 1 presents the meteorological conditions during the growth period, from planting to harvest (180 days after planting) for the trials. The total precipitation and average minimum/maximum temperatures for this period were 1410.5 mm, 22.7/30.7°C in 2017, and 1526.5 mm, 22.7/30.5°C in 2018, respectively."}]},{"head":"Plant materials and trial management","index":4,"paragraphs":[{"index":1,"size":329,"text":"Field experiments were conducted using six genotypes of white Guinea yam. TDr1649 and TDr2484 were used in the 2017 field trial, while TDr1499, TDr1649, TDr1899, TDr2029, TDr2484, and TDr2948 were used for the 2018 trial. These genotypes are part of the mini-core collection of white Guinea yam (Pachakkil et al., 2021) maintained at the IITA. Those genotypes were selected based on the tuber yield and leaf density difference (Table 1). All the genotypes were multiplied under uniform conditions in the field at IITA headquarters during the 2016 and 2017 cropping seasons to generate high-quality planting material. Plants with symptoms of viral diseases, such as yam mosaic virus, were removed from the field during the growing period. Visually assessed clean tubers with no signs of rot or pests were used as seed tuber materials for the trials. Tubers weighing approximately 1-2 kg were cut horizontally to remove the head and tail components. The tuber centre component was cut into 50 ± 10 g pieces to obtain uniform material for planting (yam setts). Yam setts were treated with a mixture of 70 g mancozeb (fungicide) and 75 mL chlorpyrifos (insecticide) dissolved in a 10 L volume of tap water for 5 min, and the setts were dried for 20 h in the shade before planting for pre-sprouting. The yam setts were planted in plastic pots (12 cm diameter × 10 cm height) filled with topsoil on 4 May 2017 and 2 May 2018. Because variation in the sprout emergence time was the main cause of variation in shoot biomass size and tuber yield within plots in yam trials (Cornet et al., 2014), seedlings that germinated simultaneously (within 14 days difference in sprout emergence date) were selected and used as experimental material in this study. Plants with uniform sprouts were transplanted into the field in a 0.5 m × 1.0 m arrangement to give 20,000 plants per hectare. A 2 m stake was provided for each plant at 30 days after planting."},{"index":2,"size":209,"text":"The plants were transplanted into the field on a ridge approximately 40 cm high and 60 cm wide. The field experiment was laid out in a split-plot randomized block design with four replications. The main plot comprised two levels of fertilizer treatments, non-fertilized (Non-F) and fertilized (+F), while the subplot consisted of genotypes. The size of the subplot was 15 m 2 . The fertilized plot (+F) received 90, 50, and 75 kg nitrogen (N), phosphorus (P), and potassium (K) per hectare, respectively. These were based on the recommendations of fertilizer amounts for low soil fertility conditions (Chude et al., 2012). In the 2017 trial, the total number of plots was 16 (two soil nutrient fertility levels × 2 genotypes × 4 replications) (detailed field design presented in Supplementary Material 1). In the 2018 trial, the total number of plots was 48 (two soil nutrient fertility levels × 6 genotypes × 4 replications) (detailed field design presented in Supplementary Material 2). Fertilizer was applied 14 days after transplanting using the side dressing method in both the 2017 and 2018 trials. To avoid fertilizer contamination, there was a 20 m distance between the non-fertilized and fertilized plots. Weeds were manually removed whenever present to maintain weed-free plots throughout the experiment."}]},{"head":"Evaluation of shoot and tuber productivity under different soil fertility","index":5,"paragraphs":[{"index":1,"size":187,"text":"At 180 days after planting, three plants from each subplot were selected randomly, excluding border plants in both the 2017 and 2018 trials, to analyse the effect of soil NPK conditions on shoot and tuber production. The total number of harvested samples was 12 (three plants × 4 replicates) for each genotype in both the 2017 and 2018 trials. Harvested plants were separated into leaves, stems, and tubers. Plant parts were rinsed with tap water. The total weight of fresh tuber was recorded. All leaves, stems, and a weighed sample of the tubers were dried in an oven at 80°C for three days. Dried samples were weighed to determine the total leaf and stem dry weights. The percent dry matter content of the tuber was calculated from the dry weight of the tuber sample in relation to its fresh weight. The total tuber dry weight was determined by multiplying the total fresh tuber weight by the percent dry matter content of the tuber. The percentage difference in dry shoot weight and dry tuber weight due to the difference in NPK conditions was calculated using the following formula:"},{"index":2,"size":63,"text":"where xlf and xhf are the mean trait values of a given genotype in non-F and +F environments, respectively. Soil fertility susceptibility index (SFSI) was calculated using the following formula (Fischer and Maurer, 1978): TABLE 1 Variation in tuber yield (g plant -1 ) and leaf density in the white Guinea yam (Dioscorea rotundata) mini-core collection and the distribution of the used genotypes."}]},{"head":"Leaf density","index":6,"paragraphs":[{"index":1,"size":7,"text":"Fresh tuber yield (g plant -1 )"},{"index":2,"size":6,"text":"Mini-core collection (n=102) Mean 1.9 1234.9"},{"index":3,"size":4,"text":"Standard deviation 0.5 493.0"},{"index":4,"size":6,"text":"Coefficient of variance (%) 26.5 39.9"},{"index":5,"size":5,"text":"Genotype selected from mini-core collection "}]},{"head":"Soil fertility susceptibility index SFSI","index":7,"paragraphs":[{"index":1,"size":49,"text":"where xlf and xhf are the mean trait values of a given genotype under the non-F and +F conditions, respectively. Ylf and Yhf are the mean trait values of all genotypes under the non-F and +F conditions, respectively, and 1 − Ylf =Yhf is the soil fertility intensity index."}]},{"head":"Nutrient uptake and use efficiency","index":8,"paragraphs":[{"index":1,"size":164,"text":"Dried leaf and tuber samples from the 2018 trial were used to determine the nutrient content in plants. Dried leaf and tuber tissues were ground separately in a Wiley mill and passed through a ≤1 mm mesh screen. As per pre-standard methods, an NC analyser (Sumigraph NCH-22, Sumika Chemical Analysis Service Ltd., Japan) was used to determine the nitrogen content in leaf and tuber tissues (Anderson and Ingram, 1993). Ground dry plant samples (100 mg) samples were pyrolyzed with 5 mL of nitric acid, and the filtered samples were analysed to determine the P and K content in leaves and tubers using inductively coupled plasma optical emission spectrometry (ICP-OES; iCAP 6000 Series, Thermo Fisher Scientific, MA, United States). The N, P, and K uptakes by the plants was determined by adding the product of the dry weight of each plant part with the elemental concentration of each plant part. In the current study, the following fertilizer efficiency parameters were used (Craswell and Godwin, 1984):"},{"index":2,"size":77,"text":"where U tF is total nutrient uptake in plants under +F condition. U tN is total nutrient uptake in plants under non-F condition; F is nutrient supply (g/plant); T wF is dry tuber weight under +F condition; T wN is dry tuber weight under non-F condition. Apparent nutrient recovery efficiency was calculated as the efficiency of nutrient capture from soil and/or fertilizer input. Physiological efficiency was calculated as the efficiency of capturing plant nutrients in tuber yield."}]},{"head":"Statistical analysis","index":9,"paragraphs":[{"index":1,"size":107,"text":"Data were analysed using the linear mixed model in the lme4 package (Bates, 2010) in the R environment version 4.0.3 for statistical computing (R Core Team, 2018). To determine a significant difference between the mean values of traits obtained from non-F and +F conditions for each genotype, ttest was performed using the R package ggpubr (Kassambara, 2020). Multiple comparison analysis using Tukey's HSD test was performed to detect statistically significant differences in the obtained traits among varieties using the agricolae package (de Mendiburu, 2021). Correlation analysis among the tested parameters was determined using Pearson correlation coefficients. In all calculations, statistical significance was set at p < 0.05."}]},{"head":"Results","index":10,"paragraphs":[]},{"head":"Soil properties at the experimental sites in 2017 and 2018","index":11,"paragraphs":[{"index":1,"size":110,"text":"The soil chemical properties of the experimental fields are presented in Table 2. Soil pH was 5.69 in 2017 and 5.98 in 2018. The organic carbon content was 0.24% and 0.39% in 2017 and 2018, respectively. No total N content change was observed between the 2017 and 2018 trials (0.04%). The available P content in 2017 (1.18 mg kg −1 ) was lower than that in the 2018 trial (2.21 mg kg −1 ). Exchangeable Ca and Mg in 2017 were higher than in those in the 2018 trial. Exchangeable K was 0.20 cmol[+] kg −1 in 2017, which was higher than that in 2018 (0.08 cmol[+] kg −1 )."}]},{"head":"Effect of fertilizer treatment on shoot and tuber production","index":12,"paragraphs":[{"index":1,"size":52,"text":"The effect of fertilizer application on dry tuber weight in the 2017 trial is presented in Figure 2. Fertilizer application increased the dry tuber weight of TDr1649 plants in 2017 experimental trial. However, there was no significant difference in the dry tuber weight of TDr2484 grown between the non-F and +F conditions."},{"index":2,"size":130,"text":"In the 2018 trial, genotype and fertilizer application interactions were significant for the dry shoot weight (Table 3 and Supplementary Material 3). Although the difference was not statistically significant, the shoot dry weight of TDr1499 was the highest among the tested genotypes. However, there was a significant difference in the dry shoot weight among the tested genotypes under +F condition, and the highest dry shoot weight was observed in TDr1499 (Table 3). The percent difference in dry shoot weight due to non-fertilizer application ranged from 14.3% (TDr2029) to 41.1% (TDr1499). SFSI for dry shoot weight ranged from 0.46 to 1.31. Among the genotypes tested under the two different fertilizer treatments, TDr1499 and TDr1649 produced significantly higher dry shoot weights in the +F condition than in the non-F state (Table 3)."},{"index":3,"size":29,"text":"Although dry tuber weight in non-F conditions ranged from 155.8 g plant -1 (TDr1899) to 260.7 g plant -1 (TDr2948), no significant difference was observed among the tested genotypes."},{"index":4,"size":114,"text":"Under the +F conditions, TDr1499 had the highest dry tuber weight (489.9 g plant -1 ), while TDr1899 had the lowest dry tuber weight (239.0 g plant -1 ) among the tested genotypes. Genotype TDr1499 showed a 51.9% reduction in dry tuber weight in the non-F condition compared to that in the +F condition. The lowest reduction in dry tuber weight of 17.2% was recorded for the genotype TDr2029. The SFSI for dry tuber weight ranged from 0.48 to 1.44. Among the genotypes tested under the two different fertilizer conditions, TDr1499 and TDr1649 produced significantly higher dry tuber weights in the +F condition than in the non-F state (Table 3 and Supplementary Material 3)."},{"index":5,"size":94,"text":"Genotype was significant for the percent dry matter content of the tuber (Table 4). The effect of genotype and fertilizer treatment interaction on tuber percent dry matter content was not observed (Table 4). Fertilizer application did not increase the percent dry matter content of the tuber in all genotypes. Under the non-F conditions, TDr1649 (31.2%), TDr1899 (32.5%), and TDr2029 (31.0%) had a significantly higher dry matter content of tuber than TDr2428 (25.5%). TDr1649 and TDr1899 showed higher percent dry matter content of tuber than TDr1499, TDr2029, TDr2484, and TDr2048 under +F conditions (Table 4)."}]},{"head":"Nutrient uptake","index":13,"paragraphs":[{"index":1,"size":74,"text":"Fertilizer treatment increased the N and K uptake of TDr1499 and TDr1649. The interaction between genotype and Effect of fertilizer treatment on dry tuber weight (g plant -1 ) in two white Guinea yam (Dioscorea rotundata) genotypes in 2017. * represents a significant difference at p< 0.05 calculated by t-test between non-fertilized (non-F) and fertilized (+F) conditions. ns; significant difference at p < 0.05 calculated by t-test between non-fertilized (non-F) and fertilized (+F) conditions."},{"index":2,"size":35,"text":"fertilizer treatment was significant for P uptake (p< 0.001) (Table 5). The difference in genotype did not affect P uptake under non-F conditions. However, P uptake varied with genotype differences under +F conditions (Table 5)."},{"index":3,"size":92,"text":"The genotypes TDr1499, TDr1649, and TDr2948 accumulated higher P than TDr1899, TDr2029, and TDr2484 under +F conditions. N uptake (g plant -1 ) by the genotypes was 7.0 to 9.7 times higher than that of P under non-F conditions, whereas N uptake was 5.8 to 7.7 times higher than that of P under the +F conditions. Similarly, the K uptake (g plant -1 ) was 5.7 to 9.5 times higher in the non-F condition compared to that of P, which was 4.7 to 6.3 times higher in the +F condition (Table 5)."}]},{"head":"Nutrient use efficiency parameters","index":14,"paragraphs":[{"index":1,"size":134,"text":"Apparent nutrient recovery efficiency and physiological efficiency that already include the response to soil nutrient level were estimated considering varietal differences. Varietal difference in the apparent nutrient recovery efficiency was observed among the tested genotypes (Figure 3). Nitrogen apparent nutrient recovery efficiency ranged from 14.7 (TDr2484) to 47.7% (TDr1499). Nitrogen apparent nutrient recovery efficiency of TDr1499 was significantly higher than that of TDr1899, TDr2029, TDr2484, and TDr2948. Similar results were observed with apparent nutrient recovery efficiency for phosphorus and potassium.  Nitrogen physiological efficiency did not differ among the tested genotypes (p = 0.141). Similar results were observed in potassium physiological efficiency (p = 0.780). Phosphorus physiological efficiency showed high values, which ranging from 462.2 to 690.7 g plant -1 ; however, varietal difference was not observed significantly among the tested genotypes (p = 0.208)."}]},{"head":"Correlation analysis among tested parameters (dry tuber weight and nutrient uptake)","index":15,"paragraphs":[{"index":1,"size":63,"text":"The correlation between dry tuber weight and nutrient uptake is presented in Figure 4. The dry tuber weight and N uptake were strongly and positively correlated in the non-F and the +F groups, respectively: r = 0.83 (p = 0.04) and 0.98 (p = 0.00). A similar trend was obtained between the relationship between the P and K uptake and dry tuber weight."}]},{"head":"Discussion","index":16,"paragraphs":[{"index":1,"size":194,"text":"Soil fertility in Nigeria has been categorized into five levels (Chude et al., 2012). Although the critical soil nutrient levels for yam cultivation have not yet been established in West Africa, yam cultivation in soils containing< 0.1% N,< 10 mg kg −1 available P, and 0.15 Cmol[+] kg −1 of exchangeable K requires external fertilizer inputs (Carsky et al., 2010). According to soil analysis performed before the experiments and previous reports by Chude et al. (2012) and Carsky et al. (2010), our experimental field in both the 2017 and 2018 trials had low soil NPK availability and could be inferred as infertile to sustain normal yam plant growth and optimize the yield Nitrogen uptake(g plant -1 ) Phosphorus uptake(g plant -1 ) Potassium uptake(g plant  (Table 2). It is, therefore, more likely to observe the positive effect of added fertilizer input in the growth and yield of yam under low soil fertility conditions. However, the fertilizer response to biomass production and tuber yield varied among the genotypes studied, suggesting that the response of soil NPK levels or fertilizer input in white Guinea yam could be genotype-specific (Figure 2, Table 3, and Supplementary Material 3)."},{"index":2,"size":125,"text":"Our results indicated differences among the white Guinea yam genotypes in the nutrient uptake (Table 5). The studied white Guinea yam genotypes absorbed N and K as primary nutrients during growth and showed an increase in tuber yield with an increase in nutrient uptake (Figure 4); this is also consistent with the results presented by Diby et al. (2011b) and Irizarry et al. (1995). The genotypes TDr1499 and TDr1649, with high response to fertilizer input, showed higher nutrient uptake than the other genotypes. In other words, the genotypes depended on the high soil fertility to exhibit high productivity. Thus, the genotypes TDr1499 and TDr1649, of Togolese origin, were responsive to fertilizer input and could be suitable candidates to maximize productivity under a high-input cultivation system."},{"index":3,"size":106,"text":"The effect of fertilizer application on yam tuber yield is variable and sometimes conflicting (Irving, 1956;Kang and Wilson, 1981;Kpeglo et al., 1981;Lyonga, 1981;Diby et al., 2009;Carsky et al., 2010;Diby et al., 2011c). Diby et al. (2009) and Carsky et al. (2010) discussed differences in the reports on the beneficial impact of fertilizer application on yam growth and yield could be attributed to the nutrient status of the cultivation area. Dare et al., (2010Dare et al., ( , 2013Dare et al., ( , 2014) ) reported the potential influence of arbuscular mycorrhizal fungi that occur naturally in the yam growing areas in some of the observed variability."},{"index":4,"size":106,"text":"This study revealed, for the first time, that there are varietal differences in the apparent recovery efficiency of white Guinea yam (Figure 3). Hgaza et al. (2019) also reported interspecific variations between the D. alata and D. rotundata genotypes in nitrogen uptake and apparent nitrogen recovery efficiency (total nitrogen uptake from fertilizer/total nitrogen rate applied × 100). Nutrient uptake in studied white Guinea yam genotypes increased with an apparent increase in nutrient recovery efficiency under +F conditions but not in physiological nutrient efficiency. This suggests the difference in nutrient recovery efficiency affected the fertilizer response or susceptibility to soil NPK condition in the white Guinea yam."},{"index":5,"size":198,"text":"Species or cultivars with a high growth rate usually respond more favorably to fertilizer application than those with low growth rates (Mengel, 1983). Our result corroborates the findings of Mengel (1983). The fertilizer-responsive genotypes, TDr1499 and TDr1649, produced more vigorous shoot biomass than the other genotypes, which did not respond to applied fertilizer (Table 3). Therefore, the size of shoot biomass or leaf density as a morphological traits, associated with plant demand for nutrients, might be a factor contributing to varietal difference in fertilizer or soil NPK responsiveness among the tested genotypes. Iseki et al. (2022) reported a positive correlation between shoot biomass and tuber yield and pointed out that shoot growth is important for final tuber yield. Therefore, increasing shoot growth by fertilizer application may improve tuber yield in white Guinea yam genotypes with a high response to fertilizer input. TDr1499 and TDr1649 were responsive to the fertilizer input with increased nutrient uptake, indicating that they can exhibit high productivity under high nutrient input. Productivity improvement in white Guinea yam in West Africa could be expected by combining appropriate fertilization techniques (Cornet et al., 2022) and genotypes that respond well to fertilizer under a high input system."},{"index":6,"size":131,"text":"In contrast to TDr1499 and TDr1649, the genotypes TDr1899, TDr2029, TDr2484, and TDr2948 showed SFSI<1 and a reduction in shoot and tuber production from +F to non-F conditions (Table 3). These results indicate that these genotypes were tolerant to low soil NPK conditions or less susceptible to soil NPK status. Among the genotypes, TDr2029 was the least sensitive to soil NPK conditions. The genotypes with a high and stable yield of marketable tubers are selection targets for breeding programs for yam (Otoo et al., 2006;Asiedu and Sartie, 2010). Hence, TDr2029, of Nigerian origin, could be a potential parent to generate varieties with a stable yield and less sensitive to soil fertility conditions or the best candidate for immediate release as a new variety for the low input system in West Africa."},{"index":7,"size":160,"text":"Likewise the tuber yield, the percent dry matter content of the tuber also varied among genotypes in white Guinea yam (Table 4). This result was consistent with Matsumoto et al. (2021b), who investigated the genotype × environment interaction on tuber quality traits on white Guinea yam. However, the application of NPK fertilizer did not affect the percent tuber dry matter content, regardless of the genotype differences in susceptibility to soil NPK conditions. This means the application of NPK increased fresh tuber yield and hence dry tuber yield without significantly influencing percent dry matter content. This is helpful to farmers because it implies that yield can be increased without reducing tuber quality by using a balanced application of soil nutrients. This is contrary to the fears expressed by some farmers that fertilizer application will reduce yam tuber quality. Our result is in line with Gizachew et al. (2022), who reported that neither organic nor mineral fertilizer application affects cassava tuber quality."},{"index":8,"size":191,"text":"In addition to fertilizer application and soil amendments, the use of cultivars with high nutrient use efficiency and responsiveness to external nutrient supply would improve productivity in the yam cultivation system under low soil fertility conditions (Asiedu and Sartie, 2010). Breeding efforts should, therefore, focus on attributes such as high yield and high nutrient use efficiency in yams. Our results highlight the genotypic variations in white Guinea yam with respect to susceptibility to soil NPK availability, nutrient uptake, and nutrient use efficiency. The wide diversity of fertilizer response or non-susceptibility to soil NPK status might be expected in the mini-core collection of white Guinea yam, confirming the wide range of tuber yield and leaf density (Pachakkil et al., 2021). The contrasting genotypes with unique characteristics of high and less susceptibility to soil nutrient conditions provide a good opportunity for further studies to elucidate the genetic and physiological bases for and the influence of genotype × environment (including soil microbes) interactions on the differential response to these major nutrients in yam. Our findings also serve as reference for breeding new and improved varieties for low and high-input cultivation systems in West Africa."}]}],"figures":[{"text":"FIGURE 1 FIGURE 1Air temperature and precipitation during the growth periods at the International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria, in 2017 and 2018. Air temperature includes a black line and grey line representing maximum and minimum air temperature averages, respectively. For precipitation, data are cumulative values for every month. "},{"text":"FIGURE 3 FIGURE 3 Apparent nutrient recovery efficiency of six genotypes in white Guinea yam (Dioscorea rotundata). Nitrogen (left box), phosphorus (centre box), and potassium physiological efficiency (right box) are shown. All values are expressed as means. Different alphabet indicates statistical significance (P< 0.05). Bars represent presents a standard error. "},{"text":"FIGURE 4 FIGURE 4Correlation between dry tuber weight and nutrient uptake under non-fertilized and fertilized conditions in six white Guinea yam (Dioscorea rotundata) genotypes. Relationships for nitrogen (left box), phosphorus (centre box), and potassium (right box) uptake are shown. "},{"text":"TABLE 2 Soil chemical properties of the experimental site at IITA Ibadan, Nigeria. Soil chemical properties 2017  2018  Soil chemical properties20172018  Mean SD (n=6) Mean SD (n=6) MeanSD (n=6)MeanSD (n=6) pH 5.69 0.21 5.98 0.04 pH5.690.215.980.04 Organic carbon (%) 0.24 0.04 0.39 0.11 Organic carbon (%)0.240.040.390.11 Total nitrogen (%) 0.04 0.02 0.04 0.00 Total nitrogen (%)0.040.020.040.00 Available phosphorus (mg kg -1 ) 1.18 0.82 2.21 0.91 Available phosphorus (mg kg -1 )1.180.822.210.91 Calcium (cmol[+] kg -1 ) 2.90 0.46 0.75 0.14 Calcium (cmol[+] kg -1 )2.900.460.750.14 Magnesium (cmol[+] kg -1 ) 0.69 0.11 0.23 0.11 Magnesium (cmol[+] kg -1 )0.690.110.230.11 Potassium (cmol[+] kg -1 ) 0.20 0.07 0.08 0.01 Potassium (cmol[+] kg -1 )0.200.070.080.01 SD, standard deviation.     SD, standard deviation.  "},{"text":"TABLE 3 Effect of genotype and fertilizer treatment on the shoot and tuber weight in six white Guinea yam (Dioscorea rotundata) genotypes In the 2018 trial.Non-F, non-fertilized; +F, fertilized. PD, percent difference due to contrasting soil mineral nutrient conditions. SFSI, soil fertility susceptibility index. Different letters in the same column indicate significant difference (p<0.05) as determined by Tukey's HSD test. ***p < 0.001, *p < 0.05, analysed by t-test between -F and +F conditions.   Dry shoot weight (g plant -1 )   Dry tuber weight (g plant -1 )  Dry shoot weight (g plant -1 )Dry tuber weight (g plant -1 )  Non-F +F Mean PD SFSI p-value Non-F +F Mean PD SFSI p-value Non-F+FMean PD SFSI p-value Non-F+FMean PD SFSI p-value TDr1499 76.9a 130.5a 103.7a 41.1 1.31 0.00 235.5a 489.9a 362.7a 51.9 1.44 0.00 TDr149976.9a130.5a103.7a41.11.310.00235.5a489.9a362.7a51.91.440.00 TDr1649 64.5a 108.1ab 86.3ab 40.3 1.28 0.01 235.8a 433.3ab 334.5ab 45.6 1.26 0.01 TDr164964.5a108.1ab86.3ab40.31.280.01235.8a433.3ab334.5ab45.61.260.01 TDr1899 45.3a 60.7c 53.0bc 25.5 0.81 0.29 155.8a 239.0d 197.4b 34.8 0.96 0.10 TDr189945.3a60.7c53.0bc25.50.810.29155.8a239.0d197.4b34.80.960.10 TDr2029 48.3a 56.4c 52.4c 14.3 0.46 0.60 260.0a 313.8bcd 286.9ab 17.2 0.48 0.41 TDr202948.3a56.4c52.4c14.30.460.60260.0a313.8bcd 286.9ab17.20.480.41 TDr2484 48.6a 63.7c 56.2bc 23.7 0.75 0.12 200.0a 262.5cd 231.2ab 23.8 0.66 0.09 TDr248448.6a63.7c56.2bc23.70.750.12200.0a262.5cd231.2ab23.80.660.09 TDr2948 57.8a 78.3bc 68.1bc 26.2 0.83 0.14 260.7a 370.3bcd 315.5ab 29.6 0.82 0.08 TDr294857.8a78.3bc68.1bc26.20.830.14260.7a370.3bcd 315.5ab29.60.820.08 Mean 56.9 83.0     224.6  351.5    Mean56.983.0224.6351.5 Type II Wald chi-square tests (Chisq)             Type II Wald chi-square tests (Chisq) Genotype (G) 30.7***      69.0***      Genotype (G)30.7***69.0*** Treatment (T) 66.2***      23.6***      Treatment (T)66.2***23.6*** Interaction G × T 12.4*      13.0*      Interaction G × T12.4*13.0*  "},{"text":"TABLE 4 Effect of genotype and fertilizer treatment on percent dry matter content of tuber (%) in six white Guinea yam (Dioscorea rotundata) genotypes in the 2018 trial. Non-F +F p-value Non-F+Fp-value  "},{"text":"TABLE 5 Effect of genotype and fertilizer treatment on the uptake of nitrogen, phosphorus, and potassium in six white Guinea yam (Dioscorea rotundata) genotypes in the 2018  "},{"text":" Non-F, non-fertilized; +F, fertilized. Different letters in the same column indicate significant differences difference (p<0.05) as determined by Tukey's HSD test. ***p < 0.001, analysed by t-test between -F and +F conditions.          -1 ) -1 )  Non-F +F p-value Non-F +F p-value Non-F +F p-value Non-F+Fp-valueNon-F+Fp-valueNon-F+Fp-value TDr1499 2.6a 4.7a 0.00 0.3a 0.8a 0.00 2.2a 3.9a 0.01 TDr14992.6a4.7a0.000.3a0.8a0.002.2a3.9a0.01 TDr1649 2.2a 3.7ab 0.01 0.3a 0.6a 0.00 2.0a 2.8b 0.10 TDr16492.2a3.7ab0.010.3a0.6a0.002.0a2.8b0.10 TDr1899 1.5a 2.3c 0.11 0.2a 0.3b 0.14 1.4a 1.9b 0.16 TDr18991.5a2.3c0.110.2a0.3b0.141.4a1.9b0.16 TDr2029 2.1a 2.9bc 0.26 0.3a 0.4b 0.12 1.7a 2.1b 0.48 TDr20292.1a2.9bc0.260.3a0.4b0.121.7a2.1b0.48 TDr2484 1.9a 2.6bc 0.07 0.2a 0.4b 0.07 1.7a 2.1b 0.29 TDr24841.9a2.6bc0.070.2a0.4b0.071.7a2.1b0.29 TDr2948 2.9a 3.5ab 0.43 0.3a 0.6a 0.01 2.1a 2.9ab 0.13 TDr29482.9a3.5ab0.430.3a0.6a0.012.1a2.9ab0.13 Mean 2.2 3.3  0.3 0.5  1.9  2.6 Mean2.23.30.30.51.92.6 Type II Wald chi-square tests (Chisq)          Type II Wald chi-square tests (Chisq) Genotype (G) 38.4***   66.8***   33.9***   Genotype (G)38.4***66.8***33.9*** Treatment (T) 31.2***   87.8***   17.1***   Treatment (T)31.2***87.8***17.1*** Interaction G x T 8.6   28.4***   9.9   Interaction G x T8.628.4***9.9  "}],"sieverID":"70655285-6f18-4ec7-b5ec-5a8af2704a28","abstract":"Yam (Dioscorea spp.) is of great importance to food security, especially in West Africa. However, the loss of soil fertility due to dwindling fallow lands with indigenous nutrient supply poses a challenge for yam cultivation. This study aimed to determine shoot and tuber biomass and nutrient use efficiency of white Guinea yam (Dioscorea rotundata) grown under low-and high-NPK conditions. Six white Guinea yam genotypes were used in field experiments conducted at Ibadan, Nigeria. Experiments were conducted with low soil NPK conditions with zero fertilizer input and high soil NPK conditions with mineral fertilizer input. Differences in response to soil NPK conditions, nutrient uptake, and nutrient use efficiency (apparent nutrient recovery efficiency) were observed among the tested genotypes. The genotypes TDr1499 and TDr1649, with high soil fertility susceptibility index (SFSI>1) and an increase in shoot and tuber biomass with fertilizer input, were recognized as susceptible to soil NPK conditions. There was a marked difference in apparent nutrient recovery efficiency; however, there was no varietal difference in physiological efficiency. Differences in apparent nutrient recovery efficiency among genotypes affected the fertilizer response (or susceptibility to soil NPK conditions) and the nutrient uptake. In contrast, the genotype TDr2029, with SFSI<1 and low reduction in shoot and tuber production between non-F and +F conditions, was recognized as a less susceptible genotype to soil NPK status. It was revealed that NPK fertilization did not reduce tuber dry matter content, regardless of genotype differences in susceptibility to soil NPK conditions. Hence, this could be helpful to farmers because it implies that yield can be increased without reducing tuber quality through a balanced application of soil nutrients. Our results highlight genotypic variation in sensitivity to the soil NPK availability, nutrient uptake, and nutrient use efficiency white Guinea yam. Differences in susceptibility to soil NPK conditions could be due to the genotypic variations in nutrient recovery efficiency white Guinea yam. Our findings could contribute to breeding programs for the development of Frontiers in Plant Science frontiersin.org 01"}

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+{"metadata":{"id":"010c3857828d23d61649183180cc782c","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/bdde2045-d48c-474b-8695-fc67e95dfbf2/retrieve"},"pageCount":4,"title":"","keywords":[],"chapters":[{"head":"Badakhshan","index":1,"paragraphs":[{"index":1,"size":316,"text":"Badakshan, one ofthe mosl remote areas in Afghanistan, is located in the northeastern comer bordering Kunar, Lagham, Kapisa, and Thakar provinces, In addition, the province borders Pakistan in the southeast, China in Ihe east, and Tajikistan in the north. It is one ofthe two major areas not under the control of the Taliban. The Panj River (Amu Darya) separates its long border with Tajikistan. The province is normally linked with the rest of country a by narrow, drivable road through the province ofTakhar on the West. Currently, after Takhar tbe road intereepts the frontline with the Taliban. The provine e is thus virtually cut offfrom Ihe rest ofthe country, On tbe eastern side, the road is línked with the Gorno-Badakshan provinee ofTajikistan through a narrow bridge over the Panj River at lshkashem. Badakshan lies in the Hindu Kush mountain range with the Wakhan rising up into tbe Pamir Mountains. The Hindu Kush mountain system is characterized by young, rugged ranges witb sharp peaks and deep valleys. The eastern half of tbe provine e lies between 1,300 meters (Darwaz) to 3,000 meters (Wakhan). The westem half is at a lower elevation, wítb Keshem, the lowest point, at 960 meters. Inside the province, mosl of the districts are isolated from each other for a greater part ofthe year by heavy snowfall in the winter, landslides in spring, and floods in the surnmer, Because of tbe rugged mounlain terrain, mueh of the land area is uninhabitable. Connecting dirt roads are either very rough or do not exist. Donkeys, horses, and walking constitute tbe major means of transport. It is eornmon for villagers to walk three to four days lo the nearest market. There is virtually no effeetive government operating in Ihe provinee at the current time. The víllages and larger towns in the province have no electricity, no running water, no sanitation facilities, few medical facilities, and poor schools."},{"index":2,"size":72,"text":"Badakshan province has historically been isolated and neglected. It has always been considered a poor province; even before ¡he war, local agricultural production met only 50% of the needs. The few development inítiatives ever started were abandoned after the eornmunist takeover and the subsequent fight between the Taliban and the Northern Allianee. 1t í5 estímated that agricultural production is down by at least 40% as a resutt of the war (UNIDATA 1966)."}]},{"head":"Agriculture","index":2,"paragraphs":[{"index":1,"size":120,"text":"The province has a highly diversified eropping system. Crop production, hortieulture, and livestock are Ihe maín sourees of income for most households. It is difficult lo obtain relíable statistics on agricultural produetion. Figures on land holdings provided by farmers during intervÍews tend 10 be grossly underestimated for fear of government taxalion and lo qualíf'y for humanitarian assistance. The majority ofhouseholds own less than one hectare, and further fragmentation ofland holdings occurs because of Ihe traditional inheritance laws. Srnaller farmers usually sharecrop Ihe land owned by farmers with relatively larger holdings (more !han two hectares). Many distrÍcts do not produce enough food, for example, surveys have shown tha! food deficits in Sheghnan, Ishkashem, and Wakhan range frorn two 10 5ix months."},{"index":2,"size":128,"text":"Autumn and spring wheat i5 Ihe main grain crop. Other crops include pulses (broad beans, vetches, field peas, grass peas) ofien grown as a companion crop with spring barley. Finger millet and chickpeas are also planted in spring. Srnall quantities of oil-seed crops such as sesarne and flax are oeeasionally grown for oil, bu! the wild mustard that giows as a weed in the wheat fields is harvesled by women and clúldren for oi! and cooking. Maize i5 grown at lower elevations (below 1600 m) from Darwaz through Shekay as a second crop after wheat. Colton is also grown in small quantities in sorne villages from Darwaz downstrearn, where it is used for stuffing quilts and pillows, and Ihe oi! extraeted from !he seed ís used for larnps."},{"index":3,"size":122,"text":"Vegetables include spinach, oníons, beans, occasionally tornatoes, carrols, squash, and a variety of herbs. Several kinds ofpotatoes ofvarying lengths of rnaturity are grown. These vegetables provide a supplementary dietduring the hungry months of spring and early surnmer before Ihe harvest. Fruit trees, particularly mulberries, are important. Olher cornmon trees inc1ude fruit trees such as walnut, apricot, plum, sour eherry, apple, and grape, and timber trees such as poplar, willow, and walnut. Several wild plants play an important role and include wild mustard, wild rhubarb, wild orclúd tuber, black cumin, licoriee, and mushrooms, in addítion lo the wild herbs of medicinal value. Opíum poppy i8 not cultivated on a cornmercíal basis, allhough small patches rnay be planted by addicts for Iheir own use."},{"index":4,"size":69,"text":"Livestock are a main source of Ihe household eeonomy in rural areas. The sale oflivestock ís the primary means for much of the population to earn income for purchase of other food and essential items, especially wheat, during the spring monlhs when they run out of food stock. The province has huge eornmon grazing areas that support herds oflivestock belonging to Ihe local people as well as to nomads."}]},{"head":"Humanitarian assistance","index":3,"paragraphs":[{"index":1,"size":44,"text":"The cMonic food-deficit situation in the province resulta in a cycle of poverty leading to hunger, and hunger leading to even greater poverty, which is very difficult to reverse. Because of its remoteness, very few assístance agencies are abre to work in the province."},{"index":2,"size":135,"text":"In response lO the food deficit in the region, FOCUS is implementing a relief programo The program has included the distribution of 10,000 tons of food aid to 250,000 people over the last years. Food rations were provided for every household in about half of the province. In sorne dístricta, food was provided in a food-for-work programo FOCUS ís able to carry out ¡ls activíties in Badakshan for several reasons: FOCUS is affiliated with the Aga Khan Development Network, wruch has been active in Tajikistan and Pakistan on the northem and southem borders of Badakshan. During the last three years, good working relationships bave been established with localleaders and wíth international organizations. A participatory model for rehabilitation comprising situation assessment, health, food assistance, village organization, agriculture, physical infrastructure, education, and economic initiatives is being considered."}]},{"head":"Agricultural interventions","index":4,"paragraphs":[{"index":1,"size":81,"text":"Agricultura[ interventions by FOCUS bave been initiated this year in the districts along the Panj River (Darwaz, Sheghaan, Ishkashem, Zebak, and Wakhan). Although Zebak is not strictly along the river basin, ita farmíng systems resemble those of Ishkashem. These districts are among the most food-deficient arcas in the province. FOCUS is able to access these areas across the river from Gomo-Badakshan in Tajikistan wherc t,l¡e Aga Khan Dcvelopment Network has a comprehensive development program, of which agriculture is an important component."},{"index":2,"size":72,"text":"The populated areas ofthe Sheghnan, Ishkashem, Wakhan, and Zebak districts are at an altitude of 2200 to 3000 meters. Population densities are low. Although there is a comparativcly Iarge area of land per capita, low temperatures, short growing seasons, low rainfalI, and poor soils combine to lower productivity. Darwaz, on the other hand, is at a lower altítude (mínimum 1300 meters) and has a longer growing season with higher rainfalI and temperatures. "}]}],"figures":[{"text":"Table 1 . Characteristics of the Target Areas  Ishkashem Zebak Wakhan Sheghnan Darwaz IshkashemZebakWakhanSheghnanDarwaz Number 01 villages 30 14 16 17 54 Number 01 villages3014161754 Households (farms) pervlllage 39 45 68 160 132 Households (farms) pervlllage394568160132 People per household 9.0 9.3 8.7 8.3 8.7 People per household9.09.38.78.38.7 Land resourc<lS: ser' per household 21 11 25 12 6 Land resourc<lS: ser' per household211125126 Number 01 animals per household 15 10 12 14 6 Number 01 animals per household151012146 Number of households surveyed 1200 635 1084 2555 2648 Number of households surveyed1200635108425552648  "}],"sieverID":"cf1d5296-80a1-4eaf-9c36-03b31482b8d6","abstract":"Security in 8adakshan, A[r¡hanistan recognized Ihat disasters, civil strife, and war pose challenges lo agricultural systems, Often, adapted crop varielies are losl and canno! be recuperaled locally. Food aid, combined with Ihe importation of often poorly adapted seed varieties, can undermíne food security and íncrease Ihe costs of donor assistance.ln such situatíons, the goal is 10 deliver seed of adapted varieties and landraces as needed to help reestablish indigenous agricultural syslems in arcas affected by disaster. In turn, Ihis can playa major role in restoring local food security,"}

data/part_5/01a932ba97b12b902c0f917cfaa1eb26.json

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+{"metadata":{"id":"01a932ba97b12b902c0f917cfaa1eb26","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/edf897b7-832f-4a52-b34a-cf81187d6ffc/retrieve"},"pageCount":5,"title":"Adding Value to Aquaculture Products: Kati Farms (Uganda) Ltd","keywords":["fish processing","supply chain management","business incubator","Uganda"],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":196,"text":"When Lovin Kobusingye first graduated from Makerere University in Uganda, she could only find a job with the Walimi Fish Farmers Cooperative Society (WAFICOS). So she started working in 2008 as a secretarial administrator for what was then a 34-member fish farmers' cooperative. Lovin was to conduct training programs helping farmers produce more fish. Very quickly the productivity of aquaculture ponds increased and the farmers started bringing in more fish to the cooperative office every day and complaining that there was no market for their fresh fish. So Lovin also started looking for marketing outlets for the farmers' fish. She approached processing plants in Uganda. None wanted to consider processing fish: small volumes, bad taste, too many bones, and irregular supply by smallholder farmers. WAFICOS managers did not wish to invest into processing but they encouraged Lovin to start her own fish processing business as a side job. That was the start of Kati Farms (U) Ltd, based in Kampala, which now buys 15 metric tons of fresh fish from Ugandan farmers every week for processing into various fish-based processed foods for the local market and for export to East, Central and Southern African neighboring countries."}]},{"head":"Company Background","index":2,"paragraphs":[{"index":1,"size":252,"text":"Uganda is an East-African land-locked country between Kenya, Tanzania, Rwanda, the Democratic Republic of Congo (DRC) and South Sudan. Uganda lies on the Eastern shore of Lake Victoria and captured fish is a usual food source for Ugandans living close by. However, based on data from the Ugandan Ministry of Agriculture, Animal Industry and Fisheries -Department of Fisheries Resources (MAAIF 2012), per capita annual consumption of fish is only 5.7 kg in Uganda. Per capita income is growing thanks to economic development in the country (World Bank data). Furthermore, governments of developing countries are being encouraged to promote sustainable fish consumption given its importance as a source of energy, protein and nutrients in these countries (FAO and WHO 2010). Therefore, it is likely that fish consumption in Uganda and in neighboring countries will increase in future. However, there are likely to be difficulties in marketing fish. First, farmed fish is not a regular diet item for a majority of Ugandans so product promotion will be needed. Second, many consumers are still dependent on very small daily wages so portions of fish products will need to be cheap enough to be bought by poor consumers. Third, sanitary infrastructure is still a luxury for most of the country so untreated waste waters end up in waterways and undermine the safety of fish raised or captured in these waterways. Thus, although there is likely to be a good market potential for farmed fish products, new products will have to be inventive to address these challenges."},{"index":2,"size":87,"text":"Lovin's idea was to use all the fish being delivered by WAFICOS farmers to launch a brand new product: fish sausages. But because she didn't know how to run a business let alone produce a sausage, she needed to build her own capacity in business management and food processing. She heard about the business incubator at the Uganda Industrial Research Institute (UIRI). It funds, trains and hosts the businesses of young entrepreneurs to develop industrial innovations. UIRI's Head of Production had never thought about fish sausages before."},{"index":3,"size":206,"text":"Lovin assured him that she had a reliable source of fish meat with the WAFICOS farmers and she prepared her business plan to complete her application for the business incubator. She got accepted at UIRI and followed a course in food processing there. UIRI also suggested a fish sausage recipe made from captured Nile perch; Lovin started experimenting around it with different types of captured fish in 34-kg batches of sausages but was unsatisfied with the result. All her savings were used up in buying the raw materials for her sausage trials until she went back to her original idea of using live farmed catfish supplied by WAFICOS farmers to produce fish sausages. Everybody who tried this batch liked it: the sausages had white flesh, the filling was clean of bones, and the texture was soft. She used this recipe to start marketing her new product. Lovin started selling her fish sausages to friends and to small itinerant street sausage roasters. Her customers were always asking: where did these fish sausages come from? Word-of-mouth created her initial customer network which allowed Kati Farms to move on to a semi-industrial scale of 500 kg of sausages per week, all the while using the food laboratory of UIRI."},{"index":4,"size":178,"text":"Lovin was lucky to be identified by the Department of Fisheries Resources which invited her to showcase her products in a trade fair. Giving out samples of her fish sausages to representatives of the media, hotels, restaurants and supermarkets made Kati Farms' fish sausages famous on the Ugandan market and opened up regular supply contracts. She got more free publicity when the Smartfish Program identified her to join a fish products trade event in Zambia where Lovin showed off her product to an international audience. At this event, Lovin received extremely useful suggestions on how to improve the labeling and packaging of her product. All agreed the sausages were delicious; she now had to work on other elements of the product. This event was Kati Farms' international breakthrough. It provided international and free press coverage of the business's fish products; this resulted in numerous phone calls from all over East and Southern Africa to start doing business. So Kati Farms now had to increase production to supply all its orders while further improving the quality of its products."},{"index":5,"size":254,"text":"Capital was needed to fund this expansion. Lovin was unsuccessful obtaining loans from commercial banks because the enterprise was considered too risky. The only people that helped out financially were the fish farmers of WAFICOS. The fish suppliers agreed to provide their raw material on credit and only be paid at the end of each week. With the fish production training program ongoing at WAFICOS and the new market outlet created by Kati Farms, the cooperative now has 1000 members. Kati Farms purchases 15 tons of fish every week, equivalent to 75% of the cooperative farmers' total production. The fish meat is processed into 1.5 tons of sausages and other products like chilled gutted whole fish, chilled fish fillets, fish samosas, fish mince for pet food, etc. To produce fish sausages, the fish purchased from farmers are cut to separate meat fillets, trimmings and fatty tissues; all are cut into small pieces. The fish fillet and fats are ground separately in 3-mm mesh. The resulting fish mince and fats are chopped together with ice, spices and food additives and chilled to +12°C. This mixture is stuffed into sausage casings of 26-28 mm diameter, and sausages are linked and twisted to form 50-g individual pieces. The sausages are packed in plastic pouches to reach retail weight of 0.5 kg (10 pieces) or 1 kg (20 pieces). Finally, the packed sausages are frozen to -18°C, at which temperature they can be stored for three to six months. It is recommended they be heat-treated only before consumption."},{"index":6,"size":63,"text":"Having started alone in the UIRI business incubator with US$800 of savings, Lovin's Kati Farms is now worth an equivalent of $400,000 USD shared between three investors and provides direct employment to 38 people. Through its extended networks of fish supply and fish product sales, Kati Farms helps support the livelihoods of at least 500 other people as well as the WAFICOS farmers."}]},{"head":"Current Management Structure","index":3,"paragraphs":[{"index":1,"size":177,"text":"Lovin is the general manager and main investor in Kati Farms holding 75% of the shares. She is also directly in charge of international sales and marketing. To help her manage the business, Jackline Ahimbisibwe holds 20% of the shares and is in charge of the domestic marketing of fish products. Jacky had experience in marketing and finance and shared a similar business-orientation mind to Lovin's. So when they met, Lovin requested her to join her in running Kati Farms. James is employed as a production manager to oversee the processing. Joseph is the accountant and makes sure that all aspects of the business stay profitable. WAFICOS is still a very close collaborator employing one person to provide technical assistance to the fish farmers and make sure that the quality of the supply to Kati Farms remains satisfactory. Finally, Lovin recognizes the invaluable contribution of the free legal advice provided by Prof. Sempebwa Fredrick. She submitted all decisions for his legal advice and is now in a position to pay for it through a legal services contract."}]},{"head":"Key Success Factors","index":4,"paragraphs":[{"index":1,"size":51,"text":"An innovative product. The most important success factor was the innovative fish sausages. Kati Farms is still the only producer of fish sausages in the region; this gives Lovin a leading advantage over potential competitors. She intends to stay ahead of other food processors by sticking to providing outstanding, quality products."},{"index":2,"size":38,"text":"Unlimited supply of main raw material. Kati Farms was lucky to have an abundance of raw materials. When Lovin started her business, nobody wanted to use farmed fish, so she had no difficulty in sourcing her main ingredient."},{"index":3,"size":125,"text":"Customizing products to customers' different tastes. Kati Farms' flagship product is fish sausages. Despite the supply contracts with supermarkets and hotels, the main customers are still informal street roasters and consumers from Kampala who buy 2/3 of the weekly production. Large fish are gutted and sold fresh to DRC consumers. Smaller fish are filleted or smoke-dried for the emerging middle class which cannot necessarily afford to buy a whole fish. Likewise, small pre-cooked fish products like samosas are cheaper than whole fish and allow fish protein to reach mouths of lower-income households. Even the leftovers and bones from the filleting are processed into fish mince for pet food. All parts of the fish supplied from the farmers are put to productive use by Kati Farms."},{"index":4,"size":139,"text":"Regular endorsements boost entrepreneur's confidence. Joining the UIRI business incubator allowed Lovin to start a business while inside an enabling environment. The incubator provided her with technical knowledge on food processing to develop the product, a working space for her firm after she completed the training, and the institutional encouragement needed by young entrepreneurs to keep going. Her selection to participate in local and international trade fairs gave her free promotion and press coverage. Lovin has won the Rising Star Award of the Uganda Women Entrepreneurs' Association in 2012. Kati Farms also won 1st prize in the 2012 African Agribusiness forum in a competition between 52 African countries sponsored by the European Markets Research Center and Rabobank. Recognition has given Lovin a sense of purpose and encouragement to keep going despite the real challenges of doing business in Africa."},{"index":5,"size":161,"text":"The trust of 1000 mainly small fish producers. Because Lovin had worked for WAFICOS, there was some mutual trust between the fish farmers and the young business entrepreneur. Lovin was the one who helped train most of the farmers in successful aquaculture fish production. She also personally started the whole marketing venture to create an outlet for the farmers. In the end, they were instrumental in getting the business growing by providing their fish on credit trusting that Lovin would pay them back at the end of each week. The private company is thus founded on its reliance on a cooperative supply base and a history of interpersonal trust. It is now in the interest of Kati Farms to help the fish farmers keep improving their quality by securing and stocking better fish feed so Lovin has invested into feed storage units where she can stock large quantities of relatively scarce good-quality fish feed ready for sale to her supplier farmers."}]},{"head":"Strategic Issue: Doing Business in Africa is Hard","index":5,"paragraphs":[{"index":1,"size":80,"text":"Especially in the land-locked country of Uganda, it is difficult to find adequate packing material, spices for the sausages or machines for processing. Everything has to be imported at high cost and high risk. Chilling and transport infrastructure is also lacking, particularly challenging to market a perishable product made from fish. Most challenging is the lack of money for investing; commercial banks seem mainly to be interested in funding projects that are safe and guarantee a return on their investment."},{"index":2,"size":88,"text":"Kati Farms thus plans to import the feed for its suppliers, processing and packing material, and exotic ingredients. It is often less risky and quicker to ship the goods by plane than by truck, overland. Lovin is also relying on new private investors who are aware of, and passionate about, the development potential of agribusinesses in Africa to help fund her expansion. One of these is Anu Frank Lawale of Gloucester Point, Virginia. He was introduced to Lovin by a common friend: Nelly Isyagi of Aquaculture Management Consultants."}]},{"head":"Looking Ahead: Replicating Successful Fish Processing for Other Untapped Raw Materials","index":6,"paragraphs":[{"index":1,"size":85,"text":"Lovin's father is a farmer with a small plot of avocadoes. Every year at harvesting season avocadoes fall down and rot because there is no processing plant to make use of all this raw material. There are thousands of other small avocado growers like Lovin's father in East Africa. Lovin is now thinking of how she could replicate what she has achieved with fish farmers on other African agrifood commodities produced in abundance but unfortunately wasted for lack of markets and processing into innovative products."}]}],"figures":[],"sieverID":"1f704037-0cc9-4071-a0cd-5a15fa278e37","abstract":"Kati Farms (U) Ltd is a fish agro-processing enterprise led by Lovin Kobusingye, a young entrepreneur linking Ugandan fish farmers with markets. Because fish farmers were having trouble finding marketing outlets for fresh farmed fish, Lovin started adding value to fish. Her breakthrough came when she thought of making sausages from fish. Lovin went through the process of developing her and her suppliers' capacities and finding markets for her innovative products with the support of Ugandan government institutions. Kati Farms and her fish suppliers now reliably deliver processed fish products to 30 local supermarkets and 23 hotels, in addition to many low-income consumers through street vendors."}

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+{"metadata":{"id":"01fc90a8d23a8ad9bd8a9b5d75d9884d","source":"gardian_index","url":"https://digitalarchive.worldfishcenter.org/bitstream/handle/20.500.12348/4127/2e92ba094bcf9fc7a49ef8dbb9638d41.pdf"},"pageCount":2,"title":"2,932 people trained of which 66 % women Topics","keywords":["Carp-mola polyculture","nutrition","leadership and childcare"],"chapters":[{"head":"","index":1,"paragraphs":[{"index":1,"size":69,"text":"The project has successfully included small fish in the regular meals of once in a week for 2,336 children from Child care Institutions and Special schools. The project was able to influence policy makers in the Fisheries and Animal Resources Department (F&ARD) and Women and Child Development Department (WCD). As a result, a proposal on inclusion of small fish in supplementary nutrition programme (SNP) of ICDS have been submitted."}]}],"figures":[],"sieverID":"2508cb8d-44ae-4211-a49b-8d7407211dc8","abstract":"Scaling innovative, nutrition-sensitive fisheries technologies and integrated approaches through partnerships in Odisha, India can improve food and nutrition securityThe goal of the project is to improve food and nutrition security in the Indian state of Odisha through increasing availability, accessibility and consumption of fish and fish products. This project will lead to the introduction of nutrition-sensitive production technologies for producing nutrient-rich fish and vegetables in selected districts of Odisha as well as increased production of high quality fresh small fish and dried fish for making fish-based products."}

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+{"metadata":{"id":"0411e299fd438003b9b580b4c8145570","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/2dc2a81c-c72e-4453-934c-660f624a4f7e/retrieve"},"pageCount":6,"title":"Getting Napier grass to dairy farmers in East Africa Dairy farming is Kenya's leading agricultural sector © ILRI/Dave Elsworth","keywords":[],"chapters":[{"head":"European funding","index":1,"paragraphs":[{"index":1,"size":54,"text":"ILRI received direct funding from the European Union, Germany, Switzerland and the United Kingdom to support their forage diversity work and forage genebank. In addition, ILRI has also been supported with unrestricted support from Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Sweden, Switzerland and the UK's Department for International Development (DFID)."}]},{"head":"Project milestones","index":2,"paragraphs":[{"index":1,"size":17,"text":"• 1992: ILRI provides material of nine promising Napier grass clones to KARI for screening in Kakamega."},{"index":2,"size":14,"text":"• 1994-1995: Kakamega I and II are developed from these ILRI accessions by KARI."},{"index":3,"size":12,"text":"• 1996-97: The two varieties are screened for smut resistance by KARI."},{"index":4,"size":15,"text":"• 2000-01: Kakamega I is screened for biomass production and nutritional value at KARI Kitale."},{"index":5,"size":14,"text":"• 2005: Kakamega I has been distributed to 1,383 farmers in smut affected areas."},{"index":6,"size":33,"text":"• 2007: 13 per cent of farmers are using Kakamega I for zero grazing systems in smut prone areas of Kenya but there are concerns over them having lower yields than susceptible varieties."},{"index":7,"size":30,"text":"• 2007: Collaboration between ILRI and KARI to develop methods for early detection of smut to speed up the search for resistant material and prevent further spread of the disease."},{"index":8,"size":33,"text":"• 2010: A probe based on ß-tubulin, an optimised extraction protocol and ITS primers are identified for laboratory use in detection of smut in Napier grass tissue using polymerase chain reaction (PCR) technology."},{"index":9,"size":17,"text":"• 2010: Smut detection technologies are transferred to two technicians from KARI for diagnostic testing in Kenya."},{"index":10,"size":18,"text":"• 2011-2012: KARI is testing more germplasm from the ILRI genebank to look for more smut tolerant lines."},{"index":11,"size":16,"text":"• 2012: ILRI sends Napier grass varieties to Brazil for breeding to improve yield and quality."}]},{"head":"Costs and benefits","index":3,"paragraphs":[{"index":1,"size":51,"text":"Based on figures from Mwendia for production losses due to smut of about 0.2 tonnes per hectare per year for zero grazing systems, the annual loss to a smallholder farmer would be equivalent to 22 days of feed for a dairy animal, a loss in income on 220-330 litres of milk."},{"index":2,"size":70,"text":"For farmers growing Napier grass for sale to dairy producers -considering the cost of Napier grass at US$15 per tonne and an estimated yield of 18 tonnes per hectare per growing season -a reduction of 40 per cent of the yield due to smut would cost a farmer US$108 in lost income from Napier grass sales. These losses can be offset by using Kakamega I rather than a susceptible variety."}]},{"head":"Multimedia material","index":4,"paragraphs":[{"index":1,"size":12,"text":"Saving Animal Feed Plants to Preserve Livelihoods Putting ILRI's Genebank to Work"}]},{"head":"More information","index":5,"paragraphs":[{"index":1,"size":12,"text":"International Livestock Research Institute -www.ilri.org/foragediversity Napier Grass Stunt and Smut Project -http://bit.ly/Qfts3v"},{"index":2,"size":70,"text":"Dairy farming, Kenya's leading livestock sector activity, is vital for the livelihoods and food security of millions of Kenyans. More than 80 per cent of milk produced and sold in Kenya comes from smallholder farmers, typically raising just one or two dairy cows on small plots of land. Women perform half of all dairy related activities in Kenya, which improves household welfare, primarily through increased household income and milk consumption."},{"index":3,"size":70,"text":"With a growing population and shrinking areas for pasture, cattle are increasingly being fed on crop residues, cultivated fodder and some concentrates. Ninety per cent of farmers now produce on-farm feeds. Being able to provide enough good quality fodder is by far the most important factor in achieving high milk quality and yield, with a well fed animal producing two or three times more milk than an averagely fed one."},{"index":4,"size":90,"text":"The high yielding fodder, Napier grass -Pennisetum purpureum -has become by far the most important due to its wide adaptation to different regions, high yield and ease of propagation and management. Napier grass constitutes between 40-80 per cent of the forage for more than 0.6 million smallholder dairy farms. With fodder in high demand, selling Napier grass as a business has good potential for improving smallholder livelihoods. According to a recent survey, up to 58 per cent of Kenyan smallholder farmers already sell fodder, including crop residues, straw or grass."},{"index":5,"size":55,"text":"However, in the early 1990s, head smut disease, caused by the fungus Ustilago kamerunensis, began to have a devastating impact on Napier grass. Spread rapidly by wind and infected plant material, smut turned valuable Napier grass into thin, shrivelled stems and reduced yields by 25-46 per cent. For smallholder farmers, the threat was very serious."},{"index":6,"size":89,"text":"Disease control using systemic fungicide in fodder crops is very expensive and therefore beyond the means of most smallholders. Using tolerant high yielding varieties is a cost effective solution and avoids the additional costs of moving to a different feeding system. The International Livestock Research Institute (ILRI) maintains an international collection of forage germplasm under the auspices of the International Treaty on Plant Genetic Resources for Food and Agriculture. The state of the art genebank, based in Ethiopia, holds over 19,000 forage accessions, including 60 genotypes of Napier grass."},{"index":7,"size":201,"text":"In 1992, scientists from the Kenya Agricultural Research Institute (KARI) and ILRI began collaborative work to screen new clones of Napier grass for improved productivity. The best lines were released as Kakamega I and II. In 1996, in response to the threat of head smut, KARI began further screening to find smut-resistant varieties of Napier grass. Kakamega I and Kakamega II were identified as both high yielding and resistant to head smut. The favourable results obtained in the laboratory were confirmed in farmers' fields and work began immediately to multiply planting material in government institutions. Within the first year, cuttings were distributed to over 10,000 smallholder farmers. Despite the success of Kakamega I and Kakamega II, some major hurdles remain. One is the danger posed by relying on just two varieties which are clonally propagated, meaning that the new plants are genetically identical to the parent. This means the chances of the resistance to smut being broken down are high. KARI scientists have already begun to screen germplasm from over 50 Napier grass accessions to select further resistant varieties. The aim is to increase the number of varieties that can be distributed and identify plants that combine smut-resistance and high yields."},{"index":8,"size":64,"text":"With feedback from farmers that Kakamega I and Kakamega II aren't quite as productive as other local Napier varieties, in August 2012 ILRI exchanged resistant lines with the Brazilian Agricultural Research Corporation (Embrapa), which has a Napier grass breeding programme with highly productive lines. Through crossbreeding, Brazilian researchers are working to develop lines with better agronomic and nutritional value as well as disease resistance."},{"index":9,"size":57,"text":"\"In Brazil we have different biomes that imply that you must have different kinds of forages well adapted to each of them. That's why having a genebank such as ILRI's is so important, not only for a country like Brazil but the whole world, in order to improve the sustainability and therefore providing more food for people.  "}]}],"figures":[{"text":" In 2001, KARI's Muguga research station received numerous requests for Kakamega root splits to multiply the material, and some schools in the area are, at the request of parents, using school gardens to multiply the material. The most productive clone, Kakamega I, is grown at bulking sites maintained by Farmer Training Centres and Parent-Teacher Associations and disseminated through KARI, the local agricultural offices and by farmer to farmer exchange.The new varieties -developed using material from Swaziland and Zimbabwe by KARI -are not quite as productive as the best of Kenya's local Napier grass varieties, but have still proven popular in smut-affected areas. By 2007, 13 per cent of farmers in smut prone areas of Kenya were using Kakamega I for zero grazing systems.Another serious disease -Napier grass stunt -was identified in Western Kenya in 2002. Affected shoots become pale yellow in colour and seriously dwarfed. Often the whole stool is affected, with yield reductions of 75 per cent and even complete loss in yield and eventual death. In badly affected areas, smallholders have lost up to 100 per cent of their Napier grass crop and are then forced to sell their animals.Between2007 and 2010, an Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA)-funded project worked to reduce the impact of smut and stunt by raising awareness of the diseases and providing information on how to manage them. ILRI, in partnership with KARI, icipe, Rothamsted Research, Tanzania's National Biological Control Program and Uganda's National Livestock Research Institute, developed improved management practices to reduce the incidence and spread in farmers' fields. For both diseases, digging up infected plants and replacing them with healthy canes is the best strategy. Smut infected plants should be burnt to kill the fungus. During this period, ILRI provided KARI with a large pool of Napier grass germplasm from the forage genebank, developed an early screening test for smut, and provided training to KARI scientists on the technology -essential when looking for Published: February 2013 Kakamega I was identified as high yielding and smut resistant © ILRI/Ramni Jamnadass \"We use Napier grass because it's nutritious for the cows and it's also high producing so it's a cheaper form of fodder. If I didn't have Napier grass on the farm probably my cows would starve.\" Patrick Mogoko, farmer, Kiambu, Kenya resistant plants -and on field diagnosis of the disease. "},{"text":" This case study has been produced by WRENmedia, funded by the Swiss Agency for Development and Cooperation (SDC) and implemented by the European Initiative on Agriculture Research for Development (EIARD). It is intended to share knowledge and promote more effective agricultural research for development (AR4D) policies and does not necessarily reflect the official position of EIARD or of individual EIARD members. on air . . . on line . . . in print . . . in person . . . WREN media "},{"text":"\" Pedro Arcuri, EmbrapaMarket: A Pilot Survey in Kiambu District for the Identification of Target Groups of Producers. Nairobi, Kenya: International Livestock Research Institute. Contact Contact Alexandra Jorge Alexandra Jorge Genebank Manager Genebank Manager International Livestock Research Institute International Livestock Research Institute PO Box 5689 PO Box 5689 Addis Ababa Addis Ababa Ethiopia Ethiopia Tel: +251 116 172 352 Tel: +251 116 172 352 Email: [email protected] Email: [email protected]  "}],"sieverID":"4406249c-083e-406d-88e3-93dfd55e027d","abstract":"To meet demand for high-yielding, disease resistant fodder from smallholder dairy farmers in East Africa, scientists from the Kenya Agricultural Research Institute (KARI) and the International Livestock Research Institute (ILRI) worked together to select and distribute smut-resistant varieties of Napier grass."}

data/part_5/04955b3878b61f6bf376fdf3c3a5697e.json

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+{"metadata":{"id":"04955b3878b61f6bf376fdf3c3a5697e","source":"gardian_index","url":"https://www.crc.uri.edu/download/WSFS2022_02_CRC_FIN508.pdf"},"pageCount":45,"title":"WOMEN SHELLFISHERS AND FOOD SECURITY PROJECT Final Technical Report on Site Based Research in Ghana and The Gambia PARTICIPATORY LAND-SEASCAPE VISIONING in Densu Estuary, Narkwa Lagoon, and Whin Estuary, Ghana","keywords":[],"chapters":[{"head":"INTRODUCTION","index":1,"paragraphs":[{"index":1,"size":185,"text":"Ghana is a West African country bordered on the north, east, south, and west by Burkina Faso, Togo, the Gulf of Guinea (Atlantic Ocean) and Côte d'Ivoire, respectively. Its coastal region is divided into three distinct geomorphological zones -the west coast covering about 95kms, the central coast covering 321kms, and the east coast estimated at 149kms (Armah, 2005). Ghana is also endowed with over 100 coastal water bodies, including closed lagoons, open lagoons, and estuaries, accompanied by mangrove vegetation, mud, and tidal flat marshes (Yeleliere et al., 2018). These resources are essential in supporting fishing and fish-related enterprises that form the primary source of livelihood to the surrounding community. Oyster fishing is among the main economic activities dominated by females (Osei, 2020;Njie and Drammeh, 2011), thus the need for clear mechanisms to promote sustainable harvesting of shellfisheries and mangrove forests in the country. A recent study by Duguma et al. (2021) suggests that Ghana has lost a net of 539 square kilometers of mangroves in the last two decades, with the 2000-2010 period recording a loss of almost five times that of the 2010-2020 period."},{"index":2,"size":178,"text":"Addressing the challenges related to degradation requires contextualized and community-led processes (Sagoe et al., 2021). The visioning process brings together communities in a well-guided process to understand the land-seascape changes, current situation, and their envisaged future. Contexts vary by location, and solution options should consider local realities. Developing and implementing such localized solutions could be costly in countries with poor financial capacity (CPI 2019). Due to the ineffectiveness and cost inefficiency of centralized government led approaches, community-based approaches dominate the African natural resources management space as noted by Roe et al. (2009). Therefore, it is crucial to devise means of designing a collective vision driven by local interests and aspirations and owned by communities who reside in the landscape for the long term. As described above, the vision should address and respond to the likely envisaged effects of climatic and non-climatic ecosystem stressors (Freduah et al., 2018). Community ownership is crucial because projects and programs are often time-bound (Thwala, 2010), and hence, there is a need for the interventions to become mainstream activities within the landscapes (Lachapelle, 2008)."},{"index":3,"size":137,"text":"Visioning is imagining, framing, and visualizing the future based on; 1) history, 2) current reality, and 3) emerging and likely priorities and interests. It is about 1) framing into context and promoting what we want to see more, and 2) designing strategies for what we want to see less in our landscapes. Broadly, visioning is based on the aspirations of the residents of the landscape and builds a future that can sustain recognizing the limitations, challenges, and opportunities existing in the land-seascape. A vision owned by local communities is the most realistic one, and it reflects their societal norms, cultural viewpoints, and social-ecological realities as McLeroy et al. (2003) argues. Developing such visions in a participatory manner has become a vital co-design process to induce the adoption of sustainable practices while reducing the intensity of destructive practices."},{"index":4,"size":165,"text":"The purpose of this study is to present the process of developing a shared land-seascape vision to tackle pertinent environmental challenges that communities relying on shellfishing identify as crucial to be addressed. To achieve this, we adopted the Focus Group Discussion (FGD) approach as the vision must be a consensus among the broader community beyond the individual households. Focus group discussions were held in Densu Estuary, Narkwa Lagoon, and Whin Estuary in Ghana. The purpose was to collect data through dialoguing and consultations with shellfishers and other communities living close to the mangrove ecosystems and in agricultural landscapes adjacent to it. The discussions focused on assessing the perceptions of shellfishing communities based on their experiences of the state of their landscape concerning agricultural, forestry and fishery resource trends in the past, present, and future prospects using the landscape visioning tool. The aim is to develop a community-owned vision to improve their land-seascapes and livelihoods by creating a vision that helps them better manage their ecosystems."}]},{"head":"METHODS","index":2,"paragraphs":[]},{"head":"Description of study sites","index":3,"paragraphs":[{"index":1,"size":251,"text":"The FGDs were conducted in five communities located along estuaries and lagoons off the Atlantic coast of Ghana, (i.e., the Gulf of Guinea), in three administrative districts. These are Tsokomey & Bortianor in Ga South District; Narkwa in Ekumfi District and Apremdo and Amanful in Kuma-Effia-Kwesimintsim sub metro of the Sekondi-Takoradi Metropolis. The socio-demographic, biophysical, and economic features of the sites surveyed are summarized in Table 1. Densu and Whin estuaries are adjacent to urban areas and economies, while Narkwa is peri-urban to rural. Infrastructural expansion is pronounced in Densu and Whin. Whin is the most populated of the three sites, with a population density of 6000/square km. National and multi-national companies undertake most of the economic activities in the agriculture, forestry, and service sector in these land-seascapes. The study sites spanned an ecological/vegetation gradient from the drier coastal savannah plains of Densu, with the coastal strand/thicket vegetation through the dense shrubby vegetation of Narkwa, to moist savannah semi-deciduous forest vegetation at Whin (Figure 1). Climate is equatorial across all sites with two rainy seasons between April and November. Temperatures vary from 22-34°C, while rainfall ranges from 790mm in the drier equatorial zone of 'Densu to over 2000mm in the moist equatorial zone of Whin. Soils are primarily orchrosol and loamy, although they could be saline in some areas. The soil supports the production of a range of vegetables, staples, fruits, pulses, and tree crops. Each area is well-endowed with at least two major water resources that drain into the sea."},{"index":2,"size":15,"text":"Figure 1: Ghana study sites: Densu, Narkwa, and Whin (Adapted from DeGraft-Johnson et al., 2010)."},{"index":3,"size":114,"text":"The three sites have agrarian communities involved in crop, livestock, and fishery activities. Whin has the least households engaged in agriculture while Narkwa has the most, with agriculture and fisheries employing over 95 percent of the labor force (www.ghanadistrcits.com). Subsistence crop production (mainly vegetables: tomato, garden eggs, pepper, onion, okra, and staples: maize, cassava, plantain) is the predominant agricultural activity, largely rain-fed and practiced under slash and burn with relatively low external inputs (Rankoana, 2017). Cash crops including pineapple, citrus, oil palm, and coconut are also produced. Livestock (sheep, goats, poultry, and pigs) is kept mainly on a subsistence basis. Artisanal sea fishing is predominantly practiced by local fisher folks in these coastal communities."},{"index":4,"size":19,"text":"Oyster harvesting is a predominant livelihood activity of women and children in the three sites (Chuku et al., 2020)."}]},{"head":"Selection of FGD participants","index":4,"paragraphs":[{"index":1,"size":90,"text":"Community contacts identified and enumerated shellfishers and invited them to participate in FGDs voluntarily. The selected community members were clustered into groups based on location and gender especially where men are also involved in shellfishing activities especially in Densu and Narkwa. In Whin, only women were involved in oyster collection, so the gender-based clustering was not relevant. A total of 115 shellfishers comprising 107 (93 percent) females and 8 (7 percent) males participated in the FGDs. Location-wise, 42 (Densu), 43 (Narkwa), and 30 (Whin) persons voluntarily participated in the FGDs."},{"index":2,"size":88,"text":"Two protocols were followed during the FGDs. The participant consent form and the COVID-19 protocols. First, participants signed informed consent forms prior to voluntarily participating in the interactive focus group discussions. Second, Covid-19 protocols included provision of a washbasin, water, soap, tissue, and sanitizer were provided for cleaning hands. A face mask was provided for each participant. The facilitator ensured each participant wore the face mask before commencing the session. The facilitators tried to get group consensus for each question or issue discussed before finalizing each issue discussed."},{"index":3,"size":168,"text":"Densu Estuary: At Densu, forty-two shellfishers comprising male (3) and female (39) participated in the FGD. According to the women, male shellfishers are in the minority and are formally organized into the Densu Oyster Pickers Association (DOPA). Members of DOPA from three communities (Tsokomey, Bortianor, and Tetegu) participated in the FGDs at a central location at Tsokomey. The largest proportion of DOPA members are from Tsokomey. Participants indicated that both Tsokomey and Tetegu communities are predominantly settlers from the Volta Region of Ghana and thus tend to be similar in characteristics in terms of ethnicity, resident status (mainly settlers) and livelihood options. Consequently, participants from Tsokomey (25) and Tetegu (3) were combined and segregated into two groups, i.e., male (5) and female (23). Participants from Bortianor (15) are the Ga people originating from the Densu area. Meetings are often held on Tuesdays as it is observed as a non-fishing day in the Densu community. It is a day of the week reserved for funerals and other social occasions."},{"index":4,"size":97,"text":"Narkwa Lagoon: Narkwa is the only community bordering the Narkwa Lagoon where shellfishing is undertaken. The shellfish collectors are not organized into a group. Hence, snowballing was used by the community contacts to first identify and list or enumerate. The people of Narkwa have not set aside any special traditional day for non-fishing activities; thus, the community engages in fishing activities every day. At Narkwa, it was not possible to subcluster the women (43 individuals) hence seats for the women group were arranged circular but roughly in three clusters (i.e., right, left, and middle) for effective engagement."},{"index":5,"size":72,"text":"Whin Estuary: The participants recruited for the Whin meeting were 30 shellfisher women from two communities, i.e., Apremdo (19) and Amanful Kuma (11). Like Narkwa, the women exploit shellfish resources from the Whin estuary every day. However, at Apremdo, the women were further grouped into subsistence and commercial collectors, where the latter was selected for this assessment to envision the landscape changes as they depend more on the lagoon and mangrove ecosystem."},{"index":6,"size":71,"text":"Based on the responses provided by the focus group respondents, qualitative summary attributes were generated to represent change trajectories as increasing (improving), no change, or decreasing (declining). Also using the qualitative details on the change trajectories, we computed proxies for understanding the relative state of the land-seascapes on a scale of 12 attributes based on the ecosystem functions and services the land-seascapes provide to people and animals (see Table 1 above)."},{"index":7,"size":105,"text":"For all the 12 attributes, communities qualitatively graded them on a trend scale of improving, no change or declining (degrading). For each FGD group, the number of declining or degrading was counted and divided by the total number of attributes ( 12). An overall site score was then calculated summing the number of attributes ranked as degrading per community focus group and dividing by the number of focus groups times 12 attributes. The value was used as a degradation proxy, representing a synthesized community perception of land-seascape degradation for each site. In the computation, 'no change' and 'improving' are considered as no sign of degradation."}]},{"head":"FINDINGS","index":5,"paragraphs":[{"index":1,"size":8,"text":"3.1 Socio-demographics and livelihood options in the land-seascapes"},{"index":2,"size":79,"text":"Participants of the FGDs included largely female shellfishers (Table 2). The shellfishers are of average ages of 43, 47, and 51 years for Densu, Narkwa, and Whin respectively. Although shellfishers generally had some formal education from basic to secondary schooling across the three sites, Densu has the highest share of people who at least had formal primary education, relative to Whin and Narkwa. These results are comparable to those reported by Agbekpornu et al. (2021) in the Densu area."},{"index":3,"size":56,"text":"Most shellfishers are born in the current areas they operate in, especially in Narkwa and Whin (Table 2). Those in Tsokomey & Tetegu in Densu are predominantly settler communities representing the Ewe people (Gbe ethnic group) originating from the Volta Region. Bortianor, Narkwa, and Whin are typically indigenous communities, with most residents originating from the area."},{"index":4,"size":142,"text":"Most shellfishers engage in more than one livelihood activity with the widest range of activities at Densu. Shellfishing is a major livelihood activity among participants across the three sites, with oyster being the most collected species. The collection of periwinkle, cockle, and other species are undertaken, but predominantly at Whin by women. Sea fish sales, trading, and farming are three major additional income-earning activities undertaken by women shellfishers. However, 1 percent, 9 percent, and 11 percent of women at Densu, Narkwa, and Whin respectively depend entirely on shellfishing for their livelihood, and thus do not engage in any other income generating activity (Table 2). These results affirm the reports by Asare et al. (2019) highlighting the supplementary livelihood contribution of shellfishing in addition to other livelihood activities. Livestock (sheep, goats, poultry and pigs) is generally reared among shellfishers across the three sites."},{"index":5,"size":41,"text":"Farming and wild fruits collection are most undertaken at Whin and Narkwa. Increasing urbanization with associated expansion in infrastructural development at Densu constrains adjacent upland areas for farming, particularly in Tsokomey and Tetegu. At Bortianor, some limited cropping is done on"},{"index":6,"size":49,"text":"areas not yet developed for infrastructural expansion. At Whin, although some cropping is done, participants were not certain about the long-term use of the land due to insecure tenure or rights to perpetual use of their farmlands due to the continual conversion of farmlands for housing and corporate purposes."}]},{"head":"Role of men and women in the land-seascapes","index":6,"paragraphs":[{"index":1,"size":156,"text":"Women shellfishers generally undertake household chores in addition to collecting, processing, and sale of oysters, crabs, periwinkles, and cockles (Whin) which they catch from the lagoon or estuary. While all men engage in collecting shellfish for their spouses or sell to other women to process for sale, those at Densu also sometimes engage in sea and estuary fishing (Table 3). Across sites, shellfishing is done in the morning when the tides are low. Usually, shellfishers return home with their catch by 8 AM. However, commercial collectors at Whin set out in the night to harvest oysters from mangroves. They return at dawn, rest awhile before performing household chores and then process their oysters for sale. At Narkwa, according to the participants, men usually supplement sea fishing, their major occupation, with farming and shellfishing, whereas women work predominantly on farms and in the lagoon. Women appear to be more affected by changes in the landscape than men."}]},{"head":"Typologies of livelihood activities in the land-seascape: current state and prospects","index":7,"paragraphs":[{"index":1,"size":217,"text":"The main livelihood activities reported by respondents across the sites and communities included sea fishing and shellfishing (Table 4). Farming was also quite common and reported in all communities except Tsokomey & Tetegu (in the Densu estuary). An additional activity, to fishing and farming, was trading. At the Whin estuary, masonry (at Amanful) and sand mining (Apremdo) were also reported. In all studied communities, respondents reported a declining trend in livelihood activities over the last ten years, except for shellfishing at Apremdo which was reported to have had no change. The major reasons underlying changes observed in fishing activities are the use of light in fishing, fine mesh nets with tiny holes, use of chemicals for fishing, and high-water tides during the rainy season. At Bortianor (in Densu basin), it was reported that the diversion of the course of the estuary also affected shellfish yields. The underlying reason for decline in trading among respondents at Narkwa was lack of financial capital. Respondents at Amanful indicated that masonry jobs had also declined due to reduction in the number of job opportunities available in recent times. Concerning mining activity at Apremdo, the underlying reason for the decline was stated by participants as related to the unavailability of lands for sand collection and the very laborious nature of the activity."},{"index":2,"size":46,"text":"Generally, communities desire their livelihood activities to be expanded in the next ten years except for sand mining which they want to eliminate. The apparent reason for the desired increase in livelihood activities or productivity was to ensure improved incomes and food security, and general wellbeing."},{"index":3,"size":83,"text":"There was more diversification in livelihood activities at Tsokomey and Tetegu than the other sites for both men and women. Narkwa was the place with fewest livelihood activities, especially for men. In this site, participants stated that the only livelihood activity common among men in the communities covered by this research was sea fishing. The most familiar livelihood activity for women was stated as fishing in the lagoon (for shellfish, crabs, periwinkle, etc.), followed by petty trading, fish porters (carrying), and fish processing."}]},{"head":"Seasonality of livelihood activities","index":8,"paragraphs":[{"index":1,"size":19,"text":"The communities pursued different livelihood activities during the year. At Densu, shellfishers undertake five main activities during the year:"},{"index":2,"size":47,"text":"1. Fishing from the sea and the estuary throughout the year 2. Oyster fishing from May till about November when the oyster season is closed 3. Fish smoking and sale throughout the year 4. Carrying fish (throughout the year) 5. Farming (Bortianor mainly) from March to December."},{"index":3,"size":100,"text":"Trading (usually small-scale businesses) is generally a secondary activity undertaken by some women shellfishers. However, in Bortianor, men may trade to augment fishery activities from January to February (Table 5). At Narkwa, shellfishing and fishing in the lagoon and fish sales are three main activities undertaken by shellfishers, mainly women, throughout the year. Farming is a secondary activity done for only four months from May to August during the major cropping season (Table 6). Women are the major gender group involved in farming while men concentrate on fishing mainly from the sea and to a lesser extent from the lagoon."},{"index":4,"size":12,"text":"At Whin, shellfishers undertake four main activities during the year as follows:"},{"index":5,"size":43,"text":"1. Fish sales except June-October when fish harvest is impossible due to heavy torrential rains. Sea fishing is done throughout the year by men at Amanful, except during periods of torrential rains. 2. Shellfishing throughout the year, with farming and trading occurring frequently."},{"index":6,"size":275,"text":"3. Farming mainly during the rainy season (April-August), especially at Apremdo 4. Trading throughout the year, especially at Amanful Trading is a major livelihood activity from the second to the fourth quarter of the year at Amanful and becomes a secondary activity undertaken by some women during the first quarter. During this period, shellfishing is done as a secondary activity and sometimes may not be pursued at all from June to October when the rains are torrential and water volume swells (Table 6). Farming is a major women's activity only during the rainy or growing season especially at Apremdo. As defined in the methods section, the state of the land-seascapes was assessed using responses from the FGDs conducted in site. The results of the perceptions of the communities show that Densu site experienced a significant degradation extent (88%), compared to what was there in the past. The figures for Narkwa and Whin are also not that very good with degradation proxies of 67% each, on average. The observed perception is quite understandable when the growing extractive pressure from the growing coastal population and communities in the adjacent landscapes is considered. The designation of Densu as one of the Ramsar sites is based on the realization that the site is extensively degraded and there is a need to reverse that. It is this degradation, as also perceived by the communities, that has led to numerous community and institutional actions to restore the landseascapes, particularly in Densu. Among the notable local actions are the mangrove planting activities by Development Action Association and the Densu Oyster Pickers Association. Table 7 summarizes the land-seascape degradation perceptions of the communities."},{"index":7,"size":54,"text":"Respondents at all sites indicated that the benefits (e.g., food, feed, fiber, income, etc.) generated from the land-seascapes are generally declining over the last decades. The trend was attributed to various causes which varied with communities. The causes for decline in sea fishing, shellfishing, and farming were similar at all the sites (Tables 8-10). "},{"index":8,"size":19,"text":"Forests and woodlands Note: The color codes and signs indicate the following: yellow-no change (=); red-declining (▼); green-improving (▲)."},{"index":9,"size":143,"text":"Crop cultivation trends over the past decade in the communities, show that similar crops were cultivated in the past and present (Table 8). However, harvest quantities declined due to reducing land sizes. To continue crop cultivation, the communities highlighted the need for agricultural inputs such as manure and fertilizer to improve yields due to depleted soils. Over the decade, Tsokomey & Tetegu completely lost the tomatoes, onions, and peppers they cultivated in the past as all idle lands previously farmed have been converted into beach resorts by private developers. Regarding the future, the responses from all communities were unanimous stating the wish to improve farming activities as an alternative livelihood option to augment their household needs. However, the main barriers or limitations to achieving this vision were listed as lack of land due to infrastructural development, depleted soil fertility, and poor rainfall patterns."},{"index":10,"size":122,"text":"Fishing (both sea fishing and shellfishing) is on the decline. For sea fishing, the causes for decline as stated by participants were the use of light fishing, use of fine mesh nets, pair trawling by boats, use of dangerous chemicals for fishing, and general overexploitation of fish. At New Amanful (in the Whin basin), the respondents believed that recent non-adherence to certain cultural practices (sacrifices to gods) that bring bumper harvests was also a cause of fish decline. For shellfishing, the causes were pollution of lagoons, siltation of the lagoon due to erosion, destruction of mangroves, creation of wooden traps in the lagoon, and high-water levels in the lagoon during the rainy season that make it impossible for women to collect shellfish."},{"index":11,"size":87,"text":"Petty trading by women has also declined in recent years. The respondents at the three sites attributed the cause of declining trading among women to a lack of financial capital. At Apremdo community in the Whin basin, the respondents attributed the leading cause of declining sand mining to the laborious nature of the work and the depletion of sand quantities from continuous mining. For other minor livelihood activities (e.g., carpentry, masonry, catering, etc.), respondents indicated that jobs were irregular, hence people with such skills experienced insecure livelihoods."},{"index":12,"size":169,"text":"Livestock rearing: Poultry and goats were the most common livestock reported to have been reared in the past in all communities (Table 8-10). It was reported that while rearing some livestock types (pigs and goats) have stopped altogether in recent times, livestock quantities have reduced drastically for other types of livestock like poultry. The main reasons for the decline are infrastructural development, leading to lack of land and inability to practice free grazing and disease outbreaks leading to the death of livestock. Those households still engaged in livestock rearing reported an increase in cost due to the need for animal shelter and feed. When asked about the future of livestock rearing, communities indicated continuing with the practice despite the overarching barriers such as lack of land due to infrastructural development, inability to practice free grazing, and disease outbreaks that cause mortality of animals. Possible solutions for these limitations are the adoption of intensive livestock rearing and the involvement of veterinary services to protect the animals from diseases and mortality."},{"index":13,"size":125,"text":"Vegetation cover and agroforestry: At all sites, respondents indicated that the vegetation cover in the past years (both natural and planted tree cover) was better than the present. The leading causes for declining tree cover and agroforestry are land sale, settlements expansion, and wood harvesting for various needs (timber, charcoal, firewood). At Bortianor, residents indicated a remnant forest area that they are currently protecting. Some respondents believed a wall should be erected around the remnant forest to protect it. They generally stated a desire to increase vegetation cover and agroforestry but recognized that a major limitation is the limited availability of land. While acknowledging the importance of forests, some people also expressed fear that forests could become a hiding place for criminals and dangerous animals. "}]},{"head":"Factors underlying the dynamics of the land-seascape attributes","index":9,"paragraphs":[{"index":1,"size":386,"text":"At Densu basin, and for both Bortianor and Tsokomey/Tetegu communities, all land-seascape attributes assessed were generally degrading or declining (see Table 8 above). The main factors causing a decline in crop production were reduced land for cultivation (see Table 11 below). The cause of livestock decline was lack of space due to settlements. The decline in the fishery was attributable to pollution and overharvesting. Also, the decline in biodiversity of aquatic animals was attributed to the use of light in fishing, the use of fine mesh nets leading to over-exploitation. The decline in biodiversity of terrestrial animals was attributed to habitat destruction and the building of settlements. Further, the cause of decline in forests and woodlands was the conversion of forests for infrastructure. The decline of mangroves was due to intensive harvesting, while the decline in soil fertility was attributed to over-cultivation of the same land over years. Increasing population and high demand for land aggravated the shrinkage in available residential spaces. Decline in freshwater availability and quality was caused by pollution (due to deposition of refuse, excretion, etc.), erosion, and sand mining. At Narkwa basin, some of the land-seascape attributes were degrading or declining while others had no change, but none was reported to be improving (Table 9). Communities reported crop production, livestock production, biodiversity of aquatic animals, soil fertility, and residential spaces were on the decline. Communities also reported agroforestry and commodity plantations, biodiversity of terrestrial animals, forests and woodlands, mangroves, freshwater volume, and quality to have remained the same. Table 12 presents the main factors communities thing might have led to the changes in Narkwa. The main factors causing decline are as follows: for crop production, it was the reduction in land for cultivation and declining soil fertility; for livestock, it was lack of space due to settlements; for fishery and biodiversity of aquatic animals, the causes were the use of light for fishing and industrial trawler fishing in marine waters. Also, the decline in soil fertility was attributed to over-cultivation of the same land over the years, while the decline in residential spaces was attributed to increasing population and high demand for land. The main threats identified included lower crop production, disease outbreaks for livestock production, over-exploitation for fishery and aquatic biodiversity, and over-cultivation of land in the case of soil fertility."},{"index":2,"size":64,"text":"At Whin basin, and for both Apremdo and Amanful communities, the land-seascape attributes were generally degrading or declining: except biodiversity of aquatic and terrestrial animals, and mangrove vegetation at Apremdo. At Amanful, more land-seascape attributes were reported to have had no change, namely, crop production, fishery, aquatic animals, and agroforestry and commodity plantations. Livestock production in the Amanful community was reported to be improving."},{"index":3,"size":16,"text":"The main factors causing a decline in the Apremdo community were reported as follows (Table 13):"},{"index":4,"size":172,"text":"for crop production, it was a reduction in land for cultivation and declining rainfall; for livestock, it was disease outbreaks and theft; for fishery, the cause was over-exploitation; for forests and woodlands as well as agroforestry and commodity plantation, the cause was harvesting of trees. Also, the decline in soil fertility at Apremdo was attributed to over-cultivation of the same land over the years, while the decrease in residential spaces was attributed to land sales for infrastructure and settlements. Further, freshwater volume and quality decline are reportedly caused by siltation from different sources, including erosion, pollution, and illegal mining. The main threats identified by respondents at Apremdo community included lower/erratic rainfall in the case of crop production, disease outbreaks for livestock production, over-exploitation for fishery, lack of land in the case of agroforestry and commodity plantations and cutting of trees in the case of forests and woodlands. Other threats to land-seascape attributes were over-cultivation in the case of soil fertility and siltation and pollution in the case of freshwater volume and quality."},{"index":5,"size":122,"text":"At the Amanful community, the main factors causing decline in livelihood activities were reported as follows: for forests and woodlands as well as mangrove vegetation, it was harvesting of trees, for decline in soil fertility, was attributed to over-cultivation of the same land over the years, while the decline in residential spaces was attributed to increasing population and needed for infrastructure. Further, freshwater volume and quality decline are reportedly caused by siltation from different sources, including erosion, pollution, and illegal sand mining. On the other hand, the cause of the reported increase in livestock production is that more people are rearing livestock. The main threats identified by respondents at Amanful community were similar to those identified at Apremdo and already enumerated above. "}]},{"head":"Understanding the envisaged change trajectories in the land-seascapes","index":10,"paragraphs":[{"index":1,"size":146,"text":"Tables 14 and 15 present the activities that need to be stopped, expanded, or introduced as perceived by the communities. At Densu basin, the main activities to be stopped at Bortianor, Tsokomey, and Tetegu were light fishing and pollution of water bodies. At Bortianor other activities were listed, including fine mesh nets, use of chemicals for fishing, cutting of mangroves, and use of wooden traps for fishing. At the Narkwa basin, the main activity to stop was light fishing. At Whin basin, the Apremdo community wanted to stop sand mining while the Amanful community wanted to stop the shoreline protection activities (e.g., seawalls and groynes) which are designed to prevent beach erosion but then do not allow access for beaching artisanal fishing canoes. For activities to expand, both communities in the Densu basin mentioned planting mangroves, while the Narkwa and Whin basin wished to expand shellfishing."},{"index":2,"size":163,"text":"For new activities to introduce, the Densu basin communities proposed introducing advanced fishing boats, mangrove planting, market linkages, and restoration of ecosystems. At Narkwa, there was a request to introduce potable water for domestic use, while a new activity common to Narkwa and Whin was the introduction of alternative livelihood options. The various proposed change trajectories have a common purpose: to improve community livelihood activities and hence people's wellbeing. The proposal to stop light fishing, pollution of water bodies (through various means), cutting of mangroves, use of chemicals in fishing, use of wooden traps, use of fine mesh nets, and sand mining are all aimed at improving sustainability in fishing activities. For instance, stopping the activities mentioned above will ensure clean water resources and prevent depletion of aquatic animals and reduced yields. Also, the proposal to expand mangrove planting activities and shellfishing would increase aquatic animal harvests. For instance, mangroves are critical for providing suitable habitats for reproducing many marine animals, including shellfish."},{"index":3,"size":72,"text":"Community aspirations to replace beach pollution with cleaning of beaches, and replace tree cutting with tree planting, would help ensure improvement in water conditions and lead to higher yields of harvests. Concerning new activities to introduce such as advanced fishing boats, mangrove planting, market linkages, restoration of ecosystems, the introduction of potable water for domestic use, and the introduction of alternative livelihood options are all geared towards improving community livelihoods and wellbeing."}]},{"head":"Stakeholder organizations and their roles in the land-seascapes","index":11,"paragraphs":[{"index":1,"size":54,"text":"Diverse stakeholders are present in the different landscapes. While the Bortianor and Tsokomey/ Tetegu communities in Densu had up to eight stakeholder organizations each, the Amanful community in Whin and Narkwa had three recognized stakeholder groups while the Apremdo community (also in the Whin basin) reported two stakeholder groups with involvement in fishing activities."},{"index":2,"size":129,"text":"The prominent roles played by the identified stakeholders varied considerably depending on the type of stakeholder (Table 16). Based on the contribution of each stakeholder to their livelihood activities, respondents at the Densu basin identified Development Action Association, a non-governmental association (NGO), as the most important stakeholder followed by the Densu Oysters Pickers Association, a community group, and then the Ministry of Fisheries. At Narkwa, the fishers' group was identified as the most important followed by the USAID/ SFMP project in the community. At New Amanful, the fishmongers group was more important followed by the USAID. Municipal and District Assemblies are key stakeholders in local governance across sites but were only mentioned at Densu, where it plays a key role in co-management of the oyster shellfishery in the estuary."},{"index":3,"size":15,"text":"3.9 Mangroves as unique elements of the land-seascape: their state, health, and contributions to shellfishing"},{"index":4,"size":238,"text":"Communities in Densu believed the mangroves are improving, while it is the opposite in Amanful in the Whin basin. At Apremdo (also in New Amanful), the community reported no change in their mangrove forest. The mangrove forests at all communities were of good health except at New Amanful (Table 17). The respondents agreed that mangroves are important for shellfishing in all the communities where mangroves currently grow. They explained that mangroves provide a habitat for shellfish to live and reproduce. In the light of this, respondents indicated a willingness to plant more mangroves to realize the associated benefits. Van Lavieren et al., (2012) and Hutchison et al., (2014) also stated the sustainability of oyster farming depends on the mangrove state. At Densu, the most important indicator of the health status of mangroves is its appearance. Where the vegetation looks physically good with vigorous growth, its attributes will be normal green leaves if the mangrove is healthy. Leaves will be curled and yellow with weak looking or degraded plants, signs of harvesting, and high sunshine in the area if the mangrove is unhealthy. A second important indicator is the presence of animal species. A healthy mangrove attracts or is inhabited by several animal species, particularly birds. The presence of high quantities of shellfish in general, oyster and crabs, are indicators of a healthy mangrove, while it is unhealthy for the reverse, where shellfish yields will be reduced (Table 18)."},{"index":5,"size":38,"text":"Participants at Whin believed that the form of the mangrove tree is the most important indicator of how healthy the stand is. The stand is healthy when the trees are growing well, with the trees resprouting when cut. "}]},{"head":"DISCUSSION","index":12,"paragraphs":[{"index":1,"size":351,"text":"The findings from this study indicate highly vulnerable communities along the coastal areas whose livelihood options are heavily restricted to land and water resources around them. Lawson et al. (2012) also reported the intertwined nature of the environment-livelihood nexus in coastal areas of Ghana and how vulnerable they could easily be if ecosystems are not managed well. With the landseascape under intense degradation pressure and with climate change directly posing a significant challenge (Atindana et al., 2020;Freduah et al., 2017)  One notable observation from the challenges the communities identified and the measures they proposed is the very strong need for regulatory measures to be implemented in their land-seascapes (Figure 2). For instance, light fishing, chemical fishing, sand mining, illegal expansion of houses and construction, etc. all need the interventions of government agencies to be actively playing their roles in minimizing degradation of the land seascapes. They could be very critical in developing legislative instruments (i.e., policies, strategies, and guidelines) as the enablers for local actions (Table 19). However, as indicated in the preceding sections of this report, governments do not always have sufficient workforce and skills to effectively implement specific measures that should happen at every locality. This is where empowering local resource governance systems through the communities could than land, owing to many challenges. They aspire to improve these land-based activities if existing barriers can be removed or circumvented. Observations of the community distribution further revealed some potential to make better use of the upland areas in the communities. Under the existing circumstance, it is possible to design and implement agroforestry interventions to improve/optimize current land use and provide additional benefits and resources to communities, including enhanced agricultural productivity, multiple ecosystem services, and human well-being (Brown et al., 2018). For instance, woodlot-based timber farming could generate substantial income on land and meet the domestic energy demands primarily in the upland parts, and as a result contribute to reducing soil erosion often resulting in siltation in the coastal areas (Eshetu et al., 2018). Another alternative is fodder tree growing for livestock rearing which could easily be combined with the zero-grazing approach."},{"index":2,"size":143,"text":"Useful multi-purpose tree species could be introduced for planting in the communities (Kankam et al., 2021). Although locals complained about inadequate spaces for planting, a close observation of the communities reveals that some tree integration is possible. For instance, trees could be planted in homesteads (home gardens), home boundaries, and on the farms of those who have land. Major constraints to the successful introduction of trees include inadequate knowledge about tree planting and nurturing, exposure of planted tree seedlings to livestock, poor technical knowledge on dealing with saline soils, lack of access to planting materials and commitment to nurture planted trees until maturity (Airoldi et al., 2021). These constraints can largely be resolved through training and capacity building for local people. Providing certain basic inputs such as tree seeds or tree seedlings would also help overcome the challenges and improve chances of success."},{"index":3,"size":135,"text":"For the expansion of livestock rearing in the communities, the major constraint is the lack of space to practice the traditional free-range approach of livestock rearing. A possibility would be to adopt a zero-grazing approach where the animals are fed in stables. Other constraints are disease outbreak and theft (Timpong-Jones et al., 2014). For diseases, involvement of veterinary services would help, while housing livestock would also minimize or eliminate theft. Successful implementation of livestockbased intervention in these communities would also require that local people be given some training on the new ways of keeping and caring for livestock. It was evident from the agricultural and wild biodiversity assessment that feedstocks for goats and sheep were primarily limited to cassava leaves and peels except for Narkwa where fodder from Ficus spp. was occasionally harvested for sheep."},{"index":4,"size":42,"text":"There is a possibility of introducing fodder species from the wild or planting on farms, in home gardens, or boundaries if livestock are put in enclosures. There may also be a need to create linkages between local people and extension service providers."}]},{"head":"SUMMARY","index":13,"paragraphs":[{"index":1,"size":50,"text":"We deployed a participatory landscape visioning process to engage coastal communities in focus group discussions to appraise resource and livelihood trends and understand the change trajectories on selected land-seascape attributes. Participants were enthusiastic in responding to the questions discussed. The major highlights and implications of the findings are summarized below."},{"index":2,"size":45,"text":"Most shellfishers were female (over 40 years of age) with a minority of male involvement. Even where males were involved in shellfishing, they performed specific tasks along the value chain, leaving certain specific aspects to females, thus highlighting gender differentiation in participation of livelihood activities."},{"index":3,"size":150,"text":"The crops cultivated across sites included staples (e.g., maize, plantain, cassava), vegetables (e.g., tomatoes, onions, okra, pepper) and fruits (pineapple), while livestock included poultry (fowls and ducks), pigs, and ruminants (goats and sheep). Across all sites and communities, changes have occurred in landscape productivity towards declining availability of resources over the past 5-10 years. Resource availability was declining for almost all resources (e.g., crops, livestock, fishery, biodiversity, soil, etc.). The drivers of resource decline did not differ much across sites and communities, and the major causes are the increasing human population and the subsequent high demand for resource supplies. For instance, increasing population led to over-exploitation/ over-utilization of lands/ soils, biodiversity, vegetation of all kinds, and water. The main threats to resource availability, going forward, were identified to include erratic/ reduced rainfall, disease outbreaks, and continuous over-exploitation of resources. These threats can further worsen the current situation in the communities."},{"index":4,"size":82,"text":"The main livelihood activities across the sites and communities were fishing and farming and were reported to be on the decline. Alternative livelihood options independent of the existing natural sources (sea and land) are almost non-existent in the study sites; only masonry, trading and sand mining were mentioned in some communities, but their scale of practice was low. This situation would invariably lead to further worsening of the already existing over-dependence on these natural resources and thus increase the vulnerabilities of inhabitants."},{"index":5,"size":104,"text":"The respondents identified some activities they wish to stop/ eliminate, expand, and replace. Additionally, some activities were identified as necessary to introduce in the communities. The activities to be stopped or replaced are those with negative consequences on resource availability and, by extension, livelihoods, including pollution of waters (sea and lagoon), causing over-exploitation of aquatic animals such as the use of light fishing and cutting of mangrove forests. Alternatively, activities to expand or introduce are perceived to impact communities and their livelihoods positively. These include planting mangrove forests, expanding shellfishing, restoring ecosystems, introducing advanced fishing boats, and providing market linkages for local fisher folks."},{"index":6,"size":130,"text":"Stakeholder organizations are actively present in the Densu basin but not in the other sites (Narkwa and Whin). The presence of relevant stakeholder organizations influences the communities in the execution of their daily livelihood activities; thus, it has been observed that the Densu basin is exposed to certain opportunities and activities that are non-existent in the Narkwa and Whin basins. For example, while the shellfishers at Densu basin have all been engaged and thus have knowledge about mangrove planting, shellfishers at Narkwa and Whin basins have no such knowledge and experience. Consequently, the mangrove stands have expanded at Densu because of planting and appears healthy from community perceptions. At Whin, mangrove stands at Apremdo have not changed but are healthy, those at Amanful have reduced in size and appear unhealthy."},{"index":7,"size":53,"text":"The indicators of healthy mangroves mentioned by respondents include physical appearance of mangrove plants, presence of indicator species like birds, presence of shellfish and crabs due to presence of mangroves. On the other hand, the indicators of unhealthy mangroves include signs of harvesting, high sunshine in the area, and reduced yield of shellfish."},{"index":8,"size":72,"text":"Generally, coastal livelihoods are increasingly threatened as water resources and the general landscape seem to be declining with overuse, infrastructural expansion, and climate change. Apart from land scarcity issues at Densu, irregular climatic patterns leading to high temperatures and unexpected drought during the main growing season negatively affect farming, particularly at Narkwa and Whin. At Narkwa, the coping strategy for some people has been digging wells for watering especially for pineapple production."}]}],"figures":[{"text":" 1. INTRODUCTION ................................................................................................................................................................................ 2. METHODS ................................................................................................................................................................................................. 2.1 DESCRIPTION OF STUDY SITES ......................................................................................................................................................... 2.2 SELECTION OF FGD PARTICIPANTS .............................................................................................................................................. 3. FINDINGS ................................................................................................................................................................................................... 3.1 SOCIO-DEMOGRAPHICS AND LIVELIHOOD OPTIONS IN THE LAND-SEASCAPES .................................................... 3.2 ROLE OF MEN AND WOMEN IN THE LAND-SEASCAPES ...................................................................................................... 3.3 TYPOLOGIES OF LIVELIHOOD ACTIVITIES IN THE LAND-SEASCAPE: CURRENT STATE AND PROSPECTS ..... 3.4 SEASONALITY OF LIVELIHOOD ACTIVITIES ............................................................................................................................... 3.5 COMMUNITY PERCEPTIONS OF THE STATE OF THE LAND-SEASCAPE ATTRIBUTES: PAST, PRESENT, AND FUTURE PERSPECTIVES ................................................................................................................................................................................. 3.6 FACTORS UNDERLYING THE DYNAMICS OF THE LAND-SEASCAPE ATTRIBUTES .................................................. 3.7 UNDERSTANDING THE ENVISAGED CHANGE TRAJECTORIES IN THE LAND-SEASCAPES .................................. 3.8 STAKEHOLDER ORGANIZATIONS AND THEIR ROLES IN THE LAND-SEASCAPES ................................................... 3.9 MANGROVES AS UNIQUE ELEMENTS OF THE LAND-SEASCAPE: THEIR STATE, HEALTH, AND CONTRIBUTIONS TO SHELLFISHING .................................................................................................................................................... 4.. DISCUSSION ........................................................................................................................................................................................ 5.. SUMMARY ............................................................................................................................................................................................. REFERENCES ............................................................................................................................................................................................... v LIST OF FIGURES Page Figure 1: Ghana study sites: Densu, Narkwa, and Whin ........................................................................................................ Figure 2: Niche based challenges identified and interventions proposed for a better land-seascape in Ghana ...................................................................................................................................................................................................................... LIST OF TABLES Page Table 1: Attributes of land-seascapes considered for assessing the degradation proxy. ................................. Table 2: Socio-demographic profile of FGD participants. ..................................................................................................... Table 3: Activity profile of shellfishers by gender. ....................................................................................................................... Table 4: Community perceptions of trends in main activities over the past decade. ........................................ Table 5: Primary and secondary livelihood activities during the year in Densu. ................................................. Table 6: Primary and secondary livelihood activities across the year in Narkwa and Whin ...................... Table 7: Participant perceptions of the state of the land-seascape in the sites from Ghana. ................... Table 8: Community perceptions of the land-seascape attributes in the Densu. ............................................. Table 9: Community perceptions of the land-seascape attributes at Narkwa. ................................................... Table 10: Community perceptions of the land-seascape attributes in Whin. ...................................................... Table 11: Community perceptions of main drivers of change and threats at Densu. ................................... Table 12: Community perceptions of main drivers of change and threats at Narkwa. ................................ Table 13: Community perceptions of main drivers of change and threats at Whin land-seascape. ... Table 14: Change trajectories envisaged and underlying reasons in Densu. ......................................................... Table 15: Change trajectories envisaged and underlying reasons in Narkwa and Whin. ............................ Table 16: Perception of communities about stakeholder type, roles and importance .................................. Table 17: Trends in mangrove forest relationship with shellfishing. ........................................................................... Table 18: Indicators and attributes of health status of mangroves. ............................................................................. Table 19: Potential enablers and inputs to implement key interventions .............................................................. "},{"text":"Figure 2 : Figure 2: Niche based challenges identified and interventions proposed for a better land-seascape in Ghana. Note: the proposed interventions are mostly from the communities, but some are also added from the researchers based on their knowledge of the area. "},{"text":"  "},{"text":"Table 1 : Attributes of land-seascapes considered for assessing the degradation proxy. Ecosystem element Attributes Ecosystem elementAttributes Production Crops; livestock; fisheries; agroforestry (Four attributes) ProductionCrops; livestock; fisheries; agroforestry (Four attributes) Biodiversity Aquatic animals; terrestrial animals (Two attributes) BiodiversityAquatic animals; terrestrial animals (Two attributes) Vegetation Forests and woodlands; mangrove (Two attributes) VegetationForests and woodlands; mangrove (Two attributes) Soil condition Soil fertility (One attribute) Soil conditionSoil fertility (One attribute) Fresh water Volume; availability and quality (Two attributes) Fresh waterVolume; availability and quality (Two attributes) Settlements Residential spaces (One attribute) SettlementsResidential spaces (One attribute)  "},{"text":"Table 2 : Socio-demographic profile of FGD participants.     Parameter Parameter  "},{"text":"Table 3 : Activity profile of shellfishers by gender. Community Men's' main activities Women's' main activities CommunityMen's' main activitiesWomen's' main activities  Estuary fishing; Preparation and dumping of  Estuary fishing; Preparation and dumping of Densu-Bortianor mangrove vegetation as fish traps into estuary water; sea fishing, oyster, collection, Household chores; oyster collection and sale Densu-Bortianormangrove vegetation as fish traps into estuary water; sea fishing, oyster, collection,Household chores; oyster collection and sale  and sale  and sale  Estuary fishing; Preparation and dumping of  Estuary fishing; Preparation and dumping of Densu-Tsokomey mangrove vegetation as fish traps into estuary water; sea fishing, collection, and sale Household chores; oyster collection and sale Densu-Tsokomeymangrove vegetation as fish traps into estuary water; sea fishing, collection, and saleHousehold chores; oyster collection and sale  of oysters  of oysters Narkwa Collection of oysters for spouses to process for sale Household chores, oyster, collection, and sale NarkwaCollection of oysters for spouses to process for saleHousehold chores, oyster, collection, and sale Whin -Apremdo N.A. Oyster collection, processing, and sale Whin -ApremdoN.A.Oyster collection, processing, and sale Whin -Amanful Collection of oysters from stone/rock surfaces for spouses to process for sale Collection from mangroves, processing, and sale of oysters Whin -AmanfulCollection of oysters from stone/rock surfaces for spouses to process for saleCollection from mangroves, processing, and sale of oysters  "},{"text":"Table 4 : Community perceptions of trends in main activities over the past decade, cause of change and future plans. Site Community Specific Trend in the last 10 years and Plans for the next 10 years SiteCommunitySpecificTrend in the last 10 years andPlans for the next 10 years   livelihood underlying reasons for the and reasons for proposed livelihoodunderlying reasons for theand reasons for proposed   activities observed trends changes activitiesobserved trendschanges Densu Tsokomey & Fishing Declining: Light fishing, nets with Increase: To provide more DensuTsokomey &FishingDeclining: Light fishing, nets withIncrease: To provide more  Tetegu  very tiny holes thus catching all fish for higher incomes Teteguvery tiny holes thus catching allfish for higher incomes    types of fish, over exploitation  types of fish, over exploitation   Farming Declining: Due to conversion of Increase: To improve food FarmingDeclining: Due to conversion ofIncrease: To improve food    vegetation for building due to security vegetation for building due tosecurity    population increase  population increase  Bortianor Fishing Declining: Pollution; Use of Increase: To improve and BortianorFishingDeclining: Pollution; Use ofIncrease: To improve and    dangerous chemicals for fishing; return to original dangerous chemicals for fishing;return to original    Diversion of estuary by  Diversion of estuary by    community  community   Overflow Expanding: Use of more Increase: Diversion of estuary OverflowExpanding: Use of moreIncrease: Diversion of estuary    advanced fishing equipment e.g., by community at a time of advanced fishing equipment e.g.,by community at a time of    light fishing and trawler fishing overflow light fishing and trawler fishingoverflow Narkwa Narkwa Shellfishing Declining: Declining: Use of Increase: To ensure return of Narkwa NarkwaShellfishingDeclining: Declining: Use ofIncrease: To ensure return of    dangerous chemicals for fishing, higher yields dangerous chemicals for fishing,higher yields    use of light for fishing  use of light for fishing   Trading Declining: Lack of finance Increase: To ensure TradingDeclining: Lack of financeIncrease: To ensure     improved livelihoods improved livelihoods   Farming Declining: Poor rains and soils Increase: To ensure higher FarmingDeclining: Poor rains and soilsIncrease: To ensure higher     yields for food security and yields for food security and     better livelihoods better livelihoods Whin Amanful Masonry Declining: Reduced job Increase: To be able to WhinAmanfulMasonryDeclining: Reduced jobIncrease: To be able to    opportunities obtain more resources for opportunitiesobtain more resources for     improved livelihood improved livelihood   Fishing Declining: Some cultural Increase: They want it to FishingDeclining: Some culturalIncrease: They want it to    practices have not been improve because there are practices have not beenimprove because there are    adhered to; filling up of the no other jobs in the area, so adhered to; filling up of theno other jobs in the area, so    lagoon especially during rainy they rely on it lagoon especially during rainythey rely on it    season results in lower harvests  season results in lower harvests    since they cannot enter;  since they cannot enter;    Declining mangroves  Declining mangroves  Apremdo Sand mining Declining: The job is difficult; Stop: It should be stopped in ApremdoSand mining Declining: The job is difficult;Stop: It should be stopped in    Land has greatly reduced so future because it is increasing Land has greatly reduced sofuture because it is increasing    sand quantities have reduced depth of the lagoon and sand quantities have reduceddepth of the lagoon and     reducing the quantities of fish reducing the quantities of fish  "},{"text":"Table 5 : Primary and secondary livelihood activities during the year in Densu. Month Tsokomey & Tetegu Bortianor MonthTsokomey & TeteguBortianor January Fishing; oyster closed season; Fish smoking and Fishing; Trading JanuaryFishing; oyster closed season; Fish smoking andFishing; Trading  sale, fish carrier  sale, fish carrier February Fishing; oyster closed season; Fish smoking and Fishing; Trading FebruaryFishing; oyster closed season; Fish smoking andFishing; Trading  sale, fish carrier  sale, fish carrier March Fishing; oyster closed season; Fish smoking and Farming; Fish smoking and sale, fish MarchFishing; oyster closed season; Fish smoking andFarming; Fish smoking and sale, fish  sale, fish carrier carrier sale, fish carriercarrier April Fishing; oyster closed season; Fish smoking and Farming; Fishing; Fish smoking and sale, AprilFishing; oyster closed season; Fish smoking andFarming; Fishing; Fish smoking and sale,  sale, fish carrier fish carrier sale, fish carrierfish carrier May Oyster (oyster season opens); Fish smoking and Farming; Oyster fishing, Fish smoking, MayOyster (oyster season opens); Fish smoking andFarming; Oyster fishing, Fish smoking,  sale, fish carrier and sale, fish carrier sale, fish carrierand sale, fish carrier June Fishing & reduced oyster fishing due to rainfall; Farming; Oyster fishing, Fish smoking, JuneFishing & reduced oyster fishing due to rainfall;Farming; Oyster fishing, Fish smoking,  Fish smoking and sale, fish carrier and sale, fish carrier Fish smoking and sale, fish carrierand sale, fish carrier July Fishing & reduced oyster fishing due to rainfall; Farming; Oyster fishing, Fish smoking, JulyFishing & reduced oyster fishing due to rainfall;Farming; Oyster fishing, Fish smoking,  smoke fish, Fish sale & fish carrier and sale, fish carrier smoke fish, Fish sale & fish carrierand sale, fish carrier August Fishing & reduced oyster fishing due to rainfall; Farming; Oyster fishing, Fish smoking, AugustFishing & reduced oyster fishing due to rainfall;Farming; Oyster fishing, Fish smoking,  smoke fish, Fish sale & fish carrier and sale, fish carrier smoke fish, Fish sale & fish carrierand sale, fish carrier September Smoke fish, Fish sale & fish carrier Farming; Oyster fishing, Fish smoking, SeptemberSmoke fish, Fish sale & fish carrierFarming; Oyster fishing, Fish smoking,   and sale, fish carrier and sale, fish carrier October Smoke fish, Fish sale & fish carrier Farming; Oyster fishing, Fish smoking, OctoberSmoke fish, Fish sale & fish carrierFarming; Oyster fishing, Fish smoking,   and sale, fish carrier and sale, fish carrier November Smoke fish, Fish sale & fish carrier Farming; Oyster fishing, Fish smoking, NovemberSmoke fish, Fish sale & fish carrierFarming; Oyster fishing, Fish smoking,   and sale, fish carrier and sale, fish carrier December Fishing only; oyster closed season; smoke fish, Farming; Fish smoking and sale, fish DecemberFishing only; oyster closed season; smoke fish,Farming; Fish smoking and sale, fish  Fish sale & fish carrier carrier Fish sale & fish carriercarrier  "},{"text":"Table 6 : Primary and secondary livelihood activities across the year in Narkwa and Whin estuaries. Month Narkwa Whin (Apremdo) Whin (Amanful) MonthNarkwaWhin (Apremdo)Whin (Amanful) January Shellfishing, fishing, fish sales Shellfishing Shellfishing; Trading JanuaryShellfishing, fishing, fish sales ShellfishingShellfishing; Trading February Shellfishing, fishing, fish sales Shellfishing Shellfishing; Trading FebruaryShellfishing, fishing, fish sales ShellfishingShellfishing; Trading March Shellfishing, fishing, fish sales Shellfishing Shellfishing; Trading MarchShellfishing, fishing, fish sales ShellfishingShellfishing; Trading April Shellfishing, fishing, fish sales Farming; Shellfishing Shellfishing; Trading AprilShellfishing, fishing, fish sales Farming; ShellfishingShellfishing; Trading May Shellfishing, fishing, fish sales; Farming; Shellfishing Trading; Shellfishing, fish sales MayShellfishing, fishing, fish sales;Farming; ShellfishingTrading; Shellfishing, fish sales  Farming   Farming June Shellfishing, fishing, fish sales; Farming; Shellfishing Trading JuneShellfishing, fishing, fish sales;Farming; ShellfishingTrading  Farming   Farming July Shellfishing, fishing, fish sales; Farming; Shellfishing Trading JulyShellfishing, fishing, fish sales;Farming; ShellfishingTrading  Farming   Farming August Shellfishing, fishing, fish sales; Farming; Shellfishing Trading AugustShellfishing, fishing, fish sales;Farming; ShellfishingTrading  Farming   Farming September Shellfishing, fishing, fish sales Shellfishing Trading SeptemberShellfishing, fishing, fish sales ShellfishingTrading October Shellfishing, fishing, fish sales Shellfishing Trading OctoberShellfishing, fishing, fish sales ShellfishingTrading November Shellfishing, fishing, fish sales Shellfishing Trading; Shellfishing, fish sales NovemberShellfishing, fishing, fish sales ShellfishingTrading; Shellfishing, fish sales December Shellfishing, fishing, fish sales Shellfishing Trading; Shellfishing, fish sales DecemberShellfishing, fishing, fish sales ShellfishingTrading; Shellfishing, fish sales 3.5 Community perceptions of the state of the land-seascape attributes: past, 3.5 Community perceptions of the state of the land-seascape attributes: past, present, and future perspectives   present, and future perspectives  "},{"text":"Table 7 : Participant perceptions of the state of the land-seascape in the sites from Ghana. Ecosystem Attributes Densu Narkwa Whin EcosystemAttributesDensuNarkwaWhin elements  Tsokomey Bortianor Narkwa Apremdo Amanful elementsTsokomey Bortianor NarkwaApremdo Amanful Production Crop ▼   ProductionCrop▼  "},{"text":"Table 8 : Community perceptions of the land-seascape attributes in the past, present and desired future at Densu. Attribute Specifics Tsokomey AttributeSpecificsTsokomey  "},{"text":"Table 9 : Community perceptions of the land-seascape attributes in the past, present and desired future at Narkwa. Ecosystem Attributes  Descriptions  EcosystemAttributesDescriptions elements  Past (5-10 years) Present Desired future (5-10 years) elementsPast (5-10 years)PresentDesired future (5-10 years) Production Crop Maize, Pineapple, Same crops Hoping for an ProductionCropMaize, Pineapple,Same cropsHoping for an   Tomatoes, Pepper production reduced improvement in future Tomatoes, Pepperproduction reducedimprovement in future    due to insufficient  due to insufficient    rains  rains  Livestock Sheep, Goats Quantities reduced Wish for the possibility to LivestockSheep, GoatsQuantities reducedWish for the possibility to     rear more rear more  Fishery High in the past Reduced quantities Hoping for higher FisheryHigh in the pastReduced quantitiesHoping for higher     production production  Agroforestry None None Wish for a possibility but AgroforestryNoneNoneWish for a possibility but     does not look likely does not look likely Biodiversity Aquatic animals Keta schoolboys, Now the quantities Wish for an increase in BiodiversityAquatic animals Keta schoolboys,Now the quantitiesWish for an increase in   Herrings, Redfish have declined quantities Herrings, Redfishhave declinedquantities  Terrestrial None None None TerrestrialNoneNoneNone  animals    animals Vegetation Forests and None None Wish for a possibility but VegetationForests andNoneNoneWish for a possibility but  woodlands   does not look likely due to woodlandsdoes not look likely due to     land limitation land limitation  Mangrove None None None MangroveNoneNoneNone Soil condition Soil fertility Was better Now declined Hoping for improvement Soil condition Soil fertilityWas betterNow declinedHoping for improvement Freshwater Volume None None None FreshwaterVolumeNoneNoneNone  Availability and None None None Availability andNoneNoneNone  Quality    Quality Settlements Residential More land in the Reduced but still None SettlementsResidentialMore land in theReduced but stillNone  spaces past manageable  spacespastmanageable  "},{"text":"Table 10 : Community perceptions of the land-seascape attributes in the past, present and desired future at Whin. Ecosystem Attributes Apremdo EcosystemAttributesApremdo elements   elements  "},{"text":"Table 11 : Community perceptions of main drivers of change and threats at Densu. Ecosystem Attributes Tsokomey EcosystemAttributesTsokomey elements   elements  "},{"text":"Table 12 : Community perceptions of main drivers of change and threats at Narkwa. Ecosystem Attributes Factors that led to degradation Possible threats in the EcosystemAttributesFactors that led to degradationPossible threats in the elements   future elementsfuture Production Crop Land shortage and declining soil fertility Lower rainfall ProductionCropLand shortage and declining soil fertility Lower rainfall  Livestock Most of the land is converted to Disease outbreaks LivestockMost of the land is converted toDisease outbreaks   infrastructure  infrastructure  Fishery The use of light fishing has negative Overexploitation FisheryThe use of light fishing has negativeOverexploitation   effects  effects  Agroforestry - - Agroforestry-- Biodiversity Aquatic animals The use of fishing trawlers leads to Overexploitation BiodiversityAquatic animalsThe use of fishing trawlers leads toOverexploitation   over-harvesting; the use of light for  over-harvesting; the use of light for   fishing on seas has reduced stock  fishing on seas has reduced stock  Terrestrial animals - Habitat destruction Terrestrial animals-Habitat destruction Vegetation Forests and - Overexploitation VegetationForests and-Overexploitation  woodlands   woodlands  Mangrove - - Mangrove-- Soil condition Soil fertility Intensive use Overcultivation Soil condition Soil fertilityIntensive useOvercultivation Freshwater Volume None Siltation of the river FreshwaterVolumeNoneSiltation of the river  Availability and None Pollution of the river Availability andNonePollution of the river  Quality   Quality Settlements Residential spaces High demand for residential lands None SettlementsResidential spacesHigh demand for residential landsNone  "},{"text":"Table 13 : Community perceptions of main drivers of change and threats at Whin land-seascape. Ecosystem Attributes Apremdo EcosystemAttributesApremdo elements   elements  "},{"text":"Table 14 : Change trajectories envisaged and underlying reasons in Densu. Location Activities to eliminate (Stop doing) and the reason Activities to expand and the reason Activities to introduce (new activities) and the reason LocationActivities to eliminate (Stop doing) and the reasonActivities to expand and the reasonActivities to introduce (new activities) and the reason Tsokomey ▪ Light fishing: Because it has ▪ Planting ▪ Introduction of Tsokomey▪ Light fishing: Because it has▪ Planting▪ Introduction of & Tetegu reduced the quantity of fish catch mangroves: advanced fishing & Tetegureduced the quantity of fish catchmangroves:advanced fishing  and affected livelihoods; Increase fish boats: to improve and affected livelihoods;Increase fishboats: to improve  ▪ Pollution of water bodies: This production fishing; ▪ Pollution of water bodies: Thisproductionfishing;  causes hygiene problems and also  ▪ Market linkages: to causes hygiene problems and also▪ Market linkages: to  affects the fish quantity and water  provide better affects the fish quantity and waterprovide better  quality  markets for locals qualitymarkets for locals Bortianor ▪ Dumping of refuse in the water: ▪ Planting ▪ Mangrove planting: to Bortianor▪ Dumping of refuse in the water:▪ Planting▪ Mangrove planting: to  Reducing yields of harvests from mangroves: to improve mangrove Reducing yields of harvests frommangroves: toimprove mangrove  the lagoon; promote spawning forests; the lagoon;promote spawningforests;  ▪ Defecating into the river: to avoid of fish; ▪ Introduce birds, crabs, ▪ Defecating into the river: to avoidof fish;▪ Introduce birds, crabs,  polluting the water; ▪ Improve school etc. to restore the polluting the water;▪ Improve schooletc. to restore the  ▪ Cutting of mangroves and other infrastructure: ecosystems: to ▪ Cutting of mangroves and otherinfrastructure:ecosystems: to  trees: To avoid degradation of the education will restore ecosystem trees: To avoid degradation of theeducation willrestore ecosystem  mangroves; promote biodiversity and mangroves;promotebiodiversity and  ▪ Light fishing (fishing with light): to cleanliness services ▪ Light fishing (fishing with light): tocleanlinessservices  avoid over-fishing;   avoid over-fishing;  ▪ Using of fine mesh nets: to avoid   ▪ Using of fine mesh nets: to avoid  catching all sizes of fish, including   catching all sizes of fish, including  juveniles;   juveniles;  ▪ Chemicals for fishing such as   ▪ Chemicals for fishing such as  carbide: to avoid killing all the   carbide: to avoid killing all the  animals present in the water;   animals present in the water;  ▪ Use of wooden traps in   ▪ Use of wooden traps in  water/trap fishing: to avoid   water/trap fishing: to avoid  catching all sizes of fish, including   catching all sizes of fish, including  juveniles   juveniles  "},{"text":"Table 15 : Change trajectories envisaged and underlying reasons in Narkwa and Whin. Location Activities to eliminate Activities to Activities to introduce (new activities) LocationActivities to eliminateActivities toActivities to introduce (new activities)  (stop doing) and the expand and the and the reason (stop doing) and theexpand and theand the reason  reason reason  reasonreason Narkwa Use of light fishing: Results Shellfishing: To Introduction of new job opportunities: NarkwaUse of light fishing: ResultsShellfishing: ToIntroduction of new job opportunities:  in low fish yields improve To prevent diseases that may occur due in low fish yieldsimproveTo prevent diseases that may occur due   production and to lack of food resulting from lack of production andto lack of food resulting from lack of   livelihoods income and livelihood benefits from the livelihoodsincome and livelihood benefits from the    land-seascapes. land-seascapes.  Water pollution: Health  Clean drinking water: To improve Water pollution: HealthClean drinking water: To improve  risks  livelihoods riskslivelihoods Whin - Sand mining: Seabed Shellfishing: Major Introduction of other livelihood options: Whin -Sand mining: SeabedShellfishing: MajorIntroduction of other livelihood options: Apremdo supporting fish is livelihood activity To diversify income sources Apremdosupporting fish islivelihood activityTo diversify income sources  disturbed* hence important  disturbed*hence important   to expand  to expand Whin - Sea defense program **: None Manage expansion of the sea Whin -Sea defense program **:NoneManage expansion of the sea Apremdo Increasing depth of the  Alternative livelihood activities: To ApremdoIncreasing depth of theAlternative livelihood activities: To  lagoon  improve livelihoods and income lagoonimprove livelihoods and income Note: * This has affected livelihood activities based on the collection of seashells for terrazzo making. ** denotes efforts Note: * This has affected livelihood activities based on the collection of seashells for terrazzo making. ** denotes efforts  to stop shoreline erosion a by using engineering techniques (e.g., building a sea wall). to stop shoreline erosion a by using engineering techniques (e.g., building a sea wall).  "},{"text":"Table 17 : Trends in mangrove forest relationship with shellfishing.   Densu Narkwa  Whin DensuNarkwaWhin Status of mangrove Bortianor Tsokomey & Tetegu Narkwa Apremdo Amanful Status of mangroveBortianorTsokomey & TeteguNarkwaApremdoAmanful The trend of mangrove Expanding Expanding None No change Declining The trend of mangroveExpandingExpandingNoneNo changeDeclining forest      forest Current mangrove health Healthy Healthy None Healthy Not Current mangrove healthHealthyHealthyNoneHealthyNot condition     healthy conditionhealthy Relation between mangrove Yes Yes None Yes Yes Relation between mangroveYesYesNoneYesYes and shellfish      and shellfish  "},{"text":"Table 18 : Indicators and attributes of health status of mangroves.     Healthy Unhealthy HealthyUnhealthy Site Community Indicators Description mangrove mangrove SiteCommunityIndicatorsDescriptionmangrovemangrove     attributes attributes attributesattributes Densu Tsokomey & Physical Good physical Mangrove stands Degraded stand; DensuTsokomey &PhysicalGood physicalMangrove standsDegraded stand;  Tetegu appearance appearance of that are not curled, yellow Teteguappearanceappearance ofthat are notcurled, yellow    mangrove vegetation degraded; green leaves mangrove vegetationdegraded; greenleaves     leaves  leaves  Bortianor Physical Healthy mangroves Normal green Yellow leaves, BortianorPhysicalHealthy mangrovesNormal greenYellow leaves,   appearance appear vigorous and leaves, vigor in curled leaves, appearanceappear vigorous andleaves, vigor incurled leaves,    not degraded growth weak looking not degradedgrowthweak looking      plants plants   Presence of If the mangrove Presence of high Low presence of Presence ofIf the mangrovePresence of highLow presence of   animal vegetation is healthy, quantities of shellfish and animalvegetation is healthy,quantities ofshellfish and   species like certain animal species shellfish and oysters species likecertain animal speciesshellfish andoysters   birds will be found oysters  birdswill be foundoysters Whin Amanful Mangrove Mangrove trees When cut, they  WhinAmanfulMangroveMangrove treesWhen cut, they   trees appear healthy and can re-sprout  treesappear healthy andcan re-sprout    growing well themselves  growing wellthemselves  "},{"text":" , the livelihood of the communities is highly threatened. Other studies such asDaniels et al. (2021) highlighted similar concerns.There are opportunities for meaningful interventions if communities have awareness about the prevailing challenges, the causes of those challenges, and what can be done to improve the status quo(Strain et al., 2019). Further, local people have a very good appraisal of what to stop doing, what to expand and what to introduce to minimize the degradation of their land-seascapes. More importantly, local people have a good mental picture of the future they desire to achieve by implementing various interventions in various niches within the land-seascape (Figure2).  "}],"sieverID":"1fabc66f-4855-46e7-bea3-1a09f4ade477","abstract":"The views expressed and opinions contained in this report are those of the Project team and are not intended as statements of policy of either USAID or the cooperating organizations. As such, the contents of this report are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government."}

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+{"metadata":{"id":"04968a023f8bc526116cae7506e5d33b","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/7fa4b15c-e05a-426b-ac8c-06d5ba45e99a/retrieve"},"pageCount":13,"title":"Molecular basis of permethrin and DDT resistance in an Anopheles funestus population from Benin","keywords":["Anopheles funestus","Insecticide resistance","Permethrin","DDT","Kpome","Resistance mechanisms"],"chapters":[{"head":"Background","index":1,"paragraphs":[{"index":1,"size":61,"text":"There were an estimated 216 million cases of malaria worldwide in 2016 and 445,000 deaths with 80% of all malaria deaths occurring in Africa [1]. Despite extensive control efforts, over half the world's population remains at risk and the disease has a massive impact on health and economic development, particularly in Africa [2]. Four species of Anopheles, An. gambiae Giles, An."},{"index":2,"size":501,"text":"coluzzii Coetzee & Wilkerson, An. arabiensis Patton and An. funestus Giles, are responsible for most of the malaria transmission in this continent. The transmission role of An. funestus (sensu stricto) is further supported by observations that in many regions of Africa, its infection rate even surpasses that of An. gambiae [3]. Long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) are the main malaria prevention interventions [4]. However, the success of these control methods is jeopardised by the development of resistance by Anopheles species to insecticides such as pyrethroids and DDT as seen in the past with loss of efficacy of dieldrin for IRS in west/central Africa [5,6]. In Benin, due to vector resistance to pyrethroids across the country [7][8][9][10], two insecticides of two different classes were used in rotation for IRS: bendiocarb (a carbamate) and pirimiphos-methyl (an organophosphate) [11]. However, DDT is still retained for use in IRS, due to the limited number of cost-effective alternatives [12]. Field studies on insecticide susceptibility carried out as baseline surveys for malaria control programs showed An. funestus to be resistant to various insecticides at various localities [9,10,13]. Metabolic resistance is the main resistance mechanism recorded, and cytochrome P450 genes are playing a major role while target-site resistance like the knockdown resistance (kdr) is absent [9,[14][15][16][17]. Target site resistance was investigated in the pyrethroid/DDT-resistant population and the fifth and sixth segments of domain II in the sodium channel sequence and no polymorphism associated with resistance was identified [9,14,16,[18][19][20] although some mutations have been detected in Cameroon [21] and Uganda [22] but with no association yet established with resistance phenotype. A significant increase in mono-oxygenase activities confers resistance to pyrethroids in An. funestus in Mozambique and South-Africa (southern Africa) [15,[23][24][25], Uganda (east Africa) [16,22,26], western Kenya (east Africa) [18,22], Cameroon (central Africa) [21] and in Senegal (west Africa) [19]. Characterisation of the molecular basis of resistance to pyrethroids and DDT in An. funestus from the coastal area of Benin [27] revealed a predominant role played by the glutathione S-transferase GSTe2. The enzyme encoded by this gene was highly upregulated in association with the presence of an L119F mutation in the substrate binding site conferring a high level of DDT and permethrin resistance [27]. Recently, An. funestus from Kpome, an inland region of Benin has also been shown to be resistant to DDT and permethrin with mortality rates of 9.1 ± 2.5% and 13.0 ± 3%, respectively [10]. However, it remains unknown if the resistant mechanism is the same as observed in Pahou, the coastal area of Benin [9] or whether different resistance mechanisms are responsible for the spread of resistance across the country. Such information is essential in designing suitable control interventions nationwide and to improve the implementation of resistance management strategies against An. funestus. In this study, using a genome-wide microarray-based transcription analysis, the underlying molecular mechanisms conferring DDT and permethrin resistance in Kpome were characterised revealing that both glutathione S-transferases, notably the GSTe2, and cytochrome P450 genes are the main drivers of resistance."}]},{"head":"Methods","index":2,"paragraphs":[]},{"head":"Area of study and mosquito collection","index":3,"paragraphs":[{"index":1,"size":138,"text":"Blood-fed adult female An. funestus mosquitoes resting indoors were collected in houses between 06:00 and 10:00 h from the rural area of Kpome (6°23'N, 2°13'E) a southern inland region of Benin from December 2013 to February 2014. Kpome is a large agricultural setting with the intensive production of tomatoes which mainly serve a commercial purpose and insecticides, mainly pyrethroids (deltamethrin and lambda-cyalothrin), used in public health are also used to protect tomatoes against pest attacks. Mosquito collections and rearing were performed as described previously [10]. Adult female mosquitoes were collected indoor between 06:00 to 10:00 h using electric aspirators. F1 adults were generated from field-collected female mosquitoes using the forced-egg laying method [16] and were randomly mixed in cages for subsequent experiments. All females used for individual oviposition were morphologically and molecularly identified as An. funestus (s.s.) [10,28]."}]},{"head":"Insecticide susceptibility assays","index":4,"paragraphs":[{"index":1,"size":129,"text":"Two to five day-old F1 adult female mosquitoes pooled from different F0 mosquitoes collected from Kpome were used for this test. Twenty to twenty-five mosquitoes per tube with at least four replicates were exposed to DDT (4%) and permethrin (0.75%) for 1 h before transferring into clean holding tube with 10% sugar solution. Final mortality was scored 24 h post-exposure. Survivors and dead mosquitoes were conserved for further analysis. In this study, we used the same mosquitoes from the previously published research results [10] to further describe the molecular basis of the observed resistance in this malaria vector population. Mosquito samples generated from the previous investigation were used for genetic analysis in this work. The susceptible strain used in this study has the same age as the exposed mosquitoes."}]},{"head":"Microarrays","index":5,"paragraphs":[{"index":1,"size":472,"text":"A genome-wide transcription profiling was performed to detect the sets of genes differentially expressed in relation to observed resistance phenotypes in An. funestus populations from Kpome. The microarray hybridisation for An. funestus was carried out using the 8 × 60 k (60 mer) Agilent An. funestus chip. This Agilent microarray chip was designed using the eArray program (Agilent, Santa Clara, CA, US ) (A-MEXP-2374) by adding the 15,527 expressed sequence tags (ESTs) generated from another transcriptome sequencing of An. funestus [29] to the previous 4 × 44 k array (A-MEXP-2245) [24]. Each array was designed using 8540 ESTs generated from An. funestus transcriptome 454 sequencing [30], 2850 An. funestus cDNAs from GenBank and a set of P450 genes from the rp1 and rp2 QTL BAC sequence [31,32], and the 13,000 transcripts of the complete An. gambiae genome. Moreover, An. gambiae detoxification genes previously present on the An. gambiae detox chip [33] were added to this. RNA was extracted from three batches of 10 An. funestus females (2-5 days-old) from the following sample sets: alive after exposure to 0.75% permethrin (Resistant permethrin: R perm ) and 4% DDT (Resistant DDT: R DDT ), un-exposed to insecticides (Control: C), and from the fully susceptible laboratory strain FANG (Susceptible: S) [a strain originating from Calueque in southern Angola in 2002 which is completely susceptible to insecticides (Hunt et al. [34]) using the Picopure RNA Isolation Kit (Arcturus, Waltham, MA, USA). The FANG colony was kept in insectary conditions (temperature of 25-28 °C with a relative humidity of 80%) at the Liverpool School of Tropical Medicine where assays were performed. RNA quantity and quality were assessed using a NanoDrop ND1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and Bioanalyzer (Agilent, Santa Clara, CA, USA), respectively. Each RNA sample was used to generate complementary RNA (cRNA) using the Agilent Quick Amp Labeling Kit (two-colour) following the manufacturer's protocol. cRNAs from the resistant samples (R) were labelled with a cy5 dye, and cRNAs from the susceptible strain FANG (S) were labelled with the cy3 dye. cRNA quantity and quality were assessed using the NanoDrop and Bioanalyzer before labelling. Labelled cRNAs were hybridised to the arrays for 17 h at 65 °C according to the manufacturer's protocol. Five hybridisations were performed for each comparison including two dyes swap per comparison. Microarray data were analysed using Genespring GX 13.0 software purchased from Agilent (Agilent). A normalisation of the data was performed with the GeneSpring feature extraction program using the lowess normalisation approach. To identify differentially expressed genes, a cut-off of 2-fold-change (FC) and a statistical significance of P ≤ 0.05 with Benjamini-Hochberg correction for multiple testing and Storey with bootstrapping (with a cut-off of 1.5-fold-change for the R-C comparison) were applied. A false discovery analysis was also performed using the Benjamini-Hochberg correction test for multiple testing as implemented in GeneSpring."}]},{"head":"Quantitative reverse transcriptase PCR","index":6,"paragraphs":[{"index":1,"size":185,"text":"A qRT-PCR was used to validate the microarrays results for seven of some upregulated detoxification genes. These genes include three cytochromes P450s (CYP6M7, CYP6P9a, CYP9K1), two glutathione transferases (GSTd1-5, GSTe2), and one aldehyde oxidase (Ald oxi). Also, the expression level of CYP6P9b previously found to be strongly associated with pyrethroid resistance [19,31,35] and GSTd1-3, a gene shown as possibly associated with pyrethroid resistance by 454 transcriptome profiling [19,30] were analysed (Table 1). One microgram of the three biological replicates for resistant (permethrin: R perm and DDT: R DDT ), control (C), and FANG (S) was used for cDNA synthesis using Superscript III (Invitrogen, Carlsbad, CA, USA) with oligodT20 and RNase H according to the manufacturer's instructions. Standard curves were established for each gene using serial dilution of cDNA. qRT-PCR amplification was performed using the MX 3005P (Agilent) system. The relative expression level and fold change (FC) of each target gene in resistant and control samples relative to susceptible were calculated according to the 2-ΔΔCT method incorporating the PCR efficiency [36] after normalisation with the housekeeping genes ribosomal protein S7 (RSP7; AFUN007153-RA), and actin (Act5C; AFUN006819)."}]},{"head":"Investigation of the role of the knockdown resistance mutation in DDT and permethrin resistance","index":7,"paragraphs":[{"index":1,"size":193,"text":"A fragment spanning a portion of the voltage-gated sodium channel gene (VGSC), including the 1014 codon (a portion of intron 19 and the entire exon 20, domain II, segment 6 of the VGSC gene) associated with resistance in An. gambiae, was amplified in eleven field-collected females of An. funestus from Kpome using the Exon 19F/KdrFunR2 primers (Exon 19F: 5'-TTT TTA AGC TCG CTA AAT CGT G-3'; KdrFunR2: 5'-CCG AAA TTT GAC AAA AGC AAA-3') [9,15,22]. The PCR products were purified using the QIAquick Purification Kit (Qiagen, Hilden, Germany) and directly sequenced. The polymorphic positions were detected through manual analysis of sequence traces using BioEdit. Sequences were aligned using ClustalW [37], and haplotype reconstruction and analysis were done using DnaSP v5.10 [38]. Kpome sequences were also compared to those previously obtained from Pahou in coastal Benin [9] and Gounougou in North Cameroon [21]. After the selection of the best model, a maximum likelihood phylogenetic tree was generated for the VGSC haplotypes in the different populations using Mega 6.06 [39]. The level of Kst of pairwise genetic differentiation between the populations was estimated in Dnasp v5.10, and the neighbour-Joining tree was built using Mega 6.06."}]},{"head":"Results","index":8,"paragraphs":[]},{"head":"Genome-wide microarray-based transcriptional profiling of permethrin resistance","index":9,"paragraphs":[{"index":1,"size":83,"text":"A set of transcripts were differentially expressed (≥ 2-fold change, P ≤ 0.05) between permethrin resistant (R perm ), susceptible (S) and control (C) mosquitoes (Fig. 1) from microarray analysis. Overall, 3669 transcripts were differentially expressed when permethrin resistant mosquitoes were compared to susceptible strains (1460 overexpressed and 2209 underexpressed), 3617 between C-S (1211 overexpressed and 2406 underexpressed) and 210 for R perm -C (91 overexpressed and 119 underexpressed). Overall, a total of 40 transcripts were differentially expressed in all the three comparisons."}]},{"head":"Genes commonly overexpressed in R perm -S, C-S and R perm -C comparisons","index":10,"paragraphs":[{"index":1,"size":42,"text":"Only the transcript, AGAP011496-PA (Anopheles gambiae str. PEST) was upregulated in R perm -S, R perm -C and C-S comparisons with FC of 2.7, 1.5 and 2.3, respectively. No detoxification gene was found in the comparison of the three groups of mosquitoes."}]},{"head":"Genes commonly overexpressed in R perm -S and C-S comparisons","index":11,"paragraphs":[{"index":1,"size":236,"text":"Several detoxification genes were commonly overexpressed in the R perm -S and C-S. Among these genes, the transcripts for glycogenin (Afun000500) and CYP6M7, belonging to the cytochrome P450 gene family, were the most commonly overexpressed detoxification gene in R perm -S (FC of 36.2 for Afun000500 and 34.6 for CYP6M7) and C-S (FC of 38.1 for Afun000500 and 28.9 for CYP6M7). A transcript from CYP6AA1 was also commonly upregulated in the R perm -S and C-S samples with FC of 5.2 and 3.8, respectively. This gene is the ortholog of CYP6AA3 in An. minimus, which was shown to metabolise pyrethroids [40]. Other upregulated detoxification genes identified in R perm -S and C-S comparisons include CYP6Y2 located in the genomic region spanning the pyrethroid resistance rp2 QTL detected in the FUMOZ-R pyrethroid-resistant laboratory strain [32], as well as CYP6M4, CYP6S1, CYP4K2 and CYP315A1 that were   expressed at lower levels (FC < 5) (Table 2). Transcripts from the glutathione S-transferase (GST) family notably from epsilon [GSTe2 (FC of 10.9 in R perm -S and 15.2 in C-S) and GSTe4 (FC of 3.9 in R perm -S and 4.7 in C-S)] and delta, GSTd1-5 (FC 2.0 and 2.6 in R perm -S and C-S, respectively) classes were upregulated. Other genes such as ABC transporter (Afun009697), alcohol dehydrogenases (Afun012461, Afun013797), carboxylesterase (Afun009492), short chain dehydrogenase (combined_c738) and chymotrypsin (Afun013921) were also overexpressed (Table 2 and Additional file 1: Table S1)."}]},{"head":"Comparative transcriptional profiling of DDT and permethrin resistance","index":12,"paragraphs":[{"index":1,"size":90,"text":"A comparative analysis of transcriptomes associated with DDT resistance in Kpome showed a total of 2158 transcripts differentially expressed (≥ 2-fold change, FC at P ≤ 0.05) in R DDT -S with 715 upregulated and 1443 downregulated. The comparison between R DDT -S, R perm -S and C-S revealed 1311 transcripts differentially expressed. A set of 212 transcripts was commonly expressed between R DDT -S and R perm -S, 958 transcripts and 270 transcripts were also expressed when comparing R perm -S and R DDT -S to C-S (Fig. 2)."}]},{"head":"Genes commonly overexpressed in R DDT -S, Rperm-S and C-S comparisons","index":13,"paragraphs":[{"index":1,"size":193,"text":"Many genes were commonly overexpressed in the three comparisons with glycogenin (Afun000500) and arginosuccinate lyase (Afun009227) been the most overexpressed and were consistent in all the three comparisons with fold change of (FC 36.2, 38.1 and 41.4) and (FC 22.2, 23.3 and 25.6) in R DDT -S, R perm -S and C-S, respectively. Among the common detoxification gene families identified, the GSTe2 gene (Afun000045) was the one that remained highly upregulated with FC of 13.3, 10.9 and 15.2 for R DDT -S, R perm -S and C-S, respectively. The CYP6M7 gene was also overexpressed in all three comparisons; FC was 34.6 in R perm -S and FC 28.9 in C-S but with lower FC of 4 in R DDT -S. Several other genes of the family of P450 such as CYP6AA1, CYP6M4, CYP6Y2, CYP4K2 and CYP315A1 were also upregulated. Other transcripts of the GST family were also upregulated such as the Gste4 (FC 4.3, 3.9 and 4.7 in R DDT -S, R perm -S and C-S, respectively) and the GSTd1 (FC 2.2, 2.0 and 2.6 in R DDT -S, R perm -S and C-S, respectively (Table 3 and Additional file 1: Table S2)."}]},{"head":"Overexpressed genes in the comparison between R DDT -S and Rperm-S","index":14,"paragraphs":[{"index":1,"size":78,"text":"A limited number of genes were commonly overexpressed in these two \"resistant\" comparisons, potentially as a result of induction or greater involvement in resistance than in control samples. The transcript of alcohol dehydrogenase (Afun011037) was the most overexpressed in R DDT -S, and R perm -S comparison with FC 6.5 and 3.7, respectively. Other genes of the P450 family including CYP4H18, CYP9K1, CYP9J3, and the esterase b1 were also overexpressed (Table 3 and Additional file 1: Table S2)."}]},{"head":"Overexpressed genes in the comparison between R DDT -S and C-S","index":15,"paragraphs":[{"index":1,"size":20,"text":"The transcript (combined_c738) belonging to the shortchain dehydrogenase gene was the most commonly   3 and Additional file 1: Table S2."}]},{"head":"Overexpressed genes in the comparison between Rperm-S and C-S","index":16,"paragraphs":[{"index":1,"size":60,"text":"Genes identified from these comparisons include the alcohol dehydrogenase gene (Afun012461 ortholog of AGAP000288 in An. gambiae) which is consistently upregulated in R perm -S (FC8.4) and C-S (FC10.9) but not in R DDT -S. Similarly, the aldehyde dehydrogenase (Afun0 08026) and the ABC transporter (Combined_c1762) were also upregulated in R perm -S and C-S exclusively although with lower FC."},{"index":2,"size":62,"text":"Overexpressed genes in the comparison between DDT resistant mosquitoes and a susceptible strain Some detoxification genes specific for R DDT -S comparison were identified in Kpome. These genes include alcohol dehydrogenase Afun013475 (FC 3.2), the most upregulated gene (FC 3.2) as well as CYP302A1 (FC 2.6), CYP4D15 (FC 2.4), CYP4J5 (FC 2.2), gb-CYP12F3 (FC 2.1) and the cytochrome P450 (Afun010630) (FC 2.1)."}]},{"head":"Overexpressed genes in the comparison between permethrin resistant mosquitoes and a susceptible strain","index":17,"paragraphs":[{"index":1,"size":42,"text":"For R perm -S comparison, the ABC transporter (Afun 007722) was the most upregulated gene with a fold change of 3.2. Also, the carboxylesterase (Afun012261) (FC 2.8), the cuticle protein (CD5773231) (FC 2.4) and the aldehyde oxidase (Afun00493) (FC 2.4) were upregulated."}]},{"head":"Quantitative reverse transcriptase PCR","index":18,"paragraphs":[{"index":1,"size":105,"text":"A qRT-PCR analysis revealed that GSTe2 is the most overexpressed genes in DDT and permethrin resistant samples (FC 16.0 and 18.1, respectively) analysed in this study. However, the expression level of the detoxification gene, CYP6M7 recorded with the qRT-PCR analysis was lower (FC 1.4 and 1.7 in permethrin and DDT resistant samples, respectively) compared to expression level from microarray analysis (FC 34.6 and 4.0 in permethrin and DDT resistant samples, respectively). The GSTd1-5 and the GSTd3 were significantly upregulated in DDT resistant samples (FC 12.5 and 6.2; P < 0.01) compared to permethrin resistant samples (FC 0.72 and 0.86; P < 0.05) (Fig. 3a, b)."},{"index":2,"size":18,"text":"Role of the knockdown resistance gene in DDT and permethrin resistance profiles recorded in An. funestus from Kpome"},{"index":3,"size":133,"text":"Amplification and sequencing of a fragment (a portion of intron 19 and the entire exon 20, domain II, segment 6) of the VGSC gene showed that both L1014F (TTAto-TTT) and L1014S (TTA-to-TCA) kdr mutation commonly found in An. gambiae are absent in this mosquito species. However, further analysis with 837 bp of common sequences generated from the eleven mosquitoes analysed revealed 12 polymorphic sites with 12 haplotypes (Table 4, Fig. 4) showing a high genetic diversity of this gene within the An. funestus mosquitoes of Kpome. No amino acid change was detected in the Benin population. Furthermore, the Tajima D and Fu and Li D* statistics (Table 4) were not statistically significant. The Neighbour-joining tree showed low genetic differentiation between Benin populations compared to Cameroon samples with respect to geographical distance (Fig. 4)."}]},{"head":"Discussion","index":19,"paragraphs":[{"index":1,"size":73,"text":"The WHO Global Plan for Insecticide Resistance Management [12] highlights the necessity to detect and monitor the development of insecticide resistance and characterise the underlying resistance mechanisms to maintain the successes in the reduction of malaria cases across Africa. In this study, we elucidated the molecular basis driving DDT and pyrethroids resistance in an inland An. funestus population from Benin. Permethrin and resistance in An. funestus from Kpome is driven by metabolic resistance"},{"index":2,"size":555,"text":"Transcriptional profiling results and the absence of kdr mutation points out to a possible metabolic mechanism driving permethrin and DDT resistance in An. funestus from Kpome notably through overexpression of genes involved in insecticide detoxification such as cytochrome P450 genes, GSTs, aldehyde oxidases, and other gene families previously associated with resistance of An. funestus to insecticides [41]. The cytochrome P450, CYP6M7 was the most overexpressed detoxification gene in permethrin exposed mosquitoes compared to the FANG susceptible strain as well as to the unexposed mosquitoes from microarrays analysis. However, this consistent information from microarray analysis was not confirmed by qRT-PCR. This discrepancy could be associated with the high genetic redundancy of some P450 genes and a high level of sequence similarity between different genes. These factors could lead to cross amplification contributing to homogenise the expression between samples. It is more likely that CYP6M7 could also be a key pyrethroid resistance gene in Benin, but further validation is required. New approaches with RNAseq could help to confirm the expression pattern of this gene. On the other hand, only one gene (AGAP011496-PA) was commonly expressed in R perm -S, C-S and R perm -C comparisons. This could be due to the high resistance recorded against permethrin suggesting a less significant difference between the resistant and the control. Such a low number of differentially expressed genes between R-S and C-S comparison is commonly observed in similar studies when the resistance level is high in the population as it is the case here in Kpome [24,27]. This is because both non-exposed (control; C) and alive mosquitoes after exposure (resistant; R) are all resistant which leads to less difference in the level of gene expression fold change between these two comparisons. The GSTe2 gene was the most upregulated gene in both DDT and permethrin resistant samples showing that this gene is likely to be involved in both DDT and permethrin resistance as previously shown in coastal Benin [27]. Hence, such genes could confer cross-resistance to both insecticides, and this represents a challenge for the success of the malaria control Fig. 3 Quantitative PCR results: differential expression by qRT-PCR of some candidate genes upregulated in microarray assays in An. funestus resistant to DDT (a) and permethrin (b). Error bars represent SD (n = 3). The presence of * on top of the two-fold changes for each gene indicates a statistically significant (P < 0.05) over-expression in resistant or non-exposed mosquitoes compared to the FANG susceptible strain; \"ns\" is shown when the difference was not significant program. The encoded by this gene was shown to be able to metabolize both DDT and permethrin [27]. QRT-PCR confirmed the expression level of GSTe2 in permethrin, DDT resistant and unexposed mosquitoes compared to the susceptible FANG. This observation is in line with the common implication of the GSTe2 gene in permethrin and DDT resistance in the An. funestus population from Pahou (Benin) reported by Riveron et al. [27]. Furthermore, the potential role of GSTe2 in permethrin resistance observed here has been shown in previous reports that suggested that orthologs of GSTe2 in other insects are associated with pyrethroids resistance. Indeed, the elevated GSTe2 expression has been associated with pyrethroid resistance by acting as a pyrethroid-binding protein and sequestering the insecticide [42] or by protecting against oxidative stress and lipid peroxidation induced by pyrethroid exposure [43]."},{"index":3,"size":40,"text":"In the yellow fever mosquito Ae. aegypti, a partial knockdown of the ortholog of GSTe2 led to increased mortality to pyrethroids (deltamethrin), indicating that GSTe2 is also associated with deltamethrin resistance in Ae. aegypti [44]. Moreover, the over-expression of GSTe2"},{"index":4,"size":562,"text":"was observed with the high frequency of the 119F resistant allele in Kpome (91% of 119F/F homozygote resistant genotype) [10]. The near fixation of 119F-GSTe2 resistant allele in Kpome, which enlarges the substrate binding sites to increase DDT metabolism [27], coupled with the high overexpression of GSTe2 highlights the key role played by this gene in DDT resistance as previously observed in Pahou. Orthologs of GSTe2 have also been shown to be associated with DDT resistance in other mosquito species such as An. gambiae and Ae. aegypti [33,44,45]. Overall, GSTe2 can confer resistance to DDT and permethrin, and this cross-resistance to pyrethroids is of significant concern for malaria control as GSTe2 could protect mosquitoes against the major insecticides used to impregnate LLINs in public health. However, the overexpression of several genes from other gene families in this study highlights the complexity of resistance mechanisms suggesting the involvement of other genes than just GSTe2. In addition, the transcription profile of the duplicated genes CYP6P9a and CYP6P9b (which can metabolise both types I and II pyrethroids as shown by Riveron et al. [27]) in pyrethroid resistance in is different to that observed for An. funestus population in southern Africa. Indeed these P450 genes were highly upregulated in pyrethroidresistant laboratory and field population from southern Africa [9,15,16,31,46,47] while a lower level of overexpression was recorded in Kpome. The level of expression of CYP6P9a and CYP6P9b recorded in Kpome An. funestus population is similar to what was observed previously in Pahou population (coastal Benin). This result suggests that the two duplicated P450 genes are not strongly associated with permethrin resistance in Benin as observed in Mozambique (southern Africa) [15,35]. Therefore, the molecular basis of the pyrethroid resistance in Benin is most likely different to that in southern Africa pointing to independent selection events of the pyrethroid resistance across Africa probably under different local selective forces. These different selective pressures could be increased of ITNs coverage across Benin, agricultural use of pesticides [48,49] and spilt petroleum products [50]. In most African urban areas, insecticides are used for domestic purposes, including the control of mosquitoes in the form of mosquito coils, fumigation bombs or sprays [51]. These insecticides are used in an uncontrolled and heterogeneous manner in term of coverage and doses of insecticides in each household. Such practices may represent an additional selective pressure favouring pyrethroid resistance. The presence of agrochemicals, or industrial pollutants and plant compounds in mosquito breeding sites could also affect insecticide tolerance by modulating mosquito detoxification systems [52]. Concerning DDT resistance, in addition to the GSTe2 gene, two other detoxification genes of GST family were also upregulated: GSTd1-5 and GSTd3. GSTd3 was shown to be upregulated in DDT-resistant An. arabiensis from an urban site in Burkina Faso [53]. GSTd1-5 have been previously implicated in coding for enzymes that directly metabolise DDT or have at least been previously associated with the DDT-resistant phenotype [54,55]. Further validation of the role of these genes in DDT resistance is required. The two duplicated cytochrome P450 genes, CYP6P9a (FC = 3.7) and CYP6P9b (FC = 3.9), which confer pyrethroid resistance in southern African populations of An. funestus [24] were also upregulated in the Benin population. However, because their encoded proteins are unable to metabolise DDT [24], these genes are not likely involved in DDT resistance observed in Kpome mosquitoes. Nevertheless, further investigation is required to validate this hypothesis."},{"index":5,"size":32,"text":"Monitoring the insecticide resistance mechanisms that occur within a population should be an essential component to all insecticide-based vector control programs and improving resistance management involves a better understanding of resistance mechanisms."},{"index":6,"size":40,"text":"These data contribute to the growing body of knowledge focussed on pyrethroid and DDT resistance in Benin. This situation emphasises the need for natural resistance and vector monitoring so that adjustments to control programmes can be made timeously and accurately."},{"index":7,"size":192,"text":"Analysis of polymorphisms of the VGSC gene supports a limited role of knockdown resistance Mechanisms of DDT and permethrin resistance are likely not associated with target site resistance as no kdr mutation was detected in analysed mosquitoes from Kpome. L1014F or L1014S change commonly associated with pyrethroid/DDT resistance in An. gambiae was not detected in this population of An. funestus, as it was also the case for all populations of this species, analysed so far [9,[19][20][21][22]. This suggests that the VGSC is probably evolving neutrally in DDT and permethrin resistance in An. funestus population. Furthermore, the neutrality tests with Tajima D and Fu and Li D* statistics revealed no signature of directional selection on the sodium channel gene suggesting the limited role of knockdown resistance in both DDT and pyrethroid resistance in An. funestus in Kpome. The neighbour-joining tree revealed that Kpome mosquitoes cluster with Pahou mosquitoes while they are different to Cameroon mosquitoes. This reveals a reduced gene flow between these populations probably through isolation by distance which can also affect the spread of insecticide resistance genes in this species as previously shown for An. funestus populations across the continent [56]."}]},{"head":"Conclusions","index":20,"paragraphs":[{"index":1,"size":165,"text":"Metabolic resistance is likely driving resistance to both pyrethroids (permethrin) and DDT in the major malaria vector An. funestus in Benin. The glutathione s-transferase gene, GSTe2 is playing a key role in DDT resistance and most likely is responsible for the observed cross-resistance to pyrethroids in An. funestus populations from Kpome and such cross-resistance should be taken into account for the implementation of future insecticide resistance management strategies. Moreover, this study provides knowledge on the resistance profile and underlying resistance mechanisms to the available insecticides in An. funestus, a less studied malaria vector in Benin, in order to develop better insecticide resistance diagnostics. Further investigation should be performed on the expression level of target genes to ascertain the role of metabolic mechanisms in DDT and permethrin resistance in this An. funestus population. Resistance mechanisms detected in this studied population appear to be different from those identified in other African regions the need to characterise mosquito populations at country-level for more appropriate and tailored control interventions."}]}],"figures":[{"text":"Fig. 1 Fig. 1 Summary of transcripts differentially expressed in permethrin resistance. The Venn diagrams show the number of transcripts significantly (P ≤ 0.05) up-or downregulated (FC ≥ 2) in each comparison as well as the commonly expressed transcripts "},{"text":"Fig. 2 Fig. 2 Summary of transcripts differentially expressed in DDT and permethrin resistance. The Venn diagrams show the number of transcripts significantly (P ≤ 0.05) up-or downregulated (FC ≥ 2) in each comparison as well as the commonly expressed transcripts permethrin "},{"text":"Fig. 4 Fig. 4 kdr polymorphism in Anopheles funestus of Kpome. a Schematic representation of haplotypes of Exon20 fragment of the voltage-gated sodium channel gene (VGSC) observed in wild type An. funestus from Kpome. Only polymorphic sites are shown and these are numbered from the beginning of each aligned sequence. Dots indicate identity with the first sequence. A number has been given to each haplotype. The column (N) indicates the number of individuals sharing the haplotype. b Maximum-likelihood tree based on kdr data for samples from Kpome, Pahou and Cameroon. c Neighbour-joining tree based on the VGSC gene data for samples of Kpome, Pahou and Cameroon. Abbreviations: Kp, Kpome; Ph, Pahou, Cam: Cameroon "},{"text":"  "},{"text":"Table 1 List of primers used for qRT-PCR Primers Forward Reverse Expected size (bp) PrimersForwardReverseExpected size (bp) CYP6M7 CCAGATACTGAAAGAGAGCCTTCG CAAGCACTGTCTTCGTACCG 102 CYP6M7CCAGATACTGAAAGAGAGCCTTCGCAAGCACTGTCTTCGTACCG102 CYP6P9a CAGCGCGTACACCAGATTGTGTAA TCACAATTTTTCCACCTTCAAGTAATTACCCGC 92 CYP6P9aCAGCGCGTACACCAGATTGTGTAATCACAATTTTTCCACCTTCAAGTAATTACCCGC92 CYP6P9b CAGCGCGTACACCAGATTGTGTAA TTACACCTTTTCTACCTTCAAGTAATTACCCGC 97 CYP6P9bCAGCGCGTACACCAGATTGTGTAATTACACCTTTTCTACCTTCAAGTAATTACCCGC97 GSTe2 GTTTGAAGCAGTTGCCATACTACGAGG TCAAGCTTTAGCATTTTCCTCCTTTTTGGC 101 GSTe2GTTTGAAGCAGTTGCCATACTACGAGGTCAAGCTTTAGCATTTTCCTCCTTTTTGGC101 GSTd3 CACGGCCAGTCCTCTTTTAG AAGCTTCTTCGCCACCAGTA 128 GSTd3CACGGCCAGTCCTCTTTTAGAAGCTTCTTCGCCACCAGTA128 GSTd1-5 TGGAGAAATACGGCAAGGAC CTTGGCGAAGATTTGTGGAT 140 GSTd1-5TGGAGAAATACGGCAAGGACCTTGGCGAAGATTTGTGGAT140 Aldehyde oxidase GCTCTGAACATTGCACCTCA TGGTGTCGAACGATTGTGTT 109 Aldehyde oxidaseGCTCTGAACATTGCACCTCATGGTGTCGAACGATTGTGTT109 CYP9K1 AGGGCTTCTGGATACGGTTC CGTACGGTTCGGTTTTGATT 103 CYP9K1AGGGCTTCTGGATACGGTTCCGTACGGTTCGGTTTTGATT103 RSP7 GTGTTCGGTTCCAAGGTGAT TCCGAGTTCATTTCCAGCTC 98 RSP7GTGTTCGGTTCCAAGGTGATTCCGAGTTCATTTCCAGCTC98 Actin TTAAACCCAAAAGCCAATCG ACCGGATGCATACAGTGACA 111 ActinTTAAACCCAAAAGCCAATCGACCGGATGCATACAGTGACA111  "},{"text":"Table 2 Top detoxification genes upregulated in Rperm-S, C-S and Rperm-C Probe name Systematic name Rperm-S fold C-S fold Rperm-C fold Ortholog in An. Description Probe nameSystematic nameRperm-S foldC-S foldRperm-C foldOrtholog in An.Description   change (FC) change (FC) change (FC) gambiae  change (FC)change (FC)change (FC)gambiae CUST_5822_PI426302897 Afun005822 2.7 2.31 1.5 AGAP011496-PA AGAP011496-PA [Anopheles CUST_5822_PI426302897 Afun0058222.72.311.5AGAP011496-PAAGAP011496-PA [Anopheles       gambiae str. PEST] gambiae str. PEST] CUST_500_PI426302897 Afun000500 36.2 38.1  na Glycogenin CUST_500_PI426302897Afun00050036.238.1naGlycogenin CUST_7663_PI426302897 Afun007663 (CYP6M7) 34.6 28.9  AGAP008213-PA Cytochrome p450 6a8 CUST_7663_PI426302897 Afun007663 (CYP6M7)34.628.9AGAP008213-PACytochrome p450 6a8 CUST_8887_PI426302897 Afun008887 34.0 16.8  AGAP011997-PA Nucleotide binding protein 2 CUST_8887_PI426302897 Afun00888734.016.8AGAP011997-PANucleotide binding protein 2 CUST_9227_PI426302897 Afun009227 23.3 25.6  AGAP008141-PA Argininosuccinate lyase CUST_9227_PI426302897 Afun00922723.325.6AGAP008141-PAArgininosuccinate lyase CUST_1459_PI406199769 combined_c738 22.0 34.5   Short-chain dehydrogenase CUST_1459_PI406199769 combined_c73822.034.5Short-chain dehydrogenase CUST_13921_PI426302897 Afun013921 19.1 21.1  AGAP006709-PA Chymotrypsin 1 CUST_13921_PI426302897 Afun01392119.121.1AGAP006709-PAChymotrypsin 1 CUST_9492_PI426302897 Afun009492 16.4 3.7  AGAP001722-PA Carboxylesterase CUST_9492_PI426302897 Afun00949216.43.7AGAP001722-PACarboxylesterase CUST_1822_PI406199769 combined_c920 11.1 11.4   Glutathione-S-transferase gst CUST_1822_PI406199769 combined_c92011.111.4Glutathione-S-transferase gst CUST_45_PI426302897 Afun000045 (GSTE2) 10.9 15.2  AGAP009194-PA Glutathione-S-transferase gst CUST_45_PI426302897Afun000045 (GSTE2)10.915.2AGAP009194-PAGlutathione-S-transferase gst CUST_4223_PI426302897 Afun004223 10.6 9.4  AGAP008358-PA Cytochrome p450 4d1 CUST_4223_PI426302897 Afun00422310.69.4AGAP008358-PACytochrome p450 4d1 CUST_12461_PI426302897 Afun012461 8.4 10.9  AGAP000288-PA Alcohol dehydrogenase CUST_12461_PI426302897 Afun0124618.410.9AGAP000288-PAAlcohol dehydrogenase CUST_3220_PI426302897 Afun003220 5.7 7.0  AGAP002867-PA Cytochrome p450 CUST_3220_PI426302897 Afun0032205.77.0AGAP002867-PACytochrome p450 CUST_8615_PI426302897 Afun008615 (CYP6AA1) 5.2 3.8  AGAP002862-PA Cytochrome p450 CUST_8615_PI426302897 Afun008615 (CYP6AA1) 5.23.8AGAP002862-PACytochrome p450 CUST_9697_PI426302897 Afun009697 4.8 6.8  AGAP006364-PA Abc transporter CUST_9697_PI426302897 Afun0096974.86.8AGAP006364-PAAbc transporter CUST_9_PI426302915 CYP6M4.seq 4.8 3.5   Cytochrome p450 CUST_9_PI426302915CYP6M4.seq4.83.5Cytochrome p450 CUST_13797_PI426302897 Afun013797 4.1 3.0  AGAP000289-PA Alcohol dehydrogenase CUST_13797_PI426302897 Afun0137974.13.0AGAP000289-PAAlcohol dehydrogenase CUST_8445_PI426302897 Afun008445 (GSTE4) 3.9 4.7  AGAP009193-PA Glutathione-S-transferase gst CUST_8445_PI426302897 Afun008445 (GSTE4)3.94.7AGAP009193-PAGlutathione-S-transferase gst CUST_25_PI426302915 CYP6Y2_rvcpl.seq 3.7 3.8   Cytochrome p450 CUST_25_PI426302915CYP6Y2_rvcpl.seq3.73.8Cytochrome p450 CUST_1392_PI426302897 Afun001392 3.3 3.4  na Glycine dehydrogenase CUST_1392_PI426302897 Afun0013923.33.4naGlycine dehydrogenase CUST_3394_PI426302897 Afun003394 (CYP315A1) 3.1 2.4  AGAP000284-PA Cytochrome p450 CUST_3394_PI426302897 Afun003394 (CYP315A1) 3.12.4AGAP000284-PACytochrome p450 CUST_8909_PI426302897 Afun008909 (CYP4K2) 2.4 2.0  AGAP002416-PA Cytochrome p450 CUST_8909_PI426302897 Afun008909 (CYP4K2)2.42.0AGAP002416-PACytochrome p450 CUST_20_PI426302915 CYP6S1.seq 2.3 2.2   Cytochrome p450 CUST_20_PI426302915CYP6S1.seq2.32.2Cytochrome p450 CUST_7499_PI426302897 Afun007499 (GSTD1) 2.0 2.6  AGAP004164-PA Glutathione transferase CUST_7499_PI426302897 Afun007499 (GSTD1)2.02.6AGAP004164-PAGlutathione transferase CUST_11942_PI426302897 Afun011942 6.1   AGAP011509-PA Carboxylesterase CUST_11942_PI426302897 Afun0119426.1AGAP011509-PACarboxylesterase CUST_4048_PI406199772 CD577343.1 5.3    Cuticle protein CUST_4048_PI406199772 CD577343.15.3Cuticle protein CUST_12343_PI426302897 Afun012343 (CYP4H18) 5.0   AGAP008358-PA Cytochrome p450 4d1 CUST_12343_PI426302897 Afun012343 (CYP4H18) 5.0AGAP008358-PACytochrome p450 4d1 CUST_10836_PI426302897 Afun010836 4.8   AGAP006228-PA Esterase b1 CUST_10836_PI426302897 Afun0108364.8AGAP006228-PAEsterase b1 CUST_11042_PI426302897 Afun011042 3.9   AGAP003321-PA Glycine dehydrogenase CUST_11042_PI426302897 Afun0110423.9AGAP003321-PAGlycine dehydrogenase CUST_11037_PI426302897 Afun011037 3.7   AGAP003581-PA Alcohol dehydrogenase CUST_11037_PI426302897 Afun0110373.7AGAP003581-PAAlcohol dehydrogenase CUST_1_PI426302915 CYP6M1a.seq 2.9    Cytochrome p450 CUST_1_PI426302915CYP6M1a.seq2.9Cytochrome p450 CUST_7501_PI406199798 AGAP007662-RA_2L 2.6   AGAP007662-RA_2L Short-chain dehydrogenase CUST_7501_PI406199798 AGAP007662-RA_2L2.6AGAP007662-RA_2L Short-chain dehydrogenase CUST_9335_PI426302897 Afun009335 (CYP6AG1) 2.4   AGAP003343-PA Cytochrome p450 CUST_9335_PI426302897 Afun009335 (CYP6AG1) 2.4AGAP003343-PACytochrome p450 CUST_7769_PI426302897 Afun007769 (CYP9K1) 2.2   AGAP000818-PA Cytochrome p450 cyp9k1 CUST_7769_PI426302897 Afun007769 (CYP9K1)2.2AGAP000818-PACytochrome p450 cyp9k1 CUST_11899_PI426302897 Afun011899 2.1   AGAP012514-PA Short-chain dehydrogenase CUST_11899_PI426302897 Afun0118992.1AGAP012514-PAShort-chain dehydrogenase CUST_4043_PI406199772 CD577345.1  3.7   Cuticle protein CUST_4043_PI406199772 CD577345.13.7Cuticle protein CUST_7722_PI426302897 Afun007722  3.2  AGAP009850-PA Abc transporter CUST_7722_PI426302897 Afun0077223.2AGAP009850-PAAbc transporter CUST_12261_PI426302897 Afun012261  2.8  AGAP005758-PA Carboxylesterase CUST_12261_PI426302897 Afun0122612.8AGAP005758-PACarboxylesterase CUST_13481_PI426302897 Afun013481 (GSTE1)  2.7  AGAP009195-PA Glutathione-S-transferase gst CUST_13481_PI426302897 Afun013481 (GSTE1)2.7AGAP009195-PAGlutathione-S-transferase gst CUST_25_PI406199775 CYP6P9a  2.3   Cytochrome p450 CUST_25_PI406199775CYP6P9a2.3Cytochrome p450 CUST_15331_PI426302897 Afun015331 (CYP307A1)  2.2  AGAP001039-PB Cytochrome p450 307a1 CUST_15331_PI426302897 Afun015331 (CYP307A1)2.2AGAP001039-PBCytochrome p450 307a1 CUST_493_PI426302897 Afun000493  2.1  AGAP006225-PA Aldehyde oxidase CUST_493_PI426302897Afun0004932.1AGAP006225-PAAldehyde oxidase  "},{"text":"Table 3 detoxification genes upregulated in R DDT -S, Rperm-S and C-S Probe name Systematic name R DDT -S fold Rperm-S fold C-S fold Ortholog in An. Description Probe nameSystematic nameR DDT -S foldRperm-S foldC-S foldOrtholog in An.Description   change (FC) change (FC) change (FC) gambiae  change (FC)change (FC)change (FC)gambiae CUST_500_PI426302897 Afun000500 36.2 38.1 41.4 na Glycogenin CUST_500_PI426302897Afun00050036.238.141.4naGlycogenin CUST_9227_PI426302897 Afun009227 22.2 23.3 25.6 AGAP008141-PA Argininosuccinate lyase CUST_9227_PI426302897Afun00922722.223.325.6AGAP008141-PA Argininosuccinate lyase CUST_45_PI426302897 Afun000045 (GSTE2) 13.3 10.9 15.2 AGAP009194-PA Glutathione-S-transferase CUST_45_PI426302897Afun000045 (GSTE2)13.310.915.2AGAP009194-PA Glutathione-S-transferase CUST_4223_PI426302897 Afun004223 12.8 10.6 9.4 AGAP008358-PA Cytochrome p450 4d1 CUST_4223_PI426302897Afun00422312.810.69.4AGAP008358-PA Cytochrome p450 4d1 CUST_9697_PI426302897 Afun009697 11.6 4.8 6.8 AGAP006364-PA Abc transporter CUST_9697_PI426302897Afun00969711.64.86.8AGAP006364-PA Abc transporter CUST_1822_PI406199769 combined_c920 11.5 11.1 11.4  Glutathione-S-transferase CUST_1822_PI406199769combined_c92011.511.111.4Glutathione-S-transferase CUST_3220_PI426302897 Afun003220 10.8 5.7 7.0 AGAP002867-PA Cytochrome p450 CUST_3220_PI426302897Afun00322010.85.77.0AGAP002867-PA Cytochrome p450 CUST_9492_PI426302897 Afun009492 7.9 16.4 3.7 AGAP001722-PA Carboxylesterase CUST_9492_PI426302897Afun0094927.916.43.7AGAP001722-PA Carboxylesterase CUST_8615_PI426302897 Afun008615 (CYP6AA1 ) 5.1 5.2 3.8 AGAP002862-PA Cytochrome p450 CUST_8615_PI426302897Afun008615 (CYP6AA1 ) 5.15.23.8AGAP002862-PA Cytochrome p450 CUST_9_PI426302915 CYP6M4.seq 4.9 4.8 3.5  Cytochrome p450 CUST_9_PI426302915CYP6M4.seq4.94.83.5Cytochrome p450 CUST_8445_PI426302897 Afun008445 (GSTE4) 4.3 3.9 4.7 AGAP009193-PA Glutathione-S-transferase CUST_8445_PI426302897Afun008445 (GSTE4)4.33.94.7AGAP009193-PA Glutathione-S-transferase CUST_6930_PI426302897 Afun006930 (CYP6M7) 4.0 34.6 28.9 AGAP008212-PA Cytochrome p450 6a8 CUST_6930_PI426302897Afun006930 (CYP6M7)4.034.628.9AGAP008212-PA Cytochrome p450 6a8 CUST_25_PI426302915 CYP6Y2_rvcpl.seq 3.4 3.7 3.8  Cytochrome p450 CUST_25_PI426302915CYP6Y2_rvcpl.seq3.43.73.8Cytochrome p450 CUST_20_PI426302915 CYP6S1.seq 3.0 2.3 2.2  Cytochrome p450 CUST_20_PI426302915CYP6S1.seq3.02.32.2Cytochrome p450 CUST_8909_PI426302897 Afun008909 (CYP4K2) 2.8 2.4 2.0 AGAP002416-PA Cytochrome p450 CUST_8909_PI426302897Afun008909 (CYP4K2)2.82.42.0AGAP002416-PA Cytochrome p450 CUST_1392_PI426302897 Afun001392 2.5 3.3 3.4 na Glycine dehydrogenase CUST_1392_PI426302897Afun0013922.53.33.4naGlycine dehydrogenase CUST_13218_PI426302897 Afun013218 (CYP315A1) 2.3 2.9 2.6 AGAP000284-PA Cytochrome p450 CUST_13218_PI426302897 Afun013218 (CYP315A1) 2.32.92.6AGAP000284-PA Cytochrome p450 CUST_7499_PI426302897 Afun007499 (GSTD1) 2.2 2.0 2.6 AGAP004164-PA Glutathione transferase CUST_7499_PI426302897Afun007499 (GSTD1)2.22.02.6AGAP004164-PA Glutathione transferase CUST_11037_PI426302897 Afun011037 6.5 3.7  AGAP003581-PA Alcohol dehydrogenase CUST_11037_PI426302897 Afun0110376.53.7AGAP003581-PA Alcohol dehydrogenase CUST_12343_PI426302897 Afun012343 (CYP4H18 ) 5.2 5.0  AGAP008358-PA Cytochrome p450 4d1 CUST_12343_PI426302897 Afun012343 (CYP4H18 ) 5.25.0AGAP008358-PA Cytochrome p450 4d1 CUST_10836_PI426302897 Afun010836 4.3 4.8  AGAP006228-PA Esterase b1 CUST_10836_PI426302897 Afun0108364.34.8AGAP006228-PA Esterase b1 CUST_7769_PI426302897 Afun007769 (CYP9K1 ) 3.0 2.2  AGAP000818-PA Cytochrome p450 cyp9k1 CUST_7769_PI426302897Afun007769 (CYP9K1 )3.02.2AGAP000818-PA Cytochrome p450 cyp9k1 CUST_7469_PI426302897 Afun007469 (CYP9J3) 2.0 2.1  AGAP012296-PA Cytochrome p450 CUST_7469_PI426302897Afun007469 (CYP9J3)2.02.1AGAP012296-PA Cytochrome p450 CUST_12461_PI426302897 Afun012461  8.4 10.9 AGAP000288-PA Alcohol dehydrogenase CUST_12461_PI426302897 Afun0124618.410.9AGAP000288-PA Alcohol dehydrogenase CUST_3489_PI406199769 combined_c1762  2.2 2.2  Abc transporter CUST_3489_PI406199769combined_c17622.22.2Abc transporter CUST_8026_PI426302897 Afun008026  2.1 2.2 AGAP003578-PA Aldehyde dehydrogenase CUST_8026_PI426302897Afun0080262.12.2AGAP003578-PA Aldehyde dehydrogenase CUST_1458_PI406199769 combined_c738 10.4  15.9  Short-chain dehydrogenase CUST_1458_PI406199769combined_c73810.415.9Short-chain dehydrogenase CUST_4043_PI406199772 CD577345.1 4.4  3.7  Cuticle protein CUST_4043_PI406199772CD577345.14.43.7Cuticle protein CUST_15331_PI426302897 Afun015331 (CYP307A1) 3.4  2.2 AGAP001039-PB Cytochrome p450 307a1 CUST_15331_PI426302897 Afun015331 (CYP307A1) 3.42.2AGAP001039-PB Cytochrome p450 307a1 CUST_25_PI406199775 CYP6P9a 2.8  2.3  Cytochrome p450 CUST_25_PI406199775CYP6P9a2.82.3Cytochrome p450 CUST_13481_PI426302897 Afun013481 (GSTE1 ) 2.5  2.7 AGAP009195-PA Glutathione-S-transferase gst CUST_13481_PI426302897 Afun013481 (GSTE1 )2.52.7AGAP009195-PA Glutathione-S-transferase gst CUST_4048_PI406199772 CD577343.1  5.3   Cuticle protein CUST_4048_PI406199772CD577343.15.3Cuticle protein CUST_11042_PI426302897 Afun011042  3.9  AGAP003321-PA Glycine dehydrogenase CUST_11042_PI426302897 Afun0110423.9AGAP003321-PA Glycine dehydrogenase CUST_1_PI426302915 CYP6M1a.seq  2.9   Cytochrome p450 CUST_1_PI426302915CYP6M1a.seq2.9Cytochrome p450 CUST_9335_PI426302897 Afun009335 (CYP6AG1)  2.4  AGAP003343-PA Cytochrome p450 CUST_9335_PI426302897Afun009335 (CYP6AG1)2.4AGAP003343-PA Cytochrome p450 CUST_13475_PI426302897 Afun013475 3.2   AGAP003582-PA Alcohol dehydrogenase CUST_13475_PI426302897 Afun0134753.2AGAP003582-PA Alcohol dehydrogenase CUST_5448_PI426302897 Afun005448 (CYP302A1) 2.6   AGAP005992-PA Cytochrome p450 CUST_5448_PI426302897Afun005448 (CYP302A1) 2.6AGAP005992-PA Cytochrome p450 CUST_4047_PI406199772 CD577343.1 2.4    Cuticle protein CUST_4047_PI406199772CD577343.12.4Cuticle protein CUST_8823_PI426302897 Afun008823 (CYP4D15 ) 2.4   AGAP002418-PA Cytochrome p450 CUST_8823_PI426302897Afun008823 (CYP4D15 ) 2.4AGAP002418-PA Cytochrome p450 CUST_7301_PI426302897 Afun007301 (CYP4J5) 2.2   AGAP006048-PA Cytochrome p450 CUST_7301_PI426302897Afun007301 (CYP4J5)2.2AGAP006048-PA Cytochrome p450 CUST_208_PI406199788 gb-CYP12F3 2.1    Cytochrome p450 CUST_208_PI406199788gb-CYP12F32.1Cytochrome p450 CUST_10630_PI426302897 Afun010630 2.1   AGAP002866-PA Cytochrome p450 CUST_10630_PI426302897 Afun0106302.1AGAP002866-PA Cytochrome p450 CUST_7722_PI426302897 Afun007722   3.2 AGAP009850-PA Abc transporter CUST_7722_PI426302897Afun0077223.2AGAP009850-PA Abc transporter  "},{"text":"Table 3 Top detoxification genes upregulated in R DDT -S, Rperm-S and C-S (Continued) Probe name Systematic name R DDT -S fold Rperm-S fold C-S fold Ortholog in An. Description Probe nameSystematic nameR DDT -S foldRperm-S foldC-S foldOrtholog in An.Description   change (FC) change (FC) change (FC) gambiae  change (FC)change (FC)change (FC)gambiae CUST_12261_PI426302897 Afun012261   2.8 AGAP005758-PA Carboxylesterase CUST_12261_PI426302897 Afun0122612.8AGAP005758-PA Carboxylesterase CUST_4088_PI406199772 CD577323.1   2.4  Cuticle protein CUST_4088_PI406199772CD577323.12.4Cuticle protein CUST_493_PI426302897 Afun000493   2.1 AGAP006225-PA Aldehyde oxidase CUST_493_PI426302897Afun0004932.1AGAP006225-PA Aldehyde oxidase  "},{"text":"Table 4 Summary statistics for polymorphism in the voltage-gated sodium channel gene in F 0 An. funestus from Kpome compared to Pahou and Cameroon population  N S π k h hd Syn Nonsyn D D* NSπkhhdSynNonsynDD*          (P-value) (P-value) (P-value)(P-value) Kpome 22 12 0.00351 2.93939 12 0.909 0 0 0.37 0.32 Kpome22120.003512.93939120.909000.370.32          (P > 0.10) (P > 0.10) (P > 0.10)(P > 0.10) Pahou 20 10 0.00260 2.17895 12 0.905 0 0 0.79 0.96 Pahou20100.002602.17895120.905000.790.96          (P > 0.10) (P > 0.10) (P > 0.10)(P > 0.10) Cameroon 40 37 0.00514 4.30128 29 0.977 2 3 1.81 2.75 Cameroon40370.005144.30128290.977231.812.75          (P > 0.10) (P > 0.10) (P > 0.10)(P > 0.10)  "}],"sieverID":"ce769dfe-96c6-4fcb-80fb-5a0d93ae551a","abstract":"Background: Insecticide resistance in Anopheles mosquitoes is threatening the success of malaria control programmes. In order to implement suitable insecticide resistance management strategies, it is necessary to understand the underlying mechanisms involved. To achieve this, the molecular basis of permethrin and DDT resistance in the principal malaria vector, Anopheles funestus from inland Benin (Kpome), was investigated.Results: Here, using a microarray-based genome-wide transcription and qRT-PCR analysis, we showed that metabolic resistance mechanisms through over-expression of cytochrome P450 and glutathione S-transferase genes (GSTs) are a major contributor to DDT and permethrin resistance in Anopheles funestus from Kpome. The GSTe2 gene was the most upregulated detoxification gene in both DDT-[fold-change (FC: 16.0)] and permethrin-resistant (FC: 18.1) mosquitoes suggesting that upregulation of this gene could contribute to DDT resistance and cross-resistance to permethrin. CYP6P9a and CYP6P9b genes that have been previously associated with pyrethroid resistance were also significantly overexpressed with FC 5.4 and 4.8, respectively, in a permethrin resistant population. Noticeably, the GSTs, GSTd1-5 and GSTd3, were more upregulated in DDT-resistant than in permethrinresistant Anopheles funestus suggesting these genes are more implicated in DDT resistance. The absence of the L1014F or L1014S kdr mutations in the voltage-gated sodium channel gene coupled with the lack of directional selection at the gene further supported that knockdown resistance plays little role in this resistance."}

data/part_5/0538b965325f841e881fff3616bf75e5.json

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+{"metadata":{"id":"0538b965325f841e881fff3616bf75e5","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/c3ad5e90-6230-411f-a4e2-e6db566e10df/retrieve"},"pageCount":4,"title":"","keywords":[],"chapters":[{"head":"MEASURING WOMEN'S EMPOWERMENT IN AGRICULTURE","index":1,"paragraphs":[{"index":1,"size":101,"text":"The Women's Empowerment in Agriculture Index (WEAI) -part of the PIM portfolio and a joint effort of the International Food Policy Research Institute (IFPRI), the Oxford Poverty and Human Development Initiative (OPHI), and USAID under the US Feed the Future Initiative -is the first comprehensive and standardized measure of women's empowerment and inclusion in the agricultural sector. Launched in 2012, the Index aims to increase understanding of the linkages between women's empowerment, agricultural productivity, and food security. By doing so, it helps to diagnose empowerment gaps, identify and prioritize interventions to close these gaps, and later test the effectiveness of interventions."},{"index":2,"size":51,"text":"The WEAI has been adopted by a wide range of research and development organizations. By 2018, 75 external users, including universities, non-profit organizations, international organizations (e.g., FAO, IFAD, UN Women), and CGIAR Centers have used the WEAI (or one of its adaptations) in 50 countries in Asia, Africa, and Latin America."},{"index":3,"size":5,"text":"To learn more, visit http://weai.ifpri.info/"}]},{"head":"ANALYSIS TO UNDERPIN MORE EFFECTIVE SOCIAL PROTECTION PROGRAMS","index":2,"paragraphs":[{"index":1,"size":131,"text":"PIM studies on social protection in Bangladesh, most notably the Transfer Modality Research Initiative (TMRI), have informed several changes to social protection programs in the country. The results of the TMRI randomized controlled trial showed that all combinations of types of transfers were useful, but that cash transfers combined with messaging about how to improve nutrition, also known as nutrition behavior change communication (BCC), had the greatest impact. Influenced by the TMRI findings, the Ministry of Women and Children incorporated the BCC component into its Vulnerable Group Development program, which has more than 1 million beneficiaries. The Agriculture, Nutrition, and Gender Linkages (ANGeL) project launched by the Bangladesh Ministry of Agriculture in 2015 alsoincludes specific BCC strategies to promote women's empowerment, nutrition, and health and maximize impacts on nutrition. A new "}]},{"head":"Impacts by 2022","index":3,"paragraphs":[]},{"head":"Where We Work","index":4,"paragraphs":[{"index":1,"size":26,"text":"Global program with special emphasis on Africa south of the Sahara, South Asia, and selected countries in East Asia, Southeast Asia, Central Asia, and Latin America"}]},{"head":"Why PIM","index":5,"paragraphs":[{"index":1,"size":35,"text":"The need for sound policies and well-functioning institutions cuts across all commodities and agri-food systems. Accordingly, PIM serves an integrative function, bringing together social science expertise and facilitating collaboration across CGIAR and with external partners."},{"index":2,"size":71,"text":"Examples include foresight modeling, work on trade and value chains, rural transformation and creation of good jobs in rural areas, assistance to the vulnerable and approaches to managing rural risks, management of critical natural resources and institutions that reduce conflict associated with competing uses, and attention to the many ways in which decisions are made by men and women, jointly and separately, creating open opportunities for both to realize their ambitions. "}]},{"head":"Technological","index":6,"paragraphs":[]}],"figures":[{"text":" Photo credit: IFPRI "},{"text":"Economywide Factors Affecting Agricultural Growth and Rural Transformation Inclusive and E cient Value Chains   Foresight modeling for  Policy options to  Making food markets Foresight modeling forPolicy options toMaking food markets Innovation and Sustainable Intensification climate change, new technologies, and shifts in demand. Policy options, investment,  promote inclusive rural growth and transformation. Agriculture as a  work for the poor. Measuring and reducing food losses. Understanding trade Innovation and Sustainable Intensificationclimate change, new technologies, and shifts in demand. Policy options, investment,promote inclusive rural growth and transformation. Agriculture as awork for the poor. Measuring and reducing food losses. Understanding trade  and regulatory reform  source of jobs as well  as a contributor to and regulatory reformsource of jobs as wellas a contributor to  to support agricultural  as food. Public  food security. Does to support agriculturalas food. Publicfood security. Does  innovation. New  investments for  certification serve innovation. Newinvestments forcertification serve  approaches to meeting  vibrant rural areas. The  sustainability? Does it approaches to meetingvibrant rural areas. Thesustainability? Does it  farmers' needs for  political economy of  crowd in or crowd out farmers' needs forpolitical economy ofcrowd in or crowd out  information.  agricultural policy  smallholders? information.agricultural policysmallholders?    reforms.  Innovations in reforms.Innovations in      insurance that work for insurance that work for      poor farmers. poor farmers. Social Protection Cash, food, or Governance of Strengthening tenure Cross-cutting Gender dimensions of Social ProtectionCash, food, orGovernance ofStrengthening tenureCross-cuttingGender dimensions of for Agriculture and Resilience vouchers-which and when? Programs that deliver more security Natural Resources over land, water, trees, and other natural resources for the poor, Gender Research and Coordination agricultural growth and rural transformation. for Agriculture and Resiliencevouchers-which and when? Programs that deliver more securityNatural Resourcesover land, water, trees, and other natural resources for the poor,Gender Research and Coordinationagricultural growth and rural transformation.  and better nutrition at  particularly women.  Women's and better nutrition atparticularly women.Women's  lower cost. Linking  Facilitating shared use  empowerment: lower cost. LinkingFacilitating shared useempowerment:  social protection and  of resources within  measurement, why it social protection andof resources withinmeasurement, why it  agricultural  landscapes.  matters, and how to agriculturallandscapes.matters, and how to  development. What  Governance of natural  foster it. Gender development. WhatGovernance of naturalfoster it. Gender  does it mean to  resources for  equality in decision does it mean toresources forequality in decision  graduate? What works  sustainability and  making, control of graduate? What workssustainability andmaking, control of  in fragile and  harmony.  assets, and benefits. in fragile andharmony.assets, and benefits.  conflict-affected    CGIAR Collaborative conflict-affectedCGIAR Collaborative  setting?    Platform for Gender setting?Platform for Gender      Research. Research.  "}],"sieverID":"adf8c7f7-079b-4ee2-bcd5-c6db69370ba0","abstract":"Sound policies, robust institutions, and well-functioning markets complement new discoveries of agricultural science to create dynamic and resilient food systems. The combination of strong agricultural science and good policy is especially important in poor rural areas, where many people depend on farming for their livelihoods. Agricultural growth creates new jobs both on and off farms as rural economies diversify. Consumers benefit from more affordable food. Landscapes recover as farmers, fishers, herders, and forest dwellers adopt better management regimes and develop new institutions for collaborative governance.Action-oriented research to provide support for policies that help poor farmers, both men and women, improve their lives; produce nutritious and affordable foods; and protect the soil, water, and biodiversity in rural landscapes.Many countries fall far short of the good policies and strong institutions needed. Our research helps diagnose problems and assess their priority, test potential options, and evaluate reforms or large scale programs. The work falls into the thematic categories of technological innovation, rural transformation, value chains, social protection, natural resource governance, and gender."}

data/part_5/0539f57037e846a14d87c3e8511133c7.json

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+{"metadata":{"id":"0539f57037e846a14d87c3e8511133c7","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/949c96a0-b067-44e9-94e9-e884dc896b7d/retrieve"},"pageCount":31,"title":"","keywords":[],"chapters":[{"head":"","index":1,"paragraphs":[{"index":1,"size":14,"text":"Some starting points  Together -researchers, communities, and development partners -know so much …"},{"index":2,"size":10,"text":" How do we create, document and share this knowledge?"},{"index":3,"size":10,"text":" How do we support learning, and share the results?"},{"index":4,"size":12,"text":" How do we enrich these processes of documenting, learning, and sharing?"},{"index":5,"size":6,"text":" Can we do R4D better?"},{"index":6,"size":9,"text":" To increase the effectiveness of R4D! Some 'answers' "}]},{"head":"Contacts","index":2,"paragraphs":[{"index":1,"size":10,"text":" KM and KS @ ILRI: Peter Ballantyne ( [email protected])"},{"index":2,"size":7,"text":" Participatory video: Beth Cullen ( [email protected])"},{"index":3,"size":7,"text":" Innovation platforms: Alan Duncan ( [email protected])"},{"index":4,"size":2,"text":" http://infoilri.wordpress.com"}]}],"figures":[{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"of livelihood activities to household income (as a percentage)  1. Co-create and co-learn in multi-stakeholder platforms 2. Document and mobilize knowledge from the (un)usual people 3. Make research knowledge, events, processes and platforms 'open' 1. Innovation platforms  spaces for diverse actors to engage in dialogue, and to jointly identify, assessment with  WOL = Observable Work + Narrating Your Work address issues 'FEAST' feed Feedback learn about and 2. Documenting (un)usual voices  Community perspectives  Beyond reports Listen and learn Participatory video Most significant changes Any observable impacts? Discussion support tools Farmer focus  Rapid value chain assessment 22% http://blog.podio.com/2011/08/01/working-out-loud-make- Participatory Discussion support tools Results in:  Promising feed interventions that might work 32% 20% 14% 6% 6% Livestock Labour Others Business -Stowe Boyd: Relationships information earlier.\" Remmitance  \"bringing activities out of closed repositories and applications [and events and processes], and pulling them into the open increases the likelihood of learning Partners, collaborators Contribution Agriculture 3. Open the knowledge 5. Engage over time Challenges 1. Co-create and co-learn in multi-stakeholder platforms 2. Document and mobilize knowledge from the (un)usual people 3. Make research knowledge, events, processes and platforms 'open' 1. Innovation platforms  spaces for diverse actors to engage in dialogue, and to jointly identify, assessment with  WOL = Observable Work + Narrating Your Work address issues 'FEAST' feed Feedback learn about and 2. Documenting (un)usual voices  Community perspectives  Beyond reports Listen and learn Participatory video Most significant changes Any observable impacts? Discussion support tools Farmer focus  Rapid value chain assessment 22% http://blog.podio.com/2011/08/01/working-out-loud-make- Participatory Discussion support tools Results in:  Promising feed interventions that might work 32% 20% 14% 6% 6% Livestock Labour Others Business -Stowe Boyd: Relationships information earlier.\" Remmitance  \"bringing activities out of closed repositories and applications [and events and processes], and pulling them into the open increases the likelihood of learning Partners, collaborators Contribution Agriculture 3. Open the knowledge 5. Engage over time Challenges 4. Engage, engage, engage … Innovating with communities communities  Narrating Your Work: journaling what you are doing Open mindsets in an open way for others to follow  Technology  Observable Work: creating / modifying / storing your work where others can see it, follow it and Social learning prioritization with contribute to it, before it is final farmers (Techfit) Social media 4. Engage, engage, engage … Innovating with communities communities  Narrating Your Work: journaling what you are doing Open mindsets in an open way for others to follow  Technology  Observable Work: creating / modifying / storing your work where others can see it, follow it and Social learning prioritization with contribute to it, before it is final farmers (Techfit) Social media  "}],"sieverID":"45639ace-6ba1-455a-b68e-6cf7c77e3ea1","abstract":""}

data/part_5/053d28e85712b885da97279968103b39.json

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+{"metadata":{"id":"053d28e85712b885da97279968103b39","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/069dca4b-0249-4eeb-9023-69d5490f4682/retrieve"},"pageCount":6,"title":"Paper 10: The effects of supplementary feeding of traditionally managed Bunaji cows","keywords":[],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":130,"text":"This paper discusses the effects of supplementary feeding of Bunaji cows on the birth weight and growth of their calves to 365 days of age. Pullan (1980) and Synge (1981) had clearly demonstrated the positive effects of such feeding under pastoral management on the Jos Plateau at sites close to ILCA's present case study areas. Synge (1981) concluded that \"the increased milk production alone was not economic while the economics in terms of increased numbers of calves was staggering. Although feeding the total herd was economic at the time of study, if the price of feedstuffs were to increase markedly with respect to cattle prices the exercise may no longer be economic. However, feeding only the breeding cows a very wide profit margin, the income being seven times the outlay.\""},{"index":2,"size":158,"text":"A theoretical analysis by Milligan and von Kaufmann (1979) demonstrated the inadequacy of natural forage in terms of an average Bunaji milking cow's requirements. ILCA concluded that the work of Pullan and Synge should be repeated in the subhumid zone but, in expectation of increasing shortages and rising prices of feedstuffs the feeding of agro-industrial byproducts should be introduced only as a precursor to rationing of improved forages (Papers 15 and 16), and should be directed towards the pregnant and lactating cows. The major objectives were to determine whether the pastoralist would accept the principle of rationing certain amounts to certain animals and, if he did, how the animals would respond in terms of increased productivity. The methods used were to follow the phases of livestock systems research (Paper 2) in order to ascertain the livestock owner's willingness to pay for supplementary feedstuffs, and the extension and input requirements necessary to support a supplementary feeding scheme (Paper 19)."}]},{"head":"Materials and methods","index":2,"paragraphs":[{"index":1,"size":93,"text":"Effects of feeding of cottonseed cake at the rate of 1 kg/cow/day or grazing of Stylosanthes fodder bank for 2.5 hours/day on the productivity of cows were studied. During the researchermanaged phase, supplementation started in November and ended in April. During the producer-managed phase, supplementation usually started in January. A 1-litre molassesurea supplement (80 g of urea) diluted to 40 litres with water and containing 30% crude protein equivalent (as in cottonseed cake) was tried in the 1980/81 dry season only. The data analysis procedures were the same as reported in Paper 6."},{"index":2,"size":50,"text":"The feeding trials followed the phases of livestock systems research outlined in Paper 2. In the early phases, under researcher management, there is much more control. In the later phases, under producer management, many complicating factors, such as farmers' whims and problems in extension supervision, affected the outcome (Paper 19)."}]},{"head":"Results and discussion","index":3,"paragraphs":[{"index":1,"size":16,"text":"This section reports on the analysis of the aggregate data from both researcher-managed and producer-managed trials."}]},{"head":"Preweaning calf growth","index":4,"paragraphs":[{"index":1,"size":126,"text":"Table 1 shows the effect of supplementary feeding of Bunaji cows on the birth weight and growth of their calves to 365 days of age. The birth weight of calves whose dams received supplements of any sort were significantly heavier than those from controls (P<0.05). When the supplements were partitioned, the birth weight of calves whose dams had received molasses-urea or grazed Stylosanthes fodder bank appeared significantly heavier (P<0.05) than that of calves from dams which had received cottonseed cake. These results differ markedly from those of Pleasants and Ginindza (1981), Hight (1966) and Ward (1968), who reported no improvement in the birth weight of calves from dams which were fed supplements. ILCA's results indicate the greater severity of undernutrition of cows in the present study."},{"index":2,"size":68,"text":"At 1 year of age, the difference in weights (8.6 kg) of calves from supplemented and nonsupplemented dams was still significant (P<0.05). As Table 2 shows, calves from dams fed molasses-urea had an advantage over those from dams fed other supplements, but this was not significant (P<0.05). The sample was small (14 cows) because pastoralists objected to the feeding of molasses when their animals became coated with it.  "}]},{"head":"Milk Yield","index":5,"paragraphs":[{"index":1,"size":170,"text":"Table 4 shows least squares means of human milk offtake from supplemented and nonsupplemented Bunaji cows. There were no significant differences (P>0.05) at any stage of the lactation up to 180 days. However, the total amount taken off supplemented dams was 9.3% higher than from the control animals. The interaction between the dry or early wet season and supplementation was significant only at 30 days after calving (P<O.05). Table 6 shows least squares means of the estimated total amount of milk produced by Bunaji cows up to 180 days after calving. That produced by cows which received supplements was 567.7 litres, only 6.4% higher than that produced by cows with no supplements. Results under researcher management The results of the two feeding trials under researcher management are summarized in Tables 7 and 8. b/ Total milk yield = human milk offtake plus milk to calf. Milk to calf was calculated from calf weight gain using a liveweight gain ratio of 11.65:1 (Drewry et al, 1959). c/ n.s. = not significant."},{"index":2,"size":184,"text":"The results of both trials indicate that significantly (P<0.01) more milk (about 77% in 1979/80 and 87% in 1980/81) was taken off from supplemented cows than from controls. Also, for cows to the first 90 days after calving, significantly (P<0.01) more milk (about 90% for 1979/80 and over 100% for 1980/81) was extracted from those which received cottonseed cake than from the controls. In the 1979/80 trials, there were no differences in the amount of milk consumed by the calf, nor in total milk yield during the first 90 days after calving. Whilst supplemented cows produced 37% more milk than controls, the differences were not significant (P>0.05). In the 1980/81 trials, however, the differences in milk consumed by the calf and in the total milk produced were very highly significant (P<0.01). Milk consumed by the calf and total milk produced averaged 43.4 and 52.0% higher respectively in the treatment group than in the control animals. The birth weights of calves from dams which had supplements for only 1 month before calving were not significantly different (P>0.05) from controls in either year of the experiment."},{"index":3,"size":81,"text":"Average daily weight gains during the first 90 days postpartum of calves from supplemented dams versus control calves for 1979/80 and 1980/81 were 0.23, 0.19, 0.33 and 0.23 kg respectively. The differences in growth rate in 1979/80 were not statistically significant (P>0.05). In the 1980/81 trial, however, the differences were significant (P<0.01). Up to 90 days of age, the calves from supplemented cows grew faster by about 21% for 1979/80, but by about 43% for 1980/81 than those of control cows."}]},{"head":"Results under producer management","index":6,"paragraphs":[{"index":1,"size":114,"text":"Owing to the greater difficulties encountered in monitoring the producer-managed trials, and because they started more recently, it is as yet too early to report the results with any confidence. Initial indications are that under producer management, the results are similar but less marked: supplementation has little effect on calving intervals, or may even lengthen them. This 'result' may be due to producers sharing the feed with animals in the herd other than those selected for supplementation, and milking their supplemented cows over longer periods than their control animals. Increased calf survival may also be keeping more cows longer in lactational anoestrus, an effect that can be countered by early weaning and calf supplementation."}]},{"head":"Conclusions","index":7,"paragraphs":[{"index":1,"size":50,"text":"The main effects of supplementation are enhanced calf viability and faster calf growth. This statistically significant effect must be due to increased milk consumption, but the estimated milk consumption was not significantly different. The method for estimating milk consumed by calves therefore needs to be re-evaluated for subhumid zone conditions."},{"index":2,"size":78,"text":"The recommended feeding regime must be amended to permit the feeding of all cows in order to break the anoestrus of non-lactating animals. It is clear from producers' responses that the extent of severe nutritional stress in individual animals had not been appreciated in the past and must be catered for. The improvement in calf viability suggests that calves should be supplemented directly, thus facilitating early weaning and possibly encouraging owners to extract more milk from their dams."}]}],"figures":[{"text":" within a column with differing superscripts are significantly different (P<0.05). "},{"text":"Table 1 . Least squares means of calf body weight (kg) from birth to 365 days, ILCA case study areas, southern Kaduna State, 1979-1982 a/  Variable Age (days) No. of records VariableAge (days)No. of records  Birth 90 180 365 Birth 90180365 Overall mean 19.4 43.6 60.2 103.6 322 Overall mean 19.4 43.6 60.2 103.6322 Supplementation:  Supplementation: No 18.6 a 42.4 56.8 a 99.3 a 218 No18.6 a 42.4 56.8 a 99.3 a218 Yes 20.1 b 44.8 63.5 b 107.9 b 104 Yes20.1 b 44.8 63.5 b 107.9 b104  "},{"text":"Table 2 . Effect of type of supplement fed to dam on body weight (kg) of Bunaji calves. a/ Supplement type Age (days) No. of records Birth 90 180 365  Cottonseed cake 18.1 a 44.7 64.2 104.0 66 Cottonseed cake 18.1 a 44.7 64.2 104.066 Molasses-urea 21.9 b 44.3 64.9 112.2 14 Molasses-urea21.9 b 44.3 64.9 112.214 Fodder bank 20.2 b 44.5 61.5 107.4 24 Fodder bank20.2 b 44.5 61.5 107.424 a/ Means with a column with differing superscripts are significantly different (P<0.05). a/ Means with a column with differing superscripts are significantly different (P<0.05). The viability of calves (Table 3) from supplemented dams was significantly superior (P<0.05) The viability of calves (Table 3) from supplemented dams was significantly superior (P<0.05) to that of calves from non-supplemented dams at all ages up to 365 days. At 365 days of age, to that of calves from non-supplemented dams at all ages up to 365 days. At 365 days of age, the viability of calves from supplemented dams averaged 88%, versus 67.2% in calves from the viability of calves from supplemented dams averaged 88%, versus 67.2% in calves from non-supplemented dams (P<0.001). This dramatic reduction in calf mortality was readily non-supplemented dams (P<0.001). This dramatic reduction in calf mortality was readily acknowledged by cooperating pastoralists.  acknowledged by cooperating pastoralists.  "},{"text":"Table 3 . Estimated least squares means of viability of calves from Bunaji cows fed supplements.  Variable   Age (days) No. of records VariableAge (days)No. of records  30 90 180 360 Mortality 3090180360 Mortality Overall mean 97.0 91.8 82.8 77.6 22.4 723 Overall mean 97.091.8 82.8 77.622.4723 Supplementation:   Supplementation: No 93.4 a 84.5 a 72.6 a 67.2 a 32.8 557 No93.4 a 84.5 a 72.6 a 67.2 a32.8557  "},{"text":"Table 4 . Least squares means of human milk offtake (litres) to 180 days.  Variable Days postpartum VariableDays postpartum  90 180 No. of records 90 180 No. of records Overall mean 56.3 108.3 585 Overall mean 56.3 108.3585 Supplementation:  Supplementation: No 54.6 103.6 430 No54.6 103.6430 Yes 58.1 113.2 155 Yes58.1 113.2155  "},{"text":"Table 5 . Least squares means of estimated milk (litres) consumed by Bunaji calves.  Variable  Days postpartum VariableDays postpartum  90 180 No. of records 90180 No. of records Overall mean 298.1 442.3 585 Overall mean 298.1 442.3585 Supplementation:  Supplementation: No 298.0 430.3 430 No298.0 430.3430 Yes 298.4 454.6 155 Yes298.4 454.6155  "},{"text":"Table 6 . Least squares means of total milk (litres) produced by Bunaji cows.  Variable  Days postpartum VariableDays postpartum  90 180 No. of records 90180 No. of records Overall mean 354.5 550.7 585 Overall mean 354.5 550.7585 Supplementation:  Supplementation: No 352.5 533.7 430 No352.5 533.7430 Yes 356.5 567.7 155 Yes356.5 567.7155  "},{"text":"Table 7 . Response of Bunaji cows to supplementary feeding (1979/1980). Production trait Control a/ Supplemented a/ Significance level  Milk offtake (ml/day ± SE) 424+77 750+85 P<0.01 Milk offtake (ml/day ± SE)424+77750+85P<0.01 Nov 1979 -Apr 1980 (22) (22)  Nov 1979 -Apr 1980(22)(22) Milk offtake during first 90 days 620+110 1519+150 P<0.01 Milk offtake during first 90 days620+1101519+150P<0.01 (ml/day ± SE) (17) (11)  (ml/day ± SE)(17)(11) Calf birth weight (kg ± SE) 18.29+1.26 (17) 22.09+0.71 (11) n.s. c/ Calf birth weight (kg ± SE)18.29+1.26 (17)22.09+0.71 (11)n.s. c/ Calf weight gain during frist 90 days (kg/day ± SE) b/ 0.19+0.02 (17) 0.23+0.03 (11) n.s. c/ Calf weight gain during frist 90 days (kg/day ± SE) b/0.19+0.02 (17)0.23+0.03 (11)n.s. c/ Total milk yield during first 90 days (ml/day ± SE) 2830+310 (17) 3880+470 (11) n.s. c/ Total milk yield during first 90 days (ml/day ± SE)2830+310 (17)3880+470 (11)n.s. c/ a/ Figures in parenthesis represent number of observations.  a/ Figures in parenthesis represent number of observations.  "},{"text":"Table 8 . Response of Bunaji cows to supplementary feeding (1980/81). a/ Production trait Control a/ Supplemented a/ Significance level  Milk offtake (ml/day ± SE) 341+54 636+54 P<0.01 Milk offtake (ml/day ± SE)341+54636+54P<0.01 Nov 1980 -Apr 1981 (14) (18)  Nov 1980 -Apr 1981(14)(18) Milk offtake during first 418+66 864+102 P<O.01 Milk offtake during first418+66864+102P<O.01 90 days (ml/day ± SE) (12) (16)  90 days (ml/day ± SE)(12)(16) Calf birth weight (kg ± SE) 19.25 (12) 18.75 (16) n.s. c/ Calf birth weight (kg ± SE)19.25 (12)18.75 (16)n.s. c/ Calf weight gain during first 0.23+0.03 0.33+0.02 P<0.01 Calf weight gain during first0.23+0.030.33+0.02P<0.01 90 days (kg/day ± SE) (12) (16)  90 days (kg/day ± SE)(12)(16) Milk consumed by calf during first 2679+318 3843+276 P<0.01 Milk consumed by calf during first2679+3183843+276P<0.01 90 days (ml/day ± SE) (12) (16)  90 days (ml/day ± SE)(12)(16) Total milk yield during first 90 days 3096+302 4707+285 P<0.01 Total milk yield during first 90 days3096+3024707+285P<0.01 (ml/day ± SE) b/ (12) (16)  (ml/day ± SE) b/(12)(16) a/ Figures in parenthesis represent number of observations.  a/ Figures in parenthesis represent number of observations.  "}],"sieverID":"cc99b37e-dcfb-4a18-80cd-e9dbc5d25cac","abstract":"Introduction Materials and methods Results and discussion Conclusions References"}

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+{"metadata":{"id":"055a387da888324372935cf2ae2d7e75","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/7b60ce55-516a-4888-809e-d188b2bcebec/retrieve"},"pageCount":21,"title":"","keywords":[],"chapters":[{"head":"Executive summary","index":1,"paragraphs":[{"index":1,"size":87,"text":"Somalia has a large livestock population, with an estimated 7.1 million camels, 5.3 million cattle, 30.9 million goats and 13.6 million sheep. The livestock sector is the backbone of the Somali economy and a significant source of livelihood for more than 70-80% of the Somalia population. The sector contributes around 80% of agricultural gross domestic product (GDP) and 45% of national GDP. Livestock exports contribute 80% of the country's foreign currency earnings. In 2021, 2.7 million camels, sheep, goats, and cattle were exported, mainly to Gulf States."},{"index":2,"size":56,"text":"However, the sector faces numerous challenges including poor infrastructure, insecurity, lack of input supplies such as feeds, veterinary services and credit and limited access to markets (including repeated bans from importing countries due to fear of transboundary animal diseases). Furthermore, the country's recurrent droughts and other climate-related challenges continue to undermine the sector's productivity and sustainability."},{"index":3,"size":71,"text":"The Somali Ministry of Livestock, Forests and Range (MoLFR) and the International Livestock Research Institute (ILRI) signed a memorandum of understanding in February 2023 agreeing to work together to support the development of the livestock sector in the country. As part of the collaboration, on 5 July 2023, MoLFR and ILRI organized, at the ILRI Nairobi Campus, a round table on the potential for investments in the livestock sector in Somalia."},{"index":4,"size":30,"text":"In addition to officials from MoLFR and staff from ILRI, the round table was attended by multilateral financial institutions, UN agencies, bilateral investors, and regional and regional and continental organizations."},{"index":5,"size":26,"text":"Delegates reviewed the livestock sector, discussed the government priorities for the sector and highlighted existing investments and identified gaps and priority areas for investment. These were:"},{"index":6,"size":1,"text":"1."},{"index":7,"size":2,"text":"Animal health"}]},{"head":"• Vaccination","index":2,"paragraphs":[{"index":1,"size":5,"text":"• Quality control of vaccines"},{"index":2,"size":2,"text":"• Regulations"}]},{"head":"•","index":3,"paragraphs":[]},{"head":"Standard operating procedures","index":4,"paragraphs":[{"index":1,"size":2,"text":"• Training"},{"index":2,"size":5,"text":"• Infrastructure (e.g., cold chain),"},{"index":3,"size":17,"text":"• Development of disease surveillance and control strategies and campaigns e.g., for peste des petits ruminants (PPR)."},{"index":4,"size":64,"text":"• Development of laboratory facilities and staff capacity However, the sector faces numerous challenges, including poor infrastructure, insecurity, lack of input supplies such as feeds, veterinary services and credit and limited access to markets (including repeated bans from importing countries due to fear of transboundary animal diseases). Furthermore, the country's recurrent droughts and other climate-related challenges continue to undermine the sector's productivity and sustainability."},{"index":5,"size":47,"text":"Investing in the livestock sector to address these challenges and improve its productivity and sustainability will not only benefit the livelihoods of people directly involved in the sector but also contribute to the country's economic development and food security. 3. Identify priorities for investment in the sector."},{"index":6,"size":11,"text":"4. Discuss the necessary legal framework for investing in the sector."},{"index":7,"size":16,"text":"5. Find solutions for mitigating the livestock sector challenges related to climate change and disease impacts."}]},{"head":"Opening remarks","index":5,"paragraphs":[{"index":1,"size":75,"text":"Appolinaire Djikeng, director general, ILRI, urged participants to reflect on and focus on avenues of development investments for the livestock sector in Somalia, stressing the importance of going beyond just identifying challenges but also identifying solutions. He commended the ILRI and Somalia government teams for their urgency and commitment in implementing the MoU they signed last year. He pointed out three areas in the MoU for the participants to bear in mind during their deliberations:"},{"index":2,"size":12,"text":"1. Solutions through research to advance animal health productivity, feed, and resilience."},{"index":3,"size":27,"text":"2. Building capacity at the country-level for impactful interventions and that can be strengthened over time, considering that ILRI has been doing capacity building for 50 years."},{"index":4,"size":29,"text":"3. Mobilizing resources, which will be the result of this round table, through engagement of donors and decision makers to co-create the future of the livestock sector in Somalia."},{"index":5,"size":57,"text":"Reiterating the importance of the round table, the director general noted that as a research organization, the impact ILRI can hope to achieve can only come from these kinds of conversations and partnerships. He urged participants to deliberate and make commitments as ambassadors of their organizations, not only for Somalia but for other countries in the region."},{"index":6,"size":83,"text":"On his part, H E. Hassan Hussien Mohamed, Somalia's Minister of Livestock, Forestry, and Range stated that the livestock sector in Somalia is the most important in the country. However, feed, vaccines and infrastructure are the biggest challenges to the sector realizing its full potential and contribution to the country's development. He reiterated the significance of signing the MoU with ILRI noting that it had opened opportunities and avenues for enhancing the partnership between ILRI and the government to develop the livestock sector."}]},{"head":"The livestock sector in Somalia","index":6,"paragraphs":[{"index":1,"size":41,"text":"Mohamed Omar, director general, Ministry of Livestock Forestry and Range, gave an overview of the livestock sector in Somalia highlighting its importance and contribution to the national economy, the challenges, the opportunities, policy and regulatory support, and the capacity building needs."},{"index":2,"size":32,"text":"He noted that Somalis keep livestock for economic, social, cultural and political purposes. It is such a critical sector that over 60% of people in Somali depend on it for their livelihoods."},{"index":3,"size":82,"text":"However, the sector faces many challenges. Despite its critical role in the development of the country, it receives the lowest contribution from the government, at less than 0.033% of the national budget. Climate shocks, loss of pastures and forage species, pests and disease outbreaks such as the Rift Valley fever outbreak in 2009 pose serious challenges to the development and productivity of the livestock sector in Somalia. These challenges are compounded by lack of human, capital, and infrastructural capacity to address them."},{"index":4,"size":37,"text":"Despite the challenges the livestock sector has great opportunities for investment, particularly in the dairy value chain (dairy farming and milk processing), in lot-feeding, in meat processing, in poultry and hatcheries, and leather production, among other areas."},{"index":5,"size":28,"text":"In an effort to provide an enabling legislative and regulatory environment to stimulate investments in the livestock sector, the government has established the following strategies, policies and regulations "}]},{"head":"Identified gaps/issues arising/ discussions","index":7,"paragraphs":[{"index":1,"size":39,"text":"During the plenary, several issues emerged on animal health, vaccines, investments in the livestock sector, fodder, feed and pastures, export of livestock and livestock products, gender and women and youth involvement in the livestock sector, poultry, and capacity development."}]},{"head":"Animal health","index":8,"paragraphs":[{"index":1,"size":103,"text":"It was observed that in Somalia, the main export commodity is livestock but it has faced numerous export bans because of animal health challenges, particularly diseases like Rift Valley fever (RVF). While Somalia has not reported any RVF outbreaks since 2009, the country has nevertheless signed MoUs with neighbouring countries-Kenya and Ethiopiato mitigate against outbreaks that are common in the two countries. Somalia has been informally collaborating with Kenyan and Ethiopian authorities in containing the outbreaks and now plans to make the collaborations formal. In addition, the country has a RVF contingency plan on how to mitigate, prevent and respond to other diseases."},{"index":2,"size":87,"text":"Despite Somalia being the leading camel producer in the region with over 7 million animals, camel health has been neglected, and consequently little is known about camel health and diseases in the country. There is an urgent need for proper determination of camel diseases, and a deliberate focus on camel research. While the country has nine universities with faculties of veterinary medicine and animal husbandry, the graduates know little about camel diseases. It was suggested that ILRI should play a larger role in camel research in Somalia."},{"index":3,"size":115,"text":"The good news is that the government research and development agencies and partners have recognized this gap and started initiatives to fill it. For instance, the MoU with ILRI offers an opportunity for refocussing energy on camel research. The government is deliberately sponsoring students to study camel deaths and has sent students to undertake graduate studies on camel health in Brazil. In addition, the Intergovernmental Authority on Development (IGAD), has published its 2023-2032 Camel Strategy for the region that has a component on camel health and camel diseases. There is also a regional Camel Diseases Taskforce, where the Food and Agricultural Organization of the United Nations (FAO), ILRI and regional governments, including Somalia, are members."},{"index":4,"size":27,"text":"Implementation of export protocols, one for live animals and one for meat, has seen Saudi Arabia lift the ban it had imposed on Somalia on meat exportation."},{"index":5,"size":51,"text":"It was noted that while emergency vaccine delivery is sometimes necessary, it is not a long-term solution to disease outbreaks. A preventative approach with routine vaccinations was therefore recommended. While it is expensive for government and donors to do routine vaccinations, it is affordable for farmers if there is supportive infrastructure."},{"index":6,"size":72,"text":"For instance, a farmer with 100 goats only needs to sell one to cover the costs of vaccinating the entire herd. With supportive infrastructure in place, the government role should only be to control rollout and quality, the private sector would provide the services, which would be paid for by farmers. Bundling of input services beyond vaccination and animal health can substantially reduce the cost of delivery, increase profitability and attract investors."},{"index":7,"size":118,"text":"There were concerns about the quality of veterinary drugs and vaccines supplied with delegates citing serious gaps in quality control mechanisms. This challenge could be overcome with the establishment of a veterinary medicine authority that would verify the quality of drugs and vaccines and how they are stored especially where disease control is through the private sector. Lack of quality control is a big problem in Somalia with some livestock keepers buying drugs and vaccines from other countries that may not be appropriate for the local animal health problems. These drugs are supplied from agrovet shops run by private sector and former pastoralists who are not veterinarians, and their prescriptions have serious implications on animal and human health."},{"index":8,"size":29,"text":"In response, the government is setting up a Commission for Vaccines that will spearhead building infrastructure and bringing together health experts and researchers to develop and supply required vaccines."}]},{"head":"Feed, fodder, and pasture management","index":9,"paragraphs":[{"index":1,"size":17,"text":"It was noted that Somalia has developed a Rangeland Management Strategy and a draft Fodder Production Strategy."},{"index":2,"size":101,"text":"There is growing interest in commercial fodder production driven by high demand and profitability. Buoyed by a conducive business landscape, Somali diaspora are now investing back home in fodder production. The growing trend of urban dairy farms in Somali cities, some with farms having close to 300 lactating camels, has created a high demand for feed and fodder. Camel farmers are also now investing in their own fodder and feed production, and storage facilities. Indeed, local production of fodder has been on the rise since 2016. For instance, farmers are growing alfalfa on over 200 acres along the Lower Shebelle river."},{"index":3,"size":47,"text":"These efforts are bearing fruit. Import of fodder has reduced over the years. For instance, the country did not import hay during the last drought. However, except for camel and cattle farmers, the price of hay during the drought was out of reach for small ruminant farmers."},{"index":4,"size":56,"text":"Underdeveloped infrastructure in the country remains a challenge, but also an investment opportunity. Invasion by Prosopis juliflora in the most productive Jubba and Shebelle riverine areas is a serious threat to commercial fodder production. Policies and strategies for its control are urgently needed otherwise that land might not be physically accessible in the next 10 years."},{"index":5,"size":23,"text":"At the same time, traditional rangeland management systems are collapsing, causing frequent local conflicts. Participatory rangeland management is now starting to be piloted."},{"index":6,"size":17,"text":"Participants of the Somalia round table held at ILRI on 5 July 2023 (photo credit: ILRI/Terry Mwenda)."}]},{"head":"The poultry subsector","index":10,"paragraphs":[{"index":1,"size":44,"text":"Virtually all rural households keep poultry, with meat and eggs being an important source of protein. Chicken, besides their economic and nutritional importance, have a cultural and social significance, a sign of respect and high esteem for visitors who have chicken slaughtered for them."},{"index":2,"size":65,"text":"Despite their economic and social significance, and the growing demand (now being imported to meet the local demand), poultry remains a neglected sub sector within the livestock sector. It is a value chain that has a lot of potential for growth and diversification. Feed supply is a challenge but there may be opportunities for novel feed sources such as insects, as the case in Kenya."}]},{"head":"The apiculture subsector","index":11,"paragraphs":[{"index":1,"size":91,"text":"This is a sub sector with double opportunities-honey, and pollinating services for crops and pastures. However, like poultry, it is a neglected sub sector, with the government, researchers and development partners focussing on cattle, camels, and small ruminants in the last 60 years. This is attributed to lack of appreciation, awareness and knowledge on apiculture and its critical role in pollination, and especially so in sustenance of pastures, and nutrition. The government is now keen to work with partners on the development and acceleration of apiculture and poultry in the country."}]},{"head":"Women and youth involvement in the livestock sector","index":12,"paragraphs":[{"index":1,"size":76,"text":"Most retailers of dairy and meat products are women. However, they undertake this on a small-scale to feed their families. There are no women in high-level livestock enterprises like export businesses, an area that they seriously need to support. They still need support even in their small-scale retail business to operate profitably as they lack the necessary infrastructure and facilities such cold storage facilities to ensure their dairy and meat products do not go to waste."},{"index":2,"size":59,"text":"The government is aware of the high youth unemployment, and their lack of interest and little involvement in the livestock sector. However, there are opportunities that the youth can leverage for employment. The meat export business, meat processing, and fodder production have enormous job opportunities for the youth. What is required is youth mobilization and sensitisation on the opportunities."},{"index":3,"size":46,"text":"The government is also keen on helping the youth build their careers in the livestock sector. Already the European Union has provided 100 scholarships for young people to undertake professional training in the livestock sector. The FAO is also supporting 120 youth in livestock related activities."},{"index":4,"size":59,"text":"However, even as development partners join in to support career development of the youth in the livestock sector, there are concerns about the low interest of youth in livestock. There are questions around the curricula of veterinary medicine and animal husbandry faculties in Somalia's universities and whether the training offered equips graduates with the requirements of the job market."}]},{"head":"Engagement of financial institutions in livestock development","index":13,"paragraphs":[{"index":1,"size":129,"text":"Engaging and convincing financial institutions to extend their services to pastoral communities and to service providers has proved challenging. They prefer to engage with businesspeople, and medium-to large-scale enterprises but not with pastoralists and small enterprises that have no collateral-no land and no houses-and who are not familiar with loan application process and requirements. There is need to come up with strategies and financial products for small-scale farmers as well as de-risking mechanisms to encourage investment from financial institutions. Training of pastoralists in business development, and loan application processes and requirements is also needed, but this could be an opportunity for women and youth to be trained as trainers. Budling of service and inputs will be important to reduced transaction cost and provide a wholistic development context for resilience."}]},{"head":"Priorities for investment","index":14,"paragraphs":[{"index":1,"size":18,"text":"Participants (in groups) deliberated on priority areas for investment in the livestock sector. These discussions are summarized as:"},{"index":2,"size":1,"text":"1."},{"index":3,"size":2,"text":"Animal health  "}]}],"figures":[{"text":"•• Development of disease surveillance and control strategies and campaigns e.g., for PPR • Development of laboratory facilities and staff capacity • Review of veterinary education curricula and investment in training, including in camel health • One Health: continue to pursue one health approaches with key stakeholders 2. Animal production • Feed and fodder production • Nurture commercial interest in feed and fodder value chain development • Promote improved and resilient feed and fodder varieties • Poultry value chain development 3. Rangeland management • Develop digital tools to monitor environment and socio-economic indicators • Mapping rangelands (ICIPE, ILRI) Development of apiculture value chain • Natural resource management including water and energy 4. Marketing • Improve market access • Market information system • Market infrastructure 5. Policy and coordination • Review of existing laws/regulations on both animal health and animal production "},{"text":"  "},{"text":"  "},{"text":" Report on round table on the potential for investment in the livestock sector in Somalia Somalia has a large livestock population, with an estimated 7.1 million camels, 5.3 million cattle, 30.9 million goats and 13.6 million sheep. The livestock sector is the backbone of the Somali economy and a significant source of livelihood for more than 70-80% of the Somalia population. It contributes around 80% of agricultural GDP and 45% of national GDP. Livestock exports contribute 80% of the country's foreign currency earnings. In 2021, 2.7 million camels, sheep, goats, and cattle were exported, mainly to Gulf States. 2. Animal production 2. Animal production • Feed and fodder production • Feed and fodder production • Nurture commercial interest in feed and fodder value chain development •Nurture commercial interest in feed and fodder value chain development • Promote improved and resilient feed and fodder varieties •Promote improved and resilient feed and fodder varieties • Poultry value chain development • Poultry value chain development 3. Rangeland management Introduction 3. Rangeland management Introduction • Develop digital tools to monitor environment and socio-economic indicators • Develop digital tools to monitor environment and socio-economic indicators • Mapping rangelands (the International Centre of Insect Physiology and Ecology [icipe]; ILRI). • Mapping rangelands (the International Centre of Insect Physiology and Ecology [icipe]; ILRI). • Development of apiculture value chain • Development of apiculture value chain • Natural resource management including water and energy • Natural resource management including water and energy 4. Marketing 4. Marketing • Improve market access • Improve market access • Market information system •Market information system • Market infrastructure •Market infrastructure 5. Policy and coordination 5. Policy and coordination Action  Responsible ActionResponsible Establish platform for coordination of investment in the livestock sector. This MoLFR Establish platform for coordination of investment in the livestock sector. ThisMoLFR can be modelled on similar platforms in other countries can be modelled on similar platforms in other countries Develop a concept note for the development of a livestock master plan for ILRI and MoLFR Develop a concept note for the development of a livestock master plan forILRI and MoLFR Somalia  Somalia Host a high-level livestock investment forum, matchmaking investors MoLFR Host a high-level livestock investment forum, matchmaking investorsMoLFR  "},{"text":" The International Livestock Research Institute (ILRI) works to improve food and nutritional security and reduce poverty in developing countries through research for efficient, safe and sustainable use of livestock. Co-hosted by Kenya and Ethiopia, it has regional or country offices and projects in East, South and Southeast Asia as well as Central, East, Southern and West Africa. ilri.org CGIAR is a global agricultural research partnership for a food-secure future. Its research is carried out by 13 research centres in collaboration with hundreds of partner organizations. cgiar.org Annex 1. Presentations, current and planned Organization Key policies and objectives in Somalia Current investments in the livestock sector Planned investments in the livestock sector Organization Key policies and objectives in Somalia Current investments in the livestock sector Planned investments in the livestock sector Annex 3. List of participants Annex 1. Presentations, current and planned Organization Key policies and objectives in Somalia Current investments in the livestock sector Planned investments in the livestock sector Organization Key policies and objectives in Somalia Current investments in the livestock sector Planned investments in the livestock sector Annex 3. List of participants • • European Union Create an inventory of existing projects and identify gaps Create an effective coordination mechanism for livestock development Contribute to stability Animal health, livestock trade, institutional USAID Somalia Targeted local institutions Inclusive Resilience in Somalia (IRiS); five-year No. Name Organization Designation partner initiatives in Somalia Organization Key policies and objectives in Somalia Current investments in the livestock sector in Somalia by extending state authority and services, promoting capacity development, value addition in livestock value chain, training livestock professionals (total investments EUR 44 million). govern in a more legitimate manner, diminishing influence USD65 million project (Jun 2022-Jun 2027). Led by DT Global. Livestock component implemented by Mercy Corps. 1 Ahmed Elbeltagy AU-IBAR Animal production, natural resource management and Women in agribusiness Feed and fodder Planned investments in the livestock sector Green energy for economic development production and processing. resilience project expert, RAFFS Project • • European Union Create an inventory of existing projects and identify gaps Create an effective coordination mechanism for livestock development Contribute to stability Animal health, livestock trade, institutional USAID Somalia Targeted local institutions Inclusive Resilience in Somalia (IRiS); five-year No. Name Organization Designation partner initiatives in Somalia Organization Key policies and objectives in Somalia Current investments in the livestock sector in Somalia by extending state authority and services, promoting capacity development, value addition in livestock value chain, training livestock professionals (total investments EUR 44 million). govern in a more legitimate manner, diminishing influence USD65 million project (Jun 2022-Jun 2027). Led by DT Global. Livestock component implemented by Mercy Corps. 1 Ahmed Elbeltagy AU-IBAR Animal production, natural resource management and Women in agribusiness Feed and fodder Planned investments in the livestock sector Green energy for economic development production and processing. resilience project expert, RAFFS Project • • 6. Financial services and business support Develop a livestock master plan to guide public and private sector investment Create awareness of investment opportunities in the livestock sector • Encourage and de-risk development of financial and business development services tailored to the needs of pastoral communities • Explore bundling of services for rural areas. 7. Cross-cutting ILRI One Health for Humans, Environment, Animals and Livelihoods (HEAL) that aims to enhance the well-being and resilience of vulnerable communities in pastoralist and agro-pastoralist areas of Ethiopia, Somalia and Kenya (2018-2022). One Health Regional Network for the whole of Africa (HORN) from 2018-2022, that aimed to local reconciliation and peacebuilding, creating Ongoing investment through the RAAISE project (mobilize investment in renewable energy, bank of violent extremist Reducing Communities' Vulnerability to Drought Improve livestock sector infrastructure to enhance 2 Ahmed Mohammed Hussein FAO-Somalia Field program coordinator organizations. Develop HORN into a EUR 7.4 million. loan to smallholders) and External Shocks (RECOVER): Three-year offtake. 3 Ameha Sebsibe ICPALD Head of livestock development inclusive economic more formal legal entity, so it can begin to attract resources as a legal entity that can then be used to support the concept of one health and one health projects in the opportunities and Additional Activities Plan 2023-opportunities Social protection (social Enable marginalized Somalis to withstand USD12.5 million project (Nov 2022-Nov 2025): implemented by FAO. Livestock market 4 Annie Lewa-Kigezo AU-IBAR Senior programs and projects officer protecting the most to support livestock sector with a focus on live cash transfer, livelihood shocks and stresses more information-digital/SMS vulnerable. animal and meat value chain packages for livestock) Icipe No current activities in Somalia effectively. market services. 5 Appolinaire Djikeng ILRI Director general But can offer the following: Renewable energy 6 Cynthia Mugo ILRI Policy and stakeholder engagement advisor solutions strengthen One Health capacity in Africa. Jameel Observatory project that is involved in food security and forecasting through satellite, imagery and community-based information region. Tools for managing vectors of livestock Strengthen government 7 Daniel Masiga ICIPE Head of animal and human health themes regulation, policies, and diseases (tsetse flies, strategies. 8 Diba Dida Wako USAID Project management specialist biting flies and ticks) • Climate change, gender, youth employment should be embedded in all investments Next steps/key recommendations Action Responsible Establish platform for coordination of investment in the livestock sector. This can be modelled on similar platforms in other countries MoLFR Develop a concept note for the development of a Livestock Masterplan for Somalia ILRI and MoLFR systems. Risk financing with work around index-based livestock insurance in the Egred Region and Enhance livestock health 9 Emily Nyambu USAID Project management specialist Apiculture-pollinators, to promote exports. pollination services and value-added products FAO Strengthening the 10 Feysal Mohamed Ali MoLFR-Federal Republic project and policy advisor Food Systems strengthening of Somalia productive sectors and broadening that out into a wide range of Mapping-rangeland resilient food systems Productive sector value chain 11 H E. Hassan Hussien Mohamed MoLFR-Federal Republic Minister financial services and products in livestock value chain development. Rangeland and forage research involves implementation of participatory rangeland management (PRM), rangeland restoration using productivity dynamics: of Somalia Infrastructure development Data science and data management for real time 12 Iain Wright ILRI Deputy director general-Integrated Sciences Institutional frameworks regulations and rangelands productivity dynamics monitoring that standards 13 Irene Nganga ILRI Research officer • • 6. Financial services and business support Develop a livestock master plan to guide public and private sector investment Create awareness of investment opportunities in the livestock sector • Encourage and de-risk development of financial and business development services tailored to the needs of pastoral communities • Explore bundling of services for rural areas. 7. Cross-cutting ILRI One Health for Humans, Environment, Animals and Livelihoods (HEAL) that aims to enhance the well-being and resilience of vulnerable communities in pastoralist and agro-pastoralist areas of Ethiopia, Somalia and Kenya (2018-2022). One Health Regional Network for the whole of Africa (HORN) from 2018-2022, that aimed to local reconciliation and peacebuilding, creating Ongoing investment through the RAAISE project (mobilize investment in renewable energy, bank of violent extremist Reducing Communities' Vulnerability to Drought Improve livestock sector infrastructure to enhance 2 Ahmed Mohammed Hussein FAO-Somalia Field program coordinator organizations. Develop HORN into a EUR 7.4 million. loan to smallholders) and External Shocks (RECOVER): Three-year offtake. 3 Ameha Sebsibe ICPALD Head of livestock development inclusive economic more formal legal entity, so it can begin to attract resources as a legal entity that can then be used to support the concept of one health and one health projects in the opportunities and Additional Activities Plan 2023-opportunities Social protection (social Enable marginalized Somalis to withstand USD12.5 million project (Nov 2022-Nov 2025): implemented by FAO. Livestock market 4 Annie Lewa-Kigezo AU-IBAR Senior programs and projects officer protecting the most to support livestock sector with a focus on live cash transfer, livelihood shocks and stresses more information-digital/SMS vulnerable. animal and meat value chain packages for livestock) Icipe No current activities in Somalia effectively. market services. 5 Appolinaire Djikeng ILRI Director general But can offer the following: Renewable energy 6 Cynthia Mugo ILRI Policy and stakeholder engagement advisor solutions strengthen One Health capacity in Africa. Jameel Observatory project that is involved in food security and forecasting through satellite, imagery and community-based information region. Tools for managing vectors of livestock Strengthen government 7 Daniel Masiga ICIPE Head of animal and human health themes regulation, policies, and diseases (tsetse flies, strategies. 8 Diba Dida Wako USAID Project management specialist biting flies and ticks) • Climate change, gender, youth employment should be embedded in all investments Next steps/key recommendations Action Responsible Establish platform for coordination of investment in the livestock sector. This can be modelled on similar platforms in other countries MoLFR Develop a concept note for the development of a Livestock Masterplan for Somalia ILRI and MoLFR systems. Risk financing with work around index-based livestock insurance in the Egred Region and Enhance livestock health 9 Emily Nyambu USAID Project management specialist Apiculture-pollinators, to promote exports. pollination services and value-added products FAO Strengthening the 10 Feysal Mohamed Ali MoLFR-Federal Republic project and policy advisor Food Systems strengthening of Somalia productive sectors and broadening that out into a wide range of Mapping-rangeland resilient food systems Productive sector value chain 11 H E. Hassan Hussien Mohamed MoLFR-Federal Republic Minister financial services and products in livestock value chain development. Rangeland and forage research involves implementation of participatory rangeland management (PRM), rangeland restoration using productivity dynamics: of Somalia Infrastructure development Data science and data management for real time 12 Iain Wright ILRI Deputy director general-Integrated Sciences Institutional frameworks regulations and rangelands productivity dynamics monitoring that standards 13 Irene Nganga ILRI Research officer Host a high-level livestock investment forum, matchmaking investors the PRM toolkit, and mathematical simulation MoLFR Trade and investments 14 Ismail Abdille FAO-Somalia Animal health and production officer includes risk and early Host a high-level livestock investment forum, matchmaking investors the PRM toolkit, and mathematical simulation MoLFR Trade and investments 14 Ismail Abdille FAO-Somalia Animal health and production officer includes risk and early 15 Japheth modelling. Climate change ICPALD Building resilience against Senior program coordinator warning components. 15Japhethmodelling. Climate change ICPALD Building resilience againstSenior program coordinatorwarning components. 16 climate change Mohamed Abdi Mohamed Capacity development: hosting graduate and research fellows from Somalia through the ILRI MoLFR-Federal Republic Natural resource management of Somalia Capacity building-training staff in vector 16climate change Mohamed Abdi MohamedCapacity development: hosting graduate and research fellows from Somalia through the ILRI MoLFR-Federal Republic Natural resource management of SomaliaCapacity building-training staff in vector Biosciences Hub. Support Somalia to review their national and regional PPR Eradication Strategy and action plan. Build capacity in participatory disease surveillance and risk-based surveillance to support the establishment of PPR episystems. Support member states to carry out PPR Disaster risk reduction MoLFR-Federal Republic prevention and control Enhance veterinary governance and of transboundary animal diseases. Enhance market access and trade of livestock and livestock products. Capacity building in quarantine operations and practices Promoting digital livestock systems in Somalia that leverage continental digital on the developed Mohamed Omar African Union Inter African Bureau for Animal Resources (AU-IBAR) 17 Director general of Somalia Early warning systems IGAD center for pastoral Areas and livestock development (ICPALD ) To complement the efforts of the Somalia livestock development Transboundary animal diseases control ( Ethiopia, Kenya, Somalia). Domestication of regional strategies (SPS, Protecting the poor and 18 PAGLIARA Luca European Union-Somalia Program manager-Productive Sectors (ILED Programme Centers of Excellence control, and screening for mastitis using the Development of robust California mastis test livestock identification system (LITs) that will support the efficiency of the red meat and live animals value Marketing Humanitarian response food security Coordination|) vulnerable from shocks Capacity building (SPS compliance; Quarantine and stresses Nutrition 19 Roba Guyo ILRI Head of JAMEEL Observatory program in Somalia and contribute towards AfCFTA; rangeland; feed and feeding; animal welfare etc. ) management Social protection 20 Rupsha Banerjee ILRI Senior scientist chains and promote improved food security Trust and transparency 21 Sarah Ossiya AU-IBAR Project officer, RAFFS Project the adoption of the African Continental Free Trade Area and livestock trade Support to export quarantines /capacity Support 22 Sayid Ali Isack Mohamed MoLFR-Federal Republic building operationalization of of Somalia surveillance. Use of appropriate laboratory mapping tools to assess national and regional laboratory capacities for diagnosis, data (AfCFTA). Feed inventory and feed balance strategies-Rangeland 23 Siboniso Moyo ILRI Deputy director general-Biosciences Management Strategy, Development of robust livestock seed industry through the Expanding livestock and meat trade through Feed and Feeding 24 Siobhan Mor ILRI Scientist market promotion, capacity building (SPS compliance; entrepreneurship; feedlot Strategy, National SPS 25 Terry Amaya ILRI Executive assistant Strategy, AfCFTA National storage and transmission, sample storage and quality control systems. establishment and management ); trust and Implementation Strategy, 26 Tobias Landmann ICIPE Integrated expert in geospatial science Africa Union Animal Resources Seed transparency Animal Welfare Strategy. 27 Wandera Ojanji ILRI Communication officer Biosciences Hub. Support Somalia to review their national and regional PPR Eradication Strategy and action plan. Build capacity in participatory disease surveillance and risk-based surveillance to support the establishment of PPR episystems. Support member states to carry out PPR Disaster risk reduction MoLFR-Federal Republic prevention and control Enhance veterinary governance and of transboundary animal diseases. Enhance market access and trade of livestock and livestock products. Capacity building in quarantine operations and practices Promoting digital livestock systems in Somalia that leverage continental digital on the developed Mohamed Omar African Union Inter African Bureau for Animal Resources (AU-IBAR) 17 Director general of Somalia Early warning systems IGAD center for pastoral Areas and livestock development (ICPALD ) To complement the efforts of the Somalia livestock development Transboundary animal diseases control ( Ethiopia, Kenya, Somalia). Domestication of regional strategies (SPS, Protecting the poor and 18 PAGLIARA Luca European Union-Somalia Program manager-Productive Sectors (ILED Programme Centers of Excellence control, and screening for mastitis using the Development of robust California mastis test livestock identification system (LITs) that will support the efficiency of the red meat and live animals value Marketing Humanitarian response food security Coordination|) vulnerable from shocks Capacity building (SPS compliance; Quarantine and stresses Nutrition 19 Roba Guyo ILRI Head of JAMEEL Observatory program in Somalia and contribute towards AfCFTA; rangeland; feed and feeding; animal welfare etc. ) management Social protection 20 Rupsha Banerjee ILRI Senior scientist chains and promote improved food security Trust and transparency 21 Sarah Ossiya AU-IBAR Project officer, RAFFS Project the adoption of the African Continental Free Trade Area and livestock trade Support to export quarantines /capacity Support 22 Sayid Ali Isack Mohamed MoLFR-Federal Republic building operationalization of of Somalia surveillance. Use of appropriate laboratory mapping tools to assess national and regional laboratory capacities for diagnosis, data (AfCFTA). Feed inventory and feed balance strategies-Rangeland 23 Siboniso Moyo ILRI Deputy director general-Biosciences Management Strategy, Development of robust livestock seed industry through the Expanding livestock and meat trade through Feed and Feeding 24 Siobhan Mor ILRI Scientist market promotion, capacity building (SPS compliance; entrepreneurship; feedlot Strategy, National SPS 25 Terry Amaya ILRI Executive assistant Strategy, AfCFTA National storage and transmission, sample storage and quality control systems. establishment and management ); trust and Implementation Strategy, 26 Tobias Landmann ICIPE Integrated expert in geospatial science Africa Union Animal Resources Seed transparency Animal Welfare Strategy. 27 Wandera Ojanji ILRI Communication officer   agricultural strategy.   Support efficient agricultural strategy.Support efficient      breeding programs breeding programs      to ensure sustainable to ensure sustainable      market supply to the market supply to the      Middle East Middle East  "}],"sieverID":"cd414358-272c-40d0-8438-b6da2ceba0a3","abstract":""}

data/part_5/05d00dc4d507bfcb8365739cae3bc05b.json

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+{"metadata":{"id":"05d00dc4d507bfcb8365739cae3bc05b","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/b12f72b1-68d9-4fcc-95b2-1ff5bc1f1f93/retrieve"},"pageCount":12,"title":"CGIAR Research Program on Forests, Trees and Agroforestry Livelihoods, Landscapes and Governance","keywords":[],"chapters":[{"head":"Research designed for impact","index":1,"paragraphs":[]},{"head":"Partnership building and collaboration","index":2,"paragraphs":[{"index":1,"size":45,"text":"By aligning the expertise of four CGIAR centers and their international networks, the program leverages partnerships to make the most of resources and knowledge and to avoid duplication and fragmentation. The centers have formal research partnerships with more than 80 global, national and subnational institutions."}]},{"head":"Capacity strengthening","index":3,"paragraphs":[{"index":1,"size":49,"text":"Capacity strengthening is built into the projects so that they develop su cient skills and information among users of research outputs to continue to generate and apply knowledge on their own. In 2012, the program conducted almost 100 workshops in nearly 40 countries for a total of 3,000 participants."}]},{"head":"Knowledge sharing","index":4,"paragraphs":[{"index":1,"size":44,"text":"The communications team employs a range of media and international networks to increase reach. In the rst year of the program, the centers published hundreds of journal articles, online reports, guidelines, toolkits, working papers and policy briefs and took part in in uential conferences."}]},{"head":"Which trees and forests?","index":5,"paragraphs":[{"index":1,"size":52,"text":"More than two-thirds of terrestrial ecosystems can be identi ed as forests and woodlands. But trees are an important element in other systems too, including agricultural landscapes, grasslands, steppes and deserts. The complexity associated with the ecological, cultural and socioeconomic variation between and within regions requires a broad diversity of research strategies."}]},{"head":"Impact-oriented research","index":6,"paragraphs":[{"index":1,"size":100,"text":"All CRP-FTA research activities are embedded in speci c impact pathways, which link research outputs to outcomes and, ultimately, impacts. How CRP-FTA research is hypothesized to lead to outcomes and impacts is described in CRP-FTA Theories of Change, which have been developed at a general level for the CRP and at a more detailed level for each agship project. The program's Monitoring, Evaluation and Impact Assessment system provides evidence to evaluate the program's e ectiveness and returns on investments. It also serves as an adaptive management mechanism to learn from what works best and where to focus future e orts."}]},{"head":"Smallholder production systems and markets","index":7,"paragraphs":[]},{"head":"Objectives","index":8,"paragraphs":[{"index":1,"size":45,"text":"To enhance the productivity of smallholder trees and forests and their contribution to income, food security and nutrition To increase smallholder participation in tree and forest product markets To strengthen policies and institutional arrangements that support smallholders in sustainable exploitation of tree and forest resources"}]},{"head":"Some of our research activities and achievements","index":9,"paragraphs":[{"index":1,"size":26,"text":"Generation and delivery of improved tree germplasm of high-value species in West Africa and India; development of new propagation methods for high-value tree species in Africa."},{"index":2,"size":23,"text":"Analysis of forest product value chains across Africa and Asia, with recommendations for improving rural livelihoods, including through greater market access for women."},{"index":3,"size":26,"text":"Development of new tools to customize tree species and management options to eld, farm and landscape niches, promoting tree diversity and more resilient livelihoods and landscapes."}]},{"head":"Management and conservation of forest and tree resources","index":10,"paragraphs":[]},{"head":"Objectives","index":11,"paragraphs":[{"index":1,"size":51,"text":"To increase the likelihood that important forest and tree resources will be available for future generations To improve the well-being of the poor whose livelihoods depend on these resources Evaluation of the compatibility of timber harvesting with the harvest of brazil nuts, which supports thousands of rural families in Western Amazonia."}]},{"head":"Some of our research activities and achievements","index":12,"paragraphs":[{"index":1,"size":16,"text":"Activities in India and Malaysia to help women gain more bene ts from tropical fruit trees."}]},{"head":"Landscape management for environmental services, biodiversity and livelihoods","index":13,"paragraphs":[]},{"head":"Objectives","index":14,"paragraphs":[{"index":1,"size":28,"text":"To determine how society can best manage multifunctional landscapes To balance the provisioning functions of ecosystem good and services with the maintenance of natural capital and social inclusiveness"}]},{"head":"Some of our research activities and achievements","index":15,"paragraphs":[{"index":1,"size":23,"text":"Synthesis of research on landscape management, focusing on greenhouse gas emissions arising from oil palm cultivation and land use change patterns in Indonesia."},{"index":2,"size":31,"text":"Overview of ecosystem services and how decision makers can be in uenced, which highlights the importance of the landscape scale for understanding trade-o s and the societal mechanisms for addressing them."},{"index":3,"size":16,"text":"Development of guidelines for combining conservation and livelihood goals around protected areas, adopted in international forums."},{"index":4,"size":13,"text":"Testing of new approaches to analyzing gender speci city in land-use decision making."}]},{"head":"Climate change adaptation and mitigation","index":16,"paragraphs":[]},{"head":"Objectives","index":17,"paragraphs":[{"index":1,"size":32,"text":"To contribute to the development of new forest and climate regimes and subnational initiatives To shape global regulatory systems as well as governance and nancing priorities for forest-related mitigation and adaptation measures"}]},{"head":"Some of our research activities and achievements","index":18,"paragraphs":[{"index":1,"size":17,"text":"Measurement of wood parameters and density to quantify the impact of climate changes and predict future climate."}]},{"head":"Analysis of human vulnerability to the impacts of climate change on forestry and agroforestry systems and livelihoods.","index":19,"paragraphs":[{"index":1,"size":30,"text":"Creation of an online platform on forests and climate change adaptation and mitigation under the auspices of \"weADAPT\", a fast-growing climate adaptation community of nearly 2000 members and 300 organizations."},{"index":2,"size":35,"text":"Engagement in a project to enable smallholders to bene t from carbon nance. More than 5000 farming households in India have adopted adaptation measures such as planting trees, changing agricultural practices and reducing energy consumption."}]},{"head":"Impacts of trade and investment on forests and people","index":20,"paragraphs":[]},{"head":"Objectives","index":21,"paragraphs":[{"index":1,"size":38,"text":"To contribute toward major shifts in trade and investment trends in forested landscapes, in order to reduce the negative impacts and enhance the positive impacts on forests and forestdependent communities by creating opportunities for sustainable and inclusive development"}]},{"head":"Some of our research activities and achievements","index":22,"paragraphs":[{"index":1,"size":24,"text":"Examination of the implications of biofuel development for forests, which markedly improved understanding of how biofuel policies a ect local socioeconomic and ecological outcomes."},{"index":2,"size":45,"text":"Identi cation of the challenges associated with the implementation of market regulations in consumer countries for supporting timber legality, such as FLEGT, and options to manage the impacts on the domestic timber sector and small/informal timber operators arising from their integration into the formal economy."},{"index":3,"size":36,"text":"Analysis of the policy and legal frameworks and corporate strategies shaping the expansion of large-scale investments across sectors that place pressures on forests (e.g. oil palm, timber, mining), and recommendation to improve nance and land governance."}]},{"head":"Success Stories Rural Resource Centers: A program innovation in seed and seedling delivery","index":23,"paragraphs":[{"index":1,"size":45,"text":"Impact study on e orts to spread the planting of high-value trees using new propagation methods, Cameroon More villagers learned about agroforestry options, thus gaining ways to diversify their livelihoods. The number of people planting high-value trees -and thereby increasing their incomes -more than doubled."}]},{"head":"Farmer-managed natural regeneration: More trees = more income","index":24,"paragraphs":[{"index":1,"size":60,"text":"Impact study on farmers undertaking natural regeneration of trees on their land; Burkina Faso, Mali, Niger and Senegal Overall, trees were found to increase crop yield by 15-30%, depending on location, species and crop type. Households practicing farmer-managed natural regeneration more intensively had higher incomes than those who did less -and the greater the tree density, the higher the incomes."}]},{"head":"Climate change mitigation: Overcoming technical hurdles and shaping international policy","index":25,"paragraphs":[{"index":1,"size":59,"text":"A stepwise approach to setting reference emission levels for REDD+, thus overcoming a major technical hurdle for countries in setting performance baselines After results were presented to policy makers at an expert workshop, the UNFCCC adopted the approach as its reference emission level framework. The approach has since been extended to the entire measuring, reporting and veri cation system."}]},{"head":"Research for the future","index":26,"paragraphs":[{"index":1,"size":35,"text":"Understanding long-term trends is essential for making well-informed decisions to shape the future. The four CGIAR centers are committed to continuing research under CRP-FTA for at least a decade, with the scale depending on funding."},{"index":2,"size":25,"text":"Returns on the resources invested are already being seen, and evidence from impact assessments has reinforced the validity and utility of the approaches being applied."},{"index":3,"size":44,"text":"Based on the sound research design and renowned international expertise at hand, and with su cient and stable funding, we are con dent that the CGIAR Research Program on Forests, Trees and Agroforestry will have a real impact on the planet and its people."}]},{"head":"Looking ahead","index":27,"paragraphs":[{"index":1,"size":65,"text":"This research was carried out by CIFOR as part of the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA). This collaborative program aims to enhance the management and use of forests, agroforestry and tree genetic resources across the landscape from forests to farms. CIFOR leads the program in partnership with Bioversity International, CIRAD, the International Center for Tropical Agriculture and the World Agroforestry Centre."}]},{"head":"cifor.org/forests-trees-agroforestry","index":28,"paragraphs":[{"index":1,"size":15,"text":"Photos by Aulia Erlangga, Dita Alangkara, Kate Evans, Neil Palmer, Ollivier Girard and Tomas Munita"}]}],"figures":[{"text":" Development of the Global Strategy for the Conservation and Use of Cacao Genetic Resources, through a consultation process that drew upon expertise of the global community. The strategy provides a clear framework for the conservation and use of cacao diversity. Analysis of how smallholder agroforestry systems contribute to farmer-based conservation of genetic resources. "}],"sieverID":"e96b26e2-3572-4d3e-9a71-8e5952ced0cc","abstract":"The research conducted under CRP-FTA tackles some of the most serious challenges facing humanity todayclimate change, food security, poverty and diminishing ecosystem services.The challenges of diminishing ecosystem services and poverty cross disciplines, sectors and institutions, and so too must research, policy and practice. CRP-FTA uses an approach to research that is strategic, targeted and collaborative to concentrate on impact and to advance knowledge. Since 2011, the program has generated a large volume of knowledge, technology, and institutional and policy innovations."}

data/part_5/05d218fa6ef68cfae0b41015364288fb.json

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+{"metadata":{"id":"05d218fa6ef68cfae0b41015364288fb","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/2c947367-2d46-4052-86b5-d416fc8fb364/retrieve"},"pageCount":28,"title":"CCAFS site atlas Usambara Tanzania CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) Site Atlas CCAFS site atlas Kollo / Fakara Niger CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)","keywords":["CCAFS Site NI01","Kollo / Fakara","Niger 2.826E 13.379N 2.826E 13.654N 2.547E 13.654N 2.547E 13.379N Sampling frame size: 30km x 30km"],"chapters":[{"head":"IV","index":1,"paragraphs":[]},{"head":"Introduction","index":2,"paragraphs":[{"index":1,"size":43,"text":"The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) seeks to promote a food-secure world through the provision of science-based efforts that support sustainable agriculture and enhance livelihoods while adapting to climate change and conserving natural resources and environmental services."},{"index":2,"size":42,"text":"Climate change is an unprecedented threat to the food security of hundreds of millions of people who depend on small-scale agriculture for their livelihoods. Climate change affects agriculture and food security, and likewise, agriculture and natural resource management affect the climate system."},{"index":3,"size":74,"text":"CCAFS has initially focused on three regions; East Africa (EA), West Africa (WA) and South Asia (SA) to carry out its research. The 15 CCAFS sites in these areas represent areas that are becoming both drier and wetter, and are focal locations that will generate results that can be applied and adapted to other regions worldwide. In this year, 2013, CCAFS is expanding its portfolio to additional sites in Latin America and South-East Asia."},{"index":4,"size":66,"text":"These sites serve as the initial focus of CCAFS partnership-building and long-term research activities falling within the following CCAFS Research Themes; Adaptation to Progressive Climate Change, Adaptation through Managing Climate Risk, Pro-Poor Climate Change Mitigation and Integration for Decision Making. At all 15 CCAFS sites, baseline surveys have been conducted, including three levels of data collection and analysis at household, village and organizational levels (see: http://ccafs.cgiar.org/resources/baseline-surveys)."},{"index":5,"size":45,"text":"More information on CCAFS work in all the three regions can be accessed at www.ccafs.cgiar.org To better understand the CCAFS sites' characteristics, a list of geospatial indicators for climate variability, bio-physical characteristics and socio-economic variables have been mapped into site atlases.      Aridity Index  Agro-Ecological Zones            "}]},{"head":"La nd us e is a de sc rip tio n of ho w pe op le ut iliz e th e lan d. It in vo lve s so cio -e co no m ic ac tiv ity , i.e . th e m an ag em en t an d m od ifi ca tio n of th e na tu ra l en vi ro nm en t in to bu ilt en vir on m en t,s uc h as ag ric ul tu ra l fie ld s an d se ttl em en ts . At an y pl ac e, th er em ay be m ul tip le lan d us es , th e do m in an t on e is pr es en te d he re .","index":3,"paragraphs":[]}],"figures":[{"text":" E ye im ag er y fr o m 2 3 -1 0 -2 0 1 0 at 5 m g ro u n d re so lu ti o n H B S = H o u se h o ld B as e lin e S u rv ey V B S = V ill ag e B as el in e S u rv ey O B S = O rg an iz at io n a l B a se lin e S "},{"text":"Fakara Hijmans et al (2005)    An nu al Ra in fa ll da ta of cu rre nt int er po lat ion s of ob se rv ed da ta , re pr es en ta tiv e of 19 50 -20 00 Citation: Jones et al(2002)     "},{"text":" Hijmans et al (2005)    An nu al Te m pe ra tu re re pr es en ts an nu al te m pe ra tu re da ta of cu rr en t in te rp ol at io ns of ob se rv ed da ta , av er ag ed fo r 19 "},{"text":"Fakara Trabucco et al (2009) Ar id ity In de x in di ca te s th e le ve l of dr yn es s, ta ki ng ev ap ot ra ns pi ra tio n in to ac co un t, at a gi ve n lo ca tio n of kn ow n ra in fa ll Jarvis et al (2008)Al tit ud e in di ca te s th e he ig ht ab ov e se a le ve l in m et er s FAO et al (2009)So il Ty pe re fe rs to th e so il gr ou p as pe r th e FA O cl as si fic at io n. So il gr ou ps ar e de fin ed by th ei r pa re nt m at er ia l an d m or ph og en et ic ch ar ac te ris tic s in te rm s of st ru ct ur al pr op er tie s an d te xt ur e (s an d, si lt an d cl ay co nt en t) , as w el l as or ga ni c m at te r co nt en t. "},{"text":" to left map Citation: FAO (2008)Ag ro -E co lo gi ca l Zo ne s in di ca te th e di vi si on of la nd ar ea s th at ha ve si m ila r ch ar ac te ris tic s re la te d to la nd su ita bi lit y, po te nt ia l ag ric ul tu ra l pr od uc tio n an d en vi ro nm en ta l im pa ct . "},{"text":" to left mapLa nd co ve r sh ow s th e ob se rv ed (b io )p hy si ca l co ve r of th e ea rt h' s su rf ac e, i.e . do m in an t ve ge ta tio n, la nd us e an d m an -m ad e fe at ur es . "},{"text":"Fakara  "},{"text":" Thornton et al (2006)    Th e Le ng th of G ro w in g Pe rio d (L G P) is de fin ed as th e nu m be r of da ys in a ye ar du rin g w hi chth er e is av ai la bl e ra in fe d so il m oi st ur e su pp ly fo r pl an t gr ow th . "},{"text":" Thornton et al (2006)    Th e Le ng th of G ro w in g Pe rio d (L G P) is de fin ed as th e nu m be r of da ys in a ye ar du rin g w hi chth er e is av ai la bl e ra in fe d so il m oi st ur e su pp ly fo r pl an t gr ow th ; he re m od el ed fo r 20 30 . "},{"text":" FAO and IIASA (2007)    C ro p S ui ta b ili ty re fe rs to th e la nd re so u rc e as se ss m en t th at co n si de rs ag ri cu lt ur al la nd us e op ti on s w ith re le va n t ag ro -e co lo gi ca l co n di ti on to es ti m at e ex pe ct ed cr op pi n g ac ti vi ti es .Li ve st oc k Pr od uc tio n Sy st em s as pa rt of ag ric ul tu ra l sy st em s ta ke ag ro -c lim at ic co nd iti on s in to ac co un t an d ar e cla ss ifi ed in te rm s of fe ed an d liv es to ck re so ur ce s; liv es to ck co m m od iti es pr od uc ed ; pr od uc tio n te ch no lo gy ; pr od uc t us e an d liv es to ck fu nc tio ns ; ar ea co ve re d; ge og ra ph ic lo ca tio ns ; an d hu m an po pu la tio ns su pp or te d.Li ve lih oo ds ar e co m pl ex an d sh ap ed by a va rie ty of fa ct or s.Th es e liv el ih oo d zo ne m ap s de lin ea te ge og ra ph ic ar ea s w ith in w hi ch pe op le br oa dl y sh ar e th e sa m e liv el ih oo d pa tt er ns in cl ud in g ac ce ss to fo od , in co m e, an d m ar ke ts . "},{"text":" Po pu la tio n D en si ty is th e gr id de d nu m be r of pe rs on s pe r km in 20 05 . "},{"text":" Tr av el tim e is a m ea su re of ac ce ss ib ili ty de te rm in ed in th e tim e (h ou rs ) ta ke n to th e ne ar es t ur ba n ce nt re , to w n or ci ty of a po pu la tio n of 50 ,0 00 pe op le or m or e (t ak in g di ffe re nt m ea ns of tr an sp or ta tio n in to ac co un t) "},{"text":" co ns tr uc te d gl ob al da ta se ts of po ve rt y th at ar e ba se d on es tim at es of su bn at io na l in fa nt m or ta lit y an d ch ild m al nu tr iti on da ta , re co gn iz in g th at bo th ar e pr ox ie s fo r po ve rt y an d w el fa re ra th er th an di re ct m ea su re s. "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "}],"sieverID":"4eb38576-c42c-4425-846d-4df0259ccc8d","abstract":"This Atlas Series has been prepared as an output for Theme 4.2 under the CCAFS program and has not been peer reviewed. Any opinions stated herein are those of the author(s) and do not necessarily reflect the policies or opinions of CCAFS, donor agencies, or partners. The geographic designation employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of CCAFS concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.All images remain the sole property of their source and may not be used for any purpose without written permission of the source."}

data/part_5/06cb3a6ad670fae04fd5fd1cf067a8bb.json

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+{"metadata":{"id":"06cb3a6ad670fae04fd5fd1cf067a8bb","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/cc8ae64d-d0ee-4d10-8866-db34a133806e/retrieve"},"pageCount":4,"title":"Realising climate gains from smallholder chicken farming in Africa CATALYSING ACTIONABLE KNOWLEDGE TO IMPLEMENT CLIMATE-SMART SOLUTIONS FOR NEXT-GENERATION ACP AGRICULTURE BLOG Chickens are among the few domestic animals that have a low environmental impact and carbon footprint","keywords":[],"chapters":[{"head":"BLOG","index":1,"paragraphs":[{"index":1,"size":80,"text":"There is hardly a document on African climate change issues that does not portray livestock husbandry in a negative light -responsible for emitting substantial quantities of greenhouse gases. While it is true that some livestock play a role in generating greenhouse gases, this is not the case across the entire sector. Chickens are among the few domestic animals that have a low environmental impact and carbon footprint, and research is moving forward to develop climate-smart poultry production for African smallholders."},{"index":2,"size":76,"text":"Aware of the huge challenges that climate change and variability pose to agriculture, experts and policy-makers are increasingly exploring approaches that can withstand the effects of climate change, contribute to productivity growth and contribute to reducing greenhouse gas emissions. Small-scale chicken production ticks all the climate-smart agriculture (CSA) boxes, though to date it has been overshadowed by a focus on the challenges and opportunities of crop agriculture and soil-water conservation in the context of watershed management."},{"index":3,"size":86,"text":"In rural sub-Saharan Africa (SSA), chickens are among the most widely reared livestock, providing valuable disposable income for poor households in general, and for women and youth in particular. They thrive in a range of environments, are efficient in converting feed into high-quality food, and have smaller environmental footprints than most other livestock. At the same time, chickens are believed to be susceptible to shifting weather patterns although, with the right breeds, it should be possible to enhance their potential to adapt to a changing climate."}]},{"head":"More productive poultry","index":2,"paragraphs":[{"index":1,"size":174,"text":"However, the overall production and productivity levels of the smallholder chicken industry in SSA are very low. Typically, a hen produces at most 45 eggs per year and takes more than six months to achieve a market live-weight of less than 1.5kg. This is in marked contrast to the developed world where, on average, a hen produces more than 300 eggs per year and meat birds attain 2 kg body weight in less than 40 days. Of late, research attention has increasingly been directed to improve local chicken breeds so they become more productive and adaptable to different agro-ecologies and farming systems. One such action research initiative is the African Chicken Genetic Gains (ACGG) project, a five-year multi-partner, multi-country initiative led by the International Livestock Research Institute (ILRI), with investment from the Bill & Melinda Gates Foundation. The project has been involved in increasing smallholder chicken production and productivity growth as a pathway out of poverty, as well serving as a platform for testing, delivering, and continuously improving tropically adapted chickens in the tropics."}]},{"head":"Adapting chickens to a changing climate","index":3,"paragraphs":[{"index":1,"size":134,"text":"ACGG and its partners have registered encouraging results in terms of testing indigenous and exotic chicken breeds and enhancing access of smallholder farmers to more productive, agroecologically appropriate, and vaccinated chicken strains. The project's genetic innovation work proceeded on the basis of farmers' preferences in different agro-ecologies. The chicken strains that ACGG made available to farmers were found to have resulted in significant productivity gains both in terms of live body weight (an average of 200 -300 per cent increase over the indigenous ones) and egg production (an average of 100 -160 per cent gain compared with the indigenous ones) for more than 6,000 farm households. ACGG has thus successfully demonstrated the viability of the science that underpins genetic innovations to improve farm-level chicken productivity in ways that are adaptable to a changing climate."},{"index":2,"size":140,"text":"It is therefore high time that action research on chicken productivity be seen from the prism of addressing the concerns of CSA. Furthermore, there is a strong case for greater appreciation of the climate credentials of the myriad practices of the chicken value chain in the context of smallholder commercial poultry systems, and the discourse on CSA should firmly embrace this sub-sector. In addition, an action research agenda on poultry could encourage chicken breeding programmes in Africa to factor in explicitly CSA parameters of adaptation, including heat/cold tolerance, so that this important sector remains viable in a changing climate. This article was created through a CTA-led process to document and share actionable knowledge on 'what works' for ACP agriculture. It capitalises on the insights, lessons and experiences of practitioners to inform and guide the implementation of agriculture for development projects."}]},{"head":"DISCLAIMER","index":4,"paragraphs":[{"index":1,"size":39,"text":"This document has been produced with the financial assistance of the European Union. The contents of this document are the sole responsibility of CTA and can under no circumstances be regarded as reflecting the position of the European Union."}]},{"head":"EXTERNAL LINKS","index":5,"paragraphs":[{"index":1,"size":30,"text":"Leveraging CTA's innovation experiences to inform next generation ACP agriculture Catalysing actionable knowledge to implement climate-smart solutions for next-generation ACP agriculture A series of interviews with the workshop participants AUTHORS"}]}],"figures":[{"text":"  "}],"sieverID":"3b007a7f-0bbd-4b56-8e45-b8abcfdfea81","abstract":""}

data/part_5/071c9a05395492b977d00eb40fe044fa.json

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+{"metadata":{"id":"071c9a05395492b977d00eb40fe044fa","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/b4aacd26-e591-4c8a-8c13-85e21e33d438/retrieve"},"pageCount":19,"title":"Income inequality within smallholder irrigation schemes in Sub-Saharan Africa","keywords":["Income inequality","agriculture","poverty","Gini coefficient","theil index"],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":93,"text":"It is estimated that 1.2 billion people across the world live in extreme poverty (UN, 2013). Alongside growth, mitigating socio-economic inequality is widely recognized as a key component of effective poverty-reduction strategies (Groll & Lambert, 2013;Kabubo-Mariara, Mwabu, & Ndeng'e, 2012). In fact, without adequate redistribution interventions, rapid development can lead to excessive economic disparities, often resulting in severe issues such as persistent poverty (Ravallion, 1997), violent crime (Hsieh & Pugh, 1993), corruption (Khagram, 2005), political instability (Alesina, 1996), worsened health (Kawachi & Kennedy, 1997) and low education levels (De Gregorio & Lee, 2002)."},{"index":2,"size":211,"text":"The interconnection between growth, poverty and inequality is especially crucial in rural areas, home to 70% of the developing world's extremely poor (Ferreira, 1996;Ortiz & Cummins, 2011;Watkins, 2013). Sub-Saharan Africa (SSA), in particular, suffers from deep and persistent poverty and inequality, which undermine the gains from technological advances, including those in agriculture (Go, Nikitin, Wang, & Zou, 2007). Most of the existing inequality literature is based on national or regional investigations (typically derived from governmental census); fewer studies exist at the level of villages or rural communities, where more detailed data collection is required (Silva, 2013). As a result of this gap, there is a need to further ABSTRACT Equitable income distribution is recognized as critical for poverty reduction, particularly in developing areas. Most of the existing literature is based on region-or country-wide data; fewer empirical studies exist at community levels. This article examines income disparities within six smallholder irrigation schemes in Zimbabwe, Tanzania and Mozambique, comparing inequality at local and national levels, as well as decomposing inequality by group and by source. The results present significant contrasts between schemes and compared to national figures. This evidences that, inadvertently, nation-wide strategies may overlook high inequality at smaller scales, and thus, development policies should be tailored to the specific areas of intervention."},{"index":3,"size":55,"text":"understand the poverty-inequality nexus at small scales, in order to define more effective and robust growth strategies (Ostry & Berg, 2011). This question is particularly critical in small-scale irrigation schemes in developing countries, where sustainable irrigation is widely recognized as a powerful tool to mitigate poverty and extreme economic inequality (Chitale, 1994;Makombe & Sampath, 1998)."},{"index":4,"size":80,"text":"This study investigates socio-economic inequality in six smallholder irrigation schemes in Zimbabwe, Tanzania and Mozambique. First, income inequality is calculated at a local level and then compared to national figures. Second, income inequality is decomposed by household economic activity -solely agricultural or diversified incomes -to assess the relative importance of the between-group and within-group components. Finally, an analysis by four different income sources determines which components contribute most to total inequality and which ones have an 'equalizing' or 'unequalizing' effect."}]},{"head":"Growth, poverty and inequality in Sub-Saharan Africa","index":2,"paragraphs":[{"index":1,"size":104,"text":"Between 1995 and 2013, SSA experienced an average annual GDP growth of 4.5%, accompanied by a 9% drop in the poverty headcount ratio (World Bank, 2014). Nevertheless, the subcontinent is still home to 30% of the word's extremely poor and undernourished population. Following the global trend, income disparity in the region has risen compared to 1980s levels, making SSA the second-most income-unequal subcontinent, after Latin America and the Caribbean (Cogneau et al., 2007). Lesotho, South Africa and Botswana are the most unequal SSA countries, with Gini coefficients above 0.63, while Niger and Ethiopia have the lowest disparities, with Gini coefficients below 0.35 (CIA, 2014)."},{"index":2,"size":115,"text":"Zimbabwe ranks among the 10 most unequal SSA countries, with a Gini coefficient of 0.50 in 2006 (CIA, 2014). Such economic disparities are partly derived from its agrarian socio-economic situation, still reflecting the legacy of the colonial era, the civil war and the reforms of the late twentieth century. Throughout the 1980s and 1990s, Zimbabweans' livelihoods deteriorated significantly, as a result of repetitive droughts and issues associated with land reform (Kinsey, 2010;Mazingi & Kamidza, 2011). The national poverty headcount ratio is 72%; in rural areas it is 84% (ZIMSTAT, 2013). Zimbabwe's Human Development Index (HDI) for 2012 was 0.397 -in the 'low' human development category -ranking 172 out of 187 countries and territories (UNDP, 2013)."},{"index":3,"size":127,"text":"Tanzania is one of the four most income-equal countries in SSA, with a Gini coefficient of 0.38 in 2007 (CIA, 2014). Its economy is largely dependent on rural activities, with agriculture, hunting and forestry accounting for 27% of GDP, second only to the service sector, at 48% (United Republic of Tanzania, 2013b). In the 1980s and early 1990s, Tanzania experienced significant economic growth, which brought lower poverty but also higher economic inequality (World Bank, 2011). Over the first decade of the 2000s, the average annual GPD grew by 7% and the national HDI rose from 163 to 151 in a world ranking of 189 countries (UNDP, 2011). The poverty headcount ratio across mainland Tanzania is 34%; in rural areas it is 38% (United Republic of Tanzania, 2009)."},{"index":4,"size":117,"text":"In Mozambique, income inequality is relatively high, with a 0.46 Gini index, above the SSA median of 0.43 (CIA, 2014). Between 1995 and 2003, agriculture was the second-largest contributor to GDP growth (1.7% out of 8.6%) and the main driver of poverty reduction. Over this period, agriculture experienced an average annual growth of 5.2%, but this mainly represented recovery from the 1977-1992 war, rather than productivity gains from innovation and investment (virtanen & Ehrenpreis, 2007). In 2008/09, the national poverty headcount ratio was 55%, with rural areas still being more affected (57%) than urban centres (50%) (Arndt, Jones, & Tarp, 2010). Worldwide, Mozambique is the tenth-least developed nation, with an HDI of 0.393 in 2013 (UN, 2014)."}]},{"head":"Data collection","index":3,"paragraphs":[{"index":1,"size":142,"text":"The countries of study in this article were selected following a scoping exercise covering nine African nations, out of which Zimbabwe, Tanzania and Mozambique were prioritized based on their local expertise, favourable policies and institutions, and potential to increase food production (Pittock, Stirzaker, Sibanda, Sullivan, & Grafton, 2013). In each of the three countries, two irrigation schemes were chosen by local research partner organizations given their institutional capacity, ability to improve agricultural practices, accessibility and the interest of local agencies in collaboration (Rhodes, Bjornlund, & Wheeler, 2014). 'Irrigation scheme' is defined as an area where crops are grown under irrigation through any method (United Republic of Tanzania, 2013a). In this article, 'irrigation scheme' will also be used to refer to an agricultural community whose members own or rent land within the same irrigated area, sharing the same water source and supply infrastructure."},{"index":2,"size":70,"text":"The selected schemes lie within semi-arid climatic areas, where, due to erratic or seasonal rainfall, irrigation is critical to achieve successful crop production (FAO, 2005). Water is abstracted from surface sources and delivered through gravity-fed methods, which is typical of smallholder schemes in semi-arid areas of the three countries. The representativeness of the chosen sites is further discussed in the respective country articles that form part of this special issue."},{"index":3,"size":67,"text":"The six schemes in this study range in size from 10 to 939 hectares, each of them having between 27 and 578 registered member households (Table 1). The average family landholding varies from 0.1 to 1.6 ha, in line with average smallholder landholdings at the respective national levels: 0.12 ha in Zimbabwe (FAO, 2006), 0.9 ha in Tanzania (FAO, 2015) and 1.4 ha in Mozambique (FAO, 2007)."},{"index":4,"size":143,"text":"While there is not one consistent definition of 'smallholder farms' , the most common approach is to consider them as those with less than 2 ha of cropland (Hazell, Poulton, Wiggins, & Dorward, 2007). Other usual smallholder characteristics (also found in the selected schemes) include low technology, reliance on household members for most of the labour, and dependence on the farm as a principal source of family income (Nagayets, 2005). The schemes in this study are subdivided into farms, each of which is cultivated by one family, with some families having more than one farm. Given the association between farm and household, and not farm and individual, the data-collection process was designed using households as the basic unit. The survey consisted of 65 structured and semi-structured questions, regarding the family members, farm characteristics, food security, asset ownership, revenue and expenses, among other questions."},{"index":5,"size":99,"text":"The surveys were conducted between May and July 2014, with sampling method varying depending on the size of the population. In the three smallest schemes -Mkoba (Zimbabwe), 25 de Setembro and Khanimambo (Mozambique) -the aim was to interview the whole population (though some farmers asked to be excused and others were absent). In the three largest schemes -Silalabuhwa (Zimbabwe), Kiwere and Magozi (Tanzania) -the population was sampled using a stratified approach. Irrigators were categorized according to the gender of the household head and wealth category (poor, medium or well-resourced) and then randomly sampled (Moyo, Moyo, & van Rooyen, 2014)."},{"index":6,"size":42,"text":"Data used in this study include household revenues and expenditure over the 12-month period prior to the interview. The information was collected according to source of revenue and type of expenditure, and was then aggregated into on-farm and off-farm categories (Table 2)."}]},{"head":"Analytical framework","index":4,"paragraphs":[]},{"head":"Defining inequality","index":5,"paragraphs":[{"index":1,"size":144,"text":"Economic inequality can be defined in many ways, but it is typically considered to be the uneven distribution of wealth, income and/or assets among individuals of a group, or between groups of individuals (McKay, 2002). While there is not one ideal measurement, the preferred indicators of poverty and living standards tend to be money metrics, i.e. income or consumption expenditure (Sahn & Stifel, 2003). Alternative non-monetary measures exist, such as those based on asset ownership (Filmer & Pritchett, 2001;McKenzie, 2005) and the Multidimensional Poverty Index, combining education, health and living standards indicators (Alkire & Santos, 2011;Kovacevic & Calderon, 2014). In this article, monetary indicators were used so as to compare local and national inequality and to investigate how various income sources contribute to total inequality. Out of a wide range of inequality measures, the section below presents a summary of the two selected indicators."}]},{"head":"Gini coefficient","index":6,"paragraphs":[{"index":1,"size":53,"text":"The Gini coefficient measures the extent to which the distribution of wealth within a group deviates from a perfectly equal distribution, with values from 0 to 1 (World Bank, 2011). Its advantages include being commonly used and relatively easy to calculate, having a visual representation, and allowing comparison between populations of different sizes."},{"index":2,"size":27,"text":"The Gini coefficient can be estimated based on the representation of the Lorenz curve, plotting cumulative income vs. cumulative population. It can also be mathematically calculated as:"},{"index":3,"size":127,"text":"where cov is the covariance between income levels y and the cumulative distribution of the same income F(y), and ȳ is average income. Lerman and Yitzhaki (1985) developed a method to decompose the Gini coefficient as the sum of the inequality contributions of all income sources: where S k is the share of income source k in total income, G k is the Gini coefficient of income source k and R k is the Gini correlation of income from source k with the distribution of total income. By calculating partial derivatives of the Gini coefficient with respect to a percentage change e in income source k, it is possible to estimate the percentage change in total inequality resulting from a small percentage change in income source k:"},{"index":4,"size":27,"text":"This property is particularly useful in this study because it allows identification of the 'equalizing' or 'unequalizing' effect of each income source on total inequality (López-Feldman, 2006)."},{"index":5,"size":57,"text":"The Gini coefficient also has several restrictions. First, it does not satisfy the properties of aggregativity and additive decomposability (Bourguignon, 1979), limiting its ability to analyze inequality between and within population subgroups. Moreover, in the presence of negative incomes, the Gini coefficient presents abnormal behaviours, as detailed in the section 'Negative Incomes and Measures of Inequality' ."}]},{"head":"Theil index","index":7,"paragraphs":[{"index":1,"size":46,"text":"The Theil is a specific case of the generalized entropy indices (Bellù & Liberati, 2006). Its lower value is zero (perfect equality), and it has no upper limit. The index is defined as: where y i is the ith observation and ȳ is the average income."},{"index":2,"size":1,"text":"(1)"},{"index":3,"size":31,"text":"One of its key advantages is being decomposable and additive into groups, thus allowing distinction of between and within sub-group inequality components. Assuming m groups, the Theil index is decomposed as:"},{"index":4,"size":31,"text":"where the first and second terms are the within-group and between-group components, respectively. Similarly, the Theil index can also be decomposed by source of income, following the expression for m sources:"},{"index":5,"size":26,"text":"In this study, the decomposition of the Theil index in between/within sub-groups and by income source was calculated by computing equations ( 5) and ( 6)."},{"index":6,"size":111,"text":"The Theil index has also some drawbacks, such as not having an intuitive representation and not being suitable for comparing populations of different sizes. Also, it does not support non-positive values, as ln(x) is undefined for x ≤ 0. As explained by Bellù and Liberati (2006) and vasilescu, Serebrenik, and van den Brand (2011), the limitation of zero values can be overcome by replacing zeros with very small values ε > 0, such that I Theil (x 1 , . . ., x n−1 , 0) ≡ I Theil (x 1 , . . ., x n−1 , ε). In this article, ε is taken as equal to 10 -10 ."}]},{"head":"Negative incomes and measures of inequality","index":8,"paragraphs":[{"index":1,"size":92,"text":"Two common measures of agricultural income are net cash income and net farm income. The former is a measure of cash flow representing the money available for debt repayment, investment or withdrawal (Statistics Canada, 2000), while the latter is the value of farm production, including cash and non-cash transactions (Edwards, 2013). Net farm income could not be used in this article because there were no records of non-monetary income transactions, e.g. depreciation, in-kind income or commodities stored. Therefore, net cash income was chosen as the measure of household income from farm sources."},{"index":2,"size":117,"text":"Across the six irrigation schemes, 30% of the households reported higher on-farm expenses than on-farm revenues, resulting in negative net cash income from farming activities. Negative incomes pose a major constraint in the study of inequality, which has been discussed in the literature, with different authors adopting different approaches. Walker and Ryan (1990) and Möllers and Buchenrieder (2011) note the existence of negative incomes in their data, yet do not discuss the implications or treatment methods for inequality calculation. Schutz (1951) and Stich (1996) indicate that negative incomes are usually excluded from the measurements of income inequality, a method that has been adopted by Cowell (2008), Cribb, Hood, Joyce, and Phillips (2013) and Sanmartin et al. (2003)."},{"index":3,"size":61,"text":"Nonetheless, disregarding households with negative net cash incomes is not ideal in this study as it would ignore almost one-third of the sample. Furthermore, this approach is undesirable for agricultural redistribution policies given that it is normal for farms to record losses (Allanson, 2005), and thus it misses out on a key feature of household incomes (Rawal, Swaminathan, & Dhar, 2008)."},{"index":4,"size":1,"text":"(5)"},{"index":5,"size":162,"text":"It is possible to calculate the Gini coefficient including zero and negative values, yet the resulting 'modified' coefficient violates several of its basic properties. First, the principle of transfers (Dalton, 1920), by which a transfer of income from a richer individual to a poorer one leads to a reduction in income inequality, is not always satisfied when the Gini coefficient includes negative incomes. Moreover, the 'modified' Gini coefficient is no longer bounded between 0 and 1, making it inaccurate as a comparison across populations or time. To correct this issue, Chen, Tsaur, and Rhai (1982) proposed a reformulation, referred to as 'normalization' , which was subsequently refined by Berrebi and Silber (1985). However, as evidenced by Raffinetti, Siletti, and vernizzi (2014), this 'normalized Gini' presents abnormal behaviours, such as providing the same inequality measure for two populations having completely different income distributions (total equality and total inequality). Furthermore, it does not allow accurate decomposition by income source (Mishra, El-Osta, & Gillespie, 2009)."},{"index":6,"size":141,"text":"The Australian Bureau of Statistics (ABS, 2006) argues that negative incomes often reflect the households' business and investment arrangements or may be a result of accidental or deliberate under-reporting. Therefore, it is inappropriate for them to have a disproportionate influence on inequality measures. Following this argument, the 'equivalization' method is proposed, in which individual income components with negative values are set to zero before computing the total income of each household (OECD, 2014). The process of equivalization has been defined by the OECD and is used by government agencies such as the Australian Bureau of Statistics and the UK Department for Work & Pensions (2014). This technique of truncating the data to report negative incomes as zeros has been applied by Seidl, Pogorelskiy, and Traub (2012) and Bray (2014), who showed consistency of results using various ways of treating negative incomes."},{"index":7,"size":54,"text":"When it comes to adopting one method or another, Smeeding, O'Higgins, and Rainwater (1990) state that each researcher is left to deal with zero and negative incomes as he or she sees fit. Similarly, Deaton (1997) notes that the choice of inequality measures can be made based on practical convenience or on theoretical preference."},{"index":8,"size":68,"text":"Given the interest in maintaining all households in the sample and in using the Gini and Theil indices, the author deemed equivalization the most suitable approach to deal with negative incomes. Thus, negative farm incomes were converted to zero, before being added to other income components to obtain the total. To test the adequacy of the chosen method, a sensitivity analysis was conducted, as described in the appendix."}]},{"head":"Results and discussion","index":9,"paragraphs":[]},{"head":"Income inequality at scheme and national levels","index":10,"paragraphs":[{"index":1,"size":47,"text":"This section describes the levels of economic inequality within six smallholder agricultural communities and compares them to their respective national figures. Household consumption expenditure and income were used at the scheme level, while family income served as the national indicator, given the available country statistics (Table 3)."},{"index":2,"size":74,"text":"Inequalities measured by expenditure are smaller than by income, which is common given that consumption expenditure tends to be more evenly distributed than income (Aguiar & Bils, 2011;Finn, Leibbrandt, & Woolard, 2009;Krueger & Perri, 2006). Income inequalities at the scheme level are generally higher than at national levels. The greatest difference is in Tanzania, where Gini income coefficients within the agricultural communities are on the order of 50-60% higher than at the national scale."},{"index":3,"size":74,"text":"The Tanzanian Ministry of Finance and Economic Affairs (United Republic of Tanzania, 2009) argues that, given the country's relatively low levels of inequality, income redistribution is not likely to be effective in achieving significant poverty reduction. Instead, it suggests that continued high rates of economic growth over the long term will be required. In contrast, this study finds that significant income inequalities exist at smaller scales, which are currently being overlooked by country-wide statistics."}]},{"head":"Income dualism between agricultural and diversified sources","index":11,"paragraphs":[{"index":1,"size":76,"text":"In rural developing areas, non-agricultural earnings represent an important part of households' incomes, but they can also create significant economic inequalities (Barrett, Reardon, & Webb, 2001;Escobal, 2001;Reardon, 1997). Hence, the aim of this section is to analyze income differences between and within two groups of households: (1) those earning incomes exclusively from agriculture (including farm income and agricultural labour); and (2) those with diversified incomes (including non-agricultural labour, regular, seasonal or self-employment, business, remittances and other)."},{"index":2,"size":53,"text":"Non-parametric tests of statistical significance, Wilcoxon rank-sum (WRS) and Kolmogorov-Smirnov (KS), were used to analyze differences in the distribution of incomes between population subgroups. Common parametric tests could not be used because they require making assumptions on parameters characterizing the populations' distributions, which was not possible given the data available for this study."},{"index":3,"size":135,"text":"In Zimbabwe, the vast majority of households have diversified incomes, while in Tanzania and Mozambique, only half obtain earnings outside of agriculture (Table 4). One common characteristic of all six communities is that households making a living exclusively from agriculture had consistently lower mean and median incomes than those with diversified incomes. The WRS and the KS tests indicated that the distribution of income is not the same in both groups and that exclusively agricultural households rank lower in the overall income distribution. The WRS test (p < .1) indicated that the null hypothesis that incomes of agricultural households are not different from diversified-income households could be rejected. Similarly, the KS test concluded that (p < .1) the hypothesis that both groups have the same distribution was also rejected in all schemes, except for Magozi."},{"index":4,"size":173,"text":"Despite the remarkable contrast between agricultural and diversified income households, the Theil index decomposition reveals that disparities within these two groups are actually the main contributor to overall inequality (Table 5). The only exception is Khanimambo, yet results from small samples should be interpreted with caution, given the low power of statistical tests (see the Limitations section). These results conclude that households with diversified earnings have higher incomes than those exclusively dedicated to agriculture, which is consistent with findings elsewhere in Africa (Barrett et al., 2001). As a result of barriers to entry, poor households typically struggle to access highly profitable non-farming activities, whereas more advantaged families tend to profit from greater returns, thus creating a negative feedback loop between poverty, inequality and diversification (Barrett, Bezuneh, & Aboud, 2001;Woldenhanna & Oskam, 2001). Furthermore, the findings in this section contribute to the existing literature by showing that the contrast between diversified and non-diversified income households only explains a minor portion of overall income inequality; disparities within each group are in fact the major driver."}]},{"head":"Relative importance of income sources in total inequality","index":12,"paragraphs":[{"index":1,"size":135,"text":"An extensive literature review by Senadza (2011) concluded that, to better understand the effects of income on inequality, it is important to distinguish between the various components of non-farm income. Hence, this section analyzes the effect on total inequality derived from four distinct income sources: agricultural, including on-farm income and agricultural labour; wages, including non-agricultural labour, regular employment and seasonal work; business and self-employment; and other, including remittances and other unspecified sources. In Tanzania, agriculture is the most important source of income, accounting for three-quarters of total earnings and ca. 80% of inequality (Table 6). In contrast, Zimbabwean schemes rely more heavily on other sources (between half and two-thirds comes from remittances), which also account for the largest portion of total income disparities. In Mozambique, incomes and inequalities are mainly split between agriculture and wages."},{"index":2,"size":130,"text":"A key rationale for understanding inequality and formulating policies is to investigate how changes in a particular income source affect overall inequality (Shariff & Azam, 2009;Singh & Dey, 2010). In order to answer this question, a Gini decomposition following equations ( 2) and (3) was carried out. For each income source, the results summarized in Table 7 indicate the marginal impact in total inequality due to a 1% increase in that particular source, holding all other sources constant. The direction and magnitude of the marginal impact are given by the % change. A negative sign indicates a tendency to reduce total inequality, while a positive sign reveals an unequalizing effect. To test the statistical significance of the marginal impacts, 99%, 95% and 90% confidence intervals were calculated using bootstrapping techniques."},{"index":3,"size":87,"text":"In four of the six schemes (Mkoba, Silalabuhwa, Magozi and 25 Setembro), agriculture has an equalizing effect that is statistically significant. Conversely, wage incomes have an unequalizing effect across the six schemes, although only two schemes (Silalabuhwa and 25 de Setembro) showed statistical significance. Little can be said about the effect of business and self-employment, as the marginal impacts are mixed across the various schemes and only statistically significant in Magozi. 'Other' income has mainly an equalizing effect, with statistical significance in Magozi and 25 de Setembro."},{"index":4,"size":59,"text":"A literature review undertaken by Lay, Mahmoud, and M'Mukaria (2008) on the equalizing or unequalizing effect of non-agricultural incomes concluded that the results of various studies were mixed and seemingly contradictory. These inconsistencies, similar to the ones  found in this study, could be reconciled by further investigating the underlying drivers of inequality that are specific to each income source."}]},{"head":"Limitations","index":13,"paragraphs":[{"index":1,"size":122,"text":"This study has three major limitations. First, the populations of study consist only of members of irrigation schemes, not the entire rural communities, comprising also dryland farmers and non-farmers. This is because the data for this study were collected as part of a research project focused on irrigated agriculture (ACIAR, 2013). Studying the entire community would not have been possible since there is no comprehensive list of all its members that would allow adequate probability sampling. On the other hand, irrigation organizations have up-to-date lists of all their members. If more data become available, future research could be extended to examine differences in income and inequality within the entire rural communities, particularly comparing irrigators and non-irrigators, as well as farmers and non-farmers."},{"index":2,"size":75,"text":"The second limitation is the large proportion of households reporting negative net cash incomes from farming activities. It is possible that farm earnings were under-reported and expenses over-reported, either accidentally or deliberately. Therefore, an improvement could have been made by identifying negative farm incomes during the interviews to then question participants about their financial losses. This would have improved the accuracy of the records and provided greater insight into why certain households experience negative incomes."},{"index":3,"size":121,"text":"The third limitation is the small population samples in Mozambique (n < 30), which undermines the robustness of statistical significance tests and can result in underestimation of the Gini coefficient (Deltas, 2003). This problem was partially addressed by using non-parametric tests, which are preferred for small samples (vickers, 2005). An alternative would have been to remove Mozambique from the study, but it was the author's choice to use the six irrigation schemes, as this article will form part of a special issue dedicated to the three countries: Zimbabwe, Tanzania and Mozambique. Moreover, despite their small size, the 25 de Setembro and Khanimambo schemes can still be considered representative examples of small-scale irrigation in Mozambique, as explained in the Data Collection section."}]},{"head":"Conclusions","index":14,"paragraphs":[{"index":1,"size":40,"text":"This article analyzed income inequality within six smallholder irrigation schemes in Zimbabwe, Tanzania and Mozambique using household survey data from 2014. The Gini and Theil indices were used to measure income inequality and decompose inequalities by activity sector and source."},{"index":2,"size":76,"text":"The results indicate that income inequality within the irrigation communities is considerably higher (20-60%) than their respective country-wide figures. Moreover, across the six schemes, exclusively agricultural households earn consistently lower incomes than those with diversified incomes. In Tanzania, the largest source of income and inequality is agriculture, while in Zimbabwe 'other' sources are predominant. In four of the six schemes, agriculture has an equalizing effect, whereas non-agricultural incomes had mixed effects that generally lack statistical significance."},{"index":3,"size":74,"text":"These findings have important policy implications. First, it is crucial to recognize the existence of high levels of income inequality at small scales. Therefore, widespread strategies should be carefully examined before being applied within local contexts, as they could overlook existing disparities and thus perpetuate, or even worsen, economic inequality. Policies incorporating income distribution considerations at local scales would be more effective in achieving poverty reduction, rather than those targeting only broad-based economic growth."},{"index":4,"size":76,"text":"Second, strategies aiming to reduce inequality within smallholder irrigation schemes should be twofold. On the one hand, removal of barriers to entry and diversification into more gainful, non-farm activates could help lift the income of poor, exclusively agricultural households. On the other hand, it is also crucial to address inequality within activity groups. A suggested approach would be to target development efforts to those households that are most severely affected by poverty within each activity group."},{"index":5,"size":75,"text":"Finally, because agriculture tends to have an equalizing effect, increasing farming productivity could also contribute to reducing income inequality in some cases. However, it is crucial to bear in mind that results from a certain community should not be generalized to larger extents without the appropriate evidence. In fact, the same strategy targeting growth in a certain activity sector could have a positive, equalizing effect in some communities, and the exact opposite (unequalizing) in others."}]},{"head":"Appendix: Sensitivity analysis","index":15,"paragraphs":[{"index":1,"size":21,"text":"This appendix provides the results of a sensitivity analysis using various methods of estimating income inequality to verify consistency of results."},{"index":2,"size":56,"text":"The five columns in Table A1 summarize income Gini coefficients calculated based on five different methods: (1) converting negative farm incomes to zero; (2) excluding households with negative farm income; (3) excluding households with negative household income; (4) using household earnings, without expenses; and (5) no data treatment (i.e. total household revenue minus total farm expenses)."},{"index":3,"size":102,"text":"Excluding households with negative incomes (Columns 2 and 3) tends to underestimate income inequality, as the bottom part of the distribution is not taken into account. The exception is the Khanimambo scheme, where there are no households with negative incomes. Using only revenue (Column 4) also provides lower values, indicating that gross revenue is more evenly distributed than net income. Finally, 'modified' Gini coefficients including negative incomes (Column 5) cannot be used for comparison because they are not bounded by a common scale (0-1). The differences across the various methods are generally consistent with those found previously in the literature (Bray, 2014)."},{"index":4,"size":72,"text":"Table A2 summarizes the marginal effects of each source of income using two alternative methods for treating negative figures: excluding housholds with negative farm incomes; and considering revenues only. In these cases, growth in agricultural income tends to reduce inequality, consistently with the results provided in the core of this study. When excluding negative farm incomes, business and self-employment income appears to have an unequalizing effect in four of the six schemes."},{"index":5,"size":36,"text":"Other treatment methods that do not eliminate negative farm incomes (i.e. exclusion of negative household incomes and no data treatment) cannot be used in marginal impact analysis, given the comparison restrictions of the 'modified' Gini coefficient."},{"index":6,"size":61,"text":"Table A3 summarizes the results of the Theil index sensitivity analysis. Theil indices calculated based on earnings (Column 4) are lower than those based on net income, as in the case of the Gini coefficients. Calculations excluding households with negative and zero incomes (Columns 2 and 3) do not allow direct comparisons with other methods because of the different population sizes.  "}]}],"figures":[{"text":"Table 1 . characteristics of the irrigation schemes and surveys undertaken.    Number of Average   Number ofAverage  Irrigation Total area irrigating household Surveyed  IrrigationTotal areairrigatinghouseholdSurveyed Country scheme (ha) households landholding households Main crops Countryscheme(ha)householdslandholdinghouseholdsMain crops Zimbabwe mkoba 10 75 0.13 68 maize, horticul- Zimbabwemkoba10750.1368maize, horticul-       ture ture  silalabuhwa 110 212 0.52 100 maize, wheat, silalabuhwa1102120.52100maize, wheat,       sugar beans, sugar beans,       vegetables vegetables tanzania Kiwere 189 199 0.95 100 vegetables, maize tanzaniaKiwere1891990.95100vegetables, maize  magozi 939 578 1.62 99 rice magozi9395781.6299rice mozambique 25 de 38 38 1.00 25 vegetables mozambique25 de38381.0025vegetables  setembro      setembro  Khanimambo 16 27 0.59 9 vegetables Khanimambo16270.599vegetables source: rhodes et al. (2014)      source: rhodes et al. (2014)  "},{"text":"Table 2 . revenue and expenditure categories used in household survey.  Revenue Expenditure RevenueExpenditure on-farm rainfed crops crop inputs on-farmrainfed cropscrop inputs  Irrigated crops Harvesting/transport Irrigated cropsHarvesting/transport  livestock sales livestock inputs livestock saleslivestock inputs  milk sales Hired labour milk salesHired labour  other Irrigation otherIrrigation   other other off-farm agricultural labour food off-farmagricultural labourfood  non-agricultural labour education non-agricultural laboureducation  regular employment Health regular employmentHealth  Business/self-employment social events Business/self-employmentsocial events  remittances Housing remittancesHousing  seasonal work personal transport seasonal workpersonal transport  other  other  "},{"text":"Table 3 . Inequality at scheme and national levels. : author's computations for scheme level; cIa (2014) for national levels. Scheme level National level Scheme levelNational level  "},{"text":"Table 4 . Income statistics by type of income. mkoba, silalabuhwa in usD; Kiwere, magozi in tZs 1000; 25 de setembro, Khanimambo in mZn. ag.: exclusively agricultural-income household; Div.: diversified-income household.          Kolmogor- Kolmogor-    Mean household Median household Wilcoxon rank- ov-Smirnov Mean householdMedian householdWilcoxon rank-ov-Smirnov  n  income* income* sum test  test nincome*income*sum testtest Scheme Ag. Div. Ag. Div. Ag. Div. Z p D p SchemeAg.Div.Ag.Div.Ag.Div.ZpDp mkoba 6 62 179 1,098 67 475 −2.52 0.012 0.66 0.009 mkoba6621791,09867475−2.520.0120.660.009 silalabuhwa 20 80 411 940 180 700 −3.55 0.000 0.48 0.001 silalabuhwa2080411940180700−3.550.0000.480.001 Kiwere 56 44 1,006 2,026 436 1,203 −3.29 0.001 0.43 0.000 Kiwere56441,0062,0264361,203−3.290.0010.430.000 magozi 48 51 1,500 2,905 1,007 1,458 −1.79 0.074 0.20 0.217 magozi48511,5002,9051,0071,458−1.790.0740.200.217 25 de setembro 14 11 40,634 187,707 27,930 84,000 −2.63 0.009 0.55 0.030 25 de setembro141140,634187,70727,93084,000−2.630.0090.550.030 Khanimambo 4 5 5,250 177,610 0 173,200 −2.49 0.013 1.00 0.016 Khanimambo455,250177,6100173,200−2.490.0131.000.016  "},{"text":"Table 5 . Household income analysis and decomposition by activity group.   Gini  Theil  GiniTheil Scheme Percentage of ag. households Ag. Div. Within Between SchemePercentage of ag. householdsAg.Div.WithinBetween mkoba 9% 0.59 0.58 92% 8% mkoba9%0.590.5892%8% silalabuhwa 20% 0.49 0.45 91% 9% silalabuhwa20%0.490.4591%9% Kiwere 56% 0.59 0.69 90% 10% Kiwere56%0.590.6990%10% magozi 48% 0.55 0.59 92% 8% magozi48%0.550.5992%8% 25 de setembro 56% 0.64 0.43 72% 28% 25 de setembro56%0.640.4372%28% Khanimambo 44% 0.56 0.54 27% 73% Khanimambo44%0.560.5427%73%  "},{"text":"Table 6 . Income and inequality decomposition by source.      Business and   Business and Scheme Agriculture Wages self-employment Other SchemeAgricultureWagesself-employmentOther  Share of Share of Share of Share of Share of Share of Share of Share of Share ofShare ofShare ofShare ofShare ofShare ofShare ofShare of  income inequality income inequality income inequality income inequality incomeinequalityincomeinequalityincomeinequalityincomeinequality mkoba 19% 2% 15% 23% 14% 17% 52% 58% mkoba19%2%15%23%14%17%52%58% silalabuhwa 34% 14% 17% 42% 5% 3% 44% 42% silalabuhwa34%14%17%42%5%3%44%42% Kiwere 79% 83% 7% 6% 11% 9% 3% 1% Kiwere79%83%7%6%11%9%3%1% magozi 66% 43% 9% 15% 23% 42% 2% 0% magozi66%43%9%15%23%42%2%0% 25 de setembro 46% 10% 47% 86% 6% 4% 1% 0% 25 de setembro46%10%47%86%6%4%1%0% Khanimambo 52% 48% 43% 47% 5% 5% 0% 0% Khanimambo52%48%43%47%5%5%0%0%  "},{"text":"Table 7 . Gini decomposition by income source and marginal effects.      Business and   Business and Scheme Agriculture  Wages self-employment  Other SchemeAgricultureWagesself-employmentOther  Gini % change Gini % change Gini % change Gini % change Gini% changeGini% changeGini% changeGini% change mkoba 0.76 −0.07*** 0.93 0.02 0.92 0.02 0.76 0.04 mkoba0.76−0.07***0.930.020.920.020.760.04 silalabuhwa 0.68 −0.07** 0.94 0.10** 0.91 −0.01 0.70 −0.01 silalabuhwa0.68−0.07**0.940.10**0.91−0.010.70−0.01 Kiwere 0.66 0.01 0.94 0.00 0.92 −0.01 0.92 −0.01 Kiwere0.660.010.940.000.92−0.010.92−0.01 magozi 0.57 −0.09** 0.95 0.02 0.91 0.08* 0.96 −0.01* magozi0.57−0.09**0.950.020.910.08*0.96−0.01* 25 de setembro 0.54 −0.13*** 0.90 0.13** 0.91 0.01 0.90 −0.01*** 25 de setembro0.54−0.13***0.900.13**0.910.010.90−0.01*** Khanimambo 0.61 −0.06 0.69 0.06 0.75 −0.01 n/a n/a Khanimambo0.61−0.060.690.060.75−0.01n/an/a ***p < .01; **p < .05; *p < .1.        ***p < .01; **p < .05; *p < .1.  "},{"text":"Table A1 . Gini coefficient sensitivity analysis  Income Gini  Income Gini  Income Gini Income GiniIncome GiniIncome Gini  adjusted for Income Gini exclud- excluding HHs Gini for HH revenue, including adjusted forIncome Gini exclud-excluding HHsGini for HH revenue,including  negative farm ing HHs with negative with negative HH not considering farm negative negative farming HHs with negativewith negative HHnot considering farmnegative  incomes farm income income expenses incomes incomesfarm incomeincomeexpensesincomes mkoba 0.60 0.51 0.58 0.57 0.63 mkoba0.600.510.580.570.63 silalabuhwa 0.48 0.44 0.46 0.42 0.52 silalabuhwa0.480.440.460.420.52 Kiwere 0.60 0.52 0.52 0.53 0.93 Kiwere0.600.520.520.530.93 magozi 0.56 0.55 0.55 0.51 0.66 magozi0.560.550.550.510.66 25 de setembro 0.65 0.62 0.62 0.64 0.85 25 de setembro 0.650.620.620.640.85 Khanimambo 0.58 0.55 0.59 0.57 0.59 Khanimambo0.580.550.590.570.59  "},{"text":"Table A2 . Gini coefficient decomposition sensitivity analysis (percentage change by method of calculation). Notes: ***p < .01; **p < .05; *p < .1. BSE = business and self-employment.  Excluding households with negative farm Revenue only (not considering farm Excluding households with negative farmRevenue only (not considering farm  income    expenses)    incomeexpenses)  Ag. Sal./wages BSE Other Ag. Sal./wages BSE Other Ag.Sal./wages BSEOtherAg.Sal./wages BSEOther mkoba −0.03*** 0.01 0.02** 0.00 −0.10*** 0.02 0.02 0.05* mkoba−0.03*** 0.010.02** 0.00−0.10*** 0.020.020.05* silalabuhwa −0.03** 0.01 0.02* 0.00** −0.10* 0.10 0.01 0.00 silalabuhwa−0.03**0.010.02*0.00**−0.10*0.100.010.00 Kiwere 0.03 −0.01 −0.01 −0.01*** −0.01 0.01 0.00 0.00 Kiwere0.03−0.01−0.01 −0.01***−0.010.010.000.00 magozi 0.00 0.00 0.01 −0.01 −0.09** 0.02 0.07*** 0.00 magozi0.000.000.01−0.01−0.09**0.020.07*** 0.00 25 de setembro −0.03*** 0.01 0.03** 0.00 −0.10 0.11* 0.00 0.00*** 25 de setembro −0.03*** 0.010.03** 0.00−0.100.11*0.000.00*** Khanimambo −0.03*** 0.01 0.03**  −0.03 0.04 −0.01  Khanimambo−0.03*** 0.010.03**−0.030.04−0.01  "},{"text":"Table A3 . theil index sensitivity analysis.    Income Theil Theil for HH Income TheilTheil for HH  Income Theil Income Theil excluding excluding HHs with revenue (not Income TheilIncome Theil excludingexcluding HHs withrevenue (not  adjusted for nega- HHs with negative and negative and zero considering farm adjusted for nega-HHs with negative andnegative and zeroconsidering farm  tive farm incomes zero farm income HH income expenses) tive farm incomeszero farm incomeHH incomeexpenses) mkoba 0.64 0.45 0.58 0.55 mkoba0.640.450.580.55 silalabuhwa 0.41 0.31 0.35 0.27 silalabuhwa0.410.310.350.27 Kiwere 0.63 0.47 0.46 0.46 Kiwere0.630.470.460.46 magozi 0.60 0.56 0.58 0.46 magozi0.600.560.580.46 25 de setembro 0.89 0.73 0.74 0.66 25 de setembro 0.890.730.740.66 Khanimambo 0.66 0.17 0.31 0.36 Khanimambo0.660.170.310.36  "}],"sieverID":"faa3ab9f-096a-4668-8665-2170c1d2a258","abstract":""}

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+{"metadata":{"id":"07f21e6925fbff1a46765e45b8a32849","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/59d6d0a5-2f9b-415c-92ef-e227283cc2a2/retrieve"},"pageCount":20,"title":"Genotype x environment interaction and yield stability of normal and biofortified maize inbred lines in stress and non-stress environments","keywords":["maize","inbred lines","zinc-enhanced","genotype x environment interaction","grain yield Author contributions Conceptualization, N.M. and M.L.","investigation, N.M.","resources, T.N. and M.L.","writing-original draft preparation, N.M.","writing-review and editing, T.N., A.v.B., M.L.","data analysis","N.M.","supervision, M.L., T.N., A.v.B.","funding acquisition, T.N. and M.L. All authors approved the manuscript"],"chapters":[{"head":"PUBLIC INTEREST STATEMENT","index":1,"paragraphs":[{"index":1,"size":115,"text":"Seed producibility of parental materials of maize hybrids is of paramount importance so as to reduce seed production costs. To identify high yielding and stable maize hybrid parents, evaluation across different growth conditions is necessary. Our study focused on identifying superior and stable maize lines that could be used to develop micronutrient-dense maize hybrids in Zimbabwe. Increasing micronutrient densities in staple crops such as maize is in line with the World Health Organization's current Sustainable Development Goals (SDGs) to end hunger in all its forms. When used as breeding materials, promising micronutrient-dense maize lines identified in this study could save millions of people in sub-Saharan Africa, currently suffering from various ailments emanating from micronutrient deficiency."}]},{"head":"Introduction","index":2,"paragraphs":[{"index":1,"size":206,"text":"Maize (Zea mays L.) is a major staple food and energy source in various parts of the world including the sub-Saharan Africa (SSA). In this region, maize and maize-based products constitute more than 38% of the food supply for both children and adults. Despite being an excellent energy source, maize is deficient in micronutrients such as zinc (Zn), iron (Fe) and provitamin A as well as essential amino acids such as lysine and tryptophan (Siwela et al., 2020). Consequently, more than two billion people from maize-based regions, including Latin America, Africa and Asia, suffer from various ailments emanating from micronutrient deficiencies, due to monotonous maize diets with limited access to diversified and fortified foods, as well as dietary supplements (Prasanna et al. 2020). The deficiency of these key micronutrients causes several health challenges with an overall impact on the physical, mental and cognitive well-being and development of humans (Bhandari & Banjara, 2015;Ma et al., 2008). Childbearing women, the elderly and pre-school children are among the worst affected, due to increased micronutrient demand (Hwalla et al., 2017). The World Health Organization (WHO) of the United Nations estimated that micronutrient deficiency accounts for more than 53% of infant mortality before the age of five (Kiran et al., 2014)."},{"index":2,"size":182,"text":"Among other nutrient deficiency mitigation measures, including food fortification and clinical supplementation, development of maize with elevated levels of grain Zn, provitamin A and tryptophan could be impactful in reducing malnutrition in rural areas of maize-based countries (Siwela et al., 2020;Temple et al., 2011). However, the development of these so-called \"biofortified\" maize cultivars relies on the importation of inbred lines that have high levels of the target nutrients (Maqbool et al., 2018). A challenge of such exotic nutrient donors is their adaptation to the new growing environment. Hence, testing the newly introduced germplasm for agronomic performance in local growing environments is necessary. Maize breeding programs aim to select new genotypes that have both high yield potential and broad adaptation (Falconer & Mackay, 1996;Sibiya et al., 2012). To achieve this, the newly introduced or developed inbred lines, hybrids or open-pollinated varieties are evaluated in multi-environment trials (METs). In METs, genotype sets are grown across several environments to evaluate both the main and interaction effects (Mafouasson et al., 2018;Yan & Tinker, 2006). This enables breeders to determine if GEI effects are significant or not."},{"index":3,"size":251,"text":"In METs, grain yield performance of a genotype across different environments can vary, and this indicates high GEI. High GEI may change the ranking of genotypes in different environments, which is known as cross-over interaction (Bocianowski et al., 2019b). This further complicates the selection of superior genotypes for target environments. In the absence of GEI, the superior genotype remains the best performing genotype in all environments (Tena et al., 2019). Therefore, GEI poses great challenges to plant breeders because it complicates efficient selection and identification of superior cultivars (Mebratu et al., 2019;Ndhlela et al., 2014). Measuring GEI is important in optimizing breeding strategies for selecting cultivars that are well adapted to specific environments (Zewdu et al., 2020). Most GEI studies, however, focus on evaluating maize testcross hybrids, while this knowledge is equally important for parental inbred lines. Understanding the grain yield performance of hybrid parental inbred lines across environments is crucial, since grain yield is directly related to the seed producibility. Highly productive inbred lines indicate a good foundation for the development of successful commercial hybrids (Worku et al., 2016). Apart from identifying high yielding and stable genotypes across environments, METs also identify experimental sites that best represent the target environment (Gasura et al., 2015;Makumbi et al., 2015). Therefore, MET experimental data can be partitioned into three variance components, the environmental (E), genotype (G) and GEI components. Although the environmental variance component is generally the largest, breeders mainly focus on the genotypic (G) main effects and GEI for efficient cultivar selection."},{"index":4,"size":354,"text":"Several statistical methods have been proposed to study GEI. Each of the methods used so far has its own advantages and limitations, and usually breeders use them interdependently. Because of this, several parametric and non-parametric statistical analyses were developed to study GEI (Farshadfar et al., 2011;Mohammadi et al., 2014). These include the additive main effect and multiplicative interaction analysis (AMMI; Gauch, 1992), site regression (SREG; Setimela et al., 2010) also known as genotype (G) main effects and genotype × environment (GE) interaction effects (thus GGE), partial least square (PLS), stability analysis and factorial regression (A. Pacheco et al., 2015). However, AMMI and GGE are the most widely used analyses (Gauch, 2006). AMMI is a hybrid model involving both additive and multiplicative components of a two-way data structure (Choukan, 2011). This model separates the additive variance from the multiplicative variance and then applies principal component analysis (PCA) to the interaction portion explaining the interaction pattern in more detail (Gauch & Zobel, 1996). Thus, AMMI, through PCA, further partitions the GEI component into individual genotypic and environmental scores (Abakemal et al., 2016;Yan et al., 2000). AMMI analysis has been reported to be effective in analysing GEI, because it captures a large portion of the GEI sum of squares, clearly separating the main and interaction effects, which allows efficient selection of stable maize cultivars (Bocianowski et al., 2019b;Haruna et al., 2017). The second most popular multivariate statistical tool for studying MET data in different breeding programmes is the site regression of genotype plus GE interaction (GGE) biplot model (Tena et al., 2019). This is a linear-bilinear model that removes the environmentally main effect and considers the genotype (G) plus the genotype × environment (GE) interaction. The main advantage of the GGE biplot model over AMMI is that it allows the detection of GEI in terms of the crossover interaction, which ultimately ranks genotypes in terms of performance across environments (Abakemal et al., 2016;Yan & Kang, 2003). Hence, the model is powerful in depicting which-won-where patterns of MET data, facilitating easy identification of stable and high yielding maize genotypes as well as environments with discriminating ability and representativeness."},{"index":5,"size":76,"text":"The objectives of this study were to (i) evaluate grain yield performance of introduced zinc (Zn) enhanced, provitamin A, normal and quality protein maize (QPM) inbred lines across stress and nonstress environments in Zimbabwe, (ii) assess the presence of GEI using AMMI and GGE biplot analysis and (iii) identify high yielding and stable inbred lines that can be used as parents for developing Znenhanced hybrids with potential success as commercial hybrids due to improved seed producibility."}]},{"head":"Materials and methods","index":3,"paragraphs":[]},{"head":"Experimental site","index":4,"paragraphs":[{"index":1,"size":46,"text":"The experiment was conducted for 2 years during both summer and winter seasons of 2019 and 2020, at 11 experimental sites in Zimbabwe with different management levels (Table 1) which were coded as E1 to E11. Combined heat and drought stress and low nitrogen (N) trials "}]},{"head":"Plant materials and experimental design","index":5,"paragraphs":[{"index":1,"size":135,"text":"Twenty-four inbred lines (coded G1 to G24) from different nutritional categories were evaluated for GEI across stress and non-stress environments, E1 to E11. Eleven inbred lines (G1 to G11) were Zn donors introduced to Zimbabwe from CIMMYT-Mexico and the International Institute of Tropical Agriculture (IITA) in Nigeria (Table 2). G12 to G18 were locally used lines from the normal, provitamin A, and quality protein maize (QPM) nutritional backgrounds, and six commercial checks G19 to G24 were included (Table 2). Genotypes in all trials were planted in an 8 × 6 alpha (0,1) lattice design (Patterson & Williams, 1976) with two replications at each site. Plots were single rows of 4 m long with spacings of 0.75 (inter-row) and 0.25 m (in-row), respectively, giving a final plant density of 53333 plants ha-1 at all experimental sites. "}]},{"head":"Trial management and data collection","index":6,"paragraphs":[{"index":1,"size":139,"text":"Standard agronomic practices were applied at all sites during the trial implementation period. Weed control was done using both pre-and post-emergence herbicides and in some cases hand weeding was applied. Supplementary irrigation was applied when necessary in all trials during the early vegetative stages. Inbred lines were exposed to drought stress by withholding irrigation 2 weeks before anthesis up to 21 days post flowering so that drought stress would coincide with flowering, which is the most sensitive growth stage. Similarly, the low N trials were grown in low N screening sites, developed by continuously depleting N to less than 7 ppm, which is estimated to cause 30% maize yield reduction (Bänziger et al., 2000). At harvesting, grain yield data was recorded using plants in the net plot area as the two border plants close to the alley were discarded."}]},{"head":"Statistical analysis","index":7,"paragraphs":[{"index":1,"size":172,"text":"The MET grain yield data was adjusted to 12.5% moisture content and analysed using the Genotype × Environment Analysis with R (GEA-R) statistical package (A. Pacheco et al., 2015) and Genstat 18 th version (VSN International, 2017) for both AMMI and SREG (GGE biplot) analyses. The AMMI model combined the conventional analysis of variance (ANOVA) and PCA into a single analysis with both additive and multiplicative variance components (Choukan, 2011;R.M. Pacheco et al., 2005). This means that in the first component (additive), the traditional ANOVA procedures were applied to estimate both genotypic and environmental main effects. The second part applied the PCA, which used the Gollob's F-test (Gollob, 1968) to determine the ratio between the mean square for axis n against the mean square error, thereby depicting the number of multiplicative terms that were retained after residuals for the main effects were removed (A. Pacheco et al., 2015). The first and second principal component axes for biplots generated by the GEA-R statistical package were referred to as Factor 1 and 2, respectively."},{"index":2,"size":8,"text":"The AMMI model used in this study is:"},{"index":3,"size":74,"text":"Where Yij is the yield of the ith genotype in the jth environment; µ is the grand mean; Gi and Ej are the genotype and environment deviations from the grand mean, respectively; βn is the eigenvalue of the PC analysis axis n; ϒin and δjn are the genotype and environment principal component scores for axis n; N is the number of principal components (factors) retained in the model and εij is the error term."},{"index":4,"size":147,"text":"The GGE biplot method was used for visualizing patterns and interactions without environmental effects. Unlike GEA-R, the first and second principal component axes of biplots generated in Genstat 18 th version statistical package were referred to as PCA1 and PCA2, respectively. The first principal component (PCA1 or Factor 1) represented responses of the genotypes that were proportional to the environments and the second principal component (PCA2 or Factor 2) showed cultivation environments that were not proportional to the environments, and those were responsible for genotype by environment crossover interaction. The GGE biplot model used is similar to the one used for AMMI. For stability analysis, four methods were used, the Francis coefficient of variation, CV% (Francis & Kannenberg, 1978), the Eberhart and Russell mean square deviation s 2 di (Eberhart & Russell, 1966), the determination coefficient R 2 (Pinthus, 1973) and the Wricke's ecovalence Wi (Wricke, 1962)."}]},{"head":"Results","index":8,"paragraphs":[]},{"head":"AMMI analysis","index":9,"paragraphs":[{"index":1,"size":127,"text":"The combined ANOVA and AMMI analysis for the 24 inbred lines evaluated over 11 environments across two seasons is shown in Table 3. The ANOVA showed that there were highly significant differences (P ≤ 0.01) for genotype, environment, and their interactions. The grain yield of the inbred lines was significantly affected by the environment, which explained 33.1% of the total variation (both additive and multiplicative effects). Genotype main effects explained 26.4% of the total variation, while the GEI captured 40.5% of the total variation. PCA was applied to further partition the GEI in the AMMI model. The first two principal component axes (Factor 1 and Factor 2) were highly significant and explained 34.2% and 25.6% of the total variation, respectively, and cumulatively 59.8% of the total variation."},{"index":2,"size":213,"text":"In the AMMI biplot (Figure 1), genotypes that are more stable are closer to the origin, and these genotypes show consistent grain yields across all the test environments. In this regard, inbred line G2 (CLWQHZN14), G4 (CLWQHZN46), G15 (CML144), G14 (TL115798), G17 (CZL16160) and G9 (ITZN344) were the most stable genotypes, implying that their performance in terms of grain yield was similar across all environments. Several authors have demonstrated that either environments or genotypes that have large negative or positive Factor 1 scores have high interactions (Abera et al., 2004;Mebratu et al., 2019). Similarly, genotypes or environments with Factor 1 scores close to zero have small interactions (Choukan, 2011;Tena et al., 2019). Using this principle, small interactions were observed in environments E5 (Kadoma RS), E7 (Gwebi opt) and E8 (Chiredzi HMDS), whereas large interactions were observed in E1 (ART opt), E9 (Chiredzi WW), E10 (Chisumbanje HMDS) and E11 (Chisumbanje WW). The distance between two genotypes or environment vectors (their end points) estimated the level of interaction between the genotypes and environments (Choukan, 2011;Mafouasson et al., 2018). Environments that had the longest vector length had the greatest discriminating ability, implying that they classify better to the genotypes. Therefore, E10 (Chisumbanje HMDS) had the longest vectors and thus had the most discriminating power (Figure 1)."},{"index":3,"size":217,"text":"Table 4 shows the grain yield means for both individual sites and across the sites of the 24 inbred lines. Genotypes that had above-average means were G1 (CLWQHZN12), G2 (CLWQHZN14), G4 (CLWQHZN19), G5 (CLWQHZN49), G8 (OBATANPA6), G10 (ITZN324), G11 (ITZN313), G16 (CZL16154) and G18 (CML546). G11 had the highest mean for grain yield of 3.5 t ha -1 across all environments. The lowest yielding genotypes were G19 (CML511), and the checks G23 (CML312) and G24 (CZL1111) with 1.32, 1.42 and 1.28 t ha -1 , respectively. In Figure 1, genotypes G9, G14, G15 and G17 were near the origin, indicating good stability, but their grain yield averages were below the grand mean. Using this criterion, the Zn donors G2 and G4 combined both high stability and high yield potential and, therefore, qualify as the most ideal inbred lines. Genotypes G11, G10, G18, G5, G16 and G8 were not stable, although they had higher average main effects for grain yield. Environments E2 (CIMMYT opt), E4 (RARS opt), E9 (Chiredzi WW) and E11 (Chisumbanje WW) had the highest individual site means (Table 4). Therefore, these environments were the most favourable in terms of grain performance for most of the genotypes. The least favourable environments for most inbred lines were E3 (CIMMYT LN), E6 (DRSS LN) and E8 (Chiredzi HMDS)."},{"index":4,"size":132,"text":"Figure 2 shows the vector view of the GGE biplot generated in GenStat for the correlation among environments. The cosine of the angle between two genotypes or environment vectors indicates the correlation between them. Environments that have an angle of less than 90° (acute) between them such as E1, E2, E3, E4 and E6 classify the genotypes in a similar manner. Similarly, obtuse angles between environments mean that these environments classified genotypes differently (negative correlation). Right angle between environments means no correlation. In this regard, E2 (CIMMYT opt) and E4 (RARS opt) were not correlated with E10 (Chisumbanje HMDS). It was observed that all the environments in the main rain season clustered together, except for E5 (Kadoma RS) and E7 (Gwebi opt), that clustered with all environments used during the winter season."}]},{"head":"\"Which-won-where\" interaction pattern","index":10,"paragraphs":[{"index":1,"size":136,"text":"The power of the GGE biplot is the ability to show which-won-where interaction patterns, making it easier for plant breeders to select superior genotypes for particular environments. The perpendicular lines (Figure 3) that radiate from the biplot origin are called sectors, and clearly show genotypes that perform better in each environment. The polygon is formed by connecting the black dotted lines and the vertex genotypes (G1, G10, G5, G11, G12, G16 and G18). These vertex genotypes have the longest vectors in their respective directions, and therefore these genotypes performed best in all the environments contained in the respective sectors. The rest of the genotypes such as G3, G7, G8, G13, G21 and G23 are contained in the polygon because they have small vectors, implying that they were less responsive to the environments contained in the sector."},{"index":2,"size":158,"text":"The polygon was divided into seven sectors, and clusters of environments that are contained in each sector are called mega-environments. Only sectors that contain one or more environments are mega-environments. Therefore, two sectors, one with G3, G5, G10, and G21 and the other one with G12 did not define any mega-environment. Hence, in this study, five mega-environments were observed. G11 (ITZN313) had the highest grain yield performance in the mega-environment that contained E9 (Chiredzi WW) and E11 (Chisumbanje WW). Similarly, G1 (CLWQHZN12) was the best performer in the mega-environment that contained mostly optimum environments E1 (ART  (%) = Percent broad sense heritability opt), E2 (CIMMYT opt), E4 (RARS opt) and E7 (Gwebi opt). G18 (CML546) had the highest yield in the mega-environment that contained stress environments E5 (Kadoma RS), E6 (DRSS LN) and E8 (Chiredzi HMDS). Although the sector that contained E10 (Chiredzi HMDS) had no vertex genotype, the Zn donor, G7 (CLWQHZN69) performed well in this environment.  "}]},{"head":"Stability analysis","index":11,"paragraphs":[{"index":1,"size":175,"text":"The GGE-biplot analysis also provides a comparison of genotypes or environments with the ideal genotype or environment (Figure 4). In this comparison biplot, the ideal genotype is located in the innermost concentric ring. The ideal genotype combined both high grain yield and stability across a wide range of environments. Ideal genotypes or environments have the longest vectors in PCA1 and smaller values for PCA2 scores. Therefore, the distance between the genotype and the centre of the innermost concentric ring depicts its stability and yield potential. Based on this, the three Zn donors G11 (ITZN313), followed by G10 (ITZN313), G5 (CLWQHZN49) and G18 (CML546) were the best genotypes showing high grain yield under both optimum and stress conditions. Similarly, the ideal or reference environment showed large PCA1 scores and zero PCA2 scores and indicates greatest discriminating ability in classifying genotypes and representativeness when compared to other environments. Environments E2 (CIMMYT opt), E4 (RARS opt), E9 (Chiredzi WW) and E11 (Chisumbanje WW) were closer to the inner concentric ring, and therefore were identified as the ideal environments."},{"index":2,"size":192,"text":"In this study, four stability parameters were used namely the Francis coefficient of variation, CV% (Francis & Kannenberg, 1978), the Eberhart and Russell mean square deviation from regression, s 2 di (Eberhart & Russell, 1966), determination coefficient, R 2 (Pinthus, 1973) and Wricke's ecovalence, Wi (Wricke, 1962). The stability coefficients are shown in Table 5. The smaller the coefficient values for all the stability parameters used except for R 2 , the more stable the genotype is. The results show that nearly all stability parameters identified G2, G4, G9 and G17 as the most stable genotypes. Comparing the efficiency of these parameters, Eberhart and Russell (s 2 di), determination coefficient (R 2 ) and Wricke's ecovalence (Wi) showed consistent results in ranking of genotypes. For instance, G12 was ranked 21 st with the Francis CV%, whereas the rest of the stability methods ranked this genotype 14 th or 15 th (Table 5). This was also observed for G21, which was ranked 12 th (CV%), but ranked in the top six stable varieties using the other stability parameters. The biplot shown in Figure 5 was constructed using the Francis coefficient of variation (CV%). "}]},{"head":"Discussion","index":12,"paragraphs":[{"index":1,"size":159,"text":"GEI studies are very important in maize breeding, since they provide a way of assessing the performance of genotypes for stability and adaptability across environments (Farshadfar et al., 2011). In this way, efficient genotypes can be identified and in the case of hybrids, recommendations for commercialization can be made based on hybrid performance in particular environments. Similarly, high yielding and stable inbred lines across environments can also give a good indication of their seed producibility or even combining ability (Worku et al., 2016). In addition, the inclusion of the GEI matrix increases the prediction accuracy in statistical models used for predicting the performance of untested genotypes and thereby saves testing costs (Bocianowski et al., 2019b;Worku et al., 2016). Although several studies have demonstrated significant GEI of grain yield performance of inbred lines (Bisawas et al., 2014;Bocianowski et al., 2019b), such studies focusing on biofortified introduced germplasm are still very limited. Currently, breeding for biofortified cereals is being promoted globally."},{"index":2,"size":55,"text":"The results of the present study demonstrated highly significant differences (P ≤ 0.01) for the genotype (G) and environmental (E) main effects. Similarly, the GEI was highly significant (P ≤ 0.01), implying that normal and biofortified genotypes responded differently across environments. The large proportion and significance of the sum of squares of the environments indicated "},{"index":3,"size":254,"text":"that the experimental sites used in this study were different and this resulted in variation of grain yield of the inbred lines (Table 3). A large contribution of the environment was reported in normal maize hybrids in earlier studies (Ertiro et al., 2017;Mafouasson et al., 2018;Sibiya et al., 2012). Large sums of squares for environments demonstrated that the inbred lines were evaluated in contrasting testing locations and therefore their performance was determined by adaptability to particular environments. Differences in environmental main effects are useful in GEI studies since they facilitate identification of high yielding and stable genotypes across varying environmental conditions. Similarly, genotype main effects were highly significant, contributing about 26.4% of the total variation. This demonstrated that grain yields for inbred lines responded differently to the environments where they were grown. Li et al. (2018) suggested that the grain yield performance of maize inbred lines varies more across environments compared to hybrids. Whilst the present study evaluated only the inbred lines, future studies should compare the stability of biofortified parental lines and hybrids across environments. The identification and selection of superior biofortified inbred line introductions with high yield potential under both stress and non-stress environments, is likely to give rise to highly productive and stable nutrient-dense hybrids that could facilitate quick adoption by stakeholders. The highly significant differences (P ≤ 0.01) for the GEI for grain yield of both normal and biofortified inbred lines justify the need for extensive testing of such germplasm over diverse growing conditions before recommendations (Badu-Apraku et al., 2003)."},{"index":4,"size":206,"text":"The results of the AMMI biplot analysis of 24 inbred lines revealed a fairly narrow range of grain yield performance (1.28 to 3.5 t ha −1 ; Table 4). The highest grain yield across sites was observed from a Zn donor line G11 (ITZN313). In addition, several Zn donors from either CIMMYT or IITA were among the best performing inbred lines in terms of grain yield (Table 4). This is quite encouraging and gives hope to plant breeders in pursuit of developing high yielding Znenhanced hybrids, since several studies have reported the inferiority of biofortified genotypes in terms of grain yield (Bänziger & Long, 2000;Maqbool et al., 2018). The high grain yield for maize inbred lines suggests good seed producibility in hybrid combinations, which reduces the cost of seed production. Although some scientists reported that heterosis is more important than midparent yield in determining potential use of inbred lines, some reported positive correlation between grain yield of parental inbred lines and their corresponding hybrids under stressed growing conditions (Zaidi et al., 2007). Therefore, the seven Zn donors G1 (CLWQHZN12), G2 (CLWQHZN14), G4 (CLWQHZN19), G5 (CLWQHZN49), G8 (OBATANPA6), G10 (ITZN324), and G11 (ITZN313) could be useful as potential parents for developing high yielding Zn-enhanced hybrids in Zimbabwe."},{"index":5,"size":244,"text":"The normal inbred lines G16 (CZL16154) and G18 (CML546) were among the highest yielding inbred lines across environments. Such inbred lines could be impactful if used as parents for developing normal stress tolerant commercial hybrids. In fact, CML546 was the winning genotype across drought, low nitrogen and random stress conditions. Through validation of the existence of tolerance genes for such stress factors, the inbred line could be recommended as a specific trait donor in different breeding programs. The polygon view of the GGE biplot was divided into seven sectors and defined into five mega-environments, as defined by Yan and Tinker (2006). Two sectors with genotypes G3 (CLWQHZN19), G5 (CLWQHZN49), G10 (ITZN324), G12 (HPYDL18190) and the provitamin A check G21 (CLHP0478) did not define any mega-environment. This implies that these inbred lines had the poorest grain yield performance in some or all environments. The existence of mega-environments reveals that inbred lines responded differently to environments, suggesting differences in mechanisms for stress tolerance (Haruna et al., 2017;Yan & Rajcan, 2002). The polygon biplot showed that G11 (ITZN313) was the winning genotype in Chiredzi and Chisumbanje under well-watered conditions and could be grown in the winter for seed production in such environments. These sites are characterized by excessively high temperatures, and based on its origin (West Africa), ITZN313 could be highly adaptable to extremely hot conditions. G1 (CLWQHZN12) performed best in terms of grain yield under optimum conditions and therefore could be less adapted to stressful conditions."},{"index":6,"size":314,"text":"The GGE biplot analysis for grain yield performance and stability of the 24 normal and biofortified inbred lines showed that PCA1 (Factor 1) explained 34.18%, whereas PCA2 (Factor 2) explained 25.57% of the total variation. Cumulatively, the two principal axes accounted for 58.75% of variation. This suggested that the two principal component axes contributed a fairly large proportion of the total sum of squares and that this contribution was sufficient to describe the GEI pattern of the environment centred data. Most of the stability parameters used in this study identified G2 (CLWQHZN14), G4 (CLWQHZN19), G8 (OBATANPA6), G11 (ITZN313) and G18 (CML546) as the most ideal genotypes (Table 5 and Figures 3 to 5). Although G11 had the highest grain yield performance across environments, it was more responsive to environmental changes when compared to G2 and G4. These Zn donors showed consistency in terms of high yield and stability and could be selected as the best parents for developing Zn-enhanced hybrids in Zimbabwe. On the other hand, the normal inbred line CML546 was less responsive to unfavourable environments. Despite their below-average means for grain yield, the QPM inbred lines G14 (TL115798) and G15 (CML144) were stable across environments, and this implies that they can be grown in both stress and non-stress environments without significant yield changes. All the provitamin A inbred lines, G12 (HPYDL18190) and G13 (CLHP0213) were similar in terms of grain yield performance and stability. Although the final selection of the inbred line parents does not solely depend on the yield performance, the seed yield producibility of the selected parents is crucial in developing successful commercial hybrids. Thus, the seed yield potential of the inbred lines reflects the potential success of their respective commercial hybrids. Therefore, based on the results of this study, the poorest inbred lines could be omitted for future hybrid development since they may not be attractive to seed producers."},{"index":7,"size":190,"text":"Five mega-environments were identified in the polygon view of the GGE biplot. Megaenvironments were for the optimum environments in the main season, the well-watered environments in the winter, random and low N stress, low N stress and HMDS environments. This suggests that the mega-environments were formed based on different management conditions and not on the growing season. This implies that trial management might have contributed more to the environmental differences as well as how different inbred lines responded to different environments (Bocianowski et al., 2019b). This shows that genotypes have different adaptive mechanisms for each of the environments (Bruce et al., 2002;Pixley & Bjarnason, 2002). Different rainfall amounts and distributions, irrigation regimes, as well as biotic and abiotic stress constraints could have been encountered in different environments (Ertiro et al., 2017;Mafouasson et al., 2018;Mebratu et al., 2019). Furthermore, most optimum environments used in this study showed similarity in terms of genotypic response and this suggests that similar information could be obtained from any one of the test environments. Hence, this could reduce multi-location evaluation costs. Similarly, all winter environments, including well-watered and combined heat and drought sites, were positively correlated."},{"index":8,"size":237,"text":"To be useful for plant breeding, a test location should have high discriminating power, providing information about the differences in performance of genotypes and representativeness to a growing environment (Mohammadi et al., 2014;Yan et al., 2007). Environments with longer vector lengths have the highest discriminating power and therefore provide more information about the performance of the genotypes (Haruna et al., 2017;Tena et al., 2019). Therefore, E10 (Chisumbanje HMDS), E9 (Chiredzi WW), and E11 (Chisumbanje WW) were all informative in terms of discriminating grain yield performance of genotypes. Since this site is used for combined heat and drought screening, E10 (Chisumbanje HMDS) could also be useful in culling drought-susceptible genotypes. In addition, environments with shorter vector lengths show a very weak correlation with environments with long vectors. Such sites could be treated as independent test environments and therefore are all useful. According to the GGE biplot (Figure 4), E11 (Chisumbanje WW) had the smallest angle with the AEC abscissa as well as the long vector and, therefore, can be identified as the most discriminating and representative test environment in Zimbabwe. Grouping of sites into mega-environments can help to identify core testing environments that can be used to identify superior genotypes and thereby reduce testing costs. However, further validation of all the findings of this study, including multiple year evaluations can help to confidently group the identified mega-environments into seed production environments (Worku et al., 2016;Yan & Tinker, 2006)."}]},{"head":"Conclusions","index":13,"paragraphs":[{"index":1,"size":189,"text":"This study showed that environment, genotype, and GEI had highly significant effects on grain yield performance of normal and biofortified inbred lines tested over stress and non-stress environments. Thus, these genotypes responded differently to changes in environments. The AMMI analysis revealed that environment main effects contributed a larger proportion of the total sum of squares when compared to the genotype main effects and the GEI. Test environments were divided into five mega-environments that clearly distinguished optimum from stress environments. The Zn donor, G1 (CLWQHZN12) was the winning genotype under optimum conditions. G18 (CML546) was well adapted to drought and low N stress environments. These inbred lines could be good sources of alleles for developing high yielding Zn-enhanced hybrids. G2 (CLWQHZN14), G4 (CLWQHZN19), G8 (OBATANPA6), G11 (ITZN313) and G18 (CML546) were the most stable and high yielding inbred lines, implying good seed producibility and reduction of seed production costs when used as parents for commercial hybrids. E11 (Chisumbanje WW) was identified as the most discriminating and representative test environment in Zimbabwe. Such sites could be useful for the identification of superior genotypes, as well as for seed production in Zimbabwe."}]},{"head":"Notes on contributors","index":14,"paragraphs":[{"index":1,"size":113,"text":"In the past decades, global breeding efforts were concentrated on developing conventional or normal maize varieties with good tolerance to multiple stress factors. Breeding for high yielding and stable biofortified maize varieties was lagging behind. Development of maize varieties with elevated levels of micronutrients (provitamin A and zinc) and essential amino acids (lysine and tryptophan) could be impactful in reducing hidden hunger in developing maize-based regions. International research organizations, namely HarvestPlus, International Maize and Wheat Improvement Centre (CIMMYT) and International Institute of Tropical Agriculture (IITA) have spearheaded maize biofortification, targeting these micronutrients. Our research was conducted at CIMMYT-Zimbabwe in collaboration with the University of the Free State, South Africa, with the following objectives:"},{"index":2,"size":14,"text":"• To out-source micronutrient-dense germplasms from abroad and integrate them into local breeding programs."},{"index":3,"size":12,"text":"• To evaluate adaptability of exotic breeding materials in local growth conditions."},{"index":4,"size":13,"text":"• To develop and commercialize biofortified maize varieties with superior and farmerpreferred traits."}]}],"figures":[{"text":"Figure Figure 2. GGE-scatterplot based on environment-focused scaling for environments.Blue and green numbers represent environments and genotypes, respectively. "},{"text":"Figure Figure 3. GGE biplot showing which-won-where pattern of the 24 normal and biofortified inbred lines (G) evaluated over across 11 environments. "},{"text":"Figure 4 . Figure 4. GGE comparison biplot based on genotype-focused scaling for comparing the 24 normal and biofortified inbred lines with the ideal genotype. "},{"text":"Figure Figure 5. Stability analysis biplot of the 24 normal and biofortified inbred lines (G) evaluated over across 11 environments using the Francis coefficient of determination (CV%). "},{"text":"Table 1 . Description of the 11 experimental sites used during 2019 and 2020 Location Ұ name Location code Latitude Longitude Altitude (masl) Management  ART E1 17°42' S 31° 5' E 1556 Optimum ARTE117°42' S31° 5' E1556Optimum CIMMYT E2 17°48' S 31°03' E 1483 Optimum CIMMYTE217°48' S31°03' E1483Optimum CIMMYT E3 17°48' S 31°03' E 1483 Low N stress CIMMYTE317°48' S31°03' E1483Low N stress RARS E4 17°48' S 31° 3' E 1369 Optimum RARSE417°48' S31° 3' E1369Optimum Kadoma E5 18°32' S 30°90' E 1149 Random stress KadomaE518°32' S30°90' E1149Random stress DRSS E6 17°13' S 31°03' E 1506 Low N stress DRSSE617°13' S31°03' E1506Low N stress Gwebi E7 17°41' S 30°32' E 1448 Optimum GwebiE717°41' S30°32' E1448Optimum Chiredzi E8 21°02' S 31°57' E 433 Drought stress ChiredziE821°02' S31°57' E433Drought stress Chiredzi E9 21°02' S 31°57' E 433 Well-watered ChiredziE921°02' S31°57' E433Well-watered Chisumbanje E10 20°47' S 32°13' E 480 Drought stress ChisumbanjeE1020°47' S32°13' E480Drought stress Chisumbanje E11 20°47' S 32°13' E 480 Well-watered ChisumbanjeE1120°47' S32°13' E480Well-watered Ұ Abbreviations: E1 = ART opt, E2 = CIMMYT opt, E3 = CIMMYT LN, E4 = RARS opt, E5 = Kadoma RS, E6 = DRSS LN, Ұ Abbreviations: E1 = ART opt, E2 = CIMMYT opt, E3 = CIMMYT LN, E4 = RARS opt, E5 = Kadoma RS, E6 = DRSS LN, E7 = Gwebi opt, E8 = Chiredzi MDS, E9 = Chiredzi WW, E10 = Chisumbanje MDS, E11 = Chisumbanje WW. E7 = Gwebi opt, E8 = Chiredzi MDS, E9 = Chiredzi WW, E10 = Chisumbanje MDS, E11 = Chisumbanje WW.  "},{"text":"Table 2 . Description of the 24 maize inbred lines evaluated for agronomic performance across 11 sites during 2019 and 2020 cropping seasons Genotype name Genotype code † Nutritional type Source  G1 CLWQHZN12 Zinc donor CIMMYT G1CLWQHZN12Zinc donorCIMMYT G2 CLWQHZN14 Zinc donor CIMMYT G2CLWQHZN14Zinc donorCIMMYT G3 CLWQHZN19 Zinc donor CIMMYT G3CLWQHZN19Zinc donorCIMMYT G4 CLWQHZN46 Zinc donor CIMMYT G4CLWQHZN46Zinc donorCIMMYT G5 CLWQHZN49 Zinc donor CIMMYT G5CLWQHZN49Zinc donorCIMMYT G6 CLWQHZN53 Zinc donor CIMMYT G6CLWQHZN53Zinc donorCIMMYT G7 CLWQHZN69 Zinc donor CIMMYT G7CLWQHZN69Zinc donorCIMMYT G8 OBATANPA6 Zinc donor IITA G8OBATANPA6Zinc donorIITA G9 ITZN344 Zinc donor IITA G9ITZN344Zinc donorIITA G10 ITZN324 Zinc donor IITA G10ITZN324Zinc donorIITA G11 ITZN313 Zinc donor IITA G11ITZN313Zinc donorIITA G12 HPYDL18190 Provitamin A CIMMYT G12HPYDL18190Provitamin ACIMMYT G13 CLHP0213 Provitamin A CIMMYT G13CLHP0213Provitamin ACIMMYT G14 TL115798 QPM CIMMYT G14TL115798QPMCIMMYT G15 CML144 QPM CIMMYT G15CML144QPMCIMMYT G16 CZL16154 Normal CIMMYT G16CZL16154NormalCIMMYT G17 CZL16160 Normal CIMMYT G17CZL16160NormalCIMMYT G18 CML546 Normal CIMMYT G18CML546NormalCIMMYT G19 CML511 Check CIMMYT G19CML511CheckCIMMYT G20 CML181 Check CIMMYT G20CML181CheckCIMMYT G21 CLHP0478 Check CIMMYT G21CLHP0478CheckCIMMYT G22 CLHP0306 Check CIMMYT G22CLHP0306CheckCIMMYT G23 CML312 Check CIMMYT G23CML312CheckCIMMYT G24 CZL1111 Check  G24CZL1111Check  "},{"text":"Table 3 . AMMI analysis of variance for 24 normal and biofortified inbred lines across 11 environments  Source DF SS MS %Variation SourceDFSSMS%Variation     explained explained Environment 10 142.07 14.21** 33.12 Environment10142.0714.21**33.12 Genotype 23 113.30 4.93** 26.41 Genotype23113.304.93**26.41 G x E interaction 230 173.61 0.75** 40.47 G x E interaction230173.610.75**40.47 Factor 1 32 60.87 1.90** 34.18 Factor 13260.871.90**34.18 Factor 2 30 45.52 1.52** 25.57 Factor 23045.521.52**25.57 Residuals 264 39.89 0.15 33.12 Residuals26439.890.1533.12 ** P ≤ 0.01 = Significance at 99% confidence level; DF = number of degrees of freedom; SS = sum of squares; ** P ≤ 0.01 = Significance at 99% confidence level; DF = number of degrees of freedom; SS = sum of squares; MS = mean squares     MS = mean squares  "},{"text":"Figure 1. AMMI biplot of Factor 1 and Factor 2 scores for 24 normal and biofortified inbred lines grown across 11 environments.   "},{"text":"Table 4 . Mean grain yield (t ha−1 ) of 24 normal and biofortified inbred lines evaluated across 11 environments in Zimbabwe  Across 1.94 1.96 1.68 2.08 2.32 1.56 1.80 2.01 1.60 2.59 3.50 1.57 1.72 1.82 1.78 2.19 1.77 2.44  1.32 1.66 1.78 1.74 1.42 1.28 (Continued) Across1.941.961.682.082.321.561.802.011.602.593.501.571.721.821.782.191.772.441.321.661.781.741.421.28(Continued) E11 2.21 2.53 1.09 2.42 2.19 3.24 2.54 2.18 2.40 2.93 4.04 2.63 2.30 2.46 2.48 2.79 2.46 3.18  2.15 2.25 2.34 2.32 1.58 1.33 E112.212.531.092.422.193.242.542.182.402.934.042.632.302.462.482.792.463.182.152.252.342.321.581.33 E10 1.17 2.20 2.56 2.32 4.08 1.44 2.71 1.25 1.15 3.92 4.28 0.23 2.13 1.75 1.50 0.27 1.48 0.25  0.33 3.02 1.97 0.43 1.69 0.39 E101.172.202.562.324.081.442.711.251.153.924.280.232.131.751.500.271.480.250.333.021.970.431.690.39 E9 1.64 2.61 1.95 2.50 3.17 2.28 2.40 1.77 2.01 3.16 5.91 2.60 3.44 2.07 2.75 3.39 2.32 2.89  2.40 2.38 2.26 1.79 1.23 1.27 E91.642.611.952.503.172.282.401.772.013.165.912.603.442.072.753.392.322.892.402.382.261.791.231.27 E8 0.75 0.99 0.76 1.44 1.33 1.63 1.20 1.84 1.31 2.39 2.16 0.80 0.76 1.36 1.26 1.17 1.08 1.45  0.76 0.44 0.29 0.74 0.29 0.89 E80.750.990.761.441.331.631.201.841.312.392.160.800.761.361.261.171.081.450.760.440.290.740.290.89 E7 1.94 2.00 2.10 1.82 2.37 1.60 1.39 2.79 1.75 2.28 2.57 1.78 1.75 1.74 1.87 2.14 2.07 1.61  2.33 1.79 1.39 1.20 1.51 1.43 E71.942.002.101.822.371.601.392.791.752.282.571.781.751.741.872.142.071.612.331.791.391.201.511.43 E6 1.77 1.31 1.45 1.35 1.01 0.66 0.89 1.17 0.76 0.54 2.27 0.95 1.17 1.50 0.97 2.44 1.59 2.03  0.55 0.73 0.92 0.81 0.91 0.96 E61.771.311.451.351.010.660.891.170.760.542.270.951.171.500.972.441.592.030.550.730.920.810.910.96 E5 1.28 1.86 2.24 2.39 1.43 2.01 1.91 1.76 1.30 1.87 1.75 1.96 1.73 2.34 1.56 1.38 1.58 2.37  1.39 1.56 1.66 1.75 1.38 1.43 E51.281.862.242.391.432.011.911.761.301.871.751.961.732.341.561.381.582.371.391.561.661.751.381.43 E4 3.49 2.36 1.44 2.41 3.42 1.34 1.36 3.51 2.26 3.75 3.58 1.94 1.85 2.19 2.92 3.06 2.25 3.78  1.63 1.68 2.51 2.99 2.06 1.53 E43.492.361.442.413.421.341.363.512.263.753.581.941.852.192.923.062.253.781.631.682.512.992.061.53 E3 0.54 1.15 1.26 1.42 1.01 0.65 0.59 1.11 0.86 0.57 3.70 1.33 0.64 1.23 0.63 1.91 1.19 2.50  0.61 1.13 1.08 1.00 1.00 0.58 E30.541.151.261.421.010.650.591.110.860.573.701.330.641.230.631.911.192.500.611.131.081.001.000.58 E2 3.25 2.70 1.34 2.84 3.35 1.55 2.93 2.95 2.06 3.70 3.51 2.18 2.02 2.25 1.55 3.27 2.17 3.86  1.47 2.08 2.60 3.31 2.45 2.01 E23.252.701.342.843.351.552.932.952.063.703.512.182.022.251.553.272.173.861.472.082.603.312.452.01 E1 3.32 1.76 2.30 1.62 2.42 0.75 1.69 2.30 1.66 3.46 2.71 0.69 1.09 1.02 1.99 2.27 1.41 2.77  0.56 1.13 2.47 2.57 1.54 1.90 E13.321.762.301.622.420.751.692.301.663.462.710.691.091.021.992.271.412.770.561.132.472.571.541.90 Genotype G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 Checks G19 G20 G21 G22 G23 G24 GenotypeG1G2G3G4G5G6G7G8G9G10G11G12G13G14G15G16G17G18ChecksG19G20G21G22G23G24  "},{"text":"Table 4   Across 1.90    Across1.90  E11 2.42 0.97 84  E112.420.9784  E10 1.77 1.49 82  E101.771.4982  E9 2.51 0.96 88  E92.510.9688  E8 1.13 0.60 89  E81.130.6089  E7 1.88 0.61 77  E71.880.6177  E6 1.19 0.53 88  E61.190.5388  E5 1.74 0.50 76  E51.740.5076  E4 2.47 0.63 93  E42.470.6393  E3 1.15 0.5 84  E31.150.584 . (Continued) Genotype E1 E2 Mean 1.89 2.56 LSD 0.75 0.58 H 2 (%) 90 92 LSD = Least Significant Difference (P ≤ 0.05); H 2 . (Continued)Genotype E1 E2Mean 1.89 2.56LSD 0.75 0.58H 2 (%) 90 92LSD = Least Significant Difference (P ≤ 0.05); H 2  "},{"text":"Table 5 . Stability analysis of 24 normal and biofortified maize inbred lines evaluated across 11 environments in Zimbabwe  Genotype CV (%) Rank s 2 di Rank R 2 Rank Wi Rank GenotypeCV (%)Ranks 2 diRankR 2RankWiRank G1 52.03 22 0.55 21 0.45 17 5.78 21 G152.03220.55210.45175.7821 G2 29.81 4 −0.05 4 0.93 1 0.25 1 G229.814−0.0540.9310.251 G3 34.30 5 0.29 15 0.01 24 5.57 19 G334.3050.29150.01245.5719 G4 24.70 1 −0.00 1 0.76 4 0.72 3 G424.701−0.0010.7640.723 G5 44.61 16 0.52 19 0.50 13 5.73 20 G544.61160.52190.50135.7320 G6 49.08 20 0.34 18 0.36 20 3.83 16 G649.08200.34180.36203.8316 G7 43.00 15 0.25 11 0.52 11 2.88 11 G743.00150.25110.52112.8811 G8 36.80 7 0.29 16 0.43 18 3.30 15 G836.8070.29160.43183.3015 G9 34.40 6 −0.02 3 0.83 2 0.52 2 G934.406−0.0230.8320.522 G10 45.49 17 0.64 22 0.54 10 7.43 23 G1045.49170.64220.54107.4323 G11 40.53 10 1.36 24 0.36 21 13.83 24 G1140.53101.36240.362113.8324 G12 50.33 21 0.29 14 0.48 15 3.26 14 G1250.33210.29140.48153.2614 G13 45.69 19 0.18 10 0.64 7 2.33 10 G1345.69190.18100.6472.3310 G14 25.61 2 0.05 5 0.51 12 1.52 7 G1425.6120.0550.51121.527 G15 38.78 8 0.10 8 0.68 6 1.55 8 G1538.7880.1080.6861.558 G16 42.45 14 0.52 20 0.38 19 5.39 18 G1642.45140.52200.38195.3918 G17 26.71 3 −0.00 2 0.72 5 0.83 4 G1726.713−0.0020.7250.834 G18 41.83 13 0.68 23 0.35 22 6.80 22 G1841.83130.68230.35226.8022 G19 59.73 24 0.27 13 0.50 14 3.12 13 G1959.73240.27130.50143.1213 G20 45.57 18 0.27 12 0.47 16 3.06 12 G2045.57180.27120.47163.0612 G21 40.98 12 0.06 6 0.78 3 1.29 5 G2140.98120.0660.7831.295 G22 54.87 23 0.34 17 0.59 8 4.08 14 G2254.87230.34170.5984.0814 G23 38.87 9 0.07 7 0.58 9 1.43 6 G2338.8790.0770.5891.436 G24 40.70 11 0.12 9 0.35 23 2.37 9 G2440.70110.1290.35232.379  "}],"sieverID":"4224d401-42c8-4d1d-8434-b06ae8f6dab4","abstract":"Breeding for nutrient-dense maize cultivars is reliant on introductions of exotic inbred lines enhanced with high levels of the targeted nutrients. Sometimes, the exotic nutrient donor germplasm may not adapt well in new growing environments, thereby reducing seed production when used in hybrid combinations. Therefore, evaluating introduced trait donors for adaptation, through genotype × environment interaction (GEI) analysis is crucial in breeding for quality traits. The objectives of this study were to (i) evaluate grain yield performance of introduced zinc-enhanced, provitamin A, normal and quality protein maize lines across stress and non-stress environments in Zimbabwe, (ii) assess the presence of GEI and (iii) identify high yielding and stable lines that could be used for developing Znenhanced hybrids with improved seed producibility. Additive main effects and multiplicative interaction (AMMI) and genotype plus genotype × environment interaction (GGE) biplot analyses were used for stability analysis. GEI effects were highly significant (P ≤ 0.01) for grain yield. Grain yields for the inbred lines ranged from 1.28 to 3.5 t ha −1 . The Zn donor G11 (ITZN313) had the highest grain yield of 3.5 t ha −1 across environments, whereas the normal check G24 (CZL1111) had the lowest grain yield. G2 (CLWQHZN14), G4 (CLWQHZN19), G8 (OBATANPA6), G11 (ITZN313) and G18 (CML546) were stable and high yielding and can be used for developing Zn-enhanced hybrids. Five mega-environments were identified, clearly separating stress and non-stress environments. E11 (Chisumbanje WW) was the most discriminating and representative test environment and could be used to identify superior genotypes."}

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+{"metadata":{"id":"080f4a53fa562ad8c780148fa1fb91e5","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/c9ca5872-b431-4592-96a0-f037fb018a19/retrieve"},"pageCount":22,"title":"Working with Farmers in Asia: Spreading New Varieties, Improved Practices and ….…. New Hope 1","keywords":["cassava","erosion control","farmer participatory research (FPR) and extension (FPE)","Thailand","Vietnam","China"],"chapters":[{"head":"INTRODUCTION","index":1,"paragraphs":[{"index":1,"size":89,"text":"Cassava (Manihot esculenta Crantz) is usually grown by smallholders in upland areas with poor soils and low or unpredictable rainfall. In some countries the crop is grown on steep slopes, but in others it is grown mainly on gentle slopes; in both cases,soil erosion can be serious. Moreover, cassava farmers seldom apply adequate amounts of fertilizers or manures to replace the nutrients removed in the harvested products. Thus, both erosion and nutrient extraction can result in a decline in soil fertility and a gradual degradation of the soil resource."},{"index":2,"size":171,"text":"The fact that farmers do not apply sufficient fertilizers and do not use soil conservation practices when the crop is grown on slopes is more a socio-economic rather than a technical problem. Research has shown many ways to maintain or improve soil fertility and reduce erosion, but farmers usually consider these practices too costly or requiring too much labor. To overcome these obstacles to adoption it is necessary to develop simple practices that are suitable for the local situation and that provide short-term benefits to the farmer as well as long-term benefits in terms of resource conservation. Being highly site specific these practices can best be developed by the farmers themselves, on their own fields, in collaboration with research and extension personnel. Thus, a project was initiated, with financial support from the Nippon Foundation in Tokyo, Japan, to develop a farmer participatory methodology for the development and dissemination of more sustainable production practices in cassava-based cropping systems, that will benefit a large number of poor farmers in the uplands of Asia."}]},{"head":"MATERIALS AND METHODS","index":2,"paragraphs":[]},{"head":"First Phase (1994-1998)","index":3,"paragraphs":[{"index":1,"size":59,"text":"The first phase of the project was conducted in four countries, i.e. China, Indonesia, Thailand and Vietnam. The project was coordinated by CIAT and implemented in collaboration with research and extension organizations in each of the four countries. During an initial training course on farmer participatory research (FPR) methodologies, each country designed a work plan to implement the project."},{"index":2,"size":37,"text":"The steps in the process, from diagnosing the problem to adoption of suitable solutions, are shown in Figure 1. The outstanding feature of this approach is that farmers participate in every step and make all important decisions. "}]},{"head":"a. Pilot site selection","index":4,"paragraphs":[{"index":1,"size":91,"text":"Suitable pilot sites were pre-selected in areas where cassava is an important crop, where it is grown on slopes and erosion is a serious problem. Detailed information obtained through Rapid Rural Appraisals (RRA) in each site have been reported by Nguyen The Dang et al. (1998), Utomo et al. (1998), Vongkasem et al. (1998) and Zhang Weite et al. (1998). After conducting the RRAs, one or two suitable pilot sites (villages or subdistricts ) were selected to work with farmers in the development and dissemination of suitable varieties and production practices."}]},{"head":"b. Demonstration plots","index":5,"paragraphs":[{"index":1,"size":84,"text":"Each year demonstration plots were laid out on an experiment station or a farmer's field to show the effect of many alternative treatments on yield, income and soil erosion. Farmers from the selected pilot sites visiting the trial were asked to discuss and score the usefulness of each treatment. From this range of many options farmers usually selected 3-4 treatments that they considered most useful for their own conditions. Some farmers then volunteered to test these treatments in FPR trials on their own fields."},{"index":2,"size":54,"text":"In both the demonstration plots and FPR erosion control trials on farmers' fields, a simple methodology was used to measure soil loss due to erosion in each treatment. Plots were laid out along the contour on a uniform slope; along the lower side of each plot a ditch was dug and covered with plastic."},{"index":3,"size":48,"text":"Small holes in the plastic allowed runoff water to seep away, while eroded sediments remained on the plastic. These sediments were collected and weighed several times during the cropping cycle. After correcting for moisture content, the amount of dry soil loss per hectare was calculated for each treatment."},{"index":4,"size":25,"text":"This simple methodology gives both a visual as well as a quantitative indication of the effectiveness of the various practices in controlling erosion (Howeler, 2001;2002)."}]},{"head":"c. FPR trials","index":6,"paragraphs":[{"index":1,"size":136,"text":"The FPR trials did not only involve soil conservation practices, but also new varieties, intercropping systems and fertilization, with the objective of developing a combination of practices that would increase farmers' income, reduce erosion and improve soil fertility. During the first phase of the project, farmers in the four countries conducted a total of 177 FPR erosion control trials, 157 variety trials, 98 fertilizer trials and 35 intercropping trials, for a total of 467 trials. At time of harvest, field days were organized in each site to harvest the various trials by the participating farmers and their neighbors. The yields of cassava and intercrops, the dry soil loss due to erosion, as well as the gross income, production costs and net income were calculated for each treatment and presented in a joint meeting to the farmers."},{"index":2,"size":35,"text":"After one or more years of testing in small plots, farmers quickly identified the best varieties and production practices for their area and started using those on larger areas of their production fields (Howeler, 2002)."}]},{"head":"Second Phase (1999-2003)","index":7,"paragraphs":[{"index":1,"size":49,"text":"The second phase of the project was conducted in collaboration with five institutions in Thailand, six in Vietnam and three in China (Table 1). During the second phase the emphasis shifted from participatory research (FPR) to extension (FPE) in order to reach more farmers and achieve more widespread adoption."},{"index":2,"size":17,"text":"Table 1. Partner institutions collaborating in the second phase of the Nippon Foundation cassava project in Asia. "}]},{"head":"Research and extension organizations in","index":8,"paragraphs":[]},{"head":"Research and extension organizations in China","index":9,"paragraphs":[{"index":1,"size":71,"text":"-Chinese Academy for Tropical Agricultural Sciences (CATAS) -Guangxi Subtropical Crops Research Institute (GSCRI) -Honghe Animal Husbandry Station of Yunnan Once farmers had selected certain practices and wanted to adopt those on their fields, the project staff tried to help them; for instance, in setting out contour lines to plant hedgerows for erosion control, or to obtain seed or vegetative planting material of the selected hedgerow species, intercrops or new cassava varieties."},{"index":2,"size":29,"text":"During both the first and second phase of the project some collaborative research continued onstation in order to develop better technologies that farmers could test on their own fields."}]},{"head":"RESULTS AND DISCUSSION","index":10,"paragraphs":[{"index":1,"size":10,"text":"First Phase (1994)(1995)(1996)(1997)(1998): Farmer Participatory Research (FPR)  et al. (2001)."}]},{"head":"a. FPR trials","index":11,"paragraphs":[]},{"head":"b. Scaling-up and adoption","index":12,"paragraphs":[{"index":1,"size":76,"text":"After having selected the most promising varieties and production practices from FPR trials, farmers generally like to test some of these on small areas of their production fields, making adaptations if necessary. Some practices may look promising on small plots, but are rejected as impractical when applied on larger areas; this may be due to lack of sufficient planting material (like vetiver grass) or lack of markets for selling the products (like pumpkin or lemon grass). "}]},{"head":"Dry","index":13,"paragraphs":[{"index":1,"size":77,"text":"Yield (t/ha) Gross Product. Net Slope soil loss -----------------income 2) costs income Treatment 1) (%) (t/ha) cassava peanut 1) -------(mil. dong/ha)------ Since the objective of the second phase was to achieve widespread adoption of more sustainable production practices by as large a number of farmers as possible, it was necessary to markedly expand the number of pilot sites and to develop farmer participatory extension (FPE) methodologies to disseminate the selected practices and varieties to many more farmers."}]},{"head":"a. Farmer participatory research (FPR)","index":14,"paragraphs":[{"index":1,"size":240,"text":"Implementing the project in collaboration with many different institutions in China, Thailand and Vietnam (Table 1), and with generous financial support from the Nippon Foundation, it was possible to expand the number of pilot sites each year. In 2001 the project was working in about 50 sites, and this further increased to 99 sites by the end of the project in 2003 (Figure 2). Once the benefits of the new technologies became clear, the number of sites increased automatically, as neighboring villages also wanted to participate in order to increase their yields and income.  Whenever the project extended to a \"new\" site, the process outlined above was re-initiated, i.e. an RRA was conducted, interested farmers visited demonstration plots and/or made a cross-visit to an already established site, they conducted FPR trials, discussed results and eventually adopted those varieties or practices they had selected as most suitable for their own conditions. Table 3 shows the number and type of FPR trials conducted in China, Thailand and Vietnam during the second phase of the project. While initially farmers were mainly interested in testing new varieties, fertilization, intercropping and erosion control practices, during the later part of the project they also wanted to test the use of organic or green manures, weed control, plant spacing and even leaf production and pig feeding. During the second phase of the project a total of 1,154 FPR trials were conducted by farmers on their own fields. "}]},{"head":"b. Farmer participatory Extension (FPE)","index":15,"paragraphs":[{"index":1,"size":36,"text":"The following farmer participatory extension methods were found to be very effective in raising farmers' interest in soil conservation, in disseminating information about improved varieties and cultural practices, and in enhancing adoption of soil conserving practices:"}]},{"head":"i. Cross-visits","index":16,"paragraphs":[{"index":1,"size":115,"text":"Farmers from new sites were usually taken to visit older sites that had already conducted FPR trials and had adopted some soil conserving technologies. These cross-visits, in which farmers from the older site could explain their reasons for adopting new technologies was a very effective way of farmer-tofarmer extension. After these cross-visits, farmers in some new sites decided to adopt some technologies immediately, while others decided to conduct FPR trials in their own fields first. In both cases, the \"FPR teams\" of the various collaborating institutions, together with provincial, district or subdistrict extension staff, helped farmers to establish the trials, or they provided seed or planting materials required for the adoption of the new technologies."}]},{"head":"ii. Field days","index":17,"paragraphs":[{"index":1,"size":56,"text":"At time of harvest, field days were organized at the site in order to harvest the trials and discuss the results. Farmers from neighboring villages were usually invited to participate in these field days, to evaluate each treatment in the various trials and to discuss the pros and cons of the various practices or varieties tested."},{"index":2,"size":77,"text":"In a few cases, large field days were also organized with participation of hundreds of neighboring farmers, school children, local and high-level officials, as well as representatives of the press and TV. The broadcasting or reporting about these events also helped to disseminate the information about suitable technologies. During the field days farmers explained the results of their own FPR trials to the other visiting farmers, while literature about the project and the results obtained was distributed."}]},{"head":"iii. Training","index":18,"paragraphs":[{"index":1,"size":53,"text":"Research and extension staff involved in the project had previously participated in Training-of-Trainers courses in FPR methodologies, including practical training sessions with farmers in some of the pilot sites. While some participants were initially skeptical, most course participants became very enthusiastic about this new approach once they started working more closely with farmers."},{"index":2,"size":104,"text":"In addition, 2-3 key farmers from each site together with their local extension agent were invited to participate in FPR training courses. The objective was to learn about the various FPR methodologies, the basics of doing experiments as well as the implementation of commonly selected technologies, such as setting out contour lines or the planting, maintenance and multiplication of hedgerow species. By spending several days together in these courses, the farmers and extensionist got to know each other well, and they were encouraged to form a local \"FPR team\" to help other farmers in their community conduct FPR trials or adopt the new technologies."}]},{"head":"iv. Community-based self-help groups","index":19,"paragraphs":[{"index":1,"size":31,"text":"Realising that effective soil conservation practices, such as planting of contour hedgerows, can best be done as a group, farmers from some sites decided to form their own \"soil conservation group\"."},{"index":2,"size":198,"text":"These community-based self-help groups are similar to \"Land Care units\", that have been very effective in promoting soil conservation in the Philippines and Australia. In Thailand, the Dept. of Agric. Extension has encouraged farmers to set up these groups as a way of organizing themselves, to conduct FPR trials, to implement the selected practices, and to manage a rotating fund, from which members of the group can borrow money for production inputs. Thus, by 2003, a total of 21 \"Cassava Development Villages\" had been set up in the pilot sites. Each group needed to have at least 40 members, elect five officers to lead the group, and establish their own bylaws about membership requirements, election of officers, use of the rotating fund, etc. The formation of these groups helped to decide on collective action and to strengthen the community, while people gained confidence and the group became more self-reliant. When necessary, the group could request help from local or national extension services, obtain information about certain production problems, or get planting material of vetiver grass or other species for hedgerows or green manures. Some groups started their own vetiver grass nurseries to have planting material available when needed."}]},{"head":"Effect of New Technologies on Cassava Yield and Soil Loss by Erosion","index":20,"paragraphs":[{"index":1,"size":55,"text":"Farmers are interested in testing new technologies only if those technologies promise substantial economic benefits over their traditional practices. Thus, strategic and applied research need to continue to produce and select still better varieties, better production practices and new utilization options. As such, some collaborative research in the area of agronomy and soil management continued."}]},{"head":"Long-term fertility maintenance:","index":21,"paragraphs":[{"index":1,"size":241,"text":"Long-term NPK trials were continued in four locations, one each in north and south Vietnam, one in Hainan island of China and one in southern Sumatra of Indonesia. Figure 3 shows the effect of annual applications of various levels of N, P, and K on the yield and starch content of two varieties during the 13 th year of continuous cropping in Hung Loc Center in south Vietnam. It is clear that, similar to most other locations, the main yield response was to the application of K, while there were minor responses to the application of N and P and mainly in the higher yielding variety SM 937-26. The combined application of 160 kg N, 80 P 2 O 5 and 160 K 2 O/ha increased yields from about 10 to 30 t/ha. Figure 4 shows the absolute and relative response to application of N, P and K as well as the change in P and K status of the soil during the entire 13-year period. Initially there was no response to any nutrient as the organic matter, P and K levels were still adequate and root yields were relatively low. With the introduction of new higher yielding varieties in the 4 th year, the root yields increased and nutrient depletion, especially K, increased, leading to an ever more pronounced response to K application. Even after 13 years soil-P remained above the critical level, which explains the lack of a P-response.  "}]},{"head":"Combined use of animal manure and chemical fertilizer","index":22,"paragraphs":[{"index":1,"size":195,"text":"Table 4 shows the effect of combining various rates of farmyard (=pig) manure (FYM) with chemical fertilizers, in this case N and K, in Thai Nguyen University in north Vietnam. Without manure or fertilizers the yield was only 3.25 t/ha; with the application of only 80 kg N and 80 K 2 O/ha yields increased to 15.47 t/ha; with a high rate of 15 t/ha of manure it was 13.11 t/ha, while the combined application of 10 t/ha of manure with N and K produced the highest yield of 18.70 t/ha. However, the combination producing the highest net income was 5 t/ha of manure with 80 kg N and 80 K 2 O/ha. The net income was much higher using chemical fertilizers alone or in combination with a modest amount of FYM as compared to using only FYM. From this and other trials it is clear that farmers can increase yields and income by reducing their application of pig manure as long as it is combined with adequate levels of N and K in chemical fertilizers. 2,300/kg manure+application 100/kg 3) Cost of cassava cultivation: 2.8 mil. dong/ha; cost of chemical fertilizer application: 0.10 mil. dong/ha"}]},{"head":"Green manures and/or chemical fertilizers","index":23,"paragraphs":[{"index":1,"size":174,"text":"Table 5 shows the results of a green manure experiment conducted for two consecutive years in Khaw Hin Sorn station in Chachoengsao province of Thailand. All green manure species were intercropped between cassava rows and planted one month after planting cassava; they were pulled out and mulched two month later. During the first year, the highest cassava yields were obtained with only chemical fertilizers applied at 25 or 75 kg/rai; all green manures competed with cassava resulting in lower yields. During the second year, the highest yield was obtained by application of 75 kg/rai of 15-7-18 fertilizers; however, the mulching and later incorporation of jackbean (Canavalia ensiformis) combined with 25 kg/rai of 15-7-18 resulted in higher yields than the same rate of fertilizer by itself. Thus, in this second year the beneficial effect of the Canavalia green manure outweighed its competitive effect. For all other green manure species, the competitive effect was still greater than the beneficial effect. Crotalaria juncea, Mucuna and cowpea were particularly competitive, while mungbean and pigeon pea were intermediately competitive."},{"index":2,"size":101,"text":"Considering all costs involved, the application of chemical fertilizers alone generally produced the highest net income, but in both years the combination of a low level of fertilizers with Canavalia green manure produced the second highest net income. These and other green manure trials (Howeler et al., 1998) indicate that green manures are seldom beneficial during the first year but their beneficial effect increases over time. 1) GM = green manure; all green manures were planted between cassava rows one month after planting cassava and were pulled up or cut off two months later and mulched; 1 ha = 6.25 rai"}]},{"head":"Effect of various soil conservation practices on cassava yield and soil loss by erosion","index":24,"paragraphs":[{"index":1,"size":349,"text":"Table 6 shows the average effect of various soil conservation practices on relative cassava yields and dry soil loss by erosion from numerous trials conducted in Thailand from 1994 to 2003. Closer plant spacing, lemon grass hedgerows and contour ridging were the most effective in both increasing yields and decreasing erosion. Most other contour hedgerow species, including vetiver grass, decreased cassava yields -mostly by reducing the area available for cropping and by competition with nearby cassava -but were very effective in reducing soil loss by erosion. Most effective in reducing erosion were vetiver grass, Paspalum atratum and lemon grass, which reduced erosion by 33 to 47%. Intercropping was usually not effective in reducing erosion, while up-and-down ridging and especially the lack of fertilization markedly increased erosion. Similar results were obtained in Vietnam (Table 7) where hedgerows of vetiver grass, Tephrosia candida and Paspalum atratum all decreased erosion by about 50%, while also increasing cassava yields 10-13%. The beneficial effects of contour hedgerows tend to increase markedly over time. Figure 5 shows the long-term effect of contour hedgerows of vetiver grass and Tephrosia candida on relative cassava yields and soil loss as compared to the check plot without hedgerows; data are average values from three FPR erosion control trials conducted for nine consecutive years in north Vietnam. Although the results are rather variable, there is a clear trend that the two types of hedgerows caused a 20-40% increase in cassava yields and reduced soil losses by erosion to 20-40% of those in the check plots without hedgerows. Vetiver grass tended to become more effective in reducing soil losses than Tephrosia, firstly because the grass is more effective in filtering out suspended soil sediments, and secondly because Tephrosia hedgerows need to be replanted every 3-4 years, in contrast to vetiver grass which is more or less permanent. While farmers claim that Tephrosia improves the fertility of the soil more so than vetiver grass, the data show that vetiver increased cassava yields more than Tephrosia, probably by reducing losses of top soil and fertilizers and improving water infiltration and soil moisture content."},{"index":2,"size":151,"text":"Figure 6 shows similar results from a soil erosion control experiment conducted for six consecutive years on about 15% slope at Hung Loc Agric. Research Center in south Vietnam. In this case, contour hedgerows of vetiver grass, Leucaena and Gliricidia all increased cassava yields as compared to the check plot without hedgerows; they also decreased soil losses by erosion. Leucaena was the most effective in increasing yields by supplying nitrogen in leaf prunings, while vetiver was the most effective in reducing erosion. Similar to the data from north Vietnam in Figure 5, the effectiveness in controlling erosion increased over time. During the 6 th year, the soil loss with vetiver hedgerows was only about 20% of that without hedgerows. These two sets of data indicate that hedgerows of vetiver grass are among the most effective ways to control erosion, and that the effectiveness of all types of hedgerows increases over time. "}]},{"head":"ADOPTION AND IMPACT","index":25,"paragraphs":[{"index":1,"size":47,"text":"After conducting their own FPR trials, or after a cross-visit to another village where those trials were being conducted, farmers often decided to adopt one or more technologies on their production fields with the hope of increasing yields or income and protecting the soil from further degradation."},{"index":2,"size":114,"text":"In Thailand, practically all of the cassava area is now planted with new varieties and about 75% of farmers apply some chemical fertilizers (TTDI, 2000), although usually not enough nor in the right proportion. As a result of the FPR fertilizer trials, farmers started to apply more K, while the official fertilizer recommendation for cassava was changed from an NPK ratio of 1:1:1 to 2:1:2. After trying various ways of controlling erosion, most farmers selected the planting of vetiver grass contour hedgerows as the most suitable. Table 8 indicates that by the end of 2003, about 1038 farmers had planted a total of 1.63 million vetiver plants, corresponding to about 145 km of hedgerows."},{"index":3,"size":641,"text":"Table 9 shows how in Vietnam the number of households in the pilot sites adopting the various technology components increased over time, with most farmers adopting new varieties. This is partially due to the testing in FPR variety trials, but is also due to the planting of new varieties by non-participating farmers in or near the pilot sites. During 2002 and 2003 farmers in Van Yen district of Yen Bai province in north Vietnam planted a total of 500 km of double hedgerows of Tephrosia candida or Paspalum atratum to control erosion, and they planted about 3000 ha of new cassava varieties with improved fertilizer practices. This increased average yields from 10 t/ha to about 30 t/ha.   10 also shows that the gross income, both per ha and per household, as a result of the adoption of soil conservation practices also increased very markedly over time. Results from both FPR trials and on-station research also indicate that the beneficial effect of contour hedgerows in terms of increasing yields and decreasing erosion increases over time (Figures 5 and 6). This is mainly because the planting of contour hedgerows, almost independent of the species used, will result in natural terrace formation, which over time reduces the slope and enhances water infiltration, thus reducing runoff and erosion. Well established hedgerows also become increasingly more effective in trapping eroded soil and fertilizers. Unfortunately, most FPR erosion control trials are conducted for only 1-2 years at the same site, so farmers do not quite appreciate the increases in beneficial effects that result over time. This, coupled with the fact that planting and maintaining hedgerows requires additional labor (and sometimes money for seed or planting material) while hedgerows take some land out of production and have initially little beneficial effect on yield, has hampered the more widespread acceptance and adoption of these soil conservation practices.  : 1999 = 9;2000=15;2001=22;2002=25;2003=34 Source: Tran Ngoc Ngoan, 2003. In order to determine the effect of this project on adoption of new technologies, an impact assessment was made by an outside consultant. He organized focus group discussions and collected data from farmers in eight representative project sites, as well as from farmers living within 10 km from those sites, who had not participated in the project. Table 11 shows the percent of households (out of 832) who had adopted various technologies. New varieties were adopted by nearly all cassava farmers in Thailand and 46% in Vietnam; the use of chemical fertilizers had been adopted by 80-90% of households; intercropping by a majority of households in Vietnam, but by very few in Thailand. Contour ridging was adopted by about 30% of households in both Vietnam and Thailand, while contour hedgerows of vetiver grass was adopted by 24% of households in Thailand and only 7% in Vietnam; most farmers in Vietnam preferred the planting of Tephrosia candida or Paspalum atratum, as these are easier to plant (from seed) and can also serve as a green manure and animal feed, respectively. Thus, it is clear that adoption of specific practices varies from site to site, depending on local conditions and traditional practices. Table 12 shows that during the past eight years the average cassava yields in all three countries increased; this increase ranged from 0.83 t/ha in China to 6.73 t/ha in Vietnam. The increased yields resulted in annual increases in gross income received by farmers of about 150 million US dollars in the three countries, and about 250 million US dollars in all of Asia. In addition, farmers in Thailand received higher prices due to the higher starch content of the new varieties. This was achieved not only by this project, but by the collaborative effort of many researchers, extensionists, factory owners and farmers, with strong support from national governments.  2) In addition, farmers also benefited from higher prices due to higher starch content"}]},{"head":"Meeting the Challenge","index":26,"paragraphs":[{"index":1,"size":122,"text":"Achieving widespread adoption of soil erosion control practices and adequate fertilization for sustainable cassava production on sloping land is a real challenge because these practices generally require additional labor, have considerable financial costs, may take land out of production or lower cassava yields due to competition, resulting in no immediate economic benefits for farmers. However, several lessons have been learned from the project described above, and steps can be taken to enhance adoption: 1. Farmers are not necessarily interested in conserving soil, but are always interested in increasing yields and income. For that reason, soil conservation practices should be combined with other technologies that do provide short-term economic benefits, such as new higher-yielding varieties, well-balanced fertilization (both organic and inorganic), and intercropping."},{"index":2,"size":46,"text":"2. The long-term beneficial effect of soil conservation practices, fertilization and the use of animal and green manures can only be shown in long-term trials. Thus, some experiments and FPR trials should be continued at the same location and with the same treatments for several years."},{"index":3,"size":49,"text":"3. Seeing is believing. By encouraging farmers to conduct simple erosion control trials on their own fields, they can see the amount of soil lost by erosion using the traditional practice; they also see how simple agronomic practices can markedly reduce these losses, while also increasing yields and income."},{"index":4,"size":74,"text":"4. What is suitable in one location is not necessarily suitable in another. Thus, we should not promote or recommend a single practice, but show farmers a range of possible options, from which they can select those that seem useful, and then test these out in small areas of their own fields before selecting the best one for adoption. 5. Farmers are not necessarily interested in increasing yields, but are most concerned about income."},{"index":5,"size":43,"text":"Thus, the data presented to farmers should include the yield, the total crop value (gross income), the best estimates of all costs of production (including farmer's own labor), as well as net income or profit. This helps farmers to make the right decisions."},{"index":6,"size":68,"text":"6. Farmers are more convinced by listening to other farmers than to researchers or extension workers; and they are more convinced by seeing another farmer using a new practice rather than seeing the same practice in a researcher-managed demonstration plot. For that reason, cross-visits, field days to see FPR trials, local FPR teams and community-based organizations are the most effective ways to disseminate new technologies and achieve adoption."},{"index":7,"size":41,"text":"7. Empower farmers and farm communities to be self-reliant, by seeking information, experimenting, developing their own location-specific technologies, and making their own decisions. Researchers and extension workers facilitate the process, but then step back to let farmers make their own choices."},{"index":8,"size":68,"text":"8. Every institution and every person has its own strength and weaknesses, but by working together they can complement each other, the private with the public sector, breeders with agronomists, researchers with extensionists, and especially local extension workers with farmers. Everyone contributes their knowledge and experience in order to achieve a common goal, a vision of sustainable and adequate food production, and improvements in the livelihoods of all."}]},{"head":"CONCLUSIONS","index":27,"paragraphs":[{"index":1,"size":137,"text":"Research on sustainable land use conducted in the past has mainly concentrated on finding solutions to the bio-physical constraints, and many solutions have been proposed for improving the long-term sustainability of the system. Still, few of these solutions have actually been adopted by farmers, mainly because they ignored the human dimension of sustainability. For new technologies to be truly sustainable they must not only maintain the productivity of the land and water resources, but they must also be economically viable and acceptable to farmers and the community. To achieve those latter objectives farmers must be directly involved in the development, adaptation and dissemination of these technologies. A farmer participatory approach to technology development was found to be very effective in developing locally appropriate and economically viable technologies, which in turn enhances their acceptance and adoption by farmers."},{"index":2,"size":86,"text":"The conducting of FPR trials is initially time consuming and costly, but once more and more people are trained and become enthusiastic about the use of this approach -including participating farmers -both the methodology and the selected improved varieties or cultural practices will spread rapidly. The selection and adoption of those farming practices that are most suitable for the local environment and in tune with local traditions will improve the long-term sustainability of the cropping system, to the benefit of both farmers and society at large."}]}],"figures":[{"text":"Figure 1 . Figure 1. Farmer participatory model used for the development of sustainable cassava-based cropping systems in Asia. "},{"text":" Thailand -Department of Agriculture (DOA) -Department of Agricultural Extension (DOAE) -Land Development Department (LDD) -Kasetsart University (KU) -The Thai Tapioca Development Institute (TTDI) 2. Research and extension organizations in Vietnam -Thai Nguyen University of Agriculture and Forestry (TNUAF) -National Institute for Soils and Fertilizers (NISF) -Vietnam Agricultural Science Institute (VASI) -Hue University of Agriculture and Forestry (HUAF) -Institute of Agricultural Sciences of South Vietnam (IAS) -Tu Duc University of Agriculture and Forestry (TDUAF) "},{"text":"Figure 2 . Figure 2. Location of FPR pilot sites in China, Thailand and Vietnam in the Nippon Foundation cassava project in 2003. "},{"text":"Figure 3 .Figure 3 .Figure 4 .Figure 4 . Figure 3. Effect of annual applications of various levels of N, P and K on the root yield and root starch content of two cassava varieties grown atHung Loc Agric. Research Center, Thong Nhat, Dongnai, Vietnam in   2002/03 (13th year). "},{"text":"Figure 5 .Figure 5 .Figure 6 .Figure 6 . Figure 5. Trend in relative yield and relative soil loss by erosion when cassava was planted with contour hedgerows of vetiver grass or Tephrosia candida during nine consecutive years of cassava cropping. Data are average values for one FPR erosion control trial in Kieu Tung and two trials in Dong Rang in North Vietnam from 1995 to 2003. "},{"text":"Table 2 shows a typical example of an FPR erosion control trial conducted by six farmers having adjacent plots on about 40% slope. It is clear that contour hedgerows of vetiver grass, Tephrosia candida or pineapple reduced erosion to about 30% of that in the check plot, while intercropping with peanut and planting vetiver hedgerows markedly increased net income. Results of many other FPR trials have been reported by Nguyen The Dang et al. (2001), Huang Jie et al. (2001), Utomo et al. (2001) and Vongkasem  "},{"text":"Table 2 . Effect of various crop management treatments on the yield of cassava and intercropped peanut as well as the gross and net income and soil loss due to erosion in a FPR erosion control trial conducted by six farmers in Kieu Tung village of Thanh Ba district, Phu Tho province, Vietnam in 1997 (3 rd year).   "},{"text":"Second Phase (1999-2003): Farmer Participatory Research (FPR) and Extension (FPE)         Farmers Farmers        ranking ranking 1. C monocult., with fertilizer, no hedgerows(TP) 40.5 106.1 19.17 - 9.58 3.72 5.86 6 1. C monocult., with fertilizer, no hedgerows(TP) 40.5 106.1 19.17-9.583.725.866 2. C+P, no fertilizer, no hedgerows 45.0 103.9 13.08 0.70 10.04 5.13 4.91 5 2. C+P, no fertilizer, no hedgerows45.0 103.9 13.080.7010.045.134.915 3. C+P, with fertilizer, no hedgerows 42.7 64.8 19.23 0.97 14.47 5.95 8.52 - 3. C+P, with fertilizer, no hedgerows42.7 64.8 19.230.9714.475.958.52- 4. C+P, with fertilizer, Tephrosia hedgerows 39.7 40.1 14.67 0.85 11.58 5.95 5.63 3 4. C+P, with fertilizer, Tephrosia hedgerows39.7 40.1 14.670.8511.585.955.633 5. C+P, with fertilizer, pineapple hedgerows 32.2 32.2 19.39 0.97 14.55 5.95 8.60 2 5. C+P, with fertilizer, pineapple hedgerows32.2 32.2 19.390.9714.555.958.602 6. C+P, with fertilizer, vetiver hedgerows 37.7 32.0 23.71 0.85 16.10 5.95 10.15 1 6. C+P, with fertilizer, vetiver hedgerows37.7 32.0 23.710.8516.105.95 10.151 7. C monocult, with fert., Tephrosia hedgerows 40.0 32.5 23.33 - 11.66 4.54 7.12 4 7. C monocult, with fert., Tephrosia hedgerows 40.0 32.5 23.33-11.664.547.124 1) Fertilizers = 60 kg N + 40 P 2 O 5 , + 120 K 2 O/ha; all plots received 10 t/ha pig manure    1) Fertilizers = 60 kg N + 40 P 2 O 5 , + 120 K 2 O/ha; all plots received 10 t/ha pig manure TP=farmer traditional practice      TP=farmer traditional practice 2) Prices: cassava (C) dong 500/kg fresh roots     2) Prices: cassava (C) dong 500/kg fresh roots peanut (P) 5000/kg dry pods      peanut (P)5000/kg dry pods 1US$ = approx. 13.000 dong      1US$ = approx. 13.000 dong  "},{"text":"Table 3 . Number of FPR trials conducted in the 2d phase of the Nippon Foundation Project in China, Thailand and Vietnam.  Country Type of FPR trial 1999 2000 2001 2002 2003 Total CountryType of FPR trial19992000200120022003Total China Varieties 9 9 20 69 20  ChinaVarieties99206920  Erosion control 3 5 8 17 -  Erosion control35817-  Fertilization - - - 4 -  Fertilization---4-  Intercropping - - - 9 -  Intercropping---9-  Pig feeding - - - 59 -  Pig feeding---59-   12 14 28 158 20  12142815820 Thailand Varieties 11 16 16 19 25  ThailandVarieties1116161925  Erosion control 14 10 6 - 11  Erosion control14106-11  Chemical fertilizers 16 6 23 17 17  Chemical fertilizers166231717  Chem.+org fertilizers - - 10 11 11  Chem.+org fertilizers--101111  Green manures - - 13 11 15  Green manures--131115  Weed control - - 17 5 10  Weed control--17510  Plant spacing - - 3 - 2  Plant spacing--3-2  Intercropping - - 16 7 -  Intercropping--167-   41 32 104 70 91  41321047091 Vietnam Varieties 12 31 36 47 35  VietnamVarieties1231364735  Erosion control 16 28 29 30 23  Erosion control1628293023  Fertilization 1 23 36 24 24  Fertilization123362424  Intercropping - 14 32 31 26  Intercropping-14323126  Weed control - 3 - - 3  Weed control-3--3  Plant spacing - 1 7 19 8  Plant spacing-17198  Leaf production - - 2 2 1  Leaf production--221  Pig feeding - - 11 16 13  Pig feeding--111613   29 100 153 169 133  29100153169133 Total  82 146 285 397 244 1,154 Total821462853972441,154  "},{"text":"Table 4 . Effect of the application of FYM 1) and chemical fertilizers on cassava yield and economic benefit at Thai Nguyen University of Agric. and Forestry in Thai Nguyen province in 2001 (2 nd year).  Gross Fert. Product. Net GrossFert.Product.Net income 2) costs 2) costs 3) income income 2)costs 2)costs 3)income  "},{"text":"Table 5 . Effect of green manures and/or chemical fertilizers on the root yield and starch content of cassava, KU 50, as well as net income when grown at Khaw Hin Sorn research station in Khaw Hin Sorn, Chachoengsao, Thailand during two consecutive years in 2002/03 and 2003/04.   Cassava yield Starch content Net income Cassava yieldStarch contentNet income  (t/ha)   (%) ('000 baht/ha) (t/ha)(%)('000 baht/ha) Treatments 1) 1 st year 2 nd year 1 st year 2 nd year 1 st year 2 nd year Treatments 1)1 st year2 nd year1 st year2 nd year1 st year2 nd year 1. Check without GM; 25 kg/rai 15-7-18 46.45 26.28 24.6 23.6 22.36 14.54 1. Check without GM; 25 kg/rai 15-7-1846.4526.2824.623.622.3614.54 2. Crotalaria juncea; 25 kg/rai 15-7-18 36.58 20.83 24.3 22.7 16.25 10.04 2. Crotalaria juncea; 25 kg/rai 15-7-1836.5820.8324.322.716.2510.04 3. Canavalia ensiformis; 25 kg/rai 15-7-18 40.35 27.07 24.9 23.1 19.06 15.16 3. Canavalia ensiformis; 25 kg/rai 15-7-1840.3527.0724.923.119.0615.16 4. Pigeon pea ICPL 304; 25 kg/rai 15-7-18 38.23 24.18 23.9 23.4 16.96 13.01 4. Pigeon pea ICPL 304; 25 kg/rai 15-7-1838.2324.1823.923.416.9613.01 5. Cowpea CP 4-2-3-1; 25 kg/rai 15-7-18 38.54 21.66 23.7 22.3 16.87 10.41 5. Cowpea CP 4-2-3-1; 25 kg/rai 15-7-1838.5421.6623.722.316.8710.41 6. Mucuna; 25 kg/rai 15-7-18 36.73 21.17 24.9 23.8 16.78 10.78 6. Mucuna; 25 kg/rai 15-7-1836.7321.1724.923.816.7810.78 7. Mungbean; 25 kg/rai 15-7-18 40.07 25.08 24.2 23.6 18.54 14.06 7. Mungbean; 25 kg/rai 15-7-1840.0725.0824.223.618.5414.06 8. Check without GM; 75 kg/rai 15-7-18 43.44 32.16 25.5 23.8 18.77 16.89 8. Check without GM; 75 kg/rai 15-7-1843.4432.1625.523.818.7716.89  "},{"text":"Table 6 . Effect of various soil conservation practices on the average 1) relative cassava yield and dry soil loss due to erosion as determined from soil erosion control experiments, FPR demonstration plots and FPR trials conducted in Thailand from 1994 to 2003.  Relative Relative RelativeRelative cassava yield dry soil loss cassava yielddry soil loss  "},{"text":"Table 7 . Effect of various soil conservation practices on the average 1) relative cassava yield and dry soil loss due to erosion as determined from soil erosion control experiments, FPR demonstration plots and FPR trials conducted in Vietnam from 1993 to 2003.  Rel. cassava yield (%) Rel. dry soil loss (%) Rel. cassava yield (%)Rel. dry soil loss (%)  "},{"text":"Table 8 . Extent of adoption of vetiver grass contour hedgerows for erosion control in various FPR pilot sites in Thailand in 2003. Cassava area with hedgerows and hedgerow length are approximate, as some hedgerows were damaged by tractor while others needed to be partially replanted because of poor establishment due to drought. Adoption of vetiver grass hedgerows Adoption of vetiver grass hedgerows  Cassava Vetiver Vetiver CassavaVetiverVetiver No. of area with (no. of hedgerows No. ofarea with(no. ofhedgerows  "},{"text":"Table 9 . Extent of adoption of soil conservation practices and the estimated increase in yield and gross income of farmers in the FPR pilot sites in Vietnam from 2000 to 2003. Data inTable 10 indicate that adoption of soil conservation practices in all sites in Vietnam increased yields, ranging from 13.5% in 2000 to 23.7% in 2002. Table  Number Area with Cassava yield (t/ha) Percent Increase in gross income NumberArea withCassava yield (t/ha)PercentIncrease in gross income  of soil conser. Farmers' With soil yield  (mil VND) 2) ofsoil conser.Farmers'With soilyield(mil VND) 2) Year farmers (ha) practice 1) conservation increase per ha total per household Yearfarmers(ha)practice 1)conservationincreaseper hatotalper household 2000 62 21.12 12.11 13.75 13.5 0.574 12.123 0.196 20006221.1212.1113.7513.50.57412.1230.196 2001 200 59.87 16.50 19.95 20.9 1.112 66.596 0.333 200120059.8716.5019.9520.91.11266.5960.333 2002 222 88.85 20.60 25.48 23.7 1.952 173.728 0.782 200222288.8520.6025.4823.71.952173.7280.782 2003 831 612.00 20.60 3) 25.48 2)  1.561 955.699 1.150 2003831612.0020.60 3)25.48 2)1.561955.6991.150 Total 831 612.00     1,208.146  Total831612.001,208.146 1) Farmers' practice includes most new technologies except soil conservation    1) Farmers' practice includes most new technologies except soil conservation 2) Fresh root price: in 2000 350 VND/kg      2) Fresh root price: in 2000 350 VND/kg   in 2001 350 VND/kg in north, 200 in central and 290 in south   in 2001350 VND/kg in north, 200 in central and 290 in south   in 2002 400 VND/kg      in 2002400 VND/kg   in 2003 320 VND/kg (estimated)     in 2003320 VND/kg (estimated) 3) Yields estimated from 2002       3) Yields estimated from 2002 Source: Tran Ngoc Ngoan, 2003       Source: Tran Ngoc Ngoan, 2003  "},{"text":"Table 10 . Trend of adoption of new cassava technologies in the Nippon Foundation project sites in Vietnam from 2000 to 2003.    Number of households adopting  Number of households adopting  -------------------------- -------------------------- Technology component 2000 2001 2002 2003 Technology component2000200120022003 1. New varieties 88 447 1,637 14,820 1. New varieties884471,63714,820 2. Improved fertilization 64 123 157 1,710 2. Improved fertilization641231571,710 3. Soil conservation practices 62 200 222 831 3. Soil conservation practices62200222831 4. Intercropping 127 360 689 4,250 4. Intercropping1273606894,250 5. Pig feeding with cassava root silage - 759 967 1,172 5. Pig feeding with cassava root silage-7599671,172 1) Number of project sites     1) Number of project sites  "},{"text":"Table 11 . Extent of adoption of new technologies by farmers participating or not directly participating in the Nippon Foundation project in Thailand and Vietnam 1) . Data are based on PRRA census forms collected at the end of the project (2003) from 439 households in Thailand and 393 households in Vietnam from farmers that had participated in FPR trials and or training courses, as well as from nearby farmers that had not directly participated in these project activities. Percentages may total more than 100% as households can adopt more than one technology simultaneously.  Participants Non-Participants  Total ParticipantsNon-ParticipantsTotal Technologies adopted Thailand Vietnam Thailand Vietnam Thailand Vietnam Technologies adoptedThailandVietnamThailandVietnamThailandVietnam Varieties       Varieties ->75% improved varieties 100 48.3 86.6 44.7 90.2 46.1 ->75% improved varieties10048.386.644.790.246.1 -about 50% improved varieties 0 34.0 0.3 20.7 0.2 25.7 -about 50% improved varieties034.00.320.70.225.7 -mainly traditional varieties 0 16.3 0 34.6 0 27.7 -mainly traditional varieties016.3034.6027.7 -no cassava 0 1.4 13.0 0 9.6 0.5 -no cassava01.413.009.60.5 Soil conservation practices       Soil conservation practices -contour ridging 53.0 31.3 22.0 28.9 30.3 29.8 -contour ridging53.031.322.028.930.329.8 -hedgerows -vetiver grass 61.5 11.6 9.6 3.7 23.5 6.6 -hedgerows -vetiver grass61.511.69.63.723.56.6 -Tephrosia candida 0 32.7 0 6.9 0 16.5 -Tephrosia candida032.706.9016.5 -Paspalum atratum 0.9 11.6 0 2.0 0.2 5.6 -Paspalum atratum0.911.602.00.25.6 -pineapple 0 2.7 0 0.8 0 1.5 -pineapple02.700.801.5 -sugarcane 1.7 0 0.6 0 0.9 0 -sugarcane1.700.600.90 -other hedgerows 3.4 7.5 0.3 1.6 1.1 3.8 -other hedgerows3.47.50.31.61.13.8 -no soil conservation 20.5 29.3 70.8 59.3 57.4 48.1 -no soil conservation20.529.370.859.357.448.1 Intercropping       Intercropping -with peanut 0.9 40.8 0.6 30.9 0.7 34.6 -with peanut0.940.80.630.90.734.6 -with beans 0 23.8 0 27.2 0 26.0 -with beans023.8027.2026.0 -with maize 10.3 2.7 2.8 3.7 4.8 3.3 -with maize10.32.72.83.74.83.3 -with green manures 20.5 0 4.0 0 8.4 0 -with green manures20.504.008.40 -other species 2.6 43.5 1.6 21.5 1.8 29.8 -other species2.643.51.621.51.829.8 -no intercropping 71.8 20.4 90.4 47.6 85.4 37.4 -no intercropping71.820.490.447.685.437.4 Fertilization       Fertilization -chemical fertilizers 98.3 79.6 84.5 80.1 88.2 79.9 -chemical fertilizers98.379.684.580.188.279.9 -farm yard or green manure 56.4 65.3 25.5 55.3 33.7 59.0 -farm yard or green manure56.465.325.555.333.759.0 -no fertilizer 0 16.3 12.4 14.2 9.1 15.0 -no fertilizer016.312.414.29.115.0 1) Source: Agrifood International, 2004       1) Source: Agrifood International, 2004  "},{"text":"Table 12 . Estimation of the annual increase in gross income due to higher cassava yields resulting from the adoption of new cassava varieties and improved practices, in China, Thailand and Vietnam, as well as in Asia as a whole.   Total cassava Cassava yield (t/ha) 1) Yield Cassava Increased gross income due Total cassavaCassava yield (t/ha) 1)YieldCassavaIncreased gross income due Country area (ha) 1) ------1994 2003 increase (t/ha) price ($/tonne) to higher yields (mil. US $) Countryarea (ha) 1)------1994 2003increase (t/ha)price ($/tonne)to higher yields (mil. US $) China Thailand 240,108 1,050,000 15.21 13.81 16.25 17.55 1.04 3.74 27 22 6.7 86.4 2) China Thailand240,108 1,050,00015.21 13.8116.25 17.551.04 3.7427 226.7 86.4 2) Vietnam 371,700 8.44 14.07 5.63 25 52.3 Vietnam371,7008.4414.075.632552.3 Asia total 3,430,688 12.95 16.12 3.17 25 271.9 Asia total3,430,68812.9516.123.1725271.9 1) Data from FAOSTAT for 2003      1) Data from FAOSTAT for 2003  "}],"sieverID":"0744e872-f2c8-485a-b94a-021e88b1ef4f","abstract":"Farmers in Asia like to grow cassava because the crop will tolerate long dry periods and poor soils, and will produce reasonable yields with little inputs. Most farmers realize, however, that cassava production on slopes can cause severe erosion, while production without fertilizer inputs may lead to a decline in soil productivity. Research has shown that cassava yields can be maintained for many years with adequate application of fertilizers, and that there are various ways to reduce erosion. Adoption of erosion control practices, however, has been minimal as farmers generally see little short-term benefits of these practices.To enhance the adoption of soil conserving practices and improve the sustainability of cassava production, a farmer participatory research (FPR) approach was used to develop not only the best soil conservation practices, but also to test new varieties, fertilization and cropping systems that tend to produce greater short-term benefits. The FPR methodology was initially developed in 2-3 sites each in China, Indonesia, Thailand and Vietnam in collaboration with various research and extension organizations in those countries; in 2003 the project had extended to about 99 villages in Thailand, Vietnam and China. The methodology includes the conducting of RRAs in each site, farmer evaluation of a wide range of practices shown in demonstration plots, FPR trials with farmer-selected treatments on their own fields, field days with discussions to select the best among the tested practices, scaling-up to larger fields, and farmer participatory dissemination to neighbors and other communities. Based on the results of these trials, farmers have readily adopted better varieties, fertilization and intercropping practices, and many farmers have now adopted the planting of contour hedgerows to control erosion. The resulting increases in cassava yields in Asia over the past ten years have increased the annual gross income of cassava farmers by an estimated 272 million US dollars."}

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+{"metadata":{"id":"0999be12512d7cc96df95628380bc1bd","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/e89cf1aa-f039-4313-9a4a-58215928cfdc/retrieve"},"pageCount":8,"title":"How vulnerable is Cali's food system to climate shocks? A historical perspective","keywords":[],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":146,"text":"Climate variability events such as El Niño and La Niña (related to heavy rains and droughts), have had historically diverse effects associated with changes in the atmosphere and ocean, which manifest themselves in detrimental impacts on the population and their livelihoods such as agriculture, livestock and fisheries (1) (2). Consequently, they threaten food systems and their sustainability. Some of the main consequences of the presence of these phenomena in global food systems are: Problems in food production, increased prices and increased food and nutritional insecurity (3)(4). It should be noted that in general terms the agricultural sector and food security are affected by climatic anomalies associated with El Niño and La Niña phenomena, since under the influence of La Niña rains flood crops and reduce their yields, while under El Niño events, the scarcity of rain and droughts reduce the production of different crops (5) (6)."},{"index":2,"size":75,"text":"In Colombia, El Niño phenomenon usually occurs during the months of July -September, while La Niña occurs during the months of November -March. There is no consensus as to the period in which it is repeated; however, experts worldwide agree that the most approximate cycle is between four and seven years and that its duration varies from 3 months to two or more years, and that its intensity can be mild, moderate or severe (7)."},{"index":3,"size":89,"text":"According to studies conducted by the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), when El Niño phenomenon occurs, precipitation volumes tend to be low in relation to the season, especially in large sectors of the Caribbean, Andean and Pacific regions; on the contrary, when La Niña is present, these same areas experience excessive precipitation. However, it should be emphasized that the occurrence of these phenomena does NOT suppress seasonality, i.e., El Niño does not suppress rainy seasons, nor does a Niña suppress dry or less rainy seasons (8)."},{"index":4,"size":75,"text":"Generally, when El Niño occurs, the crops most affected historically have been, in order: fique, cassava, African palm and barley, followed by rice, potato, corn, cotton, sugarcane, banana, cocoa and beans. It should be noted that the impact on agriculture varies from one country to another, due to several variables such as the intensity of climatic effects at the local level and the specific conditions of the production systems, life cycles, among others (2) (9)."},{"index":5,"size":81,"text":"Two of the main effects on the supply system of a city in the agricultural sector due to climatic variations in the agricultural sector are related to the reduction of food supply and the impact on food prices (10). Knowing how much El Niño/La Niña climatic phenomena affect the agricultural sector that supplies food to a city, municipality or country will help to plan strategies to mitigate these impacts, the fragility of food systems and the protection of the most vulnerable."},{"index":6,"size":59,"text":"In this sense, we are interested in knowing to what degree the supply system of the city of Cali is vulnerable to this type of phenomena, a city in which 1 out of every 2 inhabitants suffers from food insecurity, 1 out of every 2 people is poor and more than 50% of the population has nutritional deficiencies (11)."},{"index":7,"size":58,"text":"Finally, it is important to note that Cali is not a food-producing city; 80% of the products that reach the city are imported or come from other regions of the country. Thus, the objective of this study is to observe how climatic variations affect or threaten the city's food supply, but not how they influence local agricultural activity."}]},{"head":"Methodology","index":2,"paragraphs":[{"index":1,"size":24,"text":"To assess the vulnerability of historical climate shocks on Cali's food system, an exploratory time series analysis was developed where the following were studied:"},{"index":2,"size":26,"text":"1. The monthly supply 1  In Colombia, El Niño is associated with low rainfall and high temperatures, while La Niña corresponds to above normal rainfall conditions."},{"index":3,"size":34,"text":"The main objective of the analysis was to determine whether there is any causality between the ONI index and the time series of monthly food supply by food and municipality of origin to determine:"},{"index":4,"size":20,"text":"a. List of foods (and their municipality/department of origin) impacted to some extent by El Niño or La Niña events."},{"index":5,"size":24,"text":"b. Number of months (lags) following the development of an El Niño/La Niña event where an impact occurs in the food supply time series."},{"index":6,"size":34,"text":"The Granger causality statistical test was used to determine whether the ONI index can predict changes or alternations in the food supply time series with a given number of months prior to the event."},{"index":7,"size":66,"text":"The ONI index time series was defined as the predictor variable and the food supply time series was used as the response variables. As a time-window for evaluation, lags of 1 to 24 months were tested, given that the influence of ocean surface temperature warming does not translate immediately into an alteration of food supply, but takes a certain number of months to reflect this impact."},{"index":8,"size":65,"text":"To determine the statistically significant lags, a significance level of α=0.05 was used. Thus, significant lags correspond to months where the p-value of the Granger test is less than α. In other words, the statistical test detects that with a certain number of months after the occurrence of an El Niño or La Niña phenomenon, there is some important alteration in the supply time series."},{"index":9,"size":35,"text":"Finally, for each municipality (and using the information of all the foods in that municipality), the median number of subsequent months in which the food was affected by climatic shocks (El Niño/La Niña) was calculated."},{"index":10,"size":99,"text":"For the selection of foods that could have been affected by the Niño or Niña phenomenon, two main criteria were considered: a) Foods with the highest amount of food supply to Cali, which could mean a higher consumption and redistribution of these foods, b) Foods with a higher nutritional value, whose absence could represent an impact on the health and nutritional status of the population. To categorize the foods, the different food groups contemplated in the National Dietary Guidelines for Colombia -GABA were considered and the first two of each group were prioritized according to their nutritional value (12)."},{"index":11,"size":31,"text":"In the case of fruits and vegetables, the content of vitamin A and vitamin C was considered, and in the case of cereals and meats, the contribution of carbohydrates and protein."},{"index":12,"size":26,"text":"There are some unique foods per group such as milk, sugar, avocado. The knowledge of consumption in the region was also considered to assign the priority."}]},{"head":"Results","index":3,"paragraphs":[{"index":1,"size":37,"text":"According From an initial list of 30 foods that arrive in the city of Cali, 10 foods were prioritized according to the largest quantities supplied to the city and which in turn have a higher nutritional value."}]},{"head":"Event","index":4,"paragraphs":[{"index":1,"size":22,"text":"In addition, the municipalities/department of origin impacted by El Niño or La Niña phenomena in Colombia during the period studied are shown:"},{"index":2,"size":51,"text":"These results indicate that cities located in the departments of Valle, Cauca and Nariño were affected more quickly by El Niño/La Niña events, i.e., between 3 and 8 months, while cities located in the departments of Boyacá, Cundinamarca, Caquetá and Magdalena were affected more distantly, generally between 15 and 23 months."}]},{"head":"Conclusions","index":5,"paragraphs":[{"index":1,"size":67,"text":"This is a descriptive analysis, which does not allow us to observe causality to determine whether El Niño/La Niña climatic effects have any influence on Cali's food system and the diet of its population. However, the impact of El Niño/La Niña climatic phenomena on certain departments and their cities and what this may mean for their food production and distribution processes in the city is not negligible."},{"index":2,"size":65,"text":"The city is highly dependent on other areas for its supply and there is a downward trend in some of the foodstuffs reported. Although this may indicate several situations, not only of climatic origin, but it can also be deduced that what most reaches the city may be threatened by different events, whether social, climatic, related to the production characteristics of the place, among others."}]},{"head":"Limitations","index":6,"paragraphs":[{"index":1,"size":32,"text":"Some foods do not have reported values of supply for each month to be considered for the time series, in this sense the temporal period (2013 -2020) for each food may vary."},{"index":2,"size":53,"text":"Although the supply of some products such as soft drinks has an important load in the supply to the city of Cali, this was not considered because it is not a food that contains a great nutritional contribution. Therefore, this type of products (beverages, sugars, sweets, confectionery) were not considered in the analyses."},{"index":3,"size":62,"text":"The abrupt changes that can be observed in these graphs are not necessarily related to climatic impacts (it may be that there was a truck strike or some other event that produced such a change. Therefore, interpretations are cautious. In this sense, it is not possible to determine whether the changes in supply were the result of climatic variability or other factors."},{"index":4,"size":19,"text":"The time series only show the temporal pattern (ups and downs) of the supply of certain food to Cali."},{"index":5,"size":53,"text":"The Granger statistical test only allows us to identify whether the ONI index time series in any way affects the supply time series. Basically, the answer of the test is binary: yes, there is affectation or no affectation. But it does not tell us at what point in the time series it occurred."},{"index":6,"size":7,"text":"That is, we cannot see it graphically."},{"index":7,"size":75,"text":"The databases of the National Administrative Department of Statistics (DANE) were used as a secondary source of information, which reports official information in a defined period of time, this may vary from the amounts reported by the food supplies centers involved (CAVASA and Santa Elena). Additionally, changes in the methodology of data gathering could produce not very precise quantities, moreover those quantities can be underestimated due to not all the producers sell through supply centers."}]}],"figures":[{"text":" to the graph, the ONI series indicates that in Colombia there have been effects of the La Niña phenomenon in some months of the years 2012 -2014, 2017 -2018 and 2020 -2021, while the El Niño phenomenon has occurred mainly in the years 2015 -2016 and 2018 -2019. The greatest severity of El Niño occurred during 2016 and the greatest severity of La Niña occurred in late 2020 and early 2021. "},{"text":"  "},{"text":"  "}],"sieverID":"6d1c0184-1dc8-43d3-aecf-a9707cbcfa89","abstract":"In addition, for each of the 10 foods listed, supply trends were observed for the period of 2013-2020. Food City/State of origin (mainly) Period of time (ONI variable) -Significant lags Graphical representation Beef Yumbo (Valle del Cauca) Candelaria (Valle del Cauca) 5 -10, 18, 20 -24 months No significant lags Carrot Bogotá DC Tuquerres (Nariño) 15 -17 months 14 -24 months Tree tomato Medellín (Antioquia) Santa Rosa de Osos (Antioquia) 4 -8 No significant lags Plantain Belén de Umbría (Risaralda) Sevilla (Valle del Cauca) No significant lags No significant lags Milk Restrepo (Valle del Cauca) Yotoco (Valle del Cauca) 19 months 9 months Chicken Candelaria (Valle del Cauca) Ginebra (Valle del Cauca) 20 -22 months No significant lags Corn (mazorca) Pradera (Valle del Cauca) Palmira (Valle del Cauca) No significant lags 2 months Tangerine Caicedonia (Valle del Cauca) 1 -20 months Avocado Armenia (Quindío) 20 y 22 months Rice Ibague (Tolima) Espinal (Tolima) No significant lags"}

data/part_5/09aec6b3e95512c5c935fe4c2bb38f52.json

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+{"metadata":{"id":"09aec6b3e95512c5c935fe4c2bb38f52","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/d954f404-d291-448b-bcbc-e2de838510fe/retrieve"},"pageCount":89,"title":"","keywords":[],"chapters":[{"head":"","index":1,"paragraphs":[{"index":1,"size":106,"text":"Tabla 2. Accesiones y especies de lulo utilizadas en este estudio, provenientes de Centro de Investigación Corpoica, La Selva, en Rionegro, Antioquia……..………28 Tabla 3. Mezcla de reacción de la digestión del ADN……………..………………...31 Tabla 4. Mezcla de reacción de la digestión/ligación………………………………..32 Tabla 5. Mezcla de reacción para la amplificación selectiva (+1/+1) de los productos de digestión/ligación………………………………………………………...33 Tabla 6. Mezcla de reacción \"Mix 1\" para PCR (+3/+3)……………………………..34 Tabla 7. Mezcla de reacción \"Mix 2\" para PCR (+3/+3)……………………………..35 Tabla 8. Grupos obtenidos a un nivel de 0.55% en un dendograma de similaridad de Nei-Li (1978), utilizando el método de UPGMA con dos combinaciones para AFLP E-ACG/M-CAT y E-ACG/M-CTC. ………………………………………………47"}]},{"head":"LISTA DE FIGURAS","index":2,"paragraphs":[]},{"head":"Página","index":3,"paragraphs":[{"index":1,"size":363,"text":"Figura 1. Morfología del cultivo de lulo. a) hoja; b) flor; c) fruto; d)corteza y pulpa.……………………………………………………………………………………...10 Figura 2. Daño producido en la raíz de lulo por el nemátodo M. incognita………..17 Figura 4. Electroforesis en gel de agarosa al 0,8% de la extracción de ADN de algunas accesiones de lulo estudiadas. ……………………………………………..40 Figura 5. Detalle de un gel de poliacrilamida mostrando patrones de bandas de Aflp (combinación de cebadores E-ACG/M-CAT), de algunas accesiones de lulo estudiadas. El marcador de peso molecular (M) corresponde a DNA ladder 10 pb (330 a 30 pb)……………………………………………………………………………..41 Figura 6. Dendrograma de similaridad utilizando el método de UPGMA con los valores de similaridad de Nei-Li (1979) producto del análisis de los datos de las dos combinaciones usadas en los AFLP´s. E-ACG/M-CAT y E-ACG/M-CT, empleando todas las accesiones de lulo estudiadas..........................................…43 Figura 7. Fragmento del Dendrograma de similaridad para S pseudolulo utilizando el método de UPGMA con los valores de similaridad de Nei-Li (1979) producto del análisis de los datos de las dos combinaciones usadas en los AFLP……………………………………………………………………………………....50 Figura 8. Fragmento del Dendrograma de similaridad para S hirtum utilizando el método de UPGMA con los valores de similaridad de Nei-Li (1979)  Figura 9 Fragmento del Dendrograma de similaridad para S vestissimun utilizando el método de UPGMA con los valores de similaridad de Nei-Li (1979)  Figura 10 Fragmento del Dendrograma de similaridad para S quitoense utilizando el método de UPGMA con los valores de similaridad de Nei-Li (1979)  Figura 11. Fragmento del Dendrograma de similaridad para algunas especies del género solanum empleadas como grupo de comparación, utilizando el método de UPGMA con los valores de similaridad de Nei-Li producto del análisis de los datos de las dos combinaciones usadas en los AFLP.. ……………………………………55 Figura 12. Gráfico tridimensional derivado del análisis de correspondencia múltiple (ACM) de todas las accesiones evaluadas en este estudio (Vista frontal)………..58 Figura 13. Gráfico tridimensional derivado del análisis de correspondencia múltiple (ACM) de todas las accesiones evaluadas en este estudio (Vista desde arriba)……………………………………………………………………………………..59 Figura 14. Dendrograma de similaridad construido mediante el método de UPGMA con los valores de similaridad de Nei-Li, (1979) disponible en el PAUP 4.0. Los valores de 1000 réplicas del \"bootstrap\" están representados sobre las ramas……………………………………………………………………………………...61"}]},{"head":"RESUMEN","index":4,"paragraphs":[{"index":1,"size":270,"text":"El Banco de germoplasma del Estado, custodiada por Corpoica, en Rionegro (Antioquía) conserva una colección de lulo (Solanum quitoense LAM) y especies relacionadas de la seccion Lasiocarpa. En este trabajo se consideró esencial realizar un análisis de la diversidad genética a nivel molecular con AFLP (Amplified Fragment Length Polymorphism) con 159 accesiones de lulo conservadas en el Banco. De las 30 combinaciones de oligonucleótidos evaluadas, se seleccionaron dos E-ACG/M-CAT y E-ACG/M-CTC, por su alto nivel de polimorfismo con un total de 206 y 170 bandas polimórficas respectivamente. El dendograma obtenido con el análisis de similaridad de Nei-Li (1979), mostró 11 agrupamientos. La mayoría de las accesiones observadas en las especies silvestres (S. hirtum, S. pseudolulo, S. vesstissimun, S. pectinatum y S. stramonifoliun), mostraron índices de similaridad entre 75 y 98%; las especies cultivadas ( S. quitoense y S . sessiliflorum); presentan índices de similaridad entre 84-100%. Con el coeficiente de similaridad analizado, se separaron las especies andinas (S. quitoense, S. hirtum, S. pseudolulo, S. vesstissimun y S. pectinatum) de las especies Amazónicas (S. stramonifoliun y S. sessiliflorom) de la sección Lasiocarpa. Al Interior de cada especie no hubo una separación clara por origen geográfico. El análisis de correspondencia múltiple corroboró lo observado en el análisis de similaridad. Para este estudio, tres dimensiones fueron suficientes para explicar la mayor parte de la variación entre las accesiones estudiadas. El análisis neighborjoining\" a través del algoritmo \"vecino más próximo\" (\"neighbor-joining\"), mostró valores de \"bootstrap\" (análisis de remuestreo) de 100%, lo que indica que el soporte de cada rama para las especies de la sección Lasiocarpa incluidas en este estudio se encuentra bien representado."}]},{"head":"ABSTRACT","index":5,"paragraphs":[{"index":1,"size":207,"text":"The Colombian National Genebank -managed by Corpoica and based in Rionegro, Antioquía -contains a collection of \"lulo\" (Solanum quitoense LAM) and other related species from the Lasiocarpa section. The objective of this investigation implied the molecular study of the genetic diversity of 159 accessions from this collection through the use AFLP analysis. Two primer combinations, E-ACG/M-CAT (with 206 polymorphic bands) and E-ACG-M-CTC (with 170 polymorphic bands), were chosen out of 30 evaluated combinations based on their high expression levels of polymorphism. The dendrogram obtained through the Nei-li (1979) similarity analysis produced 11 clusters. The majority of the wild accessions ( S . hirtum, S . pseudolulo, S . vesstissimun, S. pectinatum y S. stramonifoliun), presented similarity coefficients ranging from 75-98%, while the cultivated species (S. quitoense y S . s essiliflorum), showed similarity indexes established between 84-100%. Within each species, intra-specific separation could not be detected with regards to the geographic origin of the accession. An analysis of multiple correspondences, where three dimensions were sufficient to explain the genetic variation between clusters, confirmed the results observed through the similarity index analysis. The neighbor-joining analysis presented \"bootstrap\" values of 100%; a result that clearly indicates that the support of each cluster for the studied species is well represented."}]},{"head":"INTRODUCCIÓN","index":6,"paragraphs":[{"index":1,"size":106,"text":"Los recursos genéticos constituyen la base biológica para la seguridad alimentarla mundial y están formados por la diversidad del material genético que contienen las variedades tradicionales y los cultivares modernos, así como por las plantas silvestres afines a las cultivadas. Estos recursos son la materia prima más Importante para la generación de nuevas variedades y el mayor aporte para la producción y la diversidad genética que emplean los agricultores. También conforman un depósito de adaptabilidad y estabilidad genética que sirve de salvaguardia ante el peligro potencial representado por los cambios medioambientales y económicos, y los patógenos, Insectos y otras plagas nuevas o sus biotipos (FAO, 1996)."},{"index":2,"size":65,"text":"En Colombia, con la creación de Corpoica en 1993 se comenzó a implementar el Sistema Nacional de Bancos de Germoplasma en la cual se desarrollan actividades como regeneración e incremento de colecciones, mantenimiento de las mismas por diferentes sistemas de conservación (mediano y largo plazo), descripción básica de los materiales almacenados, documentación de Inventarios y de valoración agregada por procesos de caracterización y premejoramiento varietal."},{"index":3,"size":220,"text":"La mayor colección ex situ Colombiana de lulo (S. quitoense) y de especies relacionadas de la sección Lasiocarpa, se encuentra en el Banco de Germoplasma del Estado, custodiada por Corpoica, en el Centro Experimental \"La Selva\", en Rionegro, Antioquia. Esta colección esta siendo caracterizada con base en caracteres morfo-agronómicos (arquitectura de la planta, presencia o ausencia de pubescencia y espinas). Sin desconocer la importancia de estos marcadores, es necesario complementar su información con marcadores de tipo molecular que a diferencia de los morfoagronómicos no son influenciados por el ambiente (Morell et al., 1995). Uno de los marcadores moleculares más utilizados para solucionar este tipo de problemas, son los AFLP (Vos et al., 1995). Los cuales presentan algunas ventajas sobre otros marcadores moleculares como los RFLP`s, los RAPD`s, los SSRs o microsatélites, debido a que esta técnica permite evaluar un gran número de loci a la vez, permitiendo un alto cubrimiento del genoma, sin requerir el conocimiento previo de la secuencia de ADN. (Ferreira y Grattapaglia, 1998). Desde su desarrollo y divulgación, los AFLP, han sido ampliamente utilizados en estudios de diversidad genética en varias especies de la familia Solanaceae (Solanum tuberosum L. (papa), Capsicum frutescens L. (pimentón) y Capsicum annum L. (ají)) (Christian et al., 1996;Saliba-Colombani et al., 2000;Rodríguez, 2000;Acquadro et al., 2002;Lanteri et al., 2003y Toquica et al., 2003)."},{"index":4,"size":53,"text":"El estudio de la diversidad genética basada en los marcadores moleculares AFLP de la colección de lulo y especies relacionadas, será de gran ayuda para establecer estrategias de conservación in situ y ex situ, para esclarecer relaciones filogenéticas y taxonómicas actualmente existentes y para dar soporte a programas de mejoramiento genético de lulo."}]},{"head":"MARCO TEÓRICO","index":7,"paragraphs":[]},{"head":"1 Generalidades","index":8,"paragraphs":[{"index":1,"size":98,"text":"S. quitoense conocido también, con el nombre de \"naranjilla de quito\" en el Ecuador, \"Morella de quito\" en el Perú y \"Lulo\" en Colombia (Morton, 1987); es una planta arbustiva que produce frutos de pulpa verde, ricos en minerales y vitamina C (Denis et al., 1985). Debido a su valor nutritivo, sabor, color y usos en la agroindustria, el lulo es una de las frutas tropicales más apetecidas (fruta fresca, pulpa congelada) en los mercados nacionales e internacionales, con amplias perspectivas para la exportación a los mercados del Japón, Estados Unidos y Comunidad Europea (Lobo y Medina, 1999)."}]},{"head":"2 Origen y Distribución","index":9,"paragraphs":[{"index":1,"size":110,"text":"S. quitoense pertenece a la familia Solanaceae. Es originario de los bosques húmedos del subtrópico, en las vertientes oriental y occidental de la cordillera de los Andes entre 1.200 y 2.500 m.s.n.m, pertenecientes a Perú, Ecuador y Colombia (Lobo et al., 1983;Heiser, 1985;Heiser & Anderson, 1999). También se encuentra en forma silvestre en Venezuela, Brasil, Costa Rica, Mesoamérica, Polinesia y en algunos países asiáticos como China y Borneo (Whalen et al., 1981). Fuera de las zonas similares a las del lugar de origen, el lulo es de difícil adaptación. En Norte América, por ejemplo, la planta llega hasta la florescencia en forma normal; pero el polen es estéril (Duran, 1988)."},{"index":2,"size":119,"text":"El lulo (S. quitoense), se adapta a temperaturas comprendidas entre 11 y 20 o C, crece entre los 500 y los 2.500 m.s.n.m, desarrollándose en forma óptima entre los 1.800 y 2.500 m.s.n.m. La precipitación adecuada entre 1.500 y 3.000 mm anuales, requiere de alta humedad relativa cerca al 80% y de de poca luminosidad, especialmente zonas de penumbra o sombreadas, de días cortos, a esta característica se le atribuye la infertilidad del polen en zonas templadas. S. quitoense variedad quitoense (sin espinas) se desarrolla mejor en alturas por debajo de los 2.000 m.s.n.m y el S. quitoense variedad septentrional (con espinas) se desarrolla bien en alturas superiores a los 2.000 m.s.n.m (Heiser & Anderson, 1999;Lobo y Medina, 2000)."},{"index":3,"size":77,"text":"En Colombia el cultivo de lulo se encuentra ampliamente distribuido en varias regiones del país. Según cifras del Ministerio de la Agricultura y Desarrollo Rural en 1998, el área cosechada fue de 4.868 hectáreas en 20 departamentos, de los cuales los más representativos se muestra en la tabla 1. No obstante, hay zonas en el país en donde el cultivo de lulo prácticamente ha desaparecido, debido a problemas fitosanitarios importantes causados por el pasador del fruto  2000)."}]},{"head":"3 Descripción Taxonómica","index":10,"paragraphs":[{"index":1,"size":60,"text":"La famila Solanaceae tiene 70 géneros y 2700 especies, la mayoría distribuidas a través de climas cálidos del Neotrópico. Se conocen 27 especies pertenecientes a siete géneros que tienen valor hortofrutícola, distribuidas en las regiones tropicales de América, África, Asia y Oceanía. El género Solanum es el más grande y extensamente distribuido de esta familia con 1.200 especies (Siesa, 2000)."},{"index":2,"size":7,"text":"De acuerdo con Whalen et al., (1981) "}]},{"head":"4 Descripción Botánica","index":11,"paragraphs":[{"index":1,"size":254,"text":"S, quitoense, puede llegar a alcanzar los 2.5 metros de altura, la corteza del tallo es de color gris. Las hojas están adheridas a las ramas por un peciolo pubescente y suculento, de aproximadamente 15 cm de largo, en la variedad septentrional, las nervaduras presentan espinas. Dependiendo de la temperatura las hojas pueden alcanzar hasta cerca de 1 metro de largo y 1 metro de ancho en su extensión, son de color verde oscuro con nervaduras de color púrpura en el haz y blancas o purpúreas en el envés (Figura 1a). Toda la planta excepto en el haz de las hojas, tiene pubescencia lanosa y todas sus partes son espinosas excepto las flores y el fruto. Las flores son de color blanco o lila claro, se agrupan en racimos en un pecíolo corto que contiene hasta 10 flores justamente debajo y frente a las hojas (Figura 1b). El fruto es una baya globosa entre 4 y 6.5 cm de diámetro, cubierta de tricomas de color amarillo o en algunos casos marrones fáciles de desprender en la cosecha (Figura 1c). La corteza del fruto es de color amarillo intenso cuando alcanza la madurez. La pulpa es de color verde oscuro, pegajosa, ácida con un pH entre 3.5 a 5.0 (Figura 1d). La planta tiene una ciclo biológico de tres a cuatro años en producción constante, iniciando fructificación a los 10-12 meses de edad, pudiéndose observar en una misma planta flores y frutos en diferentes tamaños y coloridos durante todo el año (Schulter y Cuatrecasas, 1958). "}]},{"head":"4. 1 Biología Floral y Sistema Reproductivo","index":12,"paragraphs":[{"index":1,"size":243,"text":"La flor es pentámera porque tiene 5 pétalos y es perfecta por que sus partes son iguales (hermafrodita). El cáliz es gamosépalo, esta compuesto por 5 sépalos (más o menos la tercera parte en longitud de los pétalos), el cáliz alterna con los pétalos. El sépalo mide más o menos 2 cm de largo por 1 cm de ancho. El ápice del cáliz es agudo, los sépalos presentan pubescencias de color verde por el haz y lila por el envés. La cara superior del cáliz es verde amarillenta. Los pétalos son de color crema por el haz y morado por el envés y pubescentes (entre 2 y 3 cm de longitud y 1 a 2 cm de ancho). La parte apical de la corola presenta una ligera curvatura que asemeja a una cuchara. El androceo esta compuesto por 5 estambres de color amarillo, biseldados, con dehiscencia poricida y se encuentran alternados con los pétalos. El filamento del estambre y la antera son sésiles. El pistilo es de color amarillo, el ovario es de color amarillo, súpero con 4 lóculos, con múltiples rudimentos seminales, el estigma es verde, sostenido por un pistilo corto, medio o largo (brevistilia, isostilia y longistilia) y de color amarillo. El cojín floral puede tener hasta 30 flores y casi todas las flores del extremo del racimo son estériles debido al sistema sexual que presenta (andrononoecia); el porcentaje de cuajamiento esta entre 16 y 20 % (Bernal et al., 1998)."},{"index":2,"size":121,"text":"En la familia Solanaceae, la andromonoecia la cual se define como un sistema sexual en la cual las plantas presentan conjuntamente flores hermafroditas y flores femeninas estériles (de ahora en adelante flores estaminadas) en la misma planta,. Este sistema sexual es bien conocido en el subgénero Leptostemonum, se ha reportado en 13 de 22 secciones descritas (Whalen y Costich, 1986). Aunque todos los miembros del la sección Lasiocarpa son andromonoicos, el número de flores hermafroditas y flores estaminadas, varia considerablemente entre especies y dentro de la misma especie. Algunas especies caracterizadas como débilmente andromonoicas (por ejemplo, S. hirtum y S. stramonifolium), producen relativamente pocos flores estaminadas en contraste, con otras especies caracterizadas como fuertemente andromonoicas (por ejemplo, S. quitoense , S."},{"index":3,"size":74,"text":"vestissimun, S. pectinatum y S. pseudolulo), las cuales producen muchas flores estaminadas (Whalen et al .,1981). Miller & Diggle (2003, 2004), demostraron para 6 especies de la sección Lasiocarpa (S.candidum, S. ferox, S. stramonifolium, S. pectinatum, S. pseudolulo y S. quitoense) que la producción de las flores estaminadas es fenótipicamente plástica. Por lo tanto, la variación observada en la expresión de la andromonoecia entre las especies estudiadas puede ser el resultado de esta plasticidad."},{"index":4,"size":86,"text":"Igualmente demostraron que existe dimorfismo sexual entre las flores hermafroditas y las flores estaminadas, siendo de mayor tamaño las flores hermafroditas. Este hecho ha generado especulaciones acerca de la su función y quizás sus formas adaptativas. Anderson & Symon (1989), sugieren que las corolas más grandes de flores hermafroditas promueven la polinización cruzada (alógamas) de la especie, en contraste las flores estaminadas pueden representar un ahorro del recurso substancial que se pueden invertir en la producción de la flor, crecimiento o mantenimiento vegetativo de la misma."}]},{"head":"4. 2 Citogenética","index":13,"paragraphs":[{"index":1,"size":19,"text":"Todas las especies de la sección Lasiocarpa son diploides con un numero cromosómico de 2n=24 (Bernardello et al., 1994)."}]},{"head":"5 Domesticación del Lulo","index":14,"paragraphs":[{"index":1,"size":110,"text":"El lulo se encuentra en una etapa intermedia del proceso de domesticación. Lo anterior se fundamenta en el hecho de que la planta posee una serie de características correspondientes a individuos del complejo \"maleza-silvestre\" como son: Frutos con un elevado número de semillas, dispersión de semillas ineficiente, presencia de latencia en las semillas, antocianinas en diferentes órganos, espinas en tallos, pedúnculos, pecíolos y hojas, presencia de tricomas en los frutos y posición de las hojas hacia abajo para realizar mejor la fotosíntesis (Lobo y Medina, 1999;Lobo, 2000). Heiser (1987), refiriéndose al proceso de domesticación, señala que S. quitoense fue introducido a Centro América y se ha establecido en Costa Rica."},{"index":2,"size":35,"text":"Posteriormente, Heiser (1993), indica que las plantas cultivadas actualmente deben diferir poco de las encontradas por los españoles al llegar a América debido al poco trabajo de mejoramiento que se ha tenido con esta especie."}]},{"head":"6 Métodos de Propagación","index":15,"paragraphs":[{"index":1,"size":165,"text":"La forma más común de multiplicación del cultivo de lulo es a través de semillas, también, se pueden propagar asexualmente mediante el empleo de estacas, técnica muy empleada por agricultores, la cual consiste en seleccionar estacas de 25 a 30 cm de longitud, que tengan 4 yemas, posteriormente se le quitan las hojas para evitar la traspiración para luego sembrarlas en camas de arena previamente tratadas. Sin embargo, este tipo de propagación presenta inconvenientes debido a que es muy susceptible a enfermedades fungosas (Morton, 1987). Una técnica alternativa para la propagación del lulo, es a través de las técnicas de cultivo de tejidos vegetales in vitro, utilizando meristemos. En Colombia esta técnica es utilizada en centros de investigación en la propagación de lulo, con mayores ventajas en comparación con los sistemas tradicionales de multiplicación, debido a que permiten un mayor control sobre la sanidad del material y por que han facilitado una rápida multiplicación masiva de este cultivo (Valencia y Fernández, 1998;Del Corral, 1998;Segovia, 2002)."}]},{"head":"7 Plagas y Enfermedades","index":16,"paragraphs":[{"index":1,"size":44,"text":"S. quitoense es afectado por diferentes plagas y enfermedades durante todas las etapas del desarrollo del cultivo. La incidencia de estos organismos esta influenciada en gran parte por el ambiente, esto implica estudios más complejos y específicos para cada condición climática (Tamayo, 2001;Carmona, 2003)."},{"index":2,"size":13,"text":"Entre las plagas más importantes que afectan el cultivo de lulo, se encuentran:"},{"index":3,"size":30,"text":"-El pasador del fruto ( Neoleucinodes elegantalis) lepidóptero de la familia Pyralidae, que ataca el tallo y los frutos produciendo la muerte de la planta y la caída del fruto."},{"index":4,"size":64,"text":"-La mosca blanca de los invernaderos (Trialeurodes vaporariorum) Westwood, este homóptero produce daño directo causado por los adultos e inmaduros al succionar la savia de la planta, daño indirecto por la formación de fumagina, la cual se forma al crecer el hongo Cladosporium sp y daño indirecto al transmitir virus. Este último daño es el mas importante ya que afecta el rendimiento del cultivo."},{"index":5,"size":71,"text":"-El barrenador del tallo, lepidóptero de la familia Gelechiidae, afecta directamente la producción del cultivo, las larvas hacen galerías dentro de los tallos de la planta, alimentándose del parénquima. Si la infestación es baja, la plaga pasa desapercibida. Sin embargo, cuando las larvas se presenta en tallos, la planta muestra inicialmente síntomas de marchitamiento reversible, pero cuando la infestación es severa la planta puede marchitarse por completo y morir (Carmona, 2003)."},{"index":6,"size":48,"text":"Entre las enfermedades causadas por hongos más importantes que afectan al cultivo de lulo (S. quitoense), están: La pudrición algodonosa (S. sclerotiorum Lib) y la de gota o tizón del lulo (Phytophora infestans Mont), las cuales son las causantes de las mayores pérdidas económicas de los productores. S."},{"index":7,"size":42,"text":"sclerotiorum, ataca todas las partes de la planta, especialmente los tallos y las ramas. Este hongo del suelo se introduce por las raíces y se extiende mediante micelios o rizomas; cuando ataca la base del tallo la planta se marchita y muere."},{"index":8,"size":128,"text":"P. infestans se caracteriza por atacar las hojas, los tallos y los frutos (Tamayo, 2001). Sumado a lo anterior, el cultivo del lulo también se ve afectado por enfermedades ocasionadas por nemátodos, de los cuales, el más importante es M. incognita. Este nemátodo del nudo ataca las raíces produciendo numerosos engrosamientos y agallas, dando lugar a ataques secundarios de microorganismos (Figura 2) (Tamayo, 2001). quitoense y S. sessiliflorum, llamado \"Palora\". Este híbrido presenta un fruto más grande que el híbrido \"Puyo\", no requiere de la aplicación de la fitohormona 2.4-D y el jugo empieza a oxidarse 24 horas después de preparado, mucho más tarde que el jugo preparado con el híbrido \"Puyo\", sin embargo, el consumidor prefiere el híbrido \"Puyo\" por la coloración de la pulpa (Heiser, 1993)."},{"index":9,"size":82,"text":"En Colombia, se cuenta con un híbrido mejorado, llamado lulo \"La Selva\", que fue el resultado del un proceso de hibridación interespecífica entre S. quitoense Var septentrional. La F1 obtenida del cruzamiento, fue un híbrido con las siguientes características: Planta con muchas espinas, fruto muy pequeño de pulpa amarilla e insípida, hojas más pequeñas y vellosidades en el fruto. Para eliminar las espinas se realizaron 2 retrocruzamientos con S. quitoense, sin espinas procedente del Ecuador. El híbrido resultante tiene las siguientes características:"},{"index":10,"size":67,"text":"Plantas sin espinas, frutos más grandes que los obtenidos en la F1, pero más pequeños que el lulo de castilla, tolerante a nemátodos, se puede sembrar a libre exposición, con buen comportamiento y mayor período vegetativo, la pulpa es de color verde apetecida en el mercado nacional en forma de fruta fresca, fruto de buen aroma y sabor, vellosidades en el fruto de fácil desprendimiento (Lobo, 2000)."},{"index":11,"size":51,"text":"A nivel nacional, el lulo \"La Selva\" está comercializado en los almacenes de cadena. Sin embargo, las bayas son de menor tamaño y tienen problemas de rajamiento, debido a un gene dominante con expresividad incompleta, derivado del parental silvestre S. hirtum lo cual limita su comercialización a nivel internacional (Siesa, 2000)."},{"index":12,"size":70,"text":"Actualmente los híbridos mejorados poseen características indeseables tales como el rajamiento y tamaño del fruto, coloración de la pulpa, heredadas de algunos de los parentales como consecuencia del cruce sexual. Con el avance de la biotecnología, posiblemente se podrían incorporar características deseables como por ejemplo; adaptación a plena exposición solar, resistencia a ciertas plagas y enfermedades, ausencia de espinas y frutos de buen tamaño en un solo material (Lobo, 2000)."}]},{"head":"Estudios de Caracterización","index":17,"paragraphs":[{"index":1,"size":43,"text":"Las diferentes especies de la sección Lasiocarpa, exhiben una gran variedad de formas, colores, tamaños (características morfológicas), que las distinguen entre sí (Heiser, 1972;Whalen et al., 1981;Lobo, 2000). Cualquier diferencia morfológica entre dos o más individuos sirve entonces como una \"etiqueta\" o \"Marcador"},{"index":2,"size":50,"text":"Morfológico\" que se convertirá en un rasgo característico que se puede usar para la diferenciación de individuos dentro de una misma especie. Sin embargo, algunos de estos marcadores pueden estar afectados por el medio ambiente y en ocasiones existen materiales mal caracterizados, especialmente cuando se trata de fenotipos muy similares."},{"index":3,"size":40,"text":"Se han realizado diferentes estudios basados en marcadores morfológicos, bioquímicos (isoenzimáticos), citogenéticos (cariotipos) y moleculares con las diferentes especies de la seccion Lasiocarpa (Heiser,1972(Heiser, , 1987(Heiser, , 1989;;Whalen et al., 1981;Whalen & Caruso, 1983;Bernardello et al., 1994;Bruneau et al., 1995)."},{"index":4,"size":141,"text":"Entre los estudios más importantes se destacan los siguientes: vestissimun) basados en patrones de restricción de ADN de cloroplasto los cuales fueron comparados con los estudios previos de caracterización morfológicos e izoenzimáticos (Whalen & Caruso, 1983). El análisis filogenético basado en los patrones de restricción de ADN de cloroplasto mostró claramente dos grupos, el primero incluía tres especies amazónicas y el segundo agrupaba las especies andinas y las dos especies asiáticas. La mayor correspondencia entre las tres categorías de datos (análisis morfológico, isoenzimático y patrones restricción de ADN de cloroplasto) se encontró entre los obtenidos a partir de los patrones de restricción de ADN de cloroplasto y los caracteres morfológicos. Estos resultados soportaron la hipótesis de que las dos especies asiáticas son taxa hermanas aunque se hallan originado separadamente de los dos grupos suramericanos (andinos y amazónicos) (Bruneau et al., 1995)."}]},{"head":"9.1 Caracterización Molecular Mediante AFLP","index":18,"paragraphs":[{"index":1,"size":190,"text":"El desarrollo de la biología molecular ha permitido obtener mejores estimaciones de la diversidad genética de una población determinada, debido a que el factor de error generado por el ambiente no existe ya que la información esta tomada directamente del genoma de las plantas (Morell et al., 1995). Además, se reconocen las diferencias genéticas entre individuos, al obtener un \"perfil molecular\" o \"fingerprinting\" característico para cada variedad e independiente de las condiciones de crecimiento de las plantas. Una ventaja adicional es la posibilidad de analizar loci únicos (regiones únicas en el genoma) o loci múltiples (regiones repetidas a través del genoma (Vos et al., 1995;Ferreira y Grattapaglia,1998). Uno de estos marcadores son los AFLP, los cuales presentan algunas ventajas sobre otros marcadores moleculares como los RFLPs, los RAPDs y los SSRs, debido a que esta técnica combina la especificidad, la resolución y el poder de muestreo de la digestión con enzimas de restricción con la velocidad y practicidad de detección de polimorfismo por vía PCR sin necesidad de conocer la secuencia con anterioridad, este tipo de marcadores moleculares en varias especies de la familia Solanaceae para estudios de diversidad genética."}]},{"head":"PLANTEAMIENTO DEL PROBLEMA","index":19,"paragraphs":[{"index":1,"size":13,"text":"Aún cuando el lulo es considerado como una especie promisoria desde hace 70 "}]},{"head":"OBJETIVOS","index":20,"paragraphs":[]},{"head":"1 Objetivo General","index":21,"paragraphs":[{"index":1,"size":40,"text":"Estimar el grado de diversidad genética de las accesiones de S. quitoense y seis (6) especies relacionadas de la sección Lasiocarpa existentes en la Colección Colombiana de Lulo, custodiada por Corpoica \"La Selva\" en Rionegro Antioquia, utilizando marcadores moleculares AFLP."}]},{"head":"2 Objetivos Específicos:","index":22,"paragraphs":[{"index":1,"size":15,"text":"ü Estimar la variabilidad genética entre cada una de las especies utilizadas en este estudio."},{"index":2,"size":19,"text":"ü Analizar el grado de relación exitente dentro de las diferentes accesiones queconforman las especies utilizadas en este estudio."}]},{"head":"MATERIALES Y MÉTODOS","index":23,"paragraphs":[{"index":1,"size":29,"text":"Este trabajo se realizó en los laboratorios de la Unidad de Biotecnología del Centro Internacional de Agricultura (CIAT), situado en le municipio de Palmira Departamento del Valle del Cauca."}]},{"head":"1 Material Biológico","index":24,"paragraphs":[{"index":1,"size":187,"text":"El material vegetal utilizado proviene de la Colección Colombiana de lulo, ubicado en el Centro de Investigación Corpoica, La Selva, en Rionegro, Antioquia situada a 2120 m.s.n.m., con una temperatura promedio de 17°C y una humedad relativa del 78% con precipitación anual de 1800 mm. Los sitios de colecta del germoplasma evaluado, la mayoría corresponden a Colombia (74 accesiones), especificamente al sur occidente Colombiano y a la sierra nevada de Sta Marta, también la colección cuenta con materiales traídos del Ecuador, Perú, Costa Rica, Venezuela, Brasil y del jardín botánico (Garden) en Inglaterra (Figura 3, anexo 1).Se analizaron 159 accesiones de la Colección Colombiana de Lulo. 135 corresponden a 7 especies de la sección Lasiocarpa; 14 accesiones son híbridas: 9 de origen ecuatoriano y 5 accesiones colombianas (uno es un híbrido ínterespecifico natural y los 4 restantes son clones del híbrido ¨La Selva¨). Como grupo de comparación se incluyeron 9 accesiones del mismo género (Solanum) que no pertenecen a la sección Lasiocarpa. El total de accesiones por especie se puede apreciar en la tabla 2 y sus respectivos datos de pasaporte se muestran en el anexo 1."},{"index":2,"size":15,"text":"Figura 3. Area de distribución de los materiales evaluados de la Colección Colombiana de lulo."},{"index":3,"size":114,"text":"Tabla 2. Accesiones y especies de lulo utilizadas en este estudio, provenientes de Centro de Investigación Corpoica, \"La Selva\", en Rionegro, Antioquia.  La calidad del ADN se verificó por medio de electroforesis, con geles de agarosa de 0.8% en solución tampón TBE 0.5X. Se corrieron 3 µl de ADN mezclados con 2 µl de tampón de corrida por muestra y se tiñó con bromuro de etidio. La electroforesis se realizó con el equipo Horizontal Gel electrophoresis Apparatus, (Life Technologies TM ) a una carga de 100 w/cm 2 durante 30 minutos. La concentración del ADN se midió por fluorometría utilizando el DyNA Quant 200 Flurometer (Hoefer Pharmacia Biotech Inc.), siguiendo las instrucciones del fabricante."}]},{"head":"Especies # de accesiones / Especie","index":25,"paragraphs":[]},{"head":"2. 2 Técnica de AFLP","index":26,"paragraphs":[{"index":1,"size":83,"text":"Antes de empezar con la aplicación de la técnica de Aflp se realizó una prueba de digestión con la enzima de restricción EcoRI con el fin de determinar la calidad del ADN y la no presencia de inhibidores de enzimas en las muestras. La técnica de AFLP se realizó según el método de Vos et al., (1995). En este estudio se utilizó el \"kit \" de Aflp Analysis System I (INVITROGEN™) con modificaciones, para ADN genómico de plantas como se describe a continuación."}]},{"head":"2. 2. 1 Digestión del ADN","index":27,"paragraphs":[{"index":1,"size":79,"text":"En ADN previamente aislado fue digerido con dos enzimas de restricción, una de corte raro (EcoR I) y otra de corte frecuente (Mse I). La enzima EcoR I reconoce 6 pb, mientras que la enzima Mse I tiene lugar de reconocimiento cada 4 pb (Vos et al., 1995) La reacción se incubó durante dos horas a 37°C en baño María, luego las muestras se incubaron a 70°C por 15 minutos con el fin de inactivar las enzimas de restricción."},{"index":2,"size":7,"text":"Inmediatamente, las muestras fueron colocadas en hielo."}]},{"head":"2. 2. 2 Ligación de Adaptadores","index":28,"paragraphs":[{"index":1,"size":32,"text":"A la digestión anterior se le agregaron los siguientes componentes para llevar a cabo la ligación de adaptadores complementarios a los cortes de cada una de las enzimas de restricción (Tabla 3)."},{"index":2,"size":52,"text":"Tabla 4. Mezcla de reacción de la digestión/ligación. La reacción fue incubada a 20°C ±2°C por 2 horas en baño María. Finalizada la incubación, se realizó una dilución 1:10 de la mezcla digestión / ligación con solución tampón TE (10Mm, Tris-HCL(pH 8.0), 0.1 nM EDTA), tomando 2 µl y 18 µl respectivamente."}]},{"head":"Componente","index":29,"paragraphs":[]},{"head":"2. 2. 3 Amplificación Selectiva (+1/+1)","index":30,"paragraphs":[{"index":1,"size":25,"text":"Para la amplificación selectiva (+1/+1) se utilizaron tubos eppendorf para PCR de 0.2 ml y se les adicionaron los componentes mencionados en la tabla 4."},{"index":2,"size":15,"text":"Tabla 5. Mezcla de reacción para la amplificación selectiva (+1/+1) de los productos de digestión/ligación. "}]},{"head":"Componente","index":31,"paragraphs":[]},{"head":"2. 2. 4 Amplificación Selectiva (+3/+3)","index":32,"paragraphs":[]},{"head":"2. 2. 4 .1 Selección de Combinaciones","index":33,"paragraphs":[{"index":1,"size":28,"text":"El \"kit\" de AFLP utilizado en este estudio permite utilizar 64 combinaciones posibles de cebadores con tres (3) nucleótidos seleccionantes tanto para EcoR I como para Mse I."},{"index":2,"size":101,"text":"Según trabajos previos, para estudios de diversidad genética es suficiente utilizar dos (2) combinaciones de cebadores de AFLP, ya que se obtiene la misma información genética que con un número mayor de dos combinaciones de cebadores (Caicedo et al., 1999;Tovar, 2001), por lo tanto, para este estudio se realizó un ensayo con 30 combinaciones de cebadores en 15 individuos escogidos al azar. Se eligieron las dos (2) mejores combinaciones, por presentar buena resolución de bandas, buen nivel de polimorfismo y buena reproducibilidad. Para cada combinación de cebadores se realizaron dos mezclas denominadas \"Mix 1\" (Tabla 5) y Mix 2\" (Tabla 6)."},{"index":3,"size":10,"text":"Tabla 6. Mezcla de reacción \"Mix 1\" para PCR (+3/+3)."}]},{"head":"Componente Volumen / Muestra","index":34,"paragraphs":[{"index":1,"size":23,"text":"Cebador EcoR I 0.5µl Cebador Mse I 4.5 µl Volumen total 5 µl Tabla 7. Mezcla de reacción \"Mix 2\" para PCR (+3/+3)."}]},{"head":"Componente Volumen / Muestra","index":35,"paragraphs":[{"index":1,"size":99,"text":"10X PCR Tampón + Mg 2 µl Taq ADN polymerasa (5u/µl) 0.1 µl Agua destilada estéril 7.9 µl Volumen total 10 µl La reacción de PCR final contiene 5µl del PCR +1/+1 diluido 1: 50, 5µl de la mezcla \"Mix 1\" y 10 µl de la mezcla \"Mix 2\". El programa utilizado para las amplificaciones fue el siguiente: 1 ciclo de 94°C 30 segundos, 56°C 30 segundos y 72°C 60 segundos. Durante 12 ciclos, la temperatura de alineamiento descendió 0.7 en cada ciclo. Por último durante 23 ciclos de 94°C 30 segundos, 56°C 30 segundos y 72°C 60 segundos."}]},{"head":"2. 3 Electroforesis en geles de Poliacrilamida.","index":36,"paragraphs":[{"index":1,"size":373,"text":"Los productos de la amplificación selectiva (3+3) fueron separados con un equipo de electroforesis vertical Sequi-Gen ® GT Nucleic Acid Electrophoresis Cell, (BIO-RAD) en geles denaturantes de poliacrilamida al 6% y urea 5M, con un espesor de 0.4 mm, siguiendo el protocolo sugerido por Promega (1998) con algunas modificaciones, que se describen a continuación. Inicialmente el vidrio y la cámara de electroforesis se limpiaron tres veces con etanol absoluto. El vidrio fue recubierto con una solución que contiene 3 µl de Bind Silane, (Amersham Pharmacia Biotech) y 1 ml de solución de ácido acético glacial y etanol absoluto, para que el gel se adhiriera al vidrio. La cámara fue recubierta con 350 µl de Repel Silane (Amersham Pharmacia Biotech), para que el gel no se adhiriera a esta parte. Una vez armado el equipo de electroforesis según las instrucciones del fabricante se adicionó una solución que contenía 110 ml de poliacrilamida al 6%, 600 µl de persulfato de amonio al 10% y 120 µl de Temed, dejando polimerizar por una hora y media. Al cabo de este tiempo se ensambló la cámara de electroforesis en su totalidad al colocarle los electrodos y se sirvió caliente (55°C) el tampón de corrida TBE 0.5 X, el cual permite la separación del ADN. Luego se colocó el peine de dientes de tiburón para formar los pozos y se adicionaron 3 µl de la solución de tampón de carga. Para realizar la precorrida del gel a 120 W/cm 2 se utilizó una fuente de poder Power Pac 3000 (BIO-RAD) hasta que la temperatura alcanzara 50ºC. Simultáneamente, a cada una de las reacciones de amplificación se les agregó 5 µl de solución tampón de carga (azul de bromofenol al 0.25% en agua desnaturalizada por cuatro minutos a 94ºC y colocadas en hielo hasta el momento de servir la muestra en el gel. Terminada la precorrida, se sirvieron 3 µl de cada muestra de ADN en cada uno de los pozos. Para identificar el tamaño de los productos de PCR se utilizó un patrón de peso molecular DNA ladder 10 pb (Invitrogen TM ) que tiene un rango de lectura de 330 a 30 pb. Estos productos fueron separados a 100 W/cm 2 por una hora y media aproximadamente."}]},{"head":"2. 4 Tinción y revelado de geles de poliacrilamida.","index":37,"paragraphs":[{"index":1,"size":186,"text":"La tinción y el revelado de los geles de poliacrilamida se hizo basándose en los protocolos de Bassam et al., (1991) con algunas modificaciones que se describen a continuación. Al finalizar la electroforesis, el aparato fue desmontado, el vidrio con el gel adherido se separó de la cámara y se colocó en una solución de ácido acético al 10% durante 20 minutos para la fijación del ADN. Luego se retiró el exceso de ácido lavando tres veces con agua deionizada durante dos minutos cada vez. Luego se colocó el gel en una solución que contiene nitrato de plata (1g/l) y formaldehído al 0.056% por 30 minutos, para el proceso de tinción. Inmediatamente se lavó el gel con agua deionizada durante 6 segundos. Posteriormente se revelaron los productos del PCR con una solución que contiene: carbonato de sodio (30 g/l), formaldehído al 0.056% y tiosulfato de sodio (2 mg/l) a 14ºC, hasta la visualización de las bandas. Para detener la reacción de la solución reveladora, el gel se colocó en una solución de ácido acético al 10% durante cinco minutos y finalmente se lavó con agua deionizada."},{"index":2,"size":41,"text":"La lectura de las bandas se realizó con un transiluminador tomando como guía el patrón de peso molecular incluido en los geles. Se leyeron las bandas con mejor resolución y se enumeraron consecutivamente empezando desde el primer individuo del primer carril."},{"index":3,"size":18,"text":"Una vez hecha la lectura de las bandas, cada gel fue escaneado para almacenar su patrón de bandas.."}]},{"head":"3 Análisis Estadístico","index":38,"paragraphs":[{"index":1,"size":36,"text":"A partir de la lectura de las bandas obtenidas con cada pareja de cebadores se construyó una matriz de presencia o ausencia de alelos (1 y 0) con la que se realizó el análisis de similaridad."}]},{"head":"3. 1 Análisis de Similaridad","index":39,"paragraphs":[{"index":1,"size":131,"text":"La similaridad genética entre todas las accesiones se estimó calculando el coeficiente de similaridad de Nei-Li (1979) La matriz de similaridad se construyó con el programa NTSYS versión 2.1 (Rohlf, 2000) utilizando el subprograma Simqual y el coeficiente de Dice, L. R. (1945). Los coeficientes de similaridad fueron a su vez introducidos en el subprograma SAHN para construir dendrogramas usando el método de unión media aritmética no ponderada \"UPGMA\" Adicionalmente, se realizó un Análisis de Correspondencia Múltiple (ACM), con el procedimiento \"Corresp\" del programa estadístico SAS (SAS Institute,1989), este análisis es una técnica descriptiva de análisis multivariado, que pretende encontrar estructuras en la población bajo estudio, en términos de variables e individuos representados en un espacio tridimensional. Este análisis tiene la ventaja de evaluar datos no métricos y relaciones no lineales."}]},{"head":"4 Analisis de Neighbor-Joining","index":40,"paragraphs":[{"index":1,"size":61,"text":"El análisis de \"neighbor-joining\", (vecino más próximo) se realizó a través del programa PAUP 4.0 implementado por David L. Swofford con la finalidad de determinar el intervalo de confianza de los nodos del respectivo árbol \"vecino más próximo\" (distancia). Tambien se determinó la máxima verosimilitud mediante el análisis de remuestreos \"bootstrap\", con 1.000 replicas, también disponible en el programa PAUP 4.0."}]},{"head":"RESULTADOS Y DISCUSIÓN","index":41,"paragraphs":[{"index":1,"size":98,"text":"Este es el primer trabajo de investigación a nivel molecular de la colección colombiana de lulo utilizando la técnica de AFLP. Los resultados incluyen, el análisis de similaridad de Nei-Li, el de correspondencia múltiple (ACM) y el de \"neighbor-joining\"'. Adicionalmente, los resultados son contrastados con diferentes estudios morfológicos, isoenzimáticos, cariotípicos y moleculares realizados por otros investigadores con algunas especies de la sección Lasiocarpa (Heiser,1972(Heiser, , 1987(Heiser, , 1989;;Whalen et al., 1981;Whalen y Caruso, 1983;Bernardello et al., 1994;Bruneau et al., 1995), con el fin de tener una base para el entendimiento de la estructura genética de los materiales evaluados."}]},{"head":"Extracción y digestión del ADN","index":42,"paragraphs":[{"index":1,"size":77,"text":"Con el método de extracción Dellaporta et al. (1983), se obtuvieron ADNs de buena calidad y concentración (Figura 4). El rendimiento promedio del ADN total estuvo entre 20 a 150 ng por mg de tejido, el cual fue suficiente para el análisis molecular con la técnica de AFLP. Adicionalmente, la prueba de digestión con una de las enzimas de restricción (EcoR I) empleadas en la técnica, corroboró la buena calidad de los ADNs extraídos (Datos no mostrados)."},{"index":2,"size":38,"text":"1 2 3 4 5 6 7 8 9 10 11 12 13 14 Figura 4. Electroforesis en gel de agarosa al 0,8% mostrando la calidad del ADN extraído de algunas accesiones de lulo incluidas en este estudio."}]},{"head":"Marcadores Moleculares AFLP","index":43,"paragraphs":[{"index":1,"size":38,"text":"De las 30 combinaciones de cebadores evaluadas en este estudio, se seleccionaron las combinaciones E-ACG/M-CAT y E-ACG/M-CTC, con un total de 206 y 170 bandas polimórficas, respectivamente. El rango de lectura se realizó entre 50-400 pb, (Figura 5)."},{"index":2,"size":69,"text":"Los ensayos preliminares mostraron que la técnica de los AFLP es altamente polimórfica y reproducible, confiriendo un alto grado de confiabilidad. Resultados similares han sido observados por otros investigadores empleando esta técnica con otras especies de la familia Solanaceae; (Christian et al. (1996)  (combinación de cebadores E-ACG/M-CAT), de algunas accesiones de lulo estudiadas. El marcador de peso molecular (M) corresponde a DNA ladder 10 pb (330 a 30 pb)."}]},{"head":"Análisis de Similaridad","index":44,"paragraphs":[{"index":1,"size":89,"text":"El análisis de similaridad obtenido a partir del coeficiente de Nei-Li (1979), calculado para los datos de ambas combinaciones de cebadores, mostró mayor número de sitios muestreados en el genoma que los obtenidos al utilizar cada una de las combinaciones en forma independiente. Los resultados obtenidos fueron más confiables y corroboraron la capacidad que tienen los AFLP para mostrar un gran polimorfismo entre las especies y accesiones estudiadas (Figura 6). En general, hay varios aspectos importantes de destacar que surgen de acuerdo a la estructura presentada por este análisis:"},{"index":2,"size":58,"text":"Las 7 especies de comparación llamadas así, debido a que estas no pertenecen a la sección Lasiocarpa, fueron las más alejadas de las especies de las sección lasiocarpa, a un nivel de similaridad que osciló entre 12 al 28%. Lo que indica que se encuentran genéticamente lejanas de la sección Lasiocarpa a excepción de S. stramonifoliun y S."},{"index":3,"size":26,"text":"sessiliflorum las cuales se ubicaron en un punto intermedio entre los dos grupos que conformaron las especies de comparación a un nivel del 25% de similaridad."},{"index":4,"size":120,"text":"Se observó mayor polimorfismo en las especies silvestres que en las cultivadas. Así por ejemplo, S. hirtum y S pseudolulo mostraron el mayor polimorfismo entre sus individuos, con índices de similaridad que oscilaron entre 0.76 a 0.98 y entre 0.77 a 0.96 respectivamente. En contraste con S. quitoense el cual presentó índices de similaridad entre 0.9 a 1, al igual que S sessiliflorum especie cultivada en el Amazonas con índices de similaridad entre 0.85 a 0.98. Figura 6. Dendrograma de similaridad utilizando el método de UPGMA con los valores de similaridad de Nei-Li producto del análisis de los datos de las dos combinaciones usadas en los AFLP´s. E-ACG/M-CAT y E-ACG/M-CT, empleando todas las accesiones de lulo incluidas en este estudio."}]},{"head":"S. quitoense","index":45,"paragraphs":[]},{"head":"S. hirtum","index":46,"paragraphs":[]},{"head":"Sp solanaceas","index":47,"paragraphs":[{"index":1,"size":3,"text":"Lulo \"La Selva\" "}]},{"head":"S. pseudolulo","index":48,"paragraphs":[{"index":1,"size":77,"text":"También se observó una separación marcada entre las siete especies de la sección Lasiocarpa estudiadas, esto es entendido debido a que cada una de estas especies posee una composición genética diferente, lo que le otorga una identidad propia. Estos resultados son consistentes con estudios realizados en las especies de la sección Lasiocarpa mediante análisis cladístico de caracteres morfológicos (Heiser,1972;Whalen et al. 1981), análisis isoenzimático (Whalen y Caruso, 1983;Bruneau et al. 1995) y cariotípico (Bernardello et al. 1994)."},{"index":2,"size":228,"text":"En el dendrograma de similaridad de Nei-Li mostrado en la figura 6, se visualiza una separación marcada entre las especies andinas (S. quitoense, S. hirtum, S. pseudolulo, S. vesstissimun y S. pectinatum) y las amazónicas (S. stramonifoliun y S. sessiliflorum) de la sección Lasiocarpa. Resultado similar fue encontrado por Bruneau et al. (1995). Los autores encontraron (desde el punto filogenético) dos grupos, basados en patrones de restricción de ADN de cloroplasto y comparados con los estudios de caracterización morfológico e isoenzimático realizados previamente por diferentes autores (Whalen & Caruso, 1983). El primer grupo lo conformaban las especies andinas (S. quitoense, S. hirtum, S. pseudolulo, S. vesstissimun y S. pectinatum) y el segundo grupo incluía tres especies amazónicas (S. stramonifoliun, S. sessiliflorum y Solanum albidum (Dunal) (no incluido en este estudio) Otro aspecto importante de destacar es la cercana relación entre los clones \"La Selva\" a S. quitoense, con índices de similaridad entre 0.72 a 0.74 respectivamente. Esto puede deberse a los sucesivos retrocruzamientos a partir del híbrido \"la Selva\" con el padre (S. quitoense) para la obtención de frutos más grandes y sin rajamiento característica heredada del padre (Bernal et al. 1996, Valencia y Fernández, 1998). Razón por la cual se esperaría que el híbrido \"La Selva\" se asemeje más genéticamente a S. quitoense que a S. hirtum, siendo este último el otro parental de este cruce interespecífico."}]},{"head":"3. 1 Variabilidad intraespecífica","index":49,"paragraphs":[{"index":1,"size":122,"text":"En cuanto a la variabilidad intraespecífica, es importante mencionar que el análisis no detectó una separación clara por origen geográfico para ninguna de las especies evaluadas, Esto se puede atribuir a que, en primer lugar se está trabajando con accesiones conservadas en el banco y no se ha hecho un muestreo específico para análisis de poblaciones representativas de las diferentes regiones agroecológicas y edafoclimaticas de la zona de distribución de las especies y segundo, en Colombia los agricultores de lulo se han desplazado con el cultivo de un sitio a otro, llevándose consigo el material de interes. De igual manera, agentes dispersadores de polen y semillas como aves, viento, insectos entre otros, pueden haber contribuido con esta causa (M. Lobo, comunicación personal)."},{"index":2,"size":98,"text":"Vale la pena resaltar que el análisis no mostró separación alguna entre las dos variedades de S. quitoense (variedad quitoense y septemtrional). En contraste, con los rasgos observados (fenotipo) a nivel morfológico como ausencia o presencia de espinas y pubescencia (Gracia y Garcia, 1985). Esto se puede atribuir en parte, a que los marcadores moleculares no son afectados por el medio ambiente. Sin embargo, para estudiar mas a fondo esta característica, se debería evaluar progenies que provengan de padres con espinas y padres sin espinas y el marcador más recomendado para este tipo de estudios seria los microsatites."},{"index":3,"size":82,"text":"También, es importante anotar, que los resultados de los AFLP, permitieron identificar duplicados (2) en las muestras evaluadas, Por ejemplo, tanto la accesión 120072 como la 120073 clasificadas como S. quitoense resultaron ser idénticas. Caso similar, se presentó con las accesiones 120170 y la 120151 clasificadas como S. vestissimun. Este resultado, es de gran importancia debido a que tener duplicados en los bancos de germplasma reduce la eficiencia de manejo del banco e incrementa los costos de manutención, regeneración, caracterización y documentación."}]},{"head":"3. 1 Análisis de los Agrupamientos","index":50,"paragraphs":[{"index":1,"size":155,"text":"Como se puede apreciar en el dendrograma de similaridad de Nei-Li (Figura 6), a un nivel de similaridad del 55% se pueden distinguir 11 grupos (Tabla 8). Es importante anotar, que este trabajo se orientó a caracterizar los materiales de lulo existentes en el banco de germoplasma de Corpoica \"la Selva\", por lo anterior, existieron caracterizaciones de una sola especie con una o dos accesiones y otras con más de 30 accesiones por especie, esto implica analizar los resultados con cautela, debido al sesgo introducido por el muestreo. También, es importante mencionar que la colección del banco de germoplasma de lulo, no es representativa toda la zona de distribución de cada unas de las especies involucradas en este estudio. Figura 7. Fragmento del Dendrograma de similaridad para S pseudolulo utilizando el método de UPGMA con los valores de similaridad de Nei-Li producto del análisis de los datos de las dos combinaciones usadas en los AFLP."},{"index":2,"size":93,"text":"El grupo 7. Está conformado por 7 accesiones de S. hirtum especie andina de la sección Lasiocarpa. A un nivel de similaridad del 50% esta especie se une con S pseudolulo (Figura 5). Adicionalmente esta especie fue la que mostró mayor polimorfismo entre los individuos de las especies de la sección Lasiocarpa (75 al 98%) (Figura 8). Figura 8 Fragmento del Dendrograma de similaridad para S hirtum utilizando el método de UPGMA con los valores de similaridad de Nei-Li producto del análisis de los datos de las dos combinaciones usadas en los AFLP."}]},{"head":"S. hirtum","index":51,"paragraphs":[{"index":1,"size":28,"text":"S hirtum, se encuentra ampliamente distribuida desde México hasta el norte de Colombia y Venezuela. Se caracteriza por ser resistente a nemátodos y se cruza fácilmente con S."},{"index":2,"size":19,"text":"quitoense cuando se usa como progenitor femenino (Bernal et al., 1983). Fue utilizada como parental del híbrido \"La Selva\"."},{"index":3,"size":60,"text":"El grupo 8. Presenta 7 accesiones de S. vestissimum, especie silvestre andina de la sección Lasiocarpa, conocido como lulo de la tierra baja (Heiser, 1985). Es importante notar que el análisis detectó duplicados en la colección de lulo, uno de estos casos se puede observar en las accesiones 120170 y 120151 (Figura 9), las cuales resultaron ser la misma accesión."},{"index":4,"size":74,"text":"Figura 9 Fragmento del Dendrograma de similaridad para S vestissimun utilizando el método de UPGMA con los valores de de Nei-Li producto del análisis de los datos de las dos usadas en los AFLP S. vestissimum, generalmente se encuentra desde la cordillera central de Colombia hasta la costa de Venezuela entre 1.500-2.200 m.s.m.s. Los frutos son grandes pero se encuentran recubiertos por gran cantidad de tricomas cortos y rígidos (Bernal et al., 1983;Heiser, 1985)."},{"index":5,"size":36,"text":"El grupo 9. Incluyó las 74 accesiones de S. quitoense, especie andina cultivada más conocida de la sección Lasiocarpa,.9 híbridos de Heiser y los 4 clones \"La Selva\". Como se puede apreciar en la Figura 10. "}]},{"head":"S. vestissimun","index":52,"paragraphs":[{"index":1,"size":42,"text":"Figura 10 Fragmento del Dendrograma de similaridad para S quitoense, Híbridos de Heiser y clones \"La Selva\" el método de UPGMA con los valores de similaridad de Nei-Li producto del análisis de los datos de las dos combinaciones usadas en los AFLP."},{"index":2,"size":115,"text":"Los híbridos \"La Selva\", se unen a la especie cultivada (S. quitoense) a un nivel de similaridad del 68%, este resultado puede ser explicado debido a que estos clones partieron de un híbrido interespecífico entre S. quitoense variedad septentrional colectado en Costa Rica con S. hirtum proveniente de Venezuela. Con el fin de eliminar las espinas del híbrido, se hicieron dos retrocruzamientos con S. quitoense. Este clon se caracteriza por ausencia de espinas, resistencia a la raza 2 del nemátodo M. incognita, mayor adaptabilidad a condiciones de plena exposición solar, frutos de buena calidad y menor oxidaciónen en el jugo. Sin embargo los frutos son pequeños y presentan rajamiento al madurar (Bernal et al., 1998). "}]},{"head":"0.88","index":53,"paragraphs":[{"index":1,"size":142,"text":"Los híbridos de Heiser (Ecuador) se separaron en dos grupos, uno de ellos se une a las accesiones de quitoense a un nivel de similaridad del 71% y el segundo se aleja de estas accesiones a un nivel de similaridad del 64% (Figura 10). Al analizar los datos de pasaporte se podría pensar que la separación de estos híbridos se pudo deber al grado de hibridación, debido a que los híbridos que más se acercaron a la especie cultivada, presentaron características de domesticación como buen sabor, olor, pulpa verde y pocas semillas, mientras que el otro grupo, se caracterizó por presentar espinas, fruto pequeño, pulpa anaranjada y muchas semillas. Desafortunadamente, los datos de pasaporte de estos materiales están incompletos; solamente se conoce que fueron cruzados con S. baeza que es el mismo S. quitoense var quitoense con especies de la sección Lasiocarpa."},{"index":2,"size":91,"text":"Así mismo, en la figura 10 se observa que la mayoría de las accesiones pertenecientes a S. quitoense presentaron alta similaridad entre sus individuos (88 y el 100%). Sin embargo, accesiones como 120019,120170, 1200172 entre otras, fueron las más distantes dentro de este subgrupo, este resultado es promisorio debido a que estas accesiones pueden ser tenidas en cuenta, para aumentar un poco la base genética de esta especie cultivada. Estos resultados, concuerdan con los reportados por Heiser (1972) y Whalen et al., (1981), quienes afirmaron una mimima variabiladad en especie cultivada."},{"index":3,"size":47,"text":"El grupo 10. Incluyó a un híbrido interespecifico natural donde solo se conoce que S. hirtum es uno de los padres. Este híbrido, fue incorporado en este estudio debido a que presenta características deseables tales como frutos grandes, vigor y resistencia a enfermedades (M. Lobo, comunicación personal)."},{"index":4,"size":45,"text":"El 11. contine 7 accesiones que al igual que con los grupos 1 y 2 no pertenecen a la sección Lasiocarpa, estos materiales son: S. capsiciodeo accesiones (120139 y 120077), S. mamosum (120132). S. margintum ( 120136) y Solamun. sp (120133,120134 y 120136). (Figura 11)."},{"index":5,"size":18,"text":"Figura 11 Fragmento del Dendrograma de similaridad mostrando algunas especies del género solanum, empleadas como grupo de comparación."}]},{"head":"3. 2 Variabilidad genética de S. quitoense presente en la Colección","index":54,"paragraphs":[]},{"head":"Colombiana de Lulo","index":55,"paragraphs":[{"index":1,"size":307,"text":"El conocimiento de los mecanismos reproductivos de las especies de la sección Lasiocarpa y su biología floral (que incluye la apertura de las flores-antesis, dehiscencia de anteras, viabilidad del polen y receptividad del estigma), determina fundamentalmente la estructura genética estas especies. Según trabajos de Whalen & Costich (1986);Miller & Diggle (2003) y Diggle & Miller (2004), todas las especies de la seccion Lasiocarpa presentan andromonoecia. Sin embargo, el grado de andromonoecia puede variar entre especies y dentro de la misma espeicie (plasticidad). Por lo tanto, estos conocimentos Para el caso de S. quitoense, se hubiera esperado que su base genética fuera más amplia a la observada (0.88 -1.00% de similaridad), por ser Colombia uno de los sitios de origen y de diversificación de la especie, también, por que esta especie se encuentra en un proceso intermedio de domesticación (presenta algunas carcteristicas de silvestre), Sin embargo, este hecho puede ser explicado debido a que la mayoría de las accesiones de S. quiotense, se colectaron en fincas del Sur occidente Colombiano en los departamentos del Valle de Cauca, Cauca, Nariño y Putumayo y la forma de propagacion de estos materiales fue por medio de estacas, en otras palabras, las plantas propagadas vegetativamente reproducen, toda la información genética de la planta progenitora (M. Garcia, comunicación personal). Por consiguiente las accesiones evaluadas de S. quitoense, no son representativas de toda la zona de distribución de la especie. Por lo tanto, es preciso aprovechar el germoplasma existente en los diferentes acervos genéticos intraespecíficos e Inter-específicos, Heiser (1989), presumen que S. quitoense puede hibridizarse con otras especies de la seccion Lasiocarpa como S. pectinatum, S pseudolulo, S. sessiliflorum, S. straminifolium, S. hirtum y S. vesstisimum. En algunos casos (S. quitoense x S. sessiliflorum) el porcentajes de hibridación fue superior al 50%., lo que indica que S. quitoense puede cruzarse inclusive con especies Amazonicas."},{"index":2,"size":23,"text":"También es necasario, para el enriquecimiento de la base gnetica de S. quitoense colectar en otras regiones donde la especie se ha difundido."}]},{"head":"4 Análisis de Correspondencia Múltiple (ACM)","index":56,"paragraphs":[{"index":1,"size":82,"text":"Este análisis es una técnica descriptiva de análisis multivariado permite en forma gráfica visualizar las relaciones existentes entre grupos de individuos. Una característica que resalta de este análisis es que a diferencia del análisis de similaridad que da igual peso a todas las bandas, el ACM le da más peso a las bandas únicas, este análisis corroboró gran parte lo observado en el análisis de similaridad. Para este estudio, tres dimensiones explicaron la mayor parte de la variación entre las accesiones estudiadas."},{"index":2,"size":18,"text":"Además, la estructura se visualiza mejor en un espacio tridimensional como se puede apreciar en la figura 12."},{"index":3,"size":121,"text":"En la Figura 13 (Vista desde arriba) se puede observar más evidente la distribución de las siete especies de la sección Lasiocarpa en forma de grupos discretos en un espacio tridimensional. En la primera dimensión se diferencia claramente la separación de S. sesiliflorum y S. straminifolium de las especie de comparacion. En la segunda dimensión establece una diferencia marcada de todas las especies andinas de la sección Lasiocarpa y en la tercera dimensión se puede observar la separación entre la agrupación de S. quitoense. Los Clones \"La Selva\" y los híbridos del Ecuador y las especies utilizadas como grupo de comparación Figura 12 Gráfico tridimensional derivado del análisis de correspondencia múltiple (ACM) de todas las accesiones evaluadas en este estudio (Vista"}]},{"head":"S. quitoense S. pseudolulo S. hirtum S. pectinatum S. vestissimum S. sessiliflorum S .stramonifolium","index":57,"paragraphs":[{"index":1,"size":35,"text":"Figura 14 Dendrograma de similaridad construido mediante el método de UPGMA con los valores de similaridad de Nei-Li, disponible en el PAUP 4.0. Los valores de 1000 réplicas del \"bootstrap\" están representados sobre las ramas. "}]},{"head":"Similaridad de Nei-Li (1979)","index":58,"paragraphs":[{"index":1,"size":17,"text":"Varios puntos de discusión surgen de acuerdo de acuerdo a la estructura presentada en la figura 13:"},{"index":2,"size":47,"text":"• Un aspecto importante que sobresale de este análisis es el hecho de que las 7 especies estudiadas de la sección Lasiocarpa, presentan valores de \"bootstrap\" de 100%, lo que indica que el soporte de cada rama para cada una de las especies se encuentra bien representado."},{"index":3,"size":119,"text":"• En general la estructura representada en la figura 14, es congruente con la caracterización morfológica e isoenzimática realizada por Whalen et al (1981) y Whalen & Caruso (1983), respectivamente. Sin embargo, no concuerda con el estudio realizado por Bruneau et al (1995). Estas discrepancias entre estos dos estudios pueden residir principalmente en el tipo de ADN analizado. Mientras que Bruneau et al (1995) emplearon ADNcp, una estructura altamente conservada, el cual se utiliza para obtener información más precisa acerca de la historia evolutiva de las especies utilizadas, en este estudio, se empleó ADN nuclear una estructura relativamente más dinámica que puede enmascarar procesos evolutivos, debido a que se pueden reflejar eventos de recombinación y flujo de genes. ."}]},{"head":"•","index":59,"paragraphs":[{"index":1,"size":94,"text":"El grupo formado por todas las especies andinas de la sección Lasiocarpa, presenta tres subgrupos. El primer sub-grupo conformado por S. hirtum y S pseudolulo, la cual se encuentra soportado por una rama muy fuerte (87%), lo hace suponer que estas dos especies se encuentran muy relacionadas entre sí. Resultado similar fue encontrado por diferentes estudios filogenéticos realizados por Whalen et al (1981) y Whalen & Caruso (1983). El segundo subgrupo lo conforman S. quitoense, y S vestissimum con un valor de \"bootstrap\" de 66%. Por ultimo, un tercer subgrupo que lo conformó S."},{"index":2,"size":1,"text":"pectinatum."},{"index":3,"size":116,"text":"• Es importante, mencionar que S. pectinatum no esta incluida en el análisis morfológico realizado por Whalen et al., (1981). Debido a que esta especie no poseía tricomas sobre los tallos, característica común de todos los miembros de la sección Lasiocarpa, lo cual contrasta con los trabajos de Whalen & Caruso (1983) y Bruneau et al (1995), que incluyen a esta especie. Cabe destacar que en el análisis isoenzimático realizado por Whalen & Caruso (1983), los autores encontraron que S. pectinatum esta muy relacionada con S. quitoense, hipótesis soportada por previos estudios con flavoniodes, Sin embargo, en el análisis combinado (Morfologico, isoenzimático y ADN de cloroplasto) realizado por Bruneau et al (1995), no encuentran tal relación."},{"index":4,"size":28,"text":"• Otro grupo importante que resalta de este análisis es el conformado por las especies amazónicas (S. sessiliflorum y S. stramonifolium) con un valor de \"bootstrap\" de 99%,"}]},{"head":"CONCLUSIONES","index":60,"paragraphs":[{"index":1,"size":40,"text":"• El análisis permitió detectar una importante diversidad entre las especies de la sección Lasiocarpa (Silvestres) evalaudas, resultado de gran importancia para la valoración de los recursos genéticos, dada la posibilidad de su utilización en el mejoramiento de especies cultivadas."},{"index":2,"size":41,"text":"• Tanto el anilisis de similaridad de Nei-Li como el análisis de correspondencia múltiple mostraron claramente la separación entre las especies Andinas (S. quitoense, S. hirtum, S. pseudolulo, S. vesstissimun y S. pectinatum) de las Amazónicas (S. stramonifoliun y S. sessiliflorum)."},{"index":3,"size":9,"text":"Resultado similar fue encontrado por Bruneau et al. (1995)."},{"index":4,"size":29,"text":"• Al interior de S. quitoense (Cultivada), se evidenció una tasa de similaridad de Nei entre 85 y 100%, por lo cual se hace necesario ampliar su base genética."},{"index":5,"size":82,"text":"• El análisis no permitió discriminar entre las dos variedades de S. quitoense (variedad quitoense y septentrional). Es necesario para futuros estudios el empleo de marcadores moleculares de tipo codominante (microsatélites), que permitan ampliar y profundizar si realmente existe esta discriminación entre estas dos variedades. Las accesiones de S. quitoense 120072 y 120073 y 120170 y la 120151 de S. vestissimun presentarón altas tasas de similaridad (88% -100%) con las dos combinaciones de AFLP analizadas, lo que sugiere la presencia de duplicados."},{"index":6,"size":41,"text":"• El análisis de correspondencia múltiple (ACM) corroboró lo observado en el análisis de similaridad de Nei-Li, para este estudio, tres dimensiones (Eje X, Y y Z), fueron suficientes para explicar la mayor parte de la variación entre las accesiones estudiadas."},{"index":7,"size":35,"text":"• El análisis de neighbor-Joining mostró valores de \"bootstrap\" (análisis de remuestreo) de 100%, lo que indica que el soporte de cada rama para las especies estudiadas de la sección Lasiocarpa se encuentra bien representado."}]},{"head":"PERSPECTIVAS","index":61,"paragraphs":[{"index":1,"size":112,"text":"Los resultados obtenidos en el presente trabajo proporcionaron un primer acercamiento a la caracterización molecular de 159 accesiones de la Colección Colombiana de lulo. Sin embargo es necesario ampliar y profundizar los estudios a nivel de este grupo de especies. El empleo de marcadores moleculares de tipo codominante como los microsatélites o estudios de secuenciación de genes de cloroplasto, podrían revelar nuevas visiones acerca de las relaciones filogenéticas dentro de este grupo, es recomedable que estos estudios sean continuos para dar mayor confiabilidad a los resultados. Es necesario para futuros estudios, ampliar el número de accesiones en aquellas especies silvestres de la sección Lasiocarpa que no fué posible incluir en este estudio."},{"index":2,"size":83,"text":"Es necesario disponer de una información descriptiva del material, debidamente organizada, para conocer qué existe almacenado y sus características. Sin esta información los mejoradores no podrían hacer uso de este material y estaríamos conservando por conservar. Además una información detallada también nos permitiría conocer si los nuevas accesiones que van entrando se encuentran conservadas en el banco, de no ser así, sé estaria ocupando espacio, desperdiciando dinero y recursos para la conservación de otros materiales que pueden ser de interés para el mejorador."},{"index":3,"size":63,"text":"Es necasario complementar los estudios caracterización (morfo-agronómicos ymolecular) de la Colección Colombiana de Lulo, para así lograr establecer estrategias de conservación in situ y ex situ, dilucidar relaciones filogenéticas y taxonómicas actualmente existentes. También, se podrá dar soporte a programas de mejoramiento genético y así poder contar en un futuro cercano con materiales promisorios que suplan las necesidades de consumo nacional e internacional. "}]}],"figures":[{"text":" de neighbor-joining ………………………………….....………………...39 6. RESULTADOSY DISCUSIÓN ……………………………….....………..….……..40 6.1 Extracción y digestión del ADN…………………………………………………….40 6.2 Marcadores Moleculares AFLP.…….………………………….………………….41 6.3 Análisis de Similaridad……………………………………………………………...42 6. 3. 1 Variabilidad Intraespecífica .........................................................................45 LISTA DE TABLAS Página Tabla 1. Area cosechada, producción y rendimiento del cultivo de lulo en Colombia ..................................................................................................................................7 "},{"text":"Figura 3 Figura 3 Area de distribución de los materiales evaluados de la Colección Colombiana de lulo. ...............................................................................................28 Figura 4. Electroforesis en gel de agarosa al 0,8% de la extracción de ADN de algunas accesiones de lulo estudiadas. ……………………………………………..40 "},{"text":" Figura 8. Fragmento del Dendrograma de similaridad para S hirtum utilizando el método de UPGMA con los valores de similaridad deNei-Li (1979) producto del análisis de los datos de las dos combinaciones usadas en los AFLP.,,………......................................................................................…………….51 "},{"text":" Figura 9 Fragmento del Dendrograma de similaridad para S vestissimun utilizando el método de UPGMA con los valores de similaridad deNei-Li (1979) producto del análisis de los datos de las dos combinaciones usadas en los AFLP…………………......................................................................................…….52 "},{"text":" Figura 10 Fragmento del Dendrograma de similaridad para S quitoense utilizando el método de UPGMA con los valores de similaridad deNei-Li (1979) producto del "},{"text":"Figura 1 . Figura 1. Morfología del cultivo de lulo. a) hoja; b) flor; c) fruto; d)corteza y pulpa. "},{"text":"Figura 2 . Figura 2. Daño producido en la raíz de lulo por el nemátodo M. incognita "},{"text":" años y ser Colombia parte del centro de origen, este cultivo no ha alcanzado el grado de desarrollo esperado, debido a la dificultad de obtener materiales mejorados con características deseables que puedan competir en el mercado, además, los esfuerzos de la investigación en fitomejoramiento se han canalizado a cultivos tradicionales relacionados con seguridad alimentaria y a cultivos industriales o generadores de divisas. Debido a estos inconvenientes, Colombia se ha visto en la necesidad de importar del Ecuador aproximadamente el 25% del volumen de lulo consumido por año para suplir los requerimientos nacionales. Lo anterior indica la necesidad de adelantar procesos de caracterización y evaluación de la colección colombiana de lulo (S. quitoense) y especies relacionadas de la sección Lasiocarpa, con el fin de brindar elementos indispensables en la implementación de un programa eficiente de mejoramiento de este cultivo para la obtención de materiales con características agronómicas deseables para la industria o como fruta fresca que suplan las necesidades de consumo nacional e internacional.La caracterización y evaluación de la Colección Colombiana de Lulo actualmente se realiza mediante metodologías complementarias basadas en marcadores morfológicos, bioquímicos y moleculares, para conocer la composición genética de los materiales. De los resultados que se obtengan se podrá deducir si hay necesidad de ampliar la base genética de la colección mediante acciones de colecta e introducción, esclarecer posibles relaciones filogenéticas entre las diferentes especies de la sección Lasiocarpa. Por lo tanto, El objetivo general de este estudio se centra en caracterizar la diversidad genética de la Colección Colombiana de Lulo mediante la técnica molecular AFLP. Posteriormente estos resultados serán comparados y complementados con la caracterización morfológica que se adelanta en Corpoica \"LA SELVA\". "},{"text":" de ADN, se tomaron hojas jóvenes de cada una de las accesiones de lulo. La extracción de ADN se realizó con el protocolo deDellaporta et al., (1983)  con algunas modificaciones, que se describen a continuación. Un gramo de tejido foliar de cada accesión fue macerado en nitrógeno líquido hasta obtener un polvo fino y seco y homogeneizado en 1 ml de solución de extracción, previamente calentada a 65 ºC, la cual contiene 100 mM del tampón Tris HCl (pH 8.0); 50 mM de EDTA (pH 8.0); 500 mM de NaCl; 1.25% de SDS y 0.38% de bisulfito de sodio. Las muestras se incubaron en baño María a 65 º C por 30 minutos, agitándolas cada 5 minutos. Para precipitar las proteínas se adicionó 0.4 ml de acetato de potasio 5M (pH 7.4) con agitación en frío por 30 minutos.La separación de las proteínas se realizó con centrifugado a 3.000 r.p.m. a 4º C durante 10 minutos. Posteriormente se transfirió la fase acuosa a un tubo eppendorf de 1.5 µl. Los ácidos nucleicos fueron precipitados toda la noche a -20º C al adicionar 1 volumen de isopropanol y 1/10 de volumen de acetato de sodio 3M pH 5.2. Al día siguiente, las muestras se centrifugaron a 3.000 r.p.m. a 4 ºC por 10 minutos y se descartó la fase acuosa. El precipitado resultante se lavó con 1 ml de etanol frio al 70%, se centrifugó a 3000 r.p.m durante 5 minutos y se secó a temperatura ambiente. Una vez seco el precipitado, se resuspendió en 50 µl de TE (Tris-HCl 10 mM/EDTA 1mM) y 1 µl de ARNasa (Ribonucleasa A (Type I-A) de Bovino, Sigma Chemical ® ) y se incubó a 37 ºC durante 30 minutos. "},{"text":" , también conocido como similaridad de DICE (1945entre el individuo i y j. a = Número de bandas compartidas por los individuos i y j. b = Número de bandas presentes en i pero ausente en j. c = Número de bandas presentes en j pero ausente en i. "},{"text":" ; Saliba-Colombani et al. (2000); Acquadro et al. (2002), Lanteri et al. (2003) y Toquica et al. (2003). "},{"text":"Figura 5 . Figura 5. Detalle de un gel de poliacrilamida mostrando los patrones de bandas de Aflp "},{"text":"o ACM = Análisis de correspondencia múltiple. o AFLP = Polimorfismo de la longitud de los fragmentos amplificados. o LSSRs = microsatélites. o Neighbor-Joining = Vecino más próximo. o PAUP 4.0= Phylogenetic Analysis Using Parsimony. o PCR= Reacción en cadena de la polimerasa. o RFLP`s = Polimorfismo de longitud de fragmentos de restricción. o RAPD`s = Polimorfismo de ADN amplificado al azar. "},{"text":"  "},{"text":"TABLA DE CONTENIDO Biología Floral y Sistema Reproductivo........................................................11 2. 4. 2 Citogenética………………………………………………..................…………13 Técnica de AFLP...........………………………………………………………...30 II II Página Página 4. 1. Objetivo General…………………………………………………….……………..26 4. 1. Objetivo General…………………………………………………….……………..26 Página 4. 2 Objetivos Específicos……………………………………………………………....26 Página 4. 2 Objetivos Específicos……………………………………………………………....26 RESUMEN…………………………………………………………………………………1 5. MATERIALES Y MÉTODOS……………………………………..…………………27 RESUMEN…………………………………………………………………………………1 5. MATERIALES Y MÉTODOS……………………………………..…………………27 ABSTRACT……………………………………………………………...………………...2 5. 1 Material Biológico…………………………………………………………………...27 ABSTRACT……………………………………………………………...………………...2 5. 1 Material Biológico…………………………………………………………………...27 1. INTRODUCCIÓN………………………………………………………………………3 5. 2 Análisis Molecular…………………………………………………………………. 29 1. INTRODUCCIÓN………………………………………………………………………3 5. 2 Análisis Molecular…………………………………………………………………. 29 2. MARCO TEÓRICO…….................………………………………………………..…5 5. 2. 1 Extracción de ADN….…………………………………………………………...29 2. MARCO TEÓRICO…….................………………………………………………..…5 5. 2. 1 Extracción de ADN….…………………………………………………………...29 2. 1 Generalidades…..……………………………………………………………………5 2. 2 Origen y Distribución…………………………………………………………………5 5. 2. 2 5. 2. 2. 1 Digestión de ADN …………………………………………………………….30 2. 1 Generalidades…..……………………………………………………………………5 2. 2 Origen y Distribución…………………………………………………………………5 5. 2. 2 5. 2. 2. 1 Digestión de ADN …………………………………………………………….30 2. 3 Descripción Taxonómica …………………………………………………….……..8 5. 2. 2. 2 Ligación de adaptadores……………………………………………………..31 2. 3 Descripción Taxonómica …………………………………………………….……..8 5. 2. 2. 2 Ligación de adaptadores……………………………………………………..31 2. 4 Descripción Botánica…………………………………………………..…………....9 5. 2. 2. 3 Amplificación selectiva (+1/+1) ……………………………………………..32 2. 4 Descripción Botánica…………………………………………………..…………....9 5. 2. 2. 3 Amplificación selectiva (+1/+1) ……………………………………………..32 5. 2. 2. 4 Amplificación selectiva (+3/+3) ……………………………………………..34 5. 2. 2. 4 Selección de combinaciones………………………………………………...35 2. 4 1 2. 5 Domesticación del Lulo…………………………………………………………….13 5. 2. 3 Electroforesis en geles de poliacrilamida……………………………………. 35 5. 2. 2. 4 Amplificación selectiva (+3/+3) ……………………………………………..34 5. 2. 2. 4 Selección de combinaciones………………………………………………...35 2. 4 1 2. 5 Domesticación del Lulo…………………………………………………………….13 5. 2. 3 Electroforesis en geles de poliacrilamida……………………………………. 35 2. 6 Métodos de Propagación…………………………………………………………..14 5.2. 4 Tinción y revelado en geles de poliacrilamida………………………………...36 2. 6 Métodos de Propagación…………………………………………………………..14 5.2. 4 Tinción y revelado en geles de poliacrilamida………………………………...36 2. 7 Plagas y Enfermedades……………………………………………………………15 5. 3 Análisis Estadístico…………………………………………………………………37 2. 7 Plagas y Enfermedades……………………………………………………………15 5. 3 Análisis Estadístico…………………………………………………………………37 2. 8 Mejoramiento Genético en Lulo…………………………………………………...17 2. 8 Mejoramiento Genético en Lulo…………………………………………………...17 2. 9 Estudios de Caracterización …...………………………………………………...19 2. 9 Estudios de Caracterización …...………………………………………………...19 2. 10 Caracterización Molecular mediante AFLP...………………….………………22 2. 10 Caracterización Molecular mediante AFLP...………………….………………22 3. PLANTEAMIENTO DEL PROBLEMA………………………………..…………...24 3. PLANTEAMIENTO DEL PROBLEMA………………………………..…………...24 4. OBJETIVOS…………………………………………………………..………………26 4. OBJETIVOS…………………………………………………………..………………26  "},{"text":"( Neoleucinodes elegantalis), el moho blanco (Sclerotinia sclerotiorum Lib.) y el nemátodo del nudo (Meloidogyne incógnita Kofoid & White), kilo. Los principales competidores de Colombia son Ecuador y Venezuela (Siesa, kilo. Los principales competidores de Colombia son Ecuador y Venezuela (Siesa, Tabla 1. Área cosechada, producción y rendimiento del cultivo de lulo en Tabla 1. Área cosechada, producción y rendimiento del cultivo de lulo en Colombia.    Colombia. Departamento Cosechada Ha Producción Ton Rendimiento Ton/ha DepartamentoCosechada Ha Producción Ton Rendimiento Ton/ha Huila 1,634 13,868 8.49 Huila1,63413,8688.49 Valle del Cauca 646 3,824 5.92 Valle del Cauca6463,8245.92 Cauca 380 2,496 6.57 Cauca3802,4966.57 Caquetá 357 2,664 7.46 Caquetá3572,6647.46 Nariño 273 1,636 5.99 Nariño2731,6365.99 Tolima 235 2,100 8.94 Tolima2352,1008.94 Cesar 220 1,525 6.93 Cesar2201,5256.93 Magdalena 225 1,081 4.80 Magdalena2251,0814.80 o por razones de orden público como es el caso de Frontino y Dabeiba en o por razones de orden público como es el caso de Frontino y Dabeiba en Antioquia    Antioquia En 1999, Colombia produjo 32.000 toneladas de fruta en 4.042 hectáreas que le En 1999, Colombia produjo 32.000 toneladas de fruta en 4.042 hectáreas que le representaron $62.400 millones de pesos al productor en finca. Los costos de representaron $62.400 millones de pesos al productor en finca. Los costos de producción para una hectárea de lulo y una duración de 2 años, son de $4.6 producción para una hectárea de lulo y una duración de 2 años, son de $4.6 millones de pesos. Un kilo de lulo cuesta actualmente en el supermercado $3.900 millones de pesos. Un kilo de lulo cuesta actualmente en el supermercado $3.900 pesos y un kilo de puré o pulpa congelada de lulo colombiano costaba en Nueva pesos y un kilo de puré o pulpa congelada de lulo colombiano costaba en Nueva York en 1997 $ 3.30 dólares en contraste con $1.36 dólares por el kilo de lulo York en 1997 $ 3.30 dólares en contraste con $1.36 dólares por el kilo de lulo ecuatoriano de menor calidad. A Estados Unidos entran anualmente unas 60 ecuatoriano de menor calidad. A Estados Unidos entran anualmente unas 60 toneladas de lulo congelado a un precio que oscila entre $2.20 y 3.30 dólares por toneladas de lulo congelado a un precio que oscila entre $2.20 y 3.30 dólares por  "},{"text":" Heiser en 1972,    basados en cruces entre las diferentes especies de la sección Lasiocarpa (S. Bernardello y colaboradores (1994), llevaron a cabo estudios a nivel cromosómico Bernardello y colaboradores (1994), llevaron a cabo estudios a nivel cromosómico en todas las especies de la sección Lasiocarpa. Se concluyó que todas las en todas las especies de la sección Lasiocarpa. Se concluyó que todas las especies tienen el mismo número de cromosomas (2n=24) e indicaron que la especies tienen el mismo número de cromosomas (2n=24) e indicaron que la especiación en este grupo no fue acompañada por cambios en el número de especiación en este grupo no fue acompañada por cambios en el número de cromosomas. Igualmente, señalaron que no hubo indicios de rearreglos cromosomas. Igualmente, señalaron que no hubo indicios de rearreglos cromosómicos en los taxa estudiados, lo que siguiere que la diferenciación cromosómicos en los taxa estudiados, lo que siguiere que la diferenciación candium; S felinum; S hirtum, S hyporhodium, S lasiocarpum S. pectinatum, S. morfológica no siempre está asociada con divergencia cromosómica. En este candium; S felinum; S hirtum, S hyporhodium, S lasiocarpum S. pectinatum, S. morfológica no siempre está asociada con divergencia cromosómica. En este  "},{"text":" . Es importante mencionar que al utilizar dos enzimas de restricción se generan más fragmentos de ADN que al emplear una sola enzima. La mezcla de reacción de la digestión del ADN se puede apreciar en la Tabla 2. Tabla 3. Mezcla de reacción de la digestión del ADN Tabla 3. Mezcla de reacción de la digestión del ADN Componente Volumen / Muestra ComponenteVolumen / Muestra 5x tampón de reacción* ADN (250 ng en ≤ 7 µl) Enzimas EcoR I / Mse I 2.5µl ≤7 µl 1 µl 5x tampón de reacción* ADN (250 ng en ≤ 7 µl) Enzimas EcoR I / Mse I2.5µl ≤7 µl 1 µl Agua destilada estéril Para 12.5µl Agua destilada estérilPara 12.5µl Volumen total 12.5 µl Volumen total12.5 µl  "},{"text":"Sp solanaceas S. vestissimum S. pectinatum Híbridos Ecuador Híbridos Ecuador S. sessiliflorum    {0.37} {0.55} {0.68} {0.74} {0.88} {0.37}{0.55}{0.68}{0.74}{0.88} {0.1}     {0.1} 0.00 0.25 0.50 Similaridadde Nei-Li (1979) 0.75 0.80.85 1.00 0.000.250.50 Similaridadde Nei-Li (1979) 0.750.80.851.00  "},{"text":"S. pseudolulo S. quitoense S. hirtum Sp solanaceas Lulo \"La Selva\" Sp solanaceas S. vestissimum S. pectinatum Híbridos Ecuador Híbridos Ecuador S. sessiliflorum    {0.37} {0.55} {0.68} {0.74} {0.88} {0.37}{0.55}{0.68}{0.74}{0.88} {0.1}     {0.1} 0.00 0.25 0.50 Similaridadde Nei-Li (1979) 0.75 0.80.85 1.00 0.000.250.50 Similaridadde Nei-Li (1979) 0.750.80.851.00  "},{"text":"Similaridad de Nei-Li (1979) Similaridad de Nei-Li (1979)    0.88 0.88  0.88 0.88     120186 120187 120188 120189 120190 120021 120022 120024 120025 120030 120026 12 15 120186 120187 120188 120189 120190 120021 120022 120024 120025 120030 120026 12 15 120186 120187 120188 120189 120190 120021 120022 120024 15 12 120026 120030 120025 ClonLa Selva Híbridosde Heiser ClonLa Selva Híbridosde Heiser ClonLa Selva Híbridosde Heiser 120186 120187 120188 120189 120190 120021 120022 120024 120025 120030 120026 12 15 120186 120187 120188 120189 120190 120021 120022 120024 120025 120030 120026 12 15 120186 120187 120188 120189 120190 120021 120022 120024 15 12 120026 120030 120025ClonLa Selva Híbridosde Heiser ClonLa Selva Híbridosde Heiser ClonLa Selva Híbridosde Heiser     120146 120168 120161 120174 120179 120171 120100 120101 120102 120105 120109 120093 120095 120146 120168 120161 120174 120179 120171 120100 120101 120102 120105 120109 120093 120095 120146 120168 120161 120174 120179 120171 120100 120101 120102 120105 120109 120093 120095 120146 120168 120161 120174 120179 120171 120100 120101 120102 120105 120109 120093 120095 120146 120168 120161 120174 120179 120171 120100 120101 120102 120105 120109 120093 120095 120146 120168 120161 120174 120179 120171 120100 120101 120102 120105 120109 120093 120095     120097 120098 120099 120090 120048 120052 120054 120058 120056 120064 120055 120072 120073 120074 120069 120075 120080 120085 120086 120044 120049 120045 120035 120067 120068 120008 120038 120041 120042 120107 120112 120115 120117 120032 120039 120040 120043 120084 120103 120104 120114 120116 120127 120128 120129 120118 120144 120001 12015 120010 120155 120034 120082 120172 120070 12019 120023 120028 120029 120097 120098 120099 120090 120048 120052 120054 120058 120056 120064 120055 120072 120073 120074 120069 120075 120080 120085 120086 120044 120049 120045 120035 120067 120068 120008 120038 120041 120042 120107 120112 120115 120117 120032 120039 120040 120043 120084 120103 120104 120114 120116 120127 120128 120129 120118 120144 120001 12015 120010 120155 120034 120082 120172 120070 12019 120023 120028 120029 120097 120098 120099 120090 120048 120052 120054 120058 120056 120064 120055 120072 120073 120074 120069 120075 120080 120085 120086 120044 120049 120045 120035 120067 120068 120008 120038 120041 120042 120107 120112 120115 120117 120032 120039 120040 120043 120084 120103 120104 120114 120116 120127 120128 120129 120118 120144 120001 12015 120010 120155 120029 120028 120023 12019 120070 120172 120082 120034 S. quitoense Híbridosde Heiser S. quitoense Híbridosde Heiser S. quitoense Híbridosde Heiser 120097 120098 120099 120090 120048 120052 120054 120058 120056 120064 120055 120072 120073 120074 120069 120075 120080 120085 120086 120044 120049 120045 120035 120067 120068 120008 120038 120041 120042 120107 120112 120115 120117 120032 120039 120040 120043 120084 120103 120104 120114 120116 120127 120128 120129 120118 120144 120001 12015 120010 120155 120034 120082 120172 120070 12019 120023 120028 120029 120097 120098 120099 120090 120048 120052 120054 120058 120056 120064 120055 120072 120073 120074 120069 120075 120080 120085 120086 120044 120049 120045 120035 120067 120068 120008 120038 120041 120042 120107 120112 120115 120117 120032 120039 120040 120043 120084 120103 120104 120114 120116 120127 120128 120129 120118 120144 120001 12015 120010 120155 120034 120082 120172 120070 12019 120023 120028 120029 120097 120098 120099 120090 120048 120052 120054 120058 120056 120064 120055 120072 120073 120074 120069 120075 120080 120085 120086 120044 120049 120045 120035 120067 120068 120008 120038 120041 120042 120107 120112 120115 120117 120032 120039 120040 120043 120084 120103 120104 120114 120116 120127 120128 120129 120118 120144 120001 12015 120010 120155 120029 120028 120023 12019 120070 120172 120082 120034S. quitoense Híbridosde Heiser S. quitoense Híbridosde Heiser S. quitoense Híbridosde Heiser 0. 55 0. 55 0. 55 0.75 0.75 0.75 0.80 0.85 0.80 0.85 0.80 0.85 0. 90 0. 95 0. 90 0. 95 0. 90 0. 95 1.00 1.00 1.00 0. 55 0. 55 0. 550.75 0.75 0.750.80 0.85 0.80 0.85 0.80 0.850. 90 0. 95 0. 90 0. 95 0. 90 0. 951.00 1.00 1.00 Similaridad de Nei-Li (1979) Similaridad de Nei-Li (1979)  "},{"text":" Según trabajos Bernal et al., Las especies de la sección Lasiocarpa son alógamas. aportarón criterios para entender la variabilidad presentada por estas especies tanto en aportarón criterios para entender la variabilidad presentada por estas especies tanto en condiciones naturales como de cultivo.    condiciones naturales como de cultivo.    120139 120139 120139 120139    120133 120133 120133 120133    120134 120134 120134 120134    120077 120132 120077 120132 Sp solanaceas Sp solanaceas 120077 120132 120077 120132Sp solanaceas Sp solanaceas    120136 120136 120136 120136    120138 120138 120138 120138 0.50 0.50 0.50 0.75 0.75 0.75 0.80.85 0.80.85 0.80.85 1.00 1.00 1.00 0.50 0.50 0.500.75 0.75 0.750.80.85 0.80.85 0.80.851.00 1.00 1.00 Similaridadde Nei-Li (1979) Similaridadde Nei-Li (1979)  Similaridadde Nei-Li (1979) Similaridadde Nei-Li (1979)  "}],"sieverID":"31fa0354-09e1-4132-967f-d2da0cc6a465","abstract":""}

data/part_5/09c40993cb3ecba55968d08c228a0db8.json

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+{"metadata":{"id":"09c40993cb3ecba55968d08c228a0db8","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/cacb27e0-3012-4b18-a5f2-110da2786e86/retrieve"},"pageCount":1,"title":"Study #3634 Contributing Projects: • P1591 -Policy imperatives for Southeast Asia's regional food systems under climate change","keywords":[],"chapters":[],"figures":[],"sieverID":"c4512528-6e45-4b52-bc33-70c6a715ddca","abstract":""}

data/part_5/09e9ab5de61126c549dd51f70984dceb.json

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+{"metadata":{"id":"09e9ab5de61126c549dd51f70984dceb","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/b74b4ab2-f61e-47d1-8f60-545baa1551c9/retrieve"},"pageCount":2,"title":"Amazon Region: Eco-Efficient Landscapes","keywords":[],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":90,"text":"The Amazon Basin is so diverse that one could say many Amazons exist, not just one. Indeed, its diversity is considered unique in the world. Although the Basin occupies 7% of the planet's land, it carries 25% of the world's terrestrial biodiversity. The region is so vast, it represents one-third of South America's land surface. It covers, or partly covers, nine countries: Colombia (36% of the land area), Venezuela and Guyana (6% each), Suriname and French Guiana (almost 100% each), Brazil (60%), Bolivia and Peru (75% together), and Ecuador (45%)."},{"index":2,"size":97,"text":"Yet, more than 100 million hectares of this complex of ecosystems, that is, 16%, have been deforested. With this loss, important environmental services have been degraded, for example, the conservation of biodiversity, storage of carbon dioxide, regulation of regional and global climates, and regulation of water cycles. The drive behind this deforestation has been, and still is, extensive agriculture of such low productivity that, in a few years, as soils deteriorate, it becomes unproductive. More forest is then cleared, repeating the cycle. The situation is further exacerbated by climate change phenomena and the region's vulnerability to these."}]},{"head":"CIAT's role","index":2,"paragraphs":[{"index":1,"size":67,"text":"CIAT has worked in the Amazon for more than 30 years. It has contributed towards such aspects as developing eco-efficient crop-and-livestock systems; improving markets for small farmers; monitoring and analyzing deforestation; and directing research at understanding, mitigating, and adapting to climate change. These activities were carried out in collaboration with a strong network of entities, including some strengthened by CIAT's participation in the international consortium Amazon Initiative."},{"index":2,"size":32,"text":"The pressure to conserve and sustainably use the Amazon requires strategic action. CIAT scientists, working with external actors, have formulated a strategy to provide a framework for collaborative efforts in this region."},{"index":3,"size":73,"text":"The strategy encompasses two general foci: first, to understand the dynamics of the Amazon Region. Activities will include monitoring environmental, social, and economic changes of different Amazon landscapes. Findings will then be used to visualize future scenarios in the context of climate change. The second focus is to create and use models for intensifying land use through eco-efficient production systems in those areas of the Amazon that have long since been heavily degraded."}]},{"head":"General objective","index":3,"paragraphs":[{"index":1,"size":37,"text":"CIAT's general mission is to promote eco-efficient agriculture for reducing poverty. Within this framework lies the strategy's central objective to create models that describe eco-efficient landscapes, ranging from the farm to the watershed, for the Amazon Region."}]},{"head":"Areas of action","index":4,"paragraphs":[{"index":1,"size":15,"text":"CIAT will focus its areas of action in the Amazon on three intimately linked themes:"},{"index":2,"size":46,"text":"Monitoring the Amazon: Geographical information tools such as Terra-i and IAViewer will be used to support countries in monitoring the Amazon Region. To provide elements for decision-making, the focus will be on changes in land use and on the generation of economic, environmental, and social indicators."},{"index":3,"size":89,"text":"Optimizing land-use systems: The Center will generate models of soil restoration and intensive and sustainable management of degraded areas, focusing on agroecosystems. It will use its experience in (i) the management of crops, soils, crop-forage rotations, and access to markets; and (ii) analysis of economic impact to improve farm productivity in an integrated way. CIAT will prioritize crop research on cassava, forages, rice, and fruits, which are widely grown in the Amazon. This work is expected to contribute significantly to the conservation of the Amazon Region as a whole."},{"index":4,"size":74,"text":"Improving access to markets is fundamental for generating not only eco-efficient farms (whose carbon footprints are small), but also substantially upgrading living standards for rural populations. The Center's strategy is to involve communities in participatory and interactive exercises to construct and develop valid marketing options. Markets would be visualized from the perspective of crops and their production chains, thus giving an aggregate value to the Amazon's conservation by recognizing the environmental services it provides."},{"index":5,"size":67,"text":"Mitigating and adapting to climate change: The most efficient strategy for mitigating climate change in the Amazon Region is to stop deforestation. This is the goal of options such as REDD+. CIAT's challenge, in contrast, is to recuperate areas already deforested and to mitigate climate change in systems such as extensive livestock-raising. This can be done by, for example, converting them into intensive and sustainable agrosilvopastoral systems."}]}],"figures":[],"sieverID":"5e49fea3-1fb4-4ac5-ba4d-8d161a3ce0f9","abstract":""}

data/part_5/0a1fab837245be8bedc108450fc3d7f1.json

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+{"metadata":{"id":"0a1fab837245be8bedc108450fc3d7f1","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/0525e1ce-b822-461b-909d-db1ff86e0838/retrieve"},"pageCount":4,"title":"CGIAR Initiative on Gender Equality -Inception Brief -September 2022 Challenge Objective This Initiative aims to use impactful gender research to address the four dimensions of gender inequality by applying gender-transformative approaches to harmful norms, bundling sociotechnical innovations for women's empowerment, leveraging social protection to increase women's access to and control over resources, and promoting inclusive governance and policies for increased resilience","keywords":[],"chapters":[{"head":"","index":1,"paragraphs":[{"index":1,"size":51,"text":"Gender and social inequalities are deeply entrenched within our global agrifood systems. This limits the potential of womenespecially those from agriculture-dependent communities -to be empowered to build social, economic and technological resilience to climate change. Women co-developing and co-designing solutions is essential to successfully transforming agrifood systems in a climate crisis."},{"index":2,"size":45,"text":"Structural gender inequalities such as harmful norms, unequal responsibilities and restrictive masculinities make women particularly vulnerable to shocks and stressors such as climate change, conflict, state fragility and pandemics. Although gendertransformative approaches mitigate these inequalities and can boost climate resilience among women, substantial inequalities persist."},{"index":3,"size":67,"text":"Socio-technical innovations targeting climate resilience are not adequately designed or bundled to encourage uptake by women, nor are they diffused at the pace or scale required for system transformation. Social protection systems often fail to address constraints faced by women, and agrifood system governance structures often significantly disfavor them. More research is needed on how to address these constraints and transform agrifood systems to reduce gender inequality."}]},{"head":"Activities","index":2,"paragraphs":[{"index":1,"size":6,"text":"This objective will be achieved through:"},{"index":2,"size":67,"text":"• Promoting gender transformative approaches targeting structures that create social inequalities by reducing normative constraints that limit women's capacity to build economic resilience to climate change challenges. • Co-designing and testing bundled innovations for women's empowerment as partners and drivers of climate change solutions, by identifying context-specific social and technical innovations that lead to equal uptake of and benefits for women, men and youth in agrifood systems."},{"index":3,"size":36,"text":"• Enabling gender-responsive social protection by co-designing and testing how social protection and complementary programs can inclusively address gender inequality and poverty, build resilience and support women in mitigating and adapting to effects of climate change."},{"index":4,"size":32,"text":"• Encouraging inclusive and responsive governance and policies whereby women, youth and marginalized groups are consulted and heard in the process of making policies and investments, including those related to climate change."}]},{"head":"Engagement","index":3,"paragraphs":[{"index":1,"size":32,"text":"This Initiative will work in Bangladesh, Ethiopia, India, Kenya, Malawi, Mali, Nigeria and United Republic of Tanzania as a priority and will explore work in two additional focal countries: Egypt and Vietnam."}]},{"head":"Outcomes","index":4,"paragraphs":[{"index":1,"size":4,"text":"Proposed three-year outcomes include:"},{"index":2,"size":181,"text":"1. National agencies, civil society organizations and CGIAR Initiatives in at least two low-and middle-income countries target normative constraints that limit the capacities of women food system actors to build economic resilience to climate change challenges using gender-transformative approaches. 2. Learning Labs nested in other CGIAR Initiatives and downstream partners in two low-and middle-income countries, together with this Initiative, identify and model diverse scenarios for bundling climate-smart technologies to empower women to be partners and drivers of climate change solutions. 3. Stakeholders involved in social protection programs -including governments, international NGOs, UN agencies and donors -across at least three low-and middleincome countries use this Initiative's evidence to understand how social protection systems can be better leveraged to boost rural women's climate resilience and reduce gender inequality. 4. Government, NGOs, civil society organizations and/or private sector actors in at least three low-and middle-income countries use learning and guidance from the Initiative to better understand how social innovations, organizational strategies and government and private-sector policies can increase the voice and agency of women in agrifood system governance and their resilience to climate change."}]},{"head":"Impact","index":5,"paragraphs":[{"index":1,"size":6,"text":"Projected impacts and benefits 1 include:"}]},{"head":"GENDER EQUALITY, YOUTH & SOCIAL INCLUSION","index":6,"paragraphs":[{"index":1,"size":58,"text":"Women, youth and other vulnerable groups become proactive agents of agrifood systems transformation, benefiting from enhanced agency in policy dialogues, greater participation in the co-design of innovations and programs, and a better ability to demand, access and control use of services and technologies, contributing to gender equality, empowerment and greater resilience to climate change for 3.5 million women."}]},{"head":"NUTRITION, HEALTH & FOOD SECURITY","index":7,"paragraphs":[{"index":1,"size":67,"text":"Inclusive take-up of climate-smart food production technologies, gender-responsive social protection to support women's food access and production, and strategies to increase women's voice and agency in climate-relevant nutrition and health services remove barriers to equality and elevate women's vital roles, both as entrepreneurs and producers of healthy foods and as decision-makers and consumers for their own and other household members' diets and health, benefiting 4.6 million people."}]},{"head":"POVERTY REDUCTION, LIVELIHOODS & JOBS","index":8,"paragraphs":[{"index":1,"size":46,"text":"Addressing gendered barriers to emerging from poverty and offering women opportunities to build resilience to climate change contributes to addressing key drivers of poverty and lack of livelihood opportunities and jobs in the context of climate change in target and focal countries, benefiting 5.6 million people."}]},{"head":"CLIMATE ADAPTATION & MITIGATION","index":9,"paragraphs":[{"index":1,"size":53,"text":"Women are empowered beyond accessing and using climate-smart technologies, moving towards designing and driving such technologies that include social protection and transformative solutions for 3.3 million people. Women are equipped to contribute to the development and implementation of genderresponsive actions beyond national adaptation plans and nationally determined contributions in the different target countries."}]},{"head":"ENVIRONMENTAL HEALTH & BIODIVERSITY","index":10,"paragraphs":[{"index":1,"size":42,"text":"Implementation of tried and tested socio-technical innovation bundles, which include digital support, enhanced decision-making, participatory development and application of context-specific strategies, has a positive impact on the status and management of natural resources in target sites, bringing 738,000 hectares under improved management."},{"index":2,"size":85,"text":"1 Projected Benefits are a way to illustrate reasonable orders of magnitude for impacts which could arise as a result of the impact pathways set out in the Initiative's Theories of Change. In line with the 2030 Research and Innovation Strategy, Initiatives contribute to these impact pathways, along with other partners and stakeholders. CGIAR does not deliver impact alone. These projections therefore estimate plausible levels of impact to which CGIAR, with partners, contribute. They do not estimate CGIAR's attributable share of the different impact pathways."}]},{"head":"Partners","index":11,"paragraphs":[{"index":1,"size":11,"text":"The For more details on this Initiative, visit the Initiative website."},{"index":2,"size":18,"text":"Header photo: Female farmers attending an information meeting on solar pumps in India. Photo by C. de Bode/CGIAR."}]}],"figures":[{"text":"  "},{"text":"  "},{"text":" HER+ Initiative has a wide array of demand, innovation and scaling partners, including CGIAR initiatives (EiA, Mixed Farming Systems, Aquatic Systems, PHI, LCSR, CLIMBER, Aquatic Foods, NPS, SHiFT, FRESH, and Conflict, Fragility and Migration); government ministries in Bangladesh, Ethiopia, India, Kenya, Malawi, Mali, Nigeria, United Republic of Tanzania; Overseas Development Institute (ODI) Gender Equality and Social Inclusion Programme; WorldVeg; Cornell; Wageningen University and Research; University of California -Berkeley; IPA; KALRO; ICAR; EARO; AGRA; FAO; IFAD; World Vision; WFP; Bangladesh Agricultural University; Institut de recherche pour le développement; Dadimos Development Consultants; USAID; ISEAL; ActionAid Nigeria; Women for Women Nigeria; FES; and World Bank.  "}],"sieverID":"71f7e7c4-a916-45a8-ac3d-54765af01dd7","abstract":""}

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+{"metadata":{"id":"0a2c292ed041d1a6b9079d9dbcf46461","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/3da7521c-f885-4b40-9641-268953e69e58/retrieve"},"pageCount":26,"title":"Los datos del Método 4 Celdas (M4C) fueron colectados, como parte del Proyecto GEF-Agrobiodiversidad SIPAM, por la Alianza de Bioversity International y el CIAT, con la participación de personal del proyecto GEF-Agrobiodiversidad SIPAM: Ana","keywords":[],"chapters":[{"head":"","index":1,"paragraphs":[{"index":1,"size":29,"text":"Ejemplo del grado de conservación de servicios ecosistémicos de bien público asociados con la agrobiodiversidad in situ/en chacra y el grado de equidad social bajo diferentes metas de área, "}]},{"head":"I. Antecedentes","index":2,"paragraphs":[{"index":1,"size":118,"text":"Perú es reconocido como uno de los 17 países más megadiversos del mundo 1 . Sin embargo, muchas especies/ variedades de cultivos se consideran \"gravemente amenazadas\" 2 y aún faltan estrategias para su gestión, aunque existe una Estrategia Nacional de Diversidad Biológica 3 . Es ampliamente reconocido que la diversidad de la mayoría de los cultivos es mantenida principalmente por los agricultores, in situ en chacra 4 . También, se sabe que la profusión de variedades confiere numerosos beneficios directos e indirectos para el funcionamiento y la salud de los agroecosistemas 5,6 , aunque se tiene poco conocimi sobre lo que se está conservando, lo que está en riesgo de perderse y lo que ha desaparecido 2 ."},{"index":2,"size":104,"text":"Para aquellas variedades con mercado desconocido o inexistente, el desafío está en cómo salvaguardarlas junto con el conocimiento ancestral asociado 7 , lo que constituye un bien público nacional y mundial, mientras se satisfacen las necesidades y los derechos de desarrollo de los agricultores. Durante las últimas décadas, se han implementado una serie de actividades para apoyar a los agricultores que mantienen la riqueza de cultivos y sus variedades 8 , particularmente en centros de diversidad 9 , y aunque se han evidenciado beneficios en los medios de vida de los agricultores, las ganancias en conservación de los recursos genéticos han sido menos claras."},{"index":3,"size":119,"text":"En Perú, el Ministerio del Ambiente (MINAM) y los gobiernos regionales de Puno y Cusco han mostrado un fuerte apoyo en recursos operacionales y humanos a los mecanismos de incentivos para la conservación tipo ReSCA (Retribuciones por los Servicios de Conservación de la Agrobiodiversidad) 10 . En este contexto, el Método 4 Celdas (M4C) 11 se ha utilizado para identificar y priorizar los recursos genéticos que están en riesgo de desaparecer localmente y así guiar las intervenciones ReSCA para mejorar su conservación y uso sostenible. Aquí presentamos una descripción del M4C, las bases conceptuales, y los resultados de su aplicación en las campañas agrícolas de 2019 y 2021 en cuatro regiones de la sierra sur del Perú (Figura 1)."}]},{"head":"Figura 1","index":3,"paragraphs":[{"index":1,"size":6,"text":"Sitios M4C del Proyecto GEF-Agrobiodiversidad SIPAM."},{"index":2,"size":81,"text":"El conocimiento respecto a la cantidad y distribución local de la diversidad intraespecífica de cultivos es la información básica necesaria para la gestión de la diversidad biológica en los campos de los agricultores. Las metodologías participativas adecuadas ayudan a investigadores y agricultores a entender los patrones de distribución de la diversidad. El Método 4 Celdas (M4C) es un método participativo para identificar los recursos biológicos más importantes que juegan un papel primordial en el sustento de vida de la población local."},{"index":3,"size":5,"text":"Los objetivos del M4C son:"},{"index":4,"size":7,"text":"• Identificar variedades comunes, raras y únicas."},{"index":5,"size":22,"text":"• Documentar los motivos de los agricultores respecto a la cantidad y distribución de la diversidad de los cultivos locales que mantienen."},{"index":6,"size":18,"text":"• Identificar el nivel y tipo de intervenciones necesarias para la conservación y uso sostenible de las variedades."},{"index":7,"size":150,"text":"El desarrollo del M4C es el resultado de los intentos de científicos y otros actores en determinar el riesgo de pérdida de la diversidad genética y las razones por las que una especie o variedad se encuentra en riesgo. Los métodos para caracterizar la cantidad y distribución de variedades de cultivos se han desarrollado con base en las áreas promedio de producción y el número de hogares que están produciendo cada variedad. Las variedades cultivadas por los agricultores en un lugar determinado se clasifican en grupos de variedades que ocupan áreas grandes o pequeñas (con base en el área promedio entendida localmente), y las variedades que se cultivan en muchos y pocos hogares (en función del número de hogares) (Figura 2). Este método ha sido utilizado ampliamente en una variedad de formas para entender la cantidad y distribución de la diversidad de cultivos en las comunidades. 14,15,16,17,18,19,20,21,22 MÉTODO 4 CELDAS"}]},{"head":"Figura 2","index":4,"paragraphs":[{"index":1,"size":14,"text":"Esquema del Método 4 Celdas y ejemplo de su aplicación (Taller M4C Huancavelica, 2019)."}]},{"head":"II. Método 4 Celdas (M4C): Conceptos y Desarrollo","index":5,"paragraphs":[{"index":1,"size":110,"text":"El M4C es una herramienta que ayuda a los agricultores y otros miembros de la comunidad a identificar (i) las variedades en su territorio, (ii) su estado de uso y (iii) qué variedades han desaparecido o están desapareciendo de sus campos y por qué. Es un método interactivo (Pretty, 1995 y Cornwall, 2008), 12,13 basado en la participación en grupos focales de discusión con miembros de la comunidad, inclusive productores mujeres, más jovenes/ viejos, conservacionistas, líderes y otros que mantienen chacras pequeñas. ii. La resiliencia del paisaje está asociada con los servicios regulatorios y de apoyo, que incluyen la regulación de clima, enfermedades, agua, formación de suelos, etc.) (MEA, 2005)."},{"index":2,"size":123,"text":"iii. Los conocimientos y la cultura tradicional están asociados con los servicios culturales -como la identidad y el patrimonio cultural, incluida la cultura alimentaria (MEA, 2005). iv. Faith et al. (2010) 24 consideran que los procesos evolutivos que producen la variación viva son servicios del \"evosistema\", lo que es asociado con todos los usos o servicios para los humanos que se producen a partir de los procesos evolutivos. Los valores de opción de la biodiversidad se refieren a \"la idea de que mantener la variedad mantiene nuestras opciones para beneficiarnos de usos futuros de la biodiversidad\". 24 En la economía de la biodiversidad, el valor de opción se asocia con el valor de una póliza de seguro en el contexto de la incertidumbre."},{"index":3,"size":278,"text":"v. La equidad social es un indicador de aceptabilidad social por las comunidades de los programas de conservación, lo que tiene una influencia respecto a la sustentabilidad política a largo plazo. Bajo este indicador, se puede tomar en cuenta la participación de grupos vulnerables, como mujeres, jóvenes y pobres. Además, se puede tener en cuenta el grado de distribución de los fondos de conservación entre comunidades y participantes individuales (Narloch et al., 2011). 5 El grado en que los indicadores área sembrada y número de agricultores por variedad son críticos para el estado de la agrobiodiversidad ha sido respaldado por expertos de la diversidad infraespecífica de cultivos nativos peruanos priorizados, quienes se reunieron para discutir el concepto de metas de conservación y avanzaron en el establecimiento de dichas metas de conservación para los recursos genéticos de cultivos prioritarios. 25,26,27 El área sembrada y el número de agricultores son también dos de los siete grupos principales de indicadores de relevancia para el monitoreo del estado de la agrobiodiversidad que se encuentran en la literatura (ver Nguyen y Drucker, 2013 28 ). De estos siete grupos: área, número de productores, conocimiento, semilla, medida global de diversidad, medidas ex situ y distribución espacial, indicadores de área y número de productores pueden ser considerados como los indicadores más importantes, ya que también inciden en el grado de conocimiento, la medida de diversidad en términos de uso y la cantidad de semilla producida. Los otros dos grupos de indicadores, existencia de medidas ex situ y distribución espacial en el paisaje, a pesar de ser importantes a considerar, son indicadores de riesgo que dependen de información que no es necesariamente conocida al nivel comunitario."}]},{"head":"Objetivo de conservación Resiliencia del paisaje ii","index":6,"paragraphs":[]},{"head":"Conocimientos y cultura tradicionales iii","index":7,"paragraphs":[]},{"head":"Servicios evolutivos y valores de opciones iv","index":8,"paragraphs":[{"index":1,"size":51,"text":"Equidad Social v Tabla 1. Ejemplo del grado de conservación de servicios ecosistémicos i de bien público asociados con la agrobiodiversidad in situ/en chacra y el grado de equidad social bajo diferentes metas de área, número de productores y distribución espacial. 23,24 + Impacto mínimo, ++ Impacto intermedio, +++ Impacto máximo"},{"index":2,"size":96,"text":"Las métricas principales de riesgo que se miden bajo el M4C están relacionadas con las áreas sembradas por variedad/cultivo y el número de productores por variedad. Estas métricas, junto con otra información como la distribución espacial en el paisaje, están estrechamente vinculadas con los diferentes servicios ecosistémicos de bien público asociados con la agrobiodiversidad, que es importante asegurar como el objetivo de la conservación. Cuanto mayor sea el número de agricultores participantes, las áreas de conservación y su distribución entre las comunidades/paisajes, mayor será la contribución a los diferentes tipos de servicios ecosistémicos 23 (Tabla 1)."}]},{"head":"III. Adaptación del M4C al contexto del Proyecto GEF-Agrobiodiversidad SIPAM","index":9,"paragraphs":[{"index":1,"size":43,"text":"Hay tres pasos principales en el desarrollo de los talleres M4C. El primer paso es la obtención de consentimiento, el segundo, es el proceso general a seguir durante el taller y el tercero, es la aplicación de una evaluación al final del taller."},{"index":2,"size":140,"text":"La adaptación del M4C ha incluido la puesta en relieve del importante papel que los participantes pueden desempeñar no solo en la identificación per se de los recursos genéticos amenazados en su comunidad, sino también en la evaluación del método. Esto se ha hecho mediante el fortalecimiento de los procesos de consentimiento fundamentado previo al comienzo de los talleres de M4C (=Paso 1), espacio durante el cual se compartió información sobre el papel del M4C como parte de estrategias más amplias de conservación y desarrollo; así como la aplicación de una evaluación al final del taller (=Paso 3), para visibilizar los aprendizajes de los participantes y el grado en que los principales indicadores de riesgo utilizados (número de hogares de agricultores y variedades de áreas cultivadas) son prácticos y útiles, o si los indicadores alternativos pueden considerarse en futuros talleres."}]},{"head":"IV. Aplicación del M4C en el contexto del Proyecto GEF-Agrobiodiversidad SIPAM","index":10,"paragraphs":[{"index":1,"size":41,"text":"Con el fin de obtener más información sobre las variedades que son cultivadas por pocos hogares en áreas pequeñas (celda inferior derecha -Figura 3, foto 10; y Figura 4, fotos 5 y 7), se consideraron las siguientes criterios con algunas características:"},{"index":2,"size":27,"text":"• Ecológico: grado de disimilitud -distinción de otros materiales; potencial de adaptación al cambio climático; vulnerabilidad a factores abióticos/bióticos -como el cambio climático y las enfermedades/plagas emergentes."},{"index":3,"size":18,"text":"• Económico: importancia en términos de seguridad nutricional y alimentaria (disponibilidad, acceso, uso y estabilidad) y potencial comercial."},{"index":4,"size":6,"text":"• Cultural: ceremonial, uso medicinal, otros."},{"index":5,"size":27,"text":"En la Tabla 3, se presentan los resultados de las amenazas principales identificadas por los productores respecto a los cultivos y variedades identificadas como raras en 2021."},{"index":6,"size":31,"text":"vi. Debido a la pandemia, el proceso de priorización utilizado durante 2020 fue determinado por otro método: sobre la base de los conocimientos de los grupos familiares que trabajan en ayni."},{"index":7,"size":26,"text":"vii. Siguiendo la finalización del proceso de consentimiento relativos al tratamiento de datos personales. Además, se llevó a cabo una evaluación participativa al final del taller.      "}]},{"head":"V. Resultados del M4C","index":11,"paragraphs":[{"index":1,"size":55,"text":"Como resultado de la aplicación de los ocho talleres de M4C en 2019 y 2021, los agricultores trajeron >700 muestras de variedades de siete cultivos, de las cuales 140 fueron identificadas como en riesgo, es decir, cultivadas por pocos agricultores y en áreas muy pequeñas, y por lo tanto, prioritarias para la conservación (Tabla 2)."},{"index":2,"size":28,"text":"Las razones por las que estas variedades son raras varían, e incluyen factores ecológicos y socioeconómicos (Tabla 3 • Conocer nuevas variedades de papa que nunca he visto."},{"index":3,"size":16,"text":"• Escuchar de las variedades a los vecinos de mi comunidad que participan en el taller."},{"index":4,"size":13,"text":"• Formas de preparar la oca que no sabía y nunca había escuchado."},{"index":5,"size":14,"text":"• Han aprendido y compartido conocimientos de las variedades de maíz, papa y tarwi."},{"index":6,"size":24,"text":"• Reconocieron que no habían valorado las variedades nativas de papa y maíz, porque desconocían que tienen muchas propiedades que benefician una alimentación saludable."},{"index":7,"size":12,"text":"• La participación de mujeres en el taller. Hubo equidad de género."},{"index":8,"size":11,"text":"• Conocer nuevas variedades de tarwi y quinua que no conocía."},{"index":9,"size":11,"text":"• Escuchar de las variedades a los participantes de otras comunidades."},{"index":10,"size":20,"text":"• Aprendieron de los atributos medicinales que tienen las variedades de los cuatro cultivos que se trabajaron en el taller."},{"index":11,"size":7,"text":"• Conocieron variedades que no conocían antes."},{"index":12,"size":9,"text":"• Reconocieron variedades que antes había en las comunidades."},{"index":13,"size":12,"text":"• El taller les motivó a recordar los usos de cada variedad."},{"index":14,"size":16,"text":"¿Tiene sentido considerar áreas y número de productores por variedad para identificar el grado de riesgo?"},{"index":15,"size":11,"text":"• Sí, porque hemos visto las variedades que no conocemos todos."},{"index":16,"size":16,"text":"• Sí, porque sabemos cuántas familias pueden tener esas variedades si son muchos, pocos o nadie."},{"index":17,"size":30,"text":"• La relación entre área y número de productores les está indicando que muchas de las variedades cada vez se están sembrando menos, por lo que corren riesgo de desaparecer"},{"index":18,"size":13,"text":"• Más dedican más áreas para sembrar las variedades comerciales que tienen precio."},{"index":19,"size":8,"text":"• Sí, porque hemos identificado las variedades perdidas."},{"index":20,"size":51,"text":"• La relación entre área y número de productores sí indica el grado de riesgo de una variedad, porque cada vez el área que tiene cada productor va disminuyendo porque ellos van repartiendo a sus hijos, lo que a ellos no les permite tener disponibilidad de área para sembrar variedades nativas."},{"index":21,"size":26,"text":"• Mayormente siembran cultivos que tienen mercado. El productor cultiva para comer y para vender no importa unos cuantos kilos pero vende en sus mercados locales."},{"index":22,"size":29,"text":"¿Hay otros factores que considera que se deben tener en cuenta para identificar variedades en riesgo? En caso de que sí, ¿estos fueron mencionados por alguien durante el taller?"},{"index":23,"size":12,"text":"• Sí, deben participar más mujeres. Las mujeres seleccionamos, preparamos los alimentos."},{"index":24,"size":19,"text":"• Deben participar más adultos y niños para que aprendamos todos y valoremos lo que tenemos en la comunidad."},{"index":25,"size":15,"text":"• El cambio climático afecta algunos cultivos y los productores por no perder no siembran."},{"index":26,"size":8,"text":"• Cuando no tienen mercado, dejan de sembrarlo."},{"index":27,"size":49,"text":"• Por la forma de cultivo, por ejemplo, para el tarwi se necesitan suelos arenosos ricos en materia orgánica al igual que la papa, pero lo suelos cada vez se están volviendo poco fértiles. Como no da bien, ellos ya no siembran la misma área si no que disminuyen."},{"index":28,"size":34,"text":"• El exceso de lluvias sí fue mencionado. Afecta al cultivo de la papa, se llena de gusanos por lo que los productores disminuyen las áreas y cambian por otro cultivo para sembrar otros."},{"index":29,"size":14,"text":"• También influye la tenencia de tierras. Ellos indican que tienen muy pocas áreas."},{"index":30,"size":14,"text":"• Sí, deben participar más productores de más comunidades para intercambiar más sus conocimientos."},{"index":31,"size":15,"text":"• El cambio climático afecta algunos cultivos y los productores por no perder, no siembran"},{"index":32,"size":8,"text":"• Cuando no tienen mercado, dejan de sembrarlo."},{"index":33,"size":19,"text":"• Sí, fue mencionado. Cuando hay mucha lluvia, no es bueno para los ollucos, las ocas, porque se malogran."},{"index":34,"size":11,"text":"¿Cómo se puede mejorar la realización de talleres de este tipo?"},{"index":35,"size":10,"text":"• Comunicarnos con anticipación para preparar las semillas que tenemos."},{"index":36,"size":13,"text":"• Preparar también cómo podemos vender y preparar los alimentos con estos productos."},{"index":37,"size":33,"text":"• En general, a los productores les gustó la experiencia de participar en el taller. Ellos indicaron que fue motivador, porque aprendieron de los cultivos aspectos que no conocían, por ejemplo, del tarwi."},{"index":38,"size":13,"text":"• Se manifestaron que los talleres deben tener continuidad porque así aprenden más."},{"index":39,"size":21,"text":"• Capacitación por grupos en cada comunidad y después de esos talleres recién nos juntamos en un taller todas las comunidades."},{"index":40,"size":21,"text":"• En general, a los productores les gustó la experiencia de participar en el taller. Al mismo tiempo, manifiestan lo siguiente:"},{"index":41,"size":14,"text":"• Faltaron más invitados. Ellos indican que hubiese sido bueno que participaran más productores"},{"index":42,"size":39,"text":"• Faltaron más productos/ muestras de semillas, para que ellos puedan aprender más, por lo que ellos sugirieron que este taller debe realizarse en otras fechas como, por ejemplo, después de la cosecha (meses de abril, mayo o junio)."},{"index":43,"size":3,"text":"Fuente: Elaboración propia."}]},{"head":"Figura 5","index":12,"paragraphs":[{"index":1,"size":7,"text":"Evaluación participativa postaller M4C, Lares (Cusco) 2021."},{"index":2,"size":71,"text":"Acerca de cómo se podrían mejorar los talleres en el futuro, se señaló que la participación de un mayor número de agricultores, incluso de más comunidades, aumentaría el potencial de aprendizaje; que se debería alentar a más mujeres agricultoras a participar, dada su experiencia en la selección y preparación de alimentos; y que más adultos y niños deberían participar para crear conciencia sobre el valor de la diversidad en las comunidades."},{"index":3,"size":23,"text":"Los participantes también sugirieron que los talleres podrían mejorarse celebrándolos poco después de la cosecha, lo que permitiría a los participantes traer más"},{"index":4,"size":27,"text":"semillas y muestras, aumentando así el potencial de aprendizaje entre ellos. También solicitaron actividades de capacitación en la preparación y comercialización de alimentos de las variedades identificadas."},{"index":5,"size":40,"text":"Tras la finalización de la fase de priorización, los resultados de M4C se utilizaron para informar la implementación de concursos de conservación ReSCA. Estos resultados se presentan en el Informe Final Producto 3 (ReSCAS 2019-2021 bajo el Proyecto GEF-Agrobiodiversidad SIPAM)."}]},{"head":"VI. Discusión","index":13,"paragraphs":[{"index":1,"size":144,"text":"Entre los desafíos de implementar el M4C en el contexto de altos grados de diversidad (ej., papa y maíz), está el tiempo de un solo taller en un día para identificar y clasificar el gran número de muestras que los productores traen, además de ordenar (priorizar participativamente) las muchas variedades identificadas como en riesgo, y documentar los motivos que explican por qué estas variedades son raras. Esto requiere de un gran esfuerzo de organización del equipo facilitador. Soluciones potenciales a estos desafíos podrían incluir actividades pretaller (para asegurar que las muestras traídas ya estén etiquetadas con nombres) y trabajar al nivel de razas o especies (ej., las papas amargas, muchas de las cuales ya sabemos que están en riesgo), cuando es apropiado. Las actividades postaller también podrían permitir más tiempo para profundizar las discusiones respecto a las variedades en las otras celdas (no raras)."},{"index":2,"size":64,"text":"El M4C se puede implementar en cualquier momento de la temporada agrícola antes de la siembra. Aunque en el caso de cultivos con alta diversidad, sería preferible que se implemente después de la cosecha, como algunos agricultores también lo han mencionado, ya que hay más disponibilidad de muestras de las variedades, lo que hace posible la documentación de una línea de base más completa."},{"index":3,"size":155,"text":"El M4C ha resultado en la documentación colectiva de los motivos que explican por qué ciertas variedades son raras, incluyendo fundamentos culturales. Los motivos documentados varían. Algunas variedades son raras porque se ha perdido totalmente la semilla, otras porque se está perdiendo la costumbre de consumirlas (ej., papa amarga) o debido al cambio climático y susceptibilidad a plagas y enfermedades. Hay algunas que vienen de otras provincias y regiones, y así no son reconocidas como propias del contexto local. Esto destaca la importancia de aplicar el M4C a escalas apropiadas, al nivel de paisaje y no solo de subpaisaje/comunidades cercanas, ya que la escala de análisis tiene implicaciones para la priorización. No todo lo que parece en riesgo al nivel local es necesariamente raro a escalas mayores y la configuración espacial de actividades de conservación tiene una fuerte influencia en la provisión de los servicios ecosistémicos de bien público asociados con la agrobiodiversidad (Tabla 1)."},{"index":4,"size":99,"text":"La documentación, visibilización y discusión colectiva de estos motivos, que la aplicación del M4C permite, es importante tanto para la priorización como para la identificación subsiguiente de estrategias de conservación y el empoderamiento derivado del mismo. Además, el potencial de agregar tales tipos de datos desde el nivel local/municipal hasta el nivel regional/ nacional, para informar estrategias de intervención también permitiría, con retroalimentación regular, que las comunidades locales/municipios desarrollen una perspectiva de cómo sus propios recursos genéticos y sus acciones se enmarcan en el contexto más amplio nacional y mundial y consecuentemente poder visibilizar su contribución al bien público."}]},{"head":"VII. Conclusiones e implicaciones para la identificación de estrategias e intervenciones de conservación","index":14,"paragraphs":[{"index":1,"size":103,"text":"El M4C es una herramienta de evaluación que se puede utilizar para establecer líneas de base y complementar otros métodos de monitoreo y validación como, por ejemplo, encuestas de mercados y ferias. 16 No toda variedad que parece rara puede/debe ser priorizada para intervención, por ejemplo, las que no son raras en provincias/regiones cercanas, o que no tienen un alto valor público porque no son muy distintas de otras variedades que no son raras; así como para intervenciones únicamente in situ/en chacra (ej., las que son susceptibles a plagas y enfermedades o están siendo impactadas por el cambio climático pueden conservarse ex situ)."},{"index":2,"size":1,"text":"x"},{"index":3,"size":54,"text":"Como el M4C también identifica variedades perdidas donde muchas veces no se pueden encontrar semillas in situ/en chacra, se presenta la oportunidad de colaborar con redes de bancos de semillas para enfrentar tales desafíos. Esta situación ha sido ampliamente potenciada en el caso de Huehuetenango, Guatemala 21 y podría orientar estrategias futuras en Perú."},{"index":4,"size":31,"text":"A nivel nacional, la implementación del M4C también puede apoyar la generación de datos necesarios para justificar la financiación de intervenciones relacionadas con la conservación y el uso de la agrobiodiversidad."},{"index":5,"size":62,"text":"Por ejemplo, el MINAM pretende utilizar este tipo de datos para justificar la existencia de las \"brechas\" entre el estado actual de la agrobiodiversidad y las metas de conservación establecidas como parte de los marcos regulatorios nacionales e internacionales, requisito previo para la asignación de fondos del Ministerio de Economía y Finanzas (MEF) para fines de conservación y uso de la agrobiodiversidad."},{"index":6,"size":92,"text":"Aunque en el marco del Proyecto GEF-Agrobiodiversidad SIPAM, solo ha sido posible aplicar el M4C con las comunidades dentro del número de sitios en los que este se ejecuta, el método podría aplicarse a nivel de paisaje como parte de un sistema de monitoreo de la agrobiodiversidad y podría tener un papel central en la identificación de las \"Listas Rojas\" de la agrobiodiversidad (Padulosi et al., 2014 16 ). El método es comparativamente de bajo costo, lo que facilita su sostenibilidad económica a largo plazo para la agencia competente responsable del monitoreo."},{"index":7,"size":78,"text":"Cualquier esfuerzo respecto al monitoreo tiene que hacerse con participación del estado a diferentes niveles, desde el nivel local a nacional, y por entidades imparciales. Esta es una responsabilidad compartida y más bien se deben ver los posibles mecanismos de que así ocurra. En este contexto, el Grupo Técnico de Agrobiodiversidad, que es coordinado por INIA y pertenece a la Comisión Nacional de Diversidad Biológica (CONADIB) tiene un rol, la cual a su vez está adscrita al MINAM."},{"index":8,"size":56,"text":"x. Los portafolios prioritarios de conservación identificados a través del M4C no son definidos para facilitar la captura de valores privados en primera instancia, aunque tales tipos de beneficios privados pueden resultar como un efecto secundario, como el ejemplo de la compra en Puno de chulpi por KaiPacha Alimentos para la producción de leche de quinua."}]},{"head":"cgiar.org alliancebioversityciat.org","index":15,"paragraphs":[{"index":1,"size":31,"text":"La Alianza es parte de CGIAR, un consorcio mundial de investigación para un futuro sin hambre, dedicado a transformar los sistemas alimentarios, terrestres y acuáticos en medio de una crisis climática."},{"index":2,"size":2,"text":"ISBN: 978-92-9255-279-4"}]}],"figures":[{"text":"FigurasFigura 1 . 6 Figura 2 . 7 Figura 3 . Figuras "},{"text":"Figura 4 . 13 Figura 5 . Figura 4. Taller de priorización M4C para maíz y papa, Lares (Cusco) 2019 ............................................................. 13 Figura 5. Evaluación participativa postaller M4C, Lares (Cusco) 2021 .....................................................................17 "},{"text":"8 Tabla 2 . 3 . 4 . 5 . 6 . de productores y distribución espacial ...................................................................................Talleres de M4C en cuatro regiones peruanas durante 2019 y 2021 .................................................... Tabla Clasificación de los riesgos identificados por los agricultores a través del M4C ................................. Tabla Porcentaje de variedades por áreas, número de hogares y tendencias en cuatro regiones bajo el M4C en 2021 ............................................................................................. Tabla Porcentaje de variedades por áreas y número de hogares bajo el M4C en 2019 ................................. Tabla Resultados de la evaluación posterior al taller de M4C 2021 en cuatro regiones (Discusión entre todos) ..................................................................................................................... "},{"text":"Fuente:Tabla 3 .•••• Fuente: Elaboración propia (Talleres M4C 2019 y 2021). "},{"text":"Figura 3 1 Foto 1 : Figura 3Taller de priorización M4C para papa, Laria, Huancavelíca, 2019. "},{"text":"Figura 4 Figura 4Taller de priorización M4C para maíz y papa, Lares (Cusco) 2019. "},{"text":"Fotos 1 - 3 : 5 : Documentación de la diversidad de maíz, Foto 4: Identificación de maíz chili como variedad rara, por motivos culturales, sin embargo con valor como un bien público, Foto Identificación de las variedades de maíz raras y prioritarias para la conservación, Foto 6: Documentación de la diversidad de papa, Foto 7: Identificación de las variedades de papas raras y prioritarias para la conservación. Crédito fotos A. Drucker/Bioversity International. "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"Tabla 2 . Talleres de M4C en cuatro regiones peruanas durante 2019 y 2021.  Variedades totales identificadas No. Priorizada Nombres de variedades priorizadas No. Comunidades Participantes No. Agricultores Participantes (% mujeres) Variedades totales identificadasNo. PriorizadaNombres de variedades priorizadasNo. Comunidades ParticipantesNo. Agricultores Participantes (% mujeres) Apurímac, 2021 32 8 papa Papa: 1) Emilia, 2) Maranguita, 3) Quchiacan, 4) Vacapa rurun, 5) Yana   Apurímac, 2021328 papaPapa: 1) Emilia, 2) Maranguita, 3) Quchiacan, 4) Vacapa rurun, 5) Yana  6 Oca 26 Papa  Pisqui, 6) Yanajanchi, 7) Yunca jasi, 8) Yurac Warmi  8 (25%) 6 Oca 26 PapaPisqui, 6) Yanajanchi, 7) Yunca jasi, 8) Yurac Warmi8 (25%) Apurímac, 2019 >300 Papa 28 Papa Papa: 1) Arcca, 2) Beterraga, 3) Cayhuire, 4) Ccanchi, 5) Chaudia, 6)   Apurímac, 2019>300 Papa28 PapaPapa: 1) Arcca, 2) Beterraga, 3) Cayhuire, 4) Ccanchi, 5) Chaudia, 6)    Chingos, 7) Cuchipa Acan, 8) Harina Costal, 9) Huaña, 10) Huancaina,   Chingos, 7) Cuchipa Acan, 8) Harina Costal, 9) Huaña, 10) Huancaina,    11) Huachwapan Qallun, 12) Huaytaro, 13) Ila Huaña, 14) Mariano,   11) Huachwapan Qallun, 12) Huaytaro, 13) Ila Huaña, 14) Mariano,    15) Moronguita, 16) Pepino, 17) Pichki, 18) Puca kutana, 19) Puca  25 (12%) 15) Moronguita, 16) Pepino, 17) Pichki, 18) Puca kutana, 19) Puca25 (12%)    Suytu, 20) Putis, 21) Puyae Narmi, 22) Qillo Chocclloima, 23) Runapa   Suytu, 20) Putis, 21) Puyae Narmi, 22) Qillo Chocclloima, 23) Runapa    Machin, 24) Runtos, 25) Totora, 26) Vacap Perun, 27) Wira Pasña,   Machin, 24) Runtos, 25) Totora, 26) Vacap Perun, 27) Wira Pasña,    28) Yawar Songo   28) Yawar Songo Cusco, 2021 12 3 Tarwi Tarwi: 1) Ch'eqche, 2) Chumpi quelwa, 3) Yana quellwa   Cusco, 2021123 TarwiTarwi: 1) Ch'eqche, 2) Chumpi quelwa, 3) Yana quellwa  5 Quinua    18 (33%) 5 Quinua18 (33%)  7 Tarwi     7 Tarwi Cusco, 2019 180 8 Maíz    Cusco, 20191808 Maíz  ≈80 Maíz 32 Papa viii    ≈80 Maíz32 Papa viii  ≈100 Papa     ≈100 Papa      11 (9%) 11 (9%)    Yana salseki, 29) Yanacacha huacachi, 30) Yanaquecho huacachi,   Yana salseki, 29) Yanacacha huacachi, 30) Yanaquecho huacachi,    31) Yurac beronas, 32) Yurac llamasenca   31) Yurac beronas, 32) Yurac llamasenca Huancavelica, 70 9 Maíz Maíz: 1) Occquellhuay hembra, 2) Ocquellhuay macho, 3) Miza, 4)   Huancavelica,709 MaízMaíz: 1) Occquellhuay hembra, 2) Ocquellhuay macho, 3) Miza, 4) 2021 17 Maíz 50 Papa 3 Tarwi 25 Papa 3 Tarwi Morocho, 5) Chullpi, 6) Rojo, 7) Granada, 8) Huamani, 9) Tarhuay Papa: 1) Allca suyto, 2) Butiguela, 3) Cuchipa acana, 4) Ganchillo   202117 Maíz 50 Papa 3 Tarwi25 Papa 3 TarwiMorocho, 5) Chullpi, 6) Rojo, 7) Granada, 8) Huamani, 9) Tarhuay Papa: 1) Allca suyto, 2) Butiguela, 3) Cuchipa acana, 4) Ganchillo    morado, 5) Ganchillo yurag, 6) Iscopuro, 7) Mishipa makin, 8) Muro   morado, 5) Ganchillo yurag, 6) Iscopuro, 7) Mishipa makin, 8) Muro    gaspar, 9) Papa canta; 10) Para sanray, 11) Payansa, 12) Pepino, 13) Puka gaspar, 14) Puka pasñapapa, 15) Puka sari, 16) Pumapa makin, 17)  20 (55%) gaspar, 9) Papa canta; 10) Para sanray, 11) Payansa, 12) Pepino, 13) Puka gaspar, 14) Puka pasñapapa, 15) Puka sari, 16) Pumapa makin, 17)20 (55%)    Runtos, 18) Sari, 19) Serrina, 20) Teja huasi, 21) Yana panu, 22) Yanañawi   Runtos, 18) Sari, 19) Serrina, 20) Teja huasi, 21) Yana panu, 22) Yanañawi    pasñapapa, 23) Yana passpapa, 24) Yurag llumchuy huaccachi, 25)   pasñapapa, 23) Yana passpapa, 24) Yurag llumchuy huaccachi, 25)    Yurag siri   Yurag siri    Tarwi: 1) Ancas wayta, 2) Yana tarwi, 3) Yuraq puka wayta   Tarwi: 1) Ancas wayta, 2) Yana tarwi, 3) Yuraq puka wayta Huancavelica, ≈100 papa 12 Papa: 1) Botijuela, 2) Callhuay (amargo), 3) Cauysay, 4) Ccanchillo   Huancavelica,≈100 papa12Papa: 1) Botijuela, 2) Callhuay (amargo), 3) Cauysay, 4) Ccanchillo 2019   (amargo), 5) Challhuay Siri (amargo), 6) Mashua Papa, 7) Puca Wihgullo, 8) Sumac Sonqu, 9) Yana Manua (amarga), 10) Yana Siri  37 (24%) 2019(amargo), 5) Challhuay Siri (amargo), 6) Mashua Papa, 7) Puca Wihgullo, 8) Sumac Sonqu, 9) Yana Manua (amarga), 10) Yana Siri37 (24%)    (amarga), 11) Yana Winjullo (amarga), 12) Yuracc Sari (amargo)   (amarga), 11) Yana Winjullo (amarga), 12) Yuracc Sari (amargo) Puno, 2021 17 7 Mashua: 1) Negro   Puno, 2021177Mashua: 1) Negro  1 Mashua 5 Tarwi 5 Olluco 6 Oca  Olluco: 1) Chupika olluco: Rosa; 2) Quello olluco: Amarillo Oca: 1) Chiara apilla: Negro, 2) Quello apilla: Amarillo Tarwi: 1) Cheje tarwi, 2) Chiara tarwi: Negro 11 14 (64%) 1 Mashua 5 Tarwi 5 Olluco 6 OcaOlluco: 1) Chupika olluco: Rosa; 2) Quello olluco: Amarillo Oca: 1) Chiara apilla: Negro, 2) Quello apilla: Amarillo Tarwi: 1) Cheje tarwi, 2) Chiara tarwi: Negro1114 (64%) Puno, 2019 N/A 5 Quinua: 1) Ccoito, 2) Chullpi, 3) Huariponcho, 4) Miso quinua, 5) Quello Huitullo N/A N/A ix Puno, 2019N/A5Quinua: 1) Ccoito, 2) Chullpi, 3) Huariponcho, 4) Miso quinua, 5) Quello HuitulloN/AN/A ix Total >700 140 112 raíces y tubérculos; 28 maíz, quinua y tarwi   Total>700140112 raíces y tubérculos; 28 maíz, quinua y tarwi   (2019= 85+5  40 108 (38%) (2019= 85+540108 (38%)   2021 = 55)    2021 = 55)  "},{"text":" Porcentaje* de variedades por áreas y número de hogares bajo el M4C en 2019**. Resultados de la evaluación posterior al taller de M4C 2021 en cuatro regiones (Discusión entre todos). Tabla 6. Pregunta de evaluación Apurímac   Huancavelica    Cusco Puno Tabla 6. Pregunta de evaluaciónApurímacHuancavelicaCuscoPuno ¿Cuáles son las cosas más Apurímac Cusco provincias y regiones, por lo cual no se consideran locales y no fueron priorizadas por los agricultores. Huancavelica Total % Total ¿Cuáles son las cosas másApurímacCuscoprovincias y regiones, por lo cual no se consideran locales y no fueron priorizadas por los agricultores. Huancavelica Total % Total importantes que han        importantes que han No. variedades aprendido durante el  >300  Los cuatro talleres M4C en 2021 resultaron en la 190 66 >556 No. variedades aprendido durante el>300Los cuatro talleres M4C en 2021 resultaron en la 190 66 >556 taller, que no sabían Área antes?    identificación de 130 variedades, 55 de las cuales fueron priorizadas (Tabla 2, cuarta celda). En la Tabla 4 se puede taller, que no sabían Área antes?identificación de 130 variedades, 55 de las cuales fueron priorizadas (Tabla 2, cuarta celda). En la Tabla 4 se puede  Mucha    observar que el 64% de las 130 variedades son cultivadas 14% 30% 47 8% Muchaobservar que el 64% de las 130 variedades son cultivadas 14% 30% 47 8%      por pocos productores y el 68% se cultivan en áreas muy por pocos productores y el 68% se cultivan en áreas muy  Mediana    pequeñas. Con relación a los aspectos dinámicos, el 60% 19% 3% 39 7% Medianapequeñas. Con relación a los aspectos dinámicos, el 60% 19% 3% 39 7% ). Algunas variedades son raras porque la semilla se ha perdido en gran medida, otras porque los hábitos de Pequeña 9% de los cultivos mostraban una tendencia de disminución en términos de área en comparación con hace 10 años, 47% 58% 155 28% ). Algunas variedades son raras porque la semilla se ha perdido en gran medida, otras porque los hábitos de Pequeña 9%de los cultivos mostraban una tendencia de disminución en términos de área en comparación con hace 10 años, 47% 58% 155 28% consumo están cambiando (por ejemplo, la reducción del Cero (pérdida) en donde se evidencia que Cusco y Apurímac presentan 3% 9% 12 2% consumo están cambiando (por ejemplo, la reducción del Cero (pérdida)en donde se evidencia que Cusco y Apurímac presentan 3% 9% 12 2% consumo de papa amarga) o debido al cambio climático las disminuciones más drásticas. Además, el 42% de consumo de papa amarga) o debido al cambio climáticolas disminuciones más drásticas. Además, el 42% de y otras por su susceptibilidad a plagas y enfermedades. No clasificado 91% las 12 variedades entre maíz y tarwi, identificadas en 22% 0% 313 56% y otras por su susceptibilidad a plagas y enfermedades. No clasificado 91%las 12 variedades entre maíz y tarwi, identificadas en 22% 0% 313 56% Otras son raras porque siempre fueron cultivadas por muy pocos agricultores y están asociadas principalmente Hogares Cusco, fueron consideradas como perdidas. Los datos para 2019 (Tabla 5) también indican un alto porcentaje Otras son raras porque siempre fueron cultivadas por muy pocos agricultores y están asociadas principalmente HogaresCusco, fueron consideradas como perdidas. Los datos para 2019 (Tabla 5) también indican un alto porcentaje con usos rituales y/o se consideran de mala suerte (ver Mucha de variedades cultivadas en muy pocas áreas en Cusco y 34% 53% 99 18% con usos rituales y/o se consideran de mala suerte (ver Muchade variedades cultivadas en muy pocas áreas en Cusco y 34% 53% 99 18% ejemplo de maíz chili en la Figura 4, foto No. 4). Además, Huancavelica (47% y 58%, respectivamente) y por pocos ejemplo de maíz chili en la Figura 4, foto No. 4). Además,Huancavelica (47% y 58%, respectivamente) y por pocos se identificaron variedades que provienen de otras Mediana  hogares (40% y 27%, respectivamente). 7% 11% 20 4% se identificaron variedades que provienen de otras Medianahogares (40% y 27%, respectivamente). 7% 11% 204%  Pocos  9%  40% 27%  122 22% Pocos9%40%27%12222%  Cero    3% 9%  12 2% Cero3%9%122%  No clasificados Apurímac Cusco 91%  Huancavelica 22% 0% Puno 313 Total % Total 56% No clasificadosApurímacCusco 91%Huancavelica 22%0%Puno313Total% Total 56% No. variedades 26  12 70  22  130 No. variedades26127022130 Área         Área Mucha Fuente: Elaboración propia. 38%  0% 6%  5%  15 12% MuchaFuente: Elaboración propia.38%0%6%5%1512% Mediana 31%  0% 10%  14%  18 14% Mediana31%0%10%14%1814% Pequeña 31% 58% 80%  82%  89 68% Pequeña31%58%80%82%8968% Cero (pérdida) 0%  42% 0%  0%  5 4% Cero (pérdida)0%42%0%0%54% Sin opinar o sin datos registrados La evaluación postaller M4C por los participantes 0% 0% 4% oportunidad de poder visibilizar lo que se está perdiendo; 0% 3 2% Sin opinar o sin datos registrados La evaluación postaller M4C por los participantes 0%0%4% oportunidad de poder visibilizar lo que se está perdiendo; 0% 3 2% Hogares ha revelado (Tabla 6) que los agricultores valoran la  c) la oportunidad de aumentar el conocimiento sobre Hogares ha revelado (Tabla 6) que los agricultores valoran lac) la oportunidad de aumentar el conocimiento sobre Mucha oportunidad de aprender e intercambiar conocimientos 46% 42% con sus pares de otras comunidades sobre temas 17% variedades raras; y d) la contribución al empoderamiento 9% 31 24% a través del conocimiento acerca de los recursos que se Mucha oportunidad de aprender e intercambiar conocimientos 46% 42% con sus pares de otras comunidades sobre temas17% variedades raras; y d) la contribución al empoderamiento 9% 31 24% a través del conocimiento acerca de los recursos que se Mediana asociados con sus cultivos. Los agricultores destacaron 4% 0% 4% manejan en el ámbito de comunidades vecinas, y poder 5% 5 4% Mediana asociados con sus cultivos. Los agricultores destacaron 4% 0%4% manejan en el ámbito de comunidades vecinas, y poder 5% 5 4% Pequeña a) la gran importancia de participar como parte de un 38% 17% 74% actuar de un modo u otro mediante su participación o no 86% 83 64% Pequeña a) la gran importancia de participar como parte de un 38%17%74% actuar de un modo u otro mediante su participación o no 86% 83 64% Cero grupo de pares en la identificación de las variedades 12% 42% 0% en ReSCA. Para observar un ejemplo de una evaluación 0% 8 6% Cero grupo de pares en la identificación de las variedades 12%42%0% en ReSCA. Para observar un ejemplo de una evaluación 0% 8 6% Sin opinar o sin datos registrados en riesgo, una experiencia tildada de única; b) la 0% 0% 4% posterior al taller ver la Figura 5. 0% 3 2% Sin opinar o sin datos registrados en riesgo, una experiencia tildada de única; b) la 0%0%4% posterior al taller ver la Figura 5. 0%32% Tendencia área últimos 10 años        Tendencia área últimos 10 años Aumento 0%  8% 9%  27%  13 10% Aumento0%8%9%27%1310% Sin cambio 0%  0% 50%  5%  36 28% Sin cambio0%0%50%5%3628% Disminución 100% 83% 41%  59%  78 60% Disminución100%83%41%59%7860% No se sabe 0%  8% 0%  9%  3 2% No se sabe0%8%0%9%32%  "}],"sieverID":"805d6ecd-1b88-4d2c-af86-ab82b7e0fea9","abstract":""}

data/part_5/0a2cb835e92ae0314a2affb943814960.json

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+{"metadata":{"id":"0a2cb835e92ae0314a2affb943814960","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/b5a092d5-8033-47e6-97b8-b6a2d070414a/retrieve"},"pageCount":2,"title":"Maps of abiotic stresses for rice in Africa","keywords":["No milestones associated Sub-IDOs:","10 -Closed yield gaps through improved agronomic and animal husbandry practices <Not Defined> Contributing CRPs/Platforms:","CCAFS -Climate Change, Agriculture and Food Security","Rice -Rice"],"chapters":[],"figures":[],"sieverID":"c64d834f-1ea3-4155-a9b9-c0100f687e17","abstract":"Description of the innovation: Maps showing hotspots of the stresses, the countries most affected and total potentially affected area are developed. This can be useful for (1) prioritizing research and (2) identifying stress hotspots, for directing technologies and varieties to those areas where they are most needed."}

data/part_5/0a963a2c16d17a8af99484f980efb084.json

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+{"metadata":{"id":"0a963a2c16d17a8af99484f980efb084","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/971251fa-2793-464d-8dd3-b804676d1bcf/retrieve"},"pageCount":13,"title":"CIMMYT-Eastern Africa Early-and Intermediate-Maturity Product Profiles Product Profile (with must-have traits)","keywords":[],"chapters":[{"head":"Conclusion","index":1,"paragraphs":[{"index":1,"size":71,"text":"• There is variability in tropical maize germplasm for FAW resistance. • There FAW tolerant hybrids released in Kenya, Zambia, Malawi, and South Sudan. • More than 25 FAW-resistant lines with higher GCA identified and will be used as donors to develop and deploy FAW tolerant/resistant elite maize hybrids in SSA • Possible to achieve synergies between host plant resistance and other IPM approaches for sustainable management of FAW by smallholders."}]},{"head":"Thank you for your interest!","index":2,"paragraphs":[]}],"figures":[{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"• Several of the leading varieties in this market at present are very old, and do NOT have drought tolerance or MLN resistance.  "},{"text":"in Kenya in 2017 Putative Resistant) developed from the MBR population  Screening of maize lines under FAW Screening of maize lines under FAW natural infestation at Kiboko (2017) natural infestation at Kiboko (2017)  "},{"text":"• Breeding for insect-pest resistance -requires good knowledge of the biology of the host as well as the pest -Available diversity of germplasm -Efficient insect-rearing technique -Efficient artificial infestation of the host -Breeding techniques employed High incidence of FAW in the experimental field at Mbeere, Kenya in 2017  "},{"text":"CML444 (Susceptible) Breeding for FAW resistance at CIMMYT-Kenya under artificial infestation started in 2018    Foliar Foliar Entry Name damage(1-9) EntryNamedamage(1-9) 54 CML71 4.9 54CML714.9 23 CKSBL10153 5.2 23 CKSBL101535.2 19 CKSBL10008 5.3 19 CKSBL100085.3 18 CKSBL10004 5.3 18 CKSBL100045.3 20 CKSBL10021 5.6 20 CKSBL100215.6 17 CKSBL10002 5.9 17 CKSBL100025.9 34 CML312 7.3 34CML3127.3 60 CML442 7.3 60CML4427.3 58 CML444 7.4 58CML4447.4 59 CML395 7.7 59CML3957.7  Heritability 0.8 Heritability0.8  Grand Mean 6.9 Grand Mean6.9  LSD 1.1 LSD1.1  CV 7.3 CV7.3  "},{"text":"CIMMYT-Maize germplasm evaluated under FAW artificial infestation (2018-2022) Only 4% of lines had a <4 score 23% of SX hybrids had a <4 score 10% of TWC hybrids had a <4 score  Development Development # lines # lines screened screened line=3390 line=3390 # SX # SX screened screened line=611 line=611 # TWC # TWC screened screened line=1334 line=1334  "},{"text":"of First-Generation FAW-tolerant Hybrids   "},{"text":"of FAW-tolerant Hybrids under \"No Choice\" and optimum conditions   "},{"text":"based prediction and observed F1 hybrid performance  Genetic analysis of Maize Lines Evaluated Genetic analysis of Maize Lines Evaluated under FAW Infestation   under FAW Infestation • Both additive and        • Both additive and nonadditive gene Source  GY FD1 FD2 FD3 ED ER nonadditive geneSourceGYFD1FD2FD3EDER action were Experiment I Heritability 0.53 0.32 0.33 0.35 0.65 0.54 action wereExperiment I Heritability0.530.320.330.350.650.54 important in the Baker's ratios Experiment II 0.83 0.84 0.87 0.92 0.89 0.92 important in theBaker's ratios Experiment II0.830.840.870.920.890.92 inheritance of FAW Heritability Baker's ratios 0.61 0.55 0.49 0.85 0.38 0.83 0.39 0.84 0.51 0.94 0.56 0.91 inheritance of FAWHeritability Baker's ratios0.61 0.550.49 0.850.38 0.830.39 0.840.51 0.940.56 0.91 resistance        resistance • More than 25 inbred lines with a Parent 8 Pedigree CKSBL10008 GCA 1.69 Standard_Error 0.12 T_Value 14.13 Kamweru et al 2023 Prob_T RANK 0.00 1 • More than 25 inbred lines with aParent 8Pedigree CKSBL10008GCA 1.69Standard_Error 0.12T_Value 14.13Kamweru et al 2023 Prob_T RANK 0.00 1 good general combing ability of >1 tone/ha were identified as new 22 12 16 6 19 20 10 CML338 CKSBL10153 CLRCY039 CKSBL10039 CKSPL10089 CML22 CKSBL10060 1.50 1.20 0.77 0.72 0.62 0.24 0.20 0.12 0.12 0.12 0.12 0.12 0.12 0.12 12.52 10.00 6.43 5.98 5.17 1.98 1.70  0.00 0.00 0.00 0.00 0.00 0.06 0.10 2 3 4 5 6 7 8 good general combing ability of >1 tone/ha were identified as new22 12 16 6 19 20 10CML338 CKSBL10153 CLRCY039 CKSBL10039 CKSPL10089 CML22 CKSBL100601.50 1.20 0.77 0.72 0.62 0.24 0.200.12 0.12 0.12 0.12 0.12 0.12 0.1212.52 10.00 6.43 5.98 5.17 1.98 1.700.00 0.00 0.00 0.00 0.00 0.06 0.102 3 4 5 6 7 8 donors 2 5 CKSPL10158 CKSBL10027 -1.25 -1.26 0.12 0.12 -10.47 -10.57  0.00 0.00 21 22 donors2 5CKSPL10158 CKSBL10027-1.25 -1.260.12 0.12-10.47 -10.570.00 0.0021 22  13 CKSBL10020 -1.63 0.12 -13.63  0.00 23 13CKSBL10020-1.630.12-13.630.0023  "},{"text":"Table 1 . Example of GCA/SCA in the selected diallel experiments  "},{"text":"Table 2 . General combining ability effects lines for grain yield s under artificial infestation of FAW management  "}],"sieverID":"50c8faf9-cf14-4c6c-8b74-5450e93ff200","abstract":"Emerging /value added traits Altitud e Rainfall Target Countries Estima ted Area( M/ ha) Marke t share (%) Results from screening lines to FAW under artificial infestation in 2018 CML71 CKSBL10153 Development of FAW tolerant DH lines using resistant source CKIR04005 (OPV, Rep 1) CKIR04005 (OPV, Rep 2) FAW DH line derived from FAW tolerant OPV at Kiboko DH facility"}

data/part_5/0b0027591f836af000f4766c87b48bb9.json

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+{"metadata":{"id":"0b0027591f836af000f4766c87b48bb9","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/a47be894-77a9-43d9-a1a9-4e1a4c624251/retrieve"},"pageCount":1,"title":"","keywords":[],"chapters":[],"figures":[],"sieverID":"b5fee65a-29fa-4837-a3b2-9ec1efb2438f","abstract":""}

data/part_5/0b34a903af98dbce5af7d99f69558841.json

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+{"metadata":{"id":"0b34a903af98dbce5af7d99f69558841","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/815d0887-41c8-4e4c-b01c-70918eed90be/retrieve"},"pageCount":10,"title":"Representing SSR Molecular Marker Profiles Using Concepts from Andean Khipus","keywords":[],"chapters":[{"head":"INTRODUCTION","index":1,"paragraphs":[{"index":1,"size":123,"text":"Genebanks increasingly use molecular markers for routine characterization of ex-situ collections and farmer managed diversity. CIPs genebank presently uses a SSR marker-kit [8] to assist cultivar or genotype identification through molecular profiling. Applications include the comparison of ex-situ and in-situ collections to assess effectiveness and orient conservation strategies [7] or to document provenance and attribution as in an in-situ catalog [6]. A primary motivation came from Andean potato growing communities that are in a working relationship with CIP: farmers called for support to aid in registry and identification of landraces and protect native varieties against 'biopiracy'. As a visual aid to compare SSR marker profiles between accessions in these contexts we identified the need for a compact graphical presentation similar to a 'bar-code'."},{"index":2,"size":6,"text":"A first set of criteria included:"},{"index":3,"size":7,"text":"• Amenable to standardization using bioinformatics tools"},{"index":4,"size":14,"text":"• compact presentation (e.g. a chart of maximum 2.5cm x 2cm width by height)"},{"index":5,"size":9,"text":"• all SSR marker information for a given land-race"},{"index":6,"size":14,"text":"• Convey the individuality of a genotype in comparison to diversity in a group"}]},{"head":"• Recognize the contribution of Andean farmers to the development of the potato crop","index":2,"paragraphs":[{"index":1,"size":219,"text":"To our knowledge no tool exists that would have allowed us to produce such compact graphs based on a set of informative SSR marker. In the context of the production of the first in-situ catalog ( [6] and [9]) of Andean potato landraces it seemed worthwhile to consider the use or adaptation of the traditional Andean information communication tool -the khipu (see Figure 1). Khipus consisted of a set of chords organized as a set of pendants hanging from a backbone chord. They used colors and knots to store a wide variety of information from tribute statistics (see Figure 2) to state history [2] to sins [4]. Numbers apparently were represented as groups of knots and in a top-down order from 1000s to 100s to 10s and ones [16]. Currently, khipus are not any more read by local people but some are still used for ceremonial purposes [17]. The interpretation and usage of khipus is still not fully understood [16], so we did not aim to replicate a historically accurate way of coding SSR marker information. We rather used the khipu as an inspiration to design our own version. In this paper we summarize the process, design elements and evolution, implementation, use and reception. Details on usage can be obtained from the tutorial [13] available together with the software."}]},{"head":"METHODS","index":3,"paragraphs":[]},{"head":"Process","index":4,"paragraphs":[{"index":1,"size":119,"text":"The first idea of using the khipu concept was refined by matching the properties of a set of molecular markers against the generic properties of a khipu (see details below). Then, in a first round, design criteria were consolidated and several draft designs tested on real data. Subsequently, the molecular khipu idea was presented to the general public through posters at CIP's genebank foyer, to scientists and breeders through journals [14], at conferences ( [10] and [12]), and through web sites ( [5]) as well as to farmers ( [6] and [9]) to solicit feedback over the course of several years. Recently, the molecular khipu idea was consolidated in a publicly available open source software ( [11] and [13]). "}]},{"head":"Design principle","index":5,"paragraphs":[{"index":1,"size":111,"text":"The basic design principle is described in more detail in the legend of Figure 3. In summary, SSR marker data are generated in a molecular laboratory applying each marker separately to a batch of genotypyes. In the case of the molecular marker kit for potato [8] also the set of SSR markers is defined. Therefore, the SSR data can be simply re-organized to form a molecular profile or molecular khipu. The principal equivalencies used in Figure 3 to turn the profile into a khipu are listed in Table 1. An important assumption is that only single-copy SSR markers are used. A first prototype using real data is shown in Figure 4. "}]},{"head":"Design refinements","index":6,"paragraphs":[{"index":1,"size":82,"text":"The next principal idea was to include comparative elements into each molecular khipu: how does this genotype compare to a reference genepool? To this end, we used in the second prototype the size of an elliptic dot or 'node'. Color was used to indicate to which chromosome a marker belongs as well as to indicate the range of allele sizes for each marker. See Figure 5. This prototype also tried to more closely visualize the string structure of a physical original khipu."},{"index":2,"size":27,"text":"However, eventually some of these latter elements were removed following general design principles of favoring simplicity and avoiding to depend only on color to convey meaning [15]."}]},{"head":"Software implementation and availability","index":7,"paragraphs":[{"index":1,"size":37,"text":"A first version was implemented using the programming language PHP; subsequently, the algorithm was transferred to the language R and ultimately organized as a reusable package along with a tutorial. The package can be found at: http://cran.r-project.org/web/packages/quipu/index.html. "}]},{"head":"PrePrints","index":8,"paragraphs":[]},{"head":"RESULTS","index":9,"paragraphs":[]},{"head":"Khipu features","index":10,"paragraphs":[{"index":1,"size":54,"text":"The current version of the molecular khipu or SSR marker graph is displayed and described in detail in Figure 6. In short, the standard graph has been enriched in terms of annotation and interpretation while the more playful graphical elements were removed. Many features can be customized as described in more detail in [13]."},{"index":2,"size":28,"text":"The more compact version is shown and described in Figure 7. Briefly, it remains close to prototype I in Figure 4 with a few enhancements regarding allele frequencies."}]},{"head":"DISCUSSION","index":11,"paragraphs":[]},{"head":"Related statistical graphs","index":12,"paragraphs":[{"index":1,"size":107,"text":"Perhaps a similar graph could be constructed based on a set of boxplots each showing a summary of a molecular marker with superimposed dots for each allele of an individual genotype. This would have the advantage of showing more statistical information. However, for compact figures in a catalog setting it would probably appear overly congested. Another related graph is the 'graphical genotype' chart as proposed by [18]. This latter chart also shows the molecular marker pattern across a set of genotypes but with an emphasis on explicitly comparing genotypes side-by-side whereas the molecular khipu puts an emphasis on an individual genotype. Both charts can complement each other."}]},{"head":"Reception","index":13,"paragraphs":[{"index":1,"size":133,"text":"The tool has been useful for registry of molecular passports and as a communication tool for genebanks both in-situ [6] and ex-situ as well as for breeding materials [5]: particularly, it added value to local Peruvian potato landraces, served as an example of participatory knowledge generation, and assisted in the creation of printed community potato inventories or catalogs. It was also conceived as a tool to orient conservation efforts and as an educational tool for local farmers communities and schools. However, as documented in [9] this has only been partially fulfilled: while the scientists interviewed were overall comfortable with the molecular chart, farmers in general did not 'understand' it. A different strategy would be required if the molecular khipu were to be used in dialogue on diversity with the diverse communities originally targeted."}]},{"head":"Figure 2.","index":14,"paragraphs":[{"index":1,"size":217,"text":"An example of a khipucamayoc (khipu keepers) managing apparently statistical information. Source: [3].. In the lower left corner apparently a simple counting system is shown based on pebbles or seeds to collect data. The cumulative results are summarized in a khipu.  For each genotype there may be more than one allele per lane up to the number of chromosome copies (ploidy number); only single-copy SSR markers are considered. Typically, for each gel a set of genotypes is characterized with the same SSR marker. Therefore, in order to compile data for a profile of a genotpye we will need to simply extract the lane or SSR marker information from the original gel. E.g. for genotype number 3 (marked in dark gray) we can virtually re-assemble the lanes from each original as indicated. With three simple graphical means this is transformed into a 'molecular khipu': a) the reduction of the lane to a symbolic lane, b) the addition of a top line to indicate that these markers comprise part of a set (now each lane represents a distinct gel or marker), and c) the representation of dyed fragments as dots. As a technical note: In a gel the fragments would separate according to an inverse logarithmic relationship. A direct linear scale is used in the molecuarl khipu for simplicity."},{"index":2,"size":67,"text":"Figure 4. A first prototype of the molecular khipu. Each pendant or vertical line represents a SSR marker locus. Superimposed dots represent alleles of a given size. The concept of a set is symbolized by the top horizontal backbone line. Annotations, scales, and legend are omitted to enable more minute images for display in a catalog. However, close by alleles overlap and may not be easily noticed.   "}]}],"figures":[{"text":"Figure 1 . Figure 1. An example of a traditional khipu. Source: wikipedia ([1]) "},{"text":"2 / 9 PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.522v1 | CC-BY 4.0 Open Access | rec: 3 Oct 2014, publ: 3 Oct 2014 "},{"text":"5 / 9 PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.522v1 | CC-BY 4.0 Open Access | rec: 3 Oct 2014, publ: 3 Oct 2014 "},{"text":"Figure 3 . Figure3. How the khipu is constructed. In the upper part of the figure schematic representations of four SSR marker gels are drawn. Each vertical 'bar' represents the lane in a gel where a SSR marker has been visualized. Differently located circles correspond to different alleles of a SSR marker. The higher up the circle, the more base pairs it has. For each genotype there may be more than one allele per lane up to the number of chromosome copies (ploidy number); only single-copy SSR markers are considered. Typically, for each gel a set of genotypes is characterized with the same SSR marker. Therefore, in order to compile data for a profile of a genotpye we will need to simply extract the lane or SSR marker information from the original gel. E.g. for genotype number 3 (marked in dark gray) we can virtually re-assemble the lanes from each original as indicated. With three simple graphical means this is transformed into a 'molecular khipu': a) the reduction of the lane to a symbolic lane, b) the addition of a top line to indicate that these markers comprise part of a set (now each lane represents a distinct gel or marker), and c) the representation of dyed fragments as dots. As a technical note: In a gel the fragments would separate according to an inverse logarithmic relationship. A direct linear scale is used in the molecuarl khipu for simplicity. "},{"text":"6 / 9 PrePrintsFigure 5 . 9 Figure5. A second prototype of the molecular khipu. This prototype has the same basic features as prototype I. Pendant lines represent marker loci, dots or ellipses alleles of a certain size and the horizontal top backbone highlights the set concept for this data. The most striking contrasts are the use of colors to distinguish the different chromosomes to which each marker belongs and the use of size variation of each allele according to its frequency. Both axes are labeled. The right hand side contains information about the population from which the allele frequencies were derived. Further embellishments include structuring the lines in a chord-like manner. "},{"text":"Figure 6 . 9 PrePrintsFigure 7 . 9 Figure 6. The current version of the molecular khipu as created by the corresponding R package 'khipu'. The top 'backbone' line has been modified (compared to the prototype versions) to convey additional information: that is the running number of the SSR marker locus. The pendant lines represent an individual marker locus. Upon each locus the range of the allele sizes in the reference population is superimposed as a thicker gray line. Alleles are sorted by decreasing dot radius to minimize occultation. Loci or 'lines' belonging to the same chromosome are joined by an upper thicker gray line. Both dot size and dot color can be configured to show allele frequencies in up to four classes. Marker loci are ordered from left to right by chromosome or if that information is missing by order in the given table. Along the y-axis the base pair counts are given in increasing order from bottom to top in a custom range which defaults to values from CIPs experience. The legend on the right hand has three groups. The top group shows the chosen color and size symbols corresponding to the allele frequency classes. Class breaks can also be modified. The second box displays additional information for the underlying population. Lastly, an optional logo image can be inserted into the lower right part. "},{"text":"PrePrints  "},{"text":"  "},{"text":"Table 1 . Comparison of Traditional and Molecular khipu Concepts Traditional Molecular TraditionalMolecular Set of data Set of SSR marker Set of dataSet of SSR marker Chord Marker or Locus ChordMarker or Locus Knot position Allele size Knot positionAllele size Knot color or knot size Allele frequencies Knot color or knot size Allele frequencies  "}],"sieverID":"48113700-dfd2-4e0f-ad88-3d8c8a9320c5","abstract":"Genebanks increasingly use molecular markers for routine characterization of ex-situ collections and farmer managed diversity. The International Potato Center presently uses a SSR marker-kit to create molecular profiles for potato accessions. We identified a need for a compact graphical representation that allows comparative presentation of molecular diversity and accession characteristics -thereby permitting biologists and collection curators to have a simple means to interpret molecular data. Inspired by the ancient Andean qipus we devised a graph that standardizes representation while leaving room for updates of the marker kit and the collection of accessions. The molecular khipu permits combining and annotating a set of SSR loci with allele frequency and allele size distribution information. The design is flexible to incorporate updates on genetic diversity information.Graphical means facilitate reading of allele diversity information. As a compact graphical view it facilitates information storage and exchange. The SSR khipu will be useful to other genebanks and breeders. Software to create graphics in single or batch mode is available as R package 'khipu'."}

data/part_5/0b4c5f410ae5dce28ef544aaf9915c98.json

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+{"metadata":{"id":"0b4c5f410ae5dce28ef544aaf9915c98","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/338a3626-fa30-4b07-ab50-840f0c84e1b5/retrieve"},"pageCount":1,"title":"","keywords":[],"chapters":[{"head":"Source: ENA","index":1,"paragraphs":[{"index":1,"size":2,"text":"The Challenge"}]},{"head":"•","index":2,"paragraphs":[{"index":1,"size":19,"text":"Expansion of irrigated wheat in Ethiopia can potentially lead to land degradation due to salinity from unsustainable irrigation practices."}]},{"head":"•","index":3,"paragraphs":[{"index":1,"size":11,"text":"Agroecology practices could play a great role in addressing this challenge."}]},{"head":"•","index":4,"paragraphs":[{"index":1,"size":11,"text":"Accordingly, irrigated wheat areas of Ethiopia are targeted in this project.  "}]},{"head":"Objectives and outcomes","index":5,"paragraphs":[]}],"figures":[{"text":"• Co-learning and co-design of agroecology practices for irrigated wheat farming using multi-stakeholder platforms • Policy dialog to promote agroecology transition in the agricultural landscape • Developing incentive structure and viable business models Partners Contact Lulseged Tamene, Alliance Bioversity and CIAT, [email protected] The Alliance of Bioversity International and CIAT thanks all donors & organizations which globally support its work through their contributions to the CGIAR Trust Fund. cgiar.org/funders "},{"text":"Towards supporting sustainable irrigated wheat production in Ethiopia through agroecology transition Key results   How the project contributes How the project contributes  to government priorities to government priorities  • Wheat is one of the commodities in • Wheat is one of the commodities in  the 10 by 10 program of the the 10 by 10 program of the  government, and this project government, and this project  contributes to sustainable irrigated contributes to sustainable irrigated  wheat production wheat production  • Supporting for the implementation of • Supporting for the implementation of  Climate Resilient Green Economy Climate Resilient Green Economy  (CRGE) (CRGE)  • Partnership is created with • Partnership is created with  key stakeholders key stakeholders • The project aims at developing inclusive •The project aims at developing inclusive  incentive structures and incentive structures and  investments supporting agroecological investments supporting agroecological  transitions in irrigated wheat farming in transitions in irrigated wheat farming in  Ethiopia Ethiopia  "}],"sieverID":"5b008095-8360-4258-80d6-885b5d03d35e","abstract":""}

data/part_5/0b5fe0d07ec54a6bd3225c4f95d14e97.json

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+{"metadata":{"id":"0b5fe0d07ec54a6bd3225c4f95d14e97","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/60bbad62-a5ae-4ed6-9ec0-1b75accc81f2/retrieve"},"pageCount":26,"title":"MANUAL PRACTICO: MAPEO DIGITAL DE SUELOS MANUAL: PRACTICA EN MAPEO DIGITAL DE PROPIEDADES DE SUELOS","keywords":[],"chapters":[{"head":"1) Metodología y programas requeridos","index":1,"paragraphs":[{"index":1,"size":40,"text":"En este curso será presentada una metodología de mapeo digital de suelos que utiliza lógicas difusas (Zhu, 1997;Ashtekar et al., 2014) para determinar la variabilidad espacial de propiedades de suelos según el modelo SCORPAN desarrollado por McBratney et al. (2003)."},{"index":2,"size":68,"text":" Paso 2. Corrección DEM DEM (Digital Elevation Model, por sus siglas en inglés) es un modelo digital con información de elevación, para su corrección es necesario tener en cuenta que se pueden presentar depresiones y deben corregirse. Este proceso se puede realizar desde QGIS o directamente con SAGA, ambas se relacionan entre si desde QGIS, al igual que la extensión TAUDEM que permite corregir como última instancia."}]},{"head":"DEM sin depresiones","index":2,"paragraphs":[{"index":1,"size":53,"text":"De entrada se necesitara usar el DEM, como salida se obtiene un DEM sin depresiones u ondeadas Utilizando QGIS nos dirigimos a la barra de herramientas y seleccionamos Processing /Toolbox / SAGA Geoalgorithms /Terrain analysis /Hidrology / Fill sink De igual manera el mismo enlace es posible utilizando SAGA, con la siguiente ruta."},{"index":2,"size":70,"text":"Saga GIS: Modules/Terrain analysis/Processing/Fill sink Esta es una herramienta muy útil, porque al utilizar una opción que trabaje con el flujo de escorrentía es necesario retirar las depresiones debido a que a través de ellas no habrá escurrimiento en caso de análisis hidrológico. Como se aprecia en la siguiente Figura hay un incremento en los umbrales inferiores este es un indicativo de la reacción del algoritmo de relleno de depresiones."},{"index":3,"size":39,"text":"Nota: Otra de los elementos para corrección de depresiones es la herramienta TauDEM (Terrain Analysis Using Digital Elevation Models), el cual obtiene el complemento Pic Remove; para remover depresiones y mantener la altura mínima del Modelo Digital de Elevación."}]},{"head":"3) Procesamiento de las variables ambientales","index":3,"paragraphs":[{"index":1,"size":22,"text":"Para el caso de Haití serán utilizados datos disponibles de clima, geología y topografía que si encuentran en la carpeta Terrain Attributes."}]},{"head":" TOPOGRAFIA","index":4,"paragraphs":[{"index":1,"size":22,"text":"Representada por los atributos del terrenos (TAs) desarrollados en SAGA-GIS: slope, SAGA weteness index, profile and plan curvature, normalized heigth, valley depth."},{"index":2,"size":57,"text":"Con SAGA y Q GIS es posible calcular los atributos del terreno (TAs) utilizando como base el DEM en los geoalgoritmos. Cada uno de los parámetros puede ser calculado directamente con SAGA o uno a uno en QGIS, cabe aclarar que el índice de Humedad bajo el geoalgoritmo de SAGA Wetness Index, se debe calcular por aparte."},{"index":3,"size":18,"text":"Con SAGA es posible calcular desde la herramienta de Geoprocessing/Terrain Analysis/Basic Terrain Analysis, como se muestra a continuación."},{"index":4,"size":111,"text":"Luego del despliegue de la ventana Basic Terrain Analysis, indicamos la grilla de trabajo y la elevación correspondiente al DEM previamente corregido. Damos click en OK y se generara una serie de atributos del terreno en el cual están los atributos que vamos utilizar: slope (indica el ángulo de inclinación existente entre el vector normal a la superficie de un punto y su vertical), plan curvature (analiza las crestas y los valles, valores positivos indican celdas de contorno cóncavo y negativos para celdas con contornos convexos), profile curvature (es la curvatura de la superficie en dirección a la pendiente más pronunciada) y valley depth (permite identificar diferencias verticales en el relieve)."},{"index":5,"size":27,"text":"El índice de humedad (Saga Wetness index), puede ser calculado desde la base de los geoalgoritmos de SAGA dentro de la siguiente ruta Tools/Data/Hidrology /SAGA Wetness Index."},{"index":6,"size":39,"text":"La altura normalizada es otro de los indicadores (Normalized Height), permite conocer la altura relativa del terreno y puede ser calculado desde la base de los geoalgoritmos de SAGA dentro de la siguiente ruta Tools/Data/Morphometry/Relative Heights and Slope Positions."},{"index":7,"size":46,"text":"De esta manera se consolidan los atributos del terreno que serán utilizados para conocer el comportamiento del relieve, entre otros parámetros. Es importante tener en cuenta que dentro del software SAGA la ejecución de los geoalgoritmos de SAGA wetness index y normalized height toman más tiempo."},{"index":8,"size":46,"text":"Para ayudar en la visualización del terreno vamos generar el hillshade a partir del DEM. El geoalgoritmo aplicado en QGIS para el cálculo del Hillshade también es posible seleccionamos en la barra de herramientas la opción Ráster/ Terrain Analysis/ Hillshade, como se muestra en la figura."},{"index":9,"size":50,"text":"En QGIS es posible realizar configuraciones de iluminación ideales para una mejor visualización en este caso ingresaremos los siguientes datos y generamos el Hillshade. Finalmente se tienen los atributos del terreno que se utilizaran en el paso 4 para la generación del clúster bajo parámetros de la forma del terreno."}]},{"head":" CLIMA","index":5,"paragraphs":[{"index":1,"size":66,"text":"Para identificar la variación climática que ocurre en el país vamos utilizar el índice PEI (Precipitation Effectiveness Index) Generación del PEI Thornthwaite (1931) introdujo el concepto del índice de efectividad de precipitación (PEI), índice que representa la eficacia para el crecimiento vegetal bajo estimativos de humedad; se calcula a partir de los valores mensuales de precipitación y evaporación. La evaporación es representada en términos de temperatura."}]},{"head":"\uD835\uDC43\uD835\uDC38\uD835\uDC3C =","index":6,"paragraphs":[{"index":1,"size":12,"text":"\uD835\uDC43 \uD835\uDC38 = 11.5 ( \uD835\uDC43 \uD835\uDC47 − 10 ) 10 9"},{"index":2,"size":16,"text":"Donde: \uD835\uDC43 = precipitación mensual en pulgadas (inches) y \uD835\uDC47 = temperatura media en ° F"}]},{"head":"Pasos para el cálculo de PEI en R Studio","index":7,"paragraphs":[{"index":1,"size":18,"text":"Inicialmente instalamos y llamamos las librerías para análisis de datos espaciales (ráster, sp) que necesitaremos en este caso:"},{"index":2,"size":14,"text":"Seguido llamamos los archivos ráster bajo la dirección de carpetas de almacenamiento previamente creadas."},{"index":3,"size":48,"text":"Ejemplo: Se indica en este caso la variable de precipitación direccionando a la carpeta de almacenamiento, en la cual cada uno de los rásters contiene el nombre \"preci_month_\" consecutivo de 1 a 12 equivalentes a la cantidad de meses en el año, y el indicativo del formato tif."},{"index":4,"size":17,"text":"Ecuación PEI, Thornthwaite (1931): continuando con el cálculo se procederá a calcular la ecuación teniendo en cuenta:"},{"index":5,"size":40,"text":" La temperatura debe estar en ˚F (Fahranheit) > 32 + (Tmax or Tmin in ˚C* 1.8)  La precipitación en pulgadas (inches) > Preci (mm) * 0.0394  Se debe contar con la temperatura media > (Tmax + Tmin)/2"},{"index":6,"size":1,"text":"preci=ráster(paste(\"Dir…/preci_month_\",i,\".tif\",sep=\"\"))"},{"index":7,"size":1,"text":"install.packages(\"ráster\")"},{"index":8,"size":3,"text":"install.packages(\"sp\") require(ráster) require(sp)"},{"index":9,"size":39,"text":"Continuado con el proceso se aplica la ecuación (Tener en cuenta que 10/9 = 11.1 y se guardan los archivos destino dentro de la dirección de una carpeta previamente creada con el comando writeRáster, como se muestra a continuación:"},{"index":10,"size":36,"text":"Ejecutado el código obtenemos PEI mensual, durante la ejecución del código se observara el símbolo de carga en R indicando que el proceso fue aceptado, finalizado ahora procedemos a sumar los PEI acumulados durante el año."},{"index":11,"size":35,"text":"Finalmente realizamos un promedio del Indice PEI para conocer el comportamiento anual del indice hacemos la suma de todos los meses con los PEI mensuales desde QGIS implementando la herramienta de Ráster > Ráster Calculator."},{"index":12,"size":13,"text":"PEI=((preci/(tmean-10))^1.11)*11.5) writeRáster(pei,paste(\"Dir…/pei_month_\",i,\".tif\",sep=\"\") Finalmente se crea un ciclo for que automatice los PEI mensuales."},{"index":13,"size":5,"text":"for(i in 1:12){ preci=ráster(paste(\"Dir…/preci_month_\",i,\".tif\",sep=\"\")) tmean=ráster(paste(\"Dir…/tmean_month_\",i,\".tif\",sep=\"\"))"},{"index":14,"size":3,"text":"pei=((preci/(tmean-10))^1.11)*11.5 writeRáster(pei,paste(\"Dir…/pei_month_\",i,\".tif\",sep=\"\")) }"},{"index":15,"size":25,"text":"Obtenido el comportamiento anual es posible clasificar la region dependiendo del rango en que el indice se encuentre según la clasificacion climatica de Thornthwaite (1931)."},{"index":16,"size":7,"text":"Tabla: Clasicación de regiones climáticas Thornthwaite (1931)."}]},{"head":"PE Index Climate More than 128","index":8,"paragraphs":[{"index":1,"size":4,"text":"Wet 64 -127 Humid"}]},{"head":"-63","index":9,"paragraphs":[{"index":1,"size":3,"text":"Sub-humid 16 -31"},{"index":2,"size":5,"text":"Semi-arid Less than 16 Arid"},{"index":3,"size":22,"text":"Según la clasicación de regiones climáticas Thornthwaite (1931), Tabla 1; ejecutamos el proceso de clasificación utilizando QGIS como se describe a continuación:"},{"index":4,"size":50,"text":" Abre el cuadro de diálogo propiedades de la para la capa, con clic derecho en la capa en el árbol de capas y selecciona la opción Propiedades.  Cambia a la pestaña Estilo.  Cambia el Tipo de renderizador a Singleband pseudocolor, y utiliza las opciones presentadas por defecto."},{"index":5,"size":25,"text":" Haz clic en el botón Clasificar para generar una clasificación por color nueva, y haz clic en Aceptar para aplicar esta clasificación al PEI."}]},{"head":"4) Unidades suelo-paisaje","index":10,"paragraphs":[{"index":1,"size":89,"text":"Las variables topográficas y de clima generadas anteriormente más el mapa de geología serán combinadas para crear unidades suelo-paisaje que serán utilizadas en los siguientes pasos. Los TAs serán agrupados según la forma del paisaje por medio de clustering. El análisis de agrupamiento o clustering es la asignación de un conjunto de observaciones en subconjuntos (llamados racimos) de modo que las observaciones en el mismo grupo son similares en algún sentido. Utilizando el software SAGA, bajo la siguiente ruta generamos el Clustering de agrupamiento para los atributos del terreno."},{"index":2,"size":12,"text":"Tools /Imagery /K-Means Clustering for Grids Los atributos del terreno utilizados son:"},{"index":3,"size":122,"text":"Finalmente se obtiene el Clúster con la información de los atributos de manera que las formas del terreno converjan entre sí, mostrando zonas de mayor agrupación bajo similitud y distancia. Finalizado el proceso arrojará una tabla con los indicadores de cada agrupamiento con su desviación estándar, en este caso fueron generados un total de 5 Clústeres. A partir de este punto la información es clasificada y organizada bajo la zona de estudio, el mapa se reduce debido a que la información de geomorfología obtenida no se extiende a todo el nivel nacional, de manera que se unifican las capas de factores climáticos, topográficos y geomorfológicos a la zona de estudio los datos estarán ubicados dentro de la carpeta, Data Study Zone Haiti."}]},{"head":"5) Desarrollo de reglas para mapeo usado en SoLIM (Soil Land Inference Model)","index":11,"paragraphs":[{"index":1,"size":72,"text":"SoLIM genera mapas de suelo basado en reglas y lógicas difusas, adoptando un enfoque basado en el conocimiento para predecir los valores de similitud. Los dos insumos claves para SoLIM son: datos sobre variables ambientales seleccionadas (covariables) relacionadas con las condiciones del suelo en la zona (almacenadas en la base de datos GIS) y conocimiento experto (reglas) que permiten diferenciar los distintitos suelos (unidades suelo-paisaje) según su relación con las variables ambientales."},{"index":2,"size":64,"text":"Las reglas poden ser definidas de distintas maneras dependiendo de la información disponible y conocimiento experto en las relaciones suelo-paisaje. Para este curso, las reglas serán desarrolladas utilizando la herramienta de estadística por zona (Zonal statistic) donde serán extraídos los valores del promedio y la desviación de los TAs para cada unidad suelo-paisaje. En este proceso recurrimos al software SAGA con la siguiente ruta:"},{"index":3,"size":54,"text":"Geoprocessing / Spatial and Geostatistics / Grids / Zonal Grid Statistics Nota: Es necesario tener cada variable bajo la misma resolución y extensión, por lo cual se realiza un ajuste de resolución Resampling utilizando la herramienta de SAGA. La ruta será la siguiente: Geoprocesing / Grid / Grid System/Resampling, como se muestra a continuación:"},{"index":4,"size":57,"text":"Continuando con el análisis estadístico finalmente tenemos la siguiente tabla, la cual contiene la estadística descriptiva para los TAs para cada unidad de suelo-paisaje. Es posible guardarla en formato txt, csv o dbf. Para proceder a SoLIM en el paso 6 únicamente se tendrá en cuenta la media y la desviación Estándar de los atributos del terreno."}]},{"head":"6) Creando mapas de similitud en SoLIM","index":12,"paragraphs":[{"index":1,"size":61,"text":"Inicialmente debemos crear un proyecto; indicamos nombre y dirección, posteriormente seleccionamos la opción Rule-based y damos OK SoLIM requiere para su lectura de datos, la conversión de los datos ráster a formato 3dr, para ello vamos a la barra de herramientas, seleccionamos Utilities / Data Format Conversion / Other Ráster Formats  3dr y convertimos cada uno de los factores topográficos."}]},{"head":"Finalmente indicamos la localización del archivo ráster de cada atributo del terreno e indicamos en las unidades","index":13,"paragraphs":[{"index":1,"size":50,"text":"Para explicar este ejemplo tomaremos la zona de Les Cayes (Haití), SoLIM es un software que presenta un límite en el peso de los datos de procesamiento, cuando el software no soporta este peso arrojara un mensaje de \"Out of memory\", por tal razón debemos reducir el área del ráster."},{"index":2,"size":46,"text":"Convertidos cada uno de los datos en formato ráster a formato 3dr continuamos con la creación de la GIS Database con los parámetros topográficos que serán cada una de las covariables dentro del software, añadirlas solo será dando clic derecho sobre GIS Database / Add Layer."}]},{"head":" Añadir tipos de suelos","index":14,"paragraphs":[{"index":1,"size":28,"text":"En el panel izquierdo del proyecto, haga clic con el botón derecho del ratón en el nodo \"Knowledge Base\" y seleccione \"Add Soil Type\" en el menú emergente."},{"index":2,"size":37,"text":"Es importante que establezca previamente la codificación de las unidades de suelos, realizando la clasificación para cada tipo de geología, geomorfología o clima si es el caso, y crear cada una con valores enteros concatenados entre sí."},{"index":3,"size":31,"text":"Esto muestra un cuadro de diálogo para especificar el nombre del tipo de suelo. Ingrese el número correspondiente a cada unidad de suelos: 11, 12… 45 y haga clic en \"OK\"."},{"index":4,"size":37,"text":"Cada unidad o tipo de suelo se agrega a la base de conocimientos. Desplegar el nodo de tipo de suelo, verá que se crean tres subnodos: Instancias, Ocurrencias, Exclusiones. Se usan para sostener diferentes tipos de conocimiento."},{"index":5,"size":52,"text":"La configuración ambiental tiene efecto en toda el área de mapeo, por lo que sólo se necesita una instancia para representar el conocimiento (conocimiento global) en la base de conocimientos. Haga clic con el botón derecho en el nodo \"Instancias\" bajo el nodo \"11\" y seleccione \"Añadir instancia\" en el menú emergente."},{"index":6,"size":29,"text":"Esto mostrará un cuadro de diálogo que le permitirá introducir el nombre de la instancia. Ingrese \"1\" y haga clic en \"Aceptar\", se creará una nueva instancia en blanco."}]},{"head":" Añadir reglas (Rule Based approach)","index":15,"paragraphs":[{"index":1,"size":25,"text":"Las covariables se utilizan en el conocimiento suelo-paisaje para cada tipo de suelo. Por lo tanto, la siguiente tarea es crear reglas para cada una."}]},{"head":"Ejemplo:","index":16,"paragraphs":[{"index":1,"size":35,"text":"Podemos usar la regla de rango para expresar el conocimiento en cada covariable. Procedemos con un clic derecho en el nodo \"Instance1\". En el menú emergente, seleccione \"Añadir regla\" y luego seleccione \"Regla de rango\"."},{"index":2,"size":52,"text":"Seleccione \"Choose an attached layer now\" y, a continuación, seleccione \"Slope\" en la lista desplegable \"Data Layers\" y, a continuación, haga clic en \"Next\". Esto permitirá al motor de inferencia vincular la regla definida aquí con la capa de datos GIS \"Slope\" que se definió anteriormente en la base de datos GIS."},{"index":3,"size":53,"text":"A continuación utilizamos la tabla de datos estadísticos y analizamos cada regla teniendo en cuenta la distribución normal de los datos, de manera que sea posible entender el comportamiento de la curva sea de forma Bell-shape, S-shape o Z-shape (los datos deben ser analizados desde los resultados obtenidos en el análisis stadistic zones)."}]},{"head":"Visualizacion desde SoLIM","index":17,"paragraphs":[{"index":1,"size":33,"text":"Slope: Z -Shape Saga TWI -Bell Shape Ahora hemos codificado el conocimiento sobre las condiciones ambientales del suelo como reglas. Puede repetir el proceso para los otros tipos de suelo. No olvide guardar."},{"index":2,"size":44,"text":"El siguiente paso es ejecutar una inferencia usando el conocimiento codificado para producir el mapa de similitud para cada unidad suelo-paisaje. Haga clic en el nodo \"Inferencia\" para desplegarlo. Bajo ese nodo, haga clic en \"Inferencia\", la vista cambiará a la interfaz de Inferencia."},{"index":3,"size":42,"text":"Al desplegarse la ventana se observara el listado de Unidades de suelos, en este caso se presenta la unidad 11, la opción para implementar alguna mascara y finalmente el lugar donde se guardara el mapa de inferencia. Ejecutamos y obtenemos el resultado."},{"index":4,"size":63,"text":"Para visualizar el mapa de similitud creado, puede hacer uso de la herramienta SoLIM Data Viewer adjunta en la carpeta del Software, procede a añadir el membership map, en la siguiente figura se observa el resultado obtenido anteriormente de la unidad de suelo 11. También puede utilizar la herramienta de conversión a formato ASCII para visualizar en otros software utilizando la siguiente dirección:"},{"index":5,"size":10,"text":"Utilities / Data Format Conversion / 3dr  Grid Ascii"}]},{"head":"7) Generando mapas de propiedades del suelo","index":18,"paragraphs":[{"index":1,"size":38,"text":"Seleccione en la barra de herramientas Product Derivation / Property Map, para crear el mapa de propiedades del suelo. Para el desarrollo de los mapas de propiedades es necesario tener los puntos de muestreo con valores in situ."},{"index":2,"size":9,"text":"Se deben ingresar los datos de la siguiente manera:"},{"index":3,"size":16,"text":"\"Directorio de resultados\" es el directorio donde se almacenan los Fuzzy o memberships maps anteriormente calculados."},{"index":4,"size":45,"text":"La \"Tabla de búsqueda\" (lookup table) es el archivo que contiene los valores de la propiedad de suelo que deseamos mapear para cada unidad de suelo-paisaje. La tabla de búsqueda debe digitarse así: Tipo de suelo 1 valor 1 Tipo de suelo 2 valor 2"},{"index":5,"size":42,"text":"El nombre del tipo de suelo es el nombre del mapa de similitud (sin sufijo .3dr) en \"Directorio de resultados\" y los valores corresponden a los datos de campo para la propiedad que se desea mapear dentro de cada unidad de suelo."}]},{"head":"8) Validación","index":19,"paragraphs":[{"index":1,"size":37,"text":"Seleccione Validation / Property Validation, puede crear un informe de exactitud para el mapa de propiedades, evaluándolo con puntos de muestreo en campo (usar un banco de datos distinto del usado para crear el mapa de propiedades)."},{"index":2,"size":23,"text":"El archivo de lista de puntos observados contiene información sobre las ubicaciones de las muestras. Un archivo de punto tiene el siguiente formato."},{"index":3,"size":43,"text":"La primera fila contiene los encabezados de las columnas. La primera columna contiene los identificadores asignados a los puntos de muestreo. Xs e Ys son las coordenadas de los puntos. Property Values son los valores de propiedad observados en las ubicaciones de muestra."},{"index":4,"size":33,"text":" El archivo de mapa de propiedades debe estar en formato .3dr.  El tamaño del vecindario define una ventana sobre la cual se recuperarán las propiedades medias como valor de propiedad inferido."},{"index":5,"size":19,"text":"La salida es un informe de precisión que contiene cuatro partes de información estadística y la lista de puntos:"},{"index":6,"size":13,"text":"1. RMSE (Root Mean Squared Error) 2. Agreement Coefficient 3. Mean Absolute Error"}]},{"head":"9) Estrategia de muestreo","index":20,"paragraphs":[{"index":1,"size":76,"text":"Existen distintas maneras para definir la estrategia de muestreo para mapeo digital. En este curso vamos usar la estrategia de muestreo conditioned Latin Hypercube (cLHS) según Minasny y McBratney (2006). cLHS es un procedimiento aleatorio estratificado y eficiente a la hora de muestrear variables con distribuciones multivariantes; su enfoque es basado en modelos donde prima la variación espacial y su predicción; El cLHS puede ser ejecutado en R o utilizando un plugin que funciona en ArcGIS."},{"index":2,"size":10,"text":"Es necesario tener en cuenta antes de ejecutar la herramienta:"},{"index":3,"size":10,"text":"• Todos los datos ráster deben cubrir la misma extensión"},{"index":4,"size":16,"text":"• Todos los datos ráster deben estar en la misma proyección o la herramienta fallará Procedimiento:"},{"index":5,"size":14,"text":"1. Para iniciar, seleccione el cuadro azul en la barra de herramientas TEUI principal."},{"index":6,"size":41,"text":"2. Aparecerá el cuadro de diálogo Latin Hyper Cube Generator 3. Seleccione el botón Add Data Layer de datos para agregar capas. Si tiene abierto un proyecto de TEUI Toolkit actual, la herramienta agregará automáticamente esas capas al diálogo de selección."},{"index":7,"size":21,"text":"4. Aparecerá una ventana que le permitirá navegar hasta los datos ráster de su elección. Puede seleccionar tantas capas como desee."}]}],"figures":[{"text":"Factor  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "},{"text":"  "}],"sieverID":"0bc52a66-4d5c-4f37-940b-ffbd6ffabba6","abstract":""}

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+{"metadata":{"id":"0c26a20194146ca052f9890acbaee084","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/e91e6b99-0cc5-4546-bfa9-6c5a16ebf424/retrieve"},"pageCount":10,"title":"Genetic diversity and population structure of Striga hermonthica populations from Kenya and Nigeria","keywords":["weed biology","population genetics","outlier analysis","positive selection","ecotypes"],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":314,"text":"Striga hermonthica (Delile) Benth. is an obligate hemiparasitic plant that belongs to the family Orobanchaceae. Globally, it is considered to be the most economically important parasitic weed (Parker & Riches, 1993). It infests 57% of the total area in sub-Saharan Africa under cereal production (Sauerborn, 1991) and has shown a steady increase in geographic distribution and infestation level, particularly in sub-Saharan Africa (Ejeta & Gressel, 2007), due to the dispersal mechanisms of S. hermonthica seeds and lack of knowledge and means to properly control the parasite (Berner et al., 1994). Changes in climatic conditions may further increase the geographic distribution and invasive potential of Striga, as habitats suitable for the parasite's growth are predicted to expand (Mohamed et al., 2006). Striga hermonthica parasitises cereals, causing significant reduction in growth and performance of these crops. Each S. hermonthica plant produces a large number of seeds that go through a phase called 'pre-conditioning', which is characterised by moisture and high temperature for 7-14 days (Parker & Riches, 1993), after which they germinate in response to germination stimulants exuded from a suitable host. On contact with the host root, it attaches to the root and penetrates the root cortex using haustoria. The scanning electron microscopy study by Dorr (1997) showed that invading Striga spp. cells perforate the host vascular system using a specialised structure, the osculum, to form xylem-to-xylem connections. The parasite then abstracts water and nutrients from the host roots via the haustorium. Infected host plants show stunting, chlorosis and death in severe cases (Dorr, 1997). A single plant of S. hermonthica can inflict an approximately 5% loss in yield on a host plant (Parker & Riches, 1993), and high infestations can lead to total crop failure. Average losses of maize in Kenya are estimated at 57%, while estimates of maize crop area infested are 30-40% in Togo, Mali and Nigeria (De Groote et al., 2008)."},{"index":2,"size":85,"text":"A number of control options for S. hermonthica exist. Host plant resistance is considered to be the most practical and economical approach for resourcepoor smallholder farmers (Kim, 1994). However, breeding host plants with resistance to the parasite is complicated by the ability of S. hermonthica to overcome host resistance due to high levels of genetic variation within and between populations (Koyama, 2000). This genetic variation also undermines other control methods, leading to seasonal and geographic variability in the effectiveness of S. hermonthica control (Hearne, 2009)."},{"index":3,"size":88,"text":"Striga hermonthica is expected to have enormous within-and between-population genetic variation (Bozkurt et al., 2015), which can enable the parasite to evolve and adapt to changing environmental conditions (Koyama, 2000). Therefore, knowledge of the distribution of S. hermonthica genetic diversity can facilitate selection of representative testing sites for host resistance screening and evaluation of the viability of other control options. This will allow for the development of genotypes with durable resistance and other control options for effective control across the prevalent broad range of ecotypes of the parasite."},{"index":4,"size":157,"text":"The different diversity studies conducted in various locations across Africa using a variety of markers agree on the presence of relationships between geographic distance and genetic distance among the parasite populations, but disagree on the presence or absence of host-specific S. hermonthica populations (Koyama, 2000;Gethi et al., 2005;Estep et al., 2011;Welsh & Mohamed, 2011;Bozkurt et al., 2015). This highlights the need for more extensive studies involving populations collected from different geographic areas and an array of host crops. In the present study, S. hermonthica parasitising the major staple crops in agro-ecological zones in two countries were collected and characterised using genotyping-by-sequencing derived single nucleotide polymorphisms (SNP). The populations were collected from maize, rice, pearl millet and sorghum. The main objectives of this study were to characterise the extent of diversity of S. hermonthica populations collected in Nigeria and Western Kenya and to determine the level of genetic differentiation between populations collected within and between the two countries."}]},{"head":"Methods","index":2,"paragraphs":[]},{"head":"Sample collection","index":3,"paragraphs":[{"index":1,"size":108,"text":"Striga hermonthica plants were collected from infested areas in Western Kenya and Northern Nigeria. Leaf samples were collected from infested farms (Fig. 1 and Figure S1, Table S1). In each site, S. hermonthica was simultaneously collected from different hosts including pearl millet, sorghum, maize and rice when present in the farming system. Leaves from randomly selected S. hermonthica plants were collected in each farm on hosts found at least 1 m away from each other; each sample came from a single Striga plant growing on a different individual host within the population. The samples were placed in separate sealable plastic bags containing sufficient silica gel to dry them."}]},{"head":"DNA extraction and GBS genotyping","index":4,"paragraphs":[{"index":1,"size":303,"text":"High-quality DNA (showing high molecular weight bands on agarose gel) was extracted from S. hermonthica leaf samples using a CTAB extraction method modified from Doyle and Doyle (1987). The extracted DNA samples were digested using EcoR1 to determine whether the extracted DNA can be completely digested, indicating the absence of digest inhibitors. The DNA was then lyophilised and sent to Cornell University for genotyping-by-sequencing (GBS). Genotyping-by-sequencing was performed as described in Elshire et al. (2011). In brief, genomic DNA samples of individuals were digested separately with a selective restriction enzyme known as ApeKI, a type II restriction endonuclease that recognises a degenerate 5 bp sequence (GCWGC, where W can be A or T), creates a 3 bp 5 0 overhang and is partially methylation sensitive. The digested DNA fragments were then ligated to two types of adapters: common adapters and barcoded adapters. The DNA samples were pooled and amplified using primers complementary to the adapter' sequences. DNA fragments having adapter combinations of common-common or barcoded-barcoded were not amplified, but those with a combination of common-barcoded fragments are selectively amplified. The PCR products were then purified and sequenced using Illumina Hiseq 2500 (Illumina, USA), after which the SNPs were called using the UNEAK (Universal Network Enabled Analysis Kit) GBS pipeline (Lu et al., 2013), which is part of the TASSEL 3.0 bioinformatics analysis package (Bradbury et al., 2007) (Version: 3.0.166 Date: 17 April 2014). This method does not require a reference sequence; SNP discovery is performed directly within pairs of matched sequence tags and filtered through network analysis (See Lu et al., 2013 for an exhaustive discussion and description of the method). Also, a GBS discovery pipeline, using S. hermonthica transcriptome, available on Tassel (Version: 3.0.166 Date: 17 April 2014) was used. In this instance, sequence reads were mapped to the S. hermonthica transcriptome."}]},{"head":"Genetic diversity and population structure","index":5,"paragraphs":[{"index":1,"size":334,"text":"The GBS-derived SNPs were further filtered using the TASSEL software (Bradbury et al., 2007) to retain only polymorphic SNPs with a maximum of 10% missing values and a minimum and maximum allele frequency of 0.05 and 0.95 respectively. The final filtered data comprised 1029 individuals and 1576 SNP markers for further analysis. Basic diversity indices for each population were calculated using PowerMarker (Liu & Muse, 2005) and GenAlex version 6.41 (Peakall & Smouse, 2006). The pairwise genetic distance (identity-by-state, IBS) matrix was calculated among all individuals using PLINK (Purcell et al., 2007). A Ward's minimum variance hierarchical cluster dendrogram was built from the IBS matrix using the analyses of phylogenetics and evolution (ape) package (Paradis  Core Team, 2015). Population structures of the S. hermonthica samples from Nigeria and Kenya were determined using two complementary approaches: (i) a model-based maximum likelihood estimation of ancestral subpopulations using ADMIXTURE (Alexander et al., 2009) and (ii) discriminant analysis of principal components (DAPC) (Jombart et al., 2010). The ADMIXTURE method assumes linkage equilibrium among loci and Hardy-Weinberg equilibrium within ancestral populations (Alexander et al., 2009). For ADMIXTURE analysis, the number of subpopulations, K, varied from 1 to 10. The most appropriate K value was selected after considering the 10-fold cross-validations, whereby the best K exhibits low cross-validation error compared with other K and correspondence of the results with the clustering pattern obtained by the hierarchical cluster. The clustering pattern obtained from ADMIXTURE was validated using discriminant analysis of principal components (DAPC) using the R package 'adegenet' (Jombart, 2008). DAPC involves first using K-means analysis to infer the optimal number of clusters of PCA-transformed SNP data by varying the possible number of clusters from 2 to 40 and then assessing the best supported model by Bayesian information criterion. Then, DAPC was carried out on the identified clusters using the first 55 principal components. The membership probabilities of each individual for the different groups were obtained from DAPC, and the results of DAPC analysis, ADMIXTURE and the hierarchical tree are compared."},{"index":2,"size":79,"text":"The fixation index (F ST ) and standardised F ST (F 0 ST, ) of the observed populations were assessed using analysis of molecular variance (AMOVA) implemented in GenAlex 6.41. These analyses were also performed for plants collected in Nigeria and Kenya partitioned based on their host plants and geographic locations. Correlations between pairwise linearised F ST values and geographic distance matrices were calculated using the Mantel test, after 1000 random iterations, as implemented in GenAlex software version 6.41."},{"index":3,"size":297,"text":"To detect whether there were markers under selection within the population structure observed above, we used the hierarchical Bayesian method described in Beaumont and Balding (2004) as implemented in BayeScan 2.1 software (Foll & Gaggiotti, 2008). BayeScan assumes that allele frequencies within populations follow a multinomial Dirichlet distribution (Balding & Nichols, 1995) with F ST parameters being a function of population-specific components shared among all loci (b) and of locus-specific components shared among all populations (a). For a given locus, departure from neutrality is assumed when the locusspecific component is required to explain the observed pattern of diversity. BayeScan directly infers the posterior probability of each locus to be under the effect of selection by defining and comparing two alternative models: one model includes the locus-specific component, while the other excludes it. The ratio of the model posterior probabilities is used to calculate the posterior odds (PO), which measures how much more likely the model with selection is, compared with the model without selection (Balding & Nichols, 1995). The estimation of model parameters was automatically tuned on the basis of short pilot runs (10 pilot runs, length 5000 and burn in 50 000). The sample size was set to 5000 and the thinning interval to 10 resulting in a total chain length of 100 000 iterations. False discovery rate (FDR) was used to control for multiple testing. To identify loci under selection, the posterior distribution of a i was used; a positive value suggests that locus 'i' is subject to directional selection, whereas a negative value suggests that stabilising selection is tending to homogenise allele frequencies over the populations. Loci were then ranked according to their estimated posterior probabilities. An R function (as provided in BAYESCAN) was used to identify and plot outlier loci using different criteria."}]},{"head":"Results","index":6,"paragraphs":[]},{"head":"Genetic diversity across countries","index":7,"paragraphs":[{"index":1,"size":213,"text":"A total of 1576 SNPs from GBS was used to investigate population structure in 254 S. hermonthica plants collected in Kenya and 775 plants collected in Nigeria. The plants from Kenya had a higher genetic diversity as measured by effective number of alleles, observed and expected heterozygosity and Shannon's information index in comparison with the Nigerian samples (Table 1). In contrast, we found more private alleles in the Nigerian plants (61) compared with the plants from Kenya (11) (Table 1). The hierarchical cluster analysis separated all the plants into two major groups with the first group containing all the plants from Kenya and the second group comprising plants from Nigeria (Fig. 2). The Nigerian plants were further split into three major subclusters (Fig. 2). ADMIXTURE analysis confirmed the results of hierarchical cluster analysis. At K = 2, the populations were separated by country of origin, while at K = 4, the plants from Nigeria were further divided into three genetic groups (Fig. 2). DAPC analysis also revealed the presence of four genetic groups (Fig. 3), one comprising the Kenyan plants and the other three the Nigerian plants. The results from these three analyses were consistent and showed good correspondence (Fig. 2), indicating that the population structure within the plants had been correctly identified."},{"index":2,"size":40,"text":"Analysis of molecular variance (AMOVA) revealed a moderate to high (Hamrick, 1982) level (F ST = 0.15) of genetic differentiation between S. hermonthica plants collected from Nigeria and plants from Kenya, which was statistically significant (P = 0.001) (Table 2)."}]},{"head":"Genetic structure within Kenya","index":8,"paragraphs":[{"index":1,"size":185,"text":"Analysis of molecular variance of the Kenyan plants revealed an F ST value of 0.021 (P = 0.001) among populations collected from different locations (Table 2), and Mantel's test detected a strong relationship between the geographic distance between sampling locations and their pairwise linearised F ST values (R 2 = 0.33, P = 0.01, Figure S2). When the Striga plants were grouped based on host plants, a low level of differentiation (F ST = 0.02) was observed (Table 2). The pairwise F ST value between Striga plants collected from rice and maize (0.049) and rice and sorghum (0.047) was statistically significant (P = 0.001), whereas the value between plants collected from maize and sorghum was not statistically significant (0.002, P = 0.108) (Table 3). DAPC was used to further explore the extent of genetic diversity and differentiation between plants collected from different locations and different host crops. The Kenyan plants did not form clusters based on collection sites, but when clustering was performed based on hosts, S. hermonthica plants with rice as hosts clustered away from those with maize and sorghum as hosts (Fig. 4)."}]},{"head":"Genetic structure within Nigeria","index":9,"paragraphs":[{"index":1,"size":75,"text":"The three clustering methods used to investigate the S. hermonthica plants collected in Nigeria showed the presence of three genetic groups (Fig. 2). Mantel's test detected a very weak relationship between the geographic distance between sampling locations and their pairwise linearised F ST values (R = 0.030, P = 0.05) (Figure S3). The pairwise F ST among the three genetic groups varied from 0.045 to 0.054 and was statistically significant (P = 0.001) (Table 4)."}]},{"head":"Kenya Nigeria","index":10,"paragraphs":[{"index":1,"size":220,"text":"There was significant correlation between PCA1 and longitude (P < 2 9 10 À16 , Pearson's r = À0.62), and PC 2 and latitude (P < 2 9 10 À16 , Pearson's r = 0.54) (Table 5). The latitudinal stratification is also observed in that a plot of co-ordinates of the sample collection sites shows that the distinct S. hermonthica groups are found in three separate areas within Nigeria (Fig. 1). Genetic analysis of the groups reveal that group 3 which comprised Striga plants collected mostly in north-eastern Nigeria was the most genetically diverse, followed by group 2 that consisted of plants collected     in the central part of the country, while group 1 that contained plants collected in the north-western region had the least genetic diversity (Table 6). Group 2 had the highest number of rare alleles, while group 1 had the least. It was observed that S. hermonthica collected from pearl millet in Region 2 clustered with group 1. A low level of genetic differentiation was observed when host plants were used to group the plants (F ST = 0.009, P = 0.001) (Table 2). The pairwise F ST values varied between maize and pearl millet (F ST = 0.02), and between maize and sorghum (F ST = 0.009) and between sorghum and pearl millet (F ST = 0.07)."}]},{"head":"Outlier analysis","index":11,"paragraphs":[{"index":1,"size":304,"text":"The BayeScan analysis was used to determine potential markers under selection in the genetic groups detected by population structure analysis: (i) one Kenyan and three Nigerian populations, (ii) the three Nigerian genetic groups observed by population structure analysis only, (iii) the S. hermonthica plants parasitising sorghum, rice and maize in Kenya and (iv) the entire Nigerian S. hermonthica collection taken as a whole and the Kenyan population (Figure S3). The Nigerian versus the Kenyan collection showed nine markers to be potentially under selection, FDR ≤ 3%, and log (PO) ≥ 1.05 corresponding to 'strong selection and above' on the Jeffery's scale. All the nine markers also showed positive a values and their F ST values were high (mean F ST = 0.45). Among the four genetic groups observed between Nigeria and Kenya, based on population structure analysis, 23 markers with FDR ≤ 2% and log (PO) ≥ 1.6 corresponding to 'very strong selection and above' on Jeffery's scale of evidence were identified as potentially under selection. All of the 23 markers showed positive a values and relatively high F ST (mean F ST = 0.39) indicative of positive selection, and also the three genetic groups detected within Nigeria revealed three markers to be potentially under selection, FDR ≤ 4% and log (PO) ≥ 1, corresponding to 'strong selection and above' on the Jeffery's scale, all of which also showed positive a values and relatively high F ST (mean F ST = 0.28) indicative of positive selection. Analysis of the Kenyan S. hermonthica plants parasitising maize, sorghum and rice revealed two markers to be potentially under selection (FDR ≤ 4%, and log (PO)) ≥ 1 corresponding to 'strong selection and above'. The two markers also showed positive a values; however, their F ST values were low (mean F ST = 0.08) (Figure S4, Table S2)."}]},{"head":"Discussion","index":12,"paragraphs":[]},{"head":"Genetic diversity","index":13,"paragraphs":[{"index":1,"size":75,"text":"In this study, S. hermonthica plants, collected in Kenya and Nigeria, were characterised using SNPs to determine the extent of genetic diversity existing within and between the two countries. The S. hermonthica populations exhibited a high level of genetic diversity, and the Nigerian and Kenyan populations were observed to be two genetically distinct groups, indicating that they have had limited exchange of genetic material. This is consistent with the results of Bozkurt et al. (2015)."}]},{"head":"Population structure","index":14,"paragraphs":[{"index":1,"size":219,"text":"The Kenyan population showed little or no population structure and a low-level genetic differentiation that correlated with the distance between the sampling sites; this suggests that the sampled S. hermonthica plants are   In studies on other parasitic plants like Striga gesnerioides (Lane et al., 1996) and Viscum album L. (Zuber & Widmer, 2000), host adaptation was suggested to drive race formation. However, Botanga and Timko (2006) suggested that in addition to host adaptation, geographic isolation was also a critical factor in race formation. Geography appears to be the major element structuring genetic variation and differentiation in this study, and our results suggest that S. hermonthica populations retain a rather broad host range. The rotation of crop cultivars and species, through mixed cropping, relay cropping and crop rotation systems, that is common in Striga spp. infested areas in Nigeria and Kenya (Ajeigbe et al., 2010), could provide an explanation for the maintenance of genetic variability for host range in S. hermonthica populations (Huang et al., 2012). This is because changing the crop varieties and species planted in a particular location frequently will prevent tight adaptation of Striga spp. to any one of them (Huang et al., 2011). The subpopulations observed may therefore have arisen as result of differential adaptation to environmental conditions prevalent across the locations where they are found."},{"index":2,"size":126,"text":"The results in this study show that Kenyan and Nigerian populations of S. hermonthica represent distinct ecotypes, with the Nigerian S. hermonthica divided into three genetic groups that exist in different regions in Nigeria. The results also show some host-based differentiation in the Kenyan population. It may, therefore, be useful to characterise these populations phenotypically, to determine whether they exhibit variations in their virulence characteristics and use these characteristics to identify and pyramid resistance genes that will make cereal varieties resistant to multiple ecotypes from every region and host crop in the country and across the countries. As noted by Bozkurt et al. (2015), factors driving the formation and continued existence of these ecotypes and their subpopulations need to be determined and included in breeding efforts."},{"index":3,"size":65,"text":"Testing of S. hermonthica control technologies in Nigeria should be performed at sites representing the areas of collections of the three genetic groups. This is particularly important for breeding crop varieties with broad-based resistance against different S. hermonthica populations, as the gene flow that exists within the three regions will make it difficult to develop control options that will be specifically adapted to each subpopulation."},{"index":4,"size":17,"text":"of Striga samples collected from those locations. (R 2 = 0.33, P = 0.01, r = 0.57)."},{"index":5,"size":34,"text":"Figure S3 A plot of geographic distance between sampling locations in Nigeria and linearised F ST values of Striga samples collected from those locations. (R 2 = 0.004, P = 0.2, r = 0.064)."},{"index":6,"size":27,"text":"Figure S4 Results of BayeScan analysis for 1576 SNPs genotyped in Striga hermonthica. Marker-specific F ST is plotted against the posterior odds (PO) of being under selection."},{"index":7,"size":17,"text":"Table S1 Passport data showing the location, host and GPS co-ordinates of the Striga hermonthica plants collected."},{"index":8,"size":22,"text":"Table S2 Outlier SNPs indicating positive selection in a genome scan of 1578 SNP markers using the method implemented in Bayescan 2.0."}]}],"figures":[{"text":"Fig. 1 Fig. 1 Geographic distribution of Striga hermonthica plants in Nigeria (left) and Kenya (right) analysed in this study. The middle inset is the map of Africa showing the location of Nigeria and Kenya. The right inset highlights the study region in Kenya. Each rectangle represents a plant, and the colour scheme represents the ancestral group(s) of each plant as determined by the study and shown in Fig. 2 at K = 4. "},{"text":"Fig. 2 Fig. 2 Combined Dendrogram, ADMIXTURE* and DAPC plots showing population structure correspondence between the three plots. (*ADMIXTURE at k = 2, 3 and 4 progressively separates the entire samples into Nigerian and Kenyan plants and then splits the Nigerian plants into three groups. (The groups of the DAPC plot and the arms of the dendrogram correspond to K= 4 of the ADMIXTURE plot) "},{"text":"Fig. 3 Fig. 3 DAPC plots of all the Striga hermonthica plants sampled. Points are plotted along PC1 and PC2 (A) and PC 2 and PC3 (B). The clusters in each set are consistent with the population structure (at K 4) from Fig 2. (Groups 1, 2 and 3 = Nigerian populations, group 4 = Kenyan population). "},{"text":"F ST = fixation index = (variance among populations/total variance), F 0 ST = standardised F ST = (F ST /F ST max), P = significance. "},{"text":"Fig. 4 Fig. 4 Plot of Striga hermonthica plants collected from Kenya. Plants from rice (in green) cluster separately from those from sorghum and maize. "},{"text":"Table 1 Mean allelic patterns across populations of Striga hermonthica collected in Kenya and Nigeria Population Kenya Nigeria PopulationKenyaNigeria Ne 1.405 (0.009) 1.315 (0.008) Ne1.405 (0.009)1.315 (0.008) I 0.380 (0.006) 0.324 (0.006) I0.380 (0.006)0.324 (0.006) Private Alleles 11 161 Private Alleles11161 Ho 0.284 0.210 Ho0.2840.210 He 0.245 (0.005) 0.200 (0.004) He0.245 (0.005)0.200 (0.004)  "},{"text":"Table 2 Summary of AMOVA results Striga hermonthica population F ST P F 0 ST Striga hermonthica populationF STPF 0ST Between Nigerian and Kenyan 0.147 0.001 0.203 Between Nigerian and Kenyan0.1470.0010.203 populations     populations Among Kenyan plants from 0.021 0.001 0.028 Among Kenyan plants from0.0210.0010.028 different locations     different locations Among Kenyan population 0.019 0.003 0.026 Among Kenyan population0.0190.0030.026 on different hosts     on different hosts Among the three observed 0.053 0.001 0.072 Among the three observed0.0530.0010.072 Nigerian groups     Nigerian groups Among Nigerian population 0.009 0.001 0.012 Among Nigerian population0.0090.0010.012 on different hosts     on different hosts  "},{"text":"Table 3 Pairwise F ST , P and F 0 ST of Striga hermonthica plants collected from different hosts in Kenya Hosts F ST P F 0 ST HostsF STPF 0ST Between maize and rice 0.049 0.001 0.067 Between maize and rice0.0490.0010.067 Between maize and sorghum 0.002 0.108 0.003 Between maize and sorghum0.0020.1080.003 Between rice and sorghum 0.047 0.001 0.066 Between rice and sorghum0.0470.0010.066  "},{"text":"Table 4 Pairwise F ST , P and F 0 ST of the three populations of Striga hermonthica plants observed in Nigeria ST =standardised F ST = (F ST / F ST max), P = significance. Populations F ST P F 0 ST PopulationsF STPF 0ST Between 1 (north-west) and 2 0.057 0.001 0.077 Between 1 (north-west) and 20.0570.0010.077 (central region)     (central region) Between 1 (north-west) and 3 0.061 0.001 0.081 Between 1 (north-west) and 30.0610.0010.081 (north-east)     (north-east) Between 2 (central region) 0.043 0.001 0.061 Between 2 (central region)0.0430.0010.061 and 3 (north-east)     and 3 (north-east) F ST = fixation index = (variance among populations/total vari- F ST = fixation index = (variance among populations/total vari- ance), F 0     ance), F 0  "},{"text":"Table 5 Correlation coefficients (Pearson's r) between collection location, given as latitude and longitude, and SNP PCA of the Nigerian Striga hermonthica collection  PCA1 PCA2 PCA3 PCA1PCA2PCA3 Latitude 0.1(9.74 e-08) 0.54(2.2 e-16) À0.2(3.99 e-09) Latitude0.1(9.74 e-08) 0.54(2.2 e-16) À0.2(3.99 e-09) Longitude À0.62(2.2 e-16) 0.39(2.2 e-16) 0.1(3.0 e-05) Longitude À0.62(2.2 e-16) 0.39(2.2 e-16)0.1(3.0 e-05) P value is given in parenthesis.  P value is given in parenthesis.  "},{"text":"Table 6 Standard error is given in parenthesis, Ne = no. of effective alleles, I = Shannon's information index = À1* Sum (pi * Ln (pi)), Ho = observed heterozygosity, private alleles = no. of alleles unique to a population/group, He = expected heterozygosity.© 2017 The Authors Weed Research published by John Wiley & Sons Ltd on behalf of European Weed Research Society interconnected by a stepwise exchange of genetic material among adjacent populations resulting in an isolation-by-distance pattern. The Nigerian plants, on the other hand, showed significant structuring. The S. hermonthica plants collected from Nigeria formed three groups that are similar in distribution to the distribution of three Striga gesnerioides (Willd.) Vatke biotypes observed in Nigeria byLane et al. (1996). Our study suggests the presence of two biotypes in Kenya, one adapted to rice and the other to both maize and sorghum. This is, however, not conclusive, as the observed differentiation might be due to isolation by distance. Striga hermonthica plants attached to maize, pearl millet and sorghum, collected in Nigeria, were not clearly separated. Our results strongly suggest that positive selection played a role in the divergence of the Kenyan and Nigerian populations of S. hermonthica and also in the divergence of the Nigerian populations. Information on the Kenyan maize, sorghum and rice population is, however, not conclusive because, while outlier tests indicated diversifying selection, Mantel's test also indicated the presence of isolation by distance. Mean allelic patterns across populations observed in Mean allelic patterns across populations observed in Nigeria (1 in the north-west, 2 in the central region and 3 in the Nigeria (1 in the north-west, 2 in the central region and 3 in the north-east)    north-east) Population 1 2 3 Population123 Ne 1.27(0.013) 1.31(0.012) 1.34(0.012) Ne1.27(0.013)1.31(0.012)1.34(0.012) I 0.26(0.006) 0.31(0.006) 0.3(0.006) I0.26(0.006)0.31(0.006)0.3(0.006) Private alleles 10 46 19 Private alleles104619 Ho 0.193 0.2 0.229 Ho0.1930.20.229 He 0.164(0.005) 0.195(0.004) 0.209(0.004) He0.164(0.005)0.195(0.004)0.209(0.004)  "}],"sieverID":"ce5e3084-350c-4071-a7df-757d90d883f7","abstract":"Striga hermonthica is a parasitic weed that poses a serious threat to the production of economically important cereals in sub-Saharan Africa. The existence of genetic diversity within and between S. hermonthica populations presents a challenge to the successful development and deployment of effective control technologies against this parasitic weed. Understanding the extent of diversity between S. hermonthica populations will facilitate the design and deployment of effective control technologies against the parasite. In the present study, S. hermonthica plants collected from different locations and host crops in Kenya and Nigeria were genotyped using single nucleotide polymorphisms. Statistically significant genetic differentiation (F ST = 0.15, P = 0.001) was uncovered between populations collected from the two countries. Also, the populations collected in Nigeria formed three distinct subgroups. Unique loci undergoing selection were observed between the Kenyan and Nigerian populations and among the three subgroups found in Nigeria. Striga hermonthica populations parasitising rice in Kenya appeared to be genetically distinct from those parasitising maize and sorghum. The presence of distinct populations in East and West Africa and in different regions in Nigeria highlights the importance of developing and testing Striga control technologies in multiple locations, including locations representing the geographic regions in Nigeria where genetically distinct subpopulations of the parasite were found. Efforts should also be made to develop relevant control technologies for areas infested with 'rice-specific' Striga spp. populations in Kenya."}

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data/part_5/0dad8ceb10a67659eb8e0d2de86b6fb9.json

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+{"metadata":{"id":"0dad8ceb10a67659eb8e0d2de86b6fb9","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/f8f32dac-7b23-495c-8d61-b4b3e39273d7/retrieve"},"pageCount":2,"title":"Vitamin A Orange Maize: LY1001-14","keywords":["Contributing Centers/PPA partners","Evidence link","• https","//tinyurl.com/wlf3s3o"],"chapters":[],"figures":[],"sieverID":"acc65406-2230-4dbe-859d-e1644f9fd188","abstract":"As with the 260+ biofortified varieties released in the past, these varieties not only have higher levels of micronutrients but are also high-yielding, climate-smart, and carry other attributes farmers and consumers look for."}

data/part_5/0dd7a8ff09dafc6fbf31f81002f22c01.json

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+{"metadata":{"id":"0dd7a8ff09dafc6fbf31f81002f22c01","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/d9017929-0563-4c91-9f57-aaeb65c442e5/retrieve"},"pageCount":13,"title":"Towards adequate food environment in Benin public primary schools, the challenge of food supply and hygiene practices: a case study of three municipalities","keywords":["primary school","food supply","food group score","summary hygiene index","Benin"],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":411,"text":"African countries are facing multiple burdens of malnutrition and Benin is not on the sidelines of this situation. Nearly 10% of adolescents were stunted, whereas anemia affected one-third of the school population (1). However, most studies focus on children under 5 or pregnant women while malnutrition affects young children and adolescents with serious consequences. These consequences can include a lack of productivity and academic performance, a weakened immune system with an increase in infectious diseases (2). They can also be associated with delayed maturation, poor muscle strength leading to constraints in capacity for physical work, and reduced bone density later in life (3). One way to address this problem is to act on school food environment (4). Schoolchildren spend nearly 8 hours at school; and improving the school food environment could be an approach contributing to improve the diet (5). Food environment appears to be a component of food systems and has been defined in several ways. According to Swimburn et al (6), food environment is considered as \"the collective physical, economic, policy and socio-cultural surroundings, opportunities and conditions that influence people's food and beverage choices, consumption patterns and nutritional health\". Food environment concept was seen from two angles. The personal environment includes food accessibility, promotion and quality, food affordability, convenience and sustainability, while the external environment is relevant to encompassing food availability, prices, vendor, product properties, marketing and regulation (7). These concepts show the scope and the importance of food environment in the diets of the population and call for actions focused on their pillars to improve diet quality. One of the frameworks identified by several countries in Africa to act on the food environment is the school. In our knowledge to date in Benin, few studies targeted schoolchildren diet and revealed a bad food practices and food insecurity among them (8,9). This situation highlights the important role of eating outside home for school children who spend 8 hours per day at school. Schools are a key setting to improve dietary behaviors. Moreover, a healthy school food environment is associated with lower rates of obesity (10). Among other factors, food supply influences school children's dietary status and hence their learning abilities (11). Moreover, food patterns analysis of school children in low-and-middle-income countries (LMICs) showed low intake of flesh foods, fruits and vegetables and high consumption of energy-dense processed foods (12). The cognitive performance of school children and their academic achievement depend closely on the quality of their school diets (13)."},{"index":2,"size":47,"text":"The key role that school feeding plays in the eating behaviors of school children from LMICs (11,(13)(14)(15) and how school food environment represents an effective setting to influence children's food choices, shapes their eating behavior patterns, prevent multiple burdens including obesity and undernutrition have been reported (16)."},{"index":3,"size":85,"text":"In fact, all the components of the food environment are necessary to improve the diet of schoolchildren. However, given the context where schoolchildren are assisted by a school feeding program operating in the most disadvantaged schools, this study focused on the assessment of the diversity of food supply in schools as well as the hygiene practices around public primary schools with canteens, two aspects that will also contribute significantly to improve the school food environment in order to better address schoolchildren's food and nutrition problems."}]},{"head":"Method","index":2,"paragraphs":[]},{"head":"Study area","index":3,"paragraphs":[{"index":1,"size":74,"text":"The present study is the diagnostic phase through which data were collected to assess the baseline situation. This study is conducted in three municipalities: Cotonou, Abomey-Calavi and Sèmè-Kpodji (CAS). Cotonou is an urban area while Abomey-Calavi and Sèmè-Kpodji are periurban area. The choice of these municipalities was, first, based on the fact that market gardening is mainly practiced in urban and peri-urban areas, but also on the willingness to characterize the market gardening practices."}]},{"head":"Sampling","index":4,"paragraphs":[{"index":1,"size":43,"text":"The design of the study is a cluster randomized controlled trial implemented in Public Primary Schools (PPS) with canteen (Table 1). The following formula was used to calculate the sample size at 5% level of significance, 80% power. n = 2(Zα/2 + Zβ)/δ2"},{"index":2,"size":40,"text":"The design effect was determined by the formula DE= 1+ (K -1) * ICC where K represents the number (75) of schoolchildren per cluster and ICC the Inter-Cluster Correlation fixed at 0.05. A sample size of 861 schoolchildren was calculated."},{"index":3,"size":92,"text":"The number of schools was then obtained by dividing the number of schoolchildren to be surveyed by the number of schoolchildren per cluster, i.e. 11.48 schools, hence the choice of 12 PPS. The PPS were randomly selected based on the sampling frame from the list of all PPS benefiting from the National Integrated School Feeding Program implemented by the World Food Program and Benin Government in the three municipalities. Between the 58 PPS benefiting from canteens in CAS we proceeded to a simple random selection without discount of 4 schools per municipality."}]},{"head":"Ethical considerations","index":5,"paragraphs":[{"index":1,"size":41,"text":"The present study was approved by the National Ethics and Health Research Committee (Authorization n° 53/MS/SGM/CNERS/SA) and the Ministry of Nursery (Pre-school) and Primary Education (Authorization n o 0031/MEMP/DC/DEP/ SAS/SP). Moreover, informed consent was obtained from directors of school and cookers."}]},{"head":"Data collection","index":6,"paragraphs":[{"index":1,"size":154,"text":"Data related to the diversity of the food supply and hygiene practices were mainly collected. Data among the diversity of the food supply were obtained from a collection sheet that was used to inventory all the foods available within and around each school. The list of all foods available in the school environment was compiled. Data related to hygiene around the preparation, hygiene of the handlers or cookers, presence of pests, cleanliness of the environment, presence and proximity of waste bins, hygiene of the container used for the storage of the water is kept, etc., were collected to assess the sanitary quality of foods cooked and served in canteens, through a semi-structured questionnaire administered to the cookers as well as by non-participant observation. This approach used to access the hygiene was based on the guidelines on HACCP (18). Figure 1 shows the overview of the different aspects that need to be addressed in schools."},{"index":2,"size":88,"text":"During the preparation, the interviewer observed and identified the critical points of contamination such as wearing of headgear, cleanliness of clothing, trimmed fingernails, use of apron, use of bib, coughing, nasal discharge, and wounds on the body. Observations allowed to assess the presence of animals roaming in the preparation site, the proximity of bins to '0' in the opposite case. The maximum score would be 15 for the school where all the food groups were available. The score median equal to 8 was determined to appreciate the FGS."},{"index":3,"size":59,"text":"The second indicator is related to the Summary Hygiene Index (SHI): To assess food safety, the SHI reported by Webb et al (20) has been used. This overall index is calculated from four other indexes based on the simple weighting of hygiene practices. Each of these indexes represents the different pathways food contamination may occur (Table 2), as followed:"},{"index":4,"size":1,"text":"•"},{"index":5,"size":45,"text":"Water: Source of water and contamination of stored water (Drinking Water Index, DWI); • Food: Food storage conditions (Food Index, FI); • Food: Hygiene practices related to cookers (Personal Hygiene Index, PHI) and • Environment: Presence of animals, pests and garbage (Canteen Hygiene Index, CHI)."},{"index":6,"size":16,"text":"Each index item is appreciated by \"Yes\" or \"No\", according to the assessment criteria (Table 2)."},{"index":7,"size":86,"text":"Each item was scored as 0 or 1, with 1 representing positive behavior and 0, the negative one. The indexes were calculated as the sum of the hygiene practice. A summary hygiene index (SHI) was generated from the sum of the four (04) specific indices and had ranged from 0 to 15 (20). The score median equal to 8 was used to appreciate the SHI. SHI equal or greater than the median was considered as good hygiene and less than the median as lower hygiene practices."},{"index":8,"size":104,"text":"the preparation site, the presence of the foodstuffs in the preparation site, the existence of the food storage site and the presence of pests (rats, mice, cockroaches, lizards, etc.) in the storage site. The quality of drinking water was assessed and this included source of water, cleaning of storage containers, covering of drinking and cooking water, etc. To minimize the influence of enumerator's presence on practices, they stayed at least three days in each school collecting other type of data (food consumption, anthropometry, etc.) and data related to hygiene were collected on the third day. Data were collected with tablets using Survey Solutions application."}]},{"head":"Data management","index":7,"paragraphs":[{"index":1,"size":17,"text":"After collection, data generated with the Survey Solutions server was corrected and combined to determine two indicators:"},{"index":2,"size":130,"text":"The first indicator concerns the Food group scores (FGS) which was calculated to assess the diversity of the food supply in each school using the food list. The foods available in the school environment were categorized into different food groups. The food groups considered here were adapted from those proposed by Adeleye et al (19); including cereals, vitamin A rich vegetables and tubers, white tubers, dark green leafy vegetables, other vegetables, vitamin A fruits, other fruits, organ meat, flesh meat, egg and egg products, fish, legumes/nuts/seeds, milk/milk products, oils/fats. To conform to the field realities, the group of sweet foods and drinks was added. A food group was assigned a score of '1' if it is available in the school and  of Ahossou-Gbèta has the lowest food group score (FGS=4)."}]},{"head":"Food diversity within groups and schools","index":8,"paragraphs":[{"index":1,"size":76,"text":"On the one hand, the most represented food groups available in almost all schools were cereals, legumes, nuts, seeds followed by sweet foods and drink, fish and egg and egg product. On the other hand, vitamin A rich vegetables and tubers, other vegetables, dark green leafy vegetables, vitamin A fruits, other fruits, organ meat, flesh meat, fish, milk/milk products, oils/fats, which are good sources of micronutrients, were poorly represented or not (Table 3 and Figure 3)."},{"index":2,"size":56,"text":"In all schools, cereals (rice, wheat, maize and sorghum based foods) and sweetened foods and drinks (pineapple juice, biscuits, bissap, candies) were at the top of the classification with four 4 food items. White tubers (cassava and yam based foods), legumes/nuts/ seeds (cowpea and yellow cowpea), vitamin A fruit (Carica papaya) and others fruits (Musa troglodytarum"}]},{"head":"Statistical analysis","index":9,"paragraphs":[{"index":1,"size":54,"text":"Descriptive statistics were mainly used to assess the different parameters involved in this study. Wilcoxon rank sum test was implemented to compare food group score as well as the summary hygiene index among urban and peri-urban areas. Statistical analysis was performed in Stata software (version 16) and the significance level was set at 5%."}]},{"head":"Results","index":10,"paragraphs":[]},{"head":"Diversity of the food supply","index":11,"paragraphs":[{"index":1,"size":60,"text":"Food groups score were relatively low and varied with schools (Figure 2). Only 3 schools out of the 12 achieved an FGS equal or greater than to the median score of 8, indicating a low diversity of food supply in the schools. PPS of Kpakpakame had the highest FGS (FGS=9), followed by PPSs of Lom-Nava and Dassekommey (FGS=8). The PPS    "}]},{"head":"Municipalities","index":12,"paragraphs":[]},{"head":"Hygiene practices","index":13,"paragraphs":[{"index":1,"size":287,"text":"Overall, only four (4) schools had a hygiene score higher than the median score of 8. Among these schools, PPS of Yagbé A had the highest score (SHI= 11) followed by PPS of Yagbé B (SHI= 10). PPS of Lom-Nava and Ouéga-Tokpa followed by an and Citrus Sinensis) were found in second position with 2 different items each. The groups of eggs and eggs product (boiled egg), fish, organ meat (sausage) and oil and fatty (fried tomato) had only one food item. No food item from milk and milk products, dark green leafy vegetables, other vegetables, vitamin A vegetable and tubers and flesh meat were available (Figure 4).  were also observed a study conducted in 20 schools in Bamako/Mali (22) one capital of West Africa. Authors found that the school food environment were not suitable for healthy foods. This situation could be explained by many ways: the low availability of foods rich in vitamins or micronutrients in the school environments could probably be explained by the perishability of these products due to the lack of foods storage methods in the schools (22). The lack of knowledge of cookers and food sellers about the nutritional and health benefits if eating healthy foods or different types of foods could also explain the non-diversity of food groups sold in school (23). Moreover, the cost of foods and the schoolchildren eating behaviors can affect the quality of the diet and the fact that certain type of food group is not proposed (24). Foods products like milk and fruits are relatively expensive and then less accessible to schoolchildren. The lack of policy or regulation regarding food sold in or around schools may also explain the situation observe in the 12 schools (25)."},{"index":2,"size":94,"text":"The diversity of the food supply determines food choice because, most of time, what is available is what is consumed and the availability in many food groups within school can influenced dietary habits (26). Lack of availability of a given food product affects food choices and the link between food availability SHI = 9. Moreover, 8 schools had an SHI below the median. In addition, all the schools in Cotonou had a score above or equal to the median, while in Sèmè-Kpodji and Abomey-Calavi, the majority of schools were below the median (Figure 5)."}]},{"head":"Food group score and summary hygiene index within zone","index":14,"paragraphs":[{"index":1,"size":32,"text":"The median FGS was 6.5 in schools in urban areas and 7 in schools in peri-urban areas (p= 0.722). SHI was higher in urban (9.5) than peri-urban area (6.5) (p=0.015) (Figure 6)."}]},{"head":"Discussion","index":15,"paragraphs":[{"index":1,"size":114,"text":"Results showed that food supply in both urban and peri-urban schools was still limited in terms of availability of food groups and types of foods within each group. Dark leafy vegetables, milk and milk products, other vegetables, vitamin A rich vegetables and tubers and flesh meat were poorly represented as founded in a study relative to the food supply and dietary behavior of adolescents conducted in three colleges in Cotonou (21). The limited food supply trends Similar results have been reported in southern Togo (33) and Cameroun (34) where some primary schools did not have adequate hygiene conditions and safety practices and facilities leading them to a high risk of contamination during foods preparation."},{"index":2,"size":67,"text":"We had also observed that, in the urban area, the SHI seems significantly better than in the periurban area. The lack of water supply, more observed in periurban area could explain the disparities between urban and periurban area (35). Cost of acquiring water or difficulties encounter to have drinking water is higher in periurban area than urban due to the poor water infrastructures observed in periurban area."},{"index":3,"size":42,"text":"Inadequate hygiene and sanitation practices could lead to health issues. It has been reported that poor hygiene practices in primary schools and a low level of sanitation in the school environment led to a high incidence and prevalence of childhood illnesses (36)."},{"index":4,"size":43,"text":"Poor hygiene practices in food preparation specifically can cause diarrhea and many other digestive infections (37,38). Thus, the improvement of these practices could lead to better behaviors in terms of hygiene throughout their life and minimize the risks of occurrence of diseases (39,40)."},{"index":5,"size":199,"text":"The results of the present study have made it possible to assess the diversity of the food supply in the schools surveyed as well as the hygiene practices, and to highlight the challenges to be met in terms of school feeding. From improving the range of foods and consumption is two-ways, with one influencing the other (17). At a very basic level, food availability must precede consumption and a food cannot be consumed if it is not available. Study that have investigated the role of food availability in determining dietary intake have concluded that there is a positive relationship between food availability and food consumption (27) therefore the lack of a number of food groups within schools limit their consumption by school children. Diets rich in fruits and vegetables are generally linked with an improvement in general health (28,29) due to the high amounts of fiber and phytonutrients present in fiber and vegetables (30,31). The importance of adopting healthy eating behaviors such as consuming different food groups from childhood and healthy food choices in adulthood has been reported (32). Cognitive performance, academic achievement, low prevalence of obesity, good nutritional status can be a consequences of adopting healthy eating behaviors."},{"index":6,"size":67,"text":"From these results, inadequate sanitary quality of foods prepared in all the schools was observed (Median of SHI ≤ 8) The inadequate hygiene conditions observed could be explained by many factors such as: lack of hygiene policies and sanctions against canteen cookers for non-compliance with good hygiene practices of foods, lack of knowledge about the recommended hygiene practices and low accessibility to hygiene and sanitation facilities (22).  "}]},{"head":"Limitation","index":16,"paragraphs":[{"index":1,"size":74,"text":"This study ambition is to describe in depth food supply and hygiene practices. However, we faced two majors' limitations during our investigations. Firstly, the fact that the study was focused only on public primary schools and did not consider private schools which also could have its own reality and characteristics. Secondly, it was not also practically possible for us to assess all the components of food environment such as food accessibility, commodity and sustainability."}]},{"head":"Conclusion","index":17,"paragraphs":[{"index":1,"size":148,"text":"The food supply in school environment among the targeted public primary schools in Cotonou, Abomey-Calavi and Sèmè-Kpodji is not very diversified. This school food environment is dominated by the cereals, roots and tubers, and legumes-based food groups which are available as staple foods (source of energy). The study reveals that this environment is poor in fruits, leafy vegetables and dairy products, which are sources of essential micronutrients. In addition, inadequate hygiene practices noticed, compromised the sanitary quality of the food cooked and served to school children. These findings suggest taking additional actions like, school gardens and nutritional education program to make school environment suitable for adequate and healthy diet. Further studies will be therefore needed to assess the effect of setting up school gardens combined with a nutrition education program. This approach will make it possible to make progress on the link between improving the school food environment"}]}],"figures":[{"text":"3 ) Source of cooking water Existence of food storage sites Cookers have clean hands Proximity of garbage cans Water storage container Presence of pests in the food storage site Finger nails trimmed Presence of pests Covered water storage container Clean dishes covered Wearing of headgear Roaming animals Wearing aprons Clean outfits Wearing of bibs Yes is coded 1 and No is coded 0; 1: Drinking water index; 2: Food index; 3: Personal hygiene index; 4: Canteen hygiene index. "},{"text":"Figure 2 . Figure 2. Food group score by schools. "},{"text":"Figure 3 . Figure 3. Number of schools where food group is available (offered). "},{"text":"Figure 4 . Figure 4. Variability of food within food group. "},{"text":"Figure 5 . Figure 5. Summary hygiene index by schools. PPS: Public Primary School "},{"text":"Figure 6 . Figure 6. Plots of FGS and SHI within urban and peri-urban zone. "},{"text":"Table 1 . Public primary schools per municipalities. Municipalities Cotonou Abomey-Calavi Sèmè-Kpodji MunicipalitiesCotonouAbomey-CalaviSèmè-Kpodji Public primary Toyoymé, Yagbé A, Yagbé B, Dassekommey, Ouega-Tokpa, Yagbé, Djeffa Plage, Public primaryToyoymé, Yagbé A, Yagbé B,Dassekommey, Ouega-Tokpa,Yagbé, Djeffa Plage, schools Lomnava Ahossou-Gbèta Ahouato Kpakpakame Gbakpodji schoolsLomnavaAhossou-Gbèta AhouatoKpakpakame Gbakpodji  "},{"text":"Public Primary school with canteen Figure   "},{"text":"Table 2 . Indicators of hygiene used to develop summary hygiene index.  "},{"text":"Table 3 . Foods groups available in the schools. Semè-Kpodji Yagbe Gbakpodji Kpakpakanmey wheat Wheat wheat bread, bread, bread, macaroni, macaroni, macaroni, rice, vegetable rice, rice, yoghurt made vegetable vegetable from maize yoghurt yoghurt dough made made from from maize maize dough, dough granulated fermented maize and sorghum porridge, wheat fritter \"doco\" cowpeas, cowpeas, cowpeas, yellow yellow yellow peas peas peas ---   Yam, Yam, Yam, tapioca, tapioca, tapioca, granulated granulated granulated fermented fermented fermented cassava cassava cassava Semè-KpodjiYagbe Gbakpodji Kpakpakanmeywheat Wheat wheat bread,bread, bread, macaroni,macaroni, macaroni, rice, vegetablerice, rice, yoghurt madevegetable vegetable from maizeyoghurt yoghurt doughmade made fromfrom maizemaize dough,dough granulatedfermentedmaize andsorghumporridge,wheatfritter\"doco\"cowpeas, cowpeas, cowpeas, yellowyellow yellow peas peaspeas---Yam, Yam, Yam, tapioca,tapioca, tapioca, granulatedgranulated granulated fermentedfermented fermented cassavacassava cassava Djeffa- Plage Rice, maize dough, wheat bread, wheat fritter \"doco\"           Yellow peas  -   Granulated fermented cassava, tapioca Djeffa-PlageRice, maizedough,wheatbread,wheatfritter\"doco\"Yellowpeas-Granulatedfermentedcassava,tapioca Ouega- Tokpa Maize dough, rice, wheat bread, wheat fritter \"doco\"           cowpeas, yellow peas  -   Granulated fermented cassava, tapioca Ouega-TokpaMaizedough,rice, wheatbread,wheatfritter\"doco\"cowpeas,yellow peas-Granulatedfermentedcassava,tapioca Cotonou Abomey-Calavi Yagbe B Lomnava Toyoyome Ahousougbeta Ahouato Dassekommey Wheat Rice, Wheat rice, maize Rice, maize rice, maize bread, maize bread, dough, wheat dough, dough, wheat rice, dough, macaroni, bread, wheat wheat bread, wheat wheat wheat rice, fritter \"doco\" bread, fritter \"doco\" fritter bread, granulated wheat \"doco\" wheat fermented fritter fritter maize and \"doco\" \"doco\" sorghum porridge, wheat fritter \"doco\"      cowpeas, cowpeas, cowpeas, yellow peas yellow peas cowpeas, yellow yellow yellow yellow peas peas peas peas ------   -tapioca -Granulated Granulated tapioca fermented fermented cassava, cassava, tapioca tapioca Cotonou Abomey-CalaviYagbe B Lomnava Toyoyome Ahousougbeta Ahouato DassekommeyWheat Rice, Wheat rice, maize Rice, maize rice, maizebread, maize bread, dough, wheat dough, dough, wheatrice, dough, macaroni, bread, wheat wheat bread, wheatwheat wheat rice, fritter \"doco\" bread, fritter \"doco\"fritter bread, granulated wheat\"doco\" wheat fermented fritterfritter maize and \"doco\"\"doco\" sorghumporridge,wheatfritter\"doco\"cowpeas, cowpeas, cowpeas, yellow peas yellow peas cowpeas,yellow yellow yellow yellow peaspeas peas peas-------tapioca -Granulated Granulated tapiocafermented fermentedcassava, cassava,tapioca tapioca  Yagbe A Wheat bread, rice, wheat fritter \"doco\"            cowpeas, yellow peas -   -   Yagbe AWheatbread,rice,wheatfritter\"doco\"cowpeas,yellowpeas-- Municipalities Schools Cereal                 Legume/nuts/ seeds  vitamin A vegetables and tubers white tubers   MunicipalitiesSchoolsCerealLegume/nuts/seedsvitamin Avegetables andtuberswhite tubers  "},{"text":" and the quality of diets. That will help for taking corrective measures relating to the adequate management of the school canteens. availability in term of diversity in schools to the quality of the meals served, this study was entirely justified. The presence of school canteens reinforced by the establishment of fruit and vegetable gardens and nutritional education program could improve both the diversity of the food supply and hygiene practices. Indeed, fruit and Vegetable gardens in schools accompanied by nutritional education program for school-age children are interventions that work nowadays according to United Nations System Standing Committee on Nutrition (4).  "}],"sieverID":"4dc911e4-5a7c-4224-9ba7-b90c78f618d3","abstract":"Background and aim: School food environment is a component of food system which provides the opportunity to implement interventions that lead to better nutrition. This study aimed to describe two of the five components of food environment notably food supply and food safety through hygiene practices in schools inside Cotonou, Abomey-Calavi and Sèmè-Kpodji, in Benin country. Methods: Twelve schools were randomly selected from a sampling frame of all public primary schools with canteens and that have space for school gardens and closer to the market garden sites. In the selected schools, we assessed the diversity of the Food Supply using Food Group Score (FGS) and Hygiene practices using the Summary Hygiene Index (SHI). Data were mainly collected using semi-structured questionnaire administered to foods cookers/ vendors and by observation within schools. Fifteen food groups were considered to determine the FGS and 15 for SHI. Wilcoxon test was used to compare scores among urban and peri-urban areas. Results: Food supply appeared to be limited in 9 schools (FGS< 8) over the 12 with no significant difference between periurban and urban zone (p-value = 0.72). The most represented food group which was available in all schools are cereals, legumes, nuts, seeds, followed by sweet foods and drink while others groups (source of vitamin A and micronutrients) are poorly represented. It appears in all schools a low variability of food within each group. Basically, 8 schools out of 12 have a SHI lower than the median score (08) and the urban zone has a SHI (SHI = 9.5 ± 1.29) higher than peri-urban (SHI = 6.5 ± 1.18) with p-value = 0.015. Conclusions: The food supply is not very diversified in public primary schools and hygiene practices need to be improved for a healthy food environment around schools."}

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+{"metadata":{"id":"0ddf9bf303f045fd89396d698e9f5c75","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/25efb030-7e2a-4aa3-906a-01fa9fdf6b13/retrieve"},"pageCount":13,"title":"High Public Good Values for Ecosystem Service Attributes of on-farm Quinoa Diversity Conservation in Peru","keywords":[],"chapters":[{"head":"Introduction","index":1,"paragraphs":[{"index":1,"size":97,"text":"An unprecedented, accelerating, and irreversible loss of agrobiodiversity 1 is occurring at ecosystem, species, and genetic levels throughout the world (FAO, 2015(FAO, , 2019)), even though the existence of such diversity is the basis for sustainable agriculture, food and nutrition security, ecosystem health, and adaptation to climate change (Hajjar et al., 2008;Bellon et al., 2020;Tesfaye & Tirivayi, 2020). Unlike wild diversity, agrobiodiversity is the result of thousands of years of interaction between humans and their environment, with its continued existence dependent on the maintenance of such public good \"evosystem\" (i.e., evolutionary processrelated) services (Faith et al., 2010)."},{"index":2,"size":158,"text":"Since many of the world's agrobiodiversity hotspots are in developing countries, society is presented with the fundamental conundrum of how to safeguard the biodiversity maintained in the fields of the rural poor while at the same time meeting their development needs and rights. Farmers' decisions tend to be based on how personally 'profitable' crop varieties or livestock breeds are (Bellon et al., 2020) not only in terms of providing a provisioning service but also as a means of mitigating risks and stabilising both yields and incomes (Di Falco & Chavas, 2009;Poudel & Johnsen, 2009;Kassie et al., 2017;Kremen & Merenleder, 2018;Tesfaye & Tirivayi, 2020;Maligalig et al., 2021). By contrast, the public good values associated with the maintenance of such agrobiodiversity do not necessarily feature in farmer's crop diversity maintenance decisions (Narloch et al., 2011). Global market integration, land use change, and migration also contribute to changing farmers' maintenance of crop diversity (Zimmerer & de Haan, 2017;Goldberg et al., 2021)."},{"index":3,"size":131,"text":"Failures in existing policies and markets that favour highinput, high-output \"improved\" varieties and breeds further impact farmers' changing practices and agrobiodiversity loss (Smale et al., 2004;Pascual & Perrings, 2007;Narloch et al., 2013) by driving out traditional varieties, whose range of nonmarket values are not reflected in their price (Bellon et al., 2020). In addition to environmental benefits, for example related to climate change adaptation, these include their role in maintaining cultural traditions (including food culture), local identities, and traditional knowledge (Smale et al., 2004;Nautiyal et al., 2008;George & Christopher, 2020). The existence of such non-market values leads to an under-provision of genetic diversity at national and global levels, while those farmers maintaining agrobiodiversity in their fields are often left uncompensated for their opportunity costs of providing a public good ecosystem service."},{"index":4,"size":249,"text":"The diversity of benefits results in a complex set of incentives that affect smallholder farmer preferences, which, particularly in in the Global South, result in the cultivation of multiple crop species in integrated farming systems, maintaining de facto crop diversity. Nonetheless, there is no guarantee that they will maintain socially desirable levels of diversity. Markets alone cannot be expected to reward farmers adequately for managing socially desirable levels of agrobiodiversity (Narloch et al., 2011;Drucker & Appels, 2016). Instead, complementary incentives mechanisms need to be established that reward those farmers who maintain agrobiodiversity and related ecosystem services for the public good. To justify the funding of such interventions with public money it is important to understand the values that a society places on the provision of these non-market ecosystem services (Ojea & Loureiro, 2007;Kreye et al., 2016;Zander et al., 2013). Over recent years a body of work has emerged that specifically seeks to develop and provide market-based incentives for the conservation of agrobiodiversity, such as payments for agrobiodiversity conservation services (PACS). These schemes can potentially be implemented at modest cost and designed in ways that are socially equitable (Narloch et al., 2011(Narloch et al., , 2013;;Wainwright et al., 2019;Drucker & Ramirez, 2020;Drucker et al., 2023). However, scaling-up these largely project-related PACS interventions to effectively secure national and global public goods requires government support facilitated through information regarding which ecosystem services the public value most and hence which types of conservation programme attributes to prioritise in order to maximise social welfare."},{"index":5,"size":204,"text":"It is in this context that we conducted our case-study in Peru, a megadiverse country in which there is no systematic long-term agricultural policy funding mechanism for agrobiodiversity conservation. Our aims are to assess: (1) the public's preference for ecosystem services arising from the in situ on-farm conservation of quinoa; (2) preference variation among different stakeholders; (3) preference variation under different information and motivation framing regarding a potential conservation programme; and (4) the degree to which the public's willingness to pay (WTP) for conservation is sufficient to cover the estimated costs of implementing such a diversity conservation programme. We used a total economic value (TEV) framework and conducted a choice experiment (CE) -a multi-attribute preference assessment method -with members of the Peruvian public in selected locations. The TEV framework provides a structure through which different types of benefits to society, both direct and indirect, can be aggregated to construct a comprehensive valuation. Under the framework, any public good or service may consist of both use (direct, indirect, and option) and non-use (existence, altruistic, and bequest) values (OECD, 2006). We subsequently used the CE results to inform recommendations regarding a conservation programme design that reflects the public's preferences for different types of agrobiodiversity-related ecosystem services."},{"index":6,"size":93,"text":"Under similar circumstances, stated preference methods have been widely used to elicit the value that the public places on different attributes of biodiversity, including in the specific case of agrobiodiversity (e.g., Krishna et al., 2010;Pallante et al., 2016;Botelho et al., 2018;Häfner et al., 2018). A number of these studies have also explicitly sought to demonstrate the existence of positive benefit-cost ratios, to guide the design of biodiversity policies and as a means of justifying existing or increased conservation funding (e.g., Zander et al., 2013;Martin-Collado et al., 2014;Tyack & Ščasný, 2018;Drucker & Ramirez, 2020)."}]},{"head":"Methods","index":2,"paragraphs":[]},{"head":"Research Context","index":3,"paragraphs":[{"index":1,"size":162,"text":"Peru is one of 17 megadiverse countries (OECD/ECLAC, 2017) and a centre of origin for crops important to the livelihoods of the poor, such as maize, potato, and quinoa, many of which are also of global importance. It has 184 species and hundreds of varieties of domesticated native plants, of which many species/varieties are considered \"severely threatened\" (FAO, 2015). There are over 5700 accessions of quinoa (Chenopodium quinoa Willd) conserved in seven gene banks that have been characterised into 24 races (Tapia & Fries, 2007;Tapia et al., 2014), constituting thousands of varieties. Many of these are at risk of disappearing (Kost, 2016) in large part as the national and international market is concentrated around only 15-20 mostly white varieties out of an approximate total of 3000 (Rojas et al., 2009). The resulting genetic erosion threatens Peru's food and nutritional security, the sustainability of its high-altitude production systems, and its ability to adapt to future climate change along with emerging pests and diseases."},{"index":2,"size":84,"text":"Furthermore, quinoa plays an important role in many Andean cultural traditions (Rojas et al., 2009) and its high profile in Peru in general makes it an ideal crop around which to explore its many non-market public good ecosystem service values and the general public's willingness to support its in situ on-farm conservation. Estimating the potential magnitude of such support and devising mechanisms to capture such values is critical given that poverty rates in the arid Andean rural highlands can reach over 50% (INEI, 2020)."}]},{"head":"Choice Experiment Design","index":4,"paragraphs":[{"index":1,"size":112,"text":"In a CE, respondents are presented with a series of choice tasks, known as choice sets, each containing a finite number of alternatives that describe the hypothetical environmental good or policy outcome in question (Hanley et al., 2001). CEs have been used extensively to evaluate farmer participation in schemes providing ecosystem services (e.g., biodiversity conservation: Sardaro et al., 2016;carbon sequestration: Aslam et al., 2017) or to gauge their preferences for crop traits improving livelihoods (e.g., Kassie et al., 2017;Maligalig et al., 2021); as well as to determine consumer/ general public willingness to pay for ecosystem goods and services (Zander et al., 2013;Martin-Collado et al., 2014, Blare et al., 2019;Müller et al., 2020)."},{"index":2,"size":197,"text":"The alternatives presented in a CE vary in regard to the levels associated with each of the attributes and respondents are usually asked to choose their most preferred alternatives. By making this choice, respondents trade-off the attributes and the associated costs that come with the chosen alternative. A key component of the experiment is the definition of attributes used in the choice experimental design (Johnston et al., 2017). The attributes and levels for this study drew on Zander et al. (2013) and Martin-Collado et al. (2014) and were adapted to the Peruvian crop genetic resource context in consultation with Peruvian genetic resources and agricultural experts. Each attribute represents a component of the TEV so that the sum of the separate attribute values may be used as a proxy for the TEV of the public good ecosystem service associated with the maintenance of quinoa diversity in farmers' fields. The four attributes included Andean landscape conservation (includes ecological processes and aesthetics), insuring against the risk of agricultural production loss in the context of broader food security issues, quinoa diversity conservation and the maintenance of traditional knowledge and cultural practices -the latter including aspects of food culture (see Table 1)."},{"index":3,"size":151,"text":"As a monetary value, which is required for the calculation of welfare estimates based on WTP, we selected a one-off donation (in New Peruvian Soles) to a diversity conservation programme for the crop in question. The use of oneoff payment vehicles described as donations are common when evaluating environmental goods and services through respondents' stated preferences (e.g., Veríssimo et al., 2009;Kragt & Bennett, 2011). Although one-off payments have been criticised for not being incentive compatible (Johnston et al., 2017), we opted against the use of a non-voluntary tax contribution vehicle as many respondents may fall outside the tax net. Nor did we select a repetitive payments vehicle as we did not want to make assumptions about how long payments are needed to successfully conserve crop varieties, which could potentially require support in perpetuity. The one-off payment vehicle also helps to simplify respondent understanding of the total cost of the CE alternatives."},{"index":4,"size":359,"text":"We used a generic design such that each choice set consisted of three alternatives from which respondents were asked to select their most preferred. One of the alternatives was always described as the status-quo (SQ), while two others represented different scenarios under a quinoa crop diversity conservation programme. The SQ alternative did not involve a personal cost for respondents and can be interpreted as leaving things to business-as-usual and a consequent continuing erosion of quinoa diversity. The other two scenarios involved a one-off contribution towards a conservation programme and would result in benefits associated with an increase in such diversity (or at least avoiding any further decline). Given the number of attributes and their levels (Table 1), there would have been too many possible combinations (3^3*2^1*7^1 = 378) to use all of them in the survey and hence we designed a choice experiment that only included a fraction of these combinations. The use of qualitative levels for two of the attributes (Conservation of Andean Landscape -Improve, Stable, Decrease; and Risk of Agricultural Production Loss -High, Medium, Low), as in other studies (Zander et al., 2013;Martin-Collado et al., 2014) was necessary due to the challenges of articulating potential impacts in quantitative terms with regard to such multidimensional concepts as landscapes and food security. The design was pre-tested before the main survey started. An important issue in experimental design is the identification of efficient designs 2 capable of generating statistically significant attribute combinations associated with a given sample size (Rose & Bliemer, 2008). We generated a Bayesian efficient design (see Sándor & Wedel, 2001;Ferrini & Scarpa, 2007) of 24 choice sets blocked into three blocks using the software STATA. Each respondent was assigned one block of eight choice sets each (see Fig. S1). The design was based on prior parameter estimates that we assumed after expert consultation and literature review. Using prior parameter estimates leads to more reliable parameter estimates for a given sample size, even if the information on the parameters is scant and the priors mis-specified (Bliemer et al., 2009). While we did not know the exact values of the priors, we were quite certain about the expected signs."}]},{"head":"Sampling and Data Collection","index":5,"paragraphs":[{"index":1,"size":114,"text":"With a view to exploring how public willingness to support genetic resources conservation may vary among segments of rural and urban populations as they become more geographically distant from the genetic resource in question, population samples were selected across the important Andean quinoa producing regions of Puno and Cusco. These included the regional capital cities, whose populations are respectively 135,300 and 437,500 (INEI, 2017), as well as the surrounding rural areas where incomes might be expected to be even more constrained compared to those within the cities. We also conducted surveys in the national capital, Lima (population 9.17 m (INEI, 2017)), which is distant from these quinoa producing areas but with higher average incomes."},{"index":2,"size":93,"text":"With a view to overcoming logistical and cost challenges of visiting households in Peru, we used a \"second-best\" convenience sampling method that involved enumerators randomly recruiting participants in central or communal areas, such as town squares, bus stations, and markets. Although convenience sampling can result in the risk of selection bias (Moore, 2001) and unbalanced samples, given the experimental design and randomised treatment we used here, we anticipated no major issues arising from demographic imbalances, and a significant overlap between the sample and the actual demographics can in fact be observed (Table 2)."},{"index":3,"size":183,"text":"Sample size calculation used a cluster sampling approach (Walker & Adam, 2011) considering District population, an expected WTP contributor's rate of 0.4 for Lima and 0.3 for the regional cities, a sample precision level between 0.1 (Lima and Puno) and 0.15 (Cusco) in a normal distribution z with p-value equals to 0.95. To take into consideration population heterogeneity, we considered a population heterogeneity of 0.15 in Metropolitan Lima and 0.1 in the regional capital cities. Finally, optimal cluster size was measured based on heterogeneity of population and cost of data collection. As a result, the minimum sample size was determined to be 471 (84 in Cusco,195 in Puno,and 192 in Lima). Interviews were conducted with 491 adult Peruvian resident respondents between July and September 2017 in Cusco (91), Puno (200) and Lima (200). The interviews were administered in Spanish (and occasionally in local languages Quechua and Aymara) by three groups of trained enumerators in their respective locations. Subjects were not compensated for their participation, eliminating any selection bias related to financial incentives. Only adults were interviewed, and consent was established before each interview."}]},{"head":"Questionnaire","index":6,"paragraphs":[{"index":1,"size":52,"text":"We used a three-part structured questionnaire. Respondents were first asked questions related to their familiarity with and use of different varieties of quinoa. Second, they were presented with the CE sets. In the third section, we asked for basic demographic information (gender, age, occupation, income, education, household composition, socio-economic status, and wealth)."},{"index":2,"size":101,"text":"Information was provided regarding agrobiodiversity in general. Prior to being presented with the choice sets, respondents were also reminded that achieving good conservation outcomes has a cost, that quinoa varieties are not the only crop that may require conservation funding, that there may be other good causes to support, and that their household budgets need to cover other expenses too. This so-called cheap talk script helps minimise hypothetical bias that could lead respondents to overstating their willingnessto-pay (Ladenburg & Olsen, 2014). Having provided instructions on how to interpret the choice sets and make selections, eight choice sets were then individually presented."}]},{"head":"Information Framing","index":7,"paragraphs":[{"index":1,"size":102,"text":"Framing is an effective way to increase awareness and potential WTP (Czajkowski & Hanley, 2009) because the value of an environmental good or service depends not only on their physical characteristics, but also on the context within which they are situated. In CE this refers to how the goods and services are described to respondents, in addition to their attributes. By providing different information to different sample treatment groups, respondents can be primed by the introduction of a stimuli before making their choices. This can trigger an emotional response, establish context, or change a subjects' frame of reference (Weingarten et al., 2016)."},{"index":2,"size":110,"text":"Numerous case studies have shown framing to increase WTP in specific contexts for both direct and non-direct use products. Banerji et al. (2016), for example, found that nutritional information significantly increased WTP for vitamin-fortified millet in India. Bergstrom et al. (1990) found that framing increased WTP for American wetlands when respondents were reminded how different programme attributes related to desirable consumption services. By contrast, Fox et al. (2002) found that Chinese consumers preferred to pay less for pork products when information about harmless irradiation was presented. These findings suggest that the effects of information framing can move WTP in both directions, depending on the person's perception of the information provided."},{"index":3,"size":115,"text":"We used two different framing scenarios, one about the national identity (NI) significance of quinoa and one about food security (FS). The NI framing text contained a series of historical facts detailing quinoa's history as native to Peru and attempts by Spanish colonizers to eradicate the crop in the sixteenth century (Fig. S2). We hypothesised that this stimulus involving cultural nationalism would increase the appreciation of native crops, and hence respondents' WTP. The FS framing utilised a series of questions regarding personal food security, based on the hypothesis that heightened sensitivity to potential food shocks may increase the valuation of biodiversity and hence WTP for its conservation, given its role as an informal insurance mechanism."},{"index":4,"size":53,"text":"The sample was split into three treatments with two groups of respondents being randomly presented with additional information about either the NI or the FS. A control group received neither of these additional information texts. All three treatment groups received basic information regarding what agrobiodiversity is, why it is important, and current status/threats."}]},{"head":"Data Analysis","index":8,"paragraphs":[{"index":1,"size":199,"text":"Choice experiments are based on random utility theory (Luce, 1959;McFadden, 1974) and the characteristics theory of value (Lancaster, 1966). One commonly applied method is the random parameter logit (RPL) model, which was also used here to analyse the choice data. RPL models are extensions of basic conditional logit models. Through the inclusion of random parameters, such models are less restrictive in terms of assumptions -such as the independence of irrelevant alternatives (IIA) property -and suitable to capture preference variation around the mean of the random parameters (Hensher & Greene, 2003). Being more flexible in terms of assumed distribution is useful when there is limited prior knowledge about the distribution of individual preferences. RPL models are also able to account for panel-data, such as those we obtained in this study with each respondent answering eight choice sets, allowing unobserved preference heterogeneity across individuals to be considered (see e.g., Hensher & Greene, (2003)) for detailed model specifications). All attributes were set as random parameters with a normal distribution with the exception of the cost attribute, which was restricted to a triangular distribution constrained to a non-negative range in order to avoid the possibility of negative WTP values (Hensher et al., 2015)."},{"index":2,"size":210,"text":"The RPL only captures unobserved preference heterogeneity. To better explain the source of this heterogeneity, interaction terms between relevant socio-economic variables and between the SQ alternative and these variables were also included. Such interaction terms related to location (relative to Puno), respondents' age, gender, income, and level of education (see Table 2). The variable for the framing group was interacted with the SQ alternative. We present three models, one without interaction terms (Model 1), one with interaction terms associated with the SQ alternative (Model 2), and one with interaction terms associated with the attributes (Model 3). We initially tested for all interactions, but only maintained those in the final models that were significant. We further estimated three RPL models, without interaction terms, for each of the three framing groups to gauge separate WTP estimates for these three groups in line with our aim to test if the information framing changed the amount respondents were willing to pay. For attributes with three levels (see Table 1) the reference levels were the ones of the SQ alternative. Dummy variables for the other two levels were created and included in the models so as to model the preference for the change from the SQ. All RPL models were simulated using 2000 Halton draws."},{"index":3,"size":79,"text":"The WTP estimates from the RPL model results were also calculated using simulations. The simulated distributions were obtained by dividing draws from the distributions of the attribute coefficients by draws from the distributions of the coefficient of the monetary attribute. 10,000 Halton draws were used in these calculations. This allowed mean WTP to be identified across all respondents. The simulation-based method also provided the 2.5th percentile (lower bound) and the 97.5th percentile (upper bound) for a 95% confidence interval."}]},{"head":"Results","index":9,"paragraphs":[]},{"head":"Sample Description","index":10,"paragraphs":[{"index":1,"size":188,"text":"The gender-ratio of the respondents was roughly equal (48% female) and approximated that of the whole country (Table 2). The average age was 39 (ranging between 18 and 77). More than 75% of respondents had postsecondary education, implying that they were better educated than the national average. About 39% of the sample earned less than the minimum monthly wage (US$ 258) while 65% had incomes within the average income range for Puno, Cusco, and Lima (US$183-$374/month). Respondents from Cusco had higher incomes, with 40% having at least US$607/month, compared to 5% among residents of Puno and 21% among those from Lima. Residents from Puno also had the highest share of low incomes (58% had an income of below US$259/month, compared to 23% in Cusco and 37% in Lima; (Table S1)).As per design, a third of respondents (164) received additional information about NI, a third (165) about FS, and a third (162) as a control group did not receive any of the additional information. This share was the same across all three locations. The share of women, the location, and age distributions did not significantly differ across the groups."}]},{"head":"Choice Experiment Results","index":11,"paragraphs":[{"index":1,"size":160,"text":"Almost 90% of the choices made resulted in a conservation programme alternative being selected over the SQ. Results of the baseline RPL model (Table 3) showed that respondents preferred all levels of the attributes associated with the conservation of quinoa attribute to that of the SQ, i.e., they disliked the implications for quinoa diversity conservation under the current situation (SQ). Respondents preferred the highest attribute level (90% of varieties) related to the existence of quinoa varieties in 50 years relative to rates of only 50%, which in turn was preferred to rates of only 10%. They also preferred the maintenance of cultural traditions over their loss. By contrast, respondents preferred only the medium attribute level associated with 'Risk of production loss' and 'Conservation of the Andean Landscape' over the highest level and the SQ level. The similar mean WTP and confidence intervals indicated that the difference between the medium and high levels of these two attributes were not statistically significant."},{"index":2,"size":199,"text":"Location had a significant impact on whether respondents chose the SQ alternative. Respondents from Puno were more likely to choose the SQ alternative (and hence be least likely to be WTP for conservation programmes) than those from Lima and Cusco (in that order) (Model 2 in Table 3). There was no significant difference found between the WTP of urban and rural respondents, as well as the other demographic parameters tested (income, age, gender, and education). Respondents from Lima had a higher preference for most attributes, except for the medium levels 'Risk of production loss' and 'Conservation of the Andean Landscape' (Model 3 in Table 3), compared to respondents from Puno (the reference location level) Respondents from Cusco, on the other hand, were more likely to prefer a conservation programme with a medium risk of production loss than respondents from the other two locations. People from Cusco also had a higher preference for high (90%) levels of quinoa diversity still existing in 50 years compared to people from Puno, but less so than people from Lima, as indicated by the lower coefficient of the interaction term (1.03 for the interaction with 'Cusco' compared to a coefficient of 1.73 for 'Lima')."},{"index":3,"size":83,"text":"Of the demographic variables, gender, age, and education had minor impacts on preferences. Women were less likely to prefer a conservation programme that aims at maintaining quinoa diversity with negative coefficients for both attribute levels, 90% and 50% of quinoa varieties still existing in 50 years. The older respondents were, the less likely were they to prefer the 90% level of quinoa diversity existence in 50 years. Education was positively associated with a preference for the maintenance of traditional knowledge and cultural practices."},{"index":4,"size":61,"text":"Framing had a significant impact on respondents' preferences for the SQ. Those who were informed about the importance of quinoa for Peru's national/cultural identity or for food security were less likely to choose the SQ over one of the two presented conservation programmes, i.e., those primed were more likely to be willing to pay something for conservation than the control group."},{"index":5,"size":193,"text":"Respondents had the highest WTP for securing bequest/ existence and option values (Table 4). They were WTP US$8.76 for the certainty of 90% of quinoa varieties continuing to exist in 50 years and US$8.73 for a medium level of risk associated with agricultural production loss, while relatively strong preferences were also expressed for low levels of risk (US$8.37). Similarly, landscape conservation values were also important and \"medium\" level values preferred; with respondents willing to donate US$8.15 for ensuring a stable conservation status compared to US$7.52 for improving that status conservation. WTP for maintaining traditional knowledge and cultural practices (including food culture) was US$6.15. Respondents within the 'Food Security' framing group were willing to pay more to secure the diversity of varieties in 50 years; approximately US$10 more for 50% and US$7 more for 90% varieties to be conserved (Table 4). Respondents within the 'National Identity' group were willing to pay the most for a low and medium production risk. The control group, who were not presented with an explanation about the motivations and benefits of quinoa conservation, had the lowest WTP for every attribute level except for a medium risk of production loss."}]},{"head":"The Total Economic Value of Quinoa","index":12,"paragraphs":[{"index":1,"size":130,"text":"The TEV of quinoa diversity conservation can subsequently be calculated by the summing the highest WTP values of the attributes obtained from the pooled sample RPL model without interaction terms. The TEV placed by the public on the public good ecosystem services associated with a quinoa diversity conservation programme was US$31.79 if medium levels of landscape conservation (US$8.15) and risk of production loss (US$8.73) were to be achieved, and 90% of varieties secured for the next 50 years (US$8.76), while maintaining cultural practices (US$6.15). Given that there are approximately 3,380,960 households in the three studied regions in Peru (11.86 m population -with an average household size of 3.51 persons (INEI, 2017)), this amounts to a total willingness to pay for quinoa conservation of US$107.5 m (US$31.79 × 3.38 m households)."}]},{"head":"Discussion","index":13,"paragraphs":[]},{"head":"Quinoa's Total Economic Value and Conservation Costs","index":14,"paragraphs":[{"index":1,"size":44,"text":"Most respondents revealed strong support for conservation through their dislike of the current state of quinoa diversity conservation under the SQ. Regarding the different components of TEV, respondents had the highest WTP for securing bequest/existence and option values, followed closely by stable landscape conservation."},{"index":2,"size":174,"text":"Regarding policy implications, as the WTP estimates were derived from a stated preference method, it should be noted that there nonetheless remains uncertainty as to whether all those respondents who said they would donate, would in fact do so in a non-hypothetical setting. How many people would pay has been shown to be context-specific (Kim et al., 2012). Meta-analyses have found that people would pay about 75%, of what they stated (Murphy et al., 2005); while Morrison (2000) and List and Gallett (2001) found that no more than 30% of those who stated that they would donate something, would in fact do so if given the opportunity. Zander et al. (2014) also used a similar weighting, which if we were to apply here to generate The existence of such significant non-market values also helps justify the implementation of an actual conservation programme, as relative to the costs (US$19.75 m for 2,700 varieties over 50 years, as estimated by Drucker & Ramirez, 2020), this results in a positive benefit-cost ratio of 1.22 (US$24.18 m/19.75 m)."}]},{"head":"Distance Decay","index":15,"paragraphs":[{"index":1,"size":129,"text":"Previous empirical evidence has shown that the location distance of environmental goods and services has a significant effect on the utility that individuals obtain and therefore the values they assign to them (e.g., Bateman et al., 2006;Olsen et al., 2020). This phenomenon of distance decay depends on the type of ecosystem service that primarily motivates respondents (Olsen et al., 2020). For example, for recreational and other direct use values, people living close to the associated ecosystem services have been found to obtain greater benefits and also assign higher overall protection values relative to those living further away (e.g., Bateman et al., 2006;Rolfe & Windle, 2012;Khan et al., 2019). For other values, such as cultural values, the effect of location and distance has been less clear (Olsen et al., 2020)."},{"index":2,"size":86,"text":"We found that those living in the hotspot for quinoa diversity (Puno) were more likely to choose the SQ alternative (and hence be least likely to be willing to pay for conservation programmes) than those further away from Puno and closer to the consumer markets of Lima and Cusco. This result is consistent with Zander et al. (2013), who concluded that respondents who lived close to the genetic resources in question were in fact willing to contribute less to their conservation than respondents from distant cities."},{"index":3,"size":101,"text":"In the context of Peru, this could reflect the fact that residents in Lima and Cusco have higher average disposable incomes 3 . But it could also be because those living where quinoa diversity is still locally abundant might not perceive the urgent need for conservation, thus weakening possible motivation for action (Fernández-Llamazares et al., 2016), or that such efforts would not provide them with sufficient additional non-market benefits to those already accruing to them. This suggests that when public donations are being solicited for quinoa conservation, people outside the diversity hotspot region of Puno should be a priority target group."}]},{"head":"Age, Gender, and Education","index":16,"paragraphs":[{"index":1,"size":104,"text":"Of the demographic parameters, gender had the largest effect in explaining attribute variation within the sample. In other contexts, women have been shown to be more likely to pay for conservation, as are younger people and those with higher incomes and levels of education (amongst others, Blare et al., 2019). Here we could not confirm this but found that women preferred to pay for the existence of quinoa variety diversity less than men (Table 3). Age was also negatively associated with paying for the future existence of varieties. Education was positively associated with a preference for the maintenance of traditional knowledge and cultural practices."}]},{"head":"Framing Effects","index":17,"paragraphs":[{"index":1,"size":89,"text":"The only other significant parameter was the framing group. The control group, who did not receive additional information about why quinoa conservation is so important, were willing to pay the least. This finding is in line with previous studies, showing the importance of information and knowledge in general decision-making (Banerji et al., 2016;Shreedhar & Mourato, 2019). Given the impact of framing on WTP, public-awareness campaign messages regarding quinoa diversity-related food security, national/cultural identity, and other benefits should be carefully articulated whenever soliciting donations or justifying government conservation-related tax surcharges."},{"index":2,"size":136,"text":"While those people who received information about the national/cultural identity and food security aspects of quinoa conservation were willing to pay more for all levels of the attributes 'Conservation of Andean Landscape' and 'Risk of Production Loss', they also preferred the medium levels of landscape conservation provision and risk of production loss (Table S2). A stable condition of the Andean Landscape could be preferred because the landscape is already regarded as how it should be, and respondents do not see the need to pay for further improvement. The preference for a medium level of production loss risk could indicate that the public consider quinoa to be a widely available commodity crop in Peru and are thus unconcerned about a potential decline in production and an undersupply of it, as long as that risk is not high."}]},{"head":"Study Limitation","index":18,"paragraphs":[{"index":1,"size":132,"text":"It is worth noting that the convenience sampling approach we used, while resulting in significant overlap with national socio-demographics did have an over-representation of post-secondary educated respondents along with an underrepresentation of those earning less than a minimum wage. This may have resulted in an upward bias of the stated benefits (including, albeit common in stated preference studies, because of aggregating based on all households in the study regions), leading to the results needing cautious interpretation. By contrast, our conservative WTP calculations do not account for potential service purchasers from other parts of Peru and elsewhere, nor do they account for the value of the personal ecosystem service benefits that would be generated and accrue to farmers under a conservation programme. The overall impact on the estimated total benefits is therefore ambiguous."}]},{"head":"Conclusions","index":19,"paragraphs":[{"index":1,"size":162,"text":"Megadiverse countries such as Peru are ideally placed to implement agrobiodiversity conservation strategies while both a rich range of genetic resources and accompanying traditional knowledge still exist (unlike in the Global North. In the case of quinoa diversity, the public revealed support for conservation, having the highest willingness to pay for securing bequest/existence and option values, followed closely by stable landscape conservation. Framing had an important influence on willingness-to-pay (suggesting the importance of public-awareness campaign articulation), as did distance from the quinoa diversity hotspot of Puno (suggesting the importance of targeting people in other regions too). Aggregated total economic value across the study region was found to be equivalent to just over a quarter of the market value of annual Peruvian quinoa production. The existence of such significant non-market ecosystem service values also results in a positive benefit-cost ratio for conservation intervention, which can consequently be used as an argument to justify and inform the allocation of government funds and private donations."}]}],"figures":[{"text":"Table 1 Attributes and levels used in choice experiment Conservation of Andean Improve Maintaining different Indirect Use: Conservation of AndeanImproveMaintaining differentIndirect Use: Landscape Stable varieties of quinoa can be Landscape LandscapeStablevarieties of quinoa can beLandscape  Decrease important for landscape maintenance. The absence  Decreaseimportant for landscape maintenance. The absence   of biodiversity can  of biodiversity can   negatively impact  negatively impact   ecological processes and  ecological processes and   aesthetic values.  aesthetic values. Risk of Production Loss High Medium A lack of biodiversity increases the vulnerability of Indirect Use: Option (insurance) Risk of Production LossHigh MediumA lack of biodiversity increases the vulnerability ofIndirect Use: Option (insurance)  Low crops to extreme events such  Lowcrops to extreme events such   as hail, wildlife, diseases,  as hail, wildlife, diseases,   etc. This can negatively  etc. This can negatively   impact regional food  impact regional food   security. Funding would  security. Funding would   increase incentives to plant  increase incentives to plant   more native varieties on  more native varieties on   farms to offset cost of lower  farms to offset cost of lower   market returns.  market returns. Conservation of Diversity 90%   Conservation of Diversity90% (% of Existing Quinoa 50%   (% of Existing Quinoa50% Varieties Existing in 50 Years) 10%   Varieties Existing in 50 Years)10%  "},{"text":"Table 2 Sample description (N = 491) * Censos Nacionales 2017: XII de Población, VII de Vivienda y III de Comunidades Indígenas https:// www. inei. gob. pe/ media/ MenuR ecurs ivo/ publi cacio nes_ digit ales/ Est/ Lib15 39/ libro. pdf a INEI, 2017 Characteristic Sample Statistics National Statistics CharacteristicSample StatisticsNational Statistics   (Peru, 2017 Census)* (Peru, 2017 Census)* Female (%) 48% 51% Female (%)48%51% Average age (SD) 39.4 (13.8) 32 Average age (SD)39.4 (13.8)32 Location (%)  N/A Location (%)N/A Puno 41 N/A Puno41N/A Cusco 18 N/A Cusco18N/A Lima 41  Lima41 Education (coded 1 to 3)  > 15 years old Education (coded 1 to 3)> 15 years old Primary education (1) 2% 18% Primary education (1)2%18% Secondary education (2) 22% 45% Secondary education (2)22%45% Technical post-secondary (3) 31% 14% Technical post-secondary (3)31%14% University (4) 44% 20% University (4)44%20% Income (US$/month) (coded from 1 to 6)   Income (US$/month) (coded from 1 to 6) 0-121 (1) 16%  0-121 (1)16% 122-258 (2) 23% Puno: US$ 182 122-258 (2)23%Puno: US$ 182 259-606 (3) 42% Cusco: US$ 233 259-606 (3)42%Cusco: US$ 233 607-1515 (4) 15% (Metropolitan) 607-1515 (4)15%(Metropolitan) 1516-3030 (5) 2% Lima: US$ 374 a 1516-3030 (5)2%Lima: US$ 374 a >3030 (6) 0%  >3030 (6)0%  "},{"text":"Table 3 Results of RPL model without (Model 1) and with (Model 2) interaction terms Significance at 1% (***), 5% (**) and 10% (*) levels; SQ = Status-quo Significance at 1% (***), 5% (**) and 10% (*) levels; SQ = Status-quo  "},{"text":"Table 4 WTP (US$) estimates from baseline RPL model (no interactions), for both priming groups (FS: food security and NI: national identity) and control group (no priming) a Exchange rate during the months of the survey was approximately US$ 1 = New Peruvian Soles 3.3 b Total includes 1a rather than 1b, as food security group WTP higher for the former than the latter See TableS2in Supplementary Information for the separate RPL models for each group from which these WTP were calculated Attribute WTP Pooled (95% confidence interval) WTP NI group (95% confidence interval) WTP FS group (95% confidence interval) WTP control group (95% confidence interval) AttributeWTP Pooled (95% confidence interval)WTP NI group (95% confidence interval)WTP FS group (95% confidence interval)WTP control group (95% confidence interval) Total (Soles) of highest WTP for each type of attribute (= 1b + 2a + 3b + 4) 104.9 111.90 109.2 b 89.7 Total (Soles) of highest WTP for each type of attribute (= 1b + 2a + 3b + 4)104.9111.90109.2 b89.7 Total (of highest WTP attribute values a 31.79 33.93 33.09 27.18 Total (of highest WTP attribute values a31.7933.9333.0927.18 Percentage change relative to control  24.7% 21.7%  Percentage change relative to control24.7%21.7% Percentage change relative to pooled sample  6.7% 4.1%  Percentage change relative to pooled sample6.7%4.1%  "}],"sieverID":"1103bbcd-0e73-49fe-9129-40d26497cf72","abstract":"Agrobiodiversity is associated with a range of important but poorly quantified public good ecosystem services, the conservation of which requires public support. With a view to determining the general public's willingness to pay (WTP) for such conservation, we organised interviews with 491 adult Peruvian residents in three regions a stated preference choice experiment (CE) to elicit the value they place on crop genetic resources conservation, using quinoa cultivation as a case study. Responses revealed strong support for the conservation of quinoa diversity particularly when conservation was framed in terms of conserving national cultural identity or food security. Respondents were willing to make a one-off donation of US$31.79 to an in situ on-farm quinoa crop diversity conservation programme, placing the highest values on programme attributes related to securing bequest/existence and option values, followed closely by stable landscape conservation. WTP was higher when the public was reminded that conservation also contributed to national cultural identify or food security. A conservative aggregation of the WTP estimates to the population of the three regions results in an estimated total WTP for quinoa conservation of US$24.18 m and a benefit-cost ratio of 1.22. Findings demonstrate the significant and frequently ignored social welfare benefits associated with non-market agrobiodiversity-related public good ecosystem services, in this case equivalent to just over a quarter of market production values. Such information can be used to design and prioritise quinoa genetic diversity conservation programmes with an emphasis on such attributes."}