https://hdl.handle.net/10568/1From ILRI staff and projectsSat, 09 May 2026 04:14:23 GMT2026-05-09T04:14:23Z255391https://cgspace.cgiar.org/bitstreams/9f97aa30-76b2-4ec2-a8ad-12d91b05da3e/downloadhttps://hdl.handle.net/10568/1https://hdl.handle.net/10568/182789dc.title: Soil degradation assessment across tropical grassland of Western Kenya dc.contributor.author: Quinton, J.N.; Yesuf, G.; Baldi, Germán; Gong, M.; Kinuthia, Kelvin; Fry, L.E.; Odongo, Y.; Nyakundi, B.; Hitimana, J.; Costa, Patricia de B.; Onyango, Alice A.; Leitner, Sonja; Bardgett, R.D.; Rufino, M.C. dcterms.abstract: Soils across sub-Saharan Africa are exposed to extensive degradation processes, which can reduce their ability to produce crops and support livestock. While there has been a significant research effort focussing on soil degradation in sub-Saharan croplands, less research effort has been directed towards grasslands. Here, we tested the effectiveness of remote sensing to classify the soil degradation status of smallholder grazing lands. Focussing on grasslands used by smallholders in the districts of Nyando and Kuresoi in Western Kenya, we first used remote sensing (RS) to classify grasslands as productive grazing lands, grazing lands that followed a variable trend in vegetation productivity (transition), and unstable and unproductive (degraded) grazing lands. We then tested how this classification related to measured soil parameters indicative of soil degradation. We then used this classification, which was based on a temporal analysis of Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalised Difference Water Index (NDWI) between 2013 and 2018, to identify 90 field sites across the two districts, which we then sampled and analysed for a range of physical, chemical, and biological soil properties. Only soil microbial biomass carbon (C) showed consistent alignment with the RS classification, although there was some overlap with other soil parameters at one or other of the study areas. To group the sites using the soil variables, which we split by study area and into stable (those that are slow to change) and transient (those that change rapidly in response to a changing pedological environment), K-means clustering was undertaken. Two sets of clusters were produced for each district for the stable and transient variables. For the stable variables, at Kuresoi one of these clusters included sites with higher levels of C, nitrogen (N), phosphorus (P) and pH, which aligned well with the RS classification, with seven out of 10 productive sites being assigned to this cluster. At Nyando, one of the stable variable clusters included sites with high soil C and N, but low pH and relatively low soil bulk density, and corresponded to 12 out of the 16 productive sites. For the transient variables, agreement between the clusters and the remote sensing classification was poor, indicating a lack of utility for degradation assessment. Overall, our results suggest that while the use of RS methods for classifying degraded grasslands and the soils supporting them does have significant advantages in terms of time and costs over field survey, supplementing these methods with a limited set of soil parameters related to nutrient cycling, such as microbial biomass C, soil P, percent C and N, and soil pH, could enhance our ability to identify degraded soils and target restoration efforts. cg.contributor.programAccelerator: Multifunctional Landscapes; Climate Action; Sustainable Farming Wed, 22 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1827892026-04-22T00:00:00ZQuinton, J.N.Yesuf, G.Baldi, GermánGong, M.Kinuthia, KelvinFry, L.E.Odongo, Y.Nyakundi, B.Hitimana, J.Costa, Patricia de B.Onyango, Alice A.Leitner, SonjaBardgett, R.D.Rufino, M.C.Soils across sub-Saharan Africa are exposed to extensive degradation processes, which can reduce their ability to produce crops and support livestock. While there has been a significant research effort focussing on soil degradation in sub-Saharan croplands, less research effort has been directed towards grasslands. Here, we tested the effectiveness of remote sensing to classify the soil degradation status of smallholder grazing lands. Focussing on grasslands used by smallholders in the districts of Nyando and Kuresoi in Western Kenya, we first used remote sensing (RS) to classify grasslands as productive grazing lands, grazing lands that followed a variable trend in vegetation productivity (transition), and unstable and unproductive (degraded) grazing lands. We then tested how this classification related to measured soil parameters indicative of soil degradation. We then used this classification, which was based on a temporal analysis of Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalised Difference Water Index (NDWI) between 2013 and 2018, to identify 90 field sites across the two districts, which we then sampled and analysed for a range of physical, chemical, and biological soil properties. Only soil microbial biomass carbon (C) showed consistent alignment with the RS classification, although there was some overlap with other soil parameters at one or other of the study areas. To group the sites using the soil variables, which we split by study area and into stable (those that are slow to change) and transient (those that change rapidly in response to a changing pedological environment), K-means clustering was undertaken. Two sets of clusters were produced for each district for the stable and transient variables. For the stable variables, at Kuresoi one of these clusters included sites with higher levels of C, nitrogen (N), phosphorus (P) and pH, which aligned well with the RS classification, with seven out of 10 productive sites being assigned to this cluster. At Nyando, one of the stable variable clusters included sites with high soil C and N, but low pH and relatively low soil bulk density, and corresponded to 12 out of the 16 productive sites. For the transient variables, agreement between the clusters and the remote sensing classification was poor, indicating a lack of utility for degradation assessment. Overall, our results suggest that while the use of RS methods for classifying degraded grasslands and the soils supporting them does have significant advantages in terms of time and costs over field survey, supplementing these methods with a limited set of soil parameters related to nutrient cycling, such as microbial biomass C, soil P, percent C and N, and soil pH, could enhance our ability to identify degraded soils and target restoration efforts.https://hdl.handle.net/10568/182774dc.title: Cultural domain analysis: A toolbox for collaborative research design for climate action dc.contributor.author: Habermann, Birgit; Rotich, D.; Puri, R.K. dcterms.abstract: Cultural Domain Analysis (CDA) is a tool developed to elicit the contents of particular cultural domains of interest to a community (e.g. pasture, forage or livestock diseases), explore how these items are related in local classifications, and determine their economic and cultural importance. It reveals depth of individual knowledge, identifies shared knowledge, variation among respondents, shows classification structure through pile sorting, and distinguishes core and peripheral items based on salience scores. The process begins with free listing to elicit domain items and understand local definitions, followed by pile sorting to group items based on similarity, and finally ranking to determine relative importance. CDA uses open-ended questions, encourages local terms, and relies on participatory engagement. It supports identification of culturally relevant knowledge and links between local and scientific categories. CDA is cost-effective and flexible, and requires careful facilitation, local language use, and culturally sensitive approaches to ensure accurate data collection and interpretation. cg.contributor.initiative: Livestock and Climate cg.contributor.programAccelerator: Sustainable Animal and Aquatic Foods; Climate Action Thu, 30 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1827742026-04-30T00:00:00ZHabermann, BirgitRotich, D.Puri, R.K.Cultural Domain Analysis (CDA) is a tool developed to elicit the contents of particular cultural domains of interest to a community (e.g. pasture, forage or livestock diseases), explore how these items are related in local classifications, and determine their economic and cultural importance. It reveals depth of individual knowledge, identifies shared knowledge, variation among respondents, shows classification structure through pile sorting, and distinguishes core and peripheral items based on salience scores. The process begins with free listing to elicit domain items and understand local definitions, followed by pile sorting to group items based on similarity, and finally ranking to determine relative importance. CDA uses open-ended questions, encourages local terms, and relies on participatory engagement. It supports identification of culturally relevant knowledge and links between local and scientific categories. CDA is cost-effective and flexible, and requires careful facilitation, local language use, and culturally sensitive approaches to ensure accurate data collection and interpretation.https://hdl.handle.net/10568/182738dc.title: The Asian Chicken Genetic Gains (AsCGG) costed extension project inception workshops in Vietnam and Cambodia, and the poultry development strategy workshop in Lao PDR dc.contributor.author: Alemayehu, Alula; Esatu, Wondmeneh; Hoa Hoang; Yitayih, Mulugeta; Unger, Fred; Chi Nguyen; Hung Nguyen-Viet; Frank-Lawale, Anuoluwapo Thu, 01 Jan 2026 00:00:00 GMThttps://hdl.handle.net/10568/1827382026-01-01T00:00:00ZAlemayehu, AlulaEsatu, WondmenehHoa HoangYitayih, MulugetaUnger, FredChi NguyenHung Nguyen-VietFrank-Lawale, Anuoluwapohttps://hdl.handle.net/10568/182718dc.title: Solar Irrigation and Just Energy Transitions in Agriculture: Insights from Evaluation of Gujarat’s SKY Program dc.contributor.author: Varshney, Deepak; Mukherji, Aditi; Sharma, Kriti; Banerjee, Anurag; Sikka, Alok dcterms.abstract: Set against the backdrop of reducing agricultural emissions, improving smallholder livelihoods, and promoting sustainable groundwater use, this paper evaluates the Surya Shakti Kisan Yojana (SKY)—the world's first largescale grid-connected solar irrigation pump (SIP) scheme, launched in Gujarat, India in 2018. Using real-time monitoring data from 4321 farmers and a primary survey of 2435 farmers, the study addresses three core objectives. First, it examines the determinants of SKY participation and evaluates the scheme's technical performance, financial features, and income effects. Our findings reveal that financial constraints and risk aversion among smallholder farmers hinder scheme adoption. Farmers earn up to ₹ 21,917 (~USD 257) annually from electricity sales—43 % of their crop income—even after repaying an annual loan of ₹ 105,000 (~USD 1235). The simulation suggests that extending the loan repayment period from 7 to 10 years could nearly double farmers' income from energy sales. Second, the study assesses SKY's impact on energy use for groundwater extraction. During the Rabi (dry) season, SKY-enrolled farmers show significantly slower growth in energy consumption than non-enrolled farmers, indicating more sustainable water use. No such difference is observed in the Kharif (monsoon) season. Third, it estimates SKY's climate mitigation potential. Each participant offsets about 12.34 metric tons of CO2 annually—over twice the impact of off-grid systems—yielding 53,308 metric tons of CO2 abatement across 4321 farmers. These findings demonstrates grid-connected SIP as a scalable, climate-aligned model for energy transitions in the Global South, offering practical insights for integrated energy-water-livelihood strategies. Sun, 01 Mar 2026 00:00:00 GMThttps://hdl.handle.net/10568/1827182026-03-01T00:00:00ZVarshney, DeepakMukherji, AditiSharma, KritiBanerjee, AnuragSikka, AlokSet against the backdrop of reducing agricultural emissions, improving smallholder livelihoods, and promoting sustainable groundwater use, this paper evaluates the Surya Shakti Kisan Yojana (SKY)—the world's first largescale grid-connected solar irrigation pump (SIP) scheme, launched in Gujarat, India in 2018. Using real-time monitoring data from 4321 farmers and a primary survey of 2435 farmers, the study addresses three core objectives. First, it examines the determinants of SKY participation and evaluates the scheme's technical performance, financial features, and income effects. Our findings reveal that financial constraints and risk aversion among smallholder farmers hinder scheme adoption. Farmers earn up to ₹ 21,917 (~USD 257) annually from electricity sales—43 % of their crop income—even after repaying an annual loan of ₹ 105,000 (~USD 1235). The simulation suggests that extending the loan repayment period from 7 to 10 years could nearly double farmers' income from energy sales. Second, the study assesses SKY's impact on energy use for groundwater extraction. During the Rabi (dry) season, SKY-enrolled farmers show significantly slower growth in energy consumption than non-enrolled farmers, indicating more sustainable water use. No such difference is observed in the Kharif (monsoon) season. Third, it estimates SKY's climate mitigation potential. Each participant offsets about 12.34 metric tons of CO2 annually—over twice the impact of off-grid systems—yielding 53,308 metric tons of CO2 abatement across 4321 farmers. These findings demonstrates grid-connected SIP as a scalable, climate-aligned model for energy transitions in the Global South, offering practical insights for integrated energy-water-livelihood strategies.https://hdl.handle.net/10568/182703dc.title: Gender dimensions in agricultural and livestock interventions: A review of economic modelling approaches dc.contributor.author: Mensah, Charles; Omondi, Immaculate A.; Bahta, Sirak T.; Enahoro, Dolapo K. dcterms.abstract: This study identifies innovations and gaps in quantifying gender dynamics in agricultural and livestock sectors using Computable General Equilibrium (CGE), Partial Equilibrium (PE), or micro-simulation (MS) models. These modelling approaches are widely applied to assess policy and market interventions in low- and -middle-income countries (LMICs). A semi-systematic review protocol was developed to retrieve, select, and analyse relevant literature from ScienceDirect, JSTOR, and Google Scholar. Inclusion criteria required that studies: (1) were published within a defined 40-year period, (2) applied at least one of the specified economic modelling methods, and (3) included at least one indicator related to sustainable development. From an initial pool of 17,000 articles identified through title screening; 49 were selected for abstract review, and 22 for full-text analysis. Of these, 16 CGE-based studies captured gender-specific impacts, such as women’s employment and income outcomes. Two MS-based studies tracked specific measures of women’s empowerment, while the only PE-based study linked agricultural system characteristics—such as seasonality and distribution networks - to gender-related indicators of food security and nutrition. Additionally, three studies using hybrid modelling approaches demonstrated the value of methodological integration. Such models offer enhanced capabilities to reflect the diversity of gender roles and impacts in policy analysis. The findings highlight the potential of integrated modelling approaches, not only for methodological innovation in gender-disaggregated impact analysis, but for informing evidence-based policies that prioritize gender equity. They offer actionable insights to better inform and prioritize interventions that promote women’s empowerment and equity in agricultural and livestock development in LMICs. Thu, 23 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1827032026-04-23T00:00:00ZMensah, CharlesOmondi, Immaculate A.Bahta, Sirak T.Enahoro, Dolapo K.This study identifies innovations and gaps in quantifying gender dynamics in agricultural and livestock sectors using Computable General Equilibrium (CGE), Partial Equilibrium (PE), or micro-simulation (MS) models. These modelling approaches are widely applied to assess policy and market interventions in low- and -middle-income countries (LMICs). A semi-systematic review protocol was developed to retrieve, select, and analyse relevant literature from ScienceDirect, JSTOR, and Google Scholar. Inclusion criteria required that studies: (1) were published within a defined 40-year period, (2) applied at least one of the specified economic modelling methods, and (3) included at least one indicator related to sustainable development. From an initial pool of 17,000 articles identified through title screening; 49 were selected for abstract review, and 22 for full-text analysis. Of these, 16 CGE-based studies captured gender-specific impacts, such as women’s employment and income outcomes. Two MS-based studies tracked specific measures of women’s empowerment, while the only PE-based study linked agricultural system characteristics—such as seasonality and distribution networks - to gender-related indicators of food security and nutrition. Additionally, three studies using hybrid modelling approaches demonstrated the value of methodological integration. Such models offer enhanced capabilities to reflect the diversity of gender roles and impacts in policy analysis. The findings highlight the potential of integrated modelling approaches, not only for methodological innovation in gender-disaggregated impact analysis, but for informing evidence-based policies that prioritize gender equity. They offer actionable insights to better inform and prioritize interventions that promote women’s empowerment and equity in agricultural and livestock development in LMICs.https://hdl.handle.net/10568/182698dc.title: Factors influencing adoption of climate-smart livestock practices in eastern Africa: insights from Uganda dc.contributor.author: Mugumya, R.; Omondi, Immaculate A.; Baltenweck, Isabelle; Matovu, W.; Schlecht, E.; Bateki, C.A.; Schlecht, E.; Bateki, C.A. dcterms.abstract: Agriculture accounts for 23% of the gross domestic product (GDP) on average in sub-Saharan Africa (SSA), with its contribution varying between 20 and 50% among individual countries. The sector employs about 60% of the population, with over 75% of farms owned and managed by smallholder farmers. Despite its importance, agriculture is constrained by low productivity, limited access to suitable technologies and high vulnerability to climate change impacts that threaten food security and rural livelihoods. The livestock sub-sector is particularly crucial for agriculture, contributing 5–15% of the regional GDP and nearly 60% of the agricultural GDP. Beyond income generation, the livestock sub-sector supports food and nutrition security by providing milk and meat; these are rich in protein, vitamin B12, iron, and calcium and thus provide nutrients essential for human growth and development. Climate-smart livestock (CSL) practices have the potential to boost food production while improving the resilience and environmental sustainability of agricultural systems in sub-Saharan Africa. However, their adoption rates in the region remain low. Focusing on Uganda as a case study, multivariate Probit and Tobit regression models were applied to determine the factors influencing the adoption of CSL practices by dairy farmers. Mon, 13 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1826982026-04-13T00:00:00ZMugumya, R.Omondi, Immaculate A.Baltenweck, IsabelleMatovu, W.Schlecht, E.Bateki, C.A.Schlecht, E.Bateki, C.A.Agriculture accounts for 23% of the gross domestic product (GDP) on average in sub-Saharan Africa (SSA), with its contribution varying between 20 and 50% among individual countries. The sector employs about 60% of the population, with over 75% of farms owned and managed by smallholder farmers. Despite its importance, agriculture is constrained by low productivity, limited access to suitable technologies and high vulnerability to climate change impacts that threaten food security and rural livelihoods. The livestock sub-sector is particularly crucial for agriculture, contributing 5–15% of the regional GDP and nearly 60% of the agricultural GDP. Beyond income generation, the livestock sub-sector supports food and nutrition security by providing milk and meat; these are rich in protein, vitamin B12, iron, and calcium and thus provide nutrients essential for human growth and development. Climate-smart livestock (CSL) practices have the potential to boost food production while improving the resilience and environmental sustainability of agricultural systems in sub-Saharan Africa. However, their adoption rates in the region remain low. Focusing on Uganda as a case study, multivariate Probit and Tobit regression models were applied to determine the factors influencing the adoption of CSL practices by dairy farmers.https://hdl.handle.net/10568/182697dc.title: ILRI policy framework dc.contributor.author: International Livestock Research Institute Tue, 21 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1826972026-04-21T00:00:00ZInternational Livestock Research Institutehttps://hdl.handle.net/10568/182696dc.title: Environmental dissemination of multidrug-resistant Escherichia coli across one health interfaces in Mymensingh, Bangladesh dc.contributor.author: Rahman, A.; Roy, S.; Afreen, N.; Alam, M.G.S.; Sadekuzzaman, M.; Kalam, A.; Talukdar, F.; Alam, M.B.; Alam, M.S.; Yesmin, S.M.S.; Larsen, A.R.; Cavaco, L.M.; Petersen, A.; Hoque, K.I.; Amin, M.B.; Hasan, R.; Lindahl, Johanna F.; Delamare-Deboutteville, J.; Moodley, Arshnee dcterms.abstract: Environmental dissemination of antimicrobial-resistant <i>Escherichia coli</i> is a major One Health challenge in Bangladesh. This study assessed the occurrence of third-generation cephalosporin and carbapenem-resistant <i>E. coli</i> across key environmental interfaces in Mymensingh. In May 2022, 28 water samples were collected from hospital wastewater, livestock effluents, aquaculture ponds, and the Khiro River. Samples were processed via the WHO Tricycle protocol, with 26 isolates undergoing whole-genome sequencing. Third-generation cephalosporin-resistant <i>E. coli</i> were detected in 86% of samples, while carbapenem-resistant isolates (18%) were found exclusively in hospital and river samples. Highest ESBL-producing <i>E. coli</i> concentrations occurred in hospital and poultry wastewater (mean 6.9–7.1 log₁₀CFU/ml). Sequencing identified 93 resistance genes, dominated by <i>bla</i>CTX-M-15 (79%), <i>tet(A)</i> (75%), <i>aadA1</i> (54%), <i>qnrS1</i> (50%), and <i>mph(A)</i> (50%). Carbapenem resistance was associated with blaNDM-5 in hospital and river isolates. While most isolates showed niche-specific clustering in our Phylogenetic analysis, highly conserved core-genome profiles (0 SNPs) between hospital and downstream river isolates (ST2363/ST410) provided strong genomic evidence consistent with effluent-mediated dissemination. These findings highlight the role of wastewater and livestock systems in AMR transmission, underscoring the urgent need for integrated One Health surveillance and improved waste management in Bangladesh. cg.contributor.programAccelerator: Sustainable Animal and Aquatic Foods Mon, 27 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1826962026-04-27T00:00:00ZRahman, A.Roy, S.Afreen, N.Alam, M.G.S.Sadekuzzaman, M.Kalam, A.Talukdar, F.Alam, M.B.Alam, M.S.Yesmin, S.M.S.Larsen, A.R.Cavaco, L.M.Petersen, A.Hoque, K.I.Amin, M.B.Hasan, R.Lindahl, Johanna F.Delamare-Deboutteville, J.Moodley, ArshneeEnvironmental dissemination of antimicrobial-resistant <i>Escherichia coli</i> is a major One Health challenge in Bangladesh. This study assessed the occurrence of third-generation cephalosporin and carbapenem-resistant <i>E. coli</i> across key environmental interfaces in Mymensingh. In May 2022, 28 water samples were collected from hospital wastewater, livestock effluents, aquaculture ponds, and the Khiro River. Samples were processed via the WHO Tricycle protocol, with 26 isolates undergoing whole-genome sequencing. Third-generation cephalosporin-resistant <i>E. coli</i> were detected in 86% of samples, while carbapenem-resistant isolates (18%) were found exclusively in hospital and river samples. Highest ESBL-producing <i>E. coli</i> concentrations occurred in hospital and poultry wastewater (mean 6.9–7.1 log₁₀CFU/ml). Sequencing identified 93 resistance genes, dominated by <i>bla</i>CTX-M-15 (79%), <i>tet(A)</i> (75%), <i>aadA1</i> (54%), <i>qnrS1</i> (50%), and <i>mph(A)</i> (50%). Carbapenem resistance was associated with blaNDM-5 in hospital and river isolates. While most isolates showed niche-specific clustering in our Phylogenetic analysis, highly conserved core-genome profiles (0 SNPs) between hospital and downstream river isolates (ST2363/ST410) provided strong genomic evidence consistent with effluent-mediated dissemination. These findings highlight the role of wastewater and livestock systems in AMR transmission, underscoring the urgent need for integrated One Health surveillance and improved waste management in Bangladesh.https://hdl.handle.net/10568/182689dc.title: The Nigeria Livestock Master Plan: Livestock Sector Strategy (2026–2040) dc.contributor.author: Enahoro, Dolapo K.; Bahta, Sirak T.; Asfaw, Admasu; Bamidele, Oladeji; Ferrari, S.; Chan, Derek; Alary, V.; Amole, Tunde A. Thu, 30 Apr 2026 00:00:00 GMThttps://hdl.handle.net/10568/1826892026-04-30T00:00:00ZEnahoro, Dolapo K.Bahta, Sirak T.Asfaw, AdmasuBamidele, OladejiFerrari, S.Chan, DerekAlary, V.Amole, Tunde A.https://hdl.handle.net/10568/182686dc.title: Porcine cysticercosis prevalence and risk factors in Salima district, Malawi dc.contributor.author: Chavula, Mercy; Thomas, Lian; Chatanga, Elisha; Chuzu, Elisa; Ngwili, Nicholas; Wood, Catherine; Korir, Max dcterms.abstract: This was cross-sectional study in Salima district, Malawi. 217 pigs were randomly sampled from 41 households between November 2023 to March 2024. It is quantitative data where prevalence, risk factors of porcine cysticercosis was analyzed. Samples were tested using Ag-ELISA test. The seroprevalence of porcine cysticercosis was 32%. The major risk factor identified in this study was free-range management. https://hdl.handle.net/10568/182686Chavula, MercyThomas, LianChatanga, ElishaChuzu, ElisaNgwili, NicholasWood, CatherineKorir, MaxThis was cross-sectional study in Salima district, Malawi. 217 pigs were randomly sampled from 41 households between November 2023 to March 2024. It is quantitative data where prevalence, risk factors of porcine cysticercosis was analyzed. Samples were tested using Ag-ELISA test. The seroprevalence of porcine cysticercosis was 32%. The major risk factor identified in this study was free-range management.