Introduction:Senegal's groundnut value chain covers approximately 1.25 million hectares and underpins the food security and livelihoods of a large rural population. Yet it is beset by persistent structural constraints across both supply and demand dimensions, including weak seed systems, fragmented input delivery, volatile prices, low processing capacity utilization, and climate-driven production risks, that limit its efficiency, inclusiveness, and resilience. Methods:This study combined large-scale primary data collection with a participatory Group Model Building (GMB) approach. A baseline household survey was conducted with 503 groundnut farmers across 18 villages in the peanut basin of Senegal (regions of Kaffrine, Louga, and Thiès), supplemented by 20 Focus Group Discussions and Key Informant Interviews with processors, traders, government representatives, cooperatives, and research institutions. Two GMB workshops were facilitated in Dakar in March and April 2023, convening 14 participants representing diverse value chain stakeholders. Causal Loop Diagrams were developed iteratively through structured elicitation, plenary validation, and post-workshop verification to map feedback structures and identify leverage points. Results:The household survey identified poor access to quality seed (86.77% of respondents), climate-related crop losses (75.88%), unstable market prices (79.96%), and high fertilizer costs (47.67%) as the most prevalent constraints. The GMB process revealed these challenges as components of self-reinforcing feedback loops: a low-productivity trap in the seed system rooted in insufficient ISRA breeder seed capacity; a processor marginalization loop driven by Chinese trader dominance that diverts raw material away from SONACOS (operating at 35-40% capacity); and a cross-sectoral institutional misalignment trap in which subsidy capture, weak seed quality enforcement, and misaligned export policies operate as mutually reinforcing barriers. Discussion:Policy pathways emerging from GMB deliberations include strengthening ISRA's breeder seed capacity; reforming fertilizer subsidy delivery through cooperatives; expanding scale-appropriate mechanization; integrating climate information services; establishing price stabilization mechanisms; and introducing dynamic export regulation to balance farmer income with domestic processor viability. Underpinning all recommendations is the need for a coordinated governance structure that addresses institutional misalignments and enables equitable, resilient, and sustainable growth across Senegal's groundnut value chain.

The invasion of Prosopis juliflora poses a growing threat to dryland ecosystems and pastoral livelihoods across East Africa. This study presents an integrative approach that combines satellite remote sensing, machine learning, and citizen science to detect and map the spatial extent and socio-ecological impacts of Prosopis juliflora in Baringo County, Kenya. We evaluated the performance of three satellite platforms, Sentinel-1, Sentinel-2, and PlanetScope, using a Random Forest classifier trained on field collected presence–absence data and vegetation indices. Sentinel-2 outperformed the other sensors, achieving a classification accuracy of 90.65%, with key variables including the Visible Atmospherically Resistant Index (VARI), the Ratio Vegetation Index (RVI), and red-edge bands emerging as the most important predictors. Through Participatory GIS (PGIS), a citizen-science based approach, we engaged gender-disaggregated community groups to capture local perceptions of invasion hotspots and blocked access to grazing routes and water sources, enhancing contextual understanding and validating model outputs. The comparison of satellite-derived maps and PGIS outputs revealed strong spatial congruence, particularly along water bodies, roads, and croplands. Our findings demonstrate the potential of combining Earth observation and citizen science to generate actionable knowledge for managing invasive species in data scarce dryland environments. This hybrid framework supports inclusive and spatially targeted interventions for rangeland restoration and ecosystem resilience.

This study presents a perspective on how partnerships engaging private-sector actors could support greenhouse gas mitigation in African ruminant livestock value chains. Through using value chain governance theory and illustrative examples, we highlight potential contributions of these partnerships rather than demonstrate realised mitigation effectiveness. Using illustrative examples from beef cattle grazing, intensive dairy, and mohair fibre from several countries (South Africa, Kenya, Tanzania and Rwanda), we described partnership design. Across these illustrative examples, partnerships involving agribusiness companies and livestock producers linked low-emission practices to markets through quality-based milk pricing, village milk-collection hubs, the development of certification standards for mohair, and pilot programs in rangeland restoration and manure recycling. In some examples, public and nonprofit organisations helped finance embedded services (notably, grazing-plan support and milk-quality testing for price premiums) using buyer contributions, public budgets, or donor projects, including where livestock producers could not readily capture mitigation benefits.

Gridded precipitation products (GPPs) are widely used in climate-informed agricultural analyses in data-scarce regions, yet their suitability is often assessed using meteorological performance alone. This study evaluates how differences among rainfall datasets propagate into simulated maize yield and associated economic indicators using the Agricultural Production Systems sIMulator (APSIM) in two semi-arid systems, Kaffrine (Senegal) and Kongwa (Tanzania). Six GPPs (CHIRPS, CPC, ERA5, MERRA2, MSWEP, and TAMSAT) were evaluated against station observations and used as alternative rainfall inputs in APSIM.,Rainfall performance varied across indicators and between sites, with higher agreement for seasonal totals than for onset timing, and higher detection skill for dry days than for moderate and heavy rainfall classes. Several GPPs over-represented light rainfall events and under-represented heavy rainfall extremes, consistent with spatial averaging within grid cells. When used in crop simulations, these differences translated into site-specific yield responses driven by early-season rainfall conditions. In Kaffrine, yield distributions and nitrogen responses remained similar across datasets (KGE = 0.24–0.35). In Kongwa, yield variability and estimated optimal input levels were more sensitive to dataset choice (KGE = -0.24 to 0.60), particularly under wetter conditions.,Across sites, the general shape of yield responses to nitrogen and planting density remained similar, but economically optimal input levels varied across datasets. Agreement in rainfall indicators alone was therefore insufficient to infer the stability of crop model outputs. Evaluating GPP suitability for agronomic applications requires crop model diagnostics alongside meteorological validation, with explicit consideration of environmental context.

Background: Evaluating crop suitability across diverse agro-climatic zones is crucial for mitigating risks to agricultural yield under climate change, particularly for pearl millet production in Senegal. To facilitate informed tactical or strategic decision making, aimed at enhancing productivity, a modeling approach is essential for comprehending climate impacts and formulating effective adaptation strategies. Methods: The DSSAT model's capability to simulate four pearl millet cultivars (Souna-3, SL 28, SL 423, Thialack-2) under varying plant populations and micro-dosing fertilization strategies in diverse environments is evaluated in this study. The research also examines the risk associated with management practices on millet productivity in the context of climate variability and changing conditions for the period 1990-2020. To achieve these objectives, the CERES-millet model was calibrated using field data from 2022 to 2023 growing seasons for the four cultivars and subsequently validated with independent datasets from on-farm demonstrations in Senegal under various organo-mineral fertilizer applications for the same seasons. Results: The CERES-Millet model demonstrated high accuracy, simulating key growth stages and yield components across various millet cultivars and environments. For anthesis and physiological maturity, the model achieved low RMSE values (3-5 days and 2-4 days) and acceptable d-index ranges (from 0.6 to 0.9), indicating reliable predictions of these critical developmental milestones. The model's performance extended to accurately simulating leaf area index (LAI), grain yield, and above-ground biomass, with specific RMSE values reported for different cultivars such as Souna 3 (RMSE of 443 kg ha -1 for grain yield and 747 kg ha -1 for AGB), Thialack-2 estimated RMSE of 389 kg ha -1 for grain yield and 208 kg ha -1 for AGB. SL28 recorded RMSE of 257 kg ha -1 for grain yield and 560 kg ha -1 for AGB, while SL 423 had RMSE of 698 kg ha -1 for grain and 640 kg ha -1 for AGB. A long-term 31-year seasonal simulation revealed that dual-purpose varieties consistently outperformed Souna 3 in terms of yield, despite significant inter-annual variability. Conclusion: Combining these varieties with micro-dosing fertilizer applications led to higher yield thresholds. Notably, micro-dosing fertilizer level 3 (3g/hill of NPK, 2g/hill of Urea, and 50g/hill of cow manure) demonstrated consistent stability, making it a cost-effective approach to mitigate yield variability in semi-arid regions of Senegal. Long-term simulations highlighted the promising potential of combining dual-purpose varieties with optimized micro-dosing strategies to enhance yield stability and resilience. This approach offers a practical method for mitigating the effects of climate variability in Senegal and similar environmental contexts.

Land degradation, exacerbated by climate change, is a major threat to food security, particularly in West Africa, where population growth is high. In the Sahel, including Niger, vast areas of degraded land exhibit surface hardpan soils (SHS), which restrict root penetration and hinder plant growth. These soils may be rehabilitated, as their higher clay content can retain more moisture than adjacent sandy soils. Often, village authorities allocate such lands to women farmers and groups for restoration and income generation. However, the lack of high-resolution, spatially explicit SHS maps limits efforts to prioritize and scale up interventions, especially under community-led programs like the Bio-reclamation of Degraded Lands (BDL). While remote sensing offers a scalable and cost-effective solution for SHS mapping in data-scarce regions like Niger, SHS-specific applications remain limited. To address this gap, we developed a transparent and cost-effective SHS mapping approach using Sentinel-2 imagery and logistic regression, well-suited to such environments. Eighteen logistic models were tested using 2019–2020 Sentinel-2 imagery and in-situ data processed via Google Earth Engine. For each pixel, three aggregate functions (mean, minimum, maximum) were applied to NDVI, SBI, and RI. Systematic combinations of these indices identified an optimal model NDVI-mean, SBI-min, and RI-mean which achieved an AUC of 0.895. SHS probability was mapped across croplands, bare areas, and other land covers, with classifications into high (0.85–1) and medium (0.5–0.85) probability. Estimated SHS areas were 6.05 Mha (croplands), 6.07 Mha (bare areas), and 7.69 Mha (others). The approach is transferable to other arid and semi-arid regions experiencing comparable degradation patterns, supporting broader restoration efforts.

Understanding growing period conditions is crucial for effective climate risk management strategies. Seasonal climate forecasts (SCF) are key in predicting these conditions and guiding risk management in agriculture. However, low SCF adoption rates among smallholder farmers are due to factors like uncertainty and lack of understanding. In this study, we evaluated the benefits of SCF in predicting growing season conditions, and crop performance, and developing climate risk management strategies in Kongwa district, Tanzania. We used sea surface temperature anomalies (SSTa) from the Indian and Pacific Ocean regions to predict seasonal rainfall onset dates using the k-nearest neighbor model. Contrary to traditional approaches, the study established the use of rainfall onset dates as the criterion for predicting and describing growing period conditions. We then evaluated forecast skills and the profitability of using SCF in crop management with the Agricultural Production System sIMulator (APSIM) coupled with a simple bio-economic model. Our findings show that SSTa significantly influences rainfall variability and accurately predicts rainfall onset dates. Onset dates proved more effective than traditional methods in depicting key growing period characteristics, including rainfall variability and distribution. Including SCF in climate risk management proved beneficial for maize and sorghum production both agronomically and economically. Not using SCF posed a higher risk to crop production, with an 80% probability of yield losses, especially in late-onset seasons. We conclude that while SCF has potential benefits, improvements are needed in its generation and dissemination. Enhancing the network of extension agents could facilitate better understanding and adoption by smallholder farmers.

Sub-Saharan Africa (SSA) faces significant food security risks, primarily due to low soil fertility leading to low crop yields. Climate change is expected to worsen food security issues in SSA due to a combined negative impact on crop yield and soil fertility. A common omission from climate change impact studies in SSA is the interaction between change in soil fertility and crop yield. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, is expected to increase crop yield but it is uncertain how this advantage is maintained with climate change. We explored the impact of scenarios of change in soil fertility and climate variables (temperature, rainfall, and CO2) on rainfed maize yield in four representative sites in SSA with no input and ISFM management. To do so, we used an ensemble of 15 calibrated soil-crop models. Reset and continuous simulations were performed to assess the impact of soil fertility vs climate change on crop yield. In reset simulations, SOC, soil N and soil water were reinitialized each year with the same initial conditions. In continuous simulations, SOC, soil N and soil water values of a given year were obtained from the simulation of the previous year, allowing cumulative effects on SOC and crop yields.Most models agreed that with current baseline (no input) management, yield changed by a much larger order of magnitude when considering declining soil fertility with baseline climate (-39%), compared with considering constant soil fertility but changes in temperature, rainfall and CO2 (from -12% to +5% depending on the climate variable considered). The interaction between change in soil fertility and climate variables only marginally influenced maize yield (high agreement between models). The model ensemble indicated that when accounting for soil fertility change, the benefits of ISFM systems over no-input systems increased over time (+190%). This increase in ISFM benefits was greater in sites with low initial soil fertility. We advocate for the urgent need to account for soil-crop long-term feedback in climate change studies to avoid large underestimations of climate change and ISFM impact on food production in SSA.

Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions.

The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems.

Understanding and identifying appropriate adaptation optons for cropping systems and management practices at spatial and temporal scales is an important prerequisite for scaling. Pearl millet (Pennisetum glaucum (L) R. Br.) could be regarded as a risk-reducing measure crop under climate change when coupled with tactical agronomic management practices. In this study, we assess the impacts of adaptation strategies such as cultivar type, planting windows, and fertilizer strategies on pearl millet production under rainfed farming systems over Nigeria and Senegal using the Agricultural Production Systems Simulator (APSIM) model. The impact of climate change on millet yield was evaluated using a validated APSIM-millet module that utilized yield data collected through participatory research and extension approach (PREA) in contrasting environments. The climate model projections for the mid-century period (2040–2069) were compared against a baseline period of 1980–2009 for both locations. During the simulation, two millet varieties (improved local and dual-purpose) with two sowing regimes were considered comparing traditional farmers’ sowing window (dry sowing) and agronomic sowing window (planting based on the onset of the rainfall) at three different fertilizer levels [low (23 kg N ha−1), medium (40.5 kg N ha−1), and high (68.5 kg N ha−1) respectively]. The performance of the APSIM-millet module was found to be satisfactory as indicated by the low Root Means Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) values. The range for grain yield was between 17.7% and 25.8%, while for AGB it was between 18.6% and 21.4%. The results showed that farmers’ sowing window simulated slightly higher grain yield than the agronomic sowing window for improved local millet cultivar indicating yield increased by 8–12%. However, the projected changes in the mid-century (2040–2069) resulted in a decline in yield against baseline climate for both varieties and sowing windows, indicating the negative impact of climate change (CC) on yield productivity. The comparison between dual-purpose millet and improved local millet indicates that disseminating the improved millet variety and implementing early sowing could be an effective adaptation strategy in reducing risks and losses caused by climate change. Similarly, low magnitude impacts simulated on grain yield (< −8% in Nigeria compared to > −8% in Senegal) even though both locations are in the same agroecological zone.

Experiments were conducted to evaluate the response of different sorghum varieties to micro-dosing fertilization strategies on yield and yield traits, as well as the impact on nitrogen fertilizer and water use efficiency (NUE and WUE). In addition, the benefit-cost ratio of sorghum cultivation under different fertilization strategies in the Sudan savanna zone of Nigeria was analyzed. The experiment included eight fertilizer micro-application strategies as well as two control and three sorghum varieties. Our results showed that most agronomic indicators differed significantly between years, varieties, and fertilization strategies. However, the application of 100g hill-1 poultry manure plus 3g NPK hill-1 resulted in the highest average grain yield > 2000 kg ha-1 at both study sites (BUK and Minjibir). This means that the grain yield is 86% and 132% higher than the average grain yield with zero fertilization. There were extremely significant differences between NUE and WUE fertilization strategies and varieties at the two sites. At BUK and Minjibir, NPK applied with 3 g of hill-1 had the highest NUE with an average of 37.6 and 40 kg grain/kg N. Application of 100 g of poultry manure plus 3g of NPK hill-1 resulted in the highest average WUE of 6.1 and 5.6 kg grain/mm for BUK and Minjibir, respectively. BUK (3.2) and Minjibir (3.6) had the highest net income and benefit-to-cost ratios when applying 3 grams of NPK per hill. The adoption of micro-dosing fertilization strategies by smallholder farmers provides a good opportunity to prevent long-term soil fertility limitations and thereby increase sorghum productivity and farmer incomes by recommending multiple-choice fertilization strategies for improved sorghum varieties.

The resilience capacity of smallholder households is one of the main drivers of their ability to continue to farm and make investments in the fragile dryland regions. This paper aims to assess the resilience profile of smallholder farmers in the face of climate change and the factors influencing it in three dryland sub-regions of Senegal, namely, Louga, Kaffrine, and Thies. We developed a composite index of climate resilience (CICR) using data on farmers' perceptions of climate variability and their perceived ability to withstand, adapt, and bounce back in the event of climatic shocks. Drought, strong winds, and soil fertility decline because of climate change emerged as the main climate hazards impacting smallholder farming systems. The CICR value ranged from −2 for the most vulnerable households to +2 for the most resilient households. On average, all the households were found to be vulnerable, with an average CICR value of −0.2. The LOUGA region was the most vulnerable, with an average CICR value of −0.36, followed by THIES (-0.2). The KAFFRINE region was relatively less vulnerable, with a CICR value of −0.1. Ordered logit model estimates show that the chances of improving CICR decrease with the increase of the household head's age until 59 years. Access to training on climate-smart agricultural (CSA) practices and climate information appeared to have the potential to increase by 171% the chance of the household improving its resilience status. Analysis also shows that one more woman working off-farm or in-home gardening has the potential to multiply by four times the chances of households being more resilient. This highlights the importance of empowering women to enhance household resilience to climate change. The off-farm revenue increased the chance to improve the resilience status of the farm household by 62% and the receipt of transfer revenue by 50%. This study provides a robust method for quantifying resilience or wellbeing and its drivers and enriches our understanding of the resilience ability of farmers to climate change in a West African context. It can be useful in designing effective adaptation interventions and improving the overall wellbeing of smallholder farmers.

Erratic rainfall, high evapotranspiration rates and droughts are major factors limiting crop production in semi-arid areas. Tied ridges that have crossed ties within the furrow are among the physical soil and water conservation measures. During the 2018/19 and 2019/20 seasons, we examined the efficacy of repaired tied ridges for maize crop (Zea mays) and sorghum (Sorghum bicolor) in Kongwa district of Tanzania as an alternate labour saving strategy for managing climate risks associated with variable rainfall. Treatments consisted of three tillage methods: conventional tillage (CT) which involved the preparation of a flat seedbed using handhoe, annually constructed tied ridges (ATR) and tied ridges that had been constructed during the previous season and had been repaired (residual tied ridges-RTR). Data were collected on labour requirements and crop performance. RTR increased economic returns by 29% and 80% over ATR and CT, respectively. Maize grain yield shows a trend of RTR >ATR>CT with values ranging from 2465 kg ha(-1) to 4185 kg ha(-1) (P < 0.01). While tillage and/or variety did not influence sorghum grain yield significantly (P > 0.05). The use of RTR is recommended because of low labour requirements and greater economic benefits than CT and ATR under maize cropping systems.

Many areas in the world suffer from relatively sparse soil data availability. This results in inefficient implementation of soil-related studies and inadequate recommendations for improving soil management strategies. Commonly, this problem is tackled by collecting new soil data to update legacy soil surveys. New soil data collection, however, is usually costly. In this paper, we demonstrate how to find homosoils with the objective of obtaining new soil data for a study area. Homosoils are soils that can be geographically distant but share similar soil-forming factors. We cluster the study area into homogenouse areas, and identify a homosoil to each area using distance metrics calculated in the character space spanned by the environmental covariates. In a case study in Mali, we found that large areas in India, Australia and America have similar soil-forming factors to the African Sahelian zone. We collected available soil data for these areas from the WoSIS database. Statistical analysis on the relationship between the homosoils corresponding to different areas of Mali and three soil properties (clay, sand, pH) displayed the unique variability captured by homosoils. The homosoils could explain 8% of the variation found in the soil datasets. There was a strong association between pH and homosoils corresponding to the semi-arid conditions and sedimentary parent material of Mali, whereas homosoils corresponding to other areas of Mali showed moderate association either with clay or sand. The location and spread of the group centroids were significantly different between depth-specific homosoils for the three soil properties. The approach developed in this paper shows the opportunity for identifying areas in the world with similar soils to populate areas with relatively low soil data density. The concept of homosoils is promising and we envision future applications such as transfer of soil models and agronomic experimental results between areas.

A continuously declining carbon in soils of drylands has increasingly become a source of concern and needs integrated solutions to achieve global food security and sustainability goals. This study analysed the impact and sustainability of management practices for climate change mitigation and food security in dryland tropics using long-term field trials. We compared a consortium of interventions, comprised four treatments, viz. traditional farming, improved practice, and regenerative treatments. Additionally, we presented the results of regeneration practices aimed at maintaining the soil macro and micro-aggregates. Results showed significantly higher soil organic carbon (SOC) in the topsoil layer (0–15 cm) of regeneration areas compared to the precision farming area. Our long-term experiments with a consortium of interventions resulted in a promising increase in soil carbon and crop yields. We selected shared socioeconomic pathways for scenarios in future climates and simulated the effect of improved practices in the near and distant future. Our simulation results revealed that adopting improved practices enhanced soil carbon at the rate of 0.7