Agriculture in West Africa is constrained by several yield-limiting factors, such as poor soil fertility, erratic rainfall distributions and low input systems. Projected changes in climate, thus, pose a threat since crop production is mainly rain-fed. The impact of climate change and its variation on the productivity of cereals in smallholder settings under future production systems in Navrongo, Ghana and Nioro du Rip, Senegal was assessed in this study. Data on management practices obtained from household surveys and projected agricultural development pathways (through stakeholder engagements), soil data, weather data (historical: 1980–2009 and five General Circulation Models; mid-century time slice 2040–2069 for two Representative Concentration Pathways; 4.5 and 8.5) were used for the impact assessment, employing a crop simulation model. Ensemble maize yield changes under the sustainable agricultural development pathway (SDP) were −13 and −16%, while under the unsustainable development pathway (USDP), yield changes were −19 and −20% in Navrongo and Nioro du Rip, respectively. The impact on sorghum and millet were lower than that on maize. Variations in climate change impact among smallholders were high with relative standard deviations (RSD) of between 14% and 60% across the cereals with variability being higher under the USDP, except for millet. Agricultural production systems with higher intensification but with less emphasis on soil conservation (USDP) will be more negatively impacted by climate change compared to relatively sustainable ones (SDP).

Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 2040–2069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between −9% and −39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

The production of soybean is gaining more attention in West Africa. In light of projected changes in climate, there is a need to assess the potential impacts on yield productivity and variability among farmers. An evaluated GROPGRO module of the Decision Support System for Agro-technological Transfer (DSSAT) was used to simulate soybean productivity under both historical (1980–2009) and projected climate scenarios from multiple general circulation models (GCMs) under two representative concentration pathways (RCPs): 4.5 and 8.5. Agronomic data from 90 farms, as well as multiple soil profile data, were also used for the impact assessment. Climate change leads to a reduction (3% to 13.5% across GCMs and RCPs) in the productivity of soybean in Northern Ghana. However, elevated atmospheric carbon dioxide has the potential to offset the negative impact, resulting in increased (14.8% to 31.3% across GCMs and RCPs) productivity. The impact of climate change on yield varied widely amongst farms (with relative standard deviation (RSD) ranging between 17% and 35%) and across years (RSD of between 10% and 15%). Diversity in management practices, as well as differences in soils, explained the heterogeneity in impact among farms. Variability among farms was higher than that among years. The strategic management of cultural practices provides an option to enhance the resilience of soybean productivity among smallholders.

In an era marked by climatic uncertainties and burgeoning food security challenges, climate-crop models emerge as important tools for guiding investments in climate-resilient agriculture. Here, we construct climate-crop model applications for 24 crops spanning the African food basket, including cereals, legumes, oilseeds, roots/tubers, and vegetables, in support of the Vision for Adapted Crops & Soils (VACS) program. Notably, we expand the modeling framework for 19 "opportunity" crops that have hitherto been underrepresented or overlooked but hold potential to bolster agricultural resilience across the continent. We calibrate and parameterize our process-based models using field observations and comprehensive literature reviews. We evaluate the model outputs spatially and temporally against national crop statistics across Africa, showing acceptable reproduction of yields and response to reported extreme droughts and heat waves. This work offers a scalable framework for climate-crop modeling and impact assessments related to food security in Africa and beyond.

Global dependence on a few well-researched staple crops exacerbates systemic vulnerabilities to climate change and does not encourage bio- or dietary diversity. The Vision for Adapted Crops and Soils aims to strengthen African food systems by promoting nutritious and climate-resilient “opportunity crops”, also known as "orphan crops", alongside improved land management practices. Here, the Agricultural Model Intercomparison and Improvement Project, together with the Food and Agriculture Organization, Havos.AI, and the African Orphan Crops Consortium, developed a set of 24 indicators for interdisciplinary evaluations of promising opportunity crops in Africa. We employ qualitative and quantitative evidence from climate-crop modeling, agronomy, plant breeding, nutrient composition and social sciences to assess and compare the development potential of 19 opportunity crops and five staple crops. Teff, finger millet, grass pea, pigeon pea, sesame seed, sweet potato, taro, African eggplant and okra are highlighted for their nutrient content and projected yields under climate change.

The Agricultural Model Intercomparison and Improvement Project (AgMIP) developed protocol-based methods for Regional Integrated Assessment (RIA) of agricultural systems. These methods have been applied by teams of scientists working with regional and national stakeholders across Sub-Saharan Africa and South Asia. This paper describes the data sets that were used to implement the AgMIP RIA methods for the Nioro region of Senegal. The goal of the RIA is to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin comprised of peanut, millet, maize and other minor crops and livestock, and to assess adaptations of that system to climate change, under current as well as future climate and socio-economic conditions. The data sets include: the Representative Agricultural Pathways (RAPs) developed for Nioro from 2000-2050; climate data used to implement crop yield simulations; the data used to parameterize the Agricultural Production Systems sIMulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop models, which include historical climate data and future climate scenarios; and the data used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOA-MD) economic simulation model. The analysis is structured around four AgMIP “core questions'' of climate impact assessment.