Probabilistic modeling of crop-yield loss risk under drought: a spatial showcase for sub-Saharan Africa

Abstract Assessing the risk of yield loss in African drought-affected regions is key to identify feasible solutions for stable crop production. Recent studies have demonstrated that Copula-based probabilistic methods are well suited such assessment owing reasonably inferring important properties terms exceedance probability and joint dependence different characterization. However, insufficient attention has been given quantifying determining contribution climatic factors. This study applies Copula theory describe between drought anomalies rainfed maize, millet, sorghum crops sub-Saharan Africa (SSA). The environmental policy integrated climate model, calibrated with Food Agriculture Organization country-level data, was used simulate yields across SSA (1980–2012). results showed severity due had a higher magnitude than itself. Sensitivity analysis factors contributing high-temperature stresses all amount precipitation during vegetation grain filling main driver loss, effect temperature stronger maize millet. demonstrate added value drought-impact assessment. For future studies, we recommend looking into influencing as individual/concurrent extremes.