With diverse challenges facing crop production for low income and food insecure farmers in semi-arid environments, generally there are complications when addressing food security. However, despite diverse backgrounds; the most common, critical, factors hindering agricultural production in semiarid Sub-Saharan Africa (SSA) are poor soil fertility and the variation of intraseasonal descriptors of rainfall (including onset dates, cessation dates, number of rainy days, varying intensities, and total seasonal rainfall amounts). Despite being critical throughout SSA, poor understanding and limited documentation regarding poor soils and weather conditions are still pervasive.
Comprehensive risk management strategies, referred as crop upgrading strategies (UPSs), that address these challenges must be derived from each respective local context in order to achieve a sustainable future. In this dissertation, UPSs are defined as set of good practices that secure food across local and regional food value chains. Thus, learning from successful UPSs, prioritizing according to their suitability, and testing them with farmers will extend the knowledge of how to reduce risks in crop production in challenging conditions. Knowledge and data acquired from local contexts can be integrated to regional scales through the use of crop models.
Crop models, when calibrated and validated for specific crops, provide robust opportunities for UPS optimization under current and future climate change scenarios. In this context, this dissertation comprises four peer-reviewed publications focusing on testing prioritized UPSs with farmers and integrating the respective UPSs in crop modelling to improve food security in semiarid SSA.