Sustainable Solutions to Land Degradation and Rainfall Variability in Sub-Saharan Africa: Integrating Traditional Water Management, Agricultural Intensification, and Machine Learning Approaches

This thesis examines sustainable agricultural practices designed to tackle the challenges of land degradation, climate variability, and food insecurity in sub-Saharan Africa, with a specific emphasis on rainfed agricultural systems. As the region grapples with the effects of erratic rainfall, soil degradation, and limited access to advanced technologies, this research explores solutions that blend traditional knowledge with machine learning and remote sensing. It investigates how climate variability impacts agricultural productivity and underscores the need for adaptive strategies. The thesis highlights traditional water harvesting techniques, such as stone bunds and in-situ water conservation methods, to improve soil moisture retention, reduce runoff, and boost crop yields. Furthermore, it integrates remote sensing and machine learning to enhance soil moisture predictions and inform more effective water management strategies. By combining age-old practices with innovative technologies, this research offers valuable contributions toward building resilient agricultural systems that improve productivity and sustainability in the face of climate change, providing practical insights for policymakers, practitioners, and researchers working towards lasting solutions for food security in sub-Saharan Africa.