Computational Algorithms for Climate-Smart Agriculture in Sub-Saharan Africa

Abstract

Climate-smart agriculture (CSA) provides a vital framework for enhancing food security, resilience, and mitigation in Sub-Saharan Africa (SSA), where agriculture is highly vulnerable to climate variability and shocks. By integrating practices that sustainably increase productivity and reduce greenhouse gas emissions, CSA addresses the region’s dual challenges of climate change and food insecurity. Computational algorithms offer critical tools for supporting CSA through simulation, predictive analytics, optimization, and decision-making. However, their systematic application to SSA’s agricultural systems remains limited. This study investigates how algorithm development and optimization can strengthen CSA adoption in SSA. The objectives are to examine the current status of CSA practices and adoption drivers, explore algorithmic applications in modelling and resource management, identify integration barriers, and propose scalable pathways for sustainable deployment. A systematic review was conducted across six databases, focusing on literature published between 2010 and 2023. Screening yielded 32 eligible studies, which were synthesized through narrative and thematic analysis. Results highlight the use of algorithms such as particle swarm optimization, neural networks, and evolutionary computation in domains including yield prediction, drought risk assessment, irrigation scheduling, and crop disease detection. Key barriers include budgetary constraints, weak supply chains, policy gaps, and low farmer awareness, while opportunities lie in digital connectivity, climate information services, and institutional support. The findings suggest that integrating computational algorithms into CSA frameworks can enhance adaptive capacity, optimize resource use, and accelerate resilient food system transformations in SSA.