AI-Driven Climate-Resilient Farming Systems for Smallholders: A District-Level Framework from O.R. Tambo, South Africa

Abstract

Smallholder farmers in O.R. Tambo District face increasing climate-related stress from unpredictable rainfall, frequent droughts, and rising temperatures, which threaten yields and livelihoods. This review examines how artificial intelligence (AI), ranging from remote sensing and decision support systems to genomic prediction, can support farmer adaptation and strengthen future resilience. Rather than asking whether AI can work, the paper considers how AI must be designed, delivered, and governed so that local farmers can use it effectively. Three practical contributions are presented: a farmer-centred AI adoption model, the Jeenv real-time advisory architecture, and the LOCAL data governance framework. The review emphasises low-bandwidth delivery, human mediation, and equity, and proposes staged pilots linking immediate advisories with breeding and soil investments. Rigorous mixed-methods evaluation and equity metrics, including a Social Inclusion Index, are recommended to support rapid and transparent learning. Comparative lessons from Kenya, Ethiopia, and Nigeria inform practical choices for O.R. Tambo. The paper concludes with operational recommendations for district planners, extension services, and researchers to translate the potential of AI into practical impact.