An AI-based conceptual model for climate finance, adaptation, and agriculture in small-scale farmers in Western Cape, South Africa, Osden Jokonya

Osden Jokonya
University of the Western Cape, Department of Information Systems
South Africa

Abstract

As the global population is projected to reach 9.7 billion by 2050, the demand for sustainable agricultural products and resilient food systems will intensify, placing additional pressure on the food sector. Agriculture and food systems currently contribute to nearly one-third of global emissions, while also being highly vulnerable to the impacts of climate change. Studies suggest that approximately one-third of produced food worldwide is lost or wasted, contributing to significant economic losses and food waste pollution. Smallholder farmers, who manage about 84% of global farms, are crucial for ensuring food security and advancing the Sustainable Development Goals (SDGs). The study proposes an AI-based conceptual model for an environmental pollution credit framework for small-scale farmers in the Western Cape, South Africa. The proposed model integrates a range of digital technologies and AI-driven data analytics to create a holistic, real-time system that monitors and manages environmental impacts at the farm level. Distributed across fields, soil sensors monitor key parameters such as moisture content, pH levels, and nutrient status. These sensors provide real-time data that help farmers optimize irrigation schedules, manage soil health, and minimize chemical use, reducing over-irrigation and chemical pollution. These technologies enable small-scale to make informed decisions on resource usage while generating verifiable environmental pollution credits. The framework would operate within a broader pollution credit system, where farmers earn credits for practices that reduce their footprint. The proposed AI-based conceptual model for an environmental pollution credit presents an innovative and scalable solution to the pressing challenges faced by small-scale farmers in the Western Cape. The ability to generate pollution credits creates new revenue streams for smallholder farmers. The data-driven AI model will encourage sustainable practices making farmers more resilient to climate change impacts. Linking smallholder farmers to international climate finance markets can enhance their global competitiveness. The proposed model will enable farmers to optimize resource use, enhance sustainability, and participate in global climate finance markets in support COP29 climate finance agenda. With the right framework, the model could significantly contribute to advancing the Sustainable Development Goals (SDGs) and drive meaningful environmental change in the region.