AI-Driven Contract Design Could Unlock Carbon Farming for African Smallholders

A new research paper explores how artificial intelligence, specifically reinforcement learning, can be used to design more equitable contracts for carbon farming, particularly benefiting smallholder farmers in regions like Sub-Saharan Africa. The study identifies that current profit-maximizing contract designs often exclude smallholders due to factors like high per-hectare Measurement, Reporting, and Verification (MRV) costs, which are disproportionately burdensome for smaller farms.

The researchers formulated the complex contracting problem, which involves farmers with varying adoption costs and unobserved effort over multiple seasons, as a Partially Observable Markov Decision Process (POMDP). They then employed reinforcement learning to develop a dynamic, profit-maximizing contract model. Their findings indicate that a purely profit-driven aggregator tends to exacerbate the exclusion of smallholders, achieving significantly lower adoption rates on small farms compared to large ones.

Crucially, the study suggests that if MRV costs were made purely proportional to farm area, the disparity in adoption rates between small and large farms could be eliminated. This insight is vital for policy and contract design, as it offers a pathway to ensure smallholder farmers, who are critical to scaling climate mitigation efforts through carbon farming, can access carbon income. The research provides a simulation model that can guide the development of inclusive carbon farming programs across Africa and South Asia.