YieldTrace
When a harvest fails, farmers, insurers and governments all need to know why. YieldTrace estimates what each farm would have produced under normal conditions and attributes the shortfall to its causes.
The project applies the potential outcomes framework of causal inference at the scale of an individual farm, drawing on CHIRPS and ERA5 climate data. The first study will focus on maize, cassava and tomato in Chinsali District, Muchinga Province, Zambia. The goal is to produce the kind of farm level evidence that agricultural policy commitments under the CAADP Kampala Declaration call for.
Status: Concept stage.
Darry Derrick is a Zambian researcher working on the security of AI systems and on putting trustworthy AI to work in agriculture and education across Southern Africa. His work starts from a practical question: how do we make intelligent systems safe and dependable when they run on limited compute, patchy connectivity and mobile money rails, rather than inside a well resourced data centre?
He leads Darry Lab for Secure and Applied AI, an independent research initiative. Current work includes ForgeryGuard, a guardrail that stops large language models from producing forged institutional documents; QSentry, an open source tool for securing the quantum software supply chain; LUDA, a ledger verified crop diagnostics platform for smallholder farmers; and YieldTrace, a causal approach to attributing crop losses at farm level.