Seed systems for African food security: linking molecular genetics and cultivator knowledge in West Africa

Abstract

A challenge for African countries is how to integrate new sources of knowledge on plant genetics with knowledge from farmer practice to help improve food security. This paper considers the knowledge content of farmer seed systems in the light of a distinction drawn in artificial intelligence research between supervised and unsupervised learning. Supervised learning applied to seed systems performance has a poor record in Africa. The paper discusses an alternative – unsupervised learning supported by functional genomic analysis. Recent work in West Africa on sorghum, African rice and white yam is described. Requirements for laboratory-based analytical support are outlined. A science-backed ‘farmer first’ approach – while feasible – will require a shift in policy and funding by major investors.