Accuracy of farmer-generated yield estimations of common bean in decentralised on-farm trials in sub–Saharan Africa

Improving agricultural productivity and resilience is essential to meet future food needs in sub-Saharan Africa under changing climate conditions. Achieving this will necessitate the development of high-yielding locally adapted crop varieties to mitigate the impacts of climate change. Despite advancements in crop improvement, varietal turnover in smallholder farms remains notably low. Continuous turnover of locally adapted varieties is essential, necessitating active dissemination of new varieties and withdrawal of obsolete ones across diverse target populations using participatory breeding approaches. A decentralised experimental approach, known as tricot, supported by citizen science, has proven effective in accelerating genotype selection while promoting inclusivity and diversity. However, the methodology has strongly relied on farmer-generated rankings, which provide relative performance insights but fall short in informing breeders with absolute yield data, limiting the ability to measure genetic gain or assess economic returns on breeding investments. To address this gap, we validated the accuracy of farmer-generated yield data for common bean (Phaseolus vulgaris L.), by comparing it with technician-generated volumes and researcher-generated absolute yield data. Results revealed strong correlations between farmer and technician volumes (r = 0.96, p < 0.001). The mean difference in farmer-technician log-yield was close to zero, indicating significant agreement. We further developed a predictive model to estimate absolute yields using farmer showing minimal influence from intrinsic and extrinsic factors. Our findings demonstrate that farmer-generated yield data can reliably inform breeding decisions and support the accelerated turnover of improved varieties. Integrating such data into breeding programs offers a cost-effective and scalable pathway to enhance agricultural productivity and sustainability across smallholder systems in sub-Saharan Africa. • We tested if farmer yield estimates can accurately capture absolute crop variety performance on-farm. • Farmer-generated data correlated strongly with technician-measured yields. • Yield accuracy was minimally affected by external factors; 25 farmers were enough for reliable data. • We developed a predictive model converting farmer data into accurate grain yield estimates.