Understanding Farmer Preferences through Citizen Science: Insights on Potato Varieties in Nigeria - Sharma, K.; Atieno, E.; Sousa, K. de; Etten, Jacob van; Nyawade, S. (Elsevier, 2025)
Conventional top-down potato breeding often overlooks farmer input, leading to varieties that may not be suited to local conditions, limiting their adoption and impact on smallholder farming. To address this gap, this study evaluated whether a participatory, digitally enabled approach to variety selection better reflects farmer needs and improves uptake. This study employed the tricot (Triadic Comparisons of Technology Options) approach, a participatory framework, combined with digital tools for real-time data collection and decision support. Farmers from seven Local Government Areas (LGAs) in Plateau State, Nigeria (Barikin Ladi, Bassa, Bokkos, Jos North, Jos South, Mangu, and Pankshin), evaluated anonymized potato genotypes alongside the check variety Marabel. Trial design and genotype allocation were managed through ClimMob, a cloud-based platform (https://climmob.net), enabling live streaming and real-time tracking. Data were collected via a mobile app and Open Data Kit (ODK). Assessments occurred at three crop stages: vegetative (germination, disease resistance, drought tolerance), harvest (yield, tuber appearance, marketability), and post-harvest (cooking quality, storability). Farmer preferences were analyzed using the Plackett-Luce model, a probabilistic model that estimates the relative ‘worth’ (also referred to as log-worth) of each genotype based on observed rankings across multiple comparisons. CIP clones CIP392797.22 and CIP393371.157 emerged as top-ranked, with log-worth values of 0.620 and 0.309, respectively, indicating stronger preference relative to the check. In contrast, CIP398208.29 and CIP398190.200 were least preferred (–0.085 and –0.465). Socioeconomic factors influenced selection: market-oriented farmers prioritized commercial traits, while subsistence farmers emphasized resilience. This study highlights the value of participatory, data-driven breeding that integrates local knowledge to enhance adoption, resilience, and food security.