Is farmer-generated data accurate? (journal)

The accuracy of farmer-generated data in an agricultural citizen science methodology

Participatory approaches involving on-farm experimentation have become more prevalent in agricultural research. Nevertheless, these approaches remain difficult to scale because they usually require close attention from well-trained professionals. Novel large-N participatory trials, building on recent advances in citizen science and crowdsourcing methodologies, involve large numbers of participants and little researcher supervision. This study experimentally assess the accuracy of farmer observations in trials. At five sites in Honduras, 35 farmers participated in tricot experiments. They ranked three varieties of common bean for Plant vigor, Plant architecture, Pest resistance, and Disease resistance. Reliability of farmers’ experimental observations was generally low, but aggregated observations contained information and had sufficient validity to identify the correct ranking orders of varieties. Our sample size simulation shows that low reliability can be compensated by engaging higher numbers of observers, realistic numbers of less than 200 participants can produce meaningful results for agricultural research by tricot-style trials.