Abstract
Over the last decades, 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. Reduced supervision may affect data quality, but the “Wisdom of Crowds” principle implies that many independent observations from a diverse group of people often lead to highly accurate results when taken together. In this study, we test whether farmer-generated data in agricultural citizen science are good enough to generate valid statements about the research topic. We experimentally assess the accuracy of farmer observations in trials of crowdsourced crop variety selection that use triadic comparisons of technologies (tricot). At five sites in Honduras, 35 farmers (women and men) participated in tricot experiments. They ranked three varieties of common bean (Phaseolus vulgaris L.) for Plant vigor, Plant architecture, Pest resistance, and Disease resistance. Furthermore, with a simulation approach using the empirical data, we did an order-of-magnitude estimation of the sample size of participants needed to produce relevant results. Reliability of farmers’ experimental observations was generally low (Kendall’s W 0.174 to 0.676). But aggregated observations contained information and had sufficient validity (Kendall’s tau coefficient 0.33 to 0.76) to identify the correct ranking orders of varieties by fitting Mallows-Bradley-Terry models to the data. Our sample size simulation shows that low reliability can be compensated by engaging higher numbers of observers to generate statistically meaningful results, demonstrating the usefulness of the Wisdom of Crowds principle in agricultural research. In this first study on data quality from a farmer citizen science methodology, we show that realistic numbers of less than 200 participants can produce meaningful results for agricultural research by tricot-style trials.
Highlights
Electronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.Economics, Unter den Linden 6, 10099 Berlin, Germany 3 Programa de Reconstrucción Rural, Horconcito, Santa Barbara, HondurasAgricultural research has increasingly incorporated participatory methods over the last decades in order to become more client-oriented, addressing the variable conditions and preferences of resource-poor farmers and consumers (Lilja et al 2013)
The second goal of our study is to gain insights regarding the order of magnitude of the number of participants required to produce useful findings with a large-N citizen science methodology
We apply a citizen science approach first proposed by Van Etten (2011)
Summary
Electronic supplementary material The online version of this article (doi:10.1007/s13593-017-0441-y) contains supplementary material, which is available to authorized users.Economics, Unter den Linden 6, 10099 Berlin, Germany 3 Programa de Reconstrucción Rural, Horconcito, Santa Barbara, HondurasAgricultural research has increasingly incorporated participatory methods over the last decades in order to become more client-oriented, addressing the variable conditions and preferences of resource-poor farmers and consumers (Lilja et al 2013). (2017) 37: 32 on organized farmer groups that require high levels of professional support and the establishment of collective plots (cf Witcombe et al 1996 for participatory variety selection). These approaches require that farmers stay engaged throughout the crop cycle, even when participation involves hard work, for example, weeding the plot. This often leads to a free-rider problem. Limited participation due to free-riding has led to incomplete observations, haphazard crop variety choices, and subsequent dis-adoption of selected varieties, when farmers discover the true field performance of the variety on their own plot of land (Misiko 2013)
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