From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

• Published in: PloS one Vol. 11; no. 3; p. e0150015
• Main Authors: Jiménez, Daniel, Dorado, Hugo, Cock, James, Prager, Steven D, Delerce, Sylvain, Grillon, Alexandre, Andrade Bejarano, Mercedes, Benavides, Hector, Jarvis, Andy
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-03-2016; Public Library of Science (PLoS)
• Subjects: Agricultural management; Agricultural practices; Agricultural production; Agricultural research; Agriculture; Agriculture - methods; Analysis; Biology and Life Sciences; Computer and Information Sciences; Continuous cropping; Crop management; Crop yield; Crops; Crops (Plants); Crops, Agricultural; Ecology and Environmental Sciences; Environment; Experiments; Factor analysis; Farm management; Farmers; Farms; Homology; Internet; Laboratories; Management; Models, Theoretical; People and Places; Productivity; R&D; Research & development; Researchers; Rural areas; Stability; Chile; Colombia; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0150015

Agriculture research uses "recommendation domains" to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers' experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers' fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.