Assessing weather-yield relationships in rice at local scale using data mining approaches

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dc.creatorDelerce, Sylvain Jean
dc.creatorDorado, Hugo Andres
dc.creatorGrillon, Alexandre
dc.creatorRebolledo, María Camila
dc.creatorPrager, Steven D.
dc.creatorPatiño, Victor Hugo
dc.creatorGarcés Varón, Gabriel
dc.creatorJiménez, Daniel
dc.date2016-08-25
dc.date2016-08-29T20:48:20Z
dc.date2016-08-29T20:48:20Z
dc.date.accessioned2026-06-27T14:36:11Z
dc.descriptionSeasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.
dc.identifierhttps://hdl.handle.net/10568/76628
dc.identifier.urihttp://hdl.handle.net/123456789/82907
dc.languageen
dc.publisherPublic Library of Science
dc.rightsOpen Access
dc.sourceDelerce, Sylvain; Dorado, Hugo; Grillon, Alexandre; Rebolledo, Maria Camila; Prager, Steven D.; Patiño, Victor Hugo; Garcés Varón, Gabriel; Jiménez, Daniel. 2016. Assessing weather-yield relationships in rice at local scale using data mining approaches . PloS One 11(8): e0161620.
dc.subjectrice
dc.subjectfarms
dc.subjectclimate change
dc.subjectmeteorology
dc.subjectagronomy
dc.subjectagriculture
dc.subjectarroz
dc.subjectexplotaciones agrarias
dc.subjectcambio climático
dc.subjectmeteorología
dc.subjectagronomía
dc.subjectagricultura
dc.titleAssessing weather-yield relationships in rice at local scale using data mining approaches
dc.typeJournal Article

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Centro Internacional de Agricultura Tropical (CIAT)