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

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Title Assessing weather-yield relationships in rice at local scale using data mining approaches
 
Creator Delerce, Sylvain Jean
Dorado, Hugo Andres
Grillon, Alexandre
Rebolledo, María Camila
Prager, Steven D.
Patiño, Victor Hugo
Garcés Varón, Gabriel
Jiménez, Daniel
 
Subject rice
farms
climate change
meteorology
agronomy
agriculture
arroz
explotaciones agrarias
cambio climático
meteorología
agronomía
agricultura
 
Description Seasonal 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.
 
Date 2016-08-25
2016-08-29T20:48:20Z
2016-08-29T20:48:20Z
 
Type Journal Article
 
Identifier Delerce, 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.
1932-6203
https://hdl.handle.net/10568/76628
https://doi.org/10.1371/journal.pone.0161620
https://doi.org/10.7910/DVN/7SGCCR
https://doi.org/10.7910/DVN/MGUTG3
https://doi.org/10.7910/DVN/GMTAQN
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 11(8): e0161620
 
Publisher Public Library of Science (PLoS)
 
Source PLOS ONE