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Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

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...

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Autores principales: Delerce, Sylvain, Dorado, Hugo, Grillon, Alexandre, Rebolledo, Maria Camila, Prager, Steven D., Patiño, Victor Hugo, Garcés Varón, Gabriel, Jiménez, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999131/
https://www.ncbi.nlm.nih.gov/pubmed/27560980
http://dx.doi.org/10.1371/journal.pone.0161620
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author Delerce, Sylvain
Dorado, Hugo
Grillon, Alexandre
Rebolledo, Maria Camila
Prager, Steven D.
Patiño, Victor Hugo
Garcés Varón, Gabriel
Jiménez, Daniel
author_facet Delerce, Sylvain
Dorado, Hugo
Grillon, Alexandre
Rebolledo, Maria Camila
Prager, Steven D.
Patiño, Victor Hugo
Garcés Varón, Gabriel
Jiménez, Daniel
author_sort Delerce, Sylvain
collection PubMed
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.
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spelling pubmed-49991312016-09-12 Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches Delerce, Sylvain Dorado, Hugo Grillon, Alexandre Rebolledo, Maria Camila Prager, Steven D. Patiño, Victor Hugo Garcés Varón, Gabriel Jiménez, Daniel PLoS One Research Article 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. Public Library of Science 2016-08-25 /pmc/articles/PMC4999131/ /pubmed/27560980 http://dx.doi.org/10.1371/journal.pone.0161620 Text en © 2016 Delerce et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Delerce, Sylvain
Dorado, Hugo
Grillon, Alexandre
Rebolledo, Maria Camila
Prager, Steven D.
Patiño, Victor Hugo
Garcés Varón, Gabriel
Jiménez, Daniel
Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
title Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
title_full Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
title_fullStr Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
title_full_unstemmed Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
title_short Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
title_sort assessing weather-yield relationships in rice at local scale using data mining approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999131/
https://www.ncbi.nlm.nih.gov/pubmed/27560980
http://dx.doi.org/10.1371/journal.pone.0161620
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