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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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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. |
format | Online Article Text |
id | pubmed-4999131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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