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...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Public Library of Science
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/76628 |
| _version_ | 1855521700849909760 |
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| author | Delerce, Sylvain Jean Dorado, Hugo Andres Grillon, Alexandre Rebolledo, María Camila Prager, Steven D. Patiño, Victor Hugo Garcés Varón, Gabriel Jiménez, Daniel |
| author_browse | Delerce, Sylvain Jean Dorado, Hugo Andres Garcés Varón, Gabriel Grillon, Alexandre Jiménez, Daniel Patiño, Victor Hugo Prager, Steven D. Rebolledo, María Camila |
| author_facet | Delerce, Sylvain Jean Dorado, Hugo Andres Grillon, Alexandre Rebolledo, María Camila Prager, Steven D. Patiño, Victor Hugo Garcés Varón, Gabriel Jiménez, Daniel |
| author_sort | Delerce, Sylvain Jean |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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 | Journal Article |
| id | CGSpace76628 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace766282025-03-13T09:44:08Z Assessing weather-yield relationships in rice at local scale using data mining approaches Delerce, Sylvain Jean Dorado, Hugo Andres Grillon, Alexandre Rebolledo, María Camila Prager, Steven D. Patiño, Victor Hugo Garcés Varón, Gabriel Jiménez, Daniel rice farms climate change meteorology agronomy agriculture arroz explotaciones agrarias cambio climático meteorología agronomía agricultura 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. 2016-08-25 2016-08-29T20:48:20Z 2016-08-29T20:48:20Z Journal Article https://hdl.handle.net/10568/76628 en Open Access Public Library of Science 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. |
| spellingShingle | rice farms climate change meteorology agronomy agriculture arroz explotaciones agrarias cambio climático meteorología agronomía agricultura Delerce, Sylvain Jean Dorado, Hugo Andres Grillon, Alexandre Rebolledo, María 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 | rice farms climate change meteorology agronomy agriculture arroz explotaciones agrarias cambio climático meteorología agronomía agricultura |
| url | https://hdl.handle.net/10568/76628 |
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