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Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

Although weather is a major driver of crop yield, many farmers don’t know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists,...

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Autores principales: Chapman, Ross, Cock, James, Samson, Marianne, Janetski, Noel, Janetski, Kate, Gusyana, Dadang, Dutta, Sudarshan, Oberthür, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050235/
https://www.ncbi.nlm.nih.gov/pubmed/33859261
http://dx.doi.org/10.1038/s41598-021-87520-4
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author Chapman, Ross
Cock, James
Samson, Marianne
Janetski, Noel
Janetski, Kate
Gusyana, Dadang
Dutta, Sudarshan
Oberthür, Thomas
author_facet Chapman, Ross
Cock, James
Samson, Marianne
Janetski, Noel
Janetski, Kate
Gusyana, Dadang
Dutta, Sudarshan
Oberthür, Thomas
author_sort Chapman, Ross
collection PubMed
description Although weather is a major driver of crop yield, many farmers don’t know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists, it should be possible to map ENSO Oceanic Index (ENSO OI) patterns to crop management responses without precise weather data. Time series data on cacao farm yields in Sulawesi, Indonesia, with and without fertilizer, were used to provide proof-of-concept. A machine learning approach associated 75% of cacao yield variation with the ENSO patterns up to 8 and 24 months before harvest and predicted when fertilizer applications would be worthwhile. Thus, it’s possible to relate average cacao crop performance and management response directly to ENSO patterns without weather data provided: (1) site specific data exist on crop performance over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We believe that the principles established here can readily be applied to other crops, particularly when there’s little data available on crop responses to management and weather. However, specific models will be required for each crop and every recommendation domain.
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spelling pubmed-80502352021-04-16 Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops Chapman, Ross Cock, James Samson, Marianne Janetski, Noel Janetski, Kate Gusyana, Dadang Dutta, Sudarshan Oberthür, Thomas Sci Rep Article Although weather is a major driver of crop yield, many farmers don’t know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists, it should be possible to map ENSO Oceanic Index (ENSO OI) patterns to crop management responses without precise weather data. Time series data on cacao farm yields in Sulawesi, Indonesia, with and without fertilizer, were used to provide proof-of-concept. A machine learning approach associated 75% of cacao yield variation with the ENSO patterns up to 8 and 24 months before harvest and predicted when fertilizer applications would be worthwhile. Thus, it’s possible to relate average cacao crop performance and management response directly to ENSO patterns without weather data provided: (1) site specific data exist on crop performance over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We believe that the principles established here can readily be applied to other crops, particularly when there’s little data available on crop responses to management and weather. However, specific models will be required for each crop and every recommendation domain. Nature Publishing Group UK 2021-04-15 /pmc/articles/PMC8050235/ /pubmed/33859261 http://dx.doi.org/10.1038/s41598-021-87520-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chapman, Ross
Cock, James
Samson, Marianne
Janetski, Noel
Janetski, Kate
Gusyana, Dadang
Dutta, Sudarshan
Oberthür, Thomas
Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
title Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
title_full Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
title_fullStr Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
title_full_unstemmed Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
title_short Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
title_sort crop response to el niño-southern oscillation related weather variation to help farmers manage their crops
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050235/
https://www.ncbi.nlm.nih.gov/pubmed/33859261
http://dx.doi.org/10.1038/s41598-021-87520-4
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