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Translating large-scale climate variability into crop production forecast in Europe
Studies show that climate variability drives interannual changes in meteorological variables in Europe, which directly or indirectly impacts crop production. However, there is no climate-based decision model that uses indices of atmospheric oscillation to predict agricultural production risks in Eur...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361969/ https://www.ncbi.nlm.nih.gov/pubmed/30718693 http://dx.doi.org/10.1038/s41598-018-38091-4 |
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author | Guimarães Nobre, Gabriela Hunink, Johannes E. Baruth, Bettina Aerts, Jeroen C. J. H. Ward, Philip J. |
author_facet | Guimarães Nobre, Gabriela Hunink, Johannes E. Baruth, Bettina Aerts, Jeroen C. J. H. Ward, Philip J. |
author_sort | Guimarães Nobre, Gabriela |
collection | PubMed |
description | Studies show that climate variability drives interannual changes in meteorological variables in Europe, which directly or indirectly impacts crop production. However, there is no climate-based decision model that uses indices of atmospheric oscillation to predict agricultural production risks in Europe on multiple time-scales during the growing season. We used Fast-and-Frugal trees to predict sugar beet production, applying five large-scale indices of atmospheric oscillation: El Niño Southern Oscillation, North Atlantic Oscillation, Scandinavian Pattern, East Atlantic Pattern, and East Atlantic/West Russian pattern. We found that Fast-and-Frugal trees predicted high/low sugar beet production events in 77% of the investigated regions, corresponding to 81% of total European sugar beet production. For nearly half of these regions, high/low production could be predicted six or five months before the start of the sugar beet harvesting season, which represents approximately 44% of the mean annual sugar beet produced in all investigated areas. Providing early warning of crop production shortages/excess allows decision makers to prepare in advance. Therefore, the use of the indices of climate variability to forecast crop production is a promising tool to strengthen European agricultural climate resilience. |
format | Online Article Text |
id | pubmed-6361969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63619692019-02-06 Translating large-scale climate variability into crop production forecast in Europe Guimarães Nobre, Gabriela Hunink, Johannes E. Baruth, Bettina Aerts, Jeroen C. J. H. Ward, Philip J. Sci Rep Article Studies show that climate variability drives interannual changes in meteorological variables in Europe, which directly or indirectly impacts crop production. However, there is no climate-based decision model that uses indices of atmospheric oscillation to predict agricultural production risks in Europe on multiple time-scales during the growing season. We used Fast-and-Frugal trees to predict sugar beet production, applying five large-scale indices of atmospheric oscillation: El Niño Southern Oscillation, North Atlantic Oscillation, Scandinavian Pattern, East Atlantic Pattern, and East Atlantic/West Russian pattern. We found that Fast-and-Frugal trees predicted high/low sugar beet production events in 77% of the investigated regions, corresponding to 81% of total European sugar beet production. For nearly half of these regions, high/low production could be predicted six or five months before the start of the sugar beet harvesting season, which represents approximately 44% of the mean annual sugar beet produced in all investigated areas. Providing early warning of crop production shortages/excess allows decision makers to prepare in advance. Therefore, the use of the indices of climate variability to forecast crop production is a promising tool to strengthen European agricultural climate resilience. Nature Publishing Group UK 2019-02-04 /pmc/articles/PMC6361969/ /pubmed/30718693 http://dx.doi.org/10.1038/s41598-018-38091-4 Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Guimarães Nobre, Gabriela Hunink, Johannes E. Baruth, Bettina Aerts, Jeroen C. J. H. Ward, Philip J. Translating large-scale climate variability into crop production forecast in Europe |
title | Translating large-scale climate variability into crop production forecast in Europe |
title_full | Translating large-scale climate variability into crop production forecast in Europe |
title_fullStr | Translating large-scale climate variability into crop production forecast in Europe |
title_full_unstemmed | Translating large-scale climate variability into crop production forecast in Europe |
title_short | Translating large-scale climate variability into crop production forecast in Europe |
title_sort | translating large-scale climate variability into crop production forecast in europe |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361969/ https://www.ncbi.nlm.nih.gov/pubmed/30718693 http://dx.doi.org/10.1038/s41598-018-38091-4 |
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