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Predicting county-scale maize yields with publicly available data
Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world’s maize followed by China at 22% and Brazil at 9% (https://a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486922/ https://www.ncbi.nlm.nih.gov/pubmed/32917920 http://dx.doi.org/10.1038/s41598-020-71898-8 |
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author | Jiang, Zehui Liu, Chao Ganapathysubramanian, Baskar Hayes, Dermot J. Sarkar, Soumik |
author_facet | Jiang, Zehui Liu, Chao Ganapathysubramanian, Baskar Hayes, Dermot J. Sarkar, Soumik |
author_sort | Jiang, Zehui |
collection | PubMed |
description | Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world’s maize followed by China at 22% and Brazil at 9% (https://apps.fas.usda.gov/psdonline/app/index.html#/app/home). Accurate national-scale corn yield prediction critically impacts mercantile markets through providing essential information about expected production prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. We build a deep learning model to predict corn yields, specifically focusing on county-level prediction across 10 states of the Corn-Belt in the United States, and pre-harvest prediction with monthly updates from August. The results show promising predictive power relative to existing survey-based methods and set the foundation for a publicly available county yield prediction effort that complements existing public forecasts. |
format | Online Article Text |
id | pubmed-7486922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74869222020-09-15 Predicting county-scale maize yields with publicly available data Jiang, Zehui Liu, Chao Ganapathysubramanian, Baskar Hayes, Dermot J. Sarkar, Soumik Sci Rep Article Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world’s maize followed by China at 22% and Brazil at 9% (https://apps.fas.usda.gov/psdonline/app/index.html#/app/home). Accurate national-scale corn yield prediction critically impacts mercantile markets through providing essential information about expected production prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. We build a deep learning model to predict corn yields, specifically focusing on county-level prediction across 10 states of the Corn-Belt in the United States, and pre-harvest prediction with monthly updates from August. The results show promising predictive power relative to existing survey-based methods and set the foundation for a publicly available county yield prediction effort that complements existing public forecasts. Nature Publishing Group UK 2020-09-11 /pmc/articles/PMC7486922/ /pubmed/32917920 http://dx.doi.org/10.1038/s41598-020-71898-8 Text en © The Author(s) 2020 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/. |
spellingShingle | Article Jiang, Zehui Liu, Chao Ganapathysubramanian, Baskar Hayes, Dermot J. Sarkar, Soumik Predicting county-scale maize yields with publicly available data |
title | Predicting county-scale maize yields with publicly available data |
title_full | Predicting county-scale maize yields with publicly available data |
title_fullStr | Predicting county-scale maize yields with publicly available data |
title_full_unstemmed | Predicting county-scale maize yields with publicly available data |
title_short | Predicting county-scale maize yields with publicly available data |
title_sort | predicting county-scale maize yields with publicly available data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486922/ https://www.ncbi.nlm.nih.gov/pubmed/32917920 http://dx.doi.org/10.1038/s41598-020-71898-8 |
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