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Predicting county-scale maize yields with publicly available data
Scientific Reports
  • Zehui Jiang, Iowa State University
  • Chao Liu, Tsinghua University
  • Baskar Ganapathysubramanian, Iowa State University
  • Dermot J. Hayes, Iowa State University
  • Soumik Sarkar, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
9-11-2020
DOI
10.1038/s41598-020-71898-8
Abstract

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.

Comments

This article is published as Jiang, Zehui, Chao Liu, Baskar Ganapathysubramanian, Dermot J. Hayes, and Soumik Sarkar. "Predicting county-scale maize yields with publicly available data." Scientific Reports 10 (2020): 14957. doi: 10.1038/s41598-020-71898-8.

Creative Commons License
Creative Commons Attribution 4.0 International
Copyright Owner
The Authors
Language
en
File Format
application/pdf
Citation Information
Zehui Jiang, Chao Liu, Baskar Ganapathysubramanian, Dermot J. Hayes, et al.. "Predicting county-scale maize yields with publicly available data" Scientific Reports Vol. 10 (2020) p. 14957
Available at: http://works.bepress.com/baskar-ganapathysubramanian/112/