Campus Units

Economics, Mechanical Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

9-11-2020

Journal or Book Title

Scientific Reports

Volume

10

First Page or Article ID Number

14957

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 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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