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Leveraging Important Covariate Groups for Corn Yield Prediction

Author

Listed:
  • Britta L. Schumacher

    (Department of Plants, Soils and Climate and Ecology Center, Utah State University, 4820 Old Main Hill, Logan, UT 84322-4820, USA)

  • Emily K. Burchfield

    (Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, GA 30322, USA)

  • Brennan Bean

    (Department of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT 84322-3900, USA)

  • Matt A. Yost

    (Agroclimate Extension Specialist, Department of Plants, Soils and Climate, Utah State University, 4820 Old Main Hill, Logan, UT 84322-4820, USA)

Abstract

Accurate yield information empowers farmers to adapt, their governments to adopt timely agricultural and food policy interventions, and the markets they supply to prepare for production shifts. Unfortunately, the most representative yield data in the US, provided by the US Department of Agriculture, National Agricultural Statistics Service (USDA-NASS) Surveys, are spatiotemporally patchy and inconsistent. This paper builds a more complete data product by examining the spatiotemporal efficacy of random forests (RF) in predicting county-level yields of corn—the most widely cultivated crop in the US. To meet our objective, we compare RF cross-validated prediction accuracy using several combinations of explanatory variables. We also utilize variable importance measures and partial dependence plots to compare and contextualize how key variables interact with corn yield. Results suggest that RF predicts US corn yields well using a relatively small subset of climate variables along with year and geographical location (RMSE = 17.1 bushels/acre (1.2 tons/hectare)). Of note is the insensitivity of RF prediction accuracy when removing variables traditionally thought to be predictive of yield or variables flagged as important by RF variable importance measures. Understanding what variables are needed to accurately predict corn yields provides a template for applying machine learning approaches to estimate county-level yields for other US crops.

Suggested Citation

  • Britta L. Schumacher & Emily K. Burchfield & Brennan Bean & Matt A. Yost, 2023. "Leveraging Important Covariate Groups for Corn Yield Prediction," Agriculture, MDPI, vol. 13(3), pages 1-18, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:618-:d:1087481
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    References listed on IDEAS

    as
    1. Emily Burchfield & Neil Matthews-Pennanen & Justin Schoof & Christopher Lant, 2020. "Changing yields in the Central United States under climate and technological change," Climatic Change, Springer, vol. 159(3), pages 329-346, April.
    2. Auffhammer, Maximilian & Schlenker, Wolfram, 2014. "Empirical studies on agricultural impacts and adaptation," Energy Economics, Elsevier, vol. 46(C), pages 555-561.
    3. Bigelow, Daniel & Borchers, Allison, 2017. "Major Uses of Land in the United States, 2012," Economic Information Bulletin 263079, United States Department of Agriculture, Economic Research Service.
    4. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    5. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    6. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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