Leveraging Important Covariate Groups for Corn Yield Prediction

Britta L. Schumacher (), Emily K. Burchfield, Brennan Bean and Matt A. Yost
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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

Agriculture, 2023, vol. 13, issue 3, 1-18

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.

Keywords: yield modeling; corn; random forest; data infilling; yield prediction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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