Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting U.S. Maize Yield

Seasonal agricultural production forecasting is essential for agricultural supply chain and economic prediction. However, to what extent seasonal climate prediction and remote sensing observations can improve crop yield forecasting at regional scale remains unknown. Using a statistical seasonal forecasting framework for U.S. county‐level maize yield, we demonstrated that (1) incorporating satellite‐based enhanced vegetation index (EVI) significantly improved the yield forecasting performance, compared with other climate‐only models using monthly air temperature (T), precipitation (P), and vapor pressure deficit (VPD). (2) The bias‐corrected climate prediction from the Coupled Forecast System model version 2 (CFSv2) showed better yield forecasting performance than the historical climate ensemble. (3) Using the “T + P + VPD + EVI” model with climate prediction from bias‐corrected climate prediction from CFSv2 outperformed the yield forecast in the World Agricultural Supply and Demand Estimates reports released by the United States Department of Agriculture, with root‐mean‐square error of 4.37 bushels per acre (2.79% of multiyear averaged yield) by early August.