Empirical estimation of weather-driven yield shocks using biophysical characteristics for U.S. rainfed and irrigated maize, soybeans, and winter wheat

Abstract

Agricultural yields are highly susceptible to changes in weather system patterns, including annual and sub-annual changes in temperature and precipitation. The impacts of future meteorological variable changes on crop yields have been widely studied in both empirical and process-based models. These changes in future yields can be used in economic models to adjust future crop yields or production functions to reflect the effects of changing weather conditions. This work presents an econometric approach that combines historical weather data with the biophysical growth cycles of maize, winter wheat, and soybean to predict the year-to-year weather-driven yield shocks for rainfed crops. Temperature and modeled soil moisture are taken as predictors, allowing testing of the fitted rainfed model’s ability to predict shocks to irrigated yields by assuming irrigation produces the yield-maximizing level of soil moisture. This approach enables prediction of the potential impacts of changing weather patterns on irrigated crops in areas that are currently primarily rainfed. We present the results of the empirical model, fitted with rainfed data; out-of-sample validation on irrigated crops; and projections of yield shocks under multiple future climate scenarios. Under a bias-corrected GFDL RCP8.5 scenario, this approach predicts the average of annual weather-induced yield shocks, relative to the average of 2006–2020 annual yield shocks, across U.S. counties in the 2040–2060 period of −17% and −13% for rainfed and irrigated maize, −19% and −18% for rainfed and irrigated soybean, and −4% and −2% for rainfed and irrigated winter wheat. Predicted changes in the 2070–2090 period are −30% and −29% for rainfed and irrigated maize, −33% for both rainfed and irrigated soybean, and −7% and −5%for rainfed and irrigated winter wheat. The annual yield shocks presented here will enable modeling of the economic consequences of extreme weather events and potential for irrigation to mitigate such events.