Jonathan R. McFadden
US Department of Agriculture, Economic Research Service
John A. Miranowski
Iowa State University

Although prior research has identified the effects of climate change on crop yields, there has been little consideration of outliers, structural change, information decay, and model complexity. To incorporate these features and regional productivity heterogeneity, we estimate Bayesian dynamic regressions of corn and soybean yields for Iowa, Illinois, and Nebraska. Corn yield growth of 7-26% and soybean yield growth of up to 32% over 2011 averages are forecasted by 2031. We find asymmetries in the evolution of weather effects across states and crops. Current impacts of monthly growing-season temperature and precipitation differ greatly from impacts during 1970-1999. We also observe a shift in importance from July temperatures to August temperatures, as well as a shift from average precipitation to intense precipitation. The changing time paths of weather impacts and associated yield forecasts have key adaptation implications. In turn, these could affect the long-run sustainability of the Midwestern bioeconomy.

Key words: Agricultural yields, Bayesian dynamic models, bioeconomy, climate change.