CORN YIELD DYNAMICS AND WEATHER SHOCKS: CLIMATE CHANGE IMPLICATIONS FOR THE U.S. CORN BELT

Abstract

Unmitigated climate change is projected to reduce average U.S. corn yields. Many of these projections rely on stable model coefficients, which may not hold in practice. We investigate the assumption of spatio-temporal stability of weather coefficients in regressions of U.S. Corn Belt yields during 1950–2014 and examine their implications for climate change projections. We reject the null hypothesis of time-constant weather shock coefficients for roughly one-third of sample counties and find that smoothly evolving parameters contribute to near-term prediction deterioration. We next estimate a dynamic econometric model that accounts for nonstationarities, outliers, and smoothly evolving parameters. Though average yields are projected to decline through 2050, there is considerable uncertainty when bootstrapping from a set of 30 downscaled climate models for both the RCP 4.5 and RCP 8.5 scenarios. Long-tailed distributions suggest a small number of counties could experience only small short-term yield decreases. Projections from a static analogue of our dynamic model have distributions with thinner tails, less dispersion, and higher means.