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Abstract

Growing econometric and statistical evidence points to high temperature as the main driver of large negative effects of climate change on US agriculture. This literature also suggests a limited role for precipitation in overall impacts. This paper shows this finding stems from the widespread use of calendar precipitation variables, which poorly represent water availability for rainfed crops. I rely on a state-of-the art dataset with very high spatial (14km) and temporal (1h) resolution to develop a statistical model and unpack the effects of temperature and drought stress and analyze their interactions. Using a 31-year panel of corn yields covering 70% of US production, I account for nonlinear effects of soil moisture with varying effects throughout the growing season, in addition to nonlinear temperature effects. I show that yield is highly sensitive to soil moisture toward the middle of the season around flowering time. Results show that omission of soil moisture leads to overestimation of the detrimental effects of temperature by 30%. Because climate change affects intra-seasonal soil moisture and temperature patterns differently, this omission also leads to very different impacts on US corn yields, with a much greater role for water resources in overall impacts. Under the medium warming scenario (RCP6), models omitting soil moisture overestimate yield impacts by almost 100%. The approach shows a more complete understanding that climate change impacts on agriculture are likely to be driven by both heat and drought stresses, and that their relative role can vary depending on the climate change scenario and farmer ability to adapt.

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