Event Detail

Ruiqing Miao

Geography of Climate Change Adaptation in U.S. Agriculture

Presented by:
Ruiqing Miao
Agricultural Economics and Rural Sociology
Auburn University

Friday, December 9, 2022
12:00 pm-1:15 pm
Taylor-Hibbard Seminar Room (Rm103)

Natural variation in resource endowments, climate, and geo-topographic characteristics across locations suggests non-trivial spatial heterogeneity in agricultural adaptation to a warming climate. We examine spatial heterogeneity in heat sensitivity of U.S. agriculture and its spatially in homogeneous adaptation thereto over time. Using semi-parametric methods, we model the long-run relationship between crop yields and climate while explicitly allowing its parameters to vary across geography and over time, which enables us to control for local heterogeneity that no longer needs to be restricted to be additively separable (i.e., climate-neutral), as typically done by means of county fixed effects, and to accommodate a naturally occurring spatial clustering therein. Our treatment of cross-county heterogeneity is more flexible as it can be multiplicative/non-neutral and mediate local effects of climate on agriculture. We find that the overall yield sensitivity of U.S. corn, soybeans, and cotton to overheat decreased respectively by 73%, 49%, and 51% over 1958–2019, but with significant spatial heterogeneity. For corn (soybeans, cotton), 54% (74.9%, 23%) of producing counties experienced a decrease in sensitivity to overheat, 12% (2.2%, 77%) experienced an increase, and 34% (22.9%, 0%) no change. For corn and soybeans, the adaptation mainly occurred in the Northern Great Plains and Upper Mid- west, and we do not find evidence that crop insurance or the genetically-engineered crop adoption is significantly associated with this adaptation. Further, our preferred model predicts 7.3–12.6% corn yield losses, 11.8–47.9% soybean yield losses, and 4.7–23.9% cotton yield gains by 2048–2052, which are notably smaller than projections from models neglecting spatial heterogeneity. JINGFANG ZHANG, University of Kentucky; EMIR MALIKOV, University of Nevada, Las Vegas; RUIQING MIAO, Auburn University; PRASENJIT GHOSH, University of Southern Indiana

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