Moderated by Michael Roberts, University of Hawai’i at Mānoa

Extreme Weather Events and Agricultural Total Factor Productivity Growth (Paper 1)

Presentation by Wei Zhang, Virginia Tech

Studies of the impacts of climate change on agriculture have so far disproportionately focused on the effects of changes in average seasonal temperature and precipitation on crop yields and production. These studies are limited in terms of evaluating the overall agricultural performance under a changing climate. We use a dynamic panel data model to estimate the impacts of extreme weather events on agricultural TFP growth rate. Our preliminary estimates show that extreme weather events have on average a negative and statistically significant impact on agricultural TFP growth rate. The estimated impacts are all negative across different event types and are statistically significant for storms and droughts. The long-run effects of extreme weather events on TFP growth rate are slightly smaller. Our estimates are robust to an alternative instrumental variable approach and across different subsamples.

Past and Future Climatic Impacts on U.S. Agricultural Productivity Growth (Paper 2)

Presentation by Ruixin Yang, George Mason University

Authors: Ruixin Yang, Sun Ling Wang, Qian Liu, and Mengfei Xin

Climate change is influencing our natural and social worlds in many ways. Higher temperature and the changing precipitation pattern have also affected the agricultural productivity. This study investigated the climate impacts on U.S. agricultural productivity growth using the historical climate data derived from NClimDiv database, the projected climate data from USGS, a panel of total factor productivity estimates for 48 contiguous states from Wang et al. (2022), and yields of selected crops from NASS. We apply both stepwise linear regression (SLR) with nonlinear (quadratic) terms and nonlinear regression tree models in our model building. A commonly used training and validating procedure in machine learning was applied to choose the “optimal” models with tuned hyperparameters. The preliminary results show that the climate change generally would reduce the growth rates of TFP and other major crop yields. As expected, the impacts are not uniform spatially, and crop type changes could also remedy the negative climate impacts due to heterogeneous impacts observed in the tentative results.

Climate-induced Productivity Changes and Agricultural Trade Impacts (Paper 3)

Presentation by Maros Ivanic, USDA Economic Research Service 

Authors: Jayson Beckman and Maros Ivanic, USDA Economic Research Service

We analyze the implications of the forecasted changes in the climate for the shifts in the land types across countries, and the associated changes in yields due to the change in temperature and precipitations. We simulate the changes in the countries' agricultural yields in the GTAP model, to evaluate the implications of the climate change for future trade patterns, and the additional market impacts. We find that climate change is likely to have heterogenous impact on countries' agricultural production, resulting in some countries, such as the United States, to expand their exports, while other countries’ production shrinks.

Productivity and climate change in long-term scenarios of global agriculture (Paper 4)

Presentation by Ron Sands, USDA Economic Research Service

Long-term analysis of climate change impacts on agriculture requires use of climate, crop, and economic simulation models. The economic representation of agricultural productivity becomes important in at least two ways: future productivity growth may differ from historical patterns, and output from crop models does not translate directly into economic models. Retrospective analysis of productivity change should inform the structure and parameters used in long-term economic models. Crop models provide a point estimate of change in crop yield with climate change, but economic models allow substitution among inputs and crop yield is endogenous. Future research on climate change impacts on agriculture will benefit from comparing perspectives across econometric analysis, the structure of long-term economic simulation models, and the structure of crop simulation models.