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Climate Change and Agricultural Production

Paper Session

Sunday, Jan. 9, 2022 3:45 PM - 5:45 PM (EST)

Hosted By: Agricultural and Applied Economics Association
  • Chair: Zhiyun Li, Cornell University

On the Timing of Relevant Weather Conditions in Agriculture

Zhiyun Li
,
Cornell University
Ariel Ortiz-Bobea
,
Cornell University

Abstract

A growing literature is analyzing the effects of weather fluctuations on agricultural outcomes.
When estimating these weather-agriculture linkages, researchers often make ex ante arbitrary
choices about the “season”, the time period over which weather conditions are believed to be
relevant to the agricultural outcome. In this paper, we propose a tractable approach to identify the timing of relevant weather conditions in such econometric models. The approach consists in a grid search of models based on all possible calendar periods within the year. In simulations, we find our approach is effective at recovering the “true season”, sometimes even when the selected weather variables do not match the data generating process. We also find that imposing an incorrect season introduces non-classical measurement error which biases estimates of weather impacts in either direction. We apply our approach to a panel of state-level agricultural TFP growth rates. We find considerable heterogeneity in seasonality across regions. Importantly, we find that imposing arbitrary seasons can bias weather effects in either direction, in line with our simulations.

Climate Change Impacts on Pastoral-Farm Profits and Productivity in New Zealand

Kendon Bell
,
Manaaki Whenua-Landcare Research
Simone Pieralli
,
Massey University

Abstract

We estimate the impact of climate change on the profits and productivity of New Zealand livestock farms. Assuming farmers maximize profits, we apply methods from climate econometrics, using year-to-year variation in weather, to identify the present and future impacts of climate change on productivity and profitability. Using data from approximately 70,000 tax returns and fine-scale weather data, we show that an extra day of very dry soil causes historically a loss of approximately one day's worth in annual operating profits. These results translate into relevant additional impacts of future climate change, with dairy farms projected to have 20% lower profits and sheep/beef farms projected to have 7% lower profits by the end of the century. Historical direct effects of temperature are more uncertain but potentially indicate large reductions in future profits due to climate change for sheep/beef farms, which tend to have few shade trees and not much available drinking water.

A Productivity Indicator for Adaptation to Climate Change

Moriah Bostian
,
Lewis and Clark College
Rolf Fare
,
Oregon State University
Shawna Grosskopf
,
Oregon State University
Bradley Barnhart
,
National Council for Air and Stream Improvement, Inc.
Sophia Lochner
,
University of Virginia

Abstract

This study draws on economic index theory to construct a new indicator for adaptation to changing environmental conditions, most notably climate change, which may shift the production
technology over time. Such environmental shifts are largely exogenous to firm decision making,
for instance investments in research and development, which may also lead to technology change.
Few existing measures of total factor productivity (TFP) make this distinction, between exogenous environmental shifts and shifts due to firm decision making or innovation. We introduce a nonparametric Luenberger productivity indicator for adaptation, which allows for decomposition of standard technology and efficiency change measures into both environmental and production components. We apply this framework to U.S. agricultural production in recent decades, working with data at the county level for corn and soybean production and key climate conditions. We consider both short term weather variation and longer-term climate change. We also match the production and climate data to estimates of Nitrogen loading over time, creating a unique data set that allows us to incorporate water quality into the adaptation indicator.
Discussant(s)
Spiro Stefanou
,
USDA Economic Research Service
JEL Classifications
  • Q5 - Environmental Economics