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Scientists agree that climate change is having a significant impact on U.S. agriculture production. It is difficult to devise mitigation strategies because estimates can vary widely. Two University of Illinois agricultural economists examine how the choice of statistical methodology affects climate study results. They propose a more precise and specific approach to data analysis.
The results of climate predictions on U.S. agriculture are quite different. Some scientists believe it will have a positive effect on the nation over the long-term, while others report it to be detrimental," said Sandy Dall'Erba, co-author of the study and professor in the Department of Agricultural and Consumer Economics and director of U of I's Center for Climate, Regional, Environmental and Trade Economics.
Chang Cai, a doctoral student at ACE, and Dall'Erba, the paper's principal author, review all of the academic literature on the effects of climate change on U.S. farmland revenues and values, focusing on each U.S. County. Researchers say that the county-level scale is more accurate and important for regional policymakers, as they can make county-specific decisions where climate change is expected to be a serious threat.
"There are not many commodities that are produced in the United States. Dall'Erba points out that the only way to truly understand the relationship between climate change and agriculture is to look at economic impacts, rather than focusing solely on one crop or livestock. "Averaging agricultural outcomes allows us compare the situation in every U.S. county."
Researchers examine how locations are grouped in studies for analysis and how these groups affect the results.
According to Dall'Erba, "Early research would have one degree Celsius or Fahrenheit more in Arizona to have the exact same marginal impact on agriculture as one degree in Illinois."
Recent studies have attempted to distinguish results and estimate effects based upon local conditions. One popular method is to divide the United States into rainfed and irrigated areas. This would roughly be done along a west-east partition at the 100th Meridian. Although Arizona and Illinois would be in different groups, Arizona would still be expected and likely to experience similar marginal weather effects.
Dall'Erba also used this method in his own research. Another approach is to compare low- and high-elevation areas. A third approach is grouping locations along state lines. Dall'Erba states that researchers often use this method because it is easy to estimate and relevant to policy measures. However, it doesn't produce precise results since state lines are rarely consistent with atmospheric characteristics.
All these approaches have their merits, but they all have flaws.
Cai says that the results of predicting climate change's future impacts are not always consistent. "We also discovered that no one group is better at predicting the future outcome than any other."
Cai and Dall'Erba suggest using one of three new statistical methods that provide county-specific climate impact estimates. These methods are based on data and do not assume the appearance of the groups. These methods use data to analyze the data and determine who belongs to which group. These scientific methods, known as C-Lasso and causal forest algorithm, and geographicly weighted regressions have been used in other fields such as energy conservation and labor market analysis, but have never been used in climate research.
"The data speaks for itself; you don't have to dictate anything to your model. Once you begin making decisions about how to group observations, your results will already be in one direction. The next step is to defend your decision. "We hope future researchers will be less cautious with a priori decisions," Dall'Erba says.
These new methods are being applied by Cai and Dall'Erba to an extensive analysis of climate change and U.S. agriculture production. They plan to present their findings in a paper and guide the implementation of climate change adaptation strategies that are place-tailored.
The paper "On the Evaluation Of Heterogenous Climate Change Effects on U.S. Agriculture: Does Group Member Matter?" Climatic Change published the paper.
Further research: Agricultural trade between the US states can reduce economic impacts from climate change
Chang Cai and colleagues, More information: Climatic Change (2021), Chang Cai et all, On the evaluation heterogeneous impacts of climate change on US agriculture: Does group membership matter? Information from Climatic Change Chang C. et al., On the evaluation heterogeneous climate changes impacts on US agriculture: Does group membership matter? (2021). DOI: 10.1007/s10584-021-03154-5
