The attention generated by the impact of the summer 2012 drought on the Corn Belt exemplifies how vital it is for the U.S. agricultural sector to understand climate change and how to mitigate and/or adapt to it. While there is little controversy about whether agriculture is sensitive to changes in climatic conditions, significant uncertainty exists with respect to future climate’s impact on agriculture. It is anticipated that some regions will be winners and others losers, but the location of each of the two groups is still unclear.
Furthermore, the debate on whether climate change will bring a net gain or a net loss for the U.S. agriculture as a whole is still ongoing (Mendelsohn et al., 1994; Deschênes and Greenstone, 2007). The source of uncertainty comes from the fact that production currently spans a variety of climate zones over all the lower 48 states as it occupies up to 42 percent of the U.S. territory and from the different assumptions – including on the role of farmers’ adaptation – that are used when assessing the impact of climate change.
Several contributions on the estimation process, conceptual framework and climate data quality can be brought to the literature in the hope of improvin current measurements. First, spatial externalities need to be explicitly accounted for, otherwise the estimates generated can be biased and inconsistent. Climate models intrinsically include the spatial dependence of variables such as temperature and moisture but the large majority of economic models applied to U.S. agriculture do not, which leads to biased estimates of the effects of climate on profits (or land value). The few exceptions include Polsky (2004), Seo (2008), Lippert et al. (2009), Hu (2012) and Dall’erba and Dominguez (2015). It is surprising considering that a snowpack and/or rainfall in one locality can provide water for irrigation elsewhere. Moreover, it is not reasonable to assume that all agricultural goods produced in one location will be consumed locally. For instance, 70 percent of Arizona-produced agricultural goods are for the rest of the U.S. or for export (IMPLAN, 2010).
Second, the associated issue of spatial heterogeneity has also been largely unexplored in this literature. Traditionally, if heterogeneity is not accounted for in an econometric model when it should, it leads to inconsistent estimates (Greene, 2000). A recent study by Dall’erba and Dominguez (2015) indicates how differences in elevation across South-Western U.S. counties lead to statistically significant differences in the impact of a set of climate conditions on farmland values. If heterogeneity is found for a limited sample such as theirs (n= 126), it is not difficult to assume that heterogeneity is also present in a larger sample made of the entire continental counties. Schlenker et al. (2006) distinguish high- vs. low-irrigated counties but irrigation, like elevation, is just one of the many sources of heterogeneity. Future research should explore them further as the ultimate goal of this exercise is to measure the degree of sensitivity to climate variation of every single region so that “place-tailored” adaptation strategies can be implemented.
Third, while farm subsidies represent approximately $10 billion a year and there is ample evidence that they affect U.S agriculture’s response to climate change (e.g. Lewandrowski and Brazee, 1993; Barnard et al., 1997), their role in the above literature needs further attention. On one hand, one could argue that the process of farmers adapting to climate change is counterbalanced by a government creating economic disincentives through measures such as target prices, disaster payments, and irrigation subsidies. Because increasing climate variability could result in an increasing occurrence of very good or very bad harvests, government programs could slow down farmers’ consideration of crop failures (Schimmelpfennig et al., 1996). On the other hand, it is obvious that government-funded research focusing on extending the temperature tolerances and/or reducing the water requirements of crops helps the agricultural sector prepare for climate change (National Research Council, 2002). As such, it seems important to investigate the independent role of each type of federally funded farm programs and to control for their endogenous nature to shed new light into the on-going adaptation process.
Additionally, impactful climate change literature could benefit from better integrating state-of-the-art climate data. Recent advances in the field of atmospheric sciences allow to dynamically downscale General Circulation Model (GCM) data to a much finer spatial resolution than ever before. Finer scale data allow researchers to avoid the statistical bias created by coarse measurements and to explicitly account for changes in the intensity and frequency of extreme events (cold or heat waves as well as extreme precipitation) at the local scale. Furthermore, dynamic downscaling can be applied to both past observations and projections as in Dall’erba and Dominguez (2015).
In conclusion, there has been a great deal of contributions and progress on the evaluation of the impact of climate change on U.S. agriculture over the last decades. However, the discussion can benefit from refinements on the statistical techniques used to calculate the sensitivity of each place to climate variation and from new insights into the marginal effect of farm programs on adaptation. In addition, a truly interdisciplinary approach of the problem needs to be adopted on a more regular basis so that climate change impact, like many other challenges due to human interactions with the environment, can rely on and benefit from the integration of several disciplines.
References:
Barnard, C.H., Whittaker, G., Westenbarger, D. and Ahearn, M. (1997) Evidence of capitalization of direct government payments into U.S. cropland values, American Journal of Agricultural Economics, 79, 5, 1642–1650.
Dall’erba S. and Dominguez F. (2015) The impact of climate change on agriculture in the South-West United States: the Ricardian approach revisited, Spatial Economic Analysis, 10, 4, 1-19.
Deschênes, O. and Greenstone, M. (2007) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather, The American Economic Review, 97, 1, 354–385.
Greene W. H. (2000). Econometric Analysis (4th edn.). Upper Saddle River, NJ: Prentice Hall International.
Hu Y. (2012) Climate Change, Agricultural Production Conditions and Agriculture Net Revenue: Empirical Evidences from Spatial Panel Model, in: Regional Development and Risk Management in the West of China, Zhao S. and Wang J. (Eds.), 7-11.
IMPLAN (2010) MIG, Inc., IMPLAN System (data and software), 1725 Tower Drive West, Suite 140, Stillwater, MN 55082 www.implan.com.
Lewandrowski, J.K. and Brazee, R.J. (1993) Farm programs and climate change, Climatic Change, 23, 1, 1-20.
Lippert, C., Krimly, T. and Aurbacher J. (2009) A Ricardian analysis of the impact of climate change on agriculture in Germany, Climatic Change, 97, 593–610.
Mendelsohn, R., Nordhaus, W.D. and Shaw, D. (1994) The impact of global warming on agriculture: a ricardian analysis, The American Economic Review, 84, 4, 753-771.
National Research Council (2002) Publicly Funded Agricultural Research and the Changing Structure of U.S. Agriculture, The National Academies Press.
Polsky, C. (2004) Putting space and time in ricardian climate change impact studies: the case of agriculture in the U.S. Great Plains, Annals of the Association of American Geographers, 94, 3, 549-564.
Schimmelpfennig D., Lewandrowski J., Reilly J., Tsigas M. and Parry I. (1996) Agricultural Adaptation to Climate Change: Issues of Long Range Sustainability, Agricultural Economic Report No. (AER740) 68 pp, June.
Schlenker, W., Hanemann, W.M. and Fisher, A.C. (2006) The impact of global warming on U.S. agriculture: an econometric analysis of optimal growing conditions, Review of Economics and Statistics, 88, 1, 113–125.
Seo, S.N. (2008) Assessing relative performance of econometric models in measuring the impact of climate change on agriculture using spatial autoregression, The Review of Regional Studies, 38, 2, 195–209.
Acknowledgement: This piece is based on a proposal recently funded by the US Department of Agriculture (USDA 2014-05728, Environmental and Natural Resource Economics; Aug. 2015-Aug. 2018) with the following Principal investigators: Dall’erba S., Department of Agricultural and Consumer Economics, Associate Professor; Regional Economics Applications Laboratory, Associate Director, University of Illinois; Dominguez F., Department of Atmospheric Sciences, Assistant Professor, University of Illinois; Frisvold G., Professor and Extension Specialist, Department of Agricultural and Resource Economics, University of Arizona.