Abstract
This paper measures the influence of climate normals (average long-term surface wetness and temperature) and interannual climate variance on farms in the United States and Brazil using satellite data. The paper finds that just climate normals or just climate variance variables can explain both net revenues and how much land is used for cropland. However, because they are correlated with each other, it is important to include both normals and variance in the same statistical model to get accurate measures of their individual contribution to farm outcomes. In general, higher climate variance increases the probability that land is used for cropland in both countries and higher temperatures reduce both cropland and land values. Other annual effects were not consistent across the two countries.
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References
Adams R, McCarl B (2001) Agriculture: agronomic–economic analysis. In Mendelsohn R (ed) Global warming and the American economy. Edward Elgar, Cheltenham, UK
Adams R, Glyer D, McCarl B (1989) The economic effects of climate change in US agriculture: a preliminary assessment. In Tirpak D, Smith J (eds) The potential effects of global climate change on the United States: report to congress (EPA-230-05-89-050). US Environmental Protection Agency, Washington DC
Adams R, McCarl B, Segerson K, Rosenzweig C, Bryant K, Dixon B, Conner R, Evenson R, Ojima D (1999) The economic effects of climate change on US agriculture. In Mendelsohn R, Neumann J (eds) The impact of climate change on the economy of the United States. Cambridge University Press, Cambridge, UK
Basist AN, Peterson N, Peterson T, Williams C (1998) Using the Special Sensor Microwave Imager to monitor land surface, wetness, and snow cover. J Appl Meteor 37:888–911
Basist A, Williams C Jr, Grody N, Ross TE, Shen S, Chang A, Ferraro R, Menne M (2001) Using the Special Sensor Microwave Imager to monitor surface wetness. J Hydrometeorol 2:297–308
Cline W (1996) The impact of global warming on agriculture: comment. Am Econ Rev 86:1309–1312
Darwin R (1999) Comment on the impact of global warming on Agriculture: a Ricardian analysis. Am Econ Rev 89:1049–1052
Ferraro RR, Weng F, Grody N, Basist A (1996) An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, sea ice derived from SSM/I measurements. Bull Am Meteorol Soc 77:891–905
Grody NC, Basist A (1996) Global identification of snowcover using SSM/I measurements. IEEE Trans Geosci Remote Sens 34:237–249
Houghton J, Ding Y, Griggs D, Noguer M, Van der Linden P, Dai X, Maskell K, Johnson C (eds) (2001) Climate change 2001: the scientific basis. Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge
McFarland JM, Miller R, Neale C (1990) Land surface temperature derived from the SSM/I passive microwave brightness temperatures. IEEE Trans Geosci Remote Sens 28:839–845
Mearns L, Rosenzweig C, Goldberg R (1997) Mean and variance change in climate scenarios: methods, agricultural applications, and measure of uncertainty. Clim Change 35:367–396
Mendelsohn R, Dinar A (2003) Climate, water, and agriculture. Land Econ 79:328–341
Mendelsohn R, Nordhaus W (1999) Reply to Darwin. Am Econ Rev 89:1053–1055
Mendelsohn R, Nordhaus W, Shaw D (1994) The impact of global warming on agriculture: a Ricardian analysis. Am Econ Rev 84:753–771
Mendelsohn R, Nordhaus W, Shaw D (1996) Climate impacts on aggregate farm values: Accounting for adaptation. Agric For Meteorol 80:55–67
Mendelsohn R, Dinar A, Sanghi A (2001) The effect of development on the climate sensitivity of agriculture. Environ Dev Econ 6:85–101
Neale CMU, McFarland M, Chang K (1990) Land-surface-type classification using microwave brightness temperatures from the Special Sensor Microwave/Imager. IEEE Trans Geosci Remote Sens 28:829–837
Njoku EG (1994). Surface temperature estimation over land using satellite microwave radiometry. Remote sensing of land–atmosphere interactions. St. Laury, France, pp 509–530
Prigent C, Rossow W, Matthews E (1997). Microwave land surface emissivities estimated from SSM/I. J Geophys Res 102:21867–21890
Reilly J, Baethgen W, Chege F, van de Geijn S, Enda L, Iglesias A, Kenny G, Patterson D, Rogasik J, Rotter R, Rosenzweig C, Sombroek W, Westbrook J (1996) Agriculture in a changing climate: impacts and adaptations. In Watson R, Zinyowera M, Moss R, Dokken D (eds) Climate change 1995: intergovernmental panel on climate change impacts, adaptations, and mitigation of climate change. Cambridge University Press, Cambridge, UK
Rosenzweig C, Parry M (1994) Potential impact of climate change on world food supply. Nature 367:133–138
Schneider S (1997) Integrated assessment modelling of global climate change: transparent rational tool for policy making or opaque screen hiding value-laden assumptions. Environ Model Assess 2:229–248
Schlenker W, Hanemann M, Fischer A (2005) Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am Econ Rev 95(1):395–406
United States Department of Agriculture (USDA) (1997) 1997 Census of agriculture (CD-ROM set). National Agricultural Statistics Service, Washington, DC
Vinnikov KY, Robock A, Qui S, Entin J, Owe M, Choudhury B, Hollinger S, Njoku G (1999) Satellite remote sensing of surface wetness in Illinois, United States. J Geophys Res 104:4145–4165
Weng F, Grody N (1998) Physical retrieval of land surface temperature using the Special Sensor Microwave Imager. J Geophys Res 103:8839–8848
Williams C, Basist A, Peterson T, Grody N (1999) Calibration and validation of land surface temperature anomalies derived from the SSM/I. Bull Am Meteorol Soc
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Mendelsohn, R., Basist, A., Dinar, A. et al. What explains agricultural performance: climate normals or climate variance?. Climatic Change 81, 85–99 (2007). https://doi.org/10.1007/s10584-006-9186-3
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DOI: https://doi.org/10.1007/s10584-006-9186-3