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Adaptation to climate change: changes in farmland use and stocking rate in the U.S.

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Abstract

This paper examines possible adaptations to climate change in terms of pasture and crop land use and stocking rate in the United States (U.S.). Using Agricultural Census and climate data in a statistical model, we find that as temperature and precipitation increases agricultural commodity producers respond by reducing crop land and increasing pasture land. In addition, cattle stocking rate decreases as the summer Temperature-humidity Index (THI) increases and summer precipitation decreases. Using the statistical model with climate data from four General Circulation Models (GCMs), we project that land use shifts from cropping to grazing and the stocking rate declines, and these adaptations are more pronounced in the central and the southeast regions of the U.S. Controlling for other farm production variables, crop land decreases by 6 % and pasture land increases by 33 % from the baseline. Correspondingly, the associated economic impact due to adaptation is around −14 and 29 million dollars to crop producers and pasture producers by the end of this century, respectively. The national and regional results have implications for farm programs and subsidy policies.

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Notes

  1. The Census of Agriculture reports come out each 5 years, so 2007 is the most recent report available.

  2. See Pratt and Rasmussen (2001) for definition and calculation of the stocking rate for each animal.

  3. For example, when the dependent variable in our model is from 1987, we use the seasonal averaged climate over 1985–1987, and similarly with the other four agricultural census data.

  4. \( THI = 0.8*Ta + \left( {RH/100} \right)*\left( {Ta - 14.3} \right) + 46.4 \), where\( RH = \left( {6.1121} \right)*{e^{ \wedge }}\left( {18.678 - Ta/234.5} \right)\left( {Ta/\left( {257.14 + Ta} \right)} \right) \) and Ta = temperature in °C and RH is the relative humidity. When we construct the THI index, we convert our temperature data in to Celsius degree.

  5. This assumption is strong. However, we use it to evaluate economic values as a reference level.

References

  • Adams RM, Rosenzweig C, Peart RM, Ritchie JT, McCall BA, Glyer JD, Curry RB, Jones JW, Boote KJ, Allen LH (1990) Global climate change and U.S. agriculture. Nature 345:219–224. doi:10.1038/345219a0

    Article  Google Scholar 

  • Bohmanova J, Misztal I, Cole JB (2007) Temperature-humidity indices as indicators of milk production losses due to heat stress. J Dairy Sci 90:1947–1956

    Article  Google Scholar 

  • U.S. Climate Change Science Program (2008) The effects of climate change on agriculture, land resources, water resources, and biodiversity in the United States. A report by the U.S. climate change science program and the subcommittee on global change research

  • Dikmen S, Hansen PJ (2009) Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment? J Dairy Sci 92:109–116

    Article  Google Scholar 

  • Gourieroux C, Monfort A, Trognon A (1984) Pseudo maximum likelihood methods: theory. Econometrica 52(3):681–700

    Article  Google Scholar 

  • Hahn GL, Brown-Brand T, Eigenberg RA, Gaughan JB, Mader TL, and Nienaber JA (2005) Climate change and livestock: challenges and adaptive responses of animals and production systems. Paper presented at the 17th international conference on biometeorology, Garmisch-Partenkirchen, Bavaria, Germany

  • Herrero M, Thornton PK, Kruska R, Reid RS (2008) Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030. Agr Ecosyst Environ 126:122–137

    Article  Google Scholar 

  • Hoffmann I (2010) Climate change and the characterization, breeding and conservation of animal genetic resources. Anim Genet 41(s1):32–46

    Article  Google Scholar 

  • Holden NM, Brereton AJ (2002) An assessment of the potential impact of climate change on grass yield in Ireland over the next 100 years. Ir J Agr Food Res 41:213–226

    Google Scholar 

  • IPCC (2007a) Climate Change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL(eds) Cambridge University Press, Cambridge and New York

  • IPCC (2007b) Climate change 2007: impacts, adaptation and vulnerability. In: Canziani OF, Palutikof JP, van der Linden PJ and Hanson CE (eds) Cambridge University Press, Cambridge, and New York

  • Jones PG, Thornton PK (2009) Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change. Environ Sci Pol 12:427–437

    Article  Google Scholar 

  • Koch SF (2010) Fractional multinomial response models with an application to expenditure shares. Paper Prepared for Development Policy Research Unit Conference held in Johannesburg, South Africa, October 27–29

  • Mader TL, Frank KL, Harrington JA, Hahn G, Nienaber JA (2009) Potential climate change effects on warm-season livestock production in the Great Plains. Clim Chang 97:529–541

    Article  Google Scholar 

  • McCarl BA (2007) Adaptation options for agriculture, forestry and fisheries. http://unfccc.int/files/cooperation_and_support/financial_mechanism/application/pdf/mccarl.pdf. Cited 22 March 2012

  • Mendelsohn R, Dinar A (2009) Land use and climate change interactions. Ann Rev Resource Econom 1:309–332

    Article  Google Scholar 

  • Meyer TL, Adams DC, Klopfenstein TJ, Volesky JD, Stalker LA, Funston RN (2008) Estimating livestock forage demand: defining the Animal Unit (AU). Western section. Amer Anima Soc Anima Sci Proc 59:213–216

    Google Scholar 

  • Mullahy J (2010) Multivariate fractional regression estimation of econometric share models. NBER Working Paper No. 16354. http://www.nber.org/papers/w16354. Cited 22 March 2012

  • Mullahy J, Robert SA (2010) No time to lose: time constraints and physical activity in the production of health. Rev Econ Househ 8:409–432

    Article  Google Scholar 

  • Nakićenović N, Swart R (2000) Special report on emissions scenarios. Cambridge University Press, Cambridge

    Google Scholar 

  • Neilson R, Pitelka L, Solomon A, Nathan R, Midgley G et al (2005) Forecasting regional to global plant migration in response to climate change. Bioscience 55:749–759

    Article  Google Scholar 

  • Nienaber JA, Hahn GL (2007) Livestock production system management responses to thermal challenges. Int J Biometeorol 52:149–157

    Article  Google Scholar 

  • Papke LE, Wooldridge JM (1996) Econometric methods for fractional response variables with an application to 401(k) plan participation rates. J Appl Econ 11:619–632

    Article  Google Scholar 

  • Papke LE, Wooldridge JM (2008) Panel data methods for fractional response variables with an applicaiton to test pass rates. J Econ 145:121–133

    Google Scholar 

  • Pratt M, Rasmussen GA (2001) Determining your stocking rate. Utah State University Cooperative Extension. http://extension.usu.edu/files/publications/publication/NR_RM_04.pdf. Cited 22 March 2012

  • Ramalho EA, Ramalho JS, Murteira J (2011) Alternative estimating and testing empirical strategies for fractional regression models. J Econ Surv 25:19–68

    Article  Google Scholar 

  • Redfearn DD, Bidwell TG (2011) Stocking rate: the key to successful livestock production. Oklahoma Cooperative Extension Service PSS-2871. http://www.nrem.iastate.edu/research/patchburn/pubs/PSS-2871.pdf

  • Rose SK, McCarl BA (2008) Greenhouse gas emissions, stabilization and the inevitability of adaptation: challenges for U.S. Agriculture. Choices 23

  • Schlenker W, Roberts MJ (2006) Nonlinear effects of weather on corn yields. Rev Agric Econ 28:391–398

    Article  Google Scholar 

  • Schlenker W, Hanemann WM, Fisher AC (2005) Will U.S. Agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am Econ Rev 95:395–406

    Article  Google Scholar 

  • Schlenker W, Hanemann WM, Fisher AC (2006) The impact of global warming on U.S. Agriculture: an econometric analysis of optimal growing conditions. Rev Econ Stat 88:113–125

    Google Scholar 

  • Seo SN (2010) Is an integrated farm more resilient against climate change? A microeconometric analysis of portfolio diversification in African agriculture. Food Policy 35:32–40

    Google Scholar 

  • Seo SN, Mendelsohn R (2008a) Animal husbandry in Africa: climate change impacts and adaptations. Afr J Agric Res Econom 2:65–82

    Google Scholar 

  • Seo SN, Mendelsohn R (2008b) Measuring impacts and adaptations to climate change: a structural Ricardian model of African livestock management. Agric Econ 38:151–165

    Google Scholar 

  • Seo SN, Mendelsohn R, Dinar A, Kurukulasuriya P (2009) Adapting to climate change mosaically: an analysis of african livestock management by agro-ecological zones. The B.E. J Econ Anal Policy 2. doi:10.2202/1935-1682.1955

  • Seo SN, McCarl BA, Mendelsohn R (2010) From beef cattle to sheep under global warming? An analysis of adaptation by livestock species choice in South America. Ecol Econ 69:2486–2494

    Article  Google Scholar 

  • Ye X, Banerjee A, Pendyala RM, Pinjari AR (2005) Understanding travel time expenditures around the world: how low can travel go? Paper presented at the 84th annual meeting of the transportation research board. National Research Council, Washington, DC

    Google Scholar 

  • Zilberman D, Liu X, Roland-Holst D, Sunding D (2004) The economics of climate change in agriculture. Mitig Adapt Strateg Glob Chang 9:365–382

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Agricultural Economics, Texas A&M University, College Station, TX, 77840, USA

    Jianhong E. Mu & Bruce A. McCarl

  2. Western Geographic Science Center, U.S. Geological Survey, Menlo Park, CA, 94025, USA

    Anne M. Wein

Authors
  1. Jianhong E. Mu
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  2. Bruce A. McCarl
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  3. Anne M. Wein
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Correspondence to Jianhong E. Mu.

Appendix

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Table 6 Estimated coefficients from the FMLOGIT with and without regional effects
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Mu, J.E., McCarl, B.A. & Wein, A.M. Adaptation to climate change: changes in farmland use and stocking rate in the U.S.. Mitig Adapt Strateg Glob Change 18, 713–730 (2013). https://doi.org/10.1007/s11027-012-9384-4

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