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Environmental and Resource Economics

, Volume 73, Issue 2, pp 485–513 | Cite as

Heat in the Heartland: Crop Yield and Coverage Response to Climate Change Along the Mississippi River

  • Lunyu XieEmail author
  • Sarah M. Lewis
  • Maximilian Auffhammer
  • Peter Berck
Article

Abstract

Farmers may adapt to climate change by substituting away from the crops most severely affected. In this paper we estimate the substitution caused by a moderate change in climate in the US Midwest. We pair a 10-year panel of satellite-based crop coverage with spatially explicit soil data and a fine-scale weather data set. Combining a proportion type model with local regressions, we simultaneously address the econometric issues of proportion dependent variables and spatial correlation of unobserved factors. We find the change in expected crop coverage and then we link those changes to the expected changes from an estimated climate dependent yield equation. Ceteris paribus, we find that climate induced changes in yield are offset by land coverage changes for rice and cotton but they are strongly amplified for corn and soy.

Keywords

Adaptation Climate change Crop choice Yield Production 

JEL Classification

Q15 Q54 

Notes

Acknowledgements

We are grateful to Michael Roberts and Wolfram Schlenker for sharing both their weather data and expertise. The remaining errors are those of the authors.

Funding

This project was funded by the Environmental Biosciences Institute at Berkeley and Illinois.

References

  1. Askari H, Cummings JT (1977) Estimating agricultural supply response with the nerlove model: a survey. Int Econ Rev 18(2):257–292CrossRefGoogle Scholar
  2. Baskerville G, Emin P (1969) Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50:514–517CrossRefGoogle Scholar
  3. Berry ST (1994) Estimating discrete-choice models of product differentiation. RAND J Econ 25:242–262CrossRefGoogle Scholar
  4. Braulke M (1982) A note on the nerlove model of agricultural supply response. Int Econ Rev 23(1):241–244CrossRefGoogle Scholar
  5. Burke M, Emerick K (2016) Adaptation to climate change: evidence from US agriculture. Am Econ J Econ Policy 8(3):106–140CrossRefGoogle Scholar
  6. Chavas J-P, Holt MT (1990) Acreage decisions under risk: the case of corn and soybeans. Am J Agric Econ 72(3):529–538CrossRefGoogle Scholar
  7. Chen S, Chen X, Xu J (2016) Impacts of climate change on agriculture: evidence from China. J Environ Econ Manag 76(8):105–124CrossRefGoogle Scholar
  8. Choi J-S, Helmberger PG (1993) How sensitive are crop yields to price changes and farm programs? J Agric Appl Econ 25:237–244CrossRefGoogle Scholar
  9. Hausman C (2012) Biofuels and land use change: sugarcane and soybean acreage response in Brazil. Environ Resour Econ 51(2):163–187CrossRefGoogle Scholar
  10. Hornbeck R (2012) The enduring impact of the American dust bowl: short-and long-run adjustments to environmental catastrophe. Am Econ Rev 102(4):1477–1507CrossRefGoogle Scholar
  11. Huang H, Khanna M (2010) An econometric analysis of US crop yield and cropland acreage: implications for the impact of climate change. Denver, Colorado, pp 25–27Google Scholar
  12. Just RE (1974) An investigation of the importance of risk in farmers’ decisions. Am J Agric Econ 56(1):14–25CrossRefGoogle Scholar
  13. Lichtenberg E (1989) Land quality, irrigation development, and cropping patterns in the northern high plains. Am J Agric Econ 71(1):187–194CrossRefGoogle Scholar
  14. Lin W, Dismukes R (2007) Supply response under risk: implications for counter-cyclical payments’ production impact. Appl Econ Perspect Policy 29(1):64–86Google Scholar
  15. Lobell DB, Banziger M, Magorokosho C, Vivek B (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat Clim Change 1(1):42–45CrossRefGoogle Scholar
  16. Mueller R, Seffrin R (2006) New methods and satellites: a program update on the NASS cropland data layer acreage program. In: Remote sensing support to crop yield forecast and area estimates, ISPRS archives, vol 36, no. 8, p W48Google Scholar
  17. Nerlove M (1956) Estimates of the elasticities of supply of selected agricultural commodities. J Farm Econ 38(2):496–509CrossRefGoogle Scholar
  18. Nerlove M, Bessler DA (2001) Expectations, information and dynamics. Handb Agric Econ 1:155–206CrossRefGoogle Scholar
  19. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci 106(37):15594–15598CrossRefGoogle Scholar
  20. Searchinger T et al (2008) Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319(5867):1238–1240CrossRefGoogle Scholar
  21. Wu J, Segerson K (1995) The impact of policies and land characteristics on potential groundwater pollution in Wisconsin. Am J Agric Econ 77(4):1033–1047CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Lunyu Xie
    • 1
    Email author
  • Sarah M. Lewis
    • 2
  • Maximilian Auffhammer
    • 3
  • Peter Berck
    • 3
  1. 1.Department of Energy Economics, School of Economics, Renmin University of ChinaBeijingChina
  2. 2.Envision Geo LLCOakvilleUSA
  3. 3.Department of Agricultural and Resource EconomicsUniversity of CaliforniaBerkeleyUSA

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