Interest in agricultural supply response increased dramatically after model predictions of land use change were used to estimate biofuel greenhouse gas emissions. The models divide crop response into higher yield—the intensive margin—or more land—the extensive margin. Input adjustments are assumed to drive yield response. But most yield changes result from adoption of technology. Regulators and modelers assume that increased harvested area implies conversion of land from forest or pasture to crops. With the notable exception of African countries, recent expansion of harvested area is due to more intensive use of existing agricultural land through multiple cropping and technology improvement. A lack of response at the extensive margin is consistent with inelastic estimates of land use change estimated by using time-series data. Option value is one reason for this inelastic response. Predictions of land use change based on cross-section data imply much higher land use elasticities than are consistent with recent data.


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Literature Cited

  1. Antle JM, Park SK. 1986. Economics of IPM in processing tomatoes. Calif. Agric. 40:31–32 [Google Scholar]
  2. Babcock BA, Blackmer AM. 1992. The value of reducing temporal input nonuniformities. J. Agric. Res. Econ. 17:335–47 [Google Scholar]
  3. Babcock BA, Iqbal Z. 2014. Using recent land use changes to validate land use change models. Staff Rep. 14-SR 109, Cent. Agric. Rural Dev., Iowa State Univ
  4. Barr KJ, Babcock BA, Carriquiry MC, Nassar AM, Harfuch L. 2011. Agricultural land elasticities in the United States and Brazil. Appl. Econ. Perspect. Policy 33:449–62 [Google Scholar]
  5. Berry S. 2011. Biofuels policy and the empirical inputs to the GTAP models. Indep. Rev. Rep., Calif. Air Resour. Board. http://www.arb.ca.gov/fuels/lcfs/workgroups/ewg/010511-berry-rpt.pdf
  6. Burney JA, Davis SJ, Lobell DB. 2010. Greenhouse gas mitigation by agricultural intensification. PNAS 107:12052–57 [Google Scholar]
  7. Carsky RJ, Singh BB, Oyewole B. 2001. Contribution of early season cowpea to late season maize in the savanna zone of West Africa. Biol. Agric. Hortic. 18:303–15 [Google Scholar]
  8. Caswell MF, Zilberman D. 1985. The choices of irrigation technologies in California. Am. J. Agric. Econ. 67:223–34 [Google Scholar]
  9. Cerrato ME, Blackmer AM. 1990. Comparison of models for comparing corn yield response to nitrogen fertilizer. Agron. J. 82:138–43 [Google Scholar]
  10. Cochrane W. 1958. Farm Prices: Myth and Reality. Minneapolis: Univ. Minn. Press
  11. Feder G, Just RE, Zilberman D. 1985. Adoption of agricultural innovations in developing countries: a survey. Econ. Dev. Cult. Change 33:255–98 [Google Scholar]
  12. Feinerman E, Letey J, Vaux HJ Jr. 1983. The economics of irrigation with nonuniform infiltration. Water Resour. Res. 19:1410–14 [Google Scholar]
  13. Fernandez-Cornejo J, Nehring R, Sinha E, Grube A, Vialou A. 2009. Assessing recent trends in pesticide use in US agriculture. Presented at Annu. Meet. Agric. Appl. Econ. Assoc., Milwaukee, July 26–28
  14. Foster A, Rosenzweig M. 2010. Microeconomics of technology adoption. Annu. Rev. Econ. 2:395–424 [Google Scholar]
  15. Foster WE, Babcock BA. 1993. Commodity policy, price incentives, and the growth in per-acre yields. J. Agric. Appl. Econ. 25:253–65 [Google Scholar]
  16. Fuglie KO. 2010. Accelerated productivity growth offsets decline in resource expansion in global agriculture. Amber Waves 8:46–51 [Google Scholar]
  17. Gardner B, Hardie I, Parks P. 2010. United States farm commodity programs and land use. Am. J. Agric. Econ. 92:803–20 [Google Scholar]
  18. Griliches Z. 1957. Hybrid corn: an exploration in the economics of technological change. Econometrica 25:501–22 [Google Scholar]
  19. Hayes D, Babcock B, Fabiosa J, Tokgoz S, Elobeid A et al. 2009. Biofuels: potential production capacity, effects on grain and livestock sectors, and implications for food prices and consumers. J. Agric. Appl. Econ. 41:465–91 [Google Scholar]
  20. Heady EO, Pesek J. 1954. A fertilizer production surface with specification of economic optima for corn grown on calcareous Ida silt loam. Am. J. Agric. Econ. 36:466–82 [Google Scholar]
  21. Hertel TW, Golub AA, Jones AD, O’Hare M, Plevin RJ, Kammen DM. 2010. Effects of US maize ethanol on global land use and greenhouse gas emission: estimating market mediated responses. BioScience 60:223–31 [Google Scholar]
  22. Hossain M, Pingali P. 1998. Rice research, technological progress, and impact on productivity and poverty. Impact of Rice Research Pingali P, Hossain M. 1–26 Manila: Int. Rice Res. Inst [Google Scholar]
  23. Jarvis LS. 1981. Predicting the diffusion of improved pasture in Uruguay. Am. J. Agric. Econ. 63:67–86 [Google Scholar]
  24. Keeney R, Hertel TW. 2009. The indirect land use impacts of United States biofuel policies: the importance of acreage, yield and bilateral trade responses. Am. J. Agric. Econ. 91:895–909 [Google Scholar]
  25. Laborde D. 2011. Assessing the land use change consequences of European biofuel policies. Final Subcontract. Rep., Int. Food Policy Inst., Dir.-Gen. Trade Eur. Comm., Brussels
  26. Langeveld JWA, Dixon J, van Keulen H, Foluke Quist-Wessel PM. 2013. Analyzing the effect of biofuel expansion on land use in major producing countries: evidence of increased multiple cropping. Biofuels Bioprod. Biorefin. 8:49–58 [Google Scholar]
  27. Lichtenberg E, Zilberman D. 1986. The econometrics of damage control: why specification matters. Am. J. Agric. Econ. 68:261–73 [Google Scholar]
  28. Lubowski RN, Plantinga AJ, Stavins RN. 2008. What drives land use change in the United States? A national analysis of landowner decision. Presented at 13th Coalit. Theory Netw. Worksh., FEEM, Venice, Italy, Jan. 24–25
  29. Menz KM, Pardey P. 1983. Technology and US corn yields: plateaus and price responsiveness. Am. J. Agric. Econ. 65:558–62 [Google Scholar]
  30. Nogues JJ. 2011. Agricultural export barriers and domestic prices: Argentina in the last decade. Indep. Rep., FAO
  31. OECD-FAO 2014. Feeding India: prospects and challenges in the next decade. In OECD-FAO Agricultural Outlook 2014, pp. 63–106. Paris/Rome: OECD/FAO
  32. Plantinga AJ, Mauldin T, Miller DJ. 1999. An econometric analysis of the costs of sequestering carbon in forests. Am. J. Agric. Econ. 81:812–24 [Google Scholar]
  33. Roberts MJ, Schlenker W. 2013. Identifying supply and demand elasticities of agricultural commodities: implications for the US ethanol mandate. Am. Econ. Rev. 103:2265–95 [Google Scholar]
  34. Ruttan V. 1977. The green revolution. Int. Dev. Rev. 19:16–23 [Google Scholar]
  35. Schatzki T. 2003. Options, uncertainty, and sunk costs: an empirical analysis of land use change. J. Environ. Econ. Manag. 46:86–105 [Google Scholar]
  36. Scott PT. 2013. Essays on the estimation of food supply and demand. PhD Diss., Dep. Econ., Yale Univ
  37. Song F, Zhao J, Swinton SM. 2011. Switching to perennial energy crops under uncertainty and costly reversibility. Am. J. Agric. Econ. 93:768–83 [Google Scholar]
  38. Stavins RN, Jaffe AB. 1990. Unintended impacts of public investments on private decisions: the depletion of forested wetlands. Am. Econ. Rev. 90:337–52 [Google Scholar]
  39. Taheripour F, Tyner WE. 2013. Biofuels and land use change: applying recent evidence to model estimates. Appl. Sci. 3:14–38 [Google Scholar]
  40. USDA (US Dep. Agric.) 2014. Fertilizer use and price. Econ. Res. Serv. Data, updated July 2013, Washington, DC. http://www.ers.usda.gov/data-products/fertilizer-use-and-price.aspx
  41. Wu JJ, Segerson K. 1995. The impact of policies and land characteristics on potential groundwater pollution in Wisconsin. Am. J. Agric. Econ. 77:1033–47 [Google Scholar]

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