1932

Abstract

This review seeks to survey, understand, and reconcile the widely divergent estimates of long-run global crop output, land use, and price projections in the current literature. We review the history of such projections and the different models and assumptions used in these exercises. We then introduce an analytical partial equilibrium model of the global crops sector, which provides a lens through which we can evaluate this previous work. The resulting decomposition of model responses into demand, extensive supply, and intensive supply elasticities offers important insights into the diversity of model parameterizations being employed by the existing models. Along with the methodology for implementing productivity growth, this helps explain some of the divergences in results. We employ a numerical version of the analytical model, which serves as an emulator of this entire class of models, to explore how uncertainties in the common underlying drivers and economic responses contribute to uncertain projections of output, prices, and land use in 2050. We place each of the published estimates reviewed here into the resulting empirical distribution of outcomes at mid-century. In addition, we quantify the sensitivity of these projections to model inputs. Our findings suggest that the top priority for future research should be improved estimation of agricultural factor supply elasticities, a topic that has been largely neglected in recent decades.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-resource-100815-095333
2016-10-05
2024-06-24
Loading full text...

Full text loading...

/deliver/fulltext/resource/8/1/annurev-resource-100815-095333.html?itemId=/content/journals/10.1146/annurev-resource-100815-095333&mimeType=html&fmt=ahah

Literature Cited

  1. Abbott P, Hurt C, Tyner WE. 2009. What's driving food prices? March 2009 update Iss. Rep., Farm Found., Oak Brook, IL [Google Scholar]
  2. Abbott P, Hurt C, Tyner WE. 2011. What's driving food prices in 2011? Iss. Rep., Farm Found., Oak Brook, IL [Google Scholar]
  3. Ahmed SA, Hertel T, Lubowski R. 2008. Calibration of a land cover supply function using transition probabilities Res. Memo. 14, Cent. Glob. Trade Anal. Proj., Dep. Agric. Econ., Purdue Univ., West Lafayette, IN [Google Scholar]
  4. Alexandratos N, Bruinsma J. 2012. World agriculture towards 2030/2050: the 2012 revision Work. Pap. 12-03, Food Agric. Organ., Rome [Google Scholar]
  5. Alston JM, Beddow JM, Pardey PG. 2009. Agricultural research, productivity, and food prices in the long run. Science 325:1209–10 [Google Scholar]
  6. Baldos ULC, Hertel TW. 2013. Looking back to move forward on model validation: insights from a global model of agricultural land use. Environ. Res. Lett. 8:034024 [Google Scholar]
  7. Baldos ULC, Hertel TW. 2014. Bursting the bubble: a long run perspective on crop commodity prices Work. Pap. 80, Cent. Glob. Trade Anal. Proj., Dep. Agric. Econ., Purdue Univ., West Lafayette, IN. http://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=4574 [Google Scholar]
  8. Brown L, Eckholm EP. 1974. By Bread Alone New York: Praeger [Google Scholar]
  9. Chen Y-HH, Paltsev S, Reilly JM, Morris JF, Babiker MH. 2015. The MIT EPPA6 model: economic growth, energy use, and food consumption Tech. Rep., MIT Joint Prog. Sci. Policy Glob. Change, Cambridge, MA [Google Scholar]
  10. Dietrich JP, Schmitz C, Lotze-Campen H, Popp A, Müller C. 2014. Forecasting technological change in agriculture—an endogenous implementation in a global land use model. Technol. Forecast. Soc. Change 81:236–49 [Google Scholar]
  11. Ehrilch PR. 1970. The Population Bomb New York: Sierra Club/Ballantine [Google Scholar]
  12. Foresight 2011. The future of food and farming: challenges and choices for global sustainability Rep., Gov. Off. Sci., London [Google Scholar]
  13. Fuglie KO. 2012. Productivity growth and technology capital in the global agricultural economy. Productivity Growth In Agriculture: An International Perspective KO Fuglie, SL Wang, VE Ball 335–68 Cambridge, MA: CAB Int. [Google Scholar]
  14. Fujimori S, Masui T, Matsuoka Y. 2012. AIM/CGE [basic] manual Disc. Pap. 2012-01, Cent. Social Environ. Syst. Res., Natl. Inst. Environ. Stud., Ibaraki, Jpn. [Google Scholar]
  15. Griffith R, Redding S, Reenen JV. 2004. Mapping the Two Faces of R&D: productivity growth in a panel of OECD industries. Rev. Econ. Stat. 86:883–95 [Google Scholar]
  16. Hertel TW. 2011. The global supply and demand for agricultural land in 2050: A perfect storm in the making?. Am. J. Agric. Econ. 93:259–75 [Google Scholar]
  17. IEA (Int. Energy Agency) 2014. World Energy Outlook Paris: OECD [Google Scholar]
  18. Just RE, Pope RD. 2001. The agricultural producer: theory and statistical measurement. Handbook of Agricultural Economics B Gardner, GC Rausser 631–75 New York: North-Holland [Google Scholar]
  19. Kavallari A, Conforti P, van der Mensbrugghe D. 2016. The Global Agriculture Perspectives System (GAPS): Version 1.0 ESA Work. Pap., Food Agric. Organ., Rome [Google Scholar]
  20. Keeney R, Hertel TW. 2009. Indirect land use impacts of US biofuels policies: the importance of acreage, yield and bilateral trade responses. Am. J. Agric. Econ. 91:895–909 [Google Scholar]
  21. Kriegler E, O'Neill BC, Hallegatte S, Kram T, Lempert RJ. et al. 2012. The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socio-economic pathways. Glob. Environ. Change 22:807–22 [Google Scholar]
  22. Lotze-Campen H, Müller C, Bondeau A, Rost S, Popp A, Lucht W. 2008. Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agric. Econ. 39:325–38 [Google Scholar]
  23. Ludena CE, Hertel TW, Preckel PV, Foster K, Nin A. 2007. Productivity growth and convergence in crop, ruminant, and nonruminant production: measurement and forecasts. Agric. Econ. 37:1–17 [Google Scholar]
  24. Malthus TR. 1888. An Essay on the Principle of Population Edinburgh/London: Ballantyne, 9th ed.. [Google Scholar]
  25. McCalla AF, Revoredo CL. 2001. Prospects for global food security: a critical appraisal of past projections and predictions Disc. Pap., Int. Food Policy Res. Inst., Washington, DC [Google Scholar]
  26. Meadows DH, Meadows DL, Randers J III, WWB. 1972. The Limits to Growth: A Report for the Club of Rome's Project on the Predicament of Mankind New York: Universe [Google Scholar]
  27. Monfreda C, Ramankutty N, Hertel TW. 2009. Global agricultural land use data for climate change analysis. Economic Analysis of Land Use in Global Climate Change Policy TW Hertel, S Rose, R Tol 33–48 London: Routledge [Google Scholar]
  28. Morris MD. 1991. Factorial sampling plans for preliminary computational experiments. Technometrics 33:161–74 [Google Scholar]
  29. Muhammad A, Seale JL Jr., Meade B, Regmi A. 2011. International evidence on food consumption patterns: an update using 2005 international comparison program data Tech. Bull. TB-1929, Econ. Res. Serv., US Dep. Agric., Washington, DC [Google Scholar]
  30. Narayanan GB, Aguiar A, McDougall R. 2015. Global Trade, Assistance, and Production: the GTAP 9 data base Cent. Glob. Trade Anal., Purdue Univ., West Lafayette, IN [Google Scholar]
  31. Nelson G, Rosegrant MW, Palazzo A, Gray I, Ingersoll C. et al. 2010. Food security, farming, and climate change to 2050: scenarios, results, policy options Res. Monogr., Int. Food Policy Res. Inst., Washington, DC [Google Scholar]
  32. OECD (Organ. Econ. Coop. Dev.) 2001. Market Effects of Crop Support Measures Paris: OECD [Google Scholar]
  33. O'Neill B, Kriegler E, Riahi K, Ebi K, Hallegatte S. et al. 2014. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122:387–400 [Google Scholar]
  34. Oxfam 2011. Growing a Better Future: Food Justice in a Resource-Constrained World Oxford, UK: Oxfam [Google Scholar]
  35. Pant HM 2002. Global Trade and Environment Model (GTEM): A computable general equilibrium model of the global economy and environment Rep., Aust. Bur. Agric. Resour. Econ., Canberra, Aust. [Google Scholar]
  36. Prins AG, Eickhout B, Banse M, Meijl HV, Rienks W, Woltjer G. 2011. Global impacts of European agricultural and biofuel policies. Ecol. Sci. 16:49 [Google Scholar]
  37. Ricardo D. 1817. On the Principles of Political Economy and Taxation London: John Murray [Google Scholar]
  38. Robinson S, Mason-D'Croz D, Islam S, Sulser T, Robertson R. et al. 2015. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): model description for Version 3 Disc. Pap. 01483, Int. Food Policy Res. Inst., Washington, DC [Google Scholar]
  39. Robinson S, Meijl HV, Willenbockel D, Valin H, Fujimori S. et al. 2014. Comparing supply-side specifications in models of global agriculture and the food system. Agric. Econ. 45:21–35 [Google Scholar]
  40. Sands R, Jones C, Marshall E. 2014. Global drivers of agricultural demand and supply Econ. Res. Rep. ERR-174, Econ. Res. Serv., US Dep. Agric., Washington, DC [Google Scholar]
  41. Schmitz C, van Meijl H, Kyle P, Nelson GC, Fujimori S. et al. 2014. Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric. Econ. 45:169–84 [Google Scholar]
  42. Schultz TW. 1951. A framework for land economics. The long view. J. Farm Econ. 33:204–15 [Google Scholar]
  43. Schultz TW. 1953. The declining economic importance of agricultural land. The Economic Organization of Agriculture, ed. Schultz TW. 125–45 New York: McGraw-Hill [Google Scholar]
  44. Sumner DA. 1982. The off-farm labor supply of farmers. Am. J. Agric. Econ. 64:3499–509 [Google Scholar]
  45. Tilman D, Balzer C, Hill J, Befort BL. 2011. Global food demand and the sustainable intensification of agriculture. PNAS 108:20260–64 [Google Scholar]
  46. Tweeten L, Thompson S. 2008. Long-term global agricultural output supply-demand balance and real farm and food prices Work. Pap. AEDE-WP 0044-08, Ohio State Univ., Columbus, OH [Google Scholar]
  47. Tyrchniewicz EW, Schuh GE. 1966. Regional supply of hired labor to agriculture. Am. J. Agric. Econ. 48:3537–56 [Google Scholar]
  48. Valin H, Havlík P, Forsell N, Frank S, Mosnier A. et al. 2013. Description of the GLOBIOM (IIASA) model and comparison with the MIRAGE-BioF (IFPRI) model Rep., Ecofys, Sci. Glob. Insight, E4Tech, Utrecht, Neth. [Google Scholar]
  49. Valin H, Sands RD, van der Mensbrugghe D, Nelson GC, Ahammad H. et al. 2014. The future of food demand: understanding differences in global economic models. Agric. Econ. 45:51–67 [Google Scholar]
  50. van der Mensbrugghe D. 2008. Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) Model Washington, DC: World Bank [Google Scholar]
  51. von Lampe M, Willenbockel D, Ahammad H, Blanc E, Cai Y. et al. 2014. Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agric. Econ. 45:13–20 [Google Scholar]
  52. Westhoff P. 2010. The Economics of Food: How Feeding and Fueling the Planet Affects Food Prices Upper Saddle River, NJ: Financ. Times [Google Scholar]
  53. Wise M, Calvin K. 2011. GCAM 3.0 Agriculture and land use: technical description of modeling approach Rep. PNNL-20971, Pac. Northwest Natl. Lab., US Dep. Energy, Richland, WA [Google Scholar]
  54. Woltjer G, Kuiper M. 2014. The MAGNET model Man. LEI 4-057, Wageningen Univ., Wageningen, Neth. http://www.magnet-model.org/MagnetModuleDescription.pdf [Google Scholar]
  55. World Bank 2009. Global Economic Prospects 2009: Commodities at the Crossroads Washington, DC: World Bank [Google Scholar]
/content/journals/10.1146/annurev-resource-100815-095333
Loading
/content/journals/10.1146/annurev-resource-100815-095333
Loading

Data & Media loading...

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error