Why Do Global Long-term Scenarios for Agriculture Differ? An overview of the AgMIP Global Economic Model Intercomparison

Journal Article · Mon Dec 02 04:00:00 EST 2013 · Agricultural Economics, 45(1):3-20

DOI:https://doi.org/10.1111/agec.12086· OSTI ID:1158485

von Lampe, Martin ^[1]; Willenbockel, Dirk ^[2]; Ahammad, Helal ^[3]; Blanc, Elodie ^[4]; Cai, Yongxia ^[4]; Calvin, Katherine V. ^[5]; Fujimori, Shinichiro ^[6]; Hasegawa, Tomoko ^[6]; Havlik, Petr ^[7]; Heyhoe, Edwina ^[3]; Kyle, G. Page ^[5]; Lotze-Campen, Hermann ^[8]; Mason d'Croz, Daniel ^[9]; Nelson, Gerald ^[10]; Sands, Ronald ^[11]; Schmitz, Christoph ^[8]; Tabeau, Andrzej ^[12]; Valin, Hugo ^[7]; van der Mensbrugghe, Dominique ^[13]; van Meijl, Hans ^[12]

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1. Organization for Ecomonic Co-operation and Development (OECD) (France)
2. Univ. of Sussex, Brighton (United Kingdom)
3. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra (Australia)
4. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
5. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
6. National Inst. for Environmental Studies (NIES), Tsukuba (Japan)
7. International Inst. for Applied Systems Analysis (IIASA) (Austria)
8. Postdam Inst. for Climate Impact Research (PIK), Potsdam (Germany)
9. International Food Policy Research Inst., WA (United States)
10. International Food Policy Research Inst., WA (United States); Univ. of Illinois, Champaign, IL (United States)
11. Economic Research Service (ERS), Washington, DC (United States)
12. Wageningen Univ. and Research Center (Netherlands)
13. Food and Agriculture Organization of the United Nationas (FAO), Roma (Italy)

Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. Ten global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socio-economic, climate change and bioenergy scenarios using a common set of key drivers. Results suggest that, once general assumptions are harmonized, the variability in general trends across models declines, and that several common conclusions are possible. Nonetheless, differences in basic model parameters, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. This holds for both the common reference scenario and for the various shocks applied. We conclude that agro-economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross-fertilize analyses at different scales.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1158485
• Report Number(s): PNNL-SA--96185; KP1703030
• Journal Information: Agricultural Economics, 45(1):3-20, Journal Name: Agricultural Economics, 45(1):3-20 Journal Issue: 1 Vol. 45; ISSN 0169-5150
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
C63
C68
Q11
Q16
Q24
Q42
Q54
bioenergy
climate change
computable general equilibrium
land use
meta-analysis
model intercomparison
partial equilibrium
socioeconomic pathway