Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-429-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-429-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling land use and land cover change: using a hindcast to estimate economic parameters in gcamland v2.0
Katherine V. Calvin
CORRESPONDING AUTHOR
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA
Abigail Snyder
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA
Xin Zhao
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA
Marshall Wise
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA
Related authors
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
Short summary
Short summary
GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn
Short summary
Short summary
Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Abigail Snyder, Noah Prime, Claudia Tebaldi, and Kalyn Dorheim
Earth Syst. Dynam., 15, 1301–1318, https://doi.org/10.5194/esd-15-1301-2024, https://doi.org/10.5194/esd-15-1301-2024, 2024
Short summary
Short summary
From running climate models to using their outputs to identify impacts, modeling the integrated human–Earth system is expensive. This work presents a method to identify a smaller subset of models from the full set that preserves the uncertainty characteristics of the full set. This results in a smaller number of runs that an impact modeler can use to assess how uncertainty propagates from the Earth to the human system, while still capturing the range of outcomes provided by climate models.
Claudia Tebaldi, Abigail Snyder, and Kalyn Dorheim
Earth Syst. Dynam., 13, 1557–1609, https://doi.org/10.5194/esd-13-1557-2022, https://doi.org/10.5194/esd-13-1557-2022, 2022
Short summary
Short summary
Impact modelers need many future scenarios to characterize the consequences of climate change. The climate modeling community cannot fully meet this need because of the computational cost of climate models. Emulators have fallen short of providing the entire range of inputs that modern impact models require. Our proposal, STITCHES, meets these demands in a comprehensive way and may thus support a fully integrated impact research effort and save resources for the climate modeling enterprise.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
Short summary
Short summary
GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn
Short summary
Short summary
Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
Cited articles
Ahmed, S. A., Hertel, T., and Lubowski, R.: Calibration of a land cover supply function using transition probabilities, Purdue, Indiana, available at: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2947 (last access: 2 September 2020), 2009.
Alexander, P., Prestele, R., Verburg, P. H., Arneth, A., Baranzelli, C., Batista e Silva, F., Brown, C., Butler, A., Calvin, K., Dendoncker, N., Doelman, J. C., Dunford, R., Engström, K., Eitelberg, D., Fujimori, S., Harrison, P. A., Hasegawa, T., Havlik, P., Holzhauer, S., Humpenöder, F., Jacobs-Crisioni, C., Jain, A. K., Krisztin, T., Kyle, P., Lavalle, C., Lenton, T., Liu, J., Meiyappan, P., Popp, A., Powell, T., Sands, R. D., Schaldach, R., Stehfest, E., Steinbuks, J., Tabeau, A., van Meijl, H., Wise, M. A., and Rounsevell, M. D. A.: Assessing uncertainties in land cover projections, Glob. Change Biol., 23, 767–781, https://doi.org/10.1111/gcb.13447, 2017.
Babcock, B. A.: Extensive and Intensive Agricultural Supply Response, Annu. Rev. Resour. Econ., 7, 333–348, https://doi.org/10.1146/annurev-resource-100913-012424, 2015.
Baldos, U. L. C. and Hertel, T. W.: Looking back to move forward on model validation: Insights from a global model of agricultural land use, Environ. Res. Lett., 8, 034024, https://doi.org/10.1088/1748-9326/8/3/034024, 2013.
Barr, K. J., Babcock, B. A., Carriquiry, M. A., Nassar, A. M., and Harfuch, L.: Agricultural Land Elasticities in the United States and Brazil, Appl. Econ. Perspect. P., 33, 449–462, https://doi.org/10.1093/aepp/ppr011, 2011.
Bonsch, M., Dietrich, J. P., Popp, A., Lotze-Campen, H., and Stevanovic, M.: Validation of land use models, in GTAP Conference, Shanghai, China, available at: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=4064 (last access: 2 September 2020), 2013.
Bonsch, M., Humpenöder, F., Popp, A., Bodirsky, B., Dietrich, J. P., Rolinski, S., Biewald, A., Lotze-Campen, H., Weindl, I., Gerten, D., and Stevanovic, M.: Trade-offs between land and water requirements for large-scale bioenergy production, GCB Bioenergy, 8, 11–24,, https://doi.org/10.1111/gcbb.12226, 2016.
Brovkin, V., Boysen, L., Arora, V. K., Boisier, J. P., Cadule, P., Chini, L., Claussen, M., Friedlingstein, P., Gayler, V., Van den Hurk, B. J. J. M., Hurtt, G. C., Jones, C. D., Kato, E., de Noblet-Ducoudre, N., Pacifico, F., Pongratz, J., and Weiss, M.: Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century, J. Climate, 26, 6859–6881, https://doi.org/10.1175/JCLI-D-12-00623.1, 2013.
Calvin, K., Wise, M., Kyle, P., Patel, P., Clarke, L., and Edmonds, J.: Trade-offs of different land and bioenergy policies on the path to achieving climate targets, Climatic Change, 123, 691–704, https://doi.org/10.1007/s10584-013-0897-y, 2014.
Calvin, K., Wise, M., Kyle, P., Clarke, L., and Edmonds, J.: A hindcast experiment using the GCAM 3.0 agriculture and land-use module, Clim. Chang. Econ., 8, 1750005, https://doi.org/10.1142/S2010007817500051, 2017.
Calvin, K., Link, R., and Wise, M.: gcamland v1.0 – An R Package for Modelling Land Use and Land Cover Change, J. Open Res. Softw., 7, 1–6, https://doi.org/10.5334/jors.233, 2019a.
Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019b.
Calvin, K., Mignone, B. K., Kheshgi, H. S., Snyder, A. C., Patel, P. L., Wise, M. A., Clarke, L. E., and Edmonds, J. A.: Global Market and Economic Welfare Implications of Changes in Agricultural Yields due to Climate Change, Clim. Chang. Econ., 11, 2050005, https://doi.org/10.1142/S2010007820500050, 2020a.
Calvin, K., Link, R., Snyder, A., and Sinha, E.: JGCRI/gcamland: gcamland v2.0 (v2.0), Zenodo [code], https://doi.org/10.5281/zenodo.4071797, 2020b.
Calvin, K. V., Snyder, A., Zhao, X., and Wise, M.:
META-REPOSITORY: Modeling Land Use and Land Cover Change: Using a Hindcast to Estimate Economic Parameters in gcamland v2.0, Zenodo [code],
https://doi.org/10.5281/zenodo.4631131,
2021.
Carnell, R.: lhs: Latin Hypercube Samples, available at: https://cran.r-project.org/package=lhs, last access: 4 February 2020.
Chaturvedi, V., Hejazi, M., Edmonds, J., Clarke, L., Kyle, P., Davies, E., and Wise, M.: Climate mitigation policy implications for global irrigation water demand, Mitig. Adapt. Strat. Gl., 20, 389–407, https://doi.org/10.1007/s11027-013-9497-4, 2013.
Dixon, P., van Meijl, H., Rimmer, M., Shutes, L., and Tabeau, A.: RED versus REDD: biofuel policy versus forest conservation, Econ. Model., 52, 366–374, https://doi.org/10.1016/J.ECONMOD.2015.09.014, 2016.
Engström, K., Rounsevell, M. D. A., Murray-Rust, D., Hardacre, C., Alexander, P., Cui, X., Palmer, P. I., and Arneth, A.: Applying Occam's razor to global agricultural land use change, Environ. Modell. Softw., 75, 212–229, https://doi.org/10.1016/j.envsoft.2015.10.015, 2016.
FAO: Producer Prices, FAOSTAT, available at: http://www.fao.org/faostat/en/#data/PP (last access: 4 February 2020), 2018a.
FAO: Producer Prices (old series), FAOSTAT, available at: https://www.fao.org/faostat/en/#data/PA, (last access: 4 February 2020),
2018b.
FAO: Land Cover, FAOSTAT, available at: http://www.fao.org/faostat/en/#data/LC, last access: 4 February 2020a.
FAO: Production: Crops, FAOSTAT, available at: http://www.fao.org/faostat/en/#data/QC, last access: 29 July 2020b.
Féménia, F. and Gohin, A.: Dynamic modelling of agricultural policies: The role of expectation schemes, Econ. Model., 28, 1950–1958, https://doi.org/10.1016/j.econmod.2011.03.028, 2011.
Fuglie, K. O.: Total factor productivity in the global agricultural economy: Evidence from FAO data, in: The shifting patterns of agricultural production and productivity worldwide, edited by: Alston, J. M., Babcock, D. B. A., and Pardey, P. G., The Midwest Agribusiness Trade Research and Information Center Iowa State University, Ames, Iowa, 63–95, 2010.
Hasegawa, T., Sands, R. D., Brunelle, T., Cui, Y., Frank, S., Fujimori, S., and Popp, A.: Food security under high bioenergy demand, Climatic Change, 163, 1587–1601, https://doi.org/10.1007/s10584-020-02838-8, 2020.
Hejazi, M., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J., Calvin, K., Moss, R., and Kim, S.: Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework, Technol. Forecast. Soc., 81, 205–226, https://doi.org/10.1016/j.techfore.2013.05.006, 2014a.
Hejazi, M. I., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J., and Calvin, K.: Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies, Hydrol. Earth Syst. Sci., 18, 2859–2883, https://doi.org/10.5194/hess-18-2859-2014, 2014b.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Jones, A. D., Collins, W. D., Edmonds, J., Torn, M. S., Janetos, A., Calvin, K. V., Thomson, A., Chini, L. P., Mao, J., Shi, X., Thornton, P., Hurtt, G. C., and Wise, M.: Greenhouse gas policy influences climate via direct effects of land-use change, J. Climate, 26, 3657–3670, https://doi.org/10.1175/JCLI-D-12-00377.1, 2013.
Kelly, D. L., Kolstad, C. D., and Mitchell, G. T.: Adjustment costs from environmental change, J. Environ. Econ. Manag., 50, 468–495, https://doi.org/10.1016/j.jeem.2005.02.003, 2005.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Legates, D. R. and McCabe, G. J.: Evaluating the use of “goodnessof-fit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, 1999.
Lubowski, R. N., Plantinga, A. J., and Stavins, R. N.: What Drives Land-Use Change in the United States? A National Analysis of Landowner Decisions, Land Econ., 84, 529–550, 2008.
Lundberg, L., Jonson, E., Lindgren, K., and Bryngelsson, D.: A cobweb model of land-use competition between food and bioenergy crops, J. Econ. Dyn. Control, 53, 1–14, https://doi.org/10.1016/j.jedc.2015.01.003, 2015.
Manoli, G., Meijide, A., Huth, N., Knohl, A., Kosugi, Y., Burlando, P., Ghazoul, J., and Fatichi, S.: Ecohydrological changes after tropical forest conversion to oil palm, Environ. Res. Lett., 13, 64035, https://doi.org/10.1088/1748-9326/aac54e, 2018.
McFadden, D.: Econometric models of probabilistic choice,
in: Structural Analysis of Discrete Data with Econometric
Applications, edited by: Manski, C. and McFadden, D., MIT Press, Cambridge, MA, 1981.
Mitra, S. and Boussard, J. M.: A simple model of endogenous agricultural commodity price fluctuations with storage, Agr. Econ., 43, 1–15, https://doi.org/10.1111/j.1574-0862.2011.00561.x, 2012.
Mouratiadou, I., Biewald, A., Pehl, M., Bonsch, M., Baumstark, L., Klein, D., Popp, A., Luderer, G., and Kriegler, E.: The impact of climate change mitigation on water demand for energy and food: An integrated analysis based on the Shared Socioeconomic Pathways, Environ. Sci. Policy, 64, 48–58, https://doi.org/10.1016/j.envsci.2016.06.007, 2016.
National Research Council: Advancing Land Change Modeling: Opportunities and Research Requirements, National Academies Press, Washington DC, USA, available at: http://nap.edu/18385 (last access: 2 September 2020), 2014.
Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., Elliott, J., Fujimori, S., Hasegawa, T., Heyhoe, E., Kyle, P., Von Lampe, M., Lotze-Campen, H., Mason d'Croz, D., van Meijl, H., van der Mensbrugghe, D., Müller, C., Popp, A., Robertson, R., Robinson, S., Schmid, E., Schmitz, C., Tabeau, A., and Willenbockel, D.: Climate change effects on agriculture: Economic responses to biophysical shocks, P. Natl. Acad. Sci. USA, 111, 3274–3279, https://doi.org/10.1073/pnas.1222465110, 2014.
Nerlove, M.: Adaptive Expectations and Cobweb Phenomena, Q. J. Econ., 72, 227–240, https://doi.org/10.2307/1880597, 1958.
Popp, A., Rose, S. K., Calvin, K., Van Vuuren, D. P., Dietrich, J. P., Wise, M., Stehfest, E., Humpenöder, F., Kyle, P., Van Vliet, J., Bauer, N., Lotze-Campen, H., Klein, D., and Kriegler, E.: Land-use transition for bioenergy and climate stabilization: Model comparison of drivers, impacts and interactions with other land use based mitigation options, Climatic Change, 123, 495–509, https://doi.org/10.1007/s10584-013-0926-x, 2014.
Popp, A., Calvin, K., Fujimori, S., Havlik, P.,
Humpenöder, F., Stehfest, E., Bodirsky, B. L., Dietrich,
J. P., Doelmann, J. C., Gusti, M., Hasegawa, T., Kyle,
P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H., Waldhoff,
S., Weindl, I., Wise, M., Kriegler, E., Lotze-Campen, H., Fricko,
O., Riahi, K., and Vuuren, D. P. va.: Land-use futures in
the shared socio-economic pathways, Global Environ. Chang., 42,
331–345, https://doi.org/10.1016/j.gloenvcha.2016.10.002,
2017.
R Core Team: R: A language and environment for statistical computing, 2020.
Roberts, M. J. and Schlenker, W.: Identifying supply and demand elasticities of agricultural commodities: Implications for the US ethanol mandate, Am. Econ. Rev., 103, 2265–2295, https://doi.org/10.1257/aer.103.6.2265, 2013.
Sands, R. and Leimbach, M.: Modeling agriculture and land use in an integrated assessment framework, Climatic Change, 56, 185–210, 2003.
Schmitz, C., van Meijl, H., Kyle, P., Nelson, G. C., Fujimori, S., Gurgel, A., Havlik, P., Heyhoe, E., D'Croz, D. M., Popp, A., Sands, R., Tabeau, A., van der Mensbrugghe, D., von Lampe, M., Wise, M., Blanc, E., Hasegawa, T., Kavallari, A., and Valin, H.: Land-use change trajectories up to 2050: Insights from a global agro-economic model comparison, Agr. Econ. (United Kingdom), 45, 69–84, https://doi.org/10.1111/agec.12090, 2014.
Snyder, A. C., Link, R. P., and Calvin, K. V.: Evaluation of integrated assessment model hindcast experiments: a case study of the GCAM 3.0 land use module, Geosci. Model Dev., 10, 4307–4319, https://doi.org/10.5194/gmd-10-4307-2017, 2017.
Stehfest, E., Van Zeist, W.-J., Valin, H., Havlik, P., Popp, A., Kyle, P., Tabeau, A., Mason-D'Croz, D., Hasegawa, T., and Bodirsky, B. L.: Key determinants of global land-use projections, Nat. Commun., 10, 2166, 2019.
Taheripour, F. and Tyner, W.: Biofuels and Land Use Change: Applying Recent Evidence to Model Estimates, Appl. Sci., 3, 14–38, https://doi.org/10.3390/app3010014, 2013.
Tebaldi, C., Armbruster, A., Engler, H. P., and Link, R.: Emulating climate extreme indices, Environ. Res. Lett., 15, 74006, https://doi.org/10.1088/1748-9326/ab8332, 2020.
Tilman, D., Balzer, C., Hill, J., and Befort, B. L.: Global food demand and the sustainable intensification of agriculture, P. Natl. Acad. Sci. USA, 108, 20260–20264, https://doi.org/10.1073/pnas.1116437108, 2011.
USDA: Commodity costs and returns, Econ. Res. Serv.,
available at:
https://www.ers.usda.gov/data-products/commodity-costs-and-returns.aspx,
last access: 28 January 2020a.
USDA: Farm Business Income, Farm Sect. Income Financ.,
available at: Farm Business Income Statement, https://www.ers.usda.gov/data-products/farm-income-and-wealth-statistics.aspx, last access: 3 June
2020b.
USDA: Federal Government direct farm program payments,
Farm Income Wealth Stat., available at:
https://data.ers.usda.gov/reports.aspx?ID=17833, last access: 3 June 2020c.
Von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., Mason d'Croz, D., Nelson, G. C., Sands, R. D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., and van Meijl, H.: Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison, Agr. Econ. (United Kingdom), 45, 3–20, https://doi.org/10.1111/agec.12086, 2014.
Weber, J. G. and Key, N.: How much do decoupled payments affect production? An instrumental variable approach with panel data, Am. J. Agr. Econ., 94, 52–66, 2012.
Wise, M., Calvin, K., Kyle, P., Luckow, P., and Edmonds, J.: Economic and Physical Modeling of Land Use in Gcam 3.0 and an Application To Agricultural Productivity, Land, and Terrestrial Carbon, Clim. Chang. Econ., 5, 1450003, https://doi.org/10.1142/S2010007814500031, 2014.
Young, C. E. and Westcott, P. C.: How Decoupled Is U. S. Agricultural Support for Major Crops?, Agric. Appl. Econ. Assoc., 82, 762–767, 2000.
Zhao, X., van der Mensbrugghe, D. Y., Keeney, R. M., and Tyner, W. E.: Improving the way land use change is handled in economic models, Econ. Model., 84, 13–26, https://doi.org/10.1016/j.econmod.2019.03.003, 2020a.
Zhao, X., Calvin, K. V., and Wise, M. A.: The critical role of conversion cost and comparative advantage in modeling agricultural land use change, Clim. Chang. Econ., 11, 2050004, https://doi.org/10.1142/S2010007820500049, 2020b.
Short summary
Future changes in land use and cover have important implications for agriculture, energy, water use, and climate. In this study, we demonstrate a more systematic and empirically based approach to estimating a few key parameters for an economic model of land use and land cover change, gcamland. We identify parameter combinations that best replicate historical land use in the United States.
Future changes in land use and cover have important implications for agriculture, energy, water...