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24 results on '"Phillips, Meridel"'

3. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Author

Jägermeyr, Jonas, Müller, Christoph, Ruane, Alex C., Elliott, Joshua, Balkovic, Juraj, Castillo, Oscar, Faye, Babacar, Foster, Ian, Folberth, Christian, Franke, James A., Fuchs, Kathrin, Guarin, Jose R., Heinke, Jens, Hoogenboom, Gerrit, Iizumi, Toshichika, Jain, Atul K., Kelly, David, Khabarov, Nikolay, Lange, Stefan, Lin, Tzu-Shun, Liu, Wenfeng, Mialyk, Oleksandr, Minoli, Sara, Moyer, Elisabeth J., Okada, Masashi, Phillips, Meridel, Porter, Cheryl, Rabin, Sam S., Scheer, Clemens, Schneider, Julia M., Schyns, Joep F., Skalsky, Rastislav, Smerald, Andrew, Stella, Tommaso, Stephens, Haynes, Webber, Heidi, Zabel, Florian, and Rosenzweig, Cynthia

Published
2021
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5. Strong regional influence of climatic forcing datasets on global crop model ensembles

Author

Ruane, Alex C., Phillips, Meridel, Müller, Christoph, Elliott, Joshua, Jägermeyr, Jonas, Arneth, Almut, Balkovic, Juraj, Deryng, Delphine, Folberth, Christian, Iizumi, Toshichika, Izaurralde, Roberto C., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Rosenzweig, Cynthia, Sakurai, Gen, Schmid, Erwin, Sultan, Benjamin, Wang, Xuhui, de Wit, Allard, and Yang, Hong

Published
2021
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6. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Author

Rosenzweig, Cynthia, Ruane, Alex C., Antle, John, Elliott, Joshua, Ashfaq, Muhammad, Chatta, Ashfaq Ahmad, Ewert, Frank, Folberth, Christian, Hathie, Ibrahima, Havlik, Petr, Hoogenboom, Gerrit, Lotze-Campen, Hermann, MacCarthy, Dilys S., Mason-D’Croz, Daniel, Contreras, Erik Mencos, Müller, Christoph, Perez-Dominguez, Ignacio, Phillips, Meridel, Porter, Cheryl, Raymundo, Rubi M., Sands, Ronald D., Schleussner, Carl-Friedrich, Valdivia, Roberto O., Valin, Hugo, and Wiebe, Keith

Published
2018

9. A Crop Yield Change Emulator for Use in GCAM and Similar Models: Persephone v1.0

Author

Snyder, Abigail, Calvin, Katherine V, Phillips, Meridel, and Ruane, Alex C

Subjects
Earth Resources And Remote Sensing
Abstract

Future changes in Earth system state will impact agricultural yields and, through these changed yields, can have profound impacts on the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields but are often too computationally intensive to dynamically couple into global multisector economic models, such as the Global Change Assessment Model (GCAM) and other similar-in-scale models. Yet, generalizing a faster site-specific crop model’s results to be used globally will introduce inaccuracies, and the question of which model to use is unclear given the wide variation in yield response across crop models. To examine the feedback loop among socioeconomics, Earth system changes, and crop yield changes, rapidly generated yield responses with some quantification of crop response uncertainty are desirable. The Persephone v1.0 response functions presented in this work are based on the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Climate-Crop Modeling Project (C3MP) sensitivity test data set and are focused on providing GCAM and similar models with a tractable number of rapid to evaluate dynamic yield response functions corresponding to a range of the yield response sensitivities seen in the C3MP data set. With the Persephone response functions, a new variety of agricultural impact experiments will be open to GCAM and other economic models: for example, examining the economic impacts of a multi-year drought in a key agricultural region and how economic changes in response to the drought can, in turn, impact the drought.

Published
2019
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11. Climate change signal in global agriculture emerges earlier in new generation of climate and crop models

Author

Jaegermeyr, Jonas, primary, Müller, Christoph, additional, Ruane, Alex, additional, Elliott, Joshua, additional, Balkovic, Juraj, additional, Castillo, Oscar, additional, Faye, Babacar, additional, Foster, Ian, additional, Folberth, Christian, additional, Franke, James, additional, Fuchs, Kathrin, additional, Guarin, Jose, additional, Heinke, Jens, additional, Hoogenboom, Gerrit, additional, Iizumi, Toshichika, additional, Jain, Atul, additional, Kelly, David, additional, Khabarov, Nikolay, additional, Lange, Stefan, additional, Lin, Tzu-Shun, additional, Liu, Wenfeng, additional, Mialyk, Oleksandr, additional, Minoli, Sara, additional, Moyer, Elisabeth, additional, Okada, Masashi, additional, Phillips, Meridel, additional, Porter, Cheryl, additional, Rabin, Sam, additional, Scheer, Clemens, additional, Schneider, Julia, additional, Schyns, Joep, additional, Skalský, Rastislav, additional, Smerald, Andrew, additional, Stella, Tommaso, additional, Stephens, Haynes, additional, Webber, Heidi, additional, Zabel, Florian, additional, and Rosenzweig, Cynthia, additional

Published
2021
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12. The GGCMI phase II emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Author

Franke, James, Müller, Christoph, Elliott, Joshua, Ruane, Alex C., Jägermeyr, Jonas, Snyder, Abigail, Dury, Marie, Falloon, Pete, Folberth, Christian, François, Louis, Hank, Tobias, Izaurralde, R. Cesar, Jacquemin, Ingrid, Jones, Curtis, Li, Michelle, Liu, Wenfeng, Olin, Stefan, Phillips, Meridel, Pugh, Thomas A. M., Reddy, Ashwan, Williams, Karina, Wang, Ziwei, Zabel, Florian, and Moyer, Elisabeth

Abstract

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Published
2020

13. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2 temperature, water, and nitrogen levels (protocol version 1.0)

Author

Franke, James A., Müller, Christoph, Elliott, Joshua, Ruane, Alex C., Jägermeyr, Jonas, Balkovic, Juraj, Ciais, Philippe, Dury, Marie, Falloon, Pete D., Folberth, Christian, François, Louis, Hank, Tobias, Hoffmann, Munir, Izaurralde, R. Cesar, Jacquemin, Ingrid, Jones, Curtis, Khabarov, Nikolay, Koch, Marian, Li, Michelle, Liu, Wenfeng, Olin, Stefan, Phillips, Meridel, Pugh, Thomas A. M., Reddy, Ashwan, Wang, Xuhui, Williams, Karina, Zabel, Florian, Moyer, Elisabeth J., Department of Geosciences, University of Chicago, Chicago, IL 60637, United States, Potsdam Institute for Climate Impact Research (PIK), University of Chicago, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Institute for Applied Systems Analysis [Laxenburg] (IIASA), Comenius University in Bratislava, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Peking University [Beijing], ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Unité de Modélisation du Climat et des Cycles Biogéochimiques (UMCCB), Université de Liège, Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Ludwig-Maximilians-Universität München (LMU), Tropical Plant Prodution and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, INSTITUTE OF LANDSCAPE MATTER DYNAMICS LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE AND LAND USE RESEARCH ZALF MUNCHEBERG DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geographical Sciences [College Park], University of Maryland [College Park], University of Maryland System-University of Maryland System, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Chicago, Department of Statistics, Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland and ETH Zürich, Universitätstrasse 16, CH-8092 Zürich, Switzerland, DEPARTMENT OF PHYSICAL GEOGRAPHY AND ECOSYSTEM SCIENCE LUND UNIVERSITY SWE, NASA GODDARD INSTITUTE FOR SPACE STUDIES NEW YORK USA, Columbia University [New York], School of Geography, Earth and Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham [Birmingham], LUDWIG MAXIMILIANS UNIVERSITAT MUNCHEN DEPARTMENT OF GEOGRAPHY MUNICH DEU, European Project: 641811,H2020,H2020-WATER-2014-two-stage,IMPREX(2015), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Georg-August-University [Göttingen]

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
Abstract

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

Published
2020

14. The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2 temperature, water, and nitrogen (version 1.0)

Author

Franke, James, Müller, Christoph, Elliott, Joshua, Ruane, Alex, Jägermeyr, Jonas, Snyder, Abigail, Dury, Marie, Falloon, Pete, Folberth, Christian, François, Louis, Hank, Tobias, Izaurralde, R. Cesar, Jacquemin, Ingrid, Jones, Curtis, Li, Michelle, Liu, Wenfeng, Olin, Stefan, Phillips, Meridel, Pugh, Thomas, Reddy, Ashwan, Williams, Karina, Wang, Ziwei, Zabel, Florian, Moyer, Elisabeth, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
Abstract

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Published
2020

15. Supplementary material to "The GGCMI phase II emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)"

Author

Franke, James, primary, Müller, Christoph, additional, Elliott, Joshua, additional, Ruane, Alex C., additional, Jägermeyr, Jonas, additional, Snyder, Abigail, additional, Dury, Marie, additional, Falloon, Pete, additional, Folberth, Christian, additional, François, Louis, additional, Hank, Tobias, additional, Izaurralde, R. Cesar, additional, Jacquemin, Ingrid, additional, Jones, Curtis, additional, Li, Michelle, additional, Liu, Wenfeng, additional, Olin, Stefan, additional, Phillips, Meridel, additional, Pugh, Thomas A. M., additional, Reddy, Ashwan, additional, Williams, Karina, additional, Wang, Ziwei, additional, Zabel, Florian, additional, and Moyer, Elisabeth, additional

Published
2020
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16. The GGCMI phase II emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Author

Franke, James, primary, Müller, Christoph, additional, Elliott, Joshua, additional, Ruane, Alex C., additional, Jägermeyr, Jonas, additional, Snyder, Abigail, additional, Dury, Marie, additional, Falloon, Pete, additional, Folberth, Christian, additional, François, Louis, additional, Hank, Tobias, additional, Izaurralde, R. Cesar, additional, Jacquemin, Ingrid, additional, Jones, Curtis, additional, Li, Michelle, additional, Liu, Wenfeng, additional, Olin, Stefan, additional, Phillips, Meridel, additional, Pugh, Thomas A. M., additional, Reddy, Ashwan, additional, Williams, Karina, additional, Wang, Ziwei, additional, Zabel, Florian, additional, and Moyer, Elisabeth, additional

Published
2020
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17. The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)

Author

Franke, James, Müller, Christoph, Elliott, Joshua, Ruane, Alex C., Jagermeyr, Jonas, Balkovic, Juraj, Ciais, Philippe, Dury, Marie, Falloon, Peter, Folberth, Christian, Francois, Louis, Hank, Tobias, Hoffmann, Munir, Izaurralde, R. Cesar, Jacquemin, Ingrid, Jones, Curtis, Khabarov, Nikolay, Koch, Marian, Li, Michelle, Liu, Wenfeng, Olin, Stefan, Phillips, Meridel, Pugh, Thomas A. M., Reddy, Ashwan, Wang, Xuhui, Williams, Karina, Zabel, Florian, and Moyer, Elisabeth

Abstract

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase II experimental protocol and its simulation data archive. Twelve crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (``CTWN') for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions, but is largest in high-latitude regions where crops may be grown in the future.

Published
2019

18. The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)

Author

Franke, James, primary, Müller, Christoph, additional, Elliott, Joshua, additional, Ruane, Alex C., additional, Jagermeyr, Jonas, additional, Balkovic, Juraj, additional, Ciais, Philippe, additional, Dury, Marie, additional, Falloon, Peter, additional, Folberth, Christian, additional, Francois, Louis, additional, Hank, Tobias, additional, Hoffmann, Munir, additional, Izaurralde, R. Cesar, additional, Jacquemin, Ingrid, additional, Jones, Curtis, additional, Khabarov, Nikolay, additional, Koch, Marian, additional, Li, Michelle, additional, Liu, Wenfeng, additional, Olin, Stefan, additional, Phillips, Meridel, additional, Pugh, Thomas A. M., additional, Reddy, Ashwan, additional, Wang, Xuhui, additional, Williams, Karina, additional, Zabel, Florian, additional, and Moyer, Elisabeth, additional

Published
2019
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19. Supplementary material to "The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)"

Author

Franke, James, primary, Müller, Christoph, additional, Elliott, Joshua, additional, Ruane, Alex C., additional, Jagermeyr, Jonas, additional, Balkovic, Juraj, additional, Ciais, Philippe, additional, Dury, Marie, additional, Falloon, Peter, additional, Folberth, Christian, additional, Francois, Louis, additional, Hank, Tobias, additional, Hoffmann, Munir, additional, Izaurralde, R. Cesar, additional, Jacquemin, Ingrid, additional, Jones, Curtis, additional, Khabarov, Nikolay, additional, Koch, Marian, additional, Li, Michelle, additional, Liu, Wenfeng, additional, Olin, Stefan, additional, Phillips, Meridel, additional, Pugh, Thomas A. M., additional, Reddy, Ashwan, additional, Wang, Xuhui, additional, Williams, Karina, additional, Zabel, Florian, additional, and Moyer, Elisabeth, additional

Published
2019
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20. Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments

Author

Ruane, Alex C; Antle, John; Elliott, Joshua; Folberth, Christian; Hoogenboom, Gerrit; Mason-D'Croz, Daniel; Müller, Christoph; Porter, Cheryl H.; Phillips, Meridel M.; Raymundo, Rubi M.; Sands, Ronald D.; Valdivia, Roberto; White, Jeffrey W.; Wiebe, Keith D.; Rosenzweig, Cynthia, http://orcid.org/0000-0003-0673-2301 Mason-D'Croz, Daniel; http://orcid.org/0000-0001-6035-620X Wiebe, Keith, Ruane, Alex C; Antle, John; Elliott, Joshua; Folberth, Christian; Hoogenboom, Gerrit; Mason-D'Croz, Daniel; Müller, Christoph; Porter, Cheryl H.; Phillips, Meridel M.; Raymundo, Rubi M.; Sands, Ronald D.; Valdivia, Roberto; White, Jeffrey W.; Wiebe, Keith D.; Rosenzweig, Cynthia, and http://orcid.org/0000-0003-0673-2301 Mason-D'Croz, Daniel; http://orcid.org/0000-0001-6035-620X Wiebe, Keith

Abstract

PR, IFPRI3; ISI; CRP2; CRP7; 1 Fostering Climate-Resilient and Sustainable Food Supply; Global Futures and Strategic Foresight, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5° and +2.0°C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize Zea mays experiencing the largest losses, while soy Glycine max mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat Triticum aestivum, while soy prices and area decline (results for rice Oryza sativa are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0°C scenario which has a larger climate signal and lower mitigation requirement than the +1.5°C scenario. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO2 benefits for diverse agricultural systems in crop models, as simulations without CO2 benefits show widespread production losses that raise prices and expand agricultural area.

Published
2018

21. The GGCMI phase II emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0).

Author

Franke, James, Müller, Christoph, Elliott, Joshua, Ruane, Alex C., Jägermeyr, Jonas, Snyder, Abigail, Dury, Marie, Falloon, Pete, Folberth, Christian, François, Louis, Hank, Tobias, Izaurralde, R. Cesar, Jacquemin, Ingrid, Jones, Curtis, Li, Michelle, Wenfeng Liu, Olin, Stefan, Phillips, Meridel, Pugh, Thomas A. M., and Reddy, Ashwan

Subjects
ATMOSPHERIC carbon dioxide, AGRICULTURAL climatology, CLIMATE change, CROPS, GROWING season
Abstract

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts. [ABSTRACT FROM AUTHOR]

Published
2020
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23. A crop yield change emulator for use in GCAM and similar models: Persephone v1.0.

Author

Snyder, Abigail, Calvin, Katherine V., Phillips, Meridel, and Ruane, Alex C.

Subjects
CROP yields, AGRICULTURAL productivity
Abstract

Future changes in Earth system state will impact agricultural yields and, through these changed yields, can have profound impacts on the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields, but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as GCAM and other similar-in-scale models. Yet, generalizing a faster site-specific crop model's results to be used globally will introduce inaccuracies, and the question of which model to use is unclear given the wide variation in yield response across crop models. To examine the feedback loop among socioeconomics, Earth system changes, and crop yield changes, rapidly generated yield responses with some quantification of crop response uncertainty are desirable. The Persephone v1.0 response functions presented in this work are based on the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Climate-Crop Modeling Project (C3MP) sensitivity test data set and are focused on providing GCAM and similar models with a tractable number of rapid to evaluate, dynamic yield response functions corresponding to a range of the yield response sensitivities seen in the C3MP data set. With the Persephone response functions, a new variety of agricultural impact experiments will be open to GCAM and other economic models; for example, examining the economic impacts of a multi-year drought in a key agricultural region and how economic changes in response to the drought can, in turn, impact the drought. [ABSTRACT FROM AUTHOR]

Published
2018
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24. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments.

Author

Rosenzweig C, Ruane AC, Antle J, Elliott J, Ashfaq M, Chatta AA, Ewert F, Folberth C, Hathie I, Havlik P, Hoogenboom G, Lotze-Campen H, MacCarthy DS, Mason-D'Croz D, Contreras EM, Müller C, Perez-Dominguez I, Phillips M, Porter C, Raymundo RM, Sands RD, Schleussner CF, Valdivia RO, Valin H, and Wiebe K

Abstract

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO 2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'., (© 2018 The Authors.)

Published
2018
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