An AgMIP framework for improved agricultural representation in integrated assessment models

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.

doi:10.1088/1748-9326/aa8da6

Title: An AgMIP framework for improved agricultural representation in integrated assessment models

Journal Article · Fri Nov 24 00:00:00 EST 2017 · Environmental Research Letters

DOI:https://doi.org/10.1088/1748-9326/aa8da6· OSTI ID:1410187

;

; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.;

; Snyder, Abigail; Thorburn, Peter J.

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

View Journal Article

Cite

Export

Save

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Stanford Univ., CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); United Kingdom Department for International Development

Grant/Contract Number:: AC05-76RL01830; SC0005171; AC5-76RL01830; GB-1-202108

OSTI ID:: 1410187

Alternate ID(s):: OSTI ID: 1439707; OSTI ID: 1509883

Report Number(s):: PNNL-SA-126658

Journal Information:: Environmental Research Letters, Journal Name: Environmental Research Letters Vol. 12 Journal Issue: 12; ISSN 1748-9326

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United Kingdom

Language:: English

Citation Metrics:

Cited by: 49 works

Citation information provided by
Web of Science

References (56)

Uncertainty in crop model predictions: What is the role of users? Confalonieri, Roberto; Orlando, Francesca; Paleari, Livia Environmental Modelling & Software, Vol. 81 https://doi.org/10.1016/j.envsoft.2016.04.009	journal	July 2016
Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison Rosenzweig, Cynthia; Elliott, Joshua; Deryng, Delphine Proceedings of the National Academy of Sciences, Vol. 111, Issue 9 https://doi.org/10.1073/pnas.1222463110	journal	December 2013
The parallel system for integrating impact models and sectors (pSIMS) Elliott, Joshua; Kelly, David; Chryssanthacopoulos, James Environmental Modelling & Software, Vol. 62 https://doi.org/10.1016/j.envsoft.2014.04.008	journal	December 2014
Crop modelling for integrated assessment of risk to food production from climate change Ewert, F.; Rötter, R. P.; Bindi, M. Environmental Modelling & Software, Vol. 72 https://doi.org/10.1016/j.envsoft.2014.12.003	journal	October 2015
Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces Pirttioja, N.; Carter, Tr; Fronzek, S. Climate Research, Vol. 65 https://doi.org/10.3354/cr01322	journal	September 2015
How do various maize crop models vary in their responses to climate change factors? Bassu, Simona; Brisson, Nadine; Durand, Jean-Louis Global Change Biology, Vol. 20, Issue 7 https://doi.org/10.1111/gcb.12520	journal	April 2014
A potato model intercomparison across varying climates and productivity levels Fleisher, David H.; Condori, Bruno; Quiroz, Roberto Global Change Biology, Vol. 23, Issue 3 https://doi.org/10.1111/gcb.13411	journal	October 2016
Similar estimates of temperature impacts on global wheat yield by three independent methods Liu, Bing; Asseng, Senthold; Müller, Christoph Nature Climate Change, Vol. 6, Issue 12 https://doi.org/10.1038/nclimate3115	journal	September 2016
Variability of effects of spatial climate data aggregation on regional yield simulation by crop models Hoffmann, H.; Zhao, G.; van Bussel, Lgj Climate Research, Vol. 65 https://doi.org/10.3354/cr01326	journal	September 2015
Climate change impacts on European crop yields: Do we need to consider nitrogen limitation? Webber, Heidi; Zhao, Gang; Wolf, Joost European Journal of Agronomy, Vol. 71 https://doi.org/10.1016/j.eja.2015.09.002	journal	November 2015
Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models Blanc, Élodie Agricultural and Forest Meteorology, Vol. 236 https://doi.org/10.1016/j.agrformet.2016.12.022	journal	April 2017
Climate variation explains a third of global crop yield variability Ray, Deepak K.; Gerber, James S.; MacDonald, Graham K. Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms6989	journal	January 2015
Multi-wheat-model ensemble responses to interannual climate variability Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold Environmental Modelling & Software, Vol. 81 https://doi.org/10.1016/j.envsoft.2016.03.008	journal	July 2016
The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0) Elliott, J.; Müller, C.; Deryng, D. Geoscientific Model Development, Vol. 8, Issue 2 https://doi.org/10.5194/gmd-8-261-2015	journal	January 2015
Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications Müller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James Geoscientific Model Development, Vol. 10, Issue 4 https://doi.org/10.5194/gmd-10-1403-2017	journal	January 2017
Projecting future crop productivity for global economic modeling Müller, Christoph; Robertson, Richard D. Agricultural Economics, Vol. 45, Issue 1 https://doi.org/10.1111/agec.12088	journal	December 2013
Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations Hoffmann, Holger; Zhao, Gang; Asseng, Senthold PLOS ONE, Vol. 11, Issue 4 https://doi.org/10.1371/journal.pone.0151782	journal	April 2016
MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling: MONTHLY IRRIGATED AND RAINFED CROP AREAS Portmann, Felix T.; Siebert, Stefan; Döll, Petra Global Biogeochemical Cycles, Vol. 24, Issue 1 https://doi.org/10.1029/2008GB003435	journal	March 2010
Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou Global Change Biology, Vol. 21, Issue 3 https://doi.org/10.1111/gcb.12758	journal	December 2014
Economic impacts of climate change on agriculture: a comparison of process-based and statistical yield models Moore, Frances C.; Baldos, Uris Lantz C.; Hertel, Thomas Environmental Research Letters, Vol. 12, Issue 6 https://doi.org/10.1088/1748-9326/aa6eb2	journal	May 2017
Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration Meinshausen, M.; Raper, S. C. B.; Wigley, T. M. L. Atmospheric Chemistry and Physics, Vol. 11, Issue 4 https://doi.org/10.5194/acp-11-1417-2011	journal	January 2011
Methodologies for simulating impacts of climate change on crop production White, Jeffrey W.; Hoogenboom, Gerrit; Kimball, Bruce A. Field Crops Research, Vol. 124, Issue 3 https://doi.org/10.1016/j.fcr.2011.07.001	journal	December 2011
Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change Mistry, Malcolm N.; Sue Wing, Ian; De Cian, Enrica Environmental Research Letters, Vol. 12, Issue 7 https://doi.org/10.1088/1748-9326/aa788c	journal	July 2017
Assessing Impacts of Climate Change on Food Security Worldwide Rosenzweig, Cynthia; Antle, John; Elliott, Joshua Eos, Vol. 97 https://doi.org/10.1029/2016EO047387	journal	March 2016
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops Zhao, Gang; Hoffmann, Holger; Yeluripati, Jagadeesh Environmental Modelling & Software, Vol. 80 https://doi.org/10.1016/j.envsoft.2016.02.022	journal	June 2016
Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO2 Cammarano, Davide; Rötter, Reimund P.; Asseng, Senthold Field Crops Research, Vol. 198 https://doi.org/10.1016/j.fcr.2016.08.015	journal	November 2016
Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios Wiebe, Keith; Lotze-Campen, Hermann; Sands, Ronald Environmental Research Letters, Vol. 10, Issue 8 https://doi.org/10.1088/1748-9326/10/8/085010	journal	August 2015
Crop rotation modelling—A European model intercomparison Kollas, Chris; Kersebaum, Kurt Christian; Nendel, Claas European Journal of Agronomy, Vol. 70 https://doi.org/10.1016/j.eja.2015.06.007	journal	October 2015
Modeling impacts and adaptation in global IAMs: Modeling impacts and adaptation Füssel, Hans-Martin Wiley Interdisciplinary Reviews: Climate Change, Vol. 1, Issue 2 https://doi.org/10.1002/wcc.40	journal	March 2010
Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison Webber, Heidi; Martre, Pierre; Asseng, Senthold Field Crops Research, Vol. 202 https://doi.org/10.1016/j.fcr.2015.10.009	journal	February 2017
Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates Marin, Fábio R.; Thorburn, Peter J.; Nassif, Daniel S. P. Environmental Modelling & Software, Vol. 72 https://doi.org/10.1016/j.envsoft.2015.02.019	journal	October 2015
Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation Ruane, Alex C.; Goldberg, Richard; Chryssanthacopoulos, James Agricultural and Forest Meteorology, Vol. 200 https://doi.org/10.1016/j.agrformet.2014.09.016	journal	January 2015
Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles Maiorano, Andrea; Martre, Pierre; Asseng, Senthold Field Crops Research, Vol. 202 https://doi.org/10.1016/j.fcr.2016.05.001	journal	February 2017
Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe Yin, Xiaogang; Kersebaum, Kurt Christian; Kollas, Chris European Journal of Agronomy, Vol. 84 https://doi.org/10.1016/j.eja.2016.12.009	journal	March 2017
Climate Trends and Global Crop Production Since 1980 Lobell, D. B.; Schlenker, W.; Costa-Roberts, J. Science, Vol. 333, Issue 6042 https://doi.org/10.1126/science.1204531	journal	May 2011
Selecting crop models for decision making in wheat insurance Castañeda-Vera, A.; Leffelaar, P. A.; Álvaro-Fuentes, J. European Journal of Agronomy, Vol. 68 https://doi.org/10.1016/j.eja.2015.04.008	journal	August 2015
The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science Rosenzweig, Cynthia; Jones, James W.; Hatfield, Jerry L. Handbook of Climate Change and Agroecosystems https://doi.org/10.1142/9781783265640_0001	book	March 2015
AgMIP's Transdisciplinary Agricultural Systems Approach to Regional Integrated Assessment of Climate Impacts, Vulnerability, and Adaptation Antle, John M.; Valdivia, Roberto O.; Boote, Kenneth J. Handbook of Climate Change and Agroecosystems https://doi.org/10.1142/9781783265640_0002	book	March 2015
Climate change effects on agriculture: Economic responses to biophysical shocks Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D. Proceedings of the National Academy of Sciences, Vol. 111, Issue 9 https://doi.org/10.1073/pnas.1222465110	journal	December 2013
Rapid aggregation of global gridded crop model outputs to facilitate cross-disciplinary analysis of climate change impacts in agriculture Villoria, Nelson B.; Elliott, Joshua; Müller, Christoph Environmental Modelling & Software, Vol. 75 https://doi.org/10.1016/j.envsoft.2015.10.016	journal	January 2016
Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000: GLOBAL CROP AREAS AND YIELDS IN 2000 Monfreda, Chad; Ramankutty, Navin; Foley, Jonathan A. Global Biogeochemical Cycles, Vol. 22, Issue 1 https://doi.org/10.1029/2007GB002947	journal	March 2008
Lessons from climate modeling on the design and use of ensembles for crop modeling Wallach, Daniel; Mearns, Linda O.; Ruane, Alex C. Climatic Change, Vol. 139, Issue 3-4 https://doi.org/10.1007/s10584-016-1803-1	journal	September 2016
Making the most of climate impacts ensembles Challinor, Andy; Martre, Pierre; Asseng, Senthold Nature Climate Change, Vol. 4, Issue 2 https://doi.org/10.1038/nclimate2117	journal	January 2014
A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0 Hartin, C. A.; Patel, P.; Schwarber, A. Geoscientific Model Development, Vol. 8, Issue 4 https://doi.org/10.5194/gmd-8-939-2015	journal	January 2015
Carbon-Temperature-Water change analysis for peanut production under climate change: a prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP) Ruane, Alex C.; McDermid, Sonali; Rosenzweig, Cynthia Global Change Biology, Vol. 20, Issue 2 https://doi.org/10.1111/gcb.12412	journal	December 2013
Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity Deryng, Delphine; Elliott, Joshua; Folberth, Christian Nature Climate Change, Vol. 6, Issue 8 https://doi.org/10.1038/nclimate2995	journal	April 2016
Spatial sampling of weather data for regional crop yield simulations van Bussel, Lenny G. J.; Ewert, Frank; Zhao, Gang Agricultural and Forest Meteorology, Vol. 220 https://doi.org/10.1016/j.agrformet.2016.01.014	journal	April 2016
Rising temperatures reduce global wheat production Asseng, S.; Ewert, F.; Martre, P. Nature Climate Change, Vol. 5, Issue 2 https://doi.org/10.1038/nclimate2470	journal	December 2014
Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations Asseng, S. Open Data Journal for Agricultural Research, Vol. 1, Issue 1 https://doi.org/10.18174/odjar.v1i1.14746	journal	January 2015
Crop yield response to climate change varies with cropping intensity Challinor, Andrew J.; Parkes, Ben; Ramirez-Villegas, Julian Global Change Biology, Vol. 21, Issue 4 https://doi.org/10.1111/gcb.12808	journal	January 2015
How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? Durand, Jean-Louis; Delusca, Kenel; Boote, Ken European Journal of Agronomy, Vol. 100 https://doi.org/10.1016/j.eja.2017.01.002	journal	October 2018
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies Rosenzweig, C.; Jones, J. W.; Hatfield, J. L. Agricultural and Forest Meteorology, Vol. 170 https://doi.org/10.1016/j.agrformet.2012.09.011	journal	March 2013
Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects Makowski, David; Asseng, Senthold; Ewert, Frank Handbook of Climate Change and Agroecosystems https://doi.org/10.1142/9781783265640_0011	book	March 2015
Multimodel ensembles of wheat growth: many models are better than one Martre, Pierre; Wallach, Daniel; Asseng, Senthold Global Change Biology, Vol. 21, Issue 2 https://doi.org/10.1111/gcb.12768	journal	December 2014
Analysis and classification of data sets for calibration and validation of agro-ecosystem models Kersebaum, K. C.; Boote, K. J.; Jorgenson, J. S. Environmental Modelling & Software, Vol. 72 https://doi.org/10.1016/j.envsoft.2015.05.009	journal	October 2015
The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework Warszawski, Lila; Frieler, Katja; Huber, Veronika Proceedings of the National Academy of Sciences, Vol. 111, Issue 9 https://doi.org/10.1073/pnas.1312330110	journal	December 2013

Cited By (8)

Earth Observations and Integrative Models in Support of Food and Water Security Schollaert Uz, Stephanie; Ruane, Alex C.; Duncan, Bryan N. Remote Sensing in Earth Systems Sciences, Vol. 2, Issue 1 https://doi.org/10.1007/s41976-019-0008-6	journal	February 2019
Agricultural Development and Land Use Change in India: A Scenario Analysis of Trade‐Offs Between UN Sustainable Development Goals (SDGs) Hinz, R.; Sulser, T. B.; Huefner, R. Earth's Future, Vol. 8, Issue 2 https://doi.org/10.1029/2019ef001287	journal	February 2020
Synthesis and Review: an inter-method comparison of climate change impacts on agriculture Ciscar, Juan-Carlos; Fisher-Vanden, Karen; Lobell, David B. Environmental Research Letters, Vol. 13, Issue 7 https://doi.org/10.1088/1748-9326/aac7cb	journal	June 2018
Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics Muller, Bertrand; Martre, Pierre Journal of Experimental Botany, Vol. 70, Issue 9 https://doi.org/10.1093/jxb/erz175	journal	April 2019
Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments Rosenzweig, Cynthia; Ruane, Alex C.; Antle, John Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 376, Issue 2119 https://doi.org/10.1098/rsta.2016.0455	journal	April 2018
Transmission of climate risks across sectors and borders Challinor, Andy J.; Adger, W. Neil; Benton, Tim G. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 376, Issue 2121 https://doi.org/10.1098/rsta.2017.0301	journal	April 2018
Invited review: Intergovernmental Panel on Climate Change, agriculture, and food—A case of shifting cultivation and history Porter, John R.; Challinor, Andrew J.; Henriksen, Christian Bugge Global Change Biology, Vol. 25, Issue 8 https://doi.org/10.1111/gcb.14700	journal	May 2019
The effect of univariate bias adjustment on multivariate hazard estimates Zscheischler, Jakob; Fischer, Erich M.; Lange, Stefan Earth System Dynamics, Vol. 10, Issue 1 https://doi.org/10.5194/esd-10-31-2019	journal	January 2019

Similar Records

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

Journal Article · Wed Apr 03 00:00:00 EDT 2019 · Geoscientific Model Development (Online) · OSTI ID:1410187

Snyder, Abigail; Calvin, Katherine V.; Phillips, Meridel; +1 more

Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison

Journal Article · Wed Jan 01 00:00:00 EST 2014 · Agricultural Economics, 45(1):103-116 · OSTI ID:1410187

Lotze-Campen, Hermann; von Lampe, Martin; Kyle, G. Page; +10 more

Evaluating agricultural trade-offs in the age of sustainable development

Journal Article · Sat Oct 15 00:00:00 EDT 2016 · Agricultural Systems · OSTI ID:1410187

Kanter, David R.; Musumba, Mark; Wood, Sylvia L. R.; +10 more

Related Subjects

54 ENVIRONMENTAL SCIENCES
AgMIP
climate impacts
crop models
integrated assessment models
agricultural impacts
emulators
model development

Title: An AgMIP framework for improved agricultural representation in integrated assessment models

Citation Formats

References (56)

Cited By (8)

Similar Records

Related Subjects