The AgMIP Gridded Crop Modeling Initiative

(Ag-GRID)

Main Contacts for Initiative

Christoph Müller and Jonas Jägermeyr
Contact: ag-grid@agmip.org

Brief Description of Activity

Ag-GRID is an initiative focused on crop modeling applications using formalized protocols and multi-model ensembles. We maintain an assessment framework to evaluate climate change impacts in agriculture and identify adaptation and mitigation opportunities. We work across different spatial and temporal scales and we welcome crop modeling teams to participate in our activities.

Ag-GRID was formed around the Global Gridded Crop Model Intercomparison Project (GGCMI), which is AgMIP’s global crop modeling initiative and is tied into the agricultural sector in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP; https://www.isimip.org/).

Objective

Ag-GRID provides a central organizing hub for a new generation of gridded crop modelling activities within AgMIP. We aim to develop cutting-edge crop modeling frameworks, improve the quality and access to gridded climate impact assessments, build multi-model ensemble estimates, and advance scenario development for the international climate impacts community. Ag-GRID leverages existing AgMIP capacity by bringing together AgMIP members from different teams, including climate-crop modelling, crop-specific initiatives (e.g. AgMIP-wheat), economics, Representative Agricultural Pathways (RAPs), ozone, calibration and scaling, among others.

Ag-GRID is coordinated at the Columbia University Climate School in New York, the NASA Goddard Institute for Space Studies in New York City (AgMIP Coordination Office), and the Potsdam Institute for Climate Impact Research in Germany. The Coordination Team is led by Jonas Jägermeyr and Christoph Müller. We welcome and encourage questions and interactions, and we are always interested in collaborations and new projects. You can reach us at ag-grid@agmip.org.

The Global Gridded Crop Modeling Intercomparison (GGCMI)

Among the Ag-GRID initiatives is the Global Gridded Crop Modeling Intercomparison (GGCMI). GGCMI brings together a diverse international community of crop modelers for climate impact assessment, model intercomparison and improvement at the global scale. GGCMI aims to better understand climate impacts, adaptation benefits in global agriculture. GGCMI provides a multi-model ensemble assessment framework capable to not only evaluate global warming implications, but also to address indirect food productivity responses to weather perturbations related to nuclear conflict, and solar radiation management.

In 2012, AgMIP led the first multi-model study on climate change impacts in global agriculture, a GGCMI fast-track project in coordination with the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This project contributed to a PNAS special issue and the IPCC AR5. Seven GGCMs were harmonized for the first time, providing a the state of knowledge on climate change vulnerabilities, impacts, and adaptations at the time, using the latest library of climate model outputs (CMIP5).

In 2013, GGCMI started Phase 1 with a focus on model evaluation during historical periods based on observation-based weather data. An online tool for global model evaluation was the main product of this phase.

In 2016, GGCMI Phase 2 was initiated as a multi-dimensional sensitivity study exploring crop yield responses to systematic perturbations of model inputs. This large archive of simulations from 10 GGCMs was then used to train a set of powerful light-weight crop model emulators that can be used to generate crop productivity impacts based on new climate scenarios, without the need to run expensive mechanistic crop models on a super computer.

In 2020, GGCMI studied the indirect effects of a regional nuclear war on food security using an ensemble of 6 GGCMs and a global trade model. This work highlights that global cooling primarily affects breadbaskets in the Global North, leading to larger global production losses than global warming of the same amount.

GGCMI Phase 3, the most recent activity, is aligned with ISIMIP3 and provides the largest ensemble of climate impact projections in global agriculture based on 12 GGCMS and 5 CMIP6 GCMs. The results indicate that climate impacts are more pronounced than previously expected with large losses in global maize productivity and potential gains for wheat under moderate warming. The results are highlighted in the IPCC AR6.

The upcoming focus will be the evaluation of farm-level adaptation measures. Phase 3 is ongoing and additional teams are still contributing model simulations.

Overview of Participants

Model (alphabetically)	Lead institute	Location	ISIMIP fast track	Phase 1	Phase 2	Phase 3	Nuclear Conflict
ACEA	University of Twente	Twente, The Netherlands		x
APSIM-UGOE	University of Göttingen	Göttingen, Germany		x
CARAIB	University of Liege	Liege, Belgium		x
CGMS-WOFOST	Wageningen University and Research	Wageningen, The Netherlands		x
CLM-crop	NCAR	Boulder, CO, USA		x
CROVER	NIES	Tsukuba, Japan				x
CYGMA (1p74)	National Agriculture and Food Research Organization (NARO)	Tsukuba, Japan				x
DSSAT-Pythia	University of Florida	Gainsville, FL, USA				x
EPIC-BOKU	BOKU	Vienna, Austria	x	x			x
EPIC-IIASA	IIASA	Laxemburg, Austria		x	x	x
EPIC-TAMU	TAMU	College Station, TX, USA		x	x
GAEZ in IMAGE	PBL	Den Haag, The Netherlands	x
GEPIC	EAWAG	Dübendorf, Switzerland	x	x	x		x
ISAM	University of Illinois	Urbana-Champaign, IL, USA				x
JULES	Met Office	Exeter, UK			x
LandscapeDNDC	KIT	Garmisch-Partenkirchen, Germany				x
LPJ-GUESS	KIT	Garmisch-Partenkirchen, Germany	x	x	x	x
LPJmL	PIK	Potsdam, Germany	x	x	x	x	x
ORCHIDEE-crop	LSCE	Paris, France		x	x
pAPSIM	Chicago University	Chicago, IL, USA		x
pDSSAT	Chicago University	Chicago, IL, USA	x	x	x	x	x
PEGASUS	University of East Anglia	Norwich, UK	x	x
PEPIC	EAWAG	Dübendorf, Switzerland		x	x	x	x
PROMET	Ludwig-Maximilians-University	Munich, Germany			x	x	x
PRYSBI2	NIAES	Tsukuba, Japan		x
SIMPLACE-LINTUL5+	ZALF	Müncheberg, Germany				x

Data Access for Main Simulation Phases

ISIMIP fast track on future crop yield projections under climate change (CMIP5): https://mygeohub.org/resources/agmip
Phase 1 on model evaluation: Müller et al. 2019: http://dx.doi.org/10.1038/s41597-019-0023-8
Phase 2 on systematic model sensitivities: (Franke et al. 2020a, http://dx.doi.org/10.5194/gmd-13-2315-2020) and leight-weight emulators (Franke et al. 2020b, http://dx.doi.org/10.5194/gmd-13-3995-2020)
Phase 3 (ISIMIP 3a/b) on future crop yield projections under climate change (CMIP6): https://data.isimip.org/ and https://mygeohub.org/resources/agmipagg
also see the AgMIP Zenodo Community: https://zenodo.org/communities/agmip/

Results

The GGCMI initiative has authored and contributed to over 70 publications, see e.g. our google scholar profile at https://scholar.google.com/citations?user=xr5ARjUAAAAJ.

Key publications

Müller C, Jägermeyr J, Franke JA, Ruane AC, Balkovic J, Ciais P, Dury M, Falloon P, Folberth C, Hank T, Hoffmann M, Izaurralde RC, Jacquemin I, Khabarov N, Liu W, Olin S, Pugh TAM, Wang X, Williams K, Zabel F, and Elliott JW. 2024, Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality-Based Model Evaluation, Earth’s Future, 12, e2023EF003773, doi: 10.1029/2023EF003773.

Orlov A, Jägermeyr J, Müller C, Daloz AS, Zabel F, Minoli S, Liu W, Lin T-S, Jain AK, Folberth C, Okada M, Poschlod B, Smerald A, Schneider JM, and Sillmann J. 2024, Human heat stress could offset potential economic benefits of CO2 fertilization in crop production under a high-emissions scenario, One Earth, 7, 1250-1265, doi: 10.1016/j.oneear.2024.06.012.

Jägermeyr J, Müller C, Ruane AC, Elliott J, Balkovic J, Castillo O, Faye B, Foster I, Folberth C, Franke JA, Fuchs K, Guarin JR, Heinke J, Hoogenboom G, Iizumi T, Jain AK, Kelly D, Khabarov N, Lange S, Lin T-S, Liu W, Mialyk O, Minoli S, Moyer EJ, Okada M, Phillips M, Porter C, Rabin SS, Scheer C, Schneider JM, Schyns JF, Skalsky R, Smerald A, Stella T, Stephens H, Webber H, Zabel F, and Rosenzweig C. 2021, Climate impacts on global agriculture emerge earlier in new generation of climate and crop models, Nature Food, doi: 10.1038/s43016-021-00400-y.

Müller C, Franke J, Jägermeyr J, Ruane AC, Elliott J, Moyer E, Heinke J, Falloon P, Folberth C, Francois L, Hank T, Izaurralde RC, Jacquemin I, Liu W, Olin S, Pugh T, Williams KE, and Zabel F. 2021, Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios, Environmental Research Letters, 16, 034040, doi: 10.1088/1748-9326/abd8fc.

Jägermeyr, J., Robock, A., Elliott, J., Müller, C., Xia, L., Khabarov, N., … Rosenzweig, C. (2020). A regional nuclear conflict would compromise global food security. Proceedings of the National Academy of Sciences, 117(13), 7071–7081. https://doi.org/10.1073/pnas.1919049117

Wang X, Zhao C, Müller C, Wang C, Ciais P, Janssens I, Peñuelas J, Asseng S, Li T, Elliott J, Huang Y, Li L, and Piao S. 2020, Emergent constraint on crop yield response to warmer temperature from field experiments, Nature Sustainability, doi: 10.1038/s41893-020-0569-7.

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017.

Deryng D, Elliott J, Folberth C, Müller C, Pugh TAM, Boote KJ, Conway D, Ruane AC, Gerten D, Jones JW, Khabarov N, Olin S, Schaphoff S, Schmid E, Yang H, and Rosenzweig C. 2016, Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity, Nature Clim. Change, 6, 786-790, doi: 10.1038/nclimate2995.

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015.

Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh TAM, Schmid E, Stehfest E, Yang H, and Jones JW. 2014, Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, Proceedings of the National Academy of Sciences of the United States of America, 111, 9, 3268-3273, doi: 10.1073/pnas.1222463110.