Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1639-2021
© Author(s) 2021. 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-14-1639-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
23 Mar 2021
Methods for assessment of models |
| 23 Mar 2021
| 23 Mar 2021
Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences
ISPA, Bordeaux Sciences Agro, INRAE, 33140, Villenave d'Ornon, France
Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
Thomas A. M. Pugh
School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
Nathaniel D. Mueller
Department of Earth System Science, University of California, Irvine, CA, USA
Philippe Ciais
Laboratoire de Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
Christian Folberth
Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
Wenfeng Liu
College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Philippe Debaeke
AGIR, University of Toulouse, INRAE, 31326, Castanet-Tolosan, France
Sylvain Pellerin
ISPA, Bordeaux Sciences Agro, INRAE, 33140, Villenave d'Ornon, France
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Cited
12 citations as recorded by crossref.
- Insights on Nitrogen and Phosphorus Co‐Limitation in Global Croplands From Theoretical and Modeling Fertilization Experiments B. Ringeval et al. https://doi.org/10.1029/2020GB006915
- Modelling crop yield and harvest index: the role of carbon assimilation and allocation parameters H. Camargo-Alvarez et al. https://doi.org/10.1007/s40808-022-01625-x
- Modeling wheat development under extreme weather with WOFOST-EW v1 J. Zheng et al. https://doi.org/10.5194/gmd-18-8379-2025
- Observational constraint of process crop models suggests higher risks for global maize yield under climate change X. Yin & G. Leng https://doi.org/10.1088/1748-9326/ac7ac7
- Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models T. Ippolito et al. https://doi.org/10.1016/j.jenvman.2023.118532
- Predicting planting suitability of globally important grain crops using a hybrid model Y. Ding et al. https://doi.org/10.1016/j.eja.2025.127754
- Healthy area of the penultimate leaf (F2) as a predictor of barley grain yield under foliar disease pressure in semi-arid environments H. Wazziki & B. Yousfi https://doi.org/10.1007/s42976-026-00812-1
- Future climate change significantly alters interannual wheat yield variability over half of harvested areas W. Liu et al. https://doi.org/10.1088/1748-9326/ac1fbb
- Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources Y. Zou et al. https://doi.org/10.3390/rs16040701
- Limitation of Maize Potential Yield by Phosphorus at the Global Scale B. Ringeval et al. https://doi.org/10.1111/gcb.70485
- Significant changes in global maize yield sensitivity to vapor pressure deficit during 1983–2010 L. Han & G. Leng https://doi.org/10.1016/j.agwat.2024.109107
- A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0) X. Liu et al. https://doi.org/10.5194/gmd-19-5857-2026
12 citations as recorded by crossref.
- Insights on Nitrogen and Phosphorus Co‐Limitation in Global Croplands From Theoretical and Modeling Fertilization Experiments B. Ringeval et al. https://doi.org/10.1029/2020GB006915
- Modelling crop yield and harvest index: the role of carbon assimilation and allocation parameters H. Camargo-Alvarez et al. https://doi.org/10.1007/s40808-022-01625-x
- Modeling wheat development under extreme weather with WOFOST-EW v1 J. Zheng et al. https://doi.org/10.5194/gmd-18-8379-2025
- Observational constraint of process crop models suggests higher risks for global maize yield under climate change X. Yin & G. Leng https://doi.org/10.1088/1748-9326/ac7ac7
- Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models T. Ippolito et al. https://doi.org/10.1016/j.jenvman.2023.118532
- Predicting planting suitability of globally important grain crops using a hybrid model Y. Ding et al. https://doi.org/10.1016/j.eja.2025.127754
- Healthy area of the penultimate leaf (F2) as a predictor of barley grain yield under foliar disease pressure in semi-arid environments H. Wazziki & B. Yousfi https://doi.org/10.1007/s42976-026-00812-1
- Future climate change significantly alters interannual wheat yield variability over half of harvested areas W. Liu et al. https://doi.org/10.1088/1748-9326/ac1fbb
- Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources Y. Zou et al. https://doi.org/10.3390/rs16040701
- Limitation of Maize Potential Yield by Phosphorus at the Global Scale B. Ringeval et al. https://doi.org/10.1111/gcb.70485
- Significant changes in global maize yield sensitivity to vapor pressure deficit during 1983–2010 L. Han & G. Leng https://doi.org/10.1016/j.agwat.2024.109107
- A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0) X. Liu et al. https://doi.org/10.5194/gmd-19-5857-2026
Saved (final revised paper)
Latest update: 19 Jul 2026
Short summary
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential...
