Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences

Ringeval, Bruno; Müller, Christoph; Pugh, Thomas A. M.; Mueller, Nathaniel D.; Ciais, Philippe; Folberth, Christian; Liu, Wenfeng; Debaeke, Philippe; Pellerin, Sylvain

doi:https://doi.org/10.5194/gmd-14-1639-2021

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.

https://doi.org/10.5194/gmd-14-1639-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 3

Methods for assessment of models

|

23 Mar 2021

Methods for assessment of models |

| 23 Mar 2021

Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences

Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin

Supplement

https://doi.org/10.5194/gmd-14-1639-2021-supplement

Model code and software

Process-based emulator to explain the differences in simulated potential yield between Global Gridded Crop Models, 2020, https://doi.org/10.15454/9EIJWU, Portail Data INRAE, V1 Bruno Ringeval https://doi.org/10.15454/9EIJWU

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.