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

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

doi:10.5194/gmd-14-1639-2021

Abstract

Abstract. How global gridded crop models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Intercomparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for simple mechanistic model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer–Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed-in-time fraction of net primary productivity allocated to the grains (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow for the capture of the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in a few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale.

Highlights

IntroductionPotential yield corresponds to the yield achieved when an adapted crop cultivar grows in non-limiting environmental conditions (i.e., without water and nutrient stresses and in the absence of damages from weeds, pests, and diseases) under a given crop management (e.g., plant density)
Potential yield corresponds to the yield achieved when an adapted crop cultivar grows in non-limiting environmental conditions under a given crop management
Using a fourth pair with the same C for all global gridded crop models (GGCMs) equal to its initial estimate decreased the ability of SMM to match the GGCMs only slightly and did not drastically change the radiation use efficiency (RUE) compared to the pairs where both C and RUE were calibrated

Summary

Introduction

Potential yield corresponds to the yield achieved when an adapted crop cultivar grows in non-limiting environmental conditions (i.e., without water and nutrient stresses and in the absence of damages from weeds, pests, and diseases) under a given crop management (e.g., plant density) It is determined by a reduced number of environmental variables: prevailing radiation, temperature, and atmospheric CO2 concentration. Crop simulation models provide a robust approach because they account for the interactive effects of genotype, weather, and management (van Ittersum et al, 2013) These models are mathematical representations of our current understanding of biophysical crop processes (phenology, carbon assimilation, assimilate allocation) and of crop responses to environmental factors. Crop models can be calibrated to account for local specificities, in particular for specificities related to the cultivar used at these sites (Grassini et al, 2011)

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