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A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0)

Authors

Liu,  Xinrui
External Organizations;

Gasser,  Thomas
External Organizations;

Ma,  Jianmin
External Organizations;

Liu,  Junfeng
External Organizations;

/persons/resource/jonasjae

Jägermeyr,  Jonas       
Potsdam Institute for Climate Impact Research;

/persons/resource/Christoph.Mueller

Müller,  Christoph       
Potsdam Institute for Climate Impact Research;

Folberth,  Christian
External Organizations;

Zabel,  Florian
External Organizations;

Jain,  Atul
External Organizations;

Liu,  Wenfeng
External Organizations;

Webber,  Heidi
External Organizations;

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egusphere-2025-4805.pdf
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Citation

Liu, X., Gasser, T., Ma, J., Liu, J., Jägermeyr, J., Müller, C., Folberth, C., Zabel, F., Jain, A., Liu, W., Webber, H. (in press): A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0). - Geoscientific Model Development.


Cite as: https://publications.pik-potsdam.de/pubman/item/item_34557
Abstract
This paper presents the development, validation, and preliminary application of a sub-national scale crop yield emulator to be integrated into the compact Earth system model OSCAR. The emulator simulates yields for four major food crops: maize, rice (two growing seasons), soybean, and wheat (spring and winter varieties), in alignment with the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. Key drivers include atmospheric CO2 concentration (represented as C), growing season temperature (T), water availability (W), and nitrogen fertilization (N). The emulator is trained on an ensemble of process-based crop model simulations from AgMIP’s Global Gridded Crop Model Intercomparison Projects (GGCMI), which is based on the ISIMIP Phase 3 protocol. The crop models used bias-corrected historical and future climate scenarios under fixed socioeconomic conditions, to estimate yield responses under various scenarios until the end of this century. Evaluation of the emulator against the crop model outputs demonstrates the emulator's ability to replicate complex model behavior with high fidelity. Additionally, the emulator-derived yield sensitivities to CO2 and temperature are consistent with those observed in field experiments, reinforcing its empirical robustness. Historical simulations incorporating time-varying nitrogen inputs show significantly improved agreement with FAO yield statistics, underscoring the emulator’s reliability over the historical period and its potential for future impact assessments. This study provides a computationally efficient yet empirically grounded tool for representing crop yield responses, bridging the gap between complex crop models and statistic models. The developed crop emulator facilitates probabilistic projections across large ensembles of climatic and socio-economic scenarios at policy-relevant, sub-national scales. Potential applications include integrated assessments of future food security under climate and land-use change, as well as evaluations of bioenergy with carbon capture and storage (BECCS) potential from crop residues.