| 29 Oct 2025
A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0)
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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.
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In this manuscript, the authors present a crop yield emulator of the Agricultural Model Intercomparison and Improvement Project (AgMIP) global gridded crop models (GGCMs). Their crop yield emulator was designed to be driven by CTWN output from OSCAR for novel scenarios, but was trained on and validated using publicly available model intercomparison data from GGCMs. The authors describe the model development, validation with out-of-sample GGCM results, as well as manipulative field experiments. There is a well-established need for crop yield emulators, and it is exciting to see this field growing. However, as it currently stands, I think revisions are needed to improve the clarity and readability of the manuscript. Line by line questions are included below but one overarching comment is that it is not particular clear what and how the emulator is actually connected with/related to OSCAR.
Isn’t OSCAR now on version > V3 (Gasser et al. 2020)? How does OSCAR-crop v1.0 relate to it? Is the crop emulator standalone from OSCAR, which is why it is only on v1? Will it be included in a future OSCAR release? Some clarity on whether the emulator is a module/component of OSCAR or fully independent, that is, soft-coupled with OSCAR, would be helpful. Is there a specific version of OSCAR that the emulator is compatible with? Or is it also backwards compatible with the V1 OSCAR release? These sorts of details, and the possibility of coupling the crop emulator with other RCMs, would be helpful to readers and potential users. Were any OSCAR driven emulation results included in manuscript?
Please see below for some of the questions/comments that came to mind as I was reading.
L26 : “to estimate yield responses under various scenarios” is this under various future climate scenarios? Or are socioeconomic conditions also part of the prediction process?
L33: “bridging the gap between complex crop models and statistic models” what do the atuhors mean by this gap?
L43-48: In the chunk of text starting with “In contrast” and ending with “(Folberth et al., 2025)”. As it currently reads, with where manuscripts are cited, it seems like the only other existing crop yield emulator is documented in Abramoff et al., 2023, but other crop yield emulators exist. The authors should cite more than one other crop yield emulator in this section or clarify why this emulator is so unique among them.
L56 - 60: In this section of text, the authors have been discussing a mix of crop emulators and crop yield simulations generated by the complex crop models. The second half of the paragraph is hard to follow because it is unclear what type of model is being discussed. For example, with the sentence “Despite the wide range of outcomes due to different model structures, parameterization schemes, calibration processes and input data quality (Folberth et al., 2019;Müller et al., 2024), these projections exhibit reduced uncertainty for rice and soybean and enhanced robustness for maize and wheat (Jägermeyr et al., 2021)” are the authors referring to the the complex crop models participating in GGCMI Phase 3 as having a wide range of outcomes? Or were these national crop yield emulators that the authors are building up with this work by developing a sub-national crop emulator?
L74: “It emulates crop yields at a national level for most countries, with sub-national outputs in six large-area countries (Australia, Brazil, Canada, China, Russia, and the USA).” How many regions in total? Is this enough to be considered subnational modeling capabilities? As described in L60?
L99: “The input variables provided in the repository”, what do you mean by the input variables, are you referring to the ISIMIP data that the crop emulator will use as inputs? Or are these data included in the emulator repository for emulator users?
Equations (3 & 4): Where do the weights between the regional climate and crop-specific/regional growing season crop come from? Is Oscar producing the growing-season regional temperatures and precipitation?
L145 - Why would the concatenation matter? If doing global to regional linear pattern scaling?
L175 - Is a consistent functional form used across crop types? Or is it the best emulator per region x crop type?
Figure 3: Is the y-axis RCCO2? Or is it RC? It might be helpful to include that labeling on the y-axis
Equation 9, which subtracts the perception pi control from the climate scenario, appears inconsistent with the relative perception changes described above.
~ L340 For the N fertilizer effect, could the authors clarify whether they are conducting the field experiments following the van Grinsven et al. (2022) or if they are using data published from this field experiment? Given the data limitations and the assumptions that had to be made, is it necessary to include this term in the emulator?
In Figure 9: What do the symbol makers indicate? Is it the distribution of the global average of the sub-national absolute differences? Or is it the sub-national absolute differences?
Section 5.1 is difficult to follow. Are the authors comparing emulator results with experimental observations? Is the emulator being used to predict the experimental change in yield response? Or are the field experiment results being incorporated into the emulator by “ground[ing] the emulator’s projections in real-world experimental evidence”?Furthermore, it is not entirely clear how the MC relates to the observational/field experiments.