Incorporating temperature-adapted growing periods enhances simulation of crop yield sensitivity to temperature in global gridded crop models

Abstract

Global Gridded Crop Models (GGCMs) have been widely used to simulate the impacts of global warming on crop production, but their accuracy in capturing the real-world temperature sensitivity of crop yields remains unclear. Here, we evaluated the performance of eight GGCM emulators (incorporating versus not incorporating cultivar adaptation of crop growing periods at 0.5° × 0.5° resolution) in modelling yield sensitivities to 1 K temperature increase (S_T) and optimized their ensembles against statistically-inferred S_T for maize, rice, and wheat using a Bayesian Model Averaging approach. Our results suggest that multi-GGCM ensembles assuming a fixed crop growing period (i.e., a gradually temperature-adapted crop cultivar) show higher goodness-of-fit to statistically-inferred S_T than those assuming a temperature-sensitive growing period for the crops in major food-producing countries. When setting a temperature-adapted growing period instead of a temperature-sensitive growing period in the GGCM ensembles, the R² between GGCM-simulated and statistically-inferred S_T increased from 0.63 to 0.81 for maize, 0.28 to 0.52 for rice, and 0.40 to 0.85 for wheat, meanwhile the RMSE was reduced for all three crops across their respective top 20 producing countries. The crop models may exaggerate historical responses of crop growing periods to climate warming, resulting in an overestimation of yield S_T for maize and an underestimation of yield S_T for rice and wheat in major food-producing countries. The study highlights the importance of adopting dynamic phenological parameters in GGCM simulations to reflect crop cycle adaptation under global warming.

Keywords

Crop model / GGCM / Climate change / Temperature sensitivity / Growing period / Model ensemble

CRediT authorship contribution statement

Xiaobo Wang: Writing - review & editing, Writing - original draft, Validation, Resources, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. Shaoqiang Wang: Supervision, Resources, Funding acquisition. Christian Folberth: Writing - review & editing, Supervision, Resources. Rastislav Skalsky: Supervision, Resources. Juraj Balkovic: Supervision, Resources. Florian Kraxner: Project administration. Xia Li: Funding acquisition, Conceptualization. Jinyuan Liu: Validation, Formal analysis, Data curation. Bangyou Zheng: Writing - review & editing, Supervision, Project administration.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grants No. 32301393 and 31861143015), China Postdoctoral Science Foundation (Grant No. 2023M743455), and China Scholarship Council (Grant No. 20231049003).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2026.100430.

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