The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linchao Li, Bin Wang, Puyu Feng, Jonas Jägermeyr, Senthold Asseng, Christoph Müller, Ian Macadam, De Li Liu, Cathy Waters, Yajie Zhang, Qinsi He, Yu Shi, Shang Chen, Xiaowei Guo, Yi Li, Jianqiang He, Hao Feng, Guijun Yang, Hanqin Tian, Qiang Yu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Original languageEnglish
Article number362
JournalCommunications Earth and Environment
Issue number1
StatePublished - Dec 2023


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