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

Li, L., Wang, B., Feng, P., Jägermeyr, J., Asseng, S., Müller, C., Macadam, I., Liu, D. L., Waters, C., Zhang, Y., He, Q., Shi, Y., Chen, S., Guo, X., Li, Y., He, J., Feng, H., Yang, G., Tian, H., Yu, Q. (2023): The optimization of model ensemble composition and size can enhance the robustness of crop yield projections. - Communications Earth and Environment, 4, 362.
https://doi.org/10.1038/s43247-023-01016-9

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 Creators:
Li, Linchao1, Author
Wang, Bin1, Author
Feng, Puyu1, Author
Jägermeyr, Jonas2, Author              
Asseng, Senthold1, Author
Müller, Christoph2, Author              
Macadam, Ian1, Author
Liu, De Li1, Author
Waters, Cathy1, Author
Zhang, Yajie1, Author
He, Qinsi1, Author
Shi, Yu1, Author
Chen, Shang1, Author
Guo, Xiaowei1, Author
Li, Yi1, Author
He, Jianqiang1, Author
Feng, Hao1, Author
Yang, Guijun1, Author
Tian, Hanqin1, Author
Yu, Qiang1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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Free keywords: Agriculture, Climate-change impacts, Environmental impact
 Abstract: 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.

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Language(s): eng - English
 Dates: 2023-05-032023-09-152023-10-092023-10-09
 Publication Status: Finally published
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s43247-023-01016-9
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Land Use and Resilience
MDB-ID: No data to archive
Research topic keyword: Food & Agriculture
Research topic keyword: Land use
Regional keyword: Global
Model / method: LPJmL
OATYPE: Gold Open Access
 Degree: -

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Title: Communications Earth and Environment
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 4 Sequence Number: 362 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/communications-earth-environment
Publisher: Nature