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, DL; Waters, C; Zhang, Y; He, Q; Shi, Y; Chen, S; Guo, X; Li, Y; He, J; Feng, H; Yang, G; Tian, H; Yu, Q

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, DL Waters, C Zhang, Y He, Q

Shi, Y Chen, S Guo, X Li, Y He, J Feng, H Yang, G Tian, H Yu, Q

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Publisher:: SPRINGERNATURE
Publication Type:: Journal Article
Citation:: Communications Earth and Environment, 2023, 4, (1)
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Li, L
dc.contributor.author	Wang, B
dc.contributor.author	Feng, P https://orcid.org/0000-0003-4845-9876
dc.contributor.author	Jägermeyr, J
dc.contributor.author	Asseng, S
dc.contributor.author	Müller, C
dc.contributor.author	Macadam, I
dc.contributor.author	Liu, DL
dc.contributor.author	Waters, C
dc.contributor.author	Zhang, Y
dc.contributor.author	He, Q https://orcid.org/0000-0001-9585-3716
dc.contributor.author	Shi, Y
dc.contributor.author	Chen, S
dc.contributor.author	Guo, X
dc.contributor.author	Li, Y
dc.contributor.author	He, J
dc.contributor.author	Feng, H
dc.contributor.author	Yang, G
dc.contributor.author	Tian, H
dc.contributor.author	Yu, Q
dc.date.accessioned	2024-03-11T04:05:20Z
dc.date.available	2024-03-11T04:05:20Z
dc.date.issued	2023-12-01
dc.identifier.citation	Communications Earth and Environment, 2023, 4, (1)
dc.identifier.issn	2662-4435
dc.identifier.issn	2662-4435
dc.identifier.uri	http://hdl.handle.net/10453/176462
dc.description.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.
dc.language	English
dc.publisher	SPRINGERNATURE
dc.relation.ispartof	Communications Earth and Environment
dc.relation.isbasedon	10.1038/s43247-023-01016-9
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	37 Earth sciences
dc.subject.classification	41 Environmental sciences
dc.title	The optimization of model ensemble composition and size can enhance the robustness of crop yield projections
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-11T04:05:18Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	1

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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http://hdl.handle.net/10453/176462