Pathways to identify and reduce uncertainties in agricultural climate impact assessments.

Wang, B; Jägermeyr, J; O'Leary, GJ; Wallach, D; Ruane, AC; Feng, P; Li, L; Liu, DL; Waters, C; Yu, Q; Asseng, S; Rosenzweig, C

Pathways to identify and reduce uncertainties in agricultural climate impact assessments.

Wang, B Jägermeyr, J O'Leary, GJ Wallach, D Ruane, AC Feng, P

Li, L Liu, DL Waters, C Yu, Q Asseng, S Rosenzweig, C

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Nat Food, 2024, 5, (7), pp. 550-556
Issue Date:: 2024-07

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Field	Value	Language
dc.contributor.author	Wang, B
dc.contributor.author	Jägermeyr, J
dc.contributor.author	O'Leary, GJ
dc.contributor.author	Wallach, D
dc.contributor.author	Ruane, AC
dc.contributor.author	Feng, P https://orcid.org/0000-0003-4845-9876
dc.contributor.author	Li, L
dc.contributor.author	Liu, DL
dc.contributor.author	Waters, C
dc.contributor.author	Yu, Q
dc.contributor.author	Asseng, S
dc.contributor.author	Rosenzweig, C
dc.date.accessioned	2025-01-05T21:14:05Z
dc.date.available	2024-06-14
dc.date.available	2025-01-05T21:14:05Z
dc.date.issued	2024-07
dc.identifier.citation	Nat Food, 2024, 5, (7), pp. 550-556
dc.identifier.issn	2662-1355
dc.identifier.issn	2662-1355
dc.identifier.uri	http://hdl.handle.net/10453/182961
dc.description.abstract	Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Nat Food
dc.relation.isbasedon	10.1038/s43016-024-01014-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	3002 Agriculture, land and farm management
dc.subject.classification	3006 Food sciences
dc.subject.mesh	Climate Change
dc.subject.mesh	Uncertainty
dc.subject.mesh	Agriculture
dc.subject.mesh	Crops, Agricultural
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Humans
dc.subject.mesh	Humans
dc.subject.mesh	Crops, Agricultural
dc.subject.mesh	Uncertainty
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Agriculture
dc.subject.mesh	Climate Change
dc.subject.mesh	Climate Change
dc.subject.mesh	Uncertainty
dc.subject.mesh	Agriculture
dc.subject.mesh	Crops, Agricultural
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Humans
dc.title	Pathways to identify and reduce uncertainties in agricultural climate impact assessments.
dc.type	Journal Article
utslib.citation.volume	5
utslib.location.activity	England
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	closed_access	*
dc.date.updated	2025-01-05T21:14:03Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	7

Abstract:

Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/182961