Publication Abstracts
Wang et al. 2024
Wang, B., , G.J. O'Leary, D. Wallach, , P. Feng, L. Li, D.L. Liu, C. Waters, Q. Yu, S. Asseng, and , 2024: Pathways to identify and reduce uncertainties in agricultural climate impact assessments. Nat. Food, 5, no. 7, 550-556, doi:10.1038/s43016-024-01014-w.
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.
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BibTeX Citation
@article{wa07900t,
author={Wang, B. and Jägermeyr, J. and O'Leary, G. J. and Wallach, D. and Ruane, A. C. and Feng, P. and Li, L. and Liu, D. L. and Waters, C. and Yu, Q. and Asseng, S. and Rosenzweig, C.},
title={Pathways to identify and reduce uncertainties in agricultural climate impact assessments},
year={2024},
journal={Nature Food},
volume={5},
number={7},
pages={550--556},
doi={10.1038/s43016-024-01014-w},
}
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RIS Citation
TY - JOUR ID - wa07900t AU - Wang, B. AU - Jägermeyr, J. AU - O'Leary, G. J. AU - Wallach, D. AU - Ruane, A. C. AU - Feng, P. AU - Li, L. AU - Liu, D. L. AU - Waters, C. AU - Yu, Q. AU - Asseng, S. AU - Rosenzweig, C. PY - 2024 TI - Pathways to identify and reduce uncertainties in agricultural climate impact assessments JA - Nat. Food JO - Nature Food VL - 5 IS - 7 SP - 550 EP - 556 DO - 10.1038/s43016-024-01014-w ER -
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