Abstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.
Original language | English |
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Article number | e2023EF003773 |
Number of pages | 21 |
Journal | Earth's Future |
Volume | 12 |
Issue number | 3 |
DOIs | |
Publication status | Published - 11 Mar 2024 |
Bibliographical note
AcknowledgmentsJ.J. was supported by the NASA GISS Climate Impacts Group and the Open Philanthropy Project. J.F. was supported by the NSF NRT program (Grant DGE-1735359) and the NSF Graduate Research Fellowship Program (Grant DGE-1746045). A.C.R. received support from the NASA Earth Sciences Division via the GISS Climate Impacts Group. S.O. and T.P. acknowledge support from the Swedish strong research areas BECC and MERGE, together with support from LUCCI (Lund University Centre for studies of Carbon Cycle and Climate Interactions). W.L. was supported by the National Natural Science Foundation of China (Grant 32361143871). X.W. acknowledges support from the National Natural Science Foundation of China (Grant 42171096). Open Access funding enabled and organized by Projekt DEAL.
Keywords
- global
- evaluation
- uncertainty
- crop model
- AgMIP
- sensitivity