Incorporating temperature-adapted growing periods enhances simulation of crop yield sensitivity to temperature in global gridded crop models

21447 6 113395 2 342332 document 113395 1-s2.0-S2666683926000258-main.pdf application/pdf 90ffd18107a8297853a717db4c1c0694 MD5 3196353 2026-04-07 12:10:52 https://pure.iiasa.ac.at/id/eprint/21447/1/1-s2.0-S2666683926000258-main.pdf 21447 1 1 application/pdf text en public cc_by_nc_nd_4

1-s2.0-S2666683926000258-main.pdf

published 113400 2 342361 document 113400 lightbox.jpg image/png f0e6a653b015048970f11b3f5cd75fcd MD5 49673 2026-04-07 12:20:57 https://pure.iiasa.ac.at/id/eprint/21447/2/lightbox.jpg 21447 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

lightbox.jpg

preview.jpg

medium.jpg

small.jpg

indexcodes.txt

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/113395 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/113395 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/113395 archive 353 disk0/00/02/14/47 2026-04-07 12:12:41 2026-04-07 12:12:41 2026-04-07 12:12:41 article show Wang X. Wang S. Folberth C. 2033 0000-0002-6738-5238 Skalský R. 1986 0000-0002-0983-6897 Balkovič J. 1977 0000-0003-2955-4931 Kraxner F. 7904 Li X. Liu J. Zheng B. Incorporating temperature-adapted growing periods enhances simulation of crop yield sensitivity to temperature in global gridded crop models pub BNR AFE Crop model, GGCM, Climate change, Temperature sensitivity, Growing period, Model ensemble Global Gridded Crop Models (GGCMs) have been widely used to simulate the impacts of global warming on crop production, but their accuracy in capturing the real-world temperature sensitivity of crop yields remains unclear. Here, we evaluated the performance of eight GGCM emulators (incorporating versus not incorporating cultivar adaptation of crop growing periods at 0.5° × 0.5° resolution) in modelling yield sensitivities to 1 K temperature increase (ST) and optimized their ensembles against statistically-inferred ST for maize, rice, and wheat using a Bayesian Model Averaging approach. Our results suggest that multi-GGCM ensembles assuming a fixed crop growing period (i.e., a gradually temperature-adapted crop cultivar) show higher goodness-of-fit to statistically-inferred ST than those assuming a temperature-sensitive growing period for the crops in major food-producing countries. When setting a temperature-adapted growing period instead of a temperature-sensitive growing period in the GGCM ensembles, the R2 between GGCM-simulated and statistically-inferred ST increased from 0.63 to 0.81 for maize, 0.28 to 0.52 for rice, and 0.40 to 0.85 for wheat, meanwhile the RMSE was reduced for all three crops across their respective top 20 producing countries. The crop models may exaggerate historical responses of crop growing periods to climate warming, resulting in an overestimation of yield ST for maize and an underestimation of yield ST for rice and wheat in major food-producing countries. The study highlights the importance of adopting dynamic phenological parameters in GGCM simulations to reflect crop cycle adaptation under global warming. 2026-04 published doi:10.1016/j.geosus.2026.100430 92 287 23 165 public Geography and Sustainability 7 2 e100430 1 TRUE 2666-6839 info:eu-repo/semantics/article