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• W3191926684 abstract "Global gridded crop models (GGCMs) are increasingly used for climate risk assessments and adaptation planning in agriculture. GGCM historical simulation performance is therefore crucial for such applications. However, GGCM performance is lower for rice than for other crops at an aggregated administrative unit level despite the lack of a clear difference in model performance at the site level. Here, we present key factors that need to be considered in the spatial and temporal aggregation of GGCM outputs to improve the evaluation of GGCM historical rice simulations at the country scale. The factors include an adjustment for the harvesting year in GGCM rainfed and irrigated simulations, the removal of misreports from reference data, a consideration of the quality of national crop statistics (census reports), and the explicit incorporation of a planting window. The effect of each individual factor is demonstrated by analyzing a multi-GGCM dataset and performing a planting date ensemble simulation of two GGCMs. We reveal that among others, aligning the harvesting year in the GGCM simulations with that in national reports is pivotal. Although our analysis focuses specifically on rice, the findings of this study are useful for improving the country-level evaluations of GGCM historical simulations for other crops." @default.
• W3191926684 created "2021-08-16" @default.
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• W3191926684 date "2021-10-01" @default.
• W3191926684 modified "2023-09-25" @default.
• W3191926684 title "Aligning the harvesting year in global gridded crop model simulations with that in census reports is pivotal to national-level model performance evaluations for rice" @default.
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• W3191926684 doi "https://doi.org/10.1016/j.eja.2021.126367" @default.
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