Application of high-resolution regional climate model simulations for crop yield estimation in Southern Brazil
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Cuadra, Santiago Vianna; De Oliveira, Monique Pires Gravina; De Castro Victoria, Daniel; Bender, Fabiani Denise; Bettolli, Maria Laura; Solman, Silvina; Da Rocha, Rosmeri Porfírio; Fernández Fernández, Jesús (matemático)
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2025-04-07Derechos
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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AgriEngineering, 2025, 7(4), 108
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MDPI
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Palabras clave
Regional climate models
Convection permitting
Crop growth model
Soybean
Maize
Resumen/Abstract
This study is focused on assessing the impacts of different regional climate model targeted simulations performed at convection-permitting resolution (CPRCM) in the AgS crop model yield simulations, evaluating to what extent climate model uncertainty impacts the modeled yield-considering the spatial and temporal variability of crop yield simulations over central-south Brazil. The ensemble of CPRCMs has been produced as part of a Flagship Pilot Study (FPS-SESA) framework, endorsed by the Coordinated Regional Climate Downscaling Experiment (CORDEX). The AgS simulated crop yield exhibited significant differences, in both space and time, among the simulations driven by the different CPRCMs as well as when compared with the simulations driven by observations. Rainfall showed the highest uncertainty in CPRCM simulations, particularly in its spatial variability, whereas modeled temperature and solar radiation were generally more accurate and exhibited smaller spatial and temporal differences. The results evidenced the need for multi-model simulations to account for different uncertainty, from different climate models and climate models parameterizations, in crop yield estimations. Interinstitutional collaboration and coordinated science are key aspects to address these endto-end studies in South America, since there is no single institution able to produce such CPRCM-CropModels ensembles.
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