2026-04-14T05:27:18Zhttps://recercat.cat/oai/request
oai:recercat.cat:20.500.12327/22152025-10-22T11:15:04Zcom_2072_4428com_2072_4427col_2072_487898
RECERCAT author Sándor, Renáta author Ehrhardt, Fiona author Grace, Peter author Recous, Sylvie author Smith, Pete author Snow, Val author Soussana, Jean-François author Basso, Bruno author Bhatia, Arti author Brilli, Lorenzo author Doltra, Jordi author Dorich, Christopher D. author Doro, Luca author Fitton, Nuala author Grant, Brian author Harrison, Matthew Tom author Skiba, Ute author Kirschbaum, Miko U.F. author Klumpp, Katja author Laville, Patricia author Léonard, Joel author Martin, Raphaël author Massad, Raia Silvia author Moore, Andrew author Myrgiotis, Vasileios author Pattey, Elizabeth author Rolinski, Susanne author Sharp, Joanna author Smith, Ward author Wu, Lianhai author Zhang, Qing author Bellocchi, Gianni other Producció Vegetal group Cultius Extensius Sostenibles 2025-10-22T11:15:04Z 2025-10-22T11:15:04Z 2023-01-13 Sándor, Renáta, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Val Snow, and Jean-François Soussana et al. 2023. "Residual Correlation And Ensemble Modelling To Improve Crop And Grassland Models". Environmental Modelling &Amp; Software 161: 105625. doi:10.1016/j.envsoft.2023.105625. 1364-8152 http://hdl.handle.net/20.500.12327/2215 https://doi.org/10.1016/j.envsoft.2023.105625 Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development. eng Attribution-NonCommercial-NoDerivatives 4.0 International Residual correlation and ensemble modelling to improve crop and grassland models info:eu-repo/semantics/article