Residual correlation and ensemble modelling to improve crop and grassland models | Sándor, Renáta
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Residual correlation and ensemble modelling to improve crop and grassland models

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Sándor, Renáta | Ehrhardt, Fiona | Grace, Peter | Recous, Sylvie | Smith, Pete | Snow, Val | Soussana, Jean-François | Bhatia, Arti | Brilli, Lorenzo | Doltra, Jordi | Dorich, Christopher, | Doro, Luca | Fitton, Nuala | Grant, Brian | Harrison, Matthew, | Skiba, Ute | Kirschbaum, Miko, | Klumpp, Katja | Laville, Patricia | Léonard, Joël | Martin, Raphaël | Massad, Raia Silvia | Moore, Andrew, | Myrgiotis, Vasileios | Pattey, Elizabeth | Rolinski, Susanne | Sharp, Joanna | Smith, Ward | Wu, Lianhai | Zhang, Qing | Bellocchi, Gianni

Edité par HAL CCSD ; Elsevier

International audience. Model calibration Residual plot analysis 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.

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