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Modeling soil organic carbon evolution in long-term arable experiments with AMG model

hal.structure.identifierAgroressources et Impacts environnementaux [AgroImpact]
dc.contributor.authorCLIVOT, Hugues
hal.structure.identifierAgro-Transfert Ressources et Territoires
dc.contributor.authorMOUNY, Jean-Christophe
hal.structure.identifierAgro-Transfert Ressources et Territoires
dc.contributor.authorDUPARQUE, Annie
hal.structure.identifierAgro-Transfert Ressources et Territoires
dc.contributor.authorDINH, Jean-Louis
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorDENOROY, Pascal
hal.structure.identifierEcologie fonctionnelle et écotoxicologie des agroécosystèmes [ECOSYS]
hal.structure.identifierUniversité Paris-Saclay
dc.contributor.authorHOUOT, Sabine
hal.structure.identifierSol Agro et hydrosystème Spatialisation [SAS]
dc.contributor.authorVERTÈS, Francoise
hal.structure.identifierStation Expérimentale de La Jaillière
dc.contributor.authorTROCHARD, Robert
hal.structure.identifierStation du Magneraud
dc.contributor.authorBOUTHIER, Alain
hal.structure.identifierPôle du Griffon
dc.contributor.authorSAGOT, Stéphanie
hal.structure.identifierAgroressources et Impacts environnementaux [AgroImpact]
dc.contributor.authorMARY, Bruno
dc.date.accessioned2024-04-08T11:50:03Z
dc.date.available2024-04-08T11:50:03Z
dc.date.issued2019
dc.identifier.issn1364-8152
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195367
dc.description.abstractEnReliable models predicting soil organic carbon (SOC) evolution are required to better manage cropping systems with the objectives of mitigating climate change and improving soil quality. In this study, data from 60 selected long-term field trials conducted in arable systems in France were used to evaluate a revised version of AMG model integrating a new mineralization submodel. The drivers of SOC evolution identified using Random Forest analysis were consistent with those considered in AMG. The model with its default parameterization simulated accurately the changes in SOC stocks over time, the relative model error (RRMSE = 5.3%) being comparable to the measurement error (CV = 4.3%). Model performance was little affected by the choice of plant C input estimation method, but was improved by a site specific optimization of SOC pool partitioning. AMG shows a good potential for predicting SOC evolution in scenarios varying in climate, soil properties and crop management.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enSoil carbon storage
dc.subject.enMineralization
dc.subject.enSoil organic carbon
dc.subject.enCarbon inputs
dc.subject.enAMG model
dc.titleModeling soil organic carbon evolution in long-term arable experiments with AMG model
dc.typeArticle de revue
dc.identifier.doi10.1016/j.envsoft.2019.04.004
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halSciences du Vivant [q-bio]/Sciences agricoles/Science des sols
bordeaux.journalEnvironmental Modelling and Software
bordeaux.page99-113
bordeaux.volume118
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-02161566
hal.version1
hal.popularnon
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02161566v1
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