Modeling soil organic carbon evolution in long-term arable experiments with AMG model. (August 2019)

• Record Type: Journal Article
• Authors: Clivot, Hugues; Mouny, Jean-Christophe; Duparque, Annie; Dinh, Jean-Louis; Denoroy, Pascal; Houot, Sabine; Vertès, Françoise; Trochard, Robert; Bouthier, Alain; Sagot, Stéphanie; Mary, Bruno
• Is Part Of: Environmental modelling & software. Volume 118(2019)
• Page Start: 99
• Page End: 113
• Publication Date: 2019-08
• Subjects: Soil organic carbon -- Mineralization -- Soil carbon storage -- Carbon inputs -- AMG model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.04.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Reliable 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. Highlights: SOC evolution in 60 long-term French trials was analyzed and simulated. On average, SOC stocks declined between 1970 and 2015 at a rate of −0.20 t C/ha/yr. Drivers of SOC evolution were similar in Random Forest analysis and AMG model. Implementing a new mineralization module in AMG improved SOC evolution predictions. Optimizing site-specific stable C pool could further improve model performance.