Modeling soil organic carbon evolution in long-term arable experiments with AMG model
HOUOT, Sabine
Ecologie fonctionnelle et écotoxicologie des agroécosystèmes [ECOSYS]
Université Paris-Saclay
< Reduce
Ecologie fonctionnelle et écotoxicologie des agroécosystèmes [ECOSYS]
Université Paris-Saclay
Language
en
Article de revue
This item was published in
Environmental Modelling and Software. 2019, vol. 118, p. 99-113
Elsevier
English 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 ...Read more >
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.Read less <
English Keywords
Soil carbon storage
Mineralization
Soil organic carbon
Carbon inputs
AMG model
Origin
Hal imported