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The simple AMG model accurately simulates organic carbon storage in soils after repeated application of exogenous organic matter

Abstract : Repeated application of exogenous organic matter (EOM) contributes to soil organic carbon (SOC) stocks in cropped soils. Simple and robust models such as the AMG model are useful tools for predicting the effects of various EOM practices on SOC. In AMG, EOM is characterized by a single parameter: the humification rate h, which represents the proportion of exogenous carbon that is incorporated into SOC. The AMG model has been validated for a range of pedo-climatic conditions and cropping systems, but has not yet been tested with data from long-term field experiments where EOM is regularly applied. The calibration of the EOM parameter h also remains an issue. In this study, AMG was used to simulate SOC stocks in seven long-term field experiments with EOM application. AMG predicted changes in SOC stocks with a mean RMSE of 3.0 t C ha−1 when h values were optimized. The optimized h values were highly correlated (R2= 0.62) with the indicator of remaining organic carbon (IROC), measured by laboratory analysis. The present study demonstrates (1) the ability of the AMG model to accurately simulate SOC stocks evolution in long-term field experiments with regular EOM application and (2) the ability of calibrating the model using IROC, which is routinely measured by commercial laboratories. The parameter h was determined for 26 EOM types utilizing a database of more than 600 IROC. The AMG model can thus be used to predict the SOC increase following EOM addition with a very simple calibration.
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https://hal.inrae.fr/hal-02895345
Déposant : Anaïs Durivault <>
Soumis le : jeudi 9 juillet 2020 - 16:10:55
Dernière modification le : jeudi 27 août 2020 - 03:26:11

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Florent Levavasseur, Bruno Mary, Bent Christensen, Annie Duparque, Fabien Ferchaud, et al.. The simple AMG model accurately simulates organic carbon storage in soils after repeated application of exogenous organic matter. Nutrient Cycling in Agroecosystems, Springer Verlag, 2020, 117 (2), pp.215-229. ⟨10.1007/s10705-020-10065-x⟩. ⟨hal-02895345⟩

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