Abstract. The development of soil organic C (SOC) models capable of producing accurate predictions for the long-term decomposition of exogenous organic matter (EOM) in soils is important for the effective management of organic amendments. However, reliable C modeling in amended soils requires specific optimization of current C models to take into account the high variability in EOM origin and properties. The aim of this work was to improve the prediction of C mineralization rates in amended soils by modifying the RothC model to encompass a better description of EOM quality.

The standard RothC model, involving C input to the soil only as decomposable (DPM) or resistant (RPM) organic material, was modified by introducing additional pools of decomposable (DEOM), resistant (REOM) and humified (HEOM) EOM. The partitioning factors and decomposition rates of the additional EOM pools were estimated by model fitting to the respiratory curves of amended soils. For this task, 30 EOMs from 8 contrasting groups (compost, anaerobic digestates, sewage sludge, agro-industrial waste, crop residues, bioenergy by-products, animal residues and meat and bone meals) were added to 10 soils and incubated under different conditions.

The modified RothC model was fitted to C mineralization curves in amended soils with great accuracy (mean correlation coefficient 0.995). In contrast to the standard model, the EOM-optimized RothC was able to better accommodate the large variability in EOM source and composition, as indicated by the decrease in the root mean square error of the simulations for different EOMs (from 29.9 to 3.7 % and 20.0 to 2.5 % for soils amended with bioethanol residue and household waste compost, respectively). The average decomposition rates for DEOM and REOM pools were 89 and 0.4 yr⁻¹, higher than the standard model coefficients for DPM (10 yr⁻¹) and RPM (0.3 yr⁻¹).

The results indicate that the explicit treatment of EOM heterogeneity enhances the model ability to describe amendment decomposition under laboratory conditions and provides useful information to improve C modeling on the effects of different EOM on C dynamics in agricultural soils.

Future research will involve the validation of the modified model with field data and its application in the long-term simulation of SOC patterns in amended soil at regional scales under climate change.