Modification of the RothC model to simulate soil C mineralization of exogenous organic matter

Abstract

<p><strong>Abstract.</strong> The development of soil organic C (SOC) models capable to produce accurate predictions of the long term decomposition of exogenous organic matter (EOM) in soils is important for an effective management of organic amendments. However, reliable C modelling in amended soils requires specific optimization of current C models to take into account the high variability of 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. <br><br> 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 respiratory curves of amended soils. For this task, 30 EOMs from 8 contrasting groups (compost, anaerobic digestates, sewage sludges, agro-industrial wastes, crop residues, bioenergy by-products, animal residues, meat and bone meals), were added to 10 soils and incubated under different conditions. <br><br> The modified Roth C model was fitted to C mineralization curves in amended soils with great accuracy (mean correlation coefficient: 0.995). Differently 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 squared error of the simulations for different EOMs (from 29.9 % to 3.7 % and from 20.0 % to 2.5 % for bioethanol residue and household waste compost amended soils, respectively). Average decomposition rates for DEOM and REOM pools were 89 y<sup>−1</sup> and 0.4 y<sup>−1</sup>, higher than the standard model coefficients for DPM (10 y<sup>−1</sup>) and RPM (0.3 y<sup>−1</sup>). <br><br> Results indicate that explicit treatment of EOM heterogeneity enhances the model ability to describe amendment decomposition under laboratory conditions and provides useful information to improve C modelling on the effects of different EOM on C dynamics in agricultural soils. <br><br> Future researches involve the validation of the modified model with field data and its application to long term simulation of SOC patterns in amended soil at regional scale under climate change.</p>

Simulating soil organic C dynamics in managed grasslands under humid temperate climatic conditions

“…Similarly, Mondini et al (2017) improved the prediction of SOC stocks in amended soils by fitting the RothC partitioning pools of different EOM to the respiratory curves. Such adjustment of the partition of EOM into RPM, DPM and HUM entry pools of RothC provided a successful fit and had been reproduced in other studies (e.g., Pardo et al (2016)).…”

“…The inclusion of the ruminant excreta quality in the model only slightly improved the SOC predictions in RothC_2 compared to RothC_1 (Table 4). In this context, Heitkamp et al (2012) and Mondini et al (2017) emphasised the importance of modifying the quality of residues to improve the model performance, concluding that the adjustment of DPM:RPM ratio led to better model performance than the use of default DPM: RPM values provided by the model. Comparing RothC_2 and RothC_3 versions, it could be deduced that integrating quantity and quality distinction of plant residue in RothC_3, as a primary source of SOC (Castellano et al, 2015), improved SOC predictions.…”

The crop residue conundrum: maintaining long-term soil organic carbon stocks while reinforcing the bioeconomy, compatible endeavors?

“…Organic matter decomposition involves complex processes in uenced by the biomass characteristics, eventual stabilization treatment and/or recalcitrance degree, pedoclimatic conditions, and interactions with the soil microbiota, among others. An accurate prediction of the coproducts carbon persistence in soils is therefore challenging (Lehmann et al, 2020;Mondini et al, 2017). Some soil models have been adapted or parameters have been proposed, to simulate the return of bioeconomy coproducts into soils.…”

“…This includes, for instance, RothC (Lefebvre et al, 2020;Woolf and Lehmann, 2012), Century (Dil and Oelberman), APSIM (Archontoulis et al, 2016), and EPIC (Lychuk et al, 2015) for biochar. For digestate, CTOOL (Hansen et al, n.d.), AMG (Levavasseur et al, 2020), CANDY (Witing et al, 2018), and RothC (Mondini et al, 2017) have been adapted. RothC (Mondini et al, 2017) has also ben adapted to consider bioethanol coproducts, such as the nonfermentable residue.…”

“…For digestate, CTOOL (Hansen et al, n.d.), AMG (Levavasseur et al, 2020), CANDY (Witing et al, 2018), and RothC (Mondini et al, 2017) have been adapted. RothC (Mondini et al, 2017) has also ben adapted to consider bioethanol coproducts, such as the nonfermentable residue. BioEsoil, a tool based on RothC, evaluates the effect of residues from bioenergy processes (i.e., gasi cation and incineration) on soil organic matter (Bonten et al, 2014).…”