Increasing soil organic carbon (SOC) is widely recognized as a key pathway for climate change mitigation and enhancing soil ecosystem services, but additional carbon farming practices can lead to additional GHG emissions. We propose a process-based SOC module, AresC model, integrated into an agronomic DSS already in use to monitor SOC while optimizing crop management practices and GHG emissions. AresC model estimates SOC dynamics simulating the effects of management practices, including soil tillage, under Mediterranean climatic conditions. The integrated DSS-SOC model is tested in four case studies including arable crops and orchards under different conditions, with a focus on arid Mediterranean areas. The AresC model showed good agreement with the measured values in all conditions with no significant bias or root mean square errors, and Spearman correlation factors very close to 1. A global sensitivity analysis showed that carbon inputs are the dominant driver of SOC. Finally, compared to the RothC model, AresC showed better performance under drier conditions, and no significant differences were observed between the two models in humid conditions. This study, therefore, demonstrates the potential in agricultural carbon accounting applications of the newly developed AresC model integrated into an agronomic DSS to optimize crop management while properly simulating SOC dynamics.

Decision support systems (DSSs) help farmers and decision-makers to find cropping systems which increase productivity while decreasing the use of fertilizers and irrigation; however, few DSSs exist which integrate soil carbon models, especially in Mediterranean climate. Here, we test whether RothC20_N, a version of the widely used RothC model adapted for Mediterranean and arid climates, can estimate the soil water content (SWC), soil organic carbon (SOC), and soil respiration (Rs), observed in two different cropping systems, one traditional and the other informed by the DSS by Horta Srl. The model was calibrated and tested against two long-term (8 years) field experiments in two different areas in Italy. The two sites showed characteristically dry soils during the summer period; RothC20_N was able to predict correctly the soil water content time series observed in both sites. RothC20_N could predict the measured SOC and heterotrophic respiration time series with Nash–Sutcliff efficiency ~0.3, normalized root mean squared error ~0.4, and Kling–Gupta efficiency > 0. This study shows that the multi-objective calibrated RothC20_N is an interesting candidate for inclusion in a DSS to broaden the potential of the DSS to increase the sustainability of agricultural practices by also addressing soil carbon dynamics while maintaining high agricultural productivity.