Roberta Farina

Process-based soil organic carbon (SOC) models are widely used for carbon accounting and assessments. For applications beyond experimental sites, remote sensing–derived inputs are often used to complement or replace in situ boundary conditions. This substitution introduces additional sensitivity that has rarely been explicitly quantified. Here, we propose a general framework to quantify boundary-condition sensitivity in SOC modelling, distinguishing between dynamic sensitivity, which reflects deviations in the temporal trajectory of SOC stocks, and endpoint sensitivity, which captures differences in estimated SOC changes over defined reporting periods. Sensitivity is quantified by comparing substitution simulations, in which boundary conditions are replaced one by one with remote sensing–derived spatial datasets, against a baseline simulation driven by in situ observations. The framework is applied to 16 long-term experimental sites across Europe, covering diverse land-use types and environmental conditions. Boundary-condition substitution affected both SOC trajectories and endpoint estimates, although the magnitude and form of these effects varied among inputs. Initial SOC, vegetation-derived carbon inputs, and climate data were the dominant drivers of dynamic sensitivity. Fixed endpoint sensitivity was generally small relative to measured SOC changes, supporting the use of remote sensing–derived spatial datasets as scalable boundary-condition proxies for SOC modelling. Rolling endpoint sensitivity further revealed that SOC change assessments depend not only on SOC dynamics but also on the selected reporting window. Short reporting periods were more affected by short-term variability, whereas longer periods were more susceptible to accumulated deviations. A reporting window of approximately 15–30 years provided a practical balance between these effects. Overall, the framework enables a systematic quantification of sensitivity arising from boundary-condition substitution, enabling evaluation of both trajectory and reporting robustness. These findings highlight the importance of jointly considering critical boundary conditions and reporting-window design when applying remote sensing–driven SOC modelling for regional carbon accounting.

Process-based soil organic carbon (SOC) models are widely used for carbon accounting and assessments. For applications beyond experimental sites, remote sensing–derived inputs are often used to complement or replace in situ boundary conditions. This substitution introduces additional sensitivity that has rarely been explicitly quantified. Here, we propose a general framework to quantify boundary-condition sensitivity in SOC modelling, distinguishing between dynamic sensitivity, which reflects deviations in the temporal trajectory of SOC stocks, and endpoint sensitivity, which captures differences in estimated SOC changes over defined reporting periods. Sensitivity is quantified by comparing substitution simulations, in which boundary conditions are replaced one by one with remote sensing–derived spatial datasets, against a baseline simulation driven by in situ observations. The framework is applied to 16 long-term experimental sites across Europe, covering diverse land-use types and environmental conditions. Boundary-condition substitution affected both SOC trajectories and endpoint estimates, although the magnitude and form of these effects varied among inputs. Initial SOC, vegetation-derived carbon inputs, and climate data were the dominant drivers of dynamic sensitivity. Fixed endpoint sensitivity was generally small relative to measured SOC changes, supporting the use of remote sensing–derived spatial datasets as scalable boundary-condition proxies for SOC modelling. Rolling endpoint sensitivity further revealed that SOC change assessments depend not only on SOC dynamics but also on the selected reporting window. Short reporting periods were more affected by short-term variability, whereas longer periods were more susceptible to accumulated deviations. A reporting window of approximately 15–30 years provided a practical balance between these effects. Overall, the framework enables a systematic quantification of sensitivity arising from boundary-condition substitution, enabling evaluation of both trajectory and reporting robustness. These findings highlight the importance of jointly considering critical boundary conditions and reporting-window design when applying remote sensing–driven SOC modelling for regional carbon accounting.

Mediterranean agroecosystems are highly vulnerable to climate change, soil degradation, and declining soil organic carbon (SOC), threatening long-term agricultural sustainability. Carbon farming and regenerative agriculture have emerged as complementary approaches to restore soil functionality while contributing to climate change mitigation. This review synthesizes peer-reviewed literature published between 2015 and 2025 to assess the agronomic effectiveness of key regenerative and carbon farming practices in Mediterranean systems. A structured bibliographic analysis using Scopus and Web of Science evaluated practices influencing SOC dynamics, erosion control, water regulation, and associated ecosystem services. Evidence indicates that the introduction of cover crops in the crop rotation and reduced or no-tillage are the most consistently effective practices for enhancing SOC stocks, particularly when combined with organic amendments and diversified rotations. Crop diversification, intercropping, and agroforestry further support SOC accumulation and erosion control, especially in perennial systems such as vineyards and olive orchards. Organic inputs stimulate microbial-mediated carbon stabilization, while regenerative grazing contributes to nutrient cycling under context-specific conditions. Across practices, integrated management consistently delivers greater and more stable benefits than single interventions. Regenerative agriculture thus provides a systems-based foundation for carbon farming in Mediterranean agroecosystems. Long-term field experiments and improved monitoring frameworks remain essential to quantify carbon persistence and support policy implementation.

Soil organic carbon (SOC) is a key indicator used to evaluate cropping systems, as it reflects long-term productivity, sustainability, and environmental impacts like carbon sequestration. Diversifying crops within intensive farming systems is a possible strategy for enhancing the environmental sustainability of agriculture, resulting in higher rates of SOC accumulation compared to monocultures. This study seeks to evaluate the influence of diversified cropping systems on SOC content at both the field and territorial levels. In Northern Italy, two crop management approaches—one incorporating diversification and one without—were analyzed. The ECOSSE model was employed to simulate changes in SOC content over a 30-year period of diversification, compared with monocropping. The results of the model, first run in available sampling sites, were upscaled to the field to which they belong. Then, using a machine learning approach—namely Random Forest—they were interpolated at the landscape scale, extending the information to an area with similar soil, climate, and management conditions. The maps obtained with this procedure represent valuable tools to assess the long-term effects of crop diversification with legumes on soil C at different scales and can support agricultural policymakers and planners.

Soil organic carbon (SOC) can be effective in evaluating cropping systems and their sustainability. Diversifying crops within intensive farming systems enhances the en-vironmental sustainability of agriculture. The effect of diversification in the soil-plant system can be assessed at different scales, and the field represents the first assessment level. For this purpose, both measured and modelled point data must be extrapolated to the corresponding field and subsequently extended to the broader territory in which they are situated. This study seeks to evaluate the influence of diversified cropping systems on SOC content at territorial level. In Northern Italy, two crop management approaches — one incorporating diversification and one without — were analyzed. The ECOSSE model was employed to simulate changes in SOC content over a 30-year period of diversification, compared with monocropping. The results of the model, first run in available soil profiles, were upscaled to the field to which they belong; then, using a machine learning approach, data were interpolated at the landscape scale, extending the information to several land units with fairly uniform soil, climate, and management conditions. Achieving a thorough assessment of the impact of crop diversification at landscape scale represents a valuable tool to support decisions for agricultural policy makers and planners.

The integration of different agroecological practices could significantly mitigate the impact of climate change. Therefore, a 2-year field experiment on organic zucchini was carried out to study the effects of clover (Trifolium alexandrinum L.) cover crop management (green manure, GM vs. flattening using a roller crimper, RC), compared to a control without cover (CT). This agroecological practice was tested in combination with the following different fertilizer treatments: T1. compost produced by co-composting coal mining wastes with municipal organic wastes compost plus urea; T2. compost produced with the same matrices as T1, replacing urea with lawn mowing residues; T3. non-composted mixture of the industrial matrices; T4. on-farm compost obtained from crop residues. The GM management showed the highest marketable yield and aboveground biomass of zucchini, with both values higher by approximately 38% than those recorded in CT. The T1, T2, and T3 treatments showed higher SOC values compared to T4 in both years, with a gradual increase in SOC over time. The residual effect of fertilization on SOC showed a smaller reduction in T3 and T4 than in T1 and T2, in comparison with the levels recorded during the fertilization years, indicating a higher persistence of the applied organic matter in these treatments. The findings of this study pointed out that combining organic fertilization and cover cropping is an effective agroecological practice to maintain adequate zucchini yields and enhance SOC levels in the Mediterranean environment.

Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that in a homogeneous above-ground tree cover, the heterogeneous distribution of soil microbial functional diversity and processes at the small scale is correlated with the soil’s chemical properties. From this perspective, in a typical Mediterranean holm oak (Quercus ilex L.) peri-urban forest, soil carbon dioxide (CO₂) emissions were measured with soil chambers in three different plots. In each plot, to test the linkage between above-ground and below-ground communities, soil was randomly sampled along six vertical transects (0–100 cm) to investigate soil physico-chemical parameters; microbial processes, measured using Barometric Process Separation (BaPS); and structural and functional diversity, assessed using T-RFLP and qPCR Real Time analyses. The results highlighted that the high spatial variability of CO₂ emissions—confirmed by the BaPS analysis—was associated with the microbial communities’ abundance (dominated by bacteria) and structural diversity (decreasing with soil depth), measured by H′ index. Bacteria showed higher variability than fungi and archaea at all depths examined. Such an insight showed the clear ecological and environmental implications of soil in the overall sustainability of the peri-urban forest system.

Soil and crop management influence soil organic carbon (SOC), chemical composition, and overall soil quality. In a Mediterranean region, a study initiated in 1994 examined the long-term effects of conventional tillage (CT) versus no-tillage (NT) practices. Initially focusing on continuous durum wheat cultivation until 2009, the experiment later introduced a 2-year rotation of durum wheat and Vicia faba L. cover crops in half of the CT and NT fields. SOC was monitored from 2008 to 2018, while microbial biomass (as dsDNA), soluble nitrogen (N), and enzyme activities (EAs) were monitored from 2011 to 2014 to evaluate the rotation’s impact. Between 2009 and 2018, CT yields were on average 15% higher than NT, especially during high rainfall years. NT significantly increased SOC content in the 0–30 cm soil layer, along with higher levels of soluble N, dsDNA, and EAs at 0–10 cm depth. NT led to a 23% and 10% increase in SOC stock and SOC stock per equivalent soil mass compared to CT. EAs increased by over 50% under NT, indicating enhanced biological activity. The SOC increase due to NT was limited to the top 10 cm, with a decrease at deeper depths (up to 50 cm). Introducing cover crops over 4 years did not yield significant impacts, suggesting the need for a longer period to observe noticeable effects. Overall, adopting NT practices resulted in higher SOC concentration, enhanced soil biological activity, and improved biogeochemical cycles, emphasizing the positive impact of NT on soil health and sustainability.