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Article

Evaluation of TOC Change Scenarios in Cropping Systems with and Without Diversification Across Different Scales: Insights from a Northern Italian Case Study

by
Chiara Piccini
*,
Silvia Vanino
,
Claudia Di Bene
,
Alessandro Marchetti
and
Roberta Farina
Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment (CREA-AA), Via della Navicella 2-4, 00184 Rome, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(23), 10823; https://doi.org/10.3390/su172310823
Submission received: 25 September 2025 / Revised: 17 November 2025 / Accepted: 26 November 2025 / Published: 3 December 2025
(This article belongs to the Special Issue Soil Pollution, Soil Ecology and Sustainable Land Use)
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Abstract

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.

1. Introduction

Agricultural systems, along with the challenges they pose for sustainable development, are generally complex. Among soil properties, soil organic carbon (SOC) is a key indicator of soil quality under different cropping systems and is considered a general indicator for assessing soil quality [1]. Soils act as the main terrestrial carbon (C) reservoir, holding a significant amount of the C actively cycled worldwide [2,3,4,5]. While C stored in living biomass contributes only to short-term sequestration, SOC represents a more stable, long-term C storage pathway [6]. As climate change and land use transformations intensify worldwide [7,8], understanding how SOC will respond to shifting environmental conditions has become increasingly important. Reliable information on SOC supports sustainable soil management practices that can reduce production costs, enhance crop yields, and protect the environment [9]. Such information is also especially significant in the context of Sustainable Development Goal target 15.3, which aims to achieve Land Degradation Neutrality (LDN), as established by the United Nations Convention to Combat Desertification (UNCCD; http://www.unccd.int).
Agricultural soils experience temporal variations in SOC, influenced also by management strategies, including cropping systems, fertilization, residue handling, and tillage regimes [10,11,12]. Among these, cultivation methods and crop rotations play a central role, as they strongly influence SOC levels and thus the long-term sustainability of farming systems [13,14]. In Italy, agricultural landscapes are largely characterized by cropping systems centered on winter and summer cereals, grown either in monoculture or in short rotations with other rainfed or summer-irrigated crops, such as processing tomatoes, forage crops, or various mixed sequences, which may also include periods of bare fallow [15].
Crop diversification—adding new species or varieties, or adjusting existing rotations—expands the range of crops grown in a given area and often involves integrating additional crops or reducing reliance on external inputs. Cover crops, in particular, are widely recognized as a sustainable practice that limits soil and water losses, increases organic matter, and enhances biodiversity and fertility in degraded agricultural soils [16] while also mitigating the impact of agriculture on climate change [17]. In intensive agricultural systems, crop diversification contributes to environmental sustainability and has been linked to multiple co-benefits, including higher and more stable yields, improved crop quality, and increased system resilience [18,19,20]. It can also boost farm income and contribute to poverty reduction [21]. Overall, diversified farming approaches have demonstrated positive outcomes for food production [22,23], groundwater conservation [24,25], C sequestration [26], and biodiversity protection [22,23,26,27,28]. Kamau et al. [29] found that nearly 47% of the world’s land can support profitable crop diversification strategies.
Evaluating the influence of crop diversification on soil–water–atmosphere systems begins at the field scale, where both measured and modeled point data must be scaled up to represent the entire field to understand its effects on farm yields, soil, and the environment. A broader landscape-scale perspective is also needed to support regional planning and to help with a detailed evaluation of agricultural economic and environmental aspects. Extrapolating local observations to wider areas is essential, and mapping tools are useful for identifying zones with similar characteristics. Maps can help policymakers recognize agroenvironmental, social, and economic impacts across territories. To effectively support sustainable regional development, assessments must account for both the geographic context and the diversity of cropping systems [30]. In particular, the effect of diversification in the soil–plant system can be assessed at different scales. SOC variation over time is the crucial aspect for understanding the effect of diversification, much more than single values. Upscaling consists of an increase in scale extent and commonly involves an expansion in spatial coverage accompanied by a lower resolution. Cross-scaling is suitable when the dynamics of processes at a given scale are indirectly influenced by processes occurring at other scales [31].
The use of machine learning (ML) techniques—renowned for their ability to automatically detect complex regression patterns and effectively manage outliers and missing data—has grown significantly in recent years in a variety of scientific domains. In soil science research, nonlinear models can be more effective at capturing the complexity of soil–environment interactions [32]. Thus, pedometrics employs statistical models designed to infer the spatial and temporal distribution of soil from data [33].
The H2020 Diverfarming project (http://www.diverfarming.eu/index.php/en/, accessed on 25 November 2025) intended to promote sustainable, diversified cropping systems with low-input innovative farming practices, adopting a multidisciplinary approach across Europe. In this framework, the aim of this work was to assess how diversified cropping systems can influence SOC content at the territorial level to better understand their potential to support more sustainable outcomes. A case study from Northern Italy is presented, selected to represent the most widespread irrigated and rainfed cereal-based cropping systems in these areas. Here, experimental data were upscaled from field to landscape by an ML (Random Forest) algorithm.

2. Materials and Methods

2.1. Study Area

The research was conducted on a farm situated in the Po Valley, Cremona province, Lombardy region, Northern Italy, an intensive arable land located in the Mediterranean North agroclimatic zone (farm center: 45°04′58″ N 10°26′01″ E), at about 30 m a.s.l. In this area, the mean annual temperature is 13.2 °C, and the mean annual precipitation is 760 mm, mainly occurring in the fall–winter period. Here, highly specialized professional farms for cereals, horticulture, and livestock production are present; current agricultural management practices are characterized by intensive production systems. The area is representative of a cereal-based cropping system, where tomato–durum wheat rotation is the most common farm management. Durum wheat is rainfed, while tomato is irrigated.
The soil in the experimental area has a silty loam texture. The conventional management consists of a 3-year tomato–durum wheat rotation (tomato–tomato–durum wheat), with uncovered soil periods between crop cycles. Current management practices include intense tillage, mineral fertilization, and integrated pest management. Fertilization for durum wheat consists of 40 tons per hectare of digestate, applied in October, plus a total of 100 kg N ha−1 in pre-seeding and top dressing. Tomato is irrigated with 1230 mm ha−1 year−1, distributed from June to August every 3 days, and fertilized with 165 kg N ha−1 at the transplant and as fertigation.
In 2017–2018, a leguminous crop (pea for food) was introduced in the rotation as a diversification strategy, followed by the introduction of tomato after the pea harvest as a multiple-cropping strategy. The diversified crop rotation, therefore, consisted of durum wheat–industrial tomato–pea for food/industrial tomato.

2.2. Field Data Collection and Mapping

The dataset consisted of 24 soil point samples, collected from the experimental field—which had a total area of about 0.2 km2—from the 0–30 cm layer (tilled layer). Samples were collected in October 2017 before the start of the experiment, with the process repeated at the end, in October 2019. The location of sampling points is reported in Figures 2 and 3. All the samples were analyzed for total organic carbon (TOC) by an elemental analyzer (LECO TOC Analyzer, mod. RC-612, St Joseph, MI, USA).
Field maps can support farmers and consultants in understanding the response of crop management at the field level. Usually, a probabilistic (geostatistical) approach is preferable to a deterministic estimator because it treats uncertainty explicitly. In our case, it has been verified that, given the small number of measured points and the limited spatial variability of TOC, a deterministic interpolator provides good spatial estimates with an acceptable global map error. Among deterministic interpolation methods, Inverse Distance Weighting (IDW) is the most commonly applied due to its simplicity and accessibility. It relies on the principle that unsampled point values can be approximated through a weighted average of nearby sampled points, either within a threshold distance or from a set number of closest observations, usually between 10 and 30. Weights are conventionally assigned as an inverse power of distance [34,35]. The estimated map of measured point data at the field scale was drawn by applying the IDW interpolation method to the measured data at time zero. For validating the spatial estimation, the Root Mean Square Error (RMSE) was calculated using the following formula:
R M S E = ∑ i = 1 N Z ^ ( x i ) − Z ( x i ) 2 N
where Z ^ (xi) are estimated values, Z(xi) are actual observations, and N is the number of observations. If the interpolator accurately describes the data, the RMSE should be lower or approximately equal to the standard deviation [36].

2.3. Simulation at Field Scale

Originally developed for the simulation of highly organic soils, the ECOSSE model uses a pool-type approach, describing soil organic matter as pools of inert organic matter, humus, biomass, resistant plant material, and decomposable plant material. The model accounts for all principal processes of soil C turnover, simulated by simplified equations requiring only commonly available input data [37]. This facilitates the model’s adaptation from field-scale applications to use as a national-level tool with minimal loss of accuracy. ECOSSE is able to simulate how land use, management, and climate change impact C dynamics. For a complete and more detailed description of the structure and formulation of C dynamics in the model, please refer to Smith et al. [37].
ECOSSE was then modified by adding new subroutines to account for dry soils and to consider multiple cropping, irrigation, and applications of types of manure that were not included in the original version [38]. The model was calibrated according to Smith et al. [37] in a long-term experiment in Southern Italy, observing a good agreement between observed and predicted values (RMSE < 10%) [38]. In this work, the modified version was run to predict the SOC (expressed as TOC) change in soils after a long period of diversification, compared with the same period of conventional farming. Model predictions were performed by running the model with point data corresponding to available soil profiles for a comprehensive view of the effect of crop diversification. For the simulation, we chose a projected climate scenario obtained by the Agri4Cast platform (ETHZ, Intermediate realization of climate change with markedly different precipitation patterns) [39].
In the experimental field, ECOSSE was run comparing 30 years (from 2018 to 2047) of the conventional farming system and 30 years of diversification, with a monthly time step. Initial TOC values from field measurements taken before the implementation of the diversification treatments were used, ensuring site-specific initialization. Model validation was conducted using the TOC measurements performed after three years of experiment. These simulations gave us the foreseen variations in TOC in each measured point, and these point datasets were then spatialized to the field extent with IDW for a better visual comparison.

2.4. Upscaling at Landscape Scale

Assessing the effects of crop diversification at the landscape scale is essential for informing agricultural planning. Generalization, the standard technique for translating higher-resolution maps to coarser scales, consists of aggregating areas with shared characteristics, largely guided by formal understanding and intuitive reasoning [40]. For upscaling, the support (landscape area of interest) was defined as a continuous area surrounding the experimental site with similar pedological and climatic features. The landscape extent was defined mainly based on the soil region—an area across which soils are relatively uniform—to which the experimental field belongs and on the municipalities (NUTS3) interested in the studied diversification. In our case, the landscape area comprised three provinces—Mantua, Cremona, and Brescia—in the Lombardy Region. The climate was assumed to be the same as the experimental field. In Figure 1, the graphic representation of the adopted procedure is reported.
The ECOSSE model was previously conveniently modified to run simultaneously at several spatially distributed points. A tool called MultiECOSSE was developed to run the model efficiently in a batch mode. The simulation was then extended to a set of soil profiles with the same land use (intensive agriculture) in a relatively homogeneous landscape surrounding the experimental area. The information from all the complete soil profiles available for the study area was retrieved from the European Soil Database v. 2.0 (https://esdac.jrc.ec.europa.eu/content/european-soil-database-v20-vector-and-attribute-data, accessed on 25 November 2025). The simulation was performed under two different scenarios: “present”, using the information about the current climate, and “30 years”, considering the modeled results in the next 30 years, again using the ETHZ climate scenario. Point model results for TOC variation were then interpolated at the landscape scale using an ML approach—the “ranger” function in R (version 4.3.3), a fast implementation of Random Forests particularly suitable for high-dimensional data [41]. Then, a set of 15 covariates (standard DEM derivatives of interest to soil mapping), listed in Table 1, was used as auxiliary information in the ML algorithm application. The covariates were selected based on their “importance” in predicting TOC and then converted to principal components to reduce covariance and dimensionality, as well as to fill in all missing pixels [42]. The resulting principal components were related to the target variable by means of the “ranger” function. The output was a prediction map with an associated standard error map that represents the validation step.

3. Results

In Table 2, descriptive statistics of the measured initial TOC in g kg−1 are reported.
The map of the measured TOC content in sampling points in the 0–30 cm layer at the beginning of the experiment, spatialized by means of IDW, is reported in Figure 2. Sampling locations are also indicated. The calculated RMSE of this map is 1.64, lower than the standard deviation of the measured TOC data. This supports the choice of IDW as the interpolation method.
Figure 3 presents the maps of the modeled TOC variation after 30 years in the 0–30 cm layer for the two considered scenarios—conventional and diversified management—demonstrating how diversification can mitigate C loss from the soils of the area. Such maps, based on spatial interpolation, can be readily updated as new data become available and can serve as effective tools for monitoring soil–plant system dynamics in relation to the crop management adopted by the farmer.
Upscaling involves extrapolating values over a larger space; thus, scaling efforts necessarily involve managing the transfer of information among different levels. Switching from the field scale to the landscape scale means lowering the geometric precision to achieve a broader view of the territory. A basic upscaling method consists of extending measurements obtained at a single location to a broader area, but this method does not consider spatial variability. If multiple point locations representing spatial variability are available, they may be aggregated into homogeneous spatial zones based on the type of crops and rotations, soils, and management [52]. A more accurate approach should consider the spatial variability of soil and management.
According to Hengl et al. [53], using ML instead of linear regression can considerably improve output data quality. Uncertainties are linked mainly to the quantity and quality of the input data. In Figure 4, the maps of modeled TOC variation in the 0–30 cm layer at landscape scale—both for conventional and diversified management—are shown, and in Figure 5, the maps of the corresponding standard estimation error are reported to corroborate the accuracy of the spatial estimation. Looking at these maps, it is clear how diversification can mitigate C loss from the soils of the area. The differences in soil C loss are likely not substantial, since probably just introducing a cover crop in the rotation is not sufficient to obtain a considerable reduction in such loss. Coupling crop diversification with legumes with other conservative practices (cf. https://www.fao.org/conservation-agriculture/en/, accessed 17 November 2025) can be effective not only in mitigating soil C loss but also in enhancing soil C sequestration.

4. Discussion

Field maps can be considered as a tool to determine the positive or negative impact of a specific crop diversification strategy on soil TOC at the field level. If the effect of the soil–plant system in a portion of the field does not meet the farmer’s expectations, one or more agronomic practices can be adjusted only in that sector, thereby applying a precision agriculture method (for application examples, see [54,55,56]). This can enhance the sustainability of farming, e.g., reducing inputs where they are not necessary.
At the landscape scale, approaches simply based on land use maps can be considered an efficient first-order approximation, but quantitative approaches for preserving the quality of the original information are recommended whenever feasible. Errors might be avoided if upscaling is based on a representative set of known points instead of aggregated data; thus, the availability of reliable soil data in the target area is very important for the success of the upscaling procedure. Nevertheless, there are good reasons to perform upscaling with block support (areal representation of data) because some information, like management data from regional statistics or agronomic models (scenarios), is often available only with block support [52].
Several researchers have demonstrated that the adoption of crop diversification in intensive agricultural systems can represent an important step towards sustainability. In their work, Nicholls et al. [57] found that crop diversification practices such as rotation, intercropping, and multiple cropping favor ecological interactions that help sustain soil fertility, nutrient cycling and retention, water storage, pest and disease control, and pollination, thereby reducing reliance on external inputs and mitigating the effects of—and promoting the adaptation to—climate change. Poeplau and Don [58] considered the use of cover crops as a promising and sustainable method for climate change mitigation, given their relatively high rates of carbon sequestration and the large spatial extent of areas suitable for cultivation. Laamrani et al. [14] concluded that conservation tillage combined with the inclusion of cover crops within crop rotations produced positive effects on topsoil C and may represent appropriate management strategies for enhancing soil fertility and sustainability in agricultural lands. Novara et al. [16] concluded that cover crops represent a positive contribution to the sustainability of agriculture. Locatelli et al. [59] simulated the impact of crop diversification and tillage on SOC dynamics in Brazil up to the year 2070, suggesting that diversified cropping systems may constitute a promising strategy for enhancing SOC sequestration. They also provide important insights for the improvement of current management practices.
Research by Yan et al. [60] contributed to gaining insight into the long-term performance of diversified cropping systems under real-world conditions and climate change, providing actionable information for optimizing crop productivity alongside soil quality in sustainable agriculture. Other researchers have explored the usefulness of maps in evaluating agricultural systems. For example, Heiß et al. [61] generated opportunity maps for highlighting potential improvements in ecosystem services through agricultural management changes at the field level. Oubdil et al. [62] highlighted the value of combining field observations, laboratory analyses, and spatial interpolation techniques to better capture soil variability at the regional scale. Their results confirm the relevance and applicability of mapping for supporting sustainable land management.
In our research, the maps elaborated at the landscape scale represent an additional tool for stakeholders to understand if the crop management diversification has a positive impact on soil, land, biodiversity, and economic aspects in a territory. Moreover, they can help determine if changes in techniques or different types of crops should be applied. In particular, they can show how the application of different crop diversification management can influence a large region.
A limitation of the present study could be that a fixed sampling scheme may not fully capture fine-scale spatial heterogeneity. Although the chosen sampling density is appropriate for the scale and objectives of the model validation, residual small-scale variability may still influence the extrapolation of long-term model results. Another limitation could be the relatively short period of simulation (30 years), which may not fully highlight the long-term trend of soil C variation. Thus, a longer period of observation might be necessary to better define the effect of diversification on soil properties when evaluating the maps. It should be noted that our results refer to the specific context of the Po Valley, and the findings may not be directly extended to other environments with different climatic and/or pedological conditions.

5. Conclusions

The primary objective of this study was to assess the influence of a diversified cropping system on SOC content at a territorial scale, thereby enhancing the understanding of its potential to support more sustainable agricultural outcomes. An integrated procedure was tested in a Northern Italian case study, where a legume was introduced into a cereal-based system. This procedure integrates a model for simulating C dynamics, an interpolation method to extend the results to the field scale, and an ML approach to upscale the model result to a broader landscape.
The maps obtained in this work show the effect of the studied diversification strategy, considering two periods: the present and 30 years onward, predicted by the ECOSSE model. In particular, landscape maps identify areas with similar climatic, soil, and land use characteristics. The results confirm that a spatial assessment with maps at both field and landscape scales seems to be effective in assessing the long-term effect of crop diversification with legumes on soil C at different scales. This can help stakeholders obtain a quick overview of the impact of crop diversification management on farms and on the territory. This assessment can also help to identify issues and opportunities for crop diversification to improve sustainable management towards a more resilient agriculture. Adopting crop diversification together with other conservation practices can be effective not only in mitigating soil C loss but also in enhancing soil C sequestration.
In conclusion, this work supports the role of diversified cropping systems in improving soil health and contributing to regional climate change mitigation. Since a longer period of observation may be necessary to better define the effect of diversification on soil properties, further investigation is desirable. However, the applicability of our findings to other regions should be considered with caution, and future research could be conducted applying the procedure in different pedoclimatic zones.

Author Contributions

Conceptualization, R.F., C.P., S.V., and C.D.B.; methodology, A.M., C.D.B., and C.P.; data curation, R.F. and C.D.B.; mapping, A.M. and C.P.; writing—original draft preparation, C.P.; writing—review and editing, C.D.B.; supervision, R.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Union’s Horizon 2020 Research and Innovation Programme, Diverfarming project, grant agreement No. 728003.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available in a publicly accessible repository. The original data presented in the study are openly available in Zenodo at https://zenodo.org/records/3685753 (accessed 17 November 2025).

Acknowledgments

The authors wish to thank Marianna Cerasuolo and Khadiza Begum for performing the simulations with ECOSSE.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
SOCSoil Organic Carbon
TOCTotal Organic Carbon
IDWInverse Distance Weighting
MLMachine Learning

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Figure 1. Scheme of the multi-scale spatial analysis.
Figure 1. Scheme of the multi-scale spatial analysis.
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Figure 2. Map of the measured Total Organic Carbon content in the 0–30 cm layer at the beginning of the experiment, spatialized by Inverse Distance Weighting.
Figure 2. Map of the measured Total Organic Carbon content in the 0–30 cm layer at the beginning of the experiment, spatialized by Inverse Distance Weighting.
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Figure 3. Maps of the modeled total organic carbon variation in the 0–30 cm layer after 30 years of (a) conventional management and (b) crop diversification. Spatialization is performed by inverse distance weighting.
Figure 3. Maps of the modeled total organic carbon variation in the 0–30 cm layer after 30 years of (a) conventional management and (b) crop diversification. Spatialization is performed by inverse distance weighting.
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Figure 4. Maps of the simulated total organic carbon content variation in the 0–30 cm layer at the landscape scale after 30 years of (a) conventional management and (b) crop diversification.
Figure 4. Maps of the simulated total organic carbon content variation in the 0–30 cm layer at the landscape scale after 30 years of (a) conventional management and (b) crop diversification.
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Figure 5. Maps of the standard estimation error of modeled total organic carbon content variation in the 0–30 cm layer at landscape scale after 30 years of (a) conventional management and (b) crop diversification.
Figure 5. Maps of the standard estimation error of modeled total organic carbon content variation in the 0–30 cm layer at landscape scale after 30 years of (a) conventional management and (b) crop diversification.
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Table 1. Covariates used for the Machine Learning application.
Table 1. Covariates used for the Machine Learning application.
PredictorDescriptionReference
DEMDigital elevation model, 25 mhttps://land.copernicus.eu/user-corner/publications/eu-dem-flyer/at_download/file (accessed 22 September 2025)
CPROFProfile curvature[43]
DEVMEANDeviation from the mean value—Relations of each grid cell to its neighbourhood[44]
OPENN, OPENPTopographic openness indicates the degree of dominance (positive) or enclosure (negative) at a specific site and is linked to the extent of the visible landscape from a given point[45,46,47]
SLOPESlope[43]
TWIThe “SAGA Wetness Index” is similar to the “Topographic Wetness Index”, but it relies on a modified catchment area computation that does not treat flow as a thin film. For cells located on valley floors with minimal vertical distance to a channel, it predicts higher and more realistic potential soil moisture than the conventional TWI calculation[48,49]
VBFCombination of a “multiresolution index of valley bottom flatness” (MRVBF) and the complementary “multiresolution index of the ridge top flatness” (MRRTF)[50]
VDEPTHValley depth, determined as the difference between the elevation and an interpolated ridge level[43]
NIRLandsat8 OLI band 4, 30 m[51]
REDLandsat8 OLI band 3, 30 m[51]
SW1Landsat8 OLI band 5, 30 m[51]
SW2Landsat8 OLI band 7, 30 m[51]
TREECOVERTree cover in 2000, defined as the canopy closure of all vegetation exceeding 5 m in height, 30 m[51]
BARESOILGlobal bare ground cover obtained from annual seamless composites of Landsat 7 ETM+ per band using the median reflectance of all cloud- and shadow-free observations during the growing season, 30 m[51]
Table 2. Descriptive statistics of the initial TOC (g kg−1) dataset in the experimental field.
Table 2. Descriptive statistics of the initial TOC (g kg−1) dataset in the experimental field.
CountMinMaxMeanSt DevVarSkewnessKurtosis
247.3615.8510.351.773.141.182.02
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Piccini, C.; Vanino, S.; Di Bene, C.; Marchetti, A.; Farina, R. Evaluation of TOC Change Scenarios in Cropping Systems with and Without Diversification Across Different Scales: Insights from a Northern Italian Case Study. Sustainability 2025, 17, 10823. https://doi.org/10.3390/su172310823

AMA Style

Piccini C, Vanino S, Di Bene C, Marchetti A, Farina R. Evaluation of TOC Change Scenarios in Cropping Systems with and Without Diversification Across Different Scales: Insights from a Northern Italian Case Study. Sustainability. 2025; 17(23):10823. https://doi.org/10.3390/su172310823

Chicago/Turabian Style

Piccini, Chiara, Silvia Vanino, Claudia Di Bene, Alessandro Marchetti, and Roberta Farina. 2025. "Evaluation of TOC Change Scenarios in Cropping Systems with and Without Diversification Across Different Scales: Insights from a Northern Italian Case Study" Sustainability 17, no. 23: 10823. https://doi.org/10.3390/su172310823

APA Style

Piccini, C., Vanino, S., Di Bene, C., Marchetti, A., & Farina, R. (2025). Evaluation of TOC Change Scenarios in Cropping Systems with and Without Diversification Across Different Scales: Insights from a Northern Italian Case Study. Sustainability, 17(23), 10823. https://doi.org/10.3390/su172310823

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