Do Poplar Plantations Contribute to Soil Carbon Storage? A Closer Look

Poplar plantations, a cornerstone of Italy’s wood economy, are promoted as a promising tool for carbon (C) farming and climate mitigation mainly due to their capacity to store C in the living biomass. However, it remains uncertain whether these systems are capable of having a positive impact on SOC dynamics. Thus, this study aims to investigate the role of poplar plantations in SOC sequestration, addressing key uncertainties while aligning with broader European agenda on C farming. The primary objective was to examine the entire lifecycle of poplar cultivation and its impact on SOC, employing two complementary methodologies: the SOC stock difference approach and the paired comparison method. Specifically, the study focused on: i) assessing the effect of 30 years of poplar cultivation on SOC using a diachronic approach; ii) comparing SOC levels between poplar plantations and the previous land use (e.g., cropland), and iii) assessing the impact of poplar removal and the reestablishment of cropland, both using the paired comparison method.

Leveraging a thirty-year dataset, we analyzed SOC trends at 0–30 cm soil depth in poplar plantations managed under conventional systems, using data collected from the same locations over time. These systems exhibited significant potential for increasing SOC, likely driven by reduced soil disturbance, the incorporation of organic inputs, extended rotation cycles, and improved biodiversity.

To contextualize these findings, SOC levels in poplar plantations and croplands using paired sampling methods were compared. Interestingly, while the diachronic method suggests absolute SOC accumulation over time in poplar plantations, the paired method revealed less pronounced differences between the systems. This methodological discrepancy highlights the complexity of accurately assessing SOC sequestration and raises questions in qualifying the true advantages of poplar plantations over monoculture cropping systems.

Additionally, the third part of this study focuses on the effects of converting poplar plantations back to croplands, with a focus on fields that have been converted in the past 1 to 3 years. This highlights the challenges posed by reverse land-cover changes on the permanence of SOC. The findings aim to guide the development of effective management strategies to mitigate SOC losses during such transitions, ensuring the long-term sustainability of carbon farming practices.

How to cite: Antoniella, G., Kumar, A., Chiarabaglio, P. M., Scarascia Mugnozza, G., and Chiti, T.: Do poplar plantations contribute to soil organic carbon storage? A closer look, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6114, https://doi.org/10.5194/egusphere-egu25-6114, 2025.

EGU25-6114

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Carbon farming practices, such as cover crops, are crucial for increasing soil organic carbon (SOC) content and promoting sustainable agriculture. Cover crops, including yellow mustard and Japanese oats, play a key role in improving SOC by increasing biomass inputs during the non-growing season. Accurate identification and monitoring of winter soil cover in agricultural fields is essential to assess the effectiveness of these practices. 
This study uses Sentinel-2 satellite imagery to map winter soil cover in Belgian agricultural fields. Target cover types include bare soil, grassland, winter cereals, maize residues and various cover crops. Sentinel-2’s high temporal and spatial resolution, combined with its multispectral bands, enables the differentiation of soil cover types based on their spectral signatures. Feature engineering allows to derive covariates for supervised classification models from interpolated time-series of Sentinel-2 spectral bands and indices. Ground truth data collected during four consecutive winter seasons (2021-2024) were used to train and validate the random forest model.
This research demonstrates the potential of Sentinel-2 imagery for monitoring winter soil cover in agricultural fields, allowing to assess the implementation and effectiveness of carbon farming practices. Future work will focus on refining classification methods and on integrating winter cover management practices in SOC modelling to quantify their long-term benefits, contributing to more effective carbon sequestration strategies in croplands.

How to cite: Vanongeval, F., Van Orshoven, J., and Gobin, A.: Mapping winter soil cover in Belgian agricultural fields using Sentinel-2 imagery for carbon farming monitoring., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9691, https://doi.org/10.5194/egusphere-egu25-9691, 2025.

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Soil CO₂ emissions are a critical component of the carbon cycle, serving as a key indicator of soil fertility and health. Tillage practices significantly influence soil organic matter accumulation, making the monitoring of soil CO₂ emissions crucial in the context of Carbon Farming. The Mediterranean region, identified as a climate change hotspot, faces alterations in rainfall and temperature patterns that not only affect crop yields but also impact the soil carbon cycle. In Mediterranean agriculture, a common practice is the rotation of winter (e.g., durum wheat) and summer (e.g., maize) crops, typically involving a nine-month fallow period between the harvest of the winter crop (around July) and the sowing of the summer crop (around April). Despite its importance, this fallow period remains underexplored in studies of soil CO₂ emissions.

This study quantifies soil CO₂ emissions, temperature, and moisture during the fallow period in a Mediterranean winter-summer rotation system, examining the effects of long-term tillage intensity. The research was conducted on a long-term experimental trial established in 1994 in Agugliano, Central Italy. The site features silty-clay soil (pH 8.3) with an average annual rainfall of 820 mm and a mean temperature of 15.3 °C. The long-term trial involves a split-plot design that includes three tillage levels (conventional tillage at 0–40 cm, reduced tillage at 0–10 cm, and no-tillage) and three nitrogen levels (0, 90, and 180 kg N/ha/year). This study focuses on conventional tillage (CT) and no tillage (NT) with no nitrogen fertilization to isolate tillage effects. Soil CO2 emissions were measured biweekly from July 2022 to March 2023 using a portable infrared gas analyser. Concurrently, soil temperature and moisture at 0–10 cm depth were recorded.

Results revealed that soil CO₂ emissions in NT closely followed precipitation patterns during summer, showing distinct CO₂ pulses, while emissions in winter were negligible due to low temperatures despite higher soil moisture. In spring, rising temperatures led to significantly higher emissions in NT compared to CT. Positive correlations between CO₂ emissions and soil temperature, and negative correlations with soil moisture, were observed. Cumulative CO₂ emissions during the fallow period were 0.35 and 0.55 t/ha for CT and NT, respectively.

These findings highlight the importance of extending soil CO₂ monitoring beyond the crop-growing season to the fallow period, especially in no-tillage systems. These practices are crucial for accurate carbon accounting and assigning credits in carbon markets.

How to cite: Francioni, M., D'Ottavio, P., Bianchini, M., Deligios, P. A., Ledda, L., Rivosecchi, C., Mammarella, F., Giampieri, A., and Orsini, R.: Long-term effect of no-tillage on soil CO2 emissions during the fallow period between winter and summer crops, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9948, https://doi.org/10.5194/egusphere-egu25-9948, 2025.

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Sicily's landscape features lush fields of olives, citrus trees, and vineyards, as well as vast stretches of beaches and Mount Etna. However, this landscape is changing fast, as some farmers are removing traditional crops (olives and citrus trees) to cultivate exotic ones (avocados, bananas, kiwi, mango, papaya, passion fruit, and pineapple) or abandoned agricultural/pasture areas. Facing a complex climatic scenario, especially related to a lack of water in the reservoirs and wells, the future projections indicate an increasing trend in these abandoned areas in Sicily. Land use and land cover change (LUCC) is a critical driver of carbon storage dynamics in terrestrial ecosystems, as it changes vegetation structure, soil properties, and ecological processes, which impacts the carbon balance (source and sink). This work is the first step in evaluating ecosystem services related to carbon sequestration in citrus cultivation compared to abandoned agricultural areas in semi-arid Mediterranean climate conditions, Centuripe. The modeling was carried out using the InVEST model, which simulates the net change in carbon sequestration over time and its economic value. The Centuripe region has roughly 615 thousand hectares of agricultural abandoned area. The model pointed out an abandoned area around 0,45 Mg C ha^-1 is stored below and above ground while the citrus grove stores around 7,69 Mg C ha^-1. The model simulations pointed out that citrus groves that have been around for 12 and 32 years generate US$793.34 per hectare and US$1,575.65 per hectare, respectively, for the ecosystem service of carbon sequestration. Therefore, actions should be assessed to motivate farmers and decision-makers to sustain citrus groves instead of abandoning them.

How to cite: Imbuzeiro, H., João, G., Petersen, V., Vanella, D., and Consoli, S.: How much is the value of the abandonment of a citrus grove in a semi-arid Mediterranean region - a carbon storage perspective using the InVEST model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11936, https://doi.org/10.5194/egusphere-egu25-11936, 2025.

EGU25-11936

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This study presents a robust framework for spatially explicit monitoring of soil properties and Above Ground Biomass (AGB) estimation in Mediterranean agroforestry and cropland systems by integrating remote sensing (RS) and artificial intelligence (AI). These variables are critical for assimilation into process-based models for Soil Organic Carbon (SOC) dynamics monitoring within a Monitoring, Reporting, and Verification (MRV) system. The framework was developed as part of the MRV4SOC project in Spain, aimed at designing a comprehensive, robust, and cost-effective Tier-3 approach. The primary goal is to produce high-quality geospatial layers of topsoil properties and AGB estima tion, which serve as key inputs for SOC dynamics modeling.

The methodology was tested at two long-term demonstration sites in Spain: Quercus ilex Dehesas in Extremadura (SW Spain) and rainfed cereal crops at La Canaleja experimental farm in central Spain. These agroecosystems provide diverse testing grounds for scalable and transferable SOC assessment methodologies within an MRV framework. The approach integrates multi-temporal remote sensing data (2018–2022) from Sentinel-2 and Landsat satellites with machine learning models to predict essential soil properties (SOC, Sand, Silt, Clay, pH, and Total N) and AGB. Ground truth data for AGB estimation were sourced from the Spanish National Forest Inventory (SNFI), while soil property predictions utilized the LUCAS 2018 topsoil libraries due to limited site-specific datasets for model training. A bare soil reflectance composite (2018–2022) derived from Sentinel-2 bands (B02–B12) at 20-meter resolution was employed for geospatial soil property mapping.

Given the limited availability of ground truth data, simpler models like Quantile Regression Forests (QRF) and XGBoost were selected. QRF achieved better accuracy for soil texture properties, with R² = 0.62 for clay and outperforming XGBoost for SOC (R² = 0.63) and pH (R² = 0.76) in the agroforestry site. However, XGBoost performed better for SOC (R² = 0.54) and total nitrogen in croplands, as well as for sand, silt, clay, and total nitrogen in the agroforestry site (R² = 0.61 for clay). For AGB estimation in the Dehesas area, a machine learning approach was implemented using SNFI data and remote sensing-derived transformation features. A gradient boosting algorithm (LightGBM) resulted in an R² value of 0.8. In La Canaleja, a bare soil reflectance composite was similarly employed for soil property mapping. Further analysis will be carried out to develop a bottom-up approach for monitoring SOC using these products and process-based models

Uncertainty analysis using Prediction Interval Ratio (PIR) assessment was conducted separately for landscape (L) and sub-landscape (SL) levels. While most properties showed medium to low uncertainty, sand and silt exhibited higher variability in croplands, and SOC displayed the highest uncertainty in the agroforestry site across L and SL levels.

This methodology contributes significantly to improving MRV systems by delivering high-quality geospatial layers for SOC dynamics monitoring in complex environments. Increasing ground truth data availability is essential for enhancing model accuracy and minimizing prediction uncertainties further.

How to cite: Palantza, D., Samarinas, N., Kechagias, S., Dutta, O., de la Fuente, D., Gómez-Giménez, M., Torres Fernández del Campo, J., Hernández Mateo, L., Cañellas, I., Santín, I., S. Gimeno, B., Kuehl, K., Heiden, U., and Zalidis, G.: Integrating Remote Sensing and AI modelling in Mediterranean Agroforestry and Croplands systems: A Methodological Perspective for spatial SOC monitoring in the MRV4SOC project, Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13513, https://doi.org/10.5194/egusphere-egu25-13513, 2025.

EGU25-13513

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Soil organic carbon (SOC) models are utilized to extrapolate our knowledge of SOC dynamics over time and space, allowing us to evaluate SOC stocks for entire regions of interest. Numerous research works have been implemented following different approaches to evaluate SOC dynamics on a regional, national, and international scale.

In the agricultural regions of Northern Greece, two fundamentally different approaches to SOC sequestration modeling have been employed and evaluated. Public EO data from the GEE geoprocessing platform, concerning temperature and precipitation variables (ERA5), Land Cover information from the Copernicus Global Land Service (CGLS) products, MODIS-based annual NPP and GPP (Net and Gross Primary Production), and WorldClim bioclimatic variables, representing seasonality and annual trends in climatic parameters, were utilized for this purpose. The deterministic modeling approach, which is based entirely on process-based models of multi-compartment carbon decomposition and accumulation (RothC) combined with local soil observation data from 2009 soil-mapping surveys, was used to analyze SOC spatiotemporal dynamics during the decade 2009-2018. This approach was compared with the statistical, data-driven modeling approach, which was applied to the revisited points of the LUCAS Land Use/Land Cover soil observation database in the area during the same decade. A collection of global environmental covariates, selected to reflect a variety of soil-forming factors and soil-change drivers, was assembled using GEE platform resources. These covariates were utilized in the data-driven modelling approach, to generate spatial predictions of SOC, by modelling the relationship between target and auxiliary environmental variables.

The distribution of SOC dynamics in the study area was found significantly different between the two modeling approaches. In some locations, the data-driven model that was built with LUCAS data identified substantial SOC stock losses, while RothC model simulated steadily increasing SOC stocks. This discrepancy can be attributed to the inherent limitations of the RothC process-based modelling approach.

This work was co-financed by the Interreg Euro-MED Programme within the project “Capturing and Storing Atmospheric CO2 for Improvement of Soil Quality - CARBON 4 SOIL QUALITY”.

How to cite: Karapetsas, N., Bilas, G., Righi, A., Longo, M., Morari, F., Koutsos, T., and Alexandridis, T. K.: Modeling perspectives on soil carbon sequestration in Mediterranean regions: a comparison of process-based and statistical models in the croplands of Northern Greece, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21207, https://doi.org/10.5194/egusphere-egu25-21207, 2025.

EGU25-21207

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The implementation of carbon credit policies is particularly relevant in Mediterranean agroecosystems, which are highly vulnerable to SOC losses due to climate change. However, to what extent each soil has potentials to increase the SOC content is still debated, making it necessary to determine reliable benchmark values to achieve for maximum SOC accumulation. Here, we implemented and tested different model approaches to determine topsoil SOC reference values in the Euro-Mediterranean region, including the Balkans , with data from the LUCAS soil database.. According to other studies, the reference value was identified as equivalent to those of less disturbed agroecosystems such as managed grasslands.  Three methods were tested to estimate the sequestration potential. The first method was based on the identification of pedoclimatic zones through probabilistic clustering using a Gaussian Mixture Model (GMM), where each homogeneous areas was identified based distribution of  pedological, topological and climatic conditions  (e.g., texture, air temperature , elevation and net photosynthetic production ). The second method employed random forest, a machine learning technique. The model, trained on grassland reference points, was then applied to agricultural land, estimating potential SOC levels as if every point were converted to grassland. Lastly, the RothC  model was applied to each point, simulating grassland management for 100 years, and the obtained SOC value was taken as the reference one. Each point then had three potential SOC reference values, and the median value has been taken as the final sequestration potential.
The harmonization of these three methods provided reliable sequestration potential estimates for each LUCAS point, allowing for point-specific predictions. Simultaneously, this approach enabled the delineation of geographical zones with distinct pedoclimatic properties, producing maps of reference zones for SOC sequestration. These maps will allow carbon credit policies to be tailored to the specific conditions of each region in the Euro-Balkan Mediterranean area, ensuring more effective policy implementation.

This work was co-financed by the Interreg Euro-MED Programme within the project “Capturing and Storing Atmospheric CO₂ for Improvement of Soil Quality - CARBON 4 SOIL QUALITY”.

How to cite: Righi, A., Camarotto, C., Piccoli, I., Karapetsas, N., Bilas, G., and Morari, F.: Defining soil organic carbon sequestration potential in Mediterranean agroecosystems for effective carbon credit policies: a multi-method approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21210, https://doi.org/10.5194/egusphere-egu25-21210, 2025.

EGU25-21210

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