This session examines advancements in the measurement, modelling, and prediction of SOC dynamics across spatial and temporal scales, with a focus on its interactions with ecological processes. Central to this is the scientific understanding enabled by next-generation soil carbon systems, which provide deeper insights into SOC dynamics, their ecological interactions, and responses to environmental change in both agricultural and natural systems.
Key topics include strategies to enhance SOC sequestration, the roles of plant selection and soil microbial communities in promoting long-term SOC accrual, and the integration of experimental, observational, mapping and modelling approaches to improve predictions of SOC responses to changing environmental conditions. Moreover, critical reflections on the potentials of SOC sequestration in science and policy are invited.
By bringing together diverse perspectives, this session aims to advance our understanding of SOC's role in ecosystem sustainability and resilience, fostering actionable insights for addressing global challenges.
Predicting cold ecosystem responses is crucial for global climate modeling as climate warming drives profound changes in soil biogeochemical processes. However, large uncertainties in model predictions persist, partially owing to the lack of explicit representation of microbial responses to climate warming in permafrost and seasonally frozen ground. Here, we incorporate the freeze-thaw dynamics and associated microbial adaptation strategy into the Microbial-ENzyme Decomposition (MEND) model. Using a field warming experiment in an alpine meadow with seasonally frozen ground on the Qinghai-Tibetan Plateau (QTP), we calibrated and validated the new MEND model with diverse measurements, including soil carbon (C) and nitrogen (N) fluxes, microbial stoichiometry, and enzyme kinetics. In addition to accurately simulating soil respiration and inorganic N, the model correctly predicted the warming effects on microbial carbon use efficiency (CUE) and enzyme activities. Our findings highlight the importance of microbial dormancy as survival strategies under repeated freeze-thaw stress. We also observed potential regulation of freeze-thaw processes on soil N availability, while long-term projections revealed a substantial reduction in inorganic N, suggesting intensified competition between microbial and plant N uptake under warming. This experiment-model integration framework offers improved predictive capacity for soil biogeochemical feedbacks in cold ecosystems, contributing to more accurate global climate models.
How to cite: Wang, G. and Qi, S.: Microbial dormancy under freeze-thaw cycling regulates soil biogeochemical responses in alpine meadows, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2327, https://doi.org/10.5194/egusphere-egu25-2327, 2025.
Plant- and microbial-derived compounds has been recognized as key contributors to vulnerable and stable soil organic carbon (SOC) pools. However, the relative contributions of these sources along altitudinal gradients remain unclear. This study quantified the contributions of plant- and microbial-derived carbon (C) to SOC across four distinct vegetation zones along an altitudinal gradient ranging from 2600 to 3670 m in northwest China. SOC content increased significantly along altitudinal gradients in the 0–20 cm and 20–40 cm, indicating greater C sequestration in higher elevations. Both plant lignin and microbial necromass also increased with altitude in both soil layers. Notably, in lower altitudes, SOC accumulation was predominantly driven by plant-derived C, while in higher altitudes, microbial-derived C was dominated. The substantial SOC storage in higher altitudes is more microbially processed, which contributes to greater SOC stability, as opposed to the lower altitudes, where SOC is less stable and more vulnerable to environmental change. Regression analysis and random forest modeling reveal that soil pH, moisture, and total nitrogen as the primary regulators of plant lignin and microbial necromass, surpassing the influence of plant inputs such as root biomass. In conclusion, the content of both plant lignin and microbial necromass increases with altitude, while their respective contributions to SOC follow divergent patterns. These findings have significant implications for predicting C loss as a result of global climate change, underscoring the need for targeted conservation strategies across different altitudinal zones.
How to cite: Wang, X., Mou, X., Chen, J., Yao, B., Li, Y., Liu, J., Gong, X., Lian, J., Zhu, X., and Li, Y.: Patterns of soil organic carbon accumulation and microbiological mechanisms in mountain ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5327, https://doi.org/10.5194/egusphere-egu25-5327, 2025.
Soil hosts the world’s largest terrestrial carbon pool. This is primarily held as soil organic matter (SOM), which is ~60% carbon. While plant diversity is known to influence SOM retention, the strength and mechanisms of this relationship remain uncertain in some contexts, particularly in Australia. Persistence of SOM is influenced by the interplay of physical, chemical, and biological factors. One of the proposed mechanisms involves the diversity of molecules that comprise plant inputs and SOM. It is thought that greater molecular diversity fosters persistence by imposing an energy-gain limitation on soil microbes. Additionally, that molecular diversity may be related to SOM persistence by promoting greater mineral associations, making it inaccessible to microbial decomposition.
This research aims to answer the following questions:
1) Does greater native plant diversity inhibit mineralisation of SOM by soil microbes in West Australian soils?
2) Does greater native plant diversity result in greater molecular diversity of plant inputs to soil?
3) What is the contribution of aboveground plant litter inputs to differences in microbial community structure and carbon dynamics?
4) How does molecular diversity change over time from plant inputs to stable SOM?
To address these questions, a 15-month long soil and leaf litter incubation experiment was undertaken for two soil types. Different properties were measured to assess soil carbon dynamics, microbial community functional diversity, molecular diversity of inputs, and molecular diversity of SOM. The expected outcome is that plant diversity will foster soil carbon persistence with significant implications for restoring degraded landscapes. By promoting diverse vegetation, restoration efforts can maximize soil carbon storage and support ecosystem services, such as enhanced biodiversity and improved soil health. This research will inform land managers on strategies to enhance soil carbon storage through greater plant diversity, which has additional ecological value in the provision of other ecosystem services. By linking Australia’s unique plant diversity to broader ecological restoration and carbon management frameworks, these findings provide a scalable model for leveraging biodiversity to enhance soil carbon persistence worldwide.
How to cite: Wells, N., Walden, L., Pedrini, S., and Viscarra Rossel, R.: Plant diversity as a driver of soil carbon persistence and restoration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14860, https://doi.org/10.5194/egusphere-egu25-14860, 2025.
Climate change threatens biodiversity and ecosystem functioning globally. One key consequence of these interacting global threats is decreased soil organic carbon (SOC). Yet increasing or maintaining SOC is integral to many nature-based climate change mitigation strategies. Understanding how biodiversity loss and changes to the climate, such as an increased incidence of severe drought, alter SOC simultaneously is therefore crucial to understanding the potential for such nature-based climate change mitigation strategies. One way that biodiversity may be key for SOC under severe drought is by maintaining stable soil moisture and temperature at small scales.
We manipulated planted species richness (1 and 12 mixture) and imposed drought conditions in a large-scale biodiversity and climate variability experiment (BioCliVE) at Utrecht University (Netherlands). We hypothesized that increased plant diversity would sustain aboveground biomass and moderate soil temperature and moisture, thereby preventing SOC decline under drought
Although planted species richness consistently boosted aboveground biomass, altering soil temperature and moisture, SOC did not change across diversity levels or under short-term drought treatments. These findings suggest that short-term experiments may not capture the slow, often multi-year processes by which soil carbon pools respond to biodiversity-driven ecosystem changes. Our research demonstrates the complexity of soil carbon cycling and highlights the need for longer-term monitoring to detect meaningful changes in SOC. It also raises questions about how quickly and effectively nature-based solutions can deliver the carbon sequestration benefits assumed in many climate mitigation strategies. By examining the interplay among biodiversity, drought, and SOC, our study informs ongoing debates about how global change drivers influence soil carbon stability.
How to cite: Kingsland-Mengi, A., Chen, Y., Mayr, J., Verschoor, M., Kowalchuk, G., Barel, J., and Barry, K.: Does Plant Diversity Safeguard Soil Carbon During Drought in a Large-Scale Biodiversity and Climate Experiment?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18538, https://doi.org/10.5194/egusphere-egu25-18538, 2025.
Soil organic carbon (SOC) determines multiple ecosystem services that soils provide to humanity, serving as a critical component in maintaining soil health, fertility, and climate regulation. However, changes in land use and climatic conditions may alter the current soil carbon balance, potentially converting soils from carbon sinks into sources of atmospheric CO2. Such shifts can also alter soil properties and ecosystem functions impacting environmental stability and human well-being. Using a large number of global soil profile observations, environmental datasets, and different modeling techniques, we (1) quantified the magnitude and uncertainty associated with global and regional SOC estimates, (2) evaluated projections of future SOC stock changes based on Coupled Model Intercomparison Project Phase Six (CMIP6) Earth System Models, and (3) explored the potential of machine learning (ML) techniques to address existing knowledge gaps in SOC storage and dynamics. Our findings highlight significant variability in global SOC stock estimates, both for surface soils (0–30 cm) and deeper soil profiles (0–1 m), with predictive accuracy varying across depth intervals and biomes. Projections from CMIP6 Earth System Models indicate a potential increase in global soil carbon stocks under high-emission scenarios. Meanwhile, recent advancements in ML approaches show promise in reducing uncertainties surrounding SOC storage and dynamics, offering new pathways for improved understanding and modeling. Despite these advances, critical knowledge gaps persist regarding the current distribution and future fate of global SOC stocks in the context of changing climate and land-use patterns. Addressing these uncertainties will require a coordinated and multidisciplinary effort, encompassing: (1) harmonizing SOC profile observations and collecting samples from under-represented biomes, (2) improved representation of soil-forming processes and pedogenic feedbacks within Earth System Models, and (3) leveraging advanced data-driven approaches to enhance predictive capabilities. These activities will refine our understanding of the magnitude and trajectory of SOC stocks, enabling more accurate predictions and informing sustainable management strategies for global soil resources.
How to cite: Mishra, U., Salinas, J., Qin, Z., Shi, Z., and Nyaupane, K.: Global Soil Organic Carbon Storage and Dynamics: Current knowledge and Machine Learning Potentials, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2296, https://doi.org/10.5194/egusphere-egu25-2296, 2025.
Soil organic carbon (SOC) is a fundamental component of the soil carbon pool, and its carbon stock is about 2~3 times that of vegetation carbon stock, which plays an important carbon buffering role in the context of global climate change. In agricultural production, SOC is central to soil fertility, directly impacting soil health and food security. The spatial distribution of SOC is influenced by various geographical environmental variables, including climate, topography, soil-forming parent material, and vegetation, exhibiting a covariant relationship with these factors. Understanding the spatial distribution of SOC in agricultural land and identifying the primary controlling factors is essential for maintaining soil fertility and productivity. The nonlinear relationship between SOC and environmental covariates has been widely demonstrated, but the primary controlling effects of environmental covariates on SOC content and their contribution effects are often neglected. In this study, the primary controlling factors of SOC and their effects were explored by a CatBoost model, a decision tree-based gradient boosting algorithm, to reveal the mechanism of spatial differentiation of SOC at the regional scale, utilizing multi-source data such as measured data and remote sensing. The results showed that the CatBoost model outperforms univariate linear regression models across all independent variables, achieving an overall R² of 0.828, indicating that the model could explain the variations of the target variables well. Total nitrogen (TN), available phosphorus (AP), annual lowest temperature (T), cation exchange capacity (CEC), and available potassium (AK) were, in order, the most significant factors affecting the organic carbon content, with TN ranking the highest with an influence weight of 39.10%. In addition, this study found that threshold effects on SOC were observed for the environmental covariates, and all had two thresholds. Furthermore, no two variables were independent and all had interactive negative effects. It can be concluded that the effect of environmental variables on SOC content was a complex interaction rather than a simple superposition. This indirectly proves that over-fertilization will not achieve the effect of increasing soil fertility, but will result in resource wastage and farmland ecological pollution problems. These findings underscore the importance of considering the interaction effects of environmental covariates to understand the potential processes of SOC accumulation, which are vital for sustainable agricultural development.
Keywords: Soil organic carbon (SOC), cultivated land, CatBoost, primary controlling factors, nonlinear relationships
How to cite: Guo, Y., Cheng, Y., He, J., and Zeng, K.: Spatial distribution and primary controlling factors of soil organic carbon under agricultural land based on CatBoost model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5344, https://doi.org/10.5194/egusphere-egu25-5344, 2025.
Within the soil system, biological properties, including microbial biomass, are typically more sensitive to environmental stresses than physical and chemical properties. Microbial carbon in is an important component of the soil carbon pool, essential to ecosystem functioning and soil health. This fraction is highly sensitive to environmental changes, particularly those associated with land use alterations, due to its rapid cycling and short residence time in the soil. Traditional methods for quantifying microbial carbon include chloroform fumigation, gamma-ray and, less so, microwave irradiation. The chloroform fumigation and gamma-ray methods are somewhat complex, time-consuming or expensive and there is no clear consensus on the most suitable technique, as their effectiveness depends on factors such as clay content, pH, and water-holding capacity. Each method presents advantages and challenges, influencing their precision, sensitivity, and applicability across different soil types. In this research we analysed soil microbial carbon from twelve soils with different pH, clay content, carbon concentration and land use, using four methods: the chloroform fumigation, direct application of chloroform, gamma ray irradiation with radiation doses of 5, 10, 20, 30, and 40 KGy and microwave irradiation. After applying each treatment, we incubated the soils using the MicroResp method. Our results demonstrated a strong correlation between the methodologies, however, soils with higher clay and carbon content showed more variability between methodologies. The microbial carbon measured by the microwave method was consistent with those determined by the 20 and 30 KGy gamma irradiation method. These findings highlight the importance of incorporating new, cost-effective, and time-efficient methods for measuring sensitive carbon fractions. Such approaches can enhance the accuracy of microbial carbon assessments, particularly in less studied ecosystems which are essential for advancing our understanding of microbial carbon in a global scale.
How to cite: Bravo-Escobar, A. and Viscarra-Rossel, R.: Measuring microbial carbon: A comparison of four methods for Western Australian soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8364, https://doi.org/10.5194/egusphere-egu25-8364, 2025.
Precision agriculture is defined as a sophisticated and sustainable approach to soil management, optimizing resource use while minimizing environmental impact. A key challenge in this context is the estimation of soil organic carbon (SOC), a critical parameter for assessing soil health, fertility, and carbon sequestration potential. However, traditional SOC analysis methods, while accurate, are often time-consuming and cost-prohibitive, thereby limiting scalability. Consequently, the development of rapid methods such as near-infrared spectroscopy (NIR), when combined with machine learning-based predictive models of SOC, is a promising solution for expeditious and low-cost mapping techniques. This study explores its application on a field scale to develop maps for organic carbon levels.
The calibration dataset comprises 460 soil samples obtained from northern Italy, while the validation dataset consists of 75 samples from two fields located in the Po Valley. Soil samples were collected according to a regular 50-meter grid at a depth of 30 centimeters. To map SOC concentration, these samples were analyzed by an external laboratory employing standard wet reference methods. The NIR analysis was conducted using the NIRFlex N500 (Buchi) in diffuse reflectance mode over the 1000-2500 nm range.
Different calibration models were created using three machine learning techniques: i) Locally Weighted Regression (LWR) configured with 30 local point selected from the local PLS space using 4 latent variables; ii) Gradient Boosted Tree Regression (XGBoost) with max_depth set to 4 and num_round to 300 to prevent overfitting; iii) Deep Learning Artificial Neural Network (ANNDL), implemented using TensorFlow as the framework and Rmsprop as the optimizer. The network was designed as a multilayer densely connected architecture. The spectral data were first compressed using PLS (8 latent variables) to improve training performance.
The NIR-based estimation for organic carbon content was evaluated using Root Mean Square Error (RMSE) and BIAS metrics. The machine learning calibration models showed the following results: i) RMSECV=3.53 and BIAS (cal)=0.04; ii) RMSECV=3.04 and BIAS (cal)=0.06; iii) RMSECV=3.32 and BIAS (cal)=-0.07. Moreover, the prediction demonstrated these metrics for the first field: i) RMSEP=2.14, BIAS (pred)=0.05; ii) RMSEP=2.77, BIAS (pred)=0.90; iii) RMSEP= 2.40, BIAS (pred)= 0.64. Instead, for the second field, the following predictions were made: i) RMSEP=3.05, BIAS (pred)=0.31; ii) RMSEP=2.52, BIAS (pred)=0.72; iii) RMSEP=2.05, BIAS (pred)=0.67.
To compare the practical efficiency of NIR models with reference methods, concentration maps of SOC were created by dividing them into two homogeneous zones (high and low SOC). Subsequently, the maps obtained using each NIR model were overlapped with that obtained using the reference method to calculate the percentage of consensus area. If the overlap exceeded 70%, the model was considered suitable for precision agriculture purposes.
How to cite: Gambale, C., Shchegolikhina, A., Lazzari, A., Gasparini, A., Benedini, D., Buccioli, A., and Cabassi, G.: Leveraging NIR Spectroscopy and Machine Learning Models for Estimating Organic Carbon Concentration in Agricultural Soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12226, https://doi.org/10.5194/egusphere-egu25-12226, 2025.
Soil organic carbon (SOC) sequestration is increasingly promoted as a ‘win-win’ solution to address both climate change and food security, two of the most pressing and complex contemporary global threats. Our objective is to promote critical reflection on the true potential of SOC sequestration in science and policy. Detailed analysis of the literature reveals that the existing knowledge base does not justify the current focus on SOC sequestration. We are concerned that the rapid development of unregulated voluntary carbon markets, wherein farmers get paid per ton of sequestered CO2, is unlikely to lead to a fair and effective transition to more sustainable soil management. We advocate for soil carbon research and policy to fall in line behind the wealth of knowledge showing the importance of developing locally adaptative management practices, focusing on a wide set of environmental outcomes and calling attention to social acceptability and economic viability. Framing the discussions on sustainable soil management around climate change mitigation has brought much-needed attention to soils, but we argue that this approach is ill-suited to promote the research and policy that are needed to achieve long-term sustainability goals. Therefore, we call for a shift in the narrative in soil carbon science away from climate change mitigation and towards inter- and trans-disciplinary understanding of soils.
How to cite: Moinet, G., Hijbeek, R., van Vuuren, D., and Giller, K.: Rethinking soil carbon research: beyond the mitigation-centric narrative, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1466, https://doi.org/10.5194/egusphere-egu25-1466, 2025.
Carbon dioxide removal via soil carbon sequestration is proposed as contribution to climate mitigation, as well as for compensation. The average storage durability of sequestered carbon in soil systems is uncertain, yet shorter than the adjustment time of CO2 in the atmosphere. In this study, we use the reduced-complexity climate model FaIR v2.1.0 (Finite Amplitude Impulse Response model) to quantify by how much the climate impact of carbon removals changes based on sequestration durability and time horizon (time until which the climate impacts are accounted for). We quantify the climate impact via cumulative radiative forcing, namely the well-established absolute global warming potential (AGWP) metric. We use simplified scenarios with five years of soil organic carbon accrual at a constant rate mimicking the use of cover crops, followed by 20 to 1000 years hold time before re-release of the sequestered carbon. We show that the percentage of climate benefit achieved by temporary carbon removals relative to permanent removals increases near-linearly with longer sequestration durability. However, this percentage and its increase rate also vary with chosen time horizon. The climate benefit of short-term removals diminishes rapidly with time horizon. For example, temporary removals with 20 years of durability before sudden re-release offer only about 15% of the climate benefit of permanent sequestration for a 100-year horizon, dropping below 5% for horizons longer than 400 years. For a sequestration that lasts 100 years, the full climate benefit is maintained for a 100-year horizon but still drops to less than 20% of the climate benefit of permanent sequestration for horizons longer than 400 years. These findings have significant implications with regards to compensation units: the amount of anthropogenic CO2 emissions compensated (based on AGWP equivalence) by a given temporary removal declines rapidly as we look further in time, i.e., as time horizon increases. For sequestration durations of 100 years or less, this amount drops by over 90% between horizon 2100 and a millennial horizon, whether the carbon is released abruptly or progressively following a 30-year decay pattern. Our results highlight the key role of the storage duration of carbon in soil systems on the climate impact of soil carbon sequestration over time. In the context of compensation and climate mitigation targets, we stress the need to have a physics-based accounting of the large climatic drawbacks of temporary removals relative to permanent ones. The difference in AGWP can be used for such accounting. In addition, potential drawbacks on the temperature pathway should not be overlooked.
How to cite: Desmet, F. and Leifeld, J.: Climate impact of CO2 removals in carbon farming: sequestration durability and implications for compensation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1617, https://doi.org/10.5194/egusphere-egu25-1617, 2025.
The Mediterranean region, warming 20% faster than the global average, is experiencing significant climate change impacts, including rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events. To investigate the implications of these changes on soil organic carbon (SOC) dynamics, we utilized an ensemble of four agricultural system models—EPIC, DSSAT, CropSyst, and APSIM. These models were calibrated and validated using approximately 9,000 observations of crop yields and residues, along with 110 specific SOC measurements, collected from five long-term experiments across a north-to-south pedoclimatic transect in Italy, covering the period from the 1970s to 2022. This comprehensive dataset enabled us to accurately simulate SOC dynamics under varying conditions. We examined three representative concentration pathways (RCPs): RCP2.6 (very low future emissions), RCP7.0 (high future emissions), and RCP8.5 (very high future emissions) for the period 2023-2100, utilizing three bias-corrected EURO-CORDEX climate models with local corrections. Projections revealed significant variations in SOC stocks based on location and agricultural practices. In Southern Italy, SOC stocks remained stable over time, showing only little variation according to the climate scenario. Conversely, in Central Italy, 30-cm SOC stocks increased until 2070 and then diverged according to the RCPs: a decrease under RCP8.5 (-7.1 t/ha), stabilization under RCP7.0 (+1.0 t/ha), and a sharp increase under RCP2.6 (+11.5 t/ha). In Northeast Italy, SOC stocks decreased under all scenarios, with slightly lower decreases under RCP2.6. Regarding different management systems, conservation agriculture proved to be the most effective in terms of SOC increase or stabilization, while maize and wheat monocultures were the most negatively affected by RCP7.0 and RCP8.5. This study underscores the critical need for location-specific management strategies to address the challenges posed by climate change in the Mediterranean region. Our findings highlight the importance of tailored agricultural practices to mitigate SOC losses and promote soil health under changing climatic conditions.
This research was conducted within the Agritech National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022).
How to cite: Longo, M., Piccoli, I., Berti, A., Farneselli, M., Tabaglio, V., Fiorini, A., Ventrella, D., and Morari, F.: Adapting to Climate Change: Multi-Model Insights into Soil Organic Carbon Dynamics in Mediterranean Agroecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18649, https://doi.org/10.5194/egusphere-egu25-18649, 2025.
Soil organic carbon (SOC) is a cornerstone of global carbon cycling, ecosystem health, and climate regulation. However, accurately predicting SOC storage (SOCS) and its sequestration potential under varying climatic scenarios remains a major challenge, particularly in high-altitude, climate-sensitive regions like the Qinghai-Tibet Plateau. Grasslands and croplands in this region are pivotal for carbon management, yet their dynamics remain insufficiently understood. This study addresses two core scientific questions: (1) How can SOCS dynamics be modeled accurately across large spatial scales and diverse ecosystems under future climate scenarios? (2) How to comprehensively evaluate potential of SOC sequestration and effectively guide development of targeted carbon management strategies?
Andriolo, Mary and Guérif developed a simple first-order kinetic model that relies on key controlling input data, which is ideal for application across large spatial and long temporal scales. This study improved the traditional AMG model by using NPP as the core carbon input indicator, replacing the traditional crop harvest index (HI), which is more suitable for grassland ecosystems. In addition, the model dynamically adjusted the carbon mineralization rate parameter 𝑘 to reflect the effects of temperature, precipitation and soil properties on SOC dynamics. The improved AMG model (I-AMG) generates time series data as input variables for random forest (RF) model by simulating the SOC dynamics of grassland and cropland. We further combine historical SOCS and environmental variables such as terrain and vegetation indices for training and prediction. The RF-AMG integrated model combines the process simulation capability of I-AMG model with nonlinear fitting capability of RF algorithm, which can capture complex environmental variable interaction effects and significantly improve prediction accuracy.
We used the global SOC content data provided by the Harmonized World Soil Database (HWSD) in 1980 to estimate the baseline SOCS at a resolution of 1km. The SOCS data in 2020 was provided by the National Qinghai Tibet Plateau Science Data Center using a grid dataset of soil carbon pools created through field surveys and machine learning, and was used as an observation to evaluate the simulation prediction accuracy of our improved AMG model. Meanwhile, we predicted the SOCS of cropland and grassland in Qinghai Province over the next 40 years under mild (RCP4.5) and extreme (RCP8.5) climate scenarios. And further proposed a four-quadrant method to evaluate the energy storage potential of SOC, dividing the carbon sequestration potential level of Qinghai Province in the next 40 years into four different categories based on SOC saturation deficit and change rate. This method identifies the spatial characteristics of SOC sequestration potential in Qinghai over next 40 years, which can help decision-makers gain a detailed understanding of regions with different carbon management priorities.
This study demonstrates the strength of combining process-based modeling with machine learning to address complex environmental challenges. This novel framework can be used for assessing soil carbon sequestration potential of natural ecosystems, and practical guidance for policymakers to develop tailored strategies for soil conservation, sustainable agriculture and ecosystem restoration. These efforts support global carbon neutrality goals and provide valuable insights into climate-smart land management practices.
How to cite: Gao, H., Maier, M., Gong, J., and Liu, J.: Prediction of soil organic carbon storage and future carbon sequestration potential in grassland and cropland under different climate scenarios: an integrated method combined improved AMG model and random forest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12122, https://doi.org/10.5194/egusphere-egu25-12122, 2025.
The world is grappling with the dual crises of climate change and food insecurity. The global food system, responsible for nearly one-third of anthropogenic greenhouse gas emissions, plays a pivotal role in addressing these challenges. Regenerative agriculture, which includes practices like reduced tillage, cover cropping, and crop residue retention, has been proposed as a nature-based solution with the potential to sequester carbon in agricultural soils while maintaining or enhancing food production. However, the concurrent effects of regenerative agriculture on soil carbon stocks and crop yields have not been fully explored. In particular, the extent to which regenerative agriculture will lead to trade-offs between carbon sequestration and food production, and how this relationship is modulated by environmental and agronomic conditions, remains unclear.
To address this, we conducted a global meta-analysis encompassing 5,709 paired yield and soil carbon observations from 506 sites comparing conventional systems to those incorporating one or more regenerative practices. Results show that 50% of observations exhibit significant gains in crop yields or soil carbon, with 16% achieving both (win-win). In contrast, only 7.5% show losses, and just 1.5% experience a lose-lose scenario. Importantly, the magnitude of changes in soil carbon and yields is primarily influenced by agronomic factors such as the combination of regenerative practices, nitrogen application rate, and crop type, with lesser effects from soil and climate conditions. These findings indicate that regenerative agricultural practices are unlikely to harm yields or soil carbon stocks and can be optimized to maximize win-wins by tailoring adoption to favorable conditions.
How to cite: Encarnation, D., Powell, R., and Pellegrini, A.: Regenerative Agriculture: Trade-offs and Win-Win Scenarios for Soil Carbon Sequestration and Crop Yields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12875, https://doi.org/10.5194/egusphere-egu25-12875, 2025.
Organic farming may improve agroecosystems’ resilience against external stressors, favour below-ground biodiversity, soil health, and increase soil water holding capacity. At the same time, organic farming systems are repeatedly reported to have lower average crop yields than conventional systems. To date, global meta-analyses on organic farming systems include a diverse range of crops, but none of them specifically focus on arable systems with cereal-based rotations. Further, they are not representative for specific European agro-environmental zones and often show weaknesses in the applied meta-analytical methodology.
This meta-analysis aimed at quantitatively summarizing existing knowledge and outcomes on soil organic carbon (SOC) in the topsoil (0-20/30 cm) and cereal production (i.e., yields and yield stability) in organic farming systems compared to conventional farming systems across Europe.
The database consisted of 43 independent field studies on SOC and 50 field studies on cereal yields across 16 European countries, covering nine European agro-environmental zones. Cereal-based rotations were cultivated organically and conventionally on mineral soils, up to several decades. Yields for winter rye, winter and spring wheat, spring barley and spring oats were annually measured. SOC was measured as stock or concentration at the end of the experiments. Organic farming systems relied either on animal-based or plant-based fertilizers, or on both sources of nitrogen input. Conventional systems received solely mineral fertilization in most experiments. For both farming systems conventional tillage was applied without irrigation. The meta-analysis was conducted by using Meta Win 2.0 and IBM SPSS Statistics 29. As an index of effect size, we used ln (R), i.e., relative SOC, yield or temporal yield variation. All studies were weighted by inverse variance.
The overall effect of organic farming was a 5% increase of topsoil SOC (95% CI: 1% – 9%, n=43) compared to conventional systems. Pedoclimatic factors, such as mean annual precipitation and clay content had a profound impact on SOC response under organic farming (p=0.014). With increasing annual precipitation and clay content, SOC response to organic farming was increasing, and reached 20% in areas with clayey soils and annual precipitation of 700 mm. In addition, the response of SOC to organic farming showed some positive trend with increasing soil pH (p=0.059).
Overall, cereal yield in organic farming was about 30% lower compared to conventional farming systems. However, yield performance of organic systems varied statistically significantly across farming types (p=0.021): a 20% yield gap was observed in organic systems using animal-based fertilizers, while a 35% yield gap was shown in organic systems using only legumes or mixed green manure. Moreover, the yield gap decreased with increasing average annual temperature (p=0.002). Overall, the temporal yield variation of organic farming systems was about 50% larger than in conventional systems, which was not related to any pedoclimatic factors studied.
In conclusion, organic farming systems had a positive impact on SOC in the topsoil, compared to conventional systems. The magnitude of this influence mainly depended on pedoclimatic characteristics in Europe. In terms of cereal production, organic farming had lower yields and yield stability compared to conventional farming.
How to cite: Valkama, E., Fohrafellner, J., Murugan, R., Jarosch, K., Weiss, L., Maenhout, P., Di Bene, C., Baratella, V., Diacono, M., Epifani, R., Bevivino, A., Stefanova, M., Di Gregorio, L., Rossini, E., Costanzo, M., Buttafuoco, G., Lorenzetti, R., Carboni, G., and Mereu, V.: Meta-analysis on soil organic carbon and cereal production in organic farming systems across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9318, https://doi.org/10.5194/egusphere-egu25-9318, 2025.
Perennial crops are increasingly recognized for their potential to enhance soil carbon (C) stocks, owing to their continuous C inputs from extensive root systems and reduced C degradation due to minimal tillage. Integrating perennials into traditional arable agriculture is emerging as a promising strategy for C farming. However, inconsistencies in calculation methods across studies complicate direct comparison and hinder a comprehensive assessment of the changes in soil C stocks. Soil C stock is calculated by either fixed depth (FD) or equivalent soil mass (ESM) method, and the changes are either absolutely compared to the baseline soil or relatively compared to reference plots (i.e., adjacent fields representing previous land use or annual cropping systems). Here, we conducted a meta-analysis using 1545 paired observations from 110 publications to evaluate the changes in soil C stock under perennial cropping systems as estimated by different methods.
The results revealed significant biases introduced by calculation and comparison methods. In the topsoil (0–30 cm), compared to the baseline, the ESM method estimated a 6.1% (3.3–8.8%) increase in soil C stock under perennial cropping, whereas the FD method produced an 80% higher estimate (11.0%). Meanwhile, the relative changes (10.6%) based on the ESM method was 74% higher than absolute changes. In contrast, subsoil showed no significant absolute change, with the ESM method estimating a change of 4.9% (-2.9–12.7%). The effect of perennial cropping on soil C stock varied by system type. Grass monoculture, grass mixture, and short rotation coppice increased soil C stocks (7.9–15.4%), while incorporating perennials into crop rotations led to a decrease (-5.5%). Environmental factors also influenced the changes in C stocks. Soil C stock change was positively correlated to mean annual precipitation and temperature (p < 0.05), but negatively related to initial soil C content (p < 0.05). In the medium- and low-C soils (SOC < 20 g kg-1), changes in C stocks were positively correlated to clay content and experimental duration.
Overall, our findings confirm that appropriately managed perennial cropping systems could enhance soil C stocks, with the changes primarily occurring in the topsoil. Furthermore, this study underscores the importance of selecting suitable calculation methods to ensure accurate estimates of C stock changes.
How to cite: Shang, Y., Ablos, D., Liang, Z., and Olesen, J.: Soil carbon stock accrual under perennial cropping overestimated by many methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1932, https://doi.org/10.5194/egusphere-egu25-1932, 2025.
Increasing soil organic carbon (SOC) through climate-smart land management is considered a promising nature-based solution promoting food security and climate change mitigation, particularly in arable land due to its long history of SOC loss and its intensive management. However, the achievable potential for SOC increase, e.g., at a national scale is uncertain, with conflicting predictions resulting from empirical and modelling studies and a general lack of data from realistic experimental setups. The aim of this study was to assess the potential of improved agricultural practices to increase soil C in real, commercial farming systems in Finland and to identify possible challenges involved. The study was conducted as participatory research, with 83 volunteer farms across Finland, testing various management plans with a dedicated control and treatment plot in each farm. The tested plans included eight different practices aiming to increase photosynthesis, rooting depth, or direct exogenous C inputs to soil. The efficiency of the practices to increase C inputs was evaluated using a satellite-based method and information on crop yields and harvest provided by the farmers while SOC change was measured from soil samples in the lab. Our results show that after five years, the changes observed in SOC are marginal, mainly limited by the short duration of the study. Initial SOC content was the most important driver of SOC change while C inputs, climate and clay content were less important. Despite the short duration of this study, the established network and the implemented approach can be further used, paving the way towards precise assessment of the influence of management practices on SOC changes and soil improvement under Nordic conditions over the coming decades.
How to cite: Kanari, E., Karhu, K., Vira, J., Mattila, T., Höckerstedt, L., Fer, I., Liski, J., and Heinonsalo, J.: Soil organic carbon changes after 5 years of improved carbon farming practices in Finnish arable land, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17197, https://doi.org/10.5194/egusphere-egu25-17197, 2025.
Conservation agriculture practices, such as reduced tillage and residue retention, have gained attention for their potential to enhance agricultural system resilience to climate change and to combat soil degradation. However, conventional soil-crop models usually neglect the dynamics of soil properties, limiting their ability to predict changes in soil quality on the longer time-scales relevant for sustainable soil management. One exception to this is the recently developed Uppsala model of Soil Structure and Function (USSF), which accounts for soil structure dynamics due to both physical (e.g. swell-shrink, sealing, tillage/consolidation) and biological (e.g. root growth, macro-faunal activity, soil aggregation) processes driven by changes in climate or land management. We further developed and applied the USSF model to assess the long-term impacts of conservation agriculture on soil organic matter (SOM) stocks, soil structure, water balance and crop yields.
The model was first calibrated for a winter wheat crop in Zürich, Switzerland, and then used to simulate a baseline period (1985-2015) as well as 18 future climate scenarios for the period 2020 to 2090. Simulations of two contrasting soil management systems were compared: conventional intensive tillage with residue incorporation (CIT) and no-till practices with surface residue retention (CNT), representing a conservation agriculture scenario.
Under current climate conditions, the CNT treatment was able to conserve soil moisture by reducing surface runoff and evaporation, as compared with CIT. However, yields remained similar, as under the wet site conditions, crop growth was not limited by water availability. After 30 years, SOM stocks were slightly higher under CIT, as larger amounts of above-ground biomass were incorporated through tillage compared with incorporation only by bioturbation in the case of CNT. In the future climate projections, grain yields remained stable or increased slightly under warmer site conditions . The development of SOM stocks was strongly dependent on future soil temperatures . However, after 70 years, stocks were on average ca. 14% higher under the CNT treatment compared with CIT.
Although yields did not differ between the two treatments, the USSF model projections showed increased SOM stocks and improved soil structure with no-till and surface residue retention compared with conventional practices. This suggests that conservation agriculture could be a promising strategy for sustaining soil quality and functions in the face of climate change.
How to cite: Feifel, M., Coucheney, E., Holzkämper, A., and Jarvis, N.: Modelling the long-term impacts of conservation agriculture on soil structure , soil organic matter and the water balance under climate change., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16782, https://doi.org/10.5194/egusphere-egu25-16782, 2025.
Soil microbes drive soil organic carbon (SOC) mineralization. Because microbial groups differ in metabolic efficiency and respond differently to temperature variation, it is reasonable to expect a close association of SOC mineralization and its temperature sensitivity (Q10 which is defined as the factor of the change of soil carbon mineralization induced by 10 °C temperature increase) with microbial community diversity and composition.
However, these relations have rarely been tested. Here, we conducted an incubation experiment to assess the temperature responses of microbial α diversity and the relative abundance of microbial r- and K-strategists in soils from a wide range of ecosystems across a climate gradient in the southeast Tibet. The results indicated that the instantaneous α diversity and the relative abundance of r- and K-strategists are significantly (P < 0.05) influenced by temperature, but these microbial variables are poor predictors of SOC mineralization measured at the same time. Rather, microbial community diversity and the relative abundance of r- and K-strategists of fresh soils showed consistent and significant (P < 0.05) effects on both SOC mineralization and Q10 at different incubation stages. Importantly, path analysis indicated that microbial α diversity and r- and K-strategists exerts no independent effects on SOC mineralization and Q10 when variation in climate, SOC chemistry, physical protection, and edaphic properties are accounted for. Together, our results suggest that while soil microbial community diversity and composition are a strong proxy of SOC quality and availability, they are not a fundamental determinant of SOC mineralization and Q10.
How to cite: Zheng, J., Mao, X., van Groenigen, K. J., Zhang, S., Wang, M., Guo, X., Yu, W., Chang, J., Shi, Z., and Luo, Z.: Decoupling of soil carbon mineralization and microbial community composition across a climate gradient on the Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5366, https://doi.org/10.5194/egusphere-egu25-5366, 2025.
Conservation tillage such as no-till has been recommend as a potential strategy to sequester soil organic matter (SOM) and mitigate climate change, but how this alternative farming altered SOM biochemistry remains elusive, particularly under simulated warming condition. Here, we uncovered the SOM composition and origins, and underlying microbial-mediated processes from a conservation tillage trial under warming in North China. Soil samples (i.e., 0-5, 5-15, and 15-30 cm) were collected from four treatments: moldboard plow (MP), moldboard plow with warming (MPw), no-till (NT), and no-till with warming (NTw). Considering tillage effects, NTw (cf. MPw) increased both bacterial and fungal biomass in topsoil. Further, NT (cf. MP) decreased oligotrophic K-strategists, including Chloroflexi and Gemmatimonadetes. Regardless of warming, no tillage enhanced the relative abundance of recalcitrant dissolved organic matter (DOM), but decreased biodegradable DOM compounds such as carbohydrates, proteins in topsoil. Both biomarker (higher total lignin phenols) and 13C-nuclear magnetic resonance (higher aromatic and phenolic group) collectively revealed the enhanced preservation of lignin phenols in NTw (cf. MPw). No tillage (NT and NTw) significantly increased glomalin-related soil protein (GRSP) in the surface soil, while warming have no effects on those molecules. Moreover, NT enhanced bacterial and fungal necromass relative to MP in the topsoil. Considering warming effects, warming decreased catabolic enzymes activities (β-1,4-glucosidase and leucine aminopeptidase) under two tillage systems, while exerted the strong positive influence on microbial carbon use efficiency. Collectively, NTw (cf. MPw) may have the potential to enhance the process of bacterial anabolism and in vivo turnover (reflecting by microbial necromass and GRSP), thus improve microbial carbon pump efficiency and OM formation in a future warmer world. Moreover, no-till treatments (NT, NTw) increased the fraction of refractory DOM (i.e., condensed aromatic structures and tannin) relative to conventional farming. Our work provides insights into the potential benefits of conservation agriculture for long-term carbon sequestration because no tillage improves resilience to the effects of climate warming.
How to cite: Jin, X., Wang, X., and Du, Z.: Decades of no-till coupled with warming improves topsoil soil organic matter accrual via stimulating microbial carbon use efficiency, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14558, https://doi.org/10.5194/egusphere-egu25-14558, 2025.
Microplastics (MPs) are considered a novel type of contamination that is abundant in soil due to their recalcitrant nature, potentially affecting the structure and function of the soil. However, the MPs mineralization processes in soil and their priming effects (PE) on original soil organic matter (SOM) decomposition are still poorly understood. Here, we conducted a meta-analysis of 10 published articles to estimate the mineralization rate of MPs in soil and their PE on SOM decomposition. We found that both MPs-derived carbon dioxide (CO2) and soil-derived CO2 emissions declined with incubation time. There were differences in mineralization rates among various types, sizes and concentrations of MPs, which affected their PE; however, overall, the input of MPs induced a significant positive PE in soil. In addition, MPs mineralization rate was mainly controlled by soil pH, while PE was primarily regulated by the soil carbon to nitrogen (N) ratio. Furthermore, the concentration of dissolved organic carbon and microbial biomass carbon significantly increased after MPs input, while soil nitrate concentration significantly decreased. These results indicated that MPs input may exert a positive PE by enhancing soil microbial N mineralization. Collectively, our findings provide a comprehensive assessment of MPs mineralization and how it affects soil organic carbon dynamics via stimulating PE, which is important for elucidating SOM turnover under increased MPs pollution.
How to cite: Lai, S. and Zhu, B.: Microplastics mineralization accelerates soil organic matter decomposition by positive priming effect, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7972, https://doi.org/10.5194/egusphere-egu25-7972, 2025.
As the main drivers and modifiers of soil ecosystem change, it remains unclear whether microbial metabolic limitation in sandy soil will affect its community structure and how microbial community responds to resource limitation. Hence, we studied the relationship between soil microbial community and nutrient limitation in Horqin Sandy land, a typical sandy area in northern China. The results of enzyme stoichiometric vector analysis showed that microbial carbon (C) and nitrogen (N) limitation decreased with vegetation restoration, while soil microbial phosphorus (P) limitation increased with vegetation restoration, and there was a shift from N limitation to P limitation from the semi-mobile dunes to semi-fixed dunes. We found that total N (TN) and total P (TP) were most closely related to microbial community richness, and the effects of electrical conductivity (EC), pH and topographic factors (lat, lon and dem) could not be ignored. The co-occurrence network and linear regression analysis revealed that 39.5% of the key microbes (15 key species) had significant correlation with the N/P limitation (P<0.05). The bacterial community was more sensitive to the response of the key microbial taxa than the fungal community. Microbial N/P limitation has a direct positive impact on key microbial taxa, while microbial C limitation has a negative impact on N/P limitation, and then indirectly affects the key microbial taxa, suggesting that soil microorganisms have distinct nutrient preferences and survival strategies to overcome energy restriction and nutrient stress. This study provides important insights into the response of microbial community structure to energy and nutrient constraints in sandy ecosystems.
How to cite: Yao, B., Wang, X., Li, Y., Lian, J., Mou, X., Yang, H., and Li, Y.: Responses of soil keystone microbial taxa to metabolic limitation during vegetation restoration in sandy land ecosystem, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10684, https://doi.org/10.5194/egusphere-egu25-10684, 2025.
Soil-plant interactions are critical drivers of carbon (C) cycling in terrestrial ecosystems, influencing processes from microscale rhizosphere activity to ecosystem-scale dynamics. These interactions shape ecosystem functioning, biogeochemical cycles, and soil organic carbon (SOC) sequestration. However, bridging the gap between fine-scale processes and large-scale patterns remains a key challenge in soil science.
Here, a concept is presented that compiles a number of studies and data across various spatial scales to identify mechanisms governing biogeochemical fluxes and C pools, with a focus on linking soil organic matter pools and vegetation properties under varying environmental conditions and disturbance regimes. The key research questions include: (1) how rhizosphere C inputs (e.g., exudation and rhizodeposition) affect SOC cycling, (2) which environmental factors regulate the incorporation and sequestration of phytogenic C sources (e.g., litter decomposition, particulate organic matter, dissolved organic C), and (3) which of these processes dominate along large-scale climatic gradients.
Initial findings revealed a close tie of soil respiration to microbial carbon use efficiency in rhizosphere hotspots, directly affecting pedon-scale SOC sequestration. At the ecosystem scale, direct vegetation effects were overprinted by their interaction with geomorphological gradients, controlling SOC stabilization through aggregate occlusion and mineral association. However, ecosystem SOC dynamics exhibited substantial local variance, likely driven by complex variable interactions and small-scale rhizosphere processes. Furthermore, vegetation zones remained a dominant control of plant- and microbial-derived organic matter contributions to SOC pools across large climatic gradients.
This suggests that microscale rhizosphere processes can significantly influence large-scale C budgets. However, their functioning relies on large-scale dynamics in land management, landscape modification, and environmental gradients reshaping dominant SOC storage mechanisms. These interactions become more complex on larger spatial scales, and further research is essential to fully understand and quantify their implications.
How to cite: Becker, J. N.: The Impact of Soil-Plant Interactions on Soil Organic Carbon Sequestration across spatial scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15741, https://doi.org/10.5194/egusphere-egu25-15741, 2025.
Digital soil mapping (DSM) is revolutionizing the understanding and management of soil resources by providing high-resolution spatial and temporal soil information essential for tackling environmental challenges. While integrating environmental covariates has significantly improved mapping accuracy, the potential of neighboring soil sample data remains underutilized. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive performance of spatial models. Using two open-access datasets—LUCAS Soil and Meuse—our results demonstrate that incorporating SSNI improves the accuracy of random forest models for mapping soil organic carbon density (reducing %RMSE by 3.1%), cadmium (3.6%), copper (5.9%), lead (11.5%), and zinc (7.4%). Compared to methods utilizing buffer distances or oblique geographic coordinates, SSNI consistently outperformed for both datasets. These findings highlight the potential of SSNI to enhance digital soil maps by effectively capturing neighboring soil information. Adopting SSNI could advance soil management practices and offers promising opportunities for broader applications in future research across related disciplines.
How to cite: Chen, S., Chen, Z., Wang, Z., Wang, X., and Shi, Z.: Including soil spatial neighbor information improves model performance in predictive soil mapping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7941, https://doi.org/10.5194/egusphere-egu25-7941, 2025.
Soil organic carbon (SOC) is a critical component of the global carbon cycle, serving as the largest terrestrial carbon reservoir and significantly influencing atmospheric greenhouse gas concentrations and climate dynamics. This study investigates SOC dynamics across the European Union and the United Kingdom using the LUCAS Soil datasets from 2009 and 2018, aiming to map SOC levels and assess temporal changes in response to land-use and climate variations. Two methodological approaches were employed: (1) spatiotemporal modeling, integrating data from both 2009 and 2018, and (2) baseline modeling, which used the 2009 SOC map as an environmental covariate to predict 2018 SOC density. Random Forest (RF) and Forward recursive feature selection (FRFS) combined with RF were utilized for SOC prediction. In the spatiotemporal modeling approach, RF achieved an accuracy of R² = 0.41, which improved to R² = 0.43 with FRFS. For the 2009 SOC mapping, RF accuracy was R² = 0.44, increasing to R² = 0.46 with FRFS, while for 2018, RF accuracy was R² = 0.38, improving to R² = 0.39 with FRFS. When the 2009 SOC data were incorporated as a covariate for 2018 predictions, RF achieved R² = 0.44, which further improved to R² = 0.45 with FRFS. The study highlights the impacts of land-use changes, such as afforestation, deforestation, and agricultural intensification, on SOC stocks, and evaluates the effectiveness of sustainable land management practices in enhancing carbon sequestration. The findings provide critical insights into SOC dynamics under varying land-use and climatic conditions, identifying regions where soils may transition from carbon sinks to sources of atmospheric CO2. This research contributes to evidence-based policy formulation for achieving climate neutrality and sustainable soil management, aligning with the EU Soil Strategy 2030, and underscores the importance of monitoring SOC changes to inform land-use planning and climate mitigation strategies.
How to cite: Chen, Z., Lu, R., Chen, L., Guo, H., Su, Y., Zhu, P., Arrouays, D., Schillaci, C., Richer-de-Forges, A., Ye, S., Shi, Z., and Chen, S.: Land cover changes induced spatio-temporal dynamics in soil organic carbon stock across Europe over the past decade (2009-2018), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2305, https://doi.org/10.5194/egusphere-egu25-2305, 2025.
Obtaining accurate spatial information on soil organic carbon (SOC) is essential for understanding the global carbon cycle. Digital soil mapping (DSM) has emerged as an effective approach for SOC mapping, where the selection of influential environmental covariates plays a critical role. Soil moisture (SM), which influences soil water status and the decomposition of SOC, holds great potential as a covariate for SOC estimation, particularly due to its ability to be assessed at large spatial scales using remote sensing. Previously, the normalized shortwave-infrared difference bare soil moisture indices (NSDSIs), derived from Landsat SWIR bands during bare soil periods, have been employed in SOC mapping. However, since soils are often covered by vegetation, there is a need to develop new SM indices suitable for vegetated areas and to evaluate their performance across regions with varying vegetation densities.
In this study, we introduced a novel SM index by integrating NSDSIs into the Optical TRApezoid Model, creating the OPTRAM-NSDSI. This index was compared against the original OPTRAM based on shortwave infrared transformed reflectance (OPTRAM-STR) and NSDSIs. SM indices were generated for two study areas in China: Zhuxi, Fujian (104 samples across 43.93 km², with forestland and farmland as dominant land uses) and Heshan, Heilongjiang (106 samples across 60 km², primarily farmland). The Integrated Nested Laplace Approximation combined with the Stochastic Partial Differential Equation approach was applied as the SOM prediction model.
Our results demonstrate that incorporating SM variables into commonly used environmental covariates significantly enhances prediction accuracy. The NSDSIs achieved the highest accuracy improvement of 26.8% in terms of Lin's concordance correlation coefficient in Zhuxi, while the OPTRAM-NSDSI achieved the highest improvement of 56.7% in Heshan. This suggests that OPTRAM-NSDSI is particularly effective in regions with higher vegetation density, whereas NSDSIs perform better in areas with lower vegetation density. Additionally, the optimal image acquisition dates for SM estimation appear to coincide with the vegetation "green-up" stage.
This study offers valuable insights into leveraging SM information to enhance SOC mapping, particularly in vegetated areas.
How to cite: Yang, L. and Yang, C.: Enhancing Soil Organic Carbon Mapping with Remote Sensing-Derived Soil Moisture Indices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6555, https://doi.org/10.5194/egusphere-egu25-6555, 2025.
State-of-the-art mechanistic models perform poorly to accurately capture the amount and spatial variability of global soil organic carbon (SOC) stocks compared to machine learning models. Identifying the reasons for these shortcomings using interpretable machine learning techniques is essential to advancing our understanding of SOC turnover processes and guiding future model development. In this study, we trained both mechanistic and machine learning models using approximately 37,000 global SOC observations. The machine learning models consistently outperformed the mechanistic models, achieving higher R² values and lower RMSE. To diagnose the limitations of mechanistic models, we trained random forest models with the mechanistic model inputs as predictors and either observed or modelled SOC as the target variable. Applying multiple explainable artificial intelligence (XAI) techniques including feature importance, partial dependence plots, and SHapley Additive exPlanations (SHAP), we found that while the trends in SOC responses to environmental variables were comparable between observed and modelled SOC, the magnitude of SOC sensitivity to different variables in the mechanistic models was weaker. Furthermore, the distribution of partial dependence values for observed SOC across specific variables was poorly represented by mechanistic models, even though the mean partial dependence values were similar. Notably, soil moisture and pH were significantly under-represented in the mechanistic models, highlighting the need for further research on the dependence of SOC turnover on these variables. Our study showed that XAI techniques can effectively reveal how well individual variables and their combined effects are represented in the mechanistic models, providing clear and specific direction for future model development.
How to cite: Wang, L., Wang, Y.-P., Abramowitz, G., and Pitman, A.: Why mechanistic models perform poorly in global soil organic carbon prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14630, https://doi.org/10.5194/egusphere-egu25-14630, 2025.
Carbon (C) storage in soil, coupled with concerns regarding the impact of a warming climate on its stability, has elevated soil C into a global scientific and political discourse. The Millennial model is a next-generation soil C model that reflects our recent advancements in understanding soil C dynamics, including microbial decomposition, mineral association and aggregation. However, the model's simulations and predictions remain largely uncertain due to a lack of data, including the various soil C fractions, the complex model structure and many parameters. Despite recent progress, the Millennial model has not been well-tested against measurements related to the modelled C states. With the ever-increasing availability of high-quality spatially explicit data on climate, vegetation, and soil properties (e.g. from proximal and remote sensing), there is a need to integrate these to constrain the model and improve simulations and predictions. This research aims to reduce uncertainties using data assimilation. We first reduce the Millennial model uncertainty by updating the empirical equation of maximum sorption capacity with a more realistic estimation method. The measured soil C fractions and spatially explicit forcing inputs (e.g. NPP, soil moisture, soil temperature) across different ecosystems are used to reduce the observation and forcing uncertainty. We use multiple objective global sensitivity analyses to identify influential parameters and calibrate the parameters with an efficient parameter optimisation algorithm to reduce parameter uncertainty. The site-by-site optimisation method with measured C fractions was accurate, with an RMSE of 0.2 kg C/m2 and a ρc of 0.97 for total organic carbon (TOC). Our future simulations by the Millennial model for the median changes of TOC in 2070--2100 across rangelands are 1.51 t/ha under SSP126 and 1.93 t/ha under SSP585, which are more conservative than the predicted changes by calibrated Roth-C model.
How to cite: Zhang, M. and Viscarra Rossel, R.: Enhanced soil carbon dynamics in the Millennial model with data assimilation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8472, https://doi.org/10.5194/egusphere-egu25-8472, 2025.
The preservation and enhancement of soil organic carbon (SOC) are essential for ensuring sustainable agricultural productivity, improving soil health, and addressing global environmental challenges. This study utilizes the RothC model to explore the dynamics of SOC across two distinct land uses in Wallonia, Belgium—croplands and grasslands. We used remote sensing methods to predict the necessary boundary conditions for the RothC model, focusing on precise estimations of carbon inputs from different sources based on the landuse. The research assesses the impact of varied carbon inputs by comparing traditional inputs, derived from statistical methods and existing datasets, with predictions obtained from remote sensing data. This comparison aims to illustrate discrepancies and synergies in SOC estimation and modeling, thereby providing insights into more precise and scalable methods for predicting changes in SOC. Focusing on specific demo-sites within the region, the research underscores the localized responses of SOC to diverse management practices and environmental conditions. This focus helps support the development of effective carbon sequestration strategies. Ultimately, this study not only enhances our understanding of SOC dynamics over time but also fosters the development of customized agricultural practices that enhance carbon retention and contribute to the mitigation of climate change impacts in temperate regions.
How to cite: Sabetizadeh, M., Zhou, Y., Heinesch, B., Longdoz, B., Beauclaire, Q., and van Wesemael, B.: Assessing Soil Organic Carbon Dynamics Across Croplands and Grasslands: A RothC Model Analysis with Varied Carbon Inputs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19111, https://doi.org/10.5194/egusphere-egu25-19111, 2025.
Carbon farming (CF) has gained attention as a promising tool for meeting EU-targets like mitigating climate change with the enhanced sequestration of carbon in agriculturally managed soils (Green Deal, Paris Agreement). To facilitate the inclusion of research in the design and implementation of effective CF scheme policies, a comprehensive inventory was conducted in the EU-funded EJP Soil project Road4Schemes. This inventory assessed the strengths and weaknesses of 162 existing and planned schemes for carbon farming and additional Ecosystem Services payments, including respective tools for monitoring, reporting and verification. In addition, surveys to assess stakeholders’ perspectives, compensation mechanisms, verification systems, and proposed measures were performed.
Key findings highlighted the predominance of activity-based schemes, the variability in scale, and the importance of addressing governance challenges. Based on these insights, a context-specific roadmap tailored to local and regional conditions was developed, integrating decision matrices and guidelines for a result-based scheme design, as the initial aim of the project. The roadmap provides decision-makers with a structured approach to implement CF-schemes that are adaptive, efficient, and aligned with local needs.
How to cite: Götzinger, S., Baumgarten, A., Thorsøe, M., De Cara, S., Graversgaard, M., and Smit, B.: A roadmap for carbon farming in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15782, https://doi.org/10.5194/egusphere-egu25-15782, 2025.
As global agriculture faces escalating challenges, the imperative for refined nutrient management becomes paramount. Establishing a standardized process to validate and legitimize emerging soil testing technologies offers a pathway for their acceptance by users and state regulatory bodies. The need for standardized methods for evaluating soil quality attributes is crucial in meeting the demands on global agriculture to support human and animal life while minimizing the environmental impacts of excess nutrient runoff. This makes the refinement of nutrient management protocols of critical importance. A key factor in such protocols is developing and adopting effective and accurate soil testing methods. As new soil testing technologies emerge, it becomes challenging for local and national agencies to determine whether to incorporate these technologies into their existing procedures for assessing soil quality, carbon monitoring and potential carbon and nutrient markets. Soil carbon represents a significant fraction of the global carbon cycle and is expected to be a considerable factor in future carbon management. There is a need to not only survey global soil carbon stocks but to monitor changes over time with an interest in increasing soil organic carbon (SOC). Small changes, on the order of < 1 %, need to be monitored to determine if changes in land management practices are effective. To monitor these changes economical and accurate methods are required to enable frequent and widespread analysis of soil samples. Established methods for testing and monitoring are expensive and time-consuming including Loss-on-ignition (LOI) and combustion. New innovations are emerging that are economically viable and scalable with the potential for field deployable systems. Despite the promise of these innovations, there is a need to establish a validation standard to assure accuracy and transparency. Standardization of validation methods will need to focus on accuracy and economic feasibility for assessing limits of detection and limits of quantification. The benefits of standardizing methods for validating new technologies are promoting accuracy and preventing fraud in emerging carbon and nutrient markets. The outcome of this work will foster the traceability to national metrology institutes for validation of these new technologies.
How to cite: Khalsa, S. J., Mizuta, K., and Nagel, P.: Developing a Standard for Validation of Innovative Methods in Agricultural Soil Testing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20317, https://doi.org/10.5194/egusphere-egu25-20317, 2025.
Adapting to and mitigating the various climate effects in a catchment through interventions like nature-based solutions (NbS) requires changes in land use. Changes in the spatial distributed activities over time within the catchment are a key part of these land use dynamics. These dynamics are influenced by natural processes (e.g. soil-water system) and human activities (e.g. farming, stream flow measures). The land is used by a variety of stakeholders (tenants, farmers, nature organisations), while interventions in the form of nature-based solutions are planned and implemented by water managers and spatial governments. It is crucial to understand these changes and their impact on land use dynamics when designing and implementing nature-based solutions in a catchment. In this study, we test how hydrological models (used by water managers) coupled with soil carbon models can help this discussion. The study tests the spatial effects of hydrological interventions on carbon sequestration in agriculture in a cross-border catchment (Aa of Weerijs, SE Breda, NL / BE).During the design stage, stakeholder meetings were organised to gather the different perspectives on hydrological interventions and carbon farming. Coupling relatively simple models makes the interaction between planning nature-based solutions on soil carbon transparent, and helps the discussion on future land use dynamics on NbS and carbon farming.
How to cite: Lansu, A., Huisman, P.-J., Bogatinoska, B., and Stoorvogel, J.: Carbon farming from a land use dynamics perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19985, https://doi.org/10.5194/egusphere-egu25-19985, 2025.
In recent years, Taiwan has extended irrigation services to regions previously devoid of water resources. These irrigation improvement projects incorporate sustainable engineering practices and are aligned with the objectives of Nature-based Solutions (NbS), enhancing ecosystem protection, mitigating climate impacts, and reducing economic losses associated with extreme weather events such as droughts and floods. Beyond these benefits, this study aims to investigate the additional value of these projects as they provide opportunities for carbon reduction initiatives. Should local measurements meet expectations, further development of methodologies, such as the Measurement, Reporting, and Verification (MRV) process, will be pursued. This research examines the effects of irrigation infrastructure upgrades on carbon dynamics within tea plantations, focusing on the reduction of carbon emissions and the enhancement of soil organic carbon (SOC). Three sites were selected for this purpose: two tea gardens employing different irrigation techniques and a derelict area formerly used for tea cultivation, with each site covering more than 0.5 hectares. The study assesses two crucial parameters: SOC, which indicates the carbon sequestered in the soil, serving as a carbon sink, and the carbon emissions from tea trees. The chamber method, a recognized approach for collecting greenhouse gas (GHG) emissions, was utilized to capture emissions from tea trees using transparent chambers. Field experiments are conducted monthly, with gas sampling occurring every two hours throughout the daylight hours. The collected gas samples are analyzed via Gas Chromatography-Mass Spectrometry (GC-MS) to ascertain carbon concentrations, which are subsequently used to calculate daily carbon fluxes. Preliminary results from several months of gas sampling indicate significantly lower carbon emissions in the well-irrigated tea garden, where the renovation was implemented, compared to the garden without irrigation. Furthermore, SOC levels in the irrigated garden are anticipated to show marked improvement over the previous year.
How to cite: Lai, P. C., Huang, Y. N., and Liao, K. W.: Application of the Chamber Method for Carbon Flux Measurement in Tea Plantations: Insights from Irrigation Renovation Projects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17075, https://doi.org/10.5194/egusphere-egu25-17075, 2025.
Root carbon has been shown to be one of the most dominant forms of soils carbon inputs in agricultural systems. New paradigms about the decomposition of soil organic matter suggest the role of root derived soil carbon may have been overlooked. Current data and knowledge do not allow for prediction of the fate of root derived SOC storage in agricultural soils, specifically in relation to soil-depth and the complexity of the standing crop or intercrop.
Mixed species systems are currently gaining traction Europe providing opportunities for sustainable intensification of agriculture and other ecosystem-service co-benefits. Agroforestry systems cover about 9% of the utilized agricultural area and integrated crop livestock systems are both historically and culturally important in European agriculture, as they include perennial forage grasses and grasslands. Intercropping and other mixed cash crop systems are currently less developed in the EU. The aim of the EU EJP-SOIL funded MIXROOT-C and MAXROOT-C projects (2021-2025) is to gain a management-oriented understanding of the effect of mixed-species root systems on carbon flow and organic matter accumulation in European agricultural soils.
As part of the project, we have conducted a pan-European in-situ field experiments across pedo-climatic conditions. Treatments include: (i) monoculture (1 species), (ii) low diversity (2-4 different plant species in the mix culture) and (iii) high diversity (≥ 5 different plant species in the mix culture) and different soil depths. The goal is to determine the impact of increased plant diversity organic matter breakdown to develop a trans-European decomposition index. To achieve this, we monitored the decomposition of 13C-labelled maize litter in mixed agroecosystems and in the main crop monocultures across Europe. Using a hub spoke design, a common 13C-labelled maize material was supplied to each participant and was mixed in a similar manner with the local soil from the treatment plots, packed in mesh bags and buried in the treatment plots. This was then excavated after a vegetation period of six months and returned to BOKU for analysis.
This experiment, which includes many sites, climates and cropping systems, will provide key information on the rate of litter decomposition and the inclusion of litter C in different soil OM pools depending on the climatic condition, soil type and management. Furthermore, the experiment will provide information on litter turnover and link this process to soil C storage. We tested the null hypothesis that increased plant diversity does not increase the decomposition rate in the field. Initial results suggest that decomposition rates were 40-65% across sites and that diverse cover-cropping mixtures lead to lower decomposition rates.
These data and results could be used to guide model predictions of the fate pf belowground C inputs in single and mixed species systems at different soil depths.
How to cite: Wawra, A., Hood-Nowotny, R., Schott, K., Meurer, K., Bertrand, I., Palojärvi, A., Rasmussen, J., Toleikiene, M., Navarro Cano, J. A., Louault, F., Klumpp, K., Hakl, J., Sturite, I., Chabbi, A., Piton, G., and González-Pérez, J. A.: A trans-European decomposition index study in arable soils, focusing on the impact of plant diversity using a common 13C-labelled litter., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10168, https://doi.org/10.5194/egusphere-egu25-10168, 2025.
Carbon farming aims to sequester carbon in agroecosystems by increasing soil organic matter content and reducing greenhouse gas emissions, while also improving soil health. It also affects a range of other ecosystem services, such as water regulation, nutrient cycling, and agricultural productivity. Robust methods are needed to assess the potential and effectiveness of carbon farming approaches and to analyze potential trade-offs between soil carbon sequestration and other ecosystem services. In this context, agroecosystem models that inform and optimize farmer’s practices while offering a holistic perspective for the optimization of both agronomic and environmental outcomes in current and future climatic conditions seem a promising tool. This work presents the first steps towards the development of a digital twin for a real-world farm (Damianshof, Rommerskirchen, Germany) using a spatialized version of the agroecosystem model AgroC, which simulates soil water, heat, carbon and nitrogen fluxes. The digital twin is driven by high-resolution soil, climate and farm management data from the year 2010 onwards. This enables the evaluation of different management options to increase soil carbon sequestration, with a particular focus on regenerative management practices such as the use of cover crops and different crop genotypes with more recalcitrant root systems. By simulating and analyzing these scenarios, the digital twin will provide insights for optimizing management decisions while considering multiple ecosystem services. In future studies, the digital twin will serve as a valuable tool for assessing the broader impacts of global change on the ecosystem services and soil health associated with carbon farming.
How to cite: Bauer, F. M., Brogi, C., Herbst, M., Vereecken, H., and Huisman, J. A.: Towards a farm-scale digital twin to evaluate trade-offs between ecosystem services for carbon farming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11297, https://doi.org/10.5194/egusphere-egu25-11297, 2025.
The melioration technique “fractional deep tillage” (FDT) was developed in the late 1950s to increase crop yields and crop yield security in East Germany (former GDR) by remediating subsoil compaction and increasing topsoil depth. It is characterized by the shaft-wise exchange of topsoil and subsoil material. Today, FDT is of particular interest due to its carbon accrual and CO2 sink effect. On the one hand C-rich topsoil material is shifted into 50 cm deep shafts, which were created by special plough shares in 35 to 80 cm intervals. At the same time C-poor subsoil material is lifted into the topsoil. This reduces the SOC content of the topsoil and reinforces carbon sequestration due to the induced imbalance in the C cycle of the soil-plant-system. The C accrual until topsoil equilibrium is a quite fast process (Burger et al., Geoderma 2023) – due to the admixture of highly reactive, unsaturated subsoil mineral phases. The CO2 sink potential of a soil strongly depends on the long-term fate of the SOC buried in the subsoil.
Here we present results from a unique 40 years old historical field trial in NE Germany (“Wolfshagen”). A complete erosion-deposition sequence, typical for soil landscapes of hummocky ground moraines, was studied: “Calcaric Regosol - Nudiargic Luvisol - Stagnic Luvisol – Luvic Stagnosol - Gleyic Colluvic Regosol”. As climate and farming practice along the sequence were identical soil-related factors influencing the long-term fate of buried topsoil SOC in subsoils can be identified. Soil samples were taken from the shafts and the area next to the shafts and the contents of carbon (SOC, carbonates), nitrogen, pedogenic oxides (Fe, Al, Mn) as well as soil texture were analyzed.
We found that the SOC content in the shafts was significantly increased compared to the subsoil next to the shafts. The average increase of subsoil SOC in the shafts was 224±125%. The highest increase in subsoil SOC was found in the Stagnic Luvisol with 383% and Calcaric Regosol with 345%, whereas the smallest increase was found in Gleyic Colluvic Regosol with 45%. To calculate the potential SOC increase in the subsoil (25-50 cm depth) by fractional deep tillage, the dimensions of the CarbonFarming plough currently under development and the SOC data in the shaft and subsoil after 40 years were used. The results show that SOC stocks in the subsoil could be increased by 5.7 t ha-1 on average (range: 3.2 t ha-1 in the Gleyic Colluvic Regosol, 7.7 t ha-1 in the Luvic Stagnosol).
How to cite: Gerriets, M. and Sommer, M.: Sustainable increase of subsoil SOC in loamy soils by fractional deep tillage (FDT), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2007, https://doi.org/10.5194/egusphere-egu25-2007, 2025.
To address the urgent need to reduce greenhouse gas emissions from agriculture and to promote soil carbon sequestration, innovative approaches such as the incorporation of biochar into different agricultural practices are needed. Feeding biochar to cattle is an interesting strategy that not only aims to improve animal health and productivity, but may also have a cascading effect on soil improvement and CO2 sequestration, thereby addressing different facets of modern agriculture. Analysis of the recovery efficiency of digested biochar and its structural integrity can provide insight into the potential for post-digestion biochar applications. Here, a controlled feeding trial1 with dairy cows and a 1% biochar supplement in the diet was conducted and biochar recovery and composition in manure was analysed. Quantification of biochar in manure was investigated for the first time using methods based on thermal analysis, elemental analysis and dichromate oxidation, and shows that relative quantification of biochar is possible to within ± 1%. Overall, the majority of biochar (70-90%) fed to dairy cows survived digestion. The analysis also indicated selective preservation of the most stable condensed aromatic fractions of biochar during digestion, similar to short-term aging in soil. The remaining digested biochar has an H/C ratio of 0.22 and an O/C ratio of 0.05, meeting the criteria for highly stable biochar. Our results suggest that the digested biochar is highly suitable for long-term carbon sequestration when applied to soil via manure, offering a promising and economically viable strategy for carbon farming.
Reference
1 Dittmann et al., Animal Feed Science and Technology 318 (2024) 116127
How to cite: Leifeld, J. and Walz, I.: Does feeding biochar to cattle impair its carbon sequestration efficiency?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11700, https://doi.org/10.5194/egusphere-egu25-11700, 2025.
Soil organic carbon (SOC) storage is larger than organic carbon stored in the plant and atmosphere combined, prompting interest in understanding how SOC storage respond to rising temperatures. However, the consequences of warming in regulating SOC storage are still debated, with negative, positive, and non-significant responses reported. Most existing studies focusing on SOC responses to warming manipulate either air temperature or soil temperature, although in a realistic future air warming and soil warming co-occur. Based on results of meta-analysis and numerical simulations with a mechanistic model (T&C), we separate the effects of air warming, soil warming, and whole-ecosystem warming (combination of air warming and soil warming) on SOC storage. Results shows that soil warming alone decreases SOC storage due to temperature-driven increases in decomposition. Compared with soil warming, air warming has a more complex role. Air warming can cause water stress and hence a lower net primary productivity, as well as indirectly increase soil temperature. These factors tend to decrease SOC storage. However, in certain climates air warming can stimulate net primary production and decrease soil moisture limiting SOC decomposition. Once the latter mechanisms dominate, the SOC storge can increases with air warming. Our study helps refine the understanding and quantification of SOC responses in a warming climate.
How to cite: Luo, Z., Ren, J., and Fatichi, S.: Partitioning soil and air temperature warming effects on soil organic carbon storage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5311, https://doi.org/10.5194/egusphere-egu25-5311, 2025.
Prairie pothole wetlands exhibit highly variable soil organic carbon distributions that are driven by their unique hydrological and geomorphological characteristics as well as climate variables. These depression wetlands are a characteristic landscape feature that formed during the last glacial retreat in the Canadian Prairies and Upper Midwest United States. Land management practices, such as drainage and cultivation further influence these wetland soils through changes to the hydrology, vegetation type, and organic matter inputs. Understanding how these factors contribute to wetland soil organic carbon variability is essential for assessing their carbon storage contribution across scales, from individual wetlands in a field to the overall prairie pothole wetland landscape which spans over 770,000 km2. The objectives of this study were to 1) assess within wetland variability of soil organic carbon as for prairie pothole wetlands and 2) evaluate soil organic carbon stocks of these wetlands across a climate gradient as well as under crop production with or without drainage. Soil samples were collected from 134 prairie pothole wetlands to a depth of 1 m, at 3 landscape positions (centre, toeslope, and midslope). Soil classification was conducted at each sampling point and the soil cores were divided into 4 depth increments (0-15, 15-30, 30-60, and 60-100 cm) then measured for soil organic carbon as well as various other physiochemical properties. Our findings demonstrated that the within wetland variability can be explained by the wetland hydrology and historical tillage practices that led to soil erosion from upslope positions into the wetland depressions and consequently organic matter rich buried soil horizons at depths generally near 60 cm. Drained wetlands also showed evidence of soil organic carbon variability spatially and with depth due to soil redistribution that occurred during surface drainage installation. At the landscape scale, climate regime, wetland hydrology, parent material, and land management explained up to 39% of the wetland soil organic carbon variability. Based on this understanding we can more accurately estimate the soil organic carbon stock contribution of wetlands in agricultural landscapes and prioritize the sustainable management of these areas.
How to cite: Chizen, C. and Bedard-Haughn, A.: Soil Organic Carbon in Prairie Pothole Wetlands: Assessing Variability and Stocks across Scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14412, https://doi.org/10.5194/egusphere-egu25-14412, 2025.
