Soils have a critical role in delivering ecosystem services for food security, climate action, and biodiversity conservation, among others. Alarmingly, the health of soils across the globe is deteriorating rapidly. Within the European Union alone, over 60% of soils are currently considered to be in a degraded state. In this light, it is noteworthy that, compared to environmental domains such as air and water quality, soils suffer from limited legal protection. A key barrier is the challenge of bridging the gap between how soils function - with substantial uncertainties surrounding their composition, distribution, and the most effective ways to measure and interpret key properties – and the design and performance of legal frameworks. In this interdisciplinary perspective article, we explore how law and soil science can be bridged to advance in ensuring soil health. As a case study, we critically evaluate the EU’s proposed Soil Monitoring and Resilience Directive, aimed at protecting soils through three key pillars: soil monitoring, sustainable soil management, and contamination. We identify and discuss key challenges for bridging law and soil science, and propose novel solutions for enhanced legal protection of soil. Finally, we distill lessons learned for policymakers to strengthen soil protection efforts globally.
How to cite: Alblas, E. and Helfenstein, J.: Bridging law and soil science for soil health, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11987, https://doi.org/10.5194/egusphere-egu25-11987, 2025.
In the Mediterranean region and particularly in the Near East and North Africa Mediterranean (NENA) countries, the soils and landscapes are extensively degraded, due to long-term unsustainable anthropogenic pressure and the effects of climate change. The average level of health of the soil resources is low and already inadequate to support economic development and food security targets.
In the context of the sustainable management and protection of soil resources, considering the specificities of Mediterranean environmental conditions, there is an urgent need to make soil data and soil information (SDI) data understandable and usable for the purpose of monitoring soil health and assessing soil ecosystems in the region.
Steps toward this aim are being taken within the PRIMA-funded SOIL4MED project, which focuses on monitoring soil health and developing information systems to promote sustainable soil management in Mediterranean region, aligning with the Global Soil Partnership aims and approaches.
The project starts with a comprehensive review of legacy soil point data provided by partner countries, i.e. Italy, Lebanon, Spain, France, Tunisia, Greece, Egypt, Jordan, Turkey, and Morocco.
A total of almost 9,000 soil profiles data were collected, thanks also to the contributions of some research institutes (i.e., IAO, CREA, IRD/ORSTOM). These were then subjected to detailed analysis in order to ascertain the types of survey methods employed, the different soil classification systems used and the type of data available for each country (e.g., field data, lab data).
The systematic collection of data has revealed several key findings. Firstly, that legacy data are frequently old, in non-digital format and lack homogeneity in terms of soil classification systems, field and lab methods, and data formats. Secondly, that if properly processed, such data are able to provide an overview of soil characteristics and properties in the Mediterranean area.
Therefore, to use these data systematically and effectively, they must be harmonized and digitized in order to develop an easily accessible and standardized database of soil information.
The process of collecting, evaluating, integrating multiple types of soil legacy data, homogenizing them using a single classification system (WRB, 2022), and their subsequent inclusion in a database, provides a more robust and complete view of the evidence available about soil health in the MR. It is a key step in the selection of soil health indicators and provides useful information to define past and present soil health conditions. This collaborative effort represents a crucial preparatory step for the future realization of the Soil Atlas of the Mediterranean Region.
How to cite: D'Amico, M., Masseroli, A., Demontis, R., Lorrai, E., Muscas, L., and Zucca, C.: The review of soil legacy data as a first step for the construction of a soil health monitoring system in the Mediterranean Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19239, https://doi.org/10.5194/egusphere-egu25-19239, 2025.
A proof-of-concept environmental indicator framework designed to monitor soil health and its capacity to deliver key ecosystem services was developed for England. The framework addressed trade-offs inherent in managing soils for diverse, often conflicting outcomes, such as climate regulation, food production, water regulation, and below-ground biodiversity. By integrating these ecosystem services into a unified system, the framework enables assessment of soil health at a granular scale while contextualizing results across broader land uses.
Key soil properties critical to soil health and ecosystem service delivery were identified and ranked, forming the basis of conceptual models for statistical modelling in Bayesian Belief Networks. These models were populated with national-scale datasets and expert judgment, providing probabilistic outputs that indicated the capacity of soils to support ecosystem services at the land parcel level. Results were visualized in a user-friendly dashboard app, allowing for comparison of soil health within the context of inherent soil properties and current land use.
Initial outputs demonstrated the utility of the framework in identifying trade-offs and synergies between ecosystem services, while also potential to detect emergent soil system properties. The framework’s flexibility has allowed for further iterative refinement of the models, and future incorporation of local knowledge, new soil data, and adjustment to evolving policy or scientific understanding. This adaptability ensures the framework remains relevant for diverse applications, from reporting national policy targets on soil health to supporting field-level decisions by farmers.
How to cite: Hannam, J., Harris, M., Deeks, L., Marion, S., Harris, J., Way, L., Rickson, J., and Hoskins, H.: A soil health indicator framework based on ecosystem service delivery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11623, https://doi.org/10.5194/egusphere-egu25-11623, 2025.
Soil health is crucial for enhancing food security and climate resilience in smallholder farming systems in the Global South. However, while diverse stakeholders, including researchers, private sector, governments, NGOs, extension services, and farmers, share the common goal of restoring and enhancing soil health, they have different objectives and needs. They also operate at varying spatial and temporal scales. In the context of the Global South, a significant challenge lies in the lack of consensus on effective methods for assessing and monitoring soil health across diverse agroecological contexts and farming systems. This presentation will: 1) explore the needs and challenges of soil health assessment in the Global South, 2) propose an adaptable operational framework for soil health assessment and monitoring that accommodates diverse contexts and engages key stakeholders, and 3) outline actionable steps to advance the practical implementation of the framework, supporting transitions toward sustainable food systems. Key bottlenecks to address include the development and integration of methods for assessing physical, chemical, and specifically biological soil health indicators, with a focus on ensuring their suitability and accessibility for diverse users and contexts, as well as agronomic and environmental outcomes. Furthermore, pathways to accelerate the impact of soil health decision-making will be identified. The goal is to offer essential guidance to advance integrated soil health assessment and monitoring, supporting agricultural innovations that benefit and actively include smallholder farmers and decision-makers in the Global South.
How to cite: Rasche, F. and the Soil Health Working Group of the CGIAR Excellence in Agronomy (EiA) Initiative: Advancing soil health assessment and monitoring in the Global South: a flexible framework for enhancing food security and climate resilience in smallholder farming systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9479, https://doi.org/10.5194/egusphere-egu25-9479, 2025.
The assessment of soil health needs to consider the context of soil-forming factors and land use history when identifying reference soils, and set thresholds and management targets specific to different soil types. In the Basque Country (N Spain), rural landscapes and forests have been subjected to anthropogenic transformations and uses since Antiquity, with a profound expansion of intensive forestry plantations during the 20th century. Hence, the establishment of reference soil health status is challenging but essential for guiding sustainable forest management. The aims of this study are: 1) to apply digital soil mapping for the delineation of soil monitoring units in the Basque Country, 2) set up thresholds and targets for soil indicators for managed forest soils, and 3) map the soil health condition of forest plantations. We established a framework for assessing the condition of forest plantations using three indicators suggested by the EU Soil Monitoring and Resilience Law proposal (soil organic carbon (SOC): clay, pH, and bulk density) using data from Basonet, the permanent network for forest monitoring in the Basque Country. The soil units were created applying unsupervised classification to a set of environmental covariates, proxies of the soil-forming factors (pedogenon mapping). Semi-natural native forests were used as reference for setting unit-specific thresholds for soil indicators (i.e., reference approach), and we tested the influence of the selected threshold on the soil health assessment. 61% of plots were in poor condition at the interval 0-20 cm and 90% at 20-40 cm for loss of SOC according to the EU level threshold of SOC:clay <1/13. The proportion of plantations in poor condition for loss of SOC with the reference approach ranged between 14-50% depending on the percentile used to set thresholds (5th and 25th percentiles). Forest plantations acidified the soil compared to semi-natural forests, with 15-60% of plantation plots with pH lower that the thresholds. All plantation plots were in good condition in terms of subsoil compaction with the EU criteria, but 9.6% of semi-natural plots suffered from compaction. We emphasize that the assessment of soil health needs to consider the context of soil-forming factors and inherent soil properties (e.g., mineralogy) when identifying thresholds for soil health indicators. Future work will continue the search for reference soils combining historical aerial photographs and long-term satellite imagery.
How to cite: Roman Dobarco, M., Cornu, S., McBratney, A. B., and Curiel Yuste, J.: Assessing the condition of forest soils in the Basque Country (Spain) with site-specific thresholds for soil health indicators, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10897, https://doi.org/10.5194/egusphere-egu25-10897, 2025.
Globally, soil plays a crucial role in delivering multiple soil-based ecosystem services (SESs), such as food provisioning, water regulation and purification, nutrient cycling, and others. This makes soils fundamental to achieving several Sustainable Development Goals (SDGs), including food security (SDG 2), water quality (SDG 6), and climate action (SDG 13). However, around 60 to 70% of soils in the EU are currently in an unhealthy state, highlighting the urgent need for concrete and immediate actions.
Ultimately, a variety of soil health indicators have been proposed, based on physical, chemical, and biological measurements, both in the field and in the laboratory. However, many of these indicators, being derived from single measurements at specific points in time, fail to fully capture the complexity of the diverse and interconnected ecosystem services provided by soils. Consequently, evaluating SESs in a comprehensive spatio-temporal framework remains a significant challenge.
This study introduces an integrated approach to evaluating SESs using the ARMOSA (Analysis of cRopping systems for Management Optimization and Sustainable Agriculture) process-based model. ARMOSA enables the quantification of agronomic practices' effects on a broad set of crop and soil-related variables on a daily time scale. By utilizing multiple integrated indicators rather than isolated measurements, this approach captures the dynamic interactions within the soil-plant-atmosphere system—including water, carbon, and nitrate balances—across both spatial and temporal scales. This provides a more robust framework for soil health assessment tailored to site-specific characteristics. As highlighted in this session, the approach facilitates the identification of potential soil health statuses, laying the foundation for evaluating the impacts of various management practices on diverse soil types. Furthermore, it aligns with the objectives of soil monitoring legislation by offering insights into soil district delimitation and management strategies. It also provides a consistent and comprehensive framework for evaluating multiple soil ecosystem services while addressing scaling challenges in soil health assessments.
The approach is demonstrated in a hilly area of the Campania region (southern Italy), which encompasses five distinct climatic zones where durum wheat is the predominant crop. Various indicators were assessed in relation to key SES, including yield for food production, infiltration for water regulation, carbon stock changes for climate regulation, and nitrate leaching for nutrient cycling. The results of this study highlight the potential of the integrated SESs evaluations to support sustainable soil management and inform strategies that align with global soil health objectives.
How to cite: Bancheri, M., Perego, A., Botta, M., Albrizio, R., Orefice, N., De Mascellis, R., and Basile, A.: From Indicators to Action: Modeling Soil Health for Ecosystem Service Optimization., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6406, https://doi.org/10.5194/egusphere-egu25-6406, 2025.
The soil water retention curve (SWRC) is a fundamental soil property that provides information about soil structure, soil texture, plant water availability, drainage, and compaction, and is therefore highly linked to soil functions and soil health. Current large-scale digital maps of the SWRC are typically developed indirectly through a two-step process: i) the development of pedotransfer functions (PTFs) that establish relationships between basic soil properties such as textural fractions, bulk density, organic matter content, and parameters of well-known SWRC models, and ii) the application of these PTFs to basic soil property maps at various scales. This presentation introduces a novel, physically constrained machine learning approach for directly mapping the entire SWRC from saturation to oven-dryness. Unlike previous studies, our new approach neither relies on PTFs nor is limited to a specific SWRC model. Instead, it estimates a non-specific form of the SWRC, learned from both measurements and physical constraints. Applying this method to 1,261 soil profiles across Denmark, encompassing 4,747 measured SWRCs, demonstrates its superior performance compared to established methods. The new approach enables the aggregation of datasets with sparse and incomplete SWRC measurements, which are typically unusable with conventional methods. This capability maximizes spatial coverage and reduces uncertainties in the final predicted maps. Additionally, the new approach addresses the commonly observed imbalance between wet and dry-end measurements in large SWRC datasets. Following a detailed report on the results of our approach for Denmark, we discuss ongoing efforts and progress toward applying this method to SWRC mapping at the European scale.
How to cite: Norouzi, S., Greve, M. H., Moldrup, P., Arthur, E., Lehmann, P., Robinson, D., Pesch, C., Iversen, B. V., Zaresourmanabad, M., Norgaard, T., and de Jonge, L. W.: Toward Pan-European Mapping of Soil Water Retention Curves Using a Physics-informed Machine Learning Approach: Insights from Denmark, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8362, https://doi.org/10.5194/egusphere-egu25-8362, 2025.
Monitoring of the microbiological processes in the soil is important to understand and the impacts of agricultural practices on soil health. Evaluation of microbially-mediated soil processes usually involves manual sampling followed by laboratory analysis, which is costly, time consuming, physically intensive, non-continuous, and offers limited capacity for measuring changes at a high temporal and spatial resolution. Low-cost soil sensors manufactured using printing techniques offer a potential scalable solution to these issues, allowing for high-frequency in-situ measurement of decomposition rates. Here, we tested the use of novel decomposition sensors to complement or replace conventional laboratory measurements for the evaluation of soil health.
We installed decomposition sensors in undisturbed cores from two similar soils from Cumbria UK, but with high and low nutrient status and contrasting vegetation: a winter wheat crop and a biodiverse meadow sward. We imposed a climatic stress on the cores as either a flood and drought treatment. For the flood treatment, cores were placed in a tank with rainwater collected from the site, maintained level with the top of the soil throughout the study. For the control, watering with rainwater was administered three times a week, as needed. The drought was left to dry down through the treatment phase. When the treatments were alleviated, the flooded cores were allowed to drain, and the drought treatment received one litre of fresh rainwater per core.
The decomposition sensors were able to track the recovery of biological activity through time following the alleviation of climatic stress. Drought treatments recovered rapidly, whereas recovery from flooding was less rapid in the biodiverse treatment. The flooded winter wheat treatment was less affected by flooding than the other treatments which we attribute to the plants being better adapted to waterlogging.
Our findings demonstrate the potential for the proxy measurement of soil processes in-situ using novel printed decomposition sensors, thereby supporting their potential for low-cost, high-resolution temporal and spatial monitoring of soil biological parameters and providing new insights into soil health.
How to cite: Quinton, J., Sharpe, T., Fry, E., and Whiting, G.: A Novel Biodegradable Decomposition Sensor demonstrates the dynamic recovery of soil biological activity following climatic stress , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7042, https://doi.org/10.5194/egusphere-egu25-7042, 2025.
Soil health is a comprehensive reflection of many ecosystem functions and services of soils, which covers a range of indicators including physical, chemical and biological properties. The assessment of soil health and measurement of its indicators should follow the criteria of simplicity, inexpensiveness, rapidity and reliability, but previous measurement methods seldom meet all of these requirements at the same time. Spectroscopy is a method that utilizes a light source to irradiate the measured materials and cause the movement of atoms, molecules and electron within the substance to form a spectral image. It has many advantages such as non-destructive, non-polluting, cheap, fast, reliable, and multi-indicator measurements, making it an ideal way to measure soil health indicators. This study summarizes the principles and current research situations of common spectroscopic methods (including near-infrared (NIR), visible to near-infrared (Vis-NIR), mid-infrared (MIR), Fourier transform infrared spectroscopy (FTIR), Raman (scattering) spectroscopy and X-ray fluorescence (XRF)) in the determination of various properties in soil studies. We also give our recommendations of corresponding spectroscopic methods for different assessment scenarios and purposes. Finally, we point out current research gaps and future study directions. The feasibility and advantages are confirmed for using spectroscopic techniques on soil health studies, which is conducive to the practical application and uniform assessment of soil health indicator determination in the future.
How to cite: Hu, Y.-H.: Assessment and measurement of soil health indicators using spectroscopic techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15111, https://doi.org/10.5194/egusphere-egu25-15111, 2025.
Soil color has long been used as an indicator for soil properties such as soil organic carbon and soil moisture. Recent developments of citizen science have seen increased use of smartphone cameras for soil color measurement. However, there are high errors associated with this technique, especially when used in the field. A major source of the errors is that the color in the smartphone images for a given object is strongly affected by the type of smartphone used and the environmental lighting conditions. To reduce this error, we propose to calibrate the color in a smartphone image by including reference color objects in the picture while taking the photo. To examine the validity of this calibration method, we used a commercially available color plate with 24 color squares covering a wide range in the color space as the reference color objects. The color plate was placed together with 7 Munsell soil color sheets covering the range of soil colors observed in Canada. Pictures were taken with four different smartphones under six lighting conditions. Each color square in the color plate and each color chip in the Munsell soil color sheets were measured with a FieldSpec4 SpectroRadiometer. The FieldSpec4 measurements were converted to RGB values in the RGB color space and Hue, Value, and Chroma in the Munsell color space. These values were considered as reference values (true values). Meanwhile, for each color square in the color plate and each color chip in the Munsell soil color book, the RGB values in each smartphone image were extracted. They were converted to Hue, Value, and Chroma in the Munsell color space as well. These values derived directly from the smartphone images were considered the raw values for the smartphone images. For each smartphone image and for each color parameter, a linear regression was established between the raw and reference values of the color squares on the color plate. For other objects in the same picture, the calibration was conducted by applying the regression equation to adjust the raw values to the calibrated values. The validity of the calibration method was examined by comparing the calibrated values to the FeildSpec4 measured reference values. The results show that the raw values had significant bias for some smartphones and under some lighting conditions. After calibration, the bias has been reduced for most color parameters. In particular, the variations associated with different smartphones and different lighting conditions have been reduced. With the Munsell soil color sheets, the calibrated Hue, Value matched well with the values indicated on the sheets, a substantial improvement from the raw values. However, the calibration did not seem to work well with Chroma and there was no improvement after the calibration process for Chroma.
How to cite: Zheng, F., Li, S., Koiter, A., and Kupriyanovich, Y.: Using a reference color plate to calibrate soil color measured with smartphone cameras, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7408, https://doi.org/10.5194/egusphere-egu25-7408, 2025.
Soil health is the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals and humans, with soil biodiversity playing a critical role. However, its complexity and the lack of harmonized data make it challenging to establish measurable links between soil biodiversity and soil health. In this study, we explored the use of plant-beneficial bacteria (PBB) as potential indicators of soil health. Plant-beneficial bacteria are known to support plant growth and development by providing multiple benefits, such as biocontrol, growth promotion (e.g., nitrogen fixation), and stress resistance (e.g., drought tolerance). For this study, we analyzed eDNA data from 7500 soil samples collected across various land uses and soil types in Denmark. Plant-beneficial bacteria identification was based on a global database, which links microbial taxonomy with plant-beneficial traits from existing literature based on experiments, recording 396 genera that contribute to biocontrol, stress resistance, and growth-promoting functions. Our findings show that PBB are highly variable across different land uses. For instance, agricultural areas exhibited the highest median number of different PBB genera, with values ranging from approximately 5 to 320 genera, and showed significant variation. In contrast, heathlands displayed a lower number of PBB (around 40 genera) with less variability. Additionally, the number of PBB was negatively correlated with the soil C/N ratio. The variability of PBB across and within different land uses and soil properties suggests its potential to serve as an indicator of soil health.
How to cite: Gomes, L. C., Danielsen, A.-C., Gutierrez, S., Artur, E., Pesch, C., Lystbæk Weber, P., H. Greve, M., and Wollesen de Jonge, L.: Can plant-beneficial bacteria be used as an indicator of soil health?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18270, https://doi.org/10.5194/egusphere-egu25-18270, 2025.
The quantification of soil health is typically split up in the measurement of physical, chemical, and biological indicators. Such a disciplinary approach requires a manifold of different methods that are often time consuming and are difficult to be integrated into a concise description of soil health status. A possible candidate for a comprehensive description is the extracellular enzymatic activity of the soil. The enzymatic activity as biological indicator plays a pivotal role in the physico-chemical cycles of carbon, nitrogen, phosphorus, and sulfur. In addition, it characterizes the degradation of organic matter and structure formation that controls physical processes like aeration and rainfall partitioning (infiltration and runoff). To represent various functions of enzymes and biogeochemical processes, we measured the activity of five enzymes using SEAR (Soil Enzymatic Activity Reader). SEAR determines the extracellular enzymatic activity using a fluorescent substrate. We applied this method to measure the enzymatic activity in a forest in a dry region of Switzerland that suffers from reduced rainfall amounts, resulting in hydrophobic conditions that affect rainfall partitioning and the soil water balance. Preliminary results for a pine stand show a slight decrease of the activity of enzymes that are responsible for carbon decomposition and nitrogen mineralization during the dry summer period, indicating that the soil functions are disturbed during the dry period. We will compare these findings with results from a spruce stand and discuss the soil functions and soil health status during drought periods.
How to cite: Lehmann, P., Fetzer, J., and Meller, S.: Measuring enzymatic activity to integrate biological, chemical, and physical soil health indicators – a case study in a dry coniferous forest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14934, https://doi.org/10.5194/egusphere-egu25-14934, 2025.
Understanding soil health is pivotal for the implementation of sustainable agricultural practices. Rapid and reliable field-based methods for assessing soil respiration, labile carbon (C), and stable C are needed to complement laboratory analyses and provide on-site insights into the soil. This study explores the performance and reliability of field methods for measuring these parameters across diverse land use and soil management systems, focusing on the applicability of colorimetric and spectrophotometric techniques.
Two distinct soil sample sets were analyzed. The first set included 10 agricultural sites in Eastern Austria, with samples collected from arable fields and adjacent (semi-) natural reference strips over three time points (April, May, and June). The second set involved field trials in Pyhra and Hollabrunn, employing a randomized block design to assess soil tillage (conventional, direct seeding, minimal, and reduced tillage) and cover crops (fallow, diverse, and standard). Each treatment was replicated three times and sampled once.
Field measurements of basal soil respiration were conducted by incubating fresh soil for 12 hours in containers with an agar medium and a pH-indicator, with CO₂-induced color changes captured using RGB and Lab* data. Labile C was assessed using an adapted Weil et al. (2003) method, while stable C was extracted with 0.5 M NaOH. Laboratory validation employed gas chromatography for soil respiration and photometric methods for POxC (550 nm) and NaOH extracts (400 and 600 nm).
Field and laboratory measurements correlated well for the 10 Eastern Austrian sites (r² = 0.26 for soil respiration, r² = 0.74 for labile C, and r² = 0.52 for stable C), supporting the reliability of the field methods. Results demonstrated the ability to differentiate between arable and non-arable land use systems. However, distinguishing soil management practices (e.g., tillage and cover cropping) proved challenging, with no significant differences observed across methods.
These findings highlight the potential of field-based techniques for soil quality assessment, offering practical tools that align closely with laboratory precision. However, further refinement is needed to distinguish conventional and nature-based management systems effectively.
References:
Weil, R. R., Islam, K. R., Stine, M. A., Gruver, J. B., & Samson-Liebig, S. E. (2003). Estimating active carbon for soil quality assessment: A simplified method for laboratory and field use. American Journal of Alternative Agriculture, 18(1), 3-17.
How to cite: Weinrich, F., Rosinger, C., Bodner, G., and Keiblinger, K.: Bridging the gap: Field-based soil health assessment for nature-based soil management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18347, https://doi.org/10.5194/egusphere-egu25-18347, 2025.
Soil organic carbon (SOC) is a fundamental contributor to soil functions and health. Since SOC is a strong predictor of many important soil properties, it is prominently featured in soil health assessments and monitoring targets. Yet, given its strong spatial heterogeneity, specific SOC targets are highly debated. For example, the EU Directive for Soil Monitoring and Resilience proposed the ratio between SOC and clay content (SOC:clay) as a target, with 1/13 separating “degraded” from “non-degraded” mineral agricultural soils, allowing local correction factors for diverging pedo-climatic conditions. However, SOC:clay has been criticized for inherent biases. Recent publications support this perspective, calling for regionally specific SOC benchmarks. These benchmarks are typically an expected SOC value for an approximately homogeneous area, in terms of land-use and pedoclimatic conditions. We provide an overview of three recently published protocols for deriving regionally relevant SOC targets and evaluate them on recent monitoring campaigns in Austria, using the SOC:clay ratio as a baseline.
Our results show that all regional benchmarking approaches evaluate the status of SOC in agricultural soils similarly, based on recent monitoring campaigns. Additionally, these approaches show high sensitivity to agricultural management, which the SOC:clay ratio fails to do consistently in our case study. Finally, we propose an approach for using lighthouse farms to guide SOC targets within a specific benchmark, where lighthouse farms represent the upper boundary of what is achievable in terms of soil health within a given pedoclimatic zone.
How to cite: Bernardini, L. G., Bruni, E., Emma Izquierdo-Verdiguier, E., Keiblinger, K., Rosinger, C., and Bodner, G.: Evaluating SOC status in agricultural soils: a comparison of approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17115, https://doi.org/10.5194/egusphere-egu25-17115, 2025.
Soil pH exhibits significant spatiotemporal variability due to natural factors (e.g., climate, terrain) and human activities (e.g., land use, soil management). Key drivers include soil texture, carbon content, vegetation, slope, and climate. Understanding this variability is essential for soil management. High-quality and readily interpretable soil pH maps are also needed as a covariate to investigate their relationships with complex soil properties at finer resolutions
Existing soil pH maps often lack the fine resolution required for field-scale applications, typically providing resolutions between 90 m and 250 m. To address this gap, we aimed to: (1) generate a high-resolution (10 m) soil pH map of Denmark with uncertainty estimates using machine learning, and (2) identify the main factors influencing soil pH variability across different land uses.
We analyzed 7000 topsoil samples (0–20 cm) collected from natural and agricultural sites. Soil pH was measured in H₂O and 0.01 M CaCl₂, with the delta pH calculated as their difference. Environmental layers at 10-m resolution, representing soil properties, climate, vegetation, and geomorphology, were harmonized as model inputs. We employed quantile regression forest models, splitting the dataset into 70% training and 30% testing for validation. Model accuracy was assessed using normalized root mean square error, concordance correlation coefficient, and R². We analyzed how environmental factors control the pH and the delta pH using the SHAP (SHapley Additive exPlanations) algorithm.
The pH measured in CaCl₂ achieved the highest model performance, followed by H₂O pH and delta pH. The SHAP analysis highlighted the factors driving pH variability in natural versus agricultural settings. Soil texture, climate variables, and oxalate-extractable Fe and Al were the strongest predictors. Topographical parameters related to hydrological properties also impacted the spatial distribution of the response variables. Our results indicate that soil properties and topographical features had a higher contribution than remote sensing indices representing vegetation growth patterns. This hierarchy of influence suggests that while remote sensing data is valuable, it should be complemented by high-quality topographical and soil data for optimal pH mapping outcomes.
The methodology used in this research allowed us to establish the environmental covariates affecting pH variation. These fine-resolution maps serve as valuable tools for mapping other soil properties (e.g. ion exchange capacity, soil microbial diversity, etc.), enhancing therefore, agricultural management and planning to achieve healthy soils.
How to cite: Gutierrez Diaz, J. S., Mikstas, D., Sournamabad, M., Storgaard Danielsen, A.-C., Norouzi, S., Bjørn Møller, A., de Jonge, L., Greve, M., and Gomes, L.: High-resolution mapping of soil pH for Denmark, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8276, https://doi.org/10.5194/egusphere-egu25-8276, 2025.
Soil salinization, referring to the excessive accumulation of soluble salt in soils, adversely influences nutrient cycling, microbial activity, biodiversity, plant growth, and crop production, thus affecting soil health and ecosystem functioning (Shokri et al., 2024). Soil salinity quantification is a major step toward the mitigation of its effects. Therefore, developing quantitative tools to predict soil salinity at regional and continental levels under different boundary conditions and scenarios is crucial for sustainable soil management and the security of natural resources (Hassani et al., 2020, 2021). This study proposes an AI-driven soil salinity quantification and projection approach focused on EU soils using a set of environmental covariates, which consist of soil properties, terrain attributes, climate, and remotely sensed variables. A key aspect of this study is the integration of soil salinity point data from the LUCAS survey in the AI model, complemented by the WoSIS dataset. To improve the model performance, forward feature selection technique was applied. The model achieved the training, testing, and validation accuracy, expressed in , of 0.7, 0.7, and 0.57 respectively. The analysis indicates that 4.9 and 0.6 Mha of the EU land exceeds the 1 and 2 dS/m of electrical conductivity, respectively, highlighting the regions of concern. Italy, Spain, and France show high levels of soil salinity respectively. The output of the predictive model will be a gridded dataset illustrating the spatial and temporal (yearly) distribution of soil salinity across the EU, accompanied by the corresponding uncertainty map with a spatial resolution of 1 km. This information is crucial for identifying regions with elevated salinity levels and formulating necessary action plans to mitigate the situation.
How to cite: Zarif, M. A., Hassani, A., Afshar, M. H., Panagos, P., Lebron, I., Robinson, D. A., and Shokri, N.: Spatial and Temporal Assessment of Soil Salinization Across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13293, https://doi.org/10.5194/egusphere-egu25-13293, 2025.
Environmental issues resulting from abandoned mining activities currently represent a significant challenge due to their persistence in soils, water bodies, and surrounding ecosystems. The San Quintín Mining District, located in the Valle de Alcudia (Spain), has generated substantial local geochemical anomalies of lead (Pb), zinc (Zn), calcium (Ca), manganese (Mn), iron (Fe), and copper (Cu), among other elements. This mid-20th-century mining operation has left a legacy of contamination affecting not only the soils in the area but also the surrounding vegetation, including agricultural and grazing lands.
Numerous studies have emphasized the critical role of plants as hyperaccumulators, capable of absorbing heavy metals such as Pb, Zn, Ca, Mn, Fe, and Cu from the soil. In this study, a biogeochemical assessment was conducted to determine the concentrations of these metals in soils and plants, using Quercus ilex (holm oak) as the prototype species due to its ubiquitous presence throughout the mining district.
The study employed two key parameters: the Bioaccumulation Factor (BAF), which measures the plant's capacity to accumulate heavy metals, and the Normalized Difference Vegetation Index (NDVI), used to evaluate vegetation health through multispectral imaging. The methodology combined multielement analysis via Energy-Dispersive X-ray Fluorescence (ED-XRF) for determining trace elements (EPTs) and RPAS (Remotely Piloted Aircraft Systems) technology to capture multispectral images for NDVI calculation.
The results reveal significant variability in soil geochemistry, with high levels of Fe₂O₃, Zn, Cu, and Pb, indicating localized contamination sources. Quercus ilex demonstrated varying absorption capacities depending on its specific location, with Mn and Fe showing the highest concentrations, reaching up to 26.2 mg/kg and 11.8 mg/kg, respectively, indicating substantial accumulation in certain areas. Pb and Zn concentrations displayed high coefficients of variation in soils near waste piles and tailings, supporting the hypothesis that these sources contribute to soil contamination and subsequent bioaccumulation in local vegetation.
Additionally, a deficiency in Mn, an essential nutrient for chlorophyll production and photosynthesis, could reduce chlorophyll content, thus lowering the plant's photosynthetic efficiency. This is reflected in higher NDVI values, which coincide with increased Mn concentrations in soil and plants. Finally, a negative correlation was observed between NDVI values and relatively low concentrations of metallic elements, highlighting the complex interactions between soil composition, vegetation health, and environmental contamination.
How to cite: Barquero Peralbo, J. I., Cespedes Castro, J. M., Morales Laurente, J. A., García Ordiales, E., and Higueras Higueras, P.: Biogeochemical characterization in the San Quintín mining area based on the IDV Index obtained through RPAS technology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9639, https://doi.org/10.5194/egusphere-egu25-9639, 2025.
Soil contamination is a pressing global issue, with far-reaching consequences for ecosystems, agriculture, and human health. The presence of hazardous substances such as heavy metals, hydrocarbons, and other pollutants in soils poses significant risks to biodiversity and the functioning of natural systems. In the European Union, soil remediation has emerged as a priority area, underlined by its inclusion in various environmental policies and frameworks aimed at achieving a sustainable future. Addressing soil contamination requires robust scientific approaches to assess environmental risks and inform remediation strategies.
This research presents an evaluation of environmental risks posed by contaminated soil samples collected from post-industrial and post-mining areas. The adopted research methodology is based on an assessment derived from a comprehensive ecotoxicological analysis conducted using a battery of bioassays. These biotest include representatives of various trophic levels, offering a holistic perspective on the ecological impact of the contaminants. These bioassays, representing producers, consumers, and decomposers, provided a comprehensive assessment of the ecological hazards posed by the soil contaminants. The findings underscore the importance of integrated ecotoxicological analyses in understanding the impacts of soil contamination and guiding remediation efforts, particularly within the framework of European Union initiatives aimed at sustainable soil management.
How to cite: Zgórska, A., Manier, N., Kruczek, M., and Pucheux, N.: Impact of Pollutants in Post-Industrial Soils on Soil Biota: A Comprehensive Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18586, https://doi.org/10.5194/egusphere-egu25-18586, 2025.
Microplastics (MP) smaller than 5 mm have become widespread in the environment, including agricultural soils, due to increasing production and use. MP is characterized by a large specific surface area and hydrophobicity, which makes it a carrier of organic pollutants, heavy metals, and microorganisms. Their slow degradation and small size allow it to enter the food chain, potentially threatening human health.
Fourier transform infrared spectroscopy coupled with attenuated total reflectance (FTIR-ATR) is a powerful, cost-effective, and non-destructive method for identifying MP, and analyzing its functional groups in soil samples. This study aimed to assess the applicability of FTIR-ATR spectroscopy for detecting MP in Croatian Luvisol and Eugley soils. Polyethylene (PE) and polypropylene (PP) were added to soils at concentrations of 2% and 5% and analysed in triplicate.
FTIR-ATR successfully detected PP and PE functional groups in both soils. However, in Eugley soil, strong absorption bands from minerals and inorganic soil particles overlapped the characteristic PP bands at 1454 cm⁻¹ and the 1237–720 cm⁻¹ range, whereas in Luvisol, interference occurred only within the 1237–720 cm⁻¹ range. These findings highlight FTIR-ATR strong potential for MP detection in soils, although further research is needed for MP particle quantification.
Keywords: microplastics, polyethylene, polypropylene, FTIR-ATR spectroscopy, agricultural soil
How to cite: Horvatinec, J., Reljić, M., Borghini, V. P., Svečnjak, L., Ilić, N., Nement, I., and Zovko, M.: Application of FTIR-ATR spectroscopy in the detection of microplastics in Croatian agricultural soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17266, https://doi.org/10.5194/egusphere-egu25-17266, 2025.
