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Soil health and EU soil policies: the role of the EU Soil Observatory

Nils Broothaerts

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pan-EU soil health assessment

10:00

Machine learning for soil health - Is the horizon the limit? Flaws, potentials and future challenges

Madlene Nussbaum

Machine learning is promoted as a game-changer for soil health assessment, offering new ways to model complex relationships and generate high-resolution soil property maps. However, while ML has shown promise, its application in soil science is often met with overstated expectations and underappreciated limitations. This keynote critically examines the role of ML in space-time soil mapping for soil health, highlighting both its strengths and pitfalls.

ML has certainly advanced soil mapping in an unprecedented way to achieve continental and even global maps at high resolution for numerous soil properties. Entangled soil processes and the variability of locations, all nearly having an individual set of soil-forming factors, result in complex space-time soil patterns. In the commonly used mapping approach, ML has to learn all this complexity fully data-driven from the surveyed soil samples and the environmental predictors such as remote sensing data or elevation models. For cases where no environmental predictor dataset can differentiate the observed soil property patterns, ML predictions will play save and predict the average observed value for similar locations. From a soil process knowledge perspective, the mean might often not be the best prediction. For example, a forest topsoil may be buffered by carbonates and have a pH around 8 or its pH might already have dropped to reach the aluminum buffer range of around 4. A mean pH of 5-6 likely to be predicted by ML is not often observed within unfertilized forests and, hence, is rather unlikely.

Similar limitations also appear while quantifying prediction uncertainty at each location. ML-based prediction intervals often contain value ranges that, from a soil process viewpoint, we already know are very unlikely. While certainly more field surveying is due to support unbiased mapping, it does not resolve the challenge. The marginal benefit of more data points for fully-data driven ML often decreases rapidly, more so in the presence of measurement errors. Soil sampling will always only provide a tiny fraction of the total 3D soil continuum we are interested in. Data-hungry ML techniques such as deep learning are therefore unlikely to excel in space-time soilmapping of soil health indicators.

Earth observation data for monitoring soil health

10:30

Monitoring, reporting and verification of soil health - what can we learn from the soil carbon experience

Pete Smith

Soil health indicators cover the biological, chemical and physical domains of soils. In this respect, just selecting agreed indicators of soil health, and measuring them, is difficult. Soil organic matter (which is about 58% carbon) is a headline indicator of soil health, but establishing a monitoring, verification and reporting (MRV) framework for just this one indicator is a challenge. I will present experiences of developing MRV frameworks for changes on soil carbon, and reflect upon how these could be built upon to untimately develop an MRV framework for for soil health, reflecting on the technological and scientific challenges in doing so.

I will briefly review methods and challenges of measuring SOC change directly in soils, before examining some recent novel developments that show promise for quantifying SOC. I will describe how repeat soil surveys are used to estimate changes in SOC over time, and how long-term experiments and space-for-time-substitution sites can serve as sources of knowledge and can be used to test models, and as potential benchmark sites in global frameworks to estimate SOC change. I briefly consider models that can be used to simulate and project change in SOC and examine the MRV platforms for soil organic carbon change already in use in various countries / regions. In the part of the talk, I will bring together the various components described in this review, to describe a new vision for a global framework for MRV of soil organic carbon change, and discuss how this related to soil health, to support national and international initiatives seeking to effect change in the way we manage our soils.

11:00

30min

COFFEE BREAK

HugoTECH

11:30

TBC

Tobias Bandel

12:00

60min

Discussion Panel: The Soil Data Revolution: How Earth Observation, Spectroscopy, and AI are Changing Soil Use Forever

HugoTECH

13:00

60min

LUNCH

HugoTECH

14:00

Advancing circular agriculture: The Waste4Soil Project for sustainable fertilizer development from food processing residues.

Vera Proskynitopoulou

The Waste4Soil project is focused on developing sustainable, cost-effective fertilizing products from recycled biowastes sourced from local food industries. By recycling food processing residues (FPR) into soil improvers (SI), Waste4Soil aims to reduce environmental impacts while enhancing food security across Europe. The project’s main objective is to create viable pathways for recycling biowastes within a circular, regional, and systemic framework that involves all actors in the food chain, thus closing essential nutrient, organic matter, and water loops.
The Waste4Soil approach is implemented through Living Labs (LL) established in seven EU countries: Hungary, Finland, Spain, Greece, Italy, Poland, and Slovenia. These LLs facilitate experimentation in real-life settings, engaging key stakeholders—such as food industry representatives, waste managers, fertilizer manufacturers, commercial farms, and citizens—in collaborative activities that emphasize “show me” and “ready for practice” demonstrations of best practices. Through this co-creative approach, LLs aim to provide practical examples of sustainable waste-to-fertilizer applications that are regionally adaptable and environmentally beneficial.
Central to achieving Waste4Soil’s goals is the active involvement of diverse stakeholders at every stage of the project. From co-creating solutions to participating in planning, demonstrations, dissemination, and further demonstration phases, each actor plays a critical role. The project promotes ecosystem-based collaboration among farmers and their networks, food industries, waste management companies, fertilizer producers, research and educational institutions, local and regional authorities, and civil society. By fostering these collaborations, Waste4Soil aims to develop ecosystem solutions that improve FPR management practices, making soil improvers economically viable, environmentally sustainable, and socially acceptable, thereby advancing the circular economy in agriculture.

14:00

Expanding access to soil data: SoilHive strategy to promote global collaboration

Ester Miglio

Soil health is foundational to sustainable agriculture, water resource management, and climate mitigation. However, the effectiveness of global soil health efforts is constrained by fragmented, inconsistent, and often inaccessible soil datasets. To address this, Varda has developed SoilHive, an interactive platform that aggregates and harmonizes soil data from various global sources, promoting open access and collaboration.

SoilHive is a central open repository, integrating and harmonizing diverse datasets to provide a comprehensive overview of soil data in any region. This benefits researchers, the private sector, multilateral organizations, and policymakers. The platform’s Data Availability Framework assesses the spatial distribution of soil data, aggregates it at multiple resolutions, identifies underserved areas, and guides targeted efforts to address data gaps. This approach encourages users to contribute data, fostering a culture of sharing and collaboration.

To further expand soil data access and facilitate soil data exchange, SoilHive has been developing ad hoc tools for data management and dissemination within research and collaborative projects. A private data hub enables stakeholders to securely share and access proprietary data, addressing privacy and intellectual property concerns while promoting interoperability and reusability. Each hubs allow organizations to share soil data for specific project durations while ensuring the discoverability of their metadata as well as access to to SoilHive’s broader dataset. Upon project completion, data can be released to the public domain via SoilHive.

In alignment with its broader mission, SoilHive also contributes directly to the EU-funded DeepHorizon project by enhancing data management and promoting data sharing and discoverability. This initiative involves extending existing ontologies to incorporate subsoil domains and functions, facilitating data sharing among diverse stakeholders, and creating the first European-level subsoil dataset. Furthermore, SoilHive enhances its capabilities to improve the discovery of subsoil data, including the ability to search at the horizon level.

In conclusion, SoilHive democratizes access to existing soil data while respecting data provenance and owner rights, fostering international collaboration and innovation. By engaging with both public and private entities, SoilHive enhances the breadth of available soil data, supporting global innovation and increasing our shared understanding of the soil system.

Soil Health governance: tools and methods to enhance soil literacy and facilitate the decision making processes

14:00

90min

Annalaura Vannuccini

About 60% of European soils are currently polluted, contaminated, or in many ways compromised by centuries of urbanisation, industrial and rural development, and careless waste management. The EU Mission Soil, the EU Soil Observatory, the Soil Strategy and the Soil Monitoring Law represent a soild european basis to counteract such a negative evidence and trend. However, the issue of soil health - on top of its global relevance and urgency - also embeds a deep local meaning, in that it interferes in many respects with land destination and use regulations, practices and interests – therefore with spatial planning and governance decisions that are peculiar to local and regional policy and administrations. In this context is necessary the establishment of constructive dialogues among public authority representatives, academia, citizens, businesses, associations and other Quadruple Helix stakeholders to help policy makers to understand the problems of their soils and find viable solutions together. During the SOIL HEALTH NOW! workshop the participants will simulate the undertaking of all necessary steps to reintroduce soil related issues into the political agendas as priorities in a respectful, constructive and transformative manner. The workshop will present the instruments and use the tools developed by the HuMUS project and others Mission Soil projects and beyond, to facilitate the dialogue and the decision making processes on soil health.

Expert Room 7

14:15

The quality paradigm and selecting soil indicators

David Robinson

David A. Robinson (1), Grant Campbell (2), Pete Smith (2).
(1) UK Centre for Ecology & Hydrology, (2) University of Aberdeen

Soil provides environmental, social and economic functions (Blum, 2005). Recognizing the importance of soil functions, different frameworks have been proposed to convey the importance of soils to society. Such frameworks usually have a construct based on values, where the definition of value is “a framework for identifying positive (better) or negative (worse) qualities in events, objects, or situations” (Edwards-Jones et al., 2000). Quality is something often sought after but difficult to define. Ultimately, quality matters because decision making, and subsequent actions taken, are often contingent on the interpretation of quality framings. Quality means different things in different contexts and can thus lead to frequent misunderstandings. Quality can refer to excellence (the degree of distinction or superiority), a standard (how good or bad something is), or a characteristic (a feature of something) (Cambridge Dictionary Online). Moreover, quality can be classified into five categories according to its usage, 1) exception, 2) perfection, 3) fit for purpose, 4) value for money and 5) transformative. In this presentation these framings are examined in the context of soil health and the Mission soil. Different framings are suitable for different scales and purposes, but also determine to some extent how indicators are selected, thresholds determined, and results interpreted.

Edwards-Jones, G., B. Davies, and S. Hussain. 2000. Ecological economics: An introduction. Blackwell Science, Oxford, UK

soil health indicators

14:15

Using evidence chains to predict nature’s contributions to people (NCPs) under conventional and organic farming systems across Europe

Els Dhiedt

Sustainable soil management has been proposed as an effective nature-based solution to enhance the delivery of nature’s contributions to people (NCP). Sustainable soil management principles are at the core of organic farming. The European Commission has set a target of at least 25% of the EU’s agricultural land to be under an organic farming system by 2030 under the European Green Deal. To reach this target, farmers and policy makers need to be able to make informed decisions on what sustainable soil management practices to implement.

We present a framework to understand how soil management practices and key external drivers, like climate change and soil degradation, influence the delivery of NCPs. We developed a set of conceptual evidence chains that link management practices, key drivers, soil biodiversity, ecosystem properties, functions, and goods. For each of these linkages we performed a literature search to determine the direction of effect and the strength of evidence. This set of evidence chains provided the basis for informed data-driven analyses of these linkages between individual components in the system to quantify the effects. This operationalization was constrained by the availability of observational data to train the models that make up the linkages, as well as spatial data to predict at a European scale.

Here, we illustrate this framework with a case study, where we predict the effect of converting farm management from a conventional to an organic system on the delivery of NCPs including climate regulation, water quantity and flow regulation, and food production, and their economic valuation, across Europe. These models also enable exploration of different climate and policy scenarios. Furthermore, as the models are based on the conceptual evidence chains that include strength of evidence, each model output is associated with a level of confidence, related to the scientific consensus and the availability and quality of data, that can be reported along with the results. This modelling framework and its model predictions can support farmers and policy makers in understanding trade-offs and synergies that are associated with soil management practices to make informed decisions.

soil-climate-agriculture

14:30

Developing Robust MRV Systems for Carbon Farming: Insights from the MARVIC and Credible Projects

H. Xu

Developing Robust MRV Systems for Carbon Farming: Insights from the MARVIC and Credible Projects

Xu Hui1*, Ruysschaert Greet1, D’Hose Tommy1

1 Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke-Melle, Belgium

*corresponding author

Soil Organic Carbon is a fundamental component of healthy soils and plays a crucial role in many ecological processes. With the increasing interest in carbon farming—which involves capturing and storing carbon in soils and woody plants—it's essential to develop robust systems for monitoring, reporting, and verification (MRV) and credible business models. Our study addresses this need by designing and testing a reliable MRV framework through the MARVIC project and by building a European network for carbon farming to collaboratively address current challenges through the CREDIBLE project.

Funded under the Soil Mission to support the Carbon Removal and Carbon Farming (CRCF) Regulation, MARVIC aims to create a framework for designing harmonized yet context-specific MRV systems applicable to diverse land uses such as arable land, grasslands, agroforestry/woody crops, and managed peatlands. This project explores the integration of various components like benchmark sites, sampling schemes, data layers, farm data, remote sensing, and modeling to establish comprehensive monitoring systems and operational processing chains. Additionally, the MARVIC team is actively engaged in the CRCF expert group and further contributes to discussions to achieve a good balance between minimizing risks of unjust payments, maximizing progress in the LULUCF sector, and minimizing costs.

At the 2nd Carbon Farming Summit organized by the CREDIBLE project, we will co-organize several sessions aimed at fostering collaboration between the public and private sectors on technical aspects like baseline approaches, data sharing and harmonizing. We will also engage with stakeholders—including farmers and companies—to build acceptance of the CRCF regulation, thereby promoting transparency and actionable steps.

In the conference, we will share our reflections on the Carbon Farming Summit. We will highlight key insights from our collaborative sessions on baselines, data sharing, harmonization, and strategies for farmer acceptance of the CRCF regulation.

Probability based stratified sampling for both mapping and estimating the population parameters of the soil health indicators at field scale

Thomas Gumbricht, Jasmin Fetzer, Konstantinos Karyotis, Robert Minarik, Thomas Gumbricht, Monika Zovko

Most soil properties are continuously varying over different scales in both space and time. An analysis of field sampled soil is the most accurate method for estimating the spatial distribution of soil properties and health indicators at field scale. The field scale spatiotemporal variation in properties, however, requires an effective, well selected and unbiased probability distribution based sampling framework leveraging the in-situ variability of the relevant environmental covariates. Covariates that can be used for modeling the soil properties over the entire study area. Typically such covariates are represented at spatial rasters derived from e.g. topographic data and satellite imagery. In this work, we introduce the probability based balanced stratified sampling algorithm compatible with the proposed European Soil Monitoring law. The algorithm distributes doubly balanced sampling locations over both geographical and feature space constrained by a maximum allowed error – in our case the coefficients of variation. The geographical feature space input data are selected covariates from the Soil Health Data Cube (https://shdc.ai4soilhealth.eu/). We target the covariates that reflect the distribution of soil health indicators tested or developed by the EU funded project AI4SoilHealth (https://ai4soilhealth.eu). To begin this process, a Bethel-inspired optimization approach is applied to stratify the study areas. The strata are then used for computing the approximately equal inclusion probabilities for all units and the number of samples for each strata allocated based on the available auxiliary information. The second stage of the process involves doubly balancing the algorithm, with the aim to select the optimal sampling locations over geographical and feature space with respect to the stratification. In our study, we compare the algorithm with 1) simple random sampling, 2) feature space coverage sampling and, 3) the EU wide Land Use and Cover Area frame Survey (LUCAS) algorithm using legacy data. The advantages of the novel algorithm are demonstrated in the optimized number of samples while preserving the accuracy of target estimates and mapping accuracy. In the first experiment, we subsample the legacy random grid samplings. In the second (numerical) experiment, we use the soil property maps of the Soil Health Data Cube as observed values to design new optimized sampling networks with no gridded location constraints.

in-situ measurement of soil health

Expert Room 11

14:45

Exploring key VNIR-SWIR bands for predicting nickel in an urban soil using eXML

Mahsa Nakhostinrouhi, Sibylle Itzerott, Robert Milewski, Sabine Chabrillat, Mohammadmehdi Saberioon

There is a strong need to investigate soil health due to its big role in environmental and human health. In terms of this issue, there could be some elements to be explored. Heavy metals are important among these elements since their increase from specific thresholds would lead to soil pollution and ultimately impact human health. One of the heavy metals with a high influence on soil health is nickel (Ni). It is becoming more important in urban areas because of the high potential for growth through heavy transportation. In this study, 89 surface soil samples (0-20 cm) were collected from traffic areas nearby streets in Berlin, Germany in 2018. Then they were sieved and dried in the laboratory. Next, the amount of Ni and spectra in VNIR-SWIR bands were measured in the laboratory for each sample with an ASD FieldSpec5 spectroradiometer under controlled illumination conditions. The spectral reflectance data were transformed to absorbance values as a preprocessing method. In the next step, the data were split into two groups: training (80%) and test (20%) data. The first group was fed into random forest algorithm to develop a model for predicting Ni. Then the test set was utilized to validate the predicting ability of the model. Regarding the hyperparameter tuning of the model, GridSearchCV method was applied to find the best values for n_estimators, and max_depth. What is not considered in most of the similar studies is interpretability of a machine learning model produced leaving that in a black-box situation. Hence, in this study two explainable machine learning (eXML) methods, including MDI (Mean Decrease in Impurity), and permutation were applied to determine VNIR-SWIR bands with higher importance in the modeling and predicting of Ni. When it comes to the results, the validation of the random forest model yielded an R2 of 0.74 and an RMSE of 2.01. This proves that the models’ predictions align well with the actual Ni values. Furthermore, the results achieved from MDI method specified the wavelengths 2418nm, 2127nm, and 2147nm (short wave infrared) as three most important features in the random forest model generated for Ni. On the other hand, the features 2127nm, 401nm, and 2147nm were determined the most important features by permutation method for the model trained. This information could be very useful in using remote sensing sensors in Ni prediction in large scales specifically in an urban area.

Spatiotemporal prediction of soil organic carbon density for Europe (2000--2022) in 3D+T and its uncertainty

Xuemeng Tian

This work presents a comprehensive framework for soil organic carbon density (SOCD) (kg/m3) modeling and mapping, based on spatiotemporal Random Forest (RF) and Quantile Regression Forests (QRF). 22,428 SOCD measurements and a wide range of covariate layers—particularly the 30m Landsat-based spectral indices were used to fit models and produce 30m SOCD maps for the entire EU at four-year intervals from 2000 to 2022 and for four soil depth intervals (0--20cm, 20--50cm, 50--100cm, and 100--200cm) each accompanied by per-pixel 95% probability prediction intervals (PI, between P0.025 and P0.975). The results of model evaluation indicate consistent accuracy of the predictions: based on both 5--fold spatial cross-validation with model refitting (MAE = 8.64 kg/m3, MedAE = 4.31 kg/m3, MAPE = 0.54 kg/m3 and bias = -2.95 kg/m3), and on independent testing (MAE = 7.73 kg/m3, MedAE = 3.54 kg/m3, MAPE = 0.45 kg/m3, and bias = -3.04 kg/m3), with both R2 values exceeding 0.7 and concordance correlation coefficients (CCC) greater than 0.8. Validation of PI estimation confirmed that PIs effectively capture uncertainty intervals, although with reduced accuracy for higher SOCD values. Exploratory analysis using Shapley values identified soil depth as the most important feature, with vegetation (Landsat biophysical indices) and long-term bio-climate features as the two main contributing feature groups. Although the uncertainty of the prediction per pixel is significant, further spatial aggregation has been shown to reduce the uncertainty by about 70%. Suggested uses of the data include: (1) time-series / trend analysis to detect potential land degradation hotspots, (2) optimization of sampling designs based on prediction uncertainty, and (3) prediction of future soil carbon potential by extrapolating models under different land use / climate scenarios. The data and code used are publicly available under an open license from https://doi.org/10.5281/zenodo.13754344 and https://github.com/AI4SoilHealth/SoilHealthDataCube/.

soil organic carbon

HugoTECH

15:00

Mapping Soil Issues in Southwest Europe for Sustainable Land Management Solutions

ANA ROMERO FREIRE

This study aims to identify critical soil health challenges in Southwest Europe, focusing on the Granada region, using an innovative quadruple helix approach that unites insights from scientists, local stakeholders, industry, and government entities. Given the widespread issues affecting soils in this area—including structural degradation, biodiversity loss, and reduced agricultural productivity—our project seeks a comprehensive understanding of these localized challenges to inform sustainable solutions. The first step of this work will conduct surveys with key stakeholders who rely on soil for their activities, including farmers and cooperatives in the Granada region. These surveys will aim to identify and understand the specific soil health challenges they face, such as soil degradation and nutrient loss. Additionally, we will gather information to understand basic aspects that will inform the design and implementation of effective strategies to improve soil health. By collecting this data, we aim to develop sustainable solutions tailored to local needs and enhance collaboration among the various actors involved in soil management.Findings from this study will directly contribute to the goals of SOILCRATES by supporting the development of science-backed, sustainable management strategies that address the specific needs of this region. Our approach will empower stakeholders in Granada with actionable, innovative solutions to mitigate soil degradation and protect the agricultural resources aligns with the EU Mission ‘A Soil Deal for Europe’.

15:00

Monitoring Soil Resilience: A Combined STL and Autoencoder Approach to Dynamic SWRC Prediction

Nedal Aqel

The soil water retention curve (SWRC) represents the relationship between soil water content and matric potential, explaining how soil retains and releases water under various moisture conditions. Information on matric potential is vital for assessing soil's ability to store water for plant growth, and for quantification of its mechanical stability to prevent compaction damage. However, when direct measurements of matric potential are unavailable, and only soil water content data is accessible (e.g., from satellite observations), estimating matric potential becomes particularly challenging and relies on knowledge of SWRC. This complexity is further compounded by the highly variable, site-specific nature of the relationship between soil water content and matric potential, influenced by factors such as soil structure, seasonal fluctuations, and environmental stressors like drought. As a result, conventional methods based on an unambiguous pressure-saturation relationship often fail in capturing the dynamic behavior of soil moisture over time.
In this study, we address these challenges by employing a combined approach involving Seasonal-Trend decomposition using Locally estimated smoothing (STL) and an autoencoder neural network to monitor and predict changes in SWRC. STL is utilized to isolate seasonal patterns and long-term trends in water content data, capturing how environmental factors, especially prolonged drought, impact SWRC across multiple sites in Germany. The autoencoder neural network then compresses this information into a site- and period-specific feature called the "AUV" (Autoencoder Value), which represents the soil's water-holding properties and its response to changing environmental conditions. This AUV value is subsequently used to predict shifts in SWRC by modeling changes in matric potential resulting from significant alterations in the seasonal amplitude of water content or shifts in long-term trends following dry events.
Our approach was tested across several sites, where, in some locations, prolonged drought caused a noticeable reduction in seasonal amplitude and a decrease in trend values, which led to a corresponding decline in the AUV value. This decline in AUV was found to be indicative of reduced water retention capacity and decreased soil resilience. Overall, this method offers a practical solution to (i) predict dynamic changes in matric potential using only soil water content measurements, (ii) monitor shifts in SWRC over time to reflect changes in soil health and (iii) provide a scalable tool for assessing soil resilience to climate variability, making it particularly useful in regions where direct measurements of soil matric potential are not available.

15:15

Pan-European mapping of ponding time as soil health indicator for absence of compaction and structure formation

Peter Lehmann, Annett Wania

Compaction reduces the infiltration capacity of the soil surface and may result in water ponding during heavy rainfall events. The same ponding effect is expected for soils with low organic content, limiting the formation of structural pores with high drainage capacity. The resulting presence of ponding water generates run-off and anaerobic conditions in the topsoil, increasing erosion rate and reducing the biological activity and productivity. The longer the duration of a rainfall event, the lower the infiltration capacity of the soil and the higher the risk that the infiltration capacity becomes limiting, defining the ‘ponding time’ as the time of onset of water accumulation at the surface. The ponding time is a comprehensive soil physical property, integrating aspects of soil water retention, hydraulic conductivity, and initial water content. The lower the ponding time for a certain precipitation rate, the more frequent is the expected occurrence of water ponding at the surface. In this study, we apply an analytical expression for ponding time to compute it at the Pan-European scale. Using maps of basic soil information (soil texture, organic carbon, and bulk density), we can estimate values of ponding time and thus the frequency of ponding (and run-off) by infiltration excess. This map provides a reference for the quantification of soil health by reducing ponding time by compaction and loss of organic carbon (‘unhealthy’) and increasing ponding time by structure formation (‘healthy’). Applying pedotransfer functions linking soil hydraulic properties with basic soil information and land use, we provide a sensitivity map to estimate the change in ponding time statistics by modifying land management. The generated maps have a spatial resolution of 1 km and allow a comparison with national statistics of hydromorphic constraints, revealing the relationship between infiltration excess and saturation excess. To apply ponding maps in soil health assessment and land use management, they must be developed at higher resolution. We will show the potential of providing ponding maps at higher resolution (30 m, Soil Health Data Cube for Europe) and linking them to remote sensing observations of ponding using satellite data.

pan-EU soil health assessment

15:15

Soil health monitoring of non-agricultural areas – gaps identification

Agnieszka Klimkowicz-Pawlas

One of the main objectives of Soil Mission ‘A Soil Deal for Europe’ is to develop a harmonised soil monitoring framework to assess policy impacts and trends in soil health. It is therefore necessary to integrate the current knowledge on existing monitoring programmes and harmonise the approaches used across Europe. It was emphasized in the Soil Mission implementation plan that current EU monitoring is hampered by inadequate or inactive soil monitoring programs in many EU Member States and limited availability or lack of relevant data. One of the current gaps in soil monitoring is the insufficient coverage of soils located in urban, forest or industrial areas. This is being addressed by the PREPSOIL project, whose main objective is to implement the 'A Soil Deal for Europe' mission in European regions, by helping key actors to reduce soil degradation, while increasing soil awareness and knowledge.
A detailed review of projects funded by the European Union related to soil monitoring issues and the soil health indicators used was carried out. In addition, existing national experiences on monitoring with a special focus on non-agricultural area were analysed. These results may constitute the basis for a broader discussion on future monitoring of non-agricultural soils especially in a context of implementation new directive on soil monitoring and resilience.

Acknowledgements
This research has been carried out within the framework of the PREPSOIL project ‘Preparing for the ‘Soil Deal for Europe’ Mission’ founded by European Union’s Horizon Europe programme under grant agreement No 101070045.