ITS1.23/SSS0.1.4 | Integrated Modelling Approaches and Data Integration: Exploring Ecosystems, Landscapes, Soil Health, Degradation and Living Labs
Integrated Modelling Approaches and Data Integration: Exploring Ecosystems, Landscapes, Soil Health, Degradation and Living Labs
Convener: Alina Premrov | Co-conveners: Sergio Saia, Jagadeesh Yeluripati, Calogero SchillaciECSECS, Claudio Zucca, Matthew Saunders
| Fri, 19 Apr, 16:15–18:00 (CEST)
Room 2.24
Posters on site
| Attendance Fri, 19 Apr, 10:45–12:30 (CEST) | Display Fri, 19 Apr, 08:30–12:30
Hall X3
Posters virtual
| Attendance Fri, 19 Apr, 14:00–15:45 (CEST) | Display Fri, 19 Apr, 08:30–18:00
vHall X3
Orals |
Fri, 16:15
Fri, 10:45
Fri, 14:00
Modelling is fundamental for assessing various soil processes and interactions at different scales and resolution, while healthy soils are fundamentally important in sustaining a wide range of ecosystem services. Crossing interdisciplinary borders and integrating knowledge from various fields is essential in developing more accurate and comprehensive models to better capture the complexity of soil processes/mechanisms in natural and cultivated systems, address knowledge-gaps, and tackle the challenges related to data-integration, heterogeneity and uncertainty of modelling predictions across disciplines. An interdisciplinary approach is also needed in light of recent technological advances, such as computational approaches, model-coupling, geomatics, remote sensing/earth observation, machine learning, surveying and data collection sensors/sensor platforms, real-time data-streams, all of which provide opportunities for promoting new modelling generations integrating soil science across disciplines.

Integration of various disciplines and modelling is also essential for better understanding of the role of soil health, which includes concepts soil capacity and functionality towards a wide range of ecosystem services. Several measures to support soil health and tackle soil degradation have been proposed in the scientific literature, as well as several indicators to monitor expected benefits. The need for standardized data covering the broad concept of soil health and degradation is arising, along with the lack of information on relationships between soil quality and agriculture, forest and grassland resilience, and the socio-economic and environmental impacts of these measures. The scattered data availability and their complex integration for agronomic/environmental management and policy decisions may partly be covered by many European/international/national initiatives in the frameworks of the H2020, Horizon Europe, PRIMA, FAO programs, and other programs.

This session aims to promote and enhance communication and exchange of knowledge among scientists from modelling community, soil research and various related projects, linking different disciplines, and is open to contributions in a wide range of related topics, ranging from modelling soil systems to ecosystem and landscape modelling, soil health, degradation and living labs, while striving to contribute towards tackling current research challenges, addressing the knowledge-gaps, and informing policy.

Orals: Fri, 19 Apr | Room 2.24

Chairpersons: Alina Premrov, Sergio Saia
On-site presentation
Yushu Xia, Jonathan Sanderman, Jennifer Watts, Megan Machmuller, Stephanie Ewing, Andrew Mullen, Charlotte Rivard, and Haydee Hernandez

Rangelands play a crucial role in providing various ecosystem services and have significant potential for carbon sequestration. However, monitoring soil organic carbon (SOC) stocks in rangelands is challenging due to the large size of ranches and the high spatial variability influenced by climate and management factors. To address these challenges, we have developed the Rangeland Carbon Tracking and Management (RCTM) system, which integrates remote sensing inputs, survey data sources, and both empirical and process-based SOC models. In this work, we will introduce the structure of RCTM v1.0, its data input requirements, data processing pipelines, and the resulting data outputs. Additionally, we will discuss the high-resolution soil moisture data layers, baseline SOC maps, and the targeted field sampling plan generated through an empirical digital soil mapping approach. The Bayesian calibration and validation scheme for obtaining grassland plant functional type (PFT)-specific parameters using flux tower network data will also be explained. After calibration, the RCTM system generated estimates of rangeland carbon fluxes across PFTs (R2 between 0.6 and 0.7) and surface depth SOC stocks (R2 = 0.6) with moderate accuracy at the regional scale. The visualization of modeling results associated with long-term rangeland C dynamics at different scales will be demonstrated using the Google Earth Engine platform to inform management decisions and policymaking.

How to cite: Xia, Y., Sanderman, J., Watts, J., Machmuller, M., Ewing, S., Mullen, A., Rivard, C., and Hernandez, H.: Developing a Rangeland Carbon Tracking and Monitoring System Using Remote Sensing Imagery Coupled With a Modeling Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-180,, 2024.

On-site presentation
Elizabeth Wangari, Ricky Mwanake, Tobias Houska, David Kraus, Gretchen Gettel, Ralf Kiese, Lutz Breuer, and Klaus Butterbach-Bahl

Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n = 268), thereby implementing a stratified sampling approach on a mixed-landuse landscape (∼ 5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 % to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.

How to cite: Wangari, E., Mwanake, R., Houska, T., Kraus, D., Gettel, G., Kiese, R., Breuer, L., and Butterbach-Bahl, K.: Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5750,, 2024.

On-site presentation
Sifan Yang, Blánaid White, Fiona Regan, Nigel Kent, Rebecca Hall, Felipe de Santana, and Karen Daly

             Advice for phosphorus (P) fertilisation based on soil testing using extractive methods but does not consider P sorption processes. Traditional soil P sorption capacity examined from a Langmuir isotherm batch experimental design, which is time-consuming, labour intensive and expensive. Mid-infrared (MIR) spectroscopy is a rapid analysis technique that can potentially replace the extractive technique traditionally used in soil analysis. The objective of this work was to predict the isothermal parameter of P sorption maximum capacity (Smax, mg·kg-1) from MIR spectroscopy.

              This study created spectral libraries from benchtop (Bruker) and handheld (Agilent) MIR spectrometers by scanning samples in two particle sizes, < 0.100 mm (ball-milled) and < 2 mm. The four spectral libraries created used an archive of samples with a database of sorption parameters where soils were classified into low and high sorption capacities using a threshold value of Smax = 450.03 mg·kg-1. To assess the optimal algorithmic method with highest Smax prediction accuracy, regression models were based on the partial least squares (PLS) regression, Cubist, support vector machine (SVM) regression and random forest (RF) regression algorithms. After the first derivative Savitzky-Golay smoothing, Bruker spectroscopies with both soil particle sizes yielded ‘excellent models’, with SVM predicting Smax values with high accuracy (RPIQVal = 4.50 and 4.25 for the spectral libraries of the ball-milled and <2mm samples, respectively). In comparison, the Agilent handheld spectrometer produced spectra with more noise and less resolution than the Bruker benchtop spectrometer. Unlike Bruker, for Agilent MIR spectroscopy, more homogeneous samples after ball-milling resulted in a higher accurate Smax prediction. For Agilent spectroscopy of ball-milled samples, an ‘approximate quantitative model’ (RPIQVal = 2.74) was obtained from the raw spectra using the Cubist algorithm. However, for Agilent spectroscopy of < 2 mm samples, the best performing Cubist algorithm can only achieve a ‘fair model’ (RPIQVal = 2.23) with the potential to discriminate between high and low Smax values.

              The results suggest that the Bruker bench-top spectrometer can predict the Langmuir Smax value with high accuracy without the need to ball-mill samples, highlighting the availability of the MIR spectrometer as a rapid alternative method for understanding soil P sorption capacity. However, for handheld spectrometers, the Agilent instruments can only make approximate quantitative predictions of Smax for ball milled samples. For <2mm samples, Agilent can only be used to classify low and high sorption capacity soils.

How to cite: Yang, S., White, B., Regan, F., Kent, N., Hall, R., de Santana, F., and Daly, K.: Prediction of soil phosphorus sorption capacity in agricultural soils using mid-infrared spectroscopy. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3186,, 2024.

On-site presentation
Mareike Ließ

The landscape-scale evaluation and modeling of the impact of agricultural management and climate change on soil-derived ecosystem services requires soil information at a spatial resolution addressing individual agricultural fields. A pattern recognition approach is presented that generates a nationwide data product. It agglomerates the multivariate soil parameter space into a limited number of functional soil process units (SPUs) that facilitate operating agricultural process models. Each SPU is defined by a multivariate parameter distribution along its depth profile from 0 to 100 cm. It has a depth resolution of 1 cm and a spatial resolution of 100 m. The methodological approach is based on an unsupervised classification procedure involving remote sensing, cluster analysis, and machine learning. It accounts for differences in variable types and distributions and involves genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the method is demonstrated for the agricultural soil landscape of Germany. It can be applied to other landscapes and ecosystem contexts.

How to cite: Ließ, M.: A pattern recognition approach to generate soil process units for ecosystem modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5461,, 2024.

On-site presentation
Andres Peñuela

Soil erosion is a widespread environmental challenge with far-reaching implications for agricultural productivity, water quality and ecosystem health. Addressing this complex issue requires the use of modelling tools that empower diverse stakeholders, such as researchers and decision-makers, to simulate soil erosion systems under different scenarios. For these tools to be effective, not only they need to make good predictions, but they need to be accessible and educational, so users, regardless of their technical skills and modelling expertise, can understand and even more importantly, trust the model. In traditional soil erosion modelling, the primary emphasis to build trust is by demonstrating the model’s ability to replicate past observations, and less attention is given to build trust by providing an educational and exploratory experience. We introduce a project that aims at democratizing soil erosion modelling, making it more accessible and trustworthy to researchers, educators, decision-makers, and local communities. Leveraging the versatility and accessibility of Jupyter Notebooks, we are developing iMPACT-erosion, a soil erosion modelling toolbox to support education, land management and informed decision making. A series of dedicated Notebooks not only explain and simulate the main soil erosion processes but guides users through the main steps to enhance the credibility of the model results, i.e. sensitivity analysis, model calibration, uncertainty analysis, model evaluation and scenario analysis. The integration of interactive visualization enhances this experience by facilitating exploration of both the model configuration and the soil erosion system's response under different scenarios/decisions. This model development approach is not confined to the field of soil erosion and offers the potential to facilitate knowledge transfer and collaboration between model developers and decision makers in various domains.

How to cite: Peñuela, A.: Democratizing soil erosion modelling: A Jupyter Notebook approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1907,, 2024.

On-site presentation
Eugenio Straffelini, Jian Luo, and Paolo Tarolli

Mountain grasslands play a pivotal role in delivering both economic and cultural ecosystem services, including food production, carbon sequestration, the provision of clean water, and preserving local traditions. However, these ecosystems are facing increasing threats from climate change around the world. Among the main challenges is the intensification of extreme precipitation events. They can aggravate the process of soil erosion and trigger landslides in mountain grasslands, with possible negative consequences on both ecosystems and human activities. However, the high variability of these ecosystems, as well as their wide distribution, makes it complex to adequately map their locations and investigate possible soil erosion hotspots, especially under future scenarios with varied rainfall regimes. In this context, the use of remote sensing technologies and modeling approach could open new frontiers to investigate critical areas and therefore guide mitigation solutions. The satellite Earth Observation (EO) through international space missions, coupled with cloud-based data analysis platforms like Google Earth Engine (GGE), facilitates ecosystem mapping at a resolution and frequency previously inaccessible. Furthermore, the utilization of multi-temporal models for potential soil erosion analysis in present and future scenarios can enhance our understanding of erosion dynamics attributed to climate change. In this research, we first map at high resolution the global mountain grasslands distribution taking advantage of Sentinel-based EO’s products. In such locations, we evaluate the multi-temporal soil erosion dynamics caused by water employing diverse climate scenarios (RUSLE model; 2015 vs. 2070-RCP8.5). Our findings indicate a potential global escalation in soil erosion within mountain grasslands, notably in South America and Africa, alongside identifiable localized hotspots. Remote sensing-based research paired with a modeling approach aimed at mapping critical areas and analyzing environmental challenges in ecosystems is therefore imperative. Such investigations not only delineate vulnerable regions but also guide targeted solutions crucial for safeguarding these ecosystems and their ecosystem services in the face of climate change.

How to cite: Straffelini, E., Luo, J., and Tarolli, P.: Satellite-based remote sensing and multitemporal modeling approach for mapping soil erosion hotspots in global mountain grasslands under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18161,, 2024.

On-site presentation
Nikolaos-Christos Vavlas, Lammert Kooistra, Fenny van Egmond, and Gerlinde De Deyn

The necessity of soil health monitoring is paramount in reversing soil degradation and promoting sustainable farming. Including cover crops in the crop rotation is one of the sustainable soil management practices contributing to soil health. Cover crops contribute to soil health by nutrient retention and carbon accumulation during their growth and return of organic matter to the soil upon their incorporation. During monitoring, the sampling frequency can change from annual in the case of SOC to weekly or daily for fertilization and irrigation. Remote sensing techniques offer a solution, enabling the monitoring of vegetation over time and space, thereby enhancing our understanding of the impact of cover crops on the main crop. However, this technology makes it possible to see the surface of the field which can assist with the above-ground changes of the system. Process-based modelling and data assimilation can subsequently link the above-ground component with soil functions. In-situ data collection that includes crop characteristics such as biomass and N-uptake is essential both for transforming remote sensing data into crop characteristics and for calibrating models. Using Unmanned Aerial Vehicles (UAVs) can potentially collect data at high frequency, which can be used to enhance soil process modelling. The development of this UAV-based method has the potential to be scaled up to a satellite level in the future.

In our research, we have combined the study of nutrient cycling and the effect of cover crops on soil health. To achieve this, we have used the WOFOST-SWAP-ANIMO model to simulate the varying influence of cover crop monocultures and mixtures on Soil Organic Carbon (SOC) and Nitrogen cycling in a 7-year crop rotation on sandy soil. The model simulates vegetation characteristics such as biomass, leaf area index, and yield, as well as soil moisture and mineral Nitrogen concentrations. This will give us a good estimation of the vegetation input into the soil as well as the nutrient uptake from both cover crops and main crops. Soil sampling is also important to model calibration/validation to be able to simulate the N dynamics of biological activity under the surface. Our findings suggest that the model, in conjunction with UAV data and field sensors, can effectively monitor soil health indicators crucial for field management practice selection, such as the Carbon cycle and Nitrogen use efficiency.

How to cite: Vavlas, N.-C., Kooistra, L., van Egmond, F., and De Deyn, G.: Integrating UAV data and soil-crop modelling for Enhanced Soil Health Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15537,, 2024.

On-site presentation
Valentina Mereu, Gianluca Carboni, Alessio Menini, Marta Canu, Marco Dettori, Giulia Urracci, and Serena Marras

Preserving soil health and enhancing the ecosystem services that soil produces is of primary importance in European strategies and policies. More than 60% of the European soils are unhealthy due to unsustainable land use, pollution, climate change, and extreme events. This causes loss of ecosystem services, costing the EU at least €50 billion annually. Collaboration among businesses, policymakers, public administration, and the scientific community is crucial to develop practices that recognize the essential role of soils in sustaining livelihoods, biodiversity, and climate regulation.

In this framework, the Horizon Europe funded project InBestSoil ( aims to co-create a framework for investments in soil health preservation and restoration by developing a system for the economic valuation of the ecosystem services provided by healthy soil and the impacts of soil interventions, and its incorporation into business models and incentives. To achieve this, InBestSoil has selected 7 existing Soil Health Lighthouses (LHs) and 2 Soil Health Living Labs (LLs, in different maturity stages) covering four land uses (agricultural, forestry, urban, mining) across four biogeographic regions over Europe. The LLs are collaborative initiatives focused on co-creating knowledge and innovations, while LHs represent individual sites known for exemplary performance. The LL1, located in Sardinia (Italy), is coordinated by the CMCC Foundation and Agris Sardegna Research Agency. It focuses on Mediterranean agricultural soils and aims addressing the challenges related to climate change and extreme events, soil pollution, land abandonment, and water scarcity. It includes 2 LHs on conservation agriculture managed by Agris and 9 Living Lab Experimental Sites (LLES), which evaluate the introduction of sustainable soil practices. The LHs included in the LL are two Long-Term Experiments (>20 years) on conservation agriculture (reduced and no tillage versus conventional tillage) on durum wheat in rotation with legumes, in soils with different fertility levels that are representative of Mediterranean cereal farming conditions. Conservation agriculture is among the most promising climate-smart agricultural practices because it contributes to both climate change mitigation and adaptation objectives while helping to maintain and increase farmers' incomes. However, it is important both to acquire additional information to assess the medium- to long-term effects of these practices in different environments and cropping systems as well as to disseminate the scientific evidence and support the wider application of these practices in the Mediterranean region.

The LHs aim to provide scientific evidence and disseminate knowledge and experience gained in the long-term application of conservation agriculture in Mediterranean agricultural systems.  Moreover, in the selected 9 LLES, located in different areas and including cereal, olive tree and vineyard farms, soil samplings and analyses are being conducted to measure soil indicators and provide information to assess the economic evaluation of ecosystem services provided by soils managed with sustainable agricultural practices, primarily including conservation agriculture.

We aim to create a permanent space of discussion on the topic of soil health, involving all relevant actors, from farmers to researchers to policy makers, in order to identify common solutions and innovations that can face the economic and environmental challenges the Mediterranean agriculture is facing.

How to cite: Mereu, V., Carboni, G., Menini, A., Canu, M., Dettori, M., Urracci, G., and Marras, S.: Empowering soil health in Mediterranean environments through collaborative stakeholder engagement: insights from Sardinian Living Lab of the InBestSoil project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18038,, 2024.

On-site presentation
Cenk Donmez, Carsten Hoffmann, Nikolai Svoboda, Tommy D'Hose, Xenia Specka, and Katharina Helming

Long-Term Field Experiments (LTEs) are agricultural infrastructures for studying the long-term effects of different management practices and soil and crop properties in changing climate conditions. These experiments are essential to examine the impact of management and environment on crop production and soil resources on different soil textures and types. Some of those LTEs have average times of 20-50 years, even more than 100 years. These infrastructures are thus scientific heritages with high values of agricultural data; however, LTE-related information was difficult to find since it was scattered. To close this gap, we developed a geospatial data infrastructure, including an LTE overview map to compile and analyze the meta-information of the LTEs across Europe. The map provides a spatial representation of LTEs and the meta-information, collected by extensive literature review and factsheets in collaboration with BonaRes and EJPSoil projects, clustered in different categories (management operations, land use, duration, status, etc.) (Grosse et al. 2021; Donmez et al., 2022; Blanchy et al., 2023; Donmez et al., 2023). A threshold filter with a minimum duration of 20 years was applied, which results in a total of 500 LTEs across Europe and included into the map. The clusters of LTEs were geospatially analyzed to provide inputs for the agricultural sector, scientists, farmers and policy-makers. The fertilization treatment was the major research theme of collected and studied LTEs, followed by crop rotation and tillage trials. Bringing the meta information of dispersed LTEs through the development of the LTE overview map is expected to help developing a mutual management framework of efficient agricultural production by revealing the LTE potential internationally. This will contribute to scaling up the agricultural practices from site to landscape level for increasing the climate change adaptation to agricultural yield and management.


Donmez C., Schmidt M., Cilek A., Grosse M., Paul C., Hierold W., Helming K., (2023): Climate Change Impacts on Long-Term Field Experiments in Germany. Vol.205, 103578. Agricultural Systems.

Blanchy G., D’Hose T., Donmez C., Hoffmann C., Makoschitz L., Murugan R., O’Sullivan L., Sanden T., Spiegel A., Svoboda N., Boltenstern S.Z., Klummp K., (2023): An open-source database of European long-term field experiments.  Soil Use and Management

Donmez C., Blanchy G., Svoboda N., D’Hose T., Hoffmann C., Hierold W., Klummp K., (2022): Provision of the metadata of European Agricultural Long-Term Experiments through BonaRes and EJP SOIL Collaboration. Data in Brief.

Grosse, M., Ahlborn, M.C., Hierold, W. (2021): Metadata of agricultural long-term experiments in Europe exclusive of Germany. Data in Brief 38,

How to cite: Donmez, C., Hoffmann, C., Svoboda, N., D'Hose, T., Specka, X., and Helming, K.: A Geospatial Overview of Agricultural Long-Term Field Experiments across Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11605,, 2024.

On-site presentation
Felipe de Santana, Rebecca Hall, Longnan Shi, Victoria Lowe, Jim Hodgson, and Karen Daly

Soil compaction is an important physical characteristic that affects agricultural productivity by increasing soil density, which reduces the volume of a given soil mass. Due to the higher compaction, plant roots find resistance in penetrating deeply into the soil, limiting their access to essential nutrients and moisture, impacting the plant health with lower levels of N, P and K, resulting in lower productivity. Soil compaction can also reduce soil porosity, aeration, carbon mineralisation/sequestration and increasing the production of greenhouse gases through denitrification in anaerobic sites. Besides that, soil compaction can cause surface runoff and erosion, increasing the risk of flooding and soil loss. A partial recuperation of compacted soils is an expensive and labour-intensive task. In addition, agricultural land expansion for crops is limited. Hence, mapping agricultural areas at risk of soil compaction is essential to implement strategies to mitigate the adverse effects of soil compaction.

Soil particle size and soil drainage were used to classify topsoil's (T) compaction risk class. For subsoil (S) soils (after horizon A), the subsoil particle size, packing density (bulk density + 0.009 * clay (%)), soil drainage and field capacity days were used to estimate the compaction risks. The main problem of this strategy is that these analyses are expensive and time-consuming, i.e., soil particle size analysis requires an average time of 1 month per 100 samples and costs ~ 40.00 per sample. Bulk density analysis costs ~ € 7.00 per sample and is also time-consuming; consequently, bulk density values are mainly predicted using pedo-transfer functions in mapping studies.

To speed up the analysis and minimise the costs, vibrational spectroscopy combined with chemometrics was used to determine soil particle size and bulk density. Both parameters were combined with field capacity days (obtained from 104 national wide meteorological stations) and drainage class (obtained from Irish - Environmental Protection Agency) to map soil compaction risk areas in the northern half of the Republic of Ireland with a resolution of 4 km2 (2x2km) and 1 km2 grid for regional and periurban regions, respectively (Tellus achieve). To confidentially map these regions, spectral control charts based on PCA were used to identify unrepresentative sample spectra based on the spectral models used. Only samples classified as representative were predicted by the spectral models. Using this strategy, we could predict ~ 90% (T) and ~66% (S) compaction risks in non-peat soils. The prediction results showed that ~33% (T) and ~43% (S) were classified as high risks of compaction, ~19% (T) and ~23% (S) as moderate, and ~37% (T) and <1% (S) as low risks or other classes.

How to cite: de Santana, F., Hall, R., Shi, L., Lowe, V., Hodgson, J., and Daly, K.: MIR spectroscopy combined with meteorological data can estimate soil compaction risks in top and subsoils., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10001,, 2024.

Posters on site: Fri, 19 Apr, 10:45–12:30 | Hall X3

Display time: Fri, 19 Apr 08:30–Fri, 19 Apr 12:30
Chairpersons: Alina Premrov, Sergio Saia
Bernhard Ahrens and Mohammed Ayoub Chettouh

Carbon use efficiency has recently been proposed as a central parameter that promotes soil organic carbon storage based on data assimilation with a global soil organic carbon database and a vertical, microbial explicit soil organic carbon model (Tao et al., 2023). In this research, we present a sensitivity study with a vertical soil organic carbon model, COMISSION v2.0 (Ahrens et al., 2020), that not only models microbial interactions explicitly but also represents organo-mineral interactions with a maximum capacity, Qmax, to form mineral-associated organic carbon (MAOC).

The COMISSION model represents the formation of MAOC from microbial necromass and dissolved organic carbon analogous to Langmuir sorption. Empirical studies have provided Qmax parameterizations derived from quantile or boundary line regressions with clay and silt content. For the sensitivity study, we vary Qmax along the full range of observed Qmax values while simultaneously varying carbon use efficiency (CUE). Our results highlight that CUE and Qmax promote soil organic carbon storage to similar degrees along their respective observed ranges. The remaining parameters of the COMISSION model were kept at their calibrated values from a multi-site calibration with soil organic carbon, mineral-associated organic carbon, and radiocarbon profiles (Ahrens et al., 2020). While Qmax and CUE are of similar importance for promoting soil organic carbon storage, they also interact in promoting SOC storage. Higher Qmax values strengthen the promotion of soil organic carbon storage with higher CUE. This positive interaction results from higher microbial necromass with higher CUE and the subsequent association of microbial necromass on mineral surfaces mediated through Qmax. The sensitivity study revealed that CUE is the dominant driver for microbial biomass levels. Qmax affects microbial biomass only to a small degree through 'competition' between mineral surfaces and microbial biomass for dissolved organic carbon. While the effect of Qmax on microbial biomass is small, the relationship between Qmax and microbial biomass is generally negative. At the lower end of the tested range of carbon use efficiencies (CUE < 0.15), further model experiments reveal that imposing a stronger microbial limitation of depolymerization can lead to a negative relationship between CUE and soil organic carbon storage.

Overall, our results highlight that in soil organic carbon models with microbial interactions and a limited capacity to form organo-mineral associations, both processes can be of similar importance in promoting soil organic carbon storage. The current debate in the observational realm, whether there is indeed an upper limit for mineral-associated organic carbon formation, can spark a similar debate in the modeling realm on how to represent mineral-associated organic carbon formation in models mechanistically.



Ahrens B, Guggenberger G, Rethemeyer J et al. (2020) Combination of energy limitation and sorption capacity explains 14C depth gradients. Soil Biology and Biochemistry, 148, 107912.

Tao F, Huang Y, Hungate BA et al. (2023) Microbial carbon use efficiency promotes global soil carbon storage. Nature, 618, 981-985.

Funding acknowledgment: Bernhard Ahrens has received funding through the AI4SoilHealth project. The AI4SoilHealth project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101086179.

How to cite: Ahrens, B. and Chettouh, M. A.: Carbon use efficiency and mineralogical capacity are of similar importance for promoting soil organic carbon stocks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18844,, 2024.

Matteo Longo, Ilaria Piccoli, Antonio Berti, Michela Farneselli, Vincenzo Tabaglio, Domenico Ventrella, Samuele Trestini, and Francesco Morari

Agricultural system models are widely recognized as valuable tools for identifying best management practices and addressing the challenges posed by climate change. In this context, the use of model ensembles has been recently recommended for their enhanced performance and accuracy. However, assessing their effectiveness over a large geographical area, such as national scale is often currently lacking. This study focuses on simulating soil organic carbon (SOC) dynamics using an ensemble of models comprising DSSAT, CropSyst, EPIC, and APSIM models, utilizing data derived from five Long-Term Experiments (LTEs) spread across a north-to-south pedoclimatic range transect in Italy. This region is of particular importance as it represents a significant hotspot for climate change. The LTEs featured a robust array of 63 unique experimental protocols, incorporating variation effect in fertilization rates, cropping rotations, and tillage prescriptions. This resulted in a total of 2184 years of simulated data for each model. The dataset employed included SOC stocks and crop yield and biomass. Models underwent independent calibration, with crop and SOC parameters selected based on expert knowledge. Main crop cultivars, such as maize, soybean, sugarbeet, and wheat, were further categorized and calibrated by maturity classes. A similar approach was used for cover crops. The extensive dataset enabled a nuanced exploration of the models’ performance across varied agro-ecological contexts. The models proved capable of accurately reproducing the varied pedo-climatic conditions of the Italian peninsula, contributing to the advancement of our understanding of SOC dynamics.

How to cite: Longo, M., Piccoli, I., Berti, A., Farneselli, M., Tabaglio, V., Ventrella, D., Trestini, S., and Morari, F.: Exploring soil organic carbon dynamics through a multi-model simulation of multiple long-term experiments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11219,, 2024.

Bhaskar Mitra, Jagadeesh Yeluripati, James Cash, Linda Toca, Mhairi Coyle, and Rebekka Artz

Accurately quantifying carbon dynamics in peatlands is critical to assess their role in regulating global climate. Within hotspots of peatland degradation, such as in Europe and South-east Asia, skilful assessment of the spatial and temporal impacts of climate change and different land management options is required to meet emissions reductions targets and improve regional management planning.

To address this challenge, a random forest-based metamodel was evaluated to assess its utility in simulating various greenhouse gas (CO2) emission components, including Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), and Ecosystem Respiration (ER) across two Scottish peatlands. The metamodel mimicked the complex Wetland-DNDC model at a higher level of abstraction with increased efficiency and lower computational time.

While Wetland-DNDC also simulates NEE, GPP and ER, it typically involves a considerable number of parameters related to soil properties, climate data, vegetation characteristics, biogeochemical processes, hydrology, nutrient cycling, and microbial activity. Many of these parameters (more than 100) are challenging to measure in the field, and literature values are often adopted, which may not necessarily reflect local site conditions. In essence, this multidimensional parameter space introduces high uncertainties in modelling carbon fluxes.

In contrast, random forest-based metamodel preserved the key relationships between NEE and input variables (air and soil temperature, water table, precipitation, vegetation, and soil properties) as described in the Wetland-DNDC model with lower parameter requirements (less than 20) and increased accuracy. Similar unique relationships were established for GPP and ER. The random forest-based metamodel represented the Wetland-DNDC model  within the spectrum of input values and parameters across which it was simulated.

The simulation was conducted in two locations across Scotland with contrasting contemporary carbon dynamics: a near natural blanket bog in Cross Lochs, Forsinard, currently functioning as a resilient net carbon dioxide sink (UK-CLS; Lat. = 58.37, Long. = -3.96; altitude = 207 m) and an eroding oceanic blanket bog located in the Cairngorms, currently net emitting carbon dioxide (UK-BAM; Lat. = 56.92, Long. = -3.15, altitude = 642 m). The simulation was validated against eddy covariance flux measurements under varying climate conditions.

In contrast to Wetland-DNDC (R2 = 0.43), the metamodel provided a much-improved fit to the 1:1 line for NEE (R2 = 0.83). Model accuracy was slightly lower for the former (RMSE = 0.72) compared to its metamodel version (RMSE = 0.699). Similar trends were observed for GPP and ER simulations. At a monthly resolution, Wetland-DNDC-derived NEE, GPP, and ER consistently deviated by more than 20% from the eddy covariance-derived estimates, whereas its metamodel version showed deviations of less than 10%. Currently, work is in progress to incorporate management and drought simulation within a metamodel framework, as well as to upscale carbon fluxes from tower to landscape resolution.

The simulation of carbon fluxes using the metamodel-based approach holds the promise of enhancing emission reporting to Tier 3 standards and offers a hopeful avenue for modelling carbon dynamics in peatlands.

How to cite: Mitra, B., Yeluripati, J., Cash, J., Toca, L., Coyle, M., and Artz, R.: Metamodel simulation of carbon fluxes across an eroding and pristine blanket bog in Scotland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8466,, 2024.

Alina Premrov, Jagadeesh Yeluripati, and Matthew Saunders

The Eddy covariance (EC) is a well-known technique used (among others) to investigate the ecosystem exchange of greenhouse gasses (GHGs) between the biosphere and the atmosphere (Burba et al., 2007), often required in studies on soil-plant-atmosphere interactions and GHG emissions/removals from different soil systems. The long data records from EC measurements often experience data gaps due to various reasons (BaldocchiI, 2003) resulting in  many gap-filling methods being developed over the past decades. This study is introducing the new ’miniRECgap’ (Premrov, 2024) computational tool, which is using so-called ‘classic’, traditional robust and validated modelling approaches for gap-filling the missing EC CO2 flux measurements,  based on the application of environmental temperature and light response functions (Lloyd and Taylor, 1994; Rabinowitch, 1951) in combination with empirical/semi-empirical parameter-optimisation. ‘miniRECgap’ is a very small R package that operates in a user-friendly way via GUI (Graphical User Interface) supported scripts. It is purposely designed to be simple, operating in only 5 steps. The application of ‘miniRECgap’ will be demonstrated using EC CO2 flux data from an Irish peatland site Clara Bog. Due to its simplicity, it is thought that the new tool may be beneficial for new R users and that it may allow for easier and less time-consuming testing of the potential suitability of ‘classic’ empirical/semi-empirical gap-filling on different datasets.



The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding the CO2PEAT project (2022-CE-1100) under the EPA Research Programme 2021-2030.



BaldocchiI, D.D. (2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future.  9, 479-492.

Burba, G., Anderson, D., Amen, J., (2007) Eddy Covariance Method: Overview of General Guidelines and Conventional Workflow, AGU Fall Meeting Abstracts, pp. B33D-1575.

Lloyd, J., Taylor, J.A. (1994) On the temperature dependence of soil respiration. Functional Ecology 8, 315-323.

Premrov, A., (2024) miniRECgap. R package  with GUI suported scripts for gap-filling the of Eddy Covariance CO2 flux data.  Copyright: Trinity College Dublin. URL:  'miniRECgap package will be uploaded on GitHub in near future'.

Rabinowitch, E.I. (1951) Photosynthesis and Related Processes. Interscience Publishers.

How to cite: Premrov, A., Yeluripati, J., and Saunders, M.: Introducing the ’miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO2 flux data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6475,, 2024.

Mariana Silva

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration timespans on the order of decades and thus cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre- and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. A scoping review was carried out to identify, describe, and categorise current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of eco- and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the Web of Science entire database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorised according to this review’s research interests in 7 distinct categories aggregating the papers’ research themes and model outputs. Key themes emerging from topics covered by papers in the database included: modelling restoration development from a bog growth perspective; the prioritisation of modelling GHG emissions dynamics as a part of policymaking; the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems; and the emerging prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Based on this assessment, CoupModel, DigiBog, and MPeat2D were calibrated for the case of Abbeyleix Bog, Co. Laois, Ireland (ongoing with results expected before April 2024). The exploration of subsequent simulations to represent varying peatland restoration conditions is discussed from an ecohydrological lens.

How to cite: Silva, M.: Ecohydrological modelling on peatlands: scoping review and application of three process-based models to Irish raised bog restoration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18533,, 2024.

Dimitrios D. Alexakis and Christos Polykretis

Soil erosion constitutes an increasing threat to soil productivity and food security. This work describes the potential of using Artificial Neural Networks (ANN) for upscaling soil loss outputs from medium to low scale. The Revised Universal Soil Loss Equation (RUSLE) model was implemented to calculate soil loss rates in two scales in Crete, Greece. Specifically, the RUSLE model was applied in six (6) watersheds across the island using medium spatial resolution satellite images (5m), namely Planetscope. These results were used to feed an ANN model to upscale the mesoscale outputs (5m) to regional outputs (30m-island level). The ANN system was trained using spatial environmental parameters such as the Normalized Difference Vegetation Index, Digital Elevation Model, and topographical slope angle. This "optimized" soil loss derivative later made it possible to compare it with the corresponding final derivative of Crete (regional spatial scale), which emerged from the straightforward processing of RUSLE model with the more "coarse" and generalized data as estimated from the  Landsat-8 satellite images (30m). The statistics revealed that the detailed and high-quality soil loss data, as derived from the upscaling process, provide more precise and reliable results.

How to cite: Alexakis, D. D. and Polykretis, C.: Using Artificial Neural Networks to upscale soil erosion model results from local to regional scale. An example from Crete, Greece., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14678,, 2024.

Magdeline Vlasimsky, Gerd Dercon, Hami Said Ahmed, Sarata Daraboe, Yusuf Yigini, Yuxin Tong, Yi Peng, Franck Albinet, Maria Heiling, and Christian Resch

The Soil Fertility (SoilFer) project, led by the Land and Water Division at FAO, seeks to enhance agricultural practices and resilience globally, starting with five countries (Guatemala, Honduras, Zambia, Kenya, and Ghana). The project collaborates with governments and relevant national partners to establish comprehensive national monitoring and mapping systems for soil management, catering to the diverse needs of agriculture stakeholders. The Soil and Water Management Laboratory at the Joint FAO/IAEA Center serves as a crucial hub for advancing research and technical expertise in soil and water management using nuclear and related techniques. Through its multifaceted approach in collaboration with the Land and Water Division, the laboratory contributes significantly to the SoilFer project, through the development and implementation of technical training programs for and expert advising on the application of Mid-Infrared Spectroscopy (MIRS), Cosmic Ray Neutron Sensor (CRNS), and Gamma Ray Spectroscopy (GRS) to soil monitoring and mapping.

The integration of MIRS, CRNS, and GRS technologies within the SoilFer project forms a robust framework for soil monitoring and mapping, as MIRS has been shown to provide detailed insights into soil composition and carbon content, CRNS offers real-time data on soil moisture dynamics, and GRS contributes to the analysis of radioactive isotopes and elemental composition. Given the integrated nature of landscape processes, the adoption of technological approaches must mirror this complexity. Interconnected ecological, hydrological, and geological processes within landscapes necessitate a holistic and integrated technological framework. This approach ensures that diverse data streams, derived from technologies such as remote sensing, geographic information systems (GIS), and advanced sensor networks, can be harmoniously synthesized. Only through such integration can a comprehensive understanding of landscape dynamics be achieved, facilitating informed decision-making and sustainable management practices across multifaceted environmental systems. The project emphasizes the seamless integration of these advanced technologies with soil monitoring and mapping systems, ensuring a comprehensive and effective approach to soil management practices, while improving national capacity and stakeholder engagement in data-based decision making. 

The key objectives of the SoilFer project encompass the development of robust national soil information systems, the implementation of decision support systems targeting soil health, and the promotion of sustainable soil management practices. By fostering collaboration and knowledge exchange, the project aspires to build technical, increase agricultural resilience and ensure food security in the participating countries.

How to cite: Vlasimsky, M., Dercon, G., Said Ahmed, H., Daraboe, S., Yigini, Y., Tong, Y., Peng, Y., Albinet, F., Heiling, M., and Resch, C.: The Joint FAO/IAEA Center and the Soil Fertility Project: Integrating Nuclear and Related Techniques for Modelling to Support Practical Decision Management Support, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5949,, 2024.

Ahmed S. Almalki, Marcel M. El Hajj, Kasper Johansen, and Matthew F. McCabe

The AquaCrop model is a powerful tool for crop monitoring, providing a daily estimation of soil-crop-atmosphere dynamics. The model requires a substantial number of input variables and parameters, highlighting the need for identifying those that significantly influence model outputs. Sensitivity analysis is a vital method for this purpose. A key objective of this study is to examine the performance of the AquaCrop model in simulating wheat yield and irrigation water requirement in drylands under two scenarios: first running the model employing a minimal amount of in situ data, and second using all available in situ data. A second focus is to analyze the sensitivity to all crop and soil related input variables and parameters. To do this, a pilot-scale study was undertaken, focusing on a commercial farm in the Al-Jouf province of Saudi Arabia. The farm comprised 200 center-pivot fields of mainly wheat crops. In situ data was collected to calibrate the model for two consecutive growing seasons (2019-2020 and 2020-2021). Using the variance-based Sobol technique, the sensitivity of the AquaCrop model outputs, particularly wheat yield and irrigation water requirement, to crop and soil related input variables and parameters was examined, as were the influential and non-influential inputs on these outputs. Results showed that the second scenario (all data) outperformed the first (minimal data), demonstrating more accurate wheat yield predictions with rRMSE values of 17% and 21% for the 2019-2020 and 2020-2021 growing seasons, respectively. Regarding irrigation water requirement estimations, the second scenario also exhibited lower rRMSE values of 20% and 19% for the same growing seasons. Results also demonstrated that the sensitivity indices of variables and parameters varied with model outputs and growing seasons. By synthesizing inputs sensitivities under different conditions, the influential input variables and parameters were distinguished. Overall, six variables and parameters held significant influence on the analyzed model outputs based on their total-order sensitivity indices. These included duration from sowing to senescence (senescence), duration from sowing to harvesting (maturity), duration from sowing to yield formation (HIstart), base temperature below which growth does not progress (Tbase), minimum air temperature below which pollination failure begins (Tmin_up), and shape factor describing reduction in biomass production (fshabe_b). It was revealed that most variables and parameters were non-influential, which might allow them to be fixed within their ranges to optimize model calibration. The research represents the performance assessment and sensitivity analysis of the AquaCrop model over a desert farming system and offers guidelines for model calibration by delivering information on influential and non-influential input variables and parameters.

How to cite: Almalki, A. S., El Hajj, M. M., Johansen, K., and McCabe, M. F.: A Comprehensive Assessment of the AquaCrop Model in drylands: Performance Examination and Sensitivity Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6832,, 2024.

Priscila S Matos, Johnny R Soares, Maria C S Carvalho, Beata E Madari, Bruno J R Alves, Claudia P Jantalia, Antônio C R Freitas, Bhaskar Mitraa, and Jagadeesh Yelupirati

Integrated crop-livestock (ICL) systems can have a complex of effects on soil properties that can influence greenhouse gas emissions (GHG). The ICL aim to capture atmospheric CO2 and sequester it in the soil, holding promise for reducing GHG emission intensity from livestock products. Moreover, modeling N2O emissions can help assess the potential impact of N management on the ICL system to optimize the sustainability of agriculture production. Field data were obtained from an ICL experiment of EMBRAPA-Rice and Beans, located on Capivara farm, Santo Antônio de Goiás/GO, Brazil (16°28´S; 49°17´W; 823 m alt.). The ICL experiment was evaluated for four years (2013-2016) with the following crop rotation sequence: pasture-fallow-maize, fallow-soybean, maize-fallow-maize, and beans-fallow. The N2O data was obtained from the 2013-14 season, which was measured in a static chamber during maize cultivation. The experiment consisted of 9 treatments (N sources and rates) with 5 replicates. The N2O was measured in 30 sampling events over almost 100 days. The daily N2O fluxes from the treatments control (No N), urea (UR), calcium ammonium nitrate (CAN), and ammonium sulfate (AS) at an N rate of 150 kg/ha were used to parametrize the DNDC. Model crop and soil parameters were adjusted to better simulate maize production and N2O emission according to observed data. DNDC simulated CO2 emissions, quantified as Net Ecosystem Exchange (NEE), were validated against CO2 emissions derived from eddy-covariance data, using statistical parameters such as R2, RMSE, MAE, and Bias. While data refinement is ongoing, preliminary findings indicate that DNDC shows promise for estimating CO2 emissions IPS under tropical conditions The DNDC had a satisfactory performance in predicting N2O emission in the ICL system, resulting in a significant correlation with the observed data (r = 0.63, p < 0.001), MAE of 0.024, and RMSE of 0.036. The average daily N2O-N emission observed was 0.026 kg ha-1 day-1 and simulated was 0.025 kg ha-1 day-1. The UR, CAN and AS applications showed a peak of N2O emission on 31th day after sowing (2 days after fertilization) corresponding to 0.175, 0.217, and 0.163 kg ha-1 day-1, respectively, where the model simulated N2O peaks of 0.151, 0.123, and 0.173 kg ha-1 day-1. The accumulated N2O emissions were 0.513, 1.148 1.738, and 0.890 kg ha-1 for control, UR, CAN, and AS respectively, in which the simulated by DNDC were 0. 778, 1.612, 1.391, and 1.755 kg ha-1. In general, the model had a good fit with daily N2O emissions, but it tended to overestimate the N2O emission from UR and AS, and underestimate from CAN. Further model parametrization and calibration may be necessary to better predict N2O and CO2 emissions. The DNDC satisfactory simulated the N2O emissions from different N sources applied to ICL system, which can be used to evaluate the potential emissions and mitigation according to N management in ICL.

How to cite: Matos, P. S., Soares, J. R., Carvalho, M. C. S., Madari, B. E., Alves, B. J. R., Jantalia, C. P., Freitas, A. C. R., Mitraa, B., and Yelupirati, J.: Performance of the DNDC in Estimating CO 2 and N 2 O emissions of Integrated Crop-Livestock Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19814,, 2024.

Mohammad Noor Alhamad and Shefaa Abdullah

This study employs the PHYGROW simulation model to assess the 40-year dynamics of arid grassland in Jordan, focusing on the Leaf Area Index (LAI) as a pivotal indicator of vegetation health. The observed results reveal a notable decline in LAI over the study period, with the highest recorded value in 2005 (2.27) and a subsequent reduction to 1.68 in 2021. Rigorous statistical analyses, including regression analysis, confirm the significance of this downward trend, prompting further investigation into potential contributing factors such as changes in climate, land use, and soil conditions.


Interannual variability analysis identifies specific years marked by noteworthy LAI fluctuations, providing insights into the dynamic responses of the arid grassland ecosystem. Comparison with concurrent climate data underscores the intricate relationship between LAI trends and environmental variables. The study emphasizes the importance of continuous monitoring and understanding the underlying drivers of vegetation dynamics in arid regions.

The observed decrease in LAI holds implications for the overall health and resilience of the ecosystem, highlighting the need for informed decision-making in sustainable land management practices. These findings contribute significantly to the broader understanding of arid land dynamics, guiding future research and collaborative efforts with experts in related fields. Such collaborations are essential for enhancing the robustness and applicability of the results, ultimately informing conservation and resource management strategies tailored to the unique challenges of arid environments.

How to cite: Alhamad, M. N. and Abdullah, S.: Simulation Modeling of Arid Grassland Dynamics in Jordan: A 40-Year Analysis of Leaf Area Index Trends, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4117,, 2024.

Aline de Andrade, Marco A. Z. Arruda, Sophie Miguel, Stéphanie Reynaud, and Javier Jiménez-Lamana

Plastic production worldwide has increased from 1.5 million tons in 1950 to 390.7 million tons in 2021.1 Nanoplastics (NPTs) have been considered an emergent contaminant entering the environment without any control since they can be formed by the degradation of large-sized plastic inadequately disposed of and considering that only 9% are effectively recycled.2 Just as the NPTs, nanoparticles (NPs) are considered emergent contaminants, and their application in different industrial products raises concern regarding the NPs entering the environment matrices.3 The soil bioaccessibility is an important parameter when considering the contaminants assessment evaluation with biological soil phase, and the study of soil liquid solution, which is called the soil pore water, can elucidate not only the bioaccessibility but also NPTs and NPs mobility, fate, and stability.4 The NPTs’ and NPs’ concentrations in the range of ng L-1 might be a limitation for their evaluation. However, spICP-MS can provide information on size, number concentration, and mass concentration, even in environmental conditions.5 In this study, a typical Brazilian soil used for plant cultivation (Latosol) was employed, and the soil moisture was controlled according to the field capacity determined in advance. Polystyrene (PS) nanoparticles with gold core and silver NPs (AgNPs), considering their abundance in different goods, were used as model nano-contaminants. The soil pore water was collected in two sampling points through a low-pressure lysimetric method using Rhizon® samplers once a week for 45 days of the experiment. In addition, the soil moisture was controlled by monitoring and adding more water to maintain the soil humidity, considering the three field capacity percentages studied. Results showed a downward trend in the number of particles detected in successive collections over time for both nano-contaminants. However, they also demonstrated different behaviours between them. The NPTs were bioaccessible in the pore water after the first days from the beginning of the experiments, and their concentration decreased constantly. At the same time, the NPs presented an inconstant transport through the soil column, gradually becoming bioaccessible. Finally, the concentration proved to be an important and decisive parameter, bringing essential discussion regarding the nano-contaminant's increasing concentration and behaviour in an environmental matrix, demonstrating the necessity to comprehend their interactions with the soil and between each other.


1 S. Maity, R. Guchhait, M. B. Sarkar and K. Pramanick, Plant. Cell Environ., 2022, 45, 1011–1028.
2 P. Zhou, L. Wang, J. Gao, Y. Jiang, M. Adeel and D. Hou, Soil Use Manag., 2023, 39, 13–42.
3 Q. Abbas, B. Yousaf, Amina, M. U. Ali, M. A. M. Munir, A. El-Naggar, J. Rinklebe and M. Naushad, Environ. Int., 2020, 138, 105646.
4 M. Di Bonito, N. Breward, N. Crout, B. Smith and S. Young, in Environmental Geochemistry, Elsevier, 2008, pp. 213–249.
5 J. Jiménez-Lamana, L. Marigliano, J. Allouche, B. Grassl, J. Szpunar and S. Reynaud, Anal. Chem., 2020, 92, 11664–11672.

How to cite: de Andrade, A., Arruda, M. A. Z., Miguel, S., Reynaud, S., and Jiménez-Lamana, J.: Transport and bioaccessibility of nano-contaminants in Brazilian latosol through pore water evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22341,, 2024.

Pierre Benoit, Charline Godard, Marjolaine Deschamps, Nathalie Bernet, Ghislaine Delarue, Valenti Serre, and Claire-Sophie Haudin

In the context of recycling organic waste products or irrigation by treated wastewaters (re-use), the fate of human and veterinary pharmaceuticals in agricultural soils and consequent ground-water contamination are influenced by many factors, including soil properties controlling sorption and dissipation processes (Verlicchi et al., 2015, Mejías et al., 2021, Rietra et al., 2022). Sorption coefficients are among the most sensitive parameters in models used for risk assessment. However, for different classes of pharmaceuticals, the variations in sorption among different soil types are poorly described and understood (Kodesova et al., 2015). Here we reviewed sorption parameters for different classes of pharmaceuticals and their variation with selected soil properties. We also evaluated the sorption isotherms for three pharmaceuticals, ofloxacin, tetracycline, diclofenac and a bactericide,  riclocarban and ten soils from temperate and tropical regions, and assessed the impact of soil properties on Freundlich equation parameters Kf and n. Batch experiments were set up adapting OECD protocol and using initial concentration ranges from 5 to 1000 μg/L. For strongly sorbed molecules, namely ofloxacin, tetracycline and triclocarban, there were strong technical constraints for the quantification of equilibrium concentrations by LC-MS-MS. We used this knowledge from both literature review and experimental data to build pedotransfer functions that allow predicting sorption parameters for a wide range of soils. Sorption of ionizable pharmaceuticals was, in many cases, highly affected by soil pH and CEC whereas soil organic matter content remained a driving factor of sorption for neutral molecular forms.

Kodesova, R., et al. (2015) Science of the Total Environment 511, 435–443.
Mejías, C. et al. (2021) Trends in Environmental Analytical Chemistry 30, e00125.
Rietra, R.P.P.J., et al. (2024) Heliyon 10 (2024) e23718.
Verlicchi, P. & Zambello, E., (2015) Science of The Total Environment 538, 750–767

How to cite: Benoit, P., Godard, C., Deschamps, M., Bernet, N., Delarue, G., Serre, V., and Haudin, C.-S.: Searching for pedotransfer functions to predict sorption of pharmaceuticals from soil properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21616,, 2024.

The role of EU Soil Observatory in supporting the EU policies, Research and innovation
Calogero Schillaci, Nils Broothaerts, Elise van Enyde, Timo Breure, Diana Vieira, Felipe Yunta, Cristina Arias-Navarro, Arwyn Jones, Cristiano Ballabio, Alberto Orgiazzi, Piotr Wojda, and Panos Panagos
Thomas Weninger, Irene Schwaighofer, Florian Darmann, Thomas Brunner, and Peter Strauss

The proposal for the European Soil Monitoring Law includes an integrated value of soil water holding capacity to be determined as a proxy for soil quality for whole soil districts. As this is a relatively new but interesting approach, a number of details of the assessment procedure remain open at the current stage of formulation. The aim of this study is to quantify the effects of the choice of different options on the overall result, focusing on the delineation of soil districts in different sizes, the detailed definition of the respective soil property, and the treatment of sealed areas.

High-resolution data for soil hydrological properties for two Austrian provinces are used as a basis, including different approaches to calculate soil water holding capacity. The size of the study area corresponds to the maximum size of a soil district as proposed. Thus, a variation of three size levels is possible, namely the whole area, major river catchments, and agro-geographical sub-units. The term soil water holding capacity is basically defined in the proposed EU Directive, but several options for its determination are possible. We used two different pedotransfer functions to derive soil water holding capacity values and an additional method based on averaging results from randomly located sampling points. Soil sealing is a major threat to hydrological soil functionality, and its assessment over large areas is still not standardized. Here, the European LUISA land use/land cover dataset for 2020 (1 km resolution) and a national dataset with higher resolution are used. Both datasets are optionally overlaid with the Copernicus imperviousness layer involves gradual information about surface imperviousness.

By combining all these factors with each other, different ways were evaluated to determine the target value of soil water holding capacity integrated on a regional scale. Differences in the results and their sensitivity to input variations are quantified to inform policy decisions in the implementation of the European Soil Monitoring Law in the member states.

How to cite: Weninger, T., Schwaighofer, I., Darmann, F., Brunner, T., and Strauss, P.: Soil water holding capacity as descriptor of soil health at district scale – a sensitivity study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12129,, 2024.

Marco Peli, Arianna Dada, Francesca Barisani, Vera Ventura, Michèle Pezzagno, Stefano Barontini, and Giovanna Grossi

The Horizon Europe project LOESS ‘Literacy boost through an Operational Educational Ecosystem of Societal actors on Soil health’ officially started in June 2023 involving twenty partner organizations in fifteen countries across Europe, lead by the WILA Bonn Science Shop. The final goal of the project is to raise awareness on the importance of soil and of its functions and to increase soil literacy across Europe. To do so, the first step of the project activity is designed to map and connect multiple actors in Communities of Practice (CoPs) at the national level, and engage them to provide an overview of the current level of soil related knowledge and teaching programmes and materials, in order to identify the gap between this material and the educational needs amongst different levels of the society (from pupils to students to citizens).

The Italian chapter is led by two university research groups with different expertise (civil and environmental engineering at the University of Brescia on one hand and social sciences at the University of Sassari on the other) and one NGO (Controvento) whose mission is children not-formal education. The Italian CoP, led by the University of Brescia, is composed of 62 members from both the higher education and the research community, as well as from the primary and secondary education levels (teachers and pupils), from the productive sectors (farmers and spatial planners), from the politics world (local administrators) and from the civil society (NGOs and associations).

This contribution presents the activities performed so far, viz the stakeholder mapping, the creation of the CoP and its first meetings and the community-based participatory activity which was organized on the World Soil Day 2023.

How to cite: Peli, M., Dada, A., Barisani, F., Ventura, V., Pezzagno, M., Barontini, S., and Grossi, G.: The LOESS project to boost soil health literacy across Europe: The case of Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19820,, 2024.

Amanda Matson, Maria Fantappiè, Grant A. Campbell, Jorge F. Miranda-Vélez, Jack H. Faber, Lucas Carvalho Gomes, Rudi Hessel, Marcos Lana, Stefano Mocali, Pete Smith, David Robinson, Antonio Bispo, Fenny van Egmond, Saskia Keesstra, Nicolas P.A. Saby, Bozena Smreczak, Claire Froger, Azamat Suleymanov, and Claire Chenu

Soil health is a key concept in worldwide efforts to reverse soil degradation, but to be used as a tool to improve soils, it must be definable at a policy level and quantifiable in some way. Soil indicators can be used to define soil health and quantify the degree to which soils fulfil expected functions. Indicators are assessed using target and/or threshold values, which define achievable levels of the indicators or associated soil functions. However, defining robust targets and thresholds is not a trivial task, as they should account for differences in soil type, climate, land-use, management, and history, among other factors.

We assessed (through theory and stakeholder feedback) four approaches to setting targets and thresholds: fixed values based on research, fixed proportions of natural reference values, values based on the existing range (e.g. lower quartile of the observed distribution), and targets based on relative change (e.g. a 20% increase of the indicator’s value). Three approaches (not including relative change) were then further explored using case study examples from Denmark, Italy, and France, which highlighted key strengths and weaknesses of each approach. Here, we present a selection of the assessment and case study results, as well as a framework, which facilitates both choosing the most appropriate target/threshold method for a given context, and using targets/thresholds to trigger follow-up actions to promote soil health.  

How to cite: Matson, A., Fantappiè, M., Campbell, G. A., Miranda-Vélez, J. F., Faber, J. H., Gomes, L. C., Hessel, R., Lana, M., Mocali, S., Smith, P., Robinson, D., Bispo, A., van Egmond, F., Keesstra, S., Saby, N. P. A., Smreczak, B., Froger, C., Suleymanov, A., and Chenu, C.: A framework for setting soil health targets and thresholds in agricultural soils , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20664,, 2024.

Serena D'Ambrogi, Francesca Assennato, Rocco Labadessa, Paolo Mazzetti, Valentina Rastelli, Nicola Riitano, and Cristina Tarantino

Land degradation processes have experienced a significant increase in recent decades, a trend that is projected to escalate further in the absence of any intervention. The need of adopting practices to contain, mitigate and restore degraded land have been stressed also by the new European Mission 'A Soil Deal for Europe'. To guide and support restoration actions, through a common and effective framework, an efficient monitoring approach and an adaptive ecological restoration process is needed. 

The NewLife4Drylands LIFE project provides a Protocol for design, implementation, and maintenance of restoration activities based on Nature-Based Solutions (NBS) within drylands. The Protocol, developed following the principles and inputs of some international restoration standards (SER, IUCN), is based on the identification and monitoring of degradation processes exploiting remote sensing capabilities, with the aim to integrate data derivation procedures into ecological restoration and maintenance activities. The Protocol is supported by a Decision-Making web-tool guiding trough the degradation processes, NBS along with indices/indicators with the aim to reduce the knowledge effort and helps in prioritizing options. 

The Newlife4drylands experience highlighted the heterogeneity and complexity of degradation processes, as resulted from a selected set of degraded pilot sites within Mediterranean Protected Areas, together with the issue for harmonization and standardization of ecological/physical indicators, especially those derived from satellite observations, when used as proxies of land degradation. The integrated use of both available field data (for short-term monitoring) and satellite data (for medium and long-term monitoring) have been explored to identify indicators for evaluating the effectiveness of planned restoration actions. This approach is geared, towards fostering adaptive and collaborative management of the ecological restoration process. 

Therefore, the Protocol acts as support tool for decision-makers, including public administration of Protected Areas, as well as technicians and planners. The proposed approach aims to raise awareness about the needs of drylands and opportunities provided by NBS. It serves as a guide for the identification of specific/local NBS for the restoration of drylands, beginning with the identification of degradation processes.

How to cite: D'Ambrogi, S., Assennato, F., Labadessa, R., Mazzetti, P., Rastelli, V., Riitano, N., and Tarantino, C.: NewLife4Drylands Protocol for dryland restoration in Protected Areas: an innovative tool to support restoration activities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6126,, 2024.

Posters virtual: Fri, 19 Apr, 14:00–15:45 | vHall X3

Display time: Fri, 19 Apr 08:30–Fri, 19 Apr 18:00
Chairpersons: Alina Premrov, Sergio Saia
Diara sylla, Abdoul Aziz Diouf, and Babacar Ndao

In the Sahel region, landscape configuration is closely linked to factors such as climate, ecology, soil composition, agronomy, livestock, and biology. Over the past decades, significant changes in these factors have been observed, including shorter rainy seasons, irregular precipitation, a decrease in biomass productivity, and rapid population growth, negatively impacting local agricultural and pastoral systems. In response to this pressure, mitigation strategies have been implemented to contribute to the improvement of local food, nutritional, and economic security. Agroforestry systems, involving a combination of trees, shrubs, crops, and animals in the same plot, represent one of these strategies. Therefore, characterizing these systems in the current context of climate change is crucial for sustainable natural resource management.

In this study, three agroforestry landscapes of the Senegalese Sahel were described, spanning a bioclimatic gradient from the Louga region (Ouarkhokh) in the north to the Fatick region (Niakhar) in the center, and the Tambacounda region (Koussanar) in the south. The data utilized included satellite imagery synthesis (Sentinel-2 and Spot), landscape variables (rainfall, evapotranspiration, biomass, and vegetation), spectral indices (NDVI, NDRE, GNDVI), and field data on land use and woody cover. The methodology consisted of three main approaches: (i) landscape stratification involving Sentinel image segmentation in 2021, selection of relevant landscape variables, and mixed discriminant factor analysis to establish landscape heterogeneity; (ii) land use and land cover mapping through supervised pixel-based classification using a Random Forest (RF) machine learning classifier with 500 trees; (iii) floristic diversity analysis by assessing floristic composition and calculating diversity indices (i.e., Shannon, Pielou, and Simpson indices).

Landscape stratification identified seven classes with distinct landscape characteristics. Classes (1, 2, and 4) in the Ouarkhokh site had lower average biomass, rainfall, and actual evapotranspiration values than classes (3 and 4) in the Niakhar site. Similarly, classes (5, 6, and 7) in the Koussanar area had higher average biomass, rainfall, and actual evapotranspiration values than the first two sites. Land use mapping showed vegetation predominance in the Ouarkhokh site, significance in the Koussanar site, and low presence in the Niakhar area. Other identified units (cultivated areas, built-up areas, water, and bare land) were dominant in the Niakhar area, present in the Koussanar site, and low in the Ouarkhokh area. Likewise, vegetation dominated in classes 1, 5, 6, and 7. Class 1 was exclusively found in Ouarkhokh, while classes 5, 6, and 7 were located in the Koussanar site. The majority of cultivated surfaces were in class 3, exclusively located in the Niakhar area. Species richness was higher in the Niakhar area (60 species, 21 families) and lower in the Koussanar area (56 species, 16 families) and Ouarkhokh area (31 species, 13 families). This landscape distribution of land use, landscape classes, and identified species highlights the influence of anthropogenic, soil-related, and climatic factors specific to each site.

How to cite: sylla, D., Diouf, A. A., and Ndao, B.: Variation of woody plants diversity and land use along a bioclimatic gradient of agroforestry landscapes in Senegalese Sahel, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5133,, 2024.

Cristina Tarantino, Mariella Aquilino, Saverio Vicario, Rocco Labadessa, Vito Emanuele Cambria, Christos Georgiadis, Marcello Vitale, Francesca Assennato, and Paolo Mazzetti

In the framework of the NewLife4Drylands LIFE Preparatory project (LIFE20 PRE/IT/000007, 2021-2024) the estimation of SDG 15.3.1 indicator [1], adopted in the UNCCD’s Good Practice Guidance [2], was applied for evaluating Land Degradation (LD) in different Mediterranean Protected Areas (PA). To effectively support PAs managers, joint effort was made in the evaluation of SDG 15.3.1 indicator at the local scale by using satellite Remote Sensing data in the computation of the three main sub-indicators as trend in Land Cover (LC), Primary Production (PP) and Soil Organic Carbon (SOC) stock. Where feasible, local scale sub-indicators were not sourced from open-access global/European databases due to their lack of accuracy at the site scale [3]. LD was estimated not only for the whole PA but also for specific LC classes of interest, considering additional sub-indicators related to pressures and threats affecting the class. This study focuses on the dryland Alta Murgia (IT9120007) PA, in southern Italy, and the wetland Nestos River Delta (GR1150001) PA, in Greece. For Alta Murgia site, featuring semi-natural dry grassland habitats of community interest that are frequently subjected to fire events during the summer season, the Burn Severity (BS) index was included. BS trends were measured by assessing the difference in pre/post–fire Normalized Burn Ratio (NBR) index from Landsat data during summer. Baseline data from 2004, coinciding with the establishment of a National Park within PA, was compared with 2018 for validating field data availability. Nestos River Delta hosts the largest natural riparian forest in Greece and is frequently subjected to hydrological cycle modifications, involving water scarcity due to both inappropriate river management and climate change, in turn hampering the transport of nutrient-rich sediments and the enrichment of soils being at risk of aridification. Within this framework, Hydroperiod and Soil Salinity indices were considered for LD and specific impacts on aquatic vegetation LC. Baseline data from 2017, after the dry climate conditions of 2016-2017, was compared with 2021 for validating field data availability. Both in Alta Murgia and Nestos, LC mappings were obtained by a data-driven pixel-based approach considering Landsat/Sentinel-2, respectively, multi-seasonal imagery and a multi-class Support Vector Machine (SVM) classifier trained with data from in-field campaigns and historical orthophotos interpretation. Time series of MSAVI from Landsat (which replaced standard NDVI for its soil correction benefits [4]) and PPI from Sentinel-2 by Copernicus services, respectively, were used to track grassland PP trends. Lastly, for SOC stock trends, the open-source Trends.Earth QGIS plugin [5], incorporating customized LC data and global SoilGrids product, was adopted to supplement local data limitations. According to its specification, the SDG 15.3.1 indicator was computed by integrating all the sub-indicators according to the principle “one out, all out” obtaining the 3-classes output mapping (Degradation, Improvement, Stable). The findings can support the monitoring and evaluation of LD, guiding protective measures aligned with the Agenda 2030 for Sustainable Development. They, also, highlight the importance of the integration of local scale data and sub-indicators within the UNCCD methodology.








How to cite: Tarantino, C., Aquilino, M., Vicario, S., Labadessa, R., Cambria, V. E., Georgiadis, C., Vitale, M., Assennato, F., and Mazzetti, P.: SDG 15.3.1 indicator at local scale for monitoring land degradation in protected areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10906,, 2024.