Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 3
Water accumulation is a critical challenge in arid and semi-arid regions, significantly degrading soil quality and threatening land sustainability. This study focuses on the Oued Beht watershed in Morocco, covering 6,200 km², representative of semi-arid geographical conditions. Using satellitebased Earth observation data, including Landsat 9 and SRTM, this research assesses water erosion by comparing two models: PAP/CAR, a qualitative approach, and RUSLE, a quantitative model.
Key datasets, such as NDVI, slope, and land use, were extracted from satellite imagery to calibrate and validate the models. For the RUSLE model, the rainfall erosivity factor (R) was estimated using two distinct methods. The first applies the formula developed by Renard and Freimund (1994), which links annual precipitation to erosivity. The second employs a modified formula by Rango and Arnoldus (1987), adapted to Moroccan conditions, using monthly and annual precipitation to estimate erosivity.
Rainfall data covering 65 years (1958–2023), obtained from 23 meteorological stations, were utilized to ensure robust and reliable analysis. By comparing the performance of these two RUSLE methods with the PAP/CAR model, this study aims to determine their respective effectiveness in
evaluating erosion risks.
The findings contribute to advancing knowledge on erosion processes, offering valuable insights for sustainable land management practices and mitigating land degradation in semi-arid environments. This research underscores the critical role of satellite data and modeling in
addressing natural hazards, aligning closely with the conference’s focus on leveraging Earth observation technologies for risk assessment and management.
How to cite: moutaoikil, N., Benzougagh, B., Mastere, M., Bounouira, H., El Fellah, B., Ouallali, A., and Lamrani, H.: Assessment of Water Erosion in the Semi-Arid Oued Beht WatershedUsing Satellite Data and Comparative Modeling Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2178, https://doi.org/10.5194/egusphere-egu25-2178, 2025.
In recent years, the frequency of extreme rainfall events has significantly increased worldwide, posing severe challenges to river embankments and other soil and water conservation measures. This study focused on the core disaster area of the "July 29, 2023 extreme rainfall" event—the Beizhi River Basin in Lincheng County, China. Using GIS technology, the study analyzed the damage patterns of embankments with different construction standards, the critical topographic conditions, and their protective benefits for land under extreme rainfall conditions. The results showed that: 1) River embankment damage was severe, with the affected areas primarily located in the middle reaches of the river. The overall damage proportion was significant, and embankments built to higher standards suffered less damage than those built to lower standards, indicating greater stability. 2) The damage characteristics of embankments were influenced by a combination of river slope and catchment area. The developed S-A topographic critical model indicated that high-standard embankments required higher critical topographic conditions to sustain damage, demonstrating their ability to maintain structural integrity under harsher conditions. 3) Embankments had significant soil and water conservation benefits. Compared to segments without embankments, areas with embankments experienced significantly less land damage. High-standard embankments exhibited greater efficiency in protecting land compared to low-standard embankments. This study could make an important contribution to the theory of river soil and water conservation under the backdrop of increasing extreme rainfall events due to climate change. It may provide valuable guidance for improving embankment design standards and optimizing soil and water conservation measures.
Keywords: Extreme rainfall; embankment damage; topographic critical conditions; soil and water conservation benefits
How to cite: Sun, Z., Wang, C., Shen, H., and Wang, Q.: Topographic Characteristics of River Embankment Damage and Soil and Water Conservation Benefits Under Extreme Rainfall Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9666, https://doi.org/10.5194/egusphere-egu25-9666, 2025.
The delineation of hydrological catchments and river networks is fundamental for hydrographic and hydrological information, environmental analysis, modeling, and decision-making. However, many existing datasets are limited in their spatial resolution, which can constrain their ability to accurately represent localized processes such as floodplain dynamics and soil erosion patterns. Building on the concepts of the new vector-based global river network dataset by Lin et al. (2021), Catchment Characterisation and Modelling (CCM) by the Joint Research Centre (JRC) (Vogt et al., 2003), as well as HydroSHEDS by the World Wildlife Fund US (Lehner and Grill, 2013), we aim to introduce a finer spatial scale that captures regional nuances and enhances hydrological detail. Using high-resolution digital elevation data, this study applies a hierarchical coding system to delineate nested catchments across Europe, achieving basin sizes reduced to a fine scale. The methodology ensures the accurate representation of catchments and associated river networks, with a focus on maintaining hydrological connectivity.
This delineation approach allows for the creation of a comprehensive geospatial dataset that integrates detailed catchment and river attributes. Our work complements existing large-scale datasets, providing critical insights for regional and local hydrological and environmental applications. The product/dataset will support environmental analysis by enabling the calculation of catchment-scale statistics for a wide range of environmental, soil, and land degradation parameters, including soil properties, soil erosion and land degradation, hydrological factors, ecological indicators, land use and land cover characteristics across Europe.
By generating a high-resolution, hierarchically nested dataset, this project addresses various environmental challenges at both regional and European scales, while meeting the increasing demand for spatially detailed environmental data that covers specific regional needs. The resulting data will support applications in land management, soil conservation, and environmental policy, providing a robust framework for both scientific research and practical implementation.
Acknowledgement: K.K, F.M., P.B, were funded by the European Union Horizon Europe Project Soil O-LIVE (Grant No. 101091255). P.S. was funded by the European Union Horizon Europe Project AI4SoilHealth (Grant No. 101086179).
References:
Lehner, B., & Grill, G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15), 2171-2186.
Lin, P., Pan, M., Wood, E. F., Yamazaki, D., & Allen, G. H. (2021). A new vector-based global river network dataset accounting for variable drainage density. Scientific data, 8(1), 28.
Vogt, J., Colombo, R., Paracchini, M. L., de Jager, A., & Soille, P. (2003). CMM river and catchment database. Version, 1, 1-32.
How to cite: Kaffas, K., Matthews, F., Saggau, P., and Borrelli, P.: Nested Catchment Delineation at the European Scale: A Tool for Fine-Scale Environmental Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14166, https://doi.org/10.5194/egusphere-egu25-14166, 2025.
Tilling, a common agricultural practice, is being done excessively on farms leading to about 2.35 billion tons of soil erosion from US croplands annually. This causes soil erosion, soil infertility, carbon release, nutrient runoff, and fertilizer over-usage. This paper evaluates whether optimizing tillage intensity, timing, and fertilizer quantity will address these problems. A convolutional neural network based machine learning model utilizes a camera-captured field image to determine existing tilling intensity on a 7-point scale. This machine learning output, along with soil sensor and external forecast data, flows into a 10-parameter algorithm that determines optimal tilling and fertilizer levels. A fully functional tractor prototype demonstrates the above. A 30-year simulation comparing conventionally-tilled and algorithm-tilled farms showed a reduction in carbon emission by 57%, fertilizer usage by 43%, and runoff by 86% demonstrating the transformative potential of this algorithm. Additionally, a stationary prototype was deployed in 155 farms across 5 countries.
How to cite: Magesh, S.: A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer Optimization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15186, https://doi.org/10.5194/egusphere-egu25-15186, 2025.
Landfill soils are often heavily contaminated with heavy metals (HMs), posing a significant risk of environmental pollution in surrounding areas. Historically, many landfills have been unregulated, poorly constructed, or have exceeded their design lifespans, contributing to their status as major pollution sources. Leachate generation, driven by waste degradation, microbial activity, rainfall infiltration, and groundwater intrusion, exacerbates this issue but is frequently untreated. Anthropogenic activities produce vast quantities of waste, ranging from biodegradable to hazardous materials. In rapidly urbanizing municipalities, particularly in developing countries, the challenges of solid waste management are pressing. Household waste is commonly discarded in unregulated dumpsites, waterways, and public spaces, exacerbating pollution. In contrast, developed nations typically manage municipal solid waste (MSW) more effectively due to advanced waste management infrastructure. This study investigates the classification of landfills based on waste type and evaluates the associated heavy metal (HM) contamination in soils. Representative landfill sites from various countries, including Ghana, Iran, Malaysia, China, South Africa, the Czech Republic, and Tunisia, were analyzed to determine the average concentrations of HMs in surrounding soils. Heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), cobalt (Co), lead (Pb), zinc (Zn), manganese (Mn), iron (Fe), nickel (Ni), aluminum (Al), mercury (Hg), and vanadium (V) were detected in soils adjacent to these landfills. Soil pollution was assessed using several indices. The Ecological Risk Index (Eir) and the summation of the Ecological Risk Index (ERI) quantified individual and total ecological risks, respectively. Anthropogenic pollution was evaluated through the geo-accumulation index (Igeo), pollution index (PI), and integrated pollution index (IPI), providing insights into pollution levels relative to natural elemental content in soils. Factors influencing heavy metals contamination included the proximity of the soil to the landfill, the depth of soil infiltrated by leachate, seasonal variations, and site topography. To address soil contamination, remediation strategies were proposed, including the application of biochar (BC), humic substances (HS), and iron oxide (FO) amendments to immobilize HMs effectively and other remediation techniques to remove heavy metals. These findings contribute to developing sustainable approaches for mitigating heavy metal pollution in landfill-adjacent soils.
How to cite: Shayeghi, S., Moein, B., and Asefi, M.: Heavy Metal Pollution in Soils at Various Landfills Vicinity: A Review Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1920, https://doi.org/10.5194/egusphere-egu25-1920, 2025.
The use of bioinoculants was emerging as an effective strategy to increase soil productivity, particularly in degraded areas where nutrient scarcity limits the potential of livestock systems. Inoculation with plant growth-promoting bacteria (PGPB) provides stimulation functions through the synthesis of phytohormones and available nutrients. Among PGPBs, the genus Azospirillum is known for its biostimulant capacity, while the genus Herbaspirillum includes nitrogen-fixing bacteria. Additionally, microorganisms from the genus Trichoderma are recognized for their ability to solubilize phosphorus. This study evaluates the efficacy of a bacterial consortium combining Azospirillum brasilense (A), Herbaspirillum seropedicae (AH), and Trichoderma haziarum (AT) to determine their potential as biostimulants and biofertilizers in Lolium perenne, a forage species with high nutritional value. The methodological set up included a laboratory phase where the microorganisms' ability to synthesize phytohormones was measured (Indole-3-acetic acid (IAA), cytokinins: trans-zeatin (ZT), trans-zeatin riboside (ZTR), and abscisic acid (ABA)). Also the nitrogen-fixing potential of H. seropedicae was evaluated using the acetylene reduction assay (ARA), and the phosphate-solubilizing capacity of T. harziarum was assessed using a semi-quantitative technique to measure solubilization halos. In a second phase, L. perenne seeds were sown in commercial substrate inoculated with A, AH, AT, and a control treatment (C). The following parameters were recorded: weekly longitudinal growth (WLG), at 30 days, total chlorophyll content (TCh), percentage of coverage (PC), dry weight of aerial biomass (ABiom), and root biomass (RBiom). The results showed detectable levels of growth-regulating hormone synthezed for all the microorganisms evaluated. Additionally, H. seropedicae exhibited nitrogen-fixing activity with a value of (8.33 ± 0.9) nmol C2H4 plant⁻¹ h⁻¹, while T. harziarum displayed a pH indicator shift, indicating a positive result for phosphorus solubilization. The growth parameter data demonstrated early seed emergence in inoculated treatments, with greater grass height (WLG) observed in co-inoculated treatments (C: 5; A: 7; AT: 8.5; AH: 8) cm. The consortia also showed higher root biomass development (RBiom: C: 0.76; A: 0.86; AT: 1.10; AH: 0.95) g and percentage of coverage (PC), with the H. seropedicae treatment standing out (C: 45.5%; A: 62.8%; AT: 60%; AH: 71.3%). In aerial biomass (ABiom, C: 0.89; A: 1.15; AT: 1.21; AH: 1.3 g) and total chlorophyll content (TCh, C: 0.68; A: 0.84; AT: 0.73; AH: 0.75 mg/g). Co-inoculated treatments did not show significant differences. Inoculations improved all the growth parameters studied; however, co-inoculations optimized the benefits, likely due to the combined potential to provide regulatory hormones and nutrient availability functions. In this regard, the AH combination stood out in the PC parameter, possibly due to the nitrogen-fixing ability of H. seropedicae. We conclude that joint inoculations should be further studied to optimize strategies for crop management.
How to cite: Cristóbal-Miguez, A. E. J., Galelli, M. E., Paz-Gonzalez, A., Avram, I. L., García Guzmán, E., Alegre, A. B., Curá, A. J., García, A. R., and Sarti, G. C.: Biostimulant and biofertilizer functions of a bacterial consortia in Lolium perenne, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20998, https://doi.org/10.5194/egusphere-egu25-20998, 2025.
Forest floor (FF) properties, such as thickness, mass and morphology, are critical indicators of forest ecosystem dynamics, shaped by climatic conditions, nutrient deposition and tree species composition. Despite their ecological importance, systematic assessments of the drivers and temporal changes in FF properties across spatial scales remain limited. This knowledge gap hinders the ability to extrapolate site-specific findings to broader regions, crucial for understanding and managing forests under changing environmental conditions.
We focus on identifying the drivers of FF properties and examining how these properties have changed over time at local and regional scales. Using data from inventories, such as the NFI (National Forest Inventory) 3 & 4 and the NFSI (National Forest Soil Inventory) 2 & 3, we investigate relationships between FF properties and key environmental factors, including climate variables, nutrient availability and forest management. This will involve examining spatial patterns and temporal trends in FF properties and understanding how drivers such as climate, nitrogen deposition and shifts in tree species composition influence these patterns. By leveraging statistical and geospatial modeling approaches, the project aims to refine methods for transferring plot-level data to broader scales, ensuring reliable representation of FF variability and trends. The inventory-based results on the factors influencing FF are compared with the process-oriented investigations at the study sites of the Forest Floor project (DFG FOR 5315) in order to be able to interpret the correlations found in the inventory data.
The outcomes of this research will provide crucial insights into how FF properties respond to environmental and management changes, contributing to improved forest monitoring and sustainable management strategies. By bridging the gap between localized observations and large-scale assessments, this work supports national and international efforts to evaluate FF in the context of climate change and other impacts.
How to cite: Rubin, L., Jost, P., and Puhlmann, H.: Upscaling forest floor properties: identifying drivers and assessing temporal changes on a regional scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20206, https://doi.org/10.5194/egusphere-egu25-20206, 2025.
TERRASAFE is a recent initiative that is being co-funded by the European Union and the UK Research and Innovation agency, under the Mission Soil and, more specifically, the call topic “Innovations to prevent and combat desertification” (HORIZON-MISS-2023-SOIL-01-04; grant reference 101157373), having started on 1 June 2024 with a duration of 5 years. TERRASAFE envisages to empower local communities in southern Europe and northern Africa to successfully face the escalating challenges of desertification through the adoption of nature-based, social and technological innovations. TERRASAFE’s vision will be operationalized in 5 pilot areas in Cyprus, Italy, Romania, Spain and Tunisia that strongly contrast in socio–cultural-ecological circumstances. These 5 areas were specifically selected for sharing a high vulnerability to desertification, on the one hand, and, on the other, for representing the 4 main types of desertification, i.e., depopulation, soil degradation (through organic matter loss as well as through salinization), vegetation decline and water scarcity. TERRASAFE’s vision is supported by a transdisciplinary consortium, ranging from universities to SMEs commercially exploiting innovations. TERRASAFE’s vision is implemented through a multi-actor approach that covers all WPs, in particular by setting up 5 partnerships in the 5 pilot areas. In a co-creation process, these partnerships will then: (i) define their visions on building desertification resilience and plan their ensuing TERRASAFE work; (iia) map and analyze past and ongoing desertification, identifying in each pilot area the land-cover type that is the desertification hotspot; (iib) in the case of the Italian pilot area, carry out a narrative analysis of depopulation and the role therein of social innovation, in two contrasting sub-areas; (iii) evaluate and demonstrate innovations for the above-mentioned desertification hotspots, comparing them with current and, in principle, also traditional/organic practices; (iv) elaborate policy recommendation for the wider uptake of the "TERRASAFE-certified" innovations, both within and beyond the pilot areas, taking into account lessons learnt from past and ongoing policies against desertification; (v) share their TERRASAFE’s experience with the partnerships of the other 4 pilot areas as well as other desertification-prone communities and the general public. The consortium will support the 5 partnerships not only by providing harmonized frameworks for each activity but also by providing advice on adapting these frameworks to the partnerships’ specific needs. Finally, the 5 SME partners of TERRASAFE will provide a wide offer of innovative solutions that they will tailor towards the respective desertification hotspots, in close collaboration with the partnerships. Beyond the project itself, TERRASAFE envisages to impact the combat of desertification, both within Europe and across the globe, by promoting the adoption of (part of) its approach by other desertification-prone communities as well as by fostering the widespread implementation of innovations that are both environmentally effective and economically feasible, including through business plans for the SMEs.
How to cite: Keizer, J. J., Asencio, V., Bruggeman, A., Chivers, C., Corticeiro, S., Crisan, V., Fleskens, L., Karbout, N., Loizides, M., Machado, A., Martinez, M., Mills, J., Muro, M., Nyanhongo, G. S., Pedrero Salcedo, F., Petsa, D., Quaranta, G., Salvia, R., Stolte, J., and Stringer, L.: Terrasafe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13943, https://doi.org/10.5194/egusphere-egu25-13943, 2025.
Common practices such as the use of herbicides, petrochemical plastics and excessive tillage are widely used for weed control in both horticultural and fruit crops. The use of these unfriendly environmentally techniques has led researchers around the world to focus their searches on more sustainable alternatives based on a circular economy model. These eco-friendly practices could also be extended to other systems and crops, which would be the case of seedbeds or nursery plants. In this framework, biopolymers and papers can have a proper behavior, although their use fits better to annual herbaceous crops as result of their shorter useful live. For this reason, based on preliminary laboratory tests, we implemented a field trial consisting of the application of hydromulches of different composition and characteristics on a forest tree nursery with newly transplanted seedlings in the open field in Central Spain.
The hydromulches tested were composed of by-products from agriculture and the agri-food industry (wheat straw [S]); camelina pellet [C]; pruning wood from almond [A], elm + walnut [EW], elm + walnut + camelina, [EWC]), mixed with a binder and recycled paper paste, and were applied liquidly on the ground with subsequent solidification. Additionally, two unmulched treatments were considered as control (manual weeding and a no-weeding treatments), in a randomized complete block experiment with three replications.
Periodical measurements relative to weed control (weed number, biomass, soil cover, predominant species) and the degradation of the materials (thickness, puncture resistance, soil cover, etc.) were taken. As preliminary results, and after more than 12 months after their application, all the hydromulches behaved properly, highlighting C as the treatment that best controlled weeds and which suffered a less degradation throughout the period considered, showing it as a good alternative, mainly in organic and sustainable agricultures.
Keywords: hydromulch, weeds, sustainable agriculture, circular economy.
Acknowledgements: PID2020-113865RR-C43 (HMulchCircle)/AEI/10.13039 / 501100011033 (Spanish Ministry of Science and Innovation).
How to cite: Moreno, M. M., Villena, J., López-Corral, T., Atance, C., Peco, J. D., Fernández, M. D. L. S., López-Perales, J. A., Morales-Rodríguez, P. A., and Moreno, C.: Hydromulches in nursery crops: an alternative tool to herbicides for weed control, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15840, https://doi.org/10.5194/egusphere-egu25-15840, 2025.
This study focuses on the estimation of the Winkler Index by several sources in the Oltrepò Pavese (Northern Italy) region, identified as the study area of the NODES project. The Winkler Index, also known as Thermal Sum, is useful for assessing grape ripening: the index, based exclusively on temperature, is traditionally derived from in-situ air temperature measurements.
Within the NODES Project, rather than focusing on a few sites, which could be monitored locally, we are interested in the analysis of large-scale areas. For this reason, we took into consideration global Land Surface Temperature derived from satellite data.
Three data sources are focused, in this paper:
- Air temperature observations from the ARPA monitoring stations (ARPA is the Environmental Protection Agency of the Lombardy Region), which despite their dense temporal granularity have a low spatial resolution (about one station every 92 km2 in the study area).
- Land Surface Temperature (LST) data from MODIS TERRA and AQUA satellite imagery, which provide a pixel-averaged Land Surface Temperature/Emissivity over 8 days with a spatial resolution of 1 km2.
- Daily Copernicus air temperature data, which have a spatial resolution of 0.1° x 0.1° (approximately 11 km x 8 km).
Our main objective was to develop a robust methodology to estimate air temperature from MODIS Land Surface Temperature and then evaluate the applicability of this approach to calculate the Winkler Index, using ARPA temperature data as ground truth for calibration and validation.
MODIS satellite-derived LST data were processed to derive estimated air temperatures via regression-based calibration techniques: the calibrated models were validated using statistical metrics, including root mean square error (RMSE) and p-values, to verify the accuracy and reliability of the estimates.
Lastly, we used Copernicus air temperature data to directly compute the Winkler Index.
The Winkler Index was calculated for the study area over the years 2018-2022, capturing interannual variability and trends influenced by climate conditions.
The Winkler Indices derived from MODIS-calibrated air temperatures showed a strong overall agreement with those obtained from ARPA data, demonstrating the potential of this approach for areas without dense meteorological networks. On the other hand, the Winkler Indices calculated from Copernicus are not always in excellent agreement with the ones evaluated from monitoring stations, considered as true.
The results of this study highlight the feasibility of leveraging satellite-based datasets to complement traditional meteorological observations for agricultural and climate research. By combining MODIS and Copernicus data with in-situ measurements, the study provides a scalable and cost-effective framework to estimate air temperature and calculate the Winkler Index over large spatial extents.
This approach has significant implications beyond viticulture, enabling more precise assessments of regional suitability and supporting adaptive management strategies in the context of climate change
How to cite: Rocca, M. T. and Casella, V. M.: Obtaining the Winkler Index for agricultural applications: a three-fold Assessment involving ground monitored data, MODIS-derived models and Copernicus-supplied data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20915, https://doi.org/10.5194/egusphere-egu25-20915, 2025.
Soils are a key reservoir of global biodiversity, and their fundamental role is to support soil functions and ecosystem services. Biodiversity is part of the complexity and is linked to other parameters that characterise soils, and changes in soil health status influence the provision of goods and services to its beneficiaries. Knowing the biodiversity of a soil in vineyard systems and trying to relate it to other soil characteristics helps to improve soil health, apply the more suitable NBS to reduce land degradation, to improve the ecosystem services provided by the soil and to make viticulture more sustainable.
Six vineyards were selected in Oltrepò Pavese, one of the most important high-quality wines areas in Northern Italy, in different geological contexts soils with different inter-row management techniques: permanent grass cover, tillage and alternate tillage. Soil samples were collected in each vineyard, where a 1.0 m × 2.0 m trench was dug, in order to determine the geological, chemical, agronomic and physical properties. With a multidisciplinary approach, these properties were compared with the fungal, bacterial and arthropod communities.
Environmental DNA (eDNA) was extracted, and bacterial and fungal communities were detected by NGS analysis of 16S and ITS1 DNA barcodes, respectively. Arthropod communities were described by soil biological quality (QBS-ar) and biodiversity indices, after morphological identification of the different biological forms detected.
Inter-row management techniques and geological characteristics affect bacterial, fungal and arthropod communities’ composition. Soil managed with permanent grass cover are in general richer of fungal and bacterial biodiversity. Arthropods seem to be more influenced by soil texture and consequently by the chemical and physical characteristics of the soil than by tillage or grassing in the dry season. A positive correlation was found between Fungi and Bacteria orders, a negative correlation between Arthropods and Fungi orders and a weak and not significant correlation between Arthropods and Bacteria orders. The composition of the bacterial community was radically different in soil under repeated tillage and mineral fertilisation where Bacteroidia, Bacilli, Clostridia and Fusobacteria prevailing, in permanent grass cover soils the classes Alphaproteobacteria, above all, Acidobacteria-6 and Actinobacteria prevailed. Repeated tillage results in a different composition of the prevalent fungal Classes, with a predominance of Malasseziomycetes, which are not present in permanent grass cover soils. Fungi showed a positive correlation with water content, nitrogen and organic matter, while bacteria have a positive correlation with plastic limit and pH.
The results of the study can be used to helps farmers in the selection of the best inter-row management techniques in vineyards in order to reduce the effects of climate change and mitigate the effects of erosion.
How to cite: Reguzzi, M. C., Vercesi, A., Cusaro, C. M., Mazzoni, E., Bertonazzi, M. C., Ganimede, C., Bordoni, M., Maerker, M., Capelli, E., and Meisina, C.: Arthropod, bacterial and fungal communities in vineyards with different soils and management in Oltrepò Pavese (Italy): a multidisciplinary approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19693, https://doi.org/10.5194/egusphere-egu25-19693, 2025.
Inter-row management of vineyards has various implications including on soil stability, and thus geo-
risk[2]. Two prominent classes of inter-row management are permanent grass cover (PGC), and total
tillage (TT), where the inter-row spaces are tilled to keep the soil bare. Which practice is used impacts
soil stability, and very few papers explored large-scale mapping using remotely sensed data[1]. In multi-
spectral acquisitions, reflection from vine leaves and from inter-row responses mix together, challenging
to distinguish vineyard foliage from possible inter-row vegetation. It has been indeed observed that
PGC and TT are likely more distinguishable in winter, when vines shed most of their leaves or are left
bare[1]. This increases the weight of inter-row vegetation in the spectral mix. Based on the above, here
we propose some novel discriminating features by treating the Sentinel-2 time series in winter as the
Bezier curves, which appear to increase separability.
The method has been tested on reference data collected in N-W Italy by a previous project. Data
from 130 and 141 ground truth polygons, representing PGC - and TT -managed vineyards respectively,
were collected from 10 wineries in 2015 and 2022. Sentinel-2 data from November to March with < 20%
cloud cover and ground truth for 2015 were primarily used in this work. Monthly NDVI and NDWI
data were generated using the earliest suitable S-2 acquisition each month, and their sequences of values
were used to form B`ezier curves. 5 features were considered for each index time series: arc length, area
of the bounding box, centroid of the bounding box, curvature, mean and standard deviation.
Different clustering strategies including K-Means, DBSCAN, Mean-shift, Hierarchical, and Gaussian
Mixture Model were employed. Accuracy and adjusted rand index (ARI) were used as performance
metrics. ARI ranges between [−1,1], where higher values mean better separation.
Traditional time series features such as mean, variance, maximum, average slope, ... achieve lower
accuracy levels. It can be observed from results that DBSCAN performs better with the
properties of Bezier curves in terms of accuracy and ARI. DBSCAN seems thus to be more effective at
identifying clusters of varying densities, and it is robust to noise. Hence, the proposed features generate
well-defined density-based clusters that other algorithms struggle to identify. Traditional clustering
algorithms typically assume clusters of elliptical shapes. This high disparity suggests non-spherical or
irregularly shaped clusters, where DBSCAN performs better.
This publication is part of the project NODES which has received funding from the MUR–M4C2 1.5 of PNRR funded
by the European Union-NextGenerationEU(Grant agreement no. ECS00000036).
[1] C. Garau, D. Marzi, M. Bordoni, and F. Dell’Acqua. Satellite detection of inter-row management
practices in a north-italy vineyard: Preliminary results. In IGARSS 2024-2024 IEEE International
Geoscience and Remote Sensing Symposium, pages 4325–4328. IEEE, 2024.
[2] C. Meisina, M. Bordoni, A. Vercesi, M. Maerker, C. Ganimede, M. C. Reguzzi, E. Capelli, E. Mazzoni,
S. Simoni, and E. Gagnarli. Effects of vineyard inter-row management on soils, roots and shallow
landslides probability in the apennines, lombardy, italy. In Proceedings, volume 30, page 41. MDPI,
2019.
How to cite: Dell'Acqua, F. and Mukherjee, J.: Cultivating Insights: Unsupervised Mapping of Inter-row Management inVineyards Using Bezier Curve Properties on Sentinel-2 Time Series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12158, https://doi.org/10.5194/egusphere-egu25-12158, 2025.
Soil erosion is a complex process driven by the interaction between climatic factors, soil properties, topography, vegetation, and land use. It involves detachment, transportation, and deposition of soil particles due to surface runoff and wind, causing severe environmental and economic challenges. To manage erosion, several models ranging from empirical to process-based and hybrid approaches have been developed. For example, the most widely used empirical models is the Revised Universal Soil Loss Equation (RUSLE) which estimates long-term erosion rates but not reliable in short-term assessments. Process-based models, such as the Water Erosion Prediction Project (WEPP) and the European Soil Erosion Model (EUROSEM), simulate physical erosion mechanisms but require extensive data. Hybrid models like the Sediment Delivery Distributed (SEDD) and the Limburg Soil Erosion Model (LISEM) balance usability and mechanistic accuracy but face challenges in data-scarce or complex landscapes.
This study applied a hydraulic Overland Flow (OF) model to the Oltrepò Pavese region in north-western Italy, a geologically and hydrologically diverse area influenced by natural processes and human activities. In particular, the model was applied for a rainfall event with a return period of two year in three representative mountain catchments of the region: Scuropasso, Versa, and Ardivestra, characterised by mild to steep slopes, forested areas, rural settlements and vineyards. The OF model, based on the resolution of the Shallow Water Equations (2D-SWEs) calculates hydrodynamic variables such as flow depth and velocity. Erosion-prone areas were identified through literature empirical equations employed by using the OF model output, incorporating shear stress, stream power, and sediment transport capacity. To validate the OF model, the results were compared to those generated by the RUSLE. The comparative analysis was conducted to assess spatial overlap in erosion-prone areas between the two models. To ensure consistency, minimum erosion thresholds were applied to exclude areas non-relevant to erosion, such as water bodies, rocky areas, infrastructures, and forested zones which showed negligible erosion. The thresholds optimized the alignment of erosion-prone area estimations between the two models, revealing a significant degree of overlap and demonstrating the reliability of the OF model in determine prone-erosion areas. In addition, despite uncertainties in empirical formulations, the hydraulic OF model provided prone-erosion areas by using less input information than RUSLE. This study highlights the potential of integrating hydrodynamic modelling and empirical approaches to improve soil erosion assessments. Future advancements in model dynamics, land-use representation, and climate impact analysis are essential for addressing soil conservation challenges in diverse landscapes.
Acknowledgement: This study is part of the project NODES which has received funding from the Italian Ministry of University and Research (MUR) – M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036).
How to cite: Ravazzolo, D., Persi, E., Fenocchi, A., Petaccia, G., Costabile, P., Costanzo, C., Ennouini, W., and Sibilla, S.: Assessment of soil erosion areas using a process-based overland flow modelling approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5910, https://doi.org/10.5194/egusphere-egu25-5910, 2025.
The optimal determination of the crop water requirements is the probably the most relevant operational variables the farmers managing irrigated lands must set to ensure the optimal use of water for irrigation. The absence of robust crop coefficient estimates constitutes a great limitation for maximizing the performance of agriculture in remote agricultural areas of developing countries. In such areas where despite they usually present optimal environment for cropping activities, technical and knowledge-related barriers strongly limit the development of efficient agriculture and the transference of existing knowledge on the crop coefficient. Seeking to help raise the knowledge on crop coefficient we have studied the local crop coefficient practices in the Sierra Norte of Ecuador area conducting a field research to collect the ongoing practices and farmers´ perceptions. The results from the survey reveal farmers have no information on the crop coefficients and the crop water demands and use rudimentary indicators to implement the irrigation decisions.
How to cite: Zubelzu, S., Chalacán, D., Gómez-Villarino, M. T., and López-Santiago, J.: Farmer´s perception on water crop necessities and crop coefficients in the Sierra Norte of Ecuador. Lessons learnt from field surveys., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4327, https://doi.org/10.5194/egusphere-egu25-4327, 2025.
In recent decades, the scarcity of fresh water has become a significant issue, particularly in arid regions, leading to increased competition for water among agricultural, industrial, and urban users. The widespread limitations on water for agriculture highlight the need for strategies that enhance the efficiency of irrigation water use. Pomegranates and persimmons, although considered minor fruit trees, have gained considerable attention in Spain and worldwide due to their organoleptic characteristics and health benefits. As a result, they present interesting options for diversifying fruit production in the Mediterranean basin, especially since these species are known to tolerate water stress. A three-year study investigated the agronomic responses of both crops to deficit irrigation, specifically focusing on sustained deficit irrigation (SDI) and regulated deficit irrigation (RDI). For pomegranates, RDI - where water applied is reduced to 33% of the total irrigation requirements during the flowering (RDI1) and fruit set (RDI2) periods - has been identified as a viable strategy under water-limited conditions. On the other hand, the tested SDI strategy (applying 50% of the irrigation water requirements throughout the crop cycle) should be reserved for extreme water scarcity situations. For persimmons, the tested SDI strategy, which reduces water applied to 70% of the water requirements, is recommended as it achieves a 30% water saving while maintaining production levels comparable to the control group, thereby enhancing water productivity. In contrast, RDI - where water is reduced during the flowering and fruit setting stages (60% in RDI1 and 40% in RDI2) - yielded intermediate results, providing lower water savings without increasing production relative to the SDI. In conclusion, both studies suggest that pomegranates and persimmons could serve as alternative options to citrus fruits in Valencia, considering their positive productive responses to deficit irrigation.
How to cite: Pascual-Seva, N., Porras, R., Aguilar, J. M., Baixauli, C., and Pascual, B.: Optimizing Irrigation Strategies for Pomegranates and Persimmons in Valencian Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18020, https://doi.org/10.5194/egusphere-egu25-18020, 2025.
Soil salinity is a major environmental challenge that reduces agricultural productivity and degrades soil health, especially in arid and semi-arid regions. Conventional soil salinity assessment methods involve extensive manual labor and are time-consuming In this study, we explored alternative approaches by using a combination of proximal sensing data (i.e., electromagnetic (EM) induction instruments, EM 38-MK2) with two very high-resolution multi-spectral and -sources imagery (i.e., a UAV (Unnamed Aerial Vehicle) and PlanetScope (PS)), topographic attributes, and machine learning methods to achieve field-scale soil salinity mapping under data-scarce conditions. To do so, an initial set of 26 topsoil samples (0–5 cm) were collected from a saline field in the semi-arid area of Sehb El Masjoune in Southern Morocco. and their Electrical conductivity (EC, a proxy of salinity) was determined at the lab. Then, proximal sensed data from EM38 were collected along the same field and measured apparent soil electrical conductivity (ECa – dS/m) was correlated with measured topsoil EC. We used proximal sensing technology to generate 500 EC (electrical conductivity) observations for spatialization, thereby creating a robust dataset for training four machine learning models: partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and an ensemble (stacked) model. Among these models, the RF and ensemble approaches delivered the highest accuracy, with RF outperforming all others. Performance assessments indicated that PlanetScope data achieved R² = 0.91 and RMSE = 3.47, while UAV data showed R² = 0.89 and RMSE = 3.83. These findings underscore that integrating multisource data, even in data-scarce environments, enhances reliability and robustness in soil salinity mapping at the field scale. Our results highlight a cost-effective, high-precision strategy for characterizing saline and sodic soils, offering valuable insights for targeted reclamation and management interventions in arid and semi-arid regions. We conclude that the used approach not only contributes to the scientific understanding of soil salinity dynamics but also provides practical implications for sustainable land management and agricultural planning. The research highlights the potential of combining cutting-edge technology with environmental predictors to address critical global issues.
How to cite: Chindong, M. J., Ouzemou, J.-E., Laamrani, A., El Battay, A., and Chehbouni, A.: Integrating Proximal Sensing, high-resolution Imagery, and Machine Learning for Field-Scale Soil Salinity Mapping in Semi-Arid Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14160, https://doi.org/10.5194/egusphere-egu25-14160, 2025.
Mountain livestock farming relies on meadows, by providing pasture in autumn and spring and providing hay for the winter. They are composed by different plant species from various botanical families, being a biodiverse ecosystem with high resilience.
We can classify them according to their intensification, depending on its fertilization strategy and livestock load. The most intensive meadows are fertilized by inorganic fertilizer and has high livestock load, semi extensive meadows are fertilized by manure and has lower livestock load, whereas extensive meadows are rarely fertilized and has low livestock load.
In this study, 12 meadows from the central Spanish Pyrenees where analysed, 4 meadows of each type for 2 years. Production was higher in semi extensive meadows, due to its organic fertilization, and extensive meadows had the lowest production. Looking at the quality of the hay, intensive and extensive meadows had similar protein content, being significantly higher than in semi extensive meadows. Fiber was higher in extensive meadows and the lowest was found in intensive meadows.
We used Sannon index to address biodiversity. There were significant differences between each meadow type, having extensive meadows the highest levels and intensive meadows the lowest.
High biodiversity can be kept even in high productive meadows, as it’s shown in semi extensive meadows, although they have lower protein content. Intensification practices are thought to increase productivity, with a cost of reducing biodiversity, but this study shows that lower intensive practices can have higher production.
How to cite: Jarne Casasús, A., Reiné Viñales, R., and Usón Murillo, A.: Meadow intensification, a biodiversity approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15982, https://doi.org/10.5194/egusphere-egu25-15982, 2025.
Climate change models indicate that pastoral land use in many parts of Iberia will no longer be feasible from 2050 due to rainfall decreases and desertification, thereby negatively affecting soil functioning, food security and rural livelihoods. Amending agricultural soils with biochar (carbon-based product of biomass pyrolysis) has been shown to potentially increase crop yield, mainly by improving soil pH, soil structure, water storage and exchange. The aim of this study was to quantify how biochar may alter the soil sponge function under current (100% rainfall) and future (50% rainfall.
The collaborative work between ongoing projects SOILCOMBAT, POLLINATE and TRUESOIL, aims to sustainably engineer the soil-water regulation function of Portuguese pasture soils, while minimizing detrimental effects on other soil quality parameters through the use of biochar for soil amendment. Our approach was a random block design field-trial in a real-world scenario at the Quinta da França farm (Terraprima, Portugal), a non-irrigated sown biodiverse pasture on a dystric Cambisol. The four treatments are: control 100% rainfall; control 50% rainfall; biochar (3% gravimetric) 100% rainfall; biochar (3% gravimetric) 50% rainfall; N=20). Biochar-amendment-treatments were applied at 0-20 cm depth keeping the 20-60 cm depth unaltered. It is five times replicated. Plots were equipped with soil climate sensors (volumetric moisture and temperature) recording at six depths, namely -5, -15, -25, -25, -45 & -55 cm depth (N=120).
The first 2 years of the on-going field trial at Quinta da França showed that for the treated 0-20 cm depth with 50% rainfall, the biochar plots kept 15% more moisture than the control ones, while for 100% rainfall conditions, biochar plots kept 23% more moisture. The results for deeper soil water storage (20-60 cm depth) showed that for the 50% rainfall, the biochar plots have 24% less moisture than the control ones, while for natural rainfall conditions, biochar plots have 19% less moisture than the control ones. This could indicate that the 0-20 cm depth biochar-amended soil layer, keep more water in surface (0-20 cm depth) than non-amended surface soil. Seasonal effects will be explored further.
We conclude that biochar amendments improve the soil-water regulation functions of this pasture. The results are expected to contribute to the UN Sustainable Development Goals (SDG) #13 and #15, namely sustainable food production and climate adaptation of pastoral ecosystems, while combating desertification.
Acknowledgements
We acknowledge the Portuguese Foundation for Science and Technology FCT/MCTES for the funding of CESAM (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020) through national funds, as well as of projects SOILCOMBAT (https://doi.org/10.54499/PTDC/EAM-AMB/0474/2020), POLLINATE (https://doi.org/10.54499/PTDC/EAM-AMB/1509/2021), and of authors F. Verheijen (https://doi.org/10.54499/CEECIND/02509/2018/CP1559/CT0004), A.C. Bastos (art. 23º DL57/2016 of 29 Aug amended by DL 57/2017 of 19 July, OE), P. Quinteiro (CEEC/00143/2017), B. Gholamahmadi’s (PhD grant2020.04610.BD), L Simões (PhD grant 2022.09866.BD). We also acknowledge the European Commission Joint Programme SOIL for the funding of project TRUESOIL (https://doi.org/10.54499/EJPSoils/0001/2021) and the La Caixa Foundation in collaboration with CESAM for the funding of A. Jelinčić(LCF/BQ/DI22/11940011).
How to cite: Verheijen, F. G. A., Ana Catarina, B., Zahra, K., Behrouz, G., Marjan, J., Isabel, C., Liliana, S., Antun, J., Vasco, S., Patricia, S., Paula, Q., Tiago, D., and Oscar, G.-P.: Biochar impact on the soil sponge function in sown biodiverse pastures: a 2-year whole soil profile monitoring study under 100% and 50% rainfall, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18882, https://doi.org/10.5194/egusphere-egu25-18882, 2025.
The global carbon cycle is largely controlled by the drawdown of atmospheric CO2 by plants and preservation of organic carbon at the continental margins. In the context of Himalayan rivers, previous studies explored the fate of terrestrial organic carbon (Corg)without much consideration of its preservation within the floodplains. We undertook a novel approach to investigate the spatio-temporal preservation of Corg in floodplain paleosols, which form an intermediate between the source of Corg and their subsequent deposition in the continental margin. Towards this, we sampled five 35 m long sediment cores spanning the entirety of the Ganga River Floodplain (GRF). Carbon isotopic composition of Corg and soil carbonates (SC) (δ13Corg and δ13CSC) and oxygen isotopic composition of SC (δ18OSC) along with soil texture, Al and Fe oxides (Alox and Feox) were used as predictors (n=158) of Corg preservation. The Random Forest Regression (RFR) model with the built-in feature importance tool was used to disentangle the dominant predictor of Corg across all the study sites. Our results suggest that in the upper stretch of GRF, Corg is low and preservation was predominantly controlled by the vegetation type (C3/C4) with grasslands accruing more Corg than forests. In contrast, in the lower stretch of GRF, the preservation was dominantly controlled through the formation of Alox and Feox organo-mineral complexes, with the resultant Corg being one-order higher compared to upper stretches. Previous studies suggested that rapid burial predominantly acted as a major controlling factor on the sustenance of Corg in Bay of Bengal. However, our results along with similar Al/Si vs. Corg correlations within the lower GRF compared to the previously reported values from riverine suspended load and shelf sediments suggest that the floodplains transformed the labile Corg into stable organo mineral aggregates at lower stretch of GRF before it was deposited into the Bay of Bengal. We suggest that protection of Corg in floodplain is an importantstep towards its preservation at continental shelf. In the context of the Himalayan river system and the amount of Corg effectively preserved, the role of floodplains has profound implications for the global carbon cycle.
How to cite: Adhya, S. P. and Sanyal, P.: Organo-mineral interactions in the floodplain govern the stability of buried organic carbon in continental margins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1091, https://doi.org/10.5194/egusphere-egu25-1091, 2025.
