{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.14764568", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:35Z", "type": "Other", "title": "Deliverable 2.3 - Supplementary material : Model input files for the Danube catchment modelling", "description": "This model is part of the toolbox built within the framework of the PROMISCES project (Deliverable D2.3). It contains input files for the catchment model for the Danube developped, results are presented in the deliverable D2.3. Contents of the zip-file: delwaq: Input files and batch files to run the model calculations. The batch file runallpfas.bat takes care of the whole suite of calculations. These consist of: Preparation of the emissions from the various sources in the catchment - on a per substance basis. Calculating the concentration patterns via the results of the hydrological model. Output in the form of human-readable files and netCDF files for easy visualisation. EM: Hydrological model results for the 'emissions' step. substancedata: Substance-specific data for various PFAS's and other PMT compounds wflow_danube_flow: Hydrological model schematisation for the Danube catchment.\u00a0 Input for the wflow hydrological model. WQ: Hydrological model results for the 'water quality' step.", "keywords": ["Water quality", "Emissions", "PFAS", "Catchment", "Modelling"], "contacts": [{"organization": "van Gils, Jos, Meijers, Erwin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14764568"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14764568", "name": "item", "description": "10.5281/zenodo.14764568", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14764568"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-29T00:00:00Z"}}, {"id": "10451/47259", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:58Z", "type": "Journal Article", "created": "2020-06-23", "title": "An Optimized in situ Quantification Method of Leaf H2O2 Unveils Interaction Dynamics of Pathogenic and Beneficial Bacteria in Wheat", "description": "Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for in situ quantification of relative H2O2 concentrations in wheat leaves infected with the pathogenic bacterium Pseudomonas syringae pv. atrofaciens (Psa), either alone or in the presence of the beneficial bacterium Herbaspirillum seropedicae (RAM10). This protocol involved the use of 3-3'diaminobenzidine (DAB) staining method combined with image processing to conduct deconvolution and downstream analysis of the digitalized leaf image. The application of a linear regression model allowed to relate the intensity of the pixels resulting from DAB staining with a given concentration of H2O2. Decreasing H2O2 accumulation patterns were detected at increasing distances from the site of pathogen infection, and H2O2 concentrations were different depending on the bacterial combinations tested. Notably, Psa-challenged plants in presence of RAM10 accumulated less H2O2 in the leaf and showed reduced necrotic symptoms, pointing to a potential role of RAM10 in reducing pathogen-triggered H2O2 levels in young wheat plants.", "keywords": ["biotic interactions", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "color deconvolution", "hydrogen peroxide (H2O2)", "Plant culture", "Plant Science", "3-3\u2032diaminobenzidine (DAB)", "image processing", "SB1-1110"]}, "links": [{"href": "https://repositorio.ulisboa.pt/bitstream/10451/47259/1/Carril%20et%20al%20Front%20Plant%20Sci%202020.pdf"}, {"href": "https://doi.org/10451/47259"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10451/47259", "name": "item", "description": "10451/47259", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10451/47259"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-23T00:00:00Z"}}, {"id": "10.5281/zenodo.14789120", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:35Z", "type": "Report", "title": "Deliverable D2.4 - Guidance document on fate, transport and exposure for PMT's in the environment", "description": "Executive Summary Models are used in exposure assessment for a number of reasons. They can help map the temporal and spatial variability of exposure, exposure pathways and exposure routes, and support risk assessment for water bodies where monitoring is lacking. They can be used to identify sources and pathways responsible for current exposures and to assess the impact of potential future developments of persistent, mobile, and toxic chemicals (PMT) exposures in surface water and groundwater. Such scenario assessment may include changes in PMT use, effects of pollution control measures, accidental spills or climate change. The scope of this document, produced as part of the H2020 PROMISCES project, is to provide guidance for applications of models with a specific focus on model trains for the assessment of exposure to PMTs as part of the predictive risk assessment related to surface and groundwater. This document explains the basic concepts of specific models and how best to use them in modeltrains in the framework of a tiered approach. The intention is to inform users and interested stakeholders about what needs to be considered when using different methods, what is the best use of specific models, what are the best combinations in model trains and what are their current limitations. The guidance document presents (i) \u201cscreening level\u201d models for the assessment of regional exposure of groundwater from soil pollution and for the assessment of general exposure of air, soil and water at local, regional or global scales, (ii) spatial and temporal explicit approaches for the identification of pollution plumes in the soil-groundwater continuum and (iii) model train applications for the catchment \u2013 river \u2013 river bank filtration \u2013 drinking water continuum. Exposure of surface water and groundwater to PMT depends on the use patterns and the environmental fate of the chemicals. Emission, fate and transport models incorporate driving factors into documented algorithms. The extent to which a substance persists in surface water can, for instance, be calculated with the \u201cSimpleBox - Aquatic Persistence Dashboard\u201d, based on its physical-chemical characteristics. The presented approach for deriving generic risk limits for soils shows that, depending on regional variations in geo(hydro)logical conditions, the high mobility of some PFAS could lead to strict requirements for materials applied on soil. For the soil-groundwater continuum, a novel model train is presented which accounts for the main physical and chemical processes controlling the fate and transport of PFAS. For sorption and degradation reactions, several formalisms can be used, allowing one to select the most appropriate according to the PFAS molecular properties and the characteristics of the simulateddomain. The results issued from these modelling applications indicate the key role of correctly identifying the main physical, chemical and biological processes controlling fate and transport of PFAS in the studied domain to build a robust conceptual model. To increase the robustness of the model, a thorough model calibration must be performed, preferably using time seriesmeasurements of the PFAS concentration in the pore solution at different locations of the contaminated site. The results confirm the key role of the unsaturated zone in the transfer and long-term migration of PFAS. Nonlinearity and nonideality of sorption reactions were expected for a broad range of PFAS, suggesting using more complex numerical formalism than linear isotherms. Considering the key role of capillary fringe displacement on PFAS transport in the unsaturated zone, themodel train seems to be very efficient in performing PFAS simulations, as it can explicitly describe water flow and solute transport at the interface between the unsaturated and saturated zones, avoiding the main pitfall encountered in other numerical approaches. The combination of stand-alone models in model trains expands the scope that can be covered in the context of a catchment \u2013 river \u2013 riverbank filtration \u2013 drinking water continuum for exposure assessment of surface waters and bank filtered drinking water. Model trains can combine individual models either in a complementary way or in a sequence. A complementary combination may either compare models of different complexity to find out which level of complexity (and associated effort) is needed to answer which questions, or may compare different models with their different strengths and weaknesses in parallel to assess uncertainties and/or use models for scenario evaluation according to their specific capabilities. A sequential combination facilitates a broader application in terms of content and at different spatial resolutions. Clearly defined interfaces are essential for a successful implementation. Examples of model trains for selected PFAS are presented for the catchment-river interaction in the urban context of the Berlin case and for the whole catchment \u2013 river \u2013 riverbank filtration \u2013 drinking water continuum on the scale of the Upper Danube Basin. The Berlin case demonstrates the application of the sequential model train by combining a city emission model with a city surface water fate and transport model to assess the resulting exposure to PFAS in the city surface waters. The Danube case demonstrates the application of a sequential model train for exposure assessment of bank filtered drinking water by combining large-scale catchment-scale emission models with different types of bank filtration fate and transport models for specific locations in the catchment. In addition, it also demonstrates complementary application by comparing emission models with different strengths and weaknesses for the assessment of multiple scenarios on the catchment scale and different levels of complexity for the fate and transport modelling of bank filtration. The model train has been successfully applied for 10 different PFAS-substances including the assessment of a large range of scenarios. Current limitations for exposure assessment of PFAS at river basin scale require improvement in scientific understanding as well as additional efforts in administrative data collection and inventory development. Current results of the exposure assessment show the very high relevance of legacy pollution from use of fire-fighting foams or from old municipal landfills. On the administrative level, there is a strong need for improved identification and harmonized inventorying of contaminated sites at national and international (EU) level. The lack of robust, openly available information on production, import-export and therefore use volumes of PFAS at national and EU level is strongly hampering exposure assessment. A major effort is urgently needed to provide this information, as it is decisive for a sound environmental exposure assessment, not only for surface water and groundwater. In regard to scientific advances, there is a need for more and better understanding of the extent of local groundwater pollution, particularly due to the application of fire-fighting foams or to the presence of municipal landfills. Further improvement of the scientific knowledge about the fate of PFAS in the environment, including their partitioning between different phases (air,water, solids) and the transformation of the so called \u201cprecursors\u201d into stable \u201cend-products\u201d like PFOA, PFOS and short-chain substances is needed to enlarge the number of PFAS that can be included into the exposure assessment. A reproducible and standardised analytical parameter for \u201ctotal PFAS\u201d or even \u201ctotal toxicity of PFAS\u201d would be needed to address all relevant PFAS in a combined way as it is a focus of Workpackage 1 of the H2020 PROMISCES project (Togola et al. 2024; Behnisch et al. 2024).", "keywords": ["Groundwater/chemistry", "Groundwater pollution", "emission modelling", "Surface water management", "Groundwater quality", "Per- and polyfluorinated substances (PFAS)", "environmental transport modelling", "Surface water", "environmental fate modelling", "Groundwater endangering"], "contacts": [{"organization": "Zessner, Matthias, Baldwin, Dwight, del Val Alonso, Laura, Derx, Julia, Devau, Nicolas, Janssen, Gijs, Jou Claus, S\u00f2nia, Kittlaus, Steffen, Knoche, Franziska, Liu, Meiqi, Markus, Arjen, Valstar, Johan, Meesters, Joris, Meijers, Erwin, Obeid, Ali A.A., Oudega, Thomas James, Pathak, Devanshi, Sprenger, Christoph, van Gils, Jos, Wicke, Daniel, Wintersen, Arjen, Zhiteneva, Veronika, Groot, Hans,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14789120"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14789120", "name": "item", "description": "10.5281/zenodo.14789120", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14789120"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-28T00:00:00Z"}}, {"id": "10.5281/zenodo.14825718", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:36Z", "type": "Dataset", "title": "Raw data for the manuscript: Conventional and biodegradable agricultural microplastics affecting soil properties and microbial functions across a European pedoclimatic gradient", "description": "Clay, Silt, Fine sand, Coarse sand, dry matter, bulk density, pH, electric conductivity, Water extractable organic carbon, Water extractable total nitrogen, Water extractable organic nitrogen,\u00a0Ammonium, Nitrate, Phosphate, Soil organic carbon, Soil total nitrogen, Potential ammonium oxidation, Potential ammonification, Basal respiration, Substrate-induced respiration, Remaining mass of green and black tea litter, Ergosterol concentration, Soil aggregation, Bact and fungi Chao, Bact and fungi Shannon, Bact and fungi InvSimpson, CH4, CO2, N2O", "keywords": ["Microbial community composition", "Microbial activity", "Greenhouse gases", "Teabag index", "Agricultural plastics", "eDNA", "Field experiment"], "contacts": [{"organization": "Smidova, Klara, Hofman, Jakub, Velmala, Sannakajsa, Soinne, Helena, Kim, Shin Woong, Tirroniemi, Jyri, Selonen, Salla,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14825718"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14825718", "name": "item", "description": "10.5281/zenodo.14825718", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14825718"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-06T00:00:00Z"}}, {"id": "10.5281/zenodo.14845589", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:36Z", "type": "Dataset", "title": "Data from: Comparison and evaluation of sampling and eDNA metabarcoding protocols to assess soil biodiversity in Belgian LUCAS Biopoints", "description": "Environmental DNA (eDNA) metabarcoding is emerging as a novel tool for monitoring soil biodiversity. Soil biodiversity, critical for soil health and ecosystem services, is currently under-monitored due to the lack of standardized and efficient methods. We assessed whether refinements to sampling and molecular protocols could improve soil biodiversity detection and monitoring.\u00a0Comparing the 2018 LUCAS soil biodiversity protocols with newly developed national methods, we tested sampling topsoil (0-10 cm) versus deeper layers, larger soil sample sizes for DNA-extraction, taking more subsamples for composite soil samples, and alternative primer sets across 9 Belgian Biopoints included in the LUCAS 2022 survey. The results suggest that significantly more species can be detected in upper soil layers, including the forest floor, while the diversity of taxa and eDNA in the 10\u201330 cm soil layer is insufficient for annelids and arthropods to serve as indicators of ecological change. Additionally, comparison of the universal eukaryotic primers (18S) with primer sets tailored to soil mesofauna and macrofauna, showed that universal 18S primers provide limited resolution for Collembola and Annelida. Overall, the analyses suggest that vertical soil stratification (with two sampling depths) has a greater influence on the captured diversity of soil mesofauna and macrofauna than the number of subsamples, and that the highest diversity is recovered when surface sampling (0\u201310 cm topsoil and forest floor) is combined with a greater number of subsamples and a larger sampled area. With refinement and standardization, eDNA metabarcoding, combined with optimized sampling protocols, could become a powerful and efficient tool for monitoring soil biodiversity in European soils. Description of the files This dataset includes interactive Krona taxonomy charts to visually summarize the diversity and relative read abundance of detected taxa across sampling locations and protocols. Each ring in the chart represents a taxonomic level, with the relative width of segments reflecting the proportion of reads assigned to specific taxa at that level. These charts enable exploration of taxonomic composition and allow for comparisons between the different sampled locations, sampling protocols tested, and primer sets tested. All krona charts were made in R using psadd::plot_krona. To correct for uneven sequencing depth per sample, datasets were rarefied using a random subsampling method to 27913, 31655, 1856, 19728, and 19632 reads for Annelida (Olig01), Collembola (Coll01), Fungi (ITS9mun/ITS4ngsUni), protists (18S), and Archaea (SSU1ArF/SSU1000ArR) respectively. Fauna datasets that are subsets of the total data recovered by a primer set designed to target many different phyla (e.g. 18S) were not rarefied prior to generating the krona plots. ejp_soil_annelida_olig01_27913.html contains the interactive taxonomy charts for Annelida. The data was generated using the group-specific Olig01 primer set and rarefied to 27,913 reads per sample. ejp_soil_collembola_coll01_31655.html contains the interactive taxonomy charts for Collembola. The data was generated using the group-specific Coll01 primer set and rarefied to 31,655 reads per sample. ejp_soil_arthropoda_inse01.html contains the interactive taxonomy charts for Arthropoda (Insecta, Arachnida, Chilopoda, Diplura, and Malacostraca). The data was generated using the Inse01 primer set. ejp_soil_fungi_its9mun_its4ngsuni_1856.html contains the interactive taxonomy charts for Fungi. The data was generated using the ITS9mun and ITS4ngsUni primer set and rarefied to 1,856 reads per sample. ejp_soil_protists_18s_19728.html contains the interactive taxonomy charts for protists. The data was generated using the eukaryotic 18S primer set and rarefied to 19,728 reads per sample. ejp_soil_archaea_ssu1arf_ssu1000arr_19632.html contains the interactive taxonomy charts for Archaea. The data was generated using the SSU1ArF and SSU1000ArR primer set and rarefied to 19,632 reads per sample. ejp_soil_annelida_18s.html contains the interactive taxonomy charts for Annelida. The data was generated using the eukaryotic 18S primer set. ejp_soil_collembola_18s.html contains the interactive taxonomy charts for Collembola. The data was generated using the eukaryotic 18S primer set. ejp_soil_arthropoda_18s.html contains the interactive taxonomy charts for Arthropoda. The data was generated using the eukaryotic 18S primer set. ejp_soil_metadata.csv contains metadata for the samples in this study. It includes information about the sampling locations, the sampling protocols used, the sampling depth (cm), land use type, EUNIS habitat classification, and the LUCAS-ID for each sample.", "keywords": ["soil monitoring", "metabarcoding", "LUCAS", "soil biodiversity", "eDNA"], "contacts": [{"organization": "Lambrechts, Sam, Deflem, Io Sarah, Sensalari, Cecilia, De Backer, Silke, De Beer, Berdien, Neyrinck, Sabrina, De Vos, Bruno,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14845589"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14845589", "name": "item", "description": "10.5281/zenodo.14845589", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14845589"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-10T00:00:00Z"}}, {"id": "20.500.11850/542333", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:42Z", "type": "Journal Article", "created": "2022-03-09", "title": "Improving Soil Resource Uptake by Plants Through Capitalizing on Synergies Between Root Architecture and Anatomy and Root-Associated Microorganisms", "description": "

Root architectural and anatomical phenotypes are highly diverse. Specific root phenotypes can be associated with better plant growth under low nutrient and water availability. Therefore, root ideotypes have been proposed as breeding targets for more stress-resilient and resource-efficient crops. For example, root phenotypes that correspond to the Topsoil Foraging ideotype are associated with better plant growth under suboptimal phosphorus availability, and root phenotypes that correspond to the Steep, Cheap and Deep ideotype are linked to better performance under suboptimal availability of nitrogen and water. We propose that natural variation in root phenotypes translates into a diversity of different niches for microbial associations in the rhizosphere, rhizoplane and root cortex, and that microbial traits could have synergistic effects with the beneficial effect of specific root phenotypes. Oxygen and water content, carbon rhizodeposition, nutrient availability, and root surface area are all factors that are modified by root anatomy and architecture and determine the structure and function of the associated microbial communities. Recent research results indicate that root characteristics that may modify microbial communities associated with maize include aerenchyma, rooting angle, root hairs, and lateral root branching density. Therefore, the selection of root phenotypes linked to better plant growth under specific edaphic conditions should be accompanied by investigating and selecting microbial partners better adapted to each set of conditions created by the corresponding root phenotype. Microbial traits such as nitrogen transformation, phosphorus solubilization, and water retention could have synergistic effects when correctly matched with promising plant root ideotypes for improved nutrient and water capture. We propose that elucidation of the interactive effects of root phenotypes and microbial functions on plant nutrient and water uptake offers new opportunities to increase crop yields and agroecosystem sustainability. -p dpeatdecomposition < dpeatdecomposition-backup-2025-02-24.sql Here, is the database user name. 6.3 R interface The R package \u2018dpeatdecomposition\u2019 (Teickner and Knorr 2024) provides an R interface to the database, based on the packages \u2018RMariaDB\u2019 (M\u00fcller et al. 2021), and \u2018dm\u2019 (Schieferdecker, M\u00fcller, and Bergant 2022). 7 Citation If you use data from the Peat Decomposition Database, cite the database and each of the original data sources you use. Bibliographic information on each data source are stored in table citations and linked to datasets via table citations_to_datasets. The database can be cited as: Teickner, Henning and Klaus-Holger Knorr. 2024. \u201cThe Peatland Decomposition Database.\u201d Zenodo. https://doi.org/10.5281/zenodo.11276065. Bibtex entries for each dataset can also be obtained using the \u2018dpeatdecomposition\u2019 package: # connect to database con <- RMariaDB::dbConnect( drv = RMariaDB::MariaDB(), dbname = 'dpeatdecomposition', default.file = '~/my.cnf' ) # get database as dm object dm_dpeatdecomposition <- dpeatdecomposition::dp_get_dm(con, learn_keys = TRUE) # extract bibtex entries dm_dpeatdecomposition |> dm::dm_zoom_to(datasets) |> dm::left_join(citations_to_datasets, by = 'id_dataset') |> dm::left_join(citations, by = 'id_citation') |> dm::pull_tbl() |> as.data.frame() # disconnect RMariaDB::dbDisconnect(con) A full list of references for the individual datasets is provided in Tab. 3. Table 3: Sources for each dataset in the Peatland Decomposition Database. id_dataset Source 1 Farrish and Grigal (1985) 2 Bartsch and Moore (1985) 3 Farrish and Grigal (1988) 4 Vitt (1990) 5 Hogg, Lieffers, and Wein (1992) 6 Sanger, Billett, and Cresser (1994) 7 Hiroki and Watanabe (1996) 8 Szumigalski and Bayley (1996) 9 Prevost, Belleau, and Plamondon (1997) 10 Arp, Cooper, and Stednick (1999) 11 Robbert A. Scheffer and Aerts (2000) 12 R. A. Scheffer, Van Logtestijn, and Verhoeven (2001) 13 Limpens and Berendse (2003) 14 Waddington, Rochefort, and Campeau (2003) 15 Asada, Warner, and Banner (2004) 16 Thormann, Bayley, and Currah (2001) 17 Trinder, Johnson, and Artz (2008) 18 Breeuwer et al. (2008) 19 Trinder, Johnson, and Artz (2009) 20 Bragazza and Iacumin (2009) 21 Hoorens, Stroetenga, and Aerts (2010) 22 Strakov\u00e1 et al. (2010) 22 Strakov\u00e1 et al. (2012) 23 Orwin and Ostle (2012) 24 Lieffers (1988) 25 Manninen et al. (2016) 26 Johnson and Damman (1991) 27 Bengtsson, Rydin, and H\u00e1jek (2018a) 27 Bengtsson, Rydin, and H\u00e1jek (2018b) 28 Asada and Warner (2005) 29 Bengtsson, Granath, and Rydin (2017) 29 Bengtsson, Granath, and Rydin (2016) 30 Hagemann and Moroni (2015) 30 Hagemann and Moroni (2016) 31 B. Piatkowski et al. (2021) 31 B. T. Piatkowski et al. (2021) 32 M\u00e4kil\u00e4 et al. (2018) 33 Golovatskaya and Nikonova (2017) 34 Golovatskaya and Nikonova (2017) 8 Acknowledgements Development of this database was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant no. KN 929/23-1 to Klaus-Holger Knorr and grant no. PE 1632/18-1 to Edzer Pebesma. References Arp, Christopher D., David J. Cooper, and John D. Stednick. 1999. \u201cThe Effects of Acid Rock Drainage on Carex Aquatilis Leaf Litter Decomposition in Rocky Mountain Fens.\u201d Wetlands 19 (3): 665\u201374. https://doi.org/10.1007/BF03161703. Asada, Taro, and Barry G. Warner. 2005. \u201cSurface Peat Mass and Carbon Balance in a Hypermaritime Peatland.\u201d Soil Science Society of America Journal 69 (2): 549\u201362. https://doi.org/10.2136/sssaj2005.0549. Asada, Taro, Barry G Warner, and Allen Banner. 2004. \u201cSphagnum Invasion After Clear-Cutting and Excavator Mounding in a Hypermaritime Forest of British Columbia.\u201d Canadian Journal of Forest Research 34 (8): 1730\u201346. https://doi.org/10.1139/x04-042. Bartsch, I., and T. R. Moore. 1985. \u201cA Preliminary Investigation of Primary Production and Decomposition in Four Peatlands Near Schefferville, Qu\u00e9bec.\u201d Canadian Journal of Botany 63 (7): 1241\u201348. https://doi.org/10.1139/b85-171. Bengtsson, Fia, Gustaf Granath, and H\u00e5kan Rydin. 2016. \u201cPhotosynthesis, Growth, and Decay Traits in Sphagnum \u2013 a Multispecies Comparison.\u201d Ecology and Evolution 6 (10): 3325\u201341. https://doi.org/10.1002/ece3.2119. \u2014\u2014\u2014. 2017. \u201cData from: Photosynthesis, Growth, and Decay Traits in Sphagnum \u2013 a Multispecies Comparison.\u201d Dryad. https://doi.org/10.5061/DRYAD.62054. Bengtsson, Fia, H\u00e5kan Rydin, and Tom\u00e1\u0161 H\u00e1jek. 2018a. \u201cData from: Biochemical Determinants of Litter Quality in 15 Species of Sphagnum.\u201d Dryad. https://doi.org/10.5061/DRYAD.4F8D2. \u2014\u2014\u2014. 2018b. \u201cBiochemical Determinants of Litter Quality in 15 Species of Sphagnum.\u201d Plant and Soil 425 (1-2): 161\u201376. https://doi.org/10.1007/s11104-018-3579-8. Bona, Kelly Ann, Arlene Hilger, Magdalena Burgess, Nicole Wozney, and Cindy Shaw. 2018. \u201cA Peatland Productivity and Decomposition Parameter Database.\u201d Ecology 99 (10): 2406\u20136. https://doi.org/10.1002/ecy.2462. Bragazza, Luca, and Paola Iacumin. 2009. \u201cSeasonal Variation in Carbon Isotopic Composition of Bog Plant Litter During 3 Years of Field Decomposition.\u201d Biology and Fertility of Soils 46 (1): 73\u201377. https://doi.org/10.1007/s00374-009-0406-7. Breeuwer, Angela, Monique Heijmans, Bjorn J. M. Robroek, Juul Limpens, and Frank Berendse. 2008. \u201cThe Effect of Increased Temperature and Nitrogen Deposition on Decomposition in Bogs.\u201d Oikos 117 (8): 1258\u201368. https://doi.org/10.1111/j.0030-1299.2008.16518.x. Farrish, K. W., and D. F. Grigal. 1985. \u201cMass Loss in a Forested Bog: Relation to Hummock and Hollow Microrelief.\u201d Canadian Journal of Soil Science 65 (2): 375\u201378. https://doi.org/10.4141/cjss85-042. \u2014\u2014\u2014. 1988. \u201cDecomposition in an Omrotrophic Bog and a Minerotrophic Fen in Minnesota.\u201d Soil Science 145 (5): 353\u201358. https://doi.org/10.1097/00010694-198805000-00005. Golovatskaya, E. A., and L. G. Nikonova. 2017. \u201cThe Influence of the Bog Water Level on the Transformation of Sphagnum Mosses in Peat Soils of Oligotrophic Bogs.\u201d Eurasian Soil Science 50 (5): 580\u201388. https://doi.org/10.1134/S1064229317030036. Hagemann, Ulrike, and Martin T. Moroni. 2015. \u201cMoss and Lichen Decomposition in Old-Growth and Harvested High-Boreal Forests Estimated Using the Litterbag and Minicontainer Methods.\u201d Soil Biology and Biochemistry 87 (August): 10\u201324. https://doi.org/10.1016/j.soilbio.2015.04.002. \u2014\u2014\u2014. 2016. \u201cData on Moss and Lichen Decomposition Rates and Nutrient Loss from Old-Growth and Harvested High-Boreal Forests Estimated Using the Litterbag and Minicontainer Methods.\u201d Leibniz-Zentrum f\u00fcr Agrarlandschaftsforschung (ZALF) e.V. https://doi.org/10.4228/ZALF.2007.290. Hiroki, Mikiya, and Makoto M. Watanabe. 1996. \u201cMicrobial Community and Rate of Cellulose Decomposition in Peat Soils in a Mire.\u201d Soil Science and Plant Nutrition 42 (4): 893\u2013903. https://doi.org/10.1080/00380768.1996.10416636. Hogg, Edward H., Victor J. Lieffers, and Ross W. Wein. 1992. \u201cPotential Carbon Losses from Peat Profiles: Effects of Temperature, Drought Cycles, and Fire.\u201d Ecological Applications 2 (3): 298\u2013306. https://doi.org/10.2307/1941863. Hoorens, Bart, Martin Stroetenga, and Rien Aerts. 2010. \u201cLitter Mixture Interactions at the Level of Plant Functional Types Are Additive.\u201d Ecosystems 13 (1): 90\u201398. https://doi.org/10.1007/s10021-009-9301-1. Johnson, Loretta C., and Antoni W. H. Damman. 1991. \u201cSpecies-Controlled Sphagnum Decay on a South Swedish Raised Bog.\u201d Oikos 61 (2): 234. https://doi.org/10.2307/3545341. Jones, Matthew, Margaret O\u2019Brien, Bryce Mecum, Carl Boettiger, Mark Schildhauer, Mitchell Maier, Timothy Whiteaker, Stevan Earl, and Steven Chong. 2019. \u201cEcological Metadata Language Version 2.2.0.\u201d KNB Data Repository. https://doi.org/10.5063/f11834t2. Lieffers, V. J. 1988. \u201cSphagnum and Cellulose Decomosition in Drained and Natural Areas of an Alberta Peatland.\u201d Canadian Journal of Soil Science 68 (4): 755\u201361. https://doi.org/10.4141/cjss88-073. Limpens, Juul, and Frank Berendse. 2003. \u201cHow Litter Quality Affects Mass Loss and N Loss from Decomposing Sphagnum.\u201d Oikos 103 (3): 537\u201347. https://doi.org/10.1034/j.1600-0706.2003.12707.x. M\u00e4kil\u00e4, M., H. S\u00e4\u00e4vuori, A. Grundstr\u00f6m, and T. Suomi. 2018. \u201cSphagnum Decay Patterns and Bog Microtopography in South-Eastern Finland.\u201d Mires and Peat, no. 21 (July): 1\u201312. https://doi.org/10.19189/MaP.2017.OMB.283. Manninen, S., S. Kivim\u00e4ki, I. D. Leith, S. R. Leeson, and L. J. Sheppard. 2016. \u201cNitrogen Deposition Does Not Enhance Sphagnum Decomposition.\u201d Science of The Total Environment 571 (November): 314\u201322. https://doi.org/10.1016/j.scitotenv.2016.07.152. M\u00fcller, Kirill, Jeroen Ooms, David James, Saikat DebRoy, Hadley Wickham, and Jeffrey Horner. 2021. \u201cRMariaDB: Database Interface and \u2019MariaDB\u2019 Driver.\u201d Orwin, Kate H., and Nicholas J. Ostle. 2012. \u201cMoss Species Effects on Peatland Carbon Cycling After Fire: Moss Species Effects on C Cycling After Fire.\u201d Functional Ecology 26 (4): 829\u201336. https://doi.org/10.1111/j.1365-2435.2012.01991.x. Piatkowski, Bryan T., Joseph B. Yavitt, Merritt R. Turetsky, and A. Jonathan Shaw. 2021. \u201cNatural Selection on a Carbon Cycling Trait Drives Ecosystem Engineering by Sphagnum (Peat Moss).\u201d Proceedings of the Royal Society B: Biological Sciences 288 (1957): 20210609. https://doi.org/10.1098/rspb.2021.0609. Piatkowski, Bryan, Joseph B. Yavitt, Merritt Turetsky, and A. Jonathan Shaw. 2021. \u201cOnline Data for 'Natural Selection on a Carbon Cycling Trait Drives Ecosystem Engineering by Sphagnum (Peat Moss).',\u201d August. https://doi.org/10.6084/m9.figshare.14109725.v2. Pick, Joel L., Shinichi Nakagawa, and Daniel W. A. Noble. 2018. \u201cReproducible, Flexible and High-Throughput Data Extraction from Primary Literature: The metaDigitise R Package.\u201d https://doi.org/10.1101/247775. Prevost, Marcel, Pierre Belleau, and Andr\u00e9 P. Plamondon. 1997. \u201cSubstrate Conditions in a Treed Peatland: Responses to Drainage.\u201d \u00c9coscience 4 (4): 543\u201354. https://doi.org/10.1080/11956860.1997.11682434. R Core Team. 2022. R: A Language and Environment for Statistical Computing. Manual. Vienna, Austria: R Foundation for Statistical Computing. Sanger, L. J., M. F. Billett, and M. S. Cresser. 1994. \u201cThe Effects of Acidity on Carbon Fluxes from Ombrotrophic Peat.\u201d Chemistry and Ecology 8 (4): 249\u201364. https://doi.org/10.1080/02757549408038552. Scheffer, R. A., R. S. P Van Logtestijn, and J. T. A. Verhoeven. 2001. \u201cDecomposition of Carex and Sphagnum Litter in Two Mesotrophic Fens Differing in Dominant Plant Species.\u201d Oikos 92 (1): 44\u201354. https://doi.org/10.1034/j.1600-0706.2001.920106.x. Scheffer, Robbert A., and Rien Aerts. 2000. \u201cRoot Decomposition and Soil Nutrient and Carbon Cycling in Two Temperate Fen Ecosystems.\u201d Oikos 91 (3): 541\u201349. https://doi.org/10.1034/j.1600-0706.2000.910316.x. Schieferdecker, Tobias, Kirill M\u00fcller, and Darko Bergant. 2022. \u201cdm: Relational Data Models.\u201d Strakov\u00e1, Petra, Jani Anttila, Peter Spetz, Veikko Kitunen, Tarja Tapanila, and Raija Laiho. 2010. \u201cLitter Quality and Its Response to Water Level Drawdown in Boreal Peatlands at Plant Species and Community Level.\u201d Plant and Soil 335 (1-2): 501\u201320. https://doi.org/10.1007/s11104-010-0447-6. Strakov\u00e1, Petra, Timo Penttil\u00e4, Jukka Laine, and Raija Laiho. 2012. \u201cDisentangling Direct and Indirect Effects of Water Table Drawdown on Above- and Belowground Plant Litter Decomposition: Consequences for Accumulation of Organic Matter in Boreal Peatlands.\u201d Global Change Biology 18 (1): 322\u201335. https://doi.org/10.1111/j.1365-2486.2011.02503.x. Szumigalski, Anthony R., and Suzanne E. Bayley. 1996. \u201cDecomposition Along a Bog to Rich Fen Gradient in Central Alberta, Canada.\u201d Canadian Journal of Botany 74 (4): 573\u201381. https://doi.org/10.1139/b96-073. Teickner, Henning, and Klaus-Holger Knorr. 2024. \u201cdpeatdecomposition: R Interface to the Peatland Decomposition Database.\u201d Thormann, Markus N, Suzanne E Bayley, and Randolph S Currah. 2001. \u201cComparison of Decomposition of Belowground and Aboveground Plant Litters in Peatlands of Boreal Alberta, Canada.\u201d Canadian Journal of Botany 79 (1): 9\u201322. https://doi.org/10.1139/b00-138. Trinder, Clare J., David Johnson, and Rebekka R. E. Artz. 2008. \u201cInteractions Among Fungal Community Structure, Litter Decomposition and Depth of Water Table in a Cutover Peatland.\u201d FEMS Microbiology Ecology 64 (3): 433\u201348. https://doi.org/10.1111/j.1574-6941.2008.00487.x. \u2014\u2014\u2014. 2009. \u201cLitter Type, but Not Plant Cover, Regulates Initial Litter Decomposition and Fungal Community Structure in a Recolonising Cutover Peatland.\u201d Soil Biology and Biochemistry 41 (3): 651\u201355. https://doi.org/10.1016/j.soilbio.2008.12.006. Vitt, Dale H. 1990. \u201cGrowth and Production Dynamics of Boreal Mosses over Climatic, Chemical and Topographic Gradients.\u201d Botanical Journal of the Linnean Society 104 (1-3): 35\u201359. https://doi.org/10.1111/j.1095-8339.1990.tb02210.x. Waddington, J. M., L. Rochefort, and S. Campeau. 2003. \u201cSphagnum Production and Decomposition in a Restored Cutover Peatland.\u201d Wetlands Ecology and Management 11 (1): 85\u201395. https://doi.org/10.1023/A:1022009621693.", "keywords": ["Databases", "Carex", "Sphagnum", "decomposition", "litterbag", "northern peatland", "peatland"], "contacts": [{"organization": "Teickner, Henning, Knorr, Klaus-Holger,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14917034"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14917034", "name": "item", "description": "10.5281/zenodo.14917034", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14917034"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-24T00:00:00Z"}}, {"id": "10.5281/zenodo.14926032", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:40Z", "type": "Dataset", "title": "DATASET: Climatic and edaphic drivers of soil organic carbon and pyrogenic carbon stocks across elevation and disturbance gradients in Colombian Andean forests", "description": "The dataset contains information on: entry variables in column: soil pyrogenic carbon PyC (Org) (Mg/ha), PyC (mg cm-3), NDVI(Nrmalized Difference Vegetation Index), Slope (degrees), AMT (Annual Mean Temperature in degrees Celsius), AP (Annual precipitation in mm), pH ( measure of soil acidity or alkalinity), P_available ( phosphorus available, in mg/kg), Tot_ p (Total phosphorus, in mg/kg), sand (%), clay (%), silt (%), Ca_ex (exchangeable calcium, in meq/100g), Mg_ex (exchangeable magnesium, in meq/100g), K_ex (exchangeable potassium, in meq/100g), and Na_ex (exchangeable sodium, in meq/100g). Disturbance (entries by row: low, m\u00e9dium, high, very high); Zone (entries by row: high Andes (h_And), m\u00e9dium Andes (m_And), and low Andes (l_And); and \u00a0Depth (cm), entries by row: 0-5 cm, 20-30 cm, 30-50 cm and 50-100 cm", "keywords": ["elevation gradients", "anthropogenic disturbance", "tropical ecosystem", "Agrosilvopastural systems", "mean annual temperature", " pyrogenic carbon"], "contacts": [{"organization": "Montes-Pulido, Carmen R, Bird, Michael I., Serrano, Julieth, Quesada, Carlos, Feldpausch, Ted R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14926032"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14926032", "name": "item", "description": "10.5281/zenodo.14926032", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14926032"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-21T00:00:00Z"}}, {"id": "10.5281/zenodo.15017580", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:42Z", "type": "Report", "title": "Analysis of tomato sepal fungal infection using hyperspectral imaging and deep learning", "description": "Abstract of the research on detection of fungal infection on tomato sepals using convolutional neural networks, presented at the WHISPERS conference in Helsinki, Finland, 2024.", "keywords": ["Hyperspectral", "Infection detection", "Deep learning", "Classification", "Imaging"], "contacts": [{"organization": "Filipovi\u0107, Vladan, Grbovi\u0107, \u017deljana, Chauhan, Aneesh, de Villiers, Hendrik, Panic, Marko, Brdar, Sanja,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15017580"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15017580", "name": "item", "description": "10.5281/zenodo.15017580", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15017580"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-09T00:00:00Z"}}, {"id": "10.5281/zenodo.14945573", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:40Z", "type": "Report", "title": "Deliverable D1.5 \u2013 Set of novel QSAR models/grouping/read-across and in vitro bioassay approaches predicting relevant toxicological endpoints for PFAS/PM(T) chemicals", "description": "The deliverable is a profiling of activities for pre-selected single and complex industrial mixtures of PFAS/PM(T)s with in vitro bioassay testing and QSAR modeling for a selection of most relevant toxicological endpoints. \u00a0A set of novel in silico and in vitro methods have been developed and applied for the large group of PFAS/PM(T)s. The in vitro toxicity profiling followed a stepwise approach, by first testing the potential reference compounds PFOA and PFOS as well as industrial mixtures (e.g., GenX and ADONA) with a wide range of in silico and in vitro methods (see for an overview in Annex, Table A1). Based on these initial results, the most suitable in vitro toxicity endpoints (see table 7 and table 10) have been chosen for toxicity profiling the wider set of the selected PFAS compounds as well as for toxicity testing selected complex mixture (e.g., water, soil). In the case of PMTs, at first, the potential bioactivity of several key PMTs (e.g., bisphenol A, triclosan, methyl-paraben, 5-chlorobenzotriazole, benzothiazole) have been evaluated using a range of in vitro toxicity profiling methods (see for an overview in Annex, Table A1). In a second step, the most suitable in vitro toxicity endpoints (see table 8 and 11) have been used for toxicity profiling an extended group of PMT compounds as well as for testing complex mixtures (e.g., water, soil).\u00a0 Regarding the group of PFAS, in silico modelling has been performed for a few thousands of chemicals (see Kowalski et al.,\u00a0 2023), while in case of in vitro testing the focus was on a selection of 40/2 PFAS/industrial mixtures compounds proposed by the chemical-analytical laboratories involved in PROMISCES and commercially available within a limited budget. In this study, the in silico modeling part investigated PFAS binding potencies to nuclear hormone receptors (NHRs) such as peroxisome proliferator-activated receptors (PPARs) \u03b1, \u03b2, and \u03b3 and thyroid hormone receptors (TRs) \u03b1 and \u03b2. In the first in silico step, the developed QSAR models were implemented for the screening approach of a large group of compounds (4464) from the NORMAN Database related to these 5 above mentioned NHRs. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPAR\u03b1, \u03b2, and \u03b3 only with low or moderate probability, while in the case of TR \u03b1 and \u03b2 it is similar except that those chemicals with longer chains show a moderately high probability of binding. The in silico analysis shows from the here tested 15 endpoints that most PFAS reflect a high binding probability to sex hormone receptors (anti-AR and ER). however, receptors from the peroxisome proliferator-activated (PPAR \u03b1, \u03b2, \u03b3) glucocorticoid (GR and anti-GR), liver X (LXR \u03b1, \u03b2) and retinoid X (RXR \u03b1) groups are unaffected or slightly affected by PFAS (low and moderate binding probability). Regarding the parallel in vitro bioassay toxicity analysis, an established standardised in vitro test battery (consisting of e.g., genotoxicity, neurotoxicity, cytotoxicity, obesity and early warning testing) was applied to meet specific criteria of PFAS/iPM(T) (endpoints, sensitivity and specificity). Based on existing and improved CALUX bioassays, different toxicological endpoints involving metabolic syndrome (obesity related PPAR), endocrine EATS testing, genotoxicity (p53 related) and general toxicity pathways (e.g. early warning PXR) to characterise PM(T) properties of chemicals were assessed. The in vitro bioassay groups addressed 40 PFAS compounds (e.g. all regulated 20 PFAS) and industrial standards (e.g., ADONA, GenX) with a combination of a wide range of CALUX bioassay endpoints (e.g., table 7 with 6 endpoints) and additional general toxicity in vitro bioassays (e.g., table 9 with 8 in vitro endpoints). Potency factors for selected PFAS/ iPM(T) have been established for several of the here used in vitro bioassays by two bioassay laboratories (UBA, BDS). Comparing both data sets, 80% of the relative potency factors for PFAS tested on e.g., the TTR-TRb CALUX, were found to be within an order of magnitude. Potency factors for selected PFAS have been established for each of the here used in vitro human cell-based CALUX bioassays. For example, in case of the TTR-TRb CALUX bioassay, the RPF values are rang from non-detected (e.g., several FTOH compounds) to the P37DMOA compound (RPF = up to 2.3; see tables 7 and 10). Additional up to 22 PM(T) chemicals (e.g., see table 8 and 10) have been tested with a combination of a wide range of CALUX bioassays (e.g., table 8 with 6 endpoints) and additional general toxicity in vitro bioassays (e.g., table 10 with 4 in vitro endpoints). This included i) selecting a wide range of reporter gene assays to screen the toxic profiles of a set of PM(T) chemicals, ii) selecting the most suitable bioassay panel, and iii) validating bioassay panels and cross-validating the responses obtained from the selected panel of human cell-line based bioassays Potency factors for selected iPM(T) have been established for each of the here used in vitro human cell-based CALUX bioassays. For all CALUX bioassays applied, RPF-values ranged from not-detected (e.g., several in vitro endpoints at benzotriazole) up to RPF values above 0.5 for several PMTs (e.g., galaxolide and triclosan, see table 8, or methyl-paraben in table 10 at the male hormone inhibition). In a second step, the 3 most active PFAS compounds on the in vitro TTR-TRb CALUX (PFOA with RPF = 1; P37DMOA with RPF = 2.3; 6:2 FTAB with RPF = 0.00015; see table 7) have been used to find in in silico modelling new PFAS structure-analogue compounds. In case of PFOA 12 structure-in vitro toxicity analogues were found, while in case of P37DMOA only 3 analogues and in case of 6:2 FTAB only 7 analogues were found. Our study shows that such an combined approach of in silico and in\u00a0 vitro toxicity evaluations of single PFAS congeners (from the large group of PFAS) is a promising and suitable strategy to cover a large variety of different key events in toxicology in a time- and cost-efficient way. Such early key events can lead to protein production or molecular signalling that occur in individual cells. Later events can include altered tissue or organ function. Such key events are important because after the molecular initiating event, they can characterize the progression of the toxicity. Relative potency factors (RPFs) have been determined here by a combination of in silico and vitro toxicity tools and can further help to give first toxicity indications for the handful regulated and the not yet regularly tested/regulated PFAS to be included in the testing strategy for single compounds and complex mixtures of these compounds.", "keywords": ["Deliverable", "thyroid hormone transport competition", "TTR TR CALUX", "PFAS", "in vitro assay (CALUX)", "non-animal methods (NAM)", "in silico modeling"], "contacts": [{"organization": "Behnisch, Peter, Sosnowska, Anita, Mombelli, Enrico, Kuckelkorn, Jochen,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14945573"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14945573", "name": "item", "description": "10.5281/zenodo.14945573", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14945573"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-28T00:00:00Z"}}, {"id": "10.5281/zenodo.15160426", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:24:45Z", "type": "Dataset", "title": "TABLE 1 in A report of the unusual presence of Haplotaxis cf. gordioides in a terrestrial subsoil and first isotopic analysis of its trophic position", "description": "unspecifiedTABLE 1. Soil pH, soil organic carbon and nitrogen content, texture and sorption properties of four soil layers (TN\u2014 total nitrogen, SOC\u2014soil organic matter, HA\u2014hydrolytic acidity, CEC\u2014cation exchange capacity, BS\u2014base saturation, cations, CEC and HA expressed in units of cmol(+)/kg soil). Properties0\u201315 cm15\u201330 cm30\u201345 cm45\u201360 cmpH in H 2 O4.64.955.1pH in KCl4.24.34.34.2TN (%)0.20.080.040.04SOC (%)2.260.760.340.22SOC:TN ratio11.59.87.86Sand (%)18221Silt (%)75878583Clay (%)7101315Ca 2+4.133.846.559.23K +0.320.130.180.27Mg 2+0.880.541.442.61Na +0.020.020.040.05HA14.169.428.6910.18CEC19.4213.9518.8922.35BS (%)27.5832.4548.5554.44", "keywords": ["Biodiversity", "Taxonomy"], "contacts": [{"organization": "J\u00f3zefowska, Agnieszka, Martin, Patrick, Schmidt, Olaf,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15160426"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15160426", "name": "item", "description": "10.5281/zenodo.15160426", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15160426"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-19T00:00:00Z"}}, {"id": "20.500.11850/667312", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:44Z", "type": "Journal Article", "created": "2024-03-25", "title": "Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction", "description": "ABSTRACT

Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a \uffe2\uff80\uff9ctop 10\uffe2\uff80\uff9d list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

", "keywords": ["Naturgeografi", "Earth", " Planet", "Climate Change", "Microbiology", "traits", "biogeochemistry", "Humans", "Ecosystem", "Biomedical and Clinical Sciences", "Bacteria", "biogeochemistry; modeling; traits; climate change", "modeling", "Opinion/Hypothesis", "Biodiversity", "Biological Sciences", "Medical microbiology", "Models", " Theoretical", "15. Life on land", "QR1-502", "6. Clean water", "Climate Science", "3. Good health", "Climate Action", "climate change", "Physical Geography", "Medical Microbiology", "13. Climate action", "Biochemistry and cell biology", "Biochemistry and Cell Biology", "Generic health relevance", "Klimatvetenskap"]}, "links": [{"href": "https://journals.asm.org/doi/pdf/10.1128/mbio.00455-24"}, {"href": "https://doi.org/20.500.11850/667312"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/mBio", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/667312", "name": "item", "description": "20.500.11850/667312", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/667312"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-08T00:00:00Z"}}, {"id": "20.500.11850/670813", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:44Z", "type": "Journal Article", "created": "2024-04-24", "title": "Dynamic structure-soil-structure interaction for nuclear power plants", "description": "Open AccessThe paper explores the linear and nonlinear dynamic interaction between the reactor and the auxiliary buildings of a Nuclear Power Plant on a realistic layered soil profile, aiming to evaluate the effect of the auxiliary building on the seismic response of crucial components inside the reactor building. Based on realistic geometrical assumptions, highfidelity 3D finite element (FE) models of increasing sophistication are created in the Real-ESSI Simulator. Starting with elastic soil conditions and assuming tied soil\u2500foundation interfaces, it is shown that the rocking vibration mode of the soil\u2500reactor building system is amplified by the presence of the auxiliary building through a detrimental out-of-phase rotational interaction mechanism. Adding nonlinear interfaces, which allow for soil\u2500foundation detachment during seismic shaking, introduces higher excitation frequencies (above 10 Hz) in the foundation of the reactor building, leading to amplification effects in the resonant vibration response of the biological shield wall (incl. reactor vessel) inside the reactor building. A small amount of sliding at the soil\u2500foundation interface of the auxiliary building slightly decreases its response, thus reducing its aforementioned negative effects on the reactor building. When soil nonlinearity is accounted for, the rocking vibration mode of the soil\u2500reactor building system almost vanishes, thanks to the strongly nonlinear response of the underlying soil. This leads to a beneficial out-of-phase horizontal interaction mechanism between the two buildings, reducing the spectral accelerations at critical points inside the reactor building by up to 55% for frequencies close to the resonant vibration frequency of the auxiliary building. This implies that the neighboring buildings could offer mutual seismic protection to each other, in a similar way to the recently emerged seismic resonant metamaterials, provided that they are properly tuned during the design phase, accounting for soil and soil-foundation interface nonlinearities.", "keywords": ["Structure-Soil-Structure interaction (SSSI)", "Structure-Soil-Structure interaction (SSSI); Nuclear Power Plants (NPPs); Domain reduction method (DRM); Nonlinear interface; Nonlinear soil; Seismic resonant metamaterials; Meta-SSI", "FOS: Physical sciences", "Structure-soil-structure interaction (SSSI); Nuclear power plants (NPPs); Domain reduction method (DRM); Nonlinear interface; Nonlinear soil; Seismic resonant metamaterials; Meta-SSI", "Physics - Applied Physics", "Applied Physics (physics.app-ph)", "7. Clean energy", "Domain reduction method (DRM)", "Meta-SSI", "Nuclear Power Plants (NPPs)", "Nonlinear soil", "Structure-soil-structure interaction (SSSI)", "Nuclear power plants (NPPs)", "Nonlinear interface", "Seismic resonant metamaterials"]}, "links": [{"href": "https://doi.org/20.500.11850/670813"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Dynamics%20and%20Earthquake%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/670813", "name": "item", "description": "20.500.11850/670813", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/670813"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-01T00:00:00Z"}}, {"id": "11343/310023", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:09Z", "type": "Journal Article", "created": "2021-11-28", "title": "Root\u2010to\u2010shoot iron partitioning in Arabidopsis requires IRON\u2010REGULATED TRANSPORTER1 (IRT1) protein but not its iron(II) transport function", "description": "SUMMARY

IRON\uffe2\uff80\uff90REGULATED TRANSPORTER1 (IRT1) is the root high\uffe2\uff80\uff90affinity ferrous iron (Fe) uptake system and indispensable for the completion of the life cycle of Arabidopsis thaliana without vigorous Fe supplementation. Here we provide evidence supporting a second role of IRT1 in root\uffe2\uff80\uff90to\uffe2\uff80\uff90shoot partitioning of Fe. We show that irt1 mutants overaccumulate Fe in roots, most prominently in the cortex of the differentiation zone in irt1\uffe2\uff80\uff902, compared to the wild type. Shoots of irt1\uffe2\uff80\uff902 are severely Fe\uffe2\uff80\uff90deficient according to Fe content and marker transcripts, as expected. We generated irt1\uffe2\uff80\uff902 lines producing IRT1 mutant variants carrying single amino\uffe2\uff80\uff90acid substitutions of key residues in transmembrane helices IV and V, Ser206 and His232, which are required for transport activity in yeast. Root short\uffe2\uff80\uff90term 55Fe uptake rates were uninformative concerning IRT1\uffe2\uff80\uff90mediated transport. Overall irt1\uffe2\uff80\uff90like concentrations of the secondary substrate Mn suggested that the transgenic Arabidopsis lines also remain incapable of IRT1\uffe2\uff80\uff90mediated root Fe uptake. Yet, IRT1S206A partially complements rosette dwarfing and leaf chlorosis of irt1\uffe2\uff80\uff902, as well as root\uffe2\uff80\uff90to\uffe2\uff80\uff90shoot Fe partitioning and gene expression defects of irt1\uffe2\uff80\uff902, all of which are fully complemented by wild\uffe2\uff80\uff90type IRT1. Taken together, these results suggest a regulatory function for IRT1 in root\uffe2\uff80\uff90to\uffe2\uff80\uff90shoot Fe partitioning that does not require Fe transport activity of IRT1. Among the genes of which transcript levels are partially dependent on IRT1, we identify MYB DOMAIN PROTEIN10, MYB DOMAIN PROTEIN72 and NICOTIANAMINE SYNTHASE4 as candidates for effecting IRT1\uffe2\uff80\uff90dependent Fe mobilization in roots. Understanding the biological functions of IRT1 will help to improve Fe nutrition and the nutritional quality of agricultural crops.

The wellbeing of the honey bee colonies and the health of humans are connected in numerous ways. Therefore, ensuring the wellbeing of bees is a crucial component of fostering sustainability and ecological harmony. The colony collapse disorder (CCD) phenomenon was first reported in 2006 when the majority of bee colonies in Europe died out, due to an increase in infections, contamination of hives with agrochemical pesticides, and persistent organic pollutants (POPs). Only 6 years after the emergence of CCD, more than 6.5 million premature deaths were reported, as a consequence of persistent human exposure to air pollution. The insect species such as the honey bee Apis mellifera L. and the air matrix inside the beehive can be used as tools in biomonitoring, instead of traditional monitoring methods. This may have advantages in terms of cost-effective bioindicators of the environmental health status, showing the ability to record spatial and temporal pollutant variations. In this study, we present the sustainable in-field usage of the portable membrane inlet mass spectrometry (MIMS) instrument for an instant and effective determination of the level of environmental pollution by analytical identification of hive atmosphere volatile organic compound (VOC) contaminants, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), monocyclic aromatic hydrocarbons (BTEX) compounds, and pesticides. The samples were taken from hives located in urbanized and rural regions, highlighting variations in contamination. The MIMS results were benchmarked against a conventional laboratory sampling technique, such as GC-MS.

", "keywords": ["0301 basic medicine", "03 medical and health sciences", "13. Climate action", "11. Sustainability", "01 natural sciences", "0105 earth and related environmental sciences", "12. Responsible consumption", "3. Good health"]}, "links": [{"href": "https://www.mdpi.com/2071-1050/16/5/1713/pdf"}, {"href": "https://doi.org/10.5281/zenodo.15032281"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15032281", "name": "item", "description": "10.5281/zenodo.15032281", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15032281"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-19T00:00:00Z"}}, {"id": "10.5281/zenodo.15040664", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:43Z", "type": "Dataset", "title": "Data for: Nutrient recovery from digestate: Pilot test experiments", "keywords": ["food waste", "digestate", "selective electrodialysis"], "contacts": [{"organization": "Centre for Research and Technology Hellas", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15040664"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15040664", "name": "item", "description": "10.5281/zenodo.15040664", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15040664"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15043864", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:43Z", "type": "Report", "title": "Post-processing & interactive visualisation of optimisation results. Deliverable D5.2 of the EU Horizon 2020 project OPTAIN", "description": "Deliverable report D5.2 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) Summary\u00a0Multi-objective optimisation is a powerful approach for generating a set of Pareto optimal design alternatives that decision-makers can evaluate in order to select the most-suitable configuration. In practice, however, selecting from a large number of Pareto optimal solutions can be daunting. The objective of this report is to enable researchers and stakeholders to assess the optimisation outputs produced in OPTAINs previous Task 5.2 in a structured manner, to render the results tangible and understandable, and to maximise their use for the subsequent stakeholder consultation. This report describes the tool ParetoPick-R, including how to run it, its data input requirements and the processes it employs. ParetoPick-R allows (1) to make the complex optimisation outputs understandable through various intuitive visualisation techniques, including for the links between the objective space and the decision space of Natural/Small Water Retention Measures (NSWRM) implementation plans. (2) It implements a methodology for reducing the high number of solutions from the previous optimisation to a manageable number while reducing information loss, and (3) allows to perform an Analytical Hierarchy Process for stakeholders to assign priorities based on pairwise preferences in a structured manner. This report is useful for researchers and stakeholders from OPTAIN and beyond working with complex optimisation problems who want to analyse their results in\u00a0a structured and meaningful way and render them actionable.", "keywords": ["CoMOLA", "combination", "SWAT+", "NSWRM", "post-processing", "H2020", "OPTAIN", "interactive visualisation", "stakeholder support", "R tool", "multi-objective optimization", "allocation", "Pareto solutions", "Analytical Hierarchy Process", "pareto pruning", "clustering"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15043864"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15043864", "name": "item", "description": "10.5281/zenodo.15043864", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15043864"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "10.5281/zenodo.15046019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:43Z", "type": "Report", "title": "Comparison and evaluation of sampling and eDNA metabarcoding protocols to assess soil biodiversity in Belgian LUCAS Biopoints", "description": "Environmental DNA (eDNA) metabarcoding is emerging as a novel tool for monitoring soil biodiversity. Soil biodiversity, critical to soil health and ecosystem services, remains under-monitored due to the lack of standardized and efficient methods. We evaluated whether refinements to sampling protocols (for soil invertebrates and Fungi) and molecular protocols (for soil invertebrates) could improve biodiversity detection. Comparing the 2018 LUCAS soil biodiversity protocol with newly developed national methods, we tested sampling and sequencing surface layers (0-10 cm and forest floor) versus deeper layers, larger soil sample sizes for DNA-extraction, taking more subsamples for composite soil samples, and alternative primer sets across 9 Belgian Biopoints included in the LUCAS 2022 survey. We show that the choice of sampling protocol significantly influences soil biodiversity assessments. The results show that, based on eDNA, we are able to detect significantly more species when sampling and sequencing the upper soil layers separately, while the diversity in the 10\u201330 cm soil layer is insufficient for annelids and arthropods to serve as indicators of ecological changes. Collembola and Arthropoda richness and diversity generally increased towards less intensely managed soils, when using the national (Cmon), and to a lesser extent the European (LUCAS) sampling protocols. In contrast, sampling and sequencing the 10-30 cm layer failed to capture such a pattern. Overall, the analyses suggest that soil depth has a greater influence on the soil invertebrate diversity captured than sampling intensity, and that the highest diversity is recovered when surface sampling (0\u201310 cm topsoil and forest floor) is combined with a greater number of subsamples (16 compared to 5 in LUCAS) and a larger sampled area. Additionally, comparison of the universal eukaryotic primers (18S) with primer sets tailored to important soil invertebrate groups, showed that universal 18S primers provide limited resolution for Collembola and Annelida, making them less suitable for accurately assessing the diversity of these groups as a response variable in monitoring ecological changes and biological soil health. With refinement and standardization, eDNA metabarcoding, combined with optimized sampling protocols, could become a powerful and efficient tool for monitoring soil biodiversity in European soils.", "keywords": ["EJP SOIL", "soil monitoring", "metabarcoding", "LUCAS", "soil biodiversity", "eDNA"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15046019"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15046019", "name": "item", "description": "10.5281/zenodo.15046019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15046019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "10.5281/zenodo.15044246", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:24:43Z", "type": "Journal Article", "created": "2024-09-19", "title": "Unmanned aerial vehicle-based evaluation of pollination performance employing clustering image processing technique", "description": "Abstract

The global decline of pollinator populations is posing a threat to agricultural productivity, increasingly forcing farmers to introduce pollinators to their fields. Selecting suitable pollinator species is critical for effective crop pollination. This study presents an efficient method for early pollination assessment, utilizing unmanned aerial vehicle (UAV) footage for reliable estimation and timely reactions. Twelve oilseed rape ( Brassica napus var. oleracea ) isolation cages with three pollinator treatments were set up, including the control with no pollinators. The trial employed UAV image acquisition, generating high-resolution RGB orthomosaics. A K-means clustering algorithm was implemented to identify oilseed rape flowers, a direct indicator of pollination performance. The percentage of detected oilseed rape flower coverage within each cage was the primary metric for performance assessment. These initial results demonstrated a negative correlation of 0.92 between estimated flower coverage and expert observations, affirming the efficacy of the proposed methodology. By integrating UAVs and clustering image processing, this research contributes to precision agriculture, offering a robust approach for evaluating pollination performance. The findings underscore the potential of advanced technology to support informed decision-making in agricultural practices, addressing the urgent need for sustainable pollination management in the face of declining pollinator populations.

Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al.\u00a0(2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1)\u00a0the coarser spatial resolution of ORCHIDEE; (2)\u00a0the higher temporal resolution of ORCHIDEE; and (3)\u00a0the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.

", "keywords": ["cycle du carbone", "[SDE] Environmental Sciences", "http://aims.fao.org/aos/agrovoc/c_24242", "P40 - M\u00e9t\u00e9orologie et climatologie", "mod\u00e8le de simulation", "Ips typographus", "http://aims.fao.org/aos/agrovoc/c_16411", "http://aims.fao.org/aos/agrovoc/c_2391", "http://aims.fao.org/aos/agrovoc/c_1666", "K70 - D\u00e9g\u00e2ts caus\u00e9s aux for\u00eats et leur protection", "http://aims.fao.org/aos/agrovoc/c_6111", "http://aims.fao.org/aos/agrovoc/c_4549f84e", "perturbation de l'\u00e9cosyst\u00e8me", "surveillance \u00e9pid\u00e9miologique", "mod\u00e9lisation", "s\u00e9cheresse", "changement climatique", "QE1-996.5", "http://aims.fao.org/aos/agrovoc/c_230ab86c", "U10 - Informatique", " math\u00e9matiques et statistiques", "Geology", "H10 - Ravageurs des plantes", "http://aims.fao.org/aos/agrovoc/c_331583", "s\u00e9questration du carbone", "dynamique des populations", "[SDE]Environmental Sciences", "http://aims.fao.org/aos/agrovoc/c_30153", "http://aims.fao.org/aos/agrovoc/c_17299"]}, "links": [{"href": "https://doi.org/20.500.11850/706699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/706699", "name": "item", "description": "20.500.11850/706699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/706699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-11T00:00:00Z"}}, {"id": "10.5281/zenodo.15133040", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:45Z", "type": "Dataset", "title": "InfoCarb:\u00a0Forest Inventory of the Autonomous Province of Trento (Trentino), Italy", "description": "InfoCarb: Forest Inventory of the Autonomous Province of Trento (Trentino), Italy\u00a0 Forest Carbon Stocks in the Province of Trento inventory of Trentino based on 150 plots (15 m radius) over 6200 Km2 land area. Estimation of organic carbon pools stored above and below-ground. Quantitative description of forest specific composition and structure. Year 2002.\u00a0 The dataset includes the following .csv formatted files: INFOCARB_DATASET_(ENG)_v1.0 INFOCARB_SPECIES_(ENG)_v1.0 Metadata InfoCarb (ENG) species codes_v1.0 Metadata InfoCarb categorical variables_v1.0 Metadata InfoCarb data licence and info_v1.0 Metadata InfoCarb numerical variables_v1.0 Metadata InfoCarb species codes_v1.0 \u00a0 Discalimer: The dataset refers to measurements carried out in 150 forest plots in the province of Trentino in the years 2001-2002. A severe windstorm in October 2018 (Vaia storm) caused extensive windthrows over 20 thousand hectares, in most forest areas of the province. This dataset was prepared as a contribution to the Work Package 4 of the Open Earth Monitor project (funded from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101059548 ) - https://earthmonitor.org", "keywords": ["soil organic carbon", "forest biomass", "forest inventory", "forest composition", "Trentino", "forest structure"], "contacts": [{"organization": "Belelli Marchesini, Luca, Frizzera, Lorenzo,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15133040"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15133040", "name": "item", "description": "10.5281/zenodo.15133040", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15133040"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-03T00:00:00Z"}}, {"id": "10.5281/zenodo.15166358", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:45Z", "type": "Dataset", "title": "RapidCrops: A pan-European label dataset for large-scale crop classification", "description": "Under the AI4SoilHealth project we have created a dataset (\u201cRapidCrops\u201d) to support the automatic mapping of crop types across Europe. Crop type information is essential for monitoring soil health as it provides\u00a0 systematic insights into crop rotations over time and supports efforts to detect other cropping practices that affect soil health (e.g. tillage & cover crops). The RapidCrops dataset provides approximately 99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal extent; with coverage across seven EU countries for 5-7 years. Based on parcel boundaries and crop type information reported under the EU IACS programme, our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. The dataset builds on top of the EuroCrops [1,2] crop type harmonisation initiative and the fiboa [3] open data standard for parcel boundaries. For enhanced access to the data, the dataset is also made freely available on Source Cooperative [4]. The dataset was also utilised under the Horizon Europe project Open-Earth-Monitor to perform pan-European crop identification across 51M parcels from the year 2022; classifying each parcel into one of 29 crop types [5]. Please see the license terms for underlying datasets below: Data source Data licensing terms Austria INSPIRE public access license & CC-BY-AT 4.0 Denmark INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal France Custom open license Germany Custom open licenses: NRW, Brandenberg, LS Netherlands INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license Portugal CC BY 4.0 Spain Custom open license", "keywords": ["crop classification", "earth observation", "reference data"], "contacts": [{"organization": "Holden, Piers, Davis, Timothy, Holmes, Christopher, Senaras, Caglar, Wania, Annett,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15166358"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15166358", "name": "item", "description": "10.5281/zenodo.15166358", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15166358"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.15166359", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:45Z", "type": "Dataset", "title": "RapidCrops: A pan-European label dataset for large-scale crop classification", "description": "Under the AI4SoilHealth project we have created a dataset (\u201cRapidCrops\u201d) to support the automatic mapping of crop types across Europe. Crop type information is essential for monitoring soil health as it provides\u00a0 systematic insights into crop rotations over time and supports efforts to detect other cropping practices that affect soil health (e.g. tillage & cover crops). The RapidCrops dataset provides approximately 99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal extent; with coverage across seven EU countries for 5-7 years. Based on parcel boundaries and crop type information reported under the EU IACS programme, our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. The dataset builds on top of the EuroCrops [1,2] crop type harmonisation initiative and the fiboa [3] open data standard for parcel boundaries. For enhanced access to the data, the dataset is also made freely available on Source Cooperative [4]. The dataset was also utilised under the Horizon Europe project Open-Earth-Monitor to perform pan-European crop identification across 51M parcels from the year 2022; classifying each parcel into one of 29 crop types [5]. Please see the license terms for underlying datasets below: Data source Data licensing terms Austria INSPIRE public access license & CC-BY-AT 4.0 Denmark INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal France Custom open license Germany Custom open licenses: NRW, Brandenberg, LS Netherlands INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license Portugal CC BY 4.0 Spain Custom open license", "keywords": ["crop classification", "earth observation", "reference data"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15166359"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15166359", "name": "item", "description": "10.5281/zenodo.15166359", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15166359"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.15188974", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:46Z", "type": "Report", "title": "Synthesis report of soil science capacity in Higher Education in Europe", "description": "Soils and their management are fundamental to a range of essential ecosystems, societal and climate challenges facing humanity. In Europe, 25-30% of agricultural soils are considered degraded, affecting food systems, GHG emissions, habitats and water ways. To manage soil resources for multiple uses require expertise and competence from farmers and landowners to policy makers and the private sector. This report provides a synthesis on the current state of soil science in European Higher Education (HE), as a baseline to assess competency and resources for capacity development in soil science. A total of 120 survey responses were received in 2020-2021, representing Higher Education Institutes (HEIs) in 25 European countries. Resultsshowed that only 13% of the HEIs hosted a dedicated soil science department. The majority of soil science is embedded in a department where environmental sciences, agricultural sciences and earth sciences are the main academic topics. Respondents reported an increased enrolment at BSc, and no change for MSc and PhD. Mixed trends could be seen for specific countries and universities, with both increases and decreases in student enrolment. Teaching capacity is high in soil science, with a majotrity of teachers having both PhD and training in HE teaching and learning. Yet, traditional lecture based teaching dominates soil science teaching and learning activities, both at BSc and MSc levels. At BSc level the proportion of courses that did not have any computer/modelling component was about 1/3. According to responses internationalisation is of great importance to many soil science HEI. Top three priorities for internationalisation were attracting students from abroad, providing more opportunities to send students abroad and developing strategic research partnerships. Finally, respondents\u2019 perception was that job opportunities for students have mainly increased in the past ten years, and one important explanation to this is an increased interest in soil, in relation to environmental concern, sustainability and climate change.", "keywords": ["2. Zero hunger", "13. Climate action", "4. Education", "11. Sustainability", "Soil Science", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Villa Solis, Ana, Fahlbeck, Erik, Barron, Jennie,", "roles": ["creator"]}]}, "links": [{"href": "https://pub.epsilon.slu.se/28995/1/villa-solis-a-et-al-20220928.pdf"}, {"href": "https://doi.org/10.5281/zenodo.15188974"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15188974", "name": "item", "description": "10.5281/zenodo.15188974", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15188974"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15227185", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:46Z", "type": "Dataset", "title": "Soil properties of evergreen and deciduous forests of the Southern Western Ghats, India", "description": "Soil properties of evergreen and deciduous forests of the Southern Western Ghats, India This project contains physical and chemical properties of soil collected from Anamalai Tiger Reserve of the Southern Western Ghats for a study investigating the factors influencing the distribution of evergreen-deciduous mosaics. Description of field and lab methods Soil collection: Soil samples were collected from 58 plots spread across evergreen and deciduous forests, using soil core sampler. The diameter of the core sampler was measured before soil collection. All soil samples were collected from 10 cm depth after removing all the leaf litter from the ground. From each plot, one soil column was collected for bulk density estimation and 10 soil columns were collected for analysis of chemical properties. Bulk density estimation: For each sample, all the roots above 0.5 mm diameter were separated from the soil and the length and diameter of the roots were recorded. The volume of the roots were calculated. The entire sample was dried in the oven at 120\u00b0C, for 2 hours.After drying, the soil sample was sieved with a 2mm mesh sieve to separate the stones and clumped soil. The dry mass of soil and stones for each sample was recorded. The volume of the stones from each sample was measured separately using water displacement method. The total volume of soil was calculated by deducting the volume of roots and stones from volume of the soil core sampler. Bulk density was calculated as mass of the dried soil sample by the total volume of the soil. Chemical Properties: We estimated fifteen soil chemical properties for all soil samples collected. The following parameters were analyzed by Zuari Farmhubs Laboratory: pH, electrical conductivity (E.C.) at 25\u00b0C, organic carbon (O.C.), available phosphorus (P\u2082O\u2085), available potassium (K\u2082O), available calcium (Ca), magnesium (Mg), sulfur (S), boron (B), zinc (Zn), iron (Fe), copper (Cu), and manganese (Mn). Total carbon and nitrogen were estimated using LECO elemental analyzer. More details about the data can be obtained from Bindu K. and Rohit Naniwadekar from the Nature Conservation Foundation (www.ncf-india.org).", "keywords": ["soil chemical properties", "evergreen forest", "soil physical properties", "deciduous forest", "ecology"], "contacts": [{"organization": "Lad, Himanshu, Kempegowda, Bindu, Jayanth, Arpitha, Kumar, Krishna, Page, Navendu, Ghuman, Sartaj, Naniwadekar, Rohit,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15227185"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15227185", "name": "item", "description": "10.5281/zenodo.15227185", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15227185"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8089896", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2020-10-09", "title": "Effects of water deficit and nitrogen application on leaf gas exchange, phytohormone signaling, biomass and water use efficiency of oat plants", "description": "Abstract

Background: Water and nitrogen (N) are essential resources influencing plant growth and yield. To improve their efficiencies in crop production is challenging because the physiological mechanisms of water and N coupling and their interactive effect on crop water use efficiency (WUE) are not well understood yet.

Aim: The aim of this study was to investigate the physiological responses and phytohormones signaling in oats in response to soil water status and N supply under fertigation, to explore the mechanisms regulating plant growth and WUE.

Methods: Oat plants were subjected to the factorial combination of three soil moisture regimes (50, 70, and 90% of soil water holding capacity, SWHC) and three N levels (fertilized with 74, 149, and 298 mg kg\uffe2\uff88\uff921).

Results: The stomatal conductance (gs) was significantly decreased by soil water deficit, and also by the highest N level, whereas photosynthesis rate (An) was unaffected by neither water nor N. Consequently, intrinsic WUE (WUEint, An/gs) was highest under reduced irrigation and high N fertilization. This effect at stomatal level was affirmed by responses in whole plant WUE (WUEb), which was positively correlated with shoot \uffce\uffb413C. A positive correlation between \uffce\uffb418O and \uffce\uffb413C in shoots further indicated that decreases of gs rather than changes in An contributed to the enhanced WUE.

Conclusion: Moderate soil water deficit and sufficient N supply is recommended for saving irrigation water and improving WUE on fertigated oat plants without compromising biomass accumulation to any large extent.

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

", "keywords": ["2. Zero hunger", "soil salinity; remote sensing; machine learning; predictive mapping", "soil salinity", "remote sensing", "machine learning", "13. Climate action", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "predictive mapping", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4118/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090398"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090398", "name": "item", "description": "10.5281/zenodo.8090398", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090398"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-16T00:00:00Z"}}, {"id": "10.5281/zenodo.15310473", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:48Z", "type": "Dataset", "title": "Water Analysis Data", "description": "It\u00a0 a comprehensive examination of various types of wastewater with the aim of establishing a comprehensive database on the presence of both micronutrients and micropollutants. The sources of agricultural wastewater are from UNIDEB (Hungary; fermented sludge wastewater and irrigation channel wastewater), UNIBO (Italy; surface irrigation water and agricultural drainage water) and UPWr (Poland; agricultural runoff and ditch).", "keywords": ["Water Analysis", "Micronutrients", "Wastewater", "Pesticides"], "contacts": [{"organization": "Eden Tech", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15310473"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15310473", "name": "item", "description": "10.5281/zenodo.15310473", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15310473"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-30T00:00:00Z"}}, {"id": "10.5281/zenodo.8090465", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Dataset", "title": "Data for the manuscript 'Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools'", "description": "Data for manuscript The data provided in the present document corresponds to the manuscript: Engedal, T., Magid, J., Hansen, V., Rasmussen, J., S\u00f8rensen, H., Jensen, L. S. (2023): Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools. Global Change Biology, in press. Short abstract In order to investigate the fate of cover crop-derived belowground C as rhizodeposition and, over time, into the distinct soil organic carbon pools of particulate- and mineral-associated organic carbon (POC and MAOC), a column trial was esblished with 0.25 m top soil and 0.25 m sub soil. Four cover crops were grown for 3 months and 14CO2-labelled twice a week. Four out of eight replicate columns were destructively harvested to quantify root C and the carbon lost via rhizodeposition in absolute (qClvR) and relative terms (%ClvR) in bulk soil and rhizosphere soil from top- and subsoil (t1). The other four replicate columns were harvested for undisturbed incubation for one year, before final sampling (t2). Bulk soil from both sampling times were subject to a simple fractionation protocol by size, where particles larger from 50 microns were assigned to POC and smaller than 50 microns assigned to MAOC after dispersion in NaHMP. All fractions were dried, weighed and analyzed for 14C activity as disintegrations per minute (DPM). Further details Column ID 1-16 refer to columns sampled at t1, while column ID 17-32 refer to columns sampled at t2. Underlying assumptions and detailed descriptions of the different fractions are to be found in the manuscript.", "keywords": ["2. Zero hunger", "MOAM", "POM", "MAOC", "cover crop", "15. Life on land", "soil organic fractionation", "soil organic carbon", "mineral-associated organic matter", "rhizodeposition", "root morphology", "particulate organic matter", "root carbon", "POC"], "contacts": [{"organization": "Engedal, Tine, Magid, Jakob, Hansen, Veronika, Rasmussen, Jim, S\u00f8rensen, Helle, Jensen, Lars Stoumann,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8090465"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090465", "name": "item", "description": "10.5281/zenodo.8090465", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090465"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-06-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8090556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2020-06-15", "title": "Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements", "description": "

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones\u2014that is to say, areas with the same yield level within fields over the long-term\u2014are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rost\u011bnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.

", "keywords": ["2. Zero hunger", "yield productivity zones", "precision agriculture", "Science", "Q", "Enhanced Vegetation Index", "04 agricultural and veterinary sciences", "yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index", "15. Life on land", "01 natural sciences", "yield measurements", "0401 agriculture", " forestry", " and fisheries", "satellite images", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090556"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090556", "name": "item", "description": "10.5281/zenodo.8090556", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090556"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-13T00:00:00Z"}}, {"id": "10.5281/zenodo.15345256", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Deliverable D7.11_Annual sustainability report (SOIL O-LIVE_HORIZON EUROPE ID 101091255)", "description": "D7.11. Annual sustainability summary project report.T7.3", "keywords": ["13. Climate action", "D7.11", "11. Sustainability", "15. Life on land", "soil o-live", "sustainability", "6. Clean water", "deoleo", "report", "12. Responsible consumption"], "contacts": [{"organization": "DEOLEO", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15345256"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15345256", "name": "item", "description": "10.5281/zenodo.15345256", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15345256"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-20T00:00:00Z"}}, {"id": "10.5281/zenodo.15349739", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Deliverable D5.5 - Map of potential for PM(T) substitution in one high-stake sector", "description": "This report (Deliverable D5.5), produced under the H2020 PROMISCES project, addresses the substitution of Persistent, Mobile, and Toxic (PMT) and very Persistent and very Mobile (vPvM) substances, which pose significant environmental and human health risks. Utilizing a comprehensive dataset encompassing over 120,000 substances, the report employs three distinct analytical methodologies: sectoral analysis, functional use analysis, and a case study on benzotriazoles. The findings reveal the extensive distribution of PMT substances across various sectors, highlight substantial data deficiencies, and underscore the complexities involved in identifying safer alternatives. The study emphasizes the necessity for robust data and systematic assessments to facilitate effective substitution strategies and inform regulatory decision-making.", "keywords": ["Functional use analysis", "Zero Pollution", "PROMISCES Decision Support Framework", "Substitution", "PMT substances"], "contacts": [{"organization": "BOUCARD, Pierre, Sardi, Adriana E.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15349739"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15349739", "name": "item", "description": "10.5281/zenodo.15349739", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15349739"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-06T00:00:00Z"}}, {"id": "10.5281/zenodo.15349740", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Deliverable D5.5 - Map of potential for PM(T) substitution in one high-stake sector", "description": "This report (Deliverable D5.5), produced under the H2020 PROMISCES project, addresses the substitution of Persistent, Mobile, and Toxic (PMT) and very Persistent and very Mobile (vPvM) substances, which pose significant environmental and human health risks. Utilizing a comprehensive dataset encompassing over 120,000 substances, the report employs three distinct analytical methodologies: sectoral analysis, functional use analysis, and a case study on benzotriazoles. The findings reveal the extensive distribution of PMT substances across various sectors, highlight substantial data deficiencies, and underscore the complexities involved in identifying safer alternatives. The study emphasizes the necessity for robust data and systematic assessments to facilitate effective substitution strategies and inform regulatory decision-making.", "keywords": ["Functional use analysis", "Zero Pollution", "PROMISCES Decision Support Framework", "Substitution", "PMT substances"], "contacts": [{"organization": "BOUCARD, Pierre, Sardi, Adriana E.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15349740"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15349740", "name": "item", "description": "10.5281/zenodo.15349740", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15349740"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-06T00:00:00Z"}}, {"id": "10.5281/zenodo.15363435", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Deliverable D7.1_Guidelines and specifications for soil biodiversity and soil health data capture (SOIL O-LIVE_HORIZON EUROPE ID 101091255)", "description": "D7.1. Guidelines and specifications for soil biodiversity and soil health data capture, metadating, geo-linking, and open publication. T.7.1", "keywords": ["geo-linking", "soil health", "metadata", "guidelines", "15. Life on land", "soil o-live", "olive grove", "D7.1", "biodiversity"], "contacts": [{"organization": "University of Ja\u00e9n", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15363435"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15363435", "name": "item", "description": "10.5281/zenodo.15363435", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15363435"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-20T00:00:00Z"}}, {"id": "10.5281/zenodo.15393410", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Carbon Farming Mitigation Potential: Evaluating the mitigation potential (and uncertainties) of carbon farming practices", "description": "unspecifiedThis is the updated version of the old one (version 1.0)", "keywords": ["Carbon sequestration", "Environmental benefits", "Agricultural Systems", "Greenhouse gas emissions", "Mitigation potential", "Agricultural systems", "Carbon farming"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15393410"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15393410", "name": "item", "description": "10.5281/zenodo.15393410", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15393410"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15393411", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Carbon Farming Mitigation Potential: Evaluating the mitigation potential (and uncertainties) of carbon farming practices", "description": "Open AccessThis is the updated version of the old one (version 1.0)", "keywords": ["Carbon sequestration", "Environmental benefits", "Agricultural Systems", "Greenhouse gas emissions", "Mitigation potential", "Agricultural systems", "Carbon farming"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15393411"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15393411", "name": "item", "description": "10.5281/zenodo.15393411", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15393411"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15400134", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:24:51Z", "type": "Dataset", "title": "Genomic and functional adaptation of Aristotelia chilensis across the Atacama\u2013Patagonia aridity gradient", "description": "The dataset supermatrix4.csv compiles comprehensive individual-level data for Aristotelia chilensis, a native Chilean tree species, sampled along a pronounced latitudinal aridity gradient. It comprises 225 rows (individual plants) and 51 columns (variables), integrating ecological, morphological, physiological, and environmental information. Each row represents a unique individual annotated with population and genetic cluster identifiers, spatial coordinates (latitude, longitude, altitude), and an array of functional and structural traits. Functional traits include critical photo-inactivation water content (a drought tolerance proxy), specific leaf area (SLA), and root-to-shoot biomass ratio, all of which are central to plant water-use strategies and growth efficiency. Morphometric traits\u2014such as stem diameter, plant height, number of stems, canopy width (maximum and minimum), and canopy exposure\u2014describe above-ground architectural variation. Physiological attributes include anthocyanin and phenolic concentrations, antioxidant capacity estimated via ABTS and DPPH radical scavenging assays, and germination percentage. These measures provide insight into chemical defense mechanisms and reproductive performance. Environmental predictors were extracted using point-based values corresponding to the coordinates of each sampled population, sourced from globally recognized high-resolution datasets due to their relevance to plant eco-physiology. The Aridity Index (AI), a key proxy for water availability, was calculated as the ratio of annual precipitation to potential evapotranspiration (AI = PPT / PET), following Fisher et al. (2011). Climatic variables include mean annual precipitation (PPT) and a suite of bioclimatic variables (BIO1\u2013BIO19) from the CHELSA dataset, which provides fine-resolution climatologies suitable for ecological modeling (Karger et al., 2017). Potential evapotranspiration (PET), a metric for atmospheric water demand, was also derived from the methods of Fisher et al. (2011). In addition, UV-B radiation values were extracted from the glUV dataset, which offers spatially explicit estimates of biologically effective ultraviolet exposure (Beckmann et al., 2014). Soil (edaphic) variables were obtained from the SoilGrids 2.0 global database (Poggio et al., 2021), including soil texture fractions (sand, silt, clay), water retention capacity, and a set of nutrient and structural properties such as nitrogen content, soil organic carbon (SOC), cation exchange capacity (CEC), and the proportion of coarse fragments. These variables support fine-scale trait\u2013environment analyses and help evaluate potential adaptive responses across heterogeneous climatic and soil gradients. The dataset is well-suited for integrative ecological and evolutionary research, enabling analyses of the interaction between genomic variation, phenotypic traits, and abiotic selective pressures. The accompanying maqui.vcf file contains genomic variant data in VCF v4.2 format, generated using BBMap v38.69. It includes 2,356 high-quality single nucleotide polymorphisms (SNPs) detected across 188 diploid individuals of A. chilensis, aligned to the Aristotelia chilensis v1.0 reference genome. Variant calling was based on 415,851,949 reads, with an average read length of 138.41 bp, an average total base quality of 39.99, and a mean mapping quality (MAPQ) of 41.03. SNPs are distributed across at least 37 reference contigs, ranging from approximately 63,940 to 113,184 base pairs in length. Genotypes are encoded in standard VCF format and include quality metrics per sample. This dataset enables population genomics analyses, including assessments of genetic structure, differentiation, and genotype\u2013environment associations. Funding Statement This work was supported by Fundacion para la Innovaci\u00f3n Agraria (grant PYT-2018-0138). References Beckmann, M. et al. (2014). glUV: a global UV-B radiation data set for macroecological studies. Methods in Ecology and Evolution 5, 372\u2013383. Fisher, J.B., Whittaker, R.J. & Malhi, Y. (2011). ET come home: potential evapotranspiration in geographical ecology. Global Ecology and Biogeography 20, 1\u201318.Karger, D.N. et al. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data 4, 170122.Poggio, L. et al. (2021). SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7, 217\u2013240.", "keywords": ["Aristotelia chilensis", "Nutraceuticals", "Chile"], "contacts": [{"organization": "Blinded", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15400134"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15400134", "name": "item", "description": "10.5281/zenodo.15400134", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15400134"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-14T00:00:00Z"}}, {"id": "10.5281/zenodo.15517751", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:55Z", "type": "Dataset", "title": "Atmospheric transport of microplastics in agricultural areas", "description": "Monthly total atmospheric deposition fluxes of microplastics measured at three study sites in Finland, Germany and Spain monitored over one year. The dataset includes meteorological variables during the sampling period and the characterisation of microplastic particles in terms of shape, size and polymer type.", "keywords": ["Microplastics/analysis", "Atmospheric fallout"], "contacts": [{"organization": "Kernchen, Sarmite, L\u00f6der, Martin G. J., Laforsch, Christian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15517751"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15517751", "name": "item", "description": "10.5281/zenodo.15517751", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15517751"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-26T00:00:00Z"}}, {"id": "10.5281/zenodo.8090608", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2020-01-13", "title": "Construction of ecological security pattern based on the importance of ecosystem service functions and ecological sensitivity assessment: a case study in Fengxian County of Jiangsu Province, China", "description": "Abstract

The construction of ecological security pattern is one of the important ways to alleviate the contradiction between economic development and ecological protection, as well as the important contents of ecological civilization construction. How to scientifically construct the ecological security pattern of small-scale counties, and achieve sustainable economic development based on ecological environment protection, it has become an important proposition in regulating the ecological process effectively. Taking Fengxian County of China as an example, this paper selected the importance of ecosystem service functions and ecological sensitivity to evaluate the ecological importance and identify ecological sources. Furthermore, we constructed the ecological resistance surface by various landscape assignments and nighttime lighting modifications. Through a minimum cumulative resistance model, we obtained ecological corridors and finally constructed the ecological security pattern comprehensively combining with ecological resistance surface construction. Accordingly, we further clarified the specific control measures for ecological security barriers and regional functional zoning. This case study shows that the ecological security pattern is composed of ecological sources and corridors, where the former plays an important security role, and the latter ensures the continuity of ecological functions. In terms of the spatial layout, the ecological security barriers built based on ecological security pattern and regional zoning functions are away from the urban core development area. As for the spatial distribution, ecological sources of Fengxian County are mainly located in the central and southwestern areas, which is highly coincident with the main rivers and underground drinking water source area. Moreover, key corridors and main corridors with length of approximately 115.71\uffc2\uffa0km and 26.22\uffc2\uffa0km, respectively, formed ecological corridors of Fengxian County. They are concentrated in the western and southwestern regions of the county which is far away from the built-up areas with strong human disturbance. The results will provide scientific evidence for important ecological land protection and ecological space control at a small scale in underdeveloped and plain counties. In addition, it will enrich the theoretical framework and methodological system of ecological security pattern construction. To some extent, it also makes a reference for improving the regional ecological environment carrying capacities and optimizing the ecological spatial structure in such kinds of underdeveloped small-scale counties.