_MAP.tif Products are distributed as country-wide Geotiff files with 0.00088888\u00b0 resolution (~100m). More information on product format and content can be found in the Product User Guide, available on the\u00a0SEN4LDN Deliverables web page. The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on\u00a0http://esa-sen4ldn.org/\u00a0. Click here to view the maps in an interactive Google Earth Engine application.", "keywords": ["Life Science"], "contacts": [{"organization": "Araza, Arnan, Berger, Katja, Herold, Martin, Tot\u00e9, Carolien, Van De Kerchove, Ruben,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14274476"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14274476", "name": "item", "description": "10.5281/zenodo.14274476", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14274476"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14875898", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:43Z", "type": "Other", "title": "Les mod\u00e8les de COS doivent \u00eatre valid\u00e9s par des s\u00e9ries temporelles ind\u00e9pendantes pour permettre une pr\u00e9diction fiable", "description": "Les efforts visant \u00e0 maintenir les jeux de donn\u00e9es sont imp\u00e9ratifs pour obtenir des projections et des pr\u00e9visions pr\u00e9cises en mati\u00e8re de COS.", "keywords": ["[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study"], "contacts": [{"organization": "Le No\u00eb, Julia, Manzoni, Stefano, Abramoff, Rose, B\u00f6lscher, Tobias, Bruni, Elisa, Cardinael, R\u00e9mi, Ciais, Philippe, Chenu, Claire, Clivot, Hugues, Derrien, Delphine, Ferchaud, Fabien, Garnier, Patricia, Goll, Daniel, Lashermes, Gwena\u00eblle, Martin, Manuel, Rasse, Daniel, Rees, Fr\u00e9d\u00e9ric, Sainte-Marie, Julien, Salmon, \u00c9lodie, Schiedung, Marcus, Schimel, Josh, Wieder, William, Abiven, Samuel, Barr\u00e9, Pierre, C\u00e9cillon, Lauric, Guenet, Bertrand, Delahaie, Amicie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14875898"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14875898", "name": "item", "description": "10.5281/zenodo.14875898", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14875898"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14936177", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:47Z", "type": "Dataset", "title": "Precision Liming Soil Datasets (LimeSoDa) Zenodo Repository", "description": "Overview Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper 'LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping' by Schmidinger et al. (2025). You may also use our R\u00a0and Python package likewise called LimeSoDa. \u00a0 Citation Upon usage of datasets from LimeSoDa, please cite our associated paper: Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping.", "keywords": ["Environmental sciences", "Soil Organic Carbon", "Pedometrics", "pH", "Soil Organic Matter", "Clay", "Remote sensing", "Digital Soil Mapping"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14936177"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14936177", "name": "item", "description": "10.5281/zenodo.14936177", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14936177"}, {"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.15277024", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:55Z", "type": "Dataset", "title": "Management of rice straw in rice-soybean succession in tropical lowland", "description": "This study aims to investigate alternative management practices for rice straw in tropical lowland rice-soybean systems. The goal is to twofold:\u00a0 first, to identify methods that maximize the yield of the subsequent soybean crop, and second, to quantify the effects of these practices on soil properties. 'Ten treatments (below) consisting of a combination of rice straw management (burning, removal and incorporation with disc harrow, leveling disc harrow, and knife-roller) with soybean sowing (no-tillage and conventional) were evaluated in a completely randomized design, with six replications. The disc harrow operated at 0.20-0.25 m, the leveling disc harrow at 0.10 m, and the knife-roller at 0.13 m depth. The total plot area was 600 m2 (10 m wide and 60 m long). \u00a0 Tillage treatments were: Burning (M1) Straw removal (M2) Incorporation with one pass of a disc harrow (GA) and two (M3) or three (M4) passes of a leveling harrow (GN) Incorporation with one pass of a roller knife (RF) and no-till soybean planting (M5) Incorporation with one pass of RF and two (M6) or three (M7) passes of GN Incorporation with two passes of RF and no-till soybean planting (M8) Two (M9) or three (M10) passes of GN.' 'Grain yield was determined in an area of 2.55 m\u00b2, corresponding to six 2.5 m rows spaced 0.17 m apart, which was expressed in kg ha-1, after moisture was adjusted to 13%. \u00a0 The yield components were determined: the number of panicles in one meter of the planting row; plant height, measured from the soil level to the tip of the panicle in five tillers. The HI was obtained by the ratio between grain yield and total dry matter in 1 m2. The number of grains and empty spikelets in ten panicles and the mass of 100 grains. The determination of the industrial quality of grains in 100 g samples of processed seeds. Rice and soybean grain yields were determined annually and the cumulative yields of these crops were calculated.' The experimental design is completely randomized, with six replicates. '2015 and 2017: 0-10, 10-20cm 2023: 0-10, 10-20, 20-30, 30-40cm' 'This study investigated the following chemical properties of the soil: pH, and the levels of calcium (Ca2+), magnesium (Mg2+), hydrogen (H+), aluminum (Al3+), phosphorus (P), potassium (K+), copper (Cu2+), zinc (Zn2+), iron (Fe3+), manganese (Mn2+), and organic matter. \u00a0 -Soil pH was measured in water. -Calcium and magnesium were extracted using a 1 molar potassium chloride (KCl) solution and then analyzed by atomic absorption spectroscopy. -Potential acidity (the combined amount of hydrogen and aluminum) was determined through titration with a 0.5 molar calcium acetate solution at a pH of 7. -Phosphorus, potassium, and micronutrients were extracted with Mehlich 1 solution (a mixture of 0.5 N hydrochloric acid (HCl) and 0.025 N sulfuric acid (H2SO4)) and analyzed by inductively coupled plasma atomic emission spectroscopy. -Soil organic matter (SOM) was estimated by multiplying the total organic carbon content of the soil by 1.724. This calculation was based on the chromic acid titration method. \u00a0 The specific methods used for these analyses were referenced from Teixeira et al. (2017) and Soltanpour et al. (1996).' '2015 and 2017: 0-10, 10-20cm 2023: 0-10, 10-20, 20-30, 30-40cm \u00a0 - In 2015 and 2017, soil organic carbon content was estimated indirectly by measuring soil organic matter (SOM) using the chromic acid titration method (Teixeira et al., 2017). The total soil organic carbon content was obtained by multiplying the SOM by a factor of 1.724. - In 2023, soil organic carbon content was measured directly using dry combustion with Total Organic Carbon (TOC) analysis. - Soil carbon stocks were calculated using soil bulk density. A volumetric ring was used to determine the bulk density at different depths (0-10 cm, 10-20 cm, 20-30 cm, and 30-40 cm). Since bulk density varied between treatments, estimates of soil organic carbon stocks (in Mg ha-1) were based on equivalent soil masses (Sisti et al., 2004).' The following soil physical properties were measured: bulk density, total porosity, microporosity, and macroporosity. Additionally, plant available water capacity was determined following the method of Teixeira et al. (2017). The S index, an indicator of soil physical quality, was calculated based on Dexter (2004). Finally, air capacity (AC) was assessed using the method outlined by Reynolds et al. (2002). Clay loam texture '-Sample collection: Soil samples for biological properties were collected at a depth of 0-10 cm. \u00a0 -Enzyme analysis: Betaglucosidase, aryl-sulfatase, and acid phosphatase activities were determined following the methods described by Tabatabai (1994) as modified by Lopes (2013). The analysis was performed using dried air-sampled soil.'", "keywords": ["Field crops", "plintossolo", "Plinthosol", "culturas de campo"], "contacts": [{"organization": "Ananias Soler da Silva, Mellissa, Ba\u00eata dos Santos, Alberto,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15277024"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15277024", "name": "item", "description": "10.5281/zenodo.15277024", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15277024"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-16T00:00:00Z"}}, {"id": "10.5281/zenodo.15680931", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:09Z", "type": "Journal Article", "created": "2025-06-15", "title": "Investigating the extent of PFAS contamination in the Upper Danube Basin across environmental compartments", "description": "Abstract Background Per- and polyfluoroalkyl substances (PFAS) are emerging organic pollutants widely detected in environmental systems, posing risks to human health and the ecosystem. Despite increasing efforts to monitor PFAS in river systems, knowledge gaps remain regarding sources and emissions via different pathways. This study investigates PFAS contamination across multiple environmental compartments in the Upper Danube Basin, including surface water, groundwater, wastewater, landfill leachate, surface runoff, and atmospheric deposition. The primary objectives are to assess the extent of PFAS contamination, identify key emission sources and transport pathways, and evaluate associated risks in terms of the potential exceedance of current and proposed environmental regulatory thresholds in the European Union.
Results The findings reveal a widespread presence of PFAS, with PFOA, PFOS and short-chain compounds being predominant. The Alz River and Gendorf chemical park emerge as hotspots with far-reaching effects downstream, contributing significantly to diffuse legacy contamination of PFOA and being a significant source of two industrial PFOA substitutes, ADONA and GenX. Wastewater treatment plants, old municipal landfills, and sites with a history of fire-fighting foam application are identified as key pathways or sources of legacy pollution, exhibiting higher concentrations compared to the other matrices. Notably, no significant removal is observed when comparing influent and effluent samples from conventional WWTPs. The study further demonstrates that groundwater is vulnerable to contamination from point sources and to infiltration from rivers, with bank filtration proving largely ineffective in preventing PFAS contamination.
Conclusions The study underscores the necessity for source and pathway control measures to mitigate PFAS pollution, the implementation of advanced treatment technologies to safeguard drinking water and surface water quality, and targeted remediation for legacy soil and groundwater contamination. Additionally, strong use regulations should be explored to minimize ongoing emissions. The multi-compartment monitoring proves to be a crucial approach to understand the complexity of PFAS distribution at the catchment scale. Comparative analysis and risk assessment highlight challenging situations for water management, offering an indispensable basis for emission modeling as a next step for quantitative assessment of the relevance of different sources and pathways for surface water pollution.
", "keywords": ["Emerging contaminants", "Emerging Pollutants", "PFAS", "Source identification", "Watershed management", "Environmental sciences", "Emission", "Water Framework Directive", "Environmental law", "Water pollution", "GE1-350", "K3581-3598", "Catchment monitoring", "Environmental Monitoring"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1186/s12302-025-01141-6.pdf"}, {"href": "https://doi.org/10.5281/zenodo.15680931"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Sciences%20Europe", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15680931", "name": "item", "description": "10.5281/zenodo.15680931", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15680931"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.15772619", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:11Z", "type": "Dataset", "title": "Dataset to: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Near-Infrared (NIR) Austrian Soil Spectral Library (ASSL, 680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL \u2018Towards climate-smart sustainable management of agricultural soils\u2019 (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "Spectroscopy", " Near-Infrared", "data"], "contacts": [{"organization": "Fohrafellner, Julia", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15772619"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15772619", "name": "item", "description": "10.5281/zenodo.15772619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15772619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-07T00:00:00Z"}}, {"id": "10.5281/zenodo.15797289", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:12Z", "type": "Dataset", "title": "Database of soil characteristics under specific pesticide management", "description": "Database of soil characteristics under specific pesticide management Contributors: Mark\u00e9ta Mayerov\u00e1 and Veronika \u0158ez\u00e1\u010dov\u00e1 Affiliation: Czech Agrifood Research Center, Drnovsk\u00e1 507/73, CZ-160 00 Prague 6, Czech Republic Database of soil characteristics will contribute to the realization of the project\u00b4s goal to identify appropriate and inappropriate pesticides from the point of the view of their impact on the non-target organisms and soil characteristics. Field I. The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The experiment took place at the site of the experiment that had been running there since 2013 and included 5 different herbicide treatments in 4 replications (Mayerov\u00e1 et al. 2018) The new trial area was split into 20 randomised plots with 2 different herbicide treatments in 8 replications and control without herbicides in 4 replications. Herbicide treatments differed in the mode of action (Table 1) Table 1. Summary of the herbicides and active ingredients used in the trial. Classification Group by Herbicide Resistance Action Committee (HRAC). herbicide dose formulation active ingredient content of a.i. HRAC group target weeds Agritox 50 SL 1.5 l/ha EC MCPA 500 g/l O dicot Glean 75 PX 15 g/ha WG chlorsulfuron 750 g/l B dicot + annual grasses \u00a0 The area of each plot was 100 m2 and the 10 by 10 m plots were separated from field boundaries and from each other by 2 m on all sides to eliminate interaction between plots. Herbicides were applied post emergency in spring (April 26, 2024) from the tillering crop stage to the beginning of stem elongation (BBCH 21\u201331) by the Agrio-Napa 12 sprayer. Winter wheat was grown in the experimental field in 2024. At the beginning of March, it was mineral fertilized with LAD (ammonium nitrate with dolomite - NH4NO3\u00a0+\u00a0CaMg (CO3)2; 27 % N) at a dose 100 kg/ha. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer in each replication before herbicide application (April 24, 2024), 14 days after herbicide application (May 9, 2024) and 7 weeks after herbicide application (June 14, 2024). \u00a0A total of 20 soil samples were collected at each sampling. The soil samples were subsequently dried and sieved through a 2 mm sieve, thus simultaneously homogenised. The following soil properties were determined: pH (H2O), electric conductivity, available P and K, concentration NH4 and NO3, soil organic carbon, and total organic nitrogen content. Available P and K were assessed according to the Mehlich III method (Mehlich, 1984) on an Agilant ICP-OES 5110 VDV instrument. NO3 and NH4 were determined using calcium chloride solution as extractant according to ISO 14255:1998 on automated chemistry analyser SKALAR. Soil organic carbon and soil organic matter content were determined by sulfochromic oxidation according to ISO 14235:1998. Field II The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The total area of the experiment is about 11 ha including the protective area around the entire experiment. The experimental area is divided into two halves, 120m wide and 300m long.\u00a0 One half was treated on June 17, 2024, with insecticide Decis forte (active ingredient deltamethrin) at a dose 62.5ml/ha, the other half was without insecticide treatment. Both areas are further divided into other halves. One half was treated on May 15, 2024, with herbicide Agritox (active ingredient MCPA) at a dose 1.5l/ha, the other was treated with hoeing only. We thus obtained 4 strips 60m wide with following treatment combinations: (A) herbicide + insecticide; (B) hoeing + insecticide; (C) hoeing; (D) herbicide. Spring wheat was grown in the experimental field in 2024. It was fertilized with mineral nitrogen at a dose of 150 kg N/ha before sowing and with 39 kg N/ha (DAM 390 - ammonium nitrate with urea) in the tillering phenophase. In the middle of each strip (i.e. treatment), 8 sampling sites were marked in a row, 20 m apart from each other. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer at each sampling site 14 days after herbicide application and 14 days after insecticide application. A total of 32 soil samples were collected at each sampling. Further sample processing was the same as for Field I. The database will be gradually supplemented in the following years. Funding: Development for this work is funded primarily by the Technology Agency of the Czech Republic, project SS07020100: \u201cThe impact of plant protection products on non-target biodiversity: soil microorganisms, invertebrates and wild plants\u201d, and the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO0425. The database was approved on September 2, 2025, by the Ministry of Agriculture of the Czech Republic. References: Mayerov\u00e1 M., Mikulka J., Soukup J. (2018): Effects of selective herbicide treatment on weed community in cereal crop rotation. Plant Soil Environ., 64: 413\u2013420. https://doi.org/10.17221/289/2018-PSE \u00a0Mehlich A. (1984): Mehlich 3 Soil Test Extractant. A Modification of the Mehlich 2 Extractant. Commun. Soil Sci. Plant Anal. 15, 1409-1416. http://dx.doi.org/10.1080/00103628409367568.", "keywords": ["field trial", " herbicides", " insecticides", " soil properties"], "contacts": [{"organization": "Mayerov\u00e1, Mark\u00e9ta, \u0158ez\u00e1\u010dov\u00e1, Veronika,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15797289"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15797289", "name": "item", "description": "10.5281/zenodo.15797289", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15797289"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-03T00:00:00Z"}}, {"id": "10.5281/zenodo.15835321", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:13Z", "type": "Dataset", "title": "Single- and double-cropping soybean production systems in Brazil: EPIC-IIASA GGCM simulations", "description": "Soy production in Brazil continues to expand in response to growing global demand. This expansion has been enabled by improved soy varieties and greater agricultural flexibility, allowing for practices such as double-cropping. Being able to model recent past areal expansion and productivity increase of these cropping systems requires a good assessment of their productivity and environmental externalities (e.g., soil organic carbon, soil erosion, nutrient leaching) under alternative management practices. To assess the productivity and environmental impacts of soybean production systems in Brazil, we employed a gridded modelling framework EPIC-IIASA GGCM, based on the process-based model EPIC (Environmental Policy Integrated Climate, https://epicapex.tamu.edu/about/epic). The analysis included key cropping systems and conservation practices commonly used in Brazil today: soy mono-cropping, no-till double cropping with corn, and no-till soy cultivation with pearl millet cover cropping. Additionally, we evaluated the role of irrigation and cultivar transitions as management options to support the sustainable transformation of soybean production. Simulations were conducted on a 0.5\u00b0 resolution grid covering the period from 1982 to 2016, contributing to Deliverable 6.1, \u201cNon-food biomass production based on biophysical modelling\u201d, developed under the CLEVER project (Creating Leverage to Enhance Biodiversity Outcomes of Global Biomass Trade), funded by the Horizon Europe programme (Topic: HORIZON-CL6-2021-BIODIV-01-15, Project Number: 101060765). Among other goals, the CLEVER project seeks to enhance biodiversity outcomes through improved modelling of global biomass trade. These files are included: 1) CLEVER_BRA_SOY_YLD.zip including 20 NetCDF files containing gridded soybean yield data modelled for soybean production systems, based on the simulation design described in the ReadMe file 2) CLEVER_BRA_SOY_ENVI.zip including 60 NetCDF files containing gridded environmental externality data modelled for soybean production systems, based on the simulation design described in the ReadMe file. 3) ReadMe.docx: Simulation design and metadata description.", "keywords": ["Yield (agricultural)", "Sustainable agriculture", "Computer Simulation"], "contacts": [{"organization": "Balkovic, Juraj, Skalsky, Rastislav, Folberth, Christian, Oberleitner, Thomas,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15835321"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15835321", "name": "item", "description": "10.5281/zenodo.15835321", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15835321"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-08T00:00:00Z"}}, {"id": "10.5281/zenodo.16017208", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:14Z", "type": "Dataset", "title": "Cashew orchard soil properties, Dodamarg, Northern Western Ghats, India", "description": "Soil properties of cashew orchards of the Northern Western Ghats, India This project contains chemical properties of soil collected from cashew orchards of Dodamarg, Northern Western Ghats, for a study investigating the factors influencing the effects of forest cover, flower abundance, temperature and (potentially) soil composition on cashew pollinators. Taxonomic Coverage:\u00a0Not applicable Geographic Coverage: Dodamarg, Sindhudurg District, Maharashtra, India Temporal Coverage: March 2025 \u00a0 Description of field and lab methods Soil collection: Soil samples were collected from 30 cashew orchards, 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 orchard, 10 soil columns were collected for analysis of chemical properties. Chemical Properties: We estimated thirteen 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). More details about the data can be obtained from Aditya Satish (adityasatish@ncf-india.org) and Rohit Naniwadekar (rohit@ncf-india.org) from the Nature Conservation Foundation (www.ncf-india.org). File Descriptions: Data file: Dodamarg_2025_Cashew_Soil_Properties.csv We have also included a ReadMe.txt file that explains the data file, akin to the description in the metadata. Description of the columns of the data file: Sl no: Serial number Site: Site ID Code: Site code (General location) Latitude: latitude co-ordinate of the plot (in decimal degrees, \u00b0N) Longitude: longitude co-ordinate of the plot (in decimal degrees, \u00b0E) pH: pH of the soil E.C.: Electrical Conductivity at 25\u00b0C (in dS/m) O.C.: Organic Carbon (in %) P\u2082O\u2085: Available P\u2082O\u2085 (in Kg /acre) K\u2082O: Available Potassium (in Kg /acre) Ca: Available Calcium (in mg/Kg) Mg: Available Magnesium (in mg/Kg) S: Available Sulphur (in mg/Kg) B: Available Boron (in mg/Kg) Zn: Available Zinc (in mg/Kg) Fe: Available Iron (in mg/Kg) Cu: Available Copper (in mg/Kg) Mn: Available Manganese (in mg/Kg) Funding:\u00a0 Godrej Consumer Products Limited Arvind Datar Rohini Nilekani Philanthropies", "keywords": ["Soil chemical properties", "Cashew orchards", "Ecology", "FOS: Biological sciences", "Northern Western Ghats"], "contacts": [{"organization": "Sadekar, Vishal, Satish, Aditya, Naniwadekar, Rohit,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16017208"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16017208", "name": "item", "description": "10.5281/zenodo.16017208", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16017208"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16261617", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:14Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16261617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16261617", "name": "item", "description": "10.5281/zenodo.16261617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16261617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.17941270", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:24Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17941270"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17941270", "name": "item", "description": "10.5281/zenodo.17941270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17941270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:37Z", "type": "Report", "created": "2020-03-09", "title": "Hyperspectral imaging for high resolution mapping of soil profile organic carbon distribution in an Austrian Alpine landscape", "description": " <p>Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (< 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.</p>
", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.5281/zenodo.7598122", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:55Z", "type": "Software", "title": "EJP MTE/LTE metadataset v1.0.1", "description": "Initial release of the analysis on mid-term and long-term field experiments collected within task 7.3 of EJP SOIL. Data was acquired and collected thanks to National Coordinators and LTE owners (sorted by country alphabetically) from Austria (Pia Euteneuer, Lisa Makoschitz), Belgium (Joran Barbry, Franky Coopman, Tommy D'Hose, Bruno Huyghebaert, Stijn Martens, Joris De Nies, Bert Reubens, Veerle De Rycke, Tomas Vandesande), Czech Republic (Ladislav Men\u0161\u00edk), Denmark (Bent T. Christensen, J\u00f8rgen Eriksen, Uffe J\u00f8rgensen, Lars J. Munkholm, J\u00f8rgen E. Olesen, Gitte H. Rub\u00e6k), Estonia (Alar Astover, Karin Kauer, Liina Talgre), Finland (Riitta Lemola), France (Aurore BRUT, Sabine Houot, Frida Keuper, Katja Klumpp, Frederic Launay, Frederique Louault, Thierry Morvan, Tiphaine TALLEC, Francoise Watteau), Hungary (T\u00f3th Eszter, Istv\u00e1n Henzsel, S\u00e1ndor Ko\u00f3s, Bal\u00e1zs Madar\u00e1sz, Mariann Mak\u00e1di, L\u00e1szl\u00f3 Radimszky, P\u00e9ter Rag\u00e1lyi, Anita Szab\u00f3, Melinda Tar, Nikolett Uzinger), Ireland (David Wall), Italy (Roberto Barbetti, Gianluca Carboni, Mariangela Diacono, Paolo Mul\u00e9, Gaio Cesare Pacini, Baronti Silvia, Vincenzo Tabaglio, Domenico Ventrella, Nadia Vignozzi, Laura Zavattaro), Latvia (Janis Vigovskis), Lithuania (Virginijus Feiza, Grazina Kadziene, Danute Karcauskiene, Virmantas Povilaitis), Netherlands (Phillip Ehlert, Willem van Geel, Jantine van Middelkoop, Rene Schils, Wieke Vervuurt, Marie Wesselink), Norway (Trond Maukon Henriksen, Annbj\u00f8rg \u00d8verli Kristoffersen), Poland (Jacek Nied\u017awiecki), Portugal (Nadia Castanheira, Raquel Mano), Slovakia (Jaroslava Sobocka), Slovenia (Rok Miheli\u010d), Spain (Jorge Alvaro-Fuentes, Jose A. Gomez, Carlos Garcia Izquierdo, C\u00e9sar Plaza, In\u00e9s Sant\u00edn-Montany\u00e1), Sweden (Helena Aronsson, \u00d6rjan Berglund, Sabina Braun), Turkey (Ibrahim Ortas) and UK (Catalina Estrada, Dario Fornara, Jane Hawkins, Gareth Griffith, Marecia, Andy McDonald, Jonathan Millett, Robin Pakeman, Raj Whitlock).", "keywords": ["long-term field experiment", "LTE", "EJP MTE/LTE dataset", "meta-dataset", "mid-term field experiment"], "contacts": [{"organization": "Blanchy, Guillaume, D'Hose, Tommy, Donmez, Cenk, Hoffmann, Carsten, Makoschitz, Lisa, Murugan, Rajasekaran, O'Sullivan, Lilian, Sand\u00e9n, Taru, Spiegel, Adelheid, Svoboda, Nikolai, Zechmeister-Boltenstern, Sophie, Klumpp, Katja,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7598122"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7598122", "name": "item", "description": "10.5281/zenodo.7598122", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7598122"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-02T00:00:00Z"}}, {"id": "10.5281/zenodo.8057232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:59Z", "type": "Dataset", "title": "Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning", "description": "Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: \u251c\u2500\u2500 available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
\u251c\u2500\u2500 available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
\u251c\u2500\u2500 caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
\u251c\u2500\u2500 caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
\u251c\u2500\u2500 cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 clay_30arc_30cm_us.tif [30 cm depth soil clay content]
\u251c\u2500\u2500 clay_30arc_100cm_us.tif [100 cm depth soil clay content]
\u251c\u2500\u2500 depthWT_30arc_us.tif [depth to water table]
\u251c\u2500\u2500 kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
\u251c\u2500\u2500 kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [100 cm depth soil pH]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [30 cm depth soil pH]
\u251c\u2500\u2500 pondingFre_30arc_us.tif [ponding frequency]
\u251c\u2500\u2500 sand_30arc_30cm_us.tif [30 cm depth soil sand content]
\u251c\u2500\u2500 sand_30arc_100cm_us.tif [100 cm depth soil sand content]
\u251c\u2500\u2500 silt_30arc_30cm_us.tif [30 cm depth soil silt content]
\u251c\u2500\u2500 silt_30arc_100cm_us.tif [100 cm depth soil silt content]
\u251c\u2500\u2500 water_content_30arc_30cm_us.tif [30 cm depth soil water content]
\u2514\u2500\u2500 water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: \u251c\u2500\u250030cm SOC mean.tif [30 cm depth soil SOC]
\u251c\u2500\u2500100cm SOC mean.tif [100 cm depth soil SOC]
\u251c\u2500\u250030cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
\u2514\u2500\u2500100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection: GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]]
", "keywords": ["gNATSGO", "the United States SOC", "US soil properties", "15. Life on land", "Gridded National Soil Survey Geographic Database", "International Soil Carbon Network (ISCN)"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8057232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8057232", "name": "item", "description": "10.5281/zenodo.8057232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8057232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8089699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:27:00Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8089699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089699", "name": "item", "description": "10.5281/zenodo.8089699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.7910/DVN/MIYBQE", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:00Z", "type": "Dataset", "title": "Ecogeographic land characterization map of the SADC region", "description": "With the aim of planning for the in situ and ex situ conservation of priority crop wild relatives (CWR) of the Southern African Development Community (SADC), a gap analysis at intra-specific level (i.e. ecogeographic diversity level used as a proxy of genetic diversity), was carried out. For this purpose, a generalist Ecogeographic Land Characterization (ELC) map for the SADC region was created using the ELC mapas tool of CAPFITOGEN (http://www.capfitogen.net/, Parra-Quijano et al., 2008, 2016) based on 16 ecogeographic variables from three different components (four geophysic variables, seven edaphic, and five bioclimatic; see the list below) at a resolution of 2.5 arc minutes (approximately 4.5 km at the equator). The Calinski-Harabasz (1974) criterion was applied to obtain an objective number of clusters for each bioclimatic, edaphic and geophysic multivariate analysis. The ELC map was then clipped to the SADC countries using ArcGIS 10.4.1 (ESRI, 2016). A total of 16 ecogeographic categories were identified in the SADC region with distinct ecogeographic characteristiscs (see file 'ELC_SADC_region_statistics.xlsx'). The files made available here include: the raster file of the ELC map of the SADC region (which is composed of 16 different files) and an Excel file which describes the statistics (i.e. average, median, maximum, minimum and standard deviation) of each ecogeographic category present in the map ('ELC_SADC_region_statistics.xlsx').<br> <br><b>Variables:</b> Geophysic: altitude (m) (WorldClim 1.4, http://worldclim.org), slope (\u00b0), latitude (decimal degrees), longitude (decimal degrees). Edaphic: topsoil organic carbon (% weight), topsoil pH (H2O) [-log(H+)], topsoil silt fraction (% weight), topsoil sand fraction (% weight), topsoil gravel content (% vol.), topsoil clay fraction (% weight), topsoil TEB (total exchangeable bases) (cmol/kg) (HWS Database, http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/). Bioclimatic: annual precipitation (bio_12) (mm), precipitation seasonality (coefficient of variation) (bio_15) (mm), isothermality (bio_2/bio_7) (*100) (bio_3), max temperature of warmest month (bio_5) (\u00b0C), min temperature of coldest month (bio_6) (\u00b0C) (WorldClim 1.4, http://worldclim.org).<br> <br><b>References:</b> Calinski T and Harabasz J (1974) A dendrite method for cluster analysis. Communications in Statistics, 3(1): 1\u201227. ESRI (2016) ArcGIS Desktop release Version 10.4.1. Environmental Systems Research Institute. Redlands. CA. Parra-Quijano M, Draper D and Torres E (2008) Ecogeographical representativeness in crop wild relative ex situ collections. In: Maxted N, Ford\u2010Lloyd BV, Kell SP, Iriondo JM, Dulloo E and Turok J (eds), Crop wild relative conservation and use, pp. 249\u201373. Wallingford: CAB International. Parra-Quijano M, Torres E, Iriondo JM, L\u00f3pez F and Molina A (2016) CAPFITOGEN tools user manual, version 2.0. Rome, Italy: International Treaty on Plant Genetic Resources for Food and Agriculture, FAO. Available at: http://www.capfitogen.net/en/access/manuals/ [Accessed July 2021].", "keywords": ["Agricultural Sciences", "PLANNING", "PLANT GENETIC RESOURCES", "AGROBIODIVERSITY", "GENETIC DIVERSITY AS RESOURCE"], "contacts": [{"organization": "Magos Brehm, Joana", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/MIYBQE"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/MIYBQE", "name": "item", "description": "10.7910/DVN/MIYBQE", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/MIYBQE"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/GVNJAB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:27:59Z", "type": "Dataset", "created": "2019-06-24", "title": "Physical topsoil properties in Murugusi, Western Kenya", "description": "Open Access<b>General:</b> Lab determined topsoil bulk density, contents of sand, clay and organic carbon in Murugusi, W. Kenya, together with spatial coordinates of where the soil samples were taken (rounded to the closest center point of a 250 m \u00d7 250 m raster). All lab analyses were carried out at the ILRI/CIAT lab in Nairob, Kenya. <br> <b>Soil sampling:</b> At each sample location, one composite topsoil sample was taken; three cores of 7 cm in diameter taken within an area of one square meter. The soil was taken from 0-0.2 m depth below any organic (O) horizon. <br> <b>Determination of soil properties:</b> The bulk density of the soil was determined by taking two undisturbed soil samples (0-10 cm and 10-20 cm depth) of known volume (100 cm2) and weighting them after air drying. Soil fractions of clay (<0.002 mm) and sand (0.05-2 mm) were determined by the hydrometer method (Estefan et al., 2014), using 10% sodium hexametaphosphate as the dispersing agent. Soil pH was determined potentiometrically on a soil suspension of 1:2 (soil: water). Total carbon was measured after dry combustion using an elemental analyser (Elementar Vario max cube; ISO 10694, first edition 1995-03-01) <br> <b>Reference: </b>Estefan G., Sommer R., Ryan J. (2014) Analytical Methods for Soil-Plant and Water in Dry Areas. A Manual of Relevance to the West Asia and North Africa Region. 3rd Edition, International Center for Agricultural Research in the Dry Areas, Aleppo, 255 pp. Available online at: http://repo.mel.cgiar.org:8080/handle/20.500.11766/7512?show=full. Verified: October 9, 2018. <br> <b>Acknowledgements: </b> We are deeply thankful for the good services provided by John Mukulama (soil sampling), John Yumbya Mutua (soil sampling) and Francis Mungthu Njenga (lab analyses) The project was carried out within the CGIAR Research Program on Water, Land and Ecosystems (WLE).", "keywords": ["Soil organic matter", "Agricultural Sciences", "Soil organic carbon", "sand", "Kenya", "Carbon", "Latin America and the Caribbean", "soil", "Soil", "Soil bulk density", "Sand", "soil organic matter", "Earth and Environmental Sciences", "Soil texture", "Murugusi", "Africa", "Clay", "Texture", "Western Kenya", "Agroecosystems and Sustainable Landscapes - ASL"], "contacts": [{"organization": "Piikki, Kristin, S\u00f6derstr\u00f6m, Mats, Sommer, Rolf, Da Silva, Mayesse,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/GVNJAB"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/GVNJAB", "name": "item", "description": "10.7910/DVN/GVNJAB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/GVNJAB"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/HXAH87", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:00Z", "type": "Dataset", "title": "Arbuscular and ectomycorrhizal fungi diversity in the Indian subcontinent", "description": "Mycorrhizal fungi (MF) are below-ground organisms playing a key role in terrestrial ecosystems as they regulate nutrient and carbon cycles, and influence soil structure and ecosystem multifunctionality. Arbuscular and ectomycorrhizal fungi are the two mycorrhizal types most relevant to worldwide ecosystems, but areas like the Indian sub-continent remain under-represented in global maps. The dataset presented here reports the available information regarding arbuscular and ectomycorrhizal fungi diversity in cultivated and natural ecosystems of the Indian subcontinent. We have selected studies published in English in ISI Web of Science during the years 2005 - 2020 that provided a taxonomic classification of MF and their associated abundance in terms of percentage of root colonization or number of spores per quantity of soil. From the screening of 74 studies, we have recorded: i. the scientific or common name of the plant or the generic habitat sampled for MF identification; ii the MF genus and species; iii. the location of the study with associated altitude and geographic coordinates; iv. main soil physico-chemical properties (soil pH, texture, organic Carbon, Total Nitrogen, available Phosphorus); climatic variables such as mean annual precipitation and temperature.<br><br>", "keywords": ["ecosystem management", "Asia", "Agricultural Sciences", "CGIAR Research Program on Water", " Land and Ecosystems", "Multifunctional Landscapes", "gesti\u00f3n de ecosistemas", "soil biology", "MYCORRHIZAE", "CGIAR Research Program", "Earth and Environmental Sciences", "SOIL BIOLOGY", "BIODIVERSITY", "mycorrhizae", "biolog\u00eda del suelo"], "contacts": [{"organization": "Beggi, Francesca, Dasgupta, Debarshi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/HXAH87"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/HXAH87", "name": "item", "description": "10.7910/DVN/HXAH87", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/HXAH87"}, {"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": "10.7910/DVN/T8CMAT", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:00Z", "type": "Dataset", "created": "2016-02-28", "title": "GMCSD-2. Global Mangrove Carbon, 2000 to 2012, 1 Arc-second, 1 m soil.", "description": "Open AccessGlobal Mangrove Carbon, 2000 to 2012, 1 Arc-Second, 1 m Soil, mid, EQ5. Annual stocks.
Each of these 13 years is 3TB when extracted. So that is 39 TB as a tif.
We needed to use file geodatabase format to compress enough to post on the Dataverse. Hence no TIffs.", "keywords": ["Earth and Environmental Sciences", "Raster", "ArcGIS file Geodatabase rasters", "Global Mangrove Carbon"], "contacts": [{"organization": "Hamilton, Stuart", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/T8CMAT"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/T8CMAT", "name": "item", "description": "10.7910/DVN/T8CMAT", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/T8CMAT"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/W9LSAD", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:01Z", "type": "Dataset", "created": "2015-01-01", "title": "Replication data: Zn efficient rice genotypes alter soil Zn availability, composition and Zn uptake in Zn-deficient and Zn-sufficient field soils under continuous flooding", "description": "Open Accessapplication/vnd.ms-excel, null", "keywords": ["biofortification", "Agricultural Sciences", "zinc deficiency", "Oryza sativa"], "contacts": [{"organization": "Goloran, Johnvie", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/W9LSAD"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/W9LSAD", "name": "item", "description": "10.7910/DVN/W9LSAD", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/W9LSAD"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-24T00:00:00Z"}}, {"id": "11572/255256", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:44Z", "type": "Journal Article", "created": "2019-09-23", "title": "Elastica catastrophe machine: theory, design and experiments", "description": "Open Access31 pages, 18 figures", "keywords": ["Nonlinear mechanics; Snap mechanisms; Structural instability", "0203 mechanical engineering", "FOS: Physical sciences", "02 engineering and technology", "Chaotic Dynamics (nlin.CD)", "Nonlinear Sciences - Chaotic Dynamics", "0210 nano-technology"]}, "links": [{"href": "https://iris.unitn.it/bitstream/11572/255256/1/1-s2.0-S002250961930523X-main.pdf"}, {"href": "https://doi.org/11572/255256"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20the%20Mechanics%20and%20Physics%20of%20Solids", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11572/255256", "name": "item", "description": "11572/255256", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11572/255256"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "11586/524923", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:48Z", "type": "Journal Article", "created": "2024-12-03", "title": "Addressing the environmental sustainability of plastics used in agriculture: a multi-actor perspective", "description": "Abstract
Plastics used in agriculture, commonly known as agriplastics (AP), offer numerous advantages in terrestrial agriculture, forestry, fisheries and aquaculture, but the diffusion of AP-intensive practices has led to extensive pollution. This review aims to synthesise scientific and policy discussions surrounding AP, examining evidence of their benefits and detrimental environmental and agricultural impacts. Following the proposal of a preliminary general taxonomy of AP, this paper presents the findings from a survey conducted among international experts from the plastic industry, farmer organisations, NGOs and environmental research institutes. This analysis highlights knowledge gaps, demands and perspectives for the sustainable future use of AP. Stakeholder positions vary on the options of \uffe2\uff80\uff98rejection\uffe2\uff80\uff99 or \uffe2\uff80\uff98reduction\uffe2\uff80\uff99 of AP, as well as the role of alternative materials such as (bio)degradable and compostable plastics. However, there is consensus on critical issues such as redesign, labelling, traceability, environmental safety standards, deployment and retrieval standards, as well as innovative waste management approaches. All stakeholders express concern for the environment. A \uffe2\uff80\uff98best practice\uffe2\uff80\uff99-based circular model was elaborated capturing these perspectives. In the context of global food systems increasingly reliant on AP, scientists emphasise the need to simultaneously preserve nature-based and traditional knowledge-based sustainable agricultural practices to enhance food system resilience.Abstract. Boreal fire regimes are intensifying because of climate change and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in Eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean = 127.3 cm, standard deviation (sd) = 27.7 cm) than in unburned (98.1 cm, sd = 26.9 cm) landscapes one year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained landscape positions, in open larch forest often intermixed with Scots pine, and in high severity burns. The environmental drivers, site moisture, forest type and density, and fire severity explained 73.4 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced Normalized Burn Ratio (dNBR), land surface temperature (LST), and pre-fire Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar, and found that LST displayed particularly strong correlations with post-fire thaw depth (r = 0.65, p < 0.01). Our study reveals some of the governing processes of post-fire thaw depth development and shows the capability of Landsat imagery to estimate thaw depth at a landscape scale.
", "keywords": ["Dynamic and structural geology", "QE1-996.5", "13. Climate action", "Science", "Q", "Geology", "QE500-639.5", "Deforestation", "15. Life on land", "Landsat", "Multiple linear regression", "Atmospheric temperature"]}, "links": [{"href": "https://esd.copernicus.org/articles/15/1459/2024/esd-15-1459-2024.pdf"}, {"href": "https://doi.org/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Dynamics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "name": "item", "description": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-21T00:00:00Z"}}, {"id": "1871.1/a9511e83-fead-49ba-9642-c0295015d109", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:00Z", "type": "Report", "title": "Particulate organic matter dynamics and degradation in Arctic fluvial systems", "description": "The Arctic is warming two to four times the rate of global average. The increase in air temperatures causes permafrost (i.e., perennially frozen ground) to thaw and release previously frozen organic carbon (OC) to the contemporary carbon cycle. Permafrost stores large amounts of organic carbon (~1300 \u00b1 200 Pg), which equals up to half of the belowground OC globally. Re-mineralization of the released permafrost OC can add greenhouse gases (CO2, CH4) to the atmosphere enhancing climate warming. Gradual permafrost thaw happens when the active layer (i.e., the topmost layer of permafrost that thaws during summer months) deepens due to climate warming releasing largely dissolved organic carbon (DOC). On the contrary, in permafrost regions with high ground ice-content, permafrost thaw happens abruptly (i.e., thermokarst) as landscapes subside or collapse due to melting of ice. Abrupt permafrost thaw releases dominantly particulate organic carbon (POC). While degradation of DOC has been extensively studied in Arctic fluvial systems, degradation of POC is still poorly characterized. In this study, we investigate POC composition and degradation in two different areas: i) in the thaw streams draining abrupt permafrost thaw features, retrogressive thaw slumps (RTS), on the Canadian Peel Plateau, and ii) in the Kolyma River, which is one of the major Arctic rivers draining to the Arctic Ocean. We also study carbon dynamics and water chemistry parameters in lower order streams within the Kolyma watershed in two hydrologically distinct seasons: spring freshet and summer. We use macro(molecular) methods, pyrolysis \u2013 gas chromatography mass spectrometry and lipid biomarkers (n-alkanes, n-alkanoic acids), to analyse POC composition and degradation status. For further compositional analysis, we use carbon isotopes (\u03b413C, \u039414C). Additionally, we employ spatial data analysis and statistical modelling to characterize the watersheds and POC sources. Our results indicate that POC composition is seasonally dependent, and it defines biodegradability of POC. On the Peel Plateau, POC consists largely of aromatic moieties and includes petrogenic carbon that are not easily degradable. By contrast, Kolyma River POC degrades relatively fast during summer, when it is mostly of autochthonous sources. However, freshet POC, dominated by allochthonous POC, is not readily degradable. During freshet, DOC is susceptible to adsorption to particles and/or flocculation, potentially attenuating its climate impact. The lower order streams within the Kolyma River watershed react fast to increase in air temperatures during spring freshet with increased surface water temperatures and depletion in \u03b413C-POC, suggesting early onset of primary production. Changes in water temperature and \u03b413C-POC were not as pronounced in the Kolyma River. These results suggest that lower order streams may start primary production and POC degradation earlier in the season than the larger ones and thus, start emitting greenhouse gases earlier. The degraded POC is mostly autochthonous, and more studies are needed to investigate whether degradation of autochthonous POC may stimulate degradation of allochthonous or permafrost POC. These results highlight the heterogeneity of the Arctic fluvial networks and the differences in their response to climate warming.", "keywords": ["Organische koolstof", "Afbraak", "Organische koolstof in suspensie", "Permafrost", "Flocculation", "Kolyma Rivier", "Flocculatie", "Klimaatverandering", "Particulate organic carbon", "Adsorptie", "Degradation", "Kolyma River", "Arctic", "Peel Plateau", "Climate change", "Adsorption", "Arctisch", "Organic carbon"], "contacts": [{"organization": "Keskitalo, Kirsi Helena", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/1871.1/a9511e83-fead-49ba-9642-c0295015d109"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/a9511e83-fead-49ba-9642-c0295015d109", "name": "item", "description": "1871.1/a9511e83-fead-49ba-9642-c0295015d109", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/a9511e83-fead-49ba-9642-c0295015d109"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-09T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=ie&offset=50&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=ie&offset=50&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=ie&offset=0", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=ie&offset=100", "hreflang": "en-US"}], "numberMatched": 20421, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-06-27T14:14:41.851238Z"}