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"count": 229}, {"value": "nitrous oxide", "count": 190}, {"value": "methane", "count": 190}, {"value": "nitrate", "count": 119}, {"value": "ammonia", "count": 97}, {"value": "iron", "count": 72}, {"value": "potassium", "count": 68}, {"value": "zinc", "count": 65}, {"value": "copper", "count": 50}, {"value": "mineral fertilisers", "count": 49}, {"value": "cadmium", "count": 47}, {"value": "calcium", "count": 45}, {"value": "sulphur", "count": 37}, {"value": "urea", "count": 33}, {"value": "carbon stocks", "count": 32}, {"value": "magnesium", "count": 26}, {"value": "aluminium", "count": 21}, {"value": "manganese", "count": 17}, {"value": "boron", "count": 14}, {"value": "soil carbon stocks", "count": 13}, {"value": "cation exchange capacity", "count": 11}, {"value": "nitric acid", "count": 7}, {"value": "molybdenum", "count": 3}, {"value": "nutrients", "count": 3}, {"value": "soil nutrient availability", "count": 3}, {"value": "ammonium nitrogen", "count": 2}, {"value": "base cations", "count": 1}]}, "soil_biological_properties": {"type": "terms", "property": "soil_biological_properties", "buckets": [{"value": "plants", "count": 297}, {"value": "microbial biomass", "count": 138}, {"value": "vegetation", "count": 130}, {"value": "respiration", "count": 82}, {"value": "microbiome", "count": 64}, {"value": "soil organisms", "count": 39}, {"value": "nutrient turnover", "count": 23}, {"value": "biomass production", "count": 18}, {"value": "environmental compartments", "count": 17}, {"value": "rooting", "count": 15}, {"value": "biodiversity", "count": 7}, {"value": "soil animal diversity", "count": 5}, {"value": "necromass", "count": 4}, {"value": "soil biological activity", "count": 1}]}, "soil_physical_properties": {"type": "terms", "property": "soil_physical_properties", "buckets": [{"value": "water", "count": 715}, {"value": "drainage", "count": 48}, {"value": "soil stability", "count": 41}, {"value": "bulk density", "count": 38}, {"value": "aggregate stability", "count": 22}, {"value": "hydraulic conductivity", "count": 18}, {"value": "available water capacity", "count": 3}, {"value": "water infiltration", "count": 3}, {"value": "compaction susceptibility", "count": 2}, {"value": "rainwater infiltration", "count": 1}, {"value": "soil temperature fluctuations", "count": 1}]}, "soil_classification": {"type": "terms", "property": "soil_classification", "buckets": [{"value": "agricultural soils", "count": 71}, {"value": "forest soils", "count": 47}, {"value": "sandy soils", "count": 17}, {"value": "entisols", "count": 8}, {"value": "alfisols", "count": 4}, {"value": "natural soils", "count": 3}]}, "soil_functions": {"type": "terms", "property": "soil_functions", "buckets": [{"value": "soil fertility", "count": 170}, {"value": "decomposition", "count": 123}, {"value": "ecosystem services", "count": 107}, {"value": "crop yields", "count": 60}, {"value": "soil biodiversity", "count": 53}, {"value": "land cover change", "count": 48}, {"value": "food security", "count": 45}, {"value": "water conservation", "count": 42}, {"value": "productivity", "count": 40}, {"value": "climate resilience", "count": 25}, {"value": "water purification", "count": 24}, {"value": "plant nutrients", "count": 20}, {"value": "food production", "count": 13}, {"value": "species diversity", "count": 11}, {"value": "macronutrients", "count": 4}, {"value": "high nutrient status", "count": 1}]}, "soil_threats": {"type": "terms", "property": "soil_threats", "buckets": [{"value": "soil acidification", "count": 383}, {"value": "soil erosion", "count": 135}, {"value": "soil compaction", "count": 41}, {"value": "land degradation", "count": 39}, {"value": "soil degradation", "count": 38}, {"value": "soil pollution", "count": 35}, {"value": "waterlogging", "count": 32}, {"value": "contamination", "count": 31}, {"value": "urbanisation", "count": 21}, {"value": "soil sealing", "count": 19}, {"value": "antibiotics", "count": 18}, {"value": "desertification", "count": 17}, {"value": "acidification", "count": 14}, {"value": "disturbance", "count": 12}, {"value": "contaminants", "count": 8}, {"value": "environmental degradation", "count": 8}, {"value": "wind erosion rate", "count": 8}, {"value": "anthropogenic erosion", "count": 6}, {"value": "nutrient depletion", "count": 6}, {"value": "acidic precipitation", "count": 5}, {"value": "soil excavation", "count": 3}, {"value": "degraded soils", "count": 2}, {"value": "tillage erosion", "count": 2}, {"value": "soil organic carbon losses", "count": 2}, {"value": "soil physical degradation", "count": 2}]}, "soil_processes": {"type": "terms", "property": "soil_processes", "buckets": [{"value": "sedimentation", "count": 81}, {"value": "greenhouse gas emissions", "count": 38}, {"value": "biochemical processes", "count": 7}, {"value": "soil functioning", "count": 2}]}, "soil_management": {"type": "terms", "property": "soil_management", "buckets": [{"value": "cultivation", "count": 59}, {"value": "compost", "count": 40}, {"value": "plant residues", "count": 37}, {"value": "digestate", "count": 20}, {"value": "soil restoration", "count": 16}, {"value": "biomaterials", "count": 14}, {"value": "soil protection", "count": 10}, {"value": "sewage sludge", "count": 10}, {"value": "liming", "count": 7}, {"value": "soil rehabilitation", "count": 6}, {"value": "animal manure", "count": 5}, {"value": "crop residue input", "count": 1}, {"value": "ammonium-based fertilisers", "count": 1}]}, "ecosystem_services": {"type": "terms", "property": "ecosystem_services", "buckets": [{"value": "terrestrial ecosystems", "count": 32}, {"value": "ecosystem functioning", "count": 26}, {"value": "ecosystem functions", "count": 13}, {"value": "energy transformations", "count": 10}, {"value": "hydrological cycle", "count": 9}]}}, "features": [{"id": "10.21203/rs.3.rs-5128244/v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:28Z", "type": "Report", "created": "2024-09-23", "title": "Spatiotemporal prediction of soil organic carbon density for Europe (2000--2022) in 3D+T based on Landsat-based spectral indices time-series", "description": "<title>Abstract</title>                 <p>The paper describes a comprehensive framework for soil organic carbon density (SOCD) (kg/m3) modeling and mapping, based on spatiotemporal Random Forest (RF) and Quantile Regression Forests (QRF). 22,428 SOCD measurements and a wide range of covariate layers\u2014particularly the 30m Landsat-based spectral indices were used to fit models and produce 30~m SOCD maps for the entire EU at four-year intervals from 2000 to 2022 and for four soil depth intervals (0--20cm, 20--50cm, 50--100cm, and 100--200cm) each accompanied by per-pixel 95% probability prediction intervals (PI, between P0.025 and P0.975). The results of model evaluation indicate consistent accuracy of the predictions: based on both 5--fold spatial cross-validation with model refitting (MAE = 8.64 kg/m3 , MedAE = 4.31 kg/m3 , MAPE = 0.54 kg/m3 and bias = -2.95 kg/m3 ), and on independent testing (MAE = 7.73 kg/m3 , MedAE = 3.54 kg/m3 , MAPE = 0.45 kg/m3 , and bias = -3.04 kg/m3), with both R2 values exceeding 0.7 and concordance correlation coefficients (CCC) greater than 0.8. Validation of PI estimation confirmed that PIs effectively capture uncertainty intervals, although with reduced accuracy for higher SOCD values. Exploratory analysis using Shapley values identified soil depth as the most important feature, with vegetation (Landsat biophysical indices) and long-term bio-climate features as the two main contributing feature groups. Although the uncertainty of the prediction per pixel is significant, further spatial aggregation has been shown to reduce the uncertainty by about 70%. Suggested uses of the data include: (1) time-series / trend analysis to detect potential land degradation hotspots, (2) optimization of sampling designs based on prediction uncertainty, and (3) prediction of future soil carbon potential by extrapolating models under different land use / climate scenarios. The data and code used are publicly available under an open license from https://doi.org/10.5281/zenodo.13754344 and https://github.com/AI4SoilHealth/SoilHealthDataCube/.</p>", "contacts": [{"organization": "Tian, Xuemeng, de Bruin, Sytze, Simoes, Rolf, Isik, Mustafa Serkan, Minarik, Robert, Ho, Yu-Feng, \u015eahin, Murat, Herold, Martin, Consoli, Davide, Hengl, Tomislav,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.21203/rs.3.rs-5128244/v1"}, {"rel": "self", "type": "application/geo+json", "title": "10.21203/rs.3.rs-5128244/v1", "name": "item", "description": "10.21203/rs.3.rs-5128244/v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.21203/rs.3.rs-5128244/v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-24T00:00:00Z"}}, {"id": "10.2134/agronj2010.0504", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:34Z", "type": "Journal Article", "created": "2011-07-12", "title": "Western Oregon Grass Seed Crop Rotation And Straw Residue Effects On Soil Quality", "description": "<p>Understanding the impact of crop rotation and residue management in grass seed production systems on soil quality and, in particular soil C dynamics, is critical in making long\uffe2\uff80\uff90term soil management decisions supporting farm sustainability. The effects of a 6\uffe2\uff80\uff90yr rotation and residue management (high vs. low residue) on soil quality were investigated at three locations in Oregon, each contrasting in soil drainage classification. The crop rotations were continuous perennial grass seed production, grass/legume seed production, and grass/legume/cereal seed production. The grass species grown at each location were different and represented those most commonly produced in each environment; perennial ryegrass (Lolium perenne L.), tall fescue [Schedonorus phoenix (Scop.) Holub], and creeping red fescue (Festuca rubra L.). All three grass seed crop rotations and residue methods maintained high soil quality in conventional or direct seeded soils, but under some situations, soil quality was higher with continuous grass rotation and high residue. Data suggest that straw removal for value\uffe2\uff80\uff90added use, like bioenergy production, can be accomplished in the Pacific Northwest Marine climate without appreciably affecting soil quality. Furthermore, grass seed cropping systems play an important role in soil C storage and enhancement, a valuable ecosystem service in this region where grass seed is produced on land that is not suitable for production of conventional crops that require better\uffe2\uff80\uff90drained soil. We conclude that by nature perennial grass seed crops promote high soil fertility and enriched soil C pools and consequently contribute to the tolerance of these systems to the use of less conservation\uffe2\uff80\uff90oriented crop management methods at times when crop loss could be potentially high. This attribute provides producers greater latitude in selecting soil and crop management options to address issues of soil fertility, pest, weed, or seed certification to minimize economic crop yield losses.</p>", "keywords": ["2. Zero hunger", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Gerald Whittaker, Richard P. Dick, Gary M. Banowetz, Stephen M. Griffith, George W. Mueller-Warrant,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2134/agronj2010.0504"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2134/agronj2010.0504", "name": "item", "description": "10.2134/agronj2010.0504", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2134/agronj2010.0504"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-07-01T00:00:00Z"}}, {"id": "10.2136/sssaj1995.03615995005900050022x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:44Z", "type": "Journal Article", "created": "2010-07-27", "description": "Abstract<p>Long\uffe2\uff80\uff90term N fertilization affects soil organic N reserves, N mineralization potential, and crop response to applied N, but little information is available on the influence of short\uffe2\uff80\uff90term N fertilizer (STN) management on soil organic N availability and crop response. This study was conducted to determine if STN changes soil N supplying capability to corn (Zea mays L.) after 3 yr of differential N fertilization on a Fayette silt loam soil (fine\uffe2\uff80\uff90silty, mixed, mesic Typic Hapludalf) in Wisconsin. Various rates of N fertilizer (0\uffe2\uff80\uff93402 kg N ha\uffe2\uff88\uff921) were applied to corn in 1983, 1984, and 1985, and their residual effects on corn response were evaluated in 1986. Soil profile No3\uffe2\uff80\uff90N levels in spring 1986 were very low in all plots (48 \uffc2\uffb1 4 kg ha\uffe2\uff88\uff921 [90 cm]\uffe2\uff88\uff921), yet grain yields and N uptake were significantly increased by STN applications. Corn N uptake was linearly related to the total amount of N returned to soil in crop residues during the previous 3 yr. Increased organic N availability under high STN management was equivalent to a 78 kg N ha\uffe2\uff88\uff921 rate, or 47% of the N fertilizer required for optimum crop yields. In aerobic incubations (40 wk) of spring 1986 soil (0\uffe2\uff80\uff9330 cm), STN additions increased N release only in the first few weeks. Kinetics of N mineralization were best described by a two\uffe2\uff80\uff90component model in which the active fraction (NA) of soil organic N was highly correlated with corn N uptake (r = 0.88). Simulation of field conditions showed that 95% of NA is available before crop maturity. A phosphate\uffe2\uff80\uff90borate buffer organic N availability index was significantly and consistently related to STN treatments. Relative increases in total soil organic N corresponded with the 3\uffe2\uff80\uff90yr N balance between fertilizer additions and grain removals, and were about 10 times larger than mineralizable N. These results indicate that immobilization of excess mineral N into stable soil organic N during decomposition of crop residues should be considered in determining the environmental risk of N fertilization. Although labile organic N is a small fraction of the total fertilizer N contribution to soil N, its quantification should allow a more accurate assessment of crop N needs.</p>", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.2136/sssaj1995.03615995005900050022x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Science%20Society%20of%20America%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2136/sssaj1995.03615995005900050022x", "name": "item", "description": "10.2136/sssaj1995.03615995005900050022x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2136/sssaj1995.03615995005900050022x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1995-09-01T00:00:00Z"}}, {"id": "10.2136/sssaj2007.0248", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:51Z", "type": "Journal Article", "created": "2008-05-30", "title": "Long-Term Effects Of Harvesting Maize Stover And Tillage On Soil Quality", "description": "<p>Rising concerns about greenhouse gases, increased fuel prices, and the potential for new high value agricultural products have raised interest in the use of maize stover for bioenergy production. However, residue harvest must be weighed against potential negative impacts on soil quality. This study, conducted in Chazy, NY, evaluated the long\uffe2\uff80\uff90term effects of 32 yr of maize (Zea maysL.) stover harvest vs. stover return on soil quality in the surface layer (5\uffe2\uff80\uff9366 mm) under plow till (PT) and no\uffe2\uff80\uff90till (NT) systems on a Raynham silt loam (coarse\uffe2\uff80\uff90silty, mixed, active, nonacid, mesic Aeric Epiaquept) using physical, chemical, and biological soil properties as soil quality indicators. Twenty\uffe2\uff80\uff90five soil properties were measured, including standard chemical soil tests, aggregate stability (WSA), bulk density, (\uffcf\uff81b) penetration resistance (PR), saturated hydraulic conductivity (Ks), infiltrability (Infilt), several porosity indicators (aeration pores(PO &gt; 1000), soil water potential = \uffce\uffa8 &gt; \uffe2\uff88\uff920.36 kPa; air\uffe2\uff80\uff90filled pores at field capacity (PO &gt; 30), \uffce\uffa8 &gt; \uffe2\uff88\uff9210kPa; available water capacity (AWC), \uffe2\uff88\uff921500 &lt; \uffce\uffa8 &lt; \uffe2\uff88\uff9210 kPa), total organic matter (OM), parasitic (Nemparasitic) and beneficial nematode (Nembeneficial) populations, decomposition rate (Decomp), potentially mineralizable N (PMN) and easily extractable (EEG) and total glomalin (TG). Only eight indicators were adversely affected by stover harvest, and most of these effects were significant only under NT. Almost all indicators affected by stover removal were affected equally or more adversely by tillage. A total of 15 indicators were adversely affected by tillage. Results of this study suggest that, on a silt loam soil in a temperate climate, long\uffe2\uff80\uff90term stover harvest had lower adverse impacts on soil quality than long\uffe2\uff80\uff90term tillage. Stover harvest appears to be sustainable when practiced under NT management.</p>", "keywords": ["2. Zero hunger", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.2136/sssaj2007.0248"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Science%20Society%20of%20America%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2136/sssaj2007.0248", "name": "item", "description": "10.2136/sssaj2007.0248", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2136/sssaj2007.0248"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-07-01T00:00:00Z"}}, {"id": "10.2139/ssrn.4556085", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:56Z", "type": "Journal Article", "created": "2023-08-29", "title": "A Laser Diffractometry Technique for Determining the Soil Water Stable Aggregates Index", "description": "Open AccessPeer reviewed", "keywords": ["Water stable aggregates index", "Laser diffractometry", "Wet sieving", "Soil aggregates"]}, "links": [{"href": "https://doi.org/10.2139/ssrn.4556085"}, {"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": "10.2139/ssrn.4556085", "name": "item", "description": "10.2139/ssrn.4556085", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2139/ssrn.4556085"}, {"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": "10.2307/2425191", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:03Z", "type": "Journal Article", "created": "2006-04-24", "title": "Nutrient Limitations To Plant-Growth In A California Serpentine Grassland", "description": "Nitrogen and phosphorus were identified as nutrients limiting the growth of plants in an herbaceous community on serpentine soil. Potassium, sulfur and calcium additions had little or no effect. Nutrients were added, alone and in combination, to plots in the field and to greenhouse pots of serpentine soil containing the annual grasses, Bromus mollis and Vulpia microstachys. The absence of any effect due to added calcium indicates that calcium deficiency is not a universal explanation of the low primary production on serpentine soils. INTRODUCTION Wherever they are found, serpentine soils support a vegetation that differs from that on surrounding nonserpentine soils in productivity, floristic composition and often physiognomy. It is now clear that a considerable degree of variability in physical and chemical properties is found among soils classified as serpentine. Thus the phenomenon cannot be ascribed to a single cause but rather a family of causes that vary in importance from site to site. Most notable among these are low levels of the major nutrients, nitrogen, phosphorus and potassium, low levels of calcium combined with high magnesium, and high concentrations of the potentially toxic elements, nickel, chromium and cobalt (Proctor and Woodell, 1975). Serpentine outcrops are common in the California Coastal Mountains as part of the Franciscan formation (Page, 1966). Among these is an outcrop supporting an herbaceous plant community contained within the Jasper Ridge Biological Preserve. The low productivity and floristic uniqueness of this grassland has been well-documented by McNaughton (1968). The consequences of its low productivity for foliage canopy development and potential light competition were studied by Turitzin (1978). No one, however, has reported on the causes of its low productivity. The present study was undertaken to analyze nutrient limitations to plant growth on this serpentine soil. MATERIALS AND METHODS The Jasper Ridge Biological Preserve occupies half of a low-lying ridge on the eastern side of the Santa Cruz Mountains in San Mateo Co., Calif. The crest of the ridge, which reaches an elevation of 189 m, supports an extensive area of annual grassland. A serpentine outcrop, obliquely bisecting the ridge, is occupied by a mixture of annuals and herbaceous perennials. The climate is of the Mediterranean type with warm, dry summers and cool, wet winters. Plants are active from October until May. Nutrient limitations to productivity in the field were studied on experimental plots arranged in a randomized complete blocks design (Sokal and Rohlf, 1969). Treatments were applied to nine 0.25m2 square plots arranged in 1 x 2.5 m blocks that included an untreated control plot. Blocks were replicated six times. Nutrient supplements were applied on 12 January and 12 March 1975. On each occasion nitrogen, phosphorus or potassium, or various combinations were added as solutions of reagent grade NH4NO3 (28.6g m2),. NaH2PO4 * H20 (19.4g m-2) and K2S04 (18.5g m2). Calcium applied as powdered agricultural grade gypsum (calcium sulfate, 1OOg m-2) was added in January only. Aboveground biomass contained within a 0.09 m2 quadrat was harvested from the center of each plot during late April. 'Present address: University Honors Program and School of Life and Health Sciences, University of Delaware, Newark 1971 1.", "keywords": ["0106 biological sciences", "2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences"], "contacts": [{"organization": "Stephen N. Turitzin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2307/2425191"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/American%20Midland%20Naturalist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2307/2425191", "name": "item", "description": "10.2307/2425191", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2307/2425191"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1982-01-01T00:00:00Z"}}, {"id": "10.2489/jswc.72.4.361", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:07Z", "type": "Journal Article", "created": "2017-06-24", "description": "Cover cropping is a widely promoted strategy to enhance soil health in agricultural systems. Despite a substantial body of literature demonstrating links between cover crops and soil biology, an important component of soil health, research evaluating how specific cover crop species influence soil microbial communities remains limited. This study examined the effects of eight fall-sown cover crop species grown singly and in multispecies mixtures on microbial community structure and soil biological activity using phospholipid fatty acid (PLFA) profiles and daily respiration rates, respectively. Fourteen cover crop treatments and a no cover crop control were established in August of 2011 and 2012 on adjacent fields in central Pennsylvania following spring oats (Avena sativa L.). Soil communities were sampled from bulk soil collected to a depth of 20 cm (7.9 in) in fall and spring, approximately two and nine months after cover crop planting and prior to cover crop termination. In both fall and spring, cover crops led to an increase in total PLFA concentration relative to the arable weed community present in control plots (increases of 5.37 nmol g\u22121 and 10.20 nmol g\u22121, respectively). While there was a positive correlation between aboveground plant biomass (whether from arable weeds or cover crops) and total PLFA concentration, we also found that individual cover crop species favored particular microbial functional groups. Arbuscular mycorrhizal (AM) fungi were more abundant beneath oat and cereal rye (Secale cereale L.) cover crops. Non-AM fungi were positively associated with hairy vetch (Vicia villosa L.). These cover crop-microbial group associations were present not only in monocultures, but also multispecies cover crop mixtures. Arable weed communities were associated with higher proportions of actinomycetes and Gram-positive bacteria. Soil biological activity varied by treatment and was positively correlated with both the size and composition (fungal:bacterial ratio) of the microbial community. This research establishes a clear link between cover crops, microbial communities, and soil health. We have shown that while cover crops generally promote microbial biomass and activity, there are species-specific cover crop effects on soil microbial community composition that ultimately influence soil biological activity. This discovery paves the way for intentional management of the soil microbiome to enhance soil health through cover crop selection.", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.2489/jswc.72.4.361"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Soil%20and%20Water%20Conservation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2489/jswc.72.4.361", "name": "item", "description": "10.2489/jswc.72.4.361", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2489/jswc.72.4.361"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-01T00:00:00Z"}}, {"id": "10261/216656", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:46Z", "type": "Other", "title": "ORUSCAL: RUSLE calculator for orchards", "description": "Open AccessPeer reviewed", "keywords": ["15. Life on land"], "contacts": [{"organization": "G\u00f3mez Calero, Jos\u00e9 Alfonso, Biddoccu, Marcella, Guzm\u00e1n, Gema,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10261/216656"}, {"rel": "self", "type": "application/geo+json", "title": "10261/216656", "name": "item", "description": "10261/216656", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/216656"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10261/359343", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:51Z", "type": "Dataset", "title": "Plant affinity to extreme soils and foliar sulphur mediate species-specific responses to sheep grazing in gypsum systems [Dataset V2]", "description": "Open AccessPeer reviewed", "keywords": ["Semiarid systems", "Gypsophiles", "Elemental composition", "Gypsum soils", "Herbivory", "Functional traits"], "contacts": [{"organization": "Cera, Andreu, Montserrat-Mart\u00ed, Gabriel, Luzuriaga, Arantzazu L., Pueyo, Yolanda, Palacio, Sara,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10261/359343"}, {"rel": "self", "type": "application/geo+json", "title": "10261/359343", "name": "item", "description": "10261/359343", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/359343"}, {"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": "10261/398202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:54Z", "type": "Dataset", "created": "2025-04-30", "title": "GHOST: A globally harmonised dataset of surface atmospheric composition measurements", "description": "Open AccessPeer reviewed", "contacts": [{"organization": "Bowdalo, Dene, Grodofzig, Raphael, Jaimes Palomera, M\u00f3nica, Rivera Hern\u00e1ndez, Olivia, Puchalski, Melissa, Gay, David, Klausen, J\u00f6rg, Moreno, Sergio, Netcheva, Stoyka, Tarasova, Oksana, Basart, Sara, Guevara, Marc, Jorba, Oriol, Pandolfi, Marco, Rovira, Jordi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10261/398202"}, {"rel": "self", "type": "application/geo+json", "title": "10261/398202", "name": "item", "description": "10261/398202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/398202"}, {"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": "10.33140/jgrm.07.02.03", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:14Z", "type": "Journal Article", "created": "2023-05-31", "title": "Risk-Reducing Salpingectomy And Other Strategies For Prevention Of Ovarian And Tubal Carcinoma", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Objective: To provide a review of most current evidence and data for risk-reducing strategies used in prevention of ovarian cancer. Methods of study selection: PubMed was used as a search tool for articles with key words focusing on current strategies on prevention of ovarian cancer such as \u201crisk-reducing salpingectomy, \u201crisk-reducing salpingo-oophorectomy, \u201csalpingectomy with delayed oophorectomy\u201d. General consensus and society guidelines from leading organizations such as Society of Gynecologic Oncology, American Cancer Society, and American College of Obstetricians and Gynecologists were reviewed and summarized in this review article with supporting evidence and research studies on most current riskreduction strategies for prevention of ovarian and tubal carcinoma. Result: There is growing evidence that high-grade serous ovarian carcinoma arises in the fallopian tube in the form of serous tubal intraepithelial carcinoma (STIC). Therefore, opportunistic salpingectomy has been increasingly offered at the time of routine benign gynecologic surgery. Risk-reducing bilateral salpingo-oophorectomy has been shown to reduce risk of ovarian cancer up to 90% and offered to women with high hereditary predisposition for ovarian cancer. Riskreducing salpingectomy with delayed oophorectomy (SDO) has been suggested in younger women to balance the effects of infertility and surgically induced menopause resulting from oophorectomy. Conclusion: Combined oral Contraceptive COCs confer long-term protection against ovarian cancer with reported 20% reduction for every 5 years of use, which have been cited as a confounding factor in most of the published studies. Women who used HRT (estrogen alone or combined estrogen and progesterone) carry 20% higher risk of ovarian cancer compared to never-users. The associated increased risk of cervical and breast cancer with COCs/HTR use, have recently let women prefer the RRSO over COCs for prevention of ovarian cancer. Bilateral risk reducing Salpingo-oophorectomy (RRSO) at the age of 40\u201345 in BRCA1 and 45\u201350 in BRCA2 mutation carriers is recommended to be the primary approach for risk reduction of ovarian cancer. There is well-supported evidence of lowering the risk of ovarian cancer in high-risk population by 90%. The American college of obstetrics and gynecology committee opinion, recommended opportunistic salpingectomy for the primary prevention of ovarian cancer in a woman already undergoing pelvic surgery for another indication. Bilateral salpingectomy at the time of cesarean delivery is recommended to replace the tubal ligation as the method of choice for sterilization performed with cesarean delivery. The novel alternative procedure of Risk-reducing Salpingectomy with delayed risk-reducing oophorectomy (RRSO-RRO) have growing attention as a better alternative to improve the menopause-related morbidity and quality of life.</p></article>", "keywords": ["3. Good health"]}, "links": [{"href": "https://doi.org/10.33140/jgrm.07.02.03"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Gynecology%20%26amp%3B%20Reproductive%20Medicine", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.33140/jgrm.07.02.03", "name": "item", "description": "10.33140/jgrm.07.02.03", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.33140/jgrm.07.02.03"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-17T00:00:00Z"}}, {"id": "10.3389/fmicb.2019.02597", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:19Z", "type": "Journal Article", "created": "2019-11-08", "title": "New Insights Into Cinnamoyl Esterase Activity of Oenococcus oeni.", "description": "Some strains of Oenococcus oeni possess cinnamoyl esterase activity that can be relevant in the malolactic stage of wine production liberating hydroxycinnamic acids that are precursors of volatile phenols responsible for sensory faults. The objective of this study was to better understand the basis of the differential activity between strains. After initial screening, five commercial strains of O. oeni were selected, three were found to exhibit cinnamoyl esterase activity (CE+) and two not (CE-). Although the use of functional annotation of genes revealed genotypic variations between the strains, no specific genes common only to the three CE+ strains could explain the different activities. Pasteurized wine was used as a natural source of tartrate esters in growth and metabolism experiments conducted in MRS medium, whilst commercial trans-caftaric acid was used as substrate for enzyme assays. Detoxification did not seem to be the main biological mechanism involved in the activity since unlike its phenolic cleavage products and their immediate metabolites (trans-caffeic acid and 4-ethylcatechol), trans-caftaric acid was not toxic toward O. oeni. In the case of the two CE+ strains OenosTM and CiNeTM, wine-exposed samples showed a more rapid degradation of trans-caftaric acid than the unexposed ones. The CE activity was present in all cell-free extracts of both wine-exposed and unexposed strains, except in the cell-free extracts of the CE- strain CH11TM. This activity may be constitutive rather than induced by exposure to tartrate esters. Trans-caftaric acid was totally cleaved to trans-caffeic acid by cell-free extracts of the three CE+ strains, whilst cell-free extracts of the CE- strain CH16TM showed significantly lower activity, although higher for the strains in experiments with no prior wine exposure. The EstB28 esterase gene, found in the genomes of the 5 strains, did not reveal any difference on the upstream regulation and transport functionality between the strains. This study highlights the complexity of the basis of this activity in wine related O. oeni population. Variable cinnamoyl esterases or/and membrane transport activities in the O. oeni strains analyzed and a possible implication of wine molecules could explain this phenomenon.", "keywords": ["0301 basic medicine", "0303 health sciences", "tartrate esters", "cinnamoyl esterase", "Tartrate esters", "Hydroxycinnamic acids", "Wine", "hydroxycinnamic acids", "[SDV.IDA] Life Sciences [q-bio]/Food engineering", "Microbiology", "QR1-502", "03 medical and health sciences", "Cinnamoyl esterase", "wine", "Oenococcus oeni"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2019.02597"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fmicb.2019.02597", "name": "item", "description": "10.3389/fmicb.2019.02597", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2019.02597"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-08T00:00:00Z"}}, {"id": "10.3389/fmicb.2022.983823", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:20Z", "type": "Journal Article", "created": "2022-11-08", "title": "Long-term effects of early-life rumen microbiota modulation on dairy cow production performance and methane emissions", "description": "<p>Rumen microbiota modulation during the pre-weaning period has been suggested as means to affect animal performance later in life. In this follow-up study, we examined the post-weaning rumen microbiota development differences in monozygotic twin-heifers that were inoculated (T-group) or not inoculated (C-group) (n\uffe2\uff80\uff89=\uffe2\uff80\uff894 each) with fresh adult rumen liquid during their pre-weaning period. We also assessed the treatment effect on production parameters and methane emissions of cows during their 1st lactation period. The rumen microbiota was determined by the 16S rRNA gene, 18S rRNA gene, and ITS1 amplicon sequencing. Animal weight gain and rumen fermentation parameters were monitored from 2 to 12\uffe2\uff80\uff89months of age. The weight gain was not affected by treatment, but butyrate proportion was higher in T-group in month 3 (p\uffe2\uff80\uff89=\uffe2\uff80\uff890.04). Apart from archaea (p\uffe2\uff80\uff89=\uffe2\uff80\uff890.084), the richness of bacteria (p\uffe2\uff80\uff89&amp;lt;\uffe2\uff80\uff890.0001) and ciliate protozoa increased until month 7 (p\uffe2\uff80\uff89=\uffe2\uff80\uff890.004) and anaerobic fungi until month 11 (p\uffe2\uff80\uff89=\uffe2\uff80\uff890.005). The microbiota structure, measured as Bray\uffe2\uff80\uff93Curtis distances, continued to develop until months 3, 6, 7, and 10, in archaea, ciliate protozoa, bacteria, and anaerobic fungi, respectively (for all: p\uffe2\uff80\uff89=\uffe2\uff80\uff890.001). Treatment or age \uffc3\uff97 treatment interaction had a significant (p\uffe2\uff80\uff89&amp;lt;\uffe2\uff80\uff890.05) effect on 18 bacterial, 2 archaeal, and 6 ciliate protozoan taxonomic groups, with differences occurring mostly before month 4 in bacteria, and month 3 in archaea and ciliate protozoa. Treatment stimulated earlier maturation of prokaryote community in T-group before month 4 and earlier maturation of ciliate protozoa at month 2 (Random Forest: 0.75\uffe2\uff80\uff89month for bacteria and 1.5\uffe2\uff80\uff89month for protozoa). No treatment effect on the maturity of anaerobic fungi was observed. The milk production and quality, feed efficiency, and methane emissions were monitored during cow\uffe2\uff80\uff99s 1st lactation. The T-group had lower variation in energy-corrected milk yield (p\uffe2\uff80\uff89&amp;lt;\uffe2\uff80\uff890.001), tended to differ in pattern of residual energy intake over time (p\uffe2\uff80\uff89=\uffe2\uff80\uff890.069), and had numerically lower somatic cell count throughout their 1st lactation period (p\uffe2\uff80\uff89=\uffe2\uff80\uff890.081), but no differences between the groups in methane emissions (g/d, g/kg DMI, or g/kg milk) were observed. Our results demonstrated that the orally administered microbial inoculant induced transient changes in early rumen microbiome maturation. In addition, the treatment may influence the later production performance, although the mechanisms that mediate these effects need to be further explored.</p>", "keywords": ["microbiome modulation", "0301 basic medicine", "570", "ta412", "microbiome establishment", "Heifer", "dairy cow", "Rumen function", "Animal science", " dairy science", "Microbiology", "630", "Microbiome modulation", "QR1-502", "rumen function", "Microbiome establishment", "03 medical and health sciences", "Dairy cow", "heifer"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2022.983823"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fmicb.2022.983823", "name": "item", "description": "10.3389/fmicb.2022.983823", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2022.983823"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-08T00:00:00Z"}}, {"id": "10.3390/toxins11100550", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:48Z", "type": "Journal Article", "created": "2019-09-20", "title": "Graphene-Based Sensing Platform for On-Chip Ochratoxin A Detection", "description": "<p>In this work, we report an on-chip aptasensor for ochratoxin A (OTA) toxin detection that is based on a graphene field-effect transistor (GFET). Graphene-based devices are fabricated via large-scale technology, allowing for upscaling the sensor fabrication and lowering the device cost. The sensor assembly was performed through covalent bonding of graphene\uffe2\uff80\uff99s surface with an aptamer specifically sensitive towards OTA. The results demonstrate fast (within 5 min) response to OTA exposure with a linear range of detection between 4 ng/mL and 10 pg/mL, with a detection limit of 4 pg/mL. The regeneration time constant of the sensor was found to be rather small, only 5.6 s, meaning fast sensor regeneration for multiple usages. The high reproducibility of the sensing response was demonstrated via using several recycling procedures as well as various GFETs. The applicability of the aptasensor to real samples was demonstrated for spiked red wine samples with recovery of about 105% for a 100 pM OTA concentration; the selectivity of the sensor was also confirmed via addition of another toxin, zearalenone. The developed platform opens the way for multiplex sensing of different toxins using an on-chip array of graphene sensors.</p>", "keywords": ["Communication", "graphene", "R", "aptamer", "Biosensing Techniques", "02 engineering and technology", "Aptamers", " Nucleotide", "Ochratoxins", "01 natural sciences", "7. Clean energy", "0104 chemical sciences", "12. Responsible consumption", "on-chip", "sensor", "Limit of Detection", "transistor", "Medicine", "Graphite", "0210 nano-technology", "ochratoxin A"]}, "links": [{"href": "https://www.mdpi.com/2072-6651/11/10/550/pdf"}, {"href": "https://doi.org/10.3390/toxins11100550"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Toxins", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/toxins11100550", "name": "item", "description": "10.3390/toxins11100550", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/toxins11100550"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-20T00:00:00Z"}}, {"id": "10.37501/soilsa/193074", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:54Z", "type": "Journal Article", "created": "2024-11-13", "title": "Rational management of agricultural soils under climate change", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Zr\u00f3wnowa\u017cone gospodarowanie glebami u\u017cytkowanymi rolniczo ma kluczowe znaczenie dla poprawy ich zdrowia, zwi\u0119kszenia bezpiecze\u0144stwa \u017cywno\u015bciowego, ilo\u015bci i jako\u015bci w\u00f3d powierzchniowych i gruntowych, gromadzenia w\u0119gla organicznego oraz poprawy stanu r\u00f3\u017cnorodno\u015bci biologicznej. Przyczynia si\u0119 tak\u017ce do \u0142agodzenia zmian klimatu i przystosowania si\u0119 rolnictwa do tych zmian. Celem publikacji jest przedstawienie rozwi\u0105za\u0144 dotycz\u0105cych zr\u00f3wnowa\u017conego u\u017cytkowania gleb rolniczych w warunkach zachodz\u0105cych zmian klimatycznych na przyk\u0142adzie realizacji Europejskiego Wsp\u00f3lnego Programu o Glebie (akronim EJP SOIL). EJP SOIL \u201eTowards climate-smart sustainable development of agricultural soils\u201d, w kt\u00f3rym uczestniczy 26 instytucji naukowo badawczych z 24 kraj\u00f3w Europy, rozpocz\u0105\u0142 swoj\u0105 dzia\u0142alno\u015b\u0107 w 2020 r. Rezultaty podejmowanych prac, w tym ankiety, opracowane bazy danych i analizy statystyczne oraz dialog z urz\u0119dnikami r\u00f3\u017cnego szczebla, przez ostatnie lata umo\u017cliwia\u0142y wsp\u00f3\u0142prac\u0119 ponad 400 naukowc\u00f3w w celu uzupe\u0142nienia wiedzy na temat zr\u00f3wnowa\u017conego zarz\u0105dzania glebami rolniczymi z uwzgl\u0119dnieniem stref glebowo\u2012klimatycznych Europy. Publikacja prezentuje wielow\u0105tkowo\u015b\u0107 programu EJP SOIL oraz powi\u0105zane z nim wewn\u0119trze i zewn\u0119trzne projekty badawcze, przedstawia podejmowan\u0105 problematyk\u0119 naukow\u0105 oraz wskazania dla praktyki rolniczej i kszta\u0142towania przysz\u0142ej polityki europejskiej. Ponadto, okre\u015bla kierunek podejmowanych dzia\u0142a\u0144 prowadzonych w ramach realizacji EJP SOIL, kt\u00f3re zosta\u0142y skierowane do r\u00f3\u017cnych grup interesariuszy w zakresie upowszechniania wiedzy o zdrowiu gleb i \u015bwiadczonych przez nie us\u0142ugach ekosystemowych oraz przybli\u017ca cele i zakres funkcjonowania Krajowego Hubu ds. Gleb, powsta\u0142ego w listopadzie 2023 r. z inicjatywy programu EJP SOIL oraz Misji Komisji Europejskiej \u201eTroska o gleb\u0119 to troska o \u017cycie\u201d.</p></article>", "contacts": [{"organization": "Smreczak, Bo\u017cena, Hewelke, Edyta Aleksandra, Kowalik, Monika, Ukalska-Jaruga, Aleksandra, Weber, Jerzy,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.37501/soilsa/193074"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Science%20Annual", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.37501/soilsa/193074", "name": "item", "description": "10.37501/soilsa/193074", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.37501/soilsa/193074"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-07T00:00:00Z"}}, {"id": "10.4081/ija.2012.e26", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:22:59Z", "type": "Journal Article", "created": "2012-05-31", "description": "Interest in biochar (BC) has grown dramatically in recent years, due mainly to the fact that its incorporation into soil reportedly enhances carbon sequestration and fertility. Currently, BC types most under investigation are those obtained from organic matter (OM) of plant origin. As great amounts of manure solids are expected to become available in the near future, thanks to the development of technologies for the separation of the solid fraction of animal effluents, processing of manure solids for BC production seems an interesting possibility for the recycling of OM of high nutrient value. The aim of this study was to investigate carbon (C) sequestration and nutrient dynamics in soil amended with BC from dried swine manure solids. The experiment was carried out in laboratory microcosms on a silty clay soil. The effect on nutrient dynamics of interaction between BC and fresh digestate obtained from a biogas plant was also investigated to test the hypothesis that BC can retain nutrients. A comparison was made of the following treatments: soil amended with swine manure solids (LC), soil amended with charred swine manure solids (LT), soil amended with wood chip (CC), soil amended with charred wood chip (CT), soil with no amendment as control (Cs), each one of them with and without incorporation of digestate (D) for a total of 10 treatments. Biochar was obtained by treating OM (wood chip or swine manure) with moisture content of less than 10% at 420\u00b0C in anoxic conditions. The CO2-C release and organic C, available phosphorus (P) (Olsen P, POls) and inorganic (ammonium+nitrate) nitrogen (N) (Nmin) contents at the start and three months after the start of the experiment were measured in the amended and control soils. After three months of incubation at 30\u00b0C, the CO2-C emissions from soil with BC (CT and LT, \u00b1D) were the same as those in the control soil (Cs) and were lower than those in the soils with untreated amendments (CC and LC, \u00b1D). The organic C content decreased in CT and LT to a lesser extent than in CC and LC. In soils with D (+D), the CO2-C emissions were equal to or higher than those in soils without (-D). The Nmin content increased in all treatments; the POls content decreased in the +D treatments. The incorporation of BC into soil, by reducing CO2 emissions, actually contributes to C sequestration without modifying N availability for crops. For a given N content, the BC from swine manure solids supplies much more P than the non-treated OM and, therefore, represents an interesting source of P for crops.", "keywords": ["2. Zero hunger", "S", "emissions", "Plant culture", "Agriculture", "04 agricultural and veterinary sciences", "nitrogen", "6. Clean water", "SB1-1110", "13. Climate action", "manure", "0401 agriculture", " forestry", " and fisheries", "biochar", "phosphorus"]}, "links": [{"href": "https://doi.org/10.4081/ija.2012.e26"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Italian%20Journal%20of%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4081/ija.2012.e26", "name": "item", "description": "10.4081/ija.2012.e26", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4081/ija.2012.e26"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-01T00:00:00Z"}}, {"id": "10.5061/dryad.1v87f", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:07Z", "type": "Dataset", "title": "Data from: Post-fire changes in forest carbon storage over a 300-year chronosequence of Pinus contorta-dominated forests", "description": "unspecifiedA warming climate may increase the frequency and severity of  stand-replacing wildfires, reducing carbon (C) storage in forest  ecosystems. Understanding the variability of post-fire C cycling on  heterogeneous landscapes is critical for predicting changes in C storage  with more frequent disturbance. We measured C pools and fluxes for 77  lodgepole pine (Pinus contorta Dougl. ex Loud var. latifolia Engelm.)  stands in and around Yellowstone National Park (YNP) along a 300-year  chronosequence to examine how quickly forest C pools recover after a  stand-replacing fire, their variability through time across a complex  landscape, and the role of stand structure in this variability. Carbon  accumulation after fire was rapid relative to the historical mean fire  interval of 150-300 years, recovering nearly 80% of pre-fire C in 50 years  and 90% within 100 years. Net ecosystem carbon balance (NECB) declined  monotonically from 160 g C m-2 yr-1 at age 12 to 5 g C m 2 yr-1 at age  250, but was never negative after disturbance. Decomposition and  accumulation of dead wood contributed little to NECB relative to live  biomass in this system. Aboveground net primary productivity was  correlated with leaf area for all stands, and the decline in aboveground  net primary productivity with forest age was related to a decline in both  leaf area and growth efficiency. Forest structure was an important driver  of ecosystem C, with ecosystem C, live biomass C, and organic soil C  varying with basal area or tree density in addition to forest age. Rather  than identifying a single chronosequence, we found high variability in  many components of ecosystem C stocks through time; a &gt; 50% random  subsample of the sampled stands was necessary to reliably estimate the  non-linear equation coefficients for ecosystem C. At the spatial scale of  YNP, this variability suggests that landscape C develops via many pathways  over decades and centuries, with prior stand structure, regeneration, and  within-stand disturbance all important. With fire rotation projected to be  &lt; 30 years by mid century in response to a changing climate,  forests in YNP will store substantially less C (at least 4.8 kg C/m2 or  30% less).", "keywords": ["Pinus contorta var. latifolia", "13. Climate action", "Yellowstone", "lodgepole pine", "net ecosystem carbon balance", "15. Life on land", "Carbon"]}, "links": [{"href": "https://doi.org/10.5061/dryad.1v87f"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.1v87f", "name": "item", "description": "10.5061/dryad.1v87f", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.1v87f"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-12-03T00:00:00Z"}}, {"id": "10433/20153", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:57Z", "type": "Report", "title": "Atlas mundial de los principales factores que controlan el carbono del suelo en un contexto de cambio clim\u00e1tico.", "description": "El carbono (C) es un componente esencial de la matriz del suelo que juega una funci\u00f3n vital en m\u00faltiples servicios ecosist\u00e9micos, desde la regulaci\u00f3n clim\u00e1tica hasta proporcionar suelos f\u00e9rtiles que permitan la seguridad alimentaria. Sin embargo, el cambio clim\u00e1tico y la gesti\u00f3n inadecuada del manejo del suelo est\u00e1n provocando p\u00e9rdidas aceleradas del C almacenado en los suelos de los ecosistemas terrestres, con repercusiones importantes en el clima de la Tierra. A pesar de su importancia, en la actualidad tenemos un conocimiento escaso sobre los factores que controlan los distintos componentes que forman el C almacenado en el suelo y que est\u00e1n asociados con su persistencia en un contexto de cambio clim\u00e1tico (protecci\u00f3n mineral, diversidad de la materia org\u00e1nica [SOM], recalcitrancia bioqu\u00edmica y respiraci\u00f3n heter\u00f3trofa de los microbios del suelo). En esta tesis, se investigaron los principales factores que influyen en la acumulaci\u00f3n de C a nivel global, mediante la utilizaci\u00f3n de suelos provenientes de varios muestreos estandarizados en todos los biomas terrestres. En primer lugar, nuestros resultados mostraron una menor diversidad de la SOM como consecuencia de la acumulaci\u00f3n de restos vegetales despu\u00e9s de millones de a\u00f1os de formaci\u00f3n ecosist\u00e9mica. Las correlaciones positivas entre la diversidad de la SOM y contenido de C en el suelo sugieren que el desarrollo de suelos milenarios m\u00e1s simples podr\u00eda estar asociado con las p\u00e9rdidas t\u00edpicamente observadas de las funciones ecosist\u00e9micas (incluida la acumulaci\u00f3n de C en el suelo) durante la retrogresi\u00f3n. En este contexto, el desarrollo de las comunidades vegetales es determinado por las condiciones clim\u00e1ticas. Nuestro segundo cap\u00edtulo revel\u00f3 que, independientemente del contenido de nutrientes en la capa superficial del suelo, el reservorio de la biomasa vegetal es mayor cuando las condiciones de temperatura y precipitaci\u00f3n permiten el crecimiento de las plantas. Por otra parte, frente a los bien establecidos mecanismos de persistencia, el microbioma del suelo emergi\u00f3 como el principal factor que controla las p\u00e9rdidas de C a la atm\u00f3sfera en escenarios de calentamiento. De hecho, nuestro cuarto cap\u00edtulo tambi\u00e9n revel\u00f3 que incrementar el n\u00famero de factores de cambio global est\u00e1 relacionado negativamente con el almacenamiento y los factores de persistencia del C a nivel global. Por \u00faltimo, propusimos que nuevas herramientas basadas en un enfoque microbiano podr\u00edan mejorar la diversidad de la SOM en tierras degradadas, y, por consiguiente, incrementar las reservas mundiales de C en el menor tiempo posible. En conjunto, los resultados presentados en esta tesis aportan informaci\u00f3n valiosa para orientar nuestros esfuerzos hacia medidas de gesti\u00f3n concretas y efectivas destinadas a construir y preservar el C en los ecosistemas terrestres.", "keywords": ["Carbono", "Cambio clim\u00e1tico", "Microbiolog\u00eda"], "contacts": [{"organization": "S\u00e1ez Sandino, Tadeo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10433/20153"}, {"rel": "self", "type": "application/geo+json", "title": "10433/20153", "name": "item", "description": "10433/20153", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10433/20153"}, {"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": "10.5061/dryad.3sm0340", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:08Z", "type": "Dataset", "title": "Data from: Vegetation type controls root turnover in global grasslands", "description": "unspecifiedRoot turnover in  grasslands", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Wang, Jinsong, Sun, Jian, Yu, Zhen, Li, Yong, Tian, Dashuan, Wang, Bingxue, Li, Zhaolei, Niu, Shuli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.3sm0340"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.3sm0340", "name": "item", "description": "10.5061/dryad.3sm0340", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.3sm0340"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-15T00:00:00Z"}}, {"id": "10.5061/dryad.cz8w9gj78", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:13Z", "type": "Dataset", "title": "Soil microbial relative resource limitation exhibited contrasting seasonal patterns along an elevational gradient in Yulong snow mountain", "description": "unspecified", "keywords": ["2. Zero hunger", "mountain ecosystems", "13. Climate action", "microbial metabolic mechanisms", "microbial relative C limitation", "microbial relative P limitation", "C use efficiency", "FOS: Earth and related environmental sciences", "15. Life on land", "elevations"], "contacts": [{"organization": "Zhang, Dandan, Wu, Baoyun, Li, Jinsheng, Cheng, Xiaoli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.cz8w9gj78"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.cz8w9gj78", "name": "item", "description": "10.5061/dryad.cz8w9gj78", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.cz8w9gj78"}, {"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": "10.5061/dryad.h3r16", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:15Z", "type": "Dataset", "title": "Data from: The impact of environmental heterogeneity and life stage on the hindgut microbiota of Holotrichia parallela larvae (Coleoptera: Scarabaeidae)", "description": "unspecifiedGut microbiota has diverse ecological and evolutionary effects on their  hosts. However, the ways in which it responds to environmental  heterogeneity and host physiology remain poorly understood. To this end,  we surveyed intestinal microbiota of Holotrichia parallela larvae at  different instars and from different geographic regions. Bacterial 16S  rRNA gene clone libraries were constructed and clones were subsequently  screened by DGGE and sequenced. Firmicutes and Proteobacteria were the  major phyla, and bacteria belonging to Ruminococcaceae, Lachnospiraceae,  Enterobacteriaceae, Desulfovibrionaceae and Rhodocyclaceae families were  commonly found in all natural populations. However, bacterial diversity  (Chao1 and Shannon indices) and community structure varied across host  populations, and the observed variation can be explained by soil pH,  organic carbon and total nitrogen, and the climate factors (e.g., mean  annual temperature) of the locations where the populations were sampled.  Furthermore, increases in the species richness and diversity of gut  microbiota were observed during larval growth. Bacteroidetes comprised the  dominant group in the first instar; however, Firmicutes composed the  majority of the hindgut microbiota during the second and third instars.  Our results suggest that the gut\u2019s bacterial community changes in response  to environmental heterogeneity and host\u2019s physiology, possibly to meet the  host\u2019s ecological needs or physiological demands.", "keywords": ["Holotrichia parallela", "Cenozoic era", "15. Life on land"], "contacts": [{"organization": "Huang, Shengwei, Zhang, Hongyu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.h3r16"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.h3r16", "name": "item", "description": "10.5061/dryad.h3r16", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.h3r16"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-05-20T00:00:00Z"}}, {"id": "10.5061/dryad.pb271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:17Z", "type": "Dataset", "title": "Data from: Interactions among roots, mycorrhizae and free-living microbial communities differentially impact soil carbon processes", "description": "unspecifiedPlant roots, their associated microbial community and free-living soil  microbes interact to regulate the movement of carbon from the soil to the  atmosphere, one of the most important and least understood fluxes of  terrestrial carbon. Our inadequate understanding of how plant\u2013microbial  interactions alter soil carbon decomposition may lead to poor model  predictions of terrestrial carbon feedbacks to the atmosphere. Roots,  mycorrhizal fungi and free-living soil microbes can alter soil carbon  decomposition through exudation of carbon into soil. Exudates of simple  carbon compounds can increase microbial activity because microbes are  typically carbon limited. When both roots and mycorrhizal fungi are  present in the soil, they may additively increase carbon decomposition.  However, when mycorrhizas are isolated from roots, they may limit soil  carbon decomposition by competing with free-living decomposers for  resources. We manipulated the access of roots and mycorrhizal fungi to  soil in situ in a temperate mixed deciduous forest. We added 13C-labelled  substrate to trace metabolized carbon in respiration and measured  carbon-degrading microbial extracellular enzyme activity and soil carbon  pools. We used our data in a mechanistic soil carbon decomposition model  to simulate and compare the effects of root and mycorrhizal fungal  presence on soil carbon dynamics over longer time periods. Contrary to  what we predicted, root and mycorrhizal biomass did not interact to  additively increase microbial activity and soil carbon degradation. The  metabolism of 13C-labelled starch was highest when root biomass was high  and mycorrhizal biomass was low. These results suggest that mycorrhizas  may negatively interact with the free-living microbial community to  influence soil carbon dynamics, a hypothesis supported by our enzyme  results. Our steady-state model simulations suggested that root presence  increased mineral-associated and particulate organic carbon pools, while  mycorrhizal fungal presence had a greater influence on particulate than  mineral-associated organic carbon pools. Synthesis. Our results suggest  that the activity of enzymes involved in organic matter decomposition was  contingent upon root\u2013mycorrhizal\u2013microbial interactions. Using our  experimental data in a decomposition simulation model, we show that  root\u2013mycorrhizal\u2013microbial interactions may have longer-term legacy  effects on soil carbon sequestration. Overall, our study suggests that  roots stimulate microbial activity in the short term, but contribute to  soil carbon storage over longer periods of time.", "keywords": ["2. Zero hunger", "roots", "13. Climate action", "simulation model", "carbon dynamics", "Rhizosphere", "stable isotope", "plant-soil (belowground) interactions", "15. Life on land", "extra-cellular enzyme activity", "mycorrhizae"], "contacts": [{"organization": "Moore, Jessica A. M., Jiang, Jiang, Patterson, Courtney M., Wang, Gangsheng, Mayes, Melanie A., Classen, Aim\u00e9e T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.pb271"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.pb271", "name": "item", "description": "10.5061/dryad.pb271", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.pb271"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-09-14T00:00:00Z"}}, {"id": "10.5061/dryad.wh70rxwww", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:19Z", "type": "Dataset", "created": "2024-06-19", "title": "Data from: Competition between mixo- and heterotrophic ciliates under dynamic resource supply", "description": "unspecifiedThe outcome of species competition strongly depends on the traits of the  competitors and associated trade-offs, as well as on environmental  variability. Here we investigate the relevance of consumer trait variation  for species coexistence in a ciliate consumer \u2013 microalgal prey system  under fluctuating regimes of resource supply. We focus on consumer  competition and feeding traits, and specifically on the consumer\u2019s ability  to overcome periods of resource limitation by mixotrophy, i. e. the  ability of photosynthetic carbon fixation via algal symbionts in addition  to phagotrophy. In a 48-day chemostat experiment, we investigated  competitive interactions of different heterotrophic and mixotrophic  ciliates of the genera Euplotes and Coleps under different resource  regimes, providing prey either continuously or in pulses under constant or  fluctuating light, entailing periods of resource depletion in fluctuating  environments, but overall providing the same amount of prey and light.  Although ultimate competition results remained unaffected, population  dynamics of mixotrophic and heterotrophic ciliates were significantly  altered by resource supply mode. However, the effects differed among  species combinations and changed over time. Whether mixotrophs or  heterotrophs dominated in competition strongly depended on the genera of  the competing species and thus species-specific differences in the minimum  resource requirements that are associated with feeding on shared prey,  nutrient uptake, light harvesting and access to additional resources such  as bacteria. Potential differences in the curvature of the species\u2019  resource-dependent growth functions may have further mediated the  species-specific responses to the different resource supply modes.  Overall, our study demonstrates that genus- or species-specific traits  other than related to nutritional mode may override the relevance of  acquired phototrophy by heterotrophs in competitive interactions, and that  the potential advantage of photosynthetic carbon fixation of  symbiont-bearing mixotrophs in competition with pure heterotrophs may  differ greatly among different mixotrophs, playing out under different  environmental conditions and depending on the specific requirements of the  species. Complex trophic interactions determine the outcome of  competition, which can only be understood by taking on a multidimensional  trait perspective.", "keywords": ["Ciliates", "mixotrophy", "FOS: Biological sciences", "coexistence", "resource fluctuations", "microalgae-ciliate symbiosis"], "contacts": [{"organization": "Fl\u00f6der, Sabine, Klauschies, Toni, Klaassen, Moritz, Stoffers, Tjardo, Lambrecht, Max, Moorthi, Stefanie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.wh70rxwww"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.wh70rxwww", "name": "item", "description": "10.5061/dryad.wh70rxwww", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.wh70rxwww"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-23T00:00:00Z"}}, {"id": "10.5194/acp-10-7017-2010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:21Z", "type": "Journal Article", "created": "2010-04-29", "description": "<p>Abstract. We present and discuss a new dataset of gridded emissions covering the historical period (1850\uffe2\uff80\uff932000) in decadal increments at a horizontal resolution of 0.5\uffc2\uffb0 in latitude and longitude. The primary purpose of this inventory is to provide consistent gridded emissions of reactive gases and aerosols for use in chemistry model simulations needed by climate models for the Climate Model Intercomparison Program #5 (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Our best estimate for the year 2000 inventory represents a combination of existing regional and global inventories to capture the best information available at this point; 40 regions and 12 sectors are used to combine the various sources. The historical reconstruction of each emitted compound, for each region and sector, is then forced to agree with our 2000 estimate, ensuring continuity between past and 2000 emissions. Simulations from two chemistry-climate models is used to test the ability of the emission dataset described here to capture long-term changes in atmospheric ozone, carbon monoxide and aerosol distributions. The simulated long-term change in the Northern mid-latitudes surface and mid-troposphere ozone is not quite as rapid as observed. However, stations outside this latitude band show much better agreement in both present-day and long-term trend. The model simulations indicate that the concentration of carbon monoxide is underestimated at the Mace Head station; however, the long-term trend over the limited observational period seems to be reasonably well captured. The simulated sulfate and black carbon deposition over Greenland is in very good agreement with the ice-core observations spanning the simulation period. Finally, aerosol optical depth and additional aerosol diagnostics are shown to be in good agreement with previously published estimates and observations.                         </p>", "keywords": ["info:eu-repo/classification/ddc/550", "550", "IPCC", "[SDE.MCG]Environmental Sciences/Global Changes", "Physics", "QC1-999", "emissions", "551", "01 natural sciences", "7. Clean energy", "J", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "13. Climate action", "[SDE.ES] Environmental Sciences/Environment and Society", "CMIP5", "[SDE.ES]Environmental Sciences/Environment and Society", "QD1-999", "AR5", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://pure.iiasa.ac.at/id/eprint/9279/1/acp-10-7017-2010.pdf"}, {"href": "http://pure.iiasa.ac.at/id/eprint/9279/1/acp-10-7017-2010.pdf"}, {"href": "https://doi.org/10.5194/acp-10-7017-2010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-10-7017-2010", "name": "item", "description": "10.5194/acp-10-7017-2010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-10-7017-2010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-02-19T00:00:00Z"}}, {"id": "10.5194/bg-15-1933-2018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:24Z", "type": "Journal Article", "created": "2017-11-21", "title": "Straw incorporation increases crop yield and soil organic carbon sequestration but varies under different natural conditions and farming practices in China: a system analysis", "description": "<p>Abstract. Loss of soil organic carbon (SOC) from agricultural soils is a key indicator of soil degradation associated with reductions in net primary productivity in crop production systems worldwide. Simple technical and locally appropriate solutions are required for farmers to increase SOC and to improve cropland management. In the last 30 years, straw incorporation has gradually been implemented across China in the context of agricultural intensification and rural livelihood improvement. A meta-analysis of data published before the end of 2016 was undertaken to investigate the effects of straw incorporation on crop production and SOC sequestration. The results of 68 experimental studies throughout China in different edaphic, climate regions and under different farming regimes were analyzed. Compared with straw removal, straw incorporation significantly sequestered SOC (0\uffe2\uff80\uff9320\uffe2\uff80\uff89cm depth) at the rate of 0.35 (range 0.31\uffe2\uff80\uff930.40)\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921, increased crop grain yield by 13.4\uffe2\uff80\uff89% (range 9.3\uffe2\uff80\uff89%\uffe2\uff80\uff9318.4\uffe2\uff80\uff89%) and had a conversion efficiency of the applied straw-C as 16\uffe2\uff80\uff89%\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff892\uffe2\uff80\uff89% across the whole of China. The combined straw incorporation at the rate of 3\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921 with mineral fertilizer of 200\uffe2\uff80\uff93400\uffe2\uff80\uff89kg N\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921 was demonstrated to be the best combination for farmers to use with crop yield increased by 32.7\uffe2\uff80\uff89% (range 17.9\uffe2\uff80\uff89%\uffe2\uff80\uff9356.4\uffe2\uff80\uff89%) and SOC sequestrated by the rate of 0.85 (range 0.54\uffe2\uff80\uff931.15)\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921. Straw incorporation achieved higher SOC sequestration rate and crop yield increment when applied to clay soils, under high cropping intensities, and in areas like Northeast China where the soil is being degraded. SOC responses were the greatest in the initial starting phase of straw incorporation and then declined and finally were negligible after 28\uffe2\uff80\uff9362 years, however, crop yield responses were initially low and then increased reaching their highest level at 11\uffe2\uff80\uff9315 years after straw incorporation. Overall, our study confirmed that straw incorporation did create a positive feedback loop of SOC enhancement together with increased crop production, and this is of great practical significance to straw management as agricultural intensifies in China and other regions in the world with different climate conditions.                         </p>", "keywords": ["2. Zero hunger", "QE1-996.5", "info:eu-repo/classification/ddc/550", "Ecology", "Life", "QH501-531", "0401 agriculture", " forestry", " and fisheries", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "QH540-549.5"]}, "links": [{"href": "https://doi.org/10.5194/bg-15-1933-2018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-15-1933-2018", "name": "item", "description": "10.5194/bg-15-1933-2018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-15-1933-2018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-21T00:00:00Z"}}, {"id": "10.5194/bg-19-2487-2022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:25Z", "type": "Journal Article", "created": "2022-05-13", "title": "Climatic variation drives loss and restructuring of carbon and nitrogen in boreal forest wildfire", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The boreal forest landscape covers approximately 10\u2009% of the earth's land area and accounts for almost 30\u2009% of the global annual terrestrial sink of carbon\u00a0(C). Increased emissions due to climate-change-amplified fire frequency, size, and intensity threaten to remove elements such as C and nitrogen\u00a0(N) from forest soil and vegetation at rates faster than they accumulate. This may result in large areas within the region becoming a net source of greenhouse gases, creating a positive feedback loop with a changing climate. Meter-scale estimates of area-normalized fire emissions are limited in Eurasian boreal forests, and knowledge of their relation to climate and ecosystem properties is sparse. This study sampled 50 separate Swedish wildfires, which occurred during an extreme fire season in 2018, providing quantitative estimates of C and N loss due to fire along a climate gradient. Mean annual precipitation had strong positive effects on total fuel, which was the strongest driver for increasing C and N losses. Mean annual temperature\u00a0(MAT) influenced both pre- and postfire organic layer soil bulk density and C\u2009:\u2009N ratio, which had mixed effects on C and N losses. Significant fire-induced loss of C estimated in the 50 plots was comparable to estimates in similar Eurasian forests but approximately a quarter of those found in typically more intense North American boreal wildfires. N loss was insignificant, though a large amount of fire-affected fuel was converted to a low C\u2009:\u2009N surface layer of char in proportion to increased MAT. These results reveal large quantitative differences in C and N losses between global regions and their linkage to the broad range of climate conditions within Fennoscandia. A need exists to better incorporate these factors into models to improve estimates of global emissions of C and N due to fire in future climate scenarios. Additionally, this study demonstrated a linkage between climate and the extent of charring of soil fuel and discusses its potential for altering C and N dynamics in postfire recovery.</p></article>", "keywords": ["QE1-996.5", "Ecology", "Life", "13. Climate action", "QH501-531", "Geology", "15. Life on land", "01 natural sciences", "QH540-549.5", "Climate Science", "Klimatvetenskap", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5194/bg-19-2487-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-19-2487-2022", "name": "item", "description": "10.5194/bg-19-2487-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-19-2487-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-13T00:00:00Z"}}, {"id": "10.5194/egusphere-2024-3030", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:30Z", "type": "Report", "created": "2024-10-11", "title": "Modeling of greenhouse gas emissions from paludiculture in rewetting peatlands is improved by high frequency water table data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Rewetting drained peatlands can reduce CO2 emissions but prevents traditional agriculture. Crop production under rewetted conditions may continue with flood-tolerant crops in paludiculture, but its effects on greenhouse gas (GHG) emissions compared to rewetting without further management are largely unknown This study was conducted between 2021 and 2022 on a fen peatland in central Denmark. At the study site, three harvest/fertilization management treatments were implemented on Reed Canary Grass (RCG) established in 2018. Measurements of CO2 and CH4 emissions were conducted biweekly using a transparent manual chamber connected to a gas analyzer and manipulating light intensities with four shrouding levels. Although this was a rather wet peatland (\u22128 cm mean annual WTD), the site was a CO2 source with a mean net ecosystem C balance (NECB) of 6.5 t C ha\u22121 yr\u22121 across treatments. Model simulation with the use of high temporal resolution water table depth (WTD) data was able to better capture ecosystem respiration (Reco) peaks compared to the use of mean annual WTD, which underestimated Reco. Data on pore water chemistry further improved statistical linear models of CO2 fluxes using soil temperature (Ts), WTD, ratio vegetation indices and PAR as explanatory variables. Significant differences in CO2 emissions and water chemistry parameters were found between studied blocks, with higher Reco corresponding to blocks with higher pore water nutrient concentrations. Methane emissions averaged 113 kg of CH4 ha\u22121 yr\u22121, equivalent to 11.3 % of the total carbon emission in CO2 equivalents. Because of large heterogeneity among the experimental blocks no significant treatment effect was found, however, the results indicate that biomass harvest reduces GHG emission from productive rewetted peatland areas in comparison with no management, whereas on less productive areas it is beneficial to leave the biomass unmanaged.</p></article>"}, "links": [{"href": "https://doi.org/10.5194/egusphere-2024-3030"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-2024-3030", "name": "item", "description": "10.5194/egusphere-2024-3030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-2024-3030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-11T00:00:00Z"}}, {"id": "10.5194/egusphere-2023-1681", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:29Z", "type": "Report", "created": "2023-08-14", "title": "The Effects of Land Use on Soil Carbon Stocks in the UK", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Greenhouse gas stabilisation in the atmosphere is one of the most pressing challenges of this century. Sequestering carbon in the soil by changing land use and management is increasingly proposed as part of climate mitigation strategies, but our understanding of this is limited in quantitative terms. Here we collate a substantial national and regional data set (15790 soil cores), and analyse it in an advanced statistical modelling framework. This produced new estimates of the effects of land use on soil carbon stocks in the UK, different in magnitude and ranking order from the previous best estimates. Soil carbon stocks were highest in woodlands, followed by rough grazing and semi-natural grasslands, then improved grasslands, and lowest in croplands. Estimates were smaller than the previous estimates, partly because of new data, but mainly because the effect is more reliably characterised using a logarithmic transformation of the data. With the very large data set analysed here, the uncertainty in the differences among land uses was small enough to identify consistent mean effects. However, the variability in these effects was large, and this was similar across all surveys. This has important implications for agri-environment schemes, seeking to sequester carbon in the soil by altering land use, because the effect of a given intervention is very hard to verify. We examined the validity of the 'space-for-time' substitution, and although the results were not unequivocal, we estimated that the effects are likely to be over-estimated by 5\u201333 %, depending upon land use.</p></article>", "keywords": ["2. Zero hunger", "13. Climate action", "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/10.5194/egusphere-2023-1681"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-2023-1681", "name": "item", "description": "10.5194/egusphere-2023-1681", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-2023-1681"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-14T00:00:00Z"}}, {"id": "10.5194/egusphere-2025-3788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:31Z", "type": "Report", "created": "2025-08-15", "title": "Accelerated lowland thermokarst development revealed by UAS photogrammetric surveys in the Stordalen mire, Abisko, Sweden", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The estimation of greenhouse gas (GHG) emissions from permafrost soils is challenging, as organic matter propensity to decompose depends on factors such as soil pH, temperature, and redox conditions. Over lowland permafrost soils, these conditions are directly related to the microtopography and evolve with physical degradation, i.e., lowland thermokarst development (i.e., a local collapse of the land surface due to ice-rich permafrost thaw). A dynamic quantification of thermokarst development \u2013 still poorly constrained \u2013 is therefore a critical prerequisite for predictive models of permafrost carbon balance in these areas. This requires high-resolution mapping, as lowland thermokarst development induces fine-scale spatial variability (~50 \u2013 100 cm). Here we provide such a quantification, updated for the Stordalen mire in Abisko, Sweden for the Stordalen mire, Abisko, Sweden (68\u00b021'20'N 19\u00b002'38'E), which displays a gradient from well-drained stable palsas to inundated fens, which have undergone ground subsidence. We produced RGB orthomosaics and digital elevation models from very high resolution (10 cm) unoccupied aircraft system (UAS) photogrammetry as well as a spatially continuous map of soil electrical conductivity (EC) based on electromagnetic induction (EMI) measurements. We classified the land cover following the degradation gradient and derived palsa loss rates. Our findings confirm that topography is an essential parameter for determining the evolution of palsa degradation, enhancing the overall accuracy of the classification from 41 % to 77 %, with the addition of slope allowing the detection of the early stages of degradation. We show a clear acceleration of degradation for the period 2019 \u2013 2021, with a decrease in palsa area of 0.9 \u2013 1.1 %\u00b7a\u20111 (% reduction per year relative to the entire mire) compared to previous estimates of ~0.2 %\u00b7a\u20111 (1970 \u2013 2000) and ~0.04 %\u00b7a\u20111 (2000 \u2013 2014). EMI data show that this degradation leads to an increase in soil moisture, which in turn likely decreases organic carbon geochemical stability and potentially increases methane emissions. With a palsa loss of 0.9 \u2013 1.1 %\u00b7a\u20111, we estimate accordingly that surface degradation at Stordalen might lead to a pool of 12 metric tons of organic carbon exposed annually for the topsoil (23 cm depth), of which ~25 % is mineral-interacting organic carbon. Likewise, average annual emissions would increase from ~ 7.1 g\u2011C\u00b7m\u20112\u00b7a-1 in 2019 to ~ 7.3 g\u2011C\u00b7m\u20112\u00b7a\u20111 in 2021 for the entire mire, i.e., an increase of ~1.3 %\u00b7a-1. As topography changes due to lowland thermokarst are fine-scaled and thus not possible to detect from satellite images, circumpolar up-scaling assessments are challenging. By extending the monitoring we have conducted as part of this study to other lowland areas, it would be possible to assess the spatial variability of palsa degradation/thermokarst formation rates and thus improve estimates of net ecosystem carbon dynamics.</p></article>"}, "links": [{"href": "https://doi.org/10.5194/egusphere-2025-3788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-2025-3788", "name": "item", "description": "10.5194/egusphere-2025-3788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-2025-3788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-15T00:00:00Z"}}, {"id": "10.5194/egusphere-egu2020-21951", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:31Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/10.5194/egusphere-egu2020-21951"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu2020-21951", "name": "item", "description": "10.5194/egusphere-egu2020-21951", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu2020-21951"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10.5194/soil-2020-96", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:40Z", "type": "Report", "created": "2021-02-06", "title": "Controls on heterotrophic soil respiration and carbon cycling in geochemically distinct African tropical forest soils", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Heterotrophic soil respiration is an important component of the global terrestrial carbon (C) cycle, driven by environmental factors acting from local to continental scales. For tropical Africa, these factors and their interactions remain largely unknown. Here, using samples collected along strong topographic and geochemical gradients in the East African Rift Valley, we study how soil chemistry and soil fertility, derived from the geochemical composition of soil parent material, can drive soil respiration even after many millennia of weathering and soil development. To address the drivers of soil respiration, we incubated soils from three regions with contrasting geochemistry (mafic, felsic, and mixed sedimentary) sampled along slope gradients. For three soil depths, we measured the potential maximum heterotrophic respiration under stable environmental conditions as well as the radiocarbon content (\u039414C) of the bulk soil and respired CO2. We found that soil microbial communities were able to mineralize C from fossil as well as other poor quality C sources under laboratory conditions representative of tropical topsoils. Furthermore, despite similarities in terms of climate, vegetation, and the size of soil C stocks, soil respiration showed distinct patterns with soil depth and parent material geochemistry. The topographic origin of our samples was not a main determinant of the observed respiration rates and \u039414C. In situ, however, soil hydrological conditions likely influence soil C stability by inhibiting decomposition in valley subsoils. Our study shows that soil fertility conditions are the main determinant of C stability in tropical forest soils. Further, in the presence of organic carbon sources of poor quality or the presence of strong mineral related C stabilization, microorganisms tend to discriminate against these sources in favor of more accessible forms of soil organic matter as energy sources, resulting in a slower rate of C cycling. Our results demonstrate that even in deeply weathered tropical soils, parent material has a long-lasting effect on soil chemistry that can influence and control microbial activity, the size of subsoil C stocks, and the turnover of C in soil. Soil parent material and its lasting control on soil chemistry need to be taken into account to understand and predict C stabilization and rates of C cycling in tropical forest soils.                         </p></article>", "keywords": ["2. Zero hunger", "13. Climate action", "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/10.5194/soil-2020-96"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/soil-2020-96", "name": "item", "description": "10.5194/soil-2020-96", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/soil-2020-96"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10060810", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:42Z", "type": "Dataset", "title": "SoilCompDB: Global soil compressive properties database. Version 1.0", "description": "Data collection and processing Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. \u00a0A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. \u00a0After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. \u00a0We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries. Time and place The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries.\u00a0\u00a0 Instruments The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamand\u00e9 et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006). Resources Web of Science, Scopus \u2013 literature search Endnote Web \u2013 removal of duplicates Rayyan software \u2013 initial paper selection based on title and abstract WebPlotDigitizer \u2013 data extraction from figures Microsoft Access \u2013 database platform Description of the collected data (column, unit, and description) Sample ID-\u00a0\u00a0\u00a0 A unique identification number assigned to each individual sample within the database\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Study ID- Identification number assigned to each research study in the database Reference - Research paper reference Year - Year of research paper publication \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Language - Language of the research paper \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers Soil classification (original in paper) - Soil classification described in research paper\u00a0 Soil classification (convertion to Soil Taxonomy orders) -\u00a0 Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Location - Study location country\u00a0\u00a0\u00a0 Texture classification (USDA) -\u00a0Soil textural classification according USDA Texture\u00a0 classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)\u00a0 Sand (USDA) - % - Soil sand content (weight based)\u00a0 - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available Clay - g kg-1 - Soil clay content - original in the paper Clay class upper boundary - \u00b5m - The clay class upper boundary informed in source publication Silt - g kg-1 - Silt clay content - original in the paper Silt class upper boundary - \u00b5m - The silt class upper boundary informed in source publication Sand - Soil sand content - original in the paper Sand class upper boundary - \u00b5m - The sand class upper boundary informed in source publication Particle size data flag - =0 if no issues; =1 if there are issues (summing) Sum particle size- g kg-1 - Sum of clay, silt, and sand content Soil depth FROM \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil depth TO \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Depth \u2013 cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations Particle density - Mg m-3 - Soil particle density\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial matric potential \u2013 hPa - Soil water matric potential before loading log Initial matric potential - Soil water matric potential expressed by log\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Wetness (based on initial matric potential) - \u00a01=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported Initial water content data source -\u00a0Graph or table from where the data was collected, or explanation on calculation used Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial bulk density - Mg m-3 - Soil bulk density before loading\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density Precompression stress \u2013 kPa - Precompression stress \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress (SD) \u2013 kPa - Standard deviation for precompression stress values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used Compression index - Compression index \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index (SD) - Standard deviation of compression index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index data source - Graph or table from where the data was collected, or explanation on calculation used Swelling index - Swelling index\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index (SD) - Standard deviation of swelling index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used N -\u00a0Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported Land use (paper) -\u00a0Land use described in the research paper Land use (categories) -\u00a0Land use categorized Land use standardized -\u00a0Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna Land use (number code) -\u00a0Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation Tillage system -\u00a0Tillage system Tillage system (arable soils) - Tillage system for arable soils classified as 'conventional' and 'conservation' Coordinates -\u00a0\u00a0Geographical coordinates\u00a0 of study location Climate -\u00a0Climatic region classification: temperate, tropical, subtropical Climatecod -\u00a0\u00a0\u00a0 Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical Sampling position (paper) -\u00a0Field position where soil samples were collected with details described in the paper Sampling position -\u00a0Field position where soil samples were collected standardized Treatment -\u00a0Experimental treatment type where the soil samples were collected Stress rate - \u00a0kPa - Stress applied in compressive tests\u00a0 Minimum stress \u2013\u00a0kPa - Minimum stress applied in compressive tests Maximum stress \u2013\u00a0kPa - Maximum stress applied in compressive tests Number of stress rate steps -\u00a0Number of steps in stepwise stress application procedure Stess application type -\u00a01=Stepwise stress 2=one sample per stress 3=Strain controlled Stess application type \u2013\u00a0min - Time for stress application in each step in stepwise stress application procedure Degree of deformation at the end of loading -\u00a0% - Degree of deformation at the end of compressive test Sample diameter \u2013\u00a0cm - Diameter of the soil samples Sample height \u2013\u00a0cm - Height of the soil samples Ratio sample diameter and height\u00a0-\u00a0Ratio between diameter and height of the soil samples Sample volume -\u00a0cm3 -Sample volume when the sample diameter and height are nor presented Precompression stress calculation method -\u00a0Calculation method of precompression stress Precompression stress calculation method (number code) -\u00a0Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamand\u00e9 et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006) Description of precompression stress calculation -\u00a0Brief explanation of precompression stress calculation Soil compressive curve components -\u00a0Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain.\u00a0 Soil compressive curve components (number code) -\u00a0Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio Curve components source -\u00a0Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper Compressive curve available -\u00a0Original soil compressive curve available in the paper: 1= No 2=Yes Comments -\u00a0Brief comments on the paper Issues and remarks We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 \u03bcm, utilizing the k-nearest neighbor approach (referred to as 'similarity method' by Nemes et al. (1999). \u00a0 Reference Associa\u00e7\u00e3o Brasileira de Normas T\u00e9cnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990. Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60\u201364.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954\u2013960 10.1139/T2012-054. doi:10.1139/t2012-054 Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562\u2013567. Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139\u2013151. doi:10.1016/0933-3630(95)00015-8 Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012 Lamand\u00e9, M., Schj\u00f8nning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274. Nemes, A., \u00a0W\u00f6sten, J.H.M., Lilly, A., \u00a0Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database.\u00a0Geoderma 90: 187-202. 129\u00a0 O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 \u2013 4):161 \u2013 173.", "keywords": ["2. Zero hunger", "soil compression curve", "precompression stress", "15. Life on land", "soil mechanical properties", "compression index", "soil moisture", "uniaxial compression test", "swelling index"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10060810"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10060810", "name": "item", "description": "10.5281/zenodo.10060810", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10060810"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-06T00:00:00Z"}}, {"id": "10.5281/zenodo.10404687", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:45Z", "type": "Report", "title": "D4.3 \u2013 Capacity Building Plan", "description": "Acting as a messenger and conveying awareness-raising messages to national and regional\u00a0stakeholders, the NATI00NS project will assist the Mission during most of its first \u201cinduction and\u00a0pilot\u201d phase. Capacity Building (CB) is a crucial component of the NATI00NS project. The main\u00a0objective of these activities, led by the European Network of Living Labs (ENoLL), is to support\u00a0applicants interested in applying to the 2023 and 2024 Mission calls for proposals to create consortia\u00a0of Soil Health LLs and LHsto address soil health challenges. The efficient delivery of Capacity Building\u00a0activities will support the development and submission of high-quality proposals to the first two\u00a0sets of calls for LL projects, thus directly contributing to the success of the Soil Mission.The NATI00NS Capacity Building gathers the project\u2019s training and guidance activities for the\u00a0applicants to the LL open calls, making available online support material suitable to address the\u00a0questions of stakeholders related to the Mission LL call topics, criteria for Soil Health LLs, and the\u00a0Soil Missions objectives interpreted for specific land use types.The Capacity Building support of NATI00NS will be provided at two levels:    \u00a0Direct support to applicants is foreseen through a set of training and guiding activities, including the delivery of online support material. These activities primarily include:   o Five factsheets focussing on LL basics, information on the LLs topics, and soil healthobjectives and particularities across four specific land uses (agricultural, urban,industry, and forestry);\u00a0o Four webinars focussing on the constituency of the Living Labs methodology, the LLapplication process, and updated information on the 2023 calls results;o Six thematic webinars covering co-creation experiences in cutting-edge subjects andsoil health.    In parallel, specific support and training activities are foreseen for NATI00NS partners and external intermediate actors that will indirectly further support the applicants through mentoring activities. Helping these actors become better equipped to support the applicants will ensure a harmonized, fair, high-quality, and equal approach of NATI00NS to all applicants. These activities primarily include Train-the-Trainers sessions for mentors and ad-hoc trainings to NATI00NS partners.\u00a0   Additional NATI00NS actions will contribute to expanding the knowledge base of LL applicants, complementing the specific Capacity Building activities. These activities include National Engagement Events and the implementation of a Helpdesk.This document is primarily targeted to potential future LL applicants and sister projects and itpresents the plan and strategy for the Capacity Building activities to be implemented withinNATI00NS. The document is structured in five different chapters:    Introduction which provides an overview of the role of LLs as an agent of co-creation andthe benefits of the LL methodology  \u00a0Chapter 1 \u2013 NATI00NS Capacity Building providing an overview of the Capacity Buildingstrategy, including the planned timeline for the activities and accessibility to developmaterial  Chapter 2 \u2013 NATI00NS Capacity Building Activities that describes each CB activities plannedunder NATI00NS, as well as their related content development plan and implementationstrategy  Chapter 3 \u2013 Key Performance Indicators and Impact outlining the key performanceindicators that will be monitored along the project implementation to track progress andimpact of the different CB activities, and  Chapter 4 \u2013 Conclusions and Next Steps that offers a summary of the reflections on theperformed planning, also outlining the activities to be carried out in the upcoming monthsto ensure the correct delivery of the project activities.   The plan presented in this document is to be considered as a living document that will continue to\u00a0be updated, evaluated, and revised internally based on feedbacks and requests of potential\u00a0applicants, as well as based on the evolution of the EU Soil Mission and overall landscape. The report\u00a0demonstrates that the planning and implementation process is well underway for NATI00NS\u00a0Capacity Building activities and great progress has been made so far to roll out the activities.", "keywords": ["11. Sustainability", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10404687"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10404687", "name": "item", "description": "10.5281/zenodo.10404687", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10404687"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10776892", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:47Z", "type": "Dataset", "title": "Landsat-based Spectral Indices for pan-EU 2000-2022", "description": "Description  General description  Here, we present the ARCO (analysis-ready and cloud-optimized) Landsat-based Spectral Indices data cube. Available at 30m resolution from 2000 to 2022, it includes multiple spectral indices and multi-tier predictors (bimonthly, annual, and long-term) for continental Europe, including Ukraine, the UK, and Turkey (excluding Svalbar). This data cube has a broad coverage of indices, each providing unique insights into different aspects, including: surface reflectance, vegetation, water, soil and crop. All data layers are cloud-masked and then gap-filled, ready for analysis, modeling, and mapping applications. Technical details:    Coordinate reference system: EPSG:3035  Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)  Spatial resolution: 30m  Image size: 216,700P x 153,400L  File format: Cloud Optimized Geotiff (COG) format.   Considering the data volume, only bimonthly data layers for the years 2000 and 2022 are uploaded. However, all annual and long-term layers are available. For the full data cube, please visit this catalog. Due to Zenodo's storage limits, the data layers are stored in different buckets. Use the identifier-navigation list below to access the bucket of your interest and download the corresponding layers.  Identifier navigation list  This data cube includes 4 tiers of data, depending on the processing extend in the temporal scale:    Tier-1: Bimonthly Landsat reflectance bands2000 (Jan Mar May Jul Sep Nov) 2022 (Jan Mar May Jul Sep Nov)  Tier-2: Bimonthly spectral indices2000 (Jan Mar May Jul Sep Nov) 2022 (Jan Mar May Jul Sep Nov)  Tier-3: Annual predictors    Reflectance bands, NDVI and NDWI P252000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022  Reflectance bands, NDVI and NDWI P502000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022  Reflectance bands, NDVI and NDWI P752000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022  Aggregated spectral indices2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022  Cumulative spectral indices2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Tier-4: Long-term predictors 2000-2022trend P25 P50 P75   Name convention  To ensure consistency and ease of use across the data layers, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:    generic variable name: ndti.min.slopes = the long term slope of minNDTI  variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI  Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment  Spatial support: 30m  Depth reference: s = surface  Time reference begin time: 20000101 = 2000-01-01  Time reference end time: 20221231 = 2022-12-31  Bounding box: eu = europe (without Svalbar)  EPSG code: epsg.3035  Version code: v20231218 = 2023-12-18 (creation date)   Citation  Please cite this dataset using the DOI: [10.5281/zenodo.10776891], which represents all versions of this dataset. This ensures your citation remains up to date with the latest version.  Support  If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a GitHub issue!  Long-term spectral indices trend  On this landing page of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube,\u00a0 four long-term spectral indices trend data are stored, as Zenodo doesn't allow empty buckets. Therefore, this page serves not only as the landing page for the entire dataset but also as the bucket for the long-term trend of spectral indices.", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10776892"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10776892", "name": "item", "description": "10.5281/zenodo.10776892", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10776892"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10777976", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:49Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P25 (2003)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10777976"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10777976", "name": "item", "description": "10.5281/zenodo.10777976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10777976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10777994", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:50Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P25 (2012)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10777994"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10777994", "name": "item", "description": "10.5281/zenodo.10777994", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10777994"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10864875", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:51Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P50 (2004)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10864875"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10864875", "name": "item", "description": "10.5281/zenodo.10864875", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10864875"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-25T00:00:00Z"}}, {"id": "10.5281/zenodo.10865631", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:52Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P50 (2013)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10865631"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10865631", "name": "item", "description": "10.5281/zenodo.10865631", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10865631"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-25T00:00:00Z"}}, {"id": "10.5281/zenodo.10866154", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:53Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P75 (2006)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10866154"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10866154", "name": "item", "description": "10.5281/zenodo.10866154", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10866154"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-25T00:00:00Z"}}, {"id": "10.5281/zenodo.10866295", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:53Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P75 (2010)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10866295"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10866295", "name": "item", "description": "10.5281/zenodo.10866295", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10866295"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-25T00:00:00Z"}}, {"id": "10.5281/zenodo.10866437", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:53Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P75 (2015)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10866437"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10866437", "name": "item", "description": "10.5281/zenodo.10866437", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10866437"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-25T00:00:00Z"}}, {"id": "10.5281/zenodo.10866515", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:54Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P75 (2018)", "description": "Description   This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset.   General Description   This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes:      Long-term trend (2000-2022):         The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022.    Annual Landsat P25:         Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI.    Annual Landsat P50:         Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual Landsat P75:         Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI.   Annual aggregated indices:         This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation.      Bimonthly Landsat bands:         Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands.  Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method.    Bimonthly spectral indices:         This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR.    Related identifiers      Long-term trend: 2000-2022   Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022      Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022   Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022    Data Details      Time period: 2000\u20132022   Type of data: soil health data cube, with selected indices relevant to soil health monitoring.   How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package.   Statistical methods used: band operation, time series analysis and statistics calculation   Limitations or exclusions in the data: The dataset does not include data for Svalbard.    Coordinate reference system: EPSG:3035   Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)   Spatial resolution: 30m   Image size: 216,700P x 153,400L   File format: Cloud Optimized Geotiff (COG) format.    Support   If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)   Name convention   To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:      generic variable name: ndti.min.slopes = the long term slope of minNDTI   variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI   Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment   Spatial support: 30m   Depth reference: s = surface   Time reference begin time: 20000101 = 2000-01-01   Time reference end time: 20221231 = 2022-12-31   Bounding box: go = global (without Antarctica)   EPSG code: epsg.3035   Version code: v20231218 = 2023-12-18 (creation date)", "contacts": [{"organization": "Tian, Xuemeng, Consoli, Davide, Leandro Parente, Ho, Yufeng, Hengl, Tom,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10866515"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10866515", "name": "item", "description": "10.5281/zenodo.10866515", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10866515"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-25T00:00:00Z"}}, {"id": "10.5281/zenodo.10959077", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:58Z", "type": "Dataset", "created": "2023-10-30", "title": "Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies", "description": "The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the 'Soil management effect assessment' in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ('Knowledge gap in tab'-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other", "keywords": ["Water management", "EJP SOIL", "Climate change mitigation", "Nitrogen leaching", "CH4", "Conservation agriculture", "Cropping systems", "SOMMIT", "N2O", "Organic matter inputs", "Tillage"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10959077"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10959077", "name": "item", "description": "10.5281/zenodo.10959077", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10959077"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.10991228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:23:58Z", "type": "Dataset", "title": "Water sample analysis and satellite imagery of a thermo-erosion gully and its surroundings in Adventdalen, Svalbard.", "description": "Data description  This dataset is part of the supplemental information to the paper 'Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff' by Parmentier et al. (2024). It includes the analysis of water quality in and around a thermo-erosion gully on the high-Arctic archipelago of Svalbard, and three satellite images that give an overview of the wider area around this gully in the context of a snow fence experiment (Cooper et al. 2011). More details are provided in Parmentier et al. (2024).  Background  Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. The data presented here is part of a study that showed that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially-raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1 to 3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilised aquatic carbon. Our study show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss.  Water samples  The following datafile includes the analysis of several water samples taken in and near a thermo-erosion gully on Svalbard on August 5th\u00a0and 6th, 2017. These were analyzed for dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN) content, and stable carbon isotope ratios \u03b413C-DOC and \u03b413C-POC. In addition, temperature, pH, oxygen, and electrical conductivity were measured in the field on the day of sampling. This data is provided in the following Excel file that also includes the latitude and longitude for each sample point:\u00a0    Parmentier et al - 2024 - Water Sample Analysis.xlsx   \u00a0Sample analysis  A full description of the analysis is repeated here from the supplemental information in the accompanying publication (Parmentier et al. 2024). The water samples were filtered on the day of collection through a pre-combusted glass fiber filter with pore size of 0.7 \u00b5m (Whatman, Grade GF/F). After filtration, the filters were packed in aluminum foil and frozen for later analysis of the collected particulate matter. From the filtrate, three samples of ~50 ml were taken and immediately frozen for transport.  The filtered water samples were analyzed for their dissolved organic carbon (DOC) content and their stable carbon isotope ratio \u03b413C-DOC. This combined analysis was carried out at the labs of UCLouvain, Belgium with an Aurora 1030W TOC Carbon Analyzer, from OI Analytical, coupled to an IRMS (Thermo delta V Advantage). In the Aurora 1030W, the water samples were purged with H3PO4(phosphoric acid) to remove any dissolved inorganic carbon (DIC). Afterwards, Na2S2O8\u00a0(sodium persulfate) was added to the heated sample (97 \u00b0C) to oxidize any DOC to CO2. With N2\u00a0as the carrier gas, the CO2\u00a0was transferred to the analyzing units where the total concentration and \u03b413C-DOC of the CO2\u00a0were detected. The \u03b413C-DOC samples were calibrated against the certified standard IAEA-CH-6 (-10.449 \u00b1 0.033 \u2030VPDB) and an internal sucrose standard (-26.99 +/- 0.04 \u2030). The DOC measurements were calibrated against a concentration range (n=8) of the same standards (Morana et al., 2015).  \u00a0The particulate matter retained on the filters was analyzed for particulate organic carbon (POC) and particulate nitrogen (PN) concentrations, as well as \u03b413C-POC. The glass fiber filters were subsampled and repeatedly acidified with HCl (1.5 M) in pre-combusted Ag capsules to remove carbonates. Analyses were performed at the Stable Isotope Facility of the University of California in Davis using an Elementar Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany) connected to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios of \u03b413C are reported relative to the international standard VPDB (Vienna PeeDee Belemnite).  Satellite imagery  To show the development of the thermo-erosion gully over time, we provide three high resolution satellite images from the Digital Globe constellation of satellites. The areal extent of these images covers the entire snow fence experiment in the valley of Adventdalen on Svalbard. They were acquired on August 5th, 2011, August 30th, 2013, and July 9th, 2015 by the WorldView-2, GeoEye-1 and WorldView-3 satellites, respectively. These images are provided as GeoTiffs \u2013 projected in the UTM 33X coordinate system:    SnoEco_2011AUG05_WV2_MUL_Pansharpened_bco_rcs_dobj.tif  SnoEco_2013AUG30_GE1_MUL_Pansharpened_bco_rcs_dobj.tif  SnoEco_2015JUL09_WV3_MUL_Pansharpened_bco_rcs_dobj.tif   Each of these files includes the following color bands:\u00a0    Band 1: Blue  Band 2: Green  Band 3: Red  Band 4: Near Infrared   In addition, the images are clipped to the following coordinate bounds (in UTM 33X):      xmin, xmax: 523740, 524825     ymin, ymax: 8677150, 8678100    For full details on these satellite products, we refer to DigitalGlobe/Maxar.\u00a0  Image processing  The satellite imagery was processed according to DigitalGlobe guidelines and calibration coefficient adjustment factors. The radiometrically corrected source images were first converted to top-of-the-atmosphere spectral radiance, and thereafter to top-of-the-atmosphere reflectance. Following this processing, each color band of the image was pansharpened (using Bicubic interpolation) with the RCS algorithm in the Orfeo ToolBox of QGIS 2.18 to increase the horizontal resolution to ~50 cm. To reduce haze effects, the images were further corrected through a dark object subtraction (bottom 1 percentile of the blue band) which was applied to each band separately. Subsequent negative values were set to zero.\u00a0  Acknowledgments  This research was funded by the Research Council of Norway (RCN; grant agreement 230970), and the FRAM - Terrestrial flagship (362255 and 642018). F.J.W.P. and S.W. received additional funding from the RCN (grant agreement 323945). The high-resolution satellite imagery comes courtesy of the DigitalGlobe Foundation. We thank UCLouvain and the University of California, Davis for assisting in the sample analysis.\u00a0  References  Cooper, E. J., Dullinger, S., & Semenchuk, P. (2011). Late snowmelt delays plant development and results in lower reproductive success in the High Arctic.\u00a0Plant Science, 180(1), 157\u2013167. https://doi.org/10.1016/j.plantsci.2010.09.005  Morana, C., Darchambeau, F., Roland, F. A. E., Borges, A. V., Muvundja, F., Kelemen, Z., et al. (2015). Biogeochemistry of a large and deep tropical lake (Lake Kivu, East Africa: insights from a stable isotope study covering an annual cycle.\u00a0Biogeosciences, 12(16), 4953\u20134963. https://doi.org/10.5194/bg-12-4953-2015  Parmentier, F. J. W., Nilsen, L, T\u00f8mmervik, H., Meisel, O. H., Br\u00f6der, L., Vonk, J. E., Westermann, S., Semenchuk, P. R., Cooper, E. J., Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff,\u00a0Geophysical Research Letters, In press", "contacts": [{"organization": "Parmentier, Frans-Jan W., Nilsen, Lennart, T\u00f8mmervik, Hans, Meisel, Ove H., Br\u00f6der, Lisa, Vonk, Jorien E., Westermann, Sebastian, Semenchuk, Philipp R., Cooper, Elisabeth J.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10991228"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10991228", "name": "item", "description": "10.5281/zenodo.10991228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10991228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-18T00:00:00Z"}}, {"id": "10.5281/zenodo.11071095", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:00Z", "type": "Report", "title": "D4.2. Plan for exploitation and dissemination of the project results", "description": "This document is a deliverable of the Co-UDlabs project, funded under the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 101008626.   The aim of this document is to provide the first version of the Plan for Dissemination and Exploitation of Results (PEDR), produced at M6 as part of the Work Package 4 on communication, dissemination and exploitation of results.   The aim of the PEDR is to provide the Co-UDlabs partners with guidelines on the different communication and dissemination activities that are planned and their schedule, who are the partners responsible for each activity and what tools and channels are available for dissemination. A section on exploitation will define the actions planned to achieve the exploitation of the results and impact of the project.   More specifically, in terms of dissemination and communication the PEDR will:         \u00a0Propose a communication and dissemination policy, and define the objectives of the actions;        \u00a0Identify the target audience for each objective or main result;        \u00a0List the communication and dissemination channels to be used for project promotion;        \u00a0Present a schedule of the communication and dissemination actions throughout the project duration;        \u00a0Define and monitor a series of Key Performance Indicators (KPIs) to assess the success of the implementation (e.g. number of publications, size of the audience reached, number of visits on the website, feedback received from audiences at conferences, etc.) and update the plan according to the evolution of the project.      In terms of the exploitation of the results, the PEDR will contain the following information, if applicable and when relevant, especially within the final exploitation plan to be submitted at the end of the project:      The identification of exploitable main outputs of the project;   The identification of the factors influencing exploitation and wide deployment of the project\u2019s results   The identification of new and existing measures for the project sustainability.    The document is drafted by Euronovia, which is leader of this Work Package, with inputs from all partners.   While Euronovia is the leading partner in charge of WP4, all partners have the responsibility to participate in the communication activities and dissemination of the results of the project. According to the grant agreement and unless it goes against their legitimate interests, each beneficiary must, as soon as possible, disseminate its results by disclosing them to the public by appropriate means (other than those resulting from protecting or exploiting the results), including in scientific publications.   The PEDR is an evolving document which will be updated at the end of each reporting period (October 2022, April 2024 and April 2025).", "keywords": ["Research Infrastructure", "Co-UDlabs", "Urban Drainage Systems", "12. Responsible consumption"], "contacts": [{"organization": "De Nale, Laura, Guilloteau, Lucie, Anta, Jose,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11071095"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11071095", "name": "item", "description": "10.5281/zenodo.11071095", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11071095"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-26T00:00:00Z"}}, {"id": "10.5281/zenodo.11370397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:02Z", "type": "Dataset", "title": "Coastal Foredune Belowground Biomass, Aboveground Biomass, and Species Cover", "description": "We collected data from cross-shore transects at seven sites; three categorized as unmanaged (i.e., Corolla Reserve, the northern dunes of the US Army Engineer Research and Development Center [ERDC] Field Research Facility [FRF North], and dunes in the southern reaches of that same site [FRF South]) and four categorized as managed (i.e., Southern Shores, Nags Head, Pine Island and Duck). Sediment vibracores (n = 15 managed, 10 unmanaged) were collected between September and December 2020 at the dune toe, dune face, crest and back along a single transect at each site.\u00a0Cores were stored at 4 \u00b0C and processed within one week of collection to prevent root degradation. Cores were bisected longitudinally and the sediment from each half was segmented into 30 cm sections from the ground surface (i.e., 0\u201330 cm, 31\u201360 cm, 61\u201390 cm, 91\u2013120 cm, 121\u2013150 cm). To separate belowground biomass, core sections were wet sieved using stacked 3.36 mm, 1.0 mm and 0.5 mm mesh-size sieves. Living belowground biomass was characterized as the sample portions with roots, rhizomes and belowground stems that were still flexible and did not exhibit signs of decomposition. All other biotic material was classified as non-living biomass (e.g., decayed plant material, twigs, seeds, wrack). Within the living belowground biomass component, live roots were separated from other belowground structures (rhizomes, belowground-stems) and scanned using an Epson Perfection V800 Photo electric scanner calibrated for image analysis with WinRhizo\u2122 Pro 2019a by Regent Instruments (Regent Instruments Inc, Quebec City, Quebec, Canada). Images were analyzed to quantify root surface area by diameter size class. Fine roots were defined as those with < 1 mm diameter. All living and non-living belowground biomass was oven-dried at 60 \u00b0C for 72 hours and weighed.\u00a0  Soil organic matter content was quantified by loss-on-ignition following manual removal of roots. Samples (1 g of sediment from each core section) were baked in a muffle furnace at 550 \u00b0C for 5 hours and reweighed to estimate soil organic matter content (%). Aliquots (300 mg) of each core segment were submitted to the Cornell Stable Isotope Laboratory for % carbon (C) and % nitrogen (N) analysis. Due to cost, only the top 90 cm of soils were sent for C and N analysis.  Vegetation surveys were conducted during Summer 2021. We established vegetation survey plots (0.25 m\u00ad\u00ad2 plot size) centered on each exact coring location. We estimated\u00a0vegetation cover by species (within 0.25 m2 plot), cover of bare ground, dead plant cover. Adjacent to the coring plots, aboveground biomass (within a 0.1 x 1 m plot) was collected to complement belowground biomass sampling at all sites except Duck, where grasses had been manually planted and permission to harvest aboveground biomass was not granted to maintain the vegetation on the dune face. Aboveground biomass was oven-dried at 60 \u00b0C for 72 hours, weighed and scaled to g m-2.  More details can be found in the manuscript:   White AE, Cohn N, Davis EH, Hein CJ, Zinnert JC. Coastal dune management affects above and belowground biotic characteristics. Scientific Reports (in press).", "contacts": [{"organization": "White, Andrew E., Cohn, Nicholas, Davis, Elizabeth H., Hein, Christopher J., Zinnert, Julie C.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11370397"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11370397", "name": "item", "description": "10.5281/zenodo.11370397", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11370397"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-28T00:00:00Z"}}, {"id": "10.5281/zenodo.11400540", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:02Z", "type": "Dataset", "title": "State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions", "description": "This dataset supports the State of Wildfires 2023-24 report under review at Earth System Science Data Discussions (Jones et al., under review, https://doi.org/10.5194/essd-2024-218). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2023-February 2024), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Jones et al. (2024).  Citation  Work utilising our regional summaries should\u00a0cite both Jones et al. (2024, under review, ESSD) AND the primary reference for the variable(s) of interest as follows:    Giglio et al. (2018) for MODIS MCD64A1 BA.  van der Werf et al. (2017) for GFED4.1s fire C emissions.  Kaiser er al. (2012) for GFAS fire C emissions.  van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.  Andela et al. (2019) for the Global Fire Atlas.   Input Data  Burned Area (BA)    BA data from NASA\u2019s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available at Giglio et al. (2021, https://lpdaac.usgs.gov/products/mcd64a1v061/).\u00a0    Period: 2001-February 2024  Resolution: 500m     Fire Carbon (C) Emissions    GFED4.1s fire C emissions data are extended from van der Werf and are available at\u00a0https://globalfiredata.org/.    Period: 2003-February 2024  Resolution: 0.25 degree, daily       GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available at https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation.    Period: 2003-February 2024  Resolution: 0.1 degree, daily     Global Fire Atlas (Individual Fire Atlas and Properties)    Global Fire Atlas are extended from Andela et al. (2019) and are available at Andela and Jones (2024, https://doi.org/10.5281/zenodo.11400062, last access: 31 May 2024).\u00a0    Period: 2002-February 2024  Driven by 500m MODIS BA data (collection 6.1)     Regional Analysis  We performed 'cookie-cutting' (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the 'Countries' layer).\u00a0  The statistics derived from cookie-cutting are listed below. Full details in Jones et al. (2024).         Layer    Short Form\u00a0    Source      Biomes    NA    Olson et al. (2001)      Continents    NA    ArcGIS Hub (2024)      Continental Biomes    NA    See above      Countries    NA    EU Eurostat (2020)      UC Davis Global Administrative Areas (GADM) Level 1    GADM-L1    UC Davis (2022)      Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions\u00a0    IPCC AR6 WGI Regions    IPCC (2021); SantanderMetGroup (2021)      Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions    RECCAP2 Regions    Ciais et al. (2022)      Global Fire Emissions Database (GFED) Basis Regions    GFED4.1s Regions    van der Werf et al. (2006)       \u00a0  Regional Statistics and Anomalies    Burned Area (BA)    Calculated regional totals for each fire season.  Relative and standardized anomalies from historical data (since 2001).  Ranking amongst all recorded fire seasons.  Onset, peak, and cessation based on monthly deviations from climatological means.       Carbon Emissions    Calculated regional totals for each fire season.  Relative and standardized anomalies from historical data (since 2003).  Ranking amongst all recorded fire seasons.  Onset, peak, and cessation based on monthly deviations from climatological means.  Statistics available for GFAS, GFED, and their mean.       Individual Fire Properties    Based on ignition point vectors from the Global Fire Atlas.  Calculated regional count.  Calculated regional maxima and 95th percentiles for each fire season.  Relative and standardized anomalies from historical data (since 2002).  Ranked anomalies among all recorded fire seasons.", "keywords": ["Life Science"], "contacts": [{"organization": "Jones, Matthew William, Brambleby, Esther, Andela, Niels, van der Werf, Guido, Parrington, Mark, Giglio, Louis,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11400540"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11400540", "name": "item", "description": "10.5281/zenodo.11400540", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11400540"}, {"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.11487442", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:02Z", "type": "Dataset", "title": "HAZEL.trial.ILVO: dataset of a long term variety trial with hazelnut trees at ILVO.", "description": "Nuts, including hazelnuts, are a popular and healthy substitute for animal based proteins, the production of which is known to put pressure on ecosystems around the world. Besides, there is an increasing interest in local food production to reduce food kilometers. Nowadays hazelnuts in Flanders and other West European regions are mainly imported from Turkey, producing almost 65% of the total world production (FAO). Local production of hazelnuts is limited, and research and knowledge on productive cultivars in a more temperate climate compared to Mediterranean regions is scarce.  In 2017, 168 hazel trees (Corylus avellana cvs.) of eight different varieties, were planted in a randomized design on an experimental plot at ILVO (coordinates: 50.976816, 3.778914). The eight cultivars selected were \u2018Emoa 1\u2019, \u2018Hall's Giant\u2019, \u2018Corabel\u2019, \u2018Gunslebert\u2019, \u2018Kentish cob\u2019, \u2018Gustav\u2019s Zeller\u2019, \u2018Cosford\u2019 and \u2018Tonda di Giffoni\u2019. They were planted in such a way that each cultivar was represented equally. The distance in the row (3 m) ensures an optimal light and water supply; the distance between the rows (7.5 m) allows machine mowing. This results in a planting density of 476 trees/hectare.  This hazelnut variety trial is incorporated within a silvopastoral experimental site, within which furthermore also short rotation coppice (with willows) is planted, and where impact of presence of chickens is also assessed.  The initial trial (with SRC and chickens) was installed, and several trials have been organized at the site since then. The measurements that took place include the monitoring of chicken presence (with tracking systems), chicken welfare (e.g. feather pecking, leg health, breastbone fractures,...), weather conditions (wind, temperature, rain), production of SRC (willow) and hazelnuts in relation to chicken pressure, monitoring of carbon, nitrogen (mineral and organic), phosphorus, potassium in the soil, \u2026  As for the hazelnut trial specifically, properties of the different hazelnut varieties (production, size, flavour, cracking yield, susceptibility to the hazelnut weevil, quality parameters, ...) have been assessed.", "contacts": [{"organization": "Reubens, Bert, Bracke, Jolien, Pardon, Paul,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11487442"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11487442", "name": "item", "description": "10.5281/zenodo.11487442", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11487442"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-05T00:00:00Z"}}, {"id": "10.5281/zenodo.13344265", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:08Z", "type": "Dataset", "title": "Soil Properties and Soil Macroinvertebrate Communities in Amazonian Anthropogenic Soils", "description": "About  This dataset is an update of the dataset 'A \u201cDirty\u201d Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils' previously published in Dryad repository (https://doi.org/10.5061/dryad.3tx95x6cc). This dataset now include information on soil humification index, soil carbon related to different soil minerals and soil fatty acids characterization.  Description  Soils were sampled in Brazilian Amazonia in the municipalities of Iranduba-AM, Belterra-PA and Porto Velho-RO. In each region, paired sites with anthropogenic dark earths (ADE) and nearby reference (REF) non-anthropogenic soils were sampled under three different land-use systems: native secondary vegetation (dense ombrophilous forest) classified as old secondary forest when >20 years old, or young regeneration forest when <20 years old, and agricultural systems (maize in Iranduba, soybean in Belterra, and introduced pasture in Porto Velho).  At each site, soil and litter macrofauna were collected using the Tropical Soil Biology and Fertility (TSBF) method (Anderson and Ingram, 1993) at five sampling points (soil monoliths 25x25 cm up to 30 cm depth) within a 1 ha plot, four at the corners and one on the center of a 60 x 60 m square, resulting in an \u201cX\u201d shaped sampling design. Each soil monolith was divided into surface litter and three 10 cm-thick soil layers (0-10, 10-20, 20-30 cm). Macroinvertebrates (animals with >2 mm body width) were manually hand-sorted and fixed in 92% ethanol. Earthworms, ants and termites were identified to species or genus level and other macroinvertebrates were sorted into morphospecies with higher taxonomic level assignations. Density (number of individuals) and biomass of the soil macrofauna surveyed using the TSBF method were extrapolated per square meter.  For the earthworms, ants and termites (ecosystem engineers) additional samples were performed, especially in forest sites to better estimate species richness of these taxa. Earthworms were collected at four additional cardinal points of the grid at all sites, and hand-sorted from holes of similar dimensions as the TSBF monoliths. Termites were sampled in the forest sites only forests (except one of the REF young forests at Porto Velho), in five 20 m2 (2 x 10 m) plots (close to the five main soil monoliths) by manually digging the soil and looking for termitaria in the soil, as well as in the litter and on trees using a modification of the transect method (Jones and Eggleton, 2000). Ants were sampled in 10 pitfall traps (300 ml plastic cups) set up as two 5-trap transects on the sides of each 1 ha plot, as well as in two traps to the side of each TSBF monolith (distant ~5 m) only in the forest systems of Iranduba and Belterra (not Porto Velho). Each cup was filled to a third of its volume with water, salt and detergent solution. Termites and ants were preserved in 80% ethanol and earthworms in 96% ethanol and the alcohol changed after cleaning the samples within 24 h. All the animals (earthworms, ants, termites) were identified to species level or morphospecies level (with genus assignations) by co-authors SWJ/MLCB (earthworms), AA (termites) and ACF/RMF (ants).  Soil samples for chemical and particle size analysis were collected from each TSBF monolith after the soil fauna hand-sorting. Around 2 to 3 kg soil from each depth (0-10, 10-20, 20-30 cm) and the following soil properties were evaluated according to standard methodologies (Teixeira et al., 2017): pH (CaCl2); Ca2+, Mg2+, Al3+ (KCl 1 mol L-1); K+,\u00a0P, Fe, Zn, Mn, Cu and Ni\u00a0(Mehlich-1); Pseudo-total contents of trace elements (Ba, Cd, Co, Cu, Ni, Pb, Se and ZN) were determined by acid digestion (HNO3 + HCl); Fe (sulfuric extract); total nitrogen (TN) and carbon (TC) using an element analyzer (CNHS). Base saturation and cation exchange capacity (CEC) were calculated using standard formulae (Teixeira et al., 2017) and particle size fractions (% sand, silt, clay) were obtained following standard methodologies (Teixeira et al., 2017).\u00a0Soil magnetic susceptibility (MS) and apparent electrical conductivity (ECa) (Siemens per meter \u2013 S m-1) were obtained using a KT-10 S/C magnetic susceptibility/conductivity meter (Terraplus) with 10 Hz of operating frequency.  Soil macromorphology samples were taken close to the TSBF monolith (~2 m) using a 10 x 10 x 10 cm metal frame. The collected material was separated into different fractions including: living invertebrates, litter, roots, pebbles, pottery sherds, charcoal (biochar), non-aggregated/loose soil (NA), physical aggregates (PA), root-associated aggregates (RA), and fauna-produced aggregates (FA) using the methodology proposed by Velasquez et al. (2007).  Laser-induced fluorescence spectroscopy analysis (LIFS) was performed on soil macroaggregate fraction (FA, PA, RA and NAS) from both YF and the pasture from Porto Velho to obtain the humification index of soil organic matter according to Milori et al. (2006).  Were analysed fatty acids in soil macroaggregates (PA, RA and FA) from one site in Teot\u00f4nio. The process involved extracting 2 g of each sample with a chloroform: methanol solution and a surrogate compound, 5\u03b1-cholestane. The extract was centrifuged, combined, and the solvent removed using a rotary evaporator and nitrogen. Extracts were stored at -20\u00b0C until analyzed by GC-MS. Samples were silylated, with excess silylating agent removed, followed by the addition of hexane and vortexing for GC-Q-MS analysis. The equipment used included an Agilent Technologies GC (7890B) and MS (5977A), with an autosampler and HP-5ms column. MassHunter and MSD ChemStation software facilitated analysis and quantification, respectively. Deconvolution and retention index calculations were performed using AMDIS software. Compounds were identified using NIST MS software, requiring at least three specific mass fragments per compound and a retention index deviation of less than 1.5%. Analyte intensities were normalized by dried soil sample weights and the internal standard.  Soil samples from TSBF monoliths were fractionated by dry sieving into small (<500 \u00b5m) and large (>500 \u00b5m) aggregate size classes. These were further fractionated into sand-particulate organic matter (sand-MOP) (>53 \u00b5m), silt-organic matter associated with minerals (MOM) (53-2 \u00b5m), and clay-MOM (<2 \u00b5m). Total organic carbon and nitrogen in these fractions were measured using a Vario EL III elemental analyzer. Clay-MOM samples underwent a four-step sequential extraction with hydroxylamine, sodium dithionite, sodium pyrophosphate, and sodium hydroxide to determine carbon, silicon, iron, and aluminum contents associated with different soil components. For further details see Ramalho (2020).  Soil bulk density and total porosity were determined using undisturbed core samples (0.05 m diameter, 0.05 m depth) collected at ~2 m from the TSBF samples following the method proposed by Teixeira et al. (2017).  All data is provided in excel format, and includes 14 tabs in the data file: Metadata and legend, Site description, Soil chem, BD+POR, Macromorph, Seq_ext, Biomark, HLIF, Macro_den, Macro_bio, Morpho_TSBF, Add_worm, Add_ants, Add_termites. The Metadata and legend tab provides a detailed explanation for each variable included in each table, including the units used for each. The Site description tab include a brief description of the sites sampled. Soil chem, BD+POR, Macromorph, Seq_ext, Biomark and HLIF tables contain the data on soil chemical, physical, macromorphological, organic matter related to soil minerals, fatty acids and humidification index variables, respectively. The Macro_den and Macro_bio contain the data about density and biomass on all the soil invertebrate taxa found, respectively. The Morphosp_TSBF, Add_worm, Add_ants and Add_termites tables contain the invertebrate species/morphospecies occurrence in TBSF and extra samples for earthworms, ants and termites, respectively.  References used in methods section  Anderson, J.M., Ingram, J.S.I., 1993. Tropical Soil Biology and Fertility: A handbook of methods, 2 edition. ed. Oxford University Press, Oxford. https://doi.org/10.2307/2261129  Jones, D.T., Eggleton, P., 2000. Sampling termite assemblages in tropical forests: testing a rapid biodiversity assessment protocol. Journal of Animal Ecology 37, 191\u2013203. https://doi.org/10.1046/j.1365-2664.2000.00464.x  Milori, D.M.B.P., Galeti, H.V.A., Martin-Neto, L., Dieckow, J., Gonz\u00e1lez-P\u00e9rez, M., Bayer, C., Salton, J., 2006. Organic Matter Study of Whole Soil Samples Using Laser-Induced Fluorescence Spectroscopy. Soil Science Society of America Journal. 70, 57. https://doi.org/10.2136/sssaj2004.0270  Teixeira, P.C., Donagemma, G.K., Fontana, A., Teixeira, W.G., 2017. Manual de m\u00e9todos de an\u00e1lise de solo, 3o. ed. Embrapa, Bras\u00edlia.  Ramalho. B., 2020. Caracteriza\u00e7\u00e3o das intera\u00e7\u00f5es organo-mineral em Terra Preta de \u00cdndio. Thesis. Universidade Federal do Paran\u00e1. 113p.  Velasquez, E., Pelosi, C., Brunet, D., Grimaldi, M., Martins, M., Rendeiro, A.C., Barrios, E., Lavelle, P., 2007. This ped is my ped: Visual separation and near infrared spectra allow determination of the origins of soil macroaggregates. Pedobiologia 51, 75\u201387. https://doi.org/10.1016/j.pedobi.2007.01.002  Funding  The study was supported by the Newton Fund and Funda\u00e7\u00e3o Arauc\u00e1ria (grant Nos. 45166.460.32093.02022015, NE/N000323/1), Natural Environment Research Council (NERC) UK (grant No. NE/M017656/1), a European Union Horizon 2020 Marie-Curie fellowship to LC (MSCA-IF-2014-GF-660378) and another to DWGS (No. 796877), by CAPES scholarships to WCD, ACC, TF, RFS, AF, LM, HSN, TS, AM and RSM (PVE A115/2013), Araucaria Foundation scholarships to LB, AS, ACC and ES, Post-doctoral fellowships to DWGS (NERC grant NE/M017656/1) and ES (CNPq No. 150748/2014-0), PEER (Partnerships for Enhanced Engagement in Research Science Program) NAS/USAID award number AID-OAA-A-11-0001 - project 3-188 to RMF, and by CNPq grants, scholarships and fellowships to ACF, GGB, RF, SWJ, EGN and PL (Nos.\u00a0140260/2016-1, 307486/2013-3, 302462/2016-3, 401824/2013-6, 307179/2013-3, 400533/2014-6). We thank INPA, UFOPA, Embrapa Rond\u00f4nia, Embrapa Amaz\u00f4nia Ocidental and Embrapa Amaz\u00f4nia Oriental and their staff for logistical support, and the farmers for access to and permission to sample on their properties. Sampling permit for Tapaj\u00f3s National Forest was granted by ICMBio.", "contacts": [{"organization": "Demetrio, Wilian, Brown, George,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13344265"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13344265", "name": "item", "description": "10.5281/zenodo.13344265", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13344265"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-19T00:00:00Z"}}, {"id": "10.5281/zenodo.14008412", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:17Z", "type": "Dataset", "title": "SERENA EJPSOIL BE Flanders soil sealing cookbook", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. \u00a0The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters)\u00a0combine \u201cknown\u201d sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. \u00a0The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. 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