{"type": "FeatureCollection", "features": [{"id": "10.20944/preprints202504.2122.v1", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:18:36Z", "type": "Report", "created": "2025-04-28", "title": "Citizen Science for Soil Monitoring and Protection", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Citizen science (CS) is increasingly recognized as a complementary approach for addressing soil health challenges - including erosion, pollution, nutrient imbalances, and biodiversity loss - by harnessing public participation to broaden spatial and temporal data collection. This review synthesizes findings from (i) a systematic analysis of peer-reviewed literature and grey sources, (ii) a database of 96 CS initiatives compiled by the PrepSoil project, and (iii) questionnaire surveys and workshops conducted in five European Living Labs. Our analysis indicates that volunteer-driven monitoring can enhance the volume and granularity of soil data, providing critical insights into parameters such as organic carbon content, nutrient levels, and pollutant concentrations. However, persistent challenges remain, including inconsistencies in data validation, volunteer attrition, and concerns regarding digital literacy and data privacy. Despite these challenges, ongoing efforts to standardize protocols, integrate remote sensing and sensor-based validation methods, and employ feedback mechanisms improve data reliability and participant engagement. We conclude that sustained capacity-building, transparent data governance, and stakeholder collaboration - from local communities to governmental bodies - are essential for fully realizing the potential of citizen science in soil conservation. CS can meaningfully support sustainable land management and evidence-based policymaking by aligning public-generated observations with established scientific frameworks.</p></article>", "contacts": [{"organization": "Karel Charv\u00e1t, Jaroslav \u0160mejkal, Petr Hor\u00e1k, Mark\u00e9ta Kollerov\u00e1, \u0160\u00e1rka Hor\u00e1kov\u00e1, Pierre Renault,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.20944/preprints202504.2122.v1"}, {"rel": "self", "type": "application/geo+json", "title": "10.20944/preprints202504.2122.v1", "name": "item", "description": "10.20944/preprints202504.2122.v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.20944/preprints202504.2122.v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-25T00:00:00Z"}}, {"id": "10.2111/08-106.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:36Z", "type": "Journal Article", "created": "2010-01-22", "title": "Land Use Influences Carbon Fluxes In Northern Kazakhstan", "description": "A mobile, closed-chamber system (CC) was used to measure carbon and water fluxes on four land-use types common in the Kazakh steppe ecoregion. Land uses represented crop (wheat or barley, WB), abandoned land (AL), crested wheatgrass (CW), and virgin land (VL). Measurements were conducted during the growing season of 2002 in northern Kazakhstan at three locations (blocks) 15\u201320 km apart. The CC allowed the measurement of the carbon flux components of net ecosystem exchange (NEE), ecosystem respiration (RE) and soil respiration (RS), together with evapotranspiration (ET). Nonlinear regression analyse sw ere used to model gross primary production (GPP) and ET as a function of photosynthetically active radiation (Q); RE and RS were modeled based on air (Tair) and soil (Ts) temperature, respectively. GPP, RE, RS, and ET were estimated for the entire year with the use of continuous 20-min means of Q, Tair, and Ts. Annual NEE indicated that AL gained 536 g CO2 ? m 22 , WB lost 2 191 g CO2 ? m 22 , CW was near equilibrium (2 14 g CO2 ? m 22 ), and VL exhibited considerable carbon accumulation (153 g CO2 ? m 22 ). The lower GPP values of the land-use types dominated by native species (CW and VL) compared to WB and AL were compensated by positive NEE values that were maintained during a longer growing season. As expected, VL and CW allocated a larger proportion of their carbon assimilates belowground. Non\u2013growing-season RE accounted for about 19% of annual RE in all land-use types. The results of this landscape-level study suggest that carbon lost by cultivation of VLs is partially being restored when fields are left uncultivated, and that VLs are net sinks of carbon. Estimations of carbon balances have important management implications, such as estimation of ecosystem productivity and carbon credit certification. Resumen", "keywords": ["2. Zero hunger", "abandoned fields", "13. Climate action", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.2111/08-106.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Rangeland%20Ecology%20%26amp%3B%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2111/08-106.1", "name": "item", "description": "10.2111/08-106.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2111/08-106.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-01-01T00:00:00Z"}}, {"id": "10.21203/rs.3.rs-5128244/v1", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:18:37Z", "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": "2026-08-25T16:18:41Z", "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": "10261/351480", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:22:02Z", "type": "Report", "title": "Urban greenspaces and nearby natural areas support similar levels of soil ecosystem services", "description": "Open AccessPeer reviewed"}, "links": [{"href": "https://doi.org/10261/351480"}, {"rel": "self", "type": "application/geo+json", "title": "10261/351480", "name": "item", "description": "10261/351480", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/351480"}, {"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": "10.2307/1351977", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:56Z", "type": "Journal Article", "created": "2006-04-13", "title": "Are Spartina Marshes A Replaceable Resource - A Functional-Approach To Evaluation Of Marsh Creation Efforts", "description": "Marsh creation has come into increasing use as a measure to mitigate loss of valuable wetlands. However, few programs have addressed the functional ecological equivalence of man-made marshes and their natural counterparts. This study addresses structural and functional interactions in a man-made and two natural marshes. This was done by integrating substrate characteristics and marsh utilization by organisms of two trophic levels. Sediment properties, infaunal community composition, andFundulus heteroclitus marsh utilization were compared for a man-madeSpartina salt marsh (between ages 1 to 3 yr) in Dills Creek, North Carolina, and adjacent natural marshes to the east and west. East natural marsh and planted marsh sediment grain-size distributions were more similar to each other than to the west natural marsh due to shared drainage systems, but sediment organic content of the planted marsh was much lower than in either natural marsh. This difference was reflected in macrofaunal composition. Natural marsh sediments were inhabited primarily by subsurface, deposit-feeding oligochaetes whereas planted marsh sediments were dominated by the tube-building, surface-deposit feeding polychaetesStreblospio benedicti andManayunkia aestuarina. Infaunal differences were mirrored inFundulus diets. Natural marsh diets contained more detritus and insects, because oligochaetes, though abundant, were relatively inaccessible. Polychaetes and algae were major constituents of the planted marshFundulus diet. Though naturalmarsh fish may acquire a potentially less nutritive, detritus-based diet relative to the higher animal protein diet of the planted marsh fish,Fundulus abundances were markedly lower in the planted marsh than in the natural marshes, indicating fewer fish were being supported. LowerSpartina stem densities in the planted marsh may have provided inadequate protection from predation or insufficient spawning sites for the fundulids. After three years, the planted marsh remained functionally distinct from the adjacent natural marshes. Mitigation success at Dills Creek could have been improved by increasing tidal flushing, thereby enhancing, access to marine organisms and by mulching withSpartina wrack to increase sediment organic-matter content and porosity. Results from this study indicate that salt marshes should not be treated as a replaceable resource in the short term. The extreme spatial and temporal variability inherent to salt marshes make it virtually impossible to exactly replace a marsh by planting one on another site.", "keywords": ["0106 biological sciences", "14. Life underwater", "15. Life on land", "01 natural sciences", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.2307/1351977"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Estuaries", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2307/1351977", "name": "item", "description": "10.2307/1351977", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2307/1351977"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1991-03-01T00:00:00Z"}}, {"id": "10.2136/sssaj1995.03615995005900050022x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:46Z", "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": "10261/179481", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:59Z", "type": "Journal Article", "created": "2018-07-19", "title": "Molecular Fingerprinting of14C Dated Soil Organic Matter Fractions from Archaeological Settings in NW Spain", "description": "Abstract<p>This paper evaluates the complexities of radiocarbon (14C) dates from soil organic matter (SOM) in archaeological scenarios. The aqueous NaOH-insoluble residual SOM from Neolithic to medieval sites in NW Spain produced consistently older calibrated14C ages than NaOH-extractable SOM. Using pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and thermally assisted hydrolysis and methylation (THM-GC-MS), we analyzed the molecular composition of these SOM fractions, aiming to understand the differences in14C ages and to gain insight on SOM dynamics in relation to age fractionation. The molecular composition of the NaOH-extractable SOM, which accounts for roughly two-thirds of total SOM, has a larger proportion of microbial detritus than the NaOH-insoluble SOM. This might suggest that the discrepancies between the two fractions is due to microbial rejuvenation in the extractable fraction, leading to14C results that are younger than the activity that is to be dated. However, archaeological evidence presented here unambiguously shows that the14C age of the extractable SOM provides the more accurate age for the targeted activity, and that the insoluble fraction contains inherited old carbon. After statistical data evaluation using Partial Least Squares-Regression (PLS-R), it is concluded that this inherited SOM is a mixture of Black Carbon from wild and/or domestic fires and recalcitrant aliphatic SOM.</p", "keywords": ["Radiocarbon dating", "Molecular composition", "THM-GC-MS", "SOM fractions", "0601 history and archaeology", "06 humanities and the arts", "15. Life on land", "01 natural sciences", "Py-GC-MS", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10261/179481"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Radiocarbon", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/179481", "name": "item", "description": "10261/179481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/179481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-18T00:00:00Z"}}, {"id": "10.2139/ssrn.4556085", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:54Z", "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": "10.22541/essoar.171865325.50703739/v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:56Z", "type": "Journal Article", "created": "2024-06-17", "title": "Physics-Informed Neural Networks for Estimating a Continuous Form of the Soil Water Retention Curve from Basic Soil Properties", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p id='p1'>The soil water retention curve (SWRC) is essential for describing water and energy exchange processes at the interface between the solid earth and the atmosphere. Despite its importance, measuring the SWRC using standard laboratory methods is challenging and time-consuming. This paper presents a novel physics-informed neural network (PINN) approach for developing pedotransfer functions (PTFs) to predict continuous SWRCs based on soil texture, organic carbon content, and dry bulk density. In contrast to conventional parametric PTFs developed for specific SWRC models, the PINN learns a non-specific form of the SWRC by effectively integrating both measurements and physical constraints into the training process. This approach allows the estimated SWRC to maintain its physical integrity from saturation to oven-dry conditions, even in scenarios with sparse data. The new approach is particularly effective for tackling the challenges encountered in developing PTFs on large SWRC datasets, which often have an imbalance towards the wet-end and include numerous samples with limited and unevenly distributed measurements. We compared the performance of the PINN with that of a conventional physics-agnostic neural network using a dataset of 4200 soil samples. While both networks performed similarly at the wet-end where data are abundant, the PINN excelled at the dry-end where data are sparse and unevenly distributed, achieving a normalized RMSE of 0.172 compared to 0.522 for the conventional neural network. The SWRC derived from the PINN is differentiable with respect to the matric potential and can be seamlessly integrated into the governing equations of water flow in the unsaturated zone.</p></article>", "keywords": ["Environmental sciences", "physics-constrained machine learning", "physics\u2010constrained machine learning", "soil hydraulic properties", "GE1-350", "15. Life on land", "continuous pedotransfer functions"]}, "links": [{"href": "https://doi.org/10.22541/essoar.171865325.50703739/v1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water%20Resources%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.22541/essoar.171865325.50703739/v1", "name": "item", "description": "10.22541/essoar.171865325.50703739/v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.22541/essoar.171865325.50703739/v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-17T00:00:00Z"}}, {"id": "10.2307/2265779", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:57Z", "type": "Journal Article", "created": "2006-05-09", "title": "Responses Of A C-4 Grass And Three C-3 Forbs To Variation In Nitrogen And Light In Tallgrass Prairie", "description": "<p>In tallgrass prairie, high plant species diversity results not from a large number of grass species, but from a large number of forb (nongrass, herbaceous) species. Forbs exhibit morphological, life history, and ecophysiological characteristics that contrast sharply with those of the dominant C4 grasses. Success of the subdominant forbs varies strongly with topographic position and burning regime, and landscape scale patterns of abundance are well documented. But comparatively little is known about the mechanisms determining these patterns in persistent tallgrass prairie forbs. To elucidate these mechanisms, (1) leaf\uffe2\uff80\uff94level physiological characteristics of the dominant C4 grass, Andropogon gerardii, and four co\uffe2\uff80\uff94occurring C3 forbs were measured in response to natural and experimentally manipulated gradients of N availability, and (2) seasonal light environments of forbs in contrasting topographic positions and burning regimes and their morphological and physiological responses in these environments were compared to determine whether resource availability and utilization patterns contributed to patterns of forb distribution and abundance. The effects of burning regime and topographic position on maximum rate of photosynthesis (A) and stomatal conductance to water vapor (g) measured at the leaf level were not consistent with patterns of forb abundance. Nitrogen did not appear to limit forb physiological processes, even though increased N availability resulted in higher tissue N concentrations and greater biomass. There was no consistent increase in (A) or decrease in (g) in response to fertilization. However, (A) at low light levels was as much as 67% higher in fertilized Vernonia baldwinii and A. gerardii compared to unfertilized plants. Greater light availability to forbs in the canopy was associated with lower grass biomass production in uplands compared to lowlands and in unburned compared to burned sites. Forbs did not appear to adjust morphologically (leaf area and plant height) to different light environments at different sites. As a result, as much as 90% of forb leaf area in the burned lowland was displayed in low light, whereas as little as 30% of forb leaf area was in low light in the uplands at midseason. Estimates of potential whole\uffe2\uff80\uff94plant carbon uptake, based on leaf area distribution relative to available light and (A) as a function of light availability, agreed well with patterns of forb abundance and production. Differences in light availability may account for much of the variability in forb abundance related to burning regime and topographic position by limiting carbon gain in forbs more in burned lowlands than in other sites.</p>", "keywords": ["0106 biological sciences", "2. Zero hunger", "15. Life on land", "01 natural sciences"], "contacts": [{"organization": "C. L. Turner, Alan K. Knapp,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2307/2265779"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2307/2265779", "name": "item", "description": "10.2307/2265779", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2307/2265779"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1996-09-01T00:00:00Z"}}, {"id": "10.2307/2425415", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:57Z", "type": "Journal Article", "created": "2006-04-24", "title": "The Effect Of Nitrogen-Fertilization On The Production Of Halophytes In An Inland Salt-Marsh", "description": "The effect of nitrogen fertilization on plant production, soil and plant nitrogen content, and species distribution in an Ohio salt marsh was analyzed. Seasonal measurements indicate that the three dominant species attained maximal production at different times during the growing season. Production of Salicornia europaea increased with nitrogen fertilization and it appears that reduced soil nitrogen concentrations may be responsible for the different growth forms of S. europaea found in this marsh. Shoot nitrogen concentrations of S. europaea were inversely related to the growth response to fertilization. High tissue nitrogen concentrations in Hordeum jubatum and Atriplex triangularis suggest that some factor other than nitrogen is limiting to these species. INTRODUCTION A number of studies concerning production of plants in coastal salt marsh ecosystems suggest that productivity may be limited by availability of nitrogen (Tyler, 1967; Pigott, 1969; Stewart et al., 1972, 1973; Valiela and Teal, 1974; Gallagher, 1975; Patrick and Delaune, 1976; Mendelssohn, 1979b; Haines, 1979). Smart and Barko (1980) showed that although biomass was ultimately determined by the availability of nitrogen, growth rate was affected by the salinity of the sediments. Other effects of nitrogen fertilization on coastal salt marsh vegetation include: increased allocation of resources to sexual reproduction (Jefferies and Perkins, 1977), increased levels of nitrogen in the plant material (Buresh et al., 1980; Pigott, 1969) and a change in the distribution of plants in the salt marsh (Valiela et al., 1975). While these studies suggest that nitrogen may be an important limiting factor in the productivity of coastal marshes, no studies have been done to ascertain if this is true in inland salt marshes. Studies of plant production from inland saline areas (Hadley and Buccos, 1967, Hadley, 1970) have either not examined it in relation to nitrogen limitation, or else have concentrated on the response to nitrogen additions of a single species when grown under greenhouse conditions (Cords, 1960). Lack of tidal action and differences in species composition preclude the assumption that coastal and inland salt marshes respond similarly tonitrogen addition. The Atlantic coastal marshes of the United States are generally characterized by a bay-to-upland sequence of zones consisting of perennial grasses in the low marsh, giving way to annual succulents in the higher portions of the marsh (Niering and Warren, 1980). In an Ohio salt marsh this vegetational pattern was reversed. The annual species Salicornia europaea and Atriplex triangularis form two zones bordering the center of the saline pan with the perennial grass Hordeumjubatum occurring on the outer edge of the marsh, in the area of lower salinity (Ungar et al., 1979). The objectives of this study were to determine seasonal change in plant production, the effect of the nitrate and ammonium forms of nitrogen fertilization on plant production in an inland salt marsh and to determine if nitrogen deficiencies are responsible for the difference in the growth forms of Salicornia. MATERIALS AND METHODS The study area is a saline pan located at the site of the Morton Salt Company, in the city of Rittman, Wayne Co., Ohio (long. 810 47' 30', lat. 400 57' 30':SW?4 Sec 12, T 18N, R 13W). Three vegetation zones characterize the site: a Hordeum jubatum zone, an Atriplex triangularis zone and a zone of Salicornia europaea that was divided into", "keywords": ["0106 biological sciences", "2. Zero hunger", "15. Life on land", "01 natural sciences"], "contacts": [{"organization": "Irwin A. Ungar, David G. Loveland,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2307/2425415"}, {"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": "10.2307/2425415", "name": "item", "description": "10.2307/2425415", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2307/2425415"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1983-04-01T00:00:00Z"}}, {"id": "10.23986/afsci.148486", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:59Z", "type": "Journal Article", "created": "2025-05-26", "title": "Defining critical SOC/clay thresholds for soil health in boreal croplands using satellite-based NDVI proxies for productivity and resilience", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The European Union\u2019s soil strategy underscores the necessity for establishing feasible criteria to assess the soil health condition. In this study, we developed a method to define a critical threshold value for SOC/clay ratio on the basis of crop productivity and resilience. The study integrated data from national soil monitoring (NSM) of Finnish cropland soils (n=505) with satellite-based normalized difference vegetation index (NDVI) obtained from the EcoDataCube (EDC) portal. The study area was confined to the boreal environmental zone to ensure consistent pedo-climatic conditions. The results show that the interannual variation in crop productivity increases rapidly below SOC/clay ratio of 0.09 (95% confidence intervals ranging from 0.07 to 0.16), whereas the corresponding threshold for mean productivity was 0.13 (0.09\u20130.16). The observed threshold values were found applicable for both cereals and temporary ley. The SOC/clay ratio of 1:13 (=0.08), regarded as a criterion for healthy soil in the current Soil Monitoring Law proposal, based on studies by Johannes et al. (2017) and Prout et al. (2021), is lower than the mean thresholds estimated in this study but aligns close to the lower bound of the 95% confidence intervals. In this research, Finnish agricultural land served as the case study area, but the method is easily applicable to various pedo-climatic regions and potentially to different land use types.</p></article>", "keywords": ["S", "Soil Monitoring Law", " SOC/clay ratio", " cropland", " NDVI", " satellite data", " national soil monitoring", "Agriculture (General)", "Agriculture", "S1-972"], "contacts": [{"organization": "Heikkinen, Jaakko, Keskinen, Riikka, Ylivainio, Kari,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.23986/afsci.148486"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Food%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.23986/afsci.148486", "name": "item", "description": "10.23986/afsci.148486", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.23986/afsci.148486"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-26T00:00:00Z"}}, {"id": "10.24193/subbphil.2021.2s.05", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:59Z", "type": "Journal Article", "created": "2021-11-11", "title": "EXP\u00c9RIMENTER LA PENS\u00c9E EN SCH\u00c9MAS-IMAGES. DES ADOLESCENTS S\u2019INTERROGENT \u00ab D\u2019O\u00d9 VIENNENT LES PENS\u00c9ES ? \u00bb", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Experimenting Thinking in Image Schemas. Teenagers are Wondering \u201cWhere Do Thoughts Come From?\u201d. An intellectual view of philosophy as an activity focusing on understanding abstract concepts and their relationships deprives philosophical exercise of the participation of the body and senses. If we reject the mind-body dualism, as Dewey, Johnson, etc. did, then we are constantly engaged in interactions with the world and others, and can thus consider the act of thinking from our own experiences. Inspired by an experimentalist conception of school and life, as well as the method of inquiry developed by Dewey, the Philosophy for Children program provides an inquiry process that invites participants to conceptualize and reason philosophically in a collaborative manner. Do these practices implement an embodied cognition? To find out, we selected a discussion as a case study and analyzed it based on the observation that the issue to be discussed by the participants - \u201cwhere do thoughts come from?\u201d contains two image schemas: path (come from) and source (where). We have noted a variety and a significant number of expressions (\u201cthey come from within\u201d, \u201cthey come from what happens outside\u201d, etc.) whose analysis enhances a better understanding of how an experience of understanding the origins of our thoughts fits into the discourse and contributes to a collective conceptualization of \u201cthinking\u201d.</p></article>", "keywords": ["4. Education", "B1-5802", "image schemas", " perceptual experience", " conceptualisation", " community of philosophical inquiry", " experimentalism.", "Philosophy (General)", "16. Peace & justice", "3. Good health"]}, "links": [{"href": "https://doi.org/10.24193/subbphil.2021.2s.05"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Studia%20Universitatis%20Babe%C8%99-Bolyai%20Philosophia", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.24193/subbphil.2021.2s.05", "name": "item", "description": "10.24193/subbphil.2021.2s.05", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.24193/subbphil.2021.2s.05"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-30T00:00:00Z"}}, {"id": "10.2489/jswc.72.4.361", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:00Z", "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": "10261/306235", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:01Z", "type": "Journal Article", "created": "2023-03-31", "title": "Negotiating Gender Roles in Academia: A Courageous Exploration", "description": "Open AccessPeer reviewed", "keywords": ["Women in Limnology", "5. Gender equality", "Gender Roles in Academia", "http://metadata.un.org/sdg/5", "Achieve gender equality and empower all women and girls", "16. Peace & justice"]}, "links": [{"href": "https://doi.org/10261/306235"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Limnology%20and%20Oceanography%20Bulletin", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/306235", "name": "item", "description": "10261/306235", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/306235"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-31T00:00:00Z"}}, {"id": "10261/359343", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:03Z", "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": "2026-08-25T16:22:04Z", "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": {"license": "Open Access", "updated": "2026-08-25T16:19:05Z", "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/fagro.2022.841086", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:05Z", "type": "Journal Article", "created": "2022-03-07", "title": "Laser Weeding with Small Autonomous Vehicles: Friends or Foes?", "description": "<p>Weed control is necessary to ensure a high crop yield with good quality. Herbicide application and mechanical weeding are the most common methods worldwide. The use of herbicides has led to the increasing occurrence of herbicide-resistant weeds and unwanted contamination of the environment. Mechanical weed control harms beneficial organisms, increases the degradation of organic matter, may dry out the soil, and stimulate new cohorts of weed seeds to germinate. Therefore, there is a need to develop more sustainable weed control means. We suggest using small autonomous vehicles equipped with lasers as a sustainable alternative method. Laser beams are based on electricity, which can be produced from non-fossil fuels. Deep learning methods can be used to locate and identify weed and crop plants for targeting and delivery of laser energy with robotic actuators. Given the targeted nature of laser beams, the area exposed for weed control can be reduced substantially compared to commonly used weed control methods. Therefore, the risk of affecting non-target organisms is minimized, and the soil will be kept untouched in the field, avoiding triggering weed seeds to germinate. Small autonomous vehicles may have limited weeding capacity, and precautions need to be taken as reflections from the laser beam can be harmful to humans and animals. In this paper, we discuss the pros and cons of replacing or supplementing common used weed control methods with laser weeding. The ability to use laser weeding technology is relatively new and not yet widely practiced or commercially available. Therefore, we do not discuss and compare the costs of the various methods at this early stage of the development of the technology.</p>", "keywords": ["2. Zero hunger", "S", "alternative weed control", " integrated weed management", " non-chemical weed control", " site-specific weed management", " thermal weed control", " weed killers", "non-chemical weed control", "Plant culture", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "SB1-1110", "thermal weed control", "integrated weed management", "13. Climate action", "site-specific weed management", "0401 agriculture", " forestry", " and fisheries", "weed killers", "alternative weed control"], "contacts": [{"organization": "Andreasen Christian, Scholle Karsten, Saberi Mahin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3389/fagro.2022.841086"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fagro.2022.841086", "name": "item", "description": "10.3389/fagro.2022.841086", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fagro.2022.841086"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-07T00:00:00Z"}}, {"id": "10.3389/fmicb.2016.01446", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:07Z", "type": "Journal Article", "created": "2016-09-14", "description": "Soil management is fundamental to all agricultural systems and fertilization practices have contributed substantially to the impressive increases in food production. Despite the pivotal role of soil microorganisms in agro-ecosystems, we still have a limited understanding of the complex response of the soil microbiota to organic and mineral fertilization in the very long-term. Here, we report the effects of different fertilization regimes (mineral, organic and combined mineral and organic fertilization), carried out for more than a century, on the structure and activity of the soil microbiome. Organic matter content, nutrient concentrations, and microbial biomass carbon were significantly increased by mineral, and even more strongly by organic fertilization. Pyrosequencing revealed significant differences between the structures of bacterial and fungal soil communities associated to each fertilization regime. Organic fertilization increased bacterial diversity, and stimulated microbial groups (Firmicutes, Proteobacteria, and Zygomycota) that are known to prefer nutrient-rich environments, and that are involved in the degradation of complex organic compounds. In contrast, soils not receiving manure harbored distinct microbial communities enriched in oligotrophic organisms adapted to nutrient-limited environments, as Acidobacteria. The fertilization regime also affected the relative abundances of plant beneficial and detrimental microbial taxa, which may influence productivity and stability of the agroecosystem. As expected, the activity of microbial exoenzymes involved in carbon, nitrogen, and phosphorous mineralization were enhanced by both types of fertilization. However, in contrast to comparable studies, the highest chitinase and phosphatase activities were observed in the solely mineral fertilized soil. Interestingly, these two enzymes showed also a particular high biomass-specific activities and a strong negative relation with soil pH. As many soil parameters are known to change slowly, the particularity of unchanged fertilization treatments since 1902 allows a profound assessment of linkages between management and abiotic as well as biotic soil parameters. Our study revealed that pH and TOC were the majors, while nitrogen and phosphorous pools were minors, drivers for structure and activity of the soil microbial community. Due to the long-term treatments studied, our findings likely represent permanent and stable, rather than transient, responses of soil microbial communities to fertilization.", "keywords": ["Soil nutrients", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "long-term fertilization", "microbial biomass", "15. Life on land", "microbial activity", "Microbiology", "QR1-502", "03 medical and health sciences", "13. Climate action", "soil microbial communities", "soil nutrients", "454 pyrosequencing"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2016.01446"}, {"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.2016.01446", "name": "item", "description": "10.3389/fmicb.2016.01446", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2016.01446"}, {"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": "10.3389/fmicb.2019.02597", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:07Z", "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/fpls.2017.01892", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:08Z", "type": "Journal Article", "created": "2017-11-06", "title": "Variability in Susceptibility to Anthracnose in the World Collection of Olive Cultivars of Cordoba (Spain)", "description": "Anthracnose of olive (Olea europaea ssp. europaea L.), caused by Colletotrichum species, is a serious disease causing fruit rot and branch dieback, whose epidemics are highly dependent on cultivar susceptibility and environmental conditions. Over a period of 10 years, there have been three severe epidemics in Andalusia (southern Spain) that allowed us to complete the assessment of the World Olive Germplasm Bank of C\u00f3rdoba, one of the most important cultivar collections worldwide.A total of 308 cultivars from 21 countries were evaluated, mainly Spain (174 cvs.), Syria (29 cvs.), Italy (20 cvs.), Turkey (15 cvs.), and Greece (16 cvs.). Disease assessments were performed using a 0-10 rating scale, specifically developed to estimate the incidence of symptomatic fruit in the tree canopy. Also, the susceptibility of five reference cultivars was confirmed by artificial inoculation. Because of the direct relationship between the maturity of the fruit and their susceptibility to the pathogen, evaluations were performed at the end of fruit ripening, which forced coupling assessments according to the maturity state of the trees. By applying the cluster analysis to the 308 cultivars, these were classified as follows: 66 cvs. highly susceptible (21.4%), 83 cvs. susceptible (26.9%), 66 cvs. moderately susceptible (21.4%), 61 cvs. resistant (19.8%), and 32 cvs. highly resistant (10.4%). Representative cultivars of these five categories are 'Ocal,' 'Lech\u00edn de Sevilla,' 'Arbequina,' 'Picual,' and 'Frantoio,' respectively. With some exceptions, such as cvs. Arbosana, Empeltre and Picual, most of the Spanish cultivars, such as 'Arbequina,' 'Cornicabra,' 'Hojiblanca,' 'Manzanilla de Sevilla,' 'Morisca,' 'Picudo,' 'Farga,' and 'Verdial de Hu\u00e9var' are included in the categories of moderately susceptible, susceptible or highly susceptible. The phenotypic evaluation of anthracnose reaction is a limiting factor for the selection of olive cultivars by farmers, technicians, and breeders.", "keywords": ["0301 basic medicine", "2. Zero hunger", "anthracnose", "0303 health sciences", "Plant culture", "Plant Science", "15. Life on land", "16. Peace & justice", "olive", "diseases", "SB1-1110", "03 medical and health sciences", "fruit rot", "Colletotrichum"], "contacts": [{"organization": "J. R. Viruega, Luis F. Roca, Carlos Xavi\u00e9r, Juan Caballero, Antonio Trapero, Juan Moral, Juan Moral,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3389/fpls.2017.01892"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2017.01892", "name": "item", "description": "10.3389/fpls.2017.01892", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2017.01892"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-06T00:00:00Z"}}, {"id": "10.3389/fmicb.2022.983823", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:08Z", "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": "10.3389/fpls.2019.00191", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:08Z", "type": "Journal Article", "created": "2019-02-22", "title": "Interannual and Seasonal Dynamics of Volatile Organic Compound Fluxes From the Boreal Forest Floor", "description": "In the northern hemisphere, boreal forests are a major source of biogenic volatile organic compounds (BVOCs), which drive atmospheric processes and lead to cloud formation and changes in the Earth's radiation budget. Although forest vegetation is known to be a significant source of BVOCs, the role of soil and the forest floor, and especially interannual variations in fluxes, remains largely unknown due to a lack of long-term measurements. Our aim was to determine the interannual, seasonal and diurnal dynamics of boreal forest floor volatile organic compound (VOC) fluxes and to estimate how much they contribute to ecosystem VOC fluxes. We present here an 8-year data set of forest floor VOC fluxes, measured with three automated chambers connected to the quadrupole proton transfer reaction mass spectrometer (quadrupole PTR-MS). The exceptionally long data set shows that forest floor fluxes were dominated by monoterpenes and methanol, with relatively comparable emission rates between the years. Weekly mean monoterpene fluxes from the forest floor were highest in spring and in autumn (maximum 59 and 86 \u03bcg m-2 h-1, respectively), whereas the oxygenated VOC fluxes such as methanol had highest weekly mean fluxes in spring and summer (maximum 24 and 79 \u03bcg m-2 h-1, respectively). Although the chamber locations differed from each other in emission rates, the inter-annual dynamics were very similar and systematic. Accounting for this chamber location dependent variability, temperature and relative humidity, a mixed effects linear model was able to explain 79-88% of monoterpene, methanol, acetone, and acetaldehyde fluxes from the boreal forest floor. The boreal forest floor was a significant contributor in the forest stand fluxes, but its importance varies between seasons, being most important in autumn. The forest floor emitted 2-93% of monoterpene fluxes in spring and autumn and 1-72% of methanol fluxes in spring and early summer. The forest floor covered only a few percent of the forest stand fluxes in summer.", "keywords": ["VOC EMISSIONS", "Plant Science", "ATMOSPHERIC OH", "01 natural sciences", "forest floor", "SB1-1110", "MONOTERPENE EMISSIONS", "vegetation", "biogenic volatile organic compound", "11. Sustainability", "SCOTS PINE", "EXCHANGE", "0105 earth and related environmental sciences", "decomposition", "CLIMATE-CHANGE", "seasonality", "temperature", "Plant culture", "Forestry", "15. Life on land", "SOIL", "MODEL", "Environmental sciences", "flux", "13. Climate action", "PTR-TOF", "METHANOL"]}, "links": [{"href": "https://doi.org/10.3389/fpls.2019.00191"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2019.00191", "name": "item", "description": "10.3389/fpls.2019.00191", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2019.00191"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-02-22T00:00:00Z"}}, {"id": "10.3390/molecules29204908", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:19Z", "type": "Journal Article", "created": "2024-10-17", "title": "Computational Multiscale Study of the Interaction Between the PDMS Polymer and Sunscreen-Related Pollutant Molecules", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Sunscreen molecules play a critical role in protecting skin from ultraviolet radiation, yet their efficient detection and separation pose challenges in environmental and analytical contexts. In this work, we employ a multilevel modeling approach to investigate the molecular interactions between representative sunscreen molecules and the polydimethylsiloxane (PDMS) polymer, a material widely recognized for its sorbent properties. Our goal is to explore how these interactions can be fine-tuned to facilitate the effective separation of sunscreen molecules in portable membrane inlet mass spectrometry (MIMS) systems, potentially leading to the development of new membrane materials. Using a combination of advanced computational techniques\u2014force field molecular dynamics simulations, semiempirical GFN2-xTB, and density functional theory calculations\u2014we assess the interaction strength and noncovalent interactions of sunscreen molecules, namely oxybenzone, naphthalene, benzo[a]anthracene, avobenzone, and 1,3,5-trichlorobenzene, with PDMS. Additionally, the effect of temperature on the interaction dynamics is evaluated, with the aim of extending the sorbent capacities of PDMS beyond light polar molecules to larger, polar sunscreen compounds. This study provides critical insights into the molecular-level interactions that may guide the design of novel membrane materials for efficient molecular separation.</p></article>", "keywords": ["QD241-441", "membranes", "PDMS", "MD", "Organic chemistry", "GFN2-xTB", "MIMS", "DFT", "Article"]}, "links": [{"href": "https://doi.org/10.3390/molecules29204908"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Molecules", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/molecules29204908", "name": "item", "description": "10.3390/molecules29204908", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/molecules29204908"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-17T00:00:00Z"}}, {"id": "10.3390/rs13061133", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:21Z", "type": "Journal Article", "created": "2021-03-16", "title": "Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002\u20132003, 2005\u20132006 and 2008\u20132009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers\u2019 socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.</p></article>", "keywords": ["0106 biological sciences", "2. Zero hunger", "FAO-56 soil water balance", "550", "[SDE.MCG]Environmental Sciences/Global Changes", "Science", "water", "Q", "evapotranspiration", "balance", "15. Life on land", "01 natural sciences", "630", "irrigation", "6. Clean water", "[SDE.MCG] Environmental Sciences/Global Changes", "remote sensing", "evapotranspiration; irrigation; water; remote sensing; FAO-56 soil water balance; NDVI time series", "FAO-56 soil water", "NDVI time series"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://doi.org/10.3390/rs13061133"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13061133", "name": "item", "description": "10.3390/rs13061133", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13061133"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-16T00:00:00Z"}}, {"id": "10.37501/soilsa/193074", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:28Z", "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": "2026-08-25T16:19:30Z", "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": "10.4141/p02-188", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:32Z", "type": "Journal Article", "created": "2011-04-24", "description": "<p> In the Brown soil zone of western Canada summerfallowing (F) is traditionally used to reduce the water deficit associated with cereal production, but frequent use of this practice results in soil degradation and reduces the N-supplying capacity of soils. Some scientists suggest that an annual legume green manure crop (LGM) could be used as a partial-fallow replacement to protect the soil against erosion and increase its N fertility, particularly when combined with a snow-trapping technique to replenish soil water used by the legume. We assessed this possibility by comparing yields, N economy, water use efficiency, and economic returns for hard red spring wheat (W) (Triticum aestivum L.) grown in rotation with Indianhead black lentil (Lens culinaris Medikus) green manure (i.e., LGM-W-W) vs. that obtained in a traditional F-W-W system. Further, we assessed whether a change in manage ment of the LGM crop (i.e., moving to earlier seeding and earlier turn-down) was advantageous to the overall performance of this practice. The study was conducted over 12 yr (1988\uffe2\uff80\uff9399) on a medium-textured Orthic Brown Chernozem at Swift Current, Saskatchewan. Wheat stubble was left tall to trap snow, tillage was kept to a minimum, and the wheat was fertilized based on NO3 soil tests. When we examined results after 6 yr, we concluded that by waiting until full bloom to turn down the legume (usually late July or early August) so as to maximize N2 fixation, soil water was being depleted to the detriment of yields of the following wheat crop. The change in management of the LGM crop since 1993 resulted in wheat yields following LGM equaling those after fallow (due to improved water use efficiency), a gradual and significant increase over time in grain protein and in N yield of aboveground plant biomass of wheat in the LGM-W-W compared to the F-W-W system, plus a gradual decrease in fertilizer N requirements of wheat in the LGM system accompanying an improvement in the N supplying power of the soil. These savings in N fertilizer, together with savings in tillage and herbicide costs for weed control on partial-fallow vs. conventional-fallow areas, and higher revenues from the enhanced grain protein, more than offset the added costs for seed and management of the LGM crop. Thus, our results imply that, if producers seed the LGM in April and turn it down in early July, an annual LGM-cereal rotation is a viable option in the semiarid Canadian prairies; however, one negative consequence of adopting this management strategy is the possibility of enhancing NO3 leaching. Key words: Nitrogen yields, grain protein, green fallow, summerfallow substitute, economic returns, NO3 leaching </p>", "keywords": ["0106 biological sciences", "2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water"], "contacts": [{"organization": "F. Selles, Yantai Gan, Robert P. Zentner, V. O. Biederbeck, Paul G. Jefferson, Reynald Lemke, C. A. Campbell,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.4141/p02-188"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Canadian%20Journal%20of%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4141/p02-188", "name": "item", "description": "10.4141/p02-188", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4141/p02-188"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-01-01T00:00:00Z"}}, {"id": "10433/20153", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:22:06Z", "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": "2026-08-25T16:19:36Z", "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.pb271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:42Z", "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": "2026-08-25T16:19:43Z", "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": "2026-08-25T16:19:44Z", "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": "2026-08-25T16:19:46Z", "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": "2026-08-25T16:19:47Z", "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/bg-7-315-2010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:48Z", "type": "Journal Article", "created": "2010-04-29", "description": "<p>Abstract. Global climate change in the real world always exhibits simultaneous changes in multiple factors. Prediction of ecosystem responses to multi-factor global changes in a future world strongly relies on our understanding of their interactions. However, it is still unclear how nitrogen (N) deposition and elevated atmospheric carbon dioxide concentration [CO2] would interactively influence forest floor soil respiration in subtropical China. We assessed the main and interactive effects of elevated [CO2] and N addition on soil respiration by growing tree seedlings in ten large open-top chambers under CO2 (ambient CO2 and 700 \uffce\uffbcmol mol\uffe2\uff88\uff921) and nitrogen (ambient and 100 kg N ha\uffe2\uff88\uff921 yr\uffe2\uff88\uff921) treatments. Soil respiration, soil temperature and soil moisture were measured for 30 months, as well as above-ground biomass, root biomass and soil organic matter (SOM). Results showed that soil respiration displayed strong seasonal patterns with higher values observed in the wet season (April\uffe2\uff80\uff93September) and lower values in the dry season (October\uffe2\uff80\uff93March) in all treatments. Significant exponential relationships between soil respiration rates and soil temperatures, as well as significant linear relationships between soil respiration rates and soil moistures (below 15%) were found. Both CO2 and N treatments significantly affected soil respiration, and there was significant interaction between elevated [CO2] and N addition (p&lt;0.001, p=0.003, and p=0.006, respectively). We also observed that the stimulatory effect of individual elevated [CO2] (about 29% increased) was maintained throughout the experimental period. The positive effect of N addition was found only in 2006 (8.17% increased), and then had been weakened over time. Their combined effect on soil respiration (about 50% increased) was greater than the impact of either one alone. Mean value of annual soil respiration was 5.32 \uffc2\uffb1 0.08, 4.54 \uffc2\uffb1 0.10, 3.56 \uffc2\uffb1 0.03 and 3.53 \uffc2\uffb1 0.03 kg CO2 m\uffe2\uff88\uff922 yr\uffe2\uff88\uff921 in the chambers exposed to elevated [CO2] and high N deposition (CN), elevated [CO2] and ambient N deposition (CC), ambient [CO2] and high N deposition (NN), and ambient [CO2] and ambient N deposition (CK as a control), respectively. Greater above-ground biomass and root biomass was obtained in the CN, CC and NN treatments, and higher soil organic matter was observed only in the CN treatment. In conclusion, the combined effect of elevated [CO2] and N addition on soil respiration was apparent interaction. They should be evaluated in combination in subtropical forest ecosystems in China where the atmospheric CO2 and N deposition have been increasing simultaneously and remarkably.                     </p>", "keywords": ["0106 biological sciences", "2. Zero hunger", "QE1-996.5", "Ecology", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "Life", "13. Climate action", "QH501-531", "0401 agriculture", " forestry", " and fisheries", "QH540-549.5", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qi Deng, Shuangquan Liu, Honglang Duan, Guoyi Zhou, Juxiu Liu, Dainan Zhang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5194/bg-7-315-2010"}, {"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-7-315-2010", "name": "item", "description": "10.5194/bg-7-315-2010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-7-315-2010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-08-24T00:00:00Z"}}, {"id": "10.5194/egusphere-2025-3788", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:50Z", "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-egu24-19365", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:51Z", "type": "Report", "created": "2024-03-11", "title": "PFAS variable transport behavior: insights from soil sorption experiments", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Per- and Polyfluoroalkyl Substances (PFAS) are extensively utilized chemicals owing to their desired physicochemical properties. Despite increasing efforts to limit their applications, they persist in the environment and pose a threat to drinking water production due to their persistence, mobility, and toxicity. Understanding their behavior in subsurface media is crucial for minimizing the risk of exposure in areas where groundwater is a significant source. Sorption is considered a pivotal mechanism in PFAS remediation. This study aims to explore the transport behavior of different PFAS groups in soil sorption experiments and establish a connection to field scenarios.Miscible displacement experiments were conducted on a mixture of PFAS. A 50 cm long glass column filled with sand was injected with a 2.5 &amp;#181;g/l PFAS solution. Subsequently, the column was flushed with a PFAS-free solution to examine the desorption process. A conservative tracer test was performed to determine hydrogeological properties. Samples were analyzed using liquid chromatography-mass spectrometry. Breakthrough curves were then simulated using Hydrus 1D to obtain transport parameters.The results revealed that different PFAS groups exhibit varying orders of magnitude of sorption. Some were conservative, while others were entirely retained. In addition to functional groups and chain length, hydrophobicity played a crucial role in PFAS behavior. The desorption process was inversely proportional to sorption; less desorption occurred with an increased sorption level.To simulate these behaviors, different sorption modules in Hydrus were tested. Substances with higher sorption levels required more complex sorption terms and could not be accurately simulated by assuming equilibrium sorption.</p></article>", "keywords": ["6. Clean water"]}, "links": [{"href": "https://doi.org/10.5194/egusphere-egu24-19365"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu24-19365", "name": "item", "description": "10.5194/egusphere-egu24-19365", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu24-19365"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-20T00:00:00Z"}}, {"id": "10.5194/egusphere-egu24-3127", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:51Z", "type": "Report", "created": "2024-03-08", "title": "Cracking Ground Truth Barriers: Harnessing the Power of Transfer Learning for Crop Mapping", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Transfer learning (TL) is rapidly gaining popularity in recent years in various research disciplines due to its practicality, temperate need for resources, and often quite promising results. The same principle applies for Earth observation, especially for tasks such as crop mapping where TL already showed a certain potential. Our focus in this research was on temporal transfer learning for a single agricultural region. In our study we built an initial CNN-1D crop mapping model for Vojvodina province, Serbia, using SAR satellite imagery and ground truth (GT) data collected for 2017-2020. We did it using a leave-one-year-out approach where each year served only once as a validation dataset. The top-performing model was further employed for transfer learning analysis, utilising a limited set of target season ground truth data. The aim was to diminish reliance on labour-intensive and time-consuming large-scale ground truth data collection, typically carried out through hands-on field inspections. Instead of collecting it all over Vojvodina for the 2021 season, we tried to focus on a limited area around the departure point. Three options were analysed, i.e., approximately 20, 25, and 30 km radius around the departure point for which the province capital Novi Sad was taken. From the practical standpoint, labels of these parcels are easier to record than those more distant (distributed), so it seems reasonable to visit only these locations to reduce the costs of ground truth collection. Visited parcels that fell within these radiuses served for retraining the model, and the remaining parcels (those outside 30 km radius) served for testing and accuracy assessment. For each parcel 50 randomly selected pixels were used for the analysis. After 5 retraining cycles, the average F1 score for transfer learning approach of the CNN-1D model for 20, 25 and 30 km buffer zones was 74%, 79% and 83%, respectively. Training the same CNN-1D model from scratch reached 69%, 73% and 78% respectively, i.e., approximately 5% lower score on average. Inferencing using the pre-trained model as such (without adaptations) achieved F1 score of 78%, which set 20 km radius case to the irrational use of TL, while the use of other two buffer areas were justified as they achieved comparably better results. Also, three buffer cases achieved between 3% and 9% lower F1 score than their respective pairs when the same number of parcels used for retraining were randomly distributed all over the test area. This was likely in a relationship with the restricted sampling region characteristics (uniform soil type, management practice, weather conditions) and distribution of classes in that region which may not have properly represented the entire test area. In addition, the comparison of these two approaches showed that adding more samples for retraining scaled down the difference. The viability of the presented approach was confirmed within the experiment and, therefore, practitioners are encouraged to weigh the trade-off between practicality and accuracy in their future work.</p></article>", "keywords": ["2. Zero hunger", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5194/egusphere-egu24-3127"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu24-3127", "name": "item", "description": "10.5194/egusphere-egu24-3127", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu24-3127"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-20T00:00:00Z"}}, {"id": "10.5194/essd-17-741-2025", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:52Z", "type": "Journal Article", "created": "2025-02-26", "title": "Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000\u20132022", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The production and evaluation of the analysis-ready and cloud-optimized\u00a0(ARCO) data cube for continental Europe (including Ukraine, the\u00a0UK, and T\u00fcrkiye), derived from the Landsat analysis-ready dataset version\u00a02\u00a0(ARD\u00a0V2) produced by Global Land Analysis and Discovery\u00a0(GLAD) team and covering the period from\u00a02000 to\u00a02022, is described. The data cube consists of 17\u2009TB of data at a 30\u2009m resolution and includes bimonthly, annual, and long-term spectral indices on various thematic topics, including surface reflectance bands, normalized difference vegetation index\u00a0(NDVI), soil adjusted vegetation index\u00a0(SAVI), fraction of absorbed photosynthetically active radiation\u00a0(FAPAR), normalized difference snow index\u00a0(NDSI), normalized difference water index\u00a0(NDWI), normalized difference tillage index\u00a0(NDTI), minimum normalized difference tillage index\u00a0(minNDTI), bare soil fraction\u00a0(BSF), number of seasons\u00a0(NOS), and crop duration ratio\u00a0(CDR). The data cube was developed with the intention to provide a comprehensive feature space for environmental modeling and mapping. The quality of the produced time series was assessed by (1)\u00a0assessing the accuracy of gap-filled bimonthly Landsat data with artificially created gaps; (2)\u00a0visual examination for artifacts and inconsistencies; (3)\u00a0plausibility checks with ground survey data; and (4)\u00a0predictive modeling tests, examples with soil organic carbon\u00a0(SOC) and land cover\u00a0(LC) classification. The time series reconstruction demonstrates high accuracy, with a root mean squared error (RMSE) smaller than\u00a00.05, and R2\u00a0higher than\u00a00.6, across all bands. The visual examination indicates that the product is complete and consistent, except for winter periods in northern latitudes and high-altitude areas, where high cloud and snow density introduce significant gaps and hence many artifacts remain. The plausibility check further shows that the indices logically and statistically capture the processes. The BSF index showed a strong negative correlation\u00a0(\u22120.73) with crop coverage data, while the minNDTI index had a moderate positive correlation\u00a0(0.57) with the Eurostat tillage practice survey data. The detailed temporal resolution and long-term characteristics provided by different tiers of predictors in this data cube proved to be important for both soil organic carbon regression and LC classification experiments based on 60\u2009723\u00a0LUCAS observations: long-term characteristics (tier\u00a04) were particularly valuable for predictive mapping of SOC and LC, coming out on top of variable importance assessment. Crop-specific indices (NOS and CDR) provided limited value for the tested applications, possibly due to noise or insufficient quantification methods. The data cube is made available at https://doi.org/10.5281/zenodo.10776891 (Tian et al., 2024) under a CC-BY license and will be continuously updated.                     </p></article>", "keywords": ["Environmental sciences", "QE1-996.5", "Life Science", "GE1-350", "Geology"]}, "links": [{"href": "https://doi.org/10.5194/essd-17-741-2025"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-17-741-2025", "name": "item", "description": "10.5194/essd-17-741-2025", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-17-741-2025"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-12T00:00:00Z"}}, {"id": "10.5194/soil-2020-96", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:55Z", "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": "2026-08-25T16:19:57Z", "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.10065971", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:57Z", "type": "Dataset", "title": "Database of topsoil  chemical and physical properties in Croatia", "description": "Sources Data for database is collected from four main sources:\u00a0  Data published in book 'Martinovi\u0107, J. and Vrankovi\u0107, A. (Editors), 1997. Baza podataka o hrvatskim tlima, I. Dr\u017eavna uprava za za\u0161titu prirode i okoli\u0161a, Zagreb' labeled as 'martinovic_1997' in the database.  This source consists of 2199 pedological profiles sampled from 1963 to 1996, most of which include depth to bedrock information. Data from project: 'Spatial variability of trace and toxic metals in agricultural soils of Croatia', Ministry of Science and Education and Croatian Waters. Project leader: prof.dr.sc. Marija Romi\u0107 from Faculty of Agriculture, Zagreb, labeled as 'agricultural_2013'.  Data are sampled from 'database of properties and quality of agricultural soils of Croatia' on 8x8 km grid and consists only from top soil samples (0-30 cm). There are 811 samples in this database. Data from the the project: 'Change in soil carbon stocks and calculation of trends in total nitrogen and organic carbon in soil and C: N ratio', from Ministry of Environmental Protection and Energy, carried on by Croatian Geological Institute (HGI), the Croatian Forestry Institute (H\u0160I) and the Agricultural Land Agency (APZ).  This dataset consists of two subsets:  'azo_2013' - 2519 samples of topsoil (0-25 cm), from 1994 to 2004 for making of Geochemistry Atlas of Croatia 'azo_2016' - 742 locations were revisited during 2015-2016 and new samples are taken and analyzed in horizons 0-10 cm, 10-20 cm, 20-30 cm. Network of piezometers Description of sources\u00a0 Martinovi\u0107, J. and Vrankovi\u0107, A. (Editors), 1997. Baza podataka o hrvatskim tlima (Database of Croatian Soils)\u00a0 The database contains data on soil profiles and covers the total area of the Republic of Croatia. Only data accepted by external control are entered in the Database, as well as those profiles for which there is a minimum data. External control of data reliability was performed by comparing the genetical-morphological characteristics of the soil determined by field research and the data of laboratory soil analyses. The profiles for which the field and laboratory analyses are found to differ are rejected. In addition to data on soil properties, basic data on pedogenetic factors are given. The soil profiles surveyed in the period 1963-1996 are entered in the database. The majority of data come from the Basic Pedological Map of Croatia (Osnovna pedolo\u0161ka karta Hrvatske - OPKH) project. The following are entered in the Database: 1347 profiles in volume I and 851 profiles in volume II, a total of 2198 pedological profiles Spatial variability of trace and toxic metals in agricultural soils of Croatia, Project Leader: Marija Romi\u0107\u00a0 The problem of exposure of agricultural soils to different anthropogenic inputs of toxic metals, but also of other potentially toxic substances, has acquired global dimensions in the last decades. Besides atmospheric deposition, environmental dispersion of chemicals used in agriculture is an important factor directly affecting the natural soil functions, or indirectly endangering the biosphere by bioaccumulation and inclusion into the food chain. Metal concentrations in soil can be generally predicted starting with the element abundance in the parent material. The extent to which pedogenesis affects heavy metals distribution varies according to the prevailing factors affecting soil processes. Because of the toxicity to plants and animals, it is important to determine their content, forms and distribution. Such hypotheses may be tested by total metal content determination, as well as other elements relevant for geochemical valorization of the agricultural soils of Croatia. Thus, the spatial variability and baseline of elements in soils will be determined by means of relevant statistical and geostatistical methods. The maps of toxic metal distribution will be produced and the suitability of soils for agriculture will be assessed. GIS is increasingly used in environmental assessment studies because of its ability to superimpose different spatial information and to combine them with the results of statistical analysis, enabling thus the detection of complex spatial relationships among different parameters. Geostatistics and multivariate statistics has been widely used in geochemical studies to identify pollution sources and to apportion natural vs. anthropogenic contribution, establishing a geochemical background as well. The main objectives of the investigation are: (i) to provide a geochemical database relevant to the agricultural soils in Croatia; (ii) to provide a detailed information about the natural variability of the geochemical background which is pertinent to administrative and legal issues as well as to safety food production and environmental protection; (iii) presenting the influence of human and other environmental activities on the soil quality mainly regarding the toxic and trace metal contents, and (iv) we are going to observe the influence of natural conditions on regional differences which have been widely neglected so far, and have not been taken into account while national regulations and guidelines on soil toxic metal contents have been established. Change in soil carbon stocks and calculation of trends in total nitrogen and organic carbon in soil and C: N ratio\u00a0 The project is funded by the Fund for Environmental Protection and Energy Efficiency within the Program 'Upgrading and Development of the Environmental Information System and Improving the Monitoring and Reporting System on the State of the Environment in the Republic of Croatia', Component 2: Improving the Monitoring and Reporting System on the State of the Environment Croatia; improving the system of data collection and exchange and developing methodologies for their processing in accordance with the guidelines of the UNFCCC and the Kyoto Protocol defined by the IPCC (Intergovernmental Panel on Climate Change).\u00a0 The project holder is the Ministry of Environmental Protection and Energy, and the executors are the Croatian Geological Institute, the Croatian Forestry Institute and the Agricultural Land Agency. In the period 2014-2017, field and laboratory research of soil conditions was conducted at 725 representative locations. General data on the location of sampling were collected, which contain administrative, locational, geographical and other data (relief, climatic and meteorological data, detailed data on land use and vegetation cover, description of surface soil properties). Field soil sampling for each LULUCF land use category was performed according to a modified methodology described in the EU DG JRC (Joint Research Center) 'Protocol for soil sampling to confirm changes in organic carbon stocks in the EU' by Stolbovoy et al. 2007 (Soil sampling protocol to certify the changes of organic carbon stock in mineral soil of the European Union - EU JRC). The protocol modifications aimed to ensure reporting under the UNFCCC and Kyoto protocols, i.e., to ensure compliance with the IPCC methodology. Soil sampling on forest land (FL) according to the JRC protocol is planned at two depths of 0-10 cm and 10 - 20 cm and an organic layer (list), but due to reporting requirements under the UNFCCC and Kyoto protocol, sampling was carried out at a depth of 20 - 30 cm. Land under crops (CL) was sampled at two depths (0-20 cm and 20-30 cm) and grasslands (GL), wetlands (WL), settlements (SL) and other land (OL) were sampled at three depths 0- 10, 10-20 and 20-30 cm. Geochemical analyzes were performed at depths of 0\u201310 and 20\u201330 cm for forest soils (FL) and for meadows and pastures (GL) while for soils under crops (CL) composite samples of 0\u201330 cm and 0\u201320 cm were analyzed. Network of piezometers To get a more accurate depth to bedrock parameter, positions of 812 piezometers are considered as they have at least 4 meters of depth to bedrock. Description of database Column names and descriptions: Metadata columns: site_key - unique identifier that identifies sample in source database\u00a0source_db - label of source database\u00a0source_sampled - label of organization/team who sampled and analyzed data\u00a0site_obsdate - year of taking sample\u00a0longitude_decimal_degrees - longitude in degrees in WGS84 geographical projection\u00a0latitude_decimal_degrees - latitude in degrees in WGS84 geographical projection\u00a0pedon_completeness_index - quality factor (0-100)\u00a0taxgrtgroup - classification of sample according to WBR 2014/2016 classification\u00a0 Soil properties columns: column name - property - measurement units - descriptionoc - Carbon, Organic - % wt - CMS analyte. Organic carbon is a measure of all organic forms of carbon in the soil, including organic carbon within minerals.\u00a0n_tot_ncs - Nitrogen, Total NCS - % wt - Total nitrogen is a measure of all organic and inorganic nitrogen, including that found in nitrogen minerals.ca_mehlich3 - Calcium, Mehlich3 Extractable \u00a0- mg/kg - The calcium extracted by the Mehlich III solution.\u00a0k_mehlich3 - Potassium, Mehlich3 Extractable - mg/kg - The potassium extracted by the Mehlich III solution.\u00a0mg_mehlich3 - Magnesium, Mehlich3 Extractable - mg/kg - The magnesium extracted by the Mehlich III solution.\u00a0p_mehlich3 - Phosphorus, Mehlich3 Extractable - mg/kg - The phosphorus extracted by the Mehlich III solution.\u00a0cec_sum - Cation Exchange Capacity, Summary \u00a0 cmol(+)/kg - The effective cation exchange capacity is calculated by BASE_SUM+AL_KCL. It is not calculated if soluble salts are present. It is reported as meq per 100 grams on a <2 mm base. CMS derived value default\u00a0ec_satp - Electrical Conductivity , Saturation Extract - dS/m - The electrical conductivity of the saturation extract is used to estimate the concentration of salts in a sample, and provides inferences on cation concentration in solution and osmotic pressure. It is reported as mmhos per centimeter.\u00a0caco3 - Carbonates - % wt - Carbonate in the < 2mm fraction is measured by CO2 evolution after acid treatment. It is reported as gravimetric percent CaCO3 on a <2 mm base, even though carbonates of Mg, Na, K, and Fe may be present and react with the acidph_h2o - pH, 1:1 Soil-Water Suspension - (NA) - The pH, 1:1 soil-water suspension is the pH of a sample measured in distilled water at a 1:1 soil:solution ratio. If wider ratios increase the pH, salts are indicated.\u00a0ph_kcl - The pH, 1:1 soil-KCl suspension - (NA) - The pH, 1:1 soil-KCl suspension is the pH of a sample measured in 1.0N KCl at a 1:1 soil:solution ratio. If the pH in KCl < pH in water, Al+++ is indicated.\u00a0total_clay - Clay, Total - % wt - Total clay is the soil separate with <0.002 mm particle diameter. Clay size carbonate is included. Total clay is reported as a weight percent of the <2 mm fraction.\u00a0total_silt - Silt, Total - % wt - Total silt is the soil separate with 0.002 to 0.05 mm particle size. It is reported as a gravimetric percent on a <2 mm base.\u00a0total_sand - Sand, Total - % wt - Total sand is the soil separate with 0.05 to 2.0 mm particle diameter. It is reported as a gravimetric percent on a <2 mm base.\u00a0wpg2 - Coarse fragments - % wt - The weight fraction of particles with >2 mm diameter is reported as a gravimetric percent on a whole soil base.\u00a0db_od Bulk Density, <2mm Fraction, Ovendry - g/cc - Bulk density, oven dry (105 C) is the weight per unit volume of the <2 mm fraction, with volume measured on oven dry (105 C) natural fabric (clods). It is reported as grams per cubic centimeter on a <2 mm base.\u00a0dbr - Depth to bedrock - cm - Depth to the R horizon or similar", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "16. Peace & justice", "3. Good health"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10065971"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10065971", "name": "item", "description": "10.5281/zenodo.10065971", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10065971"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-02T00:00:00Z"}}, {"id": "10.5281/zenodo.10404481", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:59Z", "type": "Report", "title": "D.4.1 \u2013 Coaching and Capacity  Building Report, Round #1", "description": "This deliverable reports on the work related to tasks 4.1 and 4.2, carried out by consortium partners from Department of Agroecology at Aarhus University and ENoLL (European Network of Living Labs), respectively. These partners provide applicants with tools and coaching (T4.1), to ease the application process and guide them through consortium building and to design sustainable and well-thought soil health improving living labs. To provide possible applicants from all over Europe with valuable advice, NATI00NS has found mentors in 18 European\u00a0countries, who can be consulted by possible applicants.  This deliverable is written to report on the implementation and execution of tasks 4.1,\u00a0Coaching Sessions, and 4.2, Capacity Building. The tasks feed into NATI00NS\u2019 main objective,\u00a0that is enhancing the possibilities of more viable and well-planned soil health improving living\u00a0lab applications under the Mission auspices, which hopefully will lead to the establishment of\u00a0well-functioning living labs in the near future. The deliverable will provide both the public and\u00a0the funding body, with knowledge on NATI00NS\u2019 initial progress and results.  In short, the function of T4.1 has been to identify candidate Soil Health Living Lab Mentors in\u00a0all EU member states and associated countries, followed up by a process aligning the\u00a0candidates' perceptions on the meaning of a living lab and understanding the topic description\u00a0in dept by participating in on-line training sessions. This concluded in mentor candidates\u00a0signing the Non-Disclosure Agreement (NDA) agreements to officially become mentors and\u00a0thereby be mandated to coach possible living lab applicants within the NATI00NS framework.  Alongside the coaching of mentors, NATI00NS\u2019 has carried out capacity building, prepared and\u00a0implemented by ENoLL, the European Network of Living Labs, a NATI00NS consortium partner,\u00a0that leads online support to bolster up stakeholders around the Soil Mission (hereafter only\u00a0described as the Mission) and broaden their understanding of what a LL is. ENoLL have for this\u00a0purpose, produced e-learning materials, including factsheets and webinars. The Capacity\u00a0Building in combination with Coaching Session activities, provide information and training,\u00a0that enhance the chances of well-conceived and relevant Soil Health Living Labs being created,\u00a0by making sure living lab applicants are not only trained well by mentors with knowledge on\u00a0living lab concepts; participants will also have capacity building material available to them in\u00a0order to design and create a strong Living Lab consortium. The materials include manuals\u00a0which they can use to design a living lab. The capacity building provided by NATI00NS does, in\u00a0general, provide applicants with hands-on capacities, whether it be factsheets or webinars on\u00a0specific living lab related questions.In supporting the applicants at national level identifying It has been important to associate\u00a0skilled mentors has been of the essence. Therefore, the NATI00NS consortium has mapped\u00a0stakeholders across EU Member States and Associated Countries during most of its first\u00a0\u2018introduction and pilot\u2019 phase, to get in contact with gatekeepers in each country.  In most countries, the National Contact Point (NCP) structure, and its responsible officers\u00a0appointed either for the Mission or the Food, Bioeconomy, Natural Resources, Agriculture and\u00a0Environment area, were primary contact points, since it is already an integrated part of their\u00a0job description, to support the Soil Health Mission calls. Consequently, many NCPs have taken\u00a0on the role of mentors themselves while others have tried assisting NATIOONS in finding\u00a0suitable mentor candidates, interested in acting as mentors within the NATI00NS framework.  NCPs are national structures associated to the framework programme. NCPs give personalised support on the spot and in applicants' own languages.  After reaching out to possible mentors, AU AGRO has continuously answered questions about\u00a0the scope of the mentoring work \u2013 such as the mentors\u2019 expected workload, responsibilities,\u00a0and for how long they are expected to commit to mentoring duties. In parallel, NATIOONS has\u00a0planned and implemented two training of trainers webinars that offered training to candidate\u00a0mentors, so they all could be aligned in terms of living lab concepts, practical circumstances\u00a0regarding the application process and confidentiality measures, after which they were able to\u00a0take an informed decision about becoming NATI00NS Mentors or not.All webinar participants, whether they joined for reasons of curiosity or already knew they\u00a0would commit to mentoring, were then briefed on, how it is necessary for them to read and\u00a0sign NDA-documents to officially become NATI00NS appointed Soil Health Living Lab mentors,\u00a0and thus appear on the NATIOONS website with name and contact details. NATIOONS have\u00a0since then continuously collected signed NDA documents and updated the website\u00a0accordingly, thereby expanding the number of mentors available to possible applicants.To carry out the work related to recruiting soil health living lab mentors and training them in\u00a0living lab-affiliated concepts, a number of Aarhus University\u2019s soil and farming systems\u00a0scientists and research support advisers, planned a training programme for mentors.  They have also been responsible for all communication and mapping of possible mentors, organising of the training of trainers event (I.e., training the mentors that will eventually offer\u00a0training to living lab applicants) webinars and gathering and handling Non-disclosure\u00a0Agreement (NDA) documents and FAQ by mentors and applicants. Content for webinars on\u00a0soil health and living labs, have been created and presented by the NATI00NS partners who\u00a0also produced the slides for the National Engagement Events \u2013 another NATIOONS activity\u00a0belonging to another work package, which will be described in its own deliverable.  Additionally, a senior officer from the Aarhus University\u2019s Research Support Office, with great\u00a0experience in providing support for framework programme applicants, provided webinar\u00a0attendants with important guidance on application practices.  The Capacity Building (CB) efforts plays a pivotal role in the NATI00NS project, to ensure the\u00a0success of the Mission. Its main objective is to guarantee the submission of high-quality\u00a0applications for the first two sets of topics aimed at establishing Living Labs (LLs) in 2023 and\u00a02024. These efforts are led by the European Network of Living Labs (ENoLL) as part of Work\u00a0Package 4, 'Supporting Proposal Applicants.'\u00a0NATI00NS\u2019 Capacity Building brings together a comprehensive range of essential training and\u00a0guidance activities tailored specifically for applicants interested in the LL topics related to the\u00a0Mission. At its core, Capacity Building provides online support materials for stakeholders\u00a0involved in the Mission. These materials include a series of e-learning resources, such as\u00a0Factsheets and recorded webinars, offering information about the criteria governing Soil\u00a0Health LLs and the objectives of the Missions within the context of various land use types. Thisinitiative sets the stage for prospective LL applicants in the future.", "keywords": ["2. Zero hunger", "9. Industry and infrastructure", "15. Life on land", "16. Peace & justice"], "contacts": [{"organization": "Krabbe, Kasper, Couture, Isabelle, Cavallo, Dolinda,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10404481"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10404481", "name": "item", "description": "10.5281/zenodo.10404481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10404481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-19T00:00:00Z"}}, {"id": "10.5281/zenodo.10776891", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:00Z", "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.10776891"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10776891", "name": "item", "description": "10.5281/zenodo.10776891", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10776891"}, {"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.10777970", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:01Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P25 (2000)", "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.10777970"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10777970", "name": "item", "description": "10.5281/zenodo.10777970", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10777970"}, {"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.10777978", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:01Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P25 (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.10777978"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10777978", "name": "item", "description": "10.5281/zenodo.10777978", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10777978"}, {"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.10778006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:02Z", "type": "Dataset", "title": "Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P25 (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.10778006"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10778006", "name": "item", "description": "10.5281/zenodo.10778006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10778006"}, {"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"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?offset=50&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?offset=50&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?offset=0", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?offset=100", "hreflang": "en-US"}], "numberMatched": 36140, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-08-25T19:57:06.696944Z"}