{"type": "FeatureCollection", "features": [{"id": "10.1016/b978-0-444-64177-9.00006-0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:15:02Z", "type": "Report", "created": "2020-04-16", "title": "SfM photogrammetry for GeoArchaeology", "description": "Geoarchaeological studies have benefits from new technological developments in remote-sensing technologies that have become an integral and important part of the archeological researches. In particular, structure-from-motion (SfM) photogrammetry is one of the most successful emerging techniques in high-resolution topography (HRT) and provides exceptionally fast, low-cost, and easy three-dimensional (3D) survey for geoscience applications. In this chapter, we present an example of SfM application for geoarchaeology. The purpose is to realize HRT digital terrain models (DTMs) of an area of prehistoric agricultural terracing together with a geoarchaeological excavation trench in the Ingram Valley, Northumberland National Park, NE England. The study area is one of the six pilot case studies of TerrACE archeological research project (ERC-2017-ADG: 787790, 2018\u20132023; https://www.terrace.no/), a 5-year European Research Council grant funded by the European Union. An integrated approach utilizing ground-based and UAV (nadir and oblique) images was used to preserve fine-grained topographic detail and permit the accurate survey of highly vegetated areas and steep or subvertical surfaces (e.g., vertical walls of terraces), while also allowing for the capture of large spatial datasets. The SfM-DTM provided an accurate and high level of detail of the terrace landscape, the archeological features, and the soil and sediment stratigraphy along the excavation trench. An additional terrace was identified that had not been recognized before due to the HRT study bringing out a level of detail that had not been previously observable in this area. The SfM 3D outputs allowed the extraction of profiles, sections, scaled plans, and orthomosaics of the terrace complex and the excavation trench, simplifying and speeding the archeologist's field and laboratory work. SfM has shown it to be a rapid, cost effective, and highly accurate technique for surveying archeological sites at both a landscape and localized scale and adding new and more accurate information in nationally important landscapes and beyond.", "keywords": ["Archeological sites; Digital terrain models; Prehistoric agricultural terraces; Structure from motion; TerrACE project; Unmanned aerial vehicles", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.1016/b978-0-444-64177-9.00006-0"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/b978-0-444-64177-9.00006-0", "name": "item", "description": "10.1016/b978-0-444-64177-9.00006-0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/b978-0-444-64177-9.00006-0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2019.114061", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:15:44Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2019.114061"}, {"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": "10.1016/j.geoderma.2019.114061", "name": "item", "description": "10.1016/j.geoderma.2019.114061", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2019.114061"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.1016/j.jenvman.2024.121882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:15:50Z", "type": "Journal Article", "created": "2024-07-17", "title": "A systematic benchmarking framework for future assessments of soil health: An example from Denmark", "description": "Based on current evidence and established critical thresholds for soil degradation indicators, it is concerning that over 60-70% of European soils are unhealthy due to unsustainable management and the impact of climate change. Despite European and national efforts to improve soil health, significant gaps remain. The proposal for a Soil Monitoring and Resilience Law, to be implemented by the European Union, seeks to establish a framework for soil monitoring and promote sustainable management practices to achieve healthy soils by 2050. This requires extensive data collection and soil monitoring systems to accurately estimate soil health across Europe, considering the diversity of soil types, climates, and land uses. To establish a framework for soil monitoring, we must understand the site-specific status of soil and the ranges of soil health indicators across specific pedoclimatic regions. In our study, we evaluated the soil status in agricultural areas in Denmark using soil health indicators and a site-specific benchmarking approach. We compiled nationally representative datasets, combining point and model-informed data of soil parameters such as organic carbon content, bulk density, pH, electrical conductivity, clay-to-soil organiccarbon ratio, water erosion, and nitrogen leaching. By categorizing Danish agricultural soils into monitoring units based on textural classes, landscape elements, and wetland types, we calculated benchmarks for these indicators, considering different cropping systems. Our approach provided detailed point-based results and a spatially explicit overview of the status of soil health indicators in Denmark. We identified areas where soil deviates from the benchmarks of different indicators. Such deviations might indicate soil functions operating outside the normal range, posing potential threats to soil health. This proposed framework could support the establishment of a baseline for assessing the directionality of future changes in soil health. Moreover, it is adaptable for implementation by other countries to support assessments of soil health.", "keywords": ["2. Zero hunger", "Conservation of Natural Resources", "European soil monitoring and resilience law", "Soil monitoring units", "Denmark", "Climate Change", "Agriculture", "15. Life on land", "6. Clean water", "12. Responsible consumption", "Soil districts", "Soil", "13. Climate action", "11. Sustainability", "Soil health indicators", "Environmental Monitoring"]}, "links": [{"href": "https://doi.org/10.1016/j.jenvman.2024.121882"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Environmental%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jenvman.2024.121882", "name": "item", "description": "10.1016/j.jenvman.2024.121882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jenvman.2024.121882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-01T00:00:00Z"}}, {"id": "10.1071/sr13043", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:16:49Z", "type": "Journal Article", "created": "2013-12-20", "title": "Impact Of Carbon Farming Practices On Soil Carbon In Northern New South Wales", "description": "

This study sought to quantify the influence of \uffe2\uff80\uff98carbon farming\uffe2\uff80\uff99 practices on soil carbon stocks, in comparison with conventional grazing and cropping, in northern New South Wales. The study had two components: assessment of impacts of organic amendments on soil carbon and biological indicators in croplands on Vertosols of the Liverpool Plains; and assessment of the impact of grazing management on soil carbon in Chromosols of the Northern Tablelands. The organic amendment sites identified for the survey had been treated with manures, composts, or microbial treatments, while the conventional management sites had received only chemical fertilisers. The rotational grazing sites had been managed so that grazing was restricted to short periods of several days, followed by long rest periods (generally several months) governed by pasture growth. These were compared with sites that were grazed continuously. No differences in total soil carbon stock, or soil carbon fractions, were observed between sites treated with organic amendments and those treated with chemical fertiliser. There was some evidence of increased soil carbon stock under rotational compared with continuous grazing, but the difference was not statistically significant. Similarly, double-stranded DNA (dsDNA) stocks were not significantly different in either of the management contrasts, but tended to show higher values in organic treatments and rotational grazing. The enzymatic activities of \uffce\uffb2-glucosidase and leucine-aminopeptidase were significantly higher in rotational than continuous grazing but statistically similar for the cropping site treatments. Relative abundance and community structure, measured on a subset of the cropping sites, showed a higher bacteria\uffe2\uff80\uff89:\uffe2\uff80\uff89fungi ratio and provided evidence that microbial process rates were significantly higher in chemically fertilised sites than organic amendment sites, suggesting enhanced mineralisation of organic matter under conventional management. The higher enzyme activity and indication of greater efficiency of microbial populations on carbon farming sites suggests a greater potential to build soil carbon under these practices. Further research is required to investigate whether the indicative trends observed reflect real effects of management.

", "keywords": ["2. Zero hunger", "Land Capability and Soil Degradation", "550", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "Carbon Sequestration Science", "04 agricultural and veterinary sciences", "15. Life on land", "Land capability and soil productivity"]}, "links": [{"href": "https://doi.org/10.1071/sr13043"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1071/sr13043", "name": "item", "description": "10.1071/sr13043", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1071/sr13043"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-01-01T00:00:00Z"}}, {"id": "10.1111/gcb.12819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:16Z", "type": "Journal Article", "created": "2014-12-05", "title": "Soil Warming And Co2 Enrichment Induce Biomass Shifts In Alpine Tree Line Vegetation", "description": "Abstract

Responses of alpine tree line ecosystems to increasing atmospheric CO2 concentrations and global warming are poorly understood. We used an experiment at the Swiss tree line to investigate changes in vegetation biomass after 9\uffc2\uffa0years of free air CO2 enrichment (+200\uffc2\uffa0ppm; 2001\uffe2\uff80\uff932009) and 6\uffc2\uffa0years of soil warming (+4\uffc2\uffa0\uffc2\uffb0C; 2007\uffe2\uff80\uff932012). The study contained two key tree line species, Larix decidua and Pinus uncinata, both approximately 40\uffc2\uffa0years old, growing in heath vegetation dominated by dwarf shrubs. In 2012, we harvested and measured biomass of all trees (including root systems), above\uffe2\uff80\uff90ground understorey vegetation and fine roots. Overall, soil warming had clearer effects on plant biomass than CO2 enrichment, and there were no interactive effects between treatments. Total plant biomass increased in warmed plots containing Pinus but not in those with Larix. This response was driven by changes in tree mass (+50%), which contributed an average of 84% (5.7\uffc2\uffa0kg\uffc2\uffa0m\uffe2\uff88\uff922) of total plant mass. Pinus coarse root mass was especially enhanced by warming (+100%), yielding an increased root mass fraction. Elevated CO2 led to an increased relative growth rate of Larix stem basal area but no change in the final biomass of either tree species. Total understorey above\uffe2\uff80\uff90ground mass was not altered by soil warming or elevated CO2. However, Vaccinium myrtillus mass increased with both treatments, graminoid mass declined with warming, and forb and nonvascular plant (moss and lichen) mass decreased with both treatments. Fine roots showed a substantial reduction under soil warming (\uffe2\uff88\uff9240% for all roots <2\uffc2\uffa0mm in diameter at 0\uffe2\uff80\uff9320\uffc2\uffa0cm soil depth) but no change with CO2 enrichment. Our findings suggest that enhanced overall productivity and shifts in biomass allocation will occur at the tree line, particularly with global warming. However, individual species and functional groups will respond differently to these environmental changes, with consequences for ecosystem structure and functioning.

", "keywords": ["0106 biological sciences", "2. Zero hunger", "Models", " Statistical", "Temperature", "Larix", "Carbon Dioxide", "15. Life on land", "Pinus", "Global Warming", "01 natural sciences", "Soil", "Species Specificity", "13. Climate action", "Biomass", "Tundra", "Switzerland"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12819", "name": "item", "description": "10.1111/gcb.12819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-01-30T00:00:00Z"}}, {"id": "10.1111/gcbb.12255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:19Z", "type": "Journal Article", "created": "2015-02-19", "title": "Bioenergy Harvest, Climate Change, And Forest Carbon In The Oregon Coast Range", "description": "Abstract

Forests provide important ecological, economic, and social services, and recent interest has emerged in the potential for using residue from timber harvest as a source of renewable woody bioenergy. The long\uffe2\uff80\uff90term consequences of such intensive harvest are unclear, particularly as forests face novel climatic conditions over the next century. We used a simulation model to project the long\uffe2\uff80\uff90term effects of management and climate change on above\uffe2\uff80\uff90 and belowground forest carbon storage in a watershed in northwestern Oregon. The multi\uffe2\uff80\uff90ownership watershed has a diverse range of current management practices, including little\uffe2\uff80\uff90to\uffe2\uff80\uff90no harvesting on federal lands, short\uffe2\uff80\uff90rotation clear\uffe2\uff80\uff90cutting on industrial land, and a mix of practices on private nonindustrial land. We simulated multiple management scenarios, varying the rate and intensity of harvest, combined with projections of climate change. Our simulations project a wide range of total ecosystem carbon storage with varying harvest rate, ranging from a 45% increase to a 16% decrease in carbon compared to current levels. Increasing the intensity of harvest for bioenergy caused a 2\uffe2\uff80\uff933% decrease in ecosystem carbon relative to conventional harvest practices. Soil carbon was relatively insensitive to harvest rotation and intensity, and accumulated slowly regardless of harvest regime. Climate change reduced carbon accumulation in soil and detrital pools due to increasing heterotrophic respiration, and had small but variable effects on aboveground live carbon and total ecosystem carbon. Overall, we conclude that current levels of ecosystem carbon storage are maintained in part due to substantial portions of the landscape (federal and some private lands) remaining unharvested or lightly managed.\uffc2\uffa0Increasing the intensity of harvest for bioenergy on currently harvested land, however,\uffc2\uffa0led to a relatively small reduction in the ability of forests to store carbon. Climate change is unlikely to substantially alter carbon storage in these forests, absent shifts in disturbance regimes.

", "keywords": ["0106 biological sciences", "Carbon dioxide mitigation", "Forest ecology -- Oregon -- Oregon Coast Range", "Forest biomass", "13. Climate action", "Carbon cycle (Biogeochemistry)", "Biomass energy", "Forest Biology", "15. Life on land", "01 natural sciences", "7. Clean energy", "Climatic change", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1111/gcbb.12255"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GCB%20Bioenergy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcbb.12255", "name": "item", "description": "10.1111/gcbb.12255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcbb.12255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-05-25T00:00:00Z"}}, {"id": "10.1111/j.1365-2486.2008.01809.x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:23Z", "type": "Journal Article", "created": "2008-11-04", "title": "Soil Organic Carbon Contents In Long-Term Experimental Grassland Plots In The Uk (Palace Leas And Park Grass) Have Not Changed Consistently In Recent Decades", "description": "Abstract

A recent report of widespread declines in soil organic C (SOC) in the UK over the 10\uffe2\uff80\uff9325 years until the early 2000s has focussed attention on the importance of resampling previously characterized sites to assess long\uffe2\uff80\uff90term trends in SOC contents and the importance of soils as a potentially volatile and globally significant reservoir of terrestrial C. We have used two sets of long\uffe2\uff80\uff90term experimental plots which have been under constant and known management for over a century and for which historical data exist that allow comparison over recent decades to determine what, if any, changes in SOC content have occurred. The plots used are the Palace Leas (PL) Meadow Hay Plots in north\uffe2\uff80\uff90east England (UK) established in 1897, and from the Park Grass (PG) Continuous Hay experiment established in 1856 at Rothamsted in south\uffe2\uff80\uff90east England. Collectively, these plots represent the only grassland sites in the UK under long\uffe2\uff80\uff90term management where changes in SOC over several decades can be assessed, and are probably unique in the world. The plots have received different manure and fertilizer treatment and have been under known management for at least 100 years. In 1982, total SOC contents were determined for the 0\uffe2\uff80\uff9327\uffe2\uff80\uff83cm layer of six of the PL plots using measurements of SOC concentrations, bulk density and soil depth. In 2006, the same six PL plots were resampled and SOC contents determined again. Four of the plots showed no net change in SOC content, but two plots showed net loss of SOC of 15% and 17% (amounting to decreases of 18 and 15\uffe2\uff80\uff83t\uffe2\uff80\uff83C\uffe2\uff80\uff83ha\uffe2\uff88\uff921) since 1982. However, these differences in total SOC content were in a similar range to the variations in bulk density (6\uffe2\uff80\uff9331%) with changing soil water content. In 1959, the soil masses and SOC concentrations to 23\uffe2\uff80\uff83cm depth were measured on six PG plots with fertilizer and manure treatments corresponding closely with those measured on PL. In 2002, the SOC concentrations on the same plots were measured again. On three of the PG plots, SOC concentrations had declined by 2\uffe2\uff80\uff9310%, but in the other three it had increased by 4\uffe2\uff80\uff938% between 1959 and 2002. If it is assumed that the soil bulk density had not changed over this period, the losses of SOC from the top soils ranged range from 10 to 3\uffe2\uff80\uff83t\uffe2\uff80\uff83C\uffe2\uff80\uff83ha\uffe2\uff88\uff921, while the gains ranged from 4 to 7\uffe2\uff80\uff83t\uffe2\uff80\uff83C\uffe2\uff80\uff83ha\uffe2\uff88\uff921. When the differences with time in SOC contents for the six PL and the six PG plots were examined using paired t\uffe2\uff80\uff90tests, that is, regarding the plots as two sets of six replicate permanent grasslands, there were no significant differences between 1982 and 2006 for the PL plots or between 1959 and 2002 for the PG plots. Thus, these independent observations on similar plots at PL and PG indicate there has been no consistent decrease in SOC stocks in surface soils under old, permanent grassland in England in recent decades, even though meteorological records for both sites indicate significant warming of the soil and air between 1980 and 2000. Because the potential influences of changes in management or land use have been definitively excluded, and measured rather than derived bulk densities have been used to convert from SOC concentrations to SOC amounts, our observations question whether for permanent grassland in England, losses in SOC in recent decades reported elsewhere can be attributed to widespread environmental change.

", "keywords": ["2. Zero hunger", "Ecology", "0401 agriculture", " forestry", " and fisheries", "biodiversity conservation", "04 agricultural and veterinary sciences", "15. Life on land", "Environmental Sciences"]}, "links": [{"href": "https://doi.org/10.1111/j.1365-2486.2008.01809.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2486.2008.01809.x", "name": "item", "description": "10.1111/j.1365-2486.2008.01809.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2486.2008.01809.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-06-05T00:00:00Z"}}, {"id": "10.1128/aem.02264-23", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:35Z", "type": "Journal Article", "created": "2024-02-19", "title": "Novel endolithic bacteria of phylum Chloroflexota reveal a myriad of potential survival strategies in the Antarctic desert", "description": "ABSTRACT

The ice-free McMurdo Dry Valleys of Antarctica are dominated by nutrient-poor mineral soil and rocky outcrops. The principal habitat for microorganisms is within rocks (endolithic). In this environment, microorganisms are provided with protection against sub-zero temperatures, rapid thermal fluctuations, extreme dryness, and ultraviolet and solar radiation. Endolithic communities include lichen, algae, fungi, and a diverse array of bacteria. Chloroflexota is among the most abundant bacterial phyla present in these communities. Among the Chloroflexota are four novel classes of bacteria, here named Candidatus Spiritibacteria class. nov. (=UBA5177), Candidatus Martimicrobia class. nov. (=UBA4733), Candidatus Tarhunnaeia class. nov. (=UBA6077), and Candidatus Uliximicrobia class. nov. (=UBA2235). We retrieved 17 high-quality metagenome-assembled genomes (MAGs) that represent these four classes. Based on genome predictions, all these bacteria are inferred to be aerobic heterotrophs that encode enzymes for the catabolism of diverse sugars. These and other organic substrates are likely derived from lichen, algae, and fungi, as metabolites (including photosynthate), cell wall components, and extracellular matrix components. The majority of MAGs encode the capacity for trace gas oxidation using high-affinity uptake hydrogenases, which could provide energy and metabolic water required for survival and persistence. Furthermore, some MAGs encode the capacity to couple the energy generated from H 2 and CO oxidation to support carbon fixation (atmospheric chemosynthesis). All encode mechanisms for the detoxification and efflux of heavy metals. Certain MAGs encode features that indicate possible interactions with other organisms, such as Tc-type toxin complexes, hemolysins, and macroglobulins.

IMPORTANCE

The ice-free McMurdo Dry Valleys of Antarctica are the coldest and most hyperarid desert on Earth. It is, therefore, the closest analog to the surface of the planet Mars. Bacteria and other microorganisms survive by inhabiting airspaces within rocks (endolithic). We identify four novel classes of phylum Chloroflexota , and, based on interrogation of 17 metagenome-assembled genomes, we predict specific metabolic and physiological adaptations that facilitate the survival of these bacteria in this harsh environment\uffe2\uff80\uff94including oxidation of trace gases and the utilization of nutrients (including sugars) derived from lichen, algae, and fungi. We propose that such adaptations allow these endolithic bacteria to eke out an existence in this cold and extremely dry habitat.

", "keywords": ["570", "Bacteria", "Fungi", "Antarctic Regions", "Chloroflexi", "15. Life on land", "Survival strategies", "Cold Temperature", "Extremophiles", "13. Climate action", "Antarctica", "Endolithic communities", "Metagenomics", "14. Life underwater", "Sugars", "Settore BIO/19 - MICROBIOLOGIA GENERALE"]}, "links": [{"href": "https://doi.org/10.1128/aem.02264-23"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20and%20Environmental%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1128/aem.02264-23", "name": "item", "description": "10.1128/aem.02264-23", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1128/aem.02264-23"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-19T00:00:00Z"}}, {"id": "10.1175/bams-d-17-0212.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:41Z", "type": "Journal Article", "created": "2018-01-09", "title": "The Global N 2 O Model Intercomparison Project", "description": "Abstract

Nitrous oxide (N2O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N2O fluxes and the key drivers of N2O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N2O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N2O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N2O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N2O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N2O dynamics; and 3) provide a benchmarking estimate of N2O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N2O estimation derived from the NMIP is a key component of the global N2O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.

", "keywords": ["[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "13. Climate action", "11. Sustainability", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "15. Life on land", "environment", "01 natural sciences", "7. Clean energy", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://journals.ametsoc.org/downloadpdf/journals/bams/99/6/bams-d-17-0212.1.xml"}, {"href": "https://doi.org/10.1175/bams-d-17-0212.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bulletin%20of%20the%20American%20Meteorological%20Society", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1175/bams-d-17-0212.1", "name": "item", "description": "10.1175/bams-d-17-0212.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1175/bams-d-17-0212.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "10.1186/s12302-025-01141-6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:41Z", "type": "Journal Article", "created": "2025-06-15", "title": "Investigating the extent of PFAS contamination in the Upper Danube Basin across environmental compartments", "description": "Abstract Background

Per- and polyfluoroalkyl substances (PFAS) are emerging organic pollutants widely detected in environmental systems, posing risks to human health and the ecosystem. Despite increasing efforts to monitor PFAS in river systems, knowledge gaps remain regarding sources and emissions via different pathways. This study investigates PFAS contamination across multiple environmental compartments in the Upper Danube Basin, including surface water, groundwater, wastewater, landfill leachate, surface runoff, and atmospheric deposition. The primary objectives are to assess the extent of PFAS contamination, identify key emission sources and transport pathways, and evaluate associated risks in terms of the potential exceedance of current and proposed environmental regulatory thresholds in the European Union.

Results

The findings reveal a widespread presence of PFAS, with PFOA, PFOS and short-chain compounds being predominant. The Alz River and Gendorf chemical park emerge as hotspots with far-reaching effects downstream, contributing significantly to diffuse legacy contamination of PFOA and being a significant source of two industrial PFOA substitutes, ADONA and GenX. Wastewater treatment plants, old municipal landfills, and sites with a history of fire-fighting foam application are identified as key pathways or sources of legacy pollution, exhibiting higher concentrations compared to the other matrices. Notably, no significant removal is observed when comparing influent and effluent samples from conventional WWTPs. The study further demonstrates that groundwater is vulnerable to contamination from point sources and to infiltration from rivers, with bank filtration proving largely ineffective in preventing PFAS contamination.

Conclusions

The study underscores the necessity for source and pathway control measures to mitigate PFAS pollution, the implementation of advanced treatment technologies to safeguard drinking water and surface water quality, and targeted remediation for legacy soil and groundwater contamination. Additionally, strong use regulations should be explored to minimize ongoing emissions. The multi-compartment monitoring proves to be a crucial approach to understand the complexity of PFAS distribution at the catchment scale. Comparative analysis and risk assessment highlight challenging situations for water management, offering an indispensable basis for emission modeling as a next step for quantitative assessment of the relevance of different sources and pathways for surface water pollution.

", "keywords": ["Emerging contaminants", "Emerging Pollutants", "PFAS", "Source identification", "Watershed management", "Environmental sciences", "Emission", "Water Framework Directive", "Environmental law", "Water pollution", "GE1-350", "K3581-3598", "Catchment monitoring", "Environmental Monitoring"]}, "links": [{"href": "https://doi.org/10.1186/s12302-025-01141-6"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Sciences%20Europe", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s12302-025-01141-6", "name": "item", "description": "10.1186/s12302-025-01141-6", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s12302-025-01141-6"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-15T00:00:00Z"}}, {"id": "10.1590/s0100-06832009000100016", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:17:57Z", "type": "Journal Article", "created": "2009-03-11", "title": "Atributos F\u00edsicos, Qu\u00edmicos E Biol\u00f3gicos De Solo De Cerrado Sob Diferentes Sistemas De Uso E Manejo", "description": "

\uffc3\uff80 medida que o conhecimento do sistema plantio direto se amplia, verifica-se que o uso de indicadores qu\uffc3\uffadmicos isolados n\uffc3\uffa3o permite melhor caracteriza\uffc3\uffa7\uffc3\uffa3o dos solos, sendo necess\uffc3\uffa1rio utilizar um conjunto de indicadores da qualidade do solo com a entrada de outros atributos, entre eles os f\uffc3\uffadsicos e os biol\uffc3\uffb3gicos. Objetivou-se avaliar os efeitos de sistemas de manejo e uso do solo nos atributos f\uffc3\uffadsicos, qu\uffc3\uffadmicos e biol\uffc3\uffb3gicos de um Latossolo Vermelho distr\uffc3\uffb3fico e um Neossolo Quartzar\uffc3\uffaanico \uffc3\uffb3rtico sob Cerrado, no entorno do Parque Nacional das Emas. Os aspectos avaliados no Latossolo foram: Cerrado nativo, pastagem, milheto em preparo convencional, nabo forrageiro em plantio direto e sorgo em plantio direto. No Neossolo: Cerrado nativo, pastagem nativa, integra\uffc3\uffa7\uffc3\uffa3o agricultura-pecu\uffc3\uffa1ria, pastagem cultivada, plantio direto com soja no ver\uffc3\uffa3o e plantio direto com milho no ver\uffc3\uffa3o. As amostras de solo foram coletadas na profundidade de 0 a 10 cm. O delineamento experimental foi o inteiramente casualizado, com cinco parcelas de 150 m\uffc2\uffb2, sendo coletadas 10 subamostras aleat\uffc3\uffb3rias. As an\uffc3\uffa1lises qu\uffc3\uffadmicas, f\uffc3\uffadsicas e biol\uffc3\uffb3gicas foram realizadas no Laborat\uffc3\uffb3rio de Solos da UFG/CJ. Os manejos promoveram altera\uffc3\uffa7\uffc3\uffb5es na densidade do solo, volume total de poros, macroporos e resist\uffc3\uffaancia do solo \uffc3\uffa0 penetra\uffc3\uffa7\uffc3\uffa3o no Neossolo e no Latossolo, excetuando-se neste o volume total de poros. Houve pequena varia\uffc3\uffa7\uffc3\uffa3o nos atributos qu\uffc3\uffadmicos nos dois solos, com o Cerrado apresentando maior acidez potencial e menor teor de c\uffc3\uffa1tions troc\uffc3\uffa1veis e P. Os atributos biol\uffc3\uffb3gicos do solo foram alterados pelos sistemas de manejo, sendo mais prejudicados em sistemas com maior revolvimento do solo. A an\uffc3\uffa1lise can\uffc3\uffb4nica dos dados demonstrou que os atributos f\uffc3\uffadsicos foram os de menor import\uffc3\uffa2ncia por apresentar maior coeficiente de pondera\uffc3\uffa7\uffc3\uffa3o nas vari\uffc3\uffa1veis can\uffc3\uffb4nicas. Os atributos do solo, isoladamente, pouco contribu\uffc3\uffadram para a avalia\uffc3\uffa7\uffc3\uffa3o da qualidade do solo: no entanto, quando se usou a an\uffc3\uffa1lise multivariada, subsidiaram a constata\uffc3\uffa7\uffc3\uffa3o dos manejos do solo mais sustent\uffc3\uffa1veis.

", "keywords": ["C fra\u00e7\u00e3o leve", "multivariate analysis", "an\u00e1lise multivariada", "plantio direto", "light carbon fraction", "0401 agriculture", " forestry", " and fisheries", "soil quality", "04 agricultural and veterinary sciences"]}, "links": [{"href": "https://doi.org/10.1590/s0100-06832009000100016"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Revista%20Brasileira%20de%20Ci%C3%AAncia%20do%20Solo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1590/s0100-06832009000100016", "name": "item", "description": "10.1590/s0100-06832009000100016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1590/s0100-06832009000100016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-02-01T00:00:00Z"}}, {"id": "10261/179481", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:21:08Z", "type": "Journal Article", "created": "2018-07-19", "title": "Molecular Fingerprinting of14C Dated Soil Organic Matter Fractions from Archaeological Settings in NW Spain", "description": "Abstract

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.

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.

", "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": "2026-09-19T16:18:29Z", "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": "

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.

", "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": "10261/306235", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:21:10Z", "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": "2026-09-19T16:21:11Z", "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": "10.3389/fmicb.2016.01446", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:18:36Z", "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.2021.758395", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:18:37Z", "type": "Journal Article", "created": "2022-01-12", "title": "Fresh rumen liquid inoculant enhances the rumen microbial community establishment in pre-weaned dairy calves", "description": "

The development of the functional rumen in calves involves a complex interplay between the host and host-related microbiome. Attempts to modulate rumen microbial community establishment may therefore have an impact on weaning success, calf health, and animal performance later in life. In this experiment, we aimed to elucidate how rumen liquid inoculum from an adult cow, provided to calves during the pre-weaning period, influences the establishment of rumen bacterial, archaeal, fungal, and ciliate protozoan communities in monozygotic twin calves (n = 6 pairs). The calves were divided into treatment (T-group) and control (C-group) groups, where the T-group received fresh rumen liquid as an oral inoculum during a 2\uffe2\uff80\uff938-week period. The C-group was not inoculated. The rumen microbial community composition was determined using bacterial and archaeal 16S ribosomal RNA (rRNA) gene, protozoal 18S rRNA gene, and fungal ITS1 region amplicon sequencing. Animal weight gain and feed intake were monitored throughout the experiment. The T-group tended to have a higher concentrate intake (Treatment: p &lt; 0.08) and had a significantly higher weekly weight gain (Treatment: p &lt; 0.05), but no significant difference in volatile fatty acid concentrations between the groups was observed. In the T-group, the inoculum stimulated the earlier establishment of mature rumen-related bacterial taxa, affecting significant differences between the groups until 6 weeks of age. The inoculum also increased the archaeal operational taxonomic unit (OTU) diversity (Treatment: p &lt; 0.05) but did not affect the archaeal quantity. Archaeal communities differed significantly between groups until week 4 (p = 0.02). Due to the inoculum, ciliate protozoa were detected in the T-group in week 2, while the C-group remained defaunated until 6 weeks of age. In week 8, Eremoplastron dilobum was the dominant ciliate protozoa in the C-group and Isotricha sp. in the T-group, respectively. The Shannon diversity of rumen anaerobic fungi reduced with age (Week: p &lt; 0.01), and community establishment was influenced by a change of diet and potential interaction with other rumen microorganisms. Our results indicate that an adult cow rumen liquid inoculum enhanced the maturation of bacterial and archaeal communities in pre-weaning calves\uffe2\uff80\uff99 rumen, whereas its effect on eukaryotic communities was less clear and requires further investigation.

", "keywords": ["0301 basic medicine", "570", "Anaerobic fungi", "microbiome establishment", "archaea", "Rumen function", "Microbiome manipulation", "Microbiology", "630", "Microbiome establishment", "ciliate protozoa", "03 medical and health sciences", "Dairy calf", "anaerobic fungi", "bacteria", "[SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology", "2. Zero hunger", "0303 health sciences", "ta412", "Bacteria", "dairy calf", "Animal science", " dairy science", "Archaea", "Ciliate protozoa", "QR1-502", "rumen function", "[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology", "microbiome manipulation"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2021.758395"}, {"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.2021.758395", "name": "item", "description": "10.3389/fmicb.2021.758395", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2021.758395"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-12T00:00:00Z"}}, {"id": "10.3390/w12082160", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:18:55Z", "type": "Journal Article", "created": "2020-08-03", "title": "Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates", "description": "

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

", "keywords": ["13. Climate action", "citizen science", "digital soil mapping", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "synthetic aperture radar (SAR)", " soil moisture", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/12/8/2160/pdf"}, {"href": "https://doi.org/10.3390/w12082160"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w12082160", "name": "item", "description": "10.3390/w12082160", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w12082160"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-30T00:00:00Z"}}, {"id": "10.4081/ija.2012.e26", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:00Z", "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": "10.5194/essd-2023-95", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:20Z", "type": "Report", "created": "2023-04-11", "title": "A global catalogue of CO2 emissions and co-emitted species from power plants at a very high spatial and temporal resolution", "description": "

Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors and emission ratios. The resulting catalog contains annual emission information for more than 16000 individual facilities at their exact geographical location. Each facility is linked to a specific temporal (i.e., monthly, day-of-the-week and hourly) and vertical distribution profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including the Carbon Monitoring for Action (CARMA) and the Emissions Database for Global Atmospheric Research (EDGAR) databases, as well as officially reported emission data. An overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/mxjo-nram, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) EU-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).

", "keywords": ["13. Climate action", "11. Sustainability", "7. Clean energy"]}, "links": [{"href": "https://doi.org/10.5194/essd-2023-95"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-2023-95", "name": "item", "description": "10.5194/essd-2023-95", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-2023-95"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-11T00:00:00Z"}}, {"id": "10.5281/zenodo.10959077", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:34Z", "type": "Dataset", "created": "2023-10-30", "title": "Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies", "description": "The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the 'Soil management effect assessment' in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ('Knowledge gap in tab'-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other", "keywords": ["Water management", "EJP SOIL", "Climate change mitigation", "Nitrogen leaching", "CH4", "Conservation agriculture", "Cropping systems", "SOMMIT", "N2O", "Organic matter inputs", "Tillage"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10959077"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10959077", "name": "item", "description": "10.5281/zenodo.10959077", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10959077"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.13952097", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:43Z", "type": "Dataset", "created": "2024-01-01", "title": "SERENA EJPSoil Soil erosion control in Tuscany (Italy)", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. One of the\u00a0objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the methodology of the SERENA Soil erosion and soil erosion control cookbook.\u00a0 Soil loss was used as an indicator for soil erosion (ST). The map of soil mass not eroded was based on the RUSLE model. Soil erosion control was calculated as the difference between potential and actual soil erosion (SES, ecosystem service of soil erosion protection, i.e. soil eroded mass retained by vegetation, Mg/ha/y). For Italy, the cookbook was applied in the Tuscany region. \u00a0 To create the soil loss map we used:\u00a0 for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990\u20132022, Lamma Consortium)\u00a0 and a local linear equation between R and mean annual precipitation (P); for C -factor, Regional Land use map 1:10.000 (2018, freely available at:\u00a0https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html) and ESDAC method (https://doi.org/10.1016/j.landusepol.2015.05.021) ; \u00a0 for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO\u2019s methodology in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function; for LS-factor, DEM 10 m of Tuscany, (freely available at\u00a0https://www502.regione.toscana.it/geoscopio/cartoteca.html99) and Desmet & Govers (1996) SAGA tool (applied at 10 m and upscaled); for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor of\u00a0 0.5\u00a0 was considered, based on expert evaluation) for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor\u00a0of\u00a0 0.5\u00a0 was considered, based on expert evaluation)\u00a0 Maps was delivered in the GeoTIFF format in the resolution of 100m. \u00a0 Delivered data will be validated by stakeholders from Italy (scientist) in October, 2024.", "keywords": ["Italy", "Tuscany", "SERENA project", "Soil-based ecosystem service", "RUSLE", "soil erosion control", "Grant n. 862695", "EJP-Soil"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13952097"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13952097", "name": "item", "description": "10.5281/zenodo.13952097", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13952097"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-18T00:00:00Z"}}, {"id": "10.5281/zenodo.14027088", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:46Z", "type": "Dataset", "created": "2024-11-01", "title": "Per- and Polyfluoroalkyl Substances (PFAS) Concentrations in the Upper Danube Catchment: Integrated Dataset from H2020 Project PROMISCES - Case Study 2", "description": "Dataset Description This dataset was produced within the framework of\u00a0Horizon 2020 Framework Programme, Project PROMISCES (Preventing Recalcitrant Organic Mobile Industrial chemicalS for Circular Economy in the Soil-sediment-water system). Project website: https://promisces.eu/ The dataset contains information on the environmental concentrations of Per- and Polyfluoroalkyl substances (PFASs) collected as part of the PROMISCES project's Case Study #2,\u00a0Subtask 2.2.4 \u2013 Large catchment scale monitoring in different environmental compartments. It also includes data gathered from various external sources. Abstract PFASs are a group of synthetic chemicals widely used in various household and industrial applications (Gl\u00fcge et al., 2020). Due to their high chemical stability, PFASs are resistant to natural degradation processes, leading to their accumulation in different environmental matrices and ultimately posing potential health risks to humans (Sunderland et al., 2019). PROMISCES CS#2 focused on understading the fate and transport of PFASs in the upper Danube catchment, covering the Danube from its source to the city of Budapest. Over approximately 1.5 years, a comprehensive monitoring campaign was conducted in this study area, across multiple environmental compartments:\u00a0 Atmopsheric Deposition:\u00a0 River water: including Danube mainstream and its tributaries. Groundwater: including bank-filtered water from the Danube, and groundwater directly influenced by the landfills Landfill leachate Surface Runoff Wastewater: Influent and effluent from municipal waterwater treatment plants (WWTPs) and direct industrial dischargers Particularly, the case study placed a special focus on the Danube and its bank filtration sites at two major cities in the Upper Danube, Vienna and Budapest. The dataset primarily consists results from targeted analysis of 32 individual PFAS substances. In addition, available data for these 32 PFASs in the study area were collected from various online resources or provided directly by project partners. For confidentiality reasons, some external data have been anonymized on names and locations.\u00a0 Partial of this dataset have already contributed to a 2023 publication (Liu et al.), which was based on preliminary data before the completion of the full monitoring campaign and external data collection. The full dataset was analysed and discussed in the publication Liu et al. (2025): https://www.doi.org/10.1186/s12302-025-01141-6 Technical Details This dataset includes: A Zip file containing .accdb Microsoft Access database A ZIP file containing .csv files structured to match the database Notice that the .accdb version is out of maintance and removed in version 3.0. The only changes compared to version 2.0 was the substance short-names for two compounds: substance with CAS number 2355-31-9 updated from \u201cMeFOSAA\u201d to \u201cN-MeFOSAA\u201d substance with CAS number 2991-50-6 updated from \u201cEtFOSAA\u201d to \u201cN-EtFOSAA Database structure One query is created to show most important information: Concentrations_PFAS: contains all PFAS concentration data.\u00a0 In addition, tables were provided with more infomation on the metadata: Table1_measurements: concentrations data with units, values, limit of quantifications (LOQs); keys indicating relationships with other tables. Table2_samplings: sample codes, sampling times (if available), sampling type, sampling techniques; key indicating relationships with Table7_analytical_methods. Table3_samples: sample names, sample sites, coordinates and coordinate systems (if available). Table4_compartments: sample matrices/compartments, more detailed sample types. Table5_compounds: CAS numbers, substance short names, Sus Dat IDs, substance names in NORMAN database, substance group short names and long names. Table6_datasources: data source names, organisations, countries, references, links. Table7_analytical_methods: laboratories, preparation methods, analytical methods, analytical method standards. References Gl\u00fcge, J., Scheringer M., Cousins I., DeWitt J., Goldenman G., Herzke D., Lohmann R., Ng A., Trier X., Wang Z (2020) An Overview of the Uses of Per- and Polyfluoroalkyl Substances (PFAS). Environmental Science: Processes & Impacts 12. https://doi.org/10.1039/D0EM00291G Liu, M., Saracevic, E., Kittlaus, S., Oudega, T., Obeid, A., Nagy-Kov\u00e1cs, Z., L\u00e1szl\u00f3, B., Krlovic, N., Saracevic, Z., Lindner, G., Rab, R., Derx, J., Zoboli, O., Zessner, M. (2023) PFAS-Belastungen im Einzugsgebiet der oberen Donau. \u00d6sterr Wasser- und Abfallw 75, 503\u2013514 . https://doi.org/10.1007/s00506-023-00973-x\u00a0 Sunderland, Elsie M., Xindi C. Hu, Clifton Dassuncao, Andrea K. Tokranov, Charlotte C. Wagner, and Joseph G. Allen. (2019) A Review of the Pathways of Human Exposure to Poly- and Perfluoroalkyl Substances (PFASs) and Present Understanding of Health Effects. Journal of Exposure Science & Environmental Epidemiology 29, no. 2 : 131\u201347. https://doi.org/10.1038/s41370-018-0094-1", "keywords": ["Water management", "Environmental sciences", "water pollution", "emerging pollutants", "PFAS", "hazardous substances", "Danube", "water quality", "Pollution", "environmental monitoring"], "contacts": [{"organization": "Liu, Meiqi", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14027088"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14027088", "name": "item", "description": "10.5281/zenodo.14027088", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14027088"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14036322", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:46Z", "type": "Dataset", "created": "2024-11-04", "title": "Geochemistry of soils and eroded suspended sediments from two large rural catchments in southern Brazil for studies on Suspended Sediment Fingerprinting", "description": "1. Introduction This dataset comes from a research project entitled 'Water and pollutants, from cropfields to cities: evaluation and improved of soil management technologies in a catchment network ' supported by the Foundation for Research Support of the State of Rio Grande do Sul (FAPERGS) and National Council for Scientific and Technological Development (CNPq) (process n\u00b010/0034-0). The project was carried out between 2010 and 2014 under the coordination of Jos\u00e9 Miguel Reichert and Danilo Rheinheimer dos Santos, professors at the Federal University of Santa Maria. One of the aims of this project was to understand the main pollutant transfer process from hillslopes to fluvial systems in large rural catchments representative of the agricultural production system in Southern Brazil. In this context, the Suspended Sediment Fingerprinting (SSF) was extremely useful for quantifying the origin of the sediment yield monitored at the outlet of these catchments. Among the various works carried out in this project, we highlight Tales Tiecher's doctoral thesis (Tiecher, 2015) that explored the SSF in many catchments, including the Concei\u00e7\u00e3o and Guapor\u00e9 river basins. 2. Material and Methods The catchments represent the magnitude of erosive and hydrological processes representative of Southern Brazil. The Concei\u00e7\u00e3o catchment has a drainage area of 804 km2 (28\u00b027\u203222\u2033S and 53\u00b058\u203224\u2033 W). According to K\u00f6ppen, the climate is Cfa type, with an annual rainfall between 1,750 and 2,000 mm. Geology is riodacithe basalt, with a formation of deep and highly weathered soils (Oxisols, Ultisols, and Alfisols). The relief is characterized by gentle slopes (6\u20139 %) on top and hillside slopes and higher steepness (10\u201314%) near the drainage channels. Farming based on the production of soybeans (Glycine max) in summer and wheat (Triticumspp.), oats (Avena strigosa), and ryegrass (Lolium multiflorum) in winter. The Guapor\u00e9 catchment has a drainage area of 1,980 km2 (28\u00b054\u203241\u2033S and 51\u00b057\u203210\u2033W), it covers part of the meridional plateau border. The climate is classified as Cfa, with annual rainfall varies between 1,400 and 2,000 mm. Geology is characterized by volcanic lava flows, and topography is undulating to hilly. Due to variations in landscape, several classes of soils (Entisols, Luvisol, Cambisol, Oxisol, Ultisol, and Chernosol). The land use is highly heterogeneous. In the upper third of the catchment, there is a predominance of soybean cultivated under no-tillage soil management. In the other two-thirds (middle and lower parts), land use and soil management are very heterogeneous. The main land uses are tobacco (Nicotiana tabacum) and maize (Zea mays) crops, Eucalyptus (Eucalyptus spp.), as well as pastures for dairy cattle. The contribution of unpaved roads is relevant to the sediment yield in both catchments (Didon\u00e9 et al., 2014). Composite samples of potential sediment sources (cropland, unpaved roads, and stream channel banks) were collected. Sediment source samples were taken from the surface soil layer (0\u20130.05 m) of cropland and unpaved roads and on exposed sites located along the river channel network. Each sample was composed of at least 10 subsamples. To obtain representative samples of suspended sediment transported in the catchment\u2019s outlet were used three strategies: (1) to collect flood suspended sediments (FSS) through the manual sampling (USDH-48) at different periods during the rising and falling stages of floods; (2) to deploy time-integrated suspended sediment samplers (TISS), by installing the device developed by Phillips et al. (2000) at different sites within the catchments; to collect fine-bed sediment (FBS) with a suction stainless sampler limiting the loss of fine material at the bed river. Source and sediment samples were oven\u2010dried at 50 \u00b0C, gently disaggregated using a pestle and mortar, and then sieved to 62,5 \u03bcm. The geochemical tracers evaluated were total organic carbon estimated by wet oxidation (K2Cr2O7 + H2SO4) and the total concentration of Al, Ba, Be, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, V, and Zn using inductively coupled plasma optical emission spectrometry after microwave\u2010assisted digestion with concentrated HCl and HNO3 (ratio 3:1) for 9.5 min at 182 \u00b0C (Tiecher, 2015; Tiecher et al. 2017, 2018). \u00a0 3. Final remarks \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The SSF results provided by this dataset (Tiecher, 2015) combined with sediment yield monitoring were very important for the assessment and modeling studies in these two catchments that took place after that (Didon\u00e9 et al., 2015; 2017). In addition, other studies have explored the same sample bank, expanding upon the array of tracer properties and increasing our understanding about the mechanisms of sediment and pollutant transfer in these catchments (Le Gall et al. 2017; Zafar et al., 2017; Ramon et al., 2020). \u00a04. References \u00a0Didon\u00e9, E. J., Minella, J. P. G., Reichert, J. M., Merten G. H., Dalbianco, L., Barros, C. A. P., Ramon, R. (2014) Impact of no-tillage agricultural systems on sediment yield in two large catchments in southern Brazil. J Soils Sediments 14:1287\u20131297. Didon\u00e9, E.J., Minella, J.P.G., Evrard, O. (2017). Measuring and modelling soil erosion and sediment yields in a large cultivated catchment under no-till of Southern Brazil. Soil Tillage Res. 174, 24-33. https://doi.org/10.1016/j.still.2017.05.011 Didon\u00e9, E. J.; Minela, J. P. G.; Merten, G. H. (2015). Quantifying soil erosion and sediment yield in a catchment in southern Brazil and implications for land conservation. J. Soils Sediments 11, 2334-2346. https://doi.org/10.1007/s11368-015-1160-0 le Gall, M., Evrard, O., Dapoigny, A., Tiecher, T., Zafar, M., Minella, J. P. G., Laceby, J. P., & Ayrault, S. (2017). Tracing sediment sources in a subtropical agricultural catchment of southern Brazil cultivated with conventional and conservation farming practices. Land Degradation and Development, 28(4). https://doi.org/10.1002/ldr.2662 Ramon, R., Evrard, O., Laceby, J. P., Caner, L., Inda, A. v., Barros, C. A. P., Minella, J. P. G., & Tiecher, T. (2020). Combining spectroscopy and magnetism with geochemical tracers to improve the discrimination of sediment sources in a homogeneous subtropical catchment. Catena, 195, 104800. https://doi.org/10.1016/j.catena.2020.104800 Tiecher, T. (2015). Fingerprinting sediment sources in agricultural catchments in Southern Brazil. Doctoral Dissertation in Soil Science. Universidade Federal de Santa Maria, Santa Maria, RS. Tiecher, T., Minella, J. P. G., Caner, L., Evrard, O., Zafar, M., Capoane, V., le Gall, M., & Santos, D. R. D. (2017). Quantifying land use contributions to suspended sediment in a large cultivated catchment of Southern Brazil (Guapor\u00e9 River, Rio Grande do Sul). Agriculture, Ecosystems and Environment, 237. https://doi.org/10.1016/j.agee.2016.12.004 Tiecher, T., Minella, J. P. G., Evrard, O., Caner, L., Merten, G. H., Capoane, V., Didon\u00e9, E. J., & dos Santos, D. R. (2018). Fingerprinting sediment sources in a large agricultural catchment under no-tillage in Southern Brazil (Concei\u00e7\u00e3o River). Land Degradation and Development, 29(4). https://doi.org/10.1002/ldr.2917. Zafar, M., Tiecher, T., Capoane, V., Troian, A., dos Santos, D.R. (2017). Characteristics, lability and distribution of phosphorus in suspended sediment from a subtropical catchment under diverse anthropic pressure in Southern Brazil. Ecol. Eng. 100, 28\u201345.", "keywords": ["No-tiilage", "Fingerprinting approach", "Soil erosion", "Sediment yield", "Sediment quality", "Brazil"], "contacts": [{"organization": "Tiecher, Tales, Minella, Jean P G, Reichert, Jos\u00e9 Miguel, dos Santos, Danilo R, Bender, Marcos, Didon\u00e9, Elizeu, Barros, Cl\u00e1udia A., Dalbianco, Leandro, Ramon, Rafael, Capone, Viviane,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14036322"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14036322", "name": "item", "description": "10.5281/zenodo.14036322", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14036322"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-04T00:00:00Z"}}, {"id": "10.5281/zenodo.14252610", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:50Z", "type": "Dataset", "title": "Data from: Turfgrass pedogenesis under low maintenance: an experimental analysis with Festuca rubra subspecies at different fertilization levels", "description": "unspecifiedThatchMatThickeness.csv (Frc = Festuca rubra commutata, Frt = Festuca rubra trichophylla, Frr = Festuca rubra rubra) spec = subspecies var = variety rep = replicate number Nfert = N fertilization (kg N ha-1) thatch1 = thatch thickness at 18-5-2018 (cm) thatch2 = thatch thickness at 26-10-2018 (cm) thatch3 = thatch thickness at 17-5-2019 (cm) thatch4 =\u00a0 thatch thickness at 29-10-2019 (cm) thatch5 = thatch thickness at 10-6-2020 (cm) thatch6 = thatch tcicknesss at 16-6-2021 (cm) mat1 = mat thickness at 18-5-2018 (cm) mat2 = mat thickness at 26-10-2018 (cm) mat3 = mat thickness at 17-5-2019 (cm) mat4 =\u00a0 mat thickness at 29-10-2019 (cm) mat5 = mat thickness at 10-6-2020 (cm) mat6 = mat tcicknesss at 16-6-2021 (cm)", "keywords": ["Festuca rubra", "festuca rubra", "fertilization", "carbon", "soil layers", "pedogenesis", "turfgrass", "microbes", "Turfgrass", "nitrogen", "Carbon", "organic matter"], "contacts": [{"organization": "Evers, Maurice, De Caluwe, Hannie, Visser, Eric J.W., De Kroon, Hans,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14252610"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14252610", "name": "item", "description": "10.5281/zenodo.14252610", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14252610"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14936177", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:58Z", "type": "Dataset", "title": "Precision Liming Soil Datasets (LimeSoDa) Zenodo Repository", "description": "Overview Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper 'LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping' by Schmidinger et al. (2025). You may also use our R\u00a0and Python package likewise called LimeSoDa. \u00a0 Citation Upon usage of datasets from LimeSoDa, please cite our associated paper: Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping.", "keywords": ["Environmental sciences", "Soil Organic Carbon", "Pedometrics", "pH", "Soil Organic Matter", "Clay", "Remote sensing", "Digital Soil Mapping"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14936177"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14936177", "name": "item", "description": "10.5281/zenodo.14936177", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14936177"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14945573", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:58Z", "type": "Report", "title": "Deliverable D1.5 \u2013 Set of novel QSAR models/grouping/read-across and in vitro bioassay approaches predicting relevant toxicological endpoints for PFAS/PM(T) chemicals", "description": "The deliverable is a profiling of activities for pre-selected single and complex industrial mixtures of PFAS/PM(T)s with in vitro bioassay testing and QSAR modeling for a selection of most relevant toxicological endpoints. \u00a0A set of novel in silico and in vitro methods have been developed and applied for the large group of PFAS/PM(T)s. The in vitro toxicity profiling followed a stepwise approach, by first testing the potential reference compounds PFOA and PFOS as well as industrial mixtures (e.g., GenX and ADONA) with a wide range of in silico and in vitro methods (see for an overview in Annex, Table A1). Based on these initial results, the most suitable in vitro toxicity endpoints (see table 7 and table 10) have been chosen for toxicity profiling the wider set of the selected PFAS compounds as well as for toxicity testing selected complex mixture (e.g., water, soil). In the case of PMTs, at first, the potential bioactivity of several key PMTs (e.g., bisphenol A, triclosan, methyl-paraben, 5-chlorobenzotriazole, benzothiazole) have been evaluated using a range of in vitro toxicity profiling methods (see for an overview in Annex, Table A1). In a second step, the most suitable in vitro toxicity endpoints (see table 8 and 11) have been used for toxicity profiling an extended group of PMT compounds as well as for testing complex mixtures (e.g., water, soil).\u00a0 Regarding the group of PFAS, in silico modelling has been performed for a few thousands of chemicals (see Kowalski et al.,\u00a0 2023), while in case of in vitro testing the focus was on a selection of 40/2 PFAS/industrial mixtures compounds proposed by the chemical-analytical laboratories involved in PROMISCES and commercially available within a limited budget. In this study, the in silico modeling part investigated PFAS binding potencies to nuclear hormone receptors (NHRs) such as peroxisome proliferator-activated receptors (PPARs) \u03b1, \u03b2, and \u03b3 and thyroid hormone receptors (TRs) \u03b1 and \u03b2. In the first in silico step, the developed QSAR models were implemented for the screening approach of a large group of compounds (4464) from the NORMAN Database related to these 5 above mentioned NHRs. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPAR\u03b1, \u03b2, and \u03b3 only with low or moderate probability, while in the case of TR \u03b1 and \u03b2 it is similar except that those chemicals with longer chains show a moderately high probability of binding. The in silico analysis shows from the here tested 15 endpoints that most PFAS reflect a high binding probability to sex hormone receptors (anti-AR and ER). however, receptors from the peroxisome proliferator-activated (PPAR \u03b1, \u03b2, \u03b3) glucocorticoid (GR and anti-GR), liver X (LXR \u03b1, \u03b2) and retinoid X (RXR \u03b1) groups are unaffected or slightly affected by PFAS (low and moderate binding probability). Regarding the parallel in vitro bioassay toxicity analysis, an established standardised in vitro test battery (consisting of e.g., genotoxicity, neurotoxicity, cytotoxicity, obesity and early warning testing) was applied to meet specific criteria of PFAS/iPM(T) (endpoints, sensitivity and specificity). Based on existing and improved CALUX bioassays, different toxicological endpoints involving metabolic syndrome (obesity related PPAR), endocrine EATS testing, genotoxicity (p53 related) and general toxicity pathways (e.g. early warning PXR) to characterise PM(T) properties of chemicals were assessed. The in vitro bioassay groups addressed 40 PFAS compounds (e.g. all regulated 20 PFAS) and industrial standards (e.g., ADONA, GenX) with a combination of a wide range of CALUX bioassay endpoints (e.g., table 7 with 6 endpoints) and additional general toxicity in vitro bioassays (e.g., table 9 with 8 in vitro endpoints). Potency factors for selected PFAS/ iPM(T) have been established for several of the here used in vitro bioassays by two bioassay laboratories (UBA, BDS). Comparing both data sets, 80% of the relative potency factors for PFAS tested on e.g., the TTR-TRb CALUX, were found to be within an order of magnitude. Potency factors for selected PFAS have been established for each of the here used in vitro human cell-based CALUX bioassays. For example, in case of the TTR-TRb CALUX bioassay, the RPF values are rang from non-detected (e.g., several FTOH compounds) to the P37DMOA compound (RPF = up to 2.3; see tables 7 and 10). Additional up to 22 PM(T) chemicals (e.g., see table 8 and 10) have been tested with a combination of a wide range of CALUX bioassays (e.g., table 8 with 6 endpoints) and additional general toxicity in vitro bioassays (e.g., table 10 with 4 in vitro endpoints). This included i) selecting a wide range of reporter gene assays to screen the toxic profiles of a set of PM(T) chemicals, ii) selecting the most suitable bioassay panel, and iii) validating bioassay panels and cross-validating the responses obtained from the selected panel of human cell-line based bioassays Potency factors for selected iPM(T) have been established for each of the here used in vitro human cell-based CALUX bioassays. For all CALUX bioassays applied, RPF-values ranged from not-detected (e.g., several in vitro endpoints at benzotriazole) up to RPF values above 0.5 for several PMTs (e.g., galaxolide and triclosan, see table 8, or methyl-paraben in table 10 at the male hormone inhibition). In a second step, the 3 most active PFAS compounds on the in vitro TTR-TRb CALUX (PFOA with RPF = 1; P37DMOA with RPF = 2.3; 6:2 FTAB with RPF = 0.00015; see table 7) have been used to find in in silico modelling new PFAS structure-analogue compounds. In case of PFOA 12 structure-in vitro toxicity analogues were found, while in case of P37DMOA only 3 analogues and in case of 6:2 FTAB only 7 analogues were found. Our study shows that such an combined approach of in silico and in\u00a0 vitro toxicity evaluations of single PFAS congeners (from the large group of PFAS) is a promising and suitable strategy to cover a large variety of different key events in toxicology in a time- and cost-efficient way. Such early key events can lead to protein production or molecular signalling that occur in individual cells. Later events can include altered tissue or organ function. Such key events are important because after the molecular initiating event, they can characterize the progression of the toxicity. Relative potency factors (RPFs) have been determined here by a combination of in silico and vitro toxicity tools and can further help to give first toxicity indications for the handful regulated and the not yet regularly tested/regulated PFAS to be included in the testing strategy for single compounds and complex mixtures of these compounds.", "keywords": ["Deliverable", "thyroid hormone transport competition", "TTR TR CALUX", "PFAS", "in vitro assay (CALUX)", "non-animal methods (NAM)", "in silico modeling"], "contacts": [{"organization": "Behnisch, Peter, Sosnowska, Anita, Mombelli, Enrico, Kuckelkorn, Jochen,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14945573"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14945573", "name": "item", "description": "10.5281/zenodo.14945573", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14945573"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-28T00:00:00Z"}}, {"id": "10.5281/zenodo.15017546", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:19:58Z", "type": "Dataset", "title": "Global Rice Land Suitability and Adaptation Strategies Under Climate Change", "description": "This study evaluated the land suitability of irrigated and rainfed rice cultivation under current and projected climate change scenarios for the year 2050 on a global scale. , the climate and soil factors utilized for this purpose were obtained from the WorldClim website (https://www.worldclim.org/) and represent the historical monthly weather data for the period between 2001 and 2021 (https://worldclim.org/data/monthlywth.html). Moreover, future climate projections were sourced from the same source for the period 2041 to 2060 (centered around 2050).The projections are based on mean value of ten Global Climate Models (GCMs) from the CMIP6 dataset and two scenarios (SSP245 and SSP585), including models such as ACCESS-CM2, CanESM5-CanOE, EC-Earth3-Veg, FIO-ESM-2-0, GISS-E2-1-H, HadGEM3-GC31-LL, INM-CM4-8, IPSL-CM6A-LR, MRI-ESM2-0, and UKESM1-0-LL (https://worldclim.org/data/cmip6/cmip6_clim2.5m.html). The future climate scenarios were developed using a new set of integrated assessment models (IAMs) that incorporate the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), as outlined by O'Neill et al. (2016). Two integrated scenarios were considered in this study. SSP245 (a combination of SSP2 with RCP4.5) and SSP585 (a combination of SSP5 with RCP8.5). SSP2 represents a scenario characterized by a continuation of existing social, economic and technological trends, with minimal deviation from historical patterns. In contrast, SSP5 depicts a pathway of fossil-fuelled development, characterized by rapid technological advancement and human capital growth. With regard to radiative forcing, RCP4.5 represents a medium scenario (4.5 W m\u207b\u00b2 by 2100), whereas RCP8.5 represents a high-emission scenario (8.5 W m\u207b\u00b2 by 2100). To ensure the highest possible spatial accuracy, 2.5 minutes (~21 km2 at the equator) spatial resolution was applied to all maps. Furthermore, maps of rice cultivation area was obtained from the MAP SPAM2020 (Spatial Production Allocation Model) website (https://www.mapspam.info), which provides detailed spatial data on global rice production.Soil parameters were then determined for each point using soil information obtained from the FAO soil map. The FAO soil map provided vital parameters such as organic carbon (OC), exchangeable sodium percentage (ESP), cation exchange capacity (CEC), CaCO3, pH, EC which extracted from the Harmonized World Soil Database (HWSD) at a scale of 1:5,000,000, sourced from the FAO Soil Portal website. (Supplementary Figure 2). The slope map was created using the digital elevation model (for more details, see Guerra et al., 2020). The SPAM2020 for rainfed and irrigated rice was used to intersect the climate zones and soil layers, focusing specifically on the areas dedicated to this area.\u00a0 The final land suitability map was generated by overlaying the suitability output with raster files representing both irrigated and rainfed harvested areas, thus creating a composite map of land suitability . This stage ensured the precise delineation of areas suitable for rice cultivation. Following the creation of land suitability maps for a variety of scenarios and adaptation strategies, the results were compared in order to identify the optimal strategy. The final map identifies the most efficacious adaptation strategy for enhancing land suitability in each region in the context of projected climate change. Also, The overview of the status of rainfed and irrigated rice worldwide.", "keywords": ["Adaptation strategy", "Climate chnage", "Rice", "land suitablity"], "contacts": [{"organization": "Dadrasi, Amir, Va\u010dk\u00e1\u0159ov\u00e1, Davina, Fatemeh, Salmani, Nguyen, Trong Can, Weinzettel, Jan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15017546"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15017546", "name": "item", "description": "10.5281/zenodo.15017546", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15017546"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-15T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:00Z", "type": "Dataset", "title": "HWSD2_Climate_and_Socioeconomic_agriculturalsoil_dataset_mainland_portugal", "description": "The study uses the Harmonized World Soil Database (HWSD v2.0) developed by FAO and IIASA for biophysical models and agroecological queries. This database consolidates information from various sources, including the European Soil Database, the 1:1 million soil map of China, and national soil maps from Afghanistan, Ghana, and T\u00fcrkiye. It has a spatial resolution of around 1 km and is revised in 2013 and 2023. HWSD v2.0 includes detailed information on soil mapping units, general soil unit information, and specific physical and chemical soil unit characteristics across seven depth layers. The database fields cover a wide range of attributes, such as soil texture, bulk density, organic carbon content, pH, and cation exchange capacity. The harmonization process ensures that data from different sources is standardized and integrated, providing a consistent and reliable dataset for various applications. However, the HWSD v2.0 has some limitations, such as combining soil inventories gathered at different times, scales, and precision, which may affect its reliability for national studies. It is recommended to use national-level harmonized soil databases for more accurate results in specific regions. For Portugal's mainland, the data presented in the HWSD v2.0 dataset is sourced from the European Soil Data Centre (ESDAC), which contains various metrics of chemical and physical soil properties. Out of the 2882 Portuguese parishes, only 22 are left out, representing 0.76% percent of the total number of parishes. The study uses several datasets to analyze land use and occupation in Portugal. The Land Use and Occupation Map (COS2007v3.0) is a detailed thematic map of land use and occupation for mainland Portugal, developed by the Directorate-General for Territory (DGT). The data is organized hierarchically and includes 83 classes of land use and occupation. The CHELSA database, maintained by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), provides bioclimatic indexes for precipitation and average temperature over various temporal intervals and variables. The National Institute of Statistics (INE) provides data on agricultural machinery distribution across different geographical locations. The dataset covers the total number of agricultural machines, as well as specific categories such as wheeled and tracked tractors, motor cultivators, power hoes, motor mowers, and combine harvesters. The dataset also examines the distribution of farms with access to irrigation based on geographical location. The burned land data from 1975 to 2023 provides a comprehensive overview of fire occurrences and their impact over time. This data is crucial for understanding long-term patterns, assessing the effectiveness of fire prevention measures, and informing future land management and policy decisions. Lastly, the population density dataset from the 2021 Census and the 2011 Census provides a decennial comparison of total population density across different geographical regions. These data are essential for understanding the evolution of land use and occupation in Portugal and their implications for environmental and agricultural consequences.", "keywords": ["Soil", "Total organic carbon", "Land use", "Soil use", "Atmospheric precipitation", "Soil type", "Organic carbon", "Land surface temperature"], "contacts": [{"organization": "Almeida Santos, R. G. F.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15096788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15096788", "name": "item", "description": "10.5281/zenodo.15096788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15096788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15680931", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:09Z", "type": "Journal Article", "created": "2025-06-15", "title": "Investigating the extent of PFAS contamination in the Upper Danube Basin across environmental compartments", "description": "Abstract Background

Per- and polyfluoroalkyl substances (PFAS) are emerging organic pollutants widely detected in environmental systems, posing risks to human health and the ecosystem. Despite increasing efforts to monitor PFAS in river systems, knowledge gaps remain regarding sources and emissions via different pathways. This study investigates PFAS contamination across multiple environmental compartments in the Upper Danube Basin, including surface water, groundwater, wastewater, landfill leachate, surface runoff, and atmospheric deposition. The primary objectives are to assess the extent of PFAS contamination, identify key emission sources and transport pathways, and evaluate associated risks in terms of the potential exceedance of current and proposed environmental regulatory thresholds in the European Union.

Results

The findings reveal a widespread presence of PFAS, with PFOA, PFOS and short-chain compounds being predominant. The Alz River and Gendorf chemical park emerge as hotspots with far-reaching effects downstream, contributing significantly to diffuse legacy contamination of PFOA and being a significant source of two industrial PFOA substitutes, ADONA and GenX. Wastewater treatment plants, old municipal landfills, and sites with a history of fire-fighting foam application are identified as key pathways or sources of legacy pollution, exhibiting higher concentrations compared to the other matrices. Notably, no significant removal is observed when comparing influent and effluent samples from conventional WWTPs. The study further demonstrates that groundwater is vulnerable to contamination from point sources and to infiltration from rivers, with bank filtration proving largely ineffective in preventing PFAS contamination.

Conclusions

The study underscores the necessity for source and pathway control measures to mitigate PFAS pollution, the implementation of advanced treatment technologies to safeguard drinking water and surface water quality, and targeted remediation for legacy soil and groundwater contamination. Additionally, strong use regulations should be explored to minimize ongoing emissions. The multi-compartment monitoring proves to be a crucial approach to understand the complexity of PFAS distribution at the catchment scale. Comparative analysis and risk assessment highlight challenging situations for water management, offering an indispensable basis for emission modeling as a next step for quantitative assessment of the relevance of different sources and pathways for surface water pollution.

", "keywords": ["Emerging contaminants", "Emerging Pollutants", "PFAS", "Source identification", "Watershed management", "Environmental sciences", "Emission", "Water Framework Directive", "Environmental law", "Water pollution", "GE1-350", "K3581-3598", "Catchment monitoring", "Environmental Monitoring"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1186/s12302-025-01141-6.pdf"}, {"href": "https://doi.org/10.5281/zenodo.15680931"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Sciences%20Europe", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15680931", "name": "item", "description": "10.5281/zenodo.15680931", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15680931"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.3247592", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:16Z", "type": "Dataset", "title": "Daily cycles in soil carbon flux", "description": "Description: Measurements of 24 hour cycles in soil CO2 flux taken from soil collars in the Belian Carbon plot at Maliau. Measurements were taken from 12 subplots over four days at 5-hourly intervals, ensuring good coverage of the complete 24 hour cycle. Air and soil temperatures, soil moisture content and CO2 flux were taken from each plot at each visit. 9 subplots only have a single total soil respiration collar, but 3 subplots also have soil flux partitioning treatments to separate contributions to total respiration from soil organic matter, mycorrhizae and roots.

This data was collected by the 2019 cohort of the Tropical Forest Ecology MRes at Imperial College London. Project: This dataset was collected as part of the following SAFE research project: MRes Tropical Forest Ecology Field Course XML metadata: GEMINI compliant metadata for this dataset is available here Files: This dataset consists of 2 files: Carbon_corrected_slopes.xlsx, raw_egm.zip Carbon_corrected_slopes.xlsx This file contains dataset metadata and 2 data tables: 24 hour observations of CO2 flux patterns (described in worksheet Carbon_flux_data) Description: Estimated CO2 flux values, soil moisture and air and soil temperatures from Carbon subplots Number of fields: 14 Number of data rows: 296 Fields: record_no: EGM 'Plot' value - record number on EGM machine for this collar (Field type: ID) plot: Carbon subplot number (Field type: Location) date: Calendar date that measurements taken (Field type: Date) time: Time that measurements taken (Field type: Time) soil_wmc: Soil water moisture content (Field type: Numeric) soil_temp: Soil temperature (Field type: Numeric) air_temp: Air temperature (Field type: Numeric) treatment: Exclusion treatments for partitioning soil respiration components (Field type: Categorical) field_flux: CO2 flux reported in the field by EGM (Field type: Numeric) Source: EGM dat file of source data used for corrected fluxes where available (Field type: File) corrected_flux: Corrected flux measurements using by eye exclusion of raw flux data (Field type: Numeric) n_points: Number of points in EGM record (Field type: Numeric) n_used: Number of points used for corrected slope estimation (Field type: Numeric) flux: Final flux values, using corrected values where available (Field type: Numeric) EGM raw data (described in worksheet EGM_raw_data) Description: Duplicates key information from raw EGM files and indicates points excluded in calculation of corrected flux values Number of fields: 7 Number of data rows: 7114 Fields: Plot: EGM recorder 'plot' code, actually just the record sequence number. (Field type: ID) RecNo: EGM record number - time points of gas measurement at a single plot (Field type: ID) Datetime: Time of gas concentration measurement (Field type: Datetime) CO2.Ref: Measured CO2 (Field type: Numeric) Input.E: EGM internal variable used in slope estimation (Field type: Numeric) Source: Original EGM dat file containing the flux data (Field type: File) ignore: Indicates where points from raw data excluded from corrected slope calculations (Field type: Categorical) raw_egm.zip Description: Zipfile of raw EGM dat files Date range: 2019-02-18 to 2019-02-21 Latitudinal extent: 4.7467 to 4.7480 Longitudinal extent: 116.9693 to 116.9704", "keywords": ["2. Zero hunger", "550", "soil CO2 flux", "24 hour cycle", "partitioning", "15. Life on land", "6. Clean water", "630"], "contacts": [{"organization": "Orme, C David L, Riutta, Terhi, Fraser, Adam, Mackintosh, Emma, Vollans, Maisie, Graves, Kristina, Roxby, Gabrielle, Baynham, Georgina, Noble, Ciar, Adzhar, Rahayu, MacLean, Catherine, Martin, Stephanie, McKinlay, Amelia, Poznansky, Frederica, Prairie, Aaron, Brasington, Ella, Wiederkehr, Fabienne,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3247592"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3247592", "name": "item", "description": "10.5281/zenodo.3247592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3247592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-17T00:00:00Z"}}, {"id": "10.5281/zenodo.3591992", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:18Z", "type": "Dataset", "title": "Organic matter content (om) soil maps of the Upper Colorado River Basin", "description": "UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY. Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds. Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal. File Name Details: ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif). Elements are separated by underscore (_) in the following sequence: property_r_depth_cm_geometry_model_additional_elements.extension Example: om_r_0_cm_2D_QRF_bt.tif Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling. The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below). _95PI_h: Indicates the layer is the upper 95% prediction interval value. _95PI_l: Indicates the layer is the lower 95% prediction interval value. _95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI. References Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma", "keywords": ["2. Zero hunger", "13. Climate action", "soil organic matter", "digital soil mapping", "15. Life on land", "6. Clean water", "predictive soil mapping", "soil property mapping"], "contacts": [{"organization": "Nauman, Travis", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3591992"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3591992", "name": "item", "description": "10.5281/zenodo.3591992", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3591992"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-28T00:00:00Z"}}, {"id": "10.5281/zenodo.4287780", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:19Z", "type": "Dataset", "title": "Forest carbon prospecting for climate change mitigation: Version 1.0", "description": "This data package includes the two 1-km resolution global maps (.tif) of tropical forests between ~23.44\u00b0N and 23.44\u00b0S produced from the study: 1) investible forest carbon (in tCO2e ha-1y-1) and 2) forest carbon return-on-investment (Net Present Value in USD ha-1y-1) over a 30-year timeframe. It also includes the R script to reproduce these layers and their uncertainties. Investible Forest Carbon: The investible forest carbon map was produced based on the total volume of CO2e associated with the three main carbon pools in the tropics, namely aboveground carbon, belowground carbon and soil organic carbon. This is followed by the application of key Verified Carbon Standard (VCS) criteria including additionality, to determine the magnitude and areas of investible forest carbon across the tropics. Aboveground carbon. A stoichiometric factor of 0.475 was applied to recent spatial data on aboveground carbon biomass to obtain carbon stock based on established carbon accounting methodologies. An uncertainty analyses was also performed to account for potential variability in stoichiometric factor. Subsequently, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO2e associated with this carbon pool. Belowground carbon. Belowground carbon biomass was firstly derived by applying two allometric equations relating to root to shoot biomass to the most recent spatial dataset on aboveground carbon biomass following established carbon accounting methodologies. The two equations are: Belowground biomass = 0.489\u00d7aboveground biomass^0.89; and Belowground biomass = 0.26\u00d7aboveground biomass A stoichiometric factor of 0.475 was subsequently applied to the estimated belowground carbon biomass to obtain the carbon stock. An uncertainty analyses was then performed to determine the mean, minimum and maximum values for belowground carbon. Following that, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO2e associated with this carbon pool. Soil Organic Carbon. Organic carbon density of the topsoil layer (0-30 cm) was obtained from the European Soil Data Centre as it represented the best data available for soil organic carbon. A conversion factor of 3.67 was subsequently applied to derive the volume of CO2e associated with this carbon pool. Applying VCS criteria. The criterion of additionality is a pre-condition for carbon credits to be certified under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. The volume of forest carbon under threat of loss was based on the best available data on predicted deforestation rates across the tropics (through to the year 2029), and annualized over predicted 15-year period. The estimated annual deforestation rates was then applied to the total volume of CO2e associated with tropical forests as estimated above, deriving the volume of CO2e that would be certifiable and thus investible under the VCS. In addition, a conservative 10-year decay estimate was assumed for the estimate of the belowground carbon pool, and lands that will likely not be certifiable for other reasons, including recently deforested areas (i.e. for the period of 2010-2017), a well as human settlements, were excluded. Lastly, the VCS requirement to set aside buffer credits of 20% was accounted for to consider the risk of non-permanence associated with Agriculture, Forestry and Other Land Use (AFOLU) projects. Return-on-Investment. From the investible forest carbon map, the relative profitability of these areas was then modelled to produce a global forest carbon return-on-investment map based on their NPV. The NPV of returns were based on several simplifying assumptions following established values from previous studies. Cost of project establishment. The cost of project establishment was estimated to be at $25 ha-1. This was based on a range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research. Cost for annual maintenance. The cost for annual maintenance was estimated to be $10 ha-1, which included aspects such as in education and communication, monitoring, sustainable livelihoods, marketing, finance and administration. Carbon price. A constant carbon price of $5.8 t-1CO\u00ad2e for the first five years was applied. This price was based on an average price of carbon for avoided deforestation projects reported recently by Forest Trends\u2019 Ecosystem Marketplace (i.e. for the period 2006 \u2013 2018). Subsequently, a 5% price appreciation was applied annually over a project timeframe of 30 years. Discount rate. We calculated NPV of annual and accumulated profits over 30 years based on a 10% risk-adjusted discount rate. Further details for these datasets and their uncertainties are presented in Koh et. al. For questions or issues on the spatial data layers, please contact Yiwen Zeng (zengyiwen@nus.edu.sg).", "keywords": ["Carbon stocks", "Climate change mitigation", "13. Climate action", "Carbon finance", "15. Life on land"], "contacts": [{"organization": "Koh, Lian Pin, Zeng, Yiwen, Sarira, Tasya Vadya, Siman, Kelly,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4287780"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4287780", "name": "item", "description": "10.5281/zenodo.4287780", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4287780"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-25T00:00:00Z"}}, {"id": "10.5281/zenodo.4896835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:21Z", "type": "Report", "title": "Virtual loads predictions of wake-affected wind turbines: Gaussian process regression and deep neural networks", "description": "Load analysis of wind turbines may be performed either via physics-based models or via direct measurement. On the first case, loads are calculated with aero-elastic models, based on significant assumptions on the mechanical and aeroelastic properties of the structure and the acting forces (wind, wave and control). Otherwise, loads can be directly measured based on a sensor network, which entails increased costs due to installation, maintenance and calibration of sensors and IT infrastructure. These costs can be manageable for a single wind turbine but become substantial on densely instrumented wind farms. In turn, the increased costs negatively affect the levelized cost of energy. This is, in fact, the main reason why stakeholders shy away from applying monitoring technologies in wind farms. To overcome the above challenges, we need an alternative way of estimating the loads which would involve a reduced number of sensors while replicating the actual load measurement scenario. To this end, we propose data-driven models to predict the loads acting on different components of a wind turbine. These models use SCADA, wind inflow and other variables to predict loads in components of interest of a wind turbine. We have already successfully demonstrated this concept in the past on simulated wind turbine Damage Equivalent Loads (DELs) based on Gaussian Process regression [1,2], and on real wind turbine data [3]. In this work, we validate this approach on actual wind turbine data from the Alpha Ventus Wind Farm obtained within the framework of the Research Alpha Ventus (RAVE) project. Two Senvion turbines are selected for this study. One of the wind turbines is used to train and validate a regression model to predict the tower base DELs based on SCADA, wind inflow and other environmental variables. Afterwards, the trained model is used to predict the loads in the second wind turbine. Load prediction is attained with two machine learning methods, the first one based on Gaussian Process Regression (GPR) and the second one based on Artificial Neural Networks (ANN). For the first one, a decision tree is used to separate the different operating modes of the wind turbine (idling, operating and transitioning). The decision tree is built on simple heuristics on a subset of SCADA variables (mean and standard deviation of the rotor RPM and blade pitch angle). Subsequently, a GPR is built for each one of the operating modes. In the second method, the SCADA variables are fed to the ANN after undergoing an initial transformation for data compression and collinearity reduction.", "keywords": ["machine learning", "structural health monitoring", "13. Climate action", "wind turbines", "virtual sensing", "7. Clean energy"], "contacts": [{"organization": "Avenda\u00f1o-Valencia, Luis David, Abdallah, Imad, Venu, Anish, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896835", "name": "item", "description": "10.5281/zenodo.4896835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7307470", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:29Z", "type": "Dataset", "title": "Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands", "description": "Open AccessTo evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat. Experimental setup The field experiment (2013 \u2013 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g\u22121 and pHKCl of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: \u201cContr\u201d). The fertilizer used were: conventional dairy cattle slurry manure (\u201cSlurry\u201d), mature compost of kitchen and garden waste (\u201cComp\u201d), dairy cattle farmyard manure (\u201cFYM\u201d), solid fraction of the cattle slurry manure (\u201cSFrac\u201d, obtained by pressurized filtration), inorganic N fertilizer (\u201cIF\u201d; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (\u201cIF+SD\u201d). Plot size was 4 \u00d7 10 m; for the Slurry treatment plots were 5.2 \u00d7 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha\u22121 yr\u22121, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of Ctotal applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K2O ha\u22121 yr\u22121 (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset). The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements. Measurements From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October. Soil biological parameters Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 \u00d7 20 \u00d7 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; St\u00f6p-Bowitz, 1969) and classified into functional groups (Bouch\u00e9, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted. Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Paloj\u00e4rvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1\u03c99, i17:0, a17:0, cy17:0, 18:1\u03c97 and cy19:0 were chosen to represent bacteria and PLFA 18:2\u03c96 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1\u03c95 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes. Soil chemical parameters A soil sample from the 0\u221210 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pHKCl), soil organic matter (SOM), total carbon (Ctotal), total nitrogen (Ntotal), total phosphorus (Ptotal) and ammonium-lactate extractable P (PAL) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40\u00b0C. Soil pHKCl was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). Ctotal was measured by incineration at 1150\u00b0C, and determination of the CO2 produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For Ntotal, evolved gasses after incineration were reduced to N2 and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). Ptotal was analysed with Fleishmann acid (Houba et al., 1997). PAL is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egn\u00e9r et al. (1960) (NEN 5793). Soil physical parameters Soil moisture was determined in April and October in a homogenized 0\u221210 cm soil sample after drying at 105\u00b0C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight. Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm2 penetration surface and 60\u00b0 apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0\u221210, 10\u221220, and 20\u221230 cm. Soil structure and rooting density were assessed in October in the 0\u221210 cm and 10\u221225 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1\u201310; 1 for no roots and 10 for above average). Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min\u22121). Grass yield and botanical composition Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, L\u00f8gst\u00f8r, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70\u00b0C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha\u22121) and herbage N yield (kg N ha\u22121) were calculated. Apparent N recovery (ANR; kg N.kg N\u22121) was calculated as (N yield(fertilized) \u2013 N yield(non-fertilized))/(N fertilization rate) (Vellinga and Andr\u00e9, 1999). In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997). Data files Data_soil_grass.csv Content: Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters. Column names and units: plot: Experimental plot number (1-42) treatment: Treatment code (see text) block: Block number (1-6) EW_species_number: Earthworm - number of species EW_totalnumber: Earthworm - total number per m2 EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2 EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2 EW_adults: Earthworm - number of adults per m2 EW_juveniles: Earthworm - number of juveniles per m2 EW_adult_epigeic: Earthworm - number of epigeic adults per m2 EW_adult_endogeic: Earthworm - number of endogeic adults per m2 EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2 EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2 EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2 EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2 EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2 EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2 EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2 EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2 EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2 EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2 EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2 EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2 EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2 EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2 EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2 EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2 EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2 EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2 EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2 EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2 EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2 EW_non_determined: Earthworm - number of non determined individuals per m2 EW_total_biomass: Earthworm - total fresh biomass per m2 Leatherjackets: number of leatherjackets per m2 Wireworms: number of wireworms per m2 TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil bactPLFA: bacterial PLFA in nmol.g-1 dry soil saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil GramMINplfa: gram negative PLFA in nmol.g-1 dry soil ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil ShannonPLFA: PLFA shannon diversity index SOM: soil organic matter in g.100 g-1 dry soil Ctotal: total C in g.100 g-1 dry soil HWC: hot water extractable C in \u03bcg.100 g-1 dry soil Ntotal: total N in g.100 g-1 dry soil Ptotal: total P2O5 in mg.100 g-1 dry soil P_AL: total P-AL in mg.100 g-1 dry soil pH_KCl: pH-KCl CN_ratio: C:N ratio C_SOM: C:SOM ratio Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton crumb_struct_cm010: percentage of crumb elements in 0-10 cm round_struct_cm011: percentage of sub-angular elements in 0-10 cm rootdensity_cm010: score (1-10) of root density in 0-10 cm crumb_struct_cm1025: percentage of crumb elements in 10-25 cm round_struct_cm1025: percentage of sub-angular elements in 10-25 cm sharp_struct_cm1025: percentage of angular elements in 10-25 cm rootdensity_cm1025: score (1-10) of root density in 10-25 cm water_infiltration: water infiltration rate in mm per minute DM_yield_year: total herbage dry matter yield in kg.ha-1 per year DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1 DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1 DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1 DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1 N_yield_year: total herbage N yield in kg.ha-1 per year N_yield_H1: herbage N yield of harvest 1 in kg.ha-1 N_yield_H2: herbage N yield of harvest 2 in kg.ha-1 N_yield_H3: herbage N yield of harvest 3 in kg.ha-1 N_yield_H4: herbage N yield of harvest 4 in kg.ha-1 DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1 ANR: apparent N recovery in kg N.kg N-1 productive_grasses: cover percentage of L. perenne and P trivialis monocotyledons: cover percentage of monocotyledons dicotyledons: cover percentage of dicotyledons plant_species: number of plant species monocot_species: number of monocotyledon species dicot_species: number of dicotyledon species Lolium_perenne: plant cover % Poa_trivialis: plant cover % Phleum_pratense: plant cover % Elytrigia_repens: plant cover % Poa_annua: plant cover % Agrostis_stolonifera: plant cover % Holcus_lanatus: plant cover % Alopecurus_pratensis: plant cover % Alopecurus_geniculatus: plant cover % Trifolium_repens: plant cover % Taraxacum_officinale: plant cover % Ranunculus_arvensis: plant cover % Rumex_obtusifolius: plant cover % Rumex_crispus: plant cover % Ranunculus_acris: plant cover % Stellaria_media: plant cover % Cardamine_pratensis: plant cover % Bellis_perennis: plant cover % Rumex_acetosa: plant cover % Ranunculus_sceleratus: plant cover % Polygonum_aviculare: plant cover % Capsella_bursa-pastoris: plant cover % Glechoma_hederacea: plant cover % Geranium_molle: plant cover % Fertilizer_input.csv Content: Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013\u22122015. Column names and units: Treatment: Treatment code (see text) Fertilizer_fresh: Applied fertilizer in Mg.ha-1 per year (fresh weight) Fertilizer_DM: Applied fertilizer in Mg.ha-1 per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer Ash: Mineral fraction in kg.ha-1 per year OM: Organic matter in kg.ha-1 per year C: Total C in kg.ha-1 per year Nmin: Mineral N in kg.ha-1 per year Norg: Organic N in kg.ha-1 per year P2O5: kg.ha-1 per year C_N_ratio: C:N ratio", "keywords": ["2. Zero hunger", "herbage production", "manure", "PLFA", "Life Science", "earthworms", "soil quality", "15. Life on land", "regenerative farming", "6. Clean water"], "contacts": [{"organization": "Deru, Joachim, Bloem, Jaap, De Goede, Ron, Brussaard, Lijbert, Van Eekeren, Nick,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307470"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307470", "name": "item", "description": "10.5281/zenodo.7307470", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307470"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:30Z", "type": "Dataset", "title": "Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis", "description": "Open AccessData description This dataset includes detailed information about five different types of land use change reported in \u201cThe effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; https://doi.org/10.1016/j.agee.2023.108402)\u201d. Lists of five different types of land use change 1) conversion of primary forest to cropland 2) conversion of primary forest to grassland 3) conversion of cropland to forest 4) conversion of grassland to forest 5) conversion of grassland to cropland Lists of detailed information Land use change (pre-LUC, post-LUC) Country, Location, Geographic position (Longitude, Latitude) Altitude (m) Climate zone Weather [rainfall (mm yr-1) and temperature (\u00b0C)] Reported time of change (years) Vegetation type (pre-LUC, post-LUC) Fertilizer (pre-LUC, post-LUC: type, application; change) Soil sampling depth (cm) Soil type [units, pre-LUC, post-LUC, change rate (%)] Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)] Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)] Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)] Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)] Soil C:N [units, pre-LUC, post-LUC, change rate (%)] Soil C:P [units, pre-LUC, post-LUC, change rate (%)] Soil N:P [units, pre-LUC, post-LUC, change rate (%)] Reference Data collection method We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland. We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases. While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation. When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents. Acknowledgements We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand\u2019s Ministry of Business, Innovation and Employment.", "keywords": ["2. Zero hunger", "13. Climate action", "land-use change", " greenhouse gas emissions", " soil", " carbon", " nitrogen", " phosphorus", " stoichiometry", " time", " temperature", " rainfall", " forest type", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7656722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7656722", "name": "item", "description": "10.5281/zenodo.7656722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7656722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7695641", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:30Z", "type": "Report", "title": "Soil and land management ontology reference document", "description": "The Soil Mission Support (SMS) project supports the European Commission and the Mission Board of the Horizon Europe
Mission in the area of Soil Health and Food in delivering its objectives and related targets. It is assumed that the
Soil Mission and its related objectives and specific targets can only be achieved through healthy soils and for that,
stakeholder engagement is needed. Healthy soils are defined as soils that are in good chemical, biological and physical
condition and thus are able to continuously provide as many ecosystem services as possible (EC, 2021a). Stakeholders
are defined as those who are affected in their interest or concern by changes in soil and land management (Brils et al.,
2022).
With multi-stakeholder processes, language and use of language is very important. The capability to understand each
other is critical. Communication difficulties originate to a large extent from the \u2018jargon\u2019 used in the different communities.
A common language facilitates \u2018learning together\u2019 which helps to build trust, develop a common view on the issues
at stake, resolve conflicts and arrive at joint solutions that are technically sound and that can be implemented in
practice. Ontology defines a common vocabulary for those who, for example, need to converse about a common issue
or share information in a specific domain.
In first instance the shared domain of discourse was defined and then at different levels of hierarchy:
\u00b7 Primary objects of relevance for the domain of discourse were selected;
\u00b7 The inter-relational links between these objects was conceptualized (conceptual model); and
\u00b7 These objects were defined in a representational vocabulary (a common language).
The domain of discourse covers soil and land management aimed to achieve the first six (of the eight) Soil Mission
objectives, which are: 1. reduce desertification, 2. conserve soil organic carbon stocks, 3. stop soil sealing and increase
re-use of urban soils, 4. reduce soil pollution and enhance restoration, 5. prevent erosion, and 6. improve soil structure
to enhance soil biodiversity.
The first level of hierarchy covers soil and land and its use. At this level the following objects have been selected, interrelated
in a conceptual model (i.e. visual of soil and land-use) and defined in a common language: soil, land, landuse
and land-use types (including: urban, industrial, agriculture, forest, nature and protected land).
The second level of hierarchy covers soil management. At his level the following objects have been selected, interrelated
in a conceptual soil management model and defined in a common language: soil management (including: soil
management strategy, measures, program of measures), soil ecosystems (including: ecosystem services, pressures,
healthy soil ecosystems), users (stakeholders) and information.
Lastly, the third level of hierarchy covers the achievement of the first six Soil Mission objectives. At this level the
most relevant objects related to each of these objectives are selected and interrelated to their position in the DPSIR
(Drivers-Pressures-State-Impact-Response) framework which is at this 3rd level superimposed on the soil management
model as used for level 2.
The remaining two Soil Mission objectives, i.e. 7. reduce the EU global footprint on soils and 8. improve soil literacy in
society, do not directly relate to the actual management of soil and land. However, also for these mission objectives
some important objects have been selected and defined in a common language.
Experts in the SMS project \u2013 jointly covering the fields of expertise related to all the 8 Soil Mission objectives \u2013 developed
this ontology. This ontology should now be used in soil policy and management practice, such as Living Labs. In
such settings, the ontology can be improved through interaction with stakeholders from different backgrounds, further
increasing its value.
The key-recommendations are:
\u00b7 use this ontology in soil policy and management practice (e.g. Living Labs)
\u00b7 soil policy makers and managers should promote its use in such practice
\u00b7 use the feedback from stakeholders to further improve the ontology
In support of the dissemination of this document a policy brief is prepared and attached as annex in this document.
Both documents are made publicly available via de SMS website: https://www.soilmissionsupport.eu/outputs", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Nougues, Laura, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695641"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695641", "name": "item", "description": "10.5281/zenodo.7695641", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695641"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.7856487", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:31Z", "type": "Dataset", "title": "HiLSS Project", "description": "This\u00a0repository is periodically updated. Historic Landscape and Soil Sustainability (MSCA-IF-2019 - Individual Fellowships) The HiLSS Project aims to investigate the relationships between sustainability and landscape heritage with particular reference to soil loss and degradation over the long term. The project will take a multidisciplinary approach that combines archaeology, Historical Landscape Characterisation (HLC), geosciences, and computer-based geospatial analysis (GIS - Geographical Information Systems) and modelling (RUSLE - Revisited Universal Soil Loss Equation). The research objectives of the HiLSS project are to quantify the impact of human activities during the Late Holocene in order to create spatial models which can inform the development of sustainable conservation strategies for rural landscape heritage. This project will focus on two mountainous regions that present historical and cultural similarities but located in different climatic zones of Europe (1- Tuscan-Emilian Apennines, Italy; 2- Northern-mid Galicia, Spain). In previous HLC studies, land-use has been evaluated from the perspective of cultural heritage, whereas RUSLE have used it as a proxy for the land-cover of an area and its effect on soil erosion. The HiLSS project will propose an innovative methodology that combines both the historic/cultural values and the environmental values of land-use to inform development of a model for the sustainable conservation. By considering the different agricultural land-use HLC types in GIS-RUSLE modelling, it will be possible to quantify the effect on soil loss for each HLC type and consequently to devise more environmentally sustainable management for each type. Environmental sustainability and historic landscape conservation are typically treated as two separate fields, but the HiLSS project will develop a transformative model for interdisciplinary research, proposing a new way to embrace both cultural and natural values as components of the same landscape management plans. HLC_RUSLE.zip The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: 'Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. -\u00a0Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)'. List of files included in HLC_RUSLE.zip: R_script_code named 'HLC_RUSLE'\u00a0in .rmd format Output folder: Figures folder: .png products of the R script code Rasters\u00a0folder: .png products of the R script code Tables\u00a0folder: .pdf\u00a0products of the R script code GeoTiff folder (.TIFF file format): Regional RUSLE\u00a0Data GPKG:\u00a0HLC dataset\u00a0and\u00a0Region Of Interest file in .gpkg format Spatial statistics to reveal patterns and connections in the historic landscape The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: '\u00a0F.\u00a0Brandolini & S.\u00a0Turner\u00a0(2022)\u00a0Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy),\u00a0Journal of Maps,\u00a0DOI:\u00a010.1080/17445647.2022.2088305.\u00a0'. It is available at:\u00a0https://doi.org/10.5281/zenodo.5907229 Supplementary material_Land _SI_Historic Landscape Evolution.zip Supplementary Materials to accompaing\u00a0the paper:\u00a0The evolution of historic agroforestry landscape in the Northern Apennines (Italy) and its consequences for slope geomorphic processes, submitted to\u00a0Land,\u00a0Special Issue\u00a0Historic Landscape Transformation. Project_Publications.zip List of .pdf file included in the folder:\u00a0 1) Brandolini F, Domingo-Ribas G, Zerboni A and Turner S. A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features [version 2; peer review: 2 approved, 1 approved with reservations]. Open Res Europe 2021,\u00a01:22\u00a0(https://doi.org/10.12688/openreseurope.13135.2) 2) Brandolini F., Turner S.\u00a0 2022 - Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy), \u00a0Journal of Maps,\u00a0 (https://doi.org/10.1080/17445647.2022.2088305) 3) Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. 2023 -\u00a0Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023), (https://doi.org/10.1038/s41598-023-31334-z) 4)\u00a0Brandolini, F., Compostella, C., Pelfini, M., and Turner, S. 2023 - 'The Evolution of Historic Agroforestry Landscape in the Northern Apennines (Italy) and Its Consequences for Slope Geomorphic Processes' Land 12, no. 5: 1054. (https://doi.org/10.3390/land12051054)", "keywords": ["2. Zero hunger", "13. Climate action", "Landscape Archaeology", "11. Sustainability", "RUSLE", "USPED", "15. Life on land", "Historic Landscape Characterisation", "Soil Sustainability", "Soil Erosion Modelling", "12. Responsible consumption"], "contacts": [{"organization": "Brandolini Filippo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7856487"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7856487", "name": "item", "description": "10.5281/zenodo.7856487", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7856487"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-10T00:00:00Z"}}, {"id": "10.5281/zenodo.8089699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:32Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8089699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089699", "name": "item", "description": "10.5281/zenodo.8089699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8407642", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:20:35Z", "type": "Report", "title": "Shedding light on the genetic diversity and evolutionary dynamics of geographic populations of Wisteria vein mosaic virus: a case study for the spread of emerging potyviruses in Europe?", "description": "Wisteria vein mosaic virus (WVMV) is a member of the genus Potyvirus associated with Wisteria mosaic disease (WMD), the most serious disease affecting Wisteria spp. In 2022, severe symptoms of WMD were observed on the leaves of a Chinese wisteria (W. sinensis) tree growing in an urban area in Apulia (Italy). The presence of WVMV was ascertained by RT-PCR analysis. Although the occurrence of WVMV in Italy had been posited in the late 1960s, no molecular information had been reported for any Italian isolate prior to this study. Subsequent phylogenetic analyses based on NIb and CP genes placed the WVMV Italian isolate within a large clade identified in the genus Potyvirus as the BCMV supergroup. Based on the increasing number of reports of the virus worldwide, we attempted an exploratory analysis of its genetic diversity and possible mechanisms that may have shaped its geographic population structure. Relying on the N-terminus of the CP, available for twenty WVMV isolates from Europe, Asia, and Oceania, sixteen different haplotypes were identified. A high haplotype diversity was found, particularly relevant in the European population. The measured dN/dS ratio led to the assumption that the target region is under purifying selection. Tests evaluating the neutrality of nucleotide variability showed different results for the European and Asian groups. The estimation of inter-population genetic differentiation showed a high level of gene flow between the two populations. Overall, our results provide a possible approach to understanding the mechanisms of WVMV emergence in Europe and draw attention to its further spread and the increasing threat of this and other neglected potyvirus species to the ornamental nursery sector.", "keywords": ["WVMV; selection pressure; population genetics; genetic diversity; gene flow; haplotype diversity; neutrality tests; FastME phylogeny", "3. Good health"], "contacts": [{"organization": "G. D'Attoma, A. Minafra", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8407642"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8407642", "name": "item", "description": "10.5281/zenodo.8407642", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8407642"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-18T00:00:00Z"}}, {"id": "10.7910/DVN/MIYBQE", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:21:03Z", "type": "Dataset", "title": "Ecogeographic land characterization map of the SADC region", "description": "With the aim of planning for the in situ and ex situ conservation of priority crop wild relatives (CWR) of the Southern African Development Community (SADC), a gap analysis at intra-specific level (i.e. ecogeographic diversity level used as a proxy of genetic diversity), was carried out. For this purpose, a generalist Ecogeographic Land Characterization (ELC) map for the SADC region was created using the ELC mapas tool of CAPFITOGEN (http://www.capfitogen.net/, Parra-Quijano et al., 2008, 2016) based on 16 ecogeographic variables from three different components (four geophysic variables, seven edaphic, and five bioclimatic; see the list below) at a resolution of 2.5 arc minutes (approximately 4.5 km at the equator). The Calinski-Harabasz (1974) criterion was applied to obtain an objective number of clusters for each bioclimatic, edaphic and geophysic multivariate analysis. The ELC map was then clipped to the SADC countries using ArcGIS 10.4.1 (ESRI, 2016). A total of 16 ecogeographic categories were identified in the SADC region with distinct ecogeographic characteristiscs (see file 'ELC_SADC_region_statistics.xlsx'). The files made available here include: the raster file of the ELC map of the SADC region (which is composed of 16 different files) and an Excel file which describes the statistics (i.e. average, median, maximum, minimum and standard deviation) of each ecogeographic category present in the map ('ELC_SADC_region_statistics.xlsx').<br> <br><b>Variables:</b> Geophysic: altitude (m) (WorldClim 1.4, http://worldclim.org), slope (\u00b0), latitude (decimal degrees), longitude (decimal degrees). Edaphic: topsoil organic carbon (% weight), topsoil pH (H2O) [-log(H+)], topsoil silt fraction (% weight), topsoil sand fraction (% weight), topsoil gravel content (% vol.), topsoil clay fraction (% weight), topsoil TEB (total exchangeable bases) (cmol/kg) (HWS Database, http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/). Bioclimatic: annual precipitation (bio_12) (mm), precipitation seasonality (coefficient of variation) (bio_15) (mm), isothermality (bio_2/bio_7) (*100) (bio_3), max temperature of warmest month (bio_5) (\u00b0C), min temperature of coldest month (bio_6) (\u00b0C) (WorldClim 1.4, http://worldclim.org).<br> <br><b>References:</b> Calinski T and Harabasz J (1974) A dendrite method for cluster analysis. Communications in Statistics, 3(1): 1\u201227. ESRI (2016) ArcGIS Desktop release Version 10.4.1. Environmental Systems Research Institute. Redlands. CA. Parra-Quijano M, Draper D and Torres E (2008) Ecogeographical representativeness in crop wild relative ex situ collections. In: Maxted N, Ford\u2010Lloyd BV, Kell SP, Iriondo JM, Dulloo E and Turok J (eds), Crop wild relative conservation and use, pp. 249\u201373. Wallingford: CAB International. Parra-Quijano M, Torres E, Iriondo JM, L\u00f3pez F and Molina A (2016) CAPFITOGEN tools user manual, version 2.0. Rome, Italy: International Treaty on Plant Genetic Resources for Food and Agriculture, FAO. Available at: http://www.capfitogen.net/en/access/manuals/ [Accessed July 2021].", "keywords": ["Agricultural Sciences", "PLANNING", "PLANT GENETIC RESOURCES", "AGROBIODIVERSITY", "GENETIC DIVERSITY AS RESOURCE"], "contacts": [{"organization": "Magos Brehm, Joana", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/MIYBQE"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/MIYBQE", "name": "item", "description": "10.7910/DVN/MIYBQE", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/MIYBQE"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "11572/255256", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:21:24Z", "type": "Journal Article", "created": "2019-09-23", "title": "Elastica catastrophe machine: theory, design and experiments", "description": "Open Access31 pages, 18 figures", "keywords": ["Nonlinear mechanics; Snap mechanisms; Structural instability", "0203 mechanical engineering", "FOS: Physical sciences", "02 engineering and technology", "Chaotic Dynamics (nlin.CD)", "Nonlinear Sciences - Chaotic Dynamics", "0210 nano-technology"]}, "links": [{"href": "https://iris.unitn.it/bitstream/11572/255256/1/1-s2.0-S002250961930523X-main.pdf"}, {"href": "https://doi.org/11572/255256"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20the%20Mechanics%20and%20Physics%20of%20Solids", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11572/255256", "name": "item", "description": "11572/255256", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11572/255256"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "11586/524923", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:21:26Z", "type": "Journal Article", "created": "2024-12-03", "title": "Addressing the environmental sustainability of plastics used in agriculture: a multi-actor perspective", "description": "Abstract

Plastics used in agriculture, commonly known as agriplastics (AP), offer numerous advantages in terrestrial agriculture, forestry, fisheries and aquaculture, but the diffusion of AP-intensive practices has led to extensive pollution. This review aims to synthesise scientific and policy discussions surrounding AP, examining evidence of their benefits and detrimental environmental and agricultural impacts. Following the proposal of a preliminary general taxonomy of AP, this paper presents the findings from a survey conducted among international experts from the plastic industry, farmer organisations, NGOs and environmental research institutes. This analysis highlights knowledge gaps, demands and perspectives for the sustainable future use of AP. Stakeholder positions vary on the options of \uffe2\uff80\uff98rejection\uffe2\uff80\uff99 or \uffe2\uff80\uff98reduction\uffe2\uff80\uff99 of AP, as well as the role of alternative materials such as (bio)degradable and compostable plastics. However, there is consensus on critical issues such as redesign, labelling, traceability, environmental safety standards, deployment and retrieval standards, as well as innovative waste management approaches. All stakeholders express concern for the environment. A \uffe2\uff80\uff98best practice\uffe2\uff80\uff99-based circular model was elaborated capturing these perspectives. In the context of global food systems increasingly reliant on AP, scientists emphasise the need to simultaneously preserve nature-based and traditional knowledge-based sustainable agricultural practices to enhance food system resilience.

Abstract. In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500\u2009m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e.\u00a0fire-related forest loss, was modelled based on the overlap between 30\u2009m forest loss data and MODIS burned area and active fire detections. Using this new 500\u2009m model, we calculated global average carbon emissions from fire of 2.1\u00b10.2 (\u00b11\u03c3 interannual variability, IAV)\u2009Pg\u2009C\u2009yr\u22121 during 2002\u20132020. Fire-related forest loss accounted for 2.6\u00b10.7\u2009% (uncertainty range =1.9\u2009%\u20133.3\u2009%) of global burned area and 24\u00b16\u2009% (uncertainty range =16\u2009%\u201331\u2009%) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon (SOC) in the boreal region and tropical peatlands accounted for 13\u00b14\u2009% of global emissions. Our global fire emissions estimate was higher than the 1.5\u2009Pg\u2009C\u2009yr\u22121 from GFED4 and similar to 2.1\u2009Pg\u2009C\u2009yr\u22121 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500\u2009m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel-consumption burned area. Recent advances in burned area detection at resolutions of 30\u2009m and finer show a substantial amount of burned area that remains undetected with 500\u2009m sensors, suggesting that global carbon emissions from fire are likely higher than our 500\u2009m estimates. The ability to model fire emissions at 500\u2009m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED.

", "keywords": ["QE1-996.5", "13. Climate action", "11. Sustainability", "Geology", "15. Life on land", "7. Clean energy", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "name": "item", "description": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-30T00:00:00Z"}}, {"id": "1887/4246123", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:21:33Z", "type": "Journal Article", "created": "2023-08-30", "title": "Inland Waters Increasingly Produce and Emit Nitrous Oxide", "description": "Nitrous oxide (N2O) is a long-lived greenhouse gas and currently contributes \u223c10% to global greenhouse warming. Studies have suggested that inland waters are a large and growing global N2O source, but whether, how, where, when, and why inland-water N2O emissions changed in the Anthropocene remains unclear. Here, we quantify global N2O formation, transport, and emission along the aquatic continuum and their changes using a spatially explicit, mechanistic, coupled biogeochemistry-hydrology model. The global inland-water N2O emission increased from 0.4 to 1.3 Tg N yr-1 during 1900-2010 due to (1) growing N2O inputs mainly from groundwater and (2) increased inland-water N2O production, largely in reservoirs. Inland waters currently contribute 7 (5-10)% to global total N2O emissions. The highest inland-water N2O emissions are typically in and downstream of reservoirs and areas with high population density and intensive agricultural activities in eastern and southern Asia, southeastern North America, and Europe. The expected continuing excessive use of nutrients, dam construction, and development of suboxic conditions in aging reservoirs imply persisting high inland-water N2O emissions.", "keywords": ["Inland waters", "N2O cycling", " long-term temporal changes", "long-term temporal changes", "Nitrous oxide", "Asia", " Southern", "Nitrous Oxide", "Integrated process-based modeling", "Water", "Agriculture", "General Chemistry", "15. Life on land", "N2O cycling", "6. Clean water", "Greenhouse gas emission", "13. Climate action", "Environmental Chemistry", "14. Life underwater", "Spatial distributions", "closed N2O budget"]}, "links": [{"href": "https://doi.org/1887/4246123"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1887/4246123", "name": "item", "description": "1887/4246123", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1887/4246123"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-30T00:00:00Z"}}, {"id": "1F20D517-4F12-4194-B9EC-544A45676839", "type": "Feature", "geometry": null, "properties": {"updated": "2020-02-11T00:00:00Z", "type": "Dataset", "language": "de", "title": "Boden\u00fcbersichtskarte 1:200.000 (B\u00dcK200) - CC7110 Mannheim", "description": "Die Boden\u00fcbersichtskarte 1:200.000 (B\u00dcK200) wird von der Bundesanstalt f\u00fcr Geowissenschaften und Rohstoffe (BGR) in Zusammenarbeit mit den Staatlichen Geologischen Diensten (SGD) der Bundesl\u00e4nder im Blattschnitt der Topographischen \u00dcbersichtskarte 1:200.000 (T\u00dcK200) erarbeitet und in 55 einzelnen Kartenbl\u00e4ttern herausgegeben. Die digitale, blattschnittfreie Datenhaltung bildet eine detaillierte, bundesweit einheitliche und fl\u00e4chendeckende Informationsgrundlage f\u00fcr L\u00e4nder \u00fcbergreifende Aussagen zu Bodennutzung und Bodenschutz. \u00dcber den aktuellen Bearbeitungsstand des Kartenwerks informieren die Internetseiten der BGR zum Thema Boden. Die Verbreitung und Vergesellschaftung der B\u00f6den auf dem Gebiet dieses Kartenblattes wird anhand von 97 Legendeneinheiten (gegliedert nach Bodenregionen und Bodengro\u00dflandschaften) beschrieben. Jede Legendeneinheit beinhaltet bodensystematische Informationen (Bodensubtyp) und Informationen zum Bodenausgangsgestein sowohl f\u00fcr die Leitb\u00f6den als auch f\u00fcr deren Begleiter. Im Rahmen der Qualit\u00e4tssicherung wurde die Linienf\u00fchrung der Bodengrenzen im gesamten Blattgebiet verbessert (Stand 12. Juli 2016).", "formats": [{"name": "PDF"}], "keywords": ["ackerbauliches-ertragspotential", "baden-wu\u0308rttemberg", "boden", "bodenart", "bodenausgangsgestein", "bodenauslaugung", "bodenbearbeitung", "bodenbelastung", "bodenbildung", "bodenbiologie", "bodenchemie", "bodendegradation", "bodendekontamination", "bodeneigenschaften", "bodenerosion", "bodenfeuchtigkeit", "bodenform", "bodenfruchtbarkeit", "bodenfunktion", "bodengefu\u0308ge", "bodengesellschaft", "bodengestaltung", "bodengruppe", "bodengu\u0308te", "bodenhorizont", "bodeninformationssystem", "bodenkarte", "bodenluft", "bodenmechanik", "bodenmineralogie", "bodennutzbarkeit", "bodennutzung", "bodenphysikalische-eigenschaften", "bodenprofil", "bodenskelett", "bodensubstrat", "bodensystematik", "bodentyp", "bodenverbreitung", "de", "durchla\u0308ssigkeit", "effektive-durchwurzelungstiefe", "fachinformationssystem", "hessen", "mannheim", "opendata", "rheinland-pfalz", "saarland", "soil"], "contacts": [{"organization": "Bundesanstalt f\u00fcr Geowissenschaften und Rohstoffe (BGR)", "roles": ["creator"]}]}, "links": [{"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/jpg/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/pdf/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/png/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/shp/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/tiff/buek200_7110.zip"}, {"href": "http://data.europa.eu/88u/dataset/1f20d517-4f12-4194-b9ec-544a45676839"}, {"rel": "self", "type": "application/geo+json", "title": "1F20D517-4F12-4194-B9EC-544A45676839", "name": "item", "description": "1F20D517-4F12-4194-B9EC-544A45676839", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1F20D517-4F12-4194-B9EC-544A45676839"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=it&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=it&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": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=it&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=it&offset=50", "hreflang": "en-US"}], "numberMatched": 11999, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-09-20T14:48:37.552612Z"}