_MAP.tif Products are distributed as country-wide Geotiff files with 0.00088888\u00b0 resolution (~100m). More information on product format and content can be found in the Product User Guide, available on the\u00a0SEN4LDN Deliverables web page. The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on\u00a0http://esa-sen4ldn.org/\u00a0. Click here to view the maps in an interactive Google Earth Engine application.", "keywords": ["Life Science"], "contacts": [{"organization": "Araza, Arnan, Berger, Katja, Herold, Martin, Tot\u00e9, Carolien, Van De Kerchove, Ruben,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14274476"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14274476", "name": "item", "description": "10.5281/zenodo.14274476", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14274476"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14875898", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:43Z", "type": "Other", "title": "Les mod\u00e8les de COS doivent \u00eatre valid\u00e9s par des s\u00e9ries temporelles ind\u00e9pendantes pour permettre une pr\u00e9diction fiable", "description": "Les efforts visant \u00e0 maintenir les jeux de donn\u00e9es sont imp\u00e9ratifs pour obtenir des projections et des pr\u00e9visions pr\u00e9cises en mati\u00e8re de COS.", "keywords": ["[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study"], "contacts": [{"organization": "Le No\u00eb, Julia, Manzoni, Stefano, Abramoff, Rose, B\u00f6lscher, Tobias, Bruni, Elisa, Cardinael, R\u00e9mi, Ciais, Philippe, Chenu, Claire, Clivot, Hugues, Derrien, Delphine, Ferchaud, Fabien, Garnier, Patricia, Goll, Daniel, Lashermes, Gwena\u00eblle, Martin, Manuel, Rasse, Daniel, Rees, Fr\u00e9d\u00e9ric, Sainte-Marie, Julien, Salmon, \u00c9lodie, Schiedung, Marcus, Schimel, Josh, Wieder, William, Abiven, Samuel, Barr\u00e9, Pierre, C\u00e9cillon, Lauric, Guenet, Bertrand, Delahaie, Amicie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14875898"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14875898", "name": "item", "description": "10.5281/zenodo.14875898", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14875898"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15395350", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-06-26T16:25:58Z", "type": "Dataset", "title": "NSW 25-ha Drone Survey Grid", "description": "NSW 25-ha Drone Survey Grid This repository provides a 25-hectare (500m x 500m) resolution spatial grid for New South Wales. This grid layer was used to align systematic drone surveys and spatially structure binomial N-mixture models for estimating the abundance of koalas at the landscape-scale. It supports presence/absence and abundance frameworks and is suitable for use in large-scale ecological monitoring programs. The grid was used in the following study: Ryan, S.A., Southwell, D.M., Beranek, C.T., Clulow, J., Jordan, N.R., Witt, R.R., 2025.\u00a0Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modellingBiological Conservation. Manuscript ID: 111207. https://doi.org/10.1016/j.biocon.2025.111207 \ud83d\udcd8 Abstract Estimating the abundance of wildlife populations at a landscape-scale is vital for conservation, but is often hampered by survey costs, data processing and imperfect detection. In this study, we developed a framework that combines a protocol for validating nocturnal thermal drone detections in real-time with N-mixture modelling to estimate the landscape-scale abundance of arboreal folivores. As a case study, we estimated the abundance of koalas (Phascolarctos cinereus) across seven reserves (673 km\u00b2) in New South Wales, Australia. We conducted thermal drone surveys of 208, 25-ha sites stratified across vegetation type and fire history, on average, three times over consecutive nights (range 1\u201312 repeats), between 18:00\u201304:00 h (May to September). All koala detections were validated by field personnel or in real-time with drones equipped with a thermal camera and searchlight. Koalas were detected on 245 occasions. We fitted N-mixture models to validated repeat count data to quantify the effect of site and observation variables on abundance and detectability. Using our top set of competing models, we estimated that 4357 koalas (95 % CI = 2319\u20138307) occupy the seven reserves, with a mean detection probability of 0.22 (95 % CI = 0.15\u20130.31) over all survey occasions. We found detection probability decreased with increases in relative humidity and temperature. Koala abundance was negatively associated with fire severity, elevation, tree height and soil clay content, and positively associated with available water content, forest cover and soil organic carbon. Our framework, which combines real-time field validated drone data while accounting for imperfect detection, improves the accuracy of abundance estimates for arboreal folivores across large-scales. \ud83d\udcc2 Contents Grid_Albers_00500m_NSW_Polys.shp and associated filesA shapefile representing 25-ha (500 m \u00d7 500 m) grid cells across New South Wales. \ud83d\uddfa\ufe0f Spatial Details CRS: GDA94 / Australian Albers (EPSG:3577) Geometry Type: Polygon Cell Size: 500 m \u00d7 500 m (25 hectares) Total Features: 3,222,693 Attribute Fields: Id (unique cell identifier) Bounding Box (minx, miny, maxx, maxy):(826250.0, \u20134212250.0, 2082750.0, \u20133181250.0) \u2705 Intended Applications Thermal drone survey planning Spatial alignment of repeatable wildlife monitoring Koala and arboreal mammal detection Binomial or Poisson N-mixture model design Landscape-scale ecological stratification \u26a0\ufe0f Data Use and Licensing This grid layer was provided by Allen Mcilwee (NSW Government) and is published with permission as open-access supplementary material to support the following paper: Ryan, S.A., Southwell, D.M., Beranek, C.T., Clulow, J., Jordan, N.R., Witt, R.R. (2025)Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modellingBiological Conservation. Manuscript ID: 111207. https://doi.org/10.1016/j.biocon.2025.111207 The dataset is made available to support open ecological research and systematic drone survey planning in New South Wales.\u00a0 Users applying this grid for survey or monitoring purposes in NSW are encouraged to submit resulting species detection records to NSW BioNet to contribute to state-wide biodiversity data and conservation efforts.", "keywords": ["spatial grid", "wildlife monitoring", "25-ha grid", "New South Wales", "koala", "spatial layer", "thermal drone survey", "abundance modelling"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15395350"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15395350", "name": "item", "description": "10.5281/zenodo.15395350", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15395350"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-04T00:00:00Z"}}, {"id": "10.5281/zenodo.16894966", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-06-26T16:26:17Z", "type": "Report", "created": "2023-02-22", "title": "Management of alternative water resources for variable rate irrigation - a Hungarian case study", "description": "Most of the climate scenarios predict increased water scarcity in arid areas, such as Hungary. However, the irrigated area in Hungary covers 2% of agricultural land, mostly with outdated irrigation technology. The aim of the research was to develop the basis of a variable rate irrigation for water-saving precision sprinkler irrigation system on an arable area (85 ha) which is located in the reference area of the Tisza Riven Basin. There is limited available water resources at the site, therefore alternative water sources utilization system was set up for irrigation to adapt to climate change and reduce fertilizers. The basis of the alternative water resources are excess water, treated wastewater, biogas fermentation sludge which is collected in a water reservoir with 114000 m3 capacity. For proper irrigation scheduling, heterogeneity of topography, hydrological, soil and crop conditions has to be explored and monitored. Therefore physically-based modelling of the water balance and remote sensing-based surplus water and  vegetation status surveying are tested to use for accurate irrigation scheduling.Shallow groundwater and/or soil compaction can also contribute to excess inland water. This may occur even if there are drought periods in a year (e.g. in the Pannonian region), resulting in spots with a low crop yield. A LiDAR-based digital elevation model was found to provide appropriate data to identify sites affected by excess inland water. The spots identified can be used as spatial input data to compile a variable rate irrigation prescription map for imposing reduced (or zero) irrigation at areas more vulnerable to the occurrence of excess inland water. The water balance was also assessed for sites with physically-based models. Hydrus was used to model soil moisture changes at the Hungarian case study site.A model concept for crop evapotranspiration estimation was also developed based on vegetation indices calculated from satellite imagery. Several combinations of sensors and remote sensing products were tested to use in ETc modelling potentially. This approach was tested both at the Hungarian case study sites. Remote sensing-based analysis of crop evapotranspiration, combined with physically-based modelling, appears to be a promising method in water balance modelling of maize fields, especially if these fields are in summer when the crop is fully developed. However, the remotely sensed information verification is essential for the proper utilization of the remote sensing data in ETc modelling and predicting the spatio-temporal dynamics of crop yield, evapotranspiration, and irrigation demands.There is a need further benchmark scenarios to improve both physically-based models and satellite-based crop evapotranspiration models to achieve more accurate and valid simulations.The abstract was funded by European Union’s Horizon 2020 “WATERAGRI Water retention and nutrient recycling in soils and steams for improved agricultural production” research and innovation programme under Grant Agreement No. 858375. This research was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16894966"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16894966", "name": "item", "description": "10.5281/zenodo.16894966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16894966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-15T00:00:00Z"}}, {"id": "10.5281/zenodo.4287780", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:32Z", "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.3591992", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-26T16:26:29Z", "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.4487144", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-06-26T16:26:33Z", "type": "Dataset", "title": "Eddy Covariance data from ICOS-associated station IT-NIV \u2013 August-November 2019", "description": "RestrictedData stored here refer to Eddy Covariance (EC) data measured in 2019 between August and November at the Alpine CZO (Critical Zone Observatory, hereafter CZO@Nivolet) which was established at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP), located in the western Italian Alps. The EC site (IT-NIV) is an ICOS-associated station. CZO@Nivolet is aimed at investigating the cross-scale interactions between climatic shifts and ecosystem functions multiple scales, involving multidisciplinary studies. The main research questions that we aim to answer are concerning: (a) the effect of bedrock lithology, soil physics and chemisty, topographic hetereogenity, biotic components and meteo-climatic parameters in modulating CO2 flux in alpine grassland; and (b) what are the controlling factors of organic C and weathering under geologic substrates and different topographic positions. The investigations started in 2017. In 2019, the EC tower was added to deeply study CO2, H20, latent and sensible heat exchanges between soil, vegetation, and atmosphere. Carbon dioxide fluxes and environmental variables are recorded during the snow-free season to estimate carbon storage and explore CO2 fluxes drivers in high-altitude grasslands. Further developments will regard the integration of different techniques (Eddy Covariance, Remote Sensing, Flux chambers) to improve both spatial and temporal extent of carbon fluxes estimates to finally assess grasslands' productivity.", "keywords": ["13. Climate action", "alpine grassland", "15. Life on land", "Gran Paradiso National Park", "Mountain", "EO_Data", "Eddy Covariance", "Net Ecosystem Exchange", "ecosystem-atmosphere carbon exchange"], "contacts": [{"organization": "Vivaldo, Gianna, Raco, Brunella, Baneschi, Ilaria, Giamberini, Maria Silvia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4487144"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4487144", "name": "item", "description": "10.5281/zenodo.4487144", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4487144"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:26:37Z", "type": "Report", "created": "2020-03-09", "title": "Hyperspectral imaging for high resolution mapping of soil profile organic carbon distribution in an Austrian Alpine landscape", "description": " <p>Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (< 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.</p>
", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.5281/zenodo.6320617", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:26:40Z", "type": "Dataset", "title": "MOSSO_SoilChemistry_AllSites_Monthly_2016-2020", "description": "Abstract The dataset provides information about the soil chemical properties at eight permanent LTER sites (named site 1, 2, 3, 6, 7, 8, 9, and 10, according to the LTER site numerations), located between 2686 (site 10) and 2854 m a.s.l. (site 6). The investigated period is 2016-2020. Details: Site 1 (coordinates: 45\ufffd\ufffd52'22.43'N, 7\ufffd\ufffd52'25.84'E; elevation: 2840 m a.s.l.), Site 2 (coordinates: 45\ufffd\ufffd52'22.17'N, 7\ufffd\ufffd52'38.07'E; elevation: 2800 m a.s.l.), Site 3 (coordinates: 45\ufffd\ufffd52'13.52'N, 7\ufffd\ufffd52'35.01'E; elevation: 2770 m a.s.l.), Site 6 (coordinates: 45\ufffd\ufffd52'32.21'N, 7\ufffd\ufffd52'31.87'E; elevation: 2854 m a.s.l.), Site 7 (coordinates: 45\ufffd\ufffd52'29.13'N, 7\ufffd\ufffd52'44.71'E; elevation: 2813 m a.s.l.), Site 8 (coordinates: 45\ufffd\ufffd52'27.74'N, 7\ufffd\ufffd52'56.86'E; elevation: 2749 m a.s.l.), Site 9 (coordinates: 45\ufffd\ufffd52'23.80'N, 7\ufffd\ufffd53'3.96'E; elevation: 2720 m a.s.l.), and Site 10 (coordinates: 45\ufffd\ufffd52'21.76'N, 7\ufffd\ufffd53'9.32'E; elevation: 2686 m a.s.l.). The bedrock is primarily micaschists, with some inclusions of amphibolites and calcschists. The vegetation of the sites is included in the \ufffd\ufffd\ufffdSiliceous alpine and boreal grasslands\ufffd\ufffd\ufffd (habitat 6150, according to the EU Habitat Directive). At each site, consisting of paired plots for soil and vegetation survey, three 9 m2 plots are established, where three topsoil samples (A horizon, 0\ufffd\ufffd\ufffd10 cm depth) are collected each month during the snow-free season. On soil samples the following analysis are performed: N-NH4, N-NO3, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), dissolved organic nitrogen (DON), microbial carbon (Cmicr), and microbial nitrogen (Nmicr). Method Description Each soil sample consists of three subsamples that are homogenised by sieving at 2 mm. An aliquot of 20 g of fresh soil is extracted with 100 mL K2SO4 0.5 M, while 10 g are fumigated using chloroform for 18 h before extraction with 50 mL K2SO4 0.5 M. The concentration of DOC in not fumigated soil extracts (extractable DOC) is determined with a TOC analyzer (Elementar, Vario TOC, Hanau, Germany) after filtration with 0.45 \ufffd\ufffdm nylon membrane filters. The microbial carbon (Cmicr) is estimated as the difference in extractable DOC between fumigated and non-fumigated samples, corrected using a recovery factor of 0.45 (Brookes et al. 1985, https://doi.org/10.1016/0038-0717(85)90144-0). Extractable N-NH4 concentration in soil extracts is measured spectrophotometrically (U-2000, Hitachi, Tokyo, Japan) using a modified Berthelot method based on the reaction with salicylate in the presence of alkaline sodium dichloroisocyanurate (Crooke and Simpson 1971, https://doi.org/10.1002/jsfa.2740220104). Extractable N-NO3 concentration in soil extracts is measured spectrophotometrically (U-2000, Hitachi, Tokyo, Japan) using the Greiss reaction (Mulvaney 1996, ISBN-10: \ufffd\ufffd\ufffd 0891188258; ISBN-13: \ufffd\ufffd\ufffd 978-0891188254) modified according to Cucu et al. (2014, https://doi.org/10.1007/s00374-013-0893-4). Extractable TDN is measured as reported for DOC. Extractable DON is determined as the difference between extractable TDN and inorganic nitrogen (extractable N-NH4 + N-NO3) in the extracts. Nmicr is estimated from the difference in extractable TDN between fumigated and non-fumigated samples corrected using a recovery factor of 0.54 (Brookes et al. 1985, https://doi.org/10.1016/0038-0717(85)90144-0). Instrumentation Spectrophotometer U-2000, Hitachi, Tokyo, Japan (N-NH4 and N-NO3) Elementar, Vario TOC, Hanau, Germany (DOC and TDN)", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Freppaz, Michele, Colombo, Nicola,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6320617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6320617", "name": "item", "description": "10.5281/zenodo.6320617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6320617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7307449", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:26:53Z", "type": "Dataset", "title": "Components of the complete budget for SAFE intensive carbon plots", "description": "Description: Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.
These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.
SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf. Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification Funding: These data were collected as part of research funded by: Sime Darby Foundation (Grant, SAFE Core data) European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131) NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1) NERC standard grant: The multi-year impacts of the 2015/2016 El Ni\u00f1o on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1) HSBC Malaysia (Grant) The University of Zurich (Grant) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. Permits: These data were collected under permit from the following authorities: Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76)) XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx SAFE_CarbonBalanceComponents.xlsx This file contains dataset metadata and 1 data tables: Carbon balance components data (described in worksheet Data) Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project Number of fields: 64 Number of data rows: 11 Fields: ForestType: Old-growth or Logged (Field type: categorical) SAFEPlotName: SAFE plot name, as in the SAFE Gazetteer (Field type: location) PlotName: Plot name (used in field work) (Field type: id) ForestPlotsCode: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id) WoodyNPP_Stem: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_CoarseRoot: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_BranchTurnover: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_Total: Total woody net primary producivity (Field type: numeric) CanopyNPP_Leaf: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Twig: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Reproductive: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Miscellaneous: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Herbivory: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Total: Total canopy net primary producivty (Field type: numeric) FineRootNPP: Fine root productivity (Field type: numeric) TotalNPP_WithoutMycorrhiza: Total net primary productivity without mycorrhiza (Field type: numeric) TotalNPP_WithMycorrhiza: Total net primary productivity including mycorrhiza (Field type: numeric) GPP_WithoutMycorrhiza: Gross primary productivity without mycorrhiza (Field type: numeric) GPP_WithMycorrhiza: Gross primary productivity including mycorrhiza (Field type: numeric) R_Stem: Respiration from woody stems (Field type: numeric) R_Leaf: Leaf Respiration (Field type: numeric) R_FineRoots: Respiration from fine roots (Field type: numeric) R_CoarseRoots: Respiration from coarse roots (Field type: numeric) R_SOM: Respiration from soil organic matter (Field type: numeric) R_Mycorrhiza: Respiration from mycorrhiza (Field type: numeric) R_Litter: Respiration from litter layer (Field type: numeric) R_Deadwood: Deadwood respiration (Field type: numeric) R_auto: Total autotrophic respiration (Field type: numeric) R_het: Total heterotrophic respiration (Field type: numeric) R_eco: Total ecosystem respiration (Field type: numeric) NEP_WithoutMycorrhiza: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) NEP_WithMycorrhiza: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) AbovegroundBiomassCarbonStock: Plot above-ground biomass carbon stock (Field type: numeric) CoarseRootBiomassCarbonStock: Biomass carbon stock of coarse roots (Field type: numeric) SE_WoodyNPP_Stem: Standard error of woody stem productivity (Field type: numeric) SE_WoodyNPP_CoarseRoot: Standard error of coarse root productivity (Field type: numeric) SE_WoodyNPP_BranchTurnover: Standard error of branch turnover productivity (Field type: numeric) SE_WoodyNPP_Total: Standard error of total woody net primary producivity (Field type: numeric) SE_CanopyNPP_Leaf: Standard error of leaf productivity (Field type: numeric) SE_CanopyNPP_Twig: Standard error of twig productivity (Field type: numeric) SE_CanopyNPP_Reproductive: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric) SE_CanopyNPP_Miscellaneous: Standard error of unidentified canopy debris (Field type: numeric) SE_CanopyNPP_Herbivory: Standard error of leaf productivity lost to herbivory (Field type: numeric) SE_CanopyNPP_Total: Standard error of total canopy net primary producivty (Field type: numeric) SE_FineRootNPP: Standard error of fine root productivity (Field type: numeric) SE_TotalNPP_WithoutMycorrhiza: Standard error of total net primary productivity without mycorrhiza (Field type: numeric) SE_TotalNPP_WithMycorrhiza: Standard error of total net primary productivity including mycorrhiza (Field type: numeric) SE_GPP_WithoutMycorrhiza: Standard error of gross primary productivity without mycorrhiza (Field type: numeric) SE_GPP_WithMycorrhiza: Standard error of gross primary productivity including mycorrhiza (Field type: numeric) SE_R_Stem: Standard error of respiration from woody stems (Field type: numeric) SE_R_Leaf: Standard error of leaf Respiration (Field type: numeric) SE_R_FineRoots: Standard error of respiration from fine roots (Field type: numeric) SE_R_CoarseRoots: Standard error of respiration from coarse roots (Field type: numeric) SE_R_SOM: Standard error of respiration from soil organic matter (Field type: numeric) SE_R_Mycorrhiza: Standard error of respiration from mycorrhiza (Field type: numeric) SE_R_Litter: Standard error of litter layer respiration (Field type: numeric) SE_R_Deadwood: Standard error of deadwood respiration (Field type: numeric) SE_R_auto: Standard error of total autotrophic respiration (Field type: numeric) SE_R_het: Standard error of total heterotrophic respiration (Field type: numeric) SE_R_eco: Standard error of total ecosystem respiration (Field type: numeric) SE_NEP_WithoutMycorrhiza: Standard error of total net ecosystem productivity (Field type: numeric) SE_NEP_WithMycorrhiza: Standard error of total net ecosystem productivity (Field type: numeric) SE_AbovegroundBiomassCarbonStock: Standard error of plot above-ground biomass carbon stock (Field type: numeric) SE_CoarseRootBiomassCarbonStock: Standard error of biomass carbon stock of coarse roots (Field type: numeric) Date range: 2011-08-25 to 2018-07-17 Latitudinal extent: 4.1830 to 5.0700 Longitudinal extent: 114.0190 to 117.8200", "keywords": ["2. Zero hunger", "Soil carbon cycle", "Soil organic matter", "Flux", "Respiration", "15. Life on land", "Carbon balance", "Autotrophic respiration", "6. Clean water", "SAFE core data", "13. Climate action", "SAFE project", "Heterotropchic respiration", "Litter", "Carbon plot", "Carbon flux", "Productivity"], "contacts": [{"organization": "Riutta, Terhi, Ewers, Robert M, Malhi, Yadvinder, Majalap, Noreen, Khoon, Kho Lip, Mills, Maria,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307449"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307449", "name": "item", "description": "10.5281/zenodo.7307449", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307449"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-09T00:00:00Z"}}, {"id": "10.5281/zenodo.7353721", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:26:53Z", "type": "Software", "title": "Algorithm to harmonize soil particle size data to the FAO/USDA system", "description": "Different countries often measure and express soil particle-size distribution using different delineations between the main textural components, clay, silt and sand content. In order to harmonize such diverse data so that a uniform textural classification system can be used, interpolation of the data is necessary. Here we provide an example algorithm written in MATLAB that helps harmonize such data country-by-country to the FAO-USDA particle-size classification system that defines clay content as the mass of solids (individual particles) that are <0.002 mm, silt as the mass of solids in the 0.002 \u2013 0.05 mm size range, and sand content as the mass of solids in the 0.05 \u2013 2 mm size range (USDA 1951; FAO 1990). This system considers particles sized above 2 mm as gravel or stones. The algorithm uses k-nearest neighbor type pattern recognition in a non-spatial context algorithm to achieve this goal (Nemes et al. 1999; Nemes et al. 2006). Note: The algorithm uses a pre-existing external reference data set to compare the current data with. That data set cannot be provided with the algorithm due to prior agreements about the use and availability of those data, but its description is provided on pages 125-127 in the report by Weynants et al. (2013), and the authors herein offer their collaboration with a future user in order to take advantage of this algorithm. References FAO, Food, and Agricultural Organization. 1990. Guidelines for Soil Profile Description. 3rd ed. Rome: FAO. Nemes, A., J. H. M. W\u00f6sten, A. Lilly, and JH Oude Voshaar. 1999. \u201cEvaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases.\u201d Geoderma 90: 187\u2013202. http://www.sciencedirect.com/science/article/pii/S0016706199000142. Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. \u201cUse of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties.\u201d Soil Science Society of America Journal 70 (2): 327\u201336. https://doi.org/10.2136/SSSAJ2005.0128. USDA, United States Department of Agriculture. 1951. Soil survey manual, U.S. Dept. Agriculture Handbook No. 18. Washington, DC. Weynants, M\u00e9lanie, Luca Montanarella, Gergely T\u00f3th, Arnold Arnoldussen, Mar\u00eda Anaya Romero, George Bilas, Trond Borresen, et al. 2013. \u201cEuropean HYdropedological Data Inventory (EU-HYDI).\u201d Luxembourg: European Commission EUR 26053 \u2013 Joint Research Centre \u2013 Institute for Environment; Sustainability; EUR \u2013 Scientific; Technical Research series \u2013 ISSN 1831-9424. https://doi.org/10.2788/5936.", "keywords": ["2. Zero hunger", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Nemes, Attila", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7353721"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7353721", "name": "item", "description": "10.5281/zenodo.7353721", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7353721"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7353722", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:26:53Z", "type": "Software", "title": "Algorithm to harmonize soil particle size data to the FAO/USDA system", "description": "Different countries often measure and express soil particle-size distribution using different delineations between the main textural components, clay, silt and sand content. In order to harmonize such diverse data so that a uniform textural classification system can be used, interpolation of the data is necessary. Here we provide an example algorithm written in MATLAB that helps harmonize such data country-by-country to the FAO-USDA particle-size classification system that defines clay content as the mass of solids (individual particles) that are <0.002 mm, silt as the mass of solids in the 0.002 \u2013 0.05 mm size range, and sand content as the mass of solids in the 0.05 \u2013 2 mm size range (USDA 1951; FAO 1990). This system considers particles sized above 2 mm as gravel or stones. The algorithm uses k-nearest neighbor type pattern recognition in a non-spatial context algorithm to achieve this goal (Nemes et al. 1999; Nemes et al. 2006). Note: The algorithm uses a pre-existing external reference data set to compare the current data with. That data set cannot be provided with the algorithm due to prior agreements about the use and availability of those data, but its description is provided on pages 125-127 in the report by Weynants et al. (2013), and the authors herein offer their collaboration with a future user in order to take advantage of this algorithm. References FAO, Food, and Agricultural Organization. 1990. Guidelines for Soil Profile Description. 3rd ed. Rome: FAO. Nemes, A., J. H. M. W\u00f6sten, A. Lilly, and JH Oude Voshaar. 1999. \u201cEvaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases.\u201d Geoderma 90: 187\u2013202. http://www.sciencedirect.com/science/article/pii/S0016706199000142. Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. \u201cUse of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties.\u201d Soil Science Society of America Journal 70 (2): 327\u201336. https://doi.org/10.2136/SSSAJ2005.0128. USDA, United States Department of Agriculture. 1951. Soil survey manual, U.S. Dept. Agriculture Handbook No. 18. Washington, DC. Weynants, M\u00e9lanie, Luca Montanarella, Gergely T\u00f3th, Arnold Arnoldussen, Mar\u00eda Anaya Romero, George Bilas, Trond Borresen, et al. 2013. \u201cEuropean HYdropedological Data Inventory (EU-HYDI).\u201d Luxembourg: European Commission EUR 26053 \u2013 Joint Research Centre \u2013 Institute for Environment; Sustainability; EUR \u2013 Scientific; Technical Research series \u2013 ISSN 1831-9424. https://doi.org/10.2788/5936.", "keywords": ["2. Zero hunger", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Nemes, Attila", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7353722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7353722", "name": "item", "description": "10.5281/zenodo.7353722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7353722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:26:55Z", "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": {"license": "Open Access", "updated": "2026-06-26T16:26:56Z", "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": {"license": "Open Access", "updated": "2026-06-26T16:26:58Z", "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.8057232", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:26:59Z", "type": "Dataset", "title": "Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning", "description": "Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: \u251c\u2500\u2500 available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
\u251c\u2500\u2500 available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
\u251c\u2500\u2500 caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
\u251c\u2500\u2500 caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
\u251c\u2500\u2500 cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 clay_30arc_30cm_us.tif [30 cm depth soil clay content]
\u251c\u2500\u2500 clay_30arc_100cm_us.tif [100 cm depth soil clay content]
\u251c\u2500\u2500 depthWT_30arc_us.tif [depth to water table]
\u251c\u2500\u2500 kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
\u251c\u2500\u2500 kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [100 cm depth soil pH]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [30 cm depth soil pH]
\u251c\u2500\u2500 pondingFre_30arc_us.tif [ponding frequency]
\u251c\u2500\u2500 sand_30arc_30cm_us.tif [30 cm depth soil sand content]
\u251c\u2500\u2500 sand_30arc_100cm_us.tif [100 cm depth soil sand content]
\u251c\u2500\u2500 silt_30arc_30cm_us.tif [30 cm depth soil silt content]
\u251c\u2500\u2500 silt_30arc_100cm_us.tif [100 cm depth soil silt content]
\u251c\u2500\u2500 water_content_30arc_30cm_us.tif [30 cm depth soil water content]
\u2514\u2500\u2500 water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: \u251c\u2500\u250030cm SOC mean.tif [30 cm depth soil SOC]
\u251c\u2500\u2500100cm SOC mean.tif [100 cm depth soil SOC]
\u251c\u2500\u250030cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
\u2514\u2500\u2500100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection: GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]]
", "keywords": ["gNATSGO", "the United States SOC", "US soil properties", "15. Life on land", "Gridded National Soil Survey Geographic Database", "International Soil Carbon Network (ISCN)"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8057232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8057232", "name": "item", "description": "10.5281/zenodo.8057232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8057232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8089699", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:27:00Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8089699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089699", "name": "item", "description": "10.5281/zenodo.8089699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8109600", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:27:02Z", "type": "Dataset", "title": "Data on soil compounds, respiration and incorporation of 13C-labeled substrate", "description": "Open AccessSee Readme.pdf", "keywords": ["2. Zero hunger", "microdialysis", "respiration rates", "compound concentration in soil solution", "PLFA and NLFA", "13C isotopic labeling", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Wiesenbauer, Julia, Kaiser, Christina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8109600"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8109600", "name": "item", "description": "10.5281/zenodo.8109600", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8109600"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-18T00:00:00Z"}}, {"id": "10.5281/zenodo.8320433", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:27:05Z", "type": "Dataset", "title": "Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type", "description": "These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. Ecol. Appl. 26, 1868\u20131880 Bright, R. M., & O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. Geoscientific Model Development, 12(9), 3975-3990. Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990\u20132020. (US Dept. Ag. For. Service, Madison, WI; https://doi.org/10.2737/FS-RU-382). Erb, Li, Sun, Paynter, Wang, & Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. Remote Sensing, 14(21), 5320. Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.", "keywords": ["climate change", "forest carbon", "13. Climate action", "15. Life on land", "Landsat", "albedo"], "contacts": [{"organization": "Healey, Sean, Yang, Zhiqiang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8320433"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8320433", "name": "item", "description": "10.5281/zenodo.8320433", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8320433"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-06T00:00:00Z"}}, {"id": "10.6084/m9.figshare.16650170", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:27:46Z", "type": "Report", "created": "2021-09-21", "title": "Additional file 2 of Trophic level drives the host microbiome of soil invertebrates at a continental scale", "description": "Additional file 1: Supplementary Text. Characterization of microbial community in the soil food web; Table S1. The information of used primers of the DNA barcoding; Table S2. Comparison of soil fauna microbiome composition using PERMANOVA (Adonis test); Table S3. Effects of the removal of Unknown OTUs from the network on network metrics; Figure S1. The potential relationship between the loss of microbial species living within the microbiome of soil fauna and soil faunal extinction; Figure S2. The distribution of sample sites across China based on different climatic zones (A suffix of 1 in the location indicates farmland, while 2 indicates forested land); Figure S3. The relative abundance of 18 most abundant bacterial families (a) and 16 most abundant bacterial species (b) in all samples, classified by sample types; Figure S4. The alpha diversity of soil and fauna microbial communities at a sequencing depth of 13551; Figure S5. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the weighted unifrac distance in each sampling site; Figure S6. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the unweighted unifrac distance in each sampling site; Figure S7. Shared OTUs between soil and soil fauna; Figure S8. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the weighted unifrac distance in each soil faunal group; Figure S9. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the unweighted unifrac distance in each soil faunal group; Figure S10. The PERMANOVA analysis revealing the relative contribution of landuse, sampling site and soil faunal species to the variation of each soil faunal group microbiome; Figure S11. Enterotyping (clustering) of each soil faunal group, which was clustered using the Jensen\u2013Shannon distance and partitioning around medoids method based on the relative abundance of bacterial genera; Figure S12. Analysis of microbial network taxa interconnectedness from the soil food web microbiome; Figure S13. Network analysis revealing microbial taxa interconnectedness at the Genus level; Figure S14. Boxplots presenting the difference of betweenness, closeness, and degree centrality values of nodes between the three network types (Original-Without Unknown, Original-Bootstrap and Without Unknown-Bootstrap) at different taxonomic levels; Figure S15. Fit of neutral model for collembola (a), nematodes (b), potworms (c), earthworms (d), oribatid mites (e) and predatory mites (g); Figure S16. Boxplot revealing natural 15N fractionation (\u03b4 15N value) of different soil fauna (centre line, median; box limits, first and third quartiles; whiskers, 1.5 \u00d7 interquartile range); Figure S17. FEAST estimations of source contribution to the sink (predatory mite) in each sampling site; Figure S18. Venn diagram revealing the shared OTUs number between different trophic level of soil fauna; Figure S19. Change in relative read abundance of 26 dominant bacterial taxa along an increase in trophic level (\u03b4 15N value) of the soil fauna; Figure S20. Random forest classification of soil fauna microbial communities (including 58 bacterial taxa which were found in more than 70% of all soil fauna samples) based on different animal types (a and b) and trophic levels (c and d); Figure S21. Changes in relative read abundance of 23 unique bacterial taxa across increased trophic level as measured by \u03b4 15N value (-0.25-16.79) in the soil fauna; Figure S22. Linear fitting diagrams revealing unique bacterial taxa that enriched with increasing \u03b4 15N value (-0.25-16.79) in the soil fauna (P < 0.001); Figure S23. Scatter diagrams revealing changes in relative read abundance of 23 unique bacterial taxa across increased trophic level as measured by \u03b4 15N value (-0.25-16.79) in the soil fauna; Figure S24. Boxplots presenting the difference of betweenness, closeness, and degree centrality values of nodes between the three network types (Original-Without Unknown, Original-Bootstrap and Without Unknown-Bootstrap) at the Genus level, which reflected effects of Unknown taxa on different trophic level soil faunal network metrics.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Zhu, Dong, Delgado-Baquerizo, Manuel, Ding, Jing, Gillings, Michael R., Zhu, Yong-Guan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.16650170"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.16650170", "name": "item", "description": "10.6084/m9.figshare.16650170", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.16650170"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.6084/m9.figshare.7987292", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:27:52Z", "type": "Dataset", "created": "2019-04-12", "title": "Dataset S1 from Convergent evolution in Arabidopsis halleri and Arabidopsis arenosa on calamine metalliferous soils.", "description": "It is a plausible hypothesis that parallel adaptation events to the same environmental challenge should result in genetic changes of similar or identical effects, depending on the underlying fitness landscapes. However, systematic testing of this is scarce. Here we examine this hypothesis in two closely related plant species, Arabidopsis halleri and Arabidopsis arenosa, which co-occur at two calamine metalliferous (M) sites harbouring toxic levels of the heavy metals zinc and cadmium. We conduct individual genome resequencing alongside soil elemental analysis for 64 plants from eight populations on M and non-metalliferous (NM) soils, and identify genomic footprints of selection and local adaptation. Selective sweep and environmental association analyses indicate a modest degree of gene as well as functional network convergence, whereby the proximal molecular factors mediating this convergence mostly differ between site pairs and species. Notably, we observe repeated selection on identical single nucleotide poly-morphisms in several A. halleri genes at two independently colonized M sites. Our data suggest that species-specific metal handling and other biological features could explain a low degree of convergence between species. The parallel establishment of plant populations on calamine M soils involves convergent evolution, which will probably be more pervasive across sites purposely chosen for maximal similarity in soil composition.This article is part of the theme issue \u2018Convergent evolution in the genomics era: new insights and directions\u2019.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Preite, Veronica, Sailer, Christian, Syllwasschy, Lara, Bray, Sian, Ahmadi, Hassan, Kr\u00e4mer, Ute, Yant, Levi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.7987292"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.7987292", "name": "item", "description": "10.6084/m9.figshare.7987292", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.7987292"}, {"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": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:28:59Z", "type": "Journal Article", "created": "2024-03-21", "title": "Environmental drivers and remote sensing proxies of post-fire thaw depth in Eastern Siberian larch forests", "description": "Abstract. Boreal fire regimes are intensifying because of climate change and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in Eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean = 127.3 cm, standard deviation (sd) = 27.7 cm) than in unburned (98.1 cm, sd = 26.9 cm) landscapes one year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained landscape positions, in open larch forest often intermixed with Scots pine, and in high severity burns. The environmental drivers, site moisture, forest type and density, and fire severity explained 73.4 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced Normalized Burn Ratio (dNBR), land surface temperature (LST), and pre-fire Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar, and found that LST displayed particularly strong correlations with post-fire thaw depth (r = 0.65, p < 0.01). Our study reveals some of the governing processes of post-fire thaw depth development and shows the capability of Landsat imagery to estimate thaw depth at a landscape scale.
", "keywords": ["Dynamic and structural geology", "QE1-996.5", "13. Climate action", "Science", "Q", "Geology", "QE500-639.5", "Deforestation", "15. Life on land", "Landsat", "Multiple linear regression", "Atmospheric temperature"]}, "links": [{"href": "https://esd.copernicus.org/articles/15/1459/2024/esd-15-1459-2024.pdf"}, {"href": "https://doi.org/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Dynamics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "name": "item", "description": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-21T00:00:00Z"}}, {"id": "1959.7/uws:72836", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:05Z", "type": "Journal Article", "created": "2023-04-24", "title": "Different Cerrado Ecotypes Show Contrasting Soil Microbial Properties, Functioning Rates, and Sensitivity to Changing Water Regimes", "description": "AbstractSoil moisture is among the most important factors regulating soil biodiversity and functioning. Models forecast changes in the precipitation regime in many areas of the planet, but how these changes will influence soil functioning, and how biotic drivers modulate such effects, is far from being understood. We evaluated the responses of C and N fluxes, and soil microbial properties to different soil water regimes in soils from the main three ecotypes of the world's largest and most diverse tropical savanna. Further, we explored the direct and indirect effects of changes in the ecotype and soil water regimes on these key soil processes. Soils from the woodland savanna showed a better nutritional status than the other ecotypes, as well as higher potential N cycling rates, N2O emissions, and soil bacterial abundance but lower bacterial richness, whereas potential CO2 emissions and CH4 uptake peaked in the intermediate savanna. The ecotype also modulated the effects of changes in the soil water regime on nutrient cycling, greenhouse gas fluxes, and soil bacterial properties, with more intense responses in the intermediate savanna. Further, we highlight the existence of multiple contrasting direct and indirect (via soil microbes and abiotic properties) effects of an intensification of the precipitation regime on soil C- and N-related processes. Our results confirm that ecotype is a fundamental driver of soil properties and functioning in the Cerrado and that it can determine the responses of key soil processes to changes in the soil water regime.Theory suggests that the diversity of molecules in soil organic matter (SOM functional diversity) provides key insights on multiple ecosystem services. We aimed to investigate how and why SOM functional diversity and composition change as topsoils develop, and its implications for key soil functions (e.g., from nutrient pool to water regulation).
LocationWe reported data on 16 soil chronosequences globally distributed in nine countries from six continents.
Time Period2016\uffe2\uff80\uff932017.
Major Taxa StudiedSoil microbes (bacteria and fungi) and vascular plants.
MethodsSOM functional diversity and composition without mineral interference were measured using diffuse reflectance mid\uffe2\uff80\uff90infrared Fourier transform spectroscopy (DRIFT). We aimed to characterize the main environmental factors related to SOM functional diversity and composition. Also, we calculated the links among SOM functional diversity and key soil functions.
ResultsWe found that SOM functional diversity declines after millions of years of soil formation (pedogenesis). We further showed that increases in plant cover and productivity led to a higher ratio of reduced (e.g., alkanes) over oxidized carbon forms (i.e., C: O\uffe2\uff80\uff90functional groups ratio), which was positively correlated to SOM functional diversity as soils age. Our findings indicated that the plant footprint (i.e., the accumulation of plant\uffe2\uff80\uff90derived material promoting the C: O\uffe2\uff80\uff90functional group ratio) would explain the reduction of SOM functional diversity as ecosystems develop. Moreover, the dissimilarity in SOM composition consistently increased with soil age, with the soil development stage emerging as the main predictor of SOM dissimilarity across contrasting biomes.
Main ConclusionsOur global survey contextualized the natural history of SOM functional diversity and composition during long\uffe2\uff80\uff90term soil development. Together, we showed how plant footprint drives the losses of SOM functional diversity with increasing age, which might provide a novel mechanism to explain typically reported losses in ecosystem functions during ecosystem retrogression.A broader knowledge of the contribution of carbon (C) released by plant roots (exudates) to soil is a prerequisite for optimizing the management of organic matter in arable soils. This is the first study to show the contribution of constantly applied13C\uffe2\uff80\uff90labelled maize and wheat exudates to water extractable organic carbon (WEOC), microbial biomass\uffe2\uff80\uff90C (MB\uffe2\uff80\uff90C), and CO2\uffe2\uff80\uff90C evolution during a 25\uffe2\uff80\uff90day incubation of agricultural soil material. The CO2\uffe2\uff80\uff90C evolution and respective \uffce\uffb413C values were measured daily. The WEOC and MB\uffe2\uff80\uff90C contents were determined weekly and a newly developed method for determining \uffce\uffb413C values in soil extracts was applied. Around 36% of exudate\uffe2\uff80\uff90C of both plants was recovered after the incubation, in the order WEOC < MB\uffe2\uff80\uff90C < CO2\uffe2\uff80\uff90C for maize and MB\uffe2\uff80\uff90C < WEOC < CO2\uffe2\uff80\uff90C for wheat. Around 64% of added exudate\uffe2\uff80\uff90C was not retrieved with the methods used here. Our results suggest that great amounts of exudates became stabilized in non\uffe2\uff80\uff90water extractable organic fractions. The amounts of MB\uffe2\uff80\uff90C stayed relatively constant over time despite a continuous exudate\uffe2\uff80\uff90C supply, which is the prerequisite for a growing microbial population. A lack of mineral nutrients might have limited microbial growth. The CO2\uffe2\uff80\uff90C mineralization rate declined during the incubation and this was probably caused by a shift in the microbial community structure. Consequently, incoming WEOC was left in the soil solution leading to rising WEOC amounts over time. In the exudate\uffe2\uff80\uff90treated soil additional amounts of soil\uffe2\uff80\uff90derived WEOC (up to 110 \uffce\uffbcg g\uffe2\uff88\uff921) and MB\uffe2\uff80\uff90C (up to 60 \uffce\uffbcg g\uffe2\uff88\uff921) relative to the control were determined. We suggest therefore that positive priming effects (i.e. accelerated turnover of soil organic matter due to the addition of organic substrates) can be explained by exchange processes between charged, soluble C\uffe2\uff80\uff90components and the soil matrix. As a result of this exchange, soil\uffe2\uff80\uff90derived WEOC becomes available for mineralization.
", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land"], "contacts": [{"organization": "A. Gattinger, F. Buegger, M. Marx, J. C. Munch, A. Zsolnay,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1111/j.1365-2389.2007.00911.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2389.2007.00911.x", "name": "item", "description": "10.1111/j.1365-2389.2007.00911.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2389.2007.00911.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2007-03-27T00:00:00Z"}}, {"id": "10.1016/j.agee.2017.04.015", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-26T16:16:45Z", "type": "Journal Article", "created": "2017-05-06", "title": "Ecosystem service delivery of agri-environment measures: A synthesis for hedgerows and grass strips on arable land", "description": "Abstract In north western Europe, agricultural systems are generally managed to maximize the potential delivery of provisioning ecosystem services. This has often been at the expense of other ecosystem services. Because the current supply of most ecosystem services is insufficient to meet the increasing demand, particular attention to ecosystem service delivery and hence multifunctionality in agriculture is vital. In this paper, we quantitatively assessed the impact of hedgerows and grass strips bordering parcels with annual arable crops on the simultaneous delivery of a set of ecosystem services and from there we identified synergies and trade-offs on virtual parcels. After a systematic literature search, mixed models were applied on observations from 60 studies and quantitative effect relationships between ecosystem service delivery and hedgerow and grass strip characteristics were developed. Next to the hedgerow, until a distance of twice the hedgerow height, arable crop yield was reduced by 29%. Beyond this distance, until 20 times the hedgerow height, crop yield was increased by 6%. Compared to a similar arable parcel without hedgerow or grass strip, soil carbon stock was 22% higher in the hedgerow, on average 6% higher in the adjacent parcel next to the hedgerow and 37% higher in the upper 30\u00a0cm soil layer in the grass strip. Both hedgerows and grass strips intercepted nitrogen from the surface (69% and 67%, respectively) and subsurface (34% and 32%, respectively) flow and phosphorus (67% and 73%, respectively) and soil sediment (91% and 90%, respectively) from the surface flow. More natural predator species were found on parcels with hedgerows, but the number of predators was unaffected. On parcels with grass strips, both predator density and diversity was higher and aphid density was reduced. Our calculations on parcel level indicate that the trade-off between arable crop yield and regulating ecosystem services depends on hedgerow width and height and parcel dimensions. A similar trade-off is found on parcels with grass strips, but increasing grass strip width results in a proportionally higher delivery of regulating ecosystem services.", "keywords": ["2. Zero hunger", "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.agee.2017.04.015"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2017.04.015", "name": "item", "description": "10.1016/j.agee.2017.04.015", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2017.04.015"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-01T00:00:00Z"}}, {"id": "10.1016/j.agsy.2016.06.007", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-26T16:16:50Z", "type": "Journal Article", "created": "2016-07-20", "title": "Greening And Producing: An Economic Assessment Framework For Integrating Trees In Cropping Systems", "description": "Abstract Environmental measures in an agricultural context often lead to extra constraints in current farming. This suggests trade-offs between the environmental objectives and profitability. Whether trade-offs exist, or may be turned into win-win, depends on creative farm options to comply new constraints. This paper concentrates on Ecological Focus Areas as a new EU Common Agricultural Policy greening requirement, and investigates profitability changes of two greening options with permanent woody elements, hedgerows and alley cropping. We predicted discounted gross margins for a hedgerow and alley cropping greening option and four market scenarios on a representative arable farm in Flanders (Belgium). Starting from the tree row, over a distance of 1.64 times the tree height, relative crop yield is 70% as compared to a treeless situation. Between 1.64 and 9.52 times the tree height, relative yield is 107%. Beyond that point, the effect is considered negligible. Discounted gross margins are calculated to account for the time horizon. Relative discounted gross margins at farm level, compared to the business as usual option, vary between 91% and 108%, depending on market conditions and policy support. The calculations show that fulfilment of the 5% ecological focus area greening requirement on arable farms with hedgerows and alley cropping only becomes economically competitive to the traditional cropping systems with extra financial stimuli (e.g. greening payments). We also show and discuss how the calculations can be fine-tuned and used in policy making, e.g. by i) getting better insights in the tree-crop interactions, ii) including the effect of e.g. crop type, tree species, tree line space and tree line orientation in the meta-information, iii) evaluating this conditional competitiveness and suggesting a better linking between subsidy level and ecological value and ecosystem services and iv) exploring novel valorization channels for wood products.", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "12. Responsible consumption", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agsy.2016.06.007"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agsy.2016.06.007", "name": "item", "description": "10.1016/j.agsy.2016.06.007", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agsy.2016.06.007"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-10-01T00:00:00Z"}}, {"id": "2901798597", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:29:51Z", "type": "Report", "created": "2018-11-16", "title": "Organic matter across subsea permafrost thaw horizons on the East Siberian Arctic Shelf", "description": "Abstract. Thaw of subsea permafrost across the Arctic Ocean shelves might promote the degradation of organic matter to CO2 and CH4, but also create conduits for transfer of deeper CH4 pools to the atmosphere and thereby amplify global warming. In this study, we describe sedimentary characteristics of three subsea permafrost cores of 21\u201356\u2009m length drilled near the current delta of the Lena River in the Buor\u2013Khaya Bay on the East Siberian Arctic Shelf, including content, origin and degradation state of organic matter around the current thaw front. Grain size distribution and optically stimulated luminescence dating suggest the alternating deposition of aeolian silt and fluvial sand over the past 160\u2009000 years. Organic matter in 3\u2009m sections across the current permafrost table was characterized by low organic carbon contents (average 0.7\u2009\u00b1\u20090.2\u2009%) as well as enriched \u03b413C values and low concentrations of the terrestrial plant biomarker lignin compared to other recent and Pleistocene deposits in the study region. The lignin phenol composition further suggests contribution of both tundra and boreal forest vegetation, at least the latter likely deposited by rivers. Our findings indicate high variability in organic matter composition of subsea permafrost even within a small study area, reflecting its development in a heterogeneous and dynamic landscape. Even with this relatively low organic carbon content, the high rates of observed subsea permafrost thaw in this area yield a thaw-out of 1.6\u2009kg\u2009OC\u2009m\u22122\u2009year\u22121, emphasizing the need to constrain the fate of the poorly described and thawing subsea permafrost organic carbon pool.
", "keywords": ["13. Climate action", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2901798597"}, {"rel": "self", "type": "application/geo+json", "title": "2901798597", "name": "item", "description": "2901798597", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2901798597"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-11-16T00:00:00Z"}}, {"id": "2995887446", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:57Z", "type": "Journal Article", "created": "2019-12-18", "title": "Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation", "description": "Trabajo desarrollado bajo la financiaci\u00f3n del proyecto \u201cSoil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems\u201d (773903), coordinado por Jos\u00e9 Alfonso G\u00f3mez Calero, investigador del Instituto de Agricultura Sostenible (IAS). Plant water deficit index (PWDI) represents the extent of water stress by relating soil moisture to the ability of a plant to take up water including consideration of the relative distribution of soil water to roots. However, for a smart irrigation decision support system, we are challenged in determining reliable thresholds of PWDI to initiate irrigation events to achieve predetermined yield and/or water use efficiency (WUE) targets. Taking drip irrigated maize and sprinkler irrigated alfalfa as examples, field experiments were conducted to investigate the choice and effects of PWDI thresholds. The results indicated that, with increasing PWDI thresholds, irrigation times and quantity of water, as well as crop transpiration, growth, and yield, were all significantly limited while WUE was enhanced except under extremely stressed conditions. To disconnect the unpredictable effects of other factors, yield and WUE were normalized to their corresponding potential values. Within the experimentally determined range of PWDI, relative yield and WUE were described with linear functions for maize, and linear and quadratic functions for alfalfa, allowing identification of the most efficient threshold value according to the objective parameter of choice. The method described can be adopted in smart irrigation decision support systems with consideration of spatial variability and after further verification and improvement under more complicated situations with various crop types and varieties, environmental conditions, cultivation modes, and wider or dynamic PWDI thresholds allowing regulated deficit irrigation. This research was supported partly by National Key Research and Development Program of China (2017YFE0118100, 2016YFD0200303), National Natural Science Foundation of China (U1706211, 51790532), Special Fund for Scientific Research in the Public Interest (201411009), and the European Union\u2019s Horizon 2020 research and innovation programme under Project SHui, grant agreement No 773903. Peer reviewed", "keywords": ["0106 biological sciences", "2. Zero hunger", "Yield", "PWDI", "Water stress", "Alfalfa", "Water use efficiency", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "Maize", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/2995887446"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2995887446", "name": "item", "description": "2995887446", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2995887446"}, {"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": "10.1002/ldr.2158", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:15:16Z", "type": "Journal Article", "created": "2012-04-03", "title": "Changes in soil organic carbon under eucalyptus plantations in brazil: a comparative analysis", "description": "ABSTRACTProper assessment of environmental quality or degradation requires knowledge of how terrestrial C pools respond to land use change. Forest plantations offer a considerable potential to sequester C in aboveground biomass. However, their impact on initial levels of soil organic carbon (SOC) varies from strong losses to gains, possibly affecting C balances in afforestation or reforestation initiatives. We compiled paired\uffe2\uff80\uff90plot studies on how SOC stocks under native vegetation change after planting fast\uffe2\uff80\uff90growth Eucalyptus species in Brazil, where these plantations are becoming increasingly important. SOC changes for the 0\uffe2\uff80\uff9320 and 0\uffe2\uff80\uff9340\uffe2\uff80\uff89cm depths varied between \uffe2\uff88\uff9225 and 42\uffe2\uff80\uff89Mg\uffe2\uff80\uff89ha\uffe2\uff88\uff921, following a normal distribution centered near zero. After replacing native vegetation by Eucalyptus plantations, mean SOC changes were \uffe2\uff88\uff921\uffc2\uffb75 and 0\uffc2\uffb73\uffe2\uff80\uff89Mg\uffe2\uff80\uff89ha\uffe2\uff88\uff921 for the 0\uffe2\uff80\uff9320 and 0\uffe2\uff80\uff9340\uffe2\uff80\uff89cm depths, respectively. These are very low figures in comparison to C stocks usually sequestered in aboveground biomass and were statistically nonsignificant as demonstrated by a t\uffe2\uff80\uff90test at p\uffe2\uff80\uff89<\uffe2\uff80\uff890\uffc2\uffb705. Similar low, nonsignificant SOC changes were estimated after data were stratified into first or second rotation cycles, soil texture and biome (savanna, rainforest or grassland). Although strong SOC losses or gains effectively occurred in some cases, their underpinning causes could not be generally identified in the present work and must be ascribed in a case basis, considering the full set of environmental and management conditions. We conclude that Eucalyptus spp. plantations in average have no net effect on SOC stocks in Brazil. Copyright \uffc2\uffa9 2012 John Wiley & Sons, Ltd.
", "keywords": ["Soil organic matter", "Carbon stocks", "Tropical soils", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Fast-growth tree plantations", "Land use change"]}, "links": [{"href": "https://doi.org/10.1002/ldr.2158"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land%20Degradation%20%26amp%3B%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ldr.2158", "name": "item", "description": "10.1002/ldr.2158", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ldr.2158"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-04-03T00:00:00Z"}}, {"id": "10.1016/j.agee.2004.04.001", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-26T16:16:35Z", "type": "Journal Article", "created": "2004-08-26", "title": "Carbon Sequestration In Tropical And Temperate Agroforestry Systems: A Review With Examples From Costa Rica And Southern Canada", "description": "Deforestation in the tropics, and fossil fuel burning in temperate regions contribute to the largest flux of CO 2 to the atmosphere. Therefore, land-use systems that increase the soil organic matter (SOM) pool and stabilize soil organic carbon (SOC) need to be implemented. Agroforestry systems have the potential to sequester atmospheric carbon (C) in trees and soil while maintaining sustainable productivity. The potential to sequester C in agroforestry systems in tropical and temperate regions is promising, but little information is available to date. The objective of this paper is to give an overview of the history of agroforestry and to outline differences in management practices between tropical and temperate systems. This review focuses on C inputs, SOC pools and SOC stabilization with highlights from Costa Rican and Canadian systems, and their role in C sequestration and trading. The potential to sequester C in aboveground components in agroforestry systems is estimated to be 2.1 \u00d7 10 9 Mg C year \u22121 in tropical and 1.9 \u00d7 10 9 Mg C year \u22121 in temperate biomes. However, the type of agroforestry systems and their capacity to sequester C vary globally. For example, alley cropping is an agroforestry practice where trees are integrated with crops, therefore storing C in the woody components of the trees and in the soil, with a continual addition of organic material from tree prunings and crop residues. Studies from Costa Rica have shown that a 10-year-old system with E. poeppigianasequestered C at a rate of 0.4 Mg C ha \u22121 year \u22121 in coarse roots and 0.3 Mg C ha \u22121 year \u22121 in tree trunks. Tree branches and leaves are added to the soil as mulch, contributing 1.4 Mg C ha \u22121 year \u22121 in addition to 3.0 Mg ha \u22121 year \u22121 from crop residues. This resulted in an annual increase of the SOC pool by 0.6 Mg ha \u22121 year \u22121 . Despite the two crop rotations in tropical agroforests, C input from crop residues is similar between the two biomes. The total organic matter input, however, is still greater in tropical systems due to the larger addition from tree prunings. This greater input does not necessarily increase the SOC pool significantly when compared to a temperate system of similar age as a result of faster turnover rates of the SOM pool. \u00a9 2004 Elsevier B.V. All rights reserved.", "keywords": ["2. Zero hunger", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2004.04.001"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2004.04.001", "name": "item", "description": "10.1016/j.agee.2004.04.001", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2004.04.001"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-12-01T00:00:00Z"}}, {"id": "10.1016/j.agee.2005.09.013", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-26T16:16:35Z", "type": "Journal Article", "created": "2005-11-18", "title": "Responses Of Soil Microbial Biomass And N Availability To Transition Strategies From Conventional To Organic Farming Systems", "description": "Abstract Organic farming can enhance soil biodiversity, alleviate environmental concerns and improve food safety through eliminating the applications of synthetic chemicals. However, yield reduction due to nutrient limitation and pest incidence in the early stages of transition from conventional to organic systems is a major concern for organic farmers, and is thus a barrier to implementing the practice of organic farming. Therefore, identifying transition strategies that minimize yield loss is critical for facilitating the implementation of organic practices. Soil microorganisms play a dominant role in nutrient cycling and pest control in organic farming systems, and their responses to changes in soil management practices may critically impact crop growth and yield. Here we examined soil microbial biomass and N supply in response to several strategies for transitioning from conventional to organic farming systems in a long-term field experiment in Goldsboro, NC, USA. The transitional strategies included one fully organic strategy (ORG) and four reduced-input strategies (withdrawal of each or gradual reduction of major conventional inputs\u2014synthetic fertilizers, pesticides (insecticides/fungicides), and herbicides), with a conventional practice (CNV) serving as a control. Microbial biomass and respiration rate were more sensitive to changes in soil management practices than total C and N. In the first 2 years, the ORG was most effective in enhancing soil microbial biomass C and N among the transition strategies, but was accompanied with high yield losses. By the third year, soil microbial biomass C and N in the reduced-input transition strategies were statistically significantly greater than those in the CNV (averaging 32 and 35% higher, respectively), although they were slightly lower than those in the ORG (averaging 13 and 17% lower, respectively). Soil microbial respiration rate and net N mineralization in all transitional systems were statistically significantly higher than those in the CNV (averagely 83 and 66% greater, respectively), with no differences among the various transition strategies. These findings suggest that the transitional strategies that partially or gradually reduce conventional inputs can serve as alternatives that could potentially minimize economic hardships as well as benefit microbial growth during the early stages of transition to organic farming systems.", "keywords": ["2. Zero hunger", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2005.09.013"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2005.09.013", "name": "item", "description": "10.1016/j.agee.2005.09.013", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2005.09.013"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-04-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Life&offset=50&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Life&offset=50&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Life&offset=0", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Life&offset=100", "hreflang": "en-US"}], "numberMatched": 13011, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-06-26T19:17:15.342162Z"}