Emerging evidence points out that the responses of soil organic carbon (SOC) to nitrogen (N) addition differ along the soil profile, highlighting the importance of synthesizing results from different soil layers. Here, using a global meta\uffe2\uff80\uff90analysis, we found that N addition significantly enhanced topsoil (0\uffe2\uff80\uff9330\uffe2\uff80\uff89cm) SOC by 3.7% (\uffc2\uffb11.4%) in forests and grasslands. In contrast, SOC in the subsoil (30\uffe2\uff80\uff93100\uffe2\uff80\uff89cm) initially increased with N addition but decreased over time. The model selection analysis revealed that experimental duration and vegetation type are among the most important predictors across a wide range of climatic, environmental, and edaphic variables. The contrasting responses of SOC to N addition indicate the importance of considering deep soil layers, particularly for long\uffe2\uff80\uff90term continuous N deposition. Finally, the lack of depth\uffe2\uff80\uff90dependent SOC responses to N addition in experimental and modeling frameworks has likely resulted in the overestimation of changes in SOC storage under enhanced N deposition.The short- and medium\uffe2\uff80\uff94long-term effects of management and hillside position on soil organic carbon (SOC) changes were studied in a centenary Mediterranean rainfed olive grove. One way to measure these changes is to analyze the soil quality, as it assesses soil degradation degree and attempts to identify management practices for sustainable soil use. In this context, the SOC stratification index (SR-COS) is one of the best indicators of soil quality to assess the degradation degree from SOC content without analyzing other soil properties. The SR-SOC was calculated in soil profiles (horizon-by-horizon) to identify the best soil management practices for sustainable use. The following time periods and soil management combinations were tested: (i) in the medium\uffe2\uff80\uff92long-term (17 years) from conventional tillage (CT) to no-tillage (NT), (ii) in the short-term (2 years) from CT to no-tillage with cover crops (NT-CC), and (iii) the effect in the short-term (from CT to NT-CC) of different topographic positions along a hillside. The results indicate that the SR-SOC increased with depth for all management practices. The SR-SOC ranged from 1.21 to 1.73 in CT0, from 1.48 to 3.01 in CT1, from 1.15 to 2.48 in CT2, from 1.22 to 2.39 in NT-CC and from 0.98 to 4.16 in NT; therefore, the soil quality from the SR-SOC index was not directly linked to the increase or loss of SOC along the soil profile. This demonstrates the time-variability of SR-SOC and that NT improves soil quality in the long-term.
", "keywords": ["2. Zero hunger", "S", "Cover crops", "Soil organic carbon", "Chronosequence", "land use", "Agriculture", "soil profile", "04 agricultural and veterinary sciences", "15. Life on land", "Soil quality", "chronosequence", "Tillage systems", "soil organic carbon", "Soil profile", "Land use", "0401 agriculture", " forestry", " and fisheries", "soil quality", "cover crops", "tillage systems"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/650/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/650/pdf"}, {"href": "https://doi.org/10.3390/agronomy11040650"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11040650", "name": "item", "description": "10.3390/agronomy11040650", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11040650"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10.5061/dryad.79cnp5htw", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-06-24T16:22:07Z", "type": "Dataset", "title": "Data from: A tipping-point in carbon storage when forest expands into tundra is related to mycorrhizal recycling of nitrogen", "description": "unspecifiedTundra ecosystems are global belowground sinks for atmospheric CO2. Ongoing warming-induced encroachment by shrubs and trees risks turning this sink into a CO2 source, resulting in a positive feedback on climate warming. To advance mechanistic understanding of how shifts in mycorrhizal types affect long-term carbon (C) and nitrogen (N) stocks, we studied small-scale soil depth profiles of fungal communities and C-N dynamics across a subarctic-alpine forest-heath vegetation gradient. Belowground organic stocks decreased abruptly at the transition from heath to forest, linked to the presence of certain tree-associateds ectomycorrhizal fungi that contribute to decomposition when mining N from organic matter. In contrast, ericoid mycorrhizal plants and fungi were associated with organic matter accumulation and slow decomposition. If climatic controls on arctic-alpine forest lines are relaxed, increased decomposition will likely outbalance increased plant productivity, decreasing the overall C sink capacity of displaced tundra.", "keywords": ["C-N dynamics", "ectomycorrhizal exploration type", "functional genes", "ergosterol", "ITS2 meta-barcoding", "Fungal community", "Arctic greening", "Climate feedback", "15. Life on land", "litter saprotrophs", "mycorrhizal type", "litter bags", "13. Climate action", "soil solution", "FOS: Biological sciences", "soil carbon storage", "quantitative PCR", "soil profiles", "Ectomycorrhizal fungal community", "Ericoid Mycorrhiza", "treeline ecotone"], "contacts": [{"organization": "Clemmensen, Karina E, Durling, Mikael B, Michelsen, Anders, Hallin, Sara, Finlay, Roger D, Lindahl, Bj\u00f6rn D,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.79cnp5htw"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.79cnp5htw", "name": "item", "description": "10.5061/dryad.79cnp5htw", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.79cnp5htw"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-28T00:00:00Z"}}, {"id": "10.5281/zenodo.3971022", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:24:11Z", "type": "Dataset", "title": "VDMBC_vertical_distribution_soil_microbial_biomass_carbon", "description": "Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables were converted to the standard international units or commonly used ones and the values were converted correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC using the equation (SOC = SOM \u00d7 0.58). Soil depth was calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer. This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.", "keywords": ["2. Zero hunger", "soil organic carbon", "13. Climate action", "deep soils", "soil clay content", "soil C/N ratio", "soil profile", "15. Life on land", "micorbial quotient"], "contacts": [{"organization": "Sun, Tingting, Wang, Yugang, Hui, Dafeng, Jing, Xin, Feng, Wenting,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3971022"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3971022", "name": "item", "description": "10.5281/zenodo.3971022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3971022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-03T00:00:00Z"}}, {"id": "10.5281/zenodo.15593990", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:23:54Z", "type": "Report", "title": "An Open Compendium of Soil Datasets: Soil Observations and Measurements", "description": "This is a public compendium of global, regional, national and sub-national\u00a0soil samples\u00a0and/or\u00a0soil profile\u00a0datasets (points with Observations and Measurements of soil properties and characteristics). Datasets listed here, assuming compatible open license, are afterwards imported into the\u00a0Global compilation of soil chemical and physical properties and soil classes\u00a0and eventually used to create a better open soil information across countries. Please feel free to contribute entries. See\u00a0GitHub repository\u00a0for more detailed instructions. To see the most up-to-date version of this compendium please visit: https://soildb.OpenLandMap.org/", "keywords": ["soil samples", "soil profiles", "15. Life on land", "soil spectroscopy", "soil mapping"], "contacts": [{"organization": "Hengl, T., Gupta, Surya,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15593990"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15593990", "name": "item", "description": "10.5281/zenodo.15593990", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15593990"}, {"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.17475742", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-06-24T16:24:02Z", "type": "Dataset", "title": "In-situ and UAV dataset with crop and soil parameters obtained from winter wheat fields", "description": "unspecifiedThe study area was located in the Danubian Plain, Bulgaria, near the town of Knezha (43.495\u00b0N, 24.081\u00b0E). Five field campaigns (November, March, April, May, and June) were conducted during the 2016-2017 agricultural season and two (November and April) during the 2017-2018 agricultural season. Each campaign took four-five days to complete. A hierarchical sampling strategy, consisting of three levels, was adopted: Unit (a single commercial agricultural field), Elementary Sampling Unit (ESU; a 20 \u00d7 20 m plot), and Elementary Sub-Sampling Unit (ESSU; a particular location in the ESU where measurement is taken). Six Units were selected for sampling during each agricultural season. Different number of ESUs were sampled in each Unit depending of its size and diversity of soils. The location of the ESUs was kept unchanged during the season except in case relocation was needed due to waterlogging. The geographic coordinates were measured each time an ESU was visited, irrespective of whether it was relocated or not and a unique ID assigned to the ESU. Thus, data from each visit are presented as a separate independent record (row) in the dataset. In each ESU there were 3 ESSUs, one at the center and two at opposite corners of the ESU. The biological yield was measured only in the central ESSU. The dataset contains only data averaged at ESU level. Two unique identifiers, Unit_ID and ESU_ID, were used across the dataset's tables, helping identify data records and connect tables. The Unit_ID consists of the season and the Unit number, for instance \u201cS16-17_U1\u201d, where \u201cS16-17\u201d is the agricultural season 2016-2017, and \u201cU1\u201d is Unit 1. The ESU_ID consists of the month and year of the field campaign, the Unit number, and the ESU number, for instance \u201capr2017_1_2\u201d, where \u201capr2017\u201d is the April 2017 field campaign, \u201c1\u201d is the Unit number, and \u201c2\u201d is the ESU number. The LAI, fIPAR, FAPAR, and fCover were measured using AccuPAR LP-80 ceptometer. The CC was measured using CCM-300. The AGB and plant nitrogen samples were collected from a 1 m x 1 m plot at the central ESSU and 50 cm x 50 cm plots in the other two ESSUs. The nitrogen (N) content of the above-ground organs was determined by the Kjeldahl method and expressed as per-cent of the dry weight. N uptake (g m-2) was determined as the product of N content and the dry AGB. Soil moisture content (W) was measured gravimetrically at the surface (0-5 cm) and, in some ESUs, at three additional depths (0-30 cm, 30-60 cm, and 60-100 cm) in 10 cm increments. The data were expressed in percent by mass. Reflectance data was collected using ASD FieldSpec 4 HiRes Field Spectrometer (ASD HH FS4 HiRes). Crop phenology was reported after Zadoks et al. (1974). The damages and weeds status were assessed using five-point rating scales. The data from the four soil profiles included morphological description of soil horizons, texture (the particle-size distribution was determined by sieving and the pipette method in accordance with ISO 11277-2020), concentration of organic carbon, pH, bulk density, water retention at suctions from 0.25 kPa (pF 0.4) to 33 kPa (pF 2.7), total porosity. Soil water retention curves (pF curves) were obtained during the drainage of the samples by using the suction plate method, pressure membrane apparatus, and desiccators methods, as described in (Rousseva et al., 2017). The field capacity (FC) was estimated by the water content retained at a suction of 10 kPa (pF = 2.0). The wilting point was estimated at a suction 1500 kPa (pF = 4.2). The water retention experimental data at different suctions were approximated with the van Genuchten equation (Van Genuchten, 1980). The crop calendar and management data included information for the studied winter wheat fields, including soil type, predecessor, pre-sowing preparation, variety, sowing date and norm, dates and type of fertilization, plant protection measures, dates of the main development stages of winter wheat, harvesting date and yield. \u00a0\u00a0 Multispectral image mosaics of the studied fields were generated using the fixed-wing drone eBee Ag furnished with Sequoia camera. The camera has Multi-channel sensor with green (550 nm), red (660 nm), red edge (735 nm), and NIR (790 nm) bands. Meteorological data were collected by an automatic telemetric agro-meteorological station ADCON addVantage Pro6.5 installed in the vicinity of the town of Knezha. The station operated between 30.11.2016 and 22.03.2017 on its initial location (Location 1: 43.521917\u00b0N, 24.098848\u00b0E) and was later moved to another location, 1.3 km southwards (Location 2: 43.511391\u00b0N, 24.092252\u00b0E), where it collected data between 26.04.2017 and 3.12.2018. The station was equipped with sensors for air temperature, relative humidity, solar radiation, wind speed, wind direction, rain, PAR, soil moisture and temperature (12 depths between 5 cm and 115 cm at 10 cm intervals). Data from all sensors were recorded at 30 min intervals. References: Zadoks, J.C., T.T. Chang, C.F. Konzak. 1974. A decimal code of the growth stages of cereals. Weed Research, 14, 415-421. Rousseva, S., Kercheva, M., Shishkov, T., Lair, G.J., Nikolaidis, N.P., Moraetis, D., Kr\u00e1m, P., Bernasconi, S.M., Blum, W.E.H., Menon, M., et al. 2017. Soil Water Characteristics of European SoilTrEC Critical Zone Observatories. In Advances in Agronomy; Academic Press: Cambridge, MA, USA, Volume 142, pp. 29\u201372. van Genuchten, M. T., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892\u2013898.", "keywords": ["Winter wheat", "Chlorophyll", "Phenology", "UAV", "Yield (agricultural)", "Soil profile", "Biomass", "Remote sensing", "Spectroscopy", "LAI"], "contacts": [{"organization": "Dimitrov, Petar, Roumenina, Eugenia, Jelev, Georgi, Filchev, Lachezar, Gikov, Alexander, Kamenova, Ilina, Ilieva, Iliana, Ganeva, Dessislava, Kercheva, Milena, Banov, Martin, Krasteva, Veneta, Kolchakov, Viktor, Dimitrov, Emil, Miteva, Nevena,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17475742"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17475742", "name": "item", "description": "10.5281/zenodo.17475742", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17475742"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-10-31T00:00:00Z"}}, {"id": "10.5281/zenodo.17618737", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:24:02Z", "type": "Dataset", "title": "The Namibian Soil Profile Database (NSPD2025): An updated, expanded and harmonised compilation of national soil data", "description": "Introduction The Namibian Soil Profile Database (NSPD2025) is a national compilation of 5,099 soil point observations and 13,425 horizons or layers, comprising site, profile, horizon, and analytical data from diverse legacy datasets. Data were verified, cleaned, and standardised to ensure compatibility with international systems.\u00a0 The spatial distribution of points shows denser sampling in agricultural and research areas, and sparser data in remote rural areas and the Namib Desert. The level of detail varies considerably, from basic site observations to full profile descriptions with analytical data. The dataset is provided as a Microsoft Excel file with four worksheets \u2013\u00a0Reg&Site, Hor_lab, Hor_field, and Metadata \u2013 and a QGIS project with the profile locations and basic geographical information. NSPD2025 is intended for use in soil classification, conventional and digital soil mapping, environmental modelling and land management in Namibia and beyond. Data Sources NSPD2025 integrates data from multiple legacy sources: National Soil Survey (1998\u20132000) and MAWRD-AEZ Campaigns (2001\u20132009) Systematic soil data collection in post-independence Namibia began with the National Soil Survey Phase I (NSS), conducted by the Ministry of Agriculture, Water and Rural Development[1] (MAWRD) in collaboration with the Cartographic Institute of Catalonia (ICC) from 1998 to 2000. It produced four soil maps and associated MS Access relational databases: NSD1000, NSD250, NSDkava, and NSDowa (ICC & MAWRD, 2000). Soil profiles were described according to the FAO Guidelines for Soil Profile Description (FAO, 1990). The data were reformatted into the Namibian Soil and Terrain Digital Database, NAMSOTER (Coetzee, 2001), based on the methodology of Van Engelen and Wen (1995), for inclusion in the Southern African SOTERSAF (FAO/ISRIC, 2003). The Agro-Ecological Zoning (AEZ) Programme of MAWRD/MAWF continued soil survey campaigns in under-sampled regions between 2000 and 2009. These datasets were consolidated and maintained within the Namibian Agricultural Resources Information System (NARIS). By 2009, NSS NARIS contained 2,155 soil records of varying completeness. All laboratory analyses for these profiles were performed by the ministerial Agriculture Laboratory (AgricLab) in Windhoek, which follows ISO-based quality management through standard operating procedures, internal standards, replicate analyses, and participation in the Agri-Laboratory Association of Southern Africa (AgriLASA) inter-laboratory proficiency testing. During the present update, additional analytical and field data were recovered from original NSS records and integrated into the NSPD2025. African Soil Microbial Genomics Project (AMP, AMP-NA, AMP-NDLS) The African Soil Microbial Genomics Project (Wild, 2016; Cowan et al., 2022) contributed 179 topsoil samples (0\u201320 cm) across three datasets. Eleven physicochemical soil properties were analysed by the University of Pretoria using AgriLASA (2004) protocols. BIOTA Project The Biodiversity Monitoring Transect Analysis in Africa (BIOTA) Programme established 11 long-term observatories across Namibia (Petersen, 2008; https://biota-africa.org/). It contributed 638 samples from 257 profiles (0\u201310 cm, 10\u201330 cm, 30\u201360 cm), analysed for texture, pH (CaCl\u2082), electrical conductivity, total nitrogen, and organic carbon by the University of Hamburg\u2019s Institute of Soil Science. GIZ Bush-SOC Project The Assessment of the Impact of Bush Encroachment and Bush Control on Soil Organic Carbon in Namibia project (Strohbach et al., 2024) contributed 162 topsoil samples (0\u201330 cm) analysed by the AgricLab for texture, pH (H\u2082O), bulk density, electrical conductivity, extractable cations, plant-available phosphorus, and organic carbon. LandPKS Project The Land Potential Knowledge System (LandPKS) (Herrick et al., 2016) developed a mobile app integrating user inputs with cloud-based geospatial data. Piloted in Namibia and Kenya, it provided 918 georeferenced observations, recording site, land use, vegetation cover, effective depth, stoniness, and field-estimated texture at seven standard depths (0\u20131 cm, 1\u201310 cm, 10\u201320 cm, 20\u201350 cm, 50\u201370 cm, 70\u2013100 cm, 100\u2013150 cm.). No laboratory data are included, and data quality varies due to contributions by non-specialists. Land Degradation Neutrality (LDN, LDN-Omusati) The Land Degradation Neutrality (LDN) Project (Nijbroek et al., 2018) contributed 319 topsoil (0\u201330 cm) and 277 subsoil (30\u2013100 cm) samples, analysed by the AgricLab for organic carbon, bulk density, and particle size. SASSCAL The Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) Phase I subproject Monitoring agricultural ecosystems with regards to climate change effects (De Bl\u00e9court et al., 2018) added 181 samples from 30 profiles across four sites. These were analysed for texture, bulk density, pH (H\u2082O and CaCl\u2082), electrical conductivity, total nitrogen, organic carbon, cation exchange capacity, and porosity by the University of Hamburg\u2019s Institute of Soil Science. Landscape and profile photographs are included. Soils4Africa (S4A) The EU Horizon 2020 Soils4Africa Project (https://www.soils4africa-h2020.eu/) contributed 139 site and descriptions, and laboratory data for 64 topsoil samples. Analyses were conducted by the South African Agricultural Research Council\u2019s Institute for Soil, Climate and Water (ARC-ISCW) (Paterson, 2021). Bulk density measurements were carried out by the AgricLab. Future Okavango (TFO) The Future Okavango Project (Pr\u00f6pper et al., 2015; https://www.future-okavango.org/) focused on knowledge-based land-use management in the Okavango catchment and contributed 484 samples from 141 profiles. These were analysed for texture, pH (H\u2082O and CaCl\u2082), bulk density, electrical conductivity, cation exchange capacity, total nitrogen, and organic carbon by the University of Hamburg and the University of Botswana\u2019s Okavango Research Institute. Landscape and profile photographs are included. Additional Contributions Smaller datasets were obtained from Simmonds & Forbes (1995), Buch (1990), Trippner (1998), Kempf (2000), and Coetzee (in Strohbach et al., 2019). Methods and Data Processing All datasets were collated in Microsoft Excel, using both automated procedures and visual inspection \u2013 based on subject knowledge \u2013 for quality-control of data. Records lacking georeferencing or containing irreparable errors were removed. Key steps included: Verification and correction of NSS and MAWF data against original field forms, and addition of previously undigitised information. Digital records could not be verified against the following missing field forms: AO, DOR, MAR, NDONGA, OMAH_A, OMAH_B, ONGO, OVI, SPERR, TSUM. Standardisation of classes, codes, and measurement units following FAO Guidelines for Soil Profile Description (FAO, 2006) and SOTER/ISRIC standards (Van Engelen & Dijkshoorn, 2013). Verification of profile identifications and horizon designations; assigning layer identifiers to non-pedogenic layers (e.g. LandPKS fixed depth intervals). Conversion of coordinates to decimal degrees and addition of the coordinate reference system. Confirmation of attribute definitions (e.g. organic matter vs organic carbon; minimum diameter of sand fraction 50 or 63 \u00b5m) and value ranges, some in combination with other attributes (e.g. the sum of particle size fractions being 100\u00b11%; horizon thickness corresponding to the difference between upper and lower horizon limits). Verification of conspicuous outliers. Addition of registration and environmental information. Where available, diagnostic elements (horizons, materials and properties) were used for classification according to the WRB (IUSS, 2015) Limitations: variable data completeness, differences in analytical methods, and unverified records for some missing field forms. However, the harmonisation process ensures national consistency and compatibility with global frameworks. Database Design The NSPD2025 dataset is provided as an MS Excel workbook, NSPD2025.xlsx, with four worksheets (see all fields below): Reg&Site contains profile registration information and site descriptions. Hor_field contains physical and morphological characteristics of the profiles and horizons. \u00a0Hor_lab contains laboratory measurements. Metadata The primary identifier of each point observation is the PRID (profile identification), with secondary identifiers for horizons/layers in the HONU field. The design embeds some redundancy: repetition of data in different formats (e.g. individual values, class ranges, class codes and descriptive text) and measuring units. For example, dates of profile description are recorded in YYYY-MM-DD, DD-Mon-YYYY, DD/MM/YYYY formats as well as in separate Year, Month and Day fields, while Organic Carbon is recorded in both % and g/kg units. This was done to facilitate integration with international soil information systems, and to accommodate users who are unfamiliar with the FAO profile description codes. The same rationale is behind the use of descriptive field names rather than codes. \u00a0 Fields of the Reg&Site table: PRID Degree Square Grazer Type Surface Sealing Hardness Desc Dataset Quarter Degree Square Human Influence Code Surface Cracks Width cm PRID Let Farm No Human Influence Desc Surface Cracks Width Code PRID Dig Farm Name Rock Outcrops Abundance/Cover Code Distance Between Surface Cracks (m) ALT PRID Location Rock Outcrops Cover % Salt (Surface Salinity) Date (YYYY-MM-DD) Diagnostic Horizon / Property / Material Rock Outcrops Cover Desc Bleached Sand Code Date (DD-Mon-YY) Present Weather Code Rock Outcrops - m between Bleached Sand % Date (DD/MM/YYYY) Present Weather Desc Rock Outcrops Type Bleached Sand Desc Year Past Weather Code Coarse Surface Fragments Cover Code Flooded >2 weeks/a Month Past Weather Desc Coarse Surface Fragments Cover % Major Landform Code (2nd level) Day Soil Temperature Regime Code Coarse Surface Fragments Cover Desc Major Landform Desc Coordinate Reference System Soil Temperature Regime Desc Coarse Surface Fragments Size Code Minor Landform desc Latitude Soil Moisture Regime Code Coarse Surface Fragments Size cm Position code Longitude Soil Moisture Regime Desc Coarse Surface Fragments Size Desc Position Desc Elevation m GPS Slope Gradient Class Surface Coarse Surface Fragments Type Vegetation Elevation m SRTM Slope Form Vert Erosion Type Code Land Cover Country ID Slope Form Hor Erosion Type Parent Material Data Origin Slope Gradient Measured % Erosion Area Affected Code Lithology Surveyor(s) Slope Gradient Desc Erosion Area Affected % Drainage Desc Status Gradient % (for landform) from SRTM Erosion Degree Code Drainage Code Profile Classification Original Slope % SRTM Erosion Degree Desc Geology Profile Classification Original Code Relief m/km Erosion Status Code Effective Depth Classification System Drainage density Erosion Status Desc Depth Class WRB2015 Reclassification RSG Present Land Use Code Surface Sealing Thickness Code Biological activity WRB2015 Reclassification Principal Qualifiers Present land Use Desc Surface Sealing Thickness mm Notes WRB2015 Reclassification Supplementary Qualifiers Past Land Use Code Surface Sealing Thickness Desc \u00a0 WRB Classification by S Nambambi Past Land Use Desc Surface Sealing Hardness Code \u00a0 \u00a0Fields of the Hor_lab table: PRID Electrical Conductivity \u00b5S/cm [2 soil:5 water suspension] (EL25) Gypsum % coSa % HONU Electrical Conductivity dS/m [2 soil:5 water suspension] (EL25) Gypsum g/kg (GYPS) meSa % Dataset Electrical Conductivity \u00b5S/cm [Saturated Paste Extract] (ELCO) Total Nitrogen mg/kg (TOTN) fiSa % Latitude Electrical Conductivity dS/m [Saturated Paste Extract] (ELCO) Total Nitrogen g/kg (TOTN) Kjeldahl vfiSa % Longitude Ca % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Total Nitrogen % coSi % Laboratory Mg % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Total Phosphorus mg/kg [Mehlich #3, ICP-AES] fiSi % Soil Lab ID K % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Total P unknown method & unit EC (1:5) mS/m Representative Profile Na % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Phosphorus [Olsen] mg/kg CaCO3 g/kg Hor thickness cm [contains >] Ca mg/kg [Extractable; 1M NH4acetate, AAS] (SOCA) P2O5 mg/kg [Olsen; P x 2.291] inorgC g/kg Upper Depth cm [contains >] Mg mg/kg [Extractable; 1M NH4acetate, AAS] (SOMG) Organic Carbon % LOI totC g/kg Lower Depth cm [contains >] K mg/kg [Extractable; 1M NH4acetate, AAS] (SOLK) Organic Carbon % WB orgC g/kg CNS analyser Hor thickness cm Na mg/kg [Extractable; 1M NH4acetate, AAS] (SONA) Organic Carbon g/kg WB exchH cmol(-)/kg Upper Depth cm Cl mg/kg [Extractable] (SOCL) Organic Matter % [OC*1.74] exchAl cmol(-)/kg Lower Depth cm Chloride % [Extractable] Mn mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES] BaseSat % ? Horizon Code Short SSO4 ppm Fe mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES] exchAcid cmol(+)/kg Dominant Sand Grade Code Sulfate % Al mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES] totMo EPA 6010C?:2007 Dominant Sand Grade Desc Soluble carbonate meq/l (SCO3) Porosity Measured totCd mg/kg EPA 6010C:2007 Sand % < 53 \u00b5m (SDTO) Total Carbonate Equivalent g/kg (TCEQ) Fe_ox mg/g acid-oxalate extr totPb mg/kg EPA 6010C:2007 Silt % [2-53\u00b5m] (STPC) Carbonate Estimate % Al_ox mg/g acid-oxalate extr totV mg/kg EPA 6020A:2007 Clay % [< 2 \u00b5m] (CLPC) Exchangeable Calcium cmol(+)/kg (EXCA) Al mg/kg? totHg mg/kg EPA 6020A:2007 Silt + Clay % Exchangeable Magnesium cmol(+)/kg (EXMG) B mg/kg? totCr mg/kg EPA 6010C:2007 Texture Code (PSCL) Exchangeable Potassium cmol(+)/kg (EXCK) Cu mg/kg? totCo mg/kg EPA 6010C:2007 Texture Desc Exchangeable Sodium cmol(+)/kg (EXNA) Fe mg/kg? totNi mg/kg EPA 6010C:2007 Bulk Density kg/dm3 CEC Effective cmol(+)/kg [Sum of Exchangeable Bases] Mn mg/kg? totCu mg/kg EPA 6010C:2007 pH CaCl2 (PHCA) [2 soil:5 CaCl2] CEC Measured cmol(+)/kg P mg/kg? totZn mg/kg EPA 6010C:2007 pH water (PHAQ) Base Saturation Estimate % [from pHwater] S mg/kg? totAs mg/kg EPA 6020A:2007 pH water other lab Base Saturation % [CECeffective/CECmeasured] Zn mg/kg? totSb mg/kg EPA 6020A:2007 pH KCl (PHKC) \u00a0 \u00a0 \u00a0 \u00a0 Fields of the Hor_field table: PRID Coarse Fragments Abundance Desc Vertic Properties Coatings Form Desc HONU Coarse Fragments Abundance % properties for classification Coatings Location Code Dataset Coarse Fragments Size Code CLAF Coatings Location Desc Latitude Coarse Fragments Size Desc LOWER LEVEL UNITS Cementation /\u00a0 Compaction Nature Code Longitude Coarse Fragments Size mm REFERENCE GROUP Cementation / Compaction Nature Desc Hor thickness cm Course Fragments Shape Code CLAV Cementation /\u00a0 Compaction Continuity Code Upper Depth cm Coarse Fragments Shape Desc ALT PRID Cementation / Compaction Continuity Desc Lower Depth cm Coarse Fragments Degree of Weathering Consistence Dry Code Cementation /\u00a0 Compaction Fabric Code Diagnostic Horizon / Property / Material Code Coarse Fragments Type Consistence Dry Desc Cementation / Compaction Fabric Desc Diagnostic Horizon Drainage Code Consistence Moist Code Mineral Concentrations Nature Code Diagnostic Property Drainage Desc Consistence Moist Desc Mineral Concentrations Nature Desc Diagnostic Material Field Texture Code Consistence Wet - Stickiness Code Mineral Concentrations Abundance Code Qualifier Field Texture Desc Consistence Wet - Stickiness\u00a0 Desc Mineral Concentrations Abundance Desc Horizon Code Short Carbonate Estimate\u00a0 %.1 Consistence Wet - Plasticity Code Mineral Concentrations Abundance % Horizon Code Original Carbonate Reaction Code Consistence Wet - Plasticity Desc Mineral Concentrations Size Code Subordinate Horizon Code Carbonate Reaction Desc Porosity Class Code Mineral Concentrations Size Desc Lithology of R/C Secondary Carbonates Code Porosity Class Desc Mineral Concentrations Shape Code Horizon Transition Distinctness Code Secondary Carbonates Desc Porosity Class % Mineral Concentrations Shape Desc Horizon Transition Distinctness Desc Munsell_col_moist Voids Type Code Mineral Concentrations Hardness Code Horizon Transition Distinctness cm Hue_moist Voids Type Desc Mineral Concentrations Hardness Desc Horizon Transition Topography Code Value_moist Voids Size Class Roots Abundance Code Horizon Transition Topography Desc Chroma_moist Voids Size Desc Roots Abundance Desc Structure Type Code Colour Moist Desc [Munsell] Voids Size mm Roots Abundance No Structure Type Desc Munsell_col_dry Voids Abundance Class Roots Diameter Code Structure Strength Code Hue_dry Voids Abundance Desc Roots Diameter Desc Structure Strength Desc Value_dry Voids Abundance Number Roots Diameter mm Structure Size Code Chroma_dry Coatings Nature Code Biological Features Code Structure Size Desc Colour Dry Desc [Munsell] Coatings Nature Desc Biological Features Desc Coarse Fragments Abundance Code Mottling Desc Coatings Form Code Notes Supporting Files and Documents GIS_zipped: QGIS project file (Namibia_Soil_Profiles.qgz) & shape files (Namibia_Soil_Points, National boundary, Regional boundaries, Trunk Roads, Main Roads, District Roads, All settlements, Larger settlements, Rivers, Etosha National Park) Lab & Field Methods: AgricLab soil analysis methods; Soils4Africa lab & field observation guidelines; Africa Micobiome Project methods; FAO Guidelines for Soil Profile Description Images: Distribution map of soil point data; Datasets in the database Usage Notes The NSPD2025 dataset is intended for: \u00a0 \u00a0 \u00a0 \u00a0Conventional soil mapping and classification \u00a0 \u00a0 \u00a0 \u00a0Digital soil mapping (DSM) and predictive modelling \u00a0 \u00a0 \u00a0 \u00a0Land evaluation and agro-ecological zoning \u00a0 \u00a0 \u00a0 \u00a0Environmental and hydrological modelling \u00a0 \u00a0 \u00a0 \u00a0Educational and research purposes Users are requested to cite the dataset when using the data. Derived products should acknowledge the original sources. Acknowledgements The author gratefully acknowledges the Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR), formerly MAWRD/MAWF/MAWLR, for providing data and documentation from the Agro-Ecological Zoning Programme. Appreciation is extended to Ms Eva Corral-Pazos-de-Provens (Universidad de Huelva, Spain) for designing and populating a prototype relational database, and to the numerous surveyors, laboratory analysts, and GIS specialists who contributed to the original fieldwork and data compilation efforts (see Metadata). References AgriLASA. (2004). AgriLASA soil handbook. Pretoria: Agri Laboratory Association of Southern Africa. Buch, M. W. (1990). Soils, soil erosion and vegetation in the Etosha National Park / Northern Namibia - Field & laboratory results of the investigations of the year 1989. Part I \u2013 field results; Part II \u2013 laboratory results. University of Regensburg (unpublished). Coetzee, M.E. (2001). NAMSOTER \u2013 A SOTER database for Namibia. Agro-Ecological Zoning Programme, MAWRD. Windhoek. Cowan, D., Lebre, P., Amon, C., Becker, R.W., Boga, H.I., Boulang\u00e9, A., Chiyaka, T.L., Coetzee, T., De Jager, P.C., Dikinya, O., Eckardt, F., Greve, M., Harris, M.A., Hopkins, D.W., Houngnandan, H.B., Houngnandan, P., Jordaan, K., Kaimoyo, E., Kambura, A.K., Kamgan-Nkuekam, G., Makhalanyane, T.P., Maggs-K\u00f6lling, G., Marais, E., Mondlane, H., Nghalipo, E., Olivier, B.W., Ortiz, M., Pertierra, L.R., Ramond, J.-B., Seely, M., Sithole-Niang, I., Valverde, A., Varliero, G., Vikram, S., Wall D.H., & Zeze, A. (2022). Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes. Microbiome 10, 131. https://doi.org/10.1186/s40168-022-01297-w De Bl\u00e9court, M., R\u00f6der, A., Gr\u00f6ngr\u00f6ft, A., Baumann, S., Frantz, D. & Eschenbach, A. (2018) Deforestation for agricultural expansion in SW Zambia and NE Namibia and the impacts on soil fertility, soil organic carbon- and nutrient levels In: Climate change and adaptive land management in southern Africa \u2013 assessments, changes, challenges, and solutions (ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & J\u00fcrgens, N.), pp. 242-250, Biodiversity & Ecology, 6, Klaus Hess Publishers, G\u00f6ttingen & Windhoek. https://doi.org/10.7809/b-e.00330 Dijkshoorn, J.A. (2003). SOTER database for Southern Africa (SOTERSAF ver. 1.0). Technical Report. Wageningen, the Netherlands: ISRIC (International Soil Reference and Information Centre). FAO. (1990). Guidelines for soil description (3rd ed.). Soil Resources, Management and Conservation Service, Land and Water Development Division, Food and Agriculture Organization of the United Nations. FAO/ISRIC. (2003). Soil and Terrain Database for Southern Africa. Land and Water Digital Media Series # 26. FAO, Rome. Herrick, J. E., Beh, A., Barrios, E., Bouvier, I., Coetzee, M., Dent, D., Elias, E., Hengl, T., Karl, J. W., Liniger, H., Matuszak, J., Neff, J. C., Ndungu, L. W., Obersteiner, M., Shepherd, K. D., Urama, K. C., Bosch, R., & Webb, N. P. (2016). The land\u2010potential knowledge system (LandPKS): mobile apps and collaboration for optimizing climate change investments. Ecosystem Health and Sustainability, 2(3).\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 https://doi.org/10.1002/ehs2.1209 ICC, MAWRD. (2000). Project to support the Agro-Ecological Zoning Programme (AEZ) in Namibia. Main report. Institut Cartogr\u00e0fic de Catalunya (ICC), & Ministry of Agriculture Water and Rural Development (MAWRD). Windhoek. IUSS (International Union of Soil Scientists) Working Group World Reference Base (WRB) (2015) World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. Update 2015. World Soil Resources Reports No 106. FAO, Rome. Kempf, J. (2000). Klimageomorphologische Studien in Zentral-Namibia: Ein Beitrag zur Morpho-, Pedo- und \u00d6kogenese. Dissertation, Univ. W\u00fcrzburg Nijbroek, R., Piikki, K., S\u00f6derstr\u00f6m, M., Kempen, B., Turner, K.G., Hengari, S., & Mutua, J. (2018). Soil organic carbon baselines for land degradation neutrality: Map accuracy and cost tradeoffs with respect to complexity in Otjozondjupa, Namibia. Sustainability, 2018(10), 1610. https://doi.org/10.3390/su10051610. Paterson, G. (2021). Guidance for the laboratory analysis - Soils4Africa Project. https://www.soils4africa-h2020.eu/serverspecific/soils4africa/images/Documents/GuidanceonLaboratoryAnalysis.pdf Petersen, A. (2008). Pedodiversity of southern African drylands. PhD dissertation. University of Hamburg, Germany. Pr\u00f6pper, M., Gr\u00f6ngr\u00f6ft, A., Finckh, M., Stirn, S., De Cauwer, V., Lages, F., Masamba, W., Murray-Hudson, M., Schmidt, L., Strohbach, B., J\u00fcrgens, N. (2015). The Future Okavango \u2013 Findings, Scenarios and Recommendations for Action. Research Project Final Synthesis Simmonds, S.E.B., Forbes Irving, T.J.M. (1995). Soils Assessment and Land Evaluation. Report prepared by Interconsult Namibia (Pty) Ltd for Africare, for Rural Water Supply Maintenance Project in Southern Kunene Region, Namibia. Strohbach, B.J., Adank, W.F., Coetzee, M.E., Jankowitz, W.J. (2019). A baseline description of the soils and vegetation of farm Klein Boesman, Khomas Region, Namibia. Namibian Journal of Environment, 3:37-55 Strohbach, B., Strydom, E., Nesongano, C., Zimmermann, I., De Cauwer, V., Coetzee, M. (2023). Final Report: Assessment of the Impact of Bush Encroachment and Bush Control on Climate Change Mitigation and Adaptation in Namibia. AHT GROUP GmbH & Perivoli Rangeland Institute, for Deutsche Gesellschaft f\u00fcr Internationale Zusammenarbeit. Windhoek, Namibia. Trippner, Christian. (1998). Semi-detailed soil survey and landscape ecological risk evaluation in the south-western and central-western parts of the Etosha National Park/N-Namibia. Part 2. Project report: DFG/GTZ Research Cooperation Project \u2018Environmental Change in the Etosha National Park/Northern Namibia\u2019 (Az. Bu 659/4-1+4-2). Van Engelen, V.W.P., & Dijkshoorn, J.A. (2013) Global and national soils and terrain databases (SOTER). Procedures manual, version 2.0. (eds.) Wageningen: ISRIC \u2013 World Soil Information. Van Engelen, V.W.P., & Wen, T.T. (eds.) (1995). Global and national soils and terrain digital databases \u2013 SOTER. Procedures manual. World Soil Resources Report 74. Rome: UNEP, ISSS, ISRIC, FAO. https://edepot.wur.nl/493802 Wild, S. (2016). Quest to map Africa\u2019s soil microbiome begins. Nature 539: 152. https://doi.org/10.1038/539152a \u00a0 Corresponding author: Marina E. Coetzee (mcoetzee@nust.na; marina.e.coetzee@gmail.com) Affiliations:\u00a0 Faculty of Engineering and the Built Environment, Namibia University of Science and Technology, Private Bag 13388, Windhoek, Namibia. Doctoral School of Environmental Sciences, Magyar Agr\u00e1r- \u00e9s \u00c9lettudom\u00e1nyi Egyetem (MATE; Hungarian University of Agriculture and Life Sciences), P\u00e1ter K\u00e1roly u. 1. H-2100 G\u00f6d\u00f6ll\u0151, Hungary. [1] Ministry's name changed over time: Ministry of Agriculture, Water and Rural Development (MAWRD; 1991-2000); Ministry of Agriculture, Water and Forestry (MAWF; 2000-2020); Ministry of Agriculture, Water and Land Reform (MAWLR; 2020-2025); Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR, since 2025).", "keywords": ["Database", "Profile Description", "Soil Horizon", "Namibia", "Soil Profile", "Laboratory Data"], "contacts": [{"organization": "Coetzee, Marina", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17618737"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17618737", "name": "item", "description": "10.5281/zenodo.17618737", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17618737"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-11-15T00:00:00Z"}}, {"id": "10.5281/zenodo.3884383", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:24:11Z", "type": "Dataset", "title": "The vertical distribution of soil microbial biomass carbon: A global dataset", "description": "Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data are collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data were also collectd including climate, ecosystem types, and edaphic factors. More specifically, we develop this dataset by compiling data from 59 papers published in the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data included in this dataset meet two creteria: 1) there are at least three soil layers along a soil profile, and 2) soil MBC is measured using the fumigation extraction method. The data were obtained from tables and texts from literature directly, and the data in figures were extracted using GetData Graph digitizer software version 2.25. When climate and soil properties are not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables are converted to the standard international units or commonly used ones and the values are converted correspondingly. For example, the value of soil organic matter (SOM) is converted to SOC using the equation (SOC = SOM \u00d7 0.58). Soil depth is calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer. This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil C models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.", "keywords": ["2. Zero hunger", "soil organic carbon", "13. Climate action", "deep soils", "soil clay content", "soil profile", "soil C/N ratio", "15. Life on land", "micorbial quotient"], "contacts": [{"organization": "Sun, Tingting, Wang, Yugang, Hui, Dafeng, Jing, Xin, Feng, Wenting,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3884383"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3884383", "name": "item", "description": "10.5281/zenodo.3884383", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3884383"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-21T00:00:00Z"}}, {"id": "10.5281/zenodo.6540710", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:24:20Z", "type": "Dataset", "title": "Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC", "description": "This dataset contains the revised version of the SISLAC database in three formats: comma-separated values (.csv), microsoft access (.mdb) and PostGIS database (.backup). This database was reviewed and the inconsistencies found in the profiles and in the description of their horizons were corrected. Consists of two tables, one for the description of the profiles and the other with the description of the horizons and their properties. The key field between both tables is the profile identifier, column profile_id.", "keywords": ["organic carbon", "soil profiles", "15. Life on land"], "contacts": [{"organization": "D\u00edaz-Guadarrama, Sergio, Guevara, Mario,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6540710"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6540710", "name": "item", "description": "10.5281/zenodo.6540710", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6540710"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-12T00:00:00Z"}}, {"id": "10.5281/zenodo.7876731", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:24:31Z", "type": "Dataset", "title": "Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC version 1.2", "description": "The SISLAC_database version 1.2 contains the revised version of the SISLAC database in comma-separated values (csv) format. This database was reviewed and the inconsistencies found in the profiles and in the description of their horizons were corrected. The key field between both tables is the profile identifier, column profile_id.", "keywords": ["Soil Organic Carbon", "soil profiles", "15. Life on land"], "contacts": [{"organization": "D\u00edaz-Guadarrama, Sergio, Guevara, Mario,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7876731"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7876731", "name": "item", "description": "10.5281/zenodo.7876731", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7876731"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8092682", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:24:34Z", "type": "Journal Article", "created": "2021-12-15", "title": "A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network", "description": "Soil salinization causes a deterioration in soil health and threatens crop growth. Rapid identification of salinization in farmlands is of great significance to improve soil functions and to maintain sustainable land management. As salt moves in soil profiles during plowing and irrigation, the commonly used protocol for measuring and monitoring salt content in topsoil does not provide a thorough assessment. In order to quantify and comprehensively evaluate the salt content in deep soil, this study developed a novel framework for monitoring total salt content in the soil profile to a depth of 1 m by combining information from time-series satellite images and machine learning. The field experiments were conducted in Alar, Southern Xinjiang, with a total of 120 soil samples and 582 measurements of EM38-MK2 apparent electrical conductivity in 2019 and 2020 to quantify the vertical variation in the salt content. A total of 42 covariates derived from time-series Sentinel-2 images, including 20 salinity indices, 10 soil indices, and 12 vegetation indices were used for modeling salinity in the soil profile. From the total covariates, 22 were selected using the Random Forest. Soil salinity which was modeled using a Temporal Convolution Network in 2019 and 2020 and forecast for 2021. The model effectively revealed the spatial and temporal variability of the salt content in the soil profile with R2 of 0.71 and 0.65 for 2019 and 2020, respectively. The proposed new framework provides an effective method to estimate the salt content in the soil profile for precision agriculture in arid and semi-arid regions.", "keywords": ["2. Zero hunger", "Soil salinity", "Random Forest", "13. Climate action", "Time-series images", "Soil profile", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "Temporal Convolution Network"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8092682"}, {"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.8092682", "name": "item", "description": "10.5281/zenodo.8092682", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092682"}, {"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": "10.7910/DVN/1PEEY0", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:25:19Z", "type": "Dataset", "created": "2015-09-30", "title": "Global High-Resolution Soil Profile Database for Crop Modeling Applications", "description": "One of the obstacles in applying advanced crop simulation models such as DSSAT at a grid-based platform is the lack of gridded soil input data at various resolutions. Recently, there has been many efforts in scientific communities to develop spatially continuous soil database across the globe. The most representative example is the SoilGrids 1km released by ISRIC in 2014. In addition recent AfSIS project put a lot of efforts to develop more accurate soil database in Africa at high spatial resolution. Taking advantage of those two available high resolution soil databases (SoilGrids 1km and ISRIC-AfSIS at 1km resolution), this project aims to develop a set of DSSAT compatible soil profiles on 5 arc-minute grid (which is HarvestChoice\u2019s standard grid). Six soil properties (bulk density, organic carbon, percentage of clay and silt, soil pH and cation exchange capacity) available from the original SoilGrids 1km or ISRIC-AfSIS were directly used as DSSAT inputs. We applied a pedo-transfer function to derive some soil hydraulic properties (saturated hydraulic conductivity, soil water content at field capacity, wilting point and saturation) which are critical to simulate crop growth. For other required variables, HarvestChoice\u2019s HC27 database are used as a reference. Final outputs are provided in *.SOL file format (DSSAT soil database) for each country at 5-min resolution. In addition, uncertainty maps for organic carbon and soil water content at wilting points at the top 15 cm soil layers were generated to provide brief idea about accuracy of the final products. The generated soil properties were evaluated by visualizing their global maps and by comparing them with IIASA-IFPRI cropland map and AfSIS-GYGA\u2019s available water content maps.", "keywords": ["Computer and Information Science", "Agricultural Sciences", "Earth and Environmental Sciences", "soil profiles", "crop modelling"], "contacts": [{"organization": "International Research Institute For Climate And Society (IRI), Michigan State University (MSU), HarvestChoice, International Food Policy Research Institute (IFPRI),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/1PEEY0"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/1PEEY0", "name": "item", "description": "10.7910/DVN/1PEEY0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/1PEEY0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-10-15T00:00:00Z"}}, {"id": "10.7910/DVN/90WJ9W", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:25:20Z", "type": "Dataset", "created": "2010-01-01", "title": "HC27 Generic Soil Profile Database", "description": "The HC27 soil profile database consists of generic soil profiles developed by John Dimes and Jawoo Koo. The 27 soil profiles were generated based on three criteria that crop models are most responsive to: texture, rooting depth (proxy of water availability), and organic carbon content (proxy of fertility). Three levels for each category were classified using the boundary conditions based on the meta-analysis of WISE 1.1 soil profiles measured at crop land areas in Sub-Saharan Africa.
< p>There are multiple ways to utilize these generic soil profiles in crop modeling applications, especially when soil measurement data is not available for the study site. For example, (1) users can choose the one that best matches the soil typically found in the study area by following the decision tree of three multiple-choice questions and use it as a starting point, or (2) users can run models with all 27 soil profiles for a given study site to create a possible range of simulation results, which can be narrowed down later as more site-specific information becomes available.These generic soil profiles does not replace existing soil mapping efforts nor site-specific soil measurements. Instead this approach addresses the need for a set of reasonably representative and prototypical soil profiles in certain types of crop modeling applications (e.g., global-scale modeling studies). Due to the nature of being 'generic,' there will be applications for which the use of HC27 is not desirable, especially where detailed soil property dynamics beyond the three criter ia are emphasized.
", "keywords": ["Earth and Environmental Sciences", "Generic Soil Profile; Prototypical Soil Profile; Crop Modeling; DSSAT; Texture; Rooting Depth; Organic Carbon Content", "Soil Profile"], "contacts": [{"organization": "Koo, Jawoo, Dimes, John,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/90WJ9W"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/90WJ9W", "name": "item", "description": "10.7910/DVN/90WJ9W", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/90WJ9W"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-01-01T00:00:00Z"}}, {"id": "31769934-038c-4873-ab14-4b6b66531103", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-157.9, -38.8], [-157.9, 29.1], [175.9, 29.1], [175.9, -38.8], [-157.9, -38.8]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Australia"}, {"id": "Bangladesh"}, {"id": "Belize"}, {"id": "Benin"}, {"id": "Brazil"}, {"id": "Cameroon"}, {"id": "China"}, {"id": "Colombia"}, {"id": "Costa Rica"}, {"id": "Dominican Republic"}, {"id": "Ecuador"}, {"id": "Egypt"}, {"id": "El Salvador"}, {"id": "French Guiana"}, {"id": "Guadeloupe"}, {"id": "Honduras"}, {"id": "Hong Kong"}, {"id": "India"}, {"id": "Indonesia"}, {"id": "Madagascar"}, {"id": "Malaysia"}, {"id": "Mexico"}, {"id": "Micronesia"}, {"id": "Mozambique"}, {"id": "New Zealand"}, {"id": "Nigeria"}, {"id": "Palau"}, {"id": "Panama"}, {"id": "Philippines"}, {"id": "Saudi Arabia"}, {"id": "Singapore"}, {"id": "South Africa"}, {"id": "Sri Lanka"}, {"id": "Taiwan"}, {"id": "Thailand"}, {"id": "United States"}, {"id": "Vietnam"}], "scheme": "Region"}], "updated": "2024-11-27T10:08:58", "type": "Dataset", "language": "eng", "title": "Global mangrove soil carbon: dataset and spatial maps", "description": "Model outputs were updated on Dec 20, 2017. This project used a machine learning data-driven model to predict the distribution of soil carbon under mangrove forests globally. Specifically this dataset contains: 1) a compilation of georeferenced and harmonized soil profile data under mangroves compiled from literature, reports and unpublished contributions 2) global mosaics of soil carbon stocks to 1m and 2m depths produced at 100 m resolution 3) tiled predictions of soil carbon stocks produced at 30 m resolution 4) shape file containing the tiling system 5) shape file containing country boundaries used for calculating national level statistics.\nFor detailed methodologies, please see the scientific paper (https://doi.org/10.1088/1748-9326/aabe1c).", "formats": [{"name": "zip"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["carbon", "soil profiles", "Soil science", "Australia", "Bangladesh", "Belize", "Benin", "Brazil", "Cameroon", "China", "Colombia", "Costa Rica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "French Guiana", "Guadeloupe", "Honduras", "Hong Kong", "India", "Indonesia", "Madagascar", "Malaysia", "Mexico", "Micronesia", "Mozambique", "New Zealand", "Nigeria", "Palau", "Panama", "Philippines", "Saudi Arabia", "Singapore", "South Africa", "Sri Lanka", "Taiwan", "Thailand", "United States", "Vietnam"], "contacts": [{"name": "Jonathan Sanderman", "organization": "Woods Hole Research Centre", "position": "Associate scientist", "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "jsanderman@whrc.org"}], "addresses": [{"deliveryPoint": [null], "city": "Falmouth, Massachusetts", "administrativeArea": null, "postalCode": "MA 02540", "country": "United States of America"}], "links": [{"href": null}]}, {"name": "Tom Hengl", "organization": "ISRIC - World Soil Information", "position": "Former staff", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "None"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "distancevalue": "30", "distanceuom": "m"}, "links": [{"href": "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OCYUIT", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://doi.org/10.1088/1748-9326/aabe1c", "name": "Scientific paper", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/other/WD-Mangroves.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "31769934-038c-4873-ab14-4b6b66531103", "name": "item", "description": "31769934-038c-4873-ab14-4b6b66531103", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/31769934-038c-4873-ab14-4b6b66531103"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1969-01-01T00:00:00Z", "2015-09-01T00:00:00Z"]}}, {"id": "10261/392191", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:25:36Z", "type": "Journal Article", "created": "2025-02-25", "title": "Herbicide and metabolite mobility in soil profiles under conventional tillage and non-tillage: A two-year comparative field experiment", "description": "A two-year field experiment was conducted to compare the impact of conventional tillage (CT) and non-tillage (NT) on the mobility in two soils (S1 and S2) of the herbicides S-metolachlor (SMOC), foramsulfuron (FORAM), and thiencarbazone-methyl (TCM), and the formation of their main metabolites. Herbicide and metabolite distribution through the soil profiles (0-50\u00a0cm) was determined over two maize cycles. After the first application, the mobility of SMOC and TCM was similar under CT conditions, with higher concentrations in S2\u00a0+\u00a0CT topsoil than in S1\u00a0+\u00a0CT due to the higher organic carbon content in S2 and its retention ability, while both herbicides were detected in the entire S1\u00a0+\u00a0CT profile over time. Under NT management, partial interception by the mulch during application reduced the amount of herbicides that initially reached the topsoil, modifying their mobility dynamics. SMOC and TCM properties facilitated their transport through the soil profile, favoured by the irrigation applied shortly after their application. The total SMOC and TCM balance in S1 and S2 profiles revealed possible leaching below 50\u00a0cm, especially in soils+CT. However, the simultaneous degradation of SMOC and TCM might also occur on the mulch and/or in soil profiles, as indicated by the continuous detection of two SMOC metabolites (ethane sulfonic acid, SMOC-ESA, and oxanilic acid, SMOC-OA) and one TCM metabolite (thiencarbazone, TCM-MET1) throughout the soil profile in all the treatments assayed. FORAM dissipated faster than SMOC and TCM in all the treatments, with a total balance in all the soil profiles <40\u00a0% after 13\u00a0days. The high water solubility and polarity of FORAM might have enhanced its leaching, although its degradation to its two main metabolites was also observed in all cases. The mobility dynamics of the three herbicides in the second experimental period were similar for both soils under CT, but differed in soils under NT compared to the first application, with higher interception by the greater amount of mulch on the soil surface in the second year.", "keywords": ["Conservation agriculture", "Herbicides", "field experimentation", "Metabolite", "Agriculture", "Field experiment", "leaching", "Soil", "herbicides", "conservation agriculture", "Soil profile", "Acetamides", "Leaching", "soil profiles", "Soil Pollutants", "Herbicide", "metabolites", "Environmental Monitoring"]}, "links": [{"href": "https://doi.org/10261/392191"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/392191", "name": "item", "description": "10261/392191", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/392191"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-01T00:00:00Z"}}, {"id": "1069afa2-e7ee-4c57-8e5f-06cf489b7623", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[12.1, 41.2], [12.1, 60.0], [41.1, 60.0], [41.1, 41.2], [12.1, 41.2]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Europe"}, {"id": "Eastern Europe"}, {"id": "Central Europe"}, {"id": "Bulgaria"}, {"id": "Czech Republic"}, {"id": "Belarus"}, {"id": "Hungary"}, {"id": "Moldova"}, {"id": "Poland"}, {"id": "Romania"}, {"id": "Russia"}, {"id": "Slovakia"}, {"id": "Ukraine"}, {"id": "Estonia"}, {"id": "Latvia"}, {"id": "Lithuania"}], "scheme": "Region"}], "license": "Attribution 3.0 International (CC BY 3.0)", "updated": "2021-07-14T11:52:16", "type": "Dataset", "language": "eng", "title": "SOTER-based soil parameter estimates (SOTWIS) for Central and Eastern Europe, version 1.0", "description": "This harmonized set of soil parameter estimates for Central and Eastern Europe has been derived from a revised version of the 1:2.5M Soil and Terrain (SOTER) Database for Central and Eastern Europe (SOVEUR ver. 1.1) and the ISRIC-WISE soil profile database.\n\nThe land surface of Central and Eastern Europe, West of the Ural Mountains, has been characterized using 8361 unique maps or SOTER units. The corresponding GIS files include some 9500 mapped polygons, including miscellaneous units. The major soils have been described using 662 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. These sources seldom hold all the physical and chemical attributes ideally required by SOTER. Gaps in the measured soil profile data have been filled using a procedure that uses taxotransfer rules, based on about 9600 soil profiles held in the WISE database, complemented with expert-rules. \n\nParameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and available water capacity (-33 to -1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. \n\nThe current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure; taxotransfer rules have been flagged to provide an indication of the confidence in the derived data.\n\nResults are presented as summary files and can be linked to the 1:2.5M scale SOVEUR map in a GIS, through the unique SOTER-unit code. The secondary data are considered appropriate for studies at the continental scale (greater than 1:2.5 million); correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.", "formats": [{"name": "zip"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["calcium", "carbon", "cation exchange capacity", "electrical conductivity", "nitrogen", "organic matter", "bulk density", "soil profiles", "pH", "salinity", "texture", "water holding capacity", "nutrients", "Soil science", "Europe", "Eastern Europe", "Central Europe", "Bulgaria", "Czech Republic", "Belarus", "Hungary", "Moldova", "Poland", "Romania", "Russia", "Slovakia", "Ukraine", "Estonia", "Latvia", "Lithuania"], "contacts": [{"name": "Niels Batjes", "organization": "ISRIC - World Soil Information", "position": "Senior Soil Scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "niels.batjes@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "denominator": "2500000"}, "links": [{"href": "https://files.isric.org/public/sotwis/SOTWIS_SOVEUR_v1.zip", "name": "Download", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://isric.org/projects/harmonized-continental-soter-derived-database-sotwis", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://isric.org/sites/default/files/isric_report_2000_02a.pdf", "name": "Report", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/sotwis/SOTWIS_SOVEUR_v2.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "1069afa2-e7ee-4c57-8e5f-06cf489b7623", "name": "item", "description": "1069afa2-e7ee-4c57-8e5f-06cf489b7623", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1069afa2-e7ee-4c57-8e5f-06cf489b7623"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1930-07-01T00:00:00Z", "1997-09-01T00:00:00Z"]}}, {"id": "1081ac75-78f7-4db3-b8cc-23b78a3aa769", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-172.5, -38.6], [-172.5, 64.9], [153.1, 64.9], [153.1, -38.6], [-172.5, -38.6]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Australia"}, {"id": "Botswana"}, {"id": "Brazil"}, {"id": "Cameroon"}, {"id": "China"}, {"id": "Colombia"}, {"id": "Costa Rica"}, {"id": "Cote d Ivoire"}, {"id": "Cuba"}, {"id": "Ecuador"}, {"id": "Finland"}, {"id": "France"}, {"id": "Gabon"}, {"id": "Germany"}, {"id": "Ghana"}, {"id": "Greece"}, {"id": "Hungary"}, {"id": "Spain"}], "scheme": "Region"}], "license": "Open Database License (ODbl) v1.0", "updated": "2021-07-14T11:52:10", "type": "Dataset", "language": "eng", "title": "A Globally Distributed Soil Spectral Library Visible Near Infrared Diffuse Reflectance Spectra", "description": "The ICRAF-ISRIC Soil VNIR Spectral Library contains visible near infrared spectra of 4,438 soils selected from the Soil Information System (ISIS) of the International Soil Reference and Information Centre (ISRIC). The samples consist of all physically archived samples at ISRIC in 2004 for which soil attribute data was available. The spectra were measured at the World Agroforestry Center's (ICRAF) Soil and Plant Spectral Diagnostic Laboratory. The samples are from 58 countries spanning Africa, Asia, Europe, North America, and South America. Associated attribute data, such as geographical coordinates, horizon (depth), and physical and chemical properties, are provided in a single relational database. The purpose of the library is to provide a resource for research and applications for sensing soil quality both in the laboratory and from space.", "formats": [{"name": "zip"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["soil profiles", "colour", "moisture", "texture", "spectroscopy data", "Soil science", "Australia", "Botswana", "Brazil", "Cameroon", "China", "Colombia", "Costa Rica", "Cote d Ivoire", "Cuba", "Ecuador", "Finland", "France", "Gabon", "Germany", "Ghana", "Greece", "Hungary", "Spain"], "contacts": [{"name": "Keith Shepherd", "organization": "World Agroforestry Centre", "position": "Senior scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "afsis.info@africasoils.net"}], "addresses": [{"deliveryPoint": ["PO Box 30677"], "city": "Nairobi", "administrativeArea": null, "postalCode": "00100", "country": "Kenya"}], "links": [{"href": null}]}, {"name": "Stephan Mantel", "organization": "ISRIC - World Soil Information", "position": "Sustainable land management", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "stephan.mantel@wur.nl"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "denominator": "100000"}, "links": [{"href": "https://files.isric.org/public/other/", "name": "Download", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://isric.org/explore/ISRIC-collections", "name": "Project webpage ISRIC", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://www.worldagroforestry.org/sd/landhealth/soil-plant-spectral-diagnostics-laboratory/soil-spectra-library", "name": "Project webpage ICRAF", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "http://worldagroforestry.org/sites/default/files/Description_ICRAF-ISRIC%20Soil%20VNIR%20Spectral%20Library.pdf", "name": "Report", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/other/ICRAF-ISRICVNIRSoilDatabase.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "1081ac75-78f7-4db3-b8cc-23b78a3aa769", "name": "item", "description": "1081ac75-78f7-4db3-b8cc-23b78a3aa769", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1081ac75-78f7-4db3-b8cc-23b78a3aa769"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1996-11-01T00:00:00Z", "2006-11-01T00:00:00Z"]}}, {"id": "1a8f1290-f75a-4c86-bf7a-7b44d03089c3", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-173.2, -78.5], [-173.2, 80.0], [178.5, 80.0], [178.5, -78.5], [-173.2, -78.5]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Global"}], "scheme": "Region"}], "license": "Licenced per profile, as specified by data provider and indicated in the data", "updated": "2024-11-26T15:42:30", "type": "Dataset", "language": "eng", "title": "WoSIS latest - Silt total", "description": "X to Y mm fraction of the fine earth fraction*; Y as specified in the analytical method description (e.g. Y = 0.05 mm) (g/100g).\n \nWoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts).\n\nBeing dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required.\n\nStatic snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024.\n\nFor general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.", "formats": [{"name": "CSV"}, {"name": "OGC:WFS"}, {"name": "WWW:LINK-1.0-http--related"}, {"name": "OGC:WMS"}], "keywords": ["silt", "soil profiles", "Soil science", "Global"], "contacts": [{"name": "Luis de Sousa", "organization": "ISRIC - World Soil Information", "position": "Guest researcher", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "luis.desousa@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Data infodesk", "organization": "ISRIC - 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The samples consist of all physically archived samples at ISRIC in 2004 for which soil attribute data was available. The spectra were measured at the World Agroforestry Center's (ICRAF) Soil and Plant Spectral Diagnostic Laboratory. The samples are from 58 countries spanning Africa, Asia, Europe, North America, and South America. Associated attribute data, such as geographical coordinates, horizon (depth), and physical and chemical properties, are provided in a single relational database. The purpose of the library is to provide a resource for research and applications for sensing soil quality both in the laboratory and from space", "formats": [{"name": "zip"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["soil profiles", "colour", "moisture", "texture", "spectroscopy data", "Soil science", "Australia", "Botswana", "Brazil", "Cameroon", "China", "Colombia", "Costa Rica", "Cote d Ivoire", "Cuba", "Ecuador", "Finland", "France", "Gabon", "Germany", "Ghana", "Greece", "Hungary", "Spain"], "contacts": [{"name": "Keith Shepherd", "organization": "World Agroforestry Centre", "position": "Senior scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "afsis.info@africasoils.net"}], "addresses": [{"deliveryPoint": ["PO Box 30677"], "city": "Nairobi", "administrativeArea": null, "postalCode": "00100", "country": "Kenya"}], "links": [{"href": null}]}, {"name": "Stephan Mantel", "organization": "ISRIC - World Soil Information", "position": "Sustainable land management", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "stephan.mantel@wur.nl"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "denominator": "100000"}, "links": [{"href": "https://files.isric.org/public/other/", "name": "Download", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://isric.org/explore/ISRIC-collections", "name": "Project webpage ISRIC", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://www.worldagroforestry.org/sd/landhealth/soil-plant-spectral-diagnostics-laboratory/soil-spectra-library", "name": "Project webpage ICRAF", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "http://worldagroforestry.org/sites/default/files/Description_ICRAF-ISRIC%20Soil%20VNIR%20Spectral%20Library.pdf", "name": "Report", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/other/ICRAF-ISRICVNIRSoilDatabase.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "1b65024a-cd9f-11e9-a8f9-a0481ca9e724", "name": "item", "description": "1b65024a-cd9f-11e9-a8f9-a0481ca9e724", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1b65024a-cd9f-11e9-a8f9-a0481ca9e724"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1996-11-01T00:00:00Z", "2006-11-01T00:00:00Z"]}}, {"id": "1e25bd5b-81d4-4b41-a3a5-2368d4f1f617", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-173.2, -78.5], [-173.2, 80.0], [178.5, 80.0], [178.5, -78.5], [-173.2, -78.5]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Global"}], "scheme": "Region"}], "license": "Licenced per profile, as specified by data provider and indicated in the data", "updated": "2024-11-26T15:18:59", "type": "Dataset", "language": "eng", "title": "WoSIS latest - Water retention gravimetric - 6 kPa", "description": "Soil moisture content by weight, at tension 6 kPa (pF 1.8) (g/100g).\n \nWoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts).\n\nBeing dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required.\n\nStatic snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024.\n\nFor general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.", "formats": [{"name": "CSV"}, {"name": "OGC:WFS"}, {"name": "WWW:LINK-1.0-http--related"}, {"name": "OGC:WMS"}], "keywords": ["water retention", "soil profiles", "Soil science", "Global"], "contacts": [{"name": "Luis de Sousa", "organization": "ISRIC - World Soil Information", "position": "Guest researcher", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "luis.desousa@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Data infodesk", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "data@isric.org"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Niels Batjes", "organization": "ISRIC - World Soil Information", "position": "Senior Soil Scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "niels.batjes@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Luis Calisto", "organization": "ISRIC - World Soil Information", "position": "Database Expert", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "luis.calisto@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "denominator": "100000"}, "links": [{"href": "https://maps.isric.org/mapserv?map=/map/wosis_latest.map", "name": ":wosis_latest_wg0006", "description": "WoSIS latest - Water retention gravimetric - 6 kPa", "protocol": "OGC:WFS", "rel": "download"}, {"href": "https://doi.org/10.5194/essd-16-4735-2024", "name": "Scientific paper", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://www.isric.org/explore/wosis/faq-wosis", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://maps.isric.org/mapserv?map=/map/wosis_latest.map", "name": "wosis_latest_wg0006", "description": "WoSIS latest - Water retention gravimetric - 6 kPa", "protocol": "OGC:WMS", "rel": null}, {"href": "https://data.isric.org/geonetwork/srv/api/records/1e25bd5b-81d4-4b41-a3a5-2368d4f1f617/attachments/WoSIS%20latest%20-%20Water%20retention%20gravimetric%20-%206%20kPa.png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "1e25bd5b-81d4-4b41-a3a5-2368d4f1f617", "name": "item", "description": "1e25bd5b-81d4-4b41-a3a5-2368d4f1f617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1e25bd5b-81d4-4b41-a3a5-2368d4f1f617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1918-01-01T00:00:00Z", "2013-02-12T00:00:00Z"]}}, {"id": "20.500.11755/fc59f818-7a90-4dbf-89a4-a82403789e1e", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:26:18Z", "type": "Journal Article", "created": "2024-03-16", "title": "Depth\u2010dependent responses of soil organic carbon under nitrogen deposition", "description": "AbstractEmerging evidence points out that the responses of soil organic carbon (SOC) to nitrogen (N) addition differ along the soil profile, highlighting the importance of synthesizing results from different soil layers. Here, using a global meta\uffe2\uff80\uff90analysis, we found that N addition significantly enhanced topsoil (0\uffe2\uff80\uff9330\uffe2\uff80\uff89cm) SOC by 3.7% (\uffc2\uffb11.4%) in forests and grasslands. In contrast, SOC in the subsoil (30\uffe2\uff80\uff93100\uffe2\uff80\uff89cm) initially increased with N addition but decreased over time. The model selection analysis revealed that experimental duration and vegetation type are among the most important predictors across a wide range of climatic, environmental, and edaphic variables. The contrasting responses of SOC to N addition indicate the importance of considering deep soil layers, particularly for long\uffe2\uff80\uff90term continuous N deposition. Finally, the lack of depth\uffe2\uff80\uff90dependent SOC responses to N addition in experimental and modeling frameworks has likely resulted in the overestimation of changes in SOC storage under enhanced N deposition.