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    Gravimetric content of less than X mm soil material in the fine earth fraction* (e.g. X = 0.002 mm as specified in the analytical method description) (g/100g). ISRIC is developing a centralized and user–focused server database, known as ISRIC World Soil Information Service (WoSIS). The aims are to: • Safeguard world soil data "as is" • Share soil data (point, polygon, grid) upon their standardization and harmonization • Provide quality-assessed input for a growing range of environmental applications. So far some 400,000 profiles have been imported into WoSIS from disparate soil databases; some 150,000 of have been standardised. The number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Further, in most source data sets, there are fewer data for soil physical as opposed to soil chemical attributes and there are fewer measurements for deeper than for superficial horizons. Generally, limited quality information is associated with the various source data. Special attention has been paid to the standardization of soil analytical method descriptions with focus on the set of soil properties considered in the GlobalSoilMap specifications. Newly developed procedures for the above, that consider the soil property, analytical method and unit of measurement, have been applied to the present set of geo-referenced soil profile data. Gradually, the quality assessed and harmonized "shared" data will be made available to the international community through several webservices. All data managed in WoSIS are handled in conformance with ISRICs data use and citation policy, respecting inherited restrictions. The most recent set of standardized attributes derived from WoSIS are available via WFS. For instructions see Procedures manual 2018, Appendix A, link below (Procedures manual 2018). * The fine earth fraction is generally defined as being less than 2 mm. However, an upper limit of 1 mm was used in the former Soviet Union and its sattelite states (Katchynsky scheme). This has been indicated in the database.

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    The Soil and Terrain database for China primary data (version 1.0), at scale 1:1 million (SOTER_China), was compiled of enhanced soil information within the framework of the FAO's program of Land Degradation Assessment in Drylands (LADA). The primary database was compiled using the SOTER methodology. The SOTER unit delineation was based on a raster format of the soil map of China, correlated and converted to FAO’s Revised Legend (1988), in combination with a SOTER landform characterization derived from Shuttle Radar Topographic Mission (SRTM) 90 m digital elevation model (DEM). Reference profiles for the dominant soil of the SOTER units has been directly linked to the polygons. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

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    Determined with a very strong acid (aqua regia and sulfuric acid/nitric acid) (mg/kg). ISRIC is developing a centralized and user–focused server database, known as ISRIC World Soil Information Service (WoSIS). The aims are to: • Safeguard world soil data "as is" • Share soil data (point, polygon, grid) upon their standardization and harmonization • Provide quality-assessed input for a growing range of environmental applications. So far some 400,000 profiles have been imported into WoSIS from disparate soil databases; some 150,000 of have been standardised. The number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Further, in most source data sets, there are fewer data for soil physical as opposed to soil chemical attributes and there are fewer measurements for deeper than for superficial horizons. Generally, limited quality information is associated with the various source data. Special attention has been paid to the standardization of soil analytical method descriptions with focus on the set of soil properties considered in the GlobalSoilMap specifications. Newly developed procedures for the above, that consider the soil property, analytical method and unit of measurement, have been applied to the present set of geo-referenced soil profile data. Gradually, the quality assessed and harmonized "shared" data will be made available to the international community through several webservices. All data managed in WoSIS are handled in conformance with ISRICs data use and citation policy, respecting inherited restrictions. The most recent set of standardized attributes derived from WoSIS are available via WFS. For instructions see Procedures manual 2018, Appendix A, link below (Procedures manual 2018)

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    Soil organic carbon content (fine earth fraction) in g per kg at 7 standard depths predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: g / kg. To visualize these layers or request a support please use www.soilgrids.org.

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    The Soil and Terrain database for Argentina primary data (version 1.0), at scale 1:1 million (SOTER_Argentina), was compiled of enhanced soil information within the framework of the FAO's program Land Degradation Assessment in Drylands (LADA). Primary soil and terrain data for Argentina were obtained from the SOTERLAC database (ver. 2) at scale 1:5 million. This update includes considerable changes in the GIS file, based on the SRTM-DEM derived surface information and on INTA's digital soil map (Instituto de Suelos), and only few changes of the attributes database. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

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    This datasets describes a harmonized set of soil property estimates for Tunisia. It has been derived from the 1:1 million scale Soil and Terrain Database for the country (SOTER_TN, ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of Tunisia, covering some 164,150 km2, has been characterized in SOTER_TN using 250 unique SOTER units. Each map unit consists of up to four different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts (see Dijkshoorn et al. 2008). Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_TN have been characterized using 100 profiles of which 44 are synthetic. The latter represent some 59 per cent of the territory. Comprehensive sets of measured attribute data are not available for most of the measured profiles (56) collated in SOTER_TN, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database ― the current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names identical to those mapped for Tunisia in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 3566). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (> 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). 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 studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range. The derived data presented here may be used for exploratory assessments at national scale or broader (< 1:1 000 000). They should be seen as best estimates based on the current, still limited, selection of soil profiles in SOTER_TN and data clustering procedure ― the type of taxotransfer rules used to fill gaps in the measured data has been flagged to provide an indication of confidence in the derived data. Citation: Batjes NH 2010. Soil property estimates for Tunisia derived from SOTER and WISE (SOTWIS-Tunisia, ver. 1.0). Report 2010/01, ISRIC - World Soil Information, Wageningen, 41 p. https://www.isric.org/sites/default/files/isric_report_2010_01.pdf

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    Nutrient clusters based on fuzzy k-means of the soil fine earth fraction and spatially predicted at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.

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    Extractable aluminium content (Al measured by Mehlich 3) in mg/kg (fine earth) at 2 depth intervals (0-20 cm and 20-50 cm) predicted using two sets of Africa soil profiles data. For details see published paper here below (Hengl T., G.B.M. Heuvelink, B. Kempen, J.G.B. Leenaars, M.G. Walsh, K.D. Shepherd, A. Sila, R.A. MacMillan, J. Mendes de Jesus, L.T. Desta, J.E. Tondoh, 2015. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6)

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    The International Soil Carbon Network (ISCN) is a science-based network that facilitates data sharing, assembles databases, identifies gaps in data coverage, and enables spatially explicit assessments of soil carbon in context of landscape, climate, land use, and biotic variables.

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    Extractable Phosphorus (P) content of the soil fine earth fraction in mg/100kg (pp100m) as measured according to the soil analytical procedure of Mehlich 3 and spatially predicted for 0-30 cm depth interval at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.