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    This data was collected to develop baselines for three Land Degradation Neutrality (LDN) indicators: land use and land cover change (LUC) for the period 2001-2017, soil organic carbon (SOC) stocks for 2017 and bush density for 2017 as a baseline for bush encroachment in Omusati region, Namibia.

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    Soil organic carbon stock in tons per ha for ICCP depth intervals 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: t / ha.

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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 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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    The Soil and Terrain database for Malawi (version 1.0), at scale 1:1 million, was compiled based on the soil map of Malawi at scale 1:250,000 (compiled by the Land Resources Evaluation Project) that was complemented with soil boundary information from the provisional soil map at scale 1:1 million. The 90m SRTM-DEM was used to define the various landform types of the SOTER units and also to adjust their boundaries. The SOTER map units were attributed with soil information obtained from the Africa Soil Profiles Database. The SOTER compilation followed the methodology described in the SOTER Procedures Manual Version 2. 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. DOI for dataset (submitted): 10.17027/isric.wdcsoils.20160002

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    Sand content (50-2000 micro meter) in g/100g (w%) at 6 standard depths 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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    Extractable Magnesium (Mg) content of the soil fine earth fraction in mg/kg (ppm) 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.

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    Following publication of the World Map of the Satus of Human-induced Land Degradation (GLASOD), at scale 1:10M, the need for more detailed and more country-specific degradation assessment became apparent. In 1993, the members of the Asian Network on Problem Soils recommended the preparation of a qualitative assessment for South and Southeast Asian at a scale of 1:5 million. The assessment was carried out using a physiographic base map, compiled according to the SOTER methodology, and a slightly modified GLASOD methodology. The information was stored in a digital database and linked to a GIS enabling preparation of thematic outputs in the form of maps, graphs and tables.

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    Measured according to the P-Olsen method: 0.5 M sodium bicarbonate (NaHCO3) solution at a pH of 8.5 to extract P from calcareous, alkaline, and neutral soils (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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    Exchangeable magnesium (Mg2+ measured in 1M NH4OAc buffered at pH 7 with part of the data converted from data measured according to Mehlich 3) in cmolc/kg (fine earth) at 2 depth intervals (0-20 cm and 20-50 cm) predicted using two Africa soil profiles datasets. 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)