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    The organic carbon content (in mass-%) map for the 0-10-cm soil layer of the United Republic of Tanzania was generated by means of digital soil mapping in a regression-kriging framework (‘Simple kriging with varying local means’) implemented in the Open Source Software R. Over 3,000 soil point observations were used to generate the map. Data sources were NAFORMA, Tanzania National Soil Survey, African Soil Profiles Database Version 1.1, and AfSIS. In addition a suite of environmental GIS data layers were used such as a land cover map, SOTER soil class map, maps of topographic attributes derived from the SRTM-DEM, maps of surface reflectance and vegetation indices derived from satellite imagery. The point observations were correlated to the environmental data layers using a linear regression model. This model was used to predict the carbon content at the nodes of a regular grid with 250 meter cell size. The regression residuals were kriged to the prediction grid nodes and added to the regression prediction to obtain the final prediction of carbon content. Map projection is UTM Zone 36S.The root mean square error, as determined through 10-fold cross-validation, is 0.89%. The map was produced by ISRIC - World Soil Information in a collaborative effort with the National Soil Survey, Ministry of Natural Resources and Tourism, Tanzania Forest Services, Sokoine University, and AfSIS.

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    Volumetric coarse fragments content (v%) of the soil whole earth, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution

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    Extractable Iron (Fe) 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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    Volumetric moisture content (v%) of the soil fine earth fraction at permanent wilting point (at h=15,000 cm or pF 4.2), aggregated over the Effective Root Zone Depth for Maize, mapped at 1km resolution

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    Extractable Zinc (Zn) 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.

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    Predicted probability in percent per class 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: probability. To visualize these layers or request a support please use www.soilgrids.org.

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    Rootable depth limiting soil factor for maize, mapped at 1km resolution

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    Bulk density (fine earth) in kg / cubic-meter 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: kg / m3. To visualize these layers or request a support please use www.soilgrids.org.

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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)

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    Soil organic carbon density in kg per cubic-m 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: kg/m3 x 10. To visualize these layers or request a support please use www.soilgrids.org.