Datasets

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Exchangeable aluminium (Al3+)

Exchangeable aluminium (Al3+ measured in 1M KCl solution) in cmolc/kg (fine earth) at two 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)

• Africa
• Soil science
• aluminium
• digital soil mapping
• +1 more

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Root zone moisture content at saturation aggregated at ERZD

Volumetric moisture content (v%) of the soil fine earth fraction at saturation (at h=1 cm or pF 0), aggregated over the Effective Root Zone Depth for Maize

• Africa
• Soil science
• digital soil mapping

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Drainage classes

Drainage classes, defined according to the Guidelines for Soil Description (FAO, 2006) predicted using the Africa Soil Profiles Database (AfSP) v1.2. LEGEND = 1:Very poor, 2:Poor, 3:Imperfect, 4:Moderate, 5:Well, 6:Somewhat excessive, 7:Excessive, 255:NODATA. 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)

• Africa

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Textural class aggregated at ERZD

Textural class (USDA) of the soil fine earth fraction, aggregated over the Effective Root Zone Depth for Maize, mapped at 1km resolution

• Africa
• Soil science
• digital soil mapping

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids nutrients - Extractable Calcium (Ca)

Extractable Calcium (Ca) 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.

FDRE - Ministry of Agriculture (MoA)

Topsoil organic carbon concentrations and stocks for Tanzania

This soil organic carbon dataset contains the following maps: soil organic carbon concentration (%) for the 0-10 cm, 10-20 cm, 20-30 cm and 0-30 cm soil layers, and bulk density (kg/m3) and soil organic carbon stock (kg/m2) for the 0-30 cm layer. These maps were produced with (geostatistical) regression-kriging models that combined soil data from the NAFORMA survey, the Tanzania National Soil Survey and the African Soil Profiles Database Version 1.1 with a suite of environmental GIS data layers including 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 regression-kriging models were used to predict carbon concentrations, stocks and bulk density at the nodes of a regular grid with 250 meter cell size covering the Tanzania. Prediction uncertainty was quantified and is available with the data as the lower and upper boundary of the 90% prediction interval. Further details about the input data, modelling framework, and cross validation results are provided in a peer-reviewed, scientific journal article. The project was funded by the UN-REDD Programme Output 2.4 “National Maps inform delivery of the REDD+ Framework” and conducted through a letter of Agreement between Food and Agricultural Organization of the United Nations (FAO) and ISRIC-World Soil Information. The maps were 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 the AfSIS project.

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Root zone moisture content at wilting point aggregated at top 30 cm

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 rootable depth and the top 30 cm, mapped at 1km resolution

• Africa
• Soil science

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Electrical conductivity (EC)

Electrical conductivity (measured in 1: x soil: water solution) in dS/m 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)

• Africa
• Soil science

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids nutrients - Extractable Boron (B)

Extractable Boron (B) 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.

FDRE - Ministry of Agriculture (MoA)

Africa SoilGrids - Exchangeable sodium (Na+)

Exchangeable sodium (Na+ 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)

• Africa