Model predictions on SOM-content under arable land( Report: https://edepot.wur.nl/556312). Predictions with a model ensemble at fixed depths of 15, 45, 80 and 120 cm. Predictions using an ensemble of Random Forests and Gradient Boosting, with covariates from the SoilGrids 2017 model (Hengl et al. 2017) and a spatial cross-validation procedure. The model...

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...

Volumetric Water Content at 33kPa suction in 10-3 cm3cm-3 (0.1 v% or 1 mm/m) at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers.

Volumetric Water Content at 10kPa suction in 10-3 cm3cm-3 (0.1 v% or 1 mm/m) at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers.

Volumetric Water Content at 1500kPa suction in 10-3 cm3cm-3 (0.1 v% or 1 mm/m) at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers.

Model predictions on SOM-content under arable land (see: Report: https://edepot.wur.nl/556312 ): 'Predictions with a model ensemble at fixed depths of 15, 45, 80 and 120 cm on a 250m grid. Predictions using an ensemble of Random Forests and Gradient Boosting, with covariates from the SoilGrids 2017 model (Hengl et al. 2017) and a spatial cross-validation...

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...

Total plant-available water holding capacity (mm) of the soil whole earth fraction (including coarse fragments), summed over the Effective Root Zone Depth for Maize, mapped at 1km resolution

Probability of occurrence of R horizon 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.

Textural class (USDA) of the soil fine earth fraction, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution

Sand content (50-2000 micro meter) mass fraction in % 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: w%.

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.

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

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...

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....

Plant available water holding capacity (v%) of the soil fine earth fraction, with field capacity defined at h=200 cm or pF 2.3, aggregated over the Effective Root Zone Depth for Maize, mapped at 1km resolution

Rootable depth (cm) for maize

Volumetric coarse fragments content (v%) of the soil whole earth, aggregated over the Effective Root Zone Depth for Maize, mapped at 1km resolution

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...

Plant available water holding capacity (v%) of the soil fine earth fraction, with field capacity defined at h=200 cm or pF 2.3, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution