Search datasets for "queryValue"
81 datasets found for "queryValue"
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Africa SoilGrids nutrients - Extractable Potassium (K) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Extractable Potassium (K) 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) . Values M = mean value...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Nutrient clusters based on fuzzy k-means N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated April 17, 2024 -
Africa SoilGrids nutrients - Extractable Zinc (Zn) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Sodium (Na) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Extractable Sodium content (Na) 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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Aluminium (Al) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Extractable Aluminium (Al) 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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Iron (Fe) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Phosphorus (P) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Manganese (Mn) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Extractable Manganese (Mn) 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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Calcium (Ca) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Magnesium (Mg) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Boron (B) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Extractable Copper (Cu) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Extractable Sodium Copper (Cu) 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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Total Nitrogen (N) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Total Nitrogen (N) content of the soil fine earth fraction in mg/kg (ppm) as measured according to the soil analytical procedure of wet oxidation 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...Created October 25, 2023 • Updated October 25, 2023 -
Africa SoilGrids nutrients - Total Phosphorus (P) N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Total Phosphorus (P) content of the soil fine earth fraction in mg/kg (ppm) as measured according to unspecified analytical methods 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...Created October 25, 2023 • Updated October 25, 2023 -
SoilGrids250m 2017-03 - Soil pH in H2O N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Soil pH x 10 in H2O at 7 standard depths (to convert to pH values divide by 10) 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: NA.Created October 25, 2023 • Updated October 25, 2023 -
SoilGrids250m 2017-03 - Soil pH x 10 in KCl N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Soil pH x 10 in KCl at 7 standard depths (to convert to pH values divide by 10) 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: NA.Created October 25, 2023 • Updated October 25, 2023 -
GeoPearl: SOM-content under arable land 250m N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated October 25, 2023 -
GeoPearl: SOM-content under arable land 1000m N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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...Created October 25, 2023 • Updated April 17, 2024 -
Africa SoilGrids - Soil pH in H2O N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Soil pH x 10 in H2O at 6 standard depths (to convert to pH values divide by 10) predicted using two sets of Africa soil profiles data. Measurement units: NA. 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,...Created October 25, 2023 • Updated October 25, 2023 -
SoilGrids250m 2017-03 - Probability of occurrence of R horizon N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)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.Created October 25, 2023 • Updated October 25, 2023
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