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Organizations: FDRE - Ministry of Agriculture (MoA)

244 datasets found

  • WoSIS snapshot - September 2019 N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the first ‘WoSIS snapshot’, in July 2016, many new soil data were shared with us, registered in the ISRIC data repository, and subsequently...
    Created October 25, 2023 Updated December 25, 2023
  • SoilGrids250m 2.0 - Volumetric Water Content at 1500kPa N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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.
    Created October 25, 2023 Updated November 25, 2023
  • SoilGrids250m 2.0 - Volumetric Water Content at 1500kPa aggregated 1000m N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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. This map is the result of resampling the mean SoilGrids 250 m predictions (Turek...
    Created October 25, 2023 Updated November 25, 2023
  • SoilGrids250m 2.0 - Volumetric Water Content at 1500kPa aggregated 5000m N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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. This map is the result of resampling the mean SoilGrids 250 m predictions (Turek...
    Created October 25, 2023 Updated November 25, 2023
  • SoilGrids250m 2.0 - Volumetric Water Content at 10kPa aggregated 1000m N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    Volumetric Water Content at 10 kPa 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. This map is the result of resampling the mean SoilGrids 250 m predictions (Turek...
    Created October 25, 2023 Updated November 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
  • SoilGrids250m 2.0 - Silt content aggregated 1000m N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    Silt content (2-50/63 micro meter) mass fraction in ‰ 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. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each...
    Created October 25, 2023 Updated October 25, 2023
  • Global Assessment of Human-induced Soil Degradation (GLASOD) N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    The GLASOD project (1987-1990), carried out for UNEP, has produced a world map of human-induced soil degradation. Data were compiled in cooperation with a large number of soil scientists throughout the world, using uniform Guidelines and international correlation. The status of soil degradation was mapped within loosely defined physiographic units...
    Created October 25, 2023 Updated October 25, 2023
  • 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 - Root zone total plant available water holding capacity agg... N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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
    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
  • Africa SoilGrids - Textural class aggregated at top 30 cm N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    Textural class (USDA) of the soil fine earth fraction, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution
    Created October 25, 2023 Updated October 25, 2023
  • SoilGrids250m 2017-03 - Sand content (50-2000 micro meter) mass fraction N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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%.
    Created October 25, 2023 Updated October 25, 2023
  • SOTER-based soil parameter estimates (SOTWIS) for Southern Africa N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    This harmonized set of soil parameter estimates for Southern Africa has been derived from the 1:2M scale Soil and Terrain Database for Southern Africa (SOTERSAF ver. 1.0) and ISRIC-WISE soil profile database. The land surface of Southern Africa has been characterized using 4022 unique SOTER units, corresponding with 6099 polygons. The major soils have...
    Created October 25, 2023 Updated October 25, 2023
  • SOTER-based soil parameter estimates (SOTWIS) for Latin America and the Carib... N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    This harmonized set of soil parameter estimates for Latin America and the Caribbean was derived from a revised version of the 1:5M Soil and Terrain Database for the region (SOTERLAC, ver. 2.0) and the ISRIC-WISE soil profile database. The land surface of Latin America and the Caribbean has been characterized using 1585 unique SOTER units, corresponding...
    Created October 25, 2023 Updated October 25, 2023
  • SoilGrids250m 2.0 - Total nitrogen aggregated 5000m N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    Total nitrogen in cg/kg 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. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each 5000 m cell.
    Created October 25, 2023 Updated October 25, 2023
  • SoilGrids250m 2017-03 - Bulk density (fine earth) N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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.
    Created October 25, 2023 Updated October 25, 2023
  • Africa SoilGrids - Root zone moisture content at wilting point aggregated at ... N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    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
    Created October 25, 2023 Updated October 25, 2023
  • SoilGrids250m 2.0 - Sand content aggregated 5000m N3 | TTL | RDF/XML | JSON-LD

    FDRE - Ministry of Agriculture (MoA)
    Sand content (50/63-2000 micro meter) mass fraction in ‰ 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. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each...
    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
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