Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
1,221–1,230 of 1,344 datasets
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.
FDRE - Ministry of Agriculture (MoA)
The Global Assessment of Land Degradation and Improvement (GLADA) project (2006–2009) was part of the GEF–UNEP–FAO program Land Degradation in Drylands. It utilized remote sensing data to identify areas experiencing land degradation as well as regions where degradation had been halted or reversed. The study relied on analyses of vegetation greenness using the Normalized Difference Vegetation Index (NDVI) as a proxy for net primary productivity, adjusted for the effects of climate variability on biomass through rain-use efficiency and energy-use efficiency. Biomass represents an integrated measure of biological productivity, and deviations from the local norm can serve as indicators of land degradation or improvement. Changes in biomass can be monitored via remote sensing of NDVI—the difference between reflected near-infrared and visible wavebands divided by their sum. NDVI is strongly and linearly correlated with the fraction of photosynthetically active radiation absorbed by vegetation, as well as with vegetation cover and above-ground net primary productivity. Baseline norms can be established by stratifying land areas based on climate, soil, terrain, and land use or vegetation type. Deviations from these norms may then be assessed regionally and aggregated globally to enable universal comparisons. Although the GLADA project concluded in 2009, its datasets and maps have been periodically updated as new remote sensing and climate data have become available, extending coverage to approximately 2021. The GLADA dataset comprises two primary raster layers presented in percentage: (1) global land degradation 1981-2021 and (2) global land improvement 1981-2021. Further details about the data can be found in the associated metadata and project documentation.
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 et al. 2021) for each 1000 m cell.
FDRE - Ministry of Agriculture (MoA)
Volumetric content of coarse fragments* in the whole soil (cm³/100cm³). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.
FDRE - Ministry of Agriculture (MoA)
Soil moisture content by weight, at tension 100 kPa (pF 3.0) (g/100g). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.
FDRE - Ministry of Agriculture (MoA)
Bulk density of the soil fine earth (measured by core method) in kg / cubic-meter (kg/m3) at 6 standard depths predicted using the Africa Soil Profiles Database (AfSP) v1.2. 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)
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 (polygons), based on expert judgement. The type, extent, degree, rate and main causes of degradation have been printed on a global map, at a scale of 1:10 million, and documented in a downloadable database. Information about the areal extent of human-induced soil degradation can be found in an explanatory note.
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 1000 m cell.
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 was applied on log-transformed observations of SOC. Models result were back- transformed and converted to SOM by multiplying SOC predictions with 1.743. Prediction
FDRE - Ministry of Agriculture (MoA)
Capacity of the fine earth fraction* to hold exchangeable cations, estimated by buffering the soil at 'pH8' (cmol(c)/kg). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.