Search datasets for "queryValue"
506 datasets found for "queryValue"
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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 -
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 -
Africa Soil Profiles Database, version 1.0 N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)ISRIC World Soil Information is compiling legacy soil profile data of Sub Saharan Africa, as a project activity of the AfSIS project (Globally integrated Africa Soil Information Service). http://africasoils.net/services/data/soil-databases/ Africa Soil Profiles database, version. 1.0 (April 2012) identifies less than 15700 unique soil profiles inventoried...Created October 25, 2023 • Updated October 25, 2023 -
WISE derived soil properties on a 5 by 5 arc-minutes global grid, version 1.2 N3 | TTL | RDF/XML | JSON-LD
FDRE - Ministry of Agriculture (MoA)Version 1.2 of describes a harmonized dataset of derived soil properties for the world. It was created using the soil distribution shown on the 1:5 million scale FAO-Unesco Soil Map of the World (DSMW), rasterised at 5 by 5 arcminutes, and soil property estimates derived from the ISRIC-WISE soil profile database, version 3.1. The dataset considers 19 soil...Created October 25, 2023 • Updated October 25, 2023 -
HTMA MPS2 Cycle 2 genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle 2 formed by inter mating selected Cycle1 genotypes genotyped with 93 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
HTMA MPS2 Cycle 1 Genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle 1 formed by inter mating selected S2 families genotypes genotyped with 84 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
HTMA MPS1 Cycle 2 genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle 2 formed by inter mating selected Cycle1 genotypes genotyped with 93 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
HTMA MPS1 Cycle 1 Genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle1 formed by inter mating selected S2 families genotypes genotyped with 84 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
Nutritious Maize for Ethiopia (NuME) - baseline survey N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaMaize is now the major food crop in Ethiopia, but it has poor nutritional quality, in particular inadequate levels of the essential amino acids lysine and tryptophan, which reduces the overall biological value of its protein. In response, quality protein maize (QPM) was developed, combining improved protein quality with storage and agronomic qualities...Created January 20, 2025 • Updated January 20, 2025 -
Spatial and temporal fluctuation of groundwater depth in Amibara, middle Awa... N3 | TTL | RDF/XML | JSON-LD
Ethiopian Institute of Agricultural Research (EIAR)Soil salinity is a threat for agriculture under irrigation as it affects the growth and development of plants and the problem is widespread in middle Awash where Amibara irrigated farms are present. Therefore, Measurements of groundwater depth were taken from thirty piezometers in the months of August, September, October, November and December to...Created August 7, 2023 • Updated December 30, 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 - Soil pH in H2O aggregated 5000m 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). 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...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 -
SoilGrids250m 2.0 - Soil pH in H2O aggregated 1000m 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). 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...Created October 25, 2023 • Updated October 25, 2023
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