Extractable Boron (mg/kg): Essential micronutrient that regulates the metabolism of carbohydrates in plants. It’s critical for new growth and assists in pollination, fertilization and more. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning models.

Cation Exchange Capacity (Cmol/kg): It is a is a measure of the soil’s ability to hold positively charged ions. It is a very important soil property influencing soil structure stability, nutrient availability, soil pH and the soil’s reaction to fertilisers and other ameliorants. The data generated by MoA-ATI (EthioSIS Project) using thousands...

Calcium Saturation (%): Secondary macronutrient that aids in the growth and development of cell walls. It’s also helpful in cell metabolism and the uptake of nitrate. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning models.

Extractable Copper (mg/kg): Essential micronutrient that activates enzymes in plants. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning models.

Extractable Iron (mg/kg): Essential micronutrient that required for formation of chlorophyll in plants. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning models.

Magnesium Saturation (%): Secondary macronutrient that contributes to the green coloration of the plants. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning models.

Total Nitrogen (%): Primary macronutrient that helps foliage to grow strong, affects the plant’s leaf development. It also gives plants their green color due to its assistance with chlorophyll production. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning...

Exchangeable Potassium (mg/kg): Primary macronutrient that strengthens plants, helps contribute to early growth and assists the plants in retaining water. It also keeps the plants from contracting diseases and insects. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine...

Soil pH: is the measure of soil acidity or alkalinity, specifically it is the inverse log of the Hydrogen ion concentration. The pH ranges from 0 (extremely acid) to 14 (extremely alkaline) with 7 being neutral. Soil pH map generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine...

Available Sulfur (mg/kg): Secondary macronutrient that helps form and grow seeds. It also aids in the production of amino acids, proteins, enzymes and vitamins. The data generated by MoA-ATI (EthioSIS Project) using thousands of field-lab data and satellite data (Covariates) as an input in machine learning models.

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. This map is the result of resampling the mean SoilGrids 250 m predictions (Turek et...

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. This map is the result of resampling the mean SoilGrids 250 m predictions (Turek et...

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...

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...

The Free Brazilian Repository for Open Soil Data – febr, www.ufsm.br/febr – is a centralized repository targeted at storing open soil data and serving it in a standardized and harmonized format. The repository infrastructure was built using open source and/or free (of cost) software, and was primarily designed for the individual management of datasets. A...

Volumetric Water Content at 33kPa 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....

This uniform soil data set for the development of pedotransfer functions was developed at the request of the Global Soil Data Task (GSDT) of the Data and Information System (DIS) of the International Geosphere Biosphere Programme (IGBP). The necessary chemical and physical soil data have been derived from ISRIC's Soil Information System (ISIS) and the...

Model outputs were updated on Dec 20, 2017. This project used a machine learning data-driven model to predict the distribution of soil carbon under mangrove forests globally. Specifically this dataset contains: 1) a compilation of georeferenced and harmonized soil profile data under mangroves compiled from literature, reports and unpublished contributions...