SoilHive: Open Soil Data Platform by Varda

AfSIS Nutrient

Africa SoilGrids nutrients

Coverage: Continental - Africa

Data type: Raster

The African Soil Information Service (AfSIS), managed by ISRIC – World Soil Information, is an initiative designed to enhance soil data and information across Sub-Saharan Africa. It aims to support sustainable land management and agricultural productivity by creating detailed soil maps and databases. AfSIS's data collection encompasses a diverse range of samples, including 18,000 specimens from 60 sentinel sites across Sub-Saharan Africa, covering soil depths from 0-20 cm and 20-50 cm. Additionally, the service has amassed 60,000 samples from 18,500 soil profiles distributed across 40 countries. This extensive dataset is further enriched by contributions from various sources such as EthioSIS, GhaSIS, NiSIS, IFDC, the One Acre Fund, the University of California, Davis, and VitalSigns, ensuring a robust and multifarious soil information system. AfSIS integrates remote sensing techniques, digital soil mapping, and geospatial data to produce detailed and actionable soil information. These technological tools enable the creation of soil maps and facilitate a better understanding of soil properties and their spatial distribution. The initiative was founded by Bill & Melinda Gates Foundation, with the support of the International Fund for Agricultural Development (IFAD), the World Bank, and the European Union.

Soil properties: Aluminium, Boron, Calcium, Copper, Iron, Potassium, Magnesium, Manganese, Nitrogen, Sodium, Phosphorus, Zinc

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AfSIS Property maps

Africa SoilGrid properties

Coverage: Continental - Africa

Data type: Raster

Over the period 2008–2014, the AfSIS project has compiled two soil profiles / samples datasets: the Africa Soil Profiles database holding legacy soil profiles data and the Sentinel Sites database holding newly collected topsoil data, jointly consisting of around 28 thousand sampling locations and a total of 85 thousands samples. Using these soil point observations & measurements and an extensive collection of global (SoilGrids1km) and continental (Africa) environmental covariates, ISRIC - World Soil Information, in collaboration with The Earth Institute, Columbia University, World Agroforestry Centre, Nairobi and the International Center for Tropical Agriculture (CIAT), has produced predictions of soil properties for the whole African continent at 250 m spatial resolution at either two or six standard soil depths.

Soil properties: Aluminium, Rock Horizon, Bulk Density, Cation Exchange Capacity, Clay, Coarse Fragments, Exchangeable acidity, Calcium, Magnesium, Sodium, Extractable Bases, Potassium, Nitrogen, Organic Carbon Content, pH, silt, sand

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Arsenic in European topsoils

Coverage: Continental - Europe

Data type: Raster

EUSO has developed a new method to model As contamination in European soils using LUCAS soil samples. They have introduced the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, they produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). They found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.

Soil properties: Arsenic

Bulk data download is not available for this data source, in order to do that, please go to Arsenic in European topsoils website

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Brazilian Coffee Farmer

Coffee farm in Brazil - field data

Coverage: National - Brazil

Data type: Vector - Polygon

Soil chemical-physical analysis on a coffe plantation in Brazil

Soil properties: Calcium Saturation, Aluminium, Magnesium Saturation, Zinc, Potassium, Exchangeable Acidity and Aluminum, Phosphorus, Calcium, Ca/Mg ratio, Mg/K ratio, Magnesium, Carbon Total, Boron, Organic Matter, Copper, Hydrogen Saturation, Aluminium Saturation, Cation Exchange Capacity, Manganese, Base Saturation, pH, Potassium Saturation, Iron, Ca/K ratio

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CAROB

CAROB Project - Soil Samples

Coverage: Global

Data type: Vector - Point

CAROB is a community project that uses a collaborative and open-source approach to standardize and compile open agricultural research data from experiments and surveys. The goal is to facilitate the further use of these data in research and development. The project compiled agricultural research data into groups with similar variables. This resource ("soil_samples") combines 10 standardized individual data sources, aggregating several soil biological, chemical, and physical parameters from 76 countries into a comprehensive dataset.

During the data cleaning process, the SoilHive team dropped some points that either didn’t have information for the soil properties we retain or fell on water. Additionally, some records in the dataset have low-precision coordinates. Users are advised to consider this limitation when working with the data and consult the FAQ for further details. The full dataset, comprehensive of data beyond the soil domain, can be found at carob-data.org.

Soil properties: pH, Organic Carbon Content, sand, Clay, silt, Nitrogen, Potassium, Phosphorus, Calcium, Magnesium, Boron, Copper, Manganese, Iron, Sulfur, Zinc, Sodium, Aluminium, Carbon Total, Electrical Conductivity, Bulk Density, Cation Exchange Capacity, Phosphorous Sorption Index, Exchangeable acidity

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CLSoilMaps

CLSoilMaps: A national soil gridded product for Chile

Coverage: National - Chile

Data type: Raster

CLSOILMAPS presents a newly gridded database of soil physical properties and soil hydraulic parameters based on digital soil mapping (DSM) techniques and a pedotransfer function (Rosetta V3) at close to 100m of spatial resolution covering the continental area of Chile and binational basins shared with Argentina for six standardized depths following GlobalSoilMap project standards. Maps were based on a newly compiled soil profile database covering different land use conditions (e.g. agricultural, forest, peatland, shrubland, and Andean grassland), and several environmental covariates based on the SCORPAN soil forming factors. DSM models showed moderate to good accuracies with R2 ranging from 0.76-0.88 for bulk density, 0.50-0.76 for clay, and 0.67-0.84 for sand. Silt maps were derived from clay and sand predictions taking advantage of the compositional nature of the particle size fraction. Field capacity, permanent wilting point, total available water capacity, and Van Genuchten´s soil hydraulic parameters were derived with Rosetta V3 algorithm.

Soil properties: Available Water Holding Capacity, Available Moisture, Field Capacity, Permanent Wilting Point, Saturated Hydraulic Conductivity, Bulk Density, Clay, sand, silt, USDA Texture

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Cadmium in European topsoils

Cadmium distribution in EU topsoils

Coverage: Continental - Europe

Data type: Raster

Cadmiun (Cd) is a naturally occurring element that can accumulate in the soil through the application of fertilisers containing cadmium and as a waste of industrial processes. Cadmium inputs in the soil have increased significantly (+50 %) during the 20th century as a result of the application of fertilisers and sewage sludge, and also due to local contamination (e.g. waste dumping, mining) and industrial emissions (e.g. zinc smelters). Using the 21,682 soil samples from the LUCAS soil survey, the JRC aimed to estimate the spatial distribution of the concentration of Cd in the European Union (EU) and UK topsoil. Applying an ensemble of machine learning models supported by a variety of environmental descriptors, they created maps of Cd distribution at a resolution of 100 m. The ensemble approach included five models and increased the prediction accuracy to R2 of 0.45 (an increase of 0.1 compared to best single model performance). The approach used resulted in a high predictive power for the general Cd distribution, while also identifying hotspots of Cd contamination. Natural factors influencing Cd levels include soil properties (pH, clay), topography, soil erosion, and leaching. As anthropogenic factors, we identified phosphorus inputs to agricultural lands as the most important for Cd levels. The application of the EU Fertiliser Directive should further limit Cd inputs and potentially the Cd content in soils.

Soil properties: Cadmium

Bulk data download is not available for this data source, in order to do that, please go to Cadmium in European topsoils website

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Downforce Technologies Limited

DownForce Technologies Limited - Soil Organic Carbon Stock in percentages for selected areas of Kenya

Coverage: National - Kenya

Data type: Raster

Taking part to the World Bank Data4SoilHealth Challenge 2025 for Kenya, Downforce Technologies Limited has shared two raster layers containing data on Organic Carbon Content in percentages for a selected of Kenya in 2022 and 2023, with a total volume of 160.000 pixels. The model has been trained using historical measurements of occ (Organic Carbon Content) across the property from 2017 to the present. Soil moisture has been observed annually. The potential for additional occ storage has been estimated based on levels observed in similar surrounding lands. Variability in occ across the property has been assessed based on its performance over time. The ongoing occ trend has been evaluated in comparison to the baseline.

Soil properties: Organic Carbon Content

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Earthworms Paraná

State of the art of studies on earthworm populations in the state of Paraná

Coverage: National - Brazil

Data type: Vector - Point

This review includes all the studies performed in the state of Paraná, Brazil, which had data on earthworms. A literature review was done for all publications (journal articles, dissertations, theses, conference proceedings, book chapters) carried out in the state of Paraná, Brazil, which had data on earthworms. The period evaluated ranged from 1986 (earliest date in the state) to 2020. Searches were performed in online databases including Sicence Direct, Scielo, CAPES and the digital collection of dissertations and theses from Brazilian Universities (BDTD).Overall 51 publications had earthworm data, including abundance, biomass, species, richness or just presence/absence. The soil chemical and physical analysis data were included in the dataset when performed in the same places as the earthworm sampling. Chemical data included: pH, H+Al, K, Ca, Mg, P, C, sum of Bases, CEC, Base saturation, N, Na. Physical data included: sand, clay and silt proportions, texture, porosity, density and resistance to penetration.
The data available in SoilHive is a subset containing only precise coordinates, containing at least three decimal points.

Soil properties: Earthworms Abundance, Earthworms Biomass, Earthworms Species Richness, pH, Aluminium, Exchangeable Acidity and Aluminum, Calcium, Magnesium, Potassium, Cation Exchange Capacity, Phosphorus, Carbon Total, Soil Organic Carbon Density, Sodium, Nitrogen, Base Saturation, Organic Matter, Available Moisture, Clay, silt, sand

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GSOCmap

Global Soil Organic Carbon Map

Coverage: Global

Data type: Raster

GSOCmap is the first global soil organic carbon map ever produced through a consultative and participatory process involving member countries, which makes this map totally new and unique. In fact, the map was prepared by member countries, under the guidance of the Intergovernmental Technical Panel on Soils and the Global Soil Partnership Secretariat. Countries agreed on the methodology to produce the map and were trained on modern tools and methodologies to develop national maps. The Global Soil Partnership then gathered all national maps to produce the final product, ensuring a thorough harmonization process.

Note: The dataset comes along with a technical report which presents methodologies and the process of compiling the Global Soil organic Carbon Map. The link to download the report is available in the on-line resources.

Soil properties: Soil Organic Carbon Stock

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Global Soil Nematode DB

A global database of soil nematode abundance and functional group composition

Coverage: Global

Data type: Vector - Point

As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. We removed all data entries that were not integers, as the origin of this information was unclear. After this cleaning process, the number of features decreased from 6,825 to 5,784. However, the original data is still accessible and can be downloaded from the other data repository available in the "service location."

Soil properties: Nematode Abundance

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HWSD

Harmonized World Soil Database

Coverage: Global

Data type: Vector - Polygon

The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to be useful for modelers and to serve as a basis for prospective studies on agroecological zoning, food security and the impacts of climate change. HWSD v2.0 also serves an educational function, illustrating the geographical distribution of soils as well as their properties globally.

Soil properties: Coarse Fragments, sand, silt, Clay, USDA Texture, Bulk Density, Organic Carbon Content, pH, Nitrogen, C:N Ratio, Cation Exchange Capacity, Extractable Bases, Base Saturation, Aluminium, Sodium, Calcium Carbonate, Gypsum, Electrical Conductivity

Bulk data download is not available for this data source, in order to do that, please go to HWSD website

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HoliSoils

Soil property datasets produced in HoliSoils project

Coverage: Continental - Europe

Data type: Raster

This dataset was prepared within the scope of the Holisoils (Holistic management practices, modelling & monitoring for European Forest Soils) project (EU Horizon 2020 Grant agreement ID: 101000289).
The soil property maps were generated at a resolution of 100m, and created with LUCAS and other continental level data using digital soil mapping. Users should assess the local ''predictive'' accuracy of the maps prior to using them for making recommendations at local (or field) level.
The datasets contain the following soil properties (for top soil (0-30 cm) depth): Soil organic carbon content, pH in water, Total nitrogen, Bulk density (oven dry), Coarse fragments (volumetric), Soil inorganic carbon content, Sand, Silt, Clay, as well as Soil organic carbon stocks, Soil inorganic carbon stocks and Soil nitrogen stocks.

Soil properties: Bulk Density, Coarse Fragments, Soil Inorganic Carbon Stock, Soil Inorganic Carbon Content, Soil Nitrogen Stock, Clay, Nitrogen, Soil Organic Carbon Stock, pH, silt, sand, Organic Carbon Content

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IITA Nigeria Soil Data

Coverage: National - Nigeria

Data type: Vector - Point

The soil data used in the database were obtained from profile observations and soil survey from IITA research sites over the last 10-15 years for Nigeria. We removed all records ISRIC, as these data have already been ingested into WoSIS. This step was taken to avoid duplication, and prevent double counting in data availability

Soil properties: pH, Nitrogen, Organic Carbon Content, Phosphorus, Calcium, Magnesium, Potassium, Sodium, Zinc, Copper, Manganese, Iron

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ISDA Africa

iSDA Africa Field Data

Coverage: Continental - Africa

Data type: Vector - Point

iSDA is an IT consulting service aiming to help smallholder farmers across Africa increase their productivity using advanced technologies while also improvIng their profitability and the well-being of their communities.

Founded by three research institutes - Rothamsted Research, the World Agroforestry Centre (ICRAF) and the International Institute of Tropical Agriculture (IITA) - iSDA is a mission-driven company carries forward the legacy of the African Soils Information Service (AfSIS).

ISDA has released a large dataset of analysed soil samples covering the African continent. These data are analysis-ready and do not require any preprocessing before usage. The dataset was collected and analysed according to protocols created by the African Soil Information Service (AfSIS) project (Vågen T-G et al., 2010). AfSIS was an initiative funded by the Bill and Melinda Gates Foundation, aimed at improving soil information across Africa. Data were collected and analysed between 2008 and 2020. In total, over 50,000 soil samples were collected across more than 15 countries.

Soil properties: Aluminium, Boron, Calcium, Organic Carbon Content, Carbon Total, Copper, Electrical Conductivity, Iron, Magnesium, Manganese, Nitrogen, pH, Phosphorus, Potassium, Sodium, Sulfur, Zinc

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KCEP-CRAL

Biophysical baseline assessment within the KCEP-CRAL action areas in Kenya, using the LDSF

Coverage: National - Kenya

Data type: Vector - Point

These data were collected within the Kenya Cereal Enhancement Programme-Climate Resilient Agricultural Livelihoods (KCEP-CRAL) Window. This study was conducted within the action sites of the Kenya Cereal Enhancement Programme-Climate Resilient Agricultural Livelihoods (KCEP-CRAL) Window, which is a programme supported by the Government of Kenya and International Fund for Agricultural Development (IFAD) within the Adaptation for Smallholder Agriculture Programme (ASAP). The project assessed key indicators of land and soil health in order to understand drivers of degradation, and monitor changes over time using the Land Degradation Surveillance Framework (LDSF) methodology. The LDSF provides a field protocol for measuring indicators of the "health" of an ecosystem. The LDSF was developed by the World Agroforestry (ICRAF) in response to the need for consistent field methods and indicator frameworks to assess land health in landscapes. The framework has been applied in projects across the global tropics, and is currently one of the largest land health databases globally with more than 30,000 observations, shared at http://landscapeportal.org. This project will benefit from existing data in the LDSF database, while at the same time contributing to these critically important global datasets through on-going data collection. Earth Observation (EO) data will be combined with the LDSF framework to develop the outputs for the project, including land degradation and soil health. Specific Activities on the ICRAF Component as Stated in the Agreement 1. Develop survey methodology detailing study design, methodology, tools, work plan and timelines documented 2. Procure LDSF field survey equipment 3. Conduct five LDSF surveys across the KCEP-CRAL action areas 4. Process, analyse and document the soil samples 5. Conduct Earth Observation-based assessment of biophysical indicators over time 6. Conduct capacity development opportunities with members of the PCU M&E staff and Government counterparts on LDSF field methodology This dataset contains LDSF field data from five LDSF sites in Kenya totalling 787 LDSF plots . It also contains the predictions using MIR Spectroscopy for 1500 Topsoil (0-20cm) and Subsoil (20-50cm) samples for six soil variables, including soil organic carbon, total nitrogen and pH. Related publications: Drivers of field-saturated soil hydraulic conductivity: Implications for restoring degraded tropical landscapes ; Drivers of topsoil saturated hydraulic conductivity in three contrasting landscapes in Kenya.

Soil properties: Organic Carbon Content, Nitrogen, pH, Extractable Bases, sand, Clay

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LUCAS 2009

Land Use and Coverage Area 2009

Coverage: Continental - Europe

Data type: Vector - Point

Following a decision of the European Parliament, the European Statistical Office (EUROSTAT) in close cooperation with the Directorate General responsible for Agriculture and the technical support of the JRC, is organising regular, harmonised surveys across all Member States to gather information on land cover and land use. This survey is known as LUCAS (Land Use/Cover Area frame statistical Survey). The name reflects the methodology used to collect the information. Estimates of the area occupied by different land use or land cover types are computed on the basis of observations taken at more than 250,000 sample points throughout the EU rather than mapping the entire area under investigation. By repeating the survey every few years, changes to land use can be identified.

In 2009, the European Commission extended the periodic Land Use/Land Cover Area Frame Survey (LUCAS) to sample and analyse the main properties of topsoil in 25 Member States of the European Union (EU). This topsoil survey represents the first attempt to build a consistent spatial database of the soil cover across the EU based on standard sampling and analytical procedures, with the analysis of all soil samples being carried out in a single laboratory. Approximately 19,967 points were selected out of the main LUCAS grid for the collection of soil samples. A standardised sampling procedure was used to collect around 0.5 kg of topsoil (0-20 cm). The samples were dispatched to a central laboratory for physical and chemical analyses.

Soil properties: Coarse Fragments, Clay, sand, silt, pH, Organic Carbon Content, Calcium Carbonate, Nitrogen, Phosphorus, Potassium, Cation Exchange Capacity

Bulk data download is not available for this data source, in order to do that, please go to LUCAS 2009 website

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LUCAS 2009 - Total P

Land Use and Coverage Area 2009 - Total P

Coverage: Continental - Europe

Data type: Vector - Point

This constitutes a segment of the LUCAS 2009 dataset. We opted to present this data separately to align with the original file structure in the ESDAC data portal.

The 'Land Use/Cover Area frame statistical Survey Soil' (LUCAS Soil) is a comprehensive and routine survey of topsoil conducted across the European Union, aimed at generating pertinent statistics regarding the impact of land management on soil properties. It involved the collection of around 45,000 soil samples during two time intervals, spanning from 2009 to 2012 and 2015. In a subsequent analysis stage, the JRC conducted laboratory assessments for Total Phosphorus (P Total) in the years 2013-14. These analytical results pertain to 21,681 samples gathered in the EU25 in 2009 and Bulgaria/Romania in 2012.

Soil properties: Phosphorus

Bulk data download is not available for this data source, in order to do that, please go to LUCAS 2009 - Total P website

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LUCAS 2015

Land Use and Coverage Area 2015

Coverage: Continental - Europe

Data type: Vector - Point

Following a decision of the European Parliament, the European Statistical Office (EUROSTAT) in close cooperation with the Directorate General responsible for Agriculture and the technical support of the JRC, is organising regular, harmonised surveys across all Member States to gather information on land cover and land use. This survey is known as LUCAS (Land Use/Cover Area frame statistical Survey). The name reflects the methodology used to collect the information. Estimates of the area occupied by different land use or land cover types are computed on the basis of observations taken at more than 250,000 sample points throughout the EU rather than mapping the entire area under investigation. By repeating the survey every few years, changes to land use can be identified.

The LUCAS 2015 Soil Module targeted a revisit to 90% of the locations sampled in the 2009 survey and expanded to cover all 28 EU Member States (MS) and locations at altitudes above 1000 m. The remaining 10% of samples were reassigned to new locations within each MS. Approximately 22,000 samples have been analysed for pH, organic carbon, nutrient concentrations (N, P, K) and electrical conductivity (new in 2015) - over 23 000 samples were analysed if countries outside the EU). Particle size has was only measured for locations that were sampled for the first time in 2015 (otherwise see 2009 data for particle size). An additional 1,000 samples (approximately) were collected and analysed from Albania, Bosnia-Herzegovina, Croatia, North Macedonia, Montenegro, Serbia and Switzerland. Data for these countries will be uploaded in the coming months.

Soil properties: Coarse Fragments, Clay, sand, silt, pH, Electrical Conductivity, Organic Carbon Content, Calcium Carbonate, Phosphorus, Nitrogen, Potassium, Cation Exchange Capacity

Bulk data download is not available for this data source, in order to do that, please go to LUCAS 2015 website

Metadata info

LUCAS 2018

Land Use and Coverage Area 2018

Coverage: Continental - Europe

Data type: Vector - Point

Following a decision of the European Parliament, the European Statistical Office (EUROSTAT) in close cooperation with the Directorate General responsible for Agriculture and the technical support of the JRC, is organising regular, harmonised surveys across all Member States to gather information on land cover and land use. This survey is known as LUCAS (Land Use/Cover Area frame statistical Survey). The name reflects the methodology used to collect the information. Estimates of the area occupied by different land use or land cover types are computed on the basis of observations taken at more than 250,000 sample points throughout the EU rather than mapping the entire area under investigation. By repeating the survey every few years, changes to land use can be identified.

The soil module of LUCAS 2018 collected soil samples at 18,984 locations, with samples taken at various depths as outlined below:

Soil samples were gathered from the 0-20 cm depth at 18,744 points. In 141 of these points, exclusively in Portugal, additional samples from the 20-30 cm depth were analyzed for OC and CaCO3.

Soil samples from the 0-10 cm depth were collected at 232 points.

Samples from the 10-20 cm depth were collected at 8 points.

Soil properties: pH, Electrical Conductivity, Organic Carbon Content, Calcium Carbonate, Phosphorus, Nitrogen, Potassium, Aluminium, Iron, Bulk Density

Bulk data download is not available for this data source, in order to do that, please go to LUCAS 2018 website

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MapBiomas SOC stock

Annual mapping of soil organic carbon stock in Brazil 1985-2022 (beta collection)

Coverage: National - Brazil

Data type: Raster

Series of raster maps of the soil (0-30 cm) organic carbon stock across Brazil, covering the period 1985-2022, with temporal resolution of one year and spatial resolution of 30 m. The maps were produced using 9650 field soil samples and 43 static and dynamic covariates. More information: https://data.mapbiomas.org/dataset.xhtml?persistentId=doi:10.58053/MapBiomas/DHAYLZ

Soil properties: Soil Organic Carbon Stock

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Mercury in European topsoils

Mercury distribution in European Union topsoils.

Coverage: Continental - Europe

Data type: Raster

Mercury (Hg) distribution in topsoil (0-20cm) is influenced by climate, soil properties, vegetation. In adiiton to the natural factor, mercury has high values close to past mining activities and coal combustion sites. High concentrations of mercury have been found close to well-known mining sites like Almaden (Asturias, Spain), Mt. Amiata (Italy), Idrija (Slovenia) and Rudnany (Slovakia). Overall, the stock of Hg in EU topsoil is estimated to c.a. 44.8 Gg with a median concentration of 38.3 μg kg−1; 10% of the area exceeds the 84.7 μg kg−1 and 209 Hg hotspots (top 1%) have been identified with concentrations >422 μg kg−1.In a more detailed investigation, 42% of the hotspots were associated with well-known mining activities while the rest can be related either to coal combustion industries or local diffuse contamination. In total 209 hotspots were identified, defined as the top percentile in Hg concentration (>422 μg kg−1). 87 sites (42% of all hotspots) were associated with known mining areas. The sources of the other hotspots could not be identified and may relate to unmined geogenic Hg or industrial pollution.

Soil properties: Mercury

Bulk data download is not available for this data source, in order to do that, please go to Mercury in European topsoils website

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OCP Kenya - Cluster 1

Coverage: National - Kenya

Data type: Vector - Point

Taking part in the World Bank Data4SoilHealth Challenge 2025 for Kenya, OCP Kenya has contributed donating soil data points collected using Mid-Infrared (MIR) spectroscopy technology. The data, coming from two distinct labs, is divided in two clusters.
5,107 data points has been ingested into SoilHive for the Cluster 1, coming from the OCP School Lab.
OCP is a global leader in the phosphate and fertilizer industry, committed to supporting sustainable agriculture through science-driven innovations. Operating in Kenya through OCP Kenya, the company partners with farmers, institutions, and local stakeholders to improve soil health and agricultural productivity.

Soil properties: pH, Organic Carbon Content, Cation Exchange Capacity, Nitrogen, Phosphorus, Potassium, Calcium, Magnesium, Sulfur, Aluminium, Copper, Iron, Silicium, Zinc, sand, Clay, USDA Texture.

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OCP Kenya - Cluster 2

Coverage: National - Kenya

Data type: Vector - Point

Taking part in the World Bank Data4SoilHealth Challenge 2025 for Kenya, OCP Kenya has contributed donating soil data points collected using Mid-Infrared (MIR) spectroscopy technology. The data, coming from two distinct labs, is divided in two clusters.
8,616 data points have been ingested from Cluster 2, originated from the Cropnuts laboratory.
OCP is a global leader in the phosphate and fertilizer industry, committed to supporting sustainable agriculture through science-driven innovations. Operating in Kenya through OCP Kenya, the company partners with farmers, institutions, and local stakeholders to improve soil health and agricultural productivity.

Soil properties: pH, Potassium, Calcium, Magnesium, Nitrogen, Organic Matter, C:N Ratio, USDA Texture.

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One Acre Fund

One Acre Fund SoilDB

Coverage: National - Tanzania

Data type: Vector - Point

One Acre Fund is a social enterprise that supplies smallholders farmers in East Africa with asset-based financing and agriculture training services to reduce hunger and poverty. Headquartered in Kakamega (Kenya) the organization works with farmers in rural villages throughout Kenya, Rwanda, Burundi, Tanzania, Uganda, Malawi, Nigeria, Zambia and Ethiopia. They envision a future in which every farm family has the knowledge and means to achieve big harvests, support healthy families, and cultivate rich soil.

As part of their activity on improving soil health, they have organised a soil sampling campaigns in Southern Tanzania. Throughout this project they have sampled 400 locations within the Mbeya, Iringa, and Kilolo Region.

Soil properties: pH, Aluminium, Boron, Copper, Iron, Manganese, Phosphorus, Sulfur, Zinc, Sodium, Calcium, Magnesium, Potassium, Extractable Bases, Electrical Conductivity, Exchangeable acidity, Nitrogen, Carbon Total

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OpenLandMap

Coverage: Global

Data type: Raster

OpenLandMap is an open-source initiative that provides high-resolution global environmental data, including soil, climate, and vegetation characteristics. This collection of 250 m resolution raster datasets provides global soil property maps, offering key insights into soil characteristics across six standard depths (0, 10, 30, 60, 100, and 200 cm). The datasets includes soil texture classes (USDA system), organic carbon content and stock, bulk density, pH, water content at different suctions, coarse fragments, silt, sand and clay fractions. These layers are derived using machine learning models trained on extensive global soil profile databases. Detailed methodologies and additional data products are available through OpenLandMap's repositories. Antarctica is not included.

Soil properties: USDA Texture, Organic Carbon Content, Soil Organic Carbon Stock, Bulk Density, pH, Water Content, sand, Clay, silt, Coarse Fragments

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SLGA

SLGA: Soil and Landscape Grid of Australia (Soil Attributes)

Coverage: National - Australia

Data type: Raster

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). Detailed information about the Soil and Landscape Grid of Australia can be found at https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Soil properties: Available Water Holding Capacity, Bulk Density, Clay, Cation Exchange Capacity, Rock Horizon, Nitrogen, pH, Phosphorus, silt, sand, Organic Carbon Content

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Soil Properties USDA-NCSS

Soil Properties USDA -NCSS

Coverage: National - USA

Data type: Raster

It is a set of maps that allows you to explore a variety of soil properties throughout the continental United States. The maps were obtained by aggregating current USDA-NCSS soil survey data (SSURGO back-filled with STATSGO where SSURGO is not available) within 800m grid cells. This data aggregation technique results in maps that may not match the original data at any given point, and is intended to depict regional trends in soil properties at the statewide scale.

Keep in mind that these layers are meant to show regional variation of properties at a coarse scale, and cell values may not be accurate at a specific location. For more information about each layer and its aggregation technique, see the metadata for the corresponding property in the Soil Properties app.

Soil properties: Calcium Carbonate, Cation Exchange Capacity, Electrical Conductivity, pH, Sodium Adsorption Ratio, Organic Matter, Available Water Holding Capacity, Coarse Fragments, Saturated Hydraulic Conductivity, sand, silt, Clay, KW Factor, Wind Erodibility Index

(Additional information not directly available on this platform: Drainage class, Hydrological group, Land capability class, Soil order, Soil temperature regime, Soil colour and the properties with the weighted average of all horizons)

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SoilGrids250m

SoilGrids — global gridded soil information

Coverage: Global

Data type: Raster

SoilGrids is designed as a globally consistent, data-driven system that predicts soil properties and classes using global covariates and globally fitted models. If you are looking for soil information on national and/or local levels we advise you, before using SoilGrids, to compare SoilGrids predictions with soil maps derived from national and local soil geographical databases. National soil maps are usually based on more detailed input soil information and therefore are often more accurate than SoilGrids (within the local coverage area). For an overview of national and regional soil databases, please refer to the Soil Geographic Databases compendium.

The selection of soil profiles underpinning SoilGrids is larger than the publicly available set ('wosis_latest') displayed here (for details see the ESSD paper). The actual number of observations for each property varies (greatly) between profiles and with depth, generally depending on the objectives of the initial soil sampling programmes. National soil survey organisations will generally maintain a wider selection of soil profiles/properties for their country in their databases.

Soil properties: Bulk Density, Cation Exchange Capacity, Coarse Fragments, Nitrogen, pH, Clay, sand, silt, Organic Carbon Content, Soil Organic Carbon Density, Soil Organic Carbon Stock, Water Content

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TAMASA Soil Data

TAMASA Tanzania. Soil data from farmers' maize fields in 2014/15 season

Coverage: National - Tanzania

Data type: Vector - Point

CIMMYT, the International Maize and Wheat Improvement Center, is a non-profit organization dedicated to agricultural research and training. Their mission is to empower farmers through scientific advancements and innovation in order to address the challenges posed by the climate crisis and ensure global food security.

One of their projects, "Taking Maize Agronomy to Scale in Africa" (TAMASA), focused on enhancing productivity and profitability for small-scale maize farmers in Ethiopia, Nigeria, and Tanzania. Throughout this project, CIMMYT collected data by sampling 140 farmer fields in Tanzania in 2014 and 2015. The data obtained from these samples were utilized to enhance nutrient management strategies, ultimately improving agricultural practices and yields for maize farmers.

For further information about the TAMASA project, please visit: https://www.cimmyt.org/projects/taking-maize-agronomy-to-scale-in-africa-tamasa/

Soil properties: Carbon Total, pH, Aluminium, Calcium, Electrical Conductivity, Sulfur, Manganese, Phosphorus, Zinc, Potassium, Magnesium, Sodium, Iron, Boron, Nitrogen

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TARI_SoilHealthProject

Coverage: National - Tanzania

Data type: Vector - Point

TARI is a leading research institution in Tanzania that is dedicated to promoting sustainable agricultural development and improving agricultural productivity. Established in 2016, TARI is the national agricultural research organization responsible for generating and disseminating scientific knowledge and innovative agricultural technologies.

As part of this mandate, TARI collaborated together with Varda and other players in a feasibility study to implement a large-scale liming program to enhance the productivity of acidic soils in Tanzania and boost soil health. Within this project they have provided a soil dataset that was used for modelling purposes. The data were cleaned,processed and transformed into Shapefile format for spatial analysis. Following this, we harmonized the data in terms of vocabulary and unit of measurement. During the harmonization process, certain parameters were excluded from the dataset as they were not necessary for the modeling activities.
More information on the data lineage can be provided under request.

Soil properties: pH, Carbon Total, Organic Carbon Content, Nitrogen, Phosphorus, Potassium, Calcium, Iron, Zinc, Aluminium, Sulfur

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Test dataset

Coverage:

Data type: Raster

Description

Soil properties: qwerty

Metadata info

WoSIS

World Soil Information Service (WoSIS)

Coverage: Global

Data type: Vector - Point

The aim of the World Soil Information Service (WoSIS) is to serve quality-assessed, georeferenced soil data (point, polygon, and grid) to the international community upon their standardisation and harmonisation. So far, the focus has been on developing procedures for legacy point data with special attention to the selection of soil analytical and physical properties considered in the GlobalSoilMap specifications (e.g. organic carbon, soil pH, soil texture (sand, silt, and clay), coarse fragments ( < 2 mm), cation exchange capacity, electrical conductivity, bulk density, and water holding capacity). Profile data managed in WoSIS were contributed by a wide range of soil data providers; the data have been described, sampled, and analysed according to methods and standards in use in the originating countries. Hence, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values, and soil analytical method descriptions.

At present, WoSIS_latest contains standardized data for 228,000 profiles. The number of measured data for each property varies between proﬁles and with depth, generally depending on the purpose of the initial studies. Further, in most source data sets, there are fewer data for soil physical as opposed to soil chemical attributes and there are fewer measurements for deeper than for superficial horizons. Generally, limited quality information is associated with the various source data.

Soil properties: Bulk Density, Cation Exchange Capacity, Coarse Fragments, Clay, Electrical Conductivity, Nitrogen, Organic Carbon Content, pH, Phosphorus, sand, silt, Calcium Carbonate, Carbon Total, Water Retention Gravimetric, Water Retention Volumetric

(Additional information not directly available in this platform: FAO soil classification, Soil Classification, Soil taxonomy, WRB Soil Classification)

Metadata info

Wyss Naibunga Soil

Wyss Academy Naibunga Soil Parameters

Coverage: National - Kenya

Data type: Vector - Point

The Wyss Academy Naibunga Soil Parameters dataset, contributed by the Wyss Academy for Nature at the University of Bern, was donated as part of the World Bank Data4SoilHealth Challenge 2025 for Kenya. Developed through a previous partnership with the Kenya Agricultural and Livestock Research Organization (KALRO), this dataset consists of 48 georeferenced soil sample points from the Naibunga Conservancy. It includes key soil health indicators such as pH, Total Nitrogen, Total Organic Carbon, Phosphorus, Potassium, Calcium, Magnesium, Manganese, Copper, Iron, Zinc, and Sodium. Last year, KALRO conducted an extensive soil characteristics study for the Wyss Academy in the Naibunga area, where semi-circular bunds were dug. More info

Soil properties: pH, Nitrogen, Carbon Total, Phosphorus, Potassium, Calcium, Magnesium, Manganese, Copper, Iron, Zinc, Sodium, Electrical Conductivity

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Yara Kenya Data Challenge

Coverage: National - Kenya

Data type: Vector - Polygon

Taking part to the World Bank Data4SoilHealth Challenge 2025 for Kenya, Yara East Africa, a subsidiary of Yara International, has shared a polygonal dataset that provides insights on several soil parameters form 2023 in Kenya.
Yara International is a leading global fertilizers and crop nutrition company. With a focus on promoting balanced and efficient fertilization practices, it helps farmers optimize their crop yields while minimizing the environmental impact. Through various initiatives and partnerships, the company seeks to equip farmers with the knowledge and skills necessary to make informed decisions about fertilizer application, crop management, and sustainable farming practices. By empowering farmers with these tools, Yara East Africa aims to contribute to the development of a strong and resilient agricultural sector in Kenya.

The data is organized using the H3 spatial indexing library, providing uniform hexagonal polygons with a spatial resolution of 0.015 km2 (1.5 hectares). This structure ensures efficient spatial analysis and integration with geospatial tools, making the dataset highly suitable for large-scale agricultural and environmental studies.

Soil properties: pH, Phosphorus, Potassium, Calcium, Magnesium, Nitrogen, Organic Matter

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Yara Tanzania

Yara Tanzania DB

Coverage: National - Tanzania

Data type: Vector - Polygon

Yara Tanzania is a subsidiary of Yara International, a leading global fertilizers and crop nutrition company. With a focus on promoting balanced and efficient fertilization practices, it helps farmers optimize their crop yields while minimizing the environmental impact. Through various initiatives and partnerships, the company seeks to equip farmers with the knowledge and skills necessary to make informed decisions about fertilizer application, crop management, and sustainable farming practices. By empowering farmers with these tools, Yara Tanzania aims to contribute to the development of a strong and resilient agricultural sector in Tanzania.

This soil data set is the result of a 10 years initiative in Tanzania. The records do not have specific coordinates, but only a reference to the different administrative levels. minimizing the environmental impact. Through various initiatives and partnerships, the company seeks to equip farmers with the knowledge and skills necessary to make informed decisions about fertilizer application, crop management, and sustainable farming practices. By empowering farmers with these tools, Yara Tanzania aims to contribute to the development of a strong and resilient agricultural sector in Tanzania.

Soil properties: Phosphorus, Potassium, Boron, Calcium, Cation Exchange Capacity, Copper, Iron, Magnesium, Manganese, Molybdenum, Sodium, pH, Sulfur, Zinc, Cobalt, Organic Matter, Organic Carbon Content

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Zinc in European topsoils

Zinc concentrations in EU topsoils

Coverage: Continental - Europe

Data type: Raster

Zinc (Zn) is essential to sustain crop production and human health, while it can be toxic when present in excess. JRC applied a machine learning model on 21,682 soil samples from the LUCAS 2009 database to assess the spatial distribution in Europe of topsoil Zn concentrations measured by aqua regia extraction, and to identify the influence of natural drivers and anthropogenic sources on topsoil Zn concentrations. As a result, a map was produced showing topsoil Zn concentrations in Europe at a resolution of 250 m. The mean predicted Zn concentration in Europe was 41 mg kg−1, with a root mean squared error of around 40 mg kg−1 calculated for independent soil samples. They identified clay content as the most important factor explaining the overall distribution of soil Zn in Europe, with lower Zn concentrations in coarser soils. Next to texture, low Zn concentrations were found in soils with low pH (e.g. Podzols), as well as in soils with pH above 8 (i.e., Calcisols). The presence of deposits and mining activities mainly explained the occurrence of relatively high Zn concentrations above 167 mg kg−1 (the one percentile highest concentrations) within 10 km from these sites. In addition, the relatively higher Zn levels found in grasslands in regions with high livestock density may point to manure as a significant source of Zn in these soils. The map developed in this study can be used as a reference to assess the eco-toxicological risks associated with soil Zn concentrations in Europe and areas with Zn deficiency. In addition, it can provide a baseline for future policies in the context of pollution, soil health, human health, and crop nutrition.

Soil properties: Zinc

Bulk data download is not available for this data source, in order to do that, please go to Zinc in European topsoils website

Metadata info

gSSURGO

Gridded Soil Survey Geographic Database

Coverage: National - USA

Data type: Raster

Gridded SSURGO (gSSURGO) is similar to the standard USDA-NRCS Soil Survey Geographic (SSURGO) Database product but in the format of an Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase. A file geodatabase has the capacity to store much more data and thus greater spatial extents than the traditional SSURGO product. This makes it possible to offer these data in statewide or even conterminous United States (CONUS) tiles. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shapefiles. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS) partnership.

The gridded SSURGO (gSSURGO) dataset was created for use in national, regional, and statewide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, including the National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer (CDL), and the National Elevation Dataset (NED).

The gSSURGO Database is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (Valu1) containing “ready to map” attributes. The gridded map layer is a file geodatabase raster in an ArcGIS file geodatabase. The raster and vector map data have a statewide extent. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

Due to file size, the raster layer for the conterminous United States is only available in a 30-meter resolution.

Soil properties: Water Content, Soil Organic Carbon Stock, Root Zone Depth, Drought Vulnerability

Metadata info

iSDAsoil

Coverage: Continental - Africa

Data type: Raster

iSDAsoil is a soil resource created by iSDA, containing soil property predictions at 30m resolution for the entire African continent.

Maps for over 20 different soil properties have been created at three different depth ranges (0-20, 20-50 and 0-200 cm). Soil property predictions were made using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples. Included in the original resource there are images of predicted soil properties, model error and satellite covariates used in the mapping process.

To explore the iSDAsoil data, it is recommended to visit the iSDAsoil homepage.
Further information can be found in the FAQ section, and technical information documentation

Soil properties: Aluminium, Zinc, Phosphorus, Potassium, Nitrogen, Sulfur, Clay, Iron, Calcium, Magnesium, silt, sand, Cation Exchange Capacity, Carbon Total, Organic Carbon Content, Bulk Density, Coarse Fragments, pH, USDA Texture, Rock Horizon

Metadata info

SoilHive: a global soil data platform

AfSIS Nutrient

Africa SoilGrids nutrients

AfSIS Property maps

Africa SoilGrid properties

Arsenic in European topsoils

Arsenic in European topsoils

Brazilian Coffee Farmer

Coffee farm in Brazil - field data

CAROB

CAROB Project - Soil Samples

CLSoilMaps

CLSoilMaps: A national soil gridded product for Chile

Cadmium in European topsoils

Cadmium distribution in EU topsoils

Downforce Technologies Limited

DownForce Technologies Limited - Soil Organic Carbon Stock in percentages for selected areas of Kenya

Earthworms Paraná

State of the art of studies on earthworm populations in the state of Paraná

GSOCmap

Global Soil Organic Carbon Map

Global Soil Nematode DB

A global database of soil nematode abundance and functional group composition

HWSD

Harmonized World Soil Database

HoliSoils

Soil property datasets produced in HoliSoils project

IITA Nigeria Soil Data

IITA Nigeria Soil Data

ISDA Africa

iSDA Africa Field Data

KCEP-CRAL

Biophysical baseline assessment within the KCEP-CRAL action areas in Kenya, using the LDSF

LUCAS 2009

Land Use and Coverage Area 2009

LUCAS 2009 - Total P

Land Use and Coverage Area 2009 - Total P

LUCAS 2015

Land Use and Coverage Area 2015

LUCAS 2018

Land Use and Coverage Area 2018

MapBiomas SOC stock

Annual mapping of soil organic carbon stock in Brazil 1985-2022 (beta collection)

Mercury in European topsoils

Mercury distribution in European Union topsoils.

OCP Kenya - Cluster 1

OCP Kenya - Cluster 1

OCP Kenya - Cluster 2

OCP Kenya - Cluster 2

One Acre Fund

One Acre Fund SoilDB

OpenLandMap

OpenLandMap

SLGA

SLGA: Soil and Landscape Grid of Australia (Soil Attributes)

Soil Properties USDA-NCSS

Soil Properties USDA -NCSS

SoilGrids250m

SoilGrids — global gridded soil information

TAMASA Soil Data

TAMASA Tanzania. Soil data from farmers' maize fields in 2014/15 season

TARI_SoilHealthProject

TARI_SoilHealthProject

Test dataset

Test dataset

WoSIS

World Soil Information Service (WoSIS)

Wyss Naibunga Soil

Wyss Academy Naibunga Soil Parameters

Yara Kenya Data Challenge

Yara Kenya Data Challenge

Yara Tanzania

Yara Tanzania DB

Zinc in European topsoils

Zinc concentrations in EU topsoils

gSSURGO

Gridded Soil Survey Geographic Database

iSDAsoil

iSDAsoil

Soil properties