| ArcGIS REST Services Directory | Login |
| Home > services > Geochemistry > IE_GSI_GEMAS_Geochemistry_Agricultural_Grazing_Land_Soil_EU_WGS84 (MapServer) | Help | API Reference |
| JSON | SOAP | WMS |
Soil is the outside layer of Earth. It is a made up of living organisms, gases, minerals, and organic matter. Knowing what elements are in the soil helps to work out where it came from and how it was made.
The GEMAS (geochemical mapping of agricultural soils and grazing land of Europe) project provides harmonised geochemical data of arable land and land under permanent grass cover across Europe.
Geological Surveys in 34 European countries collected soil samples. Soil samples were taken on arable land from the top 0–20 cm of the soil and land under permanent grass cover from the top 0–10 cm. One sample was taken per 2500 km2. The total area covered was 5.6 million km2.
Sampling took place during 2008, and early 2009 following a jointly agreed field protocol.
All samples were sent to the same lab to be tested for the chemicals that make up the soil. This was done using two different methods. The Aqua Regia (AR) method was used to extract 52 chemical elements and the X-ray Fluorescence (XRF) method was used to extract 41 chemical elements. Soil properties such as Cation exchange capacity (CEC), Total organic carbon (TOC) and pH (calcium chloride - CaCl2). In addition, the agricultural soil samples were analysed for 57 elements in a mobile metal ion (MMI®) extraction, Lead (Pb) isotopes and magnetic susceptibility.
Knowing the types of elements in the soil can point to where they came from and how the soils were made. CEC, pH and TOC tell us about the soil's ability to hold onto essential nutrients, how acid or basic the soils are, and the amount of organic carbon in the soil.
When the data is mapped, you can see the spread of elements across Europe. This allows the mapping of different soil types. Shallower soil is worth testing as it is good for showing us if any of the changes were caused by the actions of humans. It is also good for farming and the health of soil.
It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).
The data is shown as points, which shows where the sample was collected and the results for that sample.