Soil pH is one of the most important chemical parameters of soil, playing an essential role on the agricultural production and on the distribution of plants and soil biota communities. It is the expression of soil genesis that in turns is a function of soi l forming factors and influences all the chemical, physical and biological processes that occur in the soil. Thus it shapes the entire soil ecosystem. Due to any of the above reasons, mapping of soil pH becomes very important to provide harmonised soil pH data to policy makers, public bodies and researchers. In order to obtain a continuous mapping of soil pH for Europe, adopting the digital soil mapping approach, a set of continuously distribute covariates, highly correlated with pH, were selected. The esti mate of soil pH was realized using a regression procedure, coupled with the kriging of the residuals. More than 30.000 points on top soil pH (CaCl 2 ) were used, and 27 covariates were tested as predictors. The similar approach was already applied with 12.33 3 samples to produce a pH map of Europe using European Soil Profile Data in 2008 which compiles several databases from 11 differe nt sources ( Reuter et al. 2008 ). Our study was conducted to update the previous data and maps based on LUCAS (EUROSTAT - Land U se/Cover Area frame statistical Survey), BIOSOIL ( Hiederer and Durrant, 2010 ) and merged database which was used to produce previous soil pH map of Europe ( Reuter et al. 2008 ). We used a compilation of more than 30.000 soil pH measurements from 13 differen t sources to create a continuous map of soil pH across Europe using a geostatistical approach based on regression - kriging. Regression was based on the use of 27 covariates in the form of raster maps at 1km resolution to explain the differences in the distribution of soil pH in CaCl 2 and we added the kriged map of the residuals from the regression model.
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