Applications & Services

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati). This is the Metals layer.

This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution. MT2 meta-model

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): this layer displays Soil biomass productivity of croplands, values normalized to a range [0..10]

Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.

Soil Reference Group code of the STU from the World Reference Base for Soil Resources.

Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk estimation; for the whole World; R-factor map at resolutions of 30 arc-sec ((~1 km at the Equator).

This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.

This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.

This dataset (map) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great potential of soil to store more carbon. The actual SOC stock was derived from the Pan-European simulation using the biogeochemical CENTURY model (a detailed explanation can be found in the references below). The associated data can be found in ESDAC: "Pan-European SOC stock of agricultural soils" The potential SOC stock was obtained simulating a grassland land use without nitrogen limitation, since it was considered a good scenario for SOC accumulation. The scenario set-up was analogous to that described in Lugato et al (2014b, see below) for the grassland land use, namely ‘AR_GR_LUC’. However to obtain a potential SOC stock, the model was ran for 2000 years with repeated actual climate, in order to reach an equilibrium condition. The simulation involved only the agricultural soils, according to the Corine Land Cover. A value of 1 was arbitrary attributed to forest soils. The data come as a single ESRI Grid (250m resolution) in the ETRS_LAEA_10_52 Coordinate System + the two referenced articles

This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.

This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the saturated hydraulic conductivity.

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs). hd85: HadGEM2-AO, RCP 8.5

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the water content at field capacity.

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the saturated water content.

This dataset (maps) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union, and corresponds to the figures 7a and 7b from the publication "Continental-scale assessment of provisioning soil functions in Europe", Gergely Tóth, Ciro Gardi, Katalin Bódis, Éva Ivits, Ece Aksoy, Arwyn Jones, Simon Jeffrey, Thorum Petursdottir and Luca Montanarella, Ecological Processes 2013 2:32; DOI: 10.1186/2192-1709-2-32. Values scaled to the range [0..10]

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs). hd26: HadGEM2-AO, RCP 2.6

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe). This layer is SOC LUCAS only.

This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.

This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.

Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European Commission’s Soil Thematic Strategy has identified soil erosion as a relevant issue for the European Union, and has proposed an approach to monitor soil erosion. This paper presents the application of a modified version of the Revised Universal Soil Loss Equation (RUSLE) model (RUSLE2015) to estimate soil loss in Europe for the reference year 2010, within which the input factors (Rainfall erosivity, Soil erodibility, Cover- Management, Topography, Support practices) are modelled with the most recently available pan- European datasets. While RUSLE has been used before in Europe, RUSLE2015 improves the quality of estimation by introducing updated (2010), high-resolution (100 m), peer-reviewed input layers. The mean soil loss rate in the European Union’s erosion-prone lands (agricultural, forests and semi-natural areas) was found to be 2.46 t ha 1 yr 1, resulting in a total soil loss of 970 Mt annually. A major benefit of RUSLE2015 is that it can incorporate the effects of policy scenarios based on land- use changes and support practices. The impact of the Good Agricultural and Environmental Condition (GAEC) requirements of the Common Agricultural Policy (CAP) and the EU’s guidelines for soil protection can be grouped under land management (reduced/no till, plant residues, cover crops) and support practices (contour farming, maintenance of stone walls and grass margins). The policy interventions (GAEC, Soil Thematic Strategy) over the past decade have reduced the soil loss rate by 9.5% on average in Europe, and by 20% for arable lands. Special attention is given to the 4 million ha of croplands which currently have unsustainable soil loss rates of more than 5 t ha 1 yr 1, and to which policy measures should be targeted. More information about the RUSLE2015 model and the data in: Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L., Alewell, .C. 2015. The new assessment of soil loss by water erosion in Europe. Environmental Science & Policy. 54: 438-447.

The Universal Soil Loss Equation (USLE) model is the most frequently used. model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. The LS-factor dataset was calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole European Union, resulting in an improved delineation of areas at risk of soil erosion as compared to lower-resolution datasets. This combined approach of using GIS software tools with high-resolution DEMs has been successfully applied in regional assessments in the past, and is now being applied for first time at the European scale. More information about the LS-factor methodology and the data in: Panagos, P., Borrelli, P., Meusburger, K. 2015. A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water. Geosciences, 5: 117-126

Land use and management influence the magnitude of soil loss. Among the different soil erosion riskfactors, the cover-management factor (C-factor) is the one that policy makers and farmers can mostreadily influence in order to help reduce soil loss rates. The present study proposes a methodology forestimating the C-factor in the European Union (EU), using pan-European datasets (such as CORINE LandCover), biophysical attributes derived from remote sensing, and statistical data on agricultural crops andpractices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data onmanagement practices such as conservation tillage, plant residues and winter crop cover. The C-factor innon-arable lands was estimated by weighting the range of literature values found according to fractionalvegetation cover, which was estimated based on the remote sensing dataset Fcover. The mean C-factor inthe EU is estimated to be 0.1043, with an extremely high variability; forests have the lowest mean C-factor(0.00116), and arable lands and sparsely vegetated areas the highest (0.233 and 0.2651, respectively).Conservation management practices (reduced/no tillage, use of cover crops and plant residues) reducethe C-factor by on average 19.1% in arable lands.The methodology is designed to be a tool for policy makers to assess the effect of future land use andcrop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arableland expansion) and the effect of policies (such as the Common Agricultural Policy and the push to growmore renewable energy crops) can potentially be quantified with the proposed model. The C-factor dataand the statistical input data used are available from the European Soil Data Centre. More information about the LANDUM model and the data on C-factor in: Panagos, P., Borrelli, P., Meusburger, C., Alewell, C., Lugato, E., Montanarella, L., 2015. Estimating the soil erosion cover-management factor at European scale. Land Use policy 48C: 38-50.

This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database. The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha−1 MJ−1 mm−1 with a standard deviation of 0.009 t ha h ha−1 MJ−1 mm−1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed. More information about the Methodology and the results an be found in : Panagos P., Meusburger K., Ballabio C., Borrelli P., Alewell C. (2014). Soil erodibility in Europe: A high-resolution dataset based on LUCAS . Science of the Total Environment, 479-480 (1) , pp. 189-200.

Proposed European policy in the agricultural sector will place higher emphasis on soil organic carbon (SOC), both as an indicator of soil quality and as a means to offset CO2 emissions through soil carbon (C) sequestration. Despite detailed national SOC data sets in several European Union (EU) Member States, a consistent C stock estimation at EU scale remains problematic. Data are often not directly comparable, different methods have been used to obtain values (e.g. sampling, laboratory analysis) and access may be restricted. Therefore, any evolution of EU policies on C accounting and sequestration may be constrained by a lack of an accurate SOC estimation and the availability of tools to carry out scenario analysis, especially for agricultural soils. In this context, a comprehensive model platform was established at a pan-European scale (EU + Serbia, Bosnia and Herzegovina, Croatia, Montenegro, Albania, Former Yugoslav Republic of Macedonia and Norway) using the agro-ecosystem SOC model CENTURY. Almost 164 000 combinations of soil-climate-land use were computed, including the main arable crops, orchards and pasture. The model was implemented with the main management practices (e.g. irrigation, mineral and organic fertilization, tillage) derived from official statistics. The model results were tested against inventories from the European Environment and Observation Network (EIONET) and approximately 20 000 soil samples from the 2009 LUCAS survey, a monitoring project aiming at producing the first coherent, comprehensive and harmonized top-soil data set of the EU based on harmonized sampling and analytical methods. The CENTURY model estimation of the current 0–30 cm SOC stock of agricultural soils was 17.63 Gt; the model uncertainty estimation was below 36% in half of the NUTS2 regions considered. The model predicted an overall increase of this pool according to different climate-emission scenarios up to 2100, with C loss in the south and east of the area (involving 30% of the whole simulated agricultural land) compensated by a gain in central and northern regions. Generally, higher soil respiration was offset by higher C input as a consequence of increased CO2 atmospheric concentration and favourable crop growing conditions, especially in northern Europe. Considering the importance of SOC in future EU policies, this platform of simulation appears to be a very promising tool to orient future policymaking decisions. More information about the model and the data in: Lugato E., Panagos P., Bampa, F., Jones A., Montanarella L. (2014). A new baseline of organic carbon stock in European agricultural soils using a modelling approach. Global change biology. 20 (1), pp. 313-326

The JRC created a quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based on Regression-Kriging. Fifty-four (54) auxiliary variables in the form of raster maps at 1km resolution were used to explain the differences in the distribution of soil pHCaCl2 and the kriged map of the residuals from the regression model was added. The goodness of fit of the regression model was satisfactory (R2adj = 0.43) and its residuals follow a Gaussian distribution. The lowest values correspond to the soils developed on acid rock (granites, quartzite’s, sandstones, etc), while the higher values are related to the presence of calcareous sediments and basic rocks. The validation of the model shows that the model is quite accurate (R2adj = 0.56). This shows the validity of Regression-Kriging in the estimation of the distribution of soil properties when a large and adequately documented number of soil measurements are available.

The Pan-European Soil Erosion Risk Assessment - PESERA - uses a process-based and spatially distributed model to quantify soil erosion by water and assess its risk across Europe. The conceptual basis of the PESERA model can also be extended to include estimates of tillage and wind erosion. The model is intended as a regional diagnostic tool, replacing comparable existing methods, such as the Universal Soil Loss Equation (USLE), which are less suitable for European conditions and lack compatibility with higher resolution models.

CRUSTING:Soil crusting class The list of authorised codes and their corresponding meanings is given in the following table: CRUSTING:Soil crusting class Code Value -------------- 1 = Very weak 2 = Weak 3 = Moderate 4 = Strong 5 = Very strong

No abstract provided

WM1:Presence of Water Management System WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU. A water management system is intended to palliate the lack of water (dry conditions), correct a soil condition preventing agricultural use (salinity), or drain excess water in waterlogged or frequently flooded areas. In some cases, it has a double purpose, for example in zones with contrasting seasonal conditions, alternatively flooded or experiencing droughts. The most obvious, apparent, or dominant type of water management system must be chosen from the list according to the contributor's expertise. Obviously, WM1 and WM2 are inter-dependant. For example, if WM1 = 2 (no water management system) then WM2 can only have value 2. As another example, WM1 = 3 (drainage) is clearly incompatible with WM2 = 9 (flooding). The list of authorised codes and their corresponding meanings is given in the following tables for attributes WM1: WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU Code Value -------------- 0 No information 1 Not applicable (no agriculture) 2 No water management system 3 A water management system exists to alleviate waterlogging (drainage) 4 A water management system exists to alleviate drought stress (irrigation) 5 A water management system exists to alleviate salinity (drainage) 6 A water management system exists to alleviate both waterlogging and drought stress 7 A water management system exists to alleviate both waterlogging and salinity

STR_TOP:Topsoil structure The list of authorised codes and their corresponding meanings is given in the following table: STR_TOP:Topsoil structure Code Value -------------- G = Good N = Normal P = Poor H = Humic or Peaty topsoil

PARMASE1:Major group code for the secondary parent material PAR-MAT-SEC1: Major group code for the secondary parent material of the STU. PAR-MAT-SEC1: Major group code for the secondary parent material of the STU Code Value -------------- 0 No information 1 consolidated-clastic-sedimentary rocks 2 sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin) 3 igneous rocks 4 metamorphic rocks 5 unconsolidated deposits (alluvium, weathering residuum and slope deposits) 6 unconsolidated glacial deposits/glacial drift 7 eolian deposits 8 organic materials 9 anthropogenic deposits

TXSRFSE:Secondary surface textural class A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less extensive than the dominant one. Together the TXSRFDO and the TXSRFSE (Sec.surface text.class) attributes describe the lateral variability of the surface horizon texture within the STU. If there is no such variability or if information is unavailable, then the value of TXSRFDO must also be entered for TXSRFSE . TEXT-SRF-SEC: Secondary surface textural class of the STU ---------------- 0 No information 9 No mineral texture (Peat soils) 1 Coarse (18% < clay and > 65% sand) 2 Medium (18% < clay < 35% and >= 15% sand, or 18% < clay and 15% < sand < 65%) 3 Medium fine (< 35% clay and < 15% sand) 4 Fine (35% < clay < 60%) 5 Very fine (clay > 60 %)

HG:Hydro-geological class The list of authorised codes and their corresponding meanings is given in the following table: HG:Hydro-geological class Code Value -------------- 1R = 1R 1C = 1C 1S = 1S 1L = 1L 1H = 1H 1M = 1M 2 = 2 3 = 3 4W = 4W 4D = 4D

CEC_TOP: Topsoil Cation Exchange Capacity. The list of authorised codes and their corresponding meanings is given in the following table: CEC_TOP: Topsoil cation exchange capacity. Code Value -------------- H = High ( > 40 cmol(+)/kg) M = Medium (15-40 cmol(+)/kg) L = Low ( < 15 cmol(+)/kg)

EAWC_SUB: Subsoil easily available water capacity. The list of authorised codes and their corresponding meanings is given in the following table: EAWC_SUB: Subsoil easily available water capacity. Code Value -------------- VL = Very low ( ~ 0 mm/m) L = Low ( < 100 mm/m) M = Medium (100 - 140 mm/m) H = High (140 - 190 mm/m) VH = Very high ( > 190 mm/m) # = No data or not applicable

ZMIN: Minimum elevation above sea level of the STU (in metres). It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage is a generalisation of previous maps that were not very detailed themselves. The use of a Digital Elevation Model (DEM) will often palliate the lack of information for these attributes. Using a DEM will however apply to the whole Soil Mapping Unit, and not to the individual STU components, as required here. Hence the information should be provided if available from the soil survey. The range of authorised values for attributes ZMIN and ZMAX is an integer number selected in [ 999, and the interval -400 ,9999]. –999 is the code used when no information is available. The negative values in the interval are given since some areas, such as the Caspian or Dead Seas, are below the general ocean level. The following table holds the coding scheme for attributes ZMIN: ZMIN: Minimum elevation above sea level of the STU (in metres) values -------------- -999 No information -2 metres -1 metres 0 metres 1 metres 2 metres 5000 metres

AGLIM2NNI:2nd Limitation to agricultural use (without no information) The list of authorised codes and their corresponding meanings is given in the following table: AGLIM2NNI:Se2nd Limitation to agricultural use (without no information) Code Value -------------- 0 No information 1 No limitation to agricultural use 2 Gravelly (over 35% gravels diameter < 7.5 cm) 3 Stony (presence of stones diameter > 7.5 cm, impracticable mechanization) 4 Lithic (coherent and hard rock within 50 cm) 5 Concretionary (over 35% concretions diameter < 7.5 cm near the surface) 6 Petrocalcic (cemented or indurated calcic horizon within 100 cm) 7 Saline (electric conductivity > 4 mS.cm-1 within 100 cm) 8 Sodic (Na/T > 6% within 100 cm) 9 Glaciers and snow-caps 10 Soils disturbed by man 20 Fragic 21 Drained 22 Quasi permanently flooded 30 Eroded phase, erosion 31 Phreatic phase

USEDO:Dominant Land Use USE-DOM: Code for dominant land use of the STU. USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account in USE SEC. The map co-ordinator must use his expert judgement to determine what are the dominant and secondary land uses for an STU, as the soil can cover extensive surfaces in regions with different agricultural practices and crops. If there is only one land use or if the variability is unknown, then the value of USE DOM must be copied to USE SEC. Land uses that do not involve much human intervention, such as wasteland, or wildlife refuse, or land above timberline, are also listed here. The list of authorised codes and their corresponding meanings is given in the following table for attributes USE-DOM : USE-DOM: Code for dominant land use of the STU Code Value -------------- 0 No information 1 Pasture, grassland, grazing land 2 Poplars 3 Arable land, cereals 4 Wasteland, shrub 5 Forest, coppice 6 Horticulture 7 Vineyards 8 Garrigue 9 Bush, macchia 10 Moor 11 Halophile grassland 12 Arboriculture, orchard 13 Industrial crops 14 Rice 15 Cotton 16 Vegetables 17 Olive trees 18 Recreation 19 Extensive pasture, grazing, rough pasture 20 Dehesa (extensive pastoral system in forest parks in Spain) 21 Cultivos enarenados (artificial soils for orchards in SE Spain) 22 Wildlife refuge, land above timberline

The map of natural soil susceptibility to compaction was created from the evaluation of selected parameters from the European Soil Database. The soil susceptibility to compaction was divided into 4 categories. Two additional categories represent the data concerning places where this evaluation was either not relevant or could not been provided because of lack of information. In total there are 6 categories: Natural Soil Susceptibility to Compaction Code Value -------------- 0 - no soil. This represents water bodies, glaciers and rock outcrops 1 - low susceptibility to compaction 2 - medium susceptibility to compaction 3 - high susceptibility to compaction 4 - very high susceptibility to compaction 9 - no evaluation possible. This was the case of towns including also soils, soils disturbed by man and marsh.

TXEROD:Textural factor of soil erodibility The list of authorised codes and their corresponding meanings is given in the following table: TXEROD:Textural factor of soil erodibility Code Value -------------- 1 = Very favourable 2 = Favourable 3 = Medium 4 = Unfavourable 5 = Very unfavourable

ATC: Regrouped accumulated mean annual temperature class (ATC) (source: JRC-MARS) The list of authorised codes and their corresponding meanings is given in the following table: ATC: Regrouped accumulated mean annual temperature class (ATC) (source: JRC-MARS) Code Value -------------- H: High (> 3000°C) M: Medium (1800-3000°C) L: Low (< 1800°C)

USE:Regrouped land use class. The list of authorised codes and their corresponding meanings is given in the following table: USE:Regrouped land use class. Code Value -------------- HG = Halophile Grassland MG = Managed Grassland SN = Semi-natural C = Cultivated # = No information

TEXT: Dominant surface textural class (completed from dominant STU) The list of authorised codes and their corresponding meanings is given in the following table: TEXT: Dominant surface textural class (completed from dominant STU) Code Value -------------- 1 = Coarse (clay < 18 % and sand > 65 %) 2 = Medium (18% < clay < 35% and sand > 15%,\or clay < 18% and 15% < sand < 65%) 3 = Medium fine (clay < 35 % and sand < 15 %) 4 = Fine (35 % < clay < 60 %) 5 = Very fine (clay > 60 %) 7 = No texture (because of rock outcrop) 8 = No texture (because of organic layer) 6 = No texture (other cases) 0 = No information

PARMADO:Dominant Parent Material The parent material code must be selected from the list provided below. This list has evolved from a number of approximations using experiences from several pilot projects. The current version has been prepared by R. Hartwhich et al. (1999) as the reference list in the Manual for the Georeferenced Soil Database for Europe at 1 :250.000, version 1.1. It includes four levels: Major Class, Group, Type and Subtype. PAR-MAT-DOM: Code for dominant parent material of the STU Code Value -------------- 0 No information 1000 consolidated-clastic-sedimentary rocks 1100 psephite or rudite 1110 conglomerate 1111 pudding stone 1120 breccia 1200 psammite or arenite 1210 sandstone 1211 calcareous sandstone 1212 ferruginous sandstone 1213 clayey sandstone 1214 quartzitiic sandstone/orthoquartzite 1215 micaceous sandstone 1220 arkose 1230 graywacke 1231 feldspathic graywacke 1300 pelite, lutite or argilite 1310 claystone/mudstone 1311 kaolinite 1312 bentonite 1320 siltstone 1400 facies bound rock 1410 flysch 1411 sandy flisch 1412 clayey and silty flysch 1413 conglomeratic flysch 1420 molasse 2000 sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin) 2100 calcareous rocks 2110 limestone 2111 hard limestone 2112 soft limestone 2113 marly limestone 2114 chalky limestone 2115 detrital limestone 2116 carbonaceous limestone 2117 lacustrine or freshwater limestone 2118 travertine/calcareous sinter 2119 cavernous limestone 2120 dolomite 2121 cavernous dolomite 2122 calcareous dolomite 2130 marlstone 2140 marl 2141 chalk marl 2142 gypsiferous marl 2150 chalk 2200 evaporites 2210 gypsum 2220 anhydrite 2230 halite 2300 siliceous rocks 2310 chert, hornstone, flint 2320 diatomite/radiolarite 3000 igneous rocks 3100 acid to intermediate plutonic rocks 3110 granite 3120 granodiorite 3130 diorite 3131 quartz diorite 3132 gabbro diorite 3140 syenite 3200 basic plutonic rocks 3210 gabbro 3300 ultrabasic plutonic rocks 3310 peridotite 3320 pyroxenite 3400 acid to intermediate volcanic rocks 3410 rhyolite 3411 obsidian 3412 quartz porphyrite 3420 dacite 3430 andesite 3431 porphyrite (interm,) 3440 phonolite 3441 tephritic phonolite 3450 trachyte 3500 basic to ultrabasic volcanic rocks 3510 basalt 3520 diabase 3530 pikrite 3600 dike rocks 3610 aplite 3620 pegmatite 3630 lamprophyre 3700 pyroclastic rocks (tephra) 3710 tuff/tuffstone 3711 agglomeratic tuff 3712 block tuff 3713 lapilli tuff 3720 tuffite 3721 sandy tuffite 3722 silty tuffite 3723 clayey tuffite 3730 volcanic scoria/volcanic breccia 3740 volcanic ash 3750 ignimbrite 3760 pumice 4000 metamorphic rocks 4100 weakly metamorphic rocks 4110 (meta-)shale/argilite 4120 slate 4121 graphitic slate 4200 acid regional metamorphic rocks 4210 (meta-)quartzite 4211 quartzite schist 4220 phyllite 4230 micaschist 4240 gneiss 4250 granulite (sensu stricto) 4260 migmatite 4300 basic regional metamorphic rocks 4310 greenschist 4311 prasinite 4312 chlorite 4313 talc schist 4320 amphibolite 4330 eclogite 4400 ultrabasic regional metamorphic rocks 4410 serpentinite 4411 greenstone 4500 calcareous regional metamorphic rocks 4510 marble 4520 calcschist, skam 4600 rocks formed by contact metamorphism 4610 contact slate 4611 nodular slate 4620 hornfels 4630 calsilicate rocks 4700 tectogenetic metamorphism rocks or cataclasmic metamorphism 4710 tectonic breccia 4720 cataclasite 4730 mylonite 5000 unconsolidated deposits (alluvium, weathering residuum and slope deposits) 5100 marine and estuarine sands 5110 pre-quaternary sand 5111 tertiary sand 5120 quaternary sand 5121 holocene coastal sand with shells 5122 delta sand 5200 marine and estuarine clays and silts 5210 pre-quaternary clay and silt 5211 tertiary clay 5212 tertiary silt 5220 quaternary clay and silt 5221 holocene clay 5222 holocene silt 5300 fluvial sands and gravels 5310 river terrace sand or gravel 5311 river terrace sand 5312 river terrace gravel 5320 floodplain sand or gravel 5321 floodplain sand 5322 floodplain gravel 5400 fluvial clays, silts and loams 5410 river clay and silt 5411 terrace clay and silt 5412 floodplain clay and silt 5420 river loam 5421 terrace loam 5430 overbank deposit 5431 floodplain clay and silt 5432 floodplain loam 5500 lake deposits 5510 lake sand and delta sand 5520 lake marl, bog lime 5530 lake silt 5600 residual and redeposited loams from silicate rocks 5610 residual loam 5611 stony loam 5612 clayey loam 5620 redeposited loam 5621 running-ground 5700 residual and redeposited clays from calcareous rocks 5710 residual clay 5711 clay with flints 5712 ferruginous residual clay 5713 calcareous clay 5714 non-calcareous clay 5715 marly clay 5720 redeposited clay 5721 stony clay 5800 slope deposits 5810 slope-wash alluvium 5820 colluvial deposit 5830 talus scree 5831 stratified slope deposits 6000 unconsolidated glacial deposits/glacial drift 6100 morainic deposits 6110 glacial till 6111 boulder clay 6120 glacial debris 6200 glaciofluvial deposits 6210 outwash sand, glacial sand 6220 outwash gravels glacial gravels 6300 glaciolacustrine deposits 6310 varves 7000 eolian deposits 7100 loess 7110 loamy loess 7120 sandy loess 7200 eolian sands 7210 dune sand 7220 cover sand 8000 organic materials 8100 peat (mires) 8110 rainwater fed moor peat (raised bog) 8111 folic peat 8112 fibric peat 8113 terric peat 8120 groundwater fed bog peat 8200 slime and ooze deposits 8210 gyttja, sapropel 8300 carbonaceaous rocks (caustobiolite) 8310 lignite (brown coal) 8320 hard coal 8330 anthracite 9000 anthropogenic deposits 9100 redeposited natural materials 9110 sand and gravel fill 9120 loamy fill 9200 dump deposits 9210 rubble/rubbish 9220 industrial ashes and slag 9230 industrial sludge 9240 industrial waste 9300 anthropogenic organic materials

The European Soil Database WRBFU:Full soil Code Soil Adjective code of the STU taken from the World Reference Base (WRB) for Soil Resources. WRB-ADJ1: First soil adjective code of the STU from the World Reference Base (WRB) for Soil Resources Code Value ---------------- II Lamellic Iv Luvic Ix Lixic ab Albic ac Acric ad Aridic ae Aceric ah Anthropic ai Aric al Alic am Anthric an Andic ao Acroxic ap Abruptic aq Anthraquic ar Arenic au Alumic ax Alcalic az Arzic ca Calcaric cb Carbic cc Calcic ch Chernic cl Chloridic cn Carbonatic cr Chromic ct Cutanic cy Cryic dn Densic du Duric dy Dystric es Eutrisilic et Entic eu Eutric fg Fragic fi Fibric fl Ferralic fo Folic fr Ferric fu Fulvic fv Fluvic ga Garbic gc Glacic ge Gelic gi Gibbsic gl Gleyic gm Grumic gp Gypsiric gr Geric gs Glossic gt Gelistagnic gy Gypsic gz Greyic ha Haplic hg Hydragric hi Histic hk Hyperskeletic ht Hortic hu Humic hy Hydric ir Irragric le Leptic li Lithic me Melanic mg Magnesic mo Mollic ms Mesotrophic mz Mazic na Natric ni Nitic oa Oxyaquic oh Ochric om Ombric or Orthic pa Plaggic pc Petrocalcic pd Petroduric pe Pellic pf Profondic pg Petrogypsic ph Pachic pi Placic pl Plinthic pn Planic po Posic pp Petroplinthic pr Protic ps Petrosalic pt Petric rd Reductic rg Regic rh Rheic ro Rhodic rp Ruptic rs Rustic ru Rubic rz Rendzic sa Sapric sd Spodic si Silic sk Skeletic sl Siltic so Sodic sp Spolic st Stagnic su Sulphatic sz Salic tf Tephric ti Thionic tr Terric tu Turbic tx Toxic ty Takyric ub Urbic um Umbric vi Vitric vm Vermic vr Vertic vt Vetic xa Xanthic ye Yermic 1 Town 2 Soil disturbed by man 3 Water body 4 Marsh 5 Glacier 6 Rock outcrops

OC_TOP = Topsoil organic carbon content. The list of authorised codes and their corresponding meanings is given in the following table: OC_TOP = Topsoil organic carbon content. Code Value -------------- H = High ( > 6 %) M = Medium (2 - 6 %) L = Low (1 - 2 %) V = Very low ( < 1 %)

DR:Depth to rock The list of authorised codes and their corresponding meanings is given in the following table: DR:Depth to rock Code Value -------------- S = Shallow ( < 40 cm) M = Moderate (40 - 80 cm) D = Deep (80 - 120 cm) V = Very deep ( > 120 cm)

TXSUBDO:Dominant sub-surface textural class A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (TEXT-SUB-SEC) is used to indicate which sub-surface texture is less extensive than the dominant one. Together the TEXT-SUB-DOM and the TEXT-SUB-SEC attributes reflect the lateral variability of the sub-surface horizon texture within the STU. If there is no such variability or if there is no information, the value of TEXT-SUB-DOM must also be entered for TEXT-SUB-SEC. >TEXT-SUB-DOM: Dominant sub-surface textural class of the STU ---------------- 0 No information 9 No mineral texture (Peat soils) 1 Coarse (18% < clay and > 65% sand) 2 Medium (18% < clay < 35% and >= 15% sand, or 18% < clay and 15% < sand < 65%) 3 Medium fine (< 35% clay and < 15% sand) 4 Fine (35% < clay < 60%) 5 Very fine (clay > 60 %)

SLOPE-SEC: Secondary Slope class of the STU. The SLOPE SEC attribute provides an option to indicate a secondary slope class when slope variability within an STU is important and some parts of the STU fall into a different slope class than that of the dominant one. If there is no variability or if the variability is unknown, the value of SLOPE DOM must be copied to SLOPE SEC . The list of authorised codes and their corresponding meanings is given in the following tables for attributes SLOPE SEC: SLOPE-SEC: Secondary Slope class of the STU Code Value -------------- 0 No information 1 Level (dominant slope ranging from 0 to 8 %) 2 Sloping (dominant slope ranging from 8 to 15 %) 3 Moderately steep (dominant slope ranging from 15 to 25 %) 4 Steep (dominant slope over 25 %)

BS_SUB: Base saturation of the subsoil The list of authorised codes and their corresponding meanings is given in the following table: BS_SUB: Base saturation of the subsoil Code Value -------------- H = High ( > 50 %) L = Low ( < 50 %)

ALT:Elevation The list of authorised codes and their corresponding meanings is given in the following table: ALT:Elevation Code Value -------------- # = No information L = Lowlands & intermediate U = Uplands & mountains

PD_SUB:Subsoil Packing Density The list of authorised codes and their corresponding meanings is given in the following table: PD_SUB:Subsoil Packing Density Code Value -------------- L = Low M = Medium H = High

The Saline and Sodic Soils Map shows the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation of salt affected areas with a limited level of reliability (e.g. < 50 or > 50% of the area); therefore the results represented in the map should only be used for orientating purposes.In total there are 5 categories: Saline and Sodic Soils Code Value -------------- 1 - Saline > 50% of the area 2 - Sodic > 50% of the area 3 - Saline < 50% of the area 4 - Sodic < 50% of the area 5 - Potentially salt affected soils.

MIN_SUB:Subsoil Mineralogy The list of authorised codes and their corresponding meanings is given in the following table: MIN_SUB:Subsoil Mineralogy Code Value -------------- KQ = 1/1 Minerals + Quartz KX = 1/1 Min. + Oxy. & Hydroxy. MK = 2/1 & 1/1 Minerals M = 2/1 & 2/1/1 non swel. Min. MS = Swel. & non swel. 2/1 Min. S = Swelling 2/1 Minerals TV = Vitric Minerals TO = Andic Minerals NA = Not applicable

AWC_SUB: Subsoil available water capacity The list of authorised codes and their corresponding meanings is given in the following table: AWC_SUB: Subsoil available water capacity Code Value -------------- VL = Very low ( ~ 0 mm/m) L = Low ( < 100 mm/m) M = Medium (100 - 140 mm/m) H = High (140 - 190 mm/m) VH = Very high ( > 190 mm/m) # = No data or not applicable

DGH:Depth to a gleyed horizon The list of authorised codes and their corresponding meanings is given in the following table: DGH:Depth to a gleyed horizon Code Value -------------- S = Shallow ( < 40 cm) M = Moderate (40 - 80 cm) D = Deep (80 - 120 cm) V = Very deep ( > 120 cm)