Marc Van Liedekerke, Ezio Rusco, Luca Montanarella
22 July 2009
Two of the main soil threats in European soils are soil erosion and decline in soil organic matter.
The EEA and the European Commission DG Environment have identified these issues as priorities in relation to the collection of policy relevant soil data at European scale. In the context of its European
Soil Data Centre activities and in response to EEA soil data requirements, the JRC is responsible for collection and management of European soil data in collaboration with EIONET members.
Therefore, the JRC will organize through EIONET a data collection exercise with the objective of creating European-wide datasets for actual soil erosion and soil organic carbon (OC).
Data from EIONET member countries will be collected according to a grid-based approach: each country will be assigned a number of grid-cells (covering the country territory) and for each cell, the country is expected to provide a number of data according to a format that will be described. Each cell carries a unique identifier provided by JRC. Cell sizes are 1km x 1km. This concept is illustrated in
Annex-2.
The grid selected to overlay the European territory is a grid suggested by the technical co-ordinators for the implementation of the INSPIRE Directive.
For each EIONET member country x , the JRC has prepared a number of files:
- x.shp
: a shapefile containing the boundary of country x ; (source is a European Commission internal database), to be used for viewing purpose only.
- grid_x .shp: a polygon shapefile containing the grid overlaying the country x ; each grid cell is a polygon that is uniquely identified by a cell identifier and that corresponds to one entry in the attribute table of the shapefile, the attribute table contains a number of fields: the field cell_id is prefilled with the cell identifier value; the other fields refer to erosion and OC and need to be filled by the country.
- x_gc.shp and grid_x_gc .shp: x.shp and grid_x.shp
are shapefiles in a co-ordinate system
(LAEA) suggested by INSPIRE, suitable to be used for the European territory as a whole, but not necessarily suited for individual countries. Therefore, x_gc.shp
and grid_x_gc.shp
offer the same data as x.shp
and grid_x.shp
but in geographic co-ordinates.
- meta_x.xls: an excel document that serves to store meta information on the data provided in the shapefile grid_x.shp or grid_x_gc.shp
attribute table; it consists of 2 sheets: o x_erosion_meta for reporting erosion metadata o x_OC_meta for reporting organic carbon metadata grid_x.shp attribute table
Contains one column ( cell_id ), pre-filled with all cell identifiers for the country, a number of columns for reporting erosion and organic carbon data, one column for referring to erosion metadata (in the meta_x.xls
/ x_erosion_meta sheet), and one column for referring to organic carbon metadata (in the meta_x.xls
/ x_OC_meta sheet)
meta_x.xls/x_erosion_meta sheet:
Contains one column for the erosion metadata identifier and two columns for erosion metadata.
meta_x.xls/x_OC _meta sheet:
Contains one column for the OC metadata identifier and three columns for OC metadata.
Annex-1 provides a description of this table and sheets and further details on the data to be provided.

It is expected that the EIONET member country will fill the grid_x.shp
attribute table with OC and erosion data values and with identifiers referring to the meta information, which are to be reported in a separate meta_x.xls
.
Together with this document, example files for Belgium are provided:
- the country boundary: belgium.shp (in the LAEA co-ordinate system) and belgium_gc.shp (in geographic co-ordinates)
- the country grid as polygon shapefile with pre-filled attribute table: grid_belgium.shp
(in the LAEA co-ordinate system) and grid_belgium_gc.shp
(in geographic co-ordinates)
- the meta data Excel file: meta_belgium.xls
Note that for this example, the pre-filled attribute table of the shapefile contains the following values:
- the cell identifier in the cell_id field
- the value -2 (see Annex-1) for the data fields OC_30, OC_100, OC_30_per, OC_100_per, eros
- the value 0 (see Annex-1) for the metadata fields OC_m_id and eros_m_id
Prepared files for all EIONET countries will soon be made available on eusoils.jrc.ec.europa.eu and allow a first inspection of the country boundaries and grids. It should be noted that the shapefiles attribute table will show only the cell_id. Once there will be agreement among EIONET countries on the proposed additional fields, these will be added through an automatic procedure.

Annex-1
grid_x.shp attribute table
Column name cell_id
Description
Cell identifier
OC_30
OC_100
OC contents in the soil in 0-30cm, in t/ha,
(including the organic H horizons formed in conditions of saturation) for the cell
OC contents in the soil in 0-100cm, in t/ha, type string (9) example
4500_3489
OC_30_per
OC_100_per
(including the organic H horizons formed in conditions of saturation) for the cell
% of OC in 0-30cm
% of OC in 0-100cm float (0..100) 22.5 float (0..100) 38.9 eros
OC_m_id eros_m_id
Actual erosion, in t per ha per year
OC metadata identifier, referring to an entry in the meta_x.xls/x_OC_meta sheet float integer erosion metadata identifier, referring to an entry integer in the meta_x.xls
/ x_erosion_meta sheet
meta_x.xls/x_OC_meta sheet
2.4
1
1
Name
OC_m_id
BD_M description
OC metadata identifier, corresponding to values in the x_data sheet
Method used for measuring bulk density float float type
Integer
(different from 0)
String(1)
58.2
21.3 example
1
1
String(2) 02 BD_PTF
OC_D
Method used for calculating bulk density (pedotransfer function)
Description of the method and the data for calculating the OC in the cell (adding to the information reported in BD_meta_M and/or BD_meta_PTF
meta_x.xls/x_erosion_meta sheet
Name eros_m_id description erosion metadata identifier, corresponding to values in the x_data sheet eros_M eros_D
Code for the method used for soil loss assessment
Description of the method and the data used for calculating the erosion (in addition to information provided by erosion_M
Text type
Integer
(different from 0) string(2) text example
1

Soil Organic Carbon data (in grid_x.shp attribute table)
To standardize the procedures for the estimation of the stock of organic carbon with the current international standard of reference, it is proposed to calculate four separate parameters, two (OC as stock and percentage) for the section 0-30 cm mineral (OC_30 and OC_30_per) and two (OC as stock and percentage) for the section 0-100cm (OC_100 and OC_100_per).
The data on organic carbon refer to the soil only within the cell.
OC_30 : soil organic carbon content (stock) for soil in the pixel (t/ha), calculated from 0 to 30 cm.
OC_100 : soil organic carbon content (stock) for soil in the pixel (t/ha), calculated from 0 to 30 cm.
OC_30_per : percentage of organic carbon content for soil in the pixel, 0-30 cm (%), including organic H horizons formed under conditions of saturation.
OC_100_per : percentage of organic carbon content for soil in the pixel, 0-30 cm (%), including organic
H horizons formed under conditions of saturation.
In the case that OC data cannot be provided for the cell the following codes apply:
-1 : if it is not applicable to provide a value (e.g. there is no soil in the cell)
-2 : if it would be applicable to provide a value, but no data could be calculated
Soil Erosion data (in grid_x.shp attribute table)
The (actual) soil water erosion (Rill and inter-Rill erosion) are to be provided as quantitative data expressed in t/ ha/yr. The data on soil erosion refer to the soil only within the pixel.
If the data are the result of the use of models, details on the models used should be provided in the part on the metadata.
Eros : Soil water erosion in the cell (t/ha/yr)
In the case that no erosion data can be provided for the cell the following codes apply:
-1 : if it is not applicable to provide a value (e.g. there is no soil in the cell)
-2 : if it is applicable, but no data could be calculated
The meta data identifiers OC_m_id and eros_m_id (in grid_x.shp attribute table)
The (integer) values in the OC_m_id field in the grid_x.shp
attribute table should refer/correspond to values reported in the OC_m_id field of the meta_x.xls/x_OC_meta sheet.
The (integer) values in the eros_m_id field in the grid_x.shp
attribute table should refer/correspond to values reported in the eros_m_id field of the meta_x.xls/x_eros_meta sheet.
The value 0 in the OC_m_id field and/or the eros_m_id field in the grid_x.shp
attribute table can be used to indicate that no metadata is available for that cell.
The value 0 should thus NOT be used as values in the OC_m_id field or eros_m_id field of the meta_x.xls/x_OC_meta sheet or meta_x.xls/x_eros_meta sheet, to indicate meta data.
Soil Organic Carbon Metadata (in meta_x.xls/x_OC_meta sheet)
Metadata for bulk density used to calculate OC stock as t/ha should be recorded together with information on sources and methods used to assess OC content. Valid values are in the tables below.

BD_M : method used to measure bulk density is recorded with the following codes.
BD_m_M
CODE BULK DENSITY METHOD
1 Core method (in the field)
2 Excavation method
3
9
Clod method
Other method (specify in OC_D)
ISO METHOD
ISO 11272 par. 4.1
ISO 11272 par. 4.2
ISO 11272 par. 4.3
BD_PTF : pedotransfer functions used to derive bulk density
BD_m_PTF
CODE
01
02
03
04
BULK DENSITY PEDOTRANSFER FUNCTION
Alexander 1980 Alexander, E.B. 1980. Bulk densities of California soils in relation to other soil properties. Soil
Sci. Soc. Am. J. 44:689 –692.
Baumer 1992 Baumer, O.M. 1992. Predicting unsaturated hydraulic parameters. p.341
–354. In Proc. of the Int.
Benitesa 2007
Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils,
Riverside, CA. 11 –13 October 1989. Univ. of California, Riverside.
Benitesa Vinícius M., Machadob Pedro L.O.A., Fidalgoa Elaine C.C., Coelhoa Maurício R. and
Madarib Beáta E., 2007. Pedotransfer functions for estimating soil bulk density from existing soil
Bernoux 1998 survey reports in Brazil. Geoderma, Volume 139, Issues 1-2, 90-97
Bernoux, M.., D. Arrouyas, C. Cerri, B. Volkoff and C. Jolivet. 1998. Bulk densities of Brazilian
Amazon soils related to other soil properties. Soil Sci. Soc. Am. J. 62:743 –749.
05
06
Boucneau 1998 Boucneau, G., Van Meirvenne, M., and G. Hofman. 1998. Comparing pedotransfer functions to estimate soil bulk density in northern Belgium. Pedologie Themata 5:67 –70.
De Vosa 2005 Bruno De Vosa, Marc Van Meirvenne, Paul Quataerta, Jozef Deckers and Bart Muys, 2005.
Predictive Quality of Pedotransfer Functions for Estimating Bulk Density of Forest Soils. Soil Sci.
Soc. Am. J. 69:500-510.
07
08
09
10
11
12
13
Calhoun 2001
Calzolari 2001
Federer 1993
Calhoun, F.G., N.E. Smeck, B.L. Slater, J.M. Bigham, and G.F. Hall, 2001. Predicting bulk density of Ohio soils from morphology, genetic principles, and laboratory characterization data.
Soil Sci. Soc. Am. J. 65:811
–819.
Calzolari C., Ungaro, F., Busoni, E., Sanchiz P., 2001. Metodi indiretti per la stima delle proprietà fisico-idrologiche dei suoli. II. Definizione di nuove pedofunzioni Progetto SINA -
Carta Pedologica in aree a rischio ambientale, Convenzione RER SGSS - CNR IGES: "Studio del comportamento fisico-idrologico degli strati superficiali del suolo", Rapporto n. 9.2, Febbraio
2001, 42 pp.
Federer, C.A., D.E. Turcotte, and C.T. Smith. 1993. The organic fraction
—Bulk-density relationship and the expression of nutrient content in forest soils. Can. J. For. Res. 23:1026 –
1032.
Hallett 1995
Heuscher 2005 Heuscher Sonja A., Brandt Craig C. and Jardin Philip M., 2005. Using Soil Physical and
Chemical Properties to Estimate Bulk Density. Soil Sci. Soc. Am. J. 69:51-56.
Hollis 1996 Hollis, J.M., Brown, C.D. and Hallett, S.H. 1997. Coupling models and Geographical Information
Systems for environmental risk evaluation. Actes du Séminaire National; Produits
Phytosanitaires, Proces sus de Transfert et Modélisation dans les Bassins Versants:
Hydrosystèmes. Cemagref, Nancy, 22-23 Mai 1996, 203-213.
Hollis 1995
Hallett, S.H., Thanigasalam, P. and Hollis, J.M. 1995. SEISMIC: A Desktop Information System for Assessing the Fate and Behaviour of Pesticides in the Environment. Computers and
Electronics in Agriculture, 13, 3, 229-244.
Hollis, J.M., Keay, C.,A., Hallett, S.H., Gibbons, J.W. and Court, A.C. 1995. Using CatchIS to
Assess the Risk to Water Resources from Diffusely Applied Pesticides. Proceedings Pesticide
Movement to Water; British Crop Protection Council, Warwick, UK. 5-2: 345-350.
Kaur 2002
14
15
16
17
18
19
20
Leonaviciute
2000
Manrique 1991
Rawls -
Brakensiek 1985
Salifu 1999
Kätterer 2006
Rawls 1983
Kaur, R., Kumar, S., and H.P. Gurung, 2002. A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs. Austr. J. Soil Res.
40:847-857.
Leonavicˇiute, N. 2000. Predicting soil bulk and particle densities by pedotransfer functions from existing soil data in Lithuania. Geoandgrafijos metrasˇtis 33:317–330.
Manrique L.A., and C.A. Jones. 1991. Bulk density of soils in relation to soil physical and chemical properties. Soil Sci. Soc. Am. J. 55: 476-481.
Rawls, W.J. and D.L. Brakensiek. 1985. Prediction of soil water properties for hydrologic modeling. p. 293-299. In: Watershed Management in the Eighties. Eds. Jones, E and Ward, T.J.
Proceedings of a Symposium ASCE, Denver, Colorado. 30 Apr. - 2 May 1985. ASCE, New
York.
Salifu, K.F., W.L. Meyer, and H.G. Murchison. 1999. Estimating soil bulk density from organic matter content, pH, silt and clay. Tropic. For. 15:112 –120.
T. Kätterer, O. Andrén, P-E. Jansson, 2006. Pedotransfer functions for estimating plant available water and bulk density in Swedish agricultural soils. Acta Agriculturae Scandinavica,
Section B - Plant Soil Science, Volume 56, Issue 4 December 2006 , pages 263 - 276
Rawls, W.J. 1983. Estimating soil bulk-density from particle-size analysis and organic matter content. Soil Sci. 135:123 –125.

BD_m_PTF
CODE
Ungaro 2007
21
BULK DENSITY PEDOTRANSFER FUNCTION
Ungaro.F. 2007. Metodi di sti ma delle propietà fisico-idrologiche dei suoli. Definizione di nuove pedofunzioni per la stima della densità apparente dei suoli della pianura emiliano-romagnola.
Convenzione RER SGSS -
CNR IRPI: “Carta dei suoli 1: 250.000: realizzazione di strumenti per la corretta gestione del suolo nell’ambito dell’attuale politica agricola comunitaria con specifico riferimento al controllo dell’erosione idrica e dell’inquinamento delle acque”, rapporto n. 1.1,
Gennaio 2007, 69 pp. specify in OC_D 99
Other pedotransfer function

Soil Erosion Metadata (in x_erosion_meta sheet) eros_M : method used for soil loss assessment; some predictive models for erosion are listed.
Eros_M
24
25
26
27
28
29
30
98
99
16
17
18
19
20
21
22
23
07
08
09
10
11
12
13
14
15
CODE METHOD FOR SOIL LOSS ASSESSMENT
01 USLE (Wischmeier & Smith 1978)
02
03
EPIC/apex/almanac (Sharpley & Williams 1990)
RUSLE (Renard et al. 1997)
04
05
06
AGNPS (Young, R.A. et al. 1989)
MUSLE (Williams, 1975)
USPED (Mitasova et al. 1996)
CREAMS (Knisel, 1980)
SWRRB (Arnold et al.1990)
PSIAC (1968)
SPUR (Hanson et al. 1992)
SWAT/HUMUS (Arnold et al. 1995)
GLEAMS 2.1 (Knisel, 1993)
CASC2D (Julien & Saghafian 1991)
MULTSED (Simons et al. 1980)
ARMSED (Riggins et al 1989)
WEPPprof/basin (Flanagan & Nearing 1995)
SIMWE (Mitas & Mitasova, 1998)
ANSWERS (Beasley et al., 1980)
KINEROS (Woolhiser et al., 1990)
EUROSEM (Morgan et al.1993)
SHE (Abbott et al.1986a,b)
SEMMED (De Jong & Riezebos 1997)
CSEP (Kirkby and Cox, 1995)
MEDRUSH (Kirkby, 1998)
EROSION3D (Werner et al., 1997)
ACRU (New & Schulze 1996)
PISA (Bazzoffi,1993; Bazzoffi et al. 1998)
AGQA (Ciccacci et al. 1987)
CORINE erosion (EEA, 1995)
PESERA (Kirby et al., 2004)
Expert judgment
Other (specify in SL_D) eros_D: in addition to information provided by erosion_M: description of procedures used to assess soil loss in the pixel; specify for the model used: the data sources for the different parameters (e.g. rainfall erosivity, soil erodibility, topography, land cover, etc.), if there have been used functions, raw data or literature data.

Annex-2
53
27
28
38
39
40
50
51
52 to Country x are assigned cells: 3, 4 , 14, 15, 16 , 26, 27 , 28 , 38, 39 , 40 , 50, 51, 52, 53 to Country y are assigned cells: 4 , 5, 6, 7, 16 , 17 , 18 , 19, 28 , 29 , 30 , 31 , 40 , 41 , 42 , 43 , 53 , 54, 55
Only the cells indicated in blue cover 100% of the country’s territory.
Note that cells: 4, 16, 28, 40, 53 are common to both countries.
Country x Country y
Cell id % of cell belonging to country x
3
4
20
25
14
15
16
26
45
95
50
85
Cell id % of cell belonging to country y
4
5
6
7
16
17
60
70
70
60
50
100
10
100
70
99
100
90
40
80
90
18
19
28
29
30
31
40
41
42
43
53
54
55
100
95
30
100
100
100
10
100
100
100
80
90
80