1

1) Federal Institute for Geosciences and Natural Resources (BGR) 2) INTERRA3) Geological Survey of Denmark and Greenland (GEUS)

Rainer Baritz1, Dietmar Zirlewagen2, Vibeke Ernstsen3

Relevance of GEMAS for soil property mapping

2

Introduction

GEMAS samples were taken from agricultural surface-close soil layers (Ap 0-20 cm, Grazing land 0-10 cm);

Parameters also include TOC, pH, P, CEC;“Standard results“ are provided as geostatistical maps;The main objective of GEMAS is to collect information about

the spatial distribution pattern of trace elements in the rooted zone of soil;

Soil organic matter and acidity are important to interpret the potential of soil to store and release heavy metals.

3

Questions

Technical questions: What is the possible contribution of GEMAS to soil

monitoring? How can GEMAS information be integrated into different soil

inventories? How can the representativity from GEMAS be assessed?

Criteria? Are there alternative upscaling methods?

4

Upscaling method

5

Upscaling method

Spatial Regression

Stepwise multiple linear regression combined with geostatistics/kriging;

Covariates as possible impact factors on the target variables (TOC, pH, P);

Stratification is important to optimise upscaling models.

6

Upscaling method

Database

Biogeographical regions, soil regions, N deposition data (EMEP);

DEM 90m/Relief parameters (aspect, slope, curvature, topographic wetness index, potential direct radiation, etc.);

Parent material (ESDB, 2004); Land cover CORINE 2000 and 2006, and at GEMAS points LUCAS 2003 crop types (CEC, 2003); Climate (WORLDCLIM).

7

Upscaling method

Stratification

Stratum 1: Sweden Norway and Finland (‘Boreal’)

Stratum 2: United Kingdom and Ireland

Stratum 3: Eco-Regions (DMEER) with Code-Numbers 56, 27, 10 (‘High Mountainous’)

Stratum 4: the remaining European target area (‘European continent’)

8

Total organic carbon

9

TOC [%] agricultural soil

(From Baritz et al., 2014, Fig. 6.4B, p.123)

10

Influence of predictors

Step Variable Entered

Number Vars In

Partial R-Square

Model R-Square

F Value Pr > F

1 TMAX5 1 0.2015 0.2015 462.11 <.0001

2 Ap 2 0.0892 0.2907 230.09 <.0001

3 PREC11 3 0.0139 0.3046 36.58 <.0001

4 Histosol 4 0.0128 0.3174 34.31 <.0001

5 TMAX4 5 0.0059 0.3234 16.05 <.0001

6 Corine_pasture 6 0.0066 0.3299 17.91 <.0001

7 BIOCLIM_3 7 0.0041 0.3340 11.12 0.0009

8 LForm_4 8 0.0040 0.3380 11.04 0.0009

9 Corine_scrub 9 0.0037 0.3417 10.25 0.0014

10 BIOCLIM_14 10 0.0030 0.3447 8.45 0.0037

11 BIOCLIM_17 11 0.0073 0.3520 20.44 <.0001

12 TMIN7 12 0.0035 0.3555 9.78 0.0018

13 Luvisol 13 0.0033 0.3587 9.25 0.0024

14 Leptosol 14 0.0023 0.3610 6.55 0.0106

15 Podzol 15 0.0028 0.3638 7.98 0.0048

16 TEXTSRF1 16 0.0049 0.3687 13.98 0.0002

17 ECO_CODE_11 17 0.0026 0.3713 7.58 0.0060

18 climagroup4 18 0.0021 0.3734 6.09 0.0137

19 PREC8 19 0.0023 0.3758 6.82 0.0091

[All_TOC]

11

TOC

Stratum 1: Sweden, Norway and Finland (‘Boreal’)Stratum 2: British Isles and IrelandStratum 3: Eco-Regions (‘High Mountainous’)Stratum 4: remaining Europe ‘(European continent’)

TOC and crop types

(From Baritz et al., 2014, Fig. 6.5, p.124)

12

coarse medium medium fine fine very fine Potatoes Olive groves

Rye Shrubland Barley Maize Sunflower

Grassland

Durum Wheat Rape seeds

Common wheat Dry pulses

Sugar beet Other non permanent

industrial crops

Other fibre and oleaginous crops

Cotton, Oranges,

Vineyards, Nuts

LUCAS 2003 and soil texture (ESDB, 2004)

(From Baritz et al., 2014, Table 6.3, p.127)

13

pH (CaCl2)

Soil acidity

14

pH (CaCl2)agricultural soil

15

Validation and uncertainties

16

Despite low sampling density (1 sample site/2500 km2), the sample size was large enough to separate a training, and a validation set both representing well the predictive population;

Split of the data; random split inside large-scale squares stratified biogeographical region;

Regional models are derived from the training data; Prediction error is then compared to the results from running

the training set-based models with the validation data.

Validation

Method

Germany: 357,104 km2 total, 187,291 km2 , agriculture (1 site/600 km2)Europe: 10.5 million km2; agriculture: 1 site/2333 km2 (parts of Eastern Europe

and Balkans not covered)

17

Results

Response Variante R² RMSE MSE STD OBS R² RMSE MSE STD OBSTOC ALL 0,343 0,43818 0,192 0,553 1865 0,366 0,43932 0,193 0,566 1862TOC ALL_AP_STRATEN 0,267 0,38471 0,148 0,463 951 0,296 0,40988 0,168 0,502 952TOC ALL_GR_STRATEN 0,29 0,48785 0,238 0,59 911 0,316 0,46904 0,22 0,58 910TOC AP_STRATEN 0,324 0,36742 0,135 0,457 949 0,338 0,40866 0,167 0,511 948TOC AP_STRATEN_KRIGING 0,926 0,12247 0,015 0,457 949 0,33 0,41833 0,175 0,511 948TOC GR_STRATEN 0,35 0,46583 0,217 0,587 904 0,352 0,45935 0,211 0,581 907TOC GR_STRATEN_KRIGING 0,871 0,21213 0,045 0,587 904 0,35 0,46797 0,219 0,581 907TOC STRATEN 0,397 0,41833 0,175 0,549 1853 0,399 0,43359 0,188 0,57 1855

Modellskala (lognormal)Training Validierung

Spatial distribution of the inaccuracy (standard error)

Spatial distribution of the residuals

18

Outlook

19

Include N and CEC, include soil texture data; Re-upscale with the new parent material map; Condense regionally, then also improve stratification; Interpret covariates; Include integrated evaluations (e.g., potential heavy metal

release relative to SOM and acidity).

Outlook

alluvium/colluvium

calcearous rocks

clayey materials

crystalline rocks

detrital formations

glaciofluvial materials

loamy/silty

marl

other/organic

sandstone/flysch/molasse

sandy materials

schists

volcanic rocks

European Soil

Database

201 classes

(aggregated from

671 initial classes)

New BGR parent material map

(From Günther et al., 2013, Fig. 2, p.299 & Baritz et al., 2014, Fig. 6.2, p.120)

21

BGR GEMAS: N=310 (completely sampled and analysed soil profiles)

BGR soil profiles: N=1567 (agricultural land)

Regional studies

+

=Representative data set for higher

resolution evaluations, 2.5 D

The quality of the GEMAS inventory (analysis, georeferencing) is high so that satisfactory regression models can be built (950 plots for the ‘learning’ data set; stratification is important.

Option: Integration into a larger soil monitoring and soil quality assessment scheme (country-level/Europe).

Added value to facilitate a closer exchange between geoscientists and soil scientists.

Conclusions

23

Thank you for your attention!

rainer.baritz@bgr.de

d.zirlewagen@interra.biz

 ve@geus.dk 

ReferencesReferences

References

SLIDES 7, 9, 11, 12, 20:Baritz, R., Ernstsen, V. & Zirlewagen, D., 2014.  Carbon concentrations in European agricultural and grazing land soil.  Chapter 6 In:  C. Reimann, M. Birke, A. Demetriades, P. Filzmoser & P. O’Connor (Editors),  Chemistry of Europe's agricultural soils – Part B:  General background information and further analysis of the GEMAS data set.  Geologisches Jahrbuch (Reihe B 103), Schweizerbarth, 117-129.

SLIDE 6:CEC (Commission of the European Communities), 2003.  The LUCAS survey.  European statisticians monitor territory.  Theme 5:  Agriculture and fisheries, Series Office for Official Publications of the European Communities, Luxembourg, 24 pp.Corine land cover 2000 (CLC2000) seamless vector database. http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-databaseCorine Land Cover 2006 (CLC2006)s eamless vector data.  http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-versionESDB, 2004.  The European Soil Database distribution version 2.0. European Commission and the European Soil Bureau Network, CD-ROM, EUR 19945, http://eusoils.jrc.ec.europa.eu/ESDB_Archive/ESDB_Data_Distribution/ESDB_data.html . SLIDE 9: Baritz, R., Ernstsen, V. & Zirlewagen, D., 2014. Carbon concentrations in European agricultural and grazing land soil. Chapter 6 In: C. Reimann, M. Birke, A. Demetriades, P. Filzmoser & P. O’Connor (Editors), Chemistry of Europe's agricultural soils – Part B: General background information and further analysis of the GEMAS data set. Geologisches Jahrbuch (Reihe B 103), Schweizerbarth, 117-129. 

SLIDE 17:Baritz, R., D. Zirlewagen and E. Van Ranst (2006).  Methodical standards to detect forest soil carbon stocks and stock changes related to land use change and forestry – landscape scale effects.  Final report Deliverable 3.5-II. Multi-source inventory methods for quantifying carbon stocks and stock changes in European forests (CarboInvent) EU EVK2-2001-00287. SLIDE 20:Günther, A., Van Den Eeckhaut, M., Reichenbach, P., Hervás, J., Malet, J.-P., Foster, C. & Guzzetti, F., 2013.   New developments in harmonized landslide susceptibility mapping over Europe in the framework of the European Soil Thematic Strategy . Proceedings Second World Landslide Forum, 3-7 October 2011, Rome.  In:  C. Margottini, P. Canuti, K. Sassa (Editors),  Landslide Science and Practice.  Springer-Verlag, Berlin, Vol. 1, 297-301.  doi: 10.1007/978-3-642-31325-7_39.