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Applications of e-SOTER related to major soil threats
e-SOTER
Regional pilot platform as EU contribution to a
Global Soil Observing System
Simone Verzandvoort, Rudi Hessel,
Dennis Walvoort, Nanny Heidema
(Alterra, Wageningen UR)
Yusuf Yigini (JRC)
Ulrich Schuler (BGR)
Joël Daroussin (INRA)
Vít Penížek, Tereza Zadarova, Jozef Kozak (CULS)
Rachid Moussadek and colleagues (INRA-Maroc)
Workshop Towards Global Soil Information
FAO, 21 March 2012
Objectives
• To provide examples of how e-SOTER can be used to evaluate
threats to soils
• To investigate whether use of the e-SOTER database will
improve evaluation of threats to soil quality and performance
compared with using data from legacy soil maps and
databases.
Photo: Rainer Horn
Photo: Rainer Horn
soil erosion by water
soil compaction
Approach
Methods – model applications
Soil erosion
• Soil sensitivity to water
erosion (MESALES, BGR2)
• Potential soil loss (BGR1)
Soil compaction
• Inherent susceptibility to
subsoil compaction (Jones)
Input variables
Soilerosion
• Soil surface
texture
• Coarse
fragments
• Parent
material
Soilcompaction
• Subsoil
texture
• Packing
density
• Bulk density
• Clay content
Expert elicitation
Analysis
D: the maximum difference
between the cumulative
probabilities of the (discrete)
spatial distributions of model
outputs and/or expert
assessments
Results – Model outputs
Potential soil loss-CEU window
Sensitivity to water erosion – CEU window
Mesales - CEU
Sensitivity to water erosion – CEU window
Sensitivity to water erosion – WEU window
Susceptibility to soil compaction – MOR window
Sensitivity to water erosion – MOR window
Comparison model - expert
Model: 1*1 km2 pixels
Experts: NUTS3-units
Expert elicitation results
disagreement
agreement
CEU WEU
MOR
CEU WEU
MOR
Conclusions-Model results
• Different results for model applications using eSOTER versus
legacy databases
• Missing information on input variables in the eSOTER
database for considerable parts of the windows
Conclusions - Expert results
• Larger values and variation of D in the WEU window
• Larger values and variation of D for soil compaction
• No influence of area size or expert
Conclusions – model vs expert results
• Large deviation of model outputs compared to expert
responses (D up till 100%)
• Model outputs based on the eSOTER database are not always
better according to the experts than those based on legacy
databases
• D shows no differentiation according to individual experts or
the size of administrative units
Discussion
• The eSOTER database does not fully cover the administrative
units in the windows
• The comparison of the databases only refers to the input
variables of the models that differed between the databases
• Model outputs are on ordinal scales (ordered classes).
Differences between the databases providing the model
inputs may therefore be tempered.
Acknowledgements to the experts consulted
Anne-Véronique Auzet (Unistra, France)
Yves Le Bissonnais (INRA, France)
Arnd Bräunig (Sächsisches Landesamt Für Umwelt, Landwirtschaft Und
Geologie, Germany)
Miloud Chaker (Université Mohammed V, Morocco)
Tómaš Dostál (Czech Technical University, Czech Republic)
Beata Houšková (Soil Science and Conservation Research Institute,
Slovakia)
Bob Jones (NSRI Cranfield University, UK)
Ádám Kertész (Hungarian Academy of Sciences, Hungary)
Rachid Moussadek (INRA Maroc, Morocco)
Mustapha Naimi (Institut Agronomique & Vétérinaire Hassan II, Morocco)
Jane Rickson (National Soil Resources Institute (NSRI), UK)
Jan van den Akker (Alterra, Wageningen UR, The Netherlands)
Heading (24 pt)
Text (20 pt)
Spatial distributions in NUTS3-units
Similar
distributions for
legacy and
eSOTER
Larger coverasge
of high potential
soil loss classes
in eSOTER
Larger coverasge
of high potential
soil loss classes
in legacy
database

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e-SOTER Regional pilot platform as EU contribution to a Global Soil Observing System

  • 1. Applications of e-SOTER related to major soil threats e-SOTER Regional pilot platform as EU contribution to a Global Soil Observing System Simone Verzandvoort, Rudi Hessel, Dennis Walvoort, Nanny Heidema (Alterra, Wageningen UR) Yusuf Yigini (JRC) Ulrich Schuler (BGR) Joël Daroussin (INRA) Vít Penížek, Tereza Zadarova, Jozef Kozak (CULS) Rachid Moussadek and colleagues (INRA-Maroc) Workshop Towards Global Soil Information FAO, 21 March 2012
  • 2. Objectives • To provide examples of how e-SOTER can be used to evaluate threats to soils • To investigate whether use of the e-SOTER database will improve evaluation of threats to soil quality and performance compared with using data from legacy soil maps and databases. Photo: Rainer Horn Photo: Rainer Horn soil erosion by water soil compaction
  • 4. Methods – model applications Soil erosion • Soil sensitivity to water erosion (MESALES, BGR2) • Potential soil loss (BGR1) Soil compaction • Inherent susceptibility to subsoil compaction (Jones)
  • 5. Input variables Soilerosion • Soil surface texture • Coarse fragments • Parent material Soilcompaction • Subsoil texture • Packing density • Bulk density • Clay content
  • 7. Analysis D: the maximum difference between the cumulative probabilities of the (discrete) spatial distributions of model outputs and/or expert assessments
  • 10. Sensitivity to water erosion – CEU window Mesales - CEU
  • 11. Sensitivity to water erosion – CEU window
  • 12. Sensitivity to water erosion – WEU window
  • 13. Susceptibility to soil compaction – MOR window
  • 14. Sensitivity to water erosion – MOR window
  • 15. Comparison model - expert Model: 1*1 km2 pixels Experts: NUTS3-units
  • 19.
  • 20. Conclusions-Model results • Different results for model applications using eSOTER versus legacy databases • Missing information on input variables in the eSOTER database for considerable parts of the windows
  • 21. Conclusions - Expert results • Larger values and variation of D in the WEU window • Larger values and variation of D for soil compaction • No influence of area size or expert
  • 22. Conclusions – model vs expert results • Large deviation of model outputs compared to expert responses (D up till 100%) • Model outputs based on the eSOTER database are not always better according to the experts than those based on legacy databases • D shows no differentiation according to individual experts or the size of administrative units
  • 23. Discussion • The eSOTER database does not fully cover the administrative units in the windows • The comparison of the databases only refers to the input variables of the models that differed between the databases • Model outputs are on ordinal scales (ordered classes). Differences between the databases providing the model inputs may therefore be tempered.
  • 24. Acknowledgements to the experts consulted Anne-Véronique Auzet (Unistra, France) Yves Le Bissonnais (INRA, France) Arnd Bräunig (Sächsisches Landesamt Für Umwelt, Landwirtschaft Und Geologie, Germany) Miloud Chaker (Université Mohammed V, Morocco) Tómaš Dostál (Czech Technical University, Czech Republic) Beata Houšková (Soil Science and Conservation Research Institute, Slovakia) Bob Jones (NSRI Cranfield University, UK) Ádám Kertész (Hungarian Academy of Sciences, Hungary) Rachid Moussadek (INRA Maroc, Morocco) Mustapha Naimi (Institut Agronomique & Vétérinaire Hassan II, Morocco) Jane Rickson (National Soil Resources Institute (NSRI), UK) Jan van den Akker (Alterra, Wageningen UR, The Netherlands)
  • 26. Spatial distributions in NUTS3-units Similar distributions for legacy and eSOTER Larger coverasge of high potential soil loss classes in eSOTER Larger coverasge of high potential soil loss classes in legacy database