Third meeting of the International Network of Soil Information Institutions (INSII), 31 October - 1 November 2017, FAO headquarters, Rome. Composed by institutions working on soil information in FAO member countries INSII provides expertise, and facilitates soil data/information sharing. Presentation by Guillermo Federico Olmedo, FAO.

2. Data sources in the
GSOC map
Guillermo Federico Olmedo
GSP Secretariat

5. Country-driven
approach

6. 5 different Cases
• Countries who already have a map
• Countries who created their map for GSOC
• Countries who built a map after our training course
• Countries where we built the map together
• Gap filling

7. Countries who already have a map: Australia
● 5588 observations
● DSM
● Combination of
decision trees with
piecewise regression
on environmental
variables and
geostatistical modelling
of residuals.

8. Countries who
created their map
for GSOC:
Argentina
● 4255 observations
● DSM
● Support Vector
Machines + Kriging

9. Map produced after the training course:
Bolivia
● 4788 observations
● DSM
● randomForest

10. Joint efforts: South Africa
● 1257 observations
● DSM
● support vector
machines

11. Gap filling: WoSIS + ISRIC covs
● WoSIS
observations
● DSM
● randomForest
+ kknn

12. Gap filling: soilgrids.org

13. Different cases and different
methods.
How do these maps add
together?

15. Conventional Upscaling
own data +
randomForest
GF: SVM + WoSIS
GF: Ensemble
+ LUCAS
soilgrids

17. soilgrids
soilgrids
owndata
+SVM
LUCAS
+SVM
LUCAS
+SVM

19. WOSIS
own data:
SOC from soil spectroscopy
own data:
SOC from wet oxidation
own data:
SOC from wet oxidation

20. Uncertainty Assessment
and
Validation

21. Uncertainty Assessment
~ 90 countries with
uncertainties
assessment
In the end we need to
have a global
uncertainty map.

24. Many different cases
• confidence intervals for the OCS values
• standard deviation from regression kriging
• standard deviation from an ensemble of different
DSM models
• expert knowledge in percentage

25. confidence intervals

26. standard deviation from RK

27. standard deviation from an
ensemble of DSM models

28. expert knowledge (percentage)

29. Validation against WoSIS Soil
Profile Database

30. WoSIS Soil Profile Database

32. Comparison with other GSOC maps

34. R. Hiederer, M. Kochy 2012. Global Soil Organic Carbon Estimates and the Harmonized World Soil Database. EUR Scientific and
Technical Research series “ ISSN 1831-9424 (online), ISSN 1018-5593 (print), ISBN 978-92-79-23108-7, doi:10.2788/1326

35. R. Hiederer, M. Kochy 2012. Global Soil Organic Carbon Estimates and the Harmonized World Soil Database. EUR Scientific and
Technical Research series “ ISSN 1831-9424 (online), ISSN 1018-5593 (print), ISBN 978-92-79-23108-7, doi:10.2788/1326

36. R. Hiederer, M. Kochy 2012. Global Soil Organic Carbon Estimates and the Harmonized World Soil Database. EUR Scientific and
Technical Research series “ ISSN 1831-9424 (online), ISSN 1018-5593 (print), ISBN 978-92-79-23108-7, doi:10.2788/1326

37. R. Hiederer, M. Kochy 2012. Global Soil Organic Carbon Estimates and the Harmonized World Soil Database. EUR Scientific and
Technical Research series “ ISSN 1831-9424 (online), ISSN 1018-5593 (print), ISBN 978-92-79-23108-7, doi:10.2788/1326

38. R. Hiederer, M. Kochy 2012. Global Soil Organic Carbon Estimates and the Harmonized World Soil Database. EUR Scientific and
Technical Research series “ ISSN 1831-9424 (online), ISSN 1018-5593 (print), ISBN 978-92-79-23108-7, doi:10.2788/1326

39. GSOC

40. soilgrids (Hengl et at, 2017)

41. GSOC

42. HWSDa (Hiederer & Kochi, 2012)

43. ~900
R. Hiederer, M. Kochy 2012. Global Soil Organic Carbon Estimates and the Harmonized World Soil Database. EUR Scientific and
Technical Research series “ ISSN 1831-9424 (online), ISSN 1018-5593 (print), ISBN 978-92-79-23108-7, doi:10.2788/1326