This presentation summarizes the activities and results for Objective 1 of the AfSIS project - This objective aims to create and maintain a global consortium that will produce grid maps of soil properties at a fine spatial resolution of 100 m for the entire world. The slidies in this presentation highlight accomplishments and contributions towards this objective in 2010.

1. GlobalSoilMap.net: Update

2. GlobalSoilMap.net: AfSIS Objective 1 $18 million Other grants and in-kind: $8 million Total budget: $300 million

3. Objective 1: Results & Activities AfSIS Project Objective 1: Establishing the GlobalSoilMap.net global consortium Key successes (objectives achieved) Consortium agreement signed & nodes active Specifications prepared and agreed upon Soil legacy data for Africa (AfSIS) Progress and results by node New tools and initiatives Training and capacity building Fund raising Future plans

4. Consortium Agreement has been Signed Slide Credit: Alfred Hartemink

5. Nodes are Established and Active Eurasia North America EastAsia CUMERC North Africa/West and Central Asia South Asia Latin America/ Caribbean Oceania Africa (South Asia node is still pending)

6. Specifications Prepared and Agreed

7. Specifications – Soil properties (not classes) Key soil properties 1. Organic Carbon (g/kg) 2. Sand (g/kg), Silt (g/kg), Clay (g/kg) coarse fragments (g/kg) 3. pH 4. Depth to bedrock or restricting layer (cm) From these attributes, the following two properties will be predicted using pedo-transfer functions: 5. Bulk Density (Mg/m3) 6. Available Water Capacity (given in mm/m) Optional: 7. ECEC (Cations plus exchangeable acidity mmol/kg) 8. EC (Electrical conductivity mS/m) Six Depths 0 - 5 cm 5 – 15 cm 15 – 30 cm 30 – 60 cm 60-100 cm 100-200 cm Depth to bedrock and Effective depth Slide Credit: Alfred Hartemink

8. Soil Legacy Data for Africa Legacy Data Officer is locating, entering and checking data country by country 14,000 unique profiles 10,000 georeferenced 8,000 with lab data Error checked profiles Positional accuracy Data value errors Slide Credit: Johan Leenaars

9. Eurasia North America EastAsia CUMERC North Africa/West and Central Asia South Asia Latin America/ Caribbean Oceania Africa (South Asia node is still pending) Progress and Results by Node

10. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Detailed soil survey polygon data (SSURGO) and more generalized polygon data (STATSGO) have been converted to raster estimates of cumulative organic carbon content to 100 cm depth Slide Credit: Jon Hempel

11. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications Slide Credit: Nathan Odgers

12. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications Slide Credit: Nathan Odgers

13. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications Slide Credit: Nathan Odgers

14. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications Slide Credit: Nathan Odgers

15. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications Slide Credit: Nathan Odgers

16. North America Node USA is actively producing version 1.0 grid maps using polygon disaggregation methods Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications Slide Credit: Nathan Odgers

18. The compilation of the new database has been made possible as part of a National Atlas of Sustainable Ecosystem Services being developed under the leadership of the U.S. Environmental Protection Agency (EPA), along with many partner organizations including the NRCS and the U.S. Geological Survey. Slide Credit: Jon Hempel

20. The analysis of soil attributes starts on the component horizon (chorizon) table, aggregates the quantitative measures over the appropriate layers for a given analysis, and stores the result at the level of the component table (component). A weighted average of the component values is computed using the representative component percentage (comppct_r) as the area weighting factor, and the results are stored at the level of the map unit (mapunit table). These results are then copied to the spatial datasets where they are used to display maps.Slide Credit: Jon Hempel

21. Figure 11. Soil carbon content (from soil organic matter content). The carbon content is computed from the organic matter content, accounting for the bulk density, volume of rocks, and a conversion factor (0.58) for the mass of carbon per unit mass of organic matter. [om_r, dbthirdbar_r, and others] 1194 g C m-2 0 Slide Credit: Jon Hempel

22. Figure 10. Clay percentage from the ratio of the mass of clay to the mass of soil fines. [claytotal_r, dbthirdbar_r, and others] 82 0 Slide Credit: Jon Hempel

23. Figure 8. Sand percentage from the ratio of the mass of sand to the mass of soil fines (soil particles less than 2 mm diameter). [sandtotal_r, dbthirdbar_r, and others] 100 0 Slide Credit: Jon Hempel

24. Figure 9. Silt percentage from the ratio of the mass of silt to the mass of soil fines. [silttotal_r, dbthirdbar_r, and others] 92 0 Slide Credit: Jon Hempel

25. Figure 7. Bulk density is the mass of soil divided by the volume of soil for the fraction of soil with particles less than 2 mm in diameter (i.e., excluding rocks). The mass is measured on an oven-dry basis, and the volume at a water content with 33 kPa soil water tension. [dbthirdbar_r] 2.33 0.02 Slide Credit: Jon Hempel

26. Figure 2. Average soil thickness is the maximum depth of soil recorded by soil scientists or the depth to bedrock, whichever is less. There may be “county boundaries” in the data because different soil surveys used different conventions on the maximum depth of soil to record in the database (e.g., 60 inches or 80 inches). [hzdepb_r] (SSURGO + STATSGO2 12/30/2009) 457 cm 0 Slide Credit: Jon Hempel

27. SSURGO Map Units Gilpin-Laidig Pineville-Gilpin-Guyandotte Other North America Node NRCS – is developing methods to disaggregate polygon maps into component soils Each component has a single soil property value Thompson et al. 2010 Component Soils Gilpin Pineville Laidig Guyandotte Dekalb Craigsville Meckesville Cateache Shouns

28. North America Node NRCS and University of Sydney have demonstrated methods for harmonizing soil maps Original Non-harmonized Soil Maps Harmonized Soil Series Maps Slide Credit: Alex McBratney

29. North America Node USA is conducting pilot projects (with Canada) to develop and assess new methods Slide Credit: Jon Hempel

30. Oceania Node CSIRO have methods to convert polygon soil data to GlobalSoilMap.net specifications ASRIS Approach: Deriving continuous depth soil properties from a soil polygon map Slide Credit: David Jacquier

31. Oceania Node University of Sydney has developed methods for predicting properties and uncertainty The soil map Soil sample design Soil sampling and analysis Data analysis Soil map quality Upper Prediction DSM Soil Property Prediction Lower Prediction Slide Credit: Budiman Minasny

32. Oceania Node University of Sydney has developed methods for predicting properties and uncertainty 0-5cm 30-60cm 60-100cm Lower prediction limit Upper prediction limit DSM prediction Slide Credit: Alex McBratney

34. Covariate data sets

35. Capability development

36. Internat’l communicationNew Zealand S-map – new soil database Propose: S-map at 15m ↔generalise to GSM 90m Slide credit: Alan Hewitt

37. Oceania Node New Zealand has devised a strategy for applying different methods in different areas Slide credit: Alan Hewitt

38. Latin America & Caribbean Node LAC Node established a model for organizing countries at the node level Slide credit:Lou Mendonça Santos The Rio Accord Declaration

39. West & Central Asia/North Africa World’s first institute of Digital Soil Mapping opened at CUMERC December, 2010 Mahmoud AlFerihat, (2011)

40. East Asia Node A scheme showing 3-D soil mapping based on point pedon data for a pilot area in China Slide credit: Ganlin Zhang

41. East Asia Node East Asia node is evaluating 3-D SOM mapping for a pilot watershed in China Three horizontal slices at different depth: 1 cm, 50 cm, and 100 cm 3D mapping of SOM vertical slices at four transects Slide credit: Ganlin Zhang

42. Slide credit: Song Young Hong East Asia Node Korea has developed and demonstrated methods of digital soil property mapping ◇ Soil Carbon Prediction and Mapping ○ Soil Spectrum  Data Transform  Library  Prediction ○ PTF for BD  C density  Mapping Soil C Storage vertical slices at four transects Soil Carbon Prediction - Spectroscopy Soil Carbon Storage Mapping

43. Eurasia Node Eurasia node is underway now that the Consortium Agreement is signed and in force Developing two prototype cases Slovakia national database at 100 x 100 m to specifications Prepared by JRC Denmark national database at 100 x 100 m to specifications Prepared by Aarhus University Protype cases will be presented at the June, 2011 team meeting JRC is hosting the GlobalSoilMap.net team meeting in June Providing facilities, organizational and logistical support Contributed to the GlobalSoilMap.net test bed activity Developed organic carbon WMS service for testbed

44. PIXEL_id 4526_2618 SMU 1 SMU 2 Eurasia Node Eurasia node: from polygons to pixel based soil information systems GlobalSoilMap.net Exchange Format Slide credit: Luca Montanarella

45. Multiscale EUropean Soil Information System (MEUSIS) http://eusoils.jrc.it/projects/Meusis/main.html 100 km 10 km 1 km Eurasia Node Eurasia node: from polygons to pixel based soil information systems 90m Slide credit: Luca Montanarella

46. Multi-scale Soil Information system First test results in Slovakia Multi-Scale European Soil Information System (MEUSIS): A multi-scale method to derive soil indicators. Panagos, Van Liedekerke and Montanarella. Computational Geosciences, Springer (2010). Article in Press.

47. New Tools and Initiatives The project has stimulated the development of new tools and protocols for DSM University of Sydney (McBratney, Minasny & Malone) Spline function – MATLAP and R-code to fit spline to profile data Uncertainty – emperical method to estimate uncertainty CSIRO – Canberra Spline tool – stand alone Windows program for fitting the spline AfSIS Sentinel site sampling strategy - design and implementation Google mobile app for delivering on-site agronomic advice

49. Spatial Intersection of soil information with available predictive covariates

50. Data splitting: into calibration and validation datasets

51. Fuzzy clustering of feature space

52. model error estimation

53. construction of cluster prediction intervals

54. Upper and lower prediction limits

55. Construct prediction intervals

57. construct prediction intervals

58. construct continuous depth representation of predictions and uncertainties (upper and lower prediction limits:- mass preserving splinesValidate learning rules R2, RMSE etc Model training and learning Slide credit: Brendan Malone

59. Oceania Node University of Sydney has developed methods for fitting spline depth functions Estimate averages for spline at standardised depth ranges, e.g., globalsoilmap depth ranges Fit mass-preserving spline ‘Modal’ profile Fitted Spline Spline averages at specified depth ranges Sun et al.(2010)

60. Oceania Node 2 -6 6 -1.2 -2 1.0 -3 1.5 University of Sydney has developed methods for estimating uncertainty of predictions Perform fuzzy k-means with extragrades clustering on the covariates Calculate the distribution of errors for each fuzzy k-means class Class A Extragrades Class C Class A Class B Class C Class B Extragrades Malone et al.(2011)

61. Oceania Node 2 -1.2 -2 1.0 -3 1.5 University of Sydney has developed methods for fitting spline depth functions Prediction error is the weighted mean of the error Class A Class B Extragrades Class C Example Location x Predicted value = 10 mA = 0.6 mB = 0.2 mC = 0.15 mExtragrades = 0.05 Error: Lower Limit =0.6 * -2 + 0.2 * -1.2 + 0.15 * -3 + 0.05 * -6= -2.19 LPL = 10 – 2.19 = 7.81 Upper Limit = 0.6 * 2 + 0.2 * 1.0 + 0.15 * 1.5 + 0.05 * 6 = 1.93 UPL = 10 + 1.93 = 11.93 Malone et al.(2011)

62. Oceania Node CSIRO has developed a stand alone Windows program for fitting spline depth functions Slide Credit: David Jacquier

63. Africa Node (AfSIS) Sentinel Site based on the Land Degradation Surveillance Frameworka spatially stratified, hierarchical, randomized sampling framework AfSIS has devloped a design and protocols for stratified hierarchical sampling Sentinel site (100 km2) 16 Clusters (1 km2) 10 Plots (1000 m2) 4 Sub-Plots (100 m2) Slide Credit: Markus Walsh Randomization to minimize local biases that might arise from convenience sampling

64. Africa Node (AfSIS) AfSIS has devloped a design and protocols for stratified hierarchical sampling Slide Credit: Markus Walsh

65. Africa Node (AfSIS) AfSIS is developing a mobile phone based app with Google for on-site data sharing Slide Credit: Markus Walsh

66. DSM Training Efforts University of Sydney is leading efforts to devlop and deliver DSM training modules Courses delivered CSIRO – Canberra, June, 2010 JRC- Ispra, August, 2010 Embrapa – Rio, Sept, 2010 Courses planned Korea – Spring, 2011 JRC – Ispra, June, 2011 MSc course, Univ of Sydney Course modules developed Data quality checking Location accuracy checking Property value checking Bias in property values Data harmonization (methods) Standardization of methods Data harmonization (depths) Spline tool application Prediction methods Regression kriging/ANN Polygon disaggregation

67. Fund Raising Efforts Fund Raising Officer is developing node-specific fund faising strategies West & Central Asia/North Africa Node Replicate strategy from LAC Meeting at DSM/CUMERC Global In-kind Contributions North America - $925 k Oceania - $158 k WCA/NA - $219 k LAC - $25 k AfSIS - $305 k Asia - $392 Institutes – $1.2 M World Bank Washington – April 2010 50 Donor Agencies invited Prospects identified LAC Node – 3 Initiatives Google – short term $300 k Harvest legacy soil data IADB – medium term $1.5 M Country-level DSM pilots Moore – long term $15 M Operational mapping

68. Fund Raising Efforts The project is providing the impetus for significant in-kind contributions from nodes

69. Future Plans and Proposals ISRIC is implementing measures to support production and distribution of data ISRIC cyber-infrastructure development Server network Support for spatial databases ISRIC concepts and support for harmonized global mapping Open Soil Profile Database – concept, design and proposal Global Soil Information Facility - – concept, design and proposal Multi-scale modelling prediction method – concept and examples GlobalSoilMap.net coordination Full week team workshop – JRC, Ispra June, 2011

70. ISRIC – new plans & proposals ISRIC is developing cyber-infrastructure to host and serve geodata about soils ISRIC Soil Portal Slide credit: Hannes I. Reuter, 2011

71. ISRIC – new plans & proposals ISRIC Web Services: WCS – WFS - WMS GeoServer: WCS-WFS-WMS GeoNetwork – Open Source Slide credit: Hannes I. Reuter, 2011

72. ISRIC – new plans & proposals ISRIC is developing proposals for collecting and hosting global soil profile and map data Slide credit: Tom Hengl, 2011

73. ISRIC – new plans & proposals ISRIC is devloping a vision and proposal for an open soil profiles database (OSPD) Slide credit: Tom Hengl, 2011

74. ISRIC – new plans & proposals ISRIC is developing a vision and proposal for an on-line soil map geo-referencer Scan Geo-register through crowd sourcing Slide credit: Hannes I. Reuter, 2011

75. ISRIC – new plans & proposals ISRIC is developing multi-scale methods for collating & harmonizing soil property maps Slide credit: Tom Hengl, 2011

76. ISRIC – new plans & proposals ISRIC is developing multi-scale methods for collating & harmonizing soil property maps Slide credit: Tom Hengl, 2011

77. ISRIC – new proposals ISRIC is devloping multi-scale methods for harmonizing soil property maps Slide credit: Tom Hengl, 2011

78. ISRIC – new proposals ISRIC is devloping multi-scale methods for harmonizing soil property maps Slide credit: Tom Hengl, 2011

79. Conclusions ISRIC contributions Node contributions Future plans and possibilities Global training & capacity building in DSM Global standards and methods for soils Global platforms and systems for mapping soil Implications Harmonization of soil science globally

80. Recognizing our Supporters Original supporter Provided grant to suppport work in Sub-Saharan Africa To develop improved land management recommendations Support inital development of the GlobalSoilMap.net project