1. Soil Data Join Recorrelation Initiative
Overview and Background
Purpose, Issues, Objectives, Initiative
Advisory Team / Technical Team
National Instruction Highlights
Reportable Measures
FY12 and Beyond

2. Overview and Background
Chief’s decision memo regarding NASIS
Improve the database
Accelerate MLRA approach by re-correlating data joins (harmonization)
Accelerate Phase 1 of MLRA update
Goal is seamless soil survey data

3. Soil Data Join Recorrelation (SDJR) (a.k.a. Harmonization)
What is it?
Effort to provide seamless soil survey information in a timely fashion
Correlation and data enhancement using legacy soils data to provide seamless soils data
One data mapunit or consistent properties correlated to geographically consistent map units
Same named
Similar named
Uniquely named

4. SDJR Why now? It has been a SSD Director priority for at least 2 years
With the completion of SSURGO many added value products are being generated
We need to provide consistent data for USDA programs
If we don’t do this, others (non-soil scientists) will make changes to make data consistent
We have enough data to make decisions for many instances
Many soil scientists that have key knowledge for making these decisions will likely be retiring soon

5. National Soil Survey Database Harmonization Project
Why now?
Allows for SSOs and MOs to do a thorough analysis of all their data
Through this analysis long range and yearly plans, and projects can be developed and prioritized
Using Benchmark Soils, we can harmonize/make consistent a large percentage of our data

6. Division Priority FY- 2012 Soils Division Priorities
Begin a multi-year initiative to complete Soil Survey Data Join Re-correlation (often referred to as harmonization) so that soils information matches from county to county and state to state on 1 billion acres

7. Division Director Charge:
Establish Advisory and Technical teams to look at accelerating Phase I (data harmonization) of MLRA updates
Provide advice for implementation
Develop objectives, goals, and direction

8. Advisory Team Cameron Loerch Tom Weber Ken Scheffe Cleveland Watts
Paul Finnell
Dennis Williamson
Jon Gerken
Roy Vick
Dave Hoover
Jerry Schaar
Amanda Moore
Steve Park
Mike Domeier

9. Technical Team Thorson, Thor - NRCS, Portland, OR
Tallyn, Ed - NRCS, Davis, CA
Fisher, John – NRCS, Reno, NV
Mueller, Eva- NRCS, Bozeman, MT
Wehmueller, William - NRCS, Salina, KS
Hahn, Thomas - NRCS, Denver, CO
Ulmer, Mike - NRCS, Bismarck, ND
Glover, Leslie - NRCS, Phoenix, AZ
Gordon, James - NRCS, Temple, TX
Whited, Michael - NRCS, St. Paul, MN
Endres, Tonie - NRCS, Indianapolis, IN
Finn, Shawn - NRCS, Amherst, MA
Dave Kingsbury - MOL, WV
Anderson, Debbie - NRCS, Raleigh, NC
Anderson, Scott - NRCS, Auburn, AL
Mersiovsky, Edgar - NRCS, Little Rock, AR
Mark Clark – MO Leader, AK
David Gehring - NRCS, Lexington, KY
Paul Finnell, NSSC
Ken Scheffe, NSSC
Cathy Seybold, NSSC
Steve Monteith, NSSC
Zamir Libohova, NSSC
Deb Harms, NSSC
Steve Peaslee, NSSC
Sub-Committees
Database
Climate
GIS
Correlation
Interpretations
ESD
Lab Data

10. What are the issues?
Existing product developed over a time span of 60 years. Naturally incorporating differences due to technology and time.

11. What are the issues?
K factors are one interpretation dependent on texture that are dependent on map unit concept
USDA conservation programs rely on high quality, consistent data for program eligibility and conservation planning.

12. What are the issues? Same map unit name, different composition
Same named map units representing the same soil/landscape/veg relationship with differing composition result in inconsistent use and results.

13. What are the issues? Lines join, interpretations differ
Surveys that appear to join spatially, have inconsistent interpretations due to minor differences in the horizon thickness and composition data.

14. Issues: Statewide Interpretations
There are more and more data needs for broad conservation planning, such as at State Levels. Highlights the need for consistency.

15. Bulk Density, 5-20 cm (Mg m-3)
Issues: Nationwide Soil Property Data Users
Also at a National Level, soil property data is being used in Models and other planning purposes. Again highlighting the needs for complete, consistent data.
2.33
Bulk Density, 5-20 cm (Mg m-3)
0.02

16. Expectation of consistent interpretations:
What are the issues?
MLRA 75-Crete sil, 0-1%
Dwellings with Basements
This initiative is looking for positive results such as this example.
Expectation of consistent interpretations:
Before
After

17. Basic Objectives - SDJR
Support the development of seamless soils data for use with CDSI, USDA Farm Bill Programs, and added value SSURGO products
Process resulting in correlation of similar data map units taking into account existing legacy data, laboratory data, and expert knowledge

18. Basic Objectives - SDJR
Dissolve the perceived data faults in interpretations visible in geospatial presentation of soil survey information
Often resulting from minor variation in data population, horizon depths, composition, and vintage of guidance documents

19. Basic Objectives - SDJR
Improve the database
Reduces the number of DMU’s for same and similarly named soil map units
Identify priority update needs
Builds the foundation for next generation of soil survey – disaggregation

20. National Instruction

21. National Instruction Highlights
NASIS
Soil Survey Reports
Correlation Documents
Lab Data
Published Research & Documents
GIS Products
Expert Knowledge
Conducted through a review of existing data:
Map Unit Concept and Composition
Initiative focuses on evaluation and review of “existing” data, several reports and tools to access NASIS data, Published survey reports to understand the concept of the map unit and its composition, Review of correlation documents and decisions, Lab Data, any research projects or investigations, tools and products using GIS technologies (climate, geology, DEM, Land cover, STATSGO, ecoregions) to estimate distribution.

22. National Instruction Highlights
Focus on Same and Similarly named map units
Integrating Uniquely Named Map Units
SRSS/SDQS additional ideas to utilize SDJR approach
Prioritize with Initial List of MU’s
Consider Benchmark Soils
Consider Priority Landscapes

23. National Instruction Highlights
Creating SDJR Projects in NASIS
SDJR Project Milestones
Create spatial distribution maps
Compile historical data
Populate correlated map units into SDJR project
Enter pedons in NASIS
Review historical MU/DMUs
Create and populate the new MLRA MU/DMU
Document the MLRA MU/DMU
Identify/propose future field projects
Update OSD and lab characterization data
Quality control completed
Quality assurance completed
Correlation activities completed
SSURGO certification

24. National Instruction Highlights
Harmonized Soil Data is:
Linked to Same DMU
Meets Data Completeness Standards
Components Total 100%
Major and Minor Soils Populated

25. National Instruction Highlights
Lab data reviewed
The pedons will be reviewed and updated
Updating the correlated name and correlated classification for sampled pedons
OSD reviewed and updated;
Classification updated to current taxonomy if necessary
Other updates to the OSD will follow the standard operating procedures for the MLRA regional office

26. National Instruction Highlights
Legacy Data Populated and Archived
Published manuscript TUD’s
Pedon data
ESD’s
Component productivity
Component ecological site
Work with ecological site inventory specialist and local rangeland management specialist
Map unit certified by QA process through MO

27. National Instruction Highlights
Identification of project needs that require future field work and analysis
Document in NASIS as a proposed project
Brief description
Estimated extent
Areas not joining spatially across political boundaries are identified as future projects and documented
Capture ESD inventory and development needs

28. Reportable measure’s SDJR (Harmonization) projects
20% of total map unit acreage
Report when QA milestone in project has been completed.
Post to SDM when scheduled (annual)
Initial soils mapping = 100%
MLRA field projects = 100%
High priority extensive revision = 100%
20%

29. FY 2012 – SDJR
3rd Quarter
Training to MLRA SSO’s by MO (Technical Team)
4th Quarter
Develop and work on a project
Test National Instruction
Develop future SDJR projects
Other Priorities (Initial; Agreements; projects)

30. FY 13 and Beyond Priorities and goals developed
Fully engaged in SDJR
Priorities and goals developed
SSD – MO’s
MLRA Advisory and Management Teams
Complete Initial surveys before full implementation.
Support from the MO (Technical Team)

31. National Bulletin

32. Summary SDJR Process Improve/enhance/populate database
Reconcile DMU’s for same and similarly named map units
Identify future project needs
Build foundation for next generation
SDJR Process

33. Discussion
Questions?