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Soil Data Join Recorrelation Initiative

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Presentation on theme: "Soil Data Join Recorrelation Initiative"— Presentation transcript:

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 https://nrcs.sc.egov.usda.gov/ssra/nssc/default.aspx

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?


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