1. Soil Data Join Recorrelation FY14 Webinar formerly known as: MLRA Inventory and Assessment 1995-2010

2. 610.01 Policy MLRA soil survey update activities are conducted as a series of projects with…the goal of developing a seamless national product. Soil survey inventories and assessments are conducted on existing soil survey products to identify deficiencies, errors, omissions, or inappropriateness in the data or maps in order to plan and prioritize soil survey activities. The inventories and assessments are completed prior to commencing update activities for the MLRA SSO area. (See General Manual GM_430_402_A_402.5_C.) Part 610 – Updating Soil Surveys

3. SDJR Priorities  High DMU count map units  Map units mapped in multiple counties  High total map unit acres  Typically affects the most customers  Benchmark soils  151 Benchmark soils with more 1M acres each  NRCS Priority Areas  Landscape Conservation Initiative areas  StrikeForce for Rural Growth and Opportunity areas

4. Soil Data Join Recorrelation  Evaluate (historical documentation)  Harmonize (to build MLRA map unit data)  Document (future work)  National Goal – 1 billion acres evaluated in 5 years

5. 5

6. KERRVILLE, TEXAS SOIL SURVEY OFFICE

7. Kerrville, TX Soil Survey Office Jessica Jobe, MLRA Leader – 11 years, Worked in Graham, Tx and Kerrville, Tx Travis Waiser, Project Leader – 11 years, Worked in Lexington, Caldwell, Bryan, Kerrville, Tx Ashley Newsome, Soil Scientist – 4 years, Rosenberg, Bryan and Kerrville, Tx Alan Deubler, Soil Scientist – 4 years, Kerrville, Tx

8. Kerrville SSO - Area of Responsibility MLRA’s 81 A - D, and MLRA 82A 46 Counties 26,843,788 Acres 97 Series 1,067 Map Units

9. 53 SDJR Projects MU’s affected - 118 Acres claimed – 480,932 Acres affected – 2,404,660 FY2014 Workload

10. Gillespie County, TexasOakalla Oakalla silty clay loam, occasionally flooded81BR081BY335TX Loamy Bottomland 23-31" PZ flood plains, drawsflood plains Kimble County, TexasOakallaOakalla silty clay loam81BR081BY335TX Loamy Bottomland 23-31" PZ flood plains, drawsflood plains Mason County, TexasOakalla Oakalla loam, 0 to 2 percent slopes, occasionally flooded81BR081BY335TX Loamy Bottomland 23-31" PZ flood plains on perenial streams Uvalde County, TexasOakalla Oakalla silty clay loam, 0 to 1 percent slopes, occasionally flooded81BR081BY335TX Loamy Bottomland 23-31" PZ flood plains, drawsflood plains Uvalde County, TexasOakalla Oakalla silty clay loam, 1 to 3 percent slopes, occasionally flooded81BR081BY335TX Loamy Bottomland 23-31" PZ flood plains, drawsflood plains Uvalde County, TexasOakalla Oakalla silty clay loam, frequently flooded81BR081BY335TX Loamy Bottomland 23-31" PZ flood plains, drawsflood plains Bandera County, TexasOakalla Oakalla silty clay, occasionally flooded81CR081CY561TX Loamy Bottomland 29-35" PZ flood plains, drawsflood plains Kendall County, TexasOakallaOakalla silty clay loam, flooded81CR081CY561TX Loamy Bottomland 29-35" PZ flood plains, drawsflood plains Kerr County, TexasOakalla Oakalla silty clay loam, occasionally flooded81CR081CY561TX Loamy Bottomland 29-35" PZ flood plains, drawsflood plains Kerr County, TexasOakalla Urban land-Oakalla complex, rarely flooded81C flood plains, drawsflood plains Travis County, TexasOakalla Oakalla silty clay loam, 0 to 1 percent slopes, occasionally flooded81CR081CY561TX Loamy Bottomland 29-35" PZ flood plains, drawsflood plains MLRA09_Temple - Export ECO QC QA Export data v 10.78 w/nulls sm hds TX REAL Quickly identify any data inconsistencies between existing mapunits Landscape/landform position Parent material Ecosite Range production Slope Review historical pedon data in Nasis Evaluation

11. “Series Project Summary” General overview of series concept Propose mapunits to be joined in upcoming SDJR projects Account for mapunits not included in SDJR project Series extent map MLRA boundaries Soil moisture regimes Slope analysis when needed Review MUD’s and TUD’s Evaluate existing local and KSSL lab data Evaluations are then sent to SDQS, SSS and Resource SS for approval Evaluation

12. Always begin with the mapunit in which the OSD is located Review all TUD’s and OSD for the series before we start to develop ranges This provides consistency between same series map units within an MLRA and expedites the population of subsequent map units of a given series. We only have to adjust RV horizon values, component data and minor components Utilize existing local and KSSL lab data when available for horizon rv’s and ranges Harmonization

13.  Zonal stats for elevation  Slope analysis with units that have similar but not exact slope ranges  No trend analysis because we don’t have sufficient historical data to perform this type of analysis and get meaningful results Harmonization

14. CALCULATED AND STORED DATA FOR AASHTO AND UNIFIED ENGINEERING CLASSES Print date: 08/05/2014 Note: Assume Unified class is Pt for all surface organic horizons (duff layers) in well drained soils. ________________________________________________________________________________________________________________________________________ Map Symbol and | Horiz- |Depth | Calc. AASHTO from | AASHTO data in NASIS | Calc. Unified from | Unified data in NASIS Soil Name | ion | (cm) | Low RV High | RV* | Low RV High | RV* _______________________________________________________________________________________________________________________________________ 1OkcA: Oakalla |Ap |0-20 | A-6 A-7-6 A-7-6 | A-7-6*, A-6 | CL CL CH | CL*, CH |Ak |20-58 | A-4 A-7-6 A-7-6 | A-7-6*, A-6, A-4 | CL CL CH | CL*, CH |Bk1 |58-135 | A-4 A-6 A-7-6 | A-6*, A-7-6, A-4 | CL CL CH | CL*, CH |Bk2 |135-203 | A-4 A-6 A-7-6 | A-6*, A-7-6, A-4 | ML CL CH | CL*, CL-ML, ML, CH Rock outcrop |R |0-203 | BEDROCK BEDROCK BEDROCK| --- | BEDROCK BEDROCK BEDROCK| --- Dev | | | NO DATA NO DATA NO DATA| --- | NO DATA NO DATA NO DATA| --- Unnamed | | | NO DATA NO DATA NO DATA| --- | NO DATA NO DATA NO DATA| --- Check/Comparison Reports for AASHTO and Unified Land Capability Class Physical Soil Properties Chemical Soil Properties Bulk Density CEC/ECEC Fragments and Sieves Harmonization

15. Validations: Component Data mapunit Horizon Horizon Texture Group *Just because you have a validation error doesn’t mean you have an error. Investigate before changing things Harmonization

16. Areasym, Musym, Natsym Pct ENG - Dwellings W/O BasementsENG - Local Roads and StreetsENG - Septic Tank Absorption Fields Soil Name Rating class and limiting features Value Rating class and limiting features Value Rating class and limiting features Value 9-KER—1PefC—2t2md Pedernales85Somewhat limitedVery limited Shrink-swell0.78Low strength1.00Slow water movement1.00 Shrink-swell0.78Seepage, bottom layer1.00 TX604—PdB—f6fv Pedernales85Somewhat limitedVery limited Shrink-swell0.37Low strength1.00Slow water movement1.00 Shrink-swell0.37 TX453—PdC—f65w Pedernales95Somewhat limitedVery limited Shrink-swell0.50Low strength1.00Slow water movement1.00 Shrink-swell0.50 Interpretation review  Local roads streets  Septic system  Dwellings w/o basements SDJR - MLRA 81C - Pedernales fine sandy loam, 1 to 5 percent slopes Harmonization

17. QC/QA REGION 09 QUALITY CONTROL/ASSURANCE REVIEW CHECKLIST FOR NASIS DATA Project Name: MLRA SSO: MLRA Mapunit (MU) Name: MLRA DMU Description and Record ID: Name and Title of Reviewer: Date Completed: TableColumnItems to be Checked QC Checked √ QA Checked √ Review Comments Use NSSC Pangaea Query, Project/Mapunit/DMU by Project Name, to load your Selected Set. ProjectProject Name Check format: SDJR - MLRA 133B - Bowie fine sandy loam, 1 to 3 percent slopes. If using a "priority number" then format is: SDJR - MLRA 133B - 2 Bowie fine sandy loam, 1 to 3 percent slopes. (see SDJR Natl Instructions 305.5-2-ii) ProjectDescription Is description in an executive summary format? Compare with Exhibit D NI 305. ProjectApproved? Does box contains a check mark? Must not be null or contain a square. Project MLRA Soil Survey Office Area Is it populated with office assigned to work on project? Project Non-MLRA Soil Survey Area Not populated for SDJR or MLRA update projects. Check this. ProjectState ResponsibleState in which the MLRA SSO resides. Project MapunitMapunit Verify that project map units copied/pasted from the selected set of map units from official legends still have a "correlated" mapunit status. The new MLRA MU should be "provisional". See note at bottom of checklist. Project Milestone Type Name, Description, Start and Completion Dates Have all milestones (see list NI 305.5 D6) except for SDJR 11, 12, 13, and Project completed date, been populated with completion dates? Project Land Category Breakdown Land Category, Land Category Acres Are these columns populated? Project Mapping Goal Fiscal Year, Update NRCS Acres Goal Is mapping goal 20% of the Land Category Acres? Project Staff NASIS User Name, Project Leader? Are these columns populated?

18. GS 9-12 complete and QC projects using QC checklist GS 11 QC’s all projects prior to sending them to GS 12 GS 12 QC’s projects after first round of edits are made. Additional edits made when needed GS 12 submits to SDQS for Quality Assurance. If additional edits needed we start the process again. If not we create SDJR Template document.QC/QA

19. SDJR Template used to relay information to the SSS and Zone staff for review and concurrence SDJR Template

20. Results  Since 2013 we have taken 252 existing map units and harmonized them into 75 MLRA map units  Fully populated data map units to Exhibit A  Identified minor components and populated them to Exhibit B  Identified future MLRA update projects

21. All series projects for the remainder of SDJR have been created and all projects are in NASIS. Not all have been approved but the proposed FY15 projects have been submitted for approval. SDJR Projects - 30 Acres to be affected - 5,136,467 Claimable acres - 1,027,293 Future Workload

22. Future Field Projects  231 Future MLRA Projects identified so far 22

23. Future Field Projects

24. Questions

25. BELMONT, NEW YORK SOIL SURVEY OFFICE

26. Belmont NY Soil Survey Office Steve Antes, MLRA SSOL- 31 years, worked in Vinton Co., OH; numerous counties in western and central, and northern New York (MLRAs 140, 101, 127, 143) MaryEllen Cook- Project Leader- 27 years, worked in WV & VA, and NY, OH, PA (MLRA 139) Matt Havens, Project Leader- 27 years, worked in numerous counties in western and central and northern New York (MLRAs 140, 101, 127, 143) Dean Shields, Soil Scientist- 13 years, numerous counties in western and central NY (MLRAs 140, 101) Keith Shadle, Soil Scientist- 11 years, worked in Wyoming; several counties in northern and central New York (MLRAs 140, 143)

27. “The Rest of the 12-BEL Team”  Tech Teams- MLRAs 139 and 140  Soils’ expertise, priorities, etc.  Management Teams- SSS’s from NJ/NY, OH, PA  Project approvals, priorities, etc.  Regional Office 12 Staff  Goals- workflow (progressive submissions/reporting)  Timely QA  SOP- supplements the NI, provides additional specific guidance  Climatic data- zonal stats  Processing of OSD revisions  Electronic version of Exhibit A- w/ supplemental instructions

28. 12-BEL Area of Responsibility: - 2 MLRAs (139, 140) - 4 states (NJ, NY, OH, PA) - 88 survey areas - 21.6 M acres

29. Why so many projects/ DMUs?  large number of unique MU names (~3,880)  glaciated landscapes- highly variable deposits  two MLRAs- 140- predominantly Inceptisols; 139- predominantly Alfisols  wide range of correlation dates for individual survey areas (1947-2014- 67 years)  multiple states- different “standard” slope groupings, mapping concepts, etc.  including single MU/DMU projects for priority series, in order to bring all up to SDJR standards  mesic/frigid  taking a very conservative (“do no harm”) approach  field investigations/spatial analysis (as future projects) before combining MUs  more “in-house” tacit knowledge about MLRA 140 soils  more reliant on tech team for input about MLRA 139 soils

31. STEPS IN PROCESS FLOW: GS-9, GS-11, GS-12 1.ID Soil Series/catenas to be SDJR’d; project approval process 2.Run “matthews report” (MLRA12) 3.Separate into preliminary groupings based on map unit name 4.Print MUDs and TUDs for all survey areas on report 5.Read all MUDs and make notes on important issues on prelim group spread sheet 6.Set up project (usually batch of projects) in NASIS and submit for approval 7.Review all TUDs and make sure they are all entered in NASIS-if not enter them 8.Create TUD stats for key properties

32. STEPS IN PROCESS FLOW: GS-9, GS-11, GS-12 9. Review historical notes and edit prelim groups if needed 10. Identify any future projects and list on prelim group spread sheet 11. Compile any lab data and make summary stats of key properties 12. Select typical pedon based on TUD stats and lab data summary 13. Create SDJR map unit 14. Create SDJR DMU—going through checklist and running validations 15. Run SDJR quality check report 16. Submit for QC 17. QC performed by MSSO leader 18. Submit materials for QA

33. Prioritize by high DMU count/ high acreage MU’s (and catena-mates) These highest priority series also often correspond to our BM soilsPrioritize by high DMU count/ high acreage MU’s (and catena-mates) These highest priority series also often correspond to our BM soils

34. Big picture view: “Mardin is everywhere”

35. run “matthews report” (MLRA12) against National Database enter MU name and component name, using wildcards as appropriate

36. Results of Region 12-“matthews report” Right click and “Export to Excel”

37. 37 Results of “matthews report” after organizing in excel:

38. Develop Map Unit concept and decide on minor components Analyze available Lab data Select Typical Pedon (centrally located-think Big Picture) “maybe it’s time to switch OSD pedon-maybe not?”

39. Example of selected TUD stats -- Stats are used for selecting Typical Pedon and NASIS component RVs

40. Mardin Project Description-Generic Template

41. Return to preliminary grouping spread sheet- clip the columns from the project and paste in NASIS

42. Generic Map Unit Concept– A template modified for each SDJR Project map unit

43. GS-9 Pedon entry Prelim grouping Historical research GIS-Map development Lab data analysis NASIS project entry GS-11 Review prelim groups and edit if needed Review notes from historical review of MU Develop Map Unit concept Review lab data Select Typical Pedon Determine NASIS entries for key data elements Create SDJR Map unit and DMU Complete the SDJR project GS-12 Coordinates workflow Looks ahead for next project/series QC Project submittal Administrative tasks

44. Electronic version of Exhibit “A” checklist. (on the MLRA 12 sharepoint): One checklist is completed for each Project (MLRA MU/DMU/Component)

45. Future Field Projects  100 Future MLRA Projects identified so far 45

46. Often heard around the SS office  “there ain’t nothin’ clean about SDJR!”  “did you read the NI?”  “did you check the spreadsheet??”  “which spreadsheet??”  What’s the best way to complete a SDJR project? One elephant at a time!

47. WAVERLY, IA SOIL SURVEY OFFICE

48. Waverly, IA Soil Survey Office  Ryan Dermody, MLRA Leader – 15 Years, (Soil Conservationist 3 years, Plymouth and Marshall County Iowa) Soil Scientist Storm Lake IA, Washington IA, and Waverly IA. Fort Greely Alaska 2003  Lee Camp, Project Leader- 26 Year Worked in Fairfield IA, Coshocton Ohio, and Waverly Iowa  Neal Sass, Soil Scientist – 10 Years 2 in Ames Cartographic unit, 8 in Waverly Iowa “The Purpose of Soil Data Join Recorrelation SDJR is to Organize our knowledge to reveal our Ignorance.”

49. Waverly SSO Area 2 – MLRA’s 104 and 108C 3 – States Iowa, Minnesota, Wisconsin 11 – Geomorphic Areas 55 – Soil Survey areas 601 – Components Names 383 for MLRA104 218 for MLRA 108C 4620 – Map Units 12,895,160 - Acres

50. SDJR Time Line for Waverly SSO  August and September analyze and propose SDJR projects for next year ■ We try to have all projects proposed before the start of the fiscal year.  October to June Complete Approved Projects ■ This allows Soil Region Staff to finish Quality Assurance activities  June and July Dynamic Soil Properties, Soil Health projects, AND ESD’s field work  August Start Over

51. How can we Organize and use all of this data? We would have a lot of paper cuts if we had to go through this for each SDJR project!

52. Organization and Automation 3 months (Oct to Dec 2012) spent sorting out point data and scanning all county based records (3 months for 3 people almost one staff year!) (Estimated 30,000 to 40,000 pages scanned around 30 gigs of data) Series descriptions (Clinton series) County Scan 500 to 1000 pages per County 52 Fire copies have been given to the adjacent MLRA offices, Iowa state NRCS office and Iowa State University.

53. Text Recognition Software in Adobe

54. 54 All Notes IN NASIS Now!!! Mahaska Co. Notes NASIS 95 |Clinton 5 |Keswick Aggregation of all Map Unit Data into NASIS Using the computer to search the scanned documents, it takes 2 to 3 hours, per project to copy all information from every county, in to NASIS

55. Focus on Catenas and Series to maximize efficiency, and ensure data consistency Oltey SeriesLadoga SeriesClinton SeriesClinton Pedons Un-eroded Components Clinton silt loam, 2 to 5 percent slopes, Clinton silt loam, 5 to 9 percent slopes, Clinton silt loam, 9 to 14 percent slopes, Clinton silt loam, terrace, 2 to 5 % slopes, Clinton silt loam, terrace, 5 to 9 % slopes, Clinton silt loam, terrace, 9 to 14 %slopes Eroded Clinton Components Clinton silt loam, 5 to 9 % slopes, eroded, Clinton silt loam, 9 to 14 % slopes, eroded, Clinton silt loam, 14 to 18 % slopes, eroded, Clinton silt loam, terrace, 5 to 9 % slopes, eroded, Clinton silt loam, terrace, 9 to 14 %slopes, eroded, Clinton silty clay loam, 5 to 9 % slopes, eroded, Clinton silty clay loam, 9 to 14 % slopes, eroded, Clinton silty clay loam, terrace, 5 to 9 % slopes, eroded, Clinton silty clay loam, terrace, 9-4 % slopes, eroded, Severely Eroded Clinton Components Clinton silty clay loam, 5 to 9 % slopes, severely eroded Clinton silty clay loam, 9 to 14 % slopes, severely eroded Clinton silty clay loam, 14 to 18 % slopes, severely eroded 55 Clinton Series 18 SDJR Projects 73,497 Acres claimed 367,485 Acres effected Because we work on all series in a Catena, many of our minors are fully populated.

56. Building a Component  Use Pedon data to build Components  Get all TUD and Lab pedons into NASIS  Digi Pen and NASIS entry (This can take up to two weeks)  Explore and Analyze Data  Use Excel, Spatial Analysis, and Geo Statistical Analysis to evaluate Properties (1 to 2 days)  Populate NASIS Component (1 to 2 days) Goal : All data from every source possible into NASIS

57. Derive new information from existing spatial and pedon data Usually 50 to 100 Pedons per benchmark series %Clay%SiltFine SiltCo Silt%SandPh B1_DepthMin 25.853.527.817.70.73.7 B1_DepthMax 45.885.345.342.24.96.8 B1_DepthAve 33.4265.6038.0527.921.795.38 B1_DepthMedian 32.6565.938.127.91.65.4 B1_DepthMode 26.666.536.827.91.45.4 B1_DepthStandard Dev 4.817825.5891623.8627324.9090738780.8485280.791803 Simple Statistics of soil properties

58. Spatial Zonal Statistics Of Map Unit Polygons MAPUNIT__1COUNTMINMAXRANGEMEANSTDMAJORITYMINORITYMEDIAN Growing Season Days * 100 Clinton silt loam, 5 to 9 percent slopes 110111736720468310119715.84602.8101201881742219777 Temperature Celsius * 100 Clinton silt loam, 5 to 9 percent slopes 12102894111452041060.02632.33259107811371067 Precipitation in millimeters * 100 Clinton silt loam, 5 to 9 percent slopes 1210288864597681903694390.71633.866965748961494355 Elevation in Meters Clinton silt loam, 5 to 9 percent slopes 133530 5 166.251394.5257228.2747221.288626.52412 58 Clinton silt loam, 5 to 9 percent slopes – Histogram Analysis against Land Classification Raster Map from 2012. Developed Land 9% Crop and Hay Land 12% Grassland 24% Deciduous Forest 55% About 1-2 Hours to process GIS Data, Run Statistics, and analyze data for a SDJR Project

59. Geostatistical Analysis tool Trend Analysis (clay max)

60. Series Updated SDJR 2013 Kenyon 8 projects - 555,905 ac Floyd 3 projects – 581,070 ac Clyde 2 projects – 414,435 ac Liscomb 3 projects - 9010 ac Bassett 8 projects – 134,755 ac Bertram 2 projects – 5,470 ac 2014 Readlyn 3 projects – 261,505 ac Tripoli 2 projects – 200,455 ac Clinton 18 projects - 367,485 ac Ladoga 19 projects – 328,665 ac Mahaska 3 projects – 220,685 ac Oltey 8 Projects – 314,840 ac Taintor 3 projects – 159,615 ac 82 Projects 582 mapunits reduced to 82 710,779 acres claimed 3,553,895 acres affected 27.6% of Soil office area (MLRA104 & 108C) Time To SDJR a Complete Series 2 to 4 Months for one person We have populated around 85 MLRA projects in NASIS to be completed in the future. (Usually every SDJR projects spawns 1 MLRA project)

61. Future projects  Build on existing data to improve and derive new data for the Soil Survey  Use GIS and Geostatistics on existing data to plan efficient update activities and project management  See need for more data on Dynamic Soil Properties.  Collect data that adds value to the existing soil survey that meets needs of customers  Work Smarter Not Harder

62. Future Field Projects  37 Future MLRA Projects identified so far 62

63. Results of SDJR in MLRA 104 and 108C  What we have done is organize the existing data, and produced many questions.  We have identified needs in MLRA 104 and 108C, that affect the Health, Safety, and Economics of soil data user.  It is more clear now what needs to be done in Waverly as we go forward beyond SDJR.

64. WHY? What are the benefits?

65. Soil Survey Data Join Recorrelation  Purpose? Support NRCS conservation planning and State Resource Assessment planning by:  Providing consistent map unit information that flows across political boundaries for the same ‘map unit concept’  Reducing the total number of mapunits, components, and horizons in the database  Developing a complete set of soil ‘horizon’ depths and properties for the map unit component (versus ‘layer depths’)  Providing soil properties based on more recent analyzed data  Documenting map unit decisions and deficiencies  Identifying future workload

66. Support of NRCS LCI Areas