1. Databases in Soil Survey

2. Objectives Identify databases used for population, analysis, and publication of soils data Understand NASIS correlation concepts Identify correlation procedures to create fully reversible correlation

3. Soil Database History Pre-1972 – all hand written manuscripts 1972 – Soil Survey Interpretation Record 1974 – Manuscript tables computer generated 1985 – State Soil Survey Database (SSSD) 1994 – NASIS 2003 – Staging Server/SDW/SDM/SSURGO 2005 – Web Soil Survey 2015 - ????

5. 1974 – SOIL-6 Map Unit Record Used to retrieve data for manuscript development Maximum of 3 components for the map unit Horizon layer depth adjusted to match county TP

6. SOI-5 + SOI6 = MUIR –Map Unit Information Record - SSSD Same S5id number for the same component used in various map units Slight variations based on the S-5 Layer ID

7. NASIS Foundation – notice any differences?

8. Current Soil ‘Databases’ National Soil Information System (NASIS) –Pedon PC (access db) –Analysis PC (access db) Official Series Descriptions (technically not a database) Soil Classification Database (SC) Soil Characterization Database (KSSL) Soil Data Mart Database (SDM) –Spatial (shape files) –Staging Server –Soil Data Warehouse –Soil Data Mart –Web Soil Survey (portal) SSURGO database (MS Access template) U.S. General Soil Map (STATSGO)

9. Soil Correlation and Databases The first basic correlation decision is made when you decide where to dig a hole that represents the landscape concept and whether to record a complete or partial pedon description or a field note.

10. Pedon Description Properties Interpretations Lab Data Properties are collected and inferred from pedon descriptions. Properties are also obtained from laboratory data. The old photos provide evidence that little has changed over the years in the collection of soil properties.

11. Spatial Data National Soil Information System Product Development KSSL Data Pedon Data Field digitizing NASIS Transactional database

12. Correlation of Pedon Data

13. POINT-PLOT texture and MAP

14. Correlation of Pedon Data

15. POINT-Plot lab data by soil and comp layer NAT

16. ArcGIS Analysis Pedon Polygons

17. Soil Properties for Modelers albedo dry area name area symbol area type name base saturation bulk density fifteen bar bulk density one third bar bulk density oven dry caco3clay ratio calcium carbonate equivalent cec nh4oac ph7 clay total separate r coarse fragment volume comonth.month component interp component restriction component kind component name component percent r cosoilmoist.soimoiststat cosoimoistdept l drainage class ecec fine sand separate flooding duration class flooding frequency class geomorph feat name geomorph feat type name horizon depth to bottom r horizon depth to top r horizon designation horizon thickness hydrologic soil group kf factor kw factor layer depth linear extensibility percent map unit symbol mapunit acres mapunit name organic matter percent l, rv, h particle density ph 01m cacl2 ph 1to1 h2o pore quantity, shape, size restriction depth to top h restriction depth to top l rock frag 3 to 10 in rock frag > 10 sand coarse separate sand total separate sat hydraulic conductivity sieve number 4 silt total separate slope l, h soil texture and modifier sum of bases t factor water fifteen bar r water one tenth bar water one third bar water satiated

18. Data Management Point data is captured using PedonPC or NASIS Soil boundaries are captured or modified Methods are available to analyze data The map unit concept is built after data is collected, compiled, and analyzed Soil property estimates are developed using the component population collected for the specific map unit concept

19. Database Entry ‘a database lives or dies based on the consistency of the data population’ Run NASIS report: ‘PEDON - Count soil name by state’ as an example. This report attempts to identify the number of pedons captured by county using the user pedon ID.PEDON - Count soil name by state

20. NASIS Site/Pedon Entry S2005NE079001 The User Site/Pedon ID has a specific, national standard, method of population. The purpose of this field is to allow the user to place a label on the site to assist with locating the particular site record(s) in the national database. The national standard for the User Site ID is the “YYYYXXZZZ123” convention, where “YYYY” is the 4-digit year when the data or samples were collected; “XX” is the 2-character state FIPS code such as “NE” for Nebraska (for non-USA samples, use the abbreviation for the country code); “ZZZ” is the 3-digit county FIPS code (e.g. 079), and “123” is the 3-digit consecutive pedon number for that county in that year. The letter S will preface the User Site ID for soil characterization samples..

21. NASIS Entry

22. Pedon Analysis Analysis PC is designed to analyze pedons from NASIS to gather data in building components

23. Analysis PC Established relationships among the tables Built in queries available for use New queries easily written or imported Can use access queries or form analysis Can analyze data in the spatial world Can be joined with other Access databases for further analysis

24. Soil Correlation and Databases The second correlation decision is where do you draw the boundary and what soils are inside that polygon

25. Mapping/Correlation Decisions Initial Mapping –New musym and map unit concepts –Are tracked/documented until correlation –Split everything initially, lump at correlation –Reverse correlation back to the original map symbol Update Mapping –Reviewing correlated map units –Decisions on combining similar map unit concepts –Ability to track/document the origins of the map unit

26. Database Correlation Activities Involves linking tables –lmapunit –correlation –component pedon Involves documenting –lmuhistory –muhistory –text tables

27. Linking Tables

28. Create Data Mapunit

29. Link Pedons to Component

30. Link Mapunit and Datamapunit

31. Link Mapunit to a Legend

32. Documenting Correlation Decisions

33. Fully Reversible Correlation Traces current musym/mapunit to the original map field symbol Map unit correlation documentation –changing map unit name (Mapunit) –combining map units (Legend/Mapunit) –splitting map units (Legend/Mapunit) –changing map unit status (Legend) –changing map unit symbol (Legend)

34. Changing Map Unit Names

35. Changing map unit name Documented in the Mapunit table Changing the name will change it in every location it is linked. Use Mapunit History to document name change

36. Combining Map Units Using an example of an Initial survey

37. Recording Correlation Decisions Combine unit 4B consociation into unit 21B complex Combine map units Changes are recorded in the Mapunit History table.

38. Combining map units First step is to load/identify the map units in the mapunit table. The map units are highlighted, then using the icon (load related), the parameter box appears and the Mapunit table is chosen from the choice list. This will load the two map units in the Mapunit table.

39. Combining map units Combine unit 4B consociation into unit 21B complex What legends are these map unit linked to? Where’s Waldo???

40. Combining map units

41. Editing is required to insert a DMU link into the Correlation table. Therefore, the data must be “checked out”.

42. Combining map units 1.Copy the DMU link from the 4B consociation map unit and Paste into 21B complex map unit. 2.Then make sure the REP DMU box is not checked for the 4B consociation DMU. 3.Change the 4B consociation map unit to “additional”

43. Combining map units Load the related Data Mapunit and adjust the Component percentages to reflect the new map unit concept.

44. Combining map units Return to the Legend table. Notice that the map unit Status is changed in the Legend Mapunit table. Any changes made to a map unit impact those Legends where the map unit is linked. How can you identify those Legends that will be impacted?

45. Record Correlation Decisions Type note Populate the Mapunit History table

46. Linking historical map units to the MLRA map units

47. Map unit status changes

48. Map Unit Status Provisional – initial map unit concept Approved – Map unit concept approved by MLRA Project Leader and SDQS Correlated – Signed correlation document (initial) by MO Leader and State Conservationist Additional – replaced by another map unit concept

49. Documenting Decisions

50. Recording Correlation Decisions Changing map unit status in LMU History

51. Document Map Unit History Correlation Decisions the map unit

52. Role of Spatial Database in Correlation Provide visual analysis of the spatial distribution of map units This includes: 1.Soil properties 2.Components or series 3.Interpretations

53. Spatial database Spatial database allows for the analysis of the distribution of data, whether is it point data, aggregated data or map unit interpretations Supporting layers: 1.DEM’s, vegetation maps, land use, ORTHO, etc. 2.Spatial landscape and landform models 3.Analysis of spatial data (hillslope, slope gradient)

54. Summary Soil Survey publication –Historically, manuscripts –Today, databases Databases are provided for the –Population of data –Analysis of data –Publication of data Data is to be maintained in the “Corporate” database structure Why? Had all the initial documentation been in a database, you would have had all the information necessary to update.