Minimum Data Set and the Framework for Sampling Dynamic Soil Properties

1. Minimum Data Set and the Framework for Sampling Dynamic Soil Properties Arlene Tugel Soil Scientist Interpretations Staff, NSSC National State Soil Scientist Workshop March 17-21, 2008 Florence, KY

2. Outline • Overall strategy • Minimum data set • Comparison studies • Sampling framework using conceptual models • Sampling Guide • Responsibilities

3. Soil Change Leadership Team • Karl Hipple, National Leader, Interpretations • Chris Smith, National Leader, Technical Soil Services • Susan Andrews, Director, Soil Quality Team • Larry West, National Leader, Investigations • Arlene Tugel, Soil Scientist, Interpretations • Jeff Herrick, Research Soil Scientist, ARS

4. Vision Enhance NCSS data and products with information about how soils change.

5. Overall strategy • Select priority benchmark soils • Gather dynamic soil property data • Populate a point database • Develop interpretations of management effects on soil function and the consequences of change • Use models and pedotransfer functions to populate the soil map unit data base.

6. Conventions for soil change I Taxonomic classes II Phase distinctions III Models of change IV Short-interval monitoring Regular, periodic and cyclic fluctuations • Soil moisture • Temperature • Water tables Dynamic management effects on soil • Dynamic soil properties Drastically altered • Transported (Minesols) • Horizons destroyed • Horizons mixed Relatively unaltered control section • Eroded • Drained • etc.

7. Conventions for soil change I Taxonomic classes II Phase distinctions III Models of change IV Short-interval monitoring Regular, periodic and cyclic fluctuations • Soil moisture • Temperature • Water tables Dynamic management effects on soil • Dynamic soil properties Drastically altered • Transported (Minesols) • Horizons destroyed • Horizons mixed Relatively unaltered control section • Eroded • Drained • etc.

8. Conventions for soil change I Taxonomic classes II Phase distinctions III Models of change IV Short-interval monitoring Regular, periodic and cyclic fluctuations • Soil moisture • Temperature • Water tables Dynamic management effects on soil • Dynamic soil properties Drastically altered • Transported (Minesols) • Horizons destroyed • Horizons mixed Relatively unaltered control section • Eroded • Drained • etc. Change in function

9. Minimum data set of dynamic soil properties(March 15, 2008) • Organic C • pH • EC • Bulk density/Soil porosity • Structure and macro-pores • Aggregate stability (wet) • Total N (for C:N ratio) • Soil aggregate stability (field kit) OC pH EC Bulk density Structure Agg. stability N Soil aggreg. stability Ca/Mg/K/Na eCEC Pot Min N P KCl-Al Active C POM B-glucosidase Infiltration Ksat Microb-biomass Administered by ARS, Las Cruces, NM Also: Particle size Rock fragments

10. Criteria for selecting properties • Sensitive to management • Clearly represent soil function • Relatively insensitive to environmental fluctuations (or response can be modeled) • Easy to measure repeatably, and accurately • Low cost

11. Supplemental and experimental properties • Supplemental (situation-specific) • CaCO3, KCl-Al, Ksat, etc. • Experimental • Biological properties • New techniques

12. Collecting the data:Comparison studies Each horizon to 40 cm

13. Winter fat, perennial grass PGS Cheat grass AG A. Compare various management systems or plant communities Utah PilotArches National Park Weed invasion

14. B. Document the central tendency and range of variation

15. Bulk density 0-2 cm C. Report differences between the inherent potential (reference state) and other management systems or land uses. 2 cm to base of A High and low values of reference state B to 25 cm PG-S = perennial grass-shrub sub-state; AG = Annual grass (cheat grass) sub-state; n=4 = Median = Mean

16. State and transition models (STM) from Ecological Site Descriptions Cropland management models Framework to organize projects: Conceptual models

17. “A purposeful representation of reality.” Provides mental picture of how something works. Used to communicate that explanation to others. (Starfield et al., 1993) Represents key: processes, interactions, and feedbacks. (Gross, 2003) What is a conceptual model?

18. Pasture Forest Post oak/blackjack oak/little bluestem Hot summer burn and /or long-term grazing Burn, Site prep & Planting / Seeding. No grazing or limited controlled grazing Post oak/buckbrush (or similar) Lacks mid-story. Understory single species woody dominated Canopy: open 30-90% Post oak/flowering dogwood/ tick trefoil-goldenrod. Multi-story. Canopy: 30-90% Harvest, site prep, seeding Abandonment for 20+ yr with recruitment of woody natives Missouri Pilot STM by: D. Wallace, J. Robinson,B. White, M. Kennedy Pasture (improved) Non-native grass sod

19. Uses of conceptual models in soil change projects • Stratify the map unit component phase into multiple states that can be sampled; • Help verify suitable sample locations; • Facilitate data extension to similar soils; • Organize relationships among management, DSP’s, and plant communities (where present); • for interpretation, storage, and data delivery; • Serve as a decision aid for conservation planning; • illustrates alternative states and predicted soil responses to practices.

20. Soil Survey and Resource Inventory Guide for Dynamic Soil Properties and Soil Change VER. 1.1 2008 Comparisons studies Conceptual models 6 steps of a project Interpreting soil change and soil function Cropland sampling design to be added Procedures

21. Six project steps • Project Planning – what • Sampling Design -- how • Sampling Requirements – how many/where • Field Work • Data Preparation • Data Analysis and Interpretation

22. Data storage • SBAAG Ad-hoc committee (Sept. 2007) • Alan Price, Darrell Kautz, George Peacock, Susan Andrews, Arlene Tugel • Business requirements • For point data • Draft available on SBAAG sharepoint site • Work-in-progress

23. Interpretations of management effects on soil function Stable medium for plant growth Organic C N (for C:N ratio) EC pH Aggregate stability Structure Bulk density Hydrologic cycle Nutrient cycling Also Particle size Rock fragments

24. Interpretations • What is the condition of my soil? • How does it compare to what is possible? • If degraded, can it be restored it? • How long will it take (and how much $)? • What lands are at risk of irreversible (and undesirable) change? • How will soil changes affect future management options?

25. Suggested responsibilities MLRA Regional Offices (MO) and State Offices (SO) • Identify resource management needs • Select priority benchmark soils • Set project priorities • Assist with project workplan development • Assist with new interpretations • ?????

26. Suggested responsibilities MLRA Project Offices (SSO) and discipline specialists • Identify resource management needs • Develop project workplans • OJT • Collect data • Quality control • Enter and prepare data for analyses • ???? NOTE: As much of data summary and analyses as possible will be automated. Specialist assistance is needed for statistical tests.

27. Assistance roles Resource soil scientists, State discipline specialists, cooperators, NSSL/NSSC • Identify resource management needs • Workplan development • Training • New methods • Data analysis, statistical tests • Interpretations

28. Questions?Mini-session topics • Prioritizing benchmark soils (and important Ecological Sites) • Management systems to be documented • Operational questions

29. Summary • Populating the point database comes first. • Data will be collected as a part of projects • Conceptual models will be used to design projects and organize results. • Where present, vegetation data will be collected at the same time as soils data • Interpretations will emphasize management effects on soil functions. • Other discipline specialists and cooperators have important roles. • This is the orientation phase. We will improve our approach as we learn.