2. System to Evaluate Prime Farmland Reclamation Success Based on Spatial Soil Properties Applied Science Project United States Department of the Interior Office of Surface Mining Reclamation and Enforcement Cooperating and Supporting Agencies: Black Beauty Coal Company Inc. Peabody Energy Inc. Solar Sources Inc. Illinois Clean Coal Institute Natural Resources Conservation Service Illinois Department of Natural Resources Indiana Department of Natural Resources Illinois Clean Coal Institute Illinois Coal Association Indiana Coal Council

3. SMCRA Requires operator to restore mined land to pre-mine land use and level of productivity Created standards for soil replacement Authorized states "primacy" to regulate - state program no less stringent than federal rules Requires coal operator to show proof of productivity

4. Illinois AGRICULTURAL LAND PRODUCTIVITY FORMULA (ALPF) Coop. Ext Circular 1156 Soil Productivity of Illinois (1978 vintage yield data) Soils of the permit determine the potential yield target for the permit Soils in cropland in the county determine the potential county yield Ratio of County average/County Potential is the annual County Success Factor (CSF) CSF X permit target = Annual adjusted target

5. Indiana Yields are determined by the NRCS. Soils in the permit area determine the potential target yield for the permit. GROWING CROPS on a representative sample of the area using our test plot standards. A MINIMUM of 10% of the area must be planted. GROWING CROPS on ALL of the area. (WHOLE FIELD HARVEST)

6. YIELD ROOT GROWTH SOIL ENVIRONMENT CLIMATE MANAGEMENT AVAILABLE WATER AERATION BULK DENSITY ELECTRICAL CONDUCTIVITY GENETIC POTENTIAL OF THE PLANT pH Soil Strength AVAILABLE NUTRIENTS

7. 1977-1993 Funded by OSM, USDA and the Coal Industry 2000-2005 Funded by Indiana Coal Council Prime Farmland Reclamation Research Program

8. pH Action Exchange Capacity Bulk Density & Soil Strength Hydraulic Conductivity Soil Structure Soil Texture Organic Matter Fertility Minesoil Properties

9. Penetrometer * * American Society of Agricultural Engineers ASAE* 30 o Tip angle 3 cm/sec Tip force only root emulation

10. A/3 Mix TNT DM2TS/BH DM1 Yield Cisne Clarksdale PSI TS/SP DM3 TWT TLG RM1 Denmark SCR TG2 CHS

11. New Penetrometer Technology **American Society of Testing and Materials ASTM** 60 0 Tip angle 2 cm/sec Tip force Sleeve friction Soil resistivity Soil moisture

12. Project Objectives The objective of this work is to develop a soil based approach which could be used in lieu of the current yield based approach for bond release. The soil based approach will use measurable soil spatial characteristics to determine if a given reclaimed field meets the requirements of restoration of field productivity as outlined in existing federal and state regulations.

13. 2005 Sites

14. Digital Cone Penetration Testing Real Time CPT Data Acquisition Penetration at 2 cm/s Sand Clay Buried Crust Clay

16. Database Penetrometer Data –Tip Stress, Sleeve Stress, Soil Resistivity, Vol. Moisture Yield –GPS Yield Monitor Soil Fertility –GPS Grid Samples Soil Properties Topograhy Weather –Normalize yield

17. Integrated Analysis

18. Soil Test pH Soil Test P

19. Yield Data Yield monitors (combined with DGPS units) collect geo-referenced yield data.

20. Spatial Sampling: Gather observations representative of spatial distribution of variable of interest. Interpolation: Use those sample points to predict values of variable of interest at all other unsampled locations. Sampling methods: Systematic Sampling Adaptive Sampling

21. Spatial interaction model describes the amount of interactions between any of two points. Sample Point 1 Sample Point 2 Sample Point 3 Spatial interaction models

22. Systematic sampling pattern - Easy - Samples spaced uniformly at fixed X, Y intervals - Parallel lines

23. Adaptive sampling - Higher density sampling where the feature of interest is more variable. - Requires some method of estimating feature variation

24. Spatial Interpolation (Mapping spatial variability) …all interpolation algorithms assume that 1) nearby things are more alike than distant things (spatial autocorrelation), 2) appropriate sampling intensity, and 3) suitable sampling pattern. …the continuous surfaces produces map of the spatial variation in the data samples.

27. Not the first attempt….. Earlier attempts had difficulty in accounting for spatial structure. With the advent of new technology, new statistical techniques and software, and improved computer accessibility, we now have the opportunity to produce and utilize probabilistic models.