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Evaluating Prime Farmland Reclamation Success using Spatial Soil Properties

This project evaluates the success of prime farmland reclamation by analyzing spatial soil properties. It aims to develop a soil-based approach for bond release, replacing the current yield-based approach.

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Evaluating Prime Farmland Reclamation Success using Spatial Soil Properties

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  1. System to Evaluate Prime Farmland Reclamation Success Based on Spatial Soil Properties Applied Science ProjectUnited States Department of the InteriorOffice 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

  2. 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

  3. IllinoisAGRICULTURAL 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

  4. 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)

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

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

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

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

  9. A/3 Mix DM2 TS/BH DM1 TNT DM3 TS/SP TWT TLG TG2 RM1 Denmark SCR CHS Yield Cisne Clarksdale PSI

  10. NewPenetrometer Technology • ASTM** • 600Tip angle • 2 cm/sec • Tip force • Sleeve friction • Soil resistivity • Soil moisture **American Society of Testing and Materials

  11. 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.

  12. 2005 Sites

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

  14. 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

  15. Integrated Analysis

  16. Soil Test P Soil Test pH

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

  18. 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

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

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

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

  22. 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.

  23. 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.

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