1. 1 THE NATIONAL RESOURCES INVENTORY Wayne A. Fuller Sarah Nusser and Jeff Goebel Roles of Statistics in 21 st Century Monitoring and Assessment Systems Denver September, 2004

2. 2 The Natural Resources Inventory (NRI) A nationwide survey of non federal land Conducted by Natural Resources Conservation Service (NRCS) Technical support by Iowa State University

3. 3 Outline History 1982-1997 National Resources Inventories Estimation-imputed points Annual Inventory

4. 4 History Nusser and Goebel (EES, 1997) 1934 National Erosion Reconnaissanc Survey: Anecdotal (SCS) 1958 National Inventory of Soil and Water Conservation Needs (CNI): Stratified area sample of segments (40,160,640 acres) 1967 CNI: 38 Points within 160 Acre segments 1958 Segments

5. 5 History (continued) 1977 National Resources Inventory: Subsample of 1958 sample. Segment Acres for built up, farmsteads, water, streams. Three points per 160 acre segment, 70,000 segments. 1982 National Resources Inventory: New sample in 13 Northern states. 321,000 National, 44,000 local segments. Three points per 160 acres. 1987 NRI: Subsample of 108,000 of 1982 sample.

6. 6 History (continued) 1992 NRI: 300,000 segments (some retrospective 1987 data). 1997 NRI: 300,000 segments. Three points per 160 acres. 2000-200? NRI: 70,000 segments per year, 40,000 core every year. Subsamples of 1997 NRI.

7. 7 Data Collection Photo – interpretation Segment acres of built-up, water Land coveruse of points NRCS Field Offices Cropping practices Conservation Reserve Program (CRP)

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9. 9 1982-1997 NRI Longitudial survey. Almost same segments every year External controls. Large Water, Federal Combination of segment and point data Complete easy-to-use databases

10. 10 1992 – 1997 Estimation Sources of Data Sample Segment Point Auxiliary Large Water and Large Streams Federal and county area Administrative – CRP Hydrologic units (5000 HUCCOs)

11. 11 Geospatial Data for a County Federal land in 92 & 97 Census water in 92 & 97 4-digit HUA boundary County boundary PSU (Segment) Federal land in 97 only 97 92, 97 1 2 3 1 HUCCO id 92, 97

12. 12 Urban 82, 87, 92, 97 Urban 97 3 2 1 Road Segment Sketch

13. 13 Combining Segment and Point Data Table 1. Acres of segment coveruse by year. Coveruse UrbanRoadsTotal Acres 198232 3 160 198732 3160 199232 3160 199750 2160

14. 14 Table 2. Coveruse of real points. Coveruse Point 1Point 2Point 3 1982PastureCornSoybeans 1987PastureSoybeansCorn 1992PastureCornSoybeans 1997PastureSoybeansCorn

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16. 16 Table 3. Coveruse and acres of imputed points representing segment data. Imputed Point No. 4No. 5No. 6No. 7 1982UrbanSoybeansRoads 1987Urban CornRoads 1992UrbanSoybeansRoads 1997Urban RoadsUrban Acres 32 17 2 1

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19. 19 Table 4. Coveruse and acres of real points. Real Points Point 1Point 2Point 3 1982PastureCornSoybeans 1987PastureSoybeansCorn 1992PastureCornSoybeans 1997PastureSoybeansCorn Acres36

20. 20 Imputed Points Probability of selecting donor coveruse Imputed estimated total close to two-phase estimate Fuller (2003)

21. 21 1997 Data Set Set of points – ratio adjusted probability weights Acres for HUCCOs match administrative Estimates close to those of previous years

22. 22 1982-1997 Gross-Change Table (Millions of Acres) 1982 1997 CroplandDeveloped Other NF land Water & FederalTotal Cropland350.3 7.0 62.1 1.5 421.0 Developed 0.272.7 0.3 0.0 73.2 Other NF land 25.718.5 950.7 6.81001.7 Water & Federal 0.8 0.0 3.6443.8 448.2 Total377.098.31016.7452.11944.1

23. 23 Annual NRI Subsamples of 1997 NRI Supplemented Panel Design Core Panel every year Rotate supplementary panels 4-10 years to complete rotation

24. 24 Annual NRI Year Panel ObservedCore2001200220032004Remainder 1997XXXXXX 2000X 2001XX 2002XX 2003XX 2004XX

25. 25 Annual NRI Year Panel ObservedCore2001200220032004Remainder 1997XXXXXX 2000X 2001XXX 2002XX 2003XX 2004XX

26. 26 Generalized Least Squares Vector of year-panel means is “y” vector Vector of year means is “  ” vector Breidt and Fuller (JABES, 1999)

27. 27 Estimation for Annual NRI Example: Impute 2000 for 2001 supplement Nearest Neighbor donor Direct estimates of large change

28. 28 Reasons for Success Demand for information Adaptability and improvement (i) Cost structure (ii) Data: erosion, urban, CRP, wetlands (iii) Collection and processing techniques (iv) Auxiliary information-Geostatistical People and institutions