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Update of the National Commodity Crop Productivity Index

Update of the National Commodity Crop Productivity Index. Robert Dobos National Soil Survey Center 12 October 2011. Outline. A. Background, why NCCPI? B. What is it? C. How does it work? D. What is different? E. How good is it? F. Future. A. Background.

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Update of the National Commodity Crop Productivity Index

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  1. Update of the National Commodity Crop Productivity Index Robert Dobos National Soil Survey Center 12 October 2011

  2. Outline • A. Background, why NCCPI? • B. What is it? • C. How does it work? • D. What is different? • E. How good is it? • F. Future

  3. A. Background • A need existed to be able to array soils nationwide on the basis of their inherent productivity • NCCPI is not intended to replace state crop indices that work well for the area intended • This NCCPI is currently for dryland agriculture

  4. Theory • Use-invariant soil properties are a major factor in production (management is assumed to be good) • A crop is grown: 1) in/on a soil 2) on a landscape that is 3) subjected to a climate, one group of properties is not enough to make a prediction • A three-part model is needed to account for the climatic regions where crops are best adapted (frigid, mesic, thermic)

  5. Projected users FSA could use as a part of the rental rate calculation for their programs Risk Management Agency (RMA) could use to help determine premiums and detect fraud Economic Research Service could use to help in projections of productivity Real estate assessors could use to inform purchase decisions

  6. B. What is it? • NCCPI is a fuzzy system model that uses data and relationships found in the soil survey database (NASIS) to rate the properties of a soil component against a membership function

  7. Some soil, landscape, and climate parameters have greater impact on productivity and others lesser • Some soil properties are not independent • Some properties are only important in the extreme • Look at the shape of the curve

  8. Spline curve to NASIS evaluation:

  9. Data Used by NCCPI – Physical • Root Zone Available Water Holding Capacity • Bulk Density • Saturated Hydraulic Conductivity • LEP (Shrink-Swell) • Rock Fragment Content • Rooting Depth • Sand, Silt, and Clay Percentages

  10. Data Used by NCCPI – Chemical • Cation Exchange Capacity • pH • Organic Matter Content • Sodium Adsorption Ratio • Gypsum Content • Electrical Conductivity

  11. Data Used by NCCPI – Landscape • Slope Gradient and Shape • Ponding Frequency, Duration, and Timing • Flooding Frequency, Duration, and Timing • Water Table Depth, Duration, and Timing • Erosion • Surface Stones • Rock Outcrop • Other phase features (channeled, etc)

  12. Data Used by NCCPI – Climate • Mean Annual Precipitation • Mean Annual Air Temperature • Frost Free Days • Major Land Resource Area • Soil Temperature Regime (Soil Taxonomy)

  13. C. How does it work? • NCCPI looks similar to the Storie Index • Soil property scores are multiplied together • One low property score can thus drag down the overall score • Hedges modify the fuzzy numbers from the major groups: Chemical, Physical, Landscape, Water, and Climate • The highest score of the Corn and Soybeans, Small Grains, or Cotton modules is the score for a component

  14. C. What is different? • “Sufficiency” is borrowed from the Missouri productivity index for RZ AWC • The way the score from negative soil attributes is handled is improved • Seasonal soil wetness depiction in cotton growing soils is improved • pH and LEP stratified by MAP where needed • MAP stratified by MAAT where needed

  15. E. How good is it? • Smoothing Spline, Linear, and Orthogonal Fits • R-square of this is 0.41 • “Poster Child” for “data harmonization” • Also, a good way to check data

  16. Data checking • Populated yields should be supported by the properties of the soil component • Usually, frequently flooded soils are not farmed • Cotton needs at least 180 to 200 frost-free days

  17. Data checking • Sometimes the yield data needs to be updated • Other data needs to be coordinated if a component exists in a broad geographic area

  18. Data checking • The frost-free days data is the only soil/site/climate property that is different for the highlighted series

  19. Data checking

  20. Data checking As data is harmonized, the shapes, minima, and maxima of the various curves will be re-evaluated

  21. F. Future • Next step is to get NCCPI data on to the Soil Datamart • To learn more about NCCPI, look at http://soils.usda.gov/technical/ the link to the NCCPI user guide is near the bottom of the page

  22. Thanks for listening! Questions?

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