Robert Dobos National Soil Survey Center 12 October 2011

 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 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

 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)

 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

 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

 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

 Root Zone Available Water Holding Capacity  Bulk Density  Saturated Hydraulic Conductivity  LEP (Shrink-Swell)  Rock Fragment Content  Rooting Depth  Sand, Silt, and Clay Percentages

 Cation Exchange Capacity  pH  Organic Matter Content  Sodium Adsorption Ratio  Gypsum Content  Electrical Conductivity

 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)

 Mean Annual Precipitation  Mean Annual Air Temperature  Frost Free Days  Major Land Resource Area  Soil Temperature Regime (Soil Taxonomy)

 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

 “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

 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

 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

 Sometimes the yield data needs to be updated  Other data needs to be coordinated if a component exists in a broad geographic area

 The frost-free days data is the only soil/site/climate property that is different for the highlighted series

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

 Next step is to get NCCPI data on to the Soil Datamart  To learn more about NCCPI, look at technical/ the link to the NCCPI user guide is near the bottom of the page technical/