Robert DobosNational 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 http://soils.usda.gov/technical/ the link to the NCCPI user guide is near the bottom of the page