Robert DobosNational Soil Survey Center12 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 productivityNCCPI is not intended to replace state crop indices that work well for the area intendedThis 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 predictionA 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 programsRisk Management Agency (RMA) could use to help determine premiums and detect fraudEconomic Research Service could use to help in projections of productivityReal 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 lesserSome soil properties are not independentSome properties are only important in the extreme Look at the shape of the curve

Root Zone Available Water Holding CapacityBulk DensitySaturated Hydraulic ConductivityLEP (Shrink-Swell)Rock Fragment ContentRooting DepthSand, Silt, and Clay Percentages

Cation Exchange CapacitypHOrganic Matter ContentSodium Adsorption RatioGypsum ContentElectrical Conductivity

Slope Gradient and ShapePonding Frequency, Duration, and TimingFlooding Frequency, Duration, and TimingWater Table Depth, Duration, and TimingErosionSurface StonesRock OutcropOther phase features (channeled, etc)

Mean Annual PrecipitationMean Annual Air TemperatureFrost Free DaysMajor Land Resource AreaSoil Temperature Regime (Soil Taxonomy)

NCCPI looks similar to the Storie IndexSoil property scores are multiplied togetherOne low property score can thus drag down the overall scoreHedges modify the fuzzy numbers from the major groups: Chemical, Physical, Landscape, Water, and ClimateThe 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 AWCThe way the score from negative soil attributes is handled is improvedSeasonal soil wetness depiction in cotton growing soils is improvedpH and LEP stratified by MAP where neededMAP stratified by MAAT where needed

Smoothing Spline, Linear, and Orthogonal FitsR-square of this is 0.41Poster Child for data harmonizationAlso, a good way to check data

Populated yields should be supported by the properties of the soil componentUsually, frequently flooded soils are not farmedCotton needs at least 180 to 200 frost-free days

Sometimes the yield data needs to be updatedOther 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 DatamartTo 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

Thank you for taking time out of your schedules to listen in. If you have questions or comments, let me know. *I would like to spend the next few minutes talking about NCCPI and telling what it is and what it does.*A soil having superior physical and chemical properties for crop production can be too cold, too hot, too wet, or too dry to grow a crop.

The soil database is mostly use-invariant, but some properties, like pH and bulk density can vary and can have an impact on production*Take a sample of the domain of a crop and make a scatterplot of yield against the soil properties that impact crop production. Use the Russian model that says quantity has a quality all its own to find a maximum or minimum.*Also, some data elements are combined or used in ways unique to NCCPI. In this example, the logarithm of the product of the saturated hydraulic conductivity and the linear extensibility are used to model water and gas movement. In vertisols, water and gas movement occurs through cracks because the saturated hydraulic conductivity is low. The CEC of a component is handled more as the moles of exchange in a unit volume of soil, a real capacity measure instead of an intensity.*Not only did I observe the shape of the spline curve, but also the topmost points in the scattergram, because they define the upper boundary of likely production at a level of the independent variable. In this case, they are gratifyingly similar.**Not necessarily the best suite of data for agricultural climate, but it is available in the database.

MLRA is needed to pick up the Xeric or Mediterranean climate for wheat growth.Remember that fuzzy numbers range from 0.00 to 1.00, thus any power will remain less than 1.00. Raising a number to a power that is less than one is finding a root, so numbers close to one are not impacted as much as smaller numbers. *The index is just the highest of the outputs of the three submodels. Often, the submodel index is more useful.*ASSIGN suff hzdept_r < 1 AND hzdepb_r >= 1 ? suff + (awc_r/0.20 * (-0.0511789 * logn(1) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 2 AND hzdepb_r >= 2 ? suff + (awc_r/0.20 * (-0.0511789 * logn(2) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 3 AND hzdepb_r >= 3 ? suff + (awc_r/0.20 * (-0.0511789 * logn(3) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 4 AND hzdepb_r >= 4 ? suff + (awc_r/0.20 * (-0.0511789 * logn(4) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 5 AND hzdepb_r >= 5 ? suff + (awc_r/0.20 * (-0.0511789 * logn(5) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 6 AND hzdepb_r >= 6 ? suff + (awc_r/0.20 * (-0.0511789 * logn(6) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 7 AND hzdepb_r >= 7 ? suff + (awc_r/0.20 * (-0.0511789 * logn(7) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 8 AND hzdepb_r >= 8 ? suff + (awc_r/0.20 * (-0.0511789 * logn(8) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 9 AND hzdepb_r >= 9 ? suff + (awc_r/0.20 * (-0.0511789 * logn(9) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 10 AND hzdepb_r >= 10 ? suff + (awc_r/0.20 * (-0.0511789 * logn(10) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 11 AND hzdepb_r >= 11 ? suff + (awc_r/0.20 * (-0.0511789 * logn(11) + 0.270865)/10) : suff.ASSIGN suff hzdept_r < 12 AND hzdepb_r >= 12 ? suff + (awc_r/0.20 * (-0.0511789 * logn(12) + 0.270865)/10) : suff.ASSIGN suff *Even if you have your own productivity index, the various mapping unit phase criteria need to fit your preconceived notion. Thus, flooding, ponding, water table, and climate data must be in sync to get the yield indicated by the your index.*Up and down distribution is mostly differences in yield vintages or state conventions (Iowa vs Illinois, 220 vs 185 for Tama). It is the left and right variance that can be problematic, especially if slope is constant, because then it is soil property data or phase criteria that are causing the difference. The Staser series is highlighted here.*I like to pick on the climate data because the MLRA approach will need to reconcile productivity data with map unit, and thus component, use.*Erosion and slope give some of the left to right variation in Cecil. Also, the climatic distribution of Cecil is quite wide. I am not sure what is going on with Amarillo.*Uchee is interesting because the arenic surface varies in thickness, which impacts AWC, CEC, permeability and other factors. Also, there are seasonal high water table issues. *One other plug I will make is for people who want to evaluate models like this and even just to look at soil properties need to get a good statistical software package. I like JMP.*Use no-till or the ag police will get you.*