SOIL SAMPLING Dr. Dave Franzen Extension Soil Specialist North Dakota State University

Relative value of soil test for nutrient need diagnosis- NitrogenSulfur (very low value) Phosphorus Soil pH Soil EC (soluble salts) Potassium Zinc Chloride Iron Copper Manganese Boron

WHY SOIL TESTING? Often the best means of predicting crop response to the addition of mineral nutrient.

WHY SOIL TESTING? Relatively easy to do, cheap to analyze and timing is usually not as critical as in plant analysis.

To be useful, a laboratory soil testing procedure must be correlated to a crop response (yield or quality increase) and calibrated to some quantity of nutrient addition.

Researcher must establish field trials 1. Know soil test level 2. Add incremental levels of nutrients

Cate-Nelson Test

The “Return to N” model- Developed by John Sawyer, Iowa State and Emerson Nafziger, Illinois (2005, Proc. Ext-Ind Soil Fert. Conf.) This model is used in several corn-belt states, including Iowa, Illinois, Wisconsin, Minnesota, Ohio, and Michigan

Steps to implementation of Return to N in Wheat, or crops with quality factor- -Generate yield response data -Use regression model from data to predict yield at N rates from 0 to maximum reasonable rate. -Generate quality response data -Use regression model to predict quality at N rates -Generate gross return from each N rate -Generate cost of N from N rate - Subtract Cost of N from Gross return from N

Steps to implementation of Return to N- -Generate yield response data -Use regression model from data to predict yield at N rates from 0 to maximum reasonable rate. -Generate gross return from each N rate -Generate cost of N from N rate - Subtract Cost of N from Gross return from N

Different extractants often give different numbers, but mean the same thing if calibrated correctly. These both mean high levels of soil P Bray P1 25 ppm Olsen 15 ppm

Soil Testing for Sulfur Sample cores 0-24 inches in depth. Composite tests may not be representative of large areas of the field. Sample preparation may lead to overestimation of sulfur levels.

Sulfur range, lb/acre

SOIL TESTING- Depth 0-6 inch - P,K, soil pH, micros 0-2 feet - nitrate, chloride, sulfate feet – nitrate

SOIL TESTING- Technique- Automatic probes are best at depths below 6 inches Hand probes are best for surface samples

Central Tendency or Site-Specific?

Central tendency- composite

Central tendency- multiple samples with average

Site-Specific Sampling Methods -Grid Sampling -Zone Sampling

SAMPLING GRID SAMPLING Grid sampling uses sufficiently dense sampling to reveal fertility patterns.

SAMPLING GRID SAMPLING 1929

SAMPLING GRID SAMPLING Wisconsin, Wollenhaupt et al., 1994 Illinois, Franzen and Peck, 1995 Wollenhaupt et al., 1997 One sample per acre ( foot grid).

SAMPLING Zone sampling Zone sampling assumes that fertility patterns exist because of some logical, predicable reason.

Field trials in ND – Gardner , Valley City Colfax , Mandan Hunter St. Thomas Minot, Williston, Oakes, whole-field site years of data. Also Malta, MT, Crookston and Renville, MN

Patterns of mobile nutrients tend to be stable between years.

Valley City N over topography

Mobile Nutrients Move, But They Tend To Move To The Same Places.

Electrical conductivity

Electrical conductivity, EM-38

Remote imagery

Yield

1994 Yield 1995 Yield 1997 Yield 1998 Yield 2000 Yield How do you manage multiple years of yield data?

Managing multiple yield data using rank & frequency Assign rank: 1 if > average yield 0 if = average yield -1 if < average yield Assign rank for each year

Developing Frequency Map

Yield frequency map of Illinois field using corn and soybean data compared to satellite image (right).

Soil survey Order 2 1:20,000 Order 1 1:8,000

Combining the Layers

The values of layers should be a similar scale. Images range from EC’s might range from Yields from 0-5 These need to be normalized in the data set before layering. Might be easiest to transform EC and Yield to

Comparison to soil nitrate-N r Satellite0.41 Aerial0.38 Topography0.39 Yield0.47 Order 1 survey0.24 EC0.28 Correlation of delineation data layers with nitrate-N 2001

Comparison to soil nitrate-N r Satellite0.35 Aerial0.16 Topography0.41 Yield0.36 Order 1 survey0.46 EC0.24 Correlation of delineation data layers with nitrate-N 2002