MAPPING AND CUMULATIVE DISTRIBUTION FUNCTION (CDF) AS ALTERNATIVE METHODS TO ADDRESS VARIABILITY IN SOIL TEST-RESULTS

Spatial and statistical variability in soil characteristics must be ad dressed in using soil testing to guide precision nutrient management. This paper provides a case study comparing strategies using technology currently available and economically viable for farmers or their advi sors to use for this purpose. The first strategy is to divide a large area into smaller subunits for sampling, then to map results by assign ing the soil test value for each sample to the entire subunit, resulti ng in a mosaic of soil test values across the entire sampling area. An alternative approach involves collecting soil samples from known loca tions using global positioning system (GPS) technology, then mapping t he spatial distribution of soil test results. The final strategy is to use the cumulative distribution function (CDF) to find the percentage of samples with soil test values at or below certain levels irrespect ive of their location. Based on 72 soil samples from a highly variable 40 ha research site, we found that inaccuracy of GPS may limit its ap plication. Maps communicate soil test results readily, but may be diff icult to apply in fertilizer management. The CDF approach provides use ful information, but interpreting and applying the information may be difficult. Any of these methods of assessing soil test variability wil l require analysis of far more samples than composite sampling, and th e value of the added information must justify increased analytical cos ts.