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Variability in Soil Test Potassium

By Dr. Steve Phillips, IPNI North American Program Director, Huntsville, Alabama

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Variability in Soil Test Potassium


When we make a fertilizer recommendation for a farmer, what is it based on? It is usually based on a composite soil sample representing the average fertility of the entire field. When we do this, we fail to address the spatial variability of nutrients in the field resulting from changes in soil type, topography, previous cropping history and many other factors. Even precision farming strategies such as management zones fail to account for all of the spatial variability found in agricultural fields.

When I was a graduate student at Oklahoma State University, we conducted a study that demonstrated that the field element size, or the shortest distance in which a significant change in soil nutrient availability occurs, was 9 square feet. Soil samples were collected from an established bermudagrass pasture on a 1-foot-by-1-foot grid in a 490-square-foot area. Samples consisted of eight 6-inch cores per square foot. That’s right, 3,920 soil cores (see photo). Did I mention we did this for two locations?

Students Collect Soil Samples
Students collect soil samples to determine micro-variability in soil test parameters for an established
bermudagrass pasture in Oklahoma.

The mean soil test potassium (K) value for the entire area was 131 ppm, which would be considered 100 percent sufficient for bermudagrass production; therefore, no potassium fertilizer recommendation was required. However, the soil test values ranged from 12 to 301 ppm K, resulting in several zones within the test needing as much as 140 pounds of K2O per acre. A similar study was conducted in Kentucky cornfields, and found two- to three-fold differences in fertilizer recommendations based on soil test K values within a 0.22-acre area sampled on a 0.01-acre grid (Table 1).

Table 1. Spatial Variability in Soil Test K (STK)
Plot Range in STK, lbs. K/A Range in K fertilizer recommendation, lbs. K2O/A
2 144–246 30–90
5 183–334 0–70
6 159–286 30–80
9 166–303 0–80
10 185–305 0–70
14 111–232 40–110
15 128–372 0–100
17 109–218 50–100
18 158–317 0–80
20 130–371 0–100

Adapted from Wells, et al., 2000

Considering the high degree of micro-variability in agricultural fields, how can we ensure an accurate estimate of potassium levels with soil test results? I saw a great presentation at the InfoAg Conference this past summer that included an analysis of data collected by Dr. Bob Miller at Colorado State University. The results suggested that a minimum of 10 soil cores should be collected from a grid-point sampled area to minimize relative standard deviation of the mean fertility level within a management zone. This minimum number applies to any grid size, and becomes even more important in fields with lower average potassium levels.

Soil test potassium can also be highly variable for a field, depending on the timing of sample collection. Patterns in soil test potassium exist in many regions that show a decline during the growing season due to crop uptake, increasing values over winter as crop residues release K, and a subsequent decline during the next growing season. However, this cycle is often disrupted due to rainfall patterns. For example, dry conditions following harvest and throughout the winter will result in less K being released from plant residue, and lower estimates of soil test potassium than will likely be available for the next crop.

The amount and type of clay in the soil can also affect potassium levels. This is especially relevant when sampling dry soils with high 2:1 clay content. On low K-testing soils, sampling 2:1 clays when dry will result in an over-estimation of soil test potassium. Conversely, potassium will be under-estimated on high K-testing soils. This variability can also be introduced by drying samples in the laboratory prior to analyses. However, the variation due to clay content and soil moisture can be managed by using a field-moist test for K. Also presented at InfoAg were results from more than 300 corn and soybean trials conducted by Iowa State University that showed the relationship between soil test potassium and crop response to K fertilizer is much better when using a field-moist soil test (Figure 1).

Relative Corn Yield %
Source: Mallarino, et al., 2012

So, how do we ensure that our K fertilizer recommendations are as accurate as possible? By being aware of how spatial and temporal variability affects soil test potassium. Consider the following points when advising a grower on how to minimize variability:

  • Collect an adequate number of soil samples to accurately represent the field or management zone;
  • Establish consistency in the timing of sample collection;
    • Avoid unusually wet or dry periods
    • Be aware of the effect of residue decomposition on soil test potassium
  • Rely more on soil test trends rather than a single year for soil test potassium; and
  • Consider supplementing the soil test report with nutrient removal estimates from the previous crop (programs are available for the iPhone and the Internet when determining K requirements for the current crop.

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About the Author

Dr. Steve Phillips serves as a North American program director for the International Plant Nutrition Institute (IPNI). He received his B.S. degree from Cameron University in Lawton, Oklahoma, and his M.S. and Ph.D. degrees from Oklahoma State University.

Prior to joining IPNI in 2007, Dr. Phillips was an associate professor at Virginia Tech, where his program focused on variable-rate nutrient management and the development of nitrogen recommendation algorithms for sensor-based fertilization of wheat and corn in the mid-Atlantic region.

In his role at IPNI, Dr. Phillips develops and disseminates educational materials regarding the efficient and effective use of plant nutrients, with a focus on precision agriculture. He currently serves as the chair of the IPNI international workgroup on precision agriculture, the annual InfoAg Conference, and the A to Z track for practitioners at the International Conference on Precision Agriculture. He is also an active member of ASA, SSSA and ISPA.

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