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Issue link: http://gcmdigital.gcsaa.org/i/234582

MLSN potassium data 1 0.9 0.8 Probability 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 Mehlich 3 potassium (ppm) Figure 1. The cumulative distribution function for the potassium data was used to identify the MLSN guideline. At the 0.1 probability level, 10% of the samples report potassium values lower than 35 ppm (red line). This is the potassium MLSN guideline. The blue line indicates the conventional guideline of 110 ppm for potassium. Seventy-two percent (probability = 0.72 = sustainability index) of the samples report values lower than the conventional potassium guideline. MLSN phosphorus data 1 0.9 0.8 0.7 Probability with soil pH from 5.5 to 7.5. The purpose of this was to develop guidelines that would be accurate for a range of elements using the widely used Mehlich 3 soil test extractant. When soil pH is less than 5.5, we recommend application of liming materials to reduce soluble aluminum, to increase soil microbial activity and to reduce the risk of toxic soil-soluble ammonium levels. Because of that, there was no reason to include soils with a pH of less than 5.5 in the data set. In soils with a pH above 7.5, there is a high probability that the Mehlich 3 extractant may dissolve some soil minerals that contain calcium or magnesium. Such dissolution would have introduced error into the guidelines, which we avoided by selecting for a pH range at which mineral dissolution is minimal, and above which magnesium and calcium would not be defcient. After the two flters were applied, we were left with a working data set of more than 1,500 soil samples. These were from turf that performed well, had a relatively low CEC typical of golf course putting greens or relatively sandy soil, and a pH of 5.5 to 7.5. Because all of these soils were producing good turf, one could conclude that all the soils had suffcient nutrients, so anything at or above those nutrient levels would be fne. Rather than divide the data from these soils into low, medium and high classifcations, we took a different approach, in which we modeled the distribution of the data for each element (7). Nutrient concentrations in the soil are a continuous random variable with a minimum possible value of zero and a virtually unlimited maximum possible value. We analyzed the fltered data set using EasyFit distribution-ftting software from Mathwave (www.mathwave. com) and found a good ft for each element in these soil test results with a three-parameter loglogistic distribution. From this modeled distribution, based on the actual data from turfgrass sites that had good performance, we identifed the MLSN guidelines. A visual representation of the cumulative distribution function is shown for the potassium data in Figure 1 and for the phosphorus data in Figure 2. If we look at the data for potassium, for example, we see the cumulative proportion of the samples at any particular level as we go from 0 to 280 ppm. The conventional guidelines would seem to be taking a number of sites with good performance and then choosing to target the higher end of that range as a guideline. 0.6 0.5 0.4 0.3 0.2 0.1 0 0 40 80 120 160 200 240 280 320 360 400 440 480 Mehlich 3 phosphorus (ppm) Figure 2. The cumulative distribution function for the phosphorus data was used to identify the MLSN guideline. At the 0.1 probability level, 10% of the samples report phosphorus values lower than 18 ppm (red line). This is the phosphorus MLSN guideline. The blue line indicates the conventional guideline of 50 ppm for phosphorus. Fifty-nine percent (probability = 0.59 = sustainability index) of the samples report values lower than the conventional phosphorus guideline. 01.14 GOLF COURSE MANAGEMENT 135

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