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Better Golf Course Chemical Management Possible With "KTURF" Website

Date:
August 28, 1998
Source:
Kansas State University
Summary:
A website at Kansas State University may turn out to be a hot link for golf course managers. KTURF is an advanced computer program designed to predict pesticide and nitrogen leaching in the upper 20 inches of turfgrass given the soil conditions and watering regime of a particular course.

MANHATTAN, Kan.--A website at Kansas State University may turn out to be a hot link for golf course managers.

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KTURF is an advanced computer program designed to predict pesticide and nitrogen leaching in the upper 20 inches of turfgrass given the soil conditions and watering regime of a particular course.

KSU civil engineer Steven K. Starrett and electrical and computer engineer Shelli K. Starrett developed the program and placed it on the internet at http://www.eece.ksu.edu/~starret/KTURF/.

Steven Starrett will describe KTURF at the annual meeting of the American Chemical Society in Boston. He will speak at 1:30 p.m. Wednesday, Aug. 26, at the Marriott Copley Place. The paper, "KTURF: Pesticide and nitrogen leaching model," is part of a special symposium on the fate of turfgrass chemicals and pesticide management approaches, sponsored by the agrochemicals division.

On average, KTURF's predictions are within 4 percent of testing case values, Starrett said. That is, when presented with data about a setting it has never seen before, the model's estimates are close to the measured values, so it looks like a feasible modeling technique, he said. It would be useful as a screening tool for golf course turf managers.

"New golf courses are being built at the rate of 400 a year in the United States," he said.

Starrett created KTURF using research data from soil columns he collected as part of his doctoral program at Iowa State University. The Starretts used a commercially available artificial neural network toolbox to begin developing the KTURF predictive models. They entered data on soil characteristics, pesticide or nitrogen characteristics, and irrigation or precipitation conditions, and the percentages of compounds leached.

Artificial neural networks "learn" relationships that exist between input and output data, Starrett explained. They develop, without bias or guidance from the programmer, the mapping scheme between inputs and reported output. The traditional method of creating a predictive model required the modeler to develop the relationships, sometimes biasing the model.

Growing public concern about drinking water quality has forced the turfgrass industry to work toward reducing the amount of chemicals that leach down to and contaminate local groundwater supplies.

Starrett said that in 1982, course managers used more than 5.4 million kg of pesticides and nitrogen for turf maintenance. Until fairly recently, the managers could not tell how much of which chemicals they applied had leached below the turf root zone.

Starrett's graduate research was supported by the green section of the United States Golf Association. He now has a five-year $118,000 grant from them to investigate nutrient runoff from a new championship golf course, Colbert Hills, being developed in Manhattan, Kan.


Story Source:

The above story is based on materials provided by Kansas State University. Note: Materials may be edited for content and length.


Cite This Page:

Kansas State University. "Better Golf Course Chemical Management Possible With "KTURF" Website." ScienceDaily. ScienceDaily, 28 August 1998. <www.sciencedaily.com/releases/1998/08/980828072949.htm>.
Kansas State University. (1998, August 28). Better Golf Course Chemical Management Possible With "KTURF" Website. ScienceDaily. Retrieved December 22, 2014 from www.sciencedaily.com/releases/1998/08/980828072949.htm
Kansas State University. "Better Golf Course Chemical Management Possible With "KTURF" Website." ScienceDaily. www.sciencedaily.com/releases/1998/08/980828072949.htm (accessed December 22, 2014).

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