Full TGIF Record # 112574
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Web URL(s):http://www.tandfonline.com/doi/pdf/10.1080/00103629809370178
    Last checked: 10/15/2015
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    Access conditions: Item is within a limited-access website
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Author(s):Starrett, Steven K.; Starrett, Shelli K.; Najjar, Yacoub; Adams, Greg; Hill, Judy
Author Affiliation:Starrett, Steven, Najjar, Adams, and Hill: Civil Engineering Department; Starret, Shelli: Electrical and Computer Engineering Department, Kansas State University, Manhattan, Kansas
Title:Modeling pesticide leaching from golf courses using artificial neural networks
Source:Communications in Soil Science and Plant Analysis. Vol. 29, No. 19/20, November 1998, p. 3093-3106.
# of Pages:14
Publishing Information:New York, NY: Marcel Dekker
Related Web URL:http://www.eece.ksu.edu/~starret/KTURF/
    Last checked: 07/14/2006
    Notes: Interactive models
Keywords:TIC Keywords: Computer modeling; Leaching; Pesticides; Artificial Neural Networks; Solubility; Soil partition coefficient; Web sites; Irrigation
Abstract/Contents:"The objective of this work was to develop a computer model that accurately predicted pesticide leaching of pesticides applied to turfgrass areas. After much investigation, the number of inputs used to train the Artificial Neural Networks (ANN) was reduced to pesticide solubility, pesticide soil:water partitioning coefficient (Koc), time after application, and the irrigation appliation practice. For comparison reasons, 1st and 2nd order polynomial regression models were developed. An artificial neural network is a form of artificial intelligence enabling the program to learn relationships instead of the relationships being defined by the programer. The ANN proved to be a feasible modeling technique for pesticide leaching. The ANN predictions for the test cases had much less error than the 1st or 2nd order regression equations (sum of the squared error between measured and predicted values were 17.4, 528.4, and 522.3, respectively). An interactive World Wide Web (www) site has been developed where this artificial neural network can be accessed (http://www.eece.ksu.edu/~starret/KTURF/). The www site is called KTURF and is accessible through the Internet. Used as an assessment tool, KTURF can help to reduce pesticide leaching by allowing users to experiment with different pesticide/irrigation schemes. They can thus optimize their practices to reduce the likeihood of pesticide leaching beyond the rootzone."
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Starret, S. K., S. K. Starrett, Y. Najjar, G. Adams, and J. Hill. 1998. Modeling pesticide leaching from golf courses using artificial neural networks. Commun. Soil. Sci. Plant Anal. 29(19/20):p. 3093-3106.
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    Last checked: 10/15/2015
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    Access conditions: Item is within a limited-access website
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MSU catalog number: S 590 .C54
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