Accurate evaluation of nitrate leaching potential in agricultural fields is a major challenge. Field data are expensive to gather and use of existing prediction models is limited by inadequate understanding of the physical and chemical processes underlying nitrate leaching. A neural network model was developed to acquire the inherent characteristics of an experimental data set, and successfully used to simulate nitrate leaching in agricultural drainage effluent under various management systems. Simulation results indicated that: (i) sub-irrigation with a 0.5 m water table depth could reduce nitrate leaching to negligible levels, (ii) intercropping corn with ryegrass could reduce nitrate leaching by 50%, and (iii) the application of more than 180 kg N ha−1 of fertilizer may cause excessive nitrate leaching.
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Research Article|
October 01 1998
Modeling nitrate leaching using neural networks
J. W. Kaluli;
J. W. Kaluli
*Urban Systems Ltd., Suite 200-286 St. Paul Street, Kamloops, B.C. V2C 6G4, Canada
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C. A. Madramootoo;
C. A. Madramootoo
**Agricultural and Biosystems Engineering Department, McGill University, Macdonald Campus, 2111 Lakeshore Road, Ste. Anne de Bellevue, QC H9X 3V9, Canada
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Y. Djebbar
Y. Djebbar
***S&DD, Greater Vancouver Regional District, 4330 Kingsway, Burnaby, B.C. V5H 4G8, Canada
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Water Sci Technol (1998) 38 (7): 127–134.
Citation
J. W. Kaluli, C. A. Madramootoo, Y. Djebbar; Modeling nitrate leaching using neural networks. Water Sci Technol 1 October 1998; 38 (7): 127–134. doi: https://doi.org/10.2166/wst.1998.0285
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