Author(s): Y. R. Li; J. Struger; J. D. Fischer; G. H. Huang; Y. F. Li
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Keywords: Griculture; Environment; Modeling; Monitoring; Nonpoint source; Pesticide; Runoff; Watershed
Abstract: A long term pesticide monitoring program was carried out in paired sub-watersheds near Kintore Watershed, Ontario, Canada between 1988 and 1991 to determine the impact of agricultural conservation practices on pesticide delivery to surface water. Three types of water sampling schemes including grab, storm event, and continuous water sampler were employed in this study. A total of 608 water samples were analyzed for concentrations of atrazine. Atrazine concentrations exceeded national guidelines in both watersheds during runoff events. Atrazine concentrations achieved higher peak values in the control watershed. Two different statistical approaches, multiple linear regression (MLR) and the artificial neural network (ANN) model were used to estimate the runoff losses of pesticide. Four important factors were selected as inputs for the MLR and ANN models. These factors include pesticide residues, daily rainfall, soil temperature, and soil moisture conditions. An attempt is made to explain differences in stream concentrations of atrazine under different weather scenarios by examining the four independent factors. The model results were evaluated with monitoring data show the good predictive ability of ANN model with R2=0.90 and RMS=0.83 in comparison with that of MLR model with R2=0.75 and RMS=1.3.
Year: 2001