Predicting runoff-induced pesticide input in agricultural sub-catchment surface waters : linking catchment variables and contamination
Date
2002-05
Authors
Dabrowsk, James M.
Peall, Sue K.C.
Van Niekerk, Adriaan
Reinecke, Adriaan J.
Day, Jenny A.
Schulz, Ralf
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
An urgent need exists for applicable methods to predict areas at risk of pesticide contamination within agricultural
catchments. As such, an attempt was made to predict and validate contamination in nine separate sub-catchments of
the Lourens River, South Africa, through use of a geographic information system (GIS)-based runoff model, which
incorporates geographical catchment variables and physicochemical characteristics of applied pesticides. We compared
the results of the prediction with measured contamination in water and suspended sediment samples collected during
runoff conditions in tributaries discharging these sub-catchments. The most common insecticides applied and detected
in the catchment over a 3-year sampling period were azinphos-methyl (AZP), chlorpyrifos (CPF) and endosulfan
(END). AZP was predominantly found in water samples, while CPF and END were detected at higher levels in the
suspended particle samples. We found positive (po0:002) correlations between the predicted average loss and the
concentrations of the three insecticides both in water and suspended sediments (r between 0.87 and 0.94). Two sites in
the sub-catchment were identified as posing the greatest risk to the Lourens River mainstream. It is assumed that lack of
buffer strips, presence of erosion rills and high slopes are the main variables responsible for the high contamination at
these sites. We conclude that this approach to predict runoff-related surface water contamination may serve as a
powerfultool for risk assessment and management in South African orchard areas. r 2002 Elsevier Science Ltd. All
rights reserved.
Description
The original publication is available at http://www.sciencedirect.com
Keywords
Pesticide contamination -- Prediction -- South Africa, Run-off contamination -- Prediction -- South Africa, GIS-based runoff model, Lourens River (South Africa) -- Contamination, Sub-catchments -- Contamination -- South Africa
Citation
Dabrowski, J. M. et al. 2002. Predicting runoff-induced pesticide input in agricultural sub-catchment surface waters : linking catchment variables and contamination. Water Research, 36(20) 4975-4984, http://www.sciencedirect.com