Predicting runoff-induced pesticide input in agricultural sub-catchment surface waters : linking catchment variables and contamination

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dc.contributor.authorDabrowsk, James M.
dc.contributor.authorPeall, Sue K.C.
dc.contributor.authorVan Niekerk, Adriaan
dc.contributor.authorReinecke, Adriaan J.
dc.contributor.authorDay, Jenny A.
dc.contributor.authorSchulz, Ralf
dc.contributor.other0000-0002-5631-0206
dc.date.accessioned2011-08-31T13:24:29Z
dc.date.issued2002-05
dc.descriptionThe original publication is available at http://www.sciencedirect.comen_ZA
dc.description.abstractAn 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.en_ZA
dc.description.versionPublishers' Versionen_ZA
dc.embargo.lift2025-12-31
dc.embargo.terms2025-12-31en_ZA
dc.format.extentp. 4975–4984 : ill., maps
dc.identifier.citationDabrowski, 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.comen_ZA
dc.identifier.issn0043-1354 (online)
dc.identifier.urihttp://hdl.handle.net/10019.1/16330
dc.language.isoen_ZAen_ZA
dc.publisherElsevieren_ZA
dc.rights.holderElsevieren_ZA
dc.subjectPesticide contamination -- Prediction -- South Africaen_ZA
dc.subjectRun-off contamination -- Prediction -- South Africaen_ZA
dc.subjectGIS-based runoff modelen_ZA
dc.subjectLourens River (South Africa) -- Contaminationen_ZA
dc.subjectSub-catchments -- Contamination -- South Africaen_ZA
dc.titlePredicting runoff-induced pesticide input in agricultural sub-catchment surface waters : linking catchment variables and contaminationen_ZA
dc.typeArticleen_ZA
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