Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26044
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock (Forthcoming/Available Online)
Authors: Oliver, David
Bartie, Phil
Heathwaite, A Louise
Reaney, Sim M
Parnell, Jared
Quilliam, Richard
Contact Email: david.oliver@stir.ac.uk
Keywords: agriculture
decision-making
diffuse pollution
faecal indicator organism
risk screening
Issue Date: 27-Oct-2017
Citation: Oliver D, Bartie P, Heathwaite AL, Reaney SM, Parnell J & Quilliam R (2017) A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock (Forthcoming/Available Online), Science of the Total Environment.
Abstract: Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen & Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1 km2 grid cell (Ayr: r = 0.57; p < 0.001 , Lunan: r = 0.32; p < 0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P < 0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments.
DOI Link: http://dx.doi.org/10.1016/j.scitotenv.2017.10.263
Rights: © 2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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