Predicting Time Cattle Spend in Streams to Quantify Direct Deposition of Manure
Author(s) -
Shan B. Brown,
Charles D. Ikenberry,
Michelle L. Soupir,
Justin J. Bisinger,
James R. Russell
Publication year - 2014
Publication title -
applied engineering in agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.276
H-Index - 54
eISSN - 1943-7838
pISSN - 0883-8542
DOI - 10.13031/aea.30.10393
Subject(s) - streams , pasture , environmental science , manure , watershed , hydrology (agriculture) , deposition (geology) , water quality , grazing , baseflow , range (aeronautics) , livestock , current (fluid) , streamflow , ecology , drainage basin , computer science , structural basin , geography , geology , engineering , biology , computer network , paleontology , geotechnical engineering , oceanography , cartography , machine learning , aerospace engineering
. Current methods to predict bacterial loads into streams resulting from direct deposition of manure by livestock do not consider factors that influence livestock behavior. Data from three studies that monitored spatial behavior of cattle through GPS were used to develop a new method with increased temporal resolution and consideration of environmental factors to predict the time that cattle spend in streams. Information on relative location of the cattle to the pasture stream was used to calculate the number of hours a cow spent in the stream, and from that the load of bacteria deposited directly into the stream. Ultimately, four empirical equations were developed based on the pasture geometry and shaded area, and each varied as a function of the daily minimum temperature. The models were applied to the Duck Creek watershed, Iowa, (at USGS Station 05422560) to demonstrate the variation in temporal resolution when compared to standard monthly load allocation methods. Three of four models estimated fewer days of E. coli load exceeding the water quality standard than days predicted using conventional methods. While the models do not capture the entire range of cattle spatial behavior, results suggest that the models can be used as a more detailed means of calculating bacterial loads. Daily load estimations averaged over a month can be used to populate current predictive tools as an alternate to the less representative estimation method on which the current modeling tools rely.
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