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Die-Off of E. coli and Enterococci in Dairy Cowpats

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 51(6): 1987-1996. (doi: 10.13031/2013.25403) @2008
Authors:   M. L. Soupir, S. Mostaghimi, and J. Lou
Keywords:   Die-off, E coli, Enterococci, TMDL, Water quality

E. coli and enterococci re-growth and decay patterns in cowpats applied to pasturelands were monitored during the spring, summer, fall, and winter. First-order approximations were used to determine die-off rate coefficients and decimal reduction times (D-values). Higher-order approximations and weather parameters were evaluated by multiple regression analysis to identify environmental parameters impacting in-field E. coli and enterococci decay. First-order kinetics approximated E. coli and enterococci decay rates with regression coefficients ranging from 0.70 to 0.90. Die-off rate constants were greatest in cowpats applied to pasture during late winter and monitored into summer months for E. coli (k = 0.0995 d-1) and applied to the field during the summer and monitored until December for enterococci (k = 0.0978 d-1). Decay rates were lowest in cowpats applied to the pasture during the fall and monitored over the winter (k = 0.0581 d-1 for E. coli, and k = 0.0557 d-1 for enterococci). Higher-order approximations and the addition of weather variables improved regression coefficients to values ranging from 0.82 to 0.96. Statistically significant variables used in the models for predicting bacterial decay included temperature, solar radiation, rainfall, and relative humidity. Die-off rate coefficients previously reported in the literature are usually the result of laboratory-based studies and are generally higher than the field-based seasonal die-off rate coefficients presented here. To improve predictions of in-field E. coli and enterococci concentrations, this study recommends that higher-order approximations and additional parameters such as weather variables are necessary to better capture re-growth and die-off trends over extended periods of time.

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