Browsing by Author "Steele, Nicole"

Now showing 1 - 3 of 3
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    Dairy Pipeline, May 2018
    Steele, Nicole; Daubert, Jeremy (Virginia Cooperative Extension, 2018-05-09)
    This issue has two articles. The first one discusses the use of molecular technology (polymerase chain reaction) for rapid identification of mastitis pathogens in milk. The second examines the pros and cons of having heifers in herds of dairy cattle.
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    Dairy Pipeline. May 2017
    Petersson-Wolfe, Christina S.; Spurlin, Kevin; Steele, Nicole (Virginia Cooperative Extension, 2017-05-02)
    This issue has an article about the feeding and grazing behavior of cattle, and the importance of noting what cows are actually eating. It also has an article about gestational heat stress and the effects on dairy calves.
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    On-farm strategies for the prevention and detection of Gram-specific clinical mastitis in dairy cows
    Steele, Nicole (Virginia Tech, 2019-04-17)
    Controlling mastitis in dairy herds relies on good prevention and detection methods. This dissertation describes two areas of research relating to mastitis control. In the first objective, the efficacy of 2 vaccines against Escherichia coli mastitis in mid-lactation dairy cows was evaluated. Secondly, in a series of 3 studies, milk and activity sensor data were used to derive models for clinical mastitis (CM) detection, and models were tested for their ability to indicate the causative pathogen type. Primiparous and multiparous animals were vaccinated with 1 of 2 commercially available J5 vaccines (V1 or V2) or served as unvaccinated controls (CTL). Intramammary challenge with E. coli approximately 84 d later resulted in few treatment differences in the clinical and behavioral responses, except that vaccinated cows exhibited fever (≥ 39.4 °C) 3 h earlier and laid down for longer periods than CTL. Although vaccinated cows had similar severity and duration of CM, V1 cows produced more serum IgG1 and IgG2 than V2 cows. Our results indicated that the effects of vaccination were diminished in mid-lactation, and that antibodies are not the limiting factor in defending against induced E. coli mastitis. Multiple regression models, incorporating the slope changes in relevant milk and activity sensor data, were developed to indicate all CM cases (ACM), or specifically, CM due to Gram-negative (GN) or Gram-positive (GP) bacteria. Gram-specific models had greater detection accuracy (> 80%) than the ACM model (75%) when evaluated using the model training dataset, but independent evaluation demonstrated reduced sensitivity (Se) of detecting CM by all models (GN, 62%, ACM, 56%, and GP, 32% Se). Data in the 3 d prior to CM were more important in detecting GN pathogens, whereas the best GP models incorporated changes more than 1 week prior to CM detection. Still, model performance was imperfect. Next, models were rederived from a dataset that better reflected the infection distribution of the herds its use was intended for. However, the Se of detecting CM in real-time, across 2 farms, was < 21% for all models, and categorization by Gram-status had no benefit. An insufficient number of CM cases was considered to contribute to the poor detection performance of models and limited repeatability across farms. Consequently, models derived in this study were inadequate for implementation as mastitis detection tools. In the future, development of new sensors and application of more sophisticated algorithms to the field of mastitis detection may improve the accuracy of models using sensor data.