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By William Christian Kayser


Two studies were conducted to examine the impact of dietary supplementation with Saccharomyces cerevisiae boulardii (LY) on immunological, physiological and behavioral responses to experimental Mannheimia haemolytica (MH) and viral-bacterial (VB) challenges in beef cattle. In both studies, the MH and VB challenges impacted leukogram constituents, animal behavior, and body temperature responses; consistent with an acute immune response. In the MH-challenge study, LY-supplemented steers exhibited reduced (P < 0.01; 1.52 vs. 1.74, kg) ADG and G:F (P < 0.01; 0.14 vs. 0.16) compared to control steers during the 28 d prior to challenge. However, during the 28 d post challenge period LY-supplemented steers had improved ADG (P < 0.01; 1.50 vs. 1.13, kg) and G:F (P < 0.01; 0.15 vs. 0.11) compared to control steers. Furthermore, LY supplementation increased (P = 0.02) cortisol 32% throughout the study, but did not impact any other serological measures. In the VB-challenge study, LY-supplemented heifers had greater neutrophil production 16% (P = 0.02), increased monocytes (P < 0.05) on day 4, and reduced haptoglobin concentration (P < 0.05) on day 5 compared to control heifers. These results indicate LY supplementation altered immune response during disease challenge; however, the effects of LY supplementation on growth and performance requires further investigation. Animal-health monitoring systems that utilize real-time biosensor systems for preclinical disease detection are dependent upon data-processing algorithms that can accurately differentiate between healthy and morbid animals. Objectives of this research were to develop and evaluate various statistical process control (SPC) algorithms, for use in an animal-health monitoring system. For this objective, the 2 challenge studies and a field-based BRD observational study were used. The field-based BRD observational study consisted of 231 bulls on a feed efficiency test, during which 30 were identified as morbid. The SPC models were developed using 3 types of biosensor data collection systems; including, DMI and feeding behavior, ruminal temperature (RUT) and accelerometer-based behaviors. In the observational study, DMI was the most accurate (80%) of the phenotypic response variables followed by head down (HD) duration (79%), which signaled 4.8 d prior to clinical symptoms. Principal components analysis (PCA) was used to construct multivariate models using 3 feeding behavior traits with and without DMI. The feeding behavior model with DMI was 84% accurate and signaled 2 d prior to clinical symptoms. Removal of DMI did not impact the accuracy, and minimally altered signal day. In the MH-challenge study, accuracies of SPC models for DMI, BV duration and RUT were 89, 89 and 86%, respectively, which signaled 0.14, 0.13 and 0 days after the MH challenge, respectively. In the VB-challenge study, DMI was the most accurate response variable with an accuracy of 95% and signaled the day of MH inoculation, followed by rest, meal duration and RUT (89, 87 and 94%, respectively). The SPC models for DMI, feeding behaviors and RUT were consistently more accurate for monitoring the health status of beef cattle than the accelerometer-based behavior traits. These results illustrate the effectiveness of using SPC procedures coupled with remote data collection sensors for preclinical detection of BRD in beef cattle

Topics: Live yeast supplementation, bovine respiratory disease, statistical process control
Year: 2019
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