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The objectives of this study were to evaluate the effectiveness and accuracy of monitoring feeding behavior patterns using cumulative summation (CUSUM) procedures to predict the onset of bovine respiratory disease (BRD) in beef cattle. Growing bulls (N = 231) on a 70-d growth and efficiency trial were used in this study. Between days 28 and 38 of the study, 30 bulls were treated for BRD based on observed clinical signs and elevated rectal temperature (&gt;39.5 °C); remaining bulls (n = 201) were considered healthy. Clinically-ill and healthy bulls were used to evaluate sensitivity and specificity of CUSUM models, with accuracy calculated as the average of sensitivity and specificity. All data were standardized prior to generating CUSUM charts in a daily accumulative manner. Eight univariate CUSUM models were evaluated including DMI, bunk visit (BV) frequency, BV duration, head down (HD) duration, eating rate, maximal nonfeeding interval (NFI Max), SD of nonfeeding interval (NFI SD), and time to bunk (TTB). Accuracies for detection of BRD were 80.1, 69.4, 72.4, 79.1, 63.7, 64.6, 73.2, and 48.7%, respectively, and average day of detection prior to observed symptoms of BRD were 1.0, 3.2, 3.2, 4.8, 10.2, 2.7, 1.5, and 0.6 d, respectively. Principal component analysis (PCA) of all 8 univariate traits (full model) was used to construct multivariate factors that were similarly monitored with CUSUM. Two reduced multivariate models were also constructed that included the 3 best performing feeding behavior traits (BV duration, HD duration, NFI SD) with (RBD) and without DMI (RB). Accuracy of the full multivariate model was similar to the best of the univariate models (75.0%). However, both of the reduced multivariate models (RB and RBD) were more accurate (84.0%) than the full multivariate model. All 3 of the multivariate models signaled (P &lt; 0.05) 2.0 to 2.1 d prior to clinical observation. These results demonstrate that the use of PCA-derived multivariate factors in CUSUM charts was more accurate compared with univariate CUSUM charts, for pre-clinical detection of BRD. Furthermore, adding DMI to the RB model did not further improve accuracy or signal day of BRD detection. The use of PCA-based multivariate models to monitor feeding behavior traits should be more robust than relying on univariate trait models for preclinical detection of BRD. Results from this study demonstrate the value of using CUSUM procedures to monitor feeding behavior patterns to more accurately detect BRD prior to clinical symptoms in feedlot cattle.

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Evaluation of statistical process control procedures to monitor feeding behavior patterns and detect onset of bovine respiratory disease in growing bulls