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

Word, Alyssa B; Carstens, G. E.; Jackson, K. S.; Pinchak, W. E.; Banerjee, Amarnath; Fu, Yujian

doi:10.1093/jas/sky486

Cited by 20 publications

(14 citation statements)

References 37 publications

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“…For this study, initial parameter estimation was accomplished using the first 4 d (−28 through −25) of the 56-d study as a reference period to calculate baseline μ and σ for each animal. Thereafter, daily observations were used to recompute parameters in an accumulative manner (Kayser et al, 2018a;Mertens et al, 2008) using all of the available data to that time point in the data set. Therefore, every sequential time series measurement includes more data in estimating model parameters than the previous observations.…”

Section: Accumulative Parameter Estimationmentioning

confidence: 99%

“…Plotted values are the daily observation subtracted from the accumulating μ and divided by the accumulating σ. Utilizing the accumulating methodology enables the chart to behave as it would in realtime application. This procedure also allows for the rapid implementation of the monitoring process due to the relatively short reference period required for initial parameter estimation (Kayser et al, 2018a;Mertens et al, 2008). Furthermore, the accuracy of those parameters increases every day the process is monitored.…”

Section: Accumulative Parameter Estimationmentioning

confidence: 99%

“…The inverse of these classifications was categorized as false negatives (FN, chart fails to signal when steer challenged with MH) and true negatives (TN, fails to signal in PBS challenge steers), respectfully. Ratios of these classifications: sensitivity (TP/TP + FN), specificity (TN/[FP + TN]), and accuracy ([TP + TN]/[TP + FP + FN + TN]) were used to evaluate the efficacy of the monitoring systems for identifying when an animal becomes morbid (Kayser, 2018a). These diagnostic measures were calculated using PROC FREQ (SAS 9.4).…”

Section: Model Accuracy Determinationsmentioning

confidence: 99%

“…Furthermore, the CUSUM chart in this study detected morbidity 4.5 d prior to standard detection by feedyard personnel. Utilizing a CUSUM, Kayser et al (2018a) reported that DMI exhibited the greatest accuracy (80%) in detecting a naturally occurring BRD outbreak in young purebred bulls. However, in that study, HD duration was 79% accurate, exhibited greater sensitivity than DMI (77% vs. 67%) and signaled 4 d prior to observed clinical symptoms.…”

Section: Hour Relative To Inoculationmentioning

confidence: 99%

“…Statistical process control was initially applied within manufacturing industries, but their applications have also been used in the financial and healthcare industries (Montgomery, 2009;De Vries and Reneau, 2010). Previous studies have evaluated the use of SPC procedures to monitor feeding duration and ruminal temperature prior to the onset of BRD in high-risk and Holstein calves (Quimby et al, 2001;Timsit et al, 2011, Kayser et al, 2018a. However, these reports used visual observations to determine the health status of the animals, and, due to the aforementioned inaccuracy of visual observation, it is difficult to ascertain the accuracy of the SPC procedures for the detection of preclinical BRD.…”

Section: Introductionmentioning

confidence: 99%

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Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica

Word

Carstens

Parsons

et al. 2020

Journal of Animal Science

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The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with the remote continuous collection of feeding behavior patterns, accelerometer-based behaviors, and rumen temperature can accurately differentiate between animals experimentally inoculated with Mannheimia haemolytica (MH) or PBS. Thirty-six crossbred steers (BW = 352 ± 23 kg) seronegative for MH were randomly assigned to bronchoselective endoscopic inoculation with MH (n = 18) or PBS (n = 18). Electronic feed bunks were used to measure DMI and feeding behavior traits, accelerometer-based neck collars measured feeding- and activity-behavior traits, and ruminal thermo-boluses measured rumen temperature. Data were collected for 28 d prior to and following inoculation. Steers inoculated with MH exhibited elevated (P < 0.02) levels of neutrophils and rumen temperature indicating that MH challenge effectively stimulated immunologic responses. However, only nine of the MH steers exhibited increased serum haptoglobin concentrations indicative of an acute-phase protein response and one displayed clinical signs of disease. Shewhart charts (SPC procedure) were used for two analyses, and sensitivity was computed using all MH-challenged steers (n = 18), and a subset that included only MH-challenged haptoglobin-responsive steers (n = 9). Specificity was calculated using all PBS steers in both analyses. In the haptoglobin-responsive only analysis, DMI and bunk visit (BV) duration had the greatest accuracy (89%), with accuracies for head-down (HD) duration, BV frequency, time to bunk, and eating rate being less (83%, 69%, 53%, and 61%, respectively). To address the diurnal nature of rumen temperature, data were averaged over 6-h intervals, and quarterly temperature models were evaluated separately. Accuracy for the fourth quarter rumen temperature was higher (78%) than the other quarterly temperature periods (first = 56%, second = 50%, and third = 67%). In general, the accelerometer-based behavior traits were highly specific ranging from 82% for ingestion to 100% for rest, rumination, and standing. However, the sensitivity of these traits was low (0% to 50%), such that the accuracies were moderate compared with feeding behavior and rumen temperature response variables. These results indicate that Shewhart procedures can effectively identify deviations in feeding behavior and rumen temperature patterns to enable subclinical detection of BRD in beef cattle.

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Section: Accumulative Parameter Estimationmentioning

confidence: 99%

Section: Accumulative Parameter Estimationmentioning

confidence: 99%

Section: Model Accuracy Determinationsmentioning

confidence: 99%

Section: Hour Relative To Inoculationmentioning

confidence: 99%