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

2018 ◽  
Vol 97 (3) ◽  
pp. 1158-1170 ◽  
Author(s):  
William C Kayser ◽  
Gordon E Carstens ◽  
Kirby S Jackson ◽  
William E Pinchak ◽  
Amarnath Banerjee ◽  
...  
Author(s):  
W C Kayser ◽  
G E Carstens ◽  
I L Parsons ◽  
K E Washburn ◽  
S D Lawhon ◽  
...  

Abstract The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with remote continuous data collection could accurately differentiate between animals experimentally inoculated with a viral-bacterial (VB) challenge or phosphate buffer solution (PBS). Crossbred heifers (N = 38; BW = 230 ± 16.4 kg) were randomly assigned to treatments by initial weight, ADG, BHV-1 and MH serum titers. Feeding behavior, DMI, animal activity and rumen temperature were continuously monitored remotely prior to and following VB challenge. VB-challenged heifers exhibited decreased (P < 0.01) ADG and DMI, as well as increased (P < 0.01) neutrophils and rumen temperature consistent with a bovine respiratory disease (BRD) infection. However, none of the heifers displayed overt clinical signs of disease. Shewhart and cumulative summation (CUSUM) charts were evaluated, with sensitivity and specificity computed on the VB-challenged heifers (n = 19), and PBS-challenged heifers (n = 19) respectively, accuracy was determined as the average of sensitivity and specificity. To address the diurnal nature of rumen temperature responses, summary statistics (Mean, minimum, maximum) were computed for daily quartiles (6-h intervals), and these quartile temperature models were evaluated separately. In the Shewhart analysis, DMI was the most accurate (95%) at deciphering between PBS- or VB-challenged heifers, followed by rumen temperature (94%) collected in the 2 nd and 3 rd quartiles. Rest was most the accurate accelerometer-based traits (89%), and meal duration (87%) and bunk visit (BV) frequency (82%) were the most accurate feeding behavior traits. Rumen temperature collected in the 3 rd quartile signaled the earliest (2.5 d) of all the variables monitored with the Shewhart, followed by BV frequency (2.8 d), meal duration (2.8 d), DMI (3.0 d) and rest (4.0 d). Rumen temperature and DMI and remained the most accurate variables in the CUSUM at 80 and 79%, respectively. Meal duration (58%), BV frequency (71%) and rest (74%) were less accurate when monitored with the CUSUM analysis. Furthermore, signal day was greater for DMI, rumen temperature and meal duration (4.4, 5.0 and 3.7 d, respectively) in the CUSUM compared to Shewhart analysis. These results indicate that Shewhart and CUSUM charts can effectively identify deviations in feeding behavior, activity and rumen temperature patterns for the purpose of detecting sub-clinical BRD in beef cattle.


2020 ◽  
Vol 98 (2) ◽  
Author(s):  
William C Kayser ◽  
Gordon E Carstens ◽  
Ira L Parsons ◽  
Kevin E Washburn ◽  
Sara D Lawhon ◽  
...  

Abstract 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.


2012 ◽  
Vol 12 (4) ◽  
pp. 65-70 ◽  
Author(s):  
Z. Ignaszak ◽  
R. Sika

Abstract The paper presents an analysis of SPC (Statistical Process Control) procedures usability in foundry engineering. The authors pay particular attention to the processes complexity and necessity of correct preparation of data acquisition procedures. Integration of SPC systems with existing IT solutions in area of aiding and assistance during the manufacturing process is important. For each particular foundry, methodology of selective SPC application needs to prepare for supervision and control of stability of manufacturing conditions, regarding specificity of data in particular “branches” of foundry production (Sands, Pouring, Metallurgy, Quality).


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