scholarly journals Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef heifers resulting from an experimental combined viral-bacterial challenge

2021 ◽  
Author(s):  
W C Kayser ◽  
G E Carstens ◽  
I L Parsons ◽  
K E Washburn ◽  
S D Lawhon ◽  
...  
Keyword(s):  
Process Control ◽  
Feeding Behavior ◽  
Statistical Process Control ◽  
Sensitivity And Specificity ◽  
Clinical Signs ◽  
Phosphate Buffer Solution ◽  
Buffer Solution ◽  
Statistical Process ◽  
Rumen Temperature ◽  
Cusum Charts

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.

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

2020 ◽  
Vol 98 (2) ◽  
Author(s):  
William C Kayser ◽  
Gordon E Carstens ◽  
Ira L Parsons ◽  
Kevin E Washburn ◽  
Sara D Lawhon ◽  
...  
Keyword(s):  
Process Control ◽  
Feeding Behavior ◽  
Statistical Process Control ◽  
Clinical Signs ◽  
Temperature Response ◽  
Temperature Data ◽  
Mannheimia Haemolytica ◽  
Eating Rate ◽  
Statistical Process ◽  
Rumen Temperature

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.

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

2020 ◽  
Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic
Keyword(s):  
Human Capital ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Growth Models ◽  
Pearson Correlation ◽  
Small Companies ◽  
Statistical Process ◽  
Production Staff ◽  
Footwear Industry

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.

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