Capability index--a statistical process control tool to aid in udder health control in dairy herds

Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

Simple Summary

The management of body condition scores of dairy cows during the dry period affects postpartum health. It is therefore important to monitor the body condition scores so as to predict the occurrence of postpartum diseases. In this study, statistical process control technology was used to monitor body condition scores and to explore its effects on nutritional strategies and the development of postpartum subclinical ketosis. The results showed that the statistical process control technology was not a good tool for the diagnosis of postpartum subclinical ketosis, but an appropriate tool for the guidance of nutritional strategies.

Abstract

The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.

Utility of process capability indices in assessment of quality control processes at a clinical laboratory chain

BACKGROUND

To evaluate the utility of the process capability indices Cp and Cpk for assessing the quality control processes at chain laboratory facilities.

METHODS

In April 2020, the minimum Cp and Cpk values for 33 assays of a laboratory chain with 19 facilities were collected for further analysis and a total of 627 datasets (Cp and Cpk ) were compared. In addition, standard values for Cp and Cpk , defined as the lowest of the top 20%, were obtained for comparison and the indices were used to determine whether precision or trueness improvements were required for the corresponding assay.

RESULTS

A total of 627 datasets of 33 assays from 19 laboratory facilities were collected for further analysis. Based on the Cp results, 329 (52.5%), 211 (33.7%), 65 (10.3%), and 22 (3.5%) were rated as excellent, good, marginal, and poor, respectively. While the corresponding results for Cpk were 300 (47.8%), 216 (34.4%), 79 (12.6%), and 32 (5.1%). In addition, it was noteworthy that eight (Cp criteria) and six assays (Cpk criteria) were rated as excellent or good at all 19 facilities. Comparison of the process capability indices at the Jinan KingMed Center with the standard values revealed that total protein, albumin, and urea showed trueness individual improvement, precision individual improvement, and precision common improvement, respectively, while the results of other assays were stable.

CONCLUSION

Process capability indices are useful for evaluating the quality control procedures in laboratory facilities and can help improve the precision and trueness of laboratory tests.

Application of statistical process control for monitoring bulk tank milk somatic cell count of smallholder dairy farms

Background and Aim:

Consistency in producing raw milk with less variation in bulk tank milk somatic cell count (BMSCC) is important for dairy farmers as their profit is highly affected by it in the long run. Statistical process control (SPC) is widely used for monitoring and detecting variations in an industrial process. Published reports on the application of the SPC method to smallholder farm data are very limited. Thus, the purpose of this study was to assess the capability of the SPC method for monitoring the variation of BMSCC levels in milk samples collected from smallholder dairy farms.

Materials and Methods:

Bulk tank milk samples (n=1302) from 31 farms were collected 3 times/month for 14 consecutive months. The samples were analyzed to determine the BMSCC levels. The SPC charts, including the individual chart (I-chart) and the moving range chart (MR-chart), were created to determine the BMSCC variations, out of control points, and process signals for each farm every month. The interpretation of the SPC charts was reported to dairy cooperative authorities and veterinarians.

Results:

Based on a set of BMSCC values as well as their variance from SPC charts, a series of BMSCC data could be classified into different scenarios, including farms with high BMSCC values but with low variations or farms with low BMSCC values and variations. Out of control points and signals or alarms corresponding to the SPC rules, such as trend and shift signals, were observed in some of the selected farms. The information from SPC charts was used by authorities and veterinarians to communicate with dairy farmers to monitor and control BMSCC for each farm.

Conclusion:

This study showed that the SPC method can be used to monitor the variation of BMSCC in milk sampled from smallholder farms. Moreover, information obtained from the SPC charts can serve as a guideline for dairy farmers, dairy cooperative boards, and veterinarians to manage somatic cell counts in bulk tanks from smallholder dairy farms.

Application of statistical process control charts to monitor changes in animal production systems

Statistical process control (SPC) is a method of monitoring, controlling, and improving a process through statistical analysis. An important SPC tool is the control chart, which can be used to detect changes in production processes, including animal production systems, with a statistical level of confidence. This paper introduces the philosophy and types of control charts, design and performance issues, and provides a review of control chart applications in animal production systems found in the literature from 1977 to 2009. Primarily Shewhart and cumulative sum control charts have been described in animal production systems, with examples found in poultry, swine, dairy, and beef production systems. Examples include monitoring of growth, disease incidence, water intake, milk production, and reproductive performance. Most applications describe charting outcome variables, but more examples of control charts applied to input variables are needed, such as compliance to protocols, feeding practice, diet composition, and environmental factors. Common challenges for applications in animal production systems are the identification of the best statistical model for the common cause variability, grouping of data, selection of type of control chart, the cost of false alarms and lack of signals, and difficulty identifying the special causes when a change is signaled. Nevertheless, carefully constructed control charts are powerful methods to monitor animal production systems. Control charts might also supplement randomized controlled trials.

Monitoring major mastitis pathogens at the population level based on examination of bulk tank milk samples

The objective was to determine the proportions and bacterial counts of major mastitis pathogens in samples of bulk tank milk (BTM), as well as to clarify the relationship between these bacteria counts and bulk tank somatic cell count (BTSCC). The purpose was to judge the importance of the counts of mammary pathogens for BTSCC at the population level. Samples of BTM were collected from 268 randomly selected anonymous dairy herds (with approximately 29 000 dairy cows). Staphylococcus aureus, other coagulase-positive staphylococci, Streptococcus agalactiae and Streptococcus dysgalactiae were grouped as contagious pathogens, and Streptococcus uberis, Escherichia coli, Pseudomonas aeruginosa, Enterococcus faecalis and Enterococcus faecium as environmental pathogens. Based on assessment of the dominant pathogen in each herd, environmental pathogens were found to dominate. Counts of specific mammary pathogens in BTM samples did not exceed a geometric mean of 1000 cfu/ml. Significant differences were documented in BTSCC between BTM samples containing Str. agalactiae, Staph. aureus, and Str. dysgalactiae and BTM samples that were pathogen-free. Geometric means of BTSCC associated with these pathogens were noticeably higher than the overall BTSCC (Str. agalactiae 243 628; Staph. aureus 205 610; Str. dysgalactiae 203 978; overall 173 000 cells/ml). It follows that the somatic cell count (SCC) associated with these pathogens contributed substantially to the overall BTSCC. Environmental pathogens predominated in samples of BTM with SCC <300x103/ml and contagious mastitis pathogens predominated in BTM samples with SCC >300x103/ml. No correlation was detected between bacterial counts of specific pathogens and BTSCC. This study revealed that the assessment of bacterial counts of mammary pathogens in samples of BTM in relation to BTSCC is applicable for the monitoring of changes in the occurrence of major mastitis pathogens in dairy herds at a national level.

Bulk tank somatic cell counts analyzed by statistical process control tools to identify and monitor subclinical mastitis incidence

The objective of this study was to examine the relationship between monthly Dairy Herd Improvement (DHI) subclinical mastitis and new infection rate estimates and daily bulk tank somatic cell count (SCC) summarized by statistical process control tools. Dairy Herd Improvement Association test-day subclinical mastitis and new infection rate estimates along with daily or every other day bulk tank SCC data were collected for 12 mo of 2003 from 275 Upper Midwest dairy herds. Herds were divided into 5 herd production categories. A linear score [LNS = ln(BTSCC/100,000)/0.693147 + 3] was calculated for each individual bulk tank SCC. For both the raw SCC and the transformed data, the mean and sigma were calculated using the statistical quality control individual measurement and moving range chart procedure of Statistical Analysis System. One hundred eighty-three herds of the 275 herds from the study data set were then randomly selected and the raw (method 1) and transformed (method 2) bulk tank SCC mean and sigma were used to develop models for predicting subclinical mastitis and new infection rate estimates. Herd production category was also included in all models as 5 dummy variables. Models were validated by calculating estimates of subclinical mastitis and new infection rates for the remaining 92 herds and plotting them against observed values of each of the dependents. Only herd production category and bulk tank SCC mean were significant and remained in the final models. High R2 values (0.83 and 0.81 for methods 1 and 2, respectively) indicated a strong correlation between the bulk tank SCC and herd's subclinical mastitis prevalence. The standard errors of the estimate were 4.02 and 4.28% for methods 1 and 2, respectively, and decreased with increasing herd production. As a case study, Shewhart Individual Measurement Charts were plotted from the bulk tank SCC to identify shifts in mastitis incidence. Four of 5 charts examined signaled a change in bulk tank SCC before the DHI test day identified the change in subclinical mastitis prevalence. It can be concluded that applying statistical process control tools to daily bulk tank SCC can be used to estimate subclinical mastitis prevalence in the herd and observe for change in the subclinical mastitis status. Single DHI test day estimates of new infection rate were insufficient to accurately describe its dynamics.

Epidemiology: A Foundation for Dairy Production Medicine

Epidemiology in dairy production medicine strives to optimize production through the elimination and control of disease and the implementation of management practices that promote animal health, welfare, productivity, and profitability. Epidemiology in dairy production medicine is a broad discipline that encompasses many inter-related areas of activity. Quantitative epidemiology includes a range of activities, from counting clinical cases to the development of complex hierarchic decision models. Evaluation and interpretation of "tests" applied at various levels of the health management cycle serve as the basis for diagnostic and monitoring systems. Critical evaluation of the published scientific literature in the practice of evidence-based veterinary medicine facilitates the appropriate use of knowledge in support of health management decisions.

The Hazard Analysis Critical Control Point's (HACCP) concept as applied to some chemical, physical and microbiological contaminants of milk on dairy farms. A prototype

Quality management on dairy farms becomes more and more important regarding the different areas of animal health, animal welfare and food safety. Monitoring animals, farm conditions and farm records can be extended with risk identification and risk management. The hazard analysis critical control point's system is useful as an on farm strategy to control the product as well as the production process on the areas of animal health, animal welfare and food safety. This article deals in detail with the question how to develop a qualitative method where risk can be defined as an interaction between probability and impact. Two parts of the production process (milk harvest and treatment of cows) where used as an example how to apply the hazard analysis critical control point's system on chemical, physical and microbiological contaminants of milk. Not just only by summarizing the different critical checkpoints for each area but also by giving them a precise judgement of probability and impact.