Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part I: Analyzing...

Area of Science:

• Dairy Science
• Animal Health
• Food Quality

Background:

• Bulk tank somatic cell count (SCC) is a key indicator of dairy herd udder health and milk quality.
• Predicting and managing SCC levels is crucial for meeting milk quality standards and premium payment goals.

Purpose of the Study:

• To investigate the relationship between bulk tank SCC mean and variation (sigma) and the probability of exceeding established SCC standards.
• To identify factors influencing SCC levels, including seasonality and herd size.
• To develop a predictive tool for SCC standard violations.

Main Methods:

• Collected bulk tank SCC data from 1,501 dairy herds over 24 months.
• Estimated monthly SCC mean and sigma for each herd.
• Utilized Kruskal-Wallis ANOVA to compare monthly and herd production category differences.
• Employed logistic regression to model the odds of exceeding SCC standards based on month and herd size.
• Constructed a probability grid for predicting SCC standard violations.

Main Results:

• SCC mean and sigma varied significantly by month, with higher levels observed during summer months.
• Smaller herds exhibited significantly higher odds of exceeding SCC standards compared to larger herds.
• Seasonal effects and herd size were significant predictors of SCC standard violations.

Conclusions:

• Seasonal variations and smaller herd size increase the risk of exceeding bulk tank SCC standards.
• A predictive grid based on monthly SCC mean and sigma can forecast the probability of future violations.
• This tool can aid in proactive management to achieve milk quality goals and consistent performance.