Using registry data to identify individual dairy cows with abnormal patterns in routinely recorded somatic cell counts.

Data from the Danish milk recording system routinely enter the Danish Cattle Database, including somatic cell counts (SCC) for individual animals. Elevated SCC can signal intramammary inflammation, suggesting subclinical mastitis. Detecting mastitis is pivotal to limit severity, prevent pathogen spread, and target treatment or culling. This study aimed to differentiate normal and abnormal SCC patterns using recorded registry data. We used registry data from 2010 to 2020 for dairy cows in herds with 11 annual milk recordings. To create consistency across herds, we used data from 13,996 unique animals and eight different herds, selected based on the amount of data available, only selecting Holstein animals and conventional herds. We fitted log10-transformed SCC to days in milk (DIM) using the Wilmink and Wood's curve functions, originally developed for milk yield over the lactation. We used Nonlinear Least Square and Nonlinear Mixed Effect models to fit the log10-transformed SCC observations to DIM at animal level. Using mean squared residuals (MSR), we found a consistently better fit using a Wood's style function. Detection of MSR outliers in the model fitting process was used to identify animals with log10(SCC) curves deviating from the expected "normal" curve for that same animal. With this study, we propose a method to identify single animals with SCC patterns that indicate abnormalities, such as mastitis, based on registry data. This method could potentially lead to a registry data-based detection of mastitis cases in larger dairy herds.

Use of Danish National Somatic Cell Count Data to Assess the Need for Dry-Off Treatment in Holstein Dairy Cattle.

In Denmark, PCR testing of dairy cattle is commonly used to select animals for the antibacterial treatment of intramammary infection (IMI) during the dry-off period. IMI is associated with a high somatic cell count (SCC), routinely recorded for milk quality control for most commercial dairy herds. This study aimed to compare SCC curves over the lactation among dairy cows with positive vs. negative PCR test results for four major IMI pathogens. Data from 133,877 PCR-tested Holstein cows from 1364 Danish conventional dairy herds were used to fit a nonlinear mixed-effects model using a modified four-parameter Wilmink function. We stratified the data into first, second, third or fourth and later parity and fitted Wilmink curves to all SCC observations between 6 and 305 days in milk. The PCR tests were taken before dry-off at the end of the lactation to investigate which animals qualified for selective dry cow therapy. A PCR Ct-value of 37 and below was used to determine if an animal was PCR positive for any of the following IMI pathogens: Staphylococcus aureus, Streptococcus agalactiae, Str. dysgalactiae and Str. uberis. Our findings showed that mean SCC curve fits were higher for PCR-positive animals in all four parity groups and across lactations. The use of SCC data fitted to the entire lactation for multiple lactations enabled quantification of overall differences in SCC curves between cattle with and without detected IMI, adjusted for parity group and stage of lactation. These findings are relevant to the use of SCC to support treatment decisions.