Author(s): NizaRibeiro J, Noordhuizen JP, Menezes JC
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Abstract Bulk milk somatic cell count (BMSCC) averages have been used to evaluate udder health both at the individual or the herd level as well as milk quality and hygiene. The authors show that the BMSCC average is not the best tool to be used in udder health control programs and that it can be replaced with advantage by the capability index (Cpk). The Cpk is a statistical process control tool traditionally used by engineers to validate, monitor, and predict the expected behavior of processes or machines. The BMSCC data of 13 consecutive months of production from 414 dairy herds as well as SCC from all cows in the DHI program from 264 herds in the same period were collected. The Cpk and the annual BMSCC average (AAVG) of all the herds were calculated. Confronting the herd's performance explained by the Cpk and AAVG with the European Union (EU) official limit for BMSCC of 400,000 cells/mL, it was noticed that the Cpk accurately classified the compliance of the 414 farms, whereas the AAVG misclassified 166 (40\%) of the 414 selected farms. The annual prevalence of subclinical mastitis (SMP) of each herd was calculated with individual SCC data from the same 13-mo period. Cows with more than 200,000 SCC/mL were considered as having subclinical mastitis. A logistic regression model to relate the Cpk and the herd's subclinical mastitis prevalence was calculated. The model is: SMPe = 0.475 e(-0.5286 x Cpk). The validation of the model was carried out evaluating the relation between the observed SMP and the predicted SMPe, in terms of the linear correlation coefficient (R2) and the mean difference between SMP and SMPe (i.e., mean square error of prediction). The validation suggests that our model can be used to estimate the herd's SMP with the herd's Cpk. The Cpk equation relates the herd's BMSCC with the EU official SCC limit, thus the logistic regression model enables the adoption of critical limits for subclinical mastitis, taking into consideration the legal standard for SCC.
This article was published in J Dairy Sci
and referenced in Industrial Engineering & Management