The objectives of this work were 1) to examine the responsiveness of SCC,
lactose concentration, and
NAGase activity in milk to changes in bacteriological status and 2) to develop models for predicting bacteriological status of mammary glands. Data included 550 cows in 10 commercial herds. Natural logarithm
NAGase and log cell count were most responsive to changes in bacterial status. The log
NAGase was relatively more effective in identifying major from minor pathogen
infections, whereas log SCC was better able to differentiate between infected and uninfected classes. Non-transformed
NAGase, SCC, and
lactose were considerably less responsive to
infection status. Logistic regression of bacterial status on herd, lactation number, milk, log SCC, log
NAGase, and stage of lactation was performed. The least significant variables were removed in a stepwise process. Final predictors of
infection status were herd, log SCC, and log
NAGase. The role of log SCC was to discriminate
infection from no
infection, whereas log
NAGase discriminated major from minor pathogens. The log
NAGase, alone or in combination with log SCC, added substantially to the detection power of the model. Chi-square goodness of fit tests found no significant differences between observed and predicted
infection probabilities. Substitution of herd averages for log SCC and log
NAGase for the herd variables resulted in significant differences between predicted and observed herd
infection probabilities.