Objective: determine the practical use of milk electrical conductivity (EC) change detection technology at the A.U. Dairy in detecting clinical mastitis (CM). An increase in EC > 20% above baseline was a “spike.” If a spike was followed “true alarm.” If not, then a “false alarm.” A “false negative” was an episode of CM occurring without a preceding spike. Bayes’ Theorem was applied to the probability of spikes and incidence of CM: P(M|S ) = P(S|M) P(M) / P(S|M)P(M) + P(S|NM)P(NM) where P(M|S) is the probability of CM given a spike, P(S|M) is the probability of spike given there was CM (true alarms), P(S|NM) is the probability of spike but no CM (false alarms), P(M) is the probability of a cow having CM in the herd, and P(NM) is the probability of a cow not having mastitis [1- P(M)]. A spike correctly predicted CM in 29.615% of cases but false alarms were 70.385%. Correctly predicted CM was 52.294% but 47.706% of the episodes were unpredicted. Therefore, P(M|S ) = 0.1059%. Conclusion: EC change detection technology was not a reliable predictor of CM for use in the milking parlor to detect CM.
Available at: http://works.bepress.com/katherine_koudele/2/