P-01 An evaluation of electrical conductivity as a practical tool in mastitis detection
Presenter Status
Undergraduate Student, Department of Agriculture
Second Presenter Status
Center for Statistical Services
Third Presenter Status
Department of Agriculture
Preferred Session
Poster Session
Location
Buller Hallway
Start Date
1-11-2013 1:30 PM
End Date
1-11-2013 3:00 PM
Presentation Abstract
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.
P-01 An evaluation of electrical conductivity as a practical tool in mastitis detection
Buller Hallway
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.