Presentation Title

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.

This document is currently not available here.

Share

COinS
 
Nov 1st, 1:30 PM Nov 1st, 3:00 PM

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.