## Poster Session

#### 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

Poster Session

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.

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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.