Inline SCC monitoring improves clinical mastitis detection in an automatic milking system

H. Hogeveen; C. Kamphuis; R. Sherlock; J. Jago; G. Mein

Inline SCC monitoring improves clinical mastitis detection in an automatic milking system

In: Precision livestock farming '09

Authors:

H. Hogeveen

H. Hogeveen ¹Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, the Netherlands; C.Kamphuis@uu.nl
²Business Economics Group, Wageningen University, the Netherlands

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C. Kamphuis

C. Kamphuis ¹Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, the Netherlands; C.Kamphuis@uu.nl

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R. Sherlock

R. Sherlock ³SmartWork Systems Ltd, P.O. Box 36-515, Christchurch 8146, New Zealand

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J. Jago

J. Jago ⁴DairyNZ Ltd., Private Bag 3221, Hamilton, New Zealand;

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, and

G. Mein

G. Mein ⁵Sensortec Ltd., P.O. Box 11004, Hamilton, New Zealand

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Type:: Chapter

Pages:: 315–322

DOI:: https://doi.org/10.3920/9789086866632_039

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This study explored the potential value of in-line cow milk composite SCC (ISCC) monitoring as sole criterion, or in combination with quarter-based electrical conductivity (EC), for the automatic detection of clinical mastitis (CM) during automatic milking. Data, generated from a New Zealand research herd, included EC, ISCC, and observed cases of CM that were treated with antibiotics. Milk samples for ISCC were taken from the bottom of the milk receiver at the end of a cow milking. Quarters suspected of having CM were visually checked and, if CM was confirmed, sampled for bacteriological culturing and treated with antibiotics. These treated quarters were considered as gold standard positives for comparing three CM detection models: using EC alone, ISCC alone, or using a combination of these two. Different time-windows were defined in which true positive, false negative and false positive alerts were determined. Alert thresholds of all models were adjusted to achieve a sensitivity of 80%. ‘Success Rate’ (or positive predictive value) and ‘False Alert Rate’ (number of false positive alerts per 1000 cow milkings) were used to evaluate detection performances. Depending on the evaluated time-window, using EC alone as CM detection tool resulted in a Success Rate range of 6.9-11.0%, and a False Alert Rate range of 4.7-7.8. Values for the ISCC model were better with 12.7-15.6% for the Success Rate and 2.9-3.7 for the False Alert Rate. Combining sensor information to detect CM, by using a fuzzy logic algorithm, produced a two to threefold increase in the Success Rate (range 21.9-32.0%) and a two to threefold decrease in the False Alert Rate (range 1.2-2.1) when compared to both univariate models. Results suggest that the performance of a CM detection system improved when ISCC information was added to a detection model based on EC sensing.

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Precision livestock farming '09

E-Book ISBN:: 9789086866632

Publisher:: Wageningen Academic

Print Publication Date:: 30 Oct 2009

Subjects
- Life Sciences
  - General
  - Animal & Veterinary

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Inline SCC monitoring improves clinical mastitis detection in an automatic milking system

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Precision livestock farming '09

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