Quantifying degree of mastitis from common trends in a panel of indicators for mastitis in dairy cows.

This paper has 2 objectives. First, it argues that it is beneficial to regard degree of infection with respect to mastitis as a latent quantity varying continuously from 0 (truly healthy) to 1 (full-blown clinical mastitis). This quantity is denoted as degree of infection (DOI). The DOI is based on extracting common characteristics from a panel of indicators measured repeatedly over time. The indicators used in this paper are electrical conductivity (EC), somatic cell count (SCC), and the immune response related enzyme lactate dehydrogenase (LDH). Second, this paper presents a statistical model for such data and a corresponding method for estimating the DOI from a panel of indicators. An empirical proof of concept is provided. Using DOI, there was a significant difference between the DOI of mastitic and healthy control cows beginning 5 d before the mastitic cows were treated for mastitis.

[1]  C. Burvenich,et al.  Mathematical model of the acute inflammatory response to Escherichia coli in intramammary challenge. , 2006, Journal of dairy science.

[2]  T. Larsen,et al.  Estimating degree of mastitis from time-series measurements in milk: a test of a model based on lactate dehydrogenase measurements. , 2007, Journal of dairy science.

[3]  Ian T. Jolliffe,et al.  Estimating common trends in multivariate time series using dynamic factor analysis , 2003 .

[4]  H Hogeveen,et al.  Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count. , 2008, Journal of dairy science.

[5]  Martin J. Green,et al.  On distinguishing cause and consequence: do high somatic cell counts lead to lower milk yield or does high milk yield lead to lower somatic cell count? , 2006, Preventive veterinary medicine.

[6]  Joachim Krieter,et al.  Mastitis detection in dairy cows by application of neural networks , 2008 .

[7]  Rik van der Tol,et al.  Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk , 2008 .

[8]  T. Larsen,et al.  A model for detection of individual cow mastitis based on an indicator measured in milk. , 2006, Journal of dairy science.

[9]  T. Larsen.  Determination of lactate dehydrogenase (LDH) activity in milk by a fluorometric assay. , 2005, The Journal of dairy research.

[10]  J. Jensen,et al.  Potential for improving description of bovine udder health status by combined analysis of milk parameters. , 2003, Journal of dairy science.

[11]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[12]  T. Larsen,et al.  L-lactate dehydrogenase and N-acetyl-β-D-glucosaminidase activities in bovine milk as indicators of non-specific mastitis , 2006, Journal of Dairy Research.

[13]  H Hogeveen,et al.  Electrical conductivity of milk: ability to predict mastitis status. , 2004, Journal of dairy science.

[14]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[15]  P R Burton,et al.  The use of Markov chain Monte Carlo for analysis of correlated binary data: patterns of somatic cells in milk and the risk of clinical mastitis in dairy cows. , 2004, Preventive veterinary medicine.

[16]  G. Seber.  Multivariate observations / G.A.F. Seber , 1983 .