Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models

  • Jared O’Connell
  • Frede Aakmann Tøgersen
  • Nicolas C. Friggens
  • Peter Løvendahl
  • Søren Højsgaard


Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular, the ability to identify oestrus is investigated as this is of great importance to farm management. Progesterone concentration is a more accurate but more expensive method than pedometer counts, and we evaluate the added benefits of a model that includes this variable. The resulting model is biologically sensible, but validation is difficult. We utilize some auxiliary data to demonstrate the model’s performance.

Key Words

Dairy cow EM-algorithm Oestrus detection Online data Streaming data Time series 


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Copyright information

© International Biometric Society 2010

Authors and Affiliations

  • Jared O’Connell
    • 1
  • Frede Aakmann Tøgersen
    • 2
  • Nicolas C. Friggens
    • 3
  • Peter Løvendahl
    • 4
  • Søren Højsgaard
    • 4
  1. 1.Wellcome Trust Centre for Human GeneticsUniversity of OxfordOxfordUK
  2. 2.Modeling, Statistics and Risk AnalysisVestas R&DAlsvejDenmark
  3. 3.INRA UMR 791 Modélisation Systémique Appliquée aux Ruminants (MoSAR)AgroParisTechParis CedexFrance
  4. 4.Department of Genetics and BiotechnologyAarhus UniversityÅrhusDenmark

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