Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models
- 300 Downloads
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 WordsDairy cow EM-algorithm Oestrus detection Online data Streaming data Time series
Unable to display preview. Download preview PDF.
- Ferguson, J. (1980), Hidden Markov Analysis: An Introduction, Hidden Markov Models for Speech. Google Scholar
- Friggens, N. C., and Løvendahl, P. (2008), “The Potential of On-Farm Fertility Profiles: In-Line Progesterone and Activity Measurements,” in Fertility in Dairy Cows: Bridging the Gaps, British Society of Animal Science, eds. N. C. Friggens, M. D. Royal, R. Smith, Cambridge University Press, Cambridge, pp. 72–78. Google Scholar
- Holt, C. (1957), “Forecasting Trends and Seasonals by Exponentially Weighted Moving Averages,” ONR Memorandum, 52. Google Scholar
- Jonsson, R., Bjorgvinsson, T., Blanke, M., Poulsen, N., Højsgaard, S., and Munksgaard, L. (2008), “Oestrus Detection in Dairy Cows Using Likelihood Ratio Tests,” The International Federation of Automatic Control, 658–663. Google Scholar
- Meyer, D. (2002), “Naive Time Series Forecasting Methods,” R News, 2, 7–10. Google Scholar
- O’Connell, J., and Højsgaard, S. (2009a, submitted), “Hidden Semi Markov Models for Multiple Observation Sequences—The MHSMM Package for R,” Journal of Statistical Software. Google Scholar
- O’Connell, J., and Højsgaard, S. (2009b), “MHSMM: Parameter Estimation and Prediction for Hidden Markov and Semi-Markov Models for Data with Multiple Observation Sequences,” http://cran.r-project.org/web/packages/mhsmm/index.html. R package version 0.3.0.
- R Development Core Team (2008), R: A Language and Environment for Statistical Computing, Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0. Google Scholar