Bayesian updating in hierarchic Markov processes applied to the animal replacement problem

The observed level of milk yield of a dairy cow or the litter size of a sow is only partially the result of a permanent characteristic of the animal; temporary effects are also involved. Thus, we face a problem concerning the proper definition and measurement of the traits in order to give the best possible prediction of the future revenues from an animal considered for replacement. A trait model describing the underlying effects is built into a model combining a Bayesian approach with a hierarchic Markov process in order to be able to calculate optimal replacement policies under various conditions.