Milk progesterone offers an opportunity to objectively study fertility in dairy cows, in contrast to traditional measures of dairy cow fertility, which in general are highly influenced by on-farm management decisions. The aim of this thesis was to study how milk progesterone could be used as a genetic and management tool to improve fertility in dairy cows. Progesterone-based measures were influenced by different systematic factors, e.g. breed, parity, season, housing and lameness, studied in a dataset from a Swedish experimental herd. The repeatabilities were higher for progesterone-based measures compared with traditional measures of fertility based on insemination data. If a cow had an atypical progesterone profile in one lactation, the risk of an atypical profile in the next lactation was increased. Genetic parameters for progesterone measures based on different milk sampling intervals were estimated in a British dataset. Heritability estimates were moderate, but decreased with increased sampling intervals. It was shown that progesterone analysis of monthly milk samples, resembling milk sampling as in the current Swedish milk recording system, could be used to increase the accuracy of genetic evaluation for an earlier start of cyclical ovarian activity after calving. Inclusion of monthly milk sampling for progesterone analysis in predictive models could also be used to identify cows with delayed ovarian cyclicity with a high accuracy already two months after calving. This enables an earlier treatment of ovarian dysfunction and therefore, probably, a shorter calving interval. In conclusion, this thesis shows that milk progesterone may be used for improved management and genetic evaluation of dairy cow fertility
