ABSTRACT Achieving pregnancy in high-producing dairy cows in a timely and cost-effective manner is one of today's greatest management challenges. Fertility is highly influenced by management and environmental factors, but significant genetic differences exist in both male (service sire) and female (daughter) fertility. The first challenge in improving fertility through genetic selection is data collection, because an inverse relationship exists between quantity and quality. Rough measures, such as calving interval, are available for all multiparous milk-recorded cows. Insemination data (and, hence, nonreturn rates) are available for perhaps half of the population, while pregnancy examination data are available for roughly a quarter of the population. Detailed data regarding technician, type of breeding (standing or synchronized), and so on are available from selected herds, but milk progesterone data are limited to experimental studies. Statistical modeling is also a challenge, because linear models are inappropriate for binary traits, and data for continuous traits are badly skewed and frequently censored. Threshold models can be used for binary data, but survival (failure time) models may more effectively fit the complex nature of fertility traits and the genetic and environmental factors that influence them. This paper describes 2 potential approaches to genetic analysis of fertility traits based on detailed reproductive data and advanced statistical methodology. The first is a large-scale threshold model analysis that uses data regarding veterinarian-confirmed conception rates, while the second is an in-depth analysis of the management and genetic factors that influence fertility in a failure time model that properly accounts for censoring among cows that were culled or failed to conceive. The former approach can be used for large-scale analyses of service sire fertility, while the latter can be used for evaluation of reproductive management, as well as genetic improvement of daughter fertility.
(Key words: reproduction, fertility, genetic selection, dairy cattle)
2004 J. Dairy Sci. 87:(E. Suppl.):E86-E92
© 2004, by American Dairy Science Association. All rights reserved.
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