Evaluation of bull fertility in dairy and beef cattle using cow field data

Abstract

A successful outcome to a given service is a combination of both male and female fertility. Despite this, most national evaluations for fertility are generally confined to female fertility with evaluations for male fertility commonly undertaken by individual breeding organisations and generally not made public. The objective of this study was to define a pertinent male fertility trait for seasonal calving production systems, and to develop a multiple regression mixed model that may be used to evaluate male fertility at a national level. The data included in the study after editing consisted of 361,412 artificial inseminations from 206,683 cow-lactations (134,911 cows) in 2,843 commercial dairy and beef herds. Fixed effects associated with whether a successful pregnancy ensued (pregnant = 1) or not (pregnant = 0) from a given service were year by month of service, day of the week, days since calving, cow parity, level of calving difficulty experienced, whether or not the previous calving was associated with perinatal mortality, and age of the service bull at the date of insemination. Non-additive genetic effects such as heterosis and recombination loss as well as inbreeding level of the service bull, dam or mating were not associated with a successful pregnancy; there was no difference in pregnancy rate between fresh or frozen semen. Random effects included in the model were the additive genetic effect of the cow, as well as a within lactation and across lactation permanent environmental effect of the cow; pedigree group effects based on cow breed were also included via the relationship matrix. Temporal differences in the AI technician and service bull were also included as random effects. A difference in five percentage units in male fertility was evident between the average effects of different dairy and beef breeds. The correlation between raw pregnancy rates for bulls with more than 100 services (n = 431) and service bull solutions from the mixed model analysis was 0.66. The correlation between the raw pregnancy rates of 288 technicians with more than 100 services and their respective solutions from the mixed model was 0.35. These low to moderate correlations suggest considerable re-ranking among both service bulls and technicians and suggest possibly a benefit of using a statistical model to better estimate the performance of both service bulls and technicians.