Fertility Rates Following Fixed-Time Artificial Insemination in Dairy Heifers in a Practical Progesterone-Based Protocol

ABSTRACT

Background: In bovines, more efficient management practices are important for maximizing profitability. In order to increase the pregnancy rates in artificial insemination (AI) programs, several hormonal protocols were developed to synchronize the follicular wave and the moment of ovulation in beef and dairy cattle. In dairy cattle, detection of estrus can be difficult due to a number of factors including the incidence of silent estrus. Hormonal treatments designed to control both luteal and follicular function has permitting efficient synchronizations of time of ovulation. Thus, the AI can be performed in a large number of animals on a fixed schedule without the need for detection of estrus. Using these management techniques, the fixed-time artificial insemination (TAI) can overcome the problem of accurate estrus detection and help in reducing the incidence of repeat breeding. In addition, with TAI in cattle operations, it is possible to facilitate management practices and commercialization, and to reduce the time and semen wasting with animals inseminated at incorrect times. The investigation of practical and efficient TAI protocols is important for reducing the labor and animal handling of TAI in dairy cattle, as well as for increasing the profitability of the cattle management system. This study was carried out in order to investigate the effectiveness of TAI in dairy heifers treated with a practical progesterone-based protocol. Materials, Methods & Results: This experiment was conducted at the university farm located in southwestern Brazil, during May 2009. Thirty-nine cycling crossbred dairy heifers were employed in this study. All animals received a single intramuscular injection of estradiol benzoate and intravaginal progesterone releasing device in a random stage of the estrous cycle (Day 0). On day 7 the animals were treated with PGF2á analogue and on day 9 the device was removed. [...]