Uterine tone influences fertility of Merino ewes following laparoscopic artificial insemination

Abstract

Artificial insemination (AI) plays a critical role in facilitating rapid genetic and production gains within the sheep industry. However, variable rates of AI success remain a concern for the industry and a barrier to adoption. Furthermore, the degree to which female factors influence the success of intrauterine laparoscopic AI rather than natural mating remains unknown. As such, this study investigates the effect of several factors collected during the time of AI, on the success of intrauterine laparoscopic AI. Data was generously donated by artificial breeding companies and stud breeders during routine commercial AI operations. AI data was collected over 3 breeding seasons during commercial AI programs (N = 24 programs) using Merino ewes (N = 24,700). Sire ID (N = 253), time of AI following progesterone removal (approx. 43–59 h post removal), uterine tone and intra-abdominal fat (both scored 1–5) as well as age of the ewe were all recorded at the time of AI. Transcutaneous ultrasound subsequently determined pregnancy rate approximately 55 days post-AI. A multivariate regression analysis was performed and revealed pregnancy success to increase when semen was inseminated into a ewe with a uterine tone score of 4 or 5 (P < 0.001). The remaining factors fell short of significance within the multivariate model. An interclass coefficient variation matrix was also used to determine the proportion of variation contributed to AI success by random factors allocated in the model; site, sire, AI date and breeding season (45.99 %, 29.94 %, 15.15 % and 8.92 %, respectively). These results highlight the influence of uterine tone on ewe fertility following laparoscopic AI, but also that program location and the sire used can further modify this influence on pregnancy rate. These factors must now be considered in combination with semen factors per individual sire used during AI to ascertain the contribution of several factors to the success of laparoscopic AI in Australia.