A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep.

Abstract

The causes of variation in the success of laparoscopic artificial insemination (AI) in sheep are not well understood. As such, this study incorporated the contributions of multiple male and female factors relevant to the success of AI into a comprehensive prediction model for pregnancy success. Data from Merino ewes (N = 30 254) including age, uterine tone (1; pale/flaccid-5; turgid/pink), intra-abdominal fat (1; little to no fat present-5; high fat), time of insemination and sire used, were recorded during AI. A subset of semen per sire (N = 388) was thawed and assessed for volume, subjective motility, sperm concentration, and morphology. Sperm motility (CASA), viability and acrosome integrity (FITC-PNA/PI), membrane fluidity (M540/Yo-Pro), mitochondrial superoxide production (Mitosox Red/Sytox Green), lipid peroxidation (Bodipy C11), level of intracellular reactive oxygen species (H2DCFDA) and DNA fragmentation (Acridine Orange) were also assessed 0, 3 and 6h post-thaw. Logistic binomial regression revealed sperm concentration (P < 0.001), CASA parameters at 0h (PCA3; P = 0.03), viable acrosome intact sperm at 6h (P = 0.02), abnormal morphology (P < 0.001), uterine tone (P < 0.001) and intra-abdominal fat (P = 0.03) of ewes influenced likelihood of pregnancy. Results generated will help standardise the pre-screening and selection of semen and ewes prior to artificial breeding programs, reducing variation in the success of sheep AI.