The predictive value of porcine seminal parameters on fertility outcome under commercial conditions

The aim of this study was to address the question of whether differences in farrowing rate and litter size after the use of different ejaculates could be predicted using the standard semen parameters under commercial conditions. In this study, a total of 1818 sows were used to evaluate the fertility predictive value of different sperm parameters. Logistic regression analysis (univariate and multivariate) was used to correlate the dichotomous farrowing rate data to the sperm parameters. Linear regression was also used to determine the relationship between litter size and semen parameters (Pearson correlation and multiple regression). Receiver-operating curves (ROC) were used to determine the overall performance characteristics of each semen variable in the logistic regression model. Semen analysis, under commercial conditions, allows to identify ejaculates with very low fertility potential but the pre-selection of the samples, the high number of sperm per doses and the high quality of the semen used in artificial insemination (AI) programmes reduces the variability. Therefore, it is unlikely to detect fertility differences associated with seminal parameters.