Development of predictive models for boar semen quality

In this study a prognosis model is developed that predicts sperm quality characteristics based on external factors such as barn climate conditions, seasonality, semen collection frequency, age and breed of artificial insemination (AI) boars. For this a k-fold cross validation framework is used to test the prediction accuracy of a wide range of regression models that are based on different functional forms (linear, log-linear) and estimation techniques (ordinary least squares, seemingly unrelated regression, two-stage least squares estimation and three-stage least squares estimation). The dataset includes 241 boars from three barns within one boar stud located in Southern Germany, consisting of 7455 ejaculates collected during one year. The winner model predicts sperm motility with little error (Mean Absolute Percentage Error (MAPE): 4.35%), but is of limited use to predict sperm output (MAPE: 23.92%) and especially morphologically abnormal spermatozoa (MAPE: 44.67%). An estimation of marginal effects shows, that once confounding variables are controlled for, the considered barn climate variables do not have a measurable effect on sperm quality. Other factors have a more significant effect on sperm quality, like morphology-motility linkages, sperm concentration, interval between semen collections and to a lesser extent age and breed of the AI boar.