The use of machine learning methods to predict sperm quality in Holstein bulls.

The aim of this study was to develop prediction models for total sperm motility, morphological abnormalities and sperm output based on 1,551 ejaculate records of 58 Holstein bulls. The data was collected from September 2019 to November 2020 in a single artificial insemination (AI) center located in Eastern Germany. Factors considered for the prediction models include barn climate conditions, semen collector, number of false mounts, libido, semen collection frequency, breed and age (10-74 months). In this study, the prediction models Lasso, Group Lasso and Gradient Boosting were evaluated. The best model for each sperm quality parameter was chosen using cross validation. The models were estimated with five algorithms for sperm motility and sperm morphology and three algorithms for the number of total sperm per ejaculate (sperm output). For sperm motility and morphology a binary classification algorithm was applied, reaching an accuracy of over 80% for all models. For sperm output, no such classification was used and the only variable selected by all three algorithms was age. Furthermore, for sperm morphology, climate variables were frequently selected. Additionally, network diagrams from Group Lasso show the interdependencies between the major variable groups influencing sperm motility and morphology. In conclusion, the implementation of such prediction tools could help AI centers to optimize management factors and stabilize bull semen production in the future.