Predicting age at first calving of dairy breed calves using whale optimization-based ensemble learning framework

Dairy product requirement and the demand-supply gap of milk in Ethiopia have been increasing at an alarming rate due to various factors such as shortage of animal's feeds, feed staffs, feed costs, and poor genetic merits of the local breeds of the country. This problem can be lessened by selecting best breed and modern animal breeding facilities, which require technologies like big data analysis and machine learning. In this study, a prediction model that can predict age at first calving of weaned calves based on their pre-weaning and weaning parameters, including dam's parity number, season of calving, birth weight, pre-weaning health status, pre-weaning average daily weight gain (ADG), weaning age and weaning weight is developed. Primary data collected by Ardayta Dairy Research Centre; Ethiopia is used for this research. First, different pre-trained models developed using support vector regression (SVR), Linear support vector regression (LSVR) and Nu support vector regression (NuSVR) techniques with their default hyperparameter values in which SVR performed best. Second, a model was developed by tuning hyperparameters of SVR including kernel function, regularization (C-parameter) and gamma parameters, and it resulted in an accuracy of 96.46%. Next, Whale optimization technique is used to select the optimized features of the dataset. Furthermore, an ensemble of SVR, LSVR, NuSVR is designed, and the framework is trained by optimized features of data. The designed model achieved an accuracy of 98.3% superseding the other combinations.