Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring

The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair, Alpine × Hair F1 (AHF1), and Saanen × Hair F1 (SHF1) hybrid offspring at yearling age by employing early body measurement records from birth till 9th month combined with meteorological data, in an extensive natural pasture-based system. The study also included other factors such as sex, farm, doe and buck IDs, birth type, gestation length, age of the doe at birth etc. For this purpose, seven different machine learning algorithms—linear regression, artificial neural network (ANN), support vector machines (SVM), decision tree, random forest, extra gradient boosting (XGB) and ExtraTree – were applied to the data coming from 1530 goat offspring in Türkiye. Early predictions of growth and morphological traits at yearling age; such as live weight (LW), body length (BL), wither height (WH), rump height (RH), rump width (RW), leg circumference (LC), shinbone girth (SG), chest width (CW), chest girth (CG) and chest depth (CD) were performed by using birth date measurements only, up to month-3, month-6 and month-9 records. Satisfactory predictive performances were achieved once the records after 6th month were used. In extensive natural pasture-based systems, this approach may serve as an effective indirect selection method for breeders. Using month-9 records, the predictions were improved, where LW and BL were found with the highest performance in terms of coefficient of determination (R2 score of 0.81 ± 0.00) by ExtraTree. As one of the rarely applied machine learning models in animal studies, we have shown the capacity of this algorithm. Overall, the current study offers utilization of the meteorological data combined with animal records by machine learning models as an alternative decision-making tool for goat farming.