Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms.

backgroundMachine learning systems learn from historical data to forecast future outcomes. In the context of livestock farming, machine learning can be utilized to predict variables such as growth rates, milk production and breeding success by analysing data related to animal health, nutrition and environmental conditions.

objectiveThis study aimed to investigate the performance of different machine learning algorithms in predicting weaning weight based on biometric measurements of Romanov lambs at 30 days of age.

methodsThe biometric traits of the lambs, including body length (BL), chest circumference (CC), chest depth (CD), chest width (CH), withers height (WH), rump height (RH), rump width (RW) and sex were used to construct predictive models. The study employed random forest (RF), classification and regression trees (CART), gradient boosting (GB), eXtreme gradient boosting (XGBoost) and CatBoost algorithms. The data was standardized to eliminate scale differences and divided into training (80%) and test (20%) sets. GridSearchCV was utilized for hyperparameter optimization. The performance of the models was evaluated using various goodness-of-fit metrics, including RMSE, MAE, R

resultsThe gradient boosting and XGBoost models performed the highest R

conclusionThe study suggests that machine learning algorithms, particularly gradient boosting and XGBoost, show promising potential in predicting the weaning weight of lambs. These insights may facilitate more informed decision-making in animal breeding and selection, potentially contributing to enhanced livestock management practices.