Prediction of lactation milk yields using machine learning based on milk production data and reproduction performance indicators

The purpose of this research is prediction of total lactation milk yields with the application of machine learning techniques on the basis of data set containing production and reproductive performance indicators for Brown Swiss cows reared on a commercial dairy farm. The dataset includes variables age, number of lactations, number of days in milk (DIM), peak milk yield, 7-day average milk yield (DMY7), dry period, number of inseminations and calving interval, as well as the reproductive status as a categorical variable. Total lactation milk yield is used as a target variable. Performance of various machine learning algorithms was compared with 5-fold cross-validation and the separate test set using R², RMSE, MAE and MAPE criteria. Test results showed that the best prediction performance was provided by Extra Trees algorithm (R² = 0.924, RMSE = 510.27 kg, MAPE = 7.63%). The results showed that ensemble-based tree models allow to better capture non-linear dependencies between milk yield and production/reproductive performance indicators. The interpretation of the model with SHAP analysis showed that day of milking and short-term milk production indicators have the highest importance for prediction. The results suggest that machine learning-based approaches may serve as a reliable tool for decision support systems in milk yield prediction.