Stacking Ensemble Machine Learning Modelling for Milk Yield Prediction Based on Biological Characteristics and Feeding Strategies

Abstract

Knowing expected milk yield can help dairy farmers in better decision-making and management. The objective of this study was to build and compare predictive models to forecast daily milk yield over a long duration. A machine-learning pipeline was provided and five baseline models as well as a novel stacking model were developed for the prediction of milk yield on the CowNflow dataset using 414 Holstein cattle records collected from 1983 to 2019. Four different feature selection methods were performed to evaluate the essential biological characteristics and feeding-related features which affect milk yield. The results showed that the overall performance of predictive models improved after proper feature selection, with an R2 value increased to 0.811, and a root mean squared error (RMSE) decreased to 3.627. The stacking model achieved the best performance with an R2 value of 0.85, a mean absolute error (MAE) of 2.537 and an RMSE of 3.236. This research provides benchmark information for the prediction of milk yield on the CowNflow dataset and identifies useful factors such as dry matter (DM) intake and lactation month in long-term milk yield prediction.