Can machine learning algorithms perform better than multiple linear regression in predicting nitrogen excretion from lactating dairy cows

This study aims to compare the performance of multiple linear regression and machine learning algorithms for predicting manure nitrogen excretion in lactating dairy cows, and to develop new machine learning prediction models for MN excretion. Dataset used were collated from 43 total diet digestibility studies with 951 lactating dairy cows. Prediction models for MN were developed and evaluated using MLR technique and three machine learning algorithms, artificial neural networks, random forest regression and support vector regression. The ANN model produced a lower RMSE and a higher CCC, compared to the MLR, RFR and SVR model, in the tenfold cross validation. Meanwhile, a hybrid knowledge-based and data-driven approach was developed and implemented to selecting features in this study. Results showed that the performance of ANN models were greatly improved by the turning process of selection of features and learning algorithms. The proposed new ANN models for prediction of MN were developed using nitrogen intake as the primary predictor. Alternative models were also developed based on live weight and milk yield for use in the condition where nitrogen intake data are not available (e.g., in some commercial farms). These new models provide benchmark information for prediction and mitigation of nitrogen excretion under typical dairy production conditions managed within grassland-based dairy systems.

Automated summary

This study compares the performance of multiple linear regression and machine learning algorithms for predicting manure nitrogen excretion in lactating dairy cows and develops new machine learning prediction models. The results show that the artificial neural network model outperforms other models in terms of lower root mean square error and higher correlation coefficient. The performance of the artificial neural network models is greatly improved through the feature selection and learning algorithm tuning process.