A Novel Mixed Effects Random Forest Approach for Predicting Dairy Cattle Methane Emissions

Methane (CH4) emissions produced by dairy cattle (DC) are a key contributor to global warming. To assess the effectiveness of strategies designed to mitigate CH4 emissions, complex and expensive recording equipment is required. Therefore, the use of predictive models based on animal information provides a more accessible alternative. Traditionally, Statistical (SA) methods have been employed in the prediction of DC CH4 emissions. However due to the smart farming revolution, the scale and variety of complex animal information now available for the prediction of DC CH4 emissions has grown exponentially, and within them are likely to exist non-linear relationships which these traditional SA models may struggle to capture. Therefore, this research aims to explore if Machine Learning (ML) models are a viable alternative for the prediction of DC CH4 emissions, as they can handle and extract these inevitable non-linear relationships present within today's large, heterogeneous datasets. In this research, we compared a traditional SA method, a Linear Mixed Effects (ME) model, with an original ML method, a Random Forest (RF) model, as well as a novel SA/ML hybrid method, a Mixed Effects Random Forest (MERF) model, in the prediction of CH4 emissions (CH4 g/d) produced by DC across 32 experiments. The ML RF model was able to challenge the traditional SA ME model in the prediction of DC CH4 emissions, achieving a Root Mean Square Prediction Error (RMSPE) and Concordance Correlation Coefficient (CCC) of 52.73 CH4 g/d and 0.70 respectively, compared to a ME model’s 53.90 CH4 g/d and 0.71. When both the ME and RF models were combined within the novel SA/ML hybrid MERF model, a lower RMSPE and higher CCC were achieved than by each of its composite parts in isolation, 51.87 CH4 g/d and 0.73 respectively. These results demonstrate the potential of ML in the prediction of DC CH4 emissions, particularly when hybridised alongside traditional SA methods.