Rumen Fermentation Parameters Prediction Model for Dairy Cows Using a Stacking Ensemble Learning Method

Abstract

Volatile fatty acids (VFAs) and methane are the main products of rumen fermentation. Quantitative studies of rumen fermentation parameters can be performed using in vitro techniques and machine learning methods. The currently proposed models suffer from poor generalization ability due to the small number of samples. In this study, a prediction model for rumen fermentation parameters (methane, acetic acid (AA), and propionic acid (PA)) of dairy cows is established using the stacking ensemble learning method and in vitro techniques. Four factors related to the nutrient level of total mixed rations (TMRs) are selected as inputs to the model: neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM). The comparison of the prediction results of the stacking model and base learners shows that the stacking ensemble learning method has better prediction results for rumen methane (coefficient of determination (R2) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA (R2 = 0.888, RMSE = 1.975 mmol/L) and PA (R2 = 0.924, RMSE = 0.74 mmol/L). And the stacking model simulates the variation of methane and VFAs in relation to the dietary fiber content. To demonstrate the robustness of the model in the case of small samples, an independent validation experiment was conducted. The stacking model successfully simulated the transition of rumen fermentation type and the change of methane content under different concentrate-to-forage (C:F) ratios of TMR. These results suggest that the rumen fermentation parameter prediction model can be used as a decision-making basis for the optimization of dairy cow diet compositions, rapid screening of methane emission reduction, feed beneficial to dairy cow health, and improvement of feed utilization.

Development of an Alternative In Vitro Rumen Fermentation Prediction Model

“…Specifically, bovine methane emissions (excluding buffalo) represent about 75% of the total emissions from ruminants [3]. Methane released by dairy cows represents a significant contribution to greenhouse gas emissions [4]. Therefore, whether from the perspective of environmental protection or animal production, it is extremely necessary to reduce methane emissions from ruminants [5].…”

Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning

“…Shen et al [15] proposed a new method based on stochastic programming to realize a quality-related monitoring scheme for batch processing of multiple output modes through ensemble learning. Wang et al [16] established a prediction model for rumen fermentation parameters in dairy cows by a stacked ensemble learning method and in vitro technique. The comparison results show that the stacking ensemble learning method had better prediction results.…”