Development of Volatile Fatty Acid and Methane Production Prediction Model Using Ruminant Nutrition Comparison of Algorithms

Abstract

(1) Background: This study explores the correlation between volatile fatty acid (VFA) concentrations and methanogenesis in ruminants, focusing on how the nutritional composition of their diets affects these processes. (2) Methods: We developed predictive models using multiple linear regression, artificial neural networks, and k-nearest neighbor algorithms. The models are based on data extracted from 31 research papers and 16 ruminal in vitro fermentation tests to predict VFA concentrations from nutrient intake. Methane production estimates were derived by converting and clustering these predicted VFA values into molar ratios. (3) Results: This study found that acetate concentrations correlate significantly with neutral detergent fiber intake. Conversely, propionate and butyrate concentrations are highly dependent on dry matter intake. There was a notable correlation between methane production and the concentrations of acetate and butyrate. Increases in neutral detergent fiber intake were associated with higher levels of acetate, butyrate, and methane production. Among the three methods, the k-nearest neighbor algorithm performed best in terms of statistical fitting. (4) Conclusions: It is vital to determine the optimal intake levels of neutral detergent fiber to minimize methane emissions and reduce energy loss in ruminants. The predictive accuracy of VFA and methane models can be enhanced through experimental data collected from diverse environmental conditions, which will aid in determining optimal VFA and methane levels.