Nonlinear regression modeling of ruminal fermentation dynamics in buffered diets using in vitro gas production

Understanding ruminal fermentation dynamics is essential for improving feed efficiency in ruminant nutrition. This study evaluated several nonlinear regression models to describe in vitro gas production kinetics in diets with different forage-to-concentrate ratios and buffer inclusion. Fermentation experiments were conducted using total mixed rations containing various concentrate levels, both with and without dietary buffers. Cumulative gas production data were fitted to exponential, logistic, Gompertz, and Michaelis-Menten models. Model performance was assessed using statistical goodness-of-fit criteria and the biological relevance of the estimated parameters. The results showed that buffer supplementation enhanced fermentation kinetics by stabilizing pH, increasing total gas production, and modifying volatile fatty acid profiles. Although no single model consistently outperformed others, model suitability depended on the specific diet-buffer combination. Sigmoidal models generally provided better fits for buffered diets, whereas exponential models were more accurate under high-concentrate, unbuffered conditions. These findings emphasize the importance of selecting context-specific models for analyzing ruminal fermentation data. The integration of in vitro gas production and nonlinear modeling offers a reliable approach to evaluate dietary strategies that improve nutrient utilization and feed efficiency in ruminant systems.