Systematic selection of best performing mathematical models for in vitro gas production using machine learning across diverse feeds

Abstract

In vitro gas production (GP) is commonly used to evaluate ruminant feed, yet its accurate interpretation requires robust mathematical modeling. This study systematically explores a wide array of nonlinear models to explain GP dynamics across various feed types, addressing the question: how can efficient and versatile models that accurately represent GP profiles be identified? We hypothesized that distinct feed types exhibit unique GP characteristics, effectively captured by specific models, and that statistical and machine learning methodologies can streamline model selection. Utilizing a comprehensive dataset derived from 849 unique GP profiles across concentrate feed categories—including cereal and leguminous grains and processed protein feeds—21 candidate models were rigorously evaluated based on their goodness-of-fit metrics, with a particular emphasis on Bayesian Information Criterion (BIC) for model selection. A group of three models—namely Burr XII, Inverse paralogistic, and Log-logistic—consistently emerged as top performers, demonstrating high generalizability and predictive power across feed types. Notably, our analysis indicated that model type significantly influenced GP predictions, surpassing the impact of feed type characteristics. This research establishes a decision-making framework for model selection and sets the stage for further investigations linking in vitro GP parameters to in vivo digestibility, ultimately enhancing ruminant nutrition strategies.