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

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...

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Bibliographic Details
Main Authors: Hamed Ahmadi, Natascha Titze, Katharina Wild, Markus Rodehutscord
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Gas production
Mathematical models
Model selection
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-15101-w
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https://doi.org/10.1038/s41598-025-15101-w

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