Development of an Alternative In Vitro Rumen Fermentation Prediction Model

文献类型： 外文期刊

第一作者： Wang, Xinjie

作者： Wang, Xinjie;Zhou, Jianzhao;Jiang, Runjie;Wang, Yuxuan;Wu, Renbiao;Tian, Jiaxu;Wei, Xiaoli;Shen, Weizheng;Zhang, Yonggen;A, Xiaohui;Du, Haitao

作者机构：

关键词： machine learning; in vitro rumen model; rumen methane; rumen acetic acid; prediction model

期刊名称：ANIMALS （ 影响因子：3.0； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 2 期

页码：

收录情况： SCI

摘要： Simple Summary Our objective is to establish an in vitro rumen fermentation model that can dynamically simulate the fermentation process of various total mixed ration (TMR) diets in the rumen of dairy cows, enabling a quantitative investigation of rumen methane and rumen acetic acid concentrations. The models were assessed for their prediction accuracy and precision, while the independent verification experiments confirmed the models' generalization ability across different total mixed ration (TMR) ratios (C:F). These results show that in vitro rumen models constructed by machine learning methods can be used as a tool to quantify rumen fermentation parameters (methane and acetic acid) and guide the dietary structure optimization of dairy cows.Abstract The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 >= 0.83). Additionally, the root mean square errors (RMSEs) were <= 1.85 mL/g in the rumen methane model and <= 2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency.

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