Development of an Alternative In Vitro Rumen Fermentation Prediction Model.

Area of Science:

• Animal Science
• Agricultural Engineering
• Computational Biology

Background:

• Rumen fermentation is crucial for ruminant digestion and methane production.
• Accurate simulation of in vitro fermentation is needed to optimize animal nutrition and reduce environmental impact.
• Dietary nutrient composition significantly influences rumen fermentation parameters.

Purpose of the Study:

• To develop and validate machine learning models for simulating in vitro rumen fermentation.
• To quantify rumen methane and acetic acid production using different total mixed rations.
• To establish an alternative approach for predicting rumen fermentation parameters.

Main Methods:

• Dietary nutrient compositions (NDF, ADF, CP, DM) were used as input parameters.
• Three prediction models were established: Artificial Neural Network (ANN), Genetic Algorithm-BP (GA-BP), and Support Vector Machine (SVM).
• Model performance was evaluated using R-squared (R²) and Root Mean Square Error (RMSE), with independent verification.

Main Results:

• All three models demonstrated similar simulation accuracy, aligning with measured data (R² ≥ 0.83).
• Rumen methane models achieved RMSE ≤ 1.85 mL/g, and acetic acid models achieved RMSE ≤ 2.248 mmol/L.
• Models showed strong generalization capabilities in an independent verification experiment.

Conclusions:

• Machine learning-based in vitro rumen models provide a valuable tool for quantifying fermentation parameters.
• These models can guide dietary optimization for dairy cows and enhance feeding efficiency.
• The models facilitate rapid screening of feed options for methane reduction.