Development of a model to predict forage intake by grazing cattle

Area of Science:

• Animal Science
• Nutritional Physiology
• Mathematical Modeling

Background:

• Accurate prediction of dry matter intake (DMI) is crucial for optimizing livestock nutrition and performance.
• Existing models often lack the mechanistic detail to fully capture the complex interactions within the rumen influencing feed intake.

Purpose of the Study:

• To develop and evaluate a dynamic mathematical model for predicting daily DMI in cattle.
• To incorporate the effects of energy supplementation and various forage types into the intake prediction.

Main Methods:

• Adapted a dynamic rumen fermentation model for sheep and applied genetic size scaling rules for beef cattle.
• Integrated rumen fill dynamics, nutrient fraction partitioning (particulate vs. fluid passage), and differential equations for nutrient changes.
• Parameterized and validated the model using 42 data points from diverse forage sources (perennial ryegrass, wheat pasture, range grasses).

Main Results:

• The model demonstrated sensitivity to parameters like DMI-passage rate coefficients and fiber degradation rates.
• Relative insensitivity was noted for starch/protein utilization and microbial growth constants.
• The model successfully predicted feed intake based on nutrient contributions to ruminal fill.

Conclusions:

• Mechanistic models integrating ruminal fill and nutrient dynamics offer a robust approach for predicting grazing cattle feed intake.
• The developed model provides a valuable tool for understanding and managing energy supplementation strategies in cattle.