Variation in animal performance explained by the rumen microbiome or by diet composition

Area of Science:

• Animal Science
• Microbiology
• Ruminant Nutrition

Background:

• Diet composition is traditionally used to predict cattle performance.
• The rumen microbiome's role as a predictive tool for cattle performance is increasingly recognized.
• Understanding the interplay between diet and microbiome is crucial for optimizing animal production.

Purpose of the Study:

• To compare the predictive accuracy of the rumen microbiome versus diet composition for beef and dairy cattle performance.
• To develop and evaluate models using diet, microbial, and experimental variables for predicting milk yield, average daily gain, dry matter intake, and feed efficiency.
• To investigate the independent effects of diet and microbiome on cattle performance metrics.

Main Methods:

• A meta-analysis was conducted using data from 51 peer-reviewed publications.
• Linear mixed-effects regression models were developed to predict performance variables (milk yield, ADG, DMI, FE).
• Models were compared using concordance correlation coefficient (uCCC), variance estimates, and Akaike information criterion.

Main Results:

• Diet composition alone most accurately predicted average daily gain (ADG) (uCCC = 0.92).
• The rumen microbiome combined with experimental variables (M+E) outperformed diet alone in predicting dry matter intake (DMI) and dairy feed efficiency (FE).
• Milk yield was best predicted by a combination of diet, microbiome, and experimental variables (D+M+E) (uCCC = 0.71).

Conclusions:

• Both diet and rumen microbiome composition independently influence cattle performance.
• The predictive power of diet and microbiome varies depending on the specific performance metric.
• Further research is needed to elucidate the mechanisms behind the independent effects of diet and microbiome on cattle performance.