Screening and Functional Prediction of Rumen Microbiota Associated with Methane Emissions in Dairy Cows

Area of Science:

• Agricultural Science
• Microbiology
• Environmental Science

Background:

• Agricultural activities contribute significantly to global greenhouse gas emissions.
• Livestock, particularly dairy cattle, are major sources of methane emissions.
• Understanding rumen microbiota is crucial for mitigating methane production in dairy cows.

Purpose of the Study:

• To investigate the relationship between rumen microbiota composition and methane emissions in dairy cattle.
• To identify specific bacterial taxa and functional pathways associated with high methane emission (HME) and low methane emission (LME) in Chinese Holstein cows.
• To provide insights for developing strategies to reduce methane emissions in the dairy industry.

Main Methods:

• Methane emissions from 968 lactating Holstein cows were measured using a laser methane detector.
• 107 cows were selected into high (HME) and low methane-emitting (LME) groups for detailed analysis.
• Rumen microbiota composition and functional differences between HME and LME groups were analyzed using diversity indices and beta diversity analysis.

Main Results:

• Significant differences in Simpson and Pielou indices were observed between HME and LME groups.
• Beta diversity analysis indicated distinct microbial community structures between the two groups.
• Abundance of *Bacteroidales* and *Prevotellaceae* was significantly higher in the HME group, with increased activity in biosynthesis and carbohydrate metabolism pathways.

Conclusions:

• Distinct differences exist in rumen bacterial communities between high and low methane-emitting Chinese Holstein cattle.
• Specific bacteria (*Bacteroidales*, *Prevotellaceae*) and their metabolic functions are associated with higher methane production.
• These findings offer novel insights for targeted strategies to reduce methane emissions and promote sustainable dairy farming.