AUTHOR=Roques Simon , Koning Lisanne , Bossers Alex , van Gastelen Sanne , Schokker Dirkjan , Zaccaria Edoardo , Šebek Léon , Kar Soumya K. 

TITLE=Farm conditions shape microbial communities and their association with methane intensity in dairy cattle: insights from the rumen microbiome at the community level

JOURNAL=Frontiers in Microbiomes

VOLUME=Volume 4 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/microbiomes/articles/10.3389/frmbi.2025.1540197

DOI=10.3389/frmbi.2025.1540197

ISSN=2813-4338

ABSTRACT=Rumen microbial communities are known to drive methane (CH4) production, but their dynamics in variable “real-world” farming environments are less understood. This research aims to identify specific microbial taxa linked to CH4 emission in commercial dairy farms by employing 16S rRNA gene sequencing, thereby providing a more ecologically relevant understanding of CH4 production in real-world settings.Rumen fluid samples were collected from 212 cows across seventeen Dutch dairy farms. Methane production was measured from these dairy cows using the GreenFeed system and expressed as CH4 intensity (g fat- and protein-corrected milk yield−1). Rumen microbiota was analyzed using 16S rRNA gene amplicon sequencing. Analysis was performed to assess association between microbial taxa and CH4 intensity, using data from individual cattle across the dairy farm. We observed that diet and lactation stage influenced CH4 intensity, consistent with previous studies. Results showed higher CH4 intensity in cows during late lactation, and feeding type, particularly fresh grass intake, strongly influenced rumen microbiota. However, after classifying low and high CH4 emitting cows, only limited differences in microbiota composition could be measured. Few taxa, like Lachnospiraceae, were common across both groups, while Ruminoccocaceae and Rikenellaceae were more abundant in low emitters, and Oscillospiraceae in high emitters. Methanobrevibacter differentiated CH4 emission groups, but archaeal methanogen abundance may not accurately reflect CH4 variation due to methodological limitations, including reliance on relative abundance, limited taxonomic resolution, and primer bias. Using a bacterial-biased 16S rRNA approach, we observed a limited number of consistent bacterial taxa associated with CH4 intensity highlights the challenges of studying dairy farms under practical conditions, where variability in diet, genetics, and management practices complicates the identification of specific rumen microbes associated with CH4 emission. Even with control over key variables, the inherent variability of on-farm conditions impeded the detection of stable microbial patterns. In conclusion, our study clearly indicates that understanding CH4 emissions from dairy cattle in real-world settings fundamentally requires a broader ecological perspective where rumen microbes are recognized as key determinants influencing microbiota signals within multi-factorial farm settings.