A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.

Enteric methane (CH 4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH 4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Ruminants have a unique ability to derive energy from the degradation of plant polysaccharides through the activity of the rumen microbiota. Although this process is well studied in vitro, knowledge gaps remain regarding the relative contribution of the microbiota members and enzymes in vivo. The present study used RNA-sequencing to reveal both the expression of genes encoding carbohydrate-active enzymes (CAZymes) by the rumen microbiota of a lactating dairy cow and the microorganisms forming the fiber-degrading community. Functional analysis identified 12,237 CAZymes, accounting for 1% of the transcripts. The CAZyme profile was dominated by families GH94 (cellobiose-phosphorylase), GH13 (amylase), GH43 and GH10 (hemicellulases), GH9 and GH48 (cellulases), PL11 (pectinase) as well as GH2 and GH3 (oligosaccharidases). Our data support the pivotal role of the most characterized fibrolytic bacteria (Prevotella, Ruminocccus and Fibrobacter), and highlight a substantial, although most probably underestimated, contribution of fungi and ciliate protozoa to polysaccharide degradation. Particularly these results may motivate further exploration of the role and the functions of protozoa in the rumen. Moreover, an important part of the fibrolytic bacterial community remains to be characterized since one third of the CAZyme transcripts originated from distantly related strains. These findings are used to highlight limitations of current metatranscriptomics approaches to understand the functional rumen microbial community and opportunities to circumvent them.

The potential of dietary supplements of 2 live yeast strains (Saccharomyces cerevisiae) or camelina oil to lower ruminal methane (CH4) and carbon dioxide (CO2) production and the associated effects on animal performance, rumen fermentation, rumen microbial populations, nutrient metabolism, and milk fatty acid (FA) composition of cows fed grass silage-based diets were examined. Four Finnish Ayrshire cows (53±7 d in milk) fitted with rumen cannula were used in a 4×4 Latin square with four 42-d periods. Cows received a basal total mixed ration (control treatment) with a 50:50 forage-to-concentrate ratio [on a dry matter (DM) basis] containing grass silage, the same basal total mixed ration supplemented with 1 of 2 live yeasts, A or B, administered directly in the rumen at 10(10) cfu/d (treatments A and B), or supplements of 60g of camelina oil/kg of diet DM that replaced concentrate ingredients in the basal total mixed ration (treatment CO). Relative to the control, treatments A and B had no effects on DM intake, rumen fermentation, ruminal gas production, or apparent total-tract nutrient digestibility. In contrast, treatment CO lowered DM intake and ruminal CH4 and CO2 production, responses associated with numerical nonsignificant decreases in total-tract organic matter digestibility, but no alterations in rumen fermentation characteristics or changes in the total numbers of rumen bacteria, methanogens, protozoa, and fungi. Compared with the control, treatment CO decreased the yields of milk, milk fat, lactose, and protein. Relative to treatment B, treatment CO improved nitrogen utilization due to a lower crude protein intake. Treatment A had no influence on milk FA composition, whereas treatment B increased cis-9 10:1 and decreased 11-cyclohexyl 11:0 and 24:0 concentrations. Treatment CO decreased milk fat 8:0 to 16:0 and total saturated FA, and increased 18:0, 18:1, 18:2, conjugated linoleic acid, 18:3n-3, and trans FA concentrations. Decreases in ruminal CH4 production to treatment CO were related, at least in part to lowered DM intake, whereas treatments had no effect on ruminal CH4 emission intensity (g/kg of digestible organic matter intake or milk yield). Results indicated that live yeasts A and B had no influence on animal performance, ruminal gas production, rumen fermentation, or nutrient utilization in cows fed grass silage-based diets. Dietary supplements of camelina oil decreased ruminal CH4 and CO2 production, but also lowered the yields of milk and milk constituents due to an adverse effect on intake.

Microbial community analysis was carried out on ruminal digesta obtained directly via rumen fistula and buccal fluid, regurgitated digesta (bolus) and faeces of dairy cattle to assess if non-invasive samples could be used as proxies for ruminal digesta. Samples were collected from five cows receiving grass silage based diets containing no additional lipid or four different lipid supplements in a 5 x 5 Latin square design. Extracted DNA was analysed by qPCR and by sequencing 16S and 18S rRNA genes or the fungal ITS1 amplicons. Faeces contained few protozoa, and bacterial, fungal and archaeal communities were substantially different to ruminal digesta. Buccal and bolus samples gave much more similar profiles to ruminal digesta, although fewer archaea were detected in buccal and bolus samples. Bolus samples overall were most similar to ruminal samples. The differences between both buccal and bolus samples and ruminal digesta were consistent across all treatments. It can be concluded that either proxy sample type could be used as a predictor of the rumen microbial community, thereby enabling more convenient large-scale animal sampling for phenotyping and possible use in future animal breeding programs aimed at selecting cattle with a lower environmental footprint.