Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

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.

Ruminant production systems are important contributors to anthropogenic methane (CH) emissions, but there are large uncertainties in national and global livestock CH inventories. Sources of uncertainty in enteric CH emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH emission factors. There is also significant uncertainty associated with enteric CH measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

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The objectives of the present study were to compare the sulfur hexafluoride (SF₆) and respiration chamber techniques for measuring methane (CH₄) emissions from dairy cows and to determine the proportion of CH₄ that is released through the rectum. Data used were derived from 20 early lactation dairy cows in a 2 × 2 factorial design study for 4 periods with 6 wk/period. The 4 treatment diets consisted of grass silage and 2 levels of concentrate (30 and 60% dry matter basis), with or without yeast supplement. At the end of each period, CH₄ emissions were measured simultaneously using the SF₆ and respiration chamber techniques when cows were housed in chambers. The SF₆ technique was also used when cows were housed in digestibility units (barn location) before and after respiratory chamber measurements (chamber location). The simultaneous measurements in chamber location revealed that CH₄ emission estimates by the SF₆ technique were similar to those by the respiration chamber technique in the first 3 periods, although the SF₆ estimates were significantly higher in period 4. The regression of all data from the 4 periods demonstrated a linear relationship between the SF₆ and respiration chamber measurements for total CH₄ emissions (g/d, R² = 0.69) and for CH₄ emissions per unit of milk yield (g/kg, R² = 0.88), and a quadratic relationship for CH₄ emissions per unit of dry matter intake (g/kg, R² = 0.64). The CH₄ emissions from the rectum were calculated as the difference between CH₄ estimates from the SF₆ technique when cows were housed in respiratory chambers and barn locations, which was 3% of the total CH₄ emissions from the mouth, nostrils, and rectum. The SF₆ estimates in the chamber location accounted for all sources of emissions, whereas those in the barn location, like that in grazing conditions, did not include CH₄ emission from the rectum. Therefore, the SF₆ measurements for grazing cattle should be adjusted for CH₄ emissions from the rectum (3% of total). We conclude that the SF₆ technique is reasonably accurate for estimating CH₄ emissions.