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.

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The rumen microbiome occupies a central role in animal health and productivity. A better understanding of the rumen ecosystem is essential to increase productivity or decrease methane production. Samples were collected from the three main rumen environments: the solid-adherent fraction, the liquid fraction and the epithelium. For the liquid and solid fraction, two alternative sample processing protocols were compared, resulting in a total of five sample types: crude solids (S), the eluted solid-adherent fraction (Ad), free-living species in the crude rumen liquid (CRL), strained liquid samples (Lq) and epimural scrapings (Ep). The bacterial and methanogen communities of these sample types were analysed using 16S metabarcoding and qPCR. The results indicate that the liquid and solid-adherent environments are distinguished mainly by the differential abundance of specific taxonomic groups. Cellulolytic bacteria that pioneer biofilm formation, together with secondary colonisers are prevalent in solid-adherent samples, while dominant species in the fluid samples are primarily identified as consumers of soluble nutrients. Also, methanogen species are found to have a preference for either a solid-adherent or free-living occurrence. The epimural environment is characterised by a different microbial profile. Ten bacterial families and two methanogen genera are almost exclusively found in this environment.

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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.