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.

Understanding rumen microbial ecology is essential for the development of feed systems designed to improve livestock productivity, health and for methane mitigation strategies from cattle. Although rumen microbial communities have been studied previously, few studies have applied next-generation sequencing technologies to that ecosystem. The aim of this study was to characterize changes in microbial community structure arising from feeding dairy cows two widely used diets: pasture and total mixed ration (TMR). Bacterial, archaeal and protozoal communities were characterized by terminal restriction fragment length polymorphism of the amplified SSU rRNA gene and statistical analysis showed that bacterial and archaeal communities were significantly affected by diet, whereas no effect was observed for the protozoal community. Deep amplicon sequencing of the 16S rRNA gene revealed significant differences in the bacterial communities between the diets and between rumen solid and liquid content. At the family level, some important groups of rumen bacteria were clearly associated with specific diets, including the higher abundance of the Fibrobacteraceae in TMR solid samples and members of the propionate-producing Veillonelaceae in pasture samples. This study will be relevant to the study of rumen microbial ecology and livestock feed management.

The objective of the present study was to compare the enteric methane (CH4) emissions and milk production of spring-calving Holstein-Friesian cows offered either a grazed perennial ryegrass diet or a total mixed ration (TMR) diet for 10 wk in early lactation. Forty-eight spring-calving Holstein-Friesian dairy cows were randomly assigned to 1 of 2 nutritional treatments for 10 wk: 1) grass or 2) TMR. The grass group received an allocation of 17 kg of dry matter (DM) of grass per cow per day with a pre-grazing herbage mass of 1,492 kg of DM/ha. The TMR offered per cow per day was composed of maize silage (7.5 kg of DM), concentrate blend (8.6 kg of DM), grass silage (3.5 kg of DM), molasses (0.7 kg of DM), and straw (0.5 kg of DM). Daily CH4 emissions were determined via the emissions from ruminants using a calibrated tracer technique for 5 consecutive days during wk 4 and 10 of the study. Simultaneously, herbage dry matter intake (DMI) for the grass group was estimated using the n-alkane technique, whereas DMI for the TMR group was recorded using the Griffith Elder feeding system. Cows offered TMR had higher milk yield (29.5 vs. 21.1 kg/d), solids-corrected milk yield (27.7 vs. 20.1 kg/d), fat and protein (FP) yield (2.09 vs. 1.54 kg/d), bodyweight change (0.54 kg of gain/d vs. 0.37 kg of loss/d), and body condition score change (0.36 unit gain vs. 0.33 unit loss) than did the grass group over the course of the 10-wk study. Methane emissions were higher for the TMR group than the grass group (397 vs. 251 g/cow per day). The TMR group also emitted more CH4 per kg of FP (200 vs. 174 g/kg of FP) than did the grass group. They also emitted more CH4 per kg of DMI (20.28 vs. 18.06 g/kg of DMI) than did the grass group. In this study, spring-calving cows, consuming a high quality perennial ryegrass diet in the spring, produced less enteric CH4 emissions per cow, per unit of intake, and per unit of FP than did cows offered a standard TMR diet.

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data.