The impact of divergent breed types and diets on methane emissions, rumen characteristics and performance of finishing beef cattle

An integral assessment of the impact of diet and manure management on whole-farm greenhouse gas and nitrogen emissions in dairy cattle production systems using process-based models

Feed management decisions are crucial in mitigating greenhouse gas (GHG) and nitrogen (N) emissions from ruminant farming systems. However, assessing the downstream impact of diet on emissions in dairy production systems is complex, due to the multifunctional relationships between a variety of distinct but interconnected sources such as animals, housing, manure storage, and soil. Therefore, there is a need for an integral assessment of the direct and indirect GHG and N emissions that considers the underlying processes of carbon (C), N and their drivers within the system. Here we show the relevance of using a cascade of process-based (PB) models, such as Dutch Tier 3 and (Manure)-DNDC (Denitrification-Decomposition) models, for capturing the downstream influence of diet on whole-farm emissions in two contrasting case study dairy farms: a confinement system in Germany and a pasture-based system in New Zealand. Considerable variation was found in emissions on a per hectare and per head basis, and across different farm components and categories of animals. Moreover, the confinement system had a farm C emission of 1.01 kg CO2-eq kg-1 fat and protein corrected milk (FPCM), and a farm N emission of 0.0300 kg N kg-1 FPCM. In contrast, the pasture-based system had a lower farm C and N emission averaging 0.82 kg CO2-eq kg-1 FPCM and 0.006 kg N kg-1 FPCM, respectively over the 4-year period. The results demonstrate how inputs and outputs could be made compatible and exchangeable across the PB models for quantifying dietary effects on whole-farm GHG and N emissions.

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle

BACKGROUND

Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage.

RESULTS

By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits.

CONCLUSIONS

Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production

This paper reports on the use of estimates of individual animal feed intake (made using time spent feeding measurements) to predict the Feed Conversion Ratio (FCR), a measure of the amount of feed consumed to produce 1 kg of body mass, for an individual animal. Reported research to date has evaluated the ability of statistical methods to predict daily feed intake based on measurements of time spent feeding measured using electronic feeding systems. The study collated data of the time spent eating for 80 beef animals over a 56-day period as the basis for the prediction of feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and the performance of the approach was quantified. Here, feed intake predictions are used to estimate individual FCR and use this information to categorise animals into three groups based on the estimated Feed Conversion Ratio value. Results provide evidence of the feasibility of utilising the 'time spent eating' data to estimate feed intake and in turn Feed Conversion Ratio (FCR), the latter providing insights that guide farmer decisions on the optimisation of production costs.

Effect of Metabolizable Protein Supply on Milk Performance, Ruminal Fermentation, Apparent Total-Tract Digestibility, Energy and Nitrogen Utilization, and Enteric Methane Production of Ayrshire and Holstein Cows

In North America, the nutrient requirements of dairy cattle are predicted using the Cornell Net Carbohydrate and Protein System (CNCPS) or the National Research Council (NRC). As Holstein is the most predominant dairy cattle breed, these models were developed based on the phenotypic, physiological, and genetic characteristics of this breed. However, these models may not be appropriate to predict the nutrient requirements of other breeds, such as Ayrshire, that are phenotypically and genetically different from Holstein. The objective of this study was to evaluate the effects of increasing the metabolizable protein (MP) supply using CNCPS on milk performance, ruminal fermentation, apparent total-tract digestibility, energy and N utilization, and enteric methane production in Ayrshire vs. Holstein lactating dairy cows. Eighteen (nine Ayrshire; nine Holstein) lactating cows were used in a replicated 3 × 3 Latin square design (35-d periods) and fed diets formulated to meet 85%, 100%, or 115% of MP daily requirement. Except for milk production, no breed × MP supply interaction was observed for the response variables. Dry matter intake (DMI) and the yields of energy-corrected milk (ECM), fat, and protein were less (p < 0.01) in Ayrshire vs. Holstein cows. However, feed efficiency and N use efficiency for milk production did not differ between the two breeds, averaging 1.75 kg ECM/kg DMI and 33.7 g milk N/100 g N intake, respectively. Methane yield and intensity and urinary N also did not differ between the two breeds, averaging 18.8 g CH₄ /kg DMI, 10.8 g CH₄ /kg ECM, and 27.6 g N/100 g N intake, respectively. Yields of ECM and milk protein increased (p ≤ 0.01) with increasing MP supply from 85% to 100% but no or small increases occurred when MP supply increased from 100 to 115%. Feed efficiency increased linearly with an increasing MP supply. Nitrogen use efficiency (g N milk/100g N intake) decreased linearly (by up to 5.4 percentage units, (p < 0.01) whereas urinary N excretion (g/d or g/100 g N intake) increased linearly (p < 0.01) with an increasing MP supply. Methane yield and emission intensity were not affected by MP supply. This study shows that feed efficiency, N use efficiency, CH₄ (yield and intensity), and urinary N losses did not differ between Ayrshire and Holstein cows. Energy-corrected milk yield and feed efficiency increased, but N use efficiency decreased and urinary N losses increased with increasing dietary MP supply regardless of breed. Ayrshire and Holstein breeds responded similarly to increasing MP levels in the diet.

Different microbial genera drive methane emissions in beef cattle fed with two extreme diets

The ratio of forage to concentrate in cattle feeding has a major influence on the composition of the microbiota in the rumen and on the mass of methane produced. Using methane measurements and microbiota data from 26 cattle we aimed to investigate the relationships between microbial relative abundances and methane emissions, and identify potential biomarkers, in animals fed two extreme diets - a poor quality fresh cut grass diet (GRASS) or a high concentrate total mixed ration (TMR). Direct comparisons of the effects of such extreme diets on the composition of rumen microbiota have rarely been studied. Data were analyzed considering their multivariate and compositional nature. Diet had a relevant effect on methane yield of +10.6 g of methane/kg of dry matter intake for GRASS with respect to TMR, and on the centered log-ratio transformed abundance of 22 microbial genera. When predicting methane yield based on the abundance of 28 and 25 selected microbial genera in GRASS and TMR, respectively, we achieved cross-validation prediction accuracies of 66.5 ± 9% and 85 ± 8%. Only the abundance of Fibrobacter had a consistent negative association with methane yield in both diets, whereas most microbial genera were associated with methane yield in only one of the two diets. This study highlights the stark contrast in the microbiota controlling methane yield between animals fed a high concentrate diet, such as that found on intensive finishing units, and a low-quality grass forage that is often found in extensive grazing systems. This contrast must be taken into consideration when developing strategies to reduce methane emissions by manipulation of the rumen microbial composition.

Enteric methane research and mitigation strategies for pastoral-based beef cattle production systems

Ruminant livestock play a key role in global society through the conversion of lignocellulolytic plant matter into high-quality sources of protein for human consumption. However, as a consequence of the digestive physiology of ruminant species, methane (CH₄), which originates as a byproduct of enteric fermentation, is accountable for 40% of global agriculture's carbon footprint and ~6% of global greenhouse gas (GHG) emissions. Therefore, meeting the increasing demand for animal protein associated with a growing global population while reducing the GHG intensity of ruminant production will be a challenge for both the livestock industry and the research community. In recent decades, numerous strategies have been identified as having the potential to reduce the methanogenic output of livestock. Dietary supplementation with antimethanogenic compounds, targeting members of the rumen methanogen community and/or suppressing the availability of methanogenesis substrates (mainly H₂ and CO₂), may have the potential to reduce the methanogenic output of housed livestock. However, reducing the environmental impact of pasture-based beef cattle may be a challenge, but it can be achieved by enhancing the nutritional quality of grazed forage in an effort to improve animal growth rates and ultimately reduce lifetime emissions. In addition, the genetic selection of low-CH₄-emitting and/or faster-growing animals will likely benefit all beef cattle production systems by reducing the methanogenic potential of future generations of livestock. Similarly, the development of other mitigation technologies requiring minimal intervention and labor for their application, such as anti-methanogen vaccines, would likely appeal to livestock producers, with high uptake among farmers if proven effective. Therefore, the objective of this review is to give a detailed overview of the CH₄ mitigation solutions, both currently available and under development, for temperate pasture-based beef cattle production systems. A description of ruminal methanogenesis and the technologies used to estimate enteric emissions at pastures are also presented.

Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions

BACKGROUND

Healthier ruminant products can be achieved by adequate manipulation of the rumen microbiota to increase the flux of beneficial fatty acids reaching host tissues. Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored.

RESULTS

This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or cis-9, trans-11 C18:2 and trans-11 C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002/3633) of the functional core additive log-ratio transformed microbial gene abundances (alr-MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372 alr-MG were host-genomically correlated with the N3 index (n=290), CLA index (n=66) or with both (n=16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372 alr-MG, 110 were correlated with the N3 and/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (aroF and serA), carbohydrate metabolism and transport (galT, msmX), lipopolysaccharide biosynthesis (kdsA, lpxD, lpxB), or flagellar synthesis (flgB, fliN) in certain genera within the Proteobacteria phyla (e.g. Serratia, Aeromonas). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36±0.24 and 0.79±0.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g/kg of dry matter intake), we obtained a correlated mitigation response of -0.41±0.12 sd.

CONCLUSION

This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions. Video Abstract.

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Simulating grazing beef and sheep systems

CONTEXT

Ruminant livestock make an important contribution to global food security by converting feed that is unsuitable for human consumption into high value food protein, demand for which is currently increasing at an unprecedented rate because of increasing global population and income levels. Factors affecting production efficiency, product quality, and consumer acceptability, such as animal fertility, health and welfare, will ultimately define the sustainability of ruminant production systems. These more complex systems can be developed and analysed by using models that can predict system responses to environment and management.

OBJECTIVE

We present a framework that dynamically models, using a process-based and mechanistic approach, animal and grass growth, nutrient cycling and water redistribution in a soil profile taking into account the effects of animal genotype, climate, feed quality and quantity on livestock production, greenhouse gas emissions, water use and quality, and nutrient cycling in a grazing system.

METHODS

A component to estimate ruminant animal growth was developed and integrated with the existing components of the SPACSYS model. Intake of herbage and/or concentrates and partitioning of the energy and protein contained in consumed herbage and/or concentrates were simulated in the component. Simulated animal growth was validated using liveweight data from over 200 finishing beef cattle and 900 lambs collected from the North Wyke Farm Platform (NWFP) in southwest England, UK, between 2011 and 2018. Annual nitrous oxide (N₂O), ammonia, methane and carbon dioxide emissions from individual fields were simulated based on previous validated parameters.

RESULTS AND CONCLUSIONS

A series of statistical indicators demonstrated that the model could simulate liveweight gain of beef cattle and lamb. Simulated nitrogen (N) cycling estimated N input of 190 to 260 kg ha^-1, of which 37-61% was removed from the fields either as silage or animal intake, 15-26% was lost through surface runoff or lateral drainage and 1.14% was emitted to the atmosphere as N₂O. About 13% of the manure N applied to the NWFP and excreta N deposited at grazing was lost via ammonia volatilisation.

SIGNIFICANCE

The extended model has the potential to investigate the responses of the system on and consequences of a range of agronomic management and grazing strategies. However, modelling of multi-species swards needs to be validated including the dynamics of individual species in the swards, preferential selection by grazing animals and the impact on animal growth and nutrient flows.

Feeding Systems and Host Breeds Influence Ruminal Fermentation, Methane Production, Microbial Diversity and Metagenomic Gene Abundance

Our previous research revealed the advantages of separate feeding (SF) systems compared to total mixed ration (TMR) in terms of ruminal methane (CH₄) production. The purpose of this experiment was to confirm the advantage of SF as a nutritional strategy for CH₄ mitigation, and to determine the effects of different feeding systems (TMR and SF) on the rumen microbiome and associated metagenome of two different breeds and on CH₄ emissions. We randomly allocated four Holstein (305 ± 29 kg) and four Hanwoo steers (292 ± 24 kg) to two groups; the steers were fed a commercial concentrate with tall fescue (75:25) as TMR or SF, in a crossover design (two successive 22-day periods). Neither feeding systems nor cattle breeds had an effect on the total tract digestibility of nutrients. The TMR feeding system and Hanwoo steers generated significantly more CH₄ (P < 0.05) and had a higher yield [g/d and g/kg dry matter intake (DMI)] compared to the SF system and Holstein steers. A larger rumen acetate:propionate ratio was observed for the TMR than the SF diet (P < 0.05), and for Hanwoo than Holstein steers (P < 0.001), clearly reflecting a shift in the ruminal H₂ sink toward CH₄ production. The linear discriminant analysis (LDA) effect size (LEfSe) revealed a greater abundance (α < 0.05 and LDA > 2.0) of operational taxonomic units (OTUs) related to methanogenesis for Hanwoo steers compared to Holstein steers. Kendall’s correlation analysis revealed wide variation of microbial co-occurrence patterns between feeding systems, indicating differential H₂ thermodynamics in the rumen. A metagenome analysis of rumen microbes revealed the presence of 430 differentially expressed genes, among which 17 and 27 genes exhibited positive and negative associations with CH₄ production, respectively (P < 0.001). A strong interaction between feeding system and breed was observed for microbial and metagenomic abundance. Overall, these results suggest that the TMR feeding system produces more CH₄, and that Hanwoo cattle are higher CH₄ emitters than SF diet and Holstein cattle, respectively. Interestingly, host-associated microbial interactions differed within each breed depending on the feeding system, which indicated that breed-specific feeding systems should be taken into account for farm management.

Does grazing management provide opportunities to mitigate methane emissions by ruminants in pastoral ecosystems?

Agriculture, and livestock production in particular, is criticized for being a contributor to global environmental change, including emissions of greenhouse gases (GHG). Methane (CH₄) from grazing ruminants accounts for most of livestock's carbon footprint because a large share of them are reared under suboptimal grazing conditions, usually resulting in both low herbage intake and animal performance. Consequently, the CH₄ quota attributed to animal maintenance is spread across few or no animal outputs, increasing the CH₄ intensity [g CH₄/kg live weight (LW) gain or g CH₄/kg milk yield]. In this review, the generalized idea relating tropical pastures with low quality and intrinsically higher CH₄ intensity is challenged by showing evidence that emissions from animals grazing tropical pastures can equal those of temperate grasses. We demonstrate the medium-to-high mitigation potential of some grazing management strategies to mitigate CH₄ emissions from grazing ruminants and stress the predominant role that sward canopy structure (e.g., height) has over animal behavioral responses (e.g., intake rate), daily forage intake and resulting CH₄ emissions. From this ecological perspective, we identify a grazing management concept aiming to offer the best sward structure that allows animals to optimize their daily herbage intake, creating opportunities to reduce CH₄ intensity. We show the trade-off between animal performance and CH₄ intensity, stressing that mitigation is substantial when grazing management is conducted under light-to-moderate intensities and optimize herbage intake and animal performance. We conclude that optimizing LW gain of grazing sheep and cattle to a threshold of 0.14 and 0.7 kg/day, respectively, would dramatically reduce CH₄ intensity to approximately 0.2 kg CH₄/kg LW gain, as observed in some intensive feeding systems. This could represent a mitigation potential of around 55% for livestock commodities in pasture-based systems. Our results offer new insights to the debate concerning mitigation of environmental impacts of pastoral ecosystems.

Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency

In this study, Bos Taurus cattle offered one high concentrate diet (92% concentrate-8% straw) during two independent trials allowed us to classify 72 animals comprising of two cattle breeds as "Low" or "High" feed efficiency groups. Digesta samples were taken from individual beef cattle at the abattoir. After metagenomic sequencing, the rumen microbiome composition and genes were determined. Applying a targeted approach based on current biological evidence, 27 genes associated with host-microbiome interaction activities were selected. Partial least square analysis enabled the identification of the most significant genes and genera of feed efficiency (VIP > 0.8) across years of the trial and breeds when comparing all potential genes or genera together. As a result, limited number of genes explained about 40% of the variability in both feed efficiency indicators. Combining information from rumen metagenome-assembled genomes and partial least square analysis results, microbial genera carrying these genes were determined and indicated that a limited number of important genera impacting on feed efficiency. In addition, potential mechanisms explaining significant difference between Low and High feed efficiency animals were analyzed considering, based on the literature, their gastrointestinal location of action. High feed efficiency animals were associated with microbial species including several Eubacterium having the genetic capacity to form biofilm or releasing metabolites like butyrate or propionate known to provide a greater contribution to cattle energy requirements compared to acetate. Populations associated with fucose sensing or hemolysin production, both mechanisms specifically described in the lower gut by activating the immune system to compete with pathogenic colonizers, were also identified to affect feed efficiency using rumen microbiome information. Microbial mechanisms associated with low feed efficiency animals involved potential pathogens within Proteobacteria and Spirochaetales, releasing less energetic substrates (e.g., acetate) or producing sialic acid to avoid the host immune system. Therefore, this study focusing on genes known to be involved in host-microbiome interaction improved the identification of rumen microbial genetic capacities and potential mechanisms significantly impacting on feed efficiency in beef cattle fed high concentrate diet.

Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows

Reducing methane emissions from livestock production is of great importance for the sustainable management of the Earth’s environment. Rumen microbiota play an important role in producing biogenic methane. However, knowledge of how host genetics influences variation in ruminal microbiota and their joint effects on methane emission is limited. We analyzed data from 750 dairy cows, using a Bayesian model to simultaneously assess the impact of host genetics and microbiota on host methane emission. We estimated that host genetics and microbiota explained 24% and 7%, respectively, of variation in host methane levels. In this Bayesian model, one bacterial genus explained up to 1.6% of the total microbiota variance. Further analysis was performed by a mixed linear model to estimate variance explained by host genomics in abundances of microbial genera and operational taxonomic units (OTU). Highest estimates were observed for a bacterial OTU with 33%, for an archaeal OTU with 26%, and for a microbial genus with 41% heritability. However, after multiple testing correction for the number of genera and OTUs modeled, none of the effects remained significant. We also used a mixed linear model to test effects of individual host genetic markers on microbial genera and OTUs. In this analysis, genetic markers inside host genes ABS4 and DNAJC10 were found associated with microbiota composition. We show that a Bayesian model can be utilized to model complex structure and relationship between microbiota simultaneously and their interaction with host genetics on methane emission. The host genome explains a significant fraction of between-individual variation in microbial abundance. Individual microbial taxonomic groups each only explain a small amount of variation in methane emissions. The identification of genes and genetic markers suggests that it is possible to design strategies for breeding cows with desired microbiota composition associated with phenotypes.

Nuclear Magnetic Resonance to Detect Rumen Metabolites Associated with Enteric Methane Emissions from Beef Cattle

This study presents the application of metabolomics to evaluate changes in the rumen metabolites of beef cattle fed with three different diet types: forage-rich, mixed and concentrate-rich. Rumen fluid samples were analysed by ¹H-NMR spectroscopy and the resulting spectra were used to characterise and compare metabolomic profiles between diet types and assess the potential for NMR metabolite signals to be used as proxies of methane emissions (CH₄ in g/kg DMI). The dataset available consisted of 128 measurements taken from 4 experiments with CH₄ measurements taken in respiration chambers. Predictive modelling of CH₄ was conducted by partial least squares (PLS) regression, fitting calibration models either using metabolite signals only as predictors or using metabolite signals as well as other diet and animal covariates (DMI, ME, weight, BW^0.75, DMI/BW^0.75). Cross-validated R² were 0.57 and 0.70 for the two models respectively. The cattle offered the concentrate-rich diet showed increases in alanine, valerate, propionate, glucose, tyrosine, proline and isoleucine. Lower methane yield was associated with the concentrate-rich diet (p < 0.001). The results provided new insight into the relationship between rumen metabolites, CH₄ production and diets, as well as showing that metabolites alone have an acceptable association with the variation in CH₄ production from beef cattle.

Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery

Ruminants provide essential nutrition for billions of people worldwide. The rumen is a specialized stomach that is adapted to the breakdown of plant-derived complex polysaccharides. The genomes of the rumen microbiota encode thousands of enzymes adapted to digestion of the plant matter that dominates the ruminant diet. We assembled 4,941 rumen microbial metagenome-assembled genomes (MAGs) using approximately 6.5 terabases of short- and long-read sequence data from 283 ruminant cattle. We present a genome-resolved metagenomics workflow that enabled assembly of bacterial and archaeal genomes that were at least 80% complete. Of note, we obtained three single-contig, whole-chromosome assemblies of rumen bacteria, two of which represent previously unknown rumen species, assembled from long-read data. Using our rumen genome collection we predicted and annotated a large set of rumen proteins. Our set of rumen MAGs increases the rate of mapping of rumen metagenomic sequencing reads from 15% to 50-70%. These genomic and protein resources will enable a better understanding of the structure and functions of the rumen microbiota.

Creating a low enteric methane emission ruminant: what is the evidence of success to the present and prospects for developing economies?

Enteric methane emissions from livestock constitute a greater part of anthropogenic greenhouse gases (GHGs) in Africa, than in more industrialised economies, providing a strong incentive for the development of low methane phenotype ruminants. Although dietary and husbandry options already exist for lowering methane production, means of changing ‘methane status’ of animals enduringly has a strong appeal. This paper is a critical review the empirical success to date of attempts to alter this status. Introduction of reductive acetogens, defaunation, anti-methanogen vaccines, early life programming and genetic selection at both the rumen and animal level are considered in turn. It is concluded that to date, there is little in vivo evidence to support the practical success of any of these strategies, save selective breeding, and this at a high cost with unknown efficacy. Finally, it is suggested that for developing economies management and nutritional strategies to reduce emissions will have the greatest and most immediate impact, at the lowest cost.

Compositional mixed modeling of methane emissions and ruminal volatile fatty acids from individual cattle and multiple experiments

The aim of the study was to investigate the association of methane (CH) yields (g/kg DMI) with rumen VFA molar proportions and animal and diet-related covariates from individual animals and multiple experiments. The dataset available consisted of 284 measurements of CH yields for beef cattle from 6 experiments measured in indirect respiration chambers. A compositional modeling approach was employed where VFA measurements were considered as a whole, instead of in isolation, emphasizing their multivariate relative scale. The analysis revealed expected close groupings of acetate and butyrate; propionate and valerate; iso-butyrate and iso-valerate. Linear mixed models were then fitted to examine relationships between CH yield and VFA, represented by meaningful log-contrasts of components called compositional balances, while accounting for other animal and diet-related covariates and random variability between experiments. A compositional balance representing (acetate × butyrate)/propionate best explained the contribution of VFA to variation in CH yield. The covariates DMI, forage:concentrate proportion (expressed as a categorical variable diet type: high concentrate, mixed forage:concentrate or high forage), and diet ME were also statistically significant. These results provided new insights into the relative inter-relationships among VFA measurements and also between VFA and CH yield. In conclusion, VFA molar proportions as represented by compositional balances were a significant contributor to explaining variation in CH yields from individual cattle.

Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH4), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH4 emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH4 emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH4 emissions and methanogens were the microbial populations most closely correlated with CH4 emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH4 emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH4 emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH4, but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH4. Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH4 emissions across diverse production systems and environments.