Methane emissions and rumen metabolite concentrations in cattle fed two different silages.

In this study, 18 animals were fed two forage-based diets: red clover (RC) and grass silage (GS), in a crossover-design experiment in which methane (CH4) emissions were recorded in respiration chambers. Rumen samples obtained through naso-gastric sampling tubes were analysed by NMR. Methane yield (g/kg DM) was significantly lower from animals fed RC (17.8 ± 3.17) compared to GS (21.2 ± 4.61) p = 0.008. In total 42 metabolites were identified, 6 showing significant differences between diets (acetate, propionate, butyrate, valerate, 3-phenylopropionate, and 2-hydroxyvalerate). Partial least squares discriminant analysis (PLS-DA) was used to assess which metabolites were more important to distinguish between diets and partial least squares (PLS) regressions were used to assess which metabolites were more strongly associated with the variation in CH4 emissions. Acetate, butyrate and propionate along with dimethylamine were important for the distinction between diets according to the PLS-DA results. PLS regression revealed that diet and dry matter intake are key factors to explain CH4 variation when included in the model. Additionally, PLS was conducted within diet, revealing that the association between metabolites and CH4 emissions can be conditioned by diet. These results provide new insights into the methylotrophic methanogenic pathway, confirming that metabolite profiles change according to diet composition, with consequences for CH4 emissions.

The effect of sheep genetic merit and feed allowance on nitrogen partitioning and isotopic discrimination.

Animal nitrogen (N) partitioning is a key parameter for profitability and sustainability of ruminant production systems, which may be predicted from N isotopic discrimination or fractionation (Δ15N). Both animal genetics and feeding level may interact and impact on N partitioning. Therefore, this study aimed to assess the interactive effects of genetic merit (G) and feed allowance (F) on N partitioning and Δ15N in sheep. The sheep were drawn from two levels of G (high G vs. low G; based on New Zealand Sheep Improvement Limited (http://www.sil.co.nz/) dual (wool and meat) growth index) and allocated to two levels of F (1.7 (high F) vs. 1.1 (low F) times Metabolisable Energy requirement for maintenance) treatments. Twenty-four Coopworth rams were divided into four equal groups for a N balance study: high G × high F, high G × low F, low G × high F, and low G × low F. The main factors (G and F) and the interaction term were used for 2-way ANOVA and regression analysis. Higher F led to higher N excretions (urinary N (UN); faecal N (FN); manure N), retained N, N use efficiency (NUE), and urinary purine derivatives excretion (P < 0.05). On the other hand, higher UN/N intake, and plasma Δ15N were observed with the lower F (P < 0.05). Higher G led to increased UN, FN, manure N, apparent N digestibility, and urinary purine derivatives excretion (P < 0.05). Higher F only increased UN in high G sheep, with no effect on low G sheep (P < 0.05). Regression analysis results demonstrated potential to use plasma Δ15N to reflect the effects of G and F on NUE and UN/N intake. Further research is urged to study interactive effects of genetic and feeding level on sheep N partitioning.

Review: Markers and proxies to monitor ruminal function and feed efficiency in young ruminants.

Developing the rumen's capacity to utilise recalcitrant and low-value feed resources is important for ruminant production systems. Early-life nutrition and management practices have been shown to influence development of the rumen in young animals with long-term consequences on their performance. Therefore, there has been increasing interest to understand ruminal development and function in young ruminants to improve feed efficiency, health, welfare, and performance of both young and adult ruminants. However, due to the small size, rapid morphological changes and low initial microbial populations of the rumen, it is difficult to study ruminal function in young ruminants without major invasive approaches or slaughter studies. In this review, we discuss the usefulness of a range of proxies and markers to monitor ruminal function and nitrogen use efficiency (a major part of feed efficiency) in young ruminants. Breath sulphide and methane emissions showed the greatest potential as simple markers of a developing microbiota in young ruminants. However, there is only limited evidence for robust indicators of feed efficiency at this stage. The use of nitrogen isotopic discrimination based on plasma samples appeared to be the most promising proxy for feed efficiency in young ruminants. More research is needed to explore and refine potential proxies and markers to indicate ruminal function and feed efficiency in young ruminants, particularly for neonatal ruminants.

Taxonomic annotation of 16S rRNA sequences of pig intestinal samples using MG-RAST and QIIME2 generated different microbiota compositions.

Environmental microbiome studies rely on fast and accurate bioinformatics tools to characterize the taxonomic composition of samples based on the 16S rRNA gene. MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology 2 (QIIME2) are two of the most popular tools available to perform this task. Their underlying algorithms differ in many aspects, and therefore the comparison of the pipelines provides insights into their best use and interpretation of the outcomes. Both of these bioinformatics tools are based on several specialized algorithms pipelined together, but whereas MG-RAST is a user-friendly webserver that clusters rRNA sequences based on their similarity to create Operational Taxonomic Units (OTU), QIIME2 employs DADA2 in the construction of Amplicon Sequence Variants (ASV) by applying an error model that considers the abundance of each sequence and its similarity to other sequences. Taxonomic compositions obtained from the analyses of amplicon sequences of DNA from swine intestinal gut and faecal microbiota samples using MG-RAST and QIIME2 were compared at domain-, phylum-, family- and genus-levels in terms of richness, relative abundance and diversity. We found significant differences between the microbiota profiles obtained from each pipeline. At domain level, bacteria were relatively more abundant using QIIME2 than MG-RAST; at phylum level, seven taxa were identified exclusively by QIIME2; at family level, samples processed in QIIME2 showed higher evenness and richness (assessed by Shannon and Simpson indices). The genus-level compositions obtained from each pipeline were used in partial least squares-discriminant analyses (PLS-DA) to discriminate between sample collection sites (caecum, colon and faeces). The results showed that different genera were found to be significant for the models, based on the Variable Importance in Projection, e.g. when using sequencing data processed by MG-RAST, the three most important genera were Acetitomaculum, Ruminococcus and Methanosphaera, whereas when data was processed using QIIME2, these were Candidatus Methanomethylophilus, Sphaerochaeta and Anaerorhabdus. Furthermore, the application of differential filtering procedures before the PLS-DA revealed higher accuracy when using non-restricted datasets obtained from MG-RAST, whereas datasets obtained from QIIME2 resulted in more accurate discrimination of sample collection sites after removing genera with low relative abundances (<1%) from the datasets. Our results highlight the differences in taxonomic compositions of samples obtained from the two separate pipelines, while underlining the impact on downstream analyses, such as biomarkers identification.

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, BW0.75, DMI/BW0.75). 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.

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle.

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations.Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin's concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.

Nitrogen isotopic fractionation as a biomarker for nitrogen use efficiency in ruminants: a meta-analysis.

Animal proteins are naturally 15N enriched relative to the diet and the extent of this difference (Δ15Nanimal-diet or N isotopic fractionation) has been correlated to N use efficiency (NUE; N gain or milk N yield/N intake) in some recent ruminant studies. The present study used meta-analysis to investigate whether Δ15Nanimal-diet can be used as a predictor of NUE across a range of dietary conditions, particularly at the level of between-animal variation. An additional objective was to identify variables related to N partitioning explaining the link between NUE and Δ15Nanimal-diet. Individual values from eight publications reporting both NUE and Δ15Nanimal-diet for domestic ruminants were used to create a database comprising 11 experimental studies, 41 treatments and individual animal values for NUE (n=226) and Δ15Nanimal-diet (n=291). Data were analyzed by mixed-effect regression analysis taking into account experimental factors as random effects on both the intercept and slope of the model. Diets were characterized according to the INRA feeding system in terms of N utilization at the rumen, digestive and metabolic levels. These variables were used in a partial least squares regression analysis to predict separately NUE and Δ15Nanimal-diet variation, with the objective of identifying common variables linking NUE and Δ15Nanimal-diet. For individuals reared under similar conditions (within-study) and at the same time (within-period), the variance of NUE and Δ15Nanimal-diet not explained by dietary treatments (i.e. between-animal variation plus experimental error) was 35% and 55%, respectively. Mixed-effect regression analysis conducted with treatment means showed that Δ15Nanimal-diet was significantly and negatively correlated to NUE variation across diets (NUE=0.415 -0.055×Δ15Nanimal-diet). When using individual values and taking into account the random effects of study, period and diet, the relationship was also significant (NUE=0.358 -0.035×Δ15Nanimal-diet). However, there may be a biased prediction for animals close to zero, or in negative, N balance. When using a novel statistical approach, attempting to regress between-animal variation in NUE on between-animal variation in Δ15Nanimal-diet (without the influence of experimental factors), the negative relationship was still significant, highlighting the ability of Δ15Nanimal-diet to capture individual variability. Among the studied variables related to N utilization, those concerning N efficiency use at the metabolic level contributed most to predict both Δ15Nanimal-diet and NUE variation, with rumen fermentation and digestion contributing to a lesser extent. This study confirmed that on average Δ15Nanimal-diet can predict NUE variation across diets and across individuals reared under similar conditions.

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.

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions.

Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting cows require accurate and large-scale measurements of methane (CH4) emissions from individual cows. Several techniques have been developed to measure CH4 in a research setting but most are not suitable for large-scale recording on farm. Several groups have explored proxies (i.e., indicators or indirect traits) for CH4; ideally these should be accurate, inexpensive, and amenable to being recorded individually on a large scale. This review (1) systematically describes the biological basis of current potential CH4 proxies for dairy cattle; (2) assesses the accuracy and predictive power of single proxies and determines the added value of combining proxies; (3) provides a critical evaluation of the relative merit of the main proxies in terms of their simplicity, cost, accuracy, invasiveness, and throughput; and (4) discusses their suitability as selection traits. The proxies range from simple and low-cost measurements such as body weight and high-throughput milk mid-infrared spectroscopy (MIR) to more challenging measures such as rumen morphology, rumen metabolites, or microbiome profiling. Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. Proxies related to body weight or milk yield and composition, on the other hand, are relatively simple, inexpensive, and high throughput, and are easier to implement in practice. In particular, milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, and combinations of 2 or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by 15 to 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). The most important applications of CH4 proxies are in dairy cattle management and breeding for lower environmental impact. When breeding for traits of lower environmental impact, single or multiple proxies can be used as indirect criteria for the breeding objective, but care should be taken to avoid unfavorable correlated responses. Finally, although combinations of proxies appear to provide the most accurate estimates of CH4, the greatest limitation today is the lack of robustness in their general applicability. Future efforts should therefore be directed toward developing combinations of proxies that are robust and applicable across diverse production systems and environments.

Current available strategies to mitigate greenhouse gas emissions in livestock systems: an animal welfare perspective.

Livestock production is a major contributor to greenhouse gas (GHG) emissions, so will play a significant role in the mitigation effort. Recent literature highlights different strategies to mitigate GHG emissions in the livestock sector. Animal welfare is a criterion of sustainability and any strategy designed to reduce the carbon footprint of livestock production should consider animal welfare amongst other sustainability metrics. We discuss and tabulate the likely relationships and trade-offs between the GHG mitigation potential of mitigation strategies and their welfare consequences, focusing on ruminant species and on cattle in particular. The major livestock GHG mitigation strategies were classified according to their mitigation approach as reducing total emissions (inhibiting methane production in the rumen), or reducing emissions intensity (Ei; reducing CH4 per output unit without directly targeting methanogenesis). Strategies classified as antimethanogenic included chemical inhibitors, electron acceptors (i.e. nitrates), ionophores (i.e. Monensin) and dietary lipids. Increasing diet digestibility, intensive housing, improving health and welfare, increasing reproductive efficiency and breeding for higher productivity were categorized as strategies that reduce Ei. Strategies that increase productivity are very promising ways to reduce the livestock carbon footprint, though in intensive systems this is likely to be achieved at the cost of welfare. Other strategies can effectively reduce GHG emissions whilst simultaneously improving animal welfare (e.g. feed supplementation or improving health). These win-win strategies should be strongly supported as they address both environmental and ethical sustainability. In order to identify the most cost-effective measures for improving environmental sustainability of livestock production, the consequences of current and future strategies for animal welfare must be scrutinized and contrasted against their effectiveness in mitigating climate change.

The effect of dietary water soluble carbohydrate to nitrogen ratio on nitrogen partitioning and isotopic fractionation of lactating goats offered a high-nitrogen diet.

The objective of this study was to investigate the relationship between nitrogen (N) partitioning and isotopic fractionation in lactating goats consuming diets with a constant high concentration of N and increasing levels of water soluble carbohydrate (WSC). Eight lactating goats were offered four different ratios of WSC : N in the diet. A two-period incomplete cross-over design was used, with two goats assigned to each treatment in each period. N balance measurements were conducted, with measurement of feed N intake and total output of N in milk, faeces and urine. Treatment, period and infusion effects were tested using general ANOVA; the relationships between variables were analysed by linear regression. Dietary treatment and period had significant effects on dry matter (DM) intake (g/day). DM digestibility (g/kg DM) and N digestibility (g/kg N) increased as the ratio of WSC : N increased in the diet. No treatment effect was observed on milk urea N concentration (g/l) or urinary excretion of purine derivatives (mM/day). Although dietary treatment and period had significant effects on N intake, the change of N intake was small; no effect was observed for N partitioning among faeces, milk and urine. Milk, plasma and faeces were enriched in 15N compared with feed, whilst urine was depleted in 15N relative to feed. No significant relationship was established between N partitioning and isotopic fractionation. This study failed to confirm the potential to use N isotopic fractionation as an indicator of N partitioning in dairy goats when diets provided N in excess to requirements, most likely because the range of milk N output/N intake and urinary N output/N intake were narrow.

Effects of feed intake and genetics on tissue nitrogen-15 enrichment and feed conversion efficiency in sheep.

This study investigated the effects of sheep genetics and feed intake on nitrogen isotopic fractionation (ΔN) and feed conversion efficiency (FCE; live weight gain/DMI), using a 2 × 2 factorial design, with 2 levels of genetic merit for growth (high vs. low) and 2 levels of feed intake (110 vs. 170% of ME for maintenance [MEm]). No effect of genetic merit was detected for live weight gain ( = 0.64), FCE ( = 0.46), plasma urea nitrogen ( = 0.52), plasma glucose ( = 0.78), and ΔN of wool ( = 0.45), blood ( = 0.09), and plasma ( = 0.51). Sheep receiving 170% of MEm had 175% higher live weight gain ( < 0.001) and 77% higher FCE ( < 0.001) than sheep receiving 110% of MEm. There was no difference among treatments at the beginning of the study for either blood or plasma ∆N, but the treatment groups started to diverge in blood and plasma ∆N at 21 and 7 d, respectively. Blood, plasma, and wool samples were enriched in N compared with feed. There was a higher blood, plasma, and wool ∆N for the low feed intake group than the high feed intake group ( < 0.001 in all cases). Across the 4 treatment groups, higher FCE in sheep was associated with lower ∆N for plasma, blood, and wool. Overall, the results are consistent with the potential of ∆N as a rapid, low-cost biomarker of FCE in sheep, despite there being no effects of genetic treatment on FCE and ∆N.

Short communication: Relationship between the efficiency of utilization of feed nitrogen and 15N enrichment in casein from lactating dairy cows.

The objective of the present study was to evaluate the relationship between the efficiency of conversion of feed N into milk N [N-use efficiency (NUE)] and (15)N enrichment of milk casein from lactating cows fed corn silage-based diets. Samples of feeds and milk were obtained from 3 experiments with lactating dairy cows. All diets were based on corn silage and were designed to evaluate the effects of (1) diets with different ratios of effective rumen-degradable protein to fermentable metabolizable energy (experiment 1), (2) different proportions of quickly and slowly rumen-degradable protein (experiment 2), and (3) synchronizing the availability of fermentable metabolizable energy and effective rumen-degradable protein in the rumen (experiment 3). Although no significant effect of diet on casein δ(15)N values was detected, casein was more enriched than the diet in each of the experiments. Nitrogen-use efficiency was negatively related to adjusted Δ(15)N (casein δ(15)N--diet δ(15)N) for experiments 1 and 2 individually and when combining data from all 3 experiments. The relatively low values for Δ(15)N suggest that these productive animals were using dietary N efficiently, with a high proportion of N going to milk protein and less to urea. The weak, although significant, relationship between NUE and adjusted Δ(15)N, is consistent with relatively little variation in hepatic deamination and transamination, with variation in rumen efficiency having the predominant effect on NUE. The present study confirms the lower (15)N enrichment in protein when NUE is high and the potential to use N-isotope fractionation as a marker of NUE.

Changes in the ratio of tetraether to diether lipids in cattle feces in response to altered dietary ratio of grass silage and concentrates.

The distinctive membrane lipids of the archaea can contain a wide range of chemical structures. The membrane lipid composition of ruminal methanogenic archaea has not yet been characterized. In this study, we analyzed proportions of the core archaeal membrane lipids dialkyl glycerol diethers (DGDG) and glycerol dialkyl glycerol tetraether (GDGT). We analyzed the feces of beef steers consuming diets that promoted differences in ruminal conditions that were either favorable (i.e., grass silage) or challenging (i.e., concentrates) for the methanogenic archaea. There was significantly less total ether lipid in the feces of cattle consuming the concentrate diet in comparison to the grass silage diet (97 vs. 218 mg/kg DM, respectively), reflecting the inhibitory effect of dietary concentrate on methanogens. Additionally, the proportion of fecal ether lipids as GDGT was much greater in feces from cattle consuming the concentrate diet than in feces from cattle fed grass silage (90% vs. 67% GDGT). A possible explanation for this adaptation is that membrane lipids composited of GDGT lipids are less permeable to protons, thereby protecting the methanogens against low ruminal pH and helping to maintain the chemiosmotic potential (which is important for ATP production, methanogenesis, and growth). The greater proportion of fecal ether lipids as GDGT may reflect adaptation of membrane lipids within the same species, a shift toward methanogens that have a greater proportion of GDGT (e.g., Thermoplasmata), or both. The effect of ruminal environment on membrane composition means that it will be important to consider the production of both DGDG and GDGT lipids when developing a proxy for methanogenesis.

Plasma nitrogen isotopic fractionation and feed efficiency in growing beef heifers.

Fractionation of N isotopes occurs in many metabolic reactions which causes tissue proteins to become enriched in ¹5N while urea (urine) is depleted in ¹5N relative to the diet. We investigated ¹5N enrichment of whole plasma and its relationship with feed conversion efficiency (FCE) in growing beef heifers (n 84) offered 2 kg/d of concentrates with grass silage ad libitum. Heifers were on average 299 (SD 48·3) d old and weighed 311 (SD 48·8) kg. Plasma was obtained on day 79 (n 84) of the experiment and from a subset of animals (n 20) on four occasions between days 16 and 79. Silage DM intake (DMI) averaged 4·1 (SD 0·74) kg/d and concentrate DMI was 1·72 kg/d. Mean mid-test live weight was 333 (SD 47·6) kg, daily gain was 0·53 (SD 0·183) kg, FCE (g live-weight gain/g DMI) was 0·09 (SD 0·028) and residual feed intake (RFI) was 0 (SD 0·428). N isotopic fractionation (Δ¹5N; plasma δ¹5N - diet δ¹5N) averaged 3·58 ‰ on day 79 (n 84) and 3·90 ‰ for the subset of heifers. There was no relationship between Δ¹5N and RFI. Plasma δ¹5N and Δ¹5N were related to both FCE (negative) and animal weight (positive) for the whole population, and repeatable for the subset of animals over four time points. These relationships of Δ¹5N with FCE and animal weight are consistent with the anticipated negative relationship with N-use efficiency. There is potential to use Δ¹5N to provide rapid, low-cost estimates of FCE in cattle.

Technical note: Nitrogen isotopic fractionation can be used to predict nitrogen-use efficiency in dairy cows fed temperate pasture.

The objective of this study was to investigate the relationship between nitrogen isotopic fractionation (δ(15)N) and nitrogen-use efficiency (milk nitrogen/nitrogen intake; NUE) in pasture-fed dairy cows supplemented with increasing levels of urea to mimic high rumen degradable protein pastures in spring. Fifteen cows were randomly assigned to freshly cut pasture and either supplemented with 0, 250, or 336 g urea/d. Feed, milk, and plasma were analyzed for δ(15)N, milk and plasma for urea nitrogen concentration, and plasma for ammonia concentration. Treatment effects were tested using ANOVA and relationships between variables were established by linear regression. Lower dry matter intake (P = 0.002) and milk yield (P = 0.002) occurred with the highest urea supplementation (336 g urea/d) compared with the other two treatments. There was a strong linear relationship between milk δ(15)N - feed δ(15)N and NUE: [NUE (%) = 58.9 - 10.17 × milk δ(15)N - feed δ(15)N (‰) (r(2) = 0.83, P < 0.001, SE = 1.67)] and between plasma δ(15)N - feed δ(15)N and NUE: [NUE (%) = 52.4 - 8.61 × plasma δ(15)N - feed δ(15)N (‰) (r(2) = 0.85, P < 0.001, SE = 1.56)] . This study confirmed the potential use of δ(15)N to predict NUE in cows consuming different levels of rumen degradable protein.

Technical note: Comparison of biomarker and molecular biological methods for estimating methanogen abundance.

Quantitative real-time PCR (qPCR) has become a popular method for estimation of methanogen abundance in the ruminant digestive tract. However, there is no established method in terms of primer choice and quantification, which means that results are variable and not directly comparable between studies. Archaeol has been proposed as an alternative marker for methanogen abundance, as it is ubiquitous in methanogenic Archaea, and can be quantified by gas chromatography-mass spectrometry (GC-MS). The aim of this experiment was to compare total methanogen populations estimated using the new archaeol approach with estimates based on qPCR. Specific primer sets and probes were used to detect dominant ruminal methanogen species Methanobrevibacter ruminantium, Methanobrevibacter smithii, Methanosphaera stadtmanae, and total methanogen populations. There was variation in the relationships among total methanogen abundance estimates based on archaeol and qPCR. In addition, the universal methanogen primers appeared to preferentially amplify genes from M. smithii. Archaeol had the strongest relationship with the dominant rumen methanogen M. ruminantium, whereas the total methanogen primers had a comparatively weak relationship with archaeol. Archaeol analysis was a useful adjunct to molecular biology methods, but it seems that a valid specific primer for M. ruminantium would be more useful than a biased primer for total methanogens.

Chemical markers for rumen methanogens and methanogenesis.

The targeting of mcrA or 16S rRNA genes by quantitative PCR (qPCR) has become the dominant method for quantifying methanogens in rumen. There are considerable discrepancies between estimates based on different primer sets, and the literature is equivocal about the relationship with methane production. There are a number of problems with qPCR, including low primer specificity, multiple copies of genes and multiple genomes per cell. Accordingly, we have investigated alternative markers for methanogens, on the basis of the distinctive ether lipids of archaeal cell membranes. The membranes of Archaea contain dialkyl glycerol ethers such as 2,3-diphytanayl-O-sn-glycerol (archaeol), and glycerol dialkyl glycerol tetraethers (GDGTs) such as caldarchaeol (GDGT-0) in different proportions. The relationships between estimates of methanogen abundance using qPCR and archaeol measurements varied across primers. Studies in other ecosystems have identified environmental effects on the profile of ether lipids in Archaea. There is a long history of analysing easily accessible samples, such as faeces, urine and milk, to provide information about digestion and metabolism in livestock without the need for intrusive procedures. Purine derivatives in urine and odd-chain fatty acids in milk have been used to study rumen function. The association between volatile fatty acid proportions and methane production is probably the basis for empirical relationships between milk fatty acid profiles and methane production. However, these studies have not yet identified consistent predictors. We have evaluated the relationship between faecal archaeol concentration and methane production across a range of diets in studies on beef and dairy cattle. Faecal archaeol is diagnostic for ruminant faeces being below the limit of detection in faeces from non-ruminant herbivores. The relationship between faecal archaeol and methane production was significant when comparing treatment means across diets, but appears to be subject to considerable between-animal variation. This variation was also evident in the weak relationship between archaeol concentrations in rumen digesta and faeces. We speculate that variation in the distribution and kinetics of methanogens in the rumen may affect the survival and functioning of Archaea in the rumen and therefore contribute to genetic variation in methane production. Indeed, variation in the relationship between the numbers of micro-organisms present in the rumen and those leaving the rumen may explain variation in relationships between methane production and both milk fatty acid profiles and faecal archaeol. As a result, microbial markers in the faeces and milk are unlikely to relate well back to methanogenesis in the rumen. This work has also highlighted the need to describe methanogen abundance in all rumen fractions and this may explain the difficulty interpreting results on the basis of samples taken using stomach tubes or rumenocentesis.