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.

Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host’s rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host’s genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.

Individual methane (CH(4)) production was recorded repeatedly on 93 dairy cows during milking in an automatic milking system (AMS), with the aim of estimating individual cow differences in CH(4) production. Methane and CO(2) were measured with a portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection. The cows were 50 Holsteins and 43 Jerseys from mixed parities and at all stages of lactation (mean=156 d in milk). Breath was captured by the FTIR unit inlet nozzle, which was placed in front of the cow's head in each of the 2 AMS as an admixture to normal barn air. The FTIR unit was running continuously for 3 d in each of 2 AMS units, 1 with Holstein and another with Jersey cows. Air was analyzed every 20 s. From each visit of a cow to the AMS, CH(4) and CO(2) records were summarized into the mean, median, 75, and 90% quantiles. Furthermore, the ratio between CH(4) and CO(2) was used as a derived measure with the idea of using CO(2) in breath as a tracer gas to quantify the production of methane. Methane production records were analyzed with a mixed model, containing cow as random effect. Fixed effects of milk yield and daily intake of the total mixed ration and concentrates were also estimated. The repeatability of the CH(4)-to-CO(2) ratio was 0.39 for Holsteins and 0.34 for Jerseys. Both concentrate intake and total mixed ration intake were positively related to CH(4) production, whereas milk production level was not correlated with CH(4) production. In conclusion, the results from this study suggest that the CH(4)-to-CO(2) ratio measured using the noninvasive method is an asset of the individual cow and may be useful in both management and genetic evaluations.

The objective of this study was to estimate heritability of enteric methane emissions from dairy cattle. Methane (CH4) and CO2 were measured with a portable air-sampler and analyzer unit based on Fourier transform infrared detection. Data were collected on 3,121 Holstein dairy cows from 20 herds using automatic milking systems. Three CH4 phenotypes were acquired: the ratio between CH4 and CO2 in the breath of the cows (CH4_RATIO), the estimated quantified amount of CH4 (in g/d) measured over a week (CH4_GRAMSw), and CH4 intensity, defined as grams of CH4 per liter of milk produced (CH4_MILK). Fat- and protein-corrected milk (FPCM) and live weight data were also derived for the analysis. Data were analyzed using several univariate and bivariate linear animal models. The heritability of CH4_GRAMSw and CH4_MILK was 0.21 with a standard error of 0.06, and the heritability of CH4_RATIO was 0.16 with a standard error of 0.04. The 2 CH4 traits CH4_GRAMSw and CH4_RATIO were genetically highly correlated (rg=0.83) and they were strongly correlated with FPCM, meaning that, in this study, a high genetic potential for milk production will also mean a high genetic potential for CH4 production. The genetic correlation between CH4_MILK and FPCM and live weight showed similar patterns as the other CH4 phenotypes, although the correlations in general were closer to zero. The genetic correlations between the 3 CH4 phenotypes and live weight were low and only just significantly different from zero, meaning there is less indication of a genetic relationship between CH4 emission and live weight of the cow. None of the residual correlations between the ratio of CH4 and CO2, CH4 production in grams per day, FPCM, and live weight were significantly different from zero. The results from this study suggest that CH4 emission is partly under genetic control, that it is possible to decrease CH4 emission from dairy cattle through selection, and that selection for higher milk yield will lead to higher genetic merit for CH4 emission/cow per day.

Hoof diseases are a problem in many dairy herds. To study one aspect of the problem, genetic correlations between 4 hoof diseases, protein yield, clinical mastitis, number of inseminations, and days from calving to first insemination were estimated in first-parity Swedish Red cows using trivariate linear animal models. Occurrence of dermatitis, heel horn erosion, sole hemorrhage, and sole ulcer were reported by hoof trimmers. The data set contained about 314,000 animals with records on at least one of the traits; among these, about 64,000 animals had records on hoof diseases. Heritabilities were low for all hoof diseases (0.03 to 0.05). The hoof diseases fell into 2 groups: (1) dermatitis and heel horn erosion (i.e., diseases related to hygiene) and (2) sole hemorrhage and sole ulcer (i.e., diseases related to feeding). The genetic correlations between traits within the 2 groups were high (0.87 and 0.73, respectively), whereas the genetic correlations between traits in different groups were low (≤0.23). These results indicate that the 2 groups of hoof diseases are partly influenced by the same genes. All genetic correlations between hoof diseases and protein yield were low to moderate and unfavorable. Moderate and favorable genetic correlations were found between the feed-related hoof diseases and clinical mastitis (0.35 and 0.32), whereas the genetic correlations between the hygiene-related hoof diseases and clinical mastitis were low and not significantly different from zero. The genetic correlations between the hygiene-related hoof diseases and number of inseminations were low to moderate and favorable (0.32 and 0.22), and the genetic correlations between the feed-related hoof diseases and number of inseminations were low and not significantly different from zero. A moderate genetic correlation was found between sole ulcer and days from calving to first insemination (0.33), whereas the genetic correlations between days from calving to first insemination and sole hemorrhage and the hygiene-related hoof diseases were low and not significantly different from zero. In general, the 2 groups of hoof diseases showed different patterns of genetic correlations to the other functional traits, but both were unfavorably correlated to protein yield. A simulation study showed that inclusion of hoof diseases in the selection index will not only reduce the genetic decline in resistance to hoof diseases but also be favorable for other functional traits and improve overall genetic merit.

Measuring and mitigating methane (CH 4 ) emissions from livestock is of increasing importance for the environment and for policy making. Potentially, the most sustainable way of reducing enteric CH 4 emission from ruminants is through the estimation of genomic breeding values to facilitate genetic selection. There is potential for adopting genetic selection and in the future genomic selection, for reduced CH 4 emissions from ruminants. From this review it has been observed that both CH 4 emissions and production (g/day) are a heritable and repeatable trait. CH 4 emissions are strongly related to feed intake both in the short term (minutes to several hours) and over the medium term (days). When measured over the medium term, CH 4 yield (MY, g CH 4 /kg dry matter intake) is a heritable and repeatable trait albeit with less genetic variation than for CH 4 emissions. CH 4 emissions of individual animals are moderately repeatable across diets, and across feeding levels, when measured in respiration chambers. Repeatability is lower when short term measurements are used, possibly due to variation in time and amount of feed ingested prior to the measurement. However, while repeated measurements add value; it is preferable the measures be separated by at least 3 to 14 days. This temporal separation of measurements needs to be investigated further. Given the above issue can be resolved, short term (over minutes to hours) measurements of CH 4 emissions show promise, especially on systems where animals are fed ad libitum and frequency of meals is high. However, we believe that for short-term measurements to be useful for genetic evaluation, a number (between 3 and 20) of measurements will be required over an extended period of time (weeks to months). There are opportunities for using short-term measurements in standardised feeding situations such as breath 'sniffers' attached to milking parlours or total mixed ration feeding bins, to measure CH 4 . Genomic selection has the potential to reduce both CH 4 emissions and MY, but measurements on thousands of individuals will be required. This includes the need for combined resources across countries in an international effort, emphasising the need to acknowledge the impact of animal and production systems on measurement of the CH 4 trait during design of experiments.Keywords: genetics, greenhouse gases, enteric methane, ruminants ImplicationMeasuring and mitigating methane (CH 4 ) emissions from livestock is of increasing importance for the environment and for policy making. Potentially, the most sustainable way of reducing enteric CH 4 emission from ruminants is through the estimation of genomic breeding values to facilitate genetic selection. Enteric CH 4 emissions are difficult and expensive to measure, thus genomic prediction could provide significant, † E-mail: Yvette IntroductionClimate change is of growing international concern and it is well established that the release of greenhouse gases (GHG) is the driving factor (IPCC, 2006). Globally, livestock farming contributes...

It may be possible for dairy farms to improve profitability and reduce environmental impacts by selecting for higher feed efficiency and lower methane (CH4) emission traits. It remains to be clarified how CH4 emission and feed efficiency traits are related to each other, which will require direct and accurate measurements of both of these traits in large numbers of animals under the conditions in which they are expected to perform. The ranking of animals for feed efficiency and CH4 emission traits can differ depending upon the type and duration of measurement used, the trait definitions and calculations used, the period in lactation examined and the production system, as well as interactions among these factors. Because the correlation values obtained between feed efficiency and CH4 emission data are likely to be biased when either or both are expressed as ratios, therefore researchers would be well advised to maintain weighted components of the ratios in the selection index. Nutrition studies indicate that selecting low emitting animals may result in reduced efficiency of cell wall digestion, that is NDF, a key ruminant characteristic in human food production. Moreover, many interacting biological factors that are not measured directly, including digestion rate, passage rate, the rumen microbiome and rumen fermentation, may influence feed efficiency and CH4 emission. Elucidating these mechanisms may improve dairy farmers ability to select for feed efficiency and reduced CH4 emission.

Residual feed intake (RFI) is a candidate trait for feed efficiency in dairy cattle. We investigated the influence of lactation stage on the effect of energy sinks in defining RFI and the genetic parameters for RFI across lactation stages for primiparous dairy cattle. Our analysis included 747 primiparous Holstein cows, each with recordings on dry matter intake (DMI), milk yield, milk composition, and body weight (BW) over 44 lactation weeks. For each individual cow, energy-corrected milk (ECM), metabolic BW (MBW), and change in BW (ΔBW) were calculated in each week of lactation and were taken as energy sinks when defining RFI. Two RFI models were considered in the analyses; RFI model [1] was a 1-step RFI model with constant partial regression coefficients of DMI on energy sinks (ECM, MBW, and ΔBW) over lactation. In RFI model [2], data from 44 lactation weeks were divided into 11 consecutive lactation periods of 4 wk in length. The RFI model [2] was identical to model [1] except that period-specific partial regressions of DMI on ECM, MBW, and ΔBW in each lactation period were allowed across lactation. We estimated genetic parameters for RFI across lactation by both models using a random regression method. Using RFI model [2], we estimated the period-specific effects of ECM, MBW, and ΔBW on DMI in all lactation periods. Based on results from RFI model [2], the partial regression coefficients of DMI on ECM, MBW, and ΔBW differed across lactation in RFI. Constant partial regression coefficients of DMI on energy sinks over lactation was not always sufficient to account for the effects across lactation and tended to give roughly average information from all period-specific effects. Heritability for RFI over 44 lactation weeks ranged from 0.10 to 0.29 in model [1] and from 0.10 to 0.23 in model [2]. Genetic variance and heritability estimates for RFI from model [2] tended to be slightly lower and more stable across lactation than those from model [1]. In both models, RFI was genetically different over lactation, especially between early and later lactation stages. Genetic correlation estimates for RFI between early and later lactation tended to be higher when using model [2] compared with model [1]. In conclusion, partial regression coefficients of DMI on energy sinks differed across lactation when modeling RFI. Neglect of lactation stage when defining RFI could affect the assessment of RFI and the estimation of genetic parameters for RFI across lactation.