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. Methane is a potent greenhouse gas and ruminant livestock contribute a substantial amount of total methane from human activities. Variation between cows’ methane production has been found partly due to their genetics (heritable), making genetic selection a promising strategy for breeding low methane emitting cows. We hypothesized that the total methane production by a cow is affected by rumen microbes which are directly responsible for production of methane, as well as the cows’ own genetics and their interaction. We sampled the rumen contents of 750 dairy cows and found the relative abundance of some bacteria and archaea to be heritable and associated with methane production, but the majority of variation in relative abundance of rumen bacteria and archaea is due to non-genetic factors. We compared the amount of variation in methane production associated with host genetics as well as rumen bacteria and archaea and found the host genetics to explain 21% and rumen microbes 13%. Importantly, the two were largely independent of each other, so breeding for low methane emitting cows is unlikely to result in unfavorable changes in the rumen microbiome. However, further functional annotation of rumen microbiota is needed to confirm this. Strategies that target each source of variation can be conducted in parallel to optimize reduction in methane production from dairy cows.

Electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis over the last decade, and it may be considered as a potential trait in a breeding program where selection for improved udder health is included. In this study, various EC traits were investigated for their association with udder health. In total, 322 cows with 549 lactations were included in the study. Cows were classified as healthy or clinically or subclinically infected, and EC was measured repeatedly during milking on each quarter. Four EC traits were defined; the inter-quarter ratio (IQR) between the highest and lowest quarter EC values, the maximum EC level for a cow, IQR between the highest and lowest quarter EC variation, and the maximum EC variation for a cow. Values for the traits were calculated for every milking throughout the entire lactation. All EC traits increased significantly (P < 0.001) when cows were subclinically or clinically infected. A simple threshold test and discriminant function analysis was used to validate the ability of the EC traits to distinguish between cows in different health groups. Traits reflecting the level rather than variation of EC, and in particular the IQR, performed best to classify cows correctly. By using this trait, 80.6% of clinical and 45.0% of subclinical cases were classified correctly. Of the cows classified as healthy, 74.8% were classified correctly. However, some extra information about udder health status was obtained when a combination of EC traits was used.

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.

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.

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 aim of this study was to characterize patterns of energy balance through lactation of cows kept under constant feeding conditions. Danish Holstein, Danish Red, and Jersey cows were studied during consecutive lactations and remained on the same dietary treatment throughout. They were fed a normal (13.55 MJ of digestible energy/kg of dry matter) or a lower energy diet (12.88 MJ of digestible energy/kg of dry matter) ad libitum throughout lactation. Energy balance was calculated using the effective energy (EE) system in such a way that energy balance equated to body energy reserve change. In the EE system the energy values assigned to feeds are directly equivalent to the energy requirements of the animal; 1 MJ of EE supply has the same energy value as 1 MJ of lipid loss from the body. The resulting body energy change data were analyzed using a linear spline model. There was no evidence to suggest that different combinations of breed and parity required different knot placements. The Holstein mobilized significantly more body energy in early lactation than the Danish Red and Jersey breeds. Parity 1 cows mobilized significantly less than parity 2 and 3 cows. There was a significant interaction between breed and parity in the first half of lactation due to parity 1 Jersey cows having a greater mobilization than would be expected of the difference between parities in the other breeds. As lactation progressed, the differences between parities and between breeds decreased. Cows on the higher energy diet had a more positive energy balance. Within breed and parity, the following possible predictors of individual differences in body energy change were examined: fatness-corrected live weight, condition score at calving, and genotype. There was no difference in the predicted cow effect or residual energy balance profile when grouped according to quartiles of corrected live weight or according to condition score at calving. During the period of most negative energy balance (d 14) there was no significant relationship between live weight and intake, suggesting that, within diet type, the systematic patterns of body energy change through lactation in cows that were kept under stable and sufficient nutritional conditions cannot be accounted for by environmental factors such as constrained intake or condition score at calving. Thus, these patterns appear to have a genetic basis. The proportion of the phenotypic variation (remaining after accounting for fixed effects) accounted for by additive genetic effects varied through lactation from 4.2 to 13.0%. Genetic correlations between early and late lactation energy balances were low and close to zero, suggesting that body energy changes in early and late lactation are genetically independent traits.