Enteric fermentation from livestock accounts for over a quarter of the United States' methane emissions. A potent greenhouse gas, methane has 80 times the global warming potential of carbon dioxide over a 20-year period. An emerging focus of research is the incorporation of algae (e.g., kelp, seaweed or microalgae) into livestock feed, with several studies documenting dramatic suppression of enteric methane emissions in cattle. As part of a nationwide multidisciplinary study of using algae feed supplements to reduce methane emissions and improve dairy productivity, we used focus groups and individual interviews to measure organic and conventional dairy farmer's knowledge and opinions of algae-based feed supplements. Our goals were to learn what both organic and conventional dairy farmers know about algae-based feed supplements, why they do or do not feed them to their cows and if they were interested in the methane-reducing potential of these algal-based feeds. We also sought to understand where they get valued information about animal nutrition. We found most farmers were aware of algae-based feed supplements on the market, but organic farmers were more familiar with marketing claims. Farmers reported feeding algae-based feed supplements to address herd health concerns, especially reproductive issues and pink eye, but expressed rising costs of the supplements as an obstacle. Both organic and conventional farmers expressed interest in suppressing methane emissions, but only if incentives are provided. Lastly, participants receive trusted information about feed supplements from their dairy nutritionists, who help them make decisions around feed purchasing and rations.

In mitigating greenhouse gas (GHG) emissions and reducing the carbon footprint of dairy milk, the use of generic estimates in inventory and accounting methodology at farm level largely ignores variation of on-farm GHG emissions. The present study aimed to implement results of an extant dynamic, mechanistic Tier 3 model for enteric methane (CH4) (applied in Dutch national GHG inventory) in order to capture variation in enteric CH4 emission, and in faecal N and organic matter (OM) digestibility, ultimately required to predict manure CH4 and ammonia emission. Tier 3 model predictions were translated into calculation rules that could easily be implemented in an annual nutrient cycling assessment tool including GHG emissions, which is currently used by Dutch dairy farmers. Calculations focussed on (1) enteric CH4 emission, (2) apparent faecal OM digestibility and (3) apparent faecal N digestibility. Enteric CH4 was expressed in CH4 yield indicated with the term emission factor (EF; g CH4/kg DM) for individual dietary components and feedstuffs. Factors investigated to cover predicted variation in EF value included the level of feed intake, the type of roughage fed (proportions of grass silage and maize silage) and the quality of roughage fed. A minimum number of three classes of roughage type (i.e. 0. 40% and 80% maize silage in roughage DM) appeared necessary to obtain correspondence between interpolated EF values from EF lists and Tier 3 model predictions. A linear decline in EF value with 1% per kg increase in DM intake is adopted based on model simulations. The quality of roughage was represented by the effect of maturity of harvested grass or of the whole plant maize at cutting, based on a survey of modelling as well as experimental work. Also, predictions were assembled for apparent faecal OM digestibility which could be used in national inventory and in farm accounting. Apparent faecal N digestibility (as a major determinant of predicted urinary N excretion) was predicted, to support current Dutch national ammonia emission inventory and to correct the level of N digestibility in farm accounting. Compared to generic values or values retrieved from the Dutch feeding tables, predicted OM and N digestibility and enteric CH4 are better rooted in physiological principles and better reflect observed variation under experimental conditions. The present results apply for conditions with fairly intensive grassland management in temperate regions.

The combination of linseed and nitrate is known to decrease enteric methane emission in dairy cows but few studies have been carried out in fattening cattle for animal liveweight gain, enteric methane emission, animal health and presence of residues in beef products. To address this gap, 16 young bulls received a control (C) diet between weaning at 9 months and 14 months, then were split into two groups of eight balanced on feed intake, BW gain and methane emission to receive either the C diet or a diet moderately supplemented with extruded linseed and calcium nitrate (LN) for 2 months before being slaughtered. On a dry matter (DM) basis, the C diet contained 70% baled grass silage and 30% concentrate mainly made of maize, wheat and rapeseed meal. In the LN diet, rapeseed meal and a fraction of cereals were replaced by 35% extruded linseed and 6% calcium nitrate; linseed fatty acids and nitrate supply in the LN diet were 1.9% and 1.0%, respectively. Methane emission was measured continuously using the GreenFeed system. Methaemoglobin was determined every week in peripheral blood from bulls receiving the LN diet. Nitrate and nitrite concentrations were determined in rumen, liver and tongue sampled at slaughter. Dry matter intake tended to be lower for LN diet (P=0.10). Body weight gain was lower for LN diet (P=0.01; 1.60 and 1.26 kg/day for C and LN diet, respectively). Daily methane emission was 9% lower (P<0.001) for LN than C diet (249 and 271 g/day, respectively) but methane yield did not differ between diets (24.1 and 23.2 g/kg DM intake for C and LN diet, respectively, P=0.34). Methaemoglobin was under the limit of detection (<2% of total haemoglobin) for most animals and was always lower than 5.6%, suggesting an absence of risk to animal health. Nitrite and nitrate concentrations in offal did not differ between C and LN diets. In conclusion, a moderate supply of linseed and nitrate in bull feed failed to decrease enteric methane yield and impaired bull liveweight gain but without adverse effects for animal health and food safety.

Climate change is a growing international concern, and it is well established that the release of greenhouse gases (GHG) is a contributing factor. So far, within animal production, there is little or no concerted effort on long-term breeding strategies to mitigate against GHG from ruminants. In recent years, several consortia have been formed to collect and combine data for genetic evaluation. The discussion areas of these consortia focus on (1) What are methane-determining factors, (2) What are genetic parameters for methane emissions, (3) What proxies can be used, and what is their association with methane emission, and (4) How to move on with breeding for lower emitting animals? The methane-determining factors can be divided into four groups: (1) rumen microbial population, (2) feed intake and diet composition, (3) host physiology and (4) host genetics. The genetic parameters show that enteric methane is a heritable trait, and that it is highly genetically correlated with dry matter intake. So far, the most useful proxies relate to feed intake, milk mid IR spectral data and fatty acids in the milk. To be able to move on with a genetic evaluation and ranking of animals for methane emission, it is crucial to make measurements on commercial farms. In order to make that possible, it will be necessary to develop phenotypes that can be used by the farmer to optimise the production on farm level. Also, it is crucial to develop equipment that makes it possible to make measurements without interfering with everyday routines or identify proxies that are highly related to methane and which could easily be measured on a large scale. International collaboration is essential to make progress in this area. This is both in terms of sharing ideas, experiences and phenotypes, but also in terms of coming to a consensus regarding what phenotype to collect and to select for.

Measuring and mitigating methane (CH4) emissions from livestock is of increasing importance for the environment and for policy making. Potentially, the most sustainable way of reducing enteric CH4 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 CH4 emissions from ruminants. From this review it has been observed that both CH4 emissions and production (g/day) are a heritable and repeatable trait. CH4 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, CH4 yield (MY, g CH4/kg dry matter intake) is a heritable and repeatable trait albeit with less genetic variation than for CH4 emissions. CH4 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 CH4 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 CH4. Genomic selection has the potential to reduce both CH4 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 CH4 trait during design of experiments.

Methane (CH4) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH4 emissions can be substantial. The extent of variation in CH4 emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH4 emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH4 during milking on commercial dairy farms. Enteric CH4 emissions from 1964 individual cows across 21 farms were measured for at least 7 days/cow using CH4 analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH4 emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH4 emissions varied among farms. Considerable variation in CH4 emissions was observed among cows after adjusting for fixed and random effects, with the CV ranging from 22% to 67% within farms. This study confirms that enteric CH4 emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.

In the present study, following the measurement of methane emissions from 160 mature ewes three times, a subset of twenty ewes was selected for further emission and physiological studies. Ewes were selected on the basis of methane yield (MY; g CH4/kg DM intake) being low (Low MY: >1 sd below the mean; n 10) or high (High MY: >1 sd above the mean; n 10) when fed a blended chaff ration at a fixed feeding level (1·2-fold maintenance energy requirements). The difference between the Low- and High-MY groups observed at the time of selection was maintained (P= 0·001) when remeasured 1–7 months later during digesta kinetics studies. Low MY was associated with a shorter mean retention time of particulate (P< 0·01) and liquid (P< 0·001) digesta, less amounts of rumen particulate contents (P< 0·01) and a smaller rumen volume (P< 0·05), but not apparent DM digestibility (P= 0·27) or urinary allantoin excretion (P= 0·89). Computer tomography scanning of the sheep's rumens after an overnight fast revealed a trend towards the Low-MY sheep having more clearly demarcated rumen gas and liquid phases (P= 0·10). These findings indicate that the selection of ruminants for low MY may have important consequences for an animal's nutritional physiology.

Next to dry matter (DM) intake, nutritional factors cause considerable variation in methane (CH4) emitted and nitrogen (N) excreted per kg of DM intake or per kg of milk. Rumen function in particular determines CH4 emission and concomitant (amount and site) of N excretion, including the trade-offs between them with changes in nutrition and cow characteristics. Quantification of the interaction between CH4 and N emission hence requires quantification of effects on rumen function in particular. The models available to quantify CH4 emission require the same types of input. The detail of questions posed determines the choice of model and the required level of detail of model inputs needed to investigate mitigation measures and the interaction between CH4 and N emission for a specific farming case. Simulation results with a mechanistic model of enteric fermentation confirmed a profound impact of nutritional measures on both CH4 and N emission, but also demonstrated that nutritional measures to mitigate N excretion can be associated with an increase in CH4 emission. This result demonstrates the need to consider details on the rumen level when the aim is to quantify accurately the net effect on greenhouse gas emission for a specific case studied, which contrasts with applying generic values. As an alternative to models of quantification, on-farm measurement of emission might be pursued by sampling of excreta and air. The principle problem is that concentrations are measured which not necessarily reflect daily rates. Milk production rate is recorded on-farm however, which makes indicators based on milk composition just as promising candidates to estimate CH4 (milk fat) or N (milk urea) emission, provided bias by variation in milk composition unrelated to CH4 and N emission rate can be prevented.

The effects of feeding total mixed ration (TMR) or pasture forage from a perennial sward under a management intensive grazing (MIG) regimen on grain intake and enteric methane (EM) emission were measured using chambers. Chamber measurement of EM was compared with that of SF6 employed both within chamber and when cows grazed in the field. The impacts of the diet on farm gate greenhouse gas (GHG) emission were also postulated using the results of existing life cycle assessments. Emission of EM was measured in gas collection chambers in Spring and Fall. In Spring, pasture forage fiber quality was higher than that of the silage used in the TMR (47.5% v. 56.3% NDF; 24.3% v. 37.9% ADF). Higher forage quality from MIG subsequently resulted in 25% less grain use relative to TMR (0.24 v. 0.32 kg dry matter/kg milk) for MIG compared with TMR. The Fall forage fiber quality was still better, but the higher quality of MIG pasture was not as pronounced as that in Spring. Neither yield of fat-corrected milk (FCM) which averaged 28.3 kg/day, nor EM emission which averaged 18.9 g/kg dry matter intake (DMI) were significantly affected by diet in Spring. However, in the Fall, FCM from MIG (21.3 kg/day) was significantly lower than that from TMR (23.4 kg/day). Despite the differences in FCM yield, in terms of EM emission that averaged 21.9 g/kg DMI was not significantly different between the diets. In this study, grain requirement, but not EM, was a distinguishing feature of pasture and confinement systems. Considering the increased predicted GHG emissions arising from the production and use of grain needed to boost milk yield in confinement systems, EM intensity alone is a poor predictor of the potential impact of a dairy system on climate forcing.