Variation in enteric methane emissions among cows on commercial dairy farms

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.     

Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources

Milk fatty acid (FA) profile has been previously used as a predictor of enteric CH₄output in dairy cows fed diets supplemented with plant oils, which can potentially impact ruminal fermentation. The objective of this study was to investigate the relationships between milk FA and enteric CH₄ emissions in lactating dairy cows fed different types of forages in the context of commonly fed diets. A total of 81 observations from three separate 3×3 Latin square design (32-day periods) experiments including a total of 27 lactating cows (96±27 days in milk; mean±SD) were used. Dietary forages were included at 60% of ration dry matter and were as follows: (1) 100% corn silage, (2) 100% alfalfa silage, (3) 100% barley silage, (4) 100% timothy silage, (5) 50 : 50 mix of corn and alfalfa silages, (6) 50 : 50 mix of barley and corn silages and (7) 50 : 50 mix of timothy and alfalfa silages. Enteric CH₄output was measured using respiration chambers during 3 consecutive days. Milk was sampled during the last 7 days of each period and analyzed for components and FA profile. Test variables included dry matter intake (DMI; kg/day), NDF (%), ether extract (%), milk yield (kg/day), milk components (%) and individual milk FA (% of total FA). Candidate multivariate models were obtained using the Least Absolute Shrinkage and Selection Operator and Least-Angle Regression methods based on the Schwarz Bayesian Criterion. Data were then fitted into a random regression using the MIXED procedure including the random effects of cow, period and study. A positive correlation was observed between CH₄ and DMI (r=0.59,P<0.001), whereas negative associations were observed between CH₄ and cis9-17:1 (r=−0.58, P<0.001), and trans8, cis13-18:2 (r=−0.51,P<0.001). Three different candidate models were selected and the best fit candidate model predicted CH₄ with a coefficient of determination of 0.84 after correction for cow, period and study effects and was: CH₄ (g/day)=319.7−57.4×15:0−13.8×cis9-17:1−39.5×trans10-18:1−59.9×cis11-18:1−253.1×trans8, cis12-18:2−642.7×trans8, cis13-18:2−195.7×trans11, cis15-18:2+16.5×DMI. Overall and linear prediction biases of all models were not significant (P>0.19). Milk FA profile and DMI can be used to predict CH₄emissions in dairy cows across a wide range of dietary forage sources     

Evaluation of nitrogen excretion equations for ryegrass pasture-fed dairy cows

Accurate and precise estimates of nitrogen (N) excretion in faeces and urine of dairy cattle may provide direct tools to improve N management and thus, to mitigate environmental pollution from dairy production. Empirical equations of N excretion have been evaluated for indoor dairy cattle but there is no evaluation for cows fed high proportions of fresh forage. Therefore, the objective of the current study was to evaluate N excretion equations with a unique data set of zero-grazing experiments. Through literature searches, 89 predictive equations were identified from 13 studies. An independent data set was developed from seven zero-grazing experiments with, in total, 55 dairy Holstein-Friesian cows. Models’ performance was evaluated with statistics derived from a mixed-effect model and a simple regression analysis model. Squared sample correlation coefficients were used as indicators of precision and based on either the best linear unbiased predictions (R²_BLUP) or model-predicted estimates (R²_MDP) derived from the mixed model and simple regression analysis, respectively. The slope (β₀), the intercept (β₁) and the root mean square prediction error (RMSPE_m%) were calculated with the mixed-effect model and used to assess accuracy. The root mean square prediction error (RMSPE_sr%) and the decomposition of the mean square prediction error were calculated with the simple regression analysis and were used to estimate the error due to central tendency (mean bias), regression (systematic bias), and random variation. Concordance correlation coefficient (CCC) were also calculated with the simple regression analysis model and were used to simultaneously assess accuracy and precision. Considering both analysis models, results suggested that urinary N excretion (UN; R²_MDP = 0.76, R²_BLUP = 0.89, RMSPE_m% = 17.2, CCC = 0.82), total manure N excretion (ManN; R²_MDP = 0.83, R²_BLUP = 0.90, RMSPE_m% = 11.0, CCC = 0.84) and N apparently digested (NAD; R²_MDP = 0.97, R²_BLUP = 0.97, RMSPE_m% = 5.3, CCC = 0.95) were closely related to N intake. Milk N secretion was better predicted using milk yield as a single independent variable (MilkN; R²_MDP = 0.77, R²_BLUP = 0.97, RMSPE_m% = 6.0, CCC = 0.74). Additionally, DM intake was a good predictor of UN and ManN and dietary CP concentration of UN and ManN. Consequently, results suggest that several evaluated empirical equations can be used to make accurate and precise predictions concerning N excretion from dairy cows being fed on fresh forage.     

Non-invasive individual methane measurement in dairy cows

Attempts to lower the environmental footprint of milk production needs a sound understanding of the genetic and nutritional basis of methane (CH₄) emissions from the dairy production systems. This in turn requires accurate and reliable techniques for the measurement of CH₄ output from individual cows. Many of the available measurement techniques so far are either slow, expensive, labor intensive and are unsuitable for large-scale individual animal measurements. The main objectives of this study were to examine and validate a non-invasive individual cow CH₄ measurement system that is based on photoacoustic IR spectroscopy (PAS) technique implemented in a portable gas analysis equipment (F10), referred to as PAS-F10 method and to estimate the magnitude of between-animal variations in CH₄ output traits. Data were collected from 115 Nordic Red cows of the Minkiö experimental dairy farm, at the Natural Resources Institute Finland (Luke). Records on continuous daily measurements of CH₄, milk yield, feed intake and BW measurements over 2 years period were compiled for data analysis. The daily CH₄ output was calculated using carbon dioxide as a tracer method. Estimates from the non-invasive PAS-F10 technique were then tested against open-circuit indirect respiration calorimetric chamber measurements and against estimates from other widely used prediction models. Concordance analysis was used to establish agreement between the chamber and PAS-F10 methods. A linear mixed model was used for the analysis of the large continuous data. The daily CH₄ output of cows was 555 l/day and ranged from 330 to 800 l/day. Dry matter intake, level of milk production, lactation stage and diurnal variation had significant effects on daily CH₄ output. Estimates of the daily CH₄ output from PAS-F10 technique compared relatively well with the other techniques. The concordance correlation coefficient between combined weekly CH₄ output estimates of PAS-F10 and chamber was 0.84 with lower and upper confidence limits of 0.65 and 0.93, respectively. Similarly, when chamber CH₄ measurements were predicted from PAS-F10 measurements, the mean of two separate weekly PAS-F10 measurements gave the lowest prediction error variance than either of the separate weekly PAS-F10 measurements alone. This suggests that every other week PAS-F10 measurements when combined would improve the estimation of CH₄ output with PAS-F10 technique. The repeatability of daily CH₄ output from PAS-F10 technique ranged from 0.40 to 0.46 indicating that some between-animal variation exist in CH₄ output traits.     

Models for predicting enteric methane emissions from dairy cows in North America,Europe, and Australia and New Zealand

There are several models in the literature for predicting enteric methane (CH₄) emissions. These models were often developed on region or country‐specific data and may not be able to predict the emissions successfully in every region. The majority of extant models require dry matter intake (DMI) of individual animals, which is not routinely measured. The objectives of this study were to (i) evaluate performance of extant models in predicting enteric CH₄ emissions from dairy cows in North America (NA), Europe (EU), and Australia and New Zealand (AUNZ) and (ii) explore the performance using estimated DMI. Forty extant models were challenged on 55, 105, and 52 enteric CH₄ measurements (g per lactating cow per day) from NA, EU, and AUNZ, respectively. The models were ranked using root mean square prediction error as a percentage of the average observed value (RMSPE) and concordance correlation coefficient (CCC). A modified model of Nielsen et al. (Acta Agriculturae Scand Section A, 63 , 2013 and 126) using DMI, and dietary digestible neutral detergent fiber and fatty acid contents as predictor variables, were ranked highest in NA (RMSPE = 13.1% and CCC = 0.78). The gross energy intake‐based model of Yan et al. (Livestock Production Science, 64 , 2000 and 253) and the updated IPCC Tier 2 model were ranked highest in EU (RMSPE = 11.0% and CCC = 0.66) and AUNZ (RMSPE = 15.6% and CCC = 0.75), respectively. DMI of cows in NA and EU was estimated satisfactorily with body weight and fat‐corrected milk yield data (RMSPE < 12.0% and CCC > 0.60). Using estimated DMI, the Nielsen et al. (2013) (RMSPE = 12.7 and CCC = 0.79) and Yan et al. (2000) (RMSPE = 13.7 and CCC = 0.50) models still predicted emissions in respective regions well. Enteric CH₄ emissions from dairy cows can be predicted successfully (i.e., RMSPE < 15%), if DMI can be estimated with reasonable accuracy (i.e., RMSPE < 10%).     

Diet supplementation with cinnamon oil,cinnamaldehyde, or monensin does not reduce enteric methane production of dairy cows

This study was conducted to evaluate the effect of dietary addition of cinnamon oil (CIN), cinnamaldehyde (CDH), or monensin (MON) on enteric methane (CH₄) emission in dairy cows. Eight multiparous lactating Holstein cows fitted with ruminal cannulas were used in a replicated 4×4 Latin square design (28-day periods). Cows were fed (ad libitum) a total mixed ration ((TMR); 60 : 40 forage : concentrate ratio, on a dry matter (DM) basis) not supplemented (CTL), or supplemented with CIN (50 mg/kg DM intake), CDH (50 mg/kg DM intake), or monensin (24 mg/kg of DM intake). Dry matter intake (DMI), nutrient digestibility, N retention, and milk performance were measured over 6 consecutive days. Ruminal degradability of the basal diet (with no additive) was assessed using in sacco incubations (0, 2, 4, 8, 16, 24, 48, 72 and 96 h). Ruminal fermentation characteristics (pH, volatile fatty acids (VFA), and ammonia (NH₃)) and protozoa were determined over 2 days. Enteric CH₄ emissions were measured over 6 consecutive days using the sulfur hexafluoride (SF₆) tracer gas technique. Adding CIN, CDH or MON to the diet had no effects on DMI, N retention, in sacco ruminal degradation and nutrient digestibility of the diet. Ruminal fermentation characteristics and protozoa numbers were not modified by including the feed additives in the diet. Enteric CH₄ emission and CH₄ energy losses averaged 491 g/day and 6.59% of gross energy intake, respectively, and were not affected by adding CIN, CDH or MON to the diet. Results of this study indicate that CIN, CDH and MON are not viable CH₄ mitigation strategies in dairy cows.     

Long-term effect of linseed plus nitrate fed to dairy cows on enteric methane emission and nitrate and nitrite residuals in milk

A previous study showed the additive methane (CH₄)-mitigating effect of nitrate and linseed fed to non-lactating cows. Before practical application, the use of this new strategy in dairy cows requires further investigation in terms of persistency of methanogenesis reduction and absence of residuals in milk products. The objective of this experiment was to study the long-term effect of linseed plus nitrate on enteric CH₄ emission and performance in dairy cows. We also assessed the effect of this feeding strategy on the presence of nitrate residuals in milk products, total tract digestibility, nitrogen (N) balance and rumen fermentation. A total of 16 lactating Holstein cows were allocated to two groups in a randomised design conducted in parallel for 17 weeks. Diets were on a dry matter (DM) basis: (1) control (54% maize silage, 6% hay and 40% concentrate; CON) or (2) control plus 3.5% added fat from linseed and 1.8% nitrate (LIN+NIT). Diets were equivalent in terms of CP (16%), starch (28%) and NDF (33%), and were offered twice daily. Cows were fed ad libitum, except during weeks 5, 16 and 17 in which feed was restricted to 95% of dry matter intake (DMI) to ensure complete consumption of meals during measurement periods. Milk production and DMI were measured weekly. Nitrate and nitrite concentrations in milk and milk products were determined monthly. Daily CH₄ emission was quantified in open circuit respiration chambers (weeks 5 and 16). Total tract apparent digestibility, N balance and rumen fermentation parameters were determined in week 17. Daily DMI tended to be lower with LIN+NIT from week 4 to 16 (−5.1 kg/day on average). The LIN+NIT diet decreased milk production during 6 non-consecutive weeks (−2.5 kg/day on average). Nitrate or nitrite residuals were not detected in milk and associated products. The LIN+NIT diet reduced CH₄ emission to a similar extent at the beginning and end of the trial (−47%, g/day; −30%, g/kg DMI; −33%, g/kg fat- and protein-corrected milk, on average). Diets did not affect N efficiency and nutrients digestibility. In the rumen, LIN+NIT did not affect protozoa number but reduced total volatile fatty acid (−12%) and propionate (−31%) concentrations. We concluded that linseed plus nitrate may have a long-term CH₄-mitigating effect in dairy cows and that consuming milk products from cows fed nitrate may be safe in terms of nitrate and nitrite residuals. Further work is required to optimise the doses of linseed plus nitrate to avoid reduced cows performance.     

Prediction of enteric methane production,yield, and intensity in dairy cattle using an intercontinental database

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.     

Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows

This study investigates the feasibility to predict individual methane (CH₄) emissions from dairy cows using milk mid-infrared (MIR) spectra. To have a large variability of milk composition, two experiments were conducted on 11 lactating Holstein cows (two primiparous and nine multiparous). The first experiment aimed to induce a large variation in CH₄ emission by feeding two different diets: the first one was mainly composed of fresh grass and sugar beet pulp and the second one of maize silage and hay. The second experiment consisted of grass and corn silage with cracked corn, soybean meal and dried pulp. For each milking period, the milk yields were recorded twice daily and a milk sample of 50 ml was collected from each cow and analyzed by MIR spectrometry. Individual CH₄ emissions were measured daily using the sulfur hexafluoride method during a 7-day period. CH₄ daily emissions ranged from 10.2 to 47.1 g CH₄/kg of milk. The spectral data were transformed to represent an average daily milk spectrum (AMS), which was related to the recorded daily CH₄ data. By assuming a delay before the production of fermentation products in the rumen and their use to produce milk components, five different calculations were used: AMS at days 0, 0.5, 1, 1.5 and 2 compared with the CH₄ measurement. The equations were built using Partial Least Squares regression. From the calculated R²_cv, it appears that the accuracy of CH₄ prediction by MIR changed in function of the milking days. In our experimental conditions, the AMS at day 1.5 compared with the measure of CH₄ emissions gave the best results. The R² and s.e. of the cross-validation were equal to 0.79 and 5.14 g of CH₄/kg of milk. The multiple correlation analysis performed in this study showed the existence of a close relationship between milk fatty acid (FA) profile and CH₄ emission at day 1.5. The lower R² (R² = 0.76) obtained between FA profile and CH₄ emission compared with the one corresponding to the obtained calibration (R²_c = 0.87) shows the interest to apply directly the developed CH₄ equation instead of the use of correlations between FA and CH₄. In conclusion, our preliminary results suggest the feasibility of direct CH₄ prediction from milk MIR spectra. Additional research has the potential to improve the calibrations even further. This alternative method could be useful to predict the individual CH₄ emissions at farm level or at the regional scale and it also could be used to identify low-CH₄-emitting cows.     

Implications of dairy systems on enteric methane and postulated effects on total greenhouse gas emission

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.     

Mode of action of Saccharomyces cerevisiae in enteric methane mitigation in pigs

The objectives of this study were to determine the effect and mode of action of Saccharomyces cerevisiae (YST2) on enteric methane (CH₄) mitigation in pigs. A total of 12 Duroc×Landrace×Yorkshire male finisher pigs (60±1 kg), housed individually in open-circuit respiration chambers, were randomly assigned to two dietary groups: a basal diet (control); and a basal diet supplemented with 3 g/YST2 (1.8×10¹⁰ live cells/g) per kg diet. At the end of 32-day experiment, pigs were sacrificed and redox potential (Eh), pH, volatile fatty acid concentration, densities of methanogens and acetogens, and expression of methyl coenzyme-M reductase subunit A gene were determined in digesta contents from the cecum, colon and rectum. Results showed that S. cerevisiae YST2 decreased (P<0.05) the average daily enteric CH₄ production by 25.3%, lowered the pH value from 6.99 to 6.69 in the rectum, and increased the Eh value in cecum and colon by up to −55 mV (P<0.05). Fermentation patterns were also altered by supplementation of YST2 as reflected by the lower acetate, and higher propionate molar proportion in the cecum and colon (P<0.05), resulting in lower acetate : propionate ratio (P<0.05). Moreover, there was a 61% decrease in Methanobrevibacter species in the upper colon (P<0.05) and a 19% increase in the acetogen community in the cecum (P<0.05) of treated pigs. Results of our study concluded that supplementation of S. cerevisiae YST2 at 3 g/kg substantially decreased enteric CH₄ production in pigs.     

Effects of nitrogen fertilisation rate and maturity of grass silage on methane emission by lactating dairy cows

Grass silage is typically fed to dairy cows in temperate regions. However, in vivo information on methane (CH₄) emission from grass silage of varying quality is limited. We evaluated the effect of two rates of nitrogen (N) fertilisation of grassland (low fertilisation (LF), 65 kg of N/ha; and high fertilisation (HF), 150 kg of N/ha) and of three stages of maturity of grass at cutting: early maturity (EM; 28 days of regrowth), mid maturity (MM; 41 days of regrowth) and late maturity (LM; 62 days of regrowth) on CH₄ production by lactating dairy cows. In a randomised block design, 54 lactating Holstein–Friesian dairy cows (168±11 days in milk; mean±standard error of mean) received grass silage (mainly ryegrass) and compound feed at 80 : 20 on dry matter basis. Cows were adapted to the diet for 12 days and CH₄ production was measured in climate respiration chambers for 5 days. Dry matter intake (DMI; 14.9±0.56 kg/day) decreased with increasing N fertilisation and grass maturity. Production of fat- and protein-corrected milk (FPCM; 24.0±1.57 kg/day) decreased with advancing grass maturity but was not affected by N fertilisation. Apparent total-tract feed digestibility decreased with advancing grass maturity but was unaffected by N fertilisation except for an increase and decrease in N and fat digestibility with increasing N fertilisation, respectively. Total CH₄ production per cow (347±13.6 g/day) decreased with increasing N fertilisation by 4% and grass maturity by 6%. The smaller CH₄ production with advancing grass maturity was offset by a smaller FPCM and lower feed digestibility. As a result, with advancing grass maturity CH₄ emission intensity increased per units of FPCM (15.0±1.00 g CH₄/kg) by 31% and digestible organic matter intake (33.1±0.78 g CH₄/kg) by 15%. In addition, emission intensity increased per units of DMI (23.5±0.43 g CH₄/kg) by 7% and gross energy intake (7.0±0.14% CH₄) by 9%, implying an increased loss of dietary energy with advancing grass maturity. Rate of N fertilisation had no effect on CH₄ emissions per units of FPCM, DMI and gross energy intake. These results suggest that despite a lower absolute daily CH₄ production with a higher N fertilisation rate, CH₄ emission intensity remains unchanged. A significant reduction of CH₄ emission intensity can be achieved by feeding dairy cows silage of grass harvested at an earlier stage of maturity.     

Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows

Milk fatty acid (MFA) have already been used to model methane (CH₄) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH₄ expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R²_(m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R²_(v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R²_(v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH₄ emissions, the data set was split into three categories according to CH₄ emissions: LOW (lowest 25% CH₄ emissions); HIGH (highest 25% CH₄ emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH₄ emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH₄ (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH₄ (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH₄ emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.     

Relationship between rumen methanogens and methane production in dairy cows fed diets supplemented with a feed enzyme additive

Aims: To investigate the relationship between ruminal methanogen community and host enteric methane (CH₄) production in lactating dairy cows fed diets supplemented with an exogenous fibrolytic enzyme additive. Methods and Results: Ecology of ruminal methanogens from dairy cows fed with or without exogenous fibrolytic enzymes was examined using PCR–denaturing gradient gel electrophoresis (PCR–DGGE) analyses and quantitative real‐time PCR (qRT‐PCR). The density of methanogens was not affected by the enzyme additive or sampling times, and no relationship was observed between the total methanogen population and CH₄ yield (as g per head per day or g kg^?1 DMI). The PCR–DGGE profiles consisted of 26 distinctive bands, with two bands similar to Methanogenic archaeon CH1270 negatively correlated, and one band similar to Methanobrevibacter gottschalkii strain HO positively correlated, with CH₄ yield. Three bands similar to Methanogenic archaeon CH1270 or Methanobrevibacter smithii ATCC 35061 appeared after enzyme was added. Conclusions: Supplementing a dairy cow diet with an exogenous fibrolytic enzyme additive increased CH₄ yield and altered the composition of the rumen methanogen community, but not the overall density of methanogens. Significance and Impact of the Study: This is the first study to identify the correlation between methanogen ecology and host CH₄ yield from lactating dairy cows.     

The influence of grass silage-to-maize silage ratio and concentrate composition on methane emissions,performance and milk composition of dairy cows

It is well-established that altering the proportion of starch and fibre in ruminant diets can alter ruminal and post-ruminal digestion, although quantitative evidence that this reduces enteric methane (CH₄) production in dairy cattle is lacking. The objective of this study was to examine the effect of varying grass-to-maize silage ratio (70 : 30 and 30 : 70 DM basis), offered ad libitum, with either a concentrate that was high in starch or fibre, on CH₄ production, intake, performance and milk composition of dairy cows. A total of 20 cows were allocated to one of the four experimental diets in a two-by-two factorial design run as a Latin square with each period lasting 28 days. Measurements were conducted during the final 7 days of each period. Cows offered the high maize silage ration had a higher dry matter intake (DMI), milk yield, milk energy output and lower CH₄ emissions when expressed per kg DMI and per unit of ingested gross energy, but there was no difference in total CH₄ production. Several of the milk long-chain fatty acids (FA) were affected by forage treatment with the most notable being an increase in 18:0, 18:1 c9, 18:2 c9 c12 and total mono unsaturated FA, observed in cows offered the higher inclusion of maize silage, and an increase in 18:3 c9 c12 c15 when offered the higher grass silage ration. Varying the composition of the concentrate had no effect on DMI or milk production; however, when the high-starch concentrate was fed, milk protein concentration and milk FAs, 10:0, 14:1, 15:0, 16:1, increased and 18:0 decreased. Interactions were observed for milk fat concentration, being lower in cows offered high-grass silage and high-fibre concentrates compared with the high-starch concentrate, and FA 17:0, which was the highest in milk from cows fed the high-grass silage diet supplemented with the high-starch concentrate. In conclusion, increasing the proportion of maize silage in the diets of dairy cows increased intake and performance, and reduced CH₄ production, but only when expressed on a DM or energy intake basis, whereas starch-to-fibre ratio in the concentrate had little effect on performance or CH₄ production.     

Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow

Many feeding trials have been conducted to quantify enteric methane (CH₄) production in ruminants. Although a relationship between diet composition, rumen fermentation and CH₄ production is generally accepted, the efforts to quantify this relationship within the same experiment remain scarce. In the present study, a data set was compiled from the results of three intensive respiration chamber trials with lactating rumen and intestinal fistulated Holstein cows, including measurements of rumen and intestinal digestion, rumen fermentation parameters and CH₄ production. Two approaches were used to calculate CH₄ from observations: (1) a rumen organic matter (OM) balance was derived from OM intake and duodenal organic matter flow (DOM) distinguishing various nutrients and (2) a rumen carbon balance was derived from carbon intake and duodenal carbon flow (DCARB). Duodenal flow was corrected for endogenous matter, and contribution of fermentation in the large intestine was accounted for. Hydrogen (H₂) arising from fermentation was calculated using the fermentation pattern measured in rumen fluid. CH₄ was calculated from H₂ production corrected for H₂ use with biohydrogenation of fatty acids. The DOM model overestimated CH₄/kg dry matter intake (DMI) by 6.1% (R²=0.36) and the DCARB model underestimated CH₄/kg DMI by 0.4% (R²=0.43). A stepwise regression of the difference between measured and calculated daily CH₄ production was conducted to examine explanations for the deviance. Dietary carbohydrate composition and rumen carbohydrate digestion were the main sources of inaccuracies for both models. Furthermore, differences were related to rumen ammonia concentration with the DOM model and to rumen pH and dietary fat with the DCARB model. Adding these parameters to the models and performing a multiple regression against observed daily CH₄ production resulted in R² of 0.66 and 0.72 for DOM and DCARB models, respectively. The diurnal pattern of CH₄ production followed that of rumen volatile fatty acid (VFA) concentration and the CH₄ to CO₂ production ratio, but was inverse to rumen pH and the rumen hydrogen balance calculated from 4×(acetate+butyrate)/2×(propionate+valerate). In conclusion, the amount of feed fermented was the most important factor determining variations in CH₄ production between animals, diets and during the day. Interactions between feed components, VFA absorption rates and variation between animals seemed to be factors that were complicating the accurate prediction of CH₄. Using a ruminal carbon balance appeared to predict CH₄ production just as well as calculations based on rumen digestion of individual nutrients.     

Individual variation and repeatability of methane production from dairy cows estimated by the CO2 method in automatic milking system

The objectives of this study were to investigate the individual variation, repeatability and correlation of methane (CH₄) production from dairy cows measured during 2 different years. A total of 21 dairy cows with an average BW of 619±14.2 kg and average milk production of 29.1±6.5 kg/day (mean±s.d.) were used in the 1^st year. During the 2^nd year, the same cows were used with an average BW of 640±8.0 kg and average milk production of 33.4±6.0 kg/day (mean±s.d.). The cows were housed in a loose housing system fitted with an automatic milking system (AMS). A total mixed ration was fed to the cows ad libitum in both years. In addition, they were offered concentrate in the AMS based on their daily milk yield. The CH₄ and CO₂ production levels of the cows were analysed using a Gasmet DX-4030. The estimated dry matter intake (EDMI) was 19.8±0.96 and 23.1±0.78 (mean±s.d.), and the energy-corrected milk (ECM) production was 30.8±8.03 and 33.7±5.25 kg/day (mean±s.d.) during the 1^st and 2^nd year, respectively. The EDMI and ECM had a significant influence (P<0.001) on the CH₄ (l/day) yield during both years. The daily CH₄ (l/day) production was significantly higher (P<0.05) during the 2^nd year compared with the 1^st year. The EDMI (described by the ECM) appeared to be the key factor in the variation of CH₄ release. A correlation (r=0.54) of CH₄ production was observed between the years. The CH₄ (l/day) production was strongly correlated (r=0.70) between the 2 years with an adjusted ECM production (30 kg/day). The diurnal variation of CH₄ (l/h) production showed significantly lower (P<0.05) emission during the night (0000 to 0800 h). The between-cows variation of CH₄ (l/day, l/kg EDMI and l/kg ECM) was lower compared with the within-cow variation for the 1^st and 2^nd years. The repeatability of CH₄ production (l/day) was 0.51 between 2 years. In conclusion, a higher EDMI (kg/day) followed by a higher ECM (kg/day) showed a higher CH₄ production (l/day) in the 2^nd year. The variations of CH₄ (l/day) among the cows were lower than the within-cow variations. The CH₄ (l/day) production was highly repeatable and, with an adjusted ECM production, was correlated between the years.     

Changes in in vitro gas and methane production from rumen fluid from dairy cows during adaptation to feed additives in vivo

The adaptation of dairy cows to methane (CH₄)-mitigating feed additives was evaluated using the in vitro gas production (GP) technique. Nine rumen-fistulated lactating Holstein cows were grouped into three blocks and within blocks randomly assigned to one of three experimental diets: Control (CON; no feed additive), Agolin Ruminant^R (AR; 0.05 g/kg dry matter (DM)) or lauric acid (LA; 30 g/kg DM). Total mixed rations composed of maize silage, grass silage and concentrate were fed in a 40 : 30 : 30 ratio on DM basis. Rumen fluid was collected from each cow at days −4, 1, 4, 8, 15 and 22 relative to the introduction of the additives in the diets. On each of these days, a 48-h GP experiment was performed in which rumen fluid from each individual donor cow was incubated with each of the three substrates that reflected the treatment diets offered to the cows. DM intake was on average 19.8, 20.1 and 16.2 kg/day with an average fat- and protein-corrected milk production of 30.7, 31.7 and 26.2 kg/day with diet CON, AR and LA, respectively. In general, feed additives in the donor cow diet had a larger effect on gas and CH₄ production than the same additives in the incubation substrate. Incubation substrate affected asymptotic GP, half-time of asymptotic CH₄ production, total volatile fatty acid (VFA) concentration, molar proportions of propionate and butyrate and degradation of organic matter (OMD), but did not affect CH₄ production. No substrate×day interactions were observed. A significant diet×day interaction was observed for in vitro gas and CH₄ production, total VFA concentration, molar proportions of VFA and OMD. From day 4 onwards, the LA diet persistently reduced gas and CH₄ production, total VFA concentration, acetate molar proportion and OMD, and increased propionate molar proportion. In vitro CH₄ production was reduced by the AR diet on day 8, but not on days 15 and 22. In line with these findings, the molar proportion of propionate in fermentation fluid was greater, and that of acetate smaller, for the AR diet than for the CON diet on day 8, but not on days 15 and 22. Overall, the data indicate a short-term effect of AR on CH₄ production, whereas the CH₄-mitigating effect of LA persisted.