Technical note: Interchangeability and comparison of methane measurements in dairy cows with 2 noninvasive infrared systems

This study aimed to compare measurements of methane (CH₄) and carbon dioxide (CO₂) concentrations in the breath of dairy cows kept in commercial conditions using the Fourier-transform infrared spectroscopy (FTIR) and nondispersive infrared spectroscopy (NDIR) methods. The measurement systems were installed in an automated milking system. Measurements were carried out for 5 d using both systems during milkings. The measurements were averaged per milking, giving 467 observations of CH₄ and CO₂ concentrations of 44 Holstein Friesian cows. The Pearson correlation between observations from the 2 systems was 0.86 for CH₄, 0.84 for CO₂, and 0.88 for their ratio. The repeatability of FTIR (0.53 for CH₄, 0.57 for CO₂, and 0.28 for their ratio) was somewhat higher than that of NDIR (0.57 for CH₄, 0.47 for CO₂, and 0.25 for their ratio). The coefficient of individual agreement was 0.98 for CH₄, 0.89 for CO₂, and 0.89 for their ratio; the concordance correlation coefficient was 0.48 for both gases and 0.24 for their ratio. We showed that FTIR and NDIR give similar results in commercial farm conditions. They can therefore be used interchangeably to generate a larger data set, which could then be further used for genetic evaluation.     

Genetic parameters for repeatedly recorded enteric methane concentrations of dairy cows

Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH₄) concentrations, using the largest data set from long-term repeatedly recorded CH₄ on cows to date, and to evaluate (1) the accuracy of breeding values for different CH₄ traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH₄ and carbon dioxide (CO₂), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH₄ and CO₂ concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH₄/CO₂ ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH₄ per visit (0.13) but lower for logCH₄ and CH₄/CO₂ (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH₄ traits, apart from the genetic correlations with the CH₄/CO₂ trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH₄, measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH₄ (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations.     

Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk

Enteric methane (CH₄), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH₄ emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH₄:CO₂ ratio and CH₄ production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH₄:CO₂ ratio and CH₄ production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R²_Val) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH₄:CO₂ ratio, and R²_Val = 0.13 with RMSEP = 111 L/d for CH₄ production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R²_Val of 0.07 was obtained for both CH₄:CO₂ ratio and CH₄ production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH₄:CO₂ ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R²_Val increased to 0.31 with RMSEP = 0.0105. For prediction of CH₄ production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH₄ production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH₄ emission. Thus, it is not feasible to predict CH₄ emission based on FT-IR spectra alone.     

Enteric methane emission from Jersey cows during the spring transition from indoor feeding to grazing

Organic dairy cows in Denmark are often kept indoors during the winter and outside at least part time in the summer. Consequently, their diet changes by the season. We hypothesized that grazing might affect enteric CH₄ emissions due to changes in the nutrition, maintenance, and activity of the cows, and they might differentially respond to these factors. This study assessed the repeatability of enteric CH₄ emission measurements for Jersey cattle in a commercial organic dairy herd in Denmark. It also evaluated the effects of a gradual transition from indoor winter feeding to outdoor spring grazing. Further, it assessed the individual-level correlations between measurements during the consecutive feeding periods (phenotype × environment, P × E) as neither pedigrees nor genotypes were available to estimate a genotype by environment effect. Ninety-six mixed-parity lactating Jersey cows were monitored for 30 d before grazing and for 24 d while grazing. The cows spent 8 to 11 h grazing each day and had free access to an in-barn automatic milking system (AMS). For each visit to the AMS, milk yield was recorded and logged along with date and time. Monitoring equipment installed in the AMS feed bins continuously measured enteric CH₄ and CO₂ concentrations (ppm) using a noninvasive “sniffer” method. Raw enteric CH₄ and CO₂ concentrations and their ratio (CH₄:CO₂) were derived from average concentrations measured during milking and per day for each cow. We used mixed models equations to estimate variance components and adjust for the fixed and random effects influencing the analyzed gas concentrations. Univariate models were used to precorrect the gas measurements for diurnal variation and to estimate the direct effect of grazing on the analyzed concentrations. A bivariate model was used to assess the correlation between the 2 periods (in-barn vs. grazing) for each gas concentration. Grazing had a weak P × E interaction for daily average CH₄ and CO₂ gas concentrations. Bivariate repeatability estimates for average CH₄ and CO₂ concentrations and CH₄:CO₂ were 0.77 to 0.78, 0.73 to 0.80, and 0.26, respectively. Repeatability for CH₄:CO₂ was low (0.26) but indicated some between-animal variation. In conclusion, grazing does not create significant shifts compared with indoor feeding in how animals rank for average CH₄ and CO₂ concentrations and CH₄:CO₂. We found no evidence that separate evaluation is needed to quantify enteric CH₄ and CO₂ emissions from Jersey cows during in-barn and grazing periods.     

Reducing enteric methane emissions from dairy cattle: Two ways to supplement 3-nitrooxypropanol

The aim of this work was to determine the effect of 3-nitrooxypropanol (3-NOP) on the enteric methane (CH₄) emissions and performance of lactating dairy cows when mixed in with roughage or incorporated into a concentrate pellet. After 2 pretreatment weeks without 3-NOP supplementation, 30 Holstein Friesian cows were divided into 3 homogeneous treatment groups: no additive, 3-NOP mixed in with the basal diet (roughage; NOP_bas), and 3-NOP incorporated into a concentrate pellet (NOP_conc). The pretreatment period was followed by a 10-wk treatment period in which the NOP_bas and NOP_conc cows were fed 1.6 g of 3-NOP/cow per day. After the treatment period, a 2-wk washout period followed without 3-NOP supplementation. The CH₄ emissions were measured using a GreenFeed unit (C-Lock Inc., Rapid City, SD) installed in a freestall with cubicles during the entire experimental period. On average for the total treatment period and compared with the no-additive group, CH₄ production (g/d) was 28 and 23% lower for NOP_bas and NOP_conc, respectively. Methane yield (g/kg of dry matter intake) and methane intensity (g/kg of milk) were 23 and 24% lower for NOP_bas, respectively, and 21 and 22% lower for NOP_conc, respectively. No differences were found between NOP_bas and NOP_conc. Moreover, supplying 3-NOP did not affect total dry matter intake, milk production, or milk composition. The results of this experiment show that 3-NOP can reduce enteric CH₄ emissions of dairy cattle when incorporated into a concentrate pellet and that this reduction is not different from the effect of mixing in 3-NOP with the basal diet (roughage). This broadens the possibilities for using 3-NOP in the dairy sector worldwide, as it is not always feasible to provide an additive mixed in with the basal diet.     

Effect of nitrate supplementation,dietary protein supply,and genetic yield index on performance,methane emission,and nitrogen efficiency in dairy cows

The objective was to investigate the effect of nonprotein nitrogen source, dietary protein supply, and genetic yield index on methane emission, N metabolism, and ruminal fermentation in dairy cows. Forty-eight Danish Holstein dairy cows (24 primiparous cows and 24 multiparous cows) were used in a 6 × 4 incomplete Latin square design with 4 periods of 21-d duration. Cows were fed ad libitum with the following 6 experimental diets: diets with low, medium, or high rumen degradable protein (RDP):rumen undegradable protein (RUP) ratio (manipulated by changing the proportion of corn meal, corn gluten meal, and corn gluten feed) combined with either urea or nitrate (10 g NO₃⁻/kg of dry matter) as nonprotein nitrogen source. Samples of ruminal fluid and feces were collected from multiparous cows, and total-tract nutrient digestibility was estimated using TiO₂ as flow marker. Milk samples were collected from all 48 cows. Gas emission (CH₄, CO₂, and H₂) was measured by 4 GreenFeed units. We observed no significant interaction between dietary RDP:RUP ratio and nitrate supplementation, and between nitrate supplementation and genetic yield index on CH₄ emission (production, yield, intensity). As dietary RDP:RUP ratio increased, intake of crude protein, RDP, and neutral detergent fiber and total-tract digestibility of crude protein linearly increased, and RUP intake linearly decreased. Yield of milk, energy-corrected milk, and milk protein and lactose linearly decreased, whereas milk fat and milk urea nitrogen concentrations linearly increased as dietary RDP:RUP ratio increased. The increase in dietary RDP:RUP ratio resulted in a linear increase in the excretion of total purine derivatives and N in urine, but a linear decrease in N efficiency (milk N in % of N intake). Nitrate supplementation reduced dry matter intake (DMI) and increased total-tract organic matter digestibility compared with urea supplementation. Nitrate supplementation resulted in a greater reduction in DMI and daily CH₄ production and a greater increase in daily H₂ production in multiparous cows compared with primiparous cows. Nitrate supplementation also showed a greater reduction in milk protein and lactose yield in multiparous cows than in primiparous cows. Milk protein and lactose concentrations were lower for cows receiving nitrate diets compared with cows receiving urea diets. Nitrate supplementation reduced urinary purine derivatives excretion from the rumen, whereas N efficiency tended to increase. Nitrate supplementation reduced proportion of acetate and propionate in ruminal volatile fatty acids. In conclusion, no interaction was observed between dietary RDP:RUP ratio and nitrate supplementation, and no interaction between nitrate supplementation and genetic yield index on CH₄ emission (production, yield, intensity) was noted. Nitrate supplementation resulted in a greater reduction in DMI and CH₄ production, and a greater increase in H₂ production in multiparous cows than in primiparous cows. As the dietary RDP:RUP ratio increased, CH₄ emission was unaffected and RDP intake increased, but RUP intake and milk yield decreased. Genetic yield index did not affect CH₄ production, yield, or intensity.     

Persistence of differences between dairy cows categorized as low or high methane emitters,as estimated from milk mid-infrared spectra and measured by GreenFeed

Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH₄) emitting cows. We investigated the persistence of differences in CH₄ emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH₄ emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH₄ emission was calculated. Traits other than CH₄ emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH₄ production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH₄ data estimated by MIR and measured with GF for individual cows was weak (R² = 0.26). The categorization based on CH₄ production also generated differences in CH₄ emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH₄ estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH₄ production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding.     

Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide

The aims of this study were to compare methods for examining measurements of CH₄ and CO₂ emissions of dairy cows during milking and to assess repeatability and variation of CH₄ emissions among individual dairy cows. Measurements of CH₄ and CO₂ emissions from 36 cows were collected in 3 consecutive feeding periods. In the first period, cows were fed a commercial partial mixed ration (PMR) containing 69% forage. In the second and third periods, the same 36 cows were fed a high-forage PMR ration containing 75% forage, with either a high grass silage or high maize silage content. Emissions of CH₄ during each milking were examined using 2 methods. First, peaks in CH₄ concentration due to eructations during milking were quantified. Second, ratios of CH₄ and CO₂ average concentrations during milking were calculated. A linear mixed model was used to assess differences between PMR. Variation in CH₄ emissions was observed among cows after adjusting for effects of lactation number, week of lactation, diet, individual cow, and feeding period, with coefficients of variation estimated from variance components ranging from 11 to 14% across diets and methods of quantifying emissions. No significant difference was detected between the 3 PMR in CH₄ emissions estimated by either method. Emissions of CH₄ calculated from eructation peaks or as CH₄ to CO₂ ratio were positively associated with forage dry matter intake. Ranking of cows according to CH₄ emissions on different diets was correlated for both methods, although rank correlations and repeatability were greater for CH₄ concentration from eructation peaks than for CH₄-to-CO₂ ratio. We conclude that quantifying enteric CH₄ emissions either using eructation peaks in concentration or as CH₄-to-CO₂ ratio can provide highly repeatable phenotypes for ranking cows on CH₄ output.     

Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection

Mitigation of enteric methane (CH₄) emission in ruminants has become an important area of research because accumulation of CH₄ is linked to global warming. Nutritional and microbial opportunities to reduce CH₄ emissions have been extensively researched, but little is known about using natural variation to breed animals with lower CH₄ yield. Measuring CH₄ emission rates directly from animals is difficult and hinders direct selection on reduced CH₄ emission. However, improvements can be made through selection on associated traits (e.g., residual feed intake, RFI) or through selection on CH₄ predicted from feed intake and diet composition. The objective was to establish phenotypic and genetic variation in predicted CH₄ output, and to determine the potential of genetics to reduce methane emissions in dairy cattle. Experimental data were used and records on daily feed intake, weekly body weights, and weekly milk production were available from 548 heifers. Residual feed intake (MJ/d) is the difference between net energy intake and calculated net energy requirements for maintenance as a function of body weight and for fat- and protein-corrected milk production. Predicted methane emission (PME; g/d) is 6% of gross energy intake (Intergovernmental Panel on Climate Change methodology) corrected for energy content of methane (55.65 kJ/g). The estimated heritabilities for PME and RFI were 0.35 and 0.40, respectively. The positive genetic correlation between RFI and PME indicated that cows with lower RFI have lower PME (estimates ranging from 0.18 to 0.84). Hence, it is possible to decrease the methane production of a cow by selecting more-efficient cows, and the genetic variation suggests that reductions in the order of 11 to 26% in 10 yr are theoretically possible, and could be even higher in a genomic selection program. However, several uncertainties are discussed; for example, the lack of true methane measurements (and the key assumption that methane produced per unit feed is not affected by RFI level), as well as the limitations of predicting the biological consequences of selection. To overcome these limitations, an international effort is required to bring together data on feed intake and methane emissions of dairy cows.     

Validation strategy can result in an overoptimistic view of the ability of milk infrared spectra to predict methane emission of dairy cattle

Because of the environmental impact of methane (CH₄), it is of great interest to reduce CH₄ emission of dairy cattle and selective breeding might contribute to this. However, this approach requires a rapid and inexpensive measurement technique that can be used to quantify CH₄ emission for a large number of individual dairy cows. Milk infrared (IR) spectroscopy has been proposed as a predictor for CH₄ emission. In this study, we investigated the feasibility of milk IR spectra to predict breath sensor–measured CH₄ of 801 dairy cows on 10 commercial farms. To evaluate the prediction equation, we used random and block cross validation. Using random cross validation, we found a validation coefficient of determination (R²val) of 0.49, which suggests that milk IR spectra are informative in predicting CH₄ emission. However, based on block cross validation, with farms as blocks, a negligible R²val of 0.01 was obtained, indicating that milk IR spectra cannot be used to predict CH₄ emission. Random cross validation thus results in an overoptimistic view of the ability of milk IR spectra to predict CH₄ emission of dairy cows. The difference between the validation strategies could be due to the confounding of farm and date of milk IR analysis, which introduces a correlation between batch effects on the IR analyses and farm-average CH₄. Breath sensor–measured CH₄ is strongly influenced by farm-specific conditions, which magnifies the problem. Milk IR wavenumbers from water absorption regions, which are generally considered uninformative, showed moderate accuracy (R²val = 0.25) when based on random cross validation, but not when based on block cross validation (R²val = 0.03). These results indicate, therefore, that in the current study, random cross validation results in an overoptimistic view on the ability of milk IR spectra to predict CH₄ emission. We suggest prediction based on wavenumbers from water absorption regions as a negative control to identify potential dependence structures in the data.     

Technical note: Evaluation of interval between measurements and calculation method for the quantification of enteric methane emissions measured by respiration chamber

Respiration chambers share one analyzer working in parallel, and methane (CH₄) concentrations have to be measured at certain intervals. The maximum and minimum values in the kinetics of CH₄ emissions can be missed during the interval between measurements, which may influence the quantification of CH₄ emissions. Chambers must be opened for morning feeding and cleaning, which causes a loss of CH₄ data. Calculation methods are needed to estimate the lost CH₄ emission data, which may influence the estimated amount of daily CH₄ emissions. In this study, we measured the CH₄ emissions of 10 growing Chinese Holstein dairy heifers in respiration chambers. Methane concentrations were measured every 0.5 min to obtain the 23-h kinetics of CH₄ emissions, which were further selected at different intervals between measurements (i.e., 5, 30, 60, 120, 180, and 240 min) to evaluate the effects of interval on quantification of CH₄ emissions. The missing 1-h kinetics of CH₄ emissions before feeding were not measured, and 2 calculation methods were used to estimate the missing 1-h kinetics of CH₄ emissions: mean value of measuring period (the mean method) and the nearest value of measurement just before chamber opening (the nearest method). The results showed that the rates of CH₄ emission from 10 heifers varied from 4.56 to 11.42 g/h. The increment of intervals decreased maximum rate of CH₄ emission and increased minimum rate of CH₄ emission. Interval caused less than 5% of the difference in measuring CH₄ emissions. Although the mean method had greater estimated daily CH₄ emission than the nearest method, the difference was within 3%. The interval between measurements (≤3 h) and calculation method had little influence on enteric CH₄ emission measurements.     

Molecular hydrogen produced by elemental magnesium inhibits rumen fermentation and enhances methanogenesis in dairy cows

Hydrogen is a key metabolite that connects microbial fermentation and methanogenesis in the rumen. This study was to investigate the effects of elevated H₂ produced by elemental Mg on rumen fermentation and methanogenesis in dairy cows. Four nonlactating Chinese Holstein dairy cows were employed for this experiment in a replicated crossover design. The 2 dietary treatments included a basal diet supplemented with Mg(OH)₂ (14.5 g/kg of feed dry matter) or elemental Mg (6.00 g/kg of feed dry matter). When compared with Mg(OH)₂ treatment, cows fed diet with elemental Mg had similar rumen Mg²⁺ concentration, but higher rumen dissolved H₂ and methane concentrations at 2.5 h after morning feeding. Also, elemental Mg supplementation decreased feed digestibility, rumen volatile fatty acid concentration, and relative abundance of group Ruminococcaceae_UCG-014, genus Bifidobacterium, and group Mollicutes_RF9, increased acetate to propionate ratio, succinate concentration, and abundance of family Christensenellaceae. Elemental Mg supplementation increased enteric CH₄ emission, altered methanogen community with increased abundance of order Methanomassiliicoccales, 16S ribosomal RNA gene copies of methanogens, and order Methanobacteriales. In summary, the pulse of elevated dissolved H₂ after feeding produced by elemental Mg inhibited rumen fermentation and feed digestibility by decreasing the abundance of carbohydrate-degrading bacteria, promoted H₂ incorporation into succinate by increasing family Christensenellaceae and genus Bacteroidales_BS11, and increased H₂ utilization for methanogenesis by favoring growth of methanogens.     

Lactational performance,rumen fermentation,and enteric methane emission of dairy cows fed an amylase-enabled corn silage

This study investigated the effects of an amylase-enabled corn silage on lactational performance, enteric CH₄ emission, and rumen fermentation of lactating dairy cows. Following a 2-wk covariate period, 48 Holstein cows were blocked based on parity, days in milk, milk yield (MY), and CH₄ emission. Cows were randomly assigned to 1 of 2 treatments in an 8-wk randomized complete block design experiment: (1) control corn silage (CON) from an isogenic corn without α-amylase trait and (2) Enogen hybrid corn (Syngenta Seeds LLC) harvested as silage (ECS) containing a bacterial transgene expressing α-amylase (i.e., amylase-enabled) in the endosperm of the grain. The ECS and CON silages were included at 40% of the dietary dry matter (DM) and contained, on average, 43.3 and 41.8% DM and (% DM) 36.7 and 37.5% neutral detergent fiber, and 36.1 and 33.1% starch, respectively. Rumen samples were collected from a subset of 10 cows using the ororuminal sampling technique on wk 3 of the experimental period. Enteric CH₄ emission was measured using the GreenFeed system (C-Lock Inc.). Dry matter intake (DMI) was similar between treatments. Compared with CON, MY (38.8 vs. 40.8 kg/d), feed efficiency (1.47 vs. 1.55 kg of MY/kg of DMI), and milk true protein (1.20 vs. 1.25 kg/d) and lactose yields (1.89 vs. 2.00 kg/d) were increased, whereas milk urea nitrogen (14.0 vs. 12.7 mg/dL) was decreased, with the ECS diet. No effect of treatment on energy-corrected MY (ECM) was observed, but a trend was detected for increased ECM feed efficiency (1.45 vs. 1.50 kg of ECM/kg of DMI) for cows fed ECS compared with CON-fed cows. Daily CH₄ emission was not affected by treatment, but emission intensity was decreased with the ECS diet (11.1 vs. 10.3 g/kg of milk, CON and ECS, respectively); CH₄ emission intensity on ECM basis was not different between treatments. Rumen fermentation, apart from a reduced molar proportion of butyrate in ECS-fed cows, was not affected by treatment. Apparent total-tract digestibility of nutrients and urinary and fecal nitrogen excretions, apart from a trend for increased DM digestibility by ECS-fed cows, were not affected by treatment. Overall, ECS inclusion at 40% of dietary DM increased milk, milk protein, and lactose yields and feed efficiency, and tended to increase ECM feed efficiency but had no effect on ECM yield in dairy cows. The increased MY with ECS led to a decrease in enteric CH₄ emission intensity, compared with the control silage.     

The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential

This study aimed to quantify the relationship between CH₄ emission and fatty acids, volatile metabolites, and nonvolatile metabolites in milk of dairy cows fed forage-based diets. Data from 6 studies were used, including 27 dietary treatments and 123 individual observations from lactating Holstein-Friesian cows. These dietary treatments covered a large range of forage-based diets, with different qualities and proportions of grass silage and corn silage. Methane emission was measured in climate respiration chambers and expressed as production (g per day), yield (g per kg of dry matter intake; DMI), and intensity (g per kg of fat- and protein-corrected milk; FPCM). Milk samples were analyzed for fatty acids by gas chromatography, for volatile metabolites by gas chromatography-mass spectrometry, and for nonvolatile metabolites by nuclear magnetic resonance. Dry matter intake was 15.9 ± 1.90 kg/d (mean ± SD), FPCM yield was 25.2 ± 4.57 kg/d, CH₄ production was 359 ± 51.1 g/d, CH₄ yield was 22.6 ± 2.31 g/kg of DMI, and CH₄ intensity was 14.5 ± 2.59 g/kg of FPCM. The results show that changes in individual milk metabolite concentrations can be related to the ruminal CH₄ production pathways. Several of these relationships were diet driven, whereas some were partly dependent on FPCM yield. Next, prediction models were developed and subsequently evaluated based on root mean square error of prediction (RMSEP), concordance correlation coefficient (CCC) analysis, and random 10-fold cross-validation. The best models with milk fatty acids (in g/100 g of fatty acids; MFA) alone predicted CH₄ production, yield, and intensity with a RMSEP of 34 g/d, 2.0 g/kg of DMI, and 1.7 g/kg of FPCM, and with a CCC of 0.67, 0.44, and 0.75, respectively. The CH₄ prediction potential of both volatile metabolites alone and nonvolatile metabolites alone was low, regardless of the unit of CH₄ emission, as evidenced by the low CCC values (<0.35). The best models combining the 3 types of metabolites as selection variables resulted in the inclusion of only MFA for CH₄ production and CH₄ yield. For CH₄ intensity, MFA, volatile metabolites, and nonvolatile metabolites were included in the prediction model. This resulted in a small improvement in prediction potential (CCC of 0.80; RMSEP of 1.5 g/kg of FPCM) relative to MFA alone. These results indicate that volatile and nonvolatile metabolites in milk contain some information to increase our understanding of enteric CH₄ production of dairy cows, but that it is not worthwhile to determine the volatile and nonvolatile metabolites in milk to estimate CH₄ emission of dairy cows. We conclude that MFA have moderate potential to predict CH₄ emission of dairy cattle fed forage-based diets, and that the models can aid in the effort to understand and mitigate CH₄ emissions of dairy cows.     

Starch and dextrose at 2 levels of rumen-degradable protein in iso-nitrogenous diets: Effects on lactation performance,ruminal measurements,methane emission,digestibility, and nitrogen balance of dairy cows

Our objectives were to determine the effects of readily rumen-available carbohydrate source (refined starch vs. dextrose), the level of rumen-degradable protein (RDP), and their interaction on lactation performance, ruminal measurements, enteric methane (CH₄) emission, nutrient digestibility, and nitrogen (N) balance in lactating dairy cows. Eighteen mid-lactation multiparous Holstein cows were used in this split-plot study. The main plots were created by randomly assigning 9 cows to diets of 11 or 9% RDP obtained by altering the percentage of soybean meal, expeller soybean meal, and blood meal in the diet. All diets included 16.4% crude protein. In the subplots, the effects of 0:10, 5:5, and 10:0 refined starch:dextrose ratio (% of dietary dry matter) were determined in three 3 × 3 Latin squares by randomly assigning the 9 cows in each RDP level into squares. Each period lasted 4 wk, with the last 2 wk allotted for sample collection. Carbohydrate source × RDP level interaction tended to influence dry matter intake (DMI), the concentration of urinary N, and urinary urea-N. Replacing refined starch with dextrose increased DMI, the molar percentage of ruminal butyrate and valerate, daily CH₄ production (g/d), and fecal N and decreased the molar percentage of ruminal branched-chain volatile fatty acids, feed efficiency (fat- and protein-corrected milk/DMI), and N use efficiency (milk N/intake N) but did not influence nutrient digestibility. Enteric CH₄ production was negatively related to the molar percentage of ruminal propionate but positively related to the molar percentage of ruminal butyrate. Treatments did not influence milk production responses, but cows fed 9% RDP diets had lower ruminal ammonia concentration (7.2 vs. 12.3 mg/dL) and tended to excrete less urinary purine derivatives (428 vs. 493 mmol/d) compared with cows fed 11% RDP diets, suggesting lower ruminal synthesis of microbial protein. Reducing the level of RDP in iso-nitrogenous diets had no effect on nutrient apparent total-tract digestibility, manure excretion and composition, N balance, and CH₄ production. In this study, treatments did not affect yield (20.0 g of CH₄/kg of DMI) or intensity (13.1 g of CH₄/kg of fat- and protein-corrected milk), but methane production (g of CH₄/d) was 7.0% lower and N use efficiency (conversion of intake N into milk protein) was 7.8% higher for cows fed a diet of 28.1% starch and 4.6% water-soluble carbohydrate compared with diets with lower starch and higher water-soluble carbohydrate contents.     

Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows

To reduce methane (CH₄) emissions of dairy cows by animal breeding, CH₄ measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH₄, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH₄ and carbon dioxide production recorded by GreenFeed (GF) and for CH₄ and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH₄ recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH₄ production (grams/day) by GF units and of CH₄ concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH₄ records were averaged per week. For daily and weekly mean GF CH₄ the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH₄ the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH₄ production and sniffer CH₄ concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH₄ concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH₄ production (grams/day) as recorded with GF.     

Differing effects of 2 active dried yeast (Saccharomyces cerevisiae) strains on ruminal acidosis and methane production in nonlactating dairy cows

Fifteen ruminally cannulated, nonlactating Holstein cows were used to measure the effects of 2 strains of Saccharomyces cerevisiae, fed as active dried yeasts, on ruminal pH and fermentation and enteric methane (CH₄) emissions. Nonlactating cows were blocked by total duration (h) that their ruminal pH was below 5.8 during a 6-d pre-experimental period. Within each block, cows were randomly assigned to control (no yeast), yeast strain 1 (Levucell SC), or yeast strain 2 (a novel strain selected for enhanced in vitro fiber degradation), with both strains (Lallemand Animal Nutrition, Montréal, QC, Canada) providing 1 × 10¹⁰ cfu/head per day. Cows were fed once daily a total mixed ration consisting of a 50:50 forage to concentrate ratio (dry matter basis). The yeast strains were dosed via the rumen cannula daily at the time of feeding. During the 35-d experiment, ruminal pH was measured continuously for 7 d (d 22 to 28) by using an indwelling system, and CH₄ gas was measured for 4 d (d 32 to 35) using the sulfur hexafluoride tracer gas technique (with halters and yokes). Rumen contents were sampled on 2 d (d 22 and 26) at 0, 3, and 6 h after feeding. Dry matter intake, body weight, and apparent total-tract digestibility of nutrients were not affected by yeast feeding. Strain 2 decreased the average daily minimum (5.35 vs. 5.65 or 5.66), mean (5.98 vs. 6.24 or 6.34), and maximum ruminal pH (6.71 vs. 6.86 or 6.86), and prolonged the time that ruminal pH was below 5.8 (7.5 vs. 3.3 or 1.0  h/d) compared with the control or strain 1, respectively. The molar percentage of acetate was lower and that of propionate was greater in the ruminal fluid of cows receiving strain 2 compared with cows receiving no yeast or strain 1. Enteric CH₄ production adjusted for intake of dry matter or gross energy, however, did not differ between either yeast strain compared with the control but it tended to be reduced by 10% when strain 2 was compared with strain 1. The study shows that different strains of S. cerevisiae fed as active dried yeasts vary in their ability to modify the rumen fermentative pattern in nonlactating dairy cows. Because strain 2 tended (when compared with strain 1) to lower CH₄ emissions but increase the risk of acidosis, it may be prudent to further evaluate this strain in cattle fed high-forage diets, for which the risk of acidosis is low but CH₄ emissions are high.     

Genetic correlations between methane production and fertility,health, and body type traits in Danish Holstein cows

Our aim was to investigate the genetic correlations between CH₄ production and body conformation, fertility, and health traits in dairy cows. Data were collected from 10 commercial Holstein herds in Denmark, including 5,758 cows with records for body conformation traits, 7,390 for fertility traits, 7,439 for health traits, and 1,397 with individual CH₄ measurements. Methane production was measured during milking in automatic milking systems, using a sniffer approach. Correlations between CH₄ and several different traits were estimated. These traits were interval between calving and first insemination, interval between first and last insemination, number of inseminations, udder diseases, other diseases, height, body depth, chest width, dairy character, top line, and body condition score. Bivariate linear models were used to estimate the genetic parameters within and between CH₄ and the other traits. In general, the genetic correlations between CH₄ and the traits investigated were low. The heritability of CH₄ was 0.25, and ranged from 0.02 to 0.07 for fertility and health traits, and from 0.17 to 0.74 for body conformation traits. Further research with a larger data set should be performed to more accurately establish how CH₄ relates to fertility, health, and body conformation traits in dairy cattle. This will be useful in the design of future breeding goals that consider the production of CH₄.     

Comparison of methane production,intensity, and yield throughout lactation in Holstein cows

Genetic selection to reduce methane (CH₄) emissions from dairy cows is an attractive means of reducing the impact of agricultural production on climate change. In this study, we investigated the feasibility of such an approach by characterizing the interactions between CH₄ and several traits of interest in dairy cows. We measured CH₄, dry matter intake (DMI), fat- and protein-corrected milk (FPCM), body weight (BW), and body condition score (BCS) from 107 first- and second-parity Holstein cows from December 2019 to November 2021. Methane emissions were measured using a GreenFeed device and expressed in terms of production (MeP, in g/d), yield (MeY, in g/kg DMI), and intensity (MeI, in g/kg FPCM). Because of the limited number of cows, only animal parameters were estimated. Both MeP and MeI were moderately repeatable (>0.45), whereas MeY presented low repeatability, especially in early lactation. Mid lactation was the most stable and representative period of CH₄ emissions throughout lactation, with animal correlations above 0.9. The average animal correlations of MeP with DMI, FPCM, and BW were 0.62, 0.48, and 0.36, respectively. The MeI was negatively correlated with FCPM (<−0.5) and DMI (>−0.25), and positively correlated with BW and BCS. The MeY presented stable and weakly positive correlations with the 4 other traits throughout lactation, with the exception of slightly negative animal correlations with FPCM and DMI after the 35th week. The MeP, MeI, and MeY were positively correlated at all lactation stages and, assuming animal and genetic correlations do not strongly differ, selection on one trait should lead to improvements in all. Overall, selection for MeI is probably not optimal as its change would result more from CH₄ dilution in increased milk yield than from real decrease in methane emission. Instead, MeY is related to rumen function and is only weakly associated with DMI, FPCM, BW, and BCS; it thus appears to be the most promising CH₄ trait for selection, provided that this would not deteriorate feed efficiency and that a system of large-scale phenotyping is developed. The MeP is easier to measure and thus may represent an acceptable alternative, although care would need to be taken to avoid undesirable changes in FPCM and BW.