Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle

Paul E Smith; Sinead M Waters; David A Kenny; Stuart F Kirwan; Stephen Conroy; Alan K Kelly

doi:10.1093/jas/skab275

Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle

Smith, Paul E; Waters, Sinead M; Kenny, David A; Kirwan, Stuart F; Conroy, Stephen; Kelly, Alan K 2021-11-01 00:00:00 Residual expressions of enteric emissions favor a more equitable identification of an animal’s methanogenic potential compared with traditional measures of enteric emissions. The objective of this study was to investigate the effect of divergently ranking beef cattle for residual methane emissions (RME) on animal productivity, enteric emissions, and rumen fermentation. Dry matter intake (DMI), growth, feed efficiency, carcass output, and enteric emissions (GreenFeed emissions monitoring system) were recorded on 294 crossbred beef cattle (steers = 135 and heifers = 159; mean age 441 d (SD = 49); initial body weight (BW) of 476 kg (SD = 67)) at the Irish national beef cattle performance test center. Animals were offered a total mixed ration (77% concentrate and 23% forage; 12.6 MJ ME/kg of DM and 12% CP) ad libitum with emissions estimated for 21 d over a mean feed intake measurement period of 91 d. Animals had a mean daily methane emissions (DME) of 229.18 g/d (SD = 45.96), methane yield (MY) of 22.07 g/kg of DMI (SD = 4.06), methane intensity (MI) 0.70 g/kg of carcass weight (SD = 0.15), and RME 0.00 g/d (SD = 0.34). RME was computed as the residuals from a multiple regression model regressing DME on DMI and BW (R = 0.45). Animals were ranked into three groups namely high RME (>0.5 SD above the mean), medium RME (±0.5 SD above/below the mean), and low RME (>0.5 SD below the mean). Low RME animals produced 17.6% and 30.4% less ( < 0.05) P DME compared with medium and high RME animals, respectively. A ~30% reduction in MY and MI was detected in low versus high RME animals. Positive correlations were apparent among all methane traits with RME most highly associated with (r = 0.86) DME. MY and MI were correlated ( < 0.05) with DMI, P growth, feed efficiency, and carcass output. High RME had lower (P < 0.05) ruminal propionate compared with low RME animals and increased ( < 0.05) butyr P ate compared with medium and low RME animals. Propionate was negatively associated ( < 0.05) with all methane tr P aits. Greater acetate:propionate ratio was associated with higher RME ( = 0.18; r P < 0.05). Under the ad libitum feeding regime deployed here, RME was the best predictor of DME and only methane trait independent of animal productivity. Ranking animals on RME presents the opportunity to exploit interanimal variation in enteric emissions as well as providing a more equitable index of the methanogenic potential of an animal on which to investigate the underlying biological regulatory mechanisms. Key words: beef cattle, residual methane emissions © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 1 Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 2 | Journal of Animal Science, 2021, Vol. 99, No. 11 Abbreviations Selection on the basis of methane emissions expressed as a proportion of feed intake (methane yield; MY) has been ADF acid detergent fiber the traditional selection approach, as the trait was previously ADG average daily gain perceived to be free from any association with feed intake or BW body weight body weight (BW) but positively correlated with DME, when CG contemporary group open-circuit respiration chambers and restricted feed intake CH methane were utilized as reference methodology for quantifying enteric CO carbon dioxide emissions (Herd et al., 2014; Donoghue et al., 2016). However, CP crude protein the selection of animals on the basis of ratio traits has been CW carcass weight disputed by virtue of their unpredictable response to other traits DM dry matter of economic importance in beef production (Pickering et al., DMI dry matter intake 2015). In addition, a negative phenotypic correlation between DME daily methane emissions MY and feed intake has recently been observed across both EM early maturing concentrate and forage based diets under ad libitum feeding G:F gain to feed conditions with the use of the GreenFeed emissions monitoring GEM GreenFeed emissions monitoring system (Bird-Gardiner et al., 2017; Renand et al., 2019). system Consequently, due to the aforementioned shortcomings, IMF intramuscular fat there has been increasing interest in the use of the residual LM late maturing methane emissions (RME) concept to identify animals with ME metabolizable energy a greater genetic propensity for lower methane output, NDF neutral detergent fiber principally due to its ability to overcome the limitations RFI residual feed intake associated with proportional expression of methane emissions SCFA short chain fatty acids relative to other traits and by design, its lack of relationship TMR total mixed ration with feed intake. RME can be defined as the difference in the VFA volatile fatty acid animals actual and expected methane output, based on its level of feed intake and BW (Bird-Gardiner et al., 2017). First proposed by Herd et al. (2014), the trait has been observed to Introduction be phenotypically and genetically independent of feed intake and bodyweight (Herd et al., 2014; Donoghue et al., 2016; Bird- Global food production has benefited from the ability of ruminant Gardiner et al., 2017). Indeed, the independence of RME from livestock to convert plant matter into high-quality sources of animal productivity, also affords the opportunity to unravel dairy and meat protein for human consumption (Waters et al., the inherent variation in underlying biological mechanisms 2020). However, ruminant, relative to monogastric, derived food influencing methanogenesis. Currently, there is a paucity products have a much greater carbon intensity (Herrero and of information on the implications of ranking commercially Thornthon, 2013), with methane originating from domesticated representative beef cattle for RME on animal productivity, feed cattle accountable for ~4.5% of anthropogenic emissions (Gerber efficiency, and carcass output. et al., 2013). Consequently, mitigation strategies to reduce Therefore, the objectives of this study were to 1) investigate enteric methane emissions from cattle have been a key research the effects of divergently ranking beef cattle for RME on DME, priority for livestock scientists in recent decades. Numerous yield, intensity, animal productivity, and rumen fermentation; dietary interventions (strategic supplementation with various 2) examine the phenotypic relationships of RME with other feedstuffs and bioactive compounds, combined with animal traits of economic importance to beef production. management approaches) have been advocated to offer potential methane mitigation solutions to livestock producers (Hristov et al., 2013Beauc ; hemin et al., 2020; Honan et al., 2021); Materials and Methods however, a supplement with consistent antimethanogenic All animal procedures used in this study were approved by properties, and no adverse implications to animal performance, the Teagasc Animal Ethics Committee and conducted using is yet to be made commercially available. procedures consistent with the experimental license (AE19132/ Enteric methane emissions is a trait which is moderately P078) issued by the Irish Health Products Regulatory Authority in heritable (h = 0.23 to 0.30) (Pinares-Patiño et al., 2013 Dono ; ghue accordance with European Union legislation (Directive 2010/63/ et al., 2016; Manzanilla-Pech et al., 2016) with large interanimal EU), for the protection of animals used for scientific purposes. inherent variation, presenting the possibility of cumulative and permeant reductions in ruminant livestock derived emissions Animal management and performance test through genetic selection as an alternative mitigation solution (Wall et al., 2010; Pickering et al., 2015; de Hass et al., 2017; Over a period of 18 mo, data were obtained from 294 commercial Beauchemin et al., 2020). Nonetheless, determining the optimal beef cattle (steers = 135 and heifers = 159; mean age 441 d low methane phenotype, with which to select cattle, poses a (SD = 49)) enrolled in a feed efficiency performance test. Cattle challenge due to the relationship of methanogenesis with other were the progeny of AI bulls, under evaluation as part of the traits of importance to animal productivity (de Hass et al., 2017). Gene Ireland Breeding Program (https://www.icbf.com/?page_ Feed intake and daily methane emissions (DME; g/d) are both id=12900), and were recruited from commercial breeding herds, phenotypically (Herd et al., 2014) and genetically (Donoghue et al., based on factors including sire, breed, genetic merit, pedigree, 2016; Manzanilla-Pech et al., 2016) associated. As a result, the and age, and performance tested under standardized conditions implementation of breeding strategies, where DME is the targeted at the Irish Cattle Breeding Federation (ICBF) national beef bull phenotype, will likely result in a concurrent reduction to voluntarypr ogeny test station (Tully, Co. Kildare). Cattle included in this feed intake, and subsequently animal performance, in future study originated from continental late maturing (LM) beef dams generations of livestock (Herd et al., 2014 de Hass et al., ; 2017). (Charolais, Limousin, or Simmental), sired by early maturing Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 3 (EM) or LM sire breeds. The proportion of EM and LM sired of the feed was determined using a LECO 828 Series Macro animals was 25% and 75%, respectively. Combustion instrument (Leco Instruments, UK, Ltd, Stockport, Eligible cattle enter the test center in groups of 40 to 75 cattle,UK). The nitrogen concentration of the feed was multiplied by hereby referred to as “batches”, and undergo a minimum 100 d 6.25, to determine CP concentrations (g/kg DM). Neutral detergent feed efficiency performance test. Starting in January 2019 and fiber (NDF) and acid detergent fiber (ADF) concentrations were finishing in July 2020, animals from seven consecutive batches determined by the method of Van Soest et al. (1991) using the were included in this study. Upon arrival at the facility, cattle wereANK OM220 Fiber Analyzer (ANKOM Technology, Macedon, NY). allocated to indoor pens (6.1 m 4.6 m) bedded with × peat. Cattle TMR and concentrate samples were analyzed for NDF with were separated based on gender and initially penned in groups of sodium sulfite and with a heat stable amylase included for five to six depending on their initial weight and age. Cattle were both sets of samples. NDF and ADF are expressed inclusive offered a 30 d adjustment period to allow dietary acclimatization of residual ash (g/kg DM). Gross energy was determined on and adaption to the facilities. Within the first week of arrival pelletized samples using a bomb calorimeter (Parr Instrument at the test center, animals were fitted with a radio frequency Company, Moline, IL). Ether extract was determined using Soxtec identification tag (HDX EID Tag, Allflex Livestock Intelligence, instruments (Tecator, Höganäs, Sweden) and light petroleum Dallas, TX). Once tagged, pen size was increased by opening the ether. The chemical composition of the TMR and concentrate gates between adjacent pens to accommodate 11 to 30 animals ration are displayed in Table 1. per pen with animals comingled for a minimum 21 d period, prior Animal growth and ultrasonic muscle and fat to the beginning of the feed intake measurement period. This was deposition done to facilitate the measurement of enteric methane production (discussed later). After the adjustment period, animals were At the beginning of each test period, and every 21 to 28 d thereafter subjected to a mean daily feed intake measurement period of 91 until the end of the measurement period, cattle were weighed d (71 to 128 d). The mean age and BW of animals at the beginning with a calibrated scales (ID 3000 scales, Tru Test, Ireland). BW of the test was 441 d (SD = 49) and 476 kg (SD = 67), respectively. measurements were used to derive measures of feed efficiency Steers and heifers averaged 476 (SD = 46) and 410 (SD = 27) days and daily weight gain for each animal. Pre-slaughter ultrasound of age while LM and EM averaged 442 (SD = 51) and 435 (SD = 43) measurements of muscle and fat deposition and proportion days of age at the commencement of the measurement period, of intramuscular fat were collected as described by Kelly et al. respectively. Post completion of their performance test, cattle (2019). Measurements were taken with the use of the same were slaughtered in a commercial abattoir. Esaote-Pie Medical Aquila PRO Vet ultrasound scanner, with a 3.5 MHz transducer head, by a trained technician. Measurement of feed intake and chemical Carcass characteristics composition Animals were slaughtered on average 3 d after the completion Individual daily feed intake was recorded with the use of of the feed efficiency test period in a European Union licensed electronic feeding stations (RIC Feed-Weigh Trough; Hokofarm commercial facility 77 km away (Slaney Foods International, Group BV, Marknesse, The Netherlands) with a feeding event Bunclody, Co. Wexford, Ireland). Animals were slaughtered recorded with each 100 g fluctuation in weight at the feed bunk. within 1 h of arrival at the facility. Carcass weight (CW) was The mean duration of the feed intake measurement was 91 d and measured, on average, 2 h post-slaughter. After slaughter, carcass ranged from 71 to 128 d. Cattle were offered ad libitum access conformation and fat percentage were automatically recorded to the same total mixed ration (TMR) diet (77% concentrate on a 15 point scale using video imaging analysis equipment and 23% grass hay). The TMR consisted of 3 kg of hay and (VBS2000; e+v Technology GmbH & Co.KG, Oranienburg, 10 kg of concentrates, mixed with 9 kg of water. The ingredient Germany) as described by Hickey et al. (2007). composition of the concentrate was as follows (DM basis); maize meal 28%, barley 24%, soya hulls 14%, dried distillers grains Enteric methane and carbon dioxide output 10%, maize gluten meal 9%, soya bean meal 5.5%, molasses 5%, Enteric methane and carbon dioxide measurements were mineral and vitamin premix 3.75%, vegetable oil 0.7%, and yeast obtained on all animals using the GreenFeed emissions 0.05%. The concentrate was a pelleted ration, formulated to have monitoring system (GEM; C-Lock Inc., Rapid City, SD) over 21 a crude protein (CP) content of 140 g kg and had a predicted ME consecutive days throughout the feed intake measurement content of 12.6 MJ/kg DM (NRC, 2016). A fresh TMR was prepared period. The commencement of the emissions estimation period daily which was both mixed and administered via a feed wagon. ranged from days 0 to 36 of the feed intake measurement period. Feed was offered once per day and at all times animals had unrestricted access to clean drinking water. Samples of both the TMR diet and concentrates were Table 1. Details of the chemical composition of total mixed ration obtained weekly and stored at −20 °C for laboratory analysis. (TMR) and concentrates offered during feed efficiency and enteric Feed samples were defrosted overnight in a refrigeration emissions measurement periods (±SD) unit (4 °C) prior to analysis. The dry matter (DM) of TMR and Concentrate TMR concentrate samples was determined after drying at 90 °C for 16 h in a forced-air circulation oven. For chemical analysis, Chemical composition (% of DM unless stated) TMR and concentrate samples were oven dried at 40 °C for 48 h Dry matter 91.7 (0.8) 50.1 (0.9) and then ground through a 1 mm screen (Willey mill; Arthur Crude protein 13.8 (0.4) 12.2 (0.3) H. Thomas, Philadelphia, PA). After grinding, samples collected Neutral detergent fiber 21.8 (0.7) 33.5 (1.1) during each intake run were pooled, respectively. Acid detergent fiber 10.8 (0.3) 17.9 (0.6) Ash concentrations (g/kg DM) were determined by complete Either extract 3.4 (0.6) 2.3 (0.3) combustion in a muffle furnace (Nabertherm, GmbH, Lilienthal, Ash 7.4 (0.2) 7.3 (0.1) Germany) at 550 °C for 4 h. Nitrogen concentration (g/kg DM) Gross energy, MJ/kg DM 16.8 (0.3) 16.7 (0.2) Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 4 | Journal of Animal Science, 2021, Vol. 99, No. 11 A detailed description of the workings of the GEM has FFAP capillary column (Trajan Scientific, Milton Keynes, UK). The been previously described (Huhtanen et al., 2015Hammond ; initial injector temperature was 60 °C for 10 s, rising to 110 °C at et al., 2016; Patra et al., 2016; Hristov et al., 2018). Briefly, the a rate of 30 °C/min, this temperature then increased at rate of concentration of enteric gaseous emissions emitted by individual 10 °C/min to 200 °C (held for 2 min). Helium was used as a carrier animals per visit was determined by the GEM software, as a gas gas. The pressure of the column was held at 19.3 pounds per flux, from the increase in the concentration of each gas, relative square inch (psi) and the column rate was 17.2 mL/min. to background levels, accompanied by adjustments for airflow Total short chain fatty acids (SCFA) are reported as the sum rate and principles of the ideal gas law, and reported in grams total of all VFAs (mM). The percentage of acetate, propionate, and per day. The ratio of animals to a single GEM, ranged from 11 to butyrate are reported as the proportion of each individual VFA 30 depending on numbers in each intake group. relative to the total SCFA. The ratio of acetate to propionate (A:P) Each GEM was connected to both a span (0.05% methane was calculated. Estimates of theoretical hydrogen (H) production (CH ), 0.5% carbon dioxide (CO) balanced with zero grade by each animal at the time of sampling were calculated based 4 2 nitrogen gas; BOC Gas, Dublin, Ireland) and zero gas canister on the concentration of individual VFAs as described by Marty (zero grade nitrogen gas; BOC Gas, Dublin, Ireland) with auto and Demeyer (1973) with the exclusion of hydrogen gas (H). calibrations performed every 3 d. Throughout the duration of the Traits investigated and their derivations experiment, monthly CO recovery tests were performed, as per the manufactures instructions, to assess the airflow of the unit. Average dry matter intake (DMI; kg) was calculated as the A clean air filter was replaced in each unit on a weekly basis or average daily feed intake of each animal (including the GEM if airflow dropped below 27 L/s. The bait feed utilized to entice bait feed during the estimation of enteric emissions) over the animals to use the GEM, was the same pelleted concentrate course of the experiment after correcting for DM as described included in the TMR. Feed drops were weighed on a weekly basis above. Average daily gain (ADG; kg) during the test period for for each GEM unit using the average of 10 feed drops. Throughout each animal was computed as the coefficient of the linear the experimental period and across GEM units, CO recoveries regression of BW (kg) on time. The weight of the animal at the and the weight of individual feed drops averaged 99.32 ± 3.29% beginning and end of the feed intake measurement period was and 34.02 ± 4.11 g, respectively. The mean airflow for all data used to calculate initial and final live BW, respectively. Mean points utilized in this experiment was 37.1 ± 2.59 L/s. metabolic BW (MetBW; kg) was represented as average test 0.75 Previously,Arthur et al. (2017) determined a minimum of 30 BW . Pre-slaughter muscular depth (MD; mm), pre-slaughter visits to GEM, of >3 min in length, to be sufficient to accurately fat depth (FD; mm), and pre-slaughter intramuscular fat (IMF; determine enteric methane emissions for individual animals. In %) were determined using data obtained during ultrasound line with these recommendations, the GEM was programmed to measurements as previously described. Carcass conformation drop 30 g of bait feed, every 35 s, to a maximum of six drops per grade and fat class score values were scaled, with 1 representing visit for each animal. Once an animal reached the maximum the poorest conformation and 15 the best conformation in number of bait feed drops, a minimum 4 h interval was required carcass conformation grade and 1 representing the leanest before an animal could receive another drop of bait feed from value and 15 the fattest in fat class scores, respectively (Hickey the unit. et al., 2007). Gain to feed ratio (G:F) was obtained for each animal by dividing ADG by average DMI. Rumen fermentation Residual feed intake (RFI) was computed for each animal During the last week of the enteric emissions measurement and was assumed to represent the residuals from a multiple period, samples of rumen digesta were obtained from each regression model regressing DMI on ADG and MetBW. Each animal, before feeding, using a transoesophageal rumen batch of animals was subsequently treated as a contemporary sampling device (FLORA rumen scoop; Guelph, Ontario, Canada). group (CG) and included as a fixed effect in the model. The base Feed was restricted from animals for a minimum of 2 h prior model used was to sampling. After collection, ruminal fluid pH was measured immediately using a digital pH meter (Orion SA 720; Thermo Y = β + β MetBW + β ADG + CG + e , 0 1 2 j j j i j Fisher Scientific, Waltham, MA) followed by the preservation of samples via snap freezing in liquid nitrogen. On the same day of where Y is the DMI of the jth animal, β is the r egression j 0 sampling, samples were transported 61 km away to the Teagasc intercept, β is the regression coefficient on MetBW, β is the 1 2 research facility (Teagasc Grange, Dunsany, Co. Meath, Ireland) regression coefficient on ADG, CG is the fixed effect of the ith on dry ice and stored at −80 °C until analysis was conducted. batch of animals, and e is the uncontrolled error of the jth Rumen fluid samples were thawed on a laboratory bench top animal. The multiple regression model fitted for RFI explained and diluted in 50% TCA acid at a ratio of 4:5 in favor of rumen 72% of the variation in DMI while RFI averaged 0.00 kg DM/d fluid. Following the addition of acid, samples were centrifuged (SD = 0.77). RFI values ranged from −3.53 to 2.25 kg/d and for 10 min (2,000 rpm; 4 °C) after which, 250 μL of supernatant represented a difference of 5.78 kg/d between the lowest and was drawn off into a test tube and diluted with 3.75 mL of highest ranked animals for RFI. dH O and 1 mL of internal standard (0.5 g 3-metyl-n-valeric Methane DMI (MDMI; kg) was calculated as the sum total of acid in 1 liter of 0.15 M oxalic acid). Following centrifuging the combined TMR and concentrate supplementation from the for 5 min (2000 rpm; 21 °C), the dilution was filtered through GEM for each animal averaged over the methane measurement a 0.45 μm filter (Cronus Syringe filter PTFE 13mm; SMI-LabHut period. Average daily methane (DME; CH g/d) and carbon Ltd., Maisemore, Gloucester, UK) into a 2 ml GC vial (Thermo dioxide emissions (CME; CO g/d) for each animal was derived Scientific, Langerwehe, Germany) and frozen at −20 °C until VFA from the sum of emissions of each gas per spot measurement analysis. divided by the total number of these measurements as recorded One microliter of sample was injected by auto sampler on by the GEM over the test period. Only spot measurements a Varian (Saturn 2000) gas chromatograph (GC) 450 (Varian, where the visitation to the GEM was ≥3 min were included in Middelburg, The Netherlands) with a 30 m × 0.25 mm i.d. BP21 the analysis. MY (CH g/DMI kg) was calculated for each animal 4 Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 5 by dividing DME by the MDMI. The weight of individual animals and discounted in the analysis if the residual/SD was >3 for a on the 30th day of the feed intake measurement period was measurement of either gas. After the removal of outliers, 99.68% used to standardize BW for methane analysis (hereby referred of the emissions data were maintained and used for analysis. to as methane BW). Individual animal BW on day 30 was Data were checked for normality and homogeneity of calculated based on the regression analysis conducted during variance by histograms, qqplots, and formal statistical tests as the calculation of ADG. Methane per unit of BW (CH g/BW) and part of the UNIVARIATE procedure of SAS. Data from 12 animals methane intensity (MI; CH g/carcass out kg) were calculated by were not included in the analysis as visitation to the GEM was dividing DME by methane BW and CW (kg), respectively. DME below the threshold of 30 visits (n = 3) or the data from animals was also expressed per unit of ADG, using ADG calculated over were identified as statistical outliers (n = 9). This resulted in a the feed intake test period (MADG; CH g/ADG kg). final dataset of 282 animals. A mixed model ANOVA (GLIMMIX Residual methane emissions (RME; CH g/day) was computed procedure of SAS) was used to examine the effect of RME group for each animal using the equation described by Bird-Gardiner on performance, intake, feed efficiency, body composition, et al. (2017). RME was assumed to represent the residuals from methane emissions, and ruminal fermentation profiles. The a multiple regression model regressing DME on MDMI and statistical model used included the fixed effect of RME group methane BW with CG included as a fixed effect in the model. (high, medium, and low) breed maturity/genotype (LM and EM), The base model used was sex (steer and heifer), and their interactions. Non-statistically significant (P > 0.10) interactions were subsequently excluded from the final model. Age and initial bodyweight at the start of Y = β + β MDMI + β methane BW + CG + e , 0 1 2 j j j i j each performance test were included as covariates with each where Y is the DME of the jth animal, β is the r egression batch of animals treated as a CG and incorporated as a random j 0 intercept, β is the regression coefficient on MDMI, β is the effect in the statistical model. Differences among means were 1 2 regression coefficient on methane BW, CG is the fixed effect of determined by F-tests using type III sums of squares. The PDIFF the ith batch of animals, and e is the uncontrolled error of the option and the Tukey test were applied to evaluate pairwise jth animal. The multiple regression for RME explained 45% of comparisons between means. Mean values were considered the variation in DME while RME averaged 0.00 g/d (SD = 34.05). to be different when P < 0.05 and considered a tendency when RME values ranged from −114.07 to 84.99 and represented a P ≥ 0.05 and < 0.10. The associations among the traits were difference of 199.06 g/d between the lowest and highest ranked determined through partial correlations, adjusted for gender, animals for RME. SDs above and below the mean were used to breed maturity, and CG using the MANOVA/PRINTE statement group animals into high RME (RME > 0.5 SD above the mean), within the GLM procedure of SAS. Correlation coefficients were medium RME (RME ± 0.5 SD above and below the mean), and low classified as strong (r > 0.6), moderate (r between 0.4 and 0.6), or RME (RME > 0.5 SD below the mean). weak (r < 0.4), respectively. In addition, for comparative purposes among methane phenotypes, RME was calculated using the equation proposed Results by Renand et al. (2019) whereby DCE replaced DMI. RME with DCE (RME ; CH g/ day) was assumed to represent the residuals CO2 4 Animal performance, feed intake, and feed efficiency from a multiple regression model regressing DME on DCE and Summary statistics show animals on test had an average DMI of methane BW with CG included as a fixed effect in the model. 10.29 kg/d (SD = 1.46), ADG of 1.37 kg/d (SD = 0.28), G:F of 0.13 kg The base model used was of BW gain/kg of DMI (SD = 0.02), RFI of 0.00 kg DM/d (SD = 0.77), final live weight of 594.93 kg (SD = 74.25), age of slaughter of Y = β + β DCE + β methane BW + CG + e , 0 1 2 j j j i j 523.56 d (SD = 46.98), and CW of 333.14 kg (SD = 43.99). Comparisons among RME grouping, sex, and genotype (sire where Y is the DME of the jth animal, β is the r egression j 0 breed maturity), for animal performance, feed intake, and feed intercept, β is the regression coefficient on DCE, β is the 1 2 efficiency, are displayed in Table 2. In this study, there were no regression coefficient on methane BW, CG is the fixed effect of interactions detected (P > 0.05) between RME grouping, sex, the ith batch of animals, and e is the uncontrolled error of the and genotype for intake, growth, feed efficiency, or carcass jth animal. The multiple regression for RME explained 57% of CO2 composition traits. Indeed, feed intake, growth, bodyweight, the variation in DME while RME averaged 0.00 g/d (SD = 30.72). feed efficiency measures, and both CW and composition were RME values ranged from −96.76 to 94.76 and represented a not different (P > 0.05) between the high-, medium-, and low- difference of 191.52 g/d between the lowest and highest ranked ranked animals on RME. Steers relative to heifers had a heavier animals for RME . CO2 (P < 0.05) initial BW, final BW, and CW. Measures of DMI, ADG, Data and statistical analyses FCR, and RFI were not different (P > 0.05) among steers and heifers. Animals from EM sires had a greater (P < 0.05) ADG than Raw emissions data were processed by C-Lock Inc. and checked LM. LM sired animals had a heavier CW and MD, but FD and IMF for irregularities. Data were downloaded from the C-Lock Inc. were greater for the EM sired grouping (P < 0.05). website with an additional round of checks performed to identify and remove outliers as per the methods described by Coppa et al. Enteric methane and carbon dioxide output (2021). To detect outliers, the SD was calculated for both CH and CO using all spot measurements (≥3 min) supplied by C-Lock On average, 87.8% of the visits to the GEM were >3 min in length Inc. Following this, spot measurements of CH were regressed with a mean of 59 valid recordings (i.e., >3 min in length) obtained on CO and vice versa, allowing for the prediction of both gases for each animal. The mean number of valid recordings ranged using the equations generated using the REG procedure in from 54 to 70 recordings per group of cattle with the highest SAS (SAS Inst. Inc., Cary, NC; version 9.4). Residuals were then average valid recordings per animal (70) obtained at a ratio of calculated from the differences of the predicted and observed animals to GEM of 25:1. Animal visitation to the GEM averaged spot measurements for each gas. Finally, outliers were detected 2.81 times per day (SD = 0.61) during the 21 d enteric emissions Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 6 | Journal of Animal Science, 2021, Vol. 99, No. 11 Table 2. Characterization of feed intake, performance, feed efficiency, ultrasonic measurements and carcass output in finishing beef cattle ranked for residual methane emissions, sex, and genotype RME ranking Sex Genotype High, Medium, Low, Steers, Heifers, Late, Early, P-value, RME P-value, P-value, 2 3 4 5 5 5 Traits Mean SD n = 84 n = 114 n = 84 SEM n = 128 n = 154 SEM n = 219 n = 63 SEM ranking sex genotype Performance a b a b Initial weight, 475. 7 67.3 472.9 477.4 473.2 8.51 492.3 456. 7 10.3 482.8 466.2 8.0 0.82 0.02 0.04 kg a b Metabolic body 111.1 10.8 111.2 111.5 110.7 1.43 114.2 108.1 1.8 112.3 110.1 1.4 0.83 0.02 0.09 weight, kg Final weight, 594.9 74.3 599.0 598.8 592.2 11.74 617.2 576.2 15.1 602.4 590.9 11.4 0.70 0.06 0.19 kg a b Average daily 1.4 0.3 1.4 1.4 1.3 0.05 1.4 1.3 0.1 1.3 1.4 0.1 0.17 0.34 0.04 gain, kg Feed intake and efficiency Dry matter 10.29 1.46 10.56 10.29 10.26 0.19 10.52 10.22 0.22 10.21 10.53 0.18 0.29 0.36 0.1 intake, kg/d G:F, kg 0.13 0.02 0.14 0.13 0.13 0.00 0.14 0.13 0.01 0.13 0.13 0.00 0.21 0.55 0.69 a b RFI, kg DM/d 0.00 0.77 0.16 0.03 0.10 0.09 0.00 0.20 0.08 -0.08 0.28 0.08 0.48 0.12 <0.01 Ultrasonic measurements a b Fat depth, mm 5.1 1.9 5.5 5.7 5.6 0.4 4.9 6.3 0.5 4.5 6.7 0.4 0.58 0.06 <0.0001 a b Muscle depth, 76.0 7.4 74.5 75.8 75.4 1.6 76.1 74.4 2.2 78.4 72.1 1.6 0.32 0.60 <0.0001 mm a b Intramuscular 6.0 1.4 6.2 6.5 6.3 0.3 6.5 6.2 0.4 5.8 6.9 0.3 0.22 0.64 <0.0001 fat, % Carcass characteristics a b a b Carcass 333.1 44.0 328.2 334.7 331.8 7.5 345.6 317.6 9.9 340.5 322.7 7.3 0.46 0.04 <0.001 weight, kg High, RME was >0.5 SD above the mean; medium, RME was ±0.5 SD above and below the mean; low, RME was >−0.5 SD below the mean. G:F, gain to feed ratio; RFI, residual feed intake. Overall trait mean. Overall trait standard deviation. SEM, pooled standard error. a,b Least squares means within main effect and a row with different superscripts differ. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 7 measurement period. The average number of daily drops of bait feed was 19.9 drops/d/animal throughout the methane measurement period and ranged from 9.1 to 27 drops/d/animal. During the enteric emissions measurement period, animals had an average daily MDMI of 10.46 kg/d (SD = 1.53), consumed 9.84 kg/d (SD = 1.55) of TMR, and received 0.62 kg/d (SD = 0.13) of concentrate from the GEM. On average, for the high, medium, and low RME groups, the GEM supplemented concentrate account for 5.6%, 6.2%, and 6.3% of total DMI during the emissions measurement period, with no difference observed between the groups (P > 0.05). Summary statistics show a mean DME of 229.18 g/d (SD = 45.96), DCE of 8.42 kg/d (SD = 1.02), MY of 22.07 g/kg of DMI (SD = 4.06), MI 0.70 g/kg of CW (SD = 0.15), and MADG 171.67 g/kg of ADG (SD = 40.73). Summary statistics, along with comparisons among RME grouping, sex, and genotype are reported in Table 3. The diurnal pattern of enteric emissions throughout the measurement period is presented in Figure 1. No interactions were detected (P > 0.05) between RME grouping, sex, and genotype for any methane or carbon dioxide phenotypes in this study. Low RME animals produced 17.69% and 30.4% less (P < 0.05) DME in comparison to animals ranked as medium and high for RME, respectively. Similarly, the low RME group had a lower (P < 0.05) DCE than animals ranked as medium and high. Low RME animals had the lowest (P < 0.05) MY and MI of the RME groups. A difference of 29.73% and 29.63% for MY and MI was detected among the low and high RME groups, respectively. In addition, the low RME animals produced the least (P < 0.05) methane per unit of growth, of the RME groups. No differences among any of the methane phenotypes (P > 0.05), including both RME and RME , were observed among steers CO2 and heifers. No difference in DME, DCE, and RME was detected between genotypes. Association analysis among traits associated with methane output and animal productivity Correlation coefficients among the methane traits investigated in this study are presented in Table 4. The relationship of DME with RME, MI, MY, and MADG is portrayed in Figure 2. DME were positively correlated (P < .0001) with MY, MI, MADG, RME, and RME . Among the methane phenotypes, RME was the strongest CO2 predictor of daily methane output (r = 0.86; P < .0001). Between the residual methane traits RME and RME were strongly CO2 associated with each other (r = 0.86; P < .0001), but RME had the stronger correlations with MY (0.89 vs. 0.77) and MI (0.86 vs. 0.78). All three methane ratio traits (MY, MI, and MADG) were positively correlated (P < .0001). Positive associations were observed between DCE with DME, RME, and MI. Correlation analysis among methane traits with intake, growth, and feed efficiency is presented in Table 5 . The relationship of DMI with DME, DCE, MI, MY, RME, and RME is portrayed in CO2 Figure 3. The methane traits RME and RME were not associated CO2 (P > 0.10) with any of the production traits (DMI, ADG, CW, MD, FD, IMF, G:F, or RFI). MY was negatively associated ( P < 0.05) with DMI, ADG, CW, FD, IMF, G:F, and RFI. MI was positively correlated with DMI, ADG, and RFI and negatively associated with CW and MD (P < 0.05). MADG was negatively correlated ( < P 0.05) with DMI, ADG, and G:F (P < 0.05). DCE had a strong positive relationship (P < 0.05) with DMI (r = 0.78), ADG (r = 0.45), and CW (r = 0.67). Ruminal fermentation parameters Comparisons of fermentation parameters among RME grouping, sex, and animal genotype are presented in Table 6. No interactions were detected (P > 0.05) between RME grouping, Table 3. Characterization of enteric emissions and methane traits in finishing beef cattle ranked for residual methane emissions, sex, and genotype RME ranking Sex Genotype High, Medium, Low, Steers, Heifers, Late, Early, P-value, RME P-value, P-value, 2 3 4 5 5 5 Traits Mean SD n = 84 n = 114 n = 84 SEM n = 128 n = 154 SEM n = 219 n = 63 SEM ranking sex genotype a b c DME, g/d 229.2 46.0 265.0 224.0 184.4 8.8 232.0 217.0 12.1 226.4 222.5 8.7 <0.0001 0.38 0.30 a b c DCE, 8.4 1.0 8.8 8.3 8.1 0.2 8.6 8.2 0.3 8.4 8.4 0.2 <0.0001 0.39 0.92 kg/d a b c RME, g/d 0.00 34.1 38.0 -0.1 -40.3 1.8 -0.7 -0.9 1.8 0.6 -2.2 1.6 <0.0001 0.94 0.25 a b c RME , 0.00 30.2 24.6 0.7 -31.2 2.4 -1.2 -2.7 2.3 0.6 -4.6 2.1 <0.0001 0.65 0.11 CO2 g/d a b c a b MY, g/kg 22.10 4.1 25.2 21.6 17.7 0.7 21.9 21.1 1.0 21.9 21.1 0.7 <0.0001 0.59 0.01 DMI a b c a b MADG, 171.7 40.7 191.3 167.1 144.1 6.6 166.8 168.1 8.5 173.8 161.2 6.4 <0.0001 0.91 0.02 g/kg ADG a b c a b MI, g/kg 0.70 0.15 0.81 0.67 0.57 0.03 0.68 0.69 0.05 0.67 0.70 0.03 <0.0001 0.83 0.01 CW High, RME was >0.5 SD above the mean; medium, RME was±0.5 SD above and below the mean; low, RME was >−0.5 SD below the mean. DME, daily methane emissions; DCE, daily carbon dioxide emissions; RME, residual methane emissions; RME , residual methane emissions calculated with carbon dioxide; MY, methane yield; CO2 MADG, methane emissions per kg of ADG; MI, methane intensity. Overall trait mean. Overall trait standard deviation. SEM, pooled standard error. a,b,c Least squares means within main effect and a row with different superscripts differ. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 8 | Journal of Animal Science, 2021, Vol. 99, No. 11 Correlation analysis of fermentation parameters with all methane traits is reported in Table 7. Total SCFA production had a positive correlation (P < 0.05) with DME, RME, RME , MY, and CO2 MI. Acetate % was positively (P < 0.05) correlated with MY and MI. Propionate % was negatively associated (P < 0.05) with all methane traits namely DME, RME, RME , MY, MI, and MADG. CO2 Both RME and RME were positively associated with higher A:P CO2 ratio (P < 0.05). Butyrate % and theoretical H production were positively correlated (P < 0.05) with DME, RME, RME , MY, MI, CO2 and MADG. Discussion Reducing methane emissions from domesticated cattle will be key to achieving a sustainable growth in global food production. Over the past decade, there has been increased international interest in the use of genetic selection as part of a methane mitigation solution for the ruminant livestock sector (Wall et al., 2010; Pickering et al., 2015; de Hass et al., 2017; Beauchemin et al., 2020). However, while the selection of animals solely on DME has the greatest potential to decrease enteric emissions, this is likely to have ramifications for animal productivity, due to the positive relationship between methanogenesis and feed intake (Wall et al., 2010 Pic ; kering et al., 2015; de Hass et al., 2017). Consequently, researchers have proposed alternative indices for ranking the methanogenic potential of an animal. For example, RME has been advocated as having an optimal balance as a trait Figure 1. Diurnal pattern of daily methane (CH ) and carbon dioxide (CO) 4 2 in identify low emitting animals, while, due to its independence emissions. Error bars indicate SEM. from voluntary feed intake and BW, not impacting on these important drivers of profitability (Herd et al., 2014). However, prior to the completion of the current study, there was a paucity Table 4. Correlations coefficients among methane and carbon dioxide traits of information available surrounding the implications of ranking beef cattle for RME, on enteric emissions, ruminal fermentation, Traits DME DCE RME RME MY MI CO2 animal productivity, and carcass output. Multiple methane phenotypes were evaluated and the values DME − recorded in the present experiment for average DME, along with DCE 0.63*** MY and MI were consistent with previous studies investigating RME 0.86*** 0.26*** enteric emissions using the GEM technology in beef cattle fed RME 0.76*** −0.02 0.86*** CO2 under intensive ad libitum rearing conditions (Arthur et al., MY 0.61*** −0.01 0.89*** 0.77*** 2017; Bird-Gardiner et al., 2017). For example, an average DME MI 0.80*** 0.23*** 0.86*** 0.78*** 0.73*** MADG 0.48*** 0.08 0.55*** 0.57*** 0.57*** 0.49*** of 195.2 and 202.5 g/d was observed by Arthur et al. (2017) and Bird-Gardiner et al. (2017), with the slight increase in emissions DME, daily methane emissions; DCE, daily carbon dioxide observed in this study, likely due to higher proportion of forage emissions; RME, residual methane emissions; RME , residual CO2 in the diet. Additionally, daily animal visitation to the GEM emissions production calculated with carbon dioxide; MY, methane throughout the methane measurement period was within the yield; MI, methane intensity; MADG, methane emissions per kg of range (1.3 to 5.08 visits/d) reported by others (Velazco et al., 2016 ; ADG. ***P < 0.001. Alemu et al., 2017; Arthur et al., 2017; Renand et al., 2019) and further strengthens the validity of the methane recording technique implemented in this experiment. The absolute sex or animal genotype for any of the fermentation parameters range and differences in growth, performance, feed efficiency, reported in this study. and carcass data between animal sexes and genotypes were High RME animals had a greater (P < 0.05) total SCFA comparable to previous production values generated from the production in comparison to the medium and low groups. same feed efficiency performance test center over the preceding The low RME group had a greater ( P< 0.05) propionate % in 10 yr (Crowley et al., 2010K ; elly et al., 2011; Kelly et al., 2019; comparison to the high group; however, animals in the high Lahart et al., 2020). DME were positively correlated with feed group had a greater (P < 0.05) butyrate % compared with intake, growth, and carcass output, in line with previous studies both medium and low animals. No difference (P > 0.05) in (Bird-Gardiner et al., 2017Renand et ; al., 2019). There were no rumen fluid pH, acetate %, A:P ratio or rumen fluid pH was differences in DME among the sexes and genotypes, likely observed among the RME groups. Animals ranked as high had explained by the similar level of feed intake and methane the greatest (P < 0.05) theoretical H production of the RME bodyweights between the groups, with differences in MI groups. No differences in any of the fermentation associated between the breed types due to the increased carcass output variables among animal sex or genotype was found (P > 0.05). observed in LM relative to EM breeds over the finishing period. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 9 Figure 2. The relationship of daily methane emissions (DME) with residual methane emissions (RME), methane intensity (MI), methane yield (MY), and methane emissions per kg of ADG (MADG). Table 5. Correlations coefficients of intake, performance, feed efficiency traits, and body composition measures with methane traits Traits DME RME RME MY MI MADG CO2 DMI, kg 0.50*** 0.05 0.02 −0.30*** 0.13* −0.14* Average daily gain, kg 0.31*** 0.08 0.00 −0.13* 0.13* −0.63*** Carcass weight, kg 0.31*** −0.03 −0.05 −0.18** −0.29*** 0.00 Muscle depth, mm 0.13* 0.00 −0.01 −0.04 −0.21*** 0.08 Fat depth, mm 0.14* −0.03 0.00 −0.16** 0.00 −0.05 Intramuscular fat, % 0.03 −0.07 −0.07 −0.17** −0.08 −0.05 G:F −0.05 0.09 −0.03 0.14* 0.04 −0.66*** RFI 0.23*** −0.01 0.04 −0.24*** 0.31** 0.16** DME, daily methane emissions; DCE, daily carbon dioxide emissions; RME, residual methane emissions; RME , residual methane emissions CO2 calculated with carbon dioxide; MY, methane yield; MI, methane intensity; MADG, methane emissions per kg of ADG; G:F, gain to feed; RFI, residual feed intake. *P < 0.05. **P < 0.01. ***P < 0.001. Methane ratio traits, such as MY have been the traditional correlated with the individual metric of animal performance selection approach in identifying high or low emitting utilized as a denominator trait in their calculation. Therefore, animals, as the traits were observed to be independent the applicability of data generated from feed restricted animals from any associations with feed intake or BW, when open- to inform methane mitigation breeding strategies is questioned, circuit respiration chambers and restricted feed intake were due to unfavorable associations of ratio expressions of methane implemented as part of the standard operating procedure for output with economically important traits observed under ad quantifying enteric emissions (Herd et al., 2014Dono ; ghue et al., libitum feeding conditions. 2016). However, data generated as part of this study and others Alternatively, the selection and ranking of animals on (Herd et al., 2016a; Bird-Gardiner et al., 2017; Renand et al., the basis of RME as part of methane mitigation program has 2019) investigating enteric emissions under various ad libitum been suggested to overcome these limitations associated with feeding regimes, akin to that of a commercial farm setting, ratio-based methane traits and animal productivity, while also indicate the existence of an antagonistic relationship between maintaining the potential to reduce all indices of methane ratio expressions of methane output and traits of economic output (Herd et al., 2014). In support of this, RME were the importance. For example, the present study observed an only methane trait observed to be truly independent of animal unfavorable negative correlation with MY and DMI and equally, production, but positively correlated with enteric emissions in all ratio expressions of methane output (MY, MI, and MADG) were this and other studies where ad libitum feeding was employed Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 10 | Journal of Animal Science, 2021, Vol. 99, No. 11 Figure 3. The relationship of dry matter intake (DMI) with daily methane emissions (DME), daily carbon dioxide emissions (DCE), methane intensity (MI), methane yield (MY), residual methane production (RME), and residual methane production with carbon dioxide (RME ). CO2 (Bird-Gardiner et al., 2017Renand et ; al., 2019). In addition, the acceptance of any methane abatement selection program within coefficient of determination for RME in this study is similar to the livestock sector will be underpinned by its relationship with that reported for feedlot steers by Bird-Gardiner et al. (2017). on farm profitability (Beauchemin et al., 2020). The phenotypic RME were also the best predictor of DME in this experiment evidence in this study, supported by genetic correlations and and strongly associated with all traditional ratio expressions moderate heritability estimates of RME presented by others, of methane output. Animals phenotypically ranked as low for albeit under restricted feeding conditions (Donoghue et al., 2016; RME, in comparison to their high counterparts, produced 30% Manzanilla-Pech et al., 2016), suggests the ruminant livestock less DME showing that large interanimal inherent variation sector could reduce the volume of enteric methane emissions exists for this trait. Similarly, low RME animals had a lower MY in future generations of livestock, without compromising and MI, producing ~30% less methane per unit of feed intake animal productivity, through selection for low RME animals or CW, in comparison to the high RME group. The reduction in as part of a balanced breeding index or an environmentally all methane phenotypes in the low RME group occurred in the focused sub index. Indeed, any mitigation selection program absence of any adverse effect on animal performance further will further benefit from estimations of the heritability and emphasizing the merit of RME in identifying animals truly genetic correlations among methane traits under more industry divergent for methane output, irrespective of productivity. The relevant, ad libitum feeding conditions. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 11 Moreover, recently, some authors have advocated for the use of DCE as a proxy for DMI due to the linear relationship observed among both traits (Herd et al., 2016bArthur et ; al., 2018; Donoghue et al., 2020). The strong correlation with DMI, observed here and elsewhere (Arthur et al., 2018), is indicative of the potential benefit of DCE to act as a proxy for feed intake. Indeed, Renand et al. (2019), in forage fed cattle, advocated the potential to calculate RME with CO in absence of feed intake measures and reported RME to be a good CO2 predictor of RME and free from any association with DMI or BW. Concurring, in the present experiment RME maintained CO2 similar associations to that of RME with feed intake, growth, feed efficiency, carcass output, and all methane phenotypes. Due to the expense of ongoing accurate determinations of DMI and difficulties in the measurement of the trait at pasture, there may be credence for the use of DCE as a proxy for feed intake when investigating DME and RME. However, the accuracy of DCE as an indicator of feed intake will need to be further evaluated across different dietary regimes and stages of the production cycle. Ruminal methanogens primarily synthesize methane from H and CO with both substrates produced as end products of 2 2 the microbial fermentation of ingested feed (Moss et al., 2000). Methane is a known byproduct of microbial fermentation with emissions influenced by hydrogen dynamics in the rumen and shifts in microbial fermentation pathways (Janssen, 2010). Indeed, methanogenesis is believed to acquire a homeostatic role in the rumen, by preventing the accumulation of excessive amounts of H (Morgavi et al., 2010). Ruminal propionate production is considered a competitive hydrogen sink to methanogenesis, with butyrate often considered a net contributor to ruminal hydrogen (Janssen, 2010). In addition, the rumen acetate:propionate ratio is a recognized indicator of an animal’s methanogenic capabilities (Williams et al., 2019). Our data suggest, differences in microbial fermentation pathways particularly the proportion of propionate and butyrate, along with acetate: propionate profile, in the rumen to be among the definitive factors influencing divergence in methane output observed between high- and low-ranked RME animals. Members of both the bacterial and methanogen rumen community are known to influence VFA production and methanogenesis (Kittelmann et al., 2014; Shi et al., 2014; Wallace et al., 2015; Shabat et al., 2016; Auffret et al., 2017; Danielsson et al., 2017; Tapio et al., 2017) making it imperative that further efforts are implemented to identify the key ruminal microbes and methanogenic mechanisms associated with RME to facilitate a greater understanding of the trait. In addition, the increased total SCFA and theoretical H production observed in high RME suggest differences in RME could be influenced by rumen digestibility. Therefore, further studies investigating the relationship of RME with ruminal digestibility and retained energy are warranted. Conclusion RME were the best predictor of DME and were the only methane trait observed to be independent of animal productivity. Ranking cattle in terms of RME, resulted in an ~30% difference between high and low emitting animals for DME, MY, and MI. Differences in methane output among the RME groups were associated with shifts in ruminal hydrogen dynamics resulting from a varied expression of microbial fermentation pathways associated with propionate production. Further in depth rumen microbial analysis is needed to ascertain the key microbes associated with phenotypic and/or genetic divergence for RME Table 6. Characterization of rumen fermentation profile in finishing beef cattle ranked for residual methane emissions, sex, and genotype RME ranking Sex Genotype Rumen High, Medium, Low, Steers, Heifers, Late, Early, P-value, RME P-value, P-value, 2 3 3 3 fermentation Mean SD n = 84 n = 114 n = 84 SEM n = 128 n = 154 SEM n = 219 n = 63 SEM ranking sex genotype pH 6.8 0.3 6.8 6.8 6.8 0.1 6.8 6.8 0.1 6.8 6.8 0.1 0.48 0.76 0.10 a b b Total SCFA, mM 124.2 34.4 134.5 120.9 119.9 7.2 119.8 130.4 8.9 123.4 126.8 6.9 0.02 0.39 0.54 Acetate, % 74.3 6.7 73.6 73.1 73.5 1.5 71.9 74.9 1.9 73.7 73.1 1.4 0.85 0.26 0.51 a a b Propionate, % 13.0 4.3 13.0 14.0 14.5 1.2 14.4 13.2 1.5 13.7 13.9 1.1 0.04 0.59 0.66 Butyrate, % 7.8 2.6 8.0 7.8 7.1 0.7 7.7 7.5 0.9 7.5 7.7 0.69 0.10 0.89 0.56 a b b A:P 5.7 1.4 6.7 5.8 5.7 0.6 5.2 7.0 0.8 6.3 5.9 0.6 0.03 0.11 0.31 a b b Hydrogen 663.7 157.2 688.6 622.9 630.7 37.6 626.5 668.3 48.0 654.8 640.0 36.5 0.03 0.54 0.56 production, mM High, RME were >0.5 SD above the mean; medium, RME was ±0.5 SD above and below the mean; low, RME was >−0.5 SD below the mean. A:P, acetate to propionate ratio. SEM, pooled standard error. a,b Least squares means within main effect and a row with different superscripts differ. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 12 | Journal of Animal Science, 2021, Vol. 99, No. 11 Table 7. Correlations coefficients of methane traits with rumen fermentation parameters Traits pH Total SCFA, mM Acetate, % Propionate, % Butyrate, % A:P H DME 0.09 0.19* −0.08 −0.23** 0.25*** 0.07 0.20** DCE 0.05 −0.01 0.04 0.03 −0.09 −0.10 −0.03 RME 0.08 0.19* −0.08 −0.25*** 0.34*** 0.18* 0.22** RME 0.09 0.24** −0.10 −0.36*** 0.41*** 0.22** 0.24** CO2 MY 0.08 0.20** −0.19* −0.18* 0.37*** 0.09 0.23** MI 0.05 0.28*** −0.18* −0.18* 0.30*** 0.06 0.28*** MADG 0.05 0.12 −0.08 −0.26*** 0.24** 0.27*** 0.16* DME, daily methane emissions; DCE, daily carbon dioxide emissions; RME, residual methane emissions; RME , residual methane emissions CO2 calculated with carbon dioxide; MY, methane yield; MI, methane intensity; MADG, methane emissions per kg of ADG; A:P, acetate to propionate ratio; H, theoretical H production. *P < 0.05. **P < 0.01. ***P < 0.001. in order to facilitate the identification of potential microbial and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using based biomarkers associated with the trait. GreenFeed system in farm-conditions. Methods 186:59–67. doi:10.1016/j.ymeth.2020.11.004 Crowley, J. J., M. McGee, D. A. Kenny, D. H. Crews, Jr, R. D. Evans, Acknowledgments and D. P. Berry. 2010. Phenotypic and genetic parameters for This work was funded by the FACCE ERA-GAS “RumenPredict” different measures of feed efficiency in different breeds of grant (16/RD/ERAGAS/1RUMENPREDICT-ROI2017) and Horizon Irish performance-tested beef bulls. J. Anim. Sci88 . :885–894. doi:10.2527/jas.2009-1852 2020 “MASTER” grant (818368). The use of equipment purchased Danielsson, R., J. Dicksved, L. Sun, H. Gonda, B. Müller, through the Department of Agriculture, Food and the Marine A. Schnürer, and J. Bertilsson. 2017. Methane production STILMULUS fund project “GreenBreed” (17/S/235) is acknowledged. in dairy cows correlates with rumen methanogenic and PS is funded by a Teagasc Walsh Scholarship ( RMIS 0364). bacterial community structure. Front. Microbiol. 8:226. doi:10.3389/fmicb.2017.00226 de Haas, Y., M. Pszczola, H. Soyeurt, E. Wall, and J. Lassen. 2017. Conflict of interest statement Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855–870. doi:10.3168/ The authors declare no real or perceived conflicts of interest. jds.2016-11246 Donoghue, K. A., T. Bird-Gardiner, P. F. Arthur, R. M. Herd, and R. F. Hegarty. 2016. Genetic and phenotypic variance Literature Cited and covariance components for methane emission and Alemu, A. W., D. Vyas, G. Manafiazar, J. A. Basarab, and postweaning traits in Angus cattle. J. Anim. Sci 94 . :1438–1445. K. A. Beauchemin. 2017. Enteric methane emissions from doi:10.2527/jas.2015-0065 low- and high-residual feed intake beef heifers measured Donoghue, K. A., T. Bird-Gardiner, R. M. Herd, R. S. Hegarty, and using GreenFeed and respiration chamber techniques. J. P. F. Arthur. 2020. Genetic variance and covariance components Anim. Sci. 95:3727–3737. doi:10.2527/jas.2017.1501 for carbon dioxide production and postweaning traits in Angus Arthur, P. F., I. M. Barchia, C. Weber, T. Bird-Gardiner, cattleJ.. Anim. Sci. 98:253. doi:10.1093/jas/skaa253 K. A. Donoghue, R. M. Herd, and R. S. Hegarty. 2017. Gerber, P. J., H. Steinfeld, B. Henderson, A. Mottet, C. Opio, Optimizing test procedures for estimating daily methane and J. Dijkman, A. Falcucci, and G. Tempio. 2013. Tackling climate carbon dioxide emissions in cattle using short-term breath change through livestock – a global assessment of emissions measures. J. Anim. Sci. 95:645–656. doi:10.2527/jas.2016.0700 and mitigation opportunities. Rome: Food and Agriculture Arthur, P. F., T. Bird-Gardiner, I. M. Barchia, K. A. Donoghue, and Organization of the United Nations (FAO). R. M. Herd. 2018. Relationships among carbon dioxide, feed Hammond, K. J., G. C. Waghorn, and R. S. Hegarty. 2016. The GreenFeed intake, and feed efficiency traits in ad libitum fed beef cattle. system for measurement of enteric methane emission from J. Anim. Sci. 96:4859–4867. doi:10.1093/jas/sky308 cattleAnim. . Prod. Sci. 56:181–189. doi:10.1071/AN15631 Auffret, M. D., R. Stewart, R. J. Dewhurst, C. A. Duthie, J. A. Rooke, Herd, R. M., P. F. Arthur, K. A. Donoghue, S. H. Bird, T. Bird- R. J. Wallace, T. C. Freeman, T. J. Snelling, M. Watson, and Gardiner, and R. S. Hegarty. 2014. Measures of methane R. Roehe. 2017. Identification, comparison, and validation of production and their phenotypic relationships with dry robust rumen microbial biomarkers for methane emissions matter intake, growth, and body composition traits in beef using diverse Bos taurus breeds and basal diets. Front. Microbiol. cattle. J. Anim. Sci. 92:5267–5274. doi:10.2527/jas.2014-8273 8:2642. doi:10.3389/fmicb.2017.02642 Herd, R. M., J. I. Velazco, P. F. Arthur, and R. F. Hegarty. 2016a. Beauchemin, K. A., E. M. Ungerfeld, R. J. Eckard, and M. Wang. Associations among methane emission traits measured 2020. Fifty years of research on rumen methanogenesis: in the feedlot and in respiration chambers in Angus cattle lessons learned and future challenges for mitigation. Animals bred to vary in feed efficiency. J. Anim. Sci. 94:4882–4891. 14(S1):s2–s16. doi:10.1017/S1751731119003100 doi:10.2527/jas.2016-0613 Bird-Gardiner, T., P. F. Arthur, I. M. Barchia, K. A. Donoghue, Herd, R. M., J. I. Velazco, P. F. Arthur, and R. S. Hegarty. 2016b. and R. M. Herd. 2017. Phenotypic relationships among Proxies to adjust methane production rate of beef cattle methane production traits assessed under ad libitum when the quantity of feed consumed is unknown. Anim. Prod. feeding of beef cattle. J. Anim. Sci. 95:4391–4398. doi:10.2527/ Sci. 56: 213–217. doi:10.1071/AN15477 jas2017.1477 Herrero, M., and P. K. Thornton. 2013. Livestock and global change: Coppa, M., J. Jurquet, M. Eugène, T. Dechaux, Y. Rochette, emerging issues for sustainable food systems. Proc. Natl. Acad. J. M. Lamy, A. Ferlay, and C. Martin. 2021. Repeatability Sci. USA 110:20878–20881. doi:10.1073/pnas.1321844111 Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 13 Hickey, J. M., M. G. Keane, D. A. Kenny, A. R. Cromie, and National Research Council. 2016. Nutrient requirements of beef R. F. Veerkamp. 2007. Genetic parameters for EUROP carcass cattle: 2016. Washington (DC): National Academies Press. traits within different groups of cattle in Ireland. J. Anim. Sci. Patra, A. K. 2016. Recent advances in measurement and dietary 85:314–321. doi:10.2527/jas.2006-263 mitigation of enteric methane emissions in ruminants. Front. Honan, M., X. Feng, J. M. Tricarico, and E. Kebreab. 2021. Feed Vet. Sci. 3:39. doi:10.3389/fvets.2016.00039 additives as a strategic approach to reduce enteric methane Pickering, N. K., V. H. Oddy, J. Basarab, K. Cammack, B. Hayes, production in cattle: modes of action, effectiveness and R. S. Hegarty, J. Lassen, J. C. McEwan, S. Miller, C. S. Pinares- safety. Anim. Prod. Sci. doi:10.1071/AN20295 Patiño, et al. 2015. Animal board invited review: genetic Hristov, A. N., E. Kebreab, M. Niu, J. Oh, A. Bannink, A. R. Bayat, possibilities to reduce enteric methane emissions T. M. Boland, A. F. Brito, D. P. Casper, L. A. Crompton, et al. from ruminants. Animals 9:1431–1440. doi:10.1017/ 2018. Symposium review: uncertainties in enteric methane S1751731115000968 inventories, measurement techniques, and prediction Pinares-Patiño, C. S., S. M. Hickey, E. A. Young, K. G. Dodds, models. J. Dairy Sci. 101:6655–6674. doi:10.3168/jds.2017-13536 S. MacLean, G. Molano, E. Sandoval, H. Kjestrup, R. Harland, Hristov, A. N., J. Oh, J. L. Firkins, J. Dijkstra, E. Kebreab, G. Waghorn, C. Hunt, et al. 2013. Heritability estimates of methane H. P. S. Makkar, A. T. Adesogan, W. Yang, C. Lee, et al. 2013. Special emissions from sheep. Animals 7:316–321. doi:10.1017/ topics—mitigation of methane and nitrous oxide emissions from S1751731113000864 animal operations: I. A review of enteric methane mitigation Renand, G., A. Vinet, V. Decruyenaere, D. Maupetit, and D. Dozias. options. J. Anim. Sci. 91:5045–5069. doi:10.2527/jas.2013-6583 2019. Methane and carbon dioxide emission of beef heifers Huhtanen, P., E. H. Cabezas-Garcia, S. Utsumi, and S. Zimmerman. in relation with growth and feed efficiency. Animals 9:1136. 2015. Comparison of methods to determine methane doi:10.3390/ani9121136 emissions from dairy cows in farm conditions. J. Dairy Sci. Shabat, S. K., G. Sasson, A. Doron-Faigenboim, T. Durman, 98:3394–3409. doi:10.3168/jds.2014-9118 S. Yaacoby, M. E. Berg Miller, B. A. White, N. Shterzer, and Janssen, P. H. 2010. Influence of hydrogen on rumen methane I. Mizrahi. 2016. Specific microbiome-dependent mechanisms formation and fermentation balances through microbial underlie the energy harvest efficiency of ruminants. ISME J. growth kinetics and fermentation thermodynamics. Anim. 10:2958–2972. doi:10.1038/ismej.2016.62 Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002 Shi, W., C. D. Moon, S. C. Leahy, D. Kang, J. Froula, S. Kittelmann, Kelly, A. K., M. McGee, D. H. Crews, Jr, C. O. Lynch, A. R. Wylie, C. Fan, S. Deutsch, D. Gagic, H. Seedorf, et al. 2014. Methane R. D. Evans, and D. A. Kenny. 2011. Relationship between yield phenotypes linked to differential gene expression in body measurements, metabolic hormones, metabolites and the sheep rumen microbiome. Genome Res. 24:1517–1525. residual feed intake in performance tested pedigree beef doi:10.1101/gr.168245.113 bulls. Livest. Sci. 135:8–16. doi:10.1016/j.livsci.2010.05.018 Tapio, I., T. J. Snelling, F. Strozzi, and R. J. Wallace. 2017. The Kelly, D. N., C. Murphy, R. D. Sleator, M. M. Judge, S. B. Conroy, and ruminal microbiome associated with methane emissions D. P. Berry. 2019. Feed efficiency and carcass metrics in growing from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7. cattleJ.. Anim. Sci. 97:4405–4417. doi:10.1093/jas/skz316 doi:10.1186/s40104-017-0141-0 Kittelmann, S., C. S. Pinares-Patiño, H. Seedorf, M. R. Kirk, Van Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods S. Ganesh, J. C. McEwan, and P. H. Janssen. 2014. Two different for dietary fiber, neutral detergent fiber, and nonstarch bacterial community types are linked with the low-methane polysaccharides in relation to animal nutrition. J. Dairy Sci. emission trait in sheep. PLoS One 9:e103171. doi:10.1371/ 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-2 journal.pone.0103171 Velazco, J. I., D. G. Mayer, S. Zimmerman, and R. S. Hegarty. Lahart, B., R. Prendiville, F. Buckley, E. Kennedy, S. B. Conroy, 2016. Use of short-term breath measures to estimate daily T. M. Boland, and M. McGee. 2020. The repeatability of methane production by cattle. Animals 10 :25–33. doi:10.1017/ feed intake and feed efficiency in beef cattle offered high- S1751731115001603 concentrate, grass silage and pasture-based diets. Animals Wall, E., G. Simm, and D. Moran. 2010. Developing breeding 14:2288–2297. doi:10.1017/S1751731120000853 schemes to assist mitigation of greenhouse gas emissions. Manzanilla-Pech, C. I., Y. De Haas, B. J. Hayes, R. F. Veerkamp, Animal 4:366–376. doi:10.1017/S175173110999070X M. Khansefid, K. A. Donoghue, P. F. Arthur, and J. E. Pryce. Wallace, R. J., J. A. Rooke, N. McKain, C. A. Duthie, J. J. Hyslop, 2016. Genomewide association study of methane emissions D. W. Ross, A. Waterhouse, M. Watson, and R. Roehe. 2015. The in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. rumen microbial metagenome associated with high methane 94:4151–4166. doi:10.2527/jas.2016-0431 production in cattle. BMC Genomics16 :839. doi:10.1186/ Marty, R. J., and D. I. Demeyer. 1973. The effect of inhibitors s12864-015-2032-0 of methane production of fermentation pattern and Waters, S. M., D. A. Kenny, and P. E. Smith. 2020. Role of the stoichiometry in vitro using rumen contents from sheep given rumen microbiome in pasture fed ruminant production molasses. Br. J. Nutr. 30:369–376. doi:10.1079/bjn19730041 systems. In: C. McSweeney and R. Mackie, editors. Improving Morgavi, D. P., E. Forano, C. Martin, and C. J. Newbold. 2010. rumen function. Cambridge (UK): Burleigh Dodds Science Microbial ecosystem and methanogenesis in ruminants. Publishing; p. 591–650. Animal 4:1024–1036. doi:10.1017/S1751731110000546 Williams, S. R. O., M. Hannah, J. L. Jacobs, W. J. Wales, and Moss, A. R., J. P. Jouany, and J. Newbold. 2000. Methane production P. J. Moate. 2019. Volatile fatty acids in ruminal fluid can be by ruminants: its contribution to global warming. Ann. used to predict methane yield of dairy cows. Animals 9:1006. Zootechnol. 49:231–235. doi:10.1051/animres:2000119 doi:10.3390/ani9121006 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Animal Science Oxford University Press http://www.deepdyve.com/lp/oxford-university-press/effect-of-divergence-in-residual-methane-emissions-on-feed-intake-and-KGAXWLGaU1

Loading next page...

References (97)

D. Morgavi, E. Forano, Cécile Martin, C. Newbold (2010)
Microbial ecosystem and methanogenesis in ruminants.
Animal : an international journal of animal bioscience, 4 7
( Honan, M., X.Feng, J. M.Tricarico, and E.Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness and safety. Anim. Prod. Sci. doi:10.1071/AN20295)
Honan, M., X.Feng, J. M.Tricarico, and E.Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness and safety. Anim. Prod. Sci. doi:10.1071/AN20295
Honan, M., X.Feng, J. M.Tricarico, and E.Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness and safety. Anim. Prod. Sci. doi:10.1071/AN20295, Honan, M., X.Feng, J. M.Tricarico, and E.Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness and safety. Anim. Prod. Sci. doi:10.1071/AN20295
( Janssen, P. H . 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002)
Janssen, P. H . 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002
Janssen, P. H . 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002, Janssen, P. H . 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002
P. Huhtanen, E. Cabezas-Garcia, S. Utsumi, S. Zimmerman (2015)
Comparison of methods to determine methane emissions from dairy cows in farm conditions.
Journal of dairy science, 98 5
( Kelly, A. K., M.McGee, D. H.Crews, Jr, C. O.Lynch, A. R.Wylie, R. D.Evans, and D. A.Kenny. 2011. Relationship between body measurements, metabolic hormones, metabolites and residual feed intake in performance tested pedigree beef bulls. Livest. Sci. 135:8–16. doi:10.1016/j.livsci.2010.05.018)
Kelly, A. K., M.McGee, D. H.Crews, Jr, C. O.Lynch, A. R.Wylie, R. D.Evans, and D. A.Kenny. 2011. Relationship between body measurements, metabolic hormones, metabolites and residual feed intake in performance tested pedigree beef bulls. Livest. Sci. 135:8–16. doi:10.1016/j.livsci.2010.05.018
Kelly, A. K., M.McGee, D. H.Crews, Jr, C. O.Lynch, A. R.Wylie, R. D.Evans, and D. A.Kenny. 2011. Relationship between body measurements, metabolic hormones, metabolites and residual feed intake in performance tested pedigree beef bulls. Livest. Sci. 135:8–16. doi:10.1016/j.livsci.2010.05.018, Kelly, A. K., M.McGee, D. H.Crews, Jr, C. O.Lynch, A. R.Wylie, R. D.Evans, and D. A.Kenny. 2011. Relationship between body measurements, metabolic hormones, metabolites and residual feed intake in performance tested pedigree beef bulls. Livest. Sci. 135:8–16. doi:10.1016/j.livsci.2010.05.018
J. Velazco, D. Mayer, S. Zimmerman, R. Hegarty (2016)
Use of short-term breath measures to estimate daily methane production by cattle.
Animal : an international journal of animal bioscience, 10 1
( Crowley, J. J., M.McGee, D. A.Kenny, D. H.Crews, Jr, R. D.Evans, and D. P.Berry. 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls. J. Anim. Sci. 88:885–894. doi:10.2527/jas.2009-185219966161)
Crowley, J. J., M.McGee, D. A.Kenny, D. H.Crews, Jr, R. D.Evans, and D. P.Berry. 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls. J. Anim. Sci. 88:885–894. doi:10.2527/jas.2009-185219966161
Crowley, J. J., M.McGee, D. A.Kenny, D. H.Crews, Jr, R. D.Evans, and D. P.Berry. 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls. J. Anim. Sci. 88:885–894. doi:10.2527/jas.2009-185219966161, Crowley, J. J., M.McGee, D. A.Kenny, D. H.Crews, Jr, R. D.Evans, and D. P.Berry. 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls. J. Anim. Sci. 88:885–894. doi:10.2527/jas.2009-185219966161
Sheer Shabat, Goor Sasson, A. Doron-Faigenboim, Thomer Durman, Shamay Yaacoby, Margret Miller, B. White, N. Shterzer, I. Mizrahi (2016)
Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants
The ISME Journal, 10
K. Donoghue, T. Bird-Gardiner, P. Arthur, R. Herd, R. Hegarty (2016)
Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle.
Journal of animal science, 94 4
( Danielsson, R., J.Dicksved, L.Sun, H.Gonda, B.Müller, A.Schnürer, and J.Bertilsson. 2017. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 8:226. doi:10.3389/fmicb.2017.0022628261182)
Danielsson, R., J.Dicksved, L.Sun, H.Gonda, B.Müller, A.Schnürer, and J.Bertilsson. 2017. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 8:226. doi:10.3389/fmicb.2017.0022628261182
Danielsson, R., J.Dicksved, L.Sun, H.Gonda, B.Müller, A.Schnürer, and J.Bertilsson. 2017. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 8:226. doi:10.3389/fmicb.2017.0022628261182, Danielsson, R., J.Dicksved, L.Sun, H.Gonda, B.Müller, A.Schnürer, and J.Bertilsson. 2017. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 8:226. doi:10.3389/fmicb.2017.0022628261182
( Tapio, I., T. J.Snelling, F.Strozzi, and R. J.Wallace. 2017. The ruminal microbiome associated with methane emissions from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7. doi:10.1186/s40104-017-0141-028123698)
Tapio, I., T. J.Snelling, F.Strozzi, and R. J.Wallace. 2017. The ruminal microbiome associated with methane emissions from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7. doi:10.1186/s40104-017-0141-028123698
Tapio, I., T. J.Snelling, F.Strozzi, and R. J.Wallace. 2017. The ruminal microbiome associated with methane emissions from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7. doi:10.1186/s40104-017-0141-028123698, Tapio, I., T. J.Snelling, F.Strozzi, and R. J.Wallace. 2017. The ruminal microbiome associated with methane emissions from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7. doi:10.1186/s40104-017-0141-028123698
( Manzanilla-Pech, C. I., Y.De Haas, B. J.Hayes, R. F.Veerkamp, M.Khansefid, K. A.Donoghue, P. F.Arthur, and J. E.Pryce. 2016. Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. 94:4151–4166. doi:10.2527/jas.2016-043127898855)
Manzanilla-Pech, C. I., Y.De Haas, B. J.Hayes, R. F.Veerkamp, M.Khansefid, K. A.Donoghue, P. F.Arthur, and J. E.Pryce. 2016. Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. 94:4151–4166. doi:10.2527/jas.2016-043127898855
Manzanilla-Pech, C. I., Y.De Haas, B. J.Hayes, R. F.Veerkamp, M.Khansefid, K. A.Donoghue, P. F.Arthur, and J. E.Pryce. 2016. Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. 94:4151–4166. doi:10.2527/jas.2016-043127898855, Manzanilla-Pech, C. I., Y.De Haas, B. J.Hayes, R. F.Veerkamp, M.Khansefid, K. A.Donoghue, P. F.Arthur, and J. E.Pryce. 2016. Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. 94:4151–4166. doi:10.2527/jas.2016-043127898855
( Patra, A. K . 2016. Recent advances in measurement and dietary mitigation of enteric methane emissions in ruminants. Front. Vet. Sci. 3:39. doi:10.3389/fvets.2016.0003927243027)
Patra, A. K . 2016. Recent advances in measurement and dietary mitigation of enteric methane emissions in ruminants. Front. Vet. Sci. 3:39. doi:10.3389/fvets.2016.0003927243027
Patra, A. K . 2016. Recent advances in measurement and dietary mitigation of enteric methane emissions in ruminants. Front. Vet. Sci. 3:39. doi:10.3389/fvets.2016.0003927243027, Patra, A. K . 2016. Recent advances in measurement and dietary mitigation of enteric methane emissions in ruminants. Front. Vet. Sci. 3:39. doi:10.3389/fvets.2016.0003927243027
S. Williams, M. Hannah, J. Jacobs, W. Wales, P. Moate (2019)
Volatile Fatty Acids in Ruminal Fluid Can Be Used to Predict Methane Yield of Dairy Cows
Animals : an Open Access Journal from MDPI, 9
E. Finneran, P. Crosson, P. O'kiely, L. Shalloo, D. Forristal, M. Wallace (2011)
Stochastic simulation of the cost of home-produced feeds for ruminant livestock systems
The Journal of Agricultural Science, 150
( Herrero, M., and P. K.Thornton. 2013. Livestock and global change: emerging issues for sustainable food systems. Proc. Natl. Acad. Sci. USA110:20878–20881. doi:10.1073/pnas.132184411124344313)
Herrero, M., and P. K.Thornton. 2013. Livestock and global change: emerging issues for sustainable food systems. Proc. Natl. Acad. Sci. USA110:20878–20881. doi:10.1073/pnas.132184411124344313
Herrero, M., and P. K.Thornton. 2013. Livestock and global change: emerging issues for sustainable food systems. Proc. Natl. Acad. Sci. USA110:20878–20881. doi:10.1073/pnas.132184411124344313, Herrero, M., and P. K.Thornton. 2013. Livestock and global change: emerging issues for sustainable food systems. Proc. Natl. Acad. Sci. USA110:20878–20881. doi:10.1073/pnas.132184411124344313
Paul Arthur, I. Barchia, C. Weber, T. Bird-Gardiner, K. Donoghue, Robert Herd, R. Hegarty (2017)
Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures.
Journal of animal science, 95 2
D. Kelly, C. Murphy, R. Sleator, M. Judge, S. Conroy, D. Berry (2019)
Feed efficiency and carcass metrics in growing cattle.
Journal of animal science
J. Hickey, M. Keane, D. Kenny, A. Cromie, R. Veerkamp (2007)
Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland.
Journal of animal science, 85 2
( de Haas, Y., M.Pszczola, H.Soyeurt, E.Wall, and J.Lassen. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855–870. doi:10.3168/jds.2016-1124627939541)
de Haas, Y., M.Pszczola, H.Soyeurt, E.Wall, and J.Lassen. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855–870. doi:10.3168/jds.2016-1124627939541
de Haas, Y., M.Pszczola, H.Soyeurt, E.Wall, and J.Lassen. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855–870. doi:10.3168/jds.2016-1124627939541, de Haas, Y., M.Pszczola, H.Soyeurt, E.Wall, and J.Lassen. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855–870. doi:10.3168/jds.2016-1124627939541
( Pinares-Patiño, C. S., S. M.Hickey, E. A.Young, K. G.Dodds, S.MacLean, G.Molano, E.Sandoval, H.Kjestrup, R.Harland, C.Hunt, et al. 2013. Heritability estimates of methane emissions from sheep. Animals7:316–321. doi:10.1017/S1751731113000864)
Pinares-Patiño, C. S., S. M.Hickey, E. A.Young, K. G.Dodds, S.MacLean, G.Molano, E.Sandoval, H.Kjestrup, R.Harland, C.Hunt, et al. 2013. Heritability estimates of methane emissions from sheep. Animals7:316–321. doi:10.1017/S1751731113000864
Pinares-Patiño, C. S., S. M.Hickey, E. A.Young, K. G.Dodds, S.MacLean, G.Molano, E.Sandoval, H.Kjestrup, R.Harland, C.Hunt, et al. 2013. Heritability estimates of methane emissions from sheep. Animals7:316–321. doi:10.1017/S1751731113000864, Pinares-Patiño, C. S., S. M.Hickey, E. A.Young, K. G.Dodds, S.MacLean, G.Molano, E.Sandoval, H.Kjestrup, R.Harland, C.Hunt, et al. 2013. Heritability estimates of methane emissions from sheep. Animals7:316–321. doi:10.1017/S1751731113000864
P. Janssen (2010)
Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics
Animal Feed Science and Technology, 160
( Moss, A. R., J. P.Jouany, and J.Newbold. 2000. Methane production by ruminants: its contribution to global warming. Ann. Zootechnol. 49:231–235. doi:10.1051/animres:2000119)
Moss, A. R., J. P.Jouany, and J.Newbold. 2000. Methane production by ruminants: its contribution to global warming. Ann. Zootechnol. 49:231–235. doi:10.1051/animres:2000119
Moss, A. R., J. P.Jouany, and J.Newbold. 2000. Methane production by ruminants: its contribution to global warming. Ann. Zootechnol. 49:231–235. doi:10.1051/animres:2000119, Moss, A. R., J. P.Jouany, and J.Newbold. 2000. Methane production by ruminants: its contribution to global warming. Ann. Zootechnol. 49:231–235. doi:10.1051/animres:2000119
R. Herd, J. Velazco, P. Arthur, R. Hegarty (2016)
Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown
Animal Production Science, 56
A. Hristov, E. Kebreab, M. Niu, J. Oh, A. Bannink, Ali Bayat, Tommy Boland, Andre Brito, David Casper, L. Crompton, J. Dijkstra, M. Eugène, P. Garnsworthy, Nazmul Haque, A. Hellwing, P. Huhtanen, M. Kreuzer, B. Kuhla, Peter Lund, Jørgen Madsen, Cécile Martin, P. Moate, S. Muetzel, C. Muñoz, N. Peiren, J. Powell, C. Reynolds, A. Schwarm, K. Shingfield, T. Storlien, M. Weisbjerg, D. Yáñez-Ruiz, Zhongtang Yu (2018)
Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models.
Journal of dairy science, 101 7
A. Alemu, D. Vyas, G. Manafiazar, J. Basarab, K. Beauchemin (2017)
Enteric methane emissions from low- and high-residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques.
Journal of animal science, 95 8
( Wallace, R. J., J. A.Rooke, N.McKain, C. A.Duthie, J. J.Hyslop, D. W.Ross, A.Waterhouse, M.Watson, and R.Roehe. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics16:839. doi:10.1186/s12864-015-2032-026494241)
Wallace, R. J., J. A.Rooke, N.McKain, C. A.Duthie, J. J.Hyslop, D. W.Ross, A.Waterhouse, M.Watson, and R.Roehe. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics16:839. doi:10.1186/s12864-015-2032-026494241
Wallace, R. J., J. A.Rooke, N.McKain, C. A.Duthie, J. J.Hyslop, D. W.Ross, A.Waterhouse, M.Watson, and R.Roehe. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics16:839. doi:10.1186/s12864-015-2032-026494241, Wallace, R. J., J. A.Rooke, N.McKain, C. A.Duthie, J. J.Hyslop, D. W.Ross, A.Waterhouse, M.Watson, and R.Roehe. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics16:839. doi:10.1186/s12864-015-2032-026494241
Edinburgh Research Explorer Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using DiverseBreeds and Basal Diets
C. Pinares-Patiño, S. Hickey, E. Young, Ken Dodds, S. Maclean, G. Molano, E. Sandoval, H. Kjestrup, R. Harland, C. Hunt, N. Pickering, J. McEwan (2013)
Heritability estimates of methane emissions from sheep
Animal, 7
G. Renand, A. Vinet, V. Decruyenaere, D. Maupetit, D. Dozias (2019)
Methane and Carbon Dioxide Emission of Beef Heifers in Relation with Growth and Feed Efficiency
Animals : an Open Access Journal from MDPI, 9
( Alemu, A. W., D.Vyas, G.Manafiazar, J. A.Basarab, and K. A.Beauchemin. 2017. Enteric methane emissions from low- and high-residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques. J. Anim. Sci. 95:3727–3737. doi:10.2527/jas.2017.150128805902)
Alemu, A. W., D.Vyas, G.Manafiazar, J. A.Basarab, and K. A.Beauchemin. 2017. Enteric methane emissions from low- and high-residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques. J. Anim. Sci. 95:3727–3737. doi:10.2527/jas.2017.150128805902
Alemu, A. W., D.Vyas, G.Manafiazar, J. A.Basarab, and K. A.Beauchemin. 2017. Enteric methane emissions from low- and high-residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques. J. Anim. Sci. 95:3727–3737. doi:10.2527/jas.2017.150128805902, Alemu, A. W., D.Vyas, G.Manafiazar, J. A.Basarab, and K. A.Beauchemin. 2017. Enteric methane emissions from low- and high-residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques. J. Anim. Sci. 95:3727–3737. doi:10.2527/jas.2017.150128805902
B. Lahart, R. Prendiville, F. Buckley, E. Kennedy, S. Conroy, T. Boland, M. McGee (2020)
The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasture-based diets.
Animal : an international journal of animal bioscience
T. Bird-Gardiner, P. Arthur, I. Barchia, K. Donoghue, R. Herd (2017)
Phenotypic relationships among methane production traits assessed under ad libitum feeding of beef cattle.
Journal of animal science, 95 10
S. Waters, Teagasc Animal, D. Kenny, Paul Smith (2020)
Role of the rumen microbiome in pasture-fed ruminant production systems
A. Kelly, M. McGee, D. Crews, C. Lynch, A. Wylie, R. Evans, D. Kenny (2011)
Relationship between body measurements, metabolic hormones, metabolites and residual feed intake in performancetested pedigree beef bulls
Livestock Science, 135
(2016)
Nutrient requirements of beef cattle: 2016
C. Manzanilla-Pech, Y. Haas, Ben Hayes, R. Veerkamp, Majid Khansefid, Majid Khansefid, K. Donoghue, P. Arthur, J. Pryce (2016)
Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle.
Journal of animal science, 94 10
( Arthur, P. F., T.Bird-Gardiner, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2018. Relationships among carbon dioxide, feed intake, and feed efficiency traits in ad libitum fed beef cattle. J. Anim. Sci. 96:4859–4867. doi:10.1093/jas/sky30830060045)
Arthur, P. F., T.Bird-Gardiner, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2018. Relationships among carbon dioxide, feed intake, and feed efficiency traits in ad libitum fed beef cattle. J. Anim. Sci. 96:4859–4867. doi:10.1093/jas/sky30830060045
Arthur, P. F., T.Bird-Gardiner, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2018. Relationships among carbon dioxide, feed intake, and feed efficiency traits in ad libitum fed beef cattle. J. Anim. Sci. 96:4859–4867. doi:10.1093/jas/sky30830060045, Arthur, P. F., T.Bird-Gardiner, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2018. Relationships among carbon dioxide, feed intake, and feed efficiency traits in ad libitum fed beef cattle. J. Anim. Sci. 96:4859–4867. doi:10.1093/jas/sky30830060045
( Waters, S. M., D. A.Kenny, and P. E.Smith. 2020. Role of the rumen microbiome in pasture fed ruminant production systems. In: C.McSweeney and R.Mackie, editors. Improving rumen function. Cambridge (UK): Burleigh Dodds Science Publishing; p. 591–650.)
Waters, S. M., D. A.Kenny, and P. E.Smith. 2020. Role of the rumen microbiome in pasture fed ruminant production systems. In: C.McSweeney and R.Mackie, editors. Improving rumen function. Cambridge (UK): Burleigh Dodds Science Publishing; p. 591–650.
Waters, S. M., D. A.Kenny, and P. E.Smith. 2020. Role of the rumen microbiome in pasture fed ruminant production systems. In: C.McSweeney and R.Mackie, editors. Improving rumen function. Cambridge (UK): Burleigh Dodds Science Publishing; p. 591–650., Waters, S. M., D. A.Kenny, and P. E.Smith. 2020. Role of the rumen microbiome in pasture fed ruminant production systems. In: C.McSweeney and R.Mackie, editors. Improving rumen function. Cambridge (UK): Burleigh Dodds Science Publishing; p. 591–650.
( Hammond, K. J., G. C.Waghorn, and R. S.Hegarty. 2016. The GreenFeed system for measurement of enteric methane emission from cattle. Anim. Prod. Sci. 56:181–189. doi:10.1071/AN15631)
Hammond, K. J., G. C.Waghorn, and R. S.Hegarty. 2016. The GreenFeed system for measurement of enteric methane emission from cattle. Anim. Prod. Sci. 56:181–189. doi:10.1071/AN15631
Hammond, K. J., G. C.Waghorn, and R. S.Hegarty. 2016. The GreenFeed system for measurement of enteric methane emission from cattle. Anim. Prod. Sci. 56:181–189. doi:10.1071/AN15631, Hammond, K. J., G. C.Waghorn, and R. S.Hegarty. 2016. The GreenFeed system for measurement of enteric methane emission from cattle. Anim. Prod. Sci. 56:181–189. doi:10.1071/AN15631
( Kittelmann, S., C. S.Pinares-Patiño, H.Seedorf, M. R.Kirk, S.Ganesh, J. C.McEwan, and P. H.Janssen. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One9:e103171. doi:10.1371/journal.pone.010317125078564)
Kittelmann, S., C. S.Pinares-Patiño, H.Seedorf, M. R.Kirk, S.Ganesh, J. C.McEwan, and P. H.Janssen. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One9:e103171. doi:10.1371/journal.pone.010317125078564
Kittelmann, S., C. S.Pinares-Patiño, H.Seedorf, M. R.Kirk, S.Ganesh, J. C.McEwan, and P. H.Janssen. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One9:e103171. doi:10.1371/journal.pone.010317125078564, Kittelmann, S., C. S.Pinares-Patiño, H.Seedorf, M. R.Kirk, S.Ganesh, J. C.McEwan, and P. H.Janssen. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One9:e103171. doi:10.1371/journal.pone.010317125078564
( Bird-Gardiner, T., P. F.Arthur, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2017. Phenotypic relationships among methane production traits assessed under ad libitum feeding of beef cattle. J. Anim. Sci. 95:4391–4398. doi:10.2527/jas2017.147729108054)
Bird-Gardiner, T., P. F.Arthur, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2017. Phenotypic relationships among methane production traits assessed under ad libitum feeding of beef cattle. J. Anim. Sci. 95:4391–4398. doi:10.2527/jas2017.147729108054
Bird-Gardiner, T., P. F.Arthur, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2017. Phenotypic relationships among methane production traits assessed under ad libitum feeding of beef cattle. J. Anim. Sci. 95:4391–4398. doi:10.2527/jas2017.147729108054, Bird-Gardiner, T., P. F.Arthur, I. M.Barchia, K. A.Donoghue, and R. M.Herd. 2017. Phenotypic relationships among methane production traits assessed under ad libitum feeding of beef cattle. J. Anim. Sci. 95:4391–4398. doi:10.2527/jas2017.147729108054
Weibing Shi, C. Moon, S. Leahy, Dongwan Kang, J. Froula, S. Kittelmann, C. Fan, S. Deutsch, Dragana Gagic, H. Seedorf, W. Kelly, Renee Atua, C. Sang, P. Soni, Dong Li, C. Pinares-Patiño, J. McEwan, P. Janssen, F. Chen, A. Visel, Zhong Wang, G. Attwood, E. Rubin (2014)
Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome
Genome Research, 24
R. Swingle (1986)
Nutrient requirements of beef cattle
Animal Feed Science and Technology, 14
( Donoghue, K. A., T.Bird-Gardiner, R. M.Herd, R. S.Hegarty, and P. F.Arthur. 2020. Genetic variance and covariance components for carbon dioxide production and postweaning traits in Angus cattle. J. Anim. Sci. 98:253. doi:10.1093/jas/skaa253)
Donoghue, K. A., T.Bird-Gardiner, R. M.Herd, R. S.Hegarty, and P. F.Arthur. 2020. Genetic variance and covariance components for carbon dioxide production and postweaning traits in Angus cattle. J. Anim. Sci. 98:253. doi:10.1093/jas/skaa253
Donoghue, K. A., T.Bird-Gardiner, R. M.Herd, R. S.Hegarty, and P. F.Arthur. 2020. Genetic variance and covariance components for carbon dioxide production and postweaning traits in Angus cattle. J. Anim. Sci. 98:253. doi:10.1093/jas/skaa253, Donoghue, K. A., T.Bird-Gardiner, R. M.Herd, R. S.Hegarty, and P. F.Arthur. 2020. Genetic variance and covariance components for carbon dioxide production and postweaning traits in Angus cattle. J. Anim. Sci. 98:253. doi:10.1093/jas/skaa253
I. Tapio, T. Snelling, F. Strozzi, R. Wallace (2017)
The ruminal microbiome associated with methane emissions from ruminant livestock
Journal of Animal Science and Biotechnology, 8
( Shi, W., C. D.Moon, S. C.Leahy, D.Kang, J.Froula, S.Kittelmann, C.Fan, S.Deutsch, D.Gagic, H.Seedorf, et al. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. 24:1517–1525. doi:10.1101/gr.168245.11324907284)
Shi, W., C. D.Moon, S. C.Leahy, D.Kang, J.Froula, S.Kittelmann, C.Fan, S.Deutsch, D.Gagic, H.Seedorf, et al. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. 24:1517–1525. doi:10.1101/gr.168245.11324907284
Shi, W., C. D.Moon, S. C.Leahy, D.Kang, J.Froula, S.Kittelmann, C.Fan, S.Deutsch, D.Gagic, H.Seedorf, et al. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. 24:1517–1525. doi:10.1101/gr.168245.11324907284, Shi, W., C. D.Moon, S. C.Leahy, D.Kang, J.Froula, S.Kittelmann, C.Fan, S.Deutsch, D.Gagic, H.Seedorf, et al. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. 24:1517–1525. doi:10.1101/gr.168245.11324907284
( Hickey, J. M., M. G.Keane, D. A.Kenny, A. R.Cromie, and R. F.Veerkamp. 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. J. Anim. Sci. 85:314–321. doi:10.2527/jas.2006-26317235018)
Hickey, J. M., M. G.Keane, D. A.Kenny, A. R.Cromie, and R. F.Veerkamp. 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. J. Anim. Sci. 85:314–321. doi:10.2527/jas.2006-26317235018
Hickey, J. M., M. G.Keane, D. A.Kenny, A. R.Cromie, and R. F.Veerkamp. 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. J. Anim. Sci. 85:314–321. doi:10.2527/jas.2006-26317235018, Hickey, J. M., M. G.Keane, D. A.Kenny, A. R.Cromie, and R. F.Veerkamp. 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. J. Anim. Sci. 85:314–321. doi:10.2527/jas.2006-26317235018
( National Research Council. 2016. Nutrient requirements of beef cattle: 2016. Washington (DC): National Academies Press.)
National Research Council. 2016. Nutrient requirements of beef cattle: 2016. Washington (DC): National Academies Press.
National Research Council. 2016. Nutrient requirements of beef cattle: 2016. Washington (DC): National Academies Press., National Research Council. 2016. Nutrient requirements of beef cattle: 2016. Washington (DC): National Academies Press.
( Van Soest, P. J., J. B.Robertson, and B. A.Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-21660498)
Van Soest, P. J., J. B.Robertson, and B. A.Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-21660498
Van Soest, P. J., J. B.Robertson, and B. A.Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-21660498, Van Soest, P. J., J. B.Robertson, and B. A.Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-21660498
M. D. Auffret, R. Stewart, R. J. Dewhurst, C. A. Duthie, J. A. Rooke, R. J. Wallace, T. C. Freeman, T. J. Snelling, M. Watson, R. Roehe (2017)
Identification, comparison, and validation of robust rumen microbial biomarkers for methane emissions using diverse Bos taurus breeds and basal diets
Front. Microbiol, 8
( Arthur, P. F., I. M.Barchia, C.Weber, T.Bird-Gardiner, K. A.Donoghue, R. M.Herd, and R. S.Hegarty. 2017. Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures. J. Anim. Sci. 95:645–656. doi:10.2527/jas.2016.070028380597)
Arthur, P. F., I. M.Barchia, C.Weber, T.Bird-Gardiner, K. A.Donoghue, R. M.Herd, and R. S.Hegarty. 2017. Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures. J. Anim. Sci. 95:645–656. doi:10.2527/jas.2016.070028380597
Arthur, P. F., I. M.Barchia, C.Weber, T.Bird-Gardiner, K. A.Donoghue, R. M.Herd, and R. S.Hegarty. 2017. Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures. J. Anim. Sci. 95:645–656. doi:10.2527/jas.2016.070028380597, Arthur, P. F., I. M.Barchia, C.Weber, T.Bird-Gardiner, K. A.Donoghue, R. M.Herd, and R. S.Hegarty. 2017. Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures. J. Anim. Sci. 95:645–656. doi:10.2527/jas.2016.070028380597
Y. Haas, M. Pszczoła, H. Soyeurt, E. Wall, J. Lassen (2017)
Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying.
Journal of dairy science, 100 2
A. R. Moss, J. P. Jouany, J. Newbold (2000)
Methane production by ruminants: its contribution to global warming
Ann. Zootechnol, 49
R. Herd, J. Velazco, P. Arthur, R. Hegarty (2016)
Associations among methane emission traits measured in the feedlot and in respiration chambers in Angus cattle bred to vary in feed efficiency.
Journal of animal science, 94 11
K. Hammond, G. Waghorn, R. Hegarty (2016)
The GreenFeed system for measurement of enteric methane emission from cattle
Animal Production Science, 56
M. Herrero, P. Thornton (2013)
Livestock and global change: Emerging issues for sustainable food systems
Proceedings of the National Academy of Sciences, 110
M. Honan, X. Feng, J. Tricarico, E. Kebreab (2021)
Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness and safety
Animal Production Science
( Huhtanen, P., E. H.Cabezas-Garcia, S.Utsumi, and S.Zimmerman. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-911825771050)
Huhtanen, P., E. H.Cabezas-Garcia, S.Utsumi, and S.Zimmerman. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-911825771050
Huhtanen, P., E. H.Cabezas-Garcia, S.Utsumi, and S.Zimmerman. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-911825771050, Huhtanen, P., E. H.Cabezas-Garcia, S.Utsumi, and S.Zimmerman. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-911825771050
R. Marty, D. Demeyer (1973)
The effect of inhibitors of methane production of fermentation pattern and stoichiometry in vitro using rumen contents from sheep given molasses
British Journal of Nutrition, 30
R. Herd, P. Arthur, K. Donoghue, S. Bird, T. Bird-Gardiner, R. Hegarty (2014)
Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle.
Journal of animal science, 92 11
P. Soest, J. Robertson, B. Lewis (1991)
Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition.
Journal of dairy science, 74 10
( Coppa, M., J.Jurquet, M.Eugène, T.Dechaux, Y.Rochette, J. M.Lamy, A.Ferlay, and C.Martin. 2021. Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions. Methods186:59–67. doi:10.1016/j.ymeth.2020.11.00433253811)
Coppa, M., J.Jurquet, M.Eugène, T.Dechaux, Y.Rochette, J. M.Lamy, A.Ferlay, and C.Martin. 2021. Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions. Methods186:59–67. doi:10.1016/j.ymeth.2020.11.00433253811
Coppa, M., J.Jurquet, M.Eugène, T.Dechaux, Y.Rochette, J. M.Lamy, A.Ferlay, and C.Martin. 2021. Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions. Methods186:59–67. doi:10.1016/j.ymeth.2020.11.00433253811, Coppa, M., J.Jurquet, M.Eugène, T.Dechaux, Y.Rochette, J. M.Lamy, A.Ferlay, and C.Martin. 2021. Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions. Methods186:59–67. doi:10.1016/j.ymeth.2020.11.00433253811
( Morgavi, D. P., E.Forano, C.Martin, and C. J.Newbold. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal4:1024–1036. doi:10.1017/S175173111000054622444607)
Morgavi, D. P., E.Forano, C.Martin, and C. J.Newbold. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal4:1024–1036. doi:10.1017/S175173111000054622444607
Morgavi, D. P., E.Forano, C.Martin, and C. J.Newbold. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal4:1024–1036. doi:10.1017/S175173111000054622444607, Morgavi, D. P., E.Forano, C.Martin, and C. J.Newbold. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal4:1024–1036. doi:10.1017/S175173111000054622444607
( Herd, R. M., J. I.Velazco, P. F.Arthur, and R. S.Hegarty. 2016b. Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown. Anim. Prod. Sci. 56: 213–217. doi:10.1071/AN15477)
Herd, R. M., J. I.Velazco, P. F.Arthur, and R. S.Hegarty. 2016b. Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown. Anim. Prod. Sci. 56: 213–217. doi:10.1071/AN15477
Herd, R. M., J. I.Velazco, P. F.Arthur, and R. S.Hegarty. 2016b. Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown. Anim. Prod. Sci. 56: 213–217. doi:10.1071/AN15477, Herd, R. M., J. I.Velazco, P. F.Arthur, and R. S.Hegarty. 2016b. Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown. Anim. Prod. Sci. 56: 213–217. doi:10.1071/AN15477
Eileen Wall, G. Simm, Dominic Moran (2010)
Developing breeding schemes to assist mitigation of greenhouse gas emissions.
Animal : an international journal of animal bioscience, 4 3
( Velazco, J. I., D. G.Mayer, S.Zimmerman, and R. S.Hegarty. 2016. Use of short-term breath measures to estimate daily methane production by cattle. Animals10:25–33. doi:10.1017/S1751731115001603)
Velazco, J. I., D. G.Mayer, S.Zimmerman, and R. S.Hegarty. 2016. Use of short-term breath measures to estimate daily methane production by cattle. Animals10:25–33. doi:10.1017/S1751731115001603
Velazco, J. I., D. G.Mayer, S.Zimmerman, and R. S.Hegarty. 2016. Use of short-term breath measures to estimate daily methane production by cattle. Animals10:25–33. doi:10.1017/S1751731115001603, Velazco, J. I., D. G.Mayer, S.Zimmerman, and R. S.Hegarty. 2016. Use of short-term breath measures to estimate daily methane production by cattle. Animals10:25–33. doi:10.1017/S1751731115001603
J. Crowley, J. Crowley, M. McGee, D. Kenny, D. Crews, R. Evans, D. Berry (2010)
Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls.
Journal of animal science, 88 3
S. Kittelmann, C. Pinares-Patiño, H. Seedorf, M. Kirk, S. Ganesh, J. McEwan, P. Janssen (2014)
Two Different Bacterial Community Types Are Linked with the Low-Methane Emission Trait in Sheep
PLoS ONE, 9
( Auffret, M. D., R.Stewart, R. J.Dewhurst, C. A.Duthie, J. A.Rooke, R. J.Wallace, T. C.Freeman, T. J.Snelling, M.Watson, and R.Roehe. 2017. Identification, comparison, and validation of robust rumen microbial biomarkers for methane emissions using diverse Bos taurus breeds and basal diets. Front. Microbiol. 8:2642. doi:10.3389/fmicb.2017.0264229375511)
Auffret, M. D., R.Stewart, R. J.Dewhurst, C. A.Duthie, J. A.Rooke, R. J.Wallace, T. C.Freeman, T. J.Snelling, M.Watson, and R.Roehe. 2017. Identification, comparison, and validation of robust rumen microbial biomarkers for methane emissions using diverse Bos taurus breeds and basal diets. Front. Microbiol. 8:2642. doi:10.3389/fmicb.2017.0264229375511
Auffret, M. D., R.Stewart, R. J.Dewhurst, C. A.Duthie, J. A.Rooke, R. J.Wallace, T. C.Freeman, T. J.Snelling, M.Watson, and R.Roehe. 2017. Identification, comparison, and validation of robust rumen microbial biomarkers for methane emissions using diverse Bos taurus breeds and basal diets. Front. Microbiol. 8:2642. doi:10.3389/fmicb.2017.0264229375511, Auffret, M. D., R.Stewart, R. J.Dewhurst, C. A.Duthie, J. A.Rooke, R. J.Wallace, T. C.Freeman, T. J.Snelling, M.Watson, and R.Roehe. 2017. Identification, comparison, and validation of robust rumen microbial biomarkers for methane emissions using diverse Bos taurus breeds and basal diets. Front. Microbiol. 8:2642. doi:10.3389/fmicb.2017.0264229375511
( Herd, R. M., P. F.Arthur, K. A.Donoghue, S. H.Bird, T.Bird-Gardiner, and R. S.Hegarty. 2014. Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle. J. Anim. Sci. 92:5267–5274. doi:10.2527/jas.2014-827325349368)
Herd, R. M., P. F.Arthur, K. A.Donoghue, S. H.Bird, T.Bird-Gardiner, and R. S.Hegarty. 2014. Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle. J. Anim. Sci. 92:5267–5274. doi:10.2527/jas.2014-827325349368
Herd, R. M., P. F.Arthur, K. A.Donoghue, S. H.Bird, T.Bird-Gardiner, and R. S.Hegarty. 2014. Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle. J. Anim. Sci. 92:5267–5274. doi:10.2527/jas.2014-827325349368, Herd, R. M., P. F.Arthur, K. A.Donoghue, S. H.Bird, T.Bird-Gardiner, and R. S.Hegarty. 2014. Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle. J. Anim. Sci. 92:5267–5274. doi:10.2527/jas.2014-827325349368
A. Patra (2016)
Recent Advances in Measurement and Dietary Mitigation of Enteric Methane Emissions in Ruminants
Frontiers in Veterinary Science, 3
( Lahart, B., R.Prendiville, F.Buckley, E.Kennedy, S. B.Conroy, T. M.Boland, and M.McGee. 2020. The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasture-based diets. Animals14:2288–2297. doi:10.1017/S1751731120000853)
Lahart, B., R.Prendiville, F.Buckley, E.Kennedy, S. B.Conroy, T. M.Boland, and M.McGee. 2020. The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasture-based diets. Animals14:2288–2297. doi:10.1017/S1751731120000853
Lahart, B., R.Prendiville, F.Buckley, E.Kennedy, S. B.Conroy, T. M.Boland, and M.McGee. 2020. The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasture-based diets. Animals14:2288–2297. doi:10.1017/S1751731120000853, Lahart, B., R.Prendiville, F.Buckley, E.Kennedy, S. B.Conroy, T. M.Boland, and M.McGee. 2020. The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasture-based diets. Animals14:2288–2297. doi:10.1017/S1751731120000853
( Hristov, A. N., E.Kebreab, M.Niu, J.Oh, A.Bannink, A. R.Bayat, T. M.Boland, A. F.Brito, D. P.Casper, L. A.Crompton, et al. 2018. Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. J. Dairy Sci. 101:6655–6674. doi:10.3168/jds.2017-1353629680642)
Hristov, A. N., E.Kebreab, M.Niu, J.Oh, A.Bannink, A. R.Bayat, T. M.Boland, A. F.Brito, D. P.Casper, L. A.Crompton, et al. 2018. Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. J. Dairy Sci. 101:6655–6674. doi:10.3168/jds.2017-1353629680642
Hristov, A. N., E.Kebreab, M.Niu, J.Oh, A.Bannink, A. R.Bayat, T. M.Boland, A. F.Brito, D. P.Casper, L. A.Crompton, et al. 2018. Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. J. Dairy Sci. 101:6655–6674. doi:10.3168/jds.2017-1353629680642, Hristov, A. N., E.Kebreab, M.Niu, J.Oh, A.Bannink, A. R.Bayat, T. M.Boland, A. F.Brito, D. P.Casper, L. A.Crompton, et al. 2018. Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. J. Dairy Sci. 101:6655–6674. doi:10.3168/jds.2017-1353629680642
( Marty, R. J., and D. I.Demeyer. 1973. The effect of inhibitors of methane production of fermentation pattern and stoichiometry in vitro using rumen contents from sheep given molasses. Br. J. Nutr.30:369–376. doi:10.1079/bjn197300414746693)
Marty, R. J., and D. I.Demeyer. 1973. The effect of inhibitors of methane production of fermentation pattern and stoichiometry in vitro using rumen contents from sheep given molasses. Br. J. Nutr.30:369–376. doi:10.1079/bjn197300414746693
Marty, R. J., and D. I.Demeyer. 1973. The effect of inhibitors of methane production of fermentation pattern and stoichiometry in vitro using rumen contents from sheep given molasses. Br. J. Nutr.30:369–376. doi:10.1079/bjn197300414746693, Marty, R. J., and D. I.Demeyer. 1973. The effect of inhibitors of methane production of fermentation pattern and stoichiometry in vitro using rumen contents from sheep given molasses. Br. J. Nutr.30:369–376. doi:10.1079/bjn197300414746693
A. Hristov, J. Oh, J. Firkins, J. Dijkstra, E. Kebreab, G. Waghorn, H. Makkar, A. Adesogan, W. Yang, C. Lee, P. Gerber, B. Henderson, J. Tricarico (2013)
Special topics--Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options.
Journal of animal science, 91 11
A. Moss, J. Jouany, J. Newbold (2000)
Methane production by ruminants: its contribution to global warming
Annales De Zootechnie, 49
( Wall, E., G.Simm, and D.Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal4:366–376. doi:10.1017/S175173110999070X22443941)
Wall, E., G.Simm, and D.Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal4:366–376. doi:10.1017/S175173110999070X22443941
Wall, E., G.Simm, and D.Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal4:366–376. doi:10.1017/S175173110999070X22443941, Wall, E., G.Simm, and D.Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal4:366–376. doi:10.1017/S175173110999070X22443941
R. Danielsson, J. Dicksved, Li Sun, H. Gonda, Bettina Müller, A. Schnürer, J. Bertilsson (2017)
Methane Production in Dairy Cows Correlates with Rumen Methanogenic and Bacterial Community Structure
Frontiers in Microbiology, 8
( Pickering, N. K., V. H.Oddy, J.Basarab, K.Cammack, B.Hayes, R. S.Hegarty, J.Lassen, J. C.McEwan, S.Miller, C. S.Pinares-Patiño, et al. 2015. Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animals9:1431–1440. doi:10.1017/S1751731115000968)
Pickering, N. K., V. H.Oddy, J.Basarab, K.Cammack, B.Hayes, R. S.Hegarty, J.Lassen, J. C.McEwan, S.Miller, C. S.Pinares-Patiño, et al. 2015. Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animals9:1431–1440. doi:10.1017/S1751731115000968
Pickering, N. K., V. H.Oddy, J.Basarab, K.Cammack, B.Hayes, R. S.Hegarty, J.Lassen, J. C.McEwan, S.Miller, C. S.Pinares-Patiño, et al. 2015. Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animals9:1431–1440. doi:10.1017/S1751731115000968, Pickering, N. K., V. H.Oddy, J.Basarab, K.Cammack, B.Hayes, R. S.Hegarty, J.Lassen, J. C.McEwan, S.Miller, C. S.Pinares-Patiño, et al. 2015. Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animals9:1431–1440. doi:10.1017/S1751731115000968
( Shabat, S. K., G.Sasson, A.Doron-Faigenboim, T.Durman, S.Yaacoby, M. E.Berg Miller, B. A.White, N.Shterzer, and I.Mizrahi. 2016. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10:2958–2972. doi:10.1038/ismej.2016.6227152936)
Shabat, S. K., G.Sasson, A.Doron-Faigenboim, T.Durman, S.Yaacoby, M. E.Berg Miller, B. A.White, N.Shterzer, and I.Mizrahi. 2016. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10:2958–2972. doi:10.1038/ismej.2016.6227152936
Shabat, S. K., G.Sasson, A.Doron-Faigenboim, T.Durman, S.Yaacoby, M. E.Berg Miller, B. A.White, N.Shterzer, and I.Mizrahi. 2016. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10:2958–2972. doi:10.1038/ismej.2016.6227152936, Shabat, S. K., G.Sasson, A.Doron-Faigenboim, T.Durman, S.Yaacoby, M. E.Berg Miller, B. A.White, N.Shterzer, and I.Mizrahi. 2016. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10:2958–2972. doi:10.1038/ismej.2016.6227152936
M. Coppa, J. Jurquet, M. Eugène, Terrence Dechaux, Y. Rochette, J. Lamy, A. Ferlay, Cécile Martin (2020)
Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions.
Methods
( Renand, G., A.Vinet, V.Decruyenaere, D.Maupetit, and D.Dozias. 2019. Methane and carbon dioxide emission of beef heifers in relation with growth and feed efficiency. Animals9:1136. doi:10.3390/ani9121136)
Renand, G., A.Vinet, V.Decruyenaere, D.Maupetit, and D.Dozias. 2019. Methane and carbon dioxide emission of beef heifers in relation with growth and feed efficiency. Animals9:1136. doi:10.3390/ani9121136
Renand, G., A.Vinet, V.Decruyenaere, D.Maupetit, and D.Dozias. 2019. Methane and carbon dioxide emission of beef heifers in relation with growth and feed efficiency. Animals9:1136. doi:10.3390/ani9121136, Renand, G., A.Vinet, V.Decruyenaere, D.Maupetit, and D.Dozias. 2019. Methane and carbon dioxide emission of beef heifers in relation with growth and feed efficiency. Animals9:1136. doi:10.3390/ani9121136
( Hristov, A. N., J.Oh, J. L.Firkins, J.Dijkstra, E.Kebreab, G.Waghorn, H. P. S.Makkar, A. T.Adesogan, W.Yang, C.Lee, et al. 2013. Special topics—mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069. doi:10.2527/jas.2013-658324045497)
Hristov, A. N., J.Oh, J. L.Firkins, J.Dijkstra, E.Kebreab, G.Waghorn, H. P. S.Makkar, A. T.Adesogan, W.Yang, C.Lee, et al. 2013. Special topics—mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069. doi:10.2527/jas.2013-658324045497
Hristov, A. N., J.Oh, J. L.Firkins, J.Dijkstra, E.Kebreab, G.Waghorn, H. P. S.Makkar, A. T.Adesogan, W.Yang, C.Lee, et al. 2013. Special topics—mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069. doi:10.2527/jas.2013-658324045497, Hristov, A. N., J.Oh, J. L.Firkins, J.Dijkstra, E.Kebreab, G.Waghorn, H. P. S.Makkar, A. T.Adesogan, W.Yang, C.Lee, et al. 2013. Special topics—mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069. doi:10.2527/jas.2013-658324045497
( Donoghue, K. A., T.Bird-Gardiner, P. F.Arthur, R. M.Herd, and R. F.Hegarty. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas.2015-006527136003)
Donoghue, K. A., T.Bird-Gardiner, P. F.Arthur, R. M.Herd, and R. F.Hegarty. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas.2015-006527136003
Donoghue, K. A., T.Bird-Gardiner, P. F.Arthur, R. M.Herd, and R. F.Hegarty. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas.2015-006527136003, Donoghue, K. A., T.Bird-Gardiner, P. F.Arthur, R. M.Herd, and R. F.Hegarty. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas.2015-006527136003
K. Donoghue, T. Bird-Gardiner, R. Herd, R. Hegarty, P. Arthur (2020)
Genetic variance and covariance components for carbon dioxide production and postweaning traits in Angus cattle.
Journal of animal science
( Herd, R. M., J. I.Velazco, P. F.Arthur, and R. F.Hegarty. 2016a. Associations among methane emission traits measured in the feedlot and in respiration chambers in Angus cattle bred to vary in feed efficiency. J. Anim. Sci. 94:4882–4891. doi:10.2527/jas.2016-061327898960)
Herd, R. M., J. I.Velazco, P. F.Arthur, and R. F.Hegarty. 2016a. Associations among methane emission traits measured in the feedlot and in respiration chambers in Angus cattle bred to vary in feed efficiency. J. Anim. Sci. 94:4882–4891. doi:10.2527/jas.2016-061327898960
Herd, R. M., J. I.Velazco, P. F.Arthur, and R. F.Hegarty. 2016a. Associations among methane emission traits measured in the feedlot and in respiration chambers in Angus cattle bred to vary in feed efficiency. J. Anim. Sci. 94:4882–4891. doi:10.2527/jas.2016-061327898960, Herd, R. M., J. I.Velazco, P. F.Arthur, and R. F.Hegarty. 2016a. Associations among methane emission traits measured in the feedlot and in respiration chambers in Angus cattle bred to vary in feed efficiency. J. Anim. Sci. 94:4882–4891. doi:10.2527/jas.2016-061327898960
P. Arthur, T. Bird-Gardiner, I. Barchia, K. Donoghue, R. Herd (2018)
Relationships among carbon dioxide, feed intake, and feed efficiency traits in ad libitum fed beef cattle.
Journal of animal science, 96 11
( Beauchemin, K. A., E. M.Ungerfeld, R. J.Eckard, and M.Wang. 2020. Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation. Animals14(S1):s2–s16. doi:10.1017/S1751731119003100)
Beauchemin, K. A., E. M.Ungerfeld, R. J.Eckard, and M.Wang. 2020. Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation. Animals14(S1):s2–s16. doi:10.1017/S1751731119003100
Beauchemin, K. A., E. M.Ungerfeld, R. J.Eckard, and M.Wang. 2020. Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation. Animals14(S1):s2–s16. doi:10.1017/S1751731119003100, Beauchemin, K. A., E. M.Ungerfeld, R. J.Eckard, and M.Wang. 2020. Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation. Animals14(S1):s2–s16. doi:10.1017/S1751731119003100
S. M. Waters, D. A. Kenny, P. E. Smith, C. McSweeney, R. Mackie (2020)
Improving rumen function
( Gerber, P. J., H.Steinfeld, B.Henderson, A.Mottet, C.Opio, J.Dijkman, A.Falcucci, and G.Tempio. 2013. Tackling climate change through livestock – a global assessment of emissions and mitigation opportunities. Rome: Food and Agriculture Organization of the United Nations (FAO).)
Gerber, P. J., H.Steinfeld, B.Henderson, A.Mottet, C.Opio, J.Dijkman, A.Falcucci, and G.Tempio. 2013. Tackling climate change through livestock – a global assessment of emissions and mitigation opportunities. Rome: Food and Agriculture Organization of the United Nations (FAO).
Gerber, P. J., H.Steinfeld, B.Henderson, A.Mottet, C.Opio, J.Dijkman, A.Falcucci, and G.Tempio. 2013. Tackling climate change through livestock – a global assessment of emissions and mitigation opportunities. Rome: Food and Agriculture Organization of the United Nations (FAO)., Gerber, P. J., H.Steinfeld, B.Henderson, A.Mottet, C.Opio, J.Dijkman, A.Falcucci, and G.Tempio. 2013. Tackling climate change through livestock – a global assessment of emissions and mitigation opportunities. Rome: Food and Agriculture Organization of the United Nations (FAO).
( Williams, S. R. O., M.Hannah, J. L.Jacobs, W. J.Wales, and P. J.Moate. 2019. Volatile fatty acids in ruminal fluid can be used to predict methane yield of dairy cows. Animals9:1006. doi:10.3390/ani9121006)
Williams, S. R. O., M.Hannah, J. L.Jacobs, W. J.Wales, and P. J.Moate. 2019. Volatile fatty acids in ruminal fluid can be used to predict methane yield of dairy cows. Animals9:1006. doi:10.3390/ani9121006
Williams, S. R. O., M.Hannah, J. L.Jacobs, W. J.Wales, and P. J.Moate. 2019. Volatile fatty acids in ruminal fluid can be used to predict methane yield of dairy cows. Animals9:1006. doi:10.3390/ani9121006, Williams, S. R. O., M.Hannah, J. L.Jacobs, W. J.Wales, and P. J.Moate. 2019. Volatile fatty acids in ruminal fluid can be used to predict methane yield of dairy cows. Animals9:1006. doi:10.3390/ani9121006
N. Pickering, V. Oddy, J. Basarab, K. Cammack, B. Hayes, R. Hegarty, J. Lassen, J. McEwan, S. Miller, C. Pinares-Patiño, Y. Haas (2015)
Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants
Animal, 9
K. Beauchemin, E. Ungerfeld, R. Eckard, M. Wang (2020)
Review: Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation.
Animal : an international journal of animal bioscience, 14 S1
P. Gerber, H. Steinfeld, B. Henderson, A. Mottet, C. Opio, J. Dijkman, A. Falcucci, G. Tempio (2013)
Tackling climate change through livestock : a global assessment of emissions and mitigation opportunities
( Kelly, D. N., C.Murphy, R. D.Sleator, M. M.Judge, S. B.Conroy, and D. P.Berry. 2019. Feed efficiency and carcass metrics in growing cattle. J. Anim. Sci. 97:4405–4417. doi:10.1093/jas/skz31631593986)
Kelly, D. N., C.Murphy, R. D.Sleator, M. M.Judge, S. B.Conroy, and D. P.Berry. 2019. Feed efficiency and carcass metrics in growing cattle. J. Anim. Sci. 97:4405–4417. doi:10.1093/jas/skz31631593986
Kelly, D. N., C.Murphy, R. D.Sleator, M. M.Judge, S. B.Conroy, and D. P.Berry. 2019. Feed efficiency and carcass metrics in growing cattle. J. Anim. Sci. 97:4405–4417. doi:10.1093/jas/skz31631593986, Kelly, D. N., C.Murphy, R. D.Sleator, M. M.Judge, S. B.Conroy, and D. P.Berry. 2019. Feed efficiency and carcass metrics in growing cattle. J. Anim. Sci. 97:4405–4417. doi:10.1093/jas/skz31631593986
J. Rooke, N. Mckain, C. Duthie, J. Hyslop, D. Ross, A. Waterhouse, M. Watson, R. Roehe (2015)
The rumen microbial metagenome associated with high methane production in cattle
BMC Genomics, 16

Copyright: Copyright © 2022 American Society of Animal Science
ISSN: 0021-8812
eISSN: 1525-3163
DOI: 10.1093/jas/skab275
Publisher site: See Article on Publisher Site

Abstract

Residual expressions of enteric emissions favor a more equitable identification of an animal’s methanogenic potential compared with traditional measures of enteric emissions. The objective of this study was to investigate the effect of divergently ranking beef cattle for residual methane emissions (RME) on animal productivity, enteric emissions, and rumen fermentation. Dry matter intake (DMI), growth, feed efficiency, carcass output, and enteric emissions (GreenFeed emissions monitoring system) were recorded on 294 crossbred beef cattle (steers = 135 and heifers = 159; mean age 441 d (SD = 49); initial body weight (BW) of 476 kg (SD = 67)) at the Irish national beef cattle performance test center. Animals were offered a total mixed ration (77% concentrate and 23% forage; 12.6 MJ ME/kg of DM and 12% CP) ad libitum with emissions estimated for 21 d over a mean feed intake measurement period of 91 d. Animals had a mean daily methane emissions (DME) of 229.18 g/d (SD = 45.96), methane yield (MY) of 22.07 g/kg of DMI (SD = 4.06), methane intensity (MI) 0.70 g/kg of carcass weight (SD = 0.15), and RME 0.00 g/d (SD = 0.34). RME was computed as the residuals from a multiple regression model regressing DME on DMI and BW (R = 0.45). Animals were ranked into three groups namely high RME (>0.5 SD above the mean), medium RME (±0.5 SD above/below the mean), and low RME (>0.5 SD below the mean). Low RME animals produced 17.6% and 30.4% less ( < 0.05) P DME compared with medium and high RME animals, respectively. A ~30% reduction in MY and MI was detected in low versus high RME animals. Positive correlations were apparent among all methane traits with RME most highly associated with (r = 0.86) DME. MY and MI were correlated ( < 0.05) with DMI, P growth, feed efficiency, and carcass output. High RME had lower (P < 0.05) ruminal propionate compared with low RME animals and increased ( < 0.05) butyr P ate compared with medium and low RME animals. Propionate was negatively associated ( < 0.05) with all methane tr P aits. Greater acetate:propionate ratio was associated with higher RME ( = 0.18; r P < 0.05). Under the ad libitum feeding regime deployed here, RME was the best predictor of DME and only methane trait independent of animal productivity. Ranking animals on RME presents the opportunity to exploit interanimal variation in enteric emissions as well as providing a more equitable index of the methanogenic potential of an animal on which to investigate the underlying biological regulatory mechanisms. Key words: beef cattle, residual methane emissions © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 1 Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 2 | Journal of Animal Science, 2021, Vol. 99, No. 11 Abbreviations Selection on the basis of methane emissions expressed as a proportion of feed intake (methane yield; MY) has been ADF acid detergent fiber the traditional selection approach, as the trait was previously ADG average daily gain perceived to be free from any association with feed intake or BW body weight body weight (BW) but positively correlated with DME, when CG contemporary group open-circuit respiration chambers and restricted feed intake CH methane were utilized as reference methodology for quantifying enteric CO carbon dioxide emissions (Herd et al., 2014; Donoghue et al., 2016). However, CP crude protein the selection of animals on the basis of ratio traits has been CW carcass weight disputed by virtue of their unpredictable response to other traits DM dry matter of economic importance in beef production (Pickering et al., DMI dry matter intake 2015). In addition, a negative phenotypic correlation between DME daily methane emissions MY and feed intake has recently been observed across both EM early maturing concentrate and forage based diets under ad libitum feeding G:F gain to feed conditions with the use of the GreenFeed emissions monitoring GEM GreenFeed emissions monitoring system (Bird-Gardiner et al., 2017; Renand et al., 2019). system Consequently, due to the aforementioned shortcomings, IMF intramuscular fat there has been increasing interest in the use of the residual LM late maturing methane emissions (RME) concept to identify animals with ME metabolizable energy a greater genetic propensity for lower methane output, NDF neutral detergent fiber principally due to its ability to overcome the limitations RFI residual feed intake associated with proportional expression of methane emissions SCFA short chain fatty acids relative to other traits and by design, its lack of relationship TMR total mixed ration with feed intake. RME can be defined as the difference in the VFA volatile fatty acid animals actual and expected methane output, based on its level of feed intake and BW (Bird-Gardiner et al., 2017). First proposed by Herd et al. (2014), the trait has been observed to Introduction be phenotypically and genetically independent of feed intake and bodyweight (Herd et al., 2014; Donoghue et al., 2016; Bird- Global food production has benefited from the ability of ruminant Gardiner et al., 2017). Indeed, the independence of RME from livestock to convert plant matter into high-quality sources of animal productivity, also affords the opportunity to unravel dairy and meat protein for human consumption (Waters et al., the inherent variation in underlying biological mechanisms 2020). However, ruminant, relative to monogastric, derived food influencing methanogenesis. Currently, there is a paucity products have a much greater carbon intensity (Herrero and of information on the implications of ranking commercially Thornthon, 2013), with methane originating from domesticated representative beef cattle for RME on animal productivity, feed cattle accountable for ~4.5% of anthropogenic emissions (Gerber efficiency, and carcass output. et al., 2013). Consequently, mitigation strategies to reduce Therefore, the objectives of this study were to 1) investigate enteric methane emissions from cattle have been a key research the effects of divergently ranking beef cattle for RME on DME, priority for livestock scientists in recent decades. Numerous yield, intensity, animal productivity, and rumen fermentation; dietary interventions (strategic supplementation with various 2) examine the phenotypic relationships of RME with other feedstuffs and bioactive compounds, combined with animal traits of economic importance to beef production. management approaches) have been advocated to offer potential methane mitigation solutions to livestock producers (Hristov et al., 2013Beauc ; hemin et al., 2020; Honan et al., 2021); Materials and Methods however, a supplement with consistent antimethanogenic All animal procedures used in this study were approved by properties, and no adverse implications to animal performance, the Teagasc Animal Ethics Committee and conducted using is yet to be made commercially available. procedures consistent with the experimental license (AE19132/ Enteric methane emissions is a trait which is moderately P078) issued by the Irish Health Products Regulatory Authority in heritable (h = 0.23 to 0.30) (Pinares-Patiño et al., 2013 Dono ; ghue accordance with European Union legislation (Directive 2010/63/ et al., 2016; Manzanilla-Pech et al., 2016) with large interanimal EU), for the protection of animals used for scientific purposes. inherent variation, presenting the possibility of cumulative and permeant reductions in ruminant livestock derived emissions Animal management and performance test through genetic selection as an alternative mitigation solution (Wall et al., 2010; Pickering et al., 2015; de Hass et al., 2017; Over a period of 18 mo, data were obtained from 294 commercial Beauchemin et al., 2020). Nonetheless, determining the optimal beef cattle (steers = 135 and heifers = 159; mean age 441 d low methane phenotype, with which to select cattle, poses a (SD = 49)) enrolled in a feed efficiency performance test. Cattle challenge due to the relationship of methanogenesis with other were the progeny of AI bulls, under evaluation as part of the traits of importance to animal productivity (de Hass et al., 2017). Gene Ireland Breeding Program (https://www.icbf.com/?page_ Feed intake and daily methane emissions (DME; g/d) are both id=12900), and were recruited from commercial breeding herds, phenotypically (Herd et al., 2014) and genetically (Donoghue et al., based on factors including sire, breed, genetic merit, pedigree, 2016; Manzanilla-Pech et al., 2016) associated. As a result, the and age, and performance tested under standardized conditions implementation of breeding strategies, where DME is the targeted at the Irish Cattle Breeding Federation (ICBF) national beef bull phenotype, will likely result in a concurrent reduction to voluntarypr ogeny test station (Tully, Co. Kildare). Cattle included in this feed intake, and subsequently animal performance, in future study originated from continental late maturing (LM) beef dams generations of livestock (Herd et al., 2014 de Hass et al., ; 2017). (Charolais, Limousin, or Simmental), sired by early maturing Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 3 (EM) or LM sire breeds. The proportion of EM and LM sired of the feed was determined using a LECO 828 Series Macro animals was 25% and 75%, respectively. Combustion instrument (Leco Instruments, UK, Ltd, Stockport, Eligible cattle enter the test center in groups of 40 to 75 cattle,UK). The nitrogen concentration of the feed was multiplied by hereby referred to as “batches”, and undergo a minimum 100 d 6.25, to determine CP concentrations (g/kg DM). Neutral detergent feed efficiency performance test. Starting in January 2019 and fiber (NDF) and acid detergent fiber (ADF) concentrations were finishing in July 2020, animals from seven consecutive batches determined by the method of Van Soest et al. (1991) using the were included in this study. Upon arrival at the facility, cattle wereANK OM220 Fiber Analyzer (ANKOM Technology, Macedon, NY). allocated to indoor pens (6.1 m 4.6 m) bedded with × peat. Cattle TMR and concentrate samples were analyzed for NDF with were separated based on gender and initially penned in groups of sodium sulfite and with a heat stable amylase included for five to six depending on their initial weight and age. Cattle were both sets of samples. NDF and ADF are expressed inclusive offered a 30 d adjustment period to allow dietary acclimatization of residual ash (g/kg DM). Gross energy was determined on and adaption to the facilities. Within the first week of arrival pelletized samples using a bomb calorimeter (Parr Instrument at the test center, animals were fitted with a radio frequency Company, Moline, IL). Ether extract was determined using Soxtec identification tag (HDX EID Tag, Allflex Livestock Intelligence, instruments (Tecator, Höganäs, Sweden) and light petroleum Dallas, TX). Once tagged, pen size was increased by opening the ether. The chemical composition of the TMR and concentrate gates between adjacent pens to accommodate 11 to 30 animals ration are displayed in Table 1. per pen with animals comingled for a minimum 21 d period, prior Animal growth and ultrasonic muscle and fat to the beginning of the feed intake measurement period. This was deposition done to facilitate the measurement of enteric methane production (discussed later). After the adjustment period, animals were At the beginning of each test period, and every 21 to 28 d thereafter subjected to a mean daily feed intake measurement period of 91 until the end of the measurement period, cattle were weighed d (71 to 128 d). The mean age and BW of animals at the beginning with a calibrated scales (ID 3000 scales, Tru Test, Ireland). BW of the test was 441 d (SD = 49) and 476 kg (SD = 67), respectively. measurements were used to derive measures of feed efficiency Steers and heifers averaged 476 (SD = 46) and 410 (SD = 27) days and daily weight gain for each animal. Pre-slaughter ultrasound of age while LM and EM averaged 442 (SD = 51) and 435 (SD = 43) measurements of muscle and fat deposition and proportion days of age at the commencement of the measurement period, of intramuscular fat were collected as described by Kelly et al. respectively. Post completion of their performance test, cattle (2019). Measurements were taken with the use of the same were slaughtered in a commercial abattoir. Esaote-Pie Medical Aquila PRO Vet ultrasound scanner, with a 3.5 MHz transducer head, by a trained technician. Measurement of feed intake and chemical Carcass characteristics composition Animals were slaughtered on average 3 d after the completion Individual daily feed intake was recorded with the use of of the feed efficiency test period in a European Union licensed electronic feeding stations (RIC Feed-Weigh Trough; Hokofarm commercial facility 77 km away (Slaney Foods International, Group BV, Marknesse, The Netherlands) with a feeding event Bunclody, Co. Wexford, Ireland). Animals were slaughtered recorded with each 100 g fluctuation in weight at the feed bunk. within 1 h of arrival at the facility. Carcass weight (CW) was The mean duration of the feed intake measurement was 91 d and measured, on average, 2 h post-slaughter. After slaughter, carcass ranged from 71 to 128 d. Cattle were offered ad libitum access conformation and fat percentage were automatically recorded to the same total mixed ration (TMR) diet (77% concentrate on a 15 point scale using video imaging analysis equipment and 23% grass hay). The TMR consisted of 3 kg of hay and (VBS2000; e+v Technology GmbH & Co.KG, Oranienburg, 10 kg of concentrates, mixed with 9 kg of water. The ingredient Germany) as described by Hickey et al. (2007). composition of the concentrate was as follows (DM basis); maize meal 28%, barley 24%, soya hulls 14%, dried distillers grains Enteric methane and carbon dioxide output 10%, maize gluten meal 9%, soya bean meal 5.5%, molasses 5%, Enteric methane and carbon dioxide measurements were mineral and vitamin premix 3.75%, vegetable oil 0.7%, and yeast obtained on all animals using the GreenFeed emissions 0.05%. The concentrate was a pelleted ration, formulated to have monitoring system (GEM; C-Lock Inc., Rapid City, SD) over 21 a crude protein (CP) content of 140 g kg and had a predicted ME consecutive days throughout the feed intake measurement content of 12.6 MJ/kg DM (NRC, 2016). A fresh TMR was prepared period. The commencement of the emissions estimation period daily which was both mixed and administered via a feed wagon. ranged from days 0 to 36 of the feed intake measurement period. Feed was offered once per day and at all times animals had unrestricted access to clean drinking water. Samples of both the TMR diet and concentrates were Table 1. Details of the chemical composition of total mixed ration obtained weekly and stored at −20 °C for laboratory analysis. (TMR) and concentrates offered during feed efficiency and enteric Feed samples were defrosted overnight in a refrigeration emissions measurement periods (±SD) unit (4 °C) prior to analysis. The dry matter (DM) of TMR and Concentrate TMR concentrate samples was determined after drying at 90 °C for 16 h in a forced-air circulation oven. For chemical analysis, Chemical composition (% of DM unless stated) TMR and concentrate samples were oven dried at 40 °C for 48 h Dry matter 91.7 (0.8) 50.1 (0.9) and then ground through a 1 mm screen (Willey mill; Arthur Crude protein 13.8 (0.4) 12.2 (0.3) H. Thomas, Philadelphia, PA). After grinding, samples collected Neutral detergent fiber 21.8 (0.7) 33.5 (1.1) during each intake run were pooled, respectively. Acid detergent fiber 10.8 (0.3) 17.9 (0.6) Ash concentrations (g/kg DM) were determined by complete Either extract 3.4 (0.6) 2.3 (0.3) combustion in a muffle furnace (Nabertherm, GmbH, Lilienthal, Ash 7.4 (0.2) 7.3 (0.1) Germany) at 550 °C for 4 h. Nitrogen concentration (g/kg DM) Gross energy, MJ/kg DM 16.8 (0.3) 16.7 (0.2) Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 4 | Journal of Animal Science, 2021, Vol. 99, No. 11 A detailed description of the workings of the GEM has FFAP capillary column (Trajan Scientific, Milton Keynes, UK). The been previously described (Huhtanen et al., 2015Hammond ; initial injector temperature was 60 °C for 10 s, rising to 110 °C at et al., 2016; Patra et al., 2016; Hristov et al., 2018). Briefly, the a rate of 30 °C/min, this temperature then increased at rate of concentration of enteric gaseous emissions emitted by individual 10 °C/min to 200 °C (held for 2 min). Helium was used as a carrier animals per visit was determined by the GEM software, as a gas gas. The pressure of the column was held at 19.3 pounds per flux, from the increase in the concentration of each gas, relative square inch (psi) and the column rate was 17.2 mL/min. to background levels, accompanied by adjustments for airflow Total short chain fatty acids (SCFA) are reported as the sum rate and principles of the ideal gas law, and reported in grams total of all VFAs (mM). The percentage of acetate, propionate, and per day. The ratio of animals to a single GEM, ranged from 11 to butyrate are reported as the proportion of each individual VFA 30 depending on numbers in each intake group. relative to the total SCFA. The ratio of acetate to propionate (A:P) Each GEM was connected to both a span (0.05% methane was calculated. Estimates of theoretical hydrogen (H) production (CH ), 0.5% carbon dioxide (CO) balanced with zero grade by each animal at the time of sampling were calculated based 4 2 nitrogen gas; BOC Gas, Dublin, Ireland) and zero gas canister on the concentration of individual VFAs as described by Marty (zero grade nitrogen gas; BOC Gas, Dublin, Ireland) with auto and Demeyer (1973) with the exclusion of hydrogen gas (H). calibrations performed every 3 d. Throughout the duration of the Traits investigated and their derivations experiment, monthly CO recovery tests were performed, as per the manufactures instructions, to assess the airflow of the unit. Average dry matter intake (DMI; kg) was calculated as the A clean air filter was replaced in each unit on a weekly basis or average daily feed intake of each animal (including the GEM if airflow dropped below 27 L/s. The bait feed utilized to entice bait feed during the estimation of enteric emissions) over the animals to use the GEM, was the same pelleted concentrate course of the experiment after correcting for DM as described included in the TMR. Feed drops were weighed on a weekly basis above. Average daily gain (ADG; kg) during the test period for for each GEM unit using the average of 10 feed drops. Throughout each animal was computed as the coefficient of the linear the experimental period and across GEM units, CO recoveries regression of BW (kg) on time. The weight of the animal at the and the weight of individual feed drops averaged 99.32 ± 3.29% beginning and end of the feed intake measurement period was and 34.02 ± 4.11 g, respectively. The mean airflow for all data used to calculate initial and final live BW, respectively. Mean points utilized in this experiment was 37.1 ± 2.59 L/s. metabolic BW (MetBW; kg) was represented as average test 0.75 Previously,Arthur et al. (2017) determined a minimum of 30 BW . Pre-slaughter muscular depth (MD; mm), pre-slaughter visits to GEM, of >3 min in length, to be sufficient to accurately fat depth (FD; mm), and pre-slaughter intramuscular fat (IMF; determine enteric methane emissions for individual animals. In %) were determined using data obtained during ultrasound line with these recommendations, the GEM was programmed to measurements as previously described. Carcass conformation drop 30 g of bait feed, every 35 s, to a maximum of six drops per grade and fat class score values were scaled, with 1 representing visit for each animal. Once an animal reached the maximum the poorest conformation and 15 the best conformation in number of bait feed drops, a minimum 4 h interval was required carcass conformation grade and 1 representing the leanest before an animal could receive another drop of bait feed from value and 15 the fattest in fat class scores, respectively (Hickey the unit. et al., 2007). Gain to feed ratio (G:F) was obtained for each animal by dividing ADG by average DMI. Rumen fermentation Residual feed intake (RFI) was computed for each animal During the last week of the enteric emissions measurement and was assumed to represent the residuals from a multiple period, samples of rumen digesta were obtained from each regression model regressing DMI on ADG and MetBW. Each animal, before feeding, using a transoesophageal rumen batch of animals was subsequently treated as a contemporary sampling device (FLORA rumen scoop; Guelph, Ontario, Canada). group (CG) and included as a fixed effect in the model. The base Feed was restricted from animals for a minimum of 2 h prior model used was to sampling. After collection, ruminal fluid pH was measured immediately using a digital pH meter (Orion SA 720; Thermo Y = β + β MetBW + β ADG + CG + e , 0 1 2 j j j i j Fisher Scientific, Waltham, MA) followed by the preservation of samples via snap freezing in liquid nitrogen. On the same day of where Y is the DMI of the jth animal, β is the r egression j 0 sampling, samples were transported 61 km away to the Teagasc intercept, β is the regression coefficient on MetBW, β is the 1 2 research facility (Teagasc Grange, Dunsany, Co. Meath, Ireland) regression coefficient on ADG, CG is the fixed effect of the ith on dry ice and stored at −80 °C until analysis was conducted. batch of animals, and e is the uncontrolled error of the jth Rumen fluid samples were thawed on a laboratory bench top animal. The multiple regression model fitted for RFI explained and diluted in 50% TCA acid at a ratio of 4:5 in favor of rumen 72% of the variation in DMI while RFI averaged 0.00 kg DM/d fluid. Following the addition of acid, samples were centrifuged (SD = 0.77). RFI values ranged from −3.53 to 2.25 kg/d and for 10 min (2,000 rpm; 4 °C) after which, 250 μL of supernatant represented a difference of 5.78 kg/d between the lowest and was drawn off into a test tube and diluted with 3.75 mL of highest ranked animals for RFI. dH O and 1 mL of internal standard (0.5 g 3-metyl-n-valeric Methane DMI (MDMI; kg) was calculated as the sum total of acid in 1 liter of 0.15 M oxalic acid). Following centrifuging the combined TMR and concentrate supplementation from the for 5 min (2000 rpm; 21 °C), the dilution was filtered through GEM for each animal averaged over the methane measurement a 0.45 μm filter (Cronus Syringe filter PTFE 13mm; SMI-LabHut period. Average daily methane (DME; CH g/d) and carbon Ltd., Maisemore, Gloucester, UK) into a 2 ml GC vial (Thermo dioxide emissions (CME; CO g/d) for each animal was derived Scientific, Langerwehe, Germany) and frozen at −20 °C until VFA from the sum of emissions of each gas per spot measurement analysis. divided by the total number of these measurements as recorded One microliter of sample was injected by auto sampler on by the GEM over the test period. Only spot measurements a Varian (Saturn 2000) gas chromatograph (GC) 450 (Varian, where the visitation to the GEM was ≥3 min were included in Middelburg, The Netherlands) with a 30 m × 0.25 mm i.d. BP21 the analysis. MY (CH g/DMI kg) was calculated for each animal 4 Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 5 by dividing DME by the MDMI. The weight of individual animals and discounted in the analysis if the residual/SD was >3 for a on the 30th day of the feed intake measurement period was measurement of either gas. After the removal of outliers, 99.68% used to standardize BW for methane analysis (hereby referred of the emissions data were maintained and used for analysis. to as methane BW). Individual animal BW on day 30 was Data were checked for normality and homogeneity of calculated based on the regression analysis conducted during variance by histograms, qqplots, and formal statistical tests as the calculation of ADG. Methane per unit of BW (CH g/BW) and part of the UNIVARIATE procedure of SAS. Data from 12 animals methane intensity (MI; CH g/carcass out kg) were calculated by were not included in the analysis as visitation to the GEM was dividing DME by methane BW and CW (kg), respectively. DME below the threshold of 30 visits (n = 3) or the data from animals was also expressed per unit of ADG, using ADG calculated over were identified as statistical outliers (n = 9). This resulted in a the feed intake test period (MADG; CH g/ADG kg). final dataset of 282 animals. A mixed model ANOVA (GLIMMIX Residual methane emissions (RME; CH g/day) was computed procedure of SAS) was used to examine the effect of RME group for each animal using the equation described by Bird-Gardiner on performance, intake, feed efficiency, body composition, et al. (2017). RME was assumed to represent the residuals from methane emissions, and ruminal fermentation profiles. The a multiple regression model regressing DME on MDMI and statistical model used included the fixed effect of RME group methane BW with CG included as a fixed effect in the model. (high, medium, and low) breed maturity/genotype (LM and EM), The base model used was sex (steer and heifer), and their interactions. Non-statistically significant (P > 0.10) interactions were subsequently excluded from the final model. Age and initial bodyweight at the start of Y = β + β MDMI + β methane BW + CG + e , 0 1 2 j j j i j each performance test were included as covariates with each where Y is the DME of the jth animal, β is the r egression batch of animals treated as a CG and incorporated as a random j 0 intercept, β is the regression coefficient on MDMI, β is the effect in the statistical model. Differences among means were 1 2 regression coefficient on methane BW, CG is the fixed effect of determined by F-tests using type III sums of squares. The PDIFF the ith batch of animals, and e is the uncontrolled error of the option and the Tukey test were applied to evaluate pairwise jth animal. The multiple regression for RME explained 45% of comparisons between means. Mean values were considered the variation in DME while RME averaged 0.00 g/d (SD = 34.05). to be different when P < 0.05 and considered a tendency when RME values ranged from −114.07 to 84.99 and represented a P ≥ 0.05 and < 0.10. The associations among the traits were difference of 199.06 g/d between the lowest and highest ranked determined through partial correlations, adjusted for gender, animals for RME. SDs above and below the mean were used to breed maturity, and CG using the MANOVA/PRINTE statement group animals into high RME (RME > 0.5 SD above the mean), within the GLM procedure of SAS. Correlation coefficients were medium RME (RME ± 0.5 SD above and below the mean), and low classified as strong (r > 0.6), moderate (r between 0.4 and 0.6), or RME (RME > 0.5 SD below the mean). weak (r < 0.4), respectively. In addition, for comparative purposes among methane phenotypes, RME was calculated using the equation proposed Results by Renand et al. (2019) whereby DCE replaced DMI. RME with DCE (RME ; CH g/ day) was assumed to represent the residuals CO2 4 Animal performance, feed intake, and feed efficiency from a multiple regression model regressing DME on DCE and Summary statistics show animals on test had an average DMI of methane BW with CG included as a fixed effect in the model. 10.29 kg/d (SD = 1.46), ADG of 1.37 kg/d (SD = 0.28), G:F of 0.13 kg The base model used was of BW gain/kg of DMI (SD = 0.02), RFI of 0.00 kg DM/d (SD = 0.77), final live weight of 594.93 kg (SD = 74.25), age of slaughter of Y = β + β DCE + β methane BW + CG + e , 0 1 2 j j j i j 523.56 d (SD = 46.98), and CW of 333.14 kg (SD = 43.99). Comparisons among RME grouping, sex, and genotype (sire where Y is the DME of the jth animal, β is the r egression j 0 breed maturity), for animal performance, feed intake, and feed intercept, β is the regression coefficient on DCE, β is the 1 2 efficiency, are displayed in Table 2. In this study, there were no regression coefficient on methane BW, CG is the fixed effect of interactions detected (P > 0.05) between RME grouping, sex, the ith batch of animals, and e is the uncontrolled error of the and genotype for intake, growth, feed efficiency, or carcass jth animal. The multiple regression for RME explained 57% of CO2 composition traits. Indeed, feed intake, growth, bodyweight, the variation in DME while RME averaged 0.00 g/d (SD = 30.72). feed efficiency measures, and both CW and composition were RME values ranged from −96.76 to 94.76 and represented a not different (P > 0.05) between the high-, medium-, and low- difference of 191.52 g/d between the lowest and highest ranked ranked animals on RME. Steers relative to heifers had a heavier animals for RME . CO2 (P < 0.05) initial BW, final BW, and CW. Measures of DMI, ADG, Data and statistical analyses FCR, and RFI were not different (P > 0.05) among steers and heifers. Animals from EM sires had a greater (P < 0.05) ADG than Raw emissions data were processed by C-Lock Inc. and checked LM. LM sired animals had a heavier CW and MD, but FD and IMF for irregularities. Data were downloaded from the C-Lock Inc. were greater for the EM sired grouping (P < 0.05). website with an additional round of checks performed to identify and remove outliers as per the methods described by Coppa et al. Enteric methane and carbon dioxide output (2021). To detect outliers, the SD was calculated for both CH and CO using all spot measurements (≥3 min) supplied by C-Lock On average, 87.8% of the visits to the GEM were >3 min in length Inc. Following this, spot measurements of CH were regressed with a mean of 59 valid recordings (i.e., >3 min in length) obtained on CO and vice versa, allowing for the prediction of both gases for each animal. The mean number of valid recordings ranged using the equations generated using the REG procedure in from 54 to 70 recordings per group of cattle with the highest SAS (SAS Inst. Inc., Cary, NC; version 9.4). Residuals were then average valid recordings per animal (70) obtained at a ratio of calculated from the differences of the predicted and observed animals to GEM of 25:1. Animal visitation to the GEM averaged spot measurements for each gas. Finally, outliers were detected 2.81 times per day (SD = 0.61) during the 21 d enteric emissions Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 6 | Journal of Animal Science, 2021, Vol. 99, No. 11 Table 2. Characterization of feed intake, performance, feed efficiency, ultrasonic measurements and carcass output in finishing beef cattle ranked for residual methane emissions, sex, and genotype RME ranking Sex Genotype High, Medium, Low, Steers, Heifers, Late, Early, P-value, RME P-value, P-value, 2 3 4 5 5 5 Traits Mean SD n = 84 n = 114 n = 84 SEM n = 128 n = 154 SEM n = 219 n = 63 SEM ranking sex genotype Performance a b a b Initial weight, 475. 7 67.3 472.9 477.4 473.2 8.51 492.3 456. 7 10.3 482.8 466.2 8.0 0.82 0.02 0.04 kg a b Metabolic body 111.1 10.8 111.2 111.5 110.7 1.43 114.2 108.1 1.8 112.3 110.1 1.4 0.83 0.02 0.09 weight, kg Final weight, 594.9 74.3 599.0 598.8 592.2 11.74 617.2 576.2 15.1 602.4 590.9 11.4 0.70 0.06 0.19 kg a b Average daily 1.4 0.3 1.4 1.4 1.3 0.05 1.4 1.3 0.1 1.3 1.4 0.1 0.17 0.34 0.04 gain, kg Feed intake and efficiency Dry matter 10.29 1.46 10.56 10.29 10.26 0.19 10.52 10.22 0.22 10.21 10.53 0.18 0.29 0.36 0.1 intake, kg/d G:F, kg 0.13 0.02 0.14 0.13 0.13 0.00 0.14 0.13 0.01 0.13 0.13 0.00 0.21 0.55 0.69 a b RFI, kg DM/d 0.00 0.77 0.16 0.03 0.10 0.09 0.00 0.20 0.08 -0.08 0.28 0.08 0.48 0.12 <0.01 Ultrasonic measurements a b Fat depth, mm 5.1 1.9 5.5 5.7 5.6 0.4 4.9 6.3 0.5 4.5 6.7 0.4 0.58 0.06 <0.0001 a b Muscle depth, 76.0 7.4 74.5 75.8 75.4 1.6 76.1 74.4 2.2 78.4 72.1 1.6 0.32 0.60 <0.0001 mm a b Intramuscular 6.0 1.4 6.2 6.5 6.3 0.3 6.5 6.2 0.4 5.8 6.9 0.3 0.22 0.64 <0.0001 fat, % Carcass characteristics a b a b Carcass 333.1 44.0 328.2 334.7 331.8 7.5 345.6 317.6 9.9 340.5 322.7 7.3 0.46 0.04 <0.001 weight, kg High, RME was >0.5 SD above the mean; medium, RME was ±0.5 SD above and below the mean; low, RME was >−0.5 SD below the mean. G:F, gain to feed ratio; RFI, residual feed intake. Overall trait mean. Overall trait standard deviation. SEM, pooled standard error. a,b Least squares means within main effect and a row with different superscripts differ. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 7 measurement period. The average number of daily drops of bait feed was 19.9 drops/d/animal throughout the methane measurement period and ranged from 9.1 to 27 drops/d/animal. During the enteric emissions measurement period, animals had an average daily MDMI of 10.46 kg/d (SD = 1.53), consumed 9.84 kg/d (SD = 1.55) of TMR, and received 0.62 kg/d (SD = 0.13) of concentrate from the GEM. On average, for the high, medium, and low RME groups, the GEM supplemented concentrate account for 5.6%, 6.2%, and 6.3% of total DMI during the emissions measurement period, with no difference observed between the groups (P > 0.05). Summary statistics show a mean DME of 229.18 g/d (SD = 45.96), DCE of 8.42 kg/d (SD = 1.02), MY of 22.07 g/kg of DMI (SD = 4.06), MI 0.70 g/kg of CW (SD = 0.15), and MADG 171.67 g/kg of ADG (SD = 40.73). Summary statistics, along with comparisons among RME grouping, sex, and genotype are reported in Table 3. The diurnal pattern of enteric emissions throughout the measurement period is presented in Figure 1. No interactions were detected (P > 0.05) between RME grouping, sex, and genotype for any methane or carbon dioxide phenotypes in this study. Low RME animals produced 17.69% and 30.4% less (P < 0.05) DME in comparison to animals ranked as medium and high for RME, respectively. Similarly, the low RME group had a lower (P < 0.05) DCE than animals ranked as medium and high. Low RME animals had the lowest (P < 0.05) MY and MI of the RME groups. A difference of 29.73% and 29.63% for MY and MI was detected among the low and high RME groups, respectively. In addition, the low RME animals produced the least (P < 0.05) methane per unit of growth, of the RME groups. No differences among any of the methane phenotypes (P > 0.05), including both RME and RME , were observed among steers CO2 and heifers. No difference in DME, DCE, and RME was detected between genotypes. Association analysis among traits associated with methane output and animal productivity Correlation coefficients among the methane traits investigated in this study are presented in Table 4. The relationship of DME with RME, MI, MY, and MADG is portrayed in Figure 2. DME were positively correlated (P < .0001) with MY, MI, MADG, RME, and RME . Among the methane phenotypes, RME was the strongest CO2 predictor of daily methane output (r = 0.86; P < .0001). Between the residual methane traits RME and RME were strongly CO2 associated with each other (r = 0.86; P < .0001), but RME had the stronger correlations with MY (0.89 vs. 0.77) and MI (0.86 vs. 0.78). All three methane ratio traits (MY, MI, and MADG) were positively correlated (P < .0001). Positive associations were observed between DCE with DME, RME, and MI. Correlation analysis among methane traits with intake, growth, and feed efficiency is presented in Table 5 . The relationship of DMI with DME, DCE, MI, MY, RME, and RME is portrayed in CO2 Figure 3. The methane traits RME and RME were not associated CO2 (P > 0.10) with any of the production traits (DMI, ADG, CW, MD, FD, IMF, G:F, or RFI). MY was negatively associated ( P < 0.05) with DMI, ADG, CW, FD, IMF, G:F, and RFI. MI was positively correlated with DMI, ADG, and RFI and negatively associated with CW and MD (P < 0.05). MADG was negatively correlated ( < P 0.05) with DMI, ADG, and G:F (P < 0.05). DCE had a strong positive relationship (P < 0.05) with DMI (r = 0.78), ADG (r = 0.45), and CW (r = 0.67). Ruminal fermentation parameters Comparisons of fermentation parameters among RME grouping, sex, and animal genotype are presented in Table 6. No interactions were detected (P > 0.05) between RME grouping, Table 3. Characterization of enteric emissions and methane traits in finishing beef cattle ranked for residual methane emissions, sex, and genotype RME ranking Sex Genotype High, Medium, Low, Steers, Heifers, Late, Early, P-value, RME P-value, P-value, 2 3 4 5 5 5 Traits Mean SD n = 84 n = 114 n = 84 SEM n = 128 n = 154 SEM n = 219 n = 63 SEM ranking sex genotype a b c DME, g/d 229.2 46.0 265.0 224.0 184.4 8.8 232.0 217.0 12.1 226.4 222.5 8.7 <0.0001 0.38 0.30 a b c DCE, 8.4 1.0 8.8 8.3 8.1 0.2 8.6 8.2 0.3 8.4 8.4 0.2 <0.0001 0.39 0.92 kg/d a b c RME, g/d 0.00 34.1 38.0 -0.1 -40.3 1.8 -0.7 -0.9 1.8 0.6 -2.2 1.6 <0.0001 0.94 0.25 a b c RME , 0.00 30.2 24.6 0.7 -31.2 2.4 -1.2 -2.7 2.3 0.6 -4.6 2.1 <0.0001 0.65 0.11 CO2 g/d a b c a b MY, g/kg 22.10 4.1 25.2 21.6 17.7 0.7 21.9 21.1 1.0 21.9 21.1 0.7 <0.0001 0.59 0.01 DMI a b c a b MADG, 171.7 40.7 191.3 167.1 144.1 6.6 166.8 168.1 8.5 173.8 161.2 6.4 <0.0001 0.91 0.02 g/kg ADG a b c a b MI, g/kg 0.70 0.15 0.81 0.67 0.57 0.03 0.68 0.69 0.05 0.67 0.70 0.03 <0.0001 0.83 0.01 CW High, RME was >0.5 SD above the mean; medium, RME was±0.5 SD above and below the mean; low, RME was >−0.5 SD below the mean. DME, daily methane emissions; DCE, daily carbon dioxide emissions; RME, residual methane emissions; RME , residual methane emissions calculated with carbon dioxide; MY, methane yield; CO2 MADG, methane emissions per kg of ADG; MI, methane intensity. Overall trait mean. Overall trait standard deviation. SEM, pooled standard error. a,b,c Least squares means within main effect and a row with different superscripts differ. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 8 | Journal of Animal Science, 2021, Vol. 99, No. 11 Correlation analysis of fermentation parameters with all methane traits is reported in Table 7. Total SCFA production had a positive correlation (P < 0.05) with DME, RME, RME , MY, and CO2 MI. Acetate % was positively (P < 0.05) correlated with MY and MI. Propionate % was negatively associated (P < 0.05) with all methane traits namely DME, RME, RME , MY, MI, and MADG. CO2 Both RME and RME were positively associated with higher A:P CO2 ratio (P < 0.05). Butyrate % and theoretical H production were positively correlated (P < 0.05) with DME, RME, RME , MY, MI, CO2 and MADG. Discussion Reducing methane emissions from domesticated cattle will be key to achieving a sustainable growth in global food production. Over the past decade, there has been increased international interest in the use of genetic selection as part of a methane mitigation solution for the ruminant livestock sector (Wall et al., 2010; Pickering et al., 2015; de Hass et al., 2017; Beauchemin et al., 2020). However, while the selection of animals solely on DME has the greatest potential to decrease enteric emissions, this is likely to have ramifications for animal productivity, due to the positive relationship between methanogenesis and feed intake (Wall et al., 2010 Pic ; kering et al., 2015; de Hass et al., 2017). Consequently, researchers have proposed alternative indices for ranking the methanogenic potential of an animal. For example, RME has been advocated as having an optimal balance as a trait Figure 1. Diurnal pattern of daily methane (CH ) and carbon dioxide (CO) 4 2 in identify low emitting animals, while, due to its independence emissions. Error bars indicate SEM. from voluntary feed intake and BW, not impacting on these important drivers of profitability (Herd et al., 2014). However, prior to the completion of the current study, there was a paucity Table 4. Correlations coefficients among methane and carbon dioxide traits of information available surrounding the implications of ranking beef cattle for RME, on enteric emissions, ruminal fermentation, Traits DME DCE RME RME MY MI CO2 animal productivity, and carcass output. Multiple methane phenotypes were evaluated and the values DME − recorded in the present experiment for average DME, along with DCE 0.63*** MY and MI were consistent with previous studies investigating RME 0.86*** 0.26*** enteric emissions using the GEM technology in beef cattle fed RME 0.76*** −0.02 0.86*** CO2 under intensive ad libitum rearing conditions (Arthur et al., MY 0.61*** −0.01 0.89*** 0.77*** 2017; Bird-Gardiner et al., 2017). For example, an average DME MI 0.80*** 0.23*** 0.86*** 0.78*** 0.73*** MADG 0.48*** 0.08 0.55*** 0.57*** 0.57*** 0.49*** of 195.2 and 202.5 g/d was observed by Arthur et al. (2017) and Bird-Gardiner et al. (2017), with the slight increase in emissions DME, daily methane emissions; DCE, daily carbon dioxide observed in this study, likely due to higher proportion of forage emissions; RME, residual methane emissions; RME , residual CO2 in the diet. Additionally, daily animal visitation to the GEM emissions production calculated with carbon dioxide; MY, methane throughout the methane measurement period was within the yield; MI, methane intensity; MADG, methane emissions per kg of range (1.3 to 5.08 visits/d) reported by others (Velazco et al., 2016 ; ADG. ***P < 0.001. Alemu et al., 2017; Arthur et al., 2017; Renand et al., 2019) and further strengthens the validity of the methane recording technique implemented in this experiment. The absolute sex or animal genotype for any of the fermentation parameters range and differences in growth, performance, feed efficiency, reported in this study. and carcass data between animal sexes and genotypes were High RME animals had a greater (P < 0.05) total SCFA comparable to previous production values generated from the production in comparison to the medium and low groups. same feed efficiency performance test center over the preceding The low RME group had a greater ( P< 0.05) propionate % in 10 yr (Crowley et al., 2010K ; elly et al., 2011; Kelly et al., 2019; comparison to the high group; however, animals in the high Lahart et al., 2020). DME were positively correlated with feed group had a greater (P < 0.05) butyrate % compared with intake, growth, and carcass output, in line with previous studies both medium and low animals. No difference (P > 0.05) in (Bird-Gardiner et al., 2017Renand et ; al., 2019). There were no rumen fluid pH, acetate %, A:P ratio or rumen fluid pH was differences in DME among the sexes and genotypes, likely observed among the RME groups. Animals ranked as high had explained by the similar level of feed intake and methane the greatest (P < 0.05) theoretical H production of the RME bodyweights between the groups, with differences in MI groups. No differences in any of the fermentation associated between the breed types due to the increased carcass output variables among animal sex or genotype was found (P > 0.05). observed in LM relative to EM breeds over the finishing period. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 9 Figure 2. The relationship of daily methane emissions (DME) with residual methane emissions (RME), methane intensity (MI), methane yield (MY), and methane emissions per kg of ADG (MADG). Table 5. Correlations coefficients of intake, performance, feed efficiency traits, and body composition measures with methane traits Traits DME RME RME MY MI MADG CO2 DMI, kg 0.50*** 0.05 0.02 −0.30*** 0.13* −0.14* Average daily gain, kg 0.31*** 0.08 0.00 −0.13* 0.13* −0.63*** Carcass weight, kg 0.31*** −0.03 −0.05 −0.18** −0.29*** 0.00 Muscle depth, mm 0.13* 0.00 −0.01 −0.04 −0.21*** 0.08 Fat depth, mm 0.14* −0.03 0.00 −0.16** 0.00 −0.05 Intramuscular fat, % 0.03 −0.07 −0.07 −0.17** −0.08 −0.05 G:F −0.05 0.09 −0.03 0.14* 0.04 −0.66*** RFI 0.23*** −0.01 0.04 −0.24*** 0.31** 0.16** DME, daily methane emissions; DCE, daily carbon dioxide emissions; RME, residual methane emissions; RME , residual methane emissions CO2 calculated with carbon dioxide; MY, methane yield; MI, methane intensity; MADG, methane emissions per kg of ADG; G:F, gain to feed; RFI, residual feed intake. *P < 0.05. **P < 0.01. ***P < 0.001. Methane ratio traits, such as MY have been the traditional correlated with the individual metric of animal performance selection approach in identifying high or low emitting utilized as a denominator trait in their calculation. Therefore, animals, as the traits were observed to be independent the applicability of data generated from feed restricted animals from any associations with feed intake or BW, when open- to inform methane mitigation breeding strategies is questioned, circuit respiration chambers and restricted feed intake were due to unfavorable associations of ratio expressions of methane implemented as part of the standard operating procedure for output with economically important traits observed under ad quantifying enteric emissions (Herd et al., 2014Dono ; ghue et al., libitum feeding conditions. 2016). However, data generated as part of this study and others Alternatively, the selection and ranking of animals on (Herd et al., 2016a; Bird-Gardiner et al., 2017; Renand et al., the basis of RME as part of methane mitigation program has 2019) investigating enteric emissions under various ad libitum been suggested to overcome these limitations associated with feeding regimes, akin to that of a commercial farm setting, ratio-based methane traits and animal productivity, while also indicate the existence of an antagonistic relationship between maintaining the potential to reduce all indices of methane ratio expressions of methane output and traits of economic output (Herd et al., 2014). In support of this, RME were the importance. For example, the present study observed an only methane trait observed to be truly independent of animal unfavorable negative correlation with MY and DMI and equally, production, but positively correlated with enteric emissions in all ratio expressions of methane output (MY, MI, and MADG) were this and other studies where ad libitum feeding was employed Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 10 | Journal of Animal Science, 2021, Vol. 99, No. 11 Figure 3. The relationship of dry matter intake (DMI) with daily methane emissions (DME), daily carbon dioxide emissions (DCE), methane intensity (MI), methane yield (MY), residual methane production (RME), and residual methane production with carbon dioxide (RME ). CO2 (Bird-Gardiner et al., 2017Renand et ; al., 2019). In addition, the acceptance of any methane abatement selection program within coefficient of determination for RME in this study is similar to the livestock sector will be underpinned by its relationship with that reported for feedlot steers by Bird-Gardiner et al. (2017). on farm profitability (Beauchemin et al., 2020). The phenotypic RME were also the best predictor of DME in this experiment evidence in this study, supported by genetic correlations and and strongly associated with all traditional ratio expressions moderate heritability estimates of RME presented by others, of methane output. Animals phenotypically ranked as low for albeit under restricted feeding conditions (Donoghue et al., 2016; RME, in comparison to their high counterparts, produced 30% Manzanilla-Pech et al., 2016), suggests the ruminant livestock less DME showing that large interanimal inherent variation sector could reduce the volume of enteric methane emissions exists for this trait. Similarly, low RME animals had a lower MY in future generations of livestock, without compromising and MI, producing ~30% less methane per unit of feed intake animal productivity, through selection for low RME animals or CW, in comparison to the high RME group. The reduction in as part of a balanced breeding index or an environmentally all methane phenotypes in the low RME group occurred in the focused sub index. Indeed, any mitigation selection program absence of any adverse effect on animal performance further will further benefit from estimations of the heritability and emphasizing the merit of RME in identifying animals truly genetic correlations among methane traits under more industry divergent for methane output, irrespective of productivity. The relevant, ad libitum feeding conditions. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 11 Moreover, recently, some authors have advocated for the use of DCE as a proxy for DMI due to the linear relationship observed among both traits (Herd et al., 2016bArthur et ; al., 2018; Donoghue et al., 2020). The strong correlation with DMI, observed here and elsewhere (Arthur et al., 2018), is indicative of the potential benefit of DCE to act as a proxy for feed intake. Indeed, Renand et al. (2019), in forage fed cattle, advocated the potential to calculate RME with CO in absence of feed intake measures and reported RME to be a good CO2 predictor of RME and free from any association with DMI or BW. Concurring, in the present experiment RME maintained CO2 similar associations to that of RME with feed intake, growth, feed efficiency, carcass output, and all methane phenotypes. Due to the expense of ongoing accurate determinations of DMI and difficulties in the measurement of the trait at pasture, there may be credence for the use of DCE as a proxy for feed intake when investigating DME and RME. However, the accuracy of DCE as an indicator of feed intake will need to be further evaluated across different dietary regimes and stages of the production cycle. Ruminal methanogens primarily synthesize methane from H and CO with both substrates produced as end products of 2 2 the microbial fermentation of ingested feed (Moss et al., 2000). Methane is a known byproduct of microbial fermentation with emissions influenced by hydrogen dynamics in the rumen and shifts in microbial fermentation pathways (Janssen, 2010). Indeed, methanogenesis is believed to acquire a homeostatic role in the rumen, by preventing the accumulation of excessive amounts of H (Morgavi et al., 2010). Ruminal propionate production is considered a competitive hydrogen sink to methanogenesis, with butyrate often considered a net contributor to ruminal hydrogen (Janssen, 2010). In addition, the rumen acetate:propionate ratio is a recognized indicator of an animal’s methanogenic capabilities (Williams et al., 2019). Our data suggest, differences in microbial fermentation pathways particularly the proportion of propionate and butyrate, along with acetate: propionate profile, in the rumen to be among the definitive factors influencing divergence in methane output observed between high- and low-ranked RME animals. Members of both the bacterial and methanogen rumen community are known to influence VFA production and methanogenesis (Kittelmann et al., 2014; Shi et al., 2014; Wallace et al., 2015; Shabat et al., 2016; Auffret et al., 2017; Danielsson et al., 2017; Tapio et al., 2017) making it imperative that further efforts are implemented to identify the key ruminal microbes and methanogenic mechanisms associated with RME to facilitate a greater understanding of the trait. In addition, the increased total SCFA and theoretical H production observed in high RME suggest differences in RME could be influenced by rumen digestibility. Therefore, further studies investigating the relationship of RME with ruminal digestibility and retained energy are warranted. Conclusion RME were the best predictor of DME and were the only methane trait observed to be independent of animal productivity. Ranking cattle in terms of RME, resulted in an ~30% difference between high and low emitting animals for DME, MY, and MI. Differences in methane output among the RME groups were associated with shifts in ruminal hydrogen dynamics resulting from a varied expression of microbial fermentation pathways associated with propionate production. Further in depth rumen microbial analysis is needed to ascertain the key microbes associated with phenotypic and/or genetic divergence for RME Table 6. Characterization of rumen fermentation profile in finishing beef cattle ranked for residual methane emissions, sex, and genotype RME ranking Sex Genotype Rumen High, Medium, Low, Steers, Heifers, Late, Early, P-value, RME P-value, P-value, 2 3 3 3 fermentation Mean SD n = 84 n = 114 n = 84 SEM n = 128 n = 154 SEM n = 219 n = 63 SEM ranking sex genotype pH 6.8 0.3 6.8 6.8 6.8 0.1 6.8 6.8 0.1 6.8 6.8 0.1 0.48 0.76 0.10 a b b Total SCFA, mM 124.2 34.4 134.5 120.9 119.9 7.2 119.8 130.4 8.9 123.4 126.8 6.9 0.02 0.39 0.54 Acetate, % 74.3 6.7 73.6 73.1 73.5 1.5 71.9 74.9 1.9 73.7 73.1 1.4 0.85 0.26 0.51 a a b Propionate, % 13.0 4.3 13.0 14.0 14.5 1.2 14.4 13.2 1.5 13.7 13.9 1.1 0.04 0.59 0.66 Butyrate, % 7.8 2.6 8.0 7.8 7.1 0.7 7.7 7.5 0.9 7.5 7.7 0.69 0.10 0.89 0.56 a b b A:P 5.7 1.4 6.7 5.8 5.7 0.6 5.2 7.0 0.8 6.3 5.9 0.6 0.03 0.11 0.31 a b b Hydrogen 663.7 157.2 688.6 622.9 630.7 37.6 626.5 668.3 48.0 654.8 640.0 36.5 0.03 0.54 0.56 production, mM High, RME were >0.5 SD above the mean; medium, RME was ±0.5 SD above and below the mean; low, RME was >−0.5 SD below the mean. A:P, acetate to propionate ratio. SEM, pooled standard error. a,b Least squares means within main effect and a row with different superscripts differ. Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS 12 | Journal of Animal Science, 2021, Vol. 99, No. 11 Table 7. Correlations coefficients of methane traits with rumen fermentation parameters Traits pH Total SCFA, mM Acetate, % Propionate, % Butyrate, % A:P H DME 0.09 0.19* −0.08 −0.23** 0.25*** 0.07 0.20** DCE 0.05 −0.01 0.04 0.03 −0.09 −0.10 −0.03 RME 0.08 0.19* −0.08 −0.25*** 0.34*** 0.18* 0.22** RME 0.09 0.24** −0.10 −0.36*** 0.41*** 0.22** 0.24** CO2 MY 0.08 0.20** −0.19* −0.18* 0.37*** 0.09 0.23** MI 0.05 0.28*** −0.18* −0.18* 0.30*** 0.06 0.28*** MADG 0.05 0.12 −0.08 −0.26*** 0.24** 0.27*** 0.16* DME, daily methane emissions; DCE, daily carbon dioxide emissions; RME, residual methane emissions; RME , residual methane emissions CO2 calculated with carbon dioxide; MY, methane yield; MI, methane intensity; MADG, methane emissions per kg of ADG; A:P, acetate to propionate ratio; H, theoretical H production. *P < 0.05. **P < 0.01. ***P < 0.001. in order to facilitate the identification of potential microbial and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using based biomarkers associated with the trait. GreenFeed system in farm-conditions. Methods 186:59–67. doi:10.1016/j.ymeth.2020.11.004 Crowley, J. J., M. McGee, D. A. Kenny, D. H. Crews, Jr, R. D. Evans, Acknowledgments and D. P. Berry. 2010. Phenotypic and genetic parameters for This work was funded by the FACCE ERA-GAS “RumenPredict” different measures of feed efficiency in different breeds of grant (16/RD/ERAGAS/1RUMENPREDICT-ROI2017) and Horizon Irish performance-tested beef bulls. J. Anim. Sci88 . :885–894. doi:10.2527/jas.2009-1852 2020 “MASTER” grant (818368). The use of equipment purchased Danielsson, R., J. Dicksved, L. Sun, H. Gonda, B. Müller, through the Department of Agriculture, Food and the Marine A. Schnürer, and J. Bertilsson. 2017. Methane production STILMULUS fund project “GreenBreed” (17/S/235) is acknowledged. in dairy cows correlates with rumen methanogenic and PS is funded by a Teagasc Walsh Scholarship ( RMIS 0364). bacterial community structure. Front. Microbiol. 8:226. doi:10.3389/fmicb.2017.00226 de Haas, Y., M. Pszczola, H. Soyeurt, E. Wall, and J. Lassen. 2017. Conflict of interest statement Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855–870. doi:10.3168/ The authors declare no real or perceived conflicts of interest. jds.2016-11246 Donoghue, K. A., T. Bird-Gardiner, P. F. Arthur, R. M. Herd, and R. F. Hegarty. 2016. Genetic and phenotypic variance Literature Cited and covariance components for methane emission and Alemu, A. W., D. Vyas, G. Manafiazar, J. A. Basarab, and postweaning traits in Angus cattle. J. Anim. Sci 94 . :1438–1445. K. A. Beauchemin. 2017. Enteric methane emissions from doi:10.2527/jas.2015-0065 low- and high-residual feed intake beef heifers measured Donoghue, K. A., T. Bird-Gardiner, R. M. Herd, R. S. Hegarty, and using GreenFeed and respiration chamber techniques. J. P. F. Arthur. 2020. Genetic variance and covariance components Anim. Sci. 95:3727–3737. doi:10.2527/jas.2017.1501 for carbon dioxide production and postweaning traits in Angus Arthur, P. F., I. M. Barchia, C. Weber, T. Bird-Gardiner, cattleJ.. Anim. Sci. 98:253. doi:10.1093/jas/skaa253 K. A. Donoghue, R. M. Herd, and R. S. Hegarty. 2017. Gerber, P. J., H. Steinfeld, B. Henderson, A. Mottet, C. Opio, Optimizing test procedures for estimating daily methane and J. Dijkman, A. Falcucci, and G. Tempio. 2013. Tackling climate carbon dioxide emissions in cattle using short-term breath change through livestock – a global assessment of emissions measures. J. Anim. Sci. 95:645–656. doi:10.2527/jas.2016.0700 and mitigation opportunities. Rome: Food and Agriculture Arthur, P. F., T. Bird-Gardiner, I. M. Barchia, K. A. Donoghue, and Organization of the United Nations (FAO). R. M. Herd. 2018. Relationships among carbon dioxide, feed Hammond, K. J., G. C. Waghorn, and R. S. Hegarty. 2016. The GreenFeed intake, and feed efficiency traits in ad libitum fed beef cattle. system for measurement of enteric methane emission from J. Anim. Sci. 96:4859–4867. doi:10.1093/jas/sky308 cattleAnim. . Prod. Sci. 56:181–189. doi:10.1071/AN15631 Auffret, M. D., R. Stewart, R. J. Dewhurst, C. A. Duthie, J. A. Rooke, Herd, R. M., P. F. Arthur, K. A. Donoghue, S. H. Bird, T. Bird- R. J. Wallace, T. C. Freeman, T. J. Snelling, M. Watson, and Gardiner, and R. S. Hegarty. 2014. Measures of methane R. Roehe. 2017. Identification, comparison, and validation of production and their phenotypic relationships with dry robust rumen microbial biomarkers for methane emissions matter intake, growth, and body composition traits in beef using diverse Bos taurus breeds and basal diets. Front. Microbiol. cattle. J. Anim. Sci. 92:5267–5274. doi:10.2527/jas.2014-8273 8:2642. doi:10.3389/fmicb.2017.02642 Herd, R. M., J. I. Velazco, P. F. Arthur, and R. F. Hegarty. 2016a. Beauchemin, K. A., E. M. Ungerfeld, R. J. Eckard, and M. Wang. Associations among methane emission traits measured 2020. Fifty years of research on rumen methanogenesis: in the feedlot and in respiration chambers in Angus cattle lessons learned and future challenges for mitigation. Animals bred to vary in feed efficiency. J. Anim. Sci. 94:4882–4891. 14(S1):s2–s16. doi:10.1017/S1751731119003100 doi:10.2527/jas.2016-0613 Bird-Gardiner, T., P. F. Arthur, I. M. Barchia, K. A. Donoghue, Herd, R. M., J. I. Velazco, P. F. Arthur, and R. S. Hegarty. 2016b. and R. M. Herd. 2017. Phenotypic relationships among Proxies to adjust methane production rate of beef cattle methane production traits assessed under ad libitum when the quantity of feed consumed is unknown. Anim. Prod. feeding of beef cattle. J. Anim. Sci. 95:4391–4398. doi:10.2527/ Sci. 56: 213–217. doi:10.1071/AN15477 jas2017.1477 Herrero, M., and P. K. Thornton. 2013. Livestock and global change: Coppa, M., J. Jurquet, M. Eugène, T. Dechaux, Y. Rochette, emerging issues for sustainable food systems. Proc. Natl. Acad. J. M. Lamy, A. Ferlay, and C. Martin. 2021. Repeatability Sci. USA 110:20878–20881. doi:10.1073/pnas.1321844111 Downloaded from https://academic.oup.com/jas/article/99/11/skab275/6379086 by DeepDyve user on 16 July 2022 Copyedited by: AS Smith et al. | 13 Hickey, J. M., M. G. Keane, D. A. Kenny, A. R. Cromie, and National Research Council. 2016. Nutrient requirements of beef R. F. Veerkamp. 2007. Genetic parameters for EUROP carcass cattle: 2016. Washington (DC): National Academies Press. traits within different groups of cattle in Ireland. J. Anim. Sci. Patra, A. K. 2016. Recent advances in measurement and dietary 85:314–321. doi:10.2527/jas.2006-263 mitigation of enteric methane emissions in ruminants. Front. Honan, M., X. Feng, J. M. Tricarico, and E. Kebreab. 2021. Feed Vet. Sci. 3:39. doi:10.3389/fvets.2016.00039 additives as a strategic approach to reduce enteric methane Pickering, N. K., V. H. Oddy, J. Basarab, K. Cammack, B. Hayes, production in cattle: modes of action, effectiveness and R. S. Hegarty, J. Lassen, J. C. McEwan, S. Miller, C. S. Pinares- safety. Anim. Prod. Sci. doi:10.1071/AN20295 Patiño, et al. 2015. Animal board invited review: genetic Hristov, A. N., E. Kebreab, M. Niu, J. Oh, A. Bannink, A. R. Bayat, possibilities to reduce enteric methane emissions T. M. Boland, A. F. Brito, D. P. Casper, L. A. Crompton, et al. from ruminants. Animals 9:1431–1440. doi:10.1017/ 2018. Symposium review: uncertainties in enteric methane S1751731115000968 inventories, measurement techniques, and prediction Pinares-Patiño, C. S., S. M. Hickey, E. A. Young, K. G. Dodds, models. J. Dairy Sci. 101:6655–6674. doi:10.3168/jds.2017-13536 S. MacLean, G. Molano, E. Sandoval, H. Kjestrup, R. Harland, Hristov, A. N., J. Oh, J. L. Firkins, J. Dijkstra, E. Kebreab, G. Waghorn, C. Hunt, et al. 2013. Heritability estimates of methane H. P. S. Makkar, A. T. Adesogan, W. Yang, C. Lee, et al. 2013. Special emissions from sheep. Animals 7:316–321. doi:10.1017/ topics—mitigation of methane and nitrous oxide emissions from S1751731113000864 animal operations: I. A review of enteric methane mitigation Renand, G., A. Vinet, V. Decruyenaere, D. Maupetit, and D. Dozias. options. J. Anim. Sci. 91:5045–5069. doi:10.2527/jas.2013-6583 2019. Methane and carbon dioxide emission of beef heifers Huhtanen, P., E. H. Cabezas-Garcia, S. Utsumi, and S. Zimmerman. in relation with growth and feed efficiency. Animals 9:1136. 2015. Comparison of methods to determine methane doi:10.3390/ani9121136 emissions from dairy cows in farm conditions. J. Dairy Sci. Shabat, S. K., G. Sasson, A. Doron-Faigenboim, T. Durman, 98:3394–3409. doi:10.3168/jds.2014-9118 S. Yaacoby, M. E. Berg Miller, B. A. White, N. Shterzer, and Janssen, P. H. 2010. Influence of hydrogen on rumen methane I. Mizrahi. 2016. Specific microbiome-dependent mechanisms formation and fermentation balances through microbial underlie the energy harvest efficiency of ruminants. ISME J. growth kinetics and fermentation thermodynamics. Anim. 10:2958–2972. doi:10.1038/ismej.2016.62 Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002 Shi, W., C. D. Moon, S. C. Leahy, D. Kang, J. Froula, S. Kittelmann, Kelly, A. K., M. McGee, D. H. Crews, Jr, C. O. Lynch, A. R. Wylie, C. Fan, S. Deutsch, D. Gagic, H. Seedorf, et al. 2014. Methane R. D. Evans, and D. A. Kenny. 2011. Relationship between yield phenotypes linked to differential gene expression in body measurements, metabolic hormones, metabolites and the sheep rumen microbiome. Genome Res. 24:1517–1525. residual feed intake in performance tested pedigree beef doi:10.1101/gr.168245.113 bulls. Livest. Sci. 135:8–16. doi:10.1016/j.livsci.2010.05.018 Tapio, I., T. J. Snelling, F. Strozzi, and R. J. Wallace. 2017. The Kelly, D. N., C. Murphy, R. D. Sleator, M. M. Judge, S. B. Conroy, and ruminal microbiome associated with methane emissions D. P. Berry. 2019. Feed efficiency and carcass metrics in growing from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7. cattleJ.. Anim. Sci. 97:4405–4417. doi:10.1093/jas/skz316 doi:10.1186/s40104-017-0141-0 Kittelmann, S., C. S. Pinares-Patiño, H. Seedorf, M. R. Kirk, Van Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods S. Ganesh, J. C. McEwan, and P. H. Janssen. 2014. Two different for dietary fiber, neutral detergent fiber, and nonstarch bacterial community types are linked with the low-methane polysaccharides in relation to animal nutrition. J. Dairy Sci. emission trait in sheep. PLoS One 9:e103171. doi:10.1371/ 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-2 journal.pone.0103171 Velazco, J. I., D. G. Mayer, S. Zimmerman, and R. S. Hegarty. Lahart, B., R. Prendiville, F. Buckley, E. Kennedy, S. B. Conroy, 2016. Use of short-term breath measures to estimate daily T. M. Boland, and M. McGee. 2020. The repeatability of methane production by cattle. Animals 10 :25–33. doi:10.1017/ feed intake and feed efficiency in beef cattle offered high- S1751731115001603 concentrate, grass silage and pasture-based diets. Animals Wall, E., G. Simm, and D. Moran. 2010. Developing breeding 14:2288–2297. doi:10.1017/S1751731120000853 schemes to assist mitigation of greenhouse gas emissions. Manzanilla-Pech, C. I., Y. De Haas, B. J. Hayes, R. F. Veerkamp, Animal 4:366–376. doi:10.1017/S175173110999070X M. Khansefid, K. A. Donoghue, P. F. Arthur, and J. E. Pryce. Wallace, R. J., J. A. Rooke, N. McKain, C. A. Duthie, J. J. Hyslop, 2016. Genomewide association study of methane emissions D. W. Ross, A. Waterhouse, M. Watson, and R. Roehe. 2015. The in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. rumen microbial metagenome associated with high methane 94:4151–4166. doi:10.2527/jas.2016-0431 production in cattle. BMC Genomics16 :839. doi:10.1186/ Marty, R. J., and D. I. Demeyer. 1973. The effect of inhibitors s12864-015-2032-0 of methane production of fermentation pattern and Waters, S. M., D. A. Kenny, and P. E. Smith. 2020. Role of the stoichiometry in vitro using rumen contents from sheep given rumen microbiome in pasture fed ruminant production molasses. Br. J. Nutr. 30:369–376. doi:10.1079/bjn19730041 systems. In: C. McSweeney and R. Mackie, editors. Improving Morgavi, D. P., E. Forano, C. Martin, and C. J. Newbold. 2010. rumen function. Cambridge (UK): Burleigh Dodds Science Microbial ecosystem and methanogenesis in ruminants. Publishing; p. 591–650. Animal 4:1024–1036. doi:10.1017/S1751731110000546 Williams, S. R. O., M. Hannah, J. L. Jacobs, W. J. Wales, and Moss, A. R., J. P. Jouany, and J. Newbold. 2000. Methane production P. J. Moate. 2019. Volatile fatty acids in ruminal fluid can be by ruminants: its contribution to global warming. Ann. used to predict methane yield of dairy cows. Animals 9:1006. Zootechnol. 49:231–235. doi:10.1051/animres:2000119 doi:10.3390/ani9121006

Journal

Journal of Animal Science – Oxford University Press

Published: Nov 1, 2021

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle

Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle

Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle

References (97)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies