Phenotypic relationships among methane production traits assessed under ad libitum feeding of beef cattle.

Angus cattle from 2 beef cattle projects in which daily methane production (MPR) was measured were used in this study to examine the nature of the relationships among BW, DMI, and methane traits of beef cattle fed ad libitum on a roughage diet or a grain-based feedlot diet. In both projects methane was measured using the GreenFeed Emission Monitoring system, which provides multiple short-term breath measures of methane production. The data used for this study were from 119 Angus heifers over 15 d on a roughage diet and 326 Angus steers over 70 d on a feedlot diet. Mean (±SD) age, BW, and DMI were 372 ± 28 d, 355 ± 37 kg, and 8.1 ± 1.3 kg/d for the heifers and 554 ± 86 d, 577 ± 69 kg, and 13.3 ± 2.0 kg/d for the steers, respectively. The corresponding mean MPR was 212 g/d for heifers and 203 g/d for steers. Additional traits studied included methane yield (MY; MPR/DMI), methane intensity (MPR/BW), and 3 forms of residual methane production (RMP), which is a measure of actual minus predicted MPR. For RMP, RMP, and RMP predicted MPR were obtained by regression of MPR on BW, on DMI, and on both DMI and BW, respectively. The 2 data sets were analyzed separately using the same statistical procedures. For both feed types the relationships between MPR and DMI and between MPR and BW were both positive and linear. The correlation between MPR and DMI was similar to that between MPR and BW, although the correlations were stronger for the roughage diet ( = 0.75 for MPR vs. DMI; = 0.74 for MPR vs. BW) than the grain-based diet ( = 0.62 for MPR vs. DMI; = 0.66 for MPR vs. BW). The correlation between MY and DMI was negative and moderate for the roughage ( = -0.68) and grain-based ( = -0.59) diets, a finding that is different from the nonsignificant correlations reported in studies of cattle on a restricted roughage diet. The 3 RMP traits were strongly correlated ( values from 0.76 to 0.99) with each other for both the roughage and the grain-based diets, which indicates that using RMP to lower MPR could provide a result similar to using RMP in cattle. As feed intake (DMI) is more difficult to measure than BW, this result implies that under ad libitum feeding situations in which DMI cannot be measured, RMP can be used to identify higher- or lower-RMP animals with similar levels of effectiveness as RMP.

Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures.

Respiration chambers are considered the reference method for quantifying the daily CH production rate (MPR) and CO production rate (CPR) of cattle; however, they are expensive, labor intensive, cannot be used in the production environment, and can be used to assess only a limited number of animals. Alternative methods are now available, including those that provide multiple short-term measures of CH and CO, such as the GreenFeed Emission Monitoring (GEM) system. This study was conducted to provide information for optimizing test procedures for estimating MPR and CPR of cattle from multiple short-term CH and CO records. Data on 495 Angus steers on a 70-d ad libitum feedlot diet with 46,657 CH and CO records and on 121 Angus heifers on a 15-d ad libitum roughage diet with 7,927 CH and CO records were used. Mean (SD) age and BW were 554 d (SD 92) and 506 kg (SD 73), respectively, for the steers and 372 d (SD 28) and 348 kg (SD 37), respectively, for the heifers. The 2 data sets were analyzed separately but using the same procedures to examine the reduction in variance as more records are added and to evaluate the level of precision with 2 vs. 3 min as the minimum GEM visit duration for a valid record. The moving averages procedure as well as the repeated measures procedure were used to calculate variances for both CH and CO, starting with 5 records and progressively increasing to a maximum of 80 records. For both CH and CO and in both data sets, there was a sharp reduction in the variances obtained by both procedures as more records were added. However, there was no substantial reduction in the variance after 30 records had been added. Inclusion of records with a minimum of 2-min GEM visit duration resulted in reduction in precision relative to a minimum of 3 min, as indicated by significantly ( < 0.05) more heterogeneous variances for all cases except CH4 in steers. In addition, more records were required to achieve the same level of precision relative to data with minimum GEM visit durations of 3 min. For example, in the steers, 72% reduction in initial variance was achieved with 30 records for both CH and CO when minimum GEM visit duration was 3 min, relative to 45 records when data with a minimum visit duration of 2 min were included. It is concluded from this study that when using records of multiple short-term breath measures of CH or CO for the computation of an animal's MPR or CPR, a minimum of 30 records, each record obtained from a minimum GEM visit duration of 3 min, are required.

Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle.

Methane (CH) is a product of enteric fermentation in ruminants, and it represents around 17% of global CH emissions. There has been substantial effort from the livestock scientific community toward tools that can help reduce this percentage. One approach is to select for lower emitting animals. To achieve this, accurate genetic parameters and identification of the genomic basis of CH traits are required. Therefore, the objectives of this study were 1) to perform a genomewide association study to identify SNP associated with several CH traits in Angus beef cattle (1,020 animals) and validate them in a lactating Holstein population (population 1 [POP1]; 205 animals); 2) to validate significant SNP for DMI and weight at test (WT) from a second Holstein population, from a previous study (population 2 [POP2]; 903 animals), in an Angus population; and 3) to evaluate 2 different residual CH traits and determine if the genes associated with CH also control residual CH traits. Phenotypes calculated for the genotyped Angus population included CH production (MeP), CH yield (MeY), CH intensity (MI), DMI, and WT. The Holstein population (POP1) was multiparous, with phenotypes on CH traits (MeP, MeY, and MI) plus genotypes. Additionally, 2 CH traits, residual genetic CH (RGM) and residual phenotypic CH (RPM), were calculated by adjusting MeP for DMI and WT. Estimated heritabilities in the Angus population were 0.30, 0.19, and 0.15 for MeP, RGM, and RPM, respectively, and genetic correlations of MeP with DMI and WT were 0.83 and 0.80, respectively. Estimated heritabilities in Holstein POP1 were 0.23, 0.30, and 0.42 for MeP, MeY, and MI, respectively. Strong associations with MeP were found on chromosomes 4, 12, 14, 20, and 30 at < 0.001, and those chromosomes also had significant SNP for DMI in Holstein POP1. In the Angus population, the number of significant SNP for MeP at < 0.005 was 3,304, and approximately 630 of those SNP also were important for DMI and WT. When a set (approximately 3,300) of significant SNP for DMI and WT in the Angus population was used to estimate genetic parameters for MeP and MeY in Holstein POP1, the genetic variance and, consequently, the heritability slightly increased, meaning that most of the genetic variation is largely captured by these SNP. Residual traits could be a good option to include in the breeding goal, as this would facilitate selection for lower emitting animals without compromising DMI and WT.

Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle.

Ruminants contribute 80% of the global livestock greenhouse gas (GHG) emissions mainly through the production of methane, a byproduct of enteric microbial fermentation primarily in the rumen. Hence, reducing enteric methane production is essential in any GHG emissions reduction strategy in livestock. Data on 1,046 young bulls and heifers from 2 performance-recording research herds of Angus cattle were analyzed to provide genetic and phenotypic variance and covariance estimates for methane emissions and production traits and to examine the interrelationships among these traits. The cattle were fed a roughage diet at 1.2 times their estimated maintenance energy requirements and measured for methane production rate (MPR) in open circuit respiration chambers for 48 h. Traits studied included DMI during the methane measurement period, MPR, and methane yield (MY; MPR/DMI), with means of 6.1 kg/d (SD 1.3), 132 g/d (SD 25), and 22.0 g/kg (SD 2.3) DMI, respectively. Four forms of residual methane production (RMP), which is a measure of actual minus predicted MPR, were evaluated. For the first 3 forms, predicted MPR was calculated using published equations. For the fourth (RMP), predicted MPR was obtained by regression of MPR on DMI. Growth and body composition traits evaluated were birth weight (BWT), weaning weight (WWT), yearling weight (YWT), final weight (FWT), and ultrasound measures of eye muscle area, rump fat depth, rib fat depth, and intramuscular fat. Heritability estimates were moderate for MPR (0.27 [SE 0.07]), MY (0.22 [SE 0.06]), and the RMP traits (0.19 [SE 0.06] for each), indicating that genetic improvement to reduce methane emissions is possible. The RMP traits and MY were strongly genetically correlated with each other (0.99 ± 0.01). The genetic correlation of MPR with MY as well as with the RMP traits was moderate (0.32 to 0.63). The genetic correlation between MPR and the growth traits (except BWT) was strong (0.79 to 0.86). These results indicate that selection for lower MPR may have undesired effect on animal productivity. On the other hand, MY and the RMPR were either not genetically correlated or weakly correlated with BWT, YWT, and FWT (-0.06 to 0.23) and body composition traits (-0.18 to 0.18). Therefore, selection for lower MY or RMPR would lead to lower MPR without impacting animal productivity. Where the use of a ratio trait (e.g., MY) is not desirable, selection on any of the forms of RMP would be an alternative.

Genomic heritabilities and genomic estimated breeding values for methane traits in Angus cattle.

Enteric methane emissions from beef cattle are a significant component of total greenhouse gas emissions from agriculture. The variation between beef cattle in methane emissions is partly genetic, whether measured as methane production, methane yield (methane production/DMI), or residual methane production (observed methane production - expected methane production), with heritabilities ranging from 0.19 to 0.29. This suggests methane emissions could be reduced by selection. Given the high cost of measuring methane production from individual beef cattle, genomic selection is the most feasible approach to achieve this reduction in emissions. We derived genomic EBV (GEBV) for methane traits from a reference set of 747 Angus animals phenotyped for methane traits and genotyped for 630,000 SNP. The accuracy of GEBV was tested in a validation set of 273 Angus animals phenotyped for the same traits. Accuracies of GEBV ranged from 0.29 ± 0.06 for methane yield and 0.35 ± 0.06 for residual methane production. Selection on GEBV using the genomic prediction equations derived here could reduce emissions for Angus cattle by roughly 5% over 10 yr.

Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle.

Ruminants contribute up to 80% of greenhouse gas (GHG) emissions from livestock, and enteric methane production by ruminants is the main source of these GHG emissions. Hence, reducing enteric methane production is essential in any GHG emissions reduction strategy in livestock. Data from 2 performance-recording research herds of Angus cattle were used to evaluate a number of methane measures that target methane production (MPR) independent of feed intake and to examine their phenotypic relationships with growth and body composition. The data comprised 777 young bulls and heifers that were fed a roughage diet (ME of 9 MJ/kg DM) at 1.2 times their maintenance energy requirements and measured for MP in open circuit respiration chambers for 48 h. Methane traits evaluated included DMI during the methane measurement period, MPR, and methane yield (MY; MPR/DMI), with means (± SD) of 6.2 ± 1.4 kg/d, 187 ± 38 L/d, and 30.4 ± 3.5 L/kg, respectively. Four forms of residual MPR (RMP), which is a measure of actual minus predicted MPR, were evaluated. For the first 3 forms, predicted MPR was calculated using published equations. For the fourth (RMPR), predicted MPR was obtained by regression of MPR on DMI. Growth traits evaluated were BW at birth, weaning (200 d of age), yearling age (400 d of age), and 600 d of age, with means (± SD) of 34 ± 4.6, 238 ± 37, 357 ± 45, and 471 ± 53 kg, respectively. Body composition traits included ultrasound measures (600 d of age) of rib fat, rump fat, and eye muscle area, with means (± SD) of 3.8 ± 2.6 mm, 5.4 ± 3.8 mm, and 61 ± 7.7 cm(2), respectively. Methane production was positively correlated (r ± SE) with DMI (0.65 ± 0.02), MY (0.72 ± 0.02), the RMP traits (r from 0.65 to 0.79), the growth traits (r from 0.19 to 0.57), and the body composition traits (r from 0.13 to 0.29). Methane yield was, however, not correlated (r ± SE) with DMI (-0.02 ± 0.04) as well as the growth (r from -0.03 to 0.11) and body composition (r from 0.01 to 0.06) traits. All the RMP traits were strongly correlated to MY (r from 0.82 to 0.95). These results indicate that reducing MPR per se can have a negative impact on growth and body composition of cattle. Reducing MY, however, will likely have the effect of reducing MPR without impacting productivity. Where a ratio trait is undesirable, as in animal breeding, any of the RMP traits can be used instead of MY. However, where independence from DMI is desired, RMPR should be a trait worth considering.

Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle.

Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.

Comparison of methods for handling censored records in beef fertility data: simulation study.

A simulation study was conducted to compare methods for handling censored records for days to calving in beef cattle data. Days to calving was defined as the time, in days, between when a bull is turned out in the pasture and the subsequent parturition. Simulated data were generated to have data structure and genetic relationships similar to an available field data set. Records were simulated for 33,176 daughters of 4,238 sires. Data were simulated using a mixed linear model that included the fixed effects of contemporary group and sex of calf, linear and quadratic covariates for age at mating, and random effects of animal and residual error. Two methods for handling censored records were evaluated, and two censoring rates of 12 and 20% were applied to assess the influence of higher censoring rates on inferences. Censored records were assigned penalty values on a within-contemporary group basis under the first method (DCPEN). Under the second method (DCSIM), censored records were drawn from their respective predictive distributions. A Bayesian approach via Gibbs sampling was used to estimate variance components and predict breeding values. Posterior means (PM) and standard deviations (SD) of additive genetic variance for DCPEN at 12 and 20% censoring were 23.2 (3.7) and 21.0 (3.6), respectively, whereas the same estimates for DCSIM at 12 and 20% censoring were 23.7(3.3) and 21.9 (3.4), respectively. In all cases, the true value of the genetic variance was within the high posterior density (HPD) interval (95%). The PM (SD) of residual variance for DCPEN at 12 and 20% censoring were 415.7 (4.7) and 440.0 (4.8) respectively, whereas the same estimates for DCSIM at 12 and 20% censoring were 371.0 (4.3) and 365.4 (4.4), respectively. The true value of the residual variance was within the HPD (95%) for DCSIM, but it was outside this interval for DCPEN at both censoring rates, indicating a systematic bias for this parameter. Bayes Factor and Deviance Information Criteria were used for model comparisons, and both criteria indicated the superiority of the DCSIM method. However, little difference was observed between the two methods for correlations between true breeding values and posterior means of animal effects for sires, indicating that no major reranking of sires would be expected. This finding suggests that either censored data handling technique can be successfully used in a genetic evaluation for days to calving.

Comparison of methods for handling censored records in beef fertility data: field data.

The purpose of this study was to compare methods for handling censored days to calving records in beef cattle data, and verify results of an earlier simulation study. Data were records from natural service matings of 33,176 first-calf females in Australian Angus herds. Three methods for handling censored records were evaluated. Censored records (records on noncalving females) were assigned penalty values on a within-contemporary group basis under the first method (DCPEN). Under the second method (DCSIM), censored records were drawn from their respective predictive truncated normal distributions, whereas censored records were deleted under the third method (DCMISS). Data were analyzed using a mixed linear model that included the fixed effects of contemporary group and sex of calf, linear and quadratic covariates for age at mating, and random effects of animal and residual error. A Bayesian approach via Gibbs sampling was used to estimate variance components and predict breeding values. Posterior means (PM) (SD) of additive genetic variance for DCPEN, DCSIM, and DCMISS were 22.6d2 (4.2d2), 26.1d2 (3.6d2), and 13.5d2 (2.9d2), respectively. The PM (SD) of residual variance for DCPEN, DCSIM, and DCMISS were 431.4d2 (5.0d2), 371.4d2 (4.5d2), and 262.2d2 (3.4d2), respectively. The PM (SD) of heritability for DCPEN, DCSIM, and DCMISS were 0.05 (0.01), 0.07 (0.01), and 0.05 (0.01), respectively. Simulating trait records for noncalving females resulted in similar heritability to the penalty method but lower residual variance. Pearson correlations between posterior means of animal effects for sires with more than 20 daughters with records were 0.99 between DCPEN and DCSIM, 0.77 between DCPEN and DCMISS, and 0.81 between DCSIM and DCMISS. Of the 424 sires ranked in the top 10% and bottom 10% of sires in DCPEN, 91% and 89%, respectively, were also ranked in the top 10% and bottom 10% in DCSIM. Little difference was observed between DCPEN and DCSIM for correlations between posterior means of animal effects for sires, indicating that no major reranking of sires would be expected. This finding suggests little difference between these two censored data handling techniques for use in genetic evaluation of days to calving.

Genetic evaluation of calving to first insemination using natural and artificial insemination mating data.

Mating and calving records for 51,084 first-parity heifers in Australian Angus herds were used to examine the relationship between probability of calving to first insemination (CFI) in artificial insemination and natural service (NS) mating data. Calving to first insemination was defined as a binary trait for both sources of data. Two Bayesian models were employed: 1) a bivariate threshold model with CFI in AI data regarded as a trait separate from CFI in NS data and 2) a univariate threshold model with CFI regarded as the same trait for both sources of data. Posterior means (SD) of additive variance in the bivariate analysis were similar: 0.049 (0.013) and 0.075 (0.021) for CFI in AI and NS data, respectively, indicating lack of heterogeneity for this parameter. A similar trend was observed for heritability in the bivariate analysis, with posterior means (SD) of 0.025 (0.007) and 0.048 (0.012) for AI and NS data, respectively. The posterior means (SD) of the additive covariance and corresponding genetic correlation between the traits were 0.048 (0.006) and 0.821 (0.138), respectively. Differences were observed between posterior means for herd-year variance: 0.843 vs. 0.280 for AI and NS data, respectively, which may reflect the higher incidence of 100% conception rates within a herd-year class (extreme category problem) in AI data. Parameter estimates under the univariate model were close to the weighted average of the corresponding parameters under the bivariate model. Posterior means (SD) for additive, herd-year, and service sire variance and heritability under the univariate model were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013), and 0.036 (0.007), respectively. These results indicate that, genetically, cows with a higher probability of CFI when mated using AI also have a high probability of CFI when mated via NS. The high correlation between the two traits, along with the lack of heterogeneity for the additive variance, implies that a common additive variance could be used for AI and NS data. A single-trait analysis of CFI with heterogeneous variances for herd-year and service sire could be implemented. The low estimates of heritability indicate that response to selection for probability of calving to first insemination would be expected to be low.