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 naturalservice 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 andresidual 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 forDCPEN, DCSIM, and DCMISS were 431.4d²(5.0d²),371.4d² (4.5d²), and 262.2d²(3.4d²), respectively. ThePM (SD) of heritability for DCPEN, DCSIM, andDCMISS were 0.05 (0.01), 0.07 (0.01), and 0.05 (0.01),respectively. Simulating trait records for noncalvingfemales resulted in similar heritability to the penaltymethod but lower residual variance. Pearson correlationsbetween posterior means of animal effects for sireswith more than 20 daughters with records were 0.99between DCPEN and DCSIM, 0.77 between DCPENand 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, werealso ranked in the top 10% and bottom 10% in DCSIM.Little difference was observed between DCPEN andDCSIM for correlations between posterior means of animaleffects for sires, indicating that no major rerankingof sires would be expected. This finding suggests littledifference between these two censored data handlingtechniques for use in genetic evaluation of days to calving.

Docile cattle are preferred by beef cattle breeders for ease of handling and management. Genetic correlations between temperament and meat quality traits in tropically adapted breeds indicate that more docile animals have more tender meat (Kadel et al., 2006). An EBV for docility was first introduced for Australian Limousin in 2000 and genetic progress has been made. Many Australian Limousin calves are sired by imported bulls, mainly from France, USA, Canada and UK. While animals in individual and progeny test stations in France have temperament records, currently an EBV for docility is not available. French AI sires account for a significant number of calves registered each year in Australia (8% in 2004), thus future genetic improvement in Australian Limousin for docility could be boosted by the documentation of genetic merit for docility in France. The aim of this study was to investigate genetic relationships between measures of docility in Australia and France.

Genetic selection based on reduced cattle fearfulness to increase their adaptative abilities to human environments could be an important way to improve both animal welfare and breeders' working conditions. In particular, improving temperament of cattle breeds that are difficult to handle is a major concern especially in extensive ranching breeding conditions where extremes in temperament are displayed more often. Consequently, in the last decade, selection for improving temperament on farm has become one of the main objectives for the Limousin cattle reared in Australia, New Zealand or in North America. Selection has been mainly based on a crush test (Tier et al., 2001). In France, there is currently no test on farms, but Limousin bulls have been evaluated since 1990 on a "docility test" (Boivin et al., 1992) in an individual test station and in a female progeny test station for bulls going through the AI breeding program. A few studies have reported a significant sex effect on temperament traits, with females always more excitable or difficult to handle (Voisinet et al., 1997; Lanier et al.., 2000; Gauly et al., 2001). As far as we know, there had been no genetic analysis to test whether reaction to human handling was governed by the same pool of genes between sexes. Because different neuroendocrine mechanisms may be involved in the fear reactions of females and males, the aim of the study was to assess the potential genetic differences between sexes for temperament traits recorded in French and Australian Limousin 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 multi-trait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.

Ruminants emit methane, a potent greenhouse gas, as a by-product of microbial fermentation of plant material in their rumen. The objective of this study was to investigate the existence of natural genetic variation in methane yield (methane production per unit feed intake) among beef cattle in Australia. Two pedigreed, performance-recording research herds of Angus cattle were used. Methane production (MP) was measured on individual yearling-age cattle in two animal houses in different years. Each animal had MP measured while being fed a fixe d daily allowance of a roughage diet of approximately 9 MJ ME/kg dry matter (DM). The amount offered was calculated to provide 1.2-times the estimated energy requirement for maintenance based on the animal's bodyweight. In 2010, MP was measured using the SF6 tracer dilution method, while in 2011 MP was measured in individual-animal respiratory chambers. A total of 339 cattle comprising 62 bulls and 73 heifers in 2010, and 139 bulls and 65 heifers in 2011, were assessed for MP. Methane yield was calculated as MP per unit dry matter intake (DMI). There was variation in both traits, with an almost 3-fold range in methane yield being observed. Phenotypically, MP was moderately correlated with DMI (r = 0.33) and with animal bodyweight (r = 0.40). In contrast, the phenotypic correlation of methane yield with DMI (r = -0.26) and with animal bodyweight (r = -0.15) were negative and weak. There was a large range in the mean for methane yield by progeny of the sires of the cattle tested. Compared to the sires whose progeny had the lowest average methane yield, there were sires whose progeny had average methane yield that were 24%, 24%, 16%, 19% and 11% higher across the five groups of cattle tested. This study has provided preliminary evidence that naturally occurring genetic variation exists in methane yield in cattle.

Weaning weights from 83 389 Limousin calves born between 1993 and 2002 in France and the Trans-Tasman block (Australia / New Zealand) were analysed to compare different strategies for running an international genetic evaluation for the breed. These records were a subset of the complete data for both countries and comprised a sample of herds that had recorded progeny of sires used across both countries. Genetic and phenotypic parameters for weaning weight were estimated within the countries. The estimates of direct genetic heritabilities were higher in France than in the Trans-Tasman block (0.31 vs. 0.22), while direct-maternal genetic correlations were less negative in the Trans-Tasman block (−0.10) than in France (−0.21). Different strategies for an international evaluation were studied, and the correlations between the estimated breeding values (EBV) of national evaluations and these strategies were derived. The international evaluation strategies were a) an animal model on raw performance data with non unity genetic correlations and heterogeneous residual and genetic variances across countries; b) the same animal model applied to pre-corrected (for fixed effects) performance data; and c) a sire model on de-regressed proofs (MACE). Estimates of the genetic correlations between weaning weight in both countries were 0.86 (0.80) for direct (maternal) genetic effects for the first strategy. Estimation of variance components by MACE appeared to be very sensitive to the sample of bulls and their reliability approximations. Variance component estimates obtained using pre-corrected data were inconsistent with estimates on raw data. However, the EBV predicted using pre-corrected data and parameters estimated from the raw data were similar to those predicted from raw data. Correlations between national and international EBV were always high (> 0.90) for sires, whichever genetic effect (direct or maternal) or internationalevaluation model was considered. The ranking of the bulls in the top 100 is of primary interest in terms of international genetic evaluation. In this study, some re-ranking of sires was observedfor the top 100 bulls between countries and between the three international evaluation models. Thus, the origin of top sires may vary according to the implemented international evaluationstrategy.

The aim of this experiment is to investigate and demonstrate genetic variation in daily methane production (MP; g/d), methane intensity (MI; MP per unit bodyweight; g/kg) and methane yield (MY; MP per unit feed intake; g/kg). Angus cows in pedigree- and performance-recorded research herds at Industry & Investment NSW research centres at Grafton and Trangie NSW were mated in 2007 to Angus bulls that had previously been recorded for MY. Bulls that had been identified as either phenotypically high or low for MY were used as sires in the Grafton herd; unselected sires were used in the Trangie herd. In 2010 the near 2-year-old bull progeny from Trangie and heifer progeny from Grafton were measured for MP, MI and MY. There were 8 sires with progeny represented in the Trangie bull data (n=63 progeny). A wide range in least-squares (LS) sire means was observed for MP (191g/d to 233g/d), MI (0.26g/kg to 0.63g/kg) and MY (24.3g/kg to 30.2g/kg). There were 6 sires with progeny represented in the Grafton heifer data (n=79 progeny). A wide range in LS sire means was observed for MP (133g/d to 165g/d), MP (0.15g/kg to 0.55g/kg) and MY (21.5g/kg to 27.0g/kg). The differences between sires for these traits that indicate that there may be genetic variation present and provide preliminary evidence that selection on a methane production trait may be possible.

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 cm2, 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.

The main goal of the World Hereford Genetic Linkage Project was to generate genetic linkages across countries to gain a better understanding of how Hereford genetics performs in different environments and on different continents. The project was designed to establish genetic linkages between North America and Australia that could be further developed during the coming years. Four sires from each continent (two polled and two horned) were used to generate progeny in Australia and North America. Performance records on growth and scan traits were collected on all World Hereford progeny born in Australia, and submitted to BREEDPLAN. Additionally, temperament, feed efficiency and carcase data were collected for Australian steer progeny. The latest Australian Hereford BREEDPLAN EBVs for the eight sires will be available at the conference, and include all available data from Australian World Hereford progeny. Progeny born in North America have performance data for early growth traits recorded to date, and this will be included in future North American genetic evaluations, with updated EPDs available for all eight sires available at a later date.

Mating and calving records for 51,084first-parity heifers in Australian Angus herds wereused to examine the relationship between probabilityof calving to first insemination (CFI) in artificial inseminationand natural service (NS) mating data. Calvingto first insemination was defined as a binary trait forboth sources of data. Two Bayesian models were employed:1) a bivariate threshold model with CFI in AIdata regarded as a trait separate from CFI in NS dataand 2) a univariate threshold model with CFI regardedas the same trait for both sources of data. Posteriormeans (SD) of additive variance in the bivariate analysiswere similar: 0.049 (0.013) and 0.075 (0.021) forCFI in AI and NS data, respectively, indicating lack ofheterogeneity for this parameter. A similar trend wasobserved for heritability in the bivariate analysis, withposterior 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 geneticcorrelation between the traits were 0.048 (0.006)and 0.821 (0.138), respectively. Differences were observedbetween posterior means for herd-year variance:0.843 vs. 0.280 for AI and NS data, respectively, whichmay reflect the higher incidence of 100% conceptionrates within a herd-year class (extreme category problem)in AI data. Parameter estimates under the univariatemodel were close to the weighted average of thecorresponding parameters under the bivariate model.Posterior means (SD) for additive, herd-year, and servicesire variance and heritability under the univariatemodel were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013),and 0.036 (0.007), respectively. These results indicatethat, genetically, cows with a higher probability of CFIwhen mated using AI also have a high probability ofCFI when mated via NS. The high correlation betweenthe two traits, along with the lack of heterogeneity forthe additive variance, implies that a common additivevariance could be used for AI and NS data. A single-traitanalysis of CFI with heterogeneous variances forherd-year and service sire could be implemented. Thelow estimates of heritability indicate that response toselection for probability of calving to first inseminationwould be expected to be low.