Background: In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved. Methods: Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy. Results: In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included. Conclusions: While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

Background: It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models. Results: Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30–0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented. Conclusions: When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

Context: Businesses within the Australian cattle industries and associated research and advisory agencies require accurate data on production and performance of the national herd. Currently, these are derived from survey and statistical data; the latter is expected to be accurate, but the former needs to be tested in view of information suggesting significant under-reporting. Aims: The research aimed to define the structure, performance and liveweight production of the Australian cattle herd and describe changes in reproduction, growth and survival over the past 40–50 years. Methods: Interactive static herd modelling of beef and dairy herds was reconciled each year from 1976 to 2018, using slaughter and live export statistics and surveyed dairy cow numbers. A principle applied was that model performance should dictate input variables, moderating information derived from publications and professional opinion. Key results: The Australian cattle herd fluctuated in size till the mid-1980s from when it settled into a range of 30–40 million beef cattle (12–16 Mt), exceeding survey data by 56–75%. The dairy herd remained at ~10% of the beef herd. Despite consistent herd size, productivity of the cattle herd increased from ~2.5 to 4.5 Mt of liveweight annually over 35 years. Half of this change was due to reductions in mortality, though ~1 million post-weaning-age cattle still die annually, in addition to >0.5 million calves from birth to weaning. Approximately a quarter each of the change was due to increased reproductive output and to steer growth. Liveweight production per beef animal increased from 70–75 kg/year to 130–135 kg/year, while liveweight production ratio increased by 0.08 and 0.12 kg/kg of cattle in male and female beef cattle respectively, to reach 0.31 kg/kg of cattle. Conclusions: The main conclusion is the size, performance, production and productivity of the Australian cattle herd are quite different from that determined from surveys. Also, there is an on-going opportunity to derive benefit from improving cattle survival, reproduction and growth and from improving the feed base. Implications: This research may have large impact on priorities for Australian beef business and associated environmental management. It is recommended that surveys be used to derive relative values to use in combination with absolute statistical data to derive accurate herd measures.

Hip height, body condition, subcutaneous fat, eye muscle area, percentage Bos taurus, fetal age and diet digestibility data were collected at 17 372 assessments on 2181 Brahman and tropical composite (average 28% Brahman) female cattle aged between 0.5 and 7.5 years of age at five sites across Queensland. The study validated the subtraction of previously published estimates of gravid uterine weight to correct liveweight to the non-pregnant status. Hip height and liveweight were linearly related (Brahman: P < 0.001, R2 = 58%; tropical composite P < 0.001, R2 = 67%). Liveweight varied by 12–14% per body condition score (5-point scale) as cows differed from moderate condition (P < 0.01). Parallel effects were also found due to subcutaneous rump fat depth and eye muscle area, which were highly correlated with each other and body condition score (r = 0.7–0.8). Liveweight differed from average by 1.65–1.66% per mm of rump fat depth and 0.71–0.76% per cm2 of eye muscle area (P < 0.01). Estimated dry matter digestibility of pasture consumed had no consistent effect in predicting liveweight and was therefore excluded from final models. A method developed to estimate full liveweight of post-weaning age female beef cattle from the other measures taken predicted liveweight to within 10 and 23% of that recorded for 65 and 95% of cases, respectively. For a 95% chance of predicted group average liveweight (body condition score used) being within 5, 4, 3, 2 and 1% of actual group average liveweight required 23, 36, 62, 137 and 521 females, respectively, if precision and accuracy of measurements matches that used in the research. Non-pregnant Bos taurus female cattle were calculated to be 10–40% heavier than Brahmans at the same hip height and body condition, indicating a substantial conformational difference. The liveweight prediction method was applied to a validation population of 83 unrelated groups of cattle weighed in extensive commercial situations on 119 days over 18 months (20 917 assessments). Liveweight prediction in the validation population exceeded average recorded liveweight for weigh groups by an average of 19 kg (~6%) demonstrating the difficulty of achieving accurate and precise animal measurements under extensive commercial grazing conditions.

Context: High and variable fetal and calf loss in beef herds occurs across northern Australia. Reasons for the majority of these losses, a major cost to the industry, have remained unknown. Aims: The research question was, which of the 58 region-, property-, management group-, and animal-level risk factors measured are associated with pregnant females failing to wean their calf? Methods: An epidemiological study measured fetal and calf loss (consistently expressed as % points) between confirmed pregnancy and weaning and the major associated risk factors using a selected population from 55 commercial beef breeding herds representing 23 166 pregnancies in the mostly dry tropical environment of northern Australia. Key results: Median fetal and calf loss was 9.5% with large variation. Achievable levels appeared to be <10% for Northern Forest herds, and <5% for Southern Forest, Central Forest and Northern Downs herds. The risk factors most strongly associated with fetal and calf loss were low-fertility country type interacting with phosphorus inadequacy (up to 10% increase), low body condition score interacting with phosphorus inadequacy (up to 8% increase), tall cows (up to 4% increase), and high temperature–humidity index around calving interacting with country type (up to 7% increase). These are nutritional and environmental risk factors and had the combined highest individual effect and frequency. Other risk factors associated with fetal and calf loss included first-lactation cows, which interacted with mustering around calving (up to 9% increase), having not reared a calf in the previous year interacting with cow age (up to 8% increase), low mustering efficiency (up to 9% increase), perceived predation by wild dogs (4–5% increase), high prevalence of Campylobacter fetus sp. venerealis antibodies (7% increase), recent infection with bovine viral diarrhoea virus (9% increase), and inadequate dry-season protein (4% increase). Conclusions: The combined effects of environmental, nutritional and management risk factors on fetal and calf loss in northern Australia were large and additive and were much greater, collectively up to 30–40%, and more consistent than that due to either endemic infectious diseases or animal factors. Implications: Opportunity for remedial action is high and, for the effect of non-infectious risk factors, should target milk delivery to neonatal calves.