2014, Strucken, Eva, Gudex, Boyd, Gondro, Cedric, Ferdosi, Mohammad, Lee, H K, Song, K D, Gibson, John, Kelly, M, Piper, E K, Porto-Neto, L R, Lee, S H

The International Society for Animal Genetics (ISAG) proposed a panel of single nucleotide polymorphisms (SNPs) for parentage testing in cattle (a core panel of 100 SNPs and an additional list of 100 SNPs). However, markers specific to East Asian taurine cattle breeds were not included, and no information is available as to whether the ISAG panel performs adequately for these breeds. We tested ISAG's core (100 SNP) and full (200 SNP) panels on two East Asian taurine breeds: the Korean Hanwoo and the Japanese Wagyu, the latter from the Australian herd. Even though the power of exclusion was high at 0.99 for both ISAG panels, the core panel performed poorly with 3.01% false-positive assignments in the Hanwoo population and 3.57% in the Wagyu. The full ISAG panel identified all sire-offspring relations correctly in both populations with 0.02% of relations wrongly excluded in the Hanwoo population. Based on these results, we created and tested two population-specific marker panels: one for the Wagyu population, which showed no false-positive assignments with either 100 or 200 SNPs, and a second panel for the Hanwoo, which still had some false-positive assignments with 100 SNPs but no false positives using 200 SNPs. In conclusion, for parentage assignment in East Asian cattle breeds, only the full ISAG panel is adequate for parentage testing. If fewer markers should be used, it is advisable to use population-specific markers rather than the ISAG panel.

2019-01, Gudex, Boyd W, McPhee, Malcolm J, Oddy, Victor H, Walmsley, Brad J

Physiological maturity, measured as carcass ossification [10 unit increments (100, 110, 120, …)], is used by the United States Department of Agriculture and the Meat Standards Australia carcass grading systems to reflect age-associated differences in beef tenderness and determine producer payments. In most commercial cattle herds, the exact age of animals is unknown; thus, prediction of ossification in association with phenotypic prediction systems has the capacity to assist producer decision making to improve carcass and eating quality. This study developed and evaluated prediction equations that use either live animal or carcass traits to predict ossification for use in phenotypic prediction systems to predict meat quality. The average ossification in the model development dataset was 138 with a SD of 21 and a range between 100 and 200. Model development involved regressing various combinations of live animal traits: age at recording, sex, live weight (BW), average daily gain, ultrasound scanned eye muscle area, 12/13th rib and subcutaneous P8 rump fat thickness; or carcass traits: age at slaughter, sex, hot standard carcass weight (HSCW), carcass eye muscle area, marble score, rib, and P8 rump fat (CP8) thickness, against ossification. The models were challenged with data from 3 independent datasets: 1) Angus steers produced by divergent selection for visual muscle score; 2) temperate (Angus, Hereford, Shorthorn and Murray Grey) steers and heifers; and 3) tropically adapted (Brahman and Santa Gertrudis) steers and heifers. Five models with adjusted R² _adj above 0.55 were evaluated. When challenged with dataset 1, the absolute mean bias (MB) and root mean square error of prediction (RMSEP) ranged from 0.1 to 4.2, and 9.8 to 10.7, which are within the bounds of the 10 point increment on the ossification scale. When subsequently challenged with dataset 2, MB and RMSEP ranged from 2.8 to 13.4, and 19.6 to 23.7, respectively; and with dataset 3, MB and RMSEP ranged from 14.4 to 17.5, and 23.3 to 31.9, respectively. Generally, when compared in relation to the ossification scale, all evaluated models had similar accuracy. For predicting meat quality, the model containing live animal traits considered most useful was [85.35 + 0.16 × BW + 10.94 × sex – 0.09 × sex × BW (adjusted R2 = 0.59; SE = 13.51)] and the most useful model containing carcass traits was [107.15 + 11.53 × sex + 1.10 × CP8 + 0.16 × HSCW – 0.15 × sex × HSCW (adjusted R2 = 0.60; SE = 13.39)].

2014, Gudex, Boyd, Johnson, David, Singh, Kuljeet

This study investigated whether the prenatal maternal environment in dairy cattle influences the postnatal milking performance of the resulting daughters and grand-daughters. Linear mixed models were used to analyse whole season milk production from ~46000 Jersey and ~123000 Holstein Friesian cows in their 1st and 2nd lactations. Variation in the prenatal environment was associated with a small but significant (P<0.05) proportion of the total phenotypic variation (0.010 to 0.015) in all traits in Holstein Friesian cows and in the first lactation milk volume (0.011) and milk protein (0.011), and the second lactation milk fat (0.015) in the Jersey breed. This indicates that the prenatal environment does influence the adult performance of the subsequent daughter. Associations between daughter performance and dam and grand-dam traits indicative of their prenatal environment were also estimated. A one litre increase in the dam's herd test milk volume was associated with a 7.5 litre increase in the daughters' whole season milk yield and a 1% increase in either the dams' herd test milk fat or protein percentage was associated with a reduction in daughter whole season milk volume (-49.6 and -45.0 litres for dam fat and protein, respectively). Similar results between the grand-dam herd test traits ansd the daughters' whole season milk production were observed with a 1% increase in either grand-dam milk fat or protein percentage associated with a reduction in daughter whole season milk yield (-34.7 and -9.7 litres for fat and protein, respectively). This study revealed that the prenatal environment of the dam and the grand-dam can influence milk production in the subsequent daughters, though the effects are small. The similarity of the results between the dam daughter and the grand-dam daughter analyses suggests that the majority of the prenatal maternal effects are mediated by epigenetic mechanisms.

2023-07-26, Gudex, B W, Williams, P J, Walmsley, B J

BreedObject is the software used to formalise breeding objectives and create the selection indexes produced by BREEDPLAN. The BreedObject breeding objectives and selection indexes allow cattle producers to identify the most profitable cattle genetics for the beef production system modelled by each selection index. Since the release of the latest version (6.2) of the BreedObject software, eight Australian beef cattle breed organisations have implemented 29 new or revised selection indexes. This paper discusses the process by which the selection indexes were developed in conjunction with the relevant breed societies, summarises the EBV emphases applied in these new selection indexes, and discusses the breeder feedback and implications of the selection indexes in the greater industry.

2019, Lawrence, David F, Loi, Natasha M, Gudex, Boyd W

This cross-sectional study sought to investigate teachers’ experiences of work intensification and wellbeing. Using the Job Demands-Resources model as a framework, this study investigated the relationship between work intensification, satisfaction with workload, perceived organisational support, and burnout. An Australian sample of 215 high school teachers completed a confidential and anonymous online survey battery. Multiple regression analyses indicated that non-teaching-related workload was a stronger predictor of burnout than teaching-related-workload. In addition, the results indicated that perceived organisational support moderated the relationship between work intensification and the emotional exhaustion component of burnout. These findings have important implications for teacher wellbeing and highlight the importance of organisational support for staff.

2015-12, Walmsley, B J, Oddy, V H, Gudex, B W, Mayer, D G, McPhee, M J

The BeefSpecs fat calculator is a decision support tool conceived to assist beef producers with their decision making to achieve better compliance with domestic and international market specifications. BeefSpecs combines data obtained from beef cattle growth-path studies and the extensive body of knowledge contained in animal growth and body composition models with easy to record on-farm measurements to make real-time predictions of body composition. To facilitate producer acceptance and uptake, BeefSpecs makes explicit use of practical end-user knowledge, captured by the simple user interface, by translating it for incorporation into the underpinning research models and returning the outputs in producer language that is easily locatable on the interface. The current version of BeefSpecs (version 1) has three functional forms:

The primary interface acts as an educational tool to demonstrate the relationship(s) between management decisions and the performance of animal groups,

The second interface is designed to facilitate animal management on-farm by assisting drafting decisions for creating sub-groups based on predicted performance, and

The final interface optimises feeding and marketing decisions to increase profitability in both feedlots and pasture finishing systems.

The BeefSpecs calculator currently addresses consumer concerns surrounding portion size and levels of subcutaneous fat deposition by focusing on hot standard carcass weight (HSCW; kg) and carcass P8 rump fat depth (P8 fat; mm) specifications. However, other carcass attributes influence consumer perceptions of meat quality and production efficiency. Intramuscular fat content, or marbling, has been shown to have positive effects on consumer eating experiences while not receiving the negative perceptions associated with high levels of subcutaneous fat. Carcass yield, as described by lean meat yield, is associated with increased efficiency at the abattoir and remainder of the beef supply chain. These efficiency improvements are reflected in higher premiums reported by the National Livestock Reporting Scheme for higher muscled, higher yielding animals. These attributes have also been combined with other production variables to create a prediction of overall meat quality in a system known as the Meat Standards Australia index, or MSA index.

The evolution of BeefSpecs to improve compliance and the viability of beef production needs to mirror the continued evolution of market specifications to address changing consumer demands. Currently, the Meat Animal Research Centre (MARC) model underlying BeefSpecs predicts composition of empty body weight using a description of animal type and growth rate (kg/day). Current BeefSpecs inputs and the MARC model are built upon by partitioning lean and fat in the empty body into carcass and non-carcass components with fat being further partitioned into carcass fat depots allowing carcass lean and intramuscular fat to be used to predict marble score. An additional input, muscle score, is used to scale components of the MARC model to make predictions of carcass fatness and lean meat yield. In order to combine the predictions of marble score and lean meat yield with rib fat and HSCW predictions to make a prediction of eating quality using the MSA index, a prediction of ossification score was developed using current BeefSpecs inputs. These enhancements are all designed to improve the utility of all three interface versions of BeefSpecs, with muscle score being the only additional input required. These enhancements also boost the compatibility BeefSpecs has with the national carcass feedback mechanism, Livestock Data Link (LDL), which will allow the impacts that management decisions have on a wider range of carcass traits to be explored with greater emphasis on consumer requirements.

2011, Gudex, Boyd, Werf, Julius Van Der, Gondro, Cedric, Marshall, Karen

Although for most traits, there is a difference between phenotypic expression in ewes versus rams, they have been traditionally treated equally in breeding programs. This thesis has investigated whether there is any variation between them in how they inherit growth and whether there are any benefits to be gained from divergent selection objectives. A bioeconomic model was used to show that divergent selection for growth in ewes and rams could increase the feed efficiency and profitability of a self-replacing production system. The magnitude of the increase was found to be influenced by the age structure of the flock, fertility, base weight (average of both sexes) and the annual feed availability and price cycles. To test whether divergent selection for growth is possible, sex specific genetic parameters and genetic correlations between ewes and rams were estimated. Accordingly, a series of univariate, multivariate and random regression models using combinations of direct additive genetic, maternal additive genetic, maternal permanent environmental and direct permanent environmental effects were fitted. The results from these analyses revealed that ewes have larger heritability and smaller phenotypic variances than rams and that the intersex genetic correlations were less than unity and decreased with age (range 0.59 to 0.97). Furthermore, the phenotypic and BLUP selection responses predicted using these parameters revealed that rams had higher selection responses than ewes. The results for fat depth (corrected for live weight) were the sole exception to these results. Fat in ewes was deeper and had a lower heritability and higher phenotypic variances than rams. Little variation in sexual dimorphism was observed between the breeds used in this study despite variation in selection history/objectives and founder effects in each breed. In conclusion, these results reveal that it is useful to select for divergent weight, fat and eye muscle depth objectives in ewes and rams and that sexual dimorphism can have a positive effect on the feed efficiency and profitability of a self-replacing flock. Other potential uses of sexual dimorphism in livestock breeding including in species other than sheep, single sex production systems (using sexed semen), reducing product diversity (equal ewes and rams) and the influence of recording/selecting a single sex were also discussed.

2018-09-17, Loi, Natasha, Lawrence, David, Gudex, Boyd

This data measures the experience of work intensification and burnout in a sample of Australian secondary school teachers.