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)].

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.