Genetics and analysis of quantitative traits

Assessment of positive emotions in animals to improve their welfare.

http://www2.kobe-u.ac.jp/~toyoda/beef/BEEF/Ref/beef%20production_fat.pdf

Innovations in beef production systems that enhance the nutritional and health value of beef lipids and their relationship with meat quality.

The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.

/pdf/genome-wide-survey-of-snp-variation-uncovers-the-genetic-1cyjfj0k19.pdf

Genome-Wide Survey of SNP Variation Uncovers the Genetic Structure of Cattle Breeds

Consumer preference, behavior and perception about meat and meat products: an overview.

Analyses were performed to test the relationship between retail beef yield percentage (RBY) and real time ultrasound measurements taken at weaning, entry to finishing and preslaughter for animals finished under pasture and feedlot conditions to meet domestic, Korean and Japanese market specifications. The first analysis tested the power of live animal measurements (scanned P8 fat depth, scanned eye muscle area and liveweight) to predict RBY and contrasted this with a model containing these live animal measurements plus a term (HERD × KILL ) which accounted for all known classification variables. This indicated that scanned P8 fat depth, measured at slaughter, was the most useful predictor of retail beef yield, accounting for 52% of the variation in RBY for the equation containing live animal measurements alone. The power of live animal measurements to predict RBY decreased as the time between scanning and slaughter increased. Models which included HERD × KILL predicted RBY accurately (accounting for 82–86% of the variation in RBY), but live animal measurements contributed little to this result, accounting for only 8% of the variation in RBY for measurements at slaughter in the presence of the HERD × KILL term. A second analysis examined whether market category, finishing regime or breed classifications consistently influenced the relationship between the measured traits and RBY at the 3 scanning times. The magnitude of the variation between significantly different coefficients (for scanned P8 fat depth, scanned eye muscle area and liveweight) was generally small, though the results suggested that in some instances, developing separate equations for animals of different classifications would marginally improve the accuracy of RBY prediction. The final analysis investigated the improvement in RBY prediction when measurements from entry to finishing were included with those taken before slaughter. HERD × KILL was included in the model to account for all known classification variables. Measurements of both P8 fat depth and EMA from the earlier measurement time were significant predictors of RBY in the presence of the corresponding measurement at slaughter, but accounted for an increase in R 2 of only 0.0007. It was concluded that a single scan and liveweight measurement, close to slaughter, would provide the best live animal measurements for RBY prediction, and that no improvement in accuracy would be achieved by additional scans taken earlier in an animal’s life.

/pdf/the-prediction-of-retail-beef-yield-from-real-time-2az2ukl0wv.pdf

The prediction of retail beef yield from real time ultrasound measurements on live animals at three stages through growout and finishing

Strategic sourcing of cattle that are more likely to meet market specification is a critical challenge for buyers purchasing animals for feedlot finishing Missing target specifications has the potential to result in a large discount in carcass value Costs associated with carcass weight and external fat depth that is out of specification impact on both the ability of suppliers to meet customer requirements and levels of productivity or slaughter rates that are nearer to the processor’s cost-minimising level of production This analysis used a subset of Australian beef industry feedlot data from two long-fed feedlots to assess both the costs and causes of carcasses out-of-specification The economic value associated with products that are out-of-specification can be illustrated by the Taguchi Quadratic Loss Function, carcass specifications, and industry grid prices At the nominated carcass weight specification (300kg - 400kg) in the short-fed export market, there were 28 per cent outside of specification, while in the long-fed export market (380kg - 450kg), 29 per cent were outside specification A P8 fat depth specification of 10mm-26mm showed 16 per cent outside of specification in the short-fed market At the nominated marbling score specification (3+) in the long-fed market, 70 per cent were outside of

The cost of non-compliance to beef market specifications

The purpose of CAT scanning in some animal science experiments is to provide estimates of the proportion of the tissues, fat, muscle, and bone present in an individual body, and compare some of the density characteristics. In this paper we present an extension to the hierarchical Bayesian Normal mixture model, which incorporates some of the information provided by the neighbouring pixels in a CAT scan image. This neighbour information is included in the model through the use of a Markov random field for the component allocation variable. This extended mixture model provides a more responsive fit to the local likelihood of the data than that of the independent mixture model. The effectiveness of this modelling technique is illustrated by comparing its performance with that of a Normal mixture model and a fixed boundary method in 3 examples. In these examples it is shown that the extended mixture model we propose is most useful in situations that involve only slight separation of components. The advantages of the model decline as the separation of components increases.

Extending the Bayesian Mixture Model to Incorporate Spatial Information in Analysing Sheep CAT Scan Images

Cooperative Research Centre for Beef Genetic Technologies 1 NSW Department of Primary Industries, Beef Industry Centre, Armidale, NSW, 2351 2 Division of Animal Science, University of New England, Armidale, NSW, 2351 3 NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Camden, NSW, 2570 4 NSW Department of Primary Industries, Tamworth Agricultural Institute, Tamworth, NSW, 2340 5 NSW Department of Primary Industries, Agricultural Research Institute, Trangie, NSW, 2823

http://www.livestocklibrary.com.au/bitstream/handle/1234/6103/herd476.pdf?sequence=1

John Mitchell Thompson

Papers

The prediction of retail beef yield from real time ultrasound measurements on live animals at three stages through growout and finishing

The cost of non-compliance to beef market specifications

Extending the Bayesian Mixture Model to Incorporate Spatial Information in Analysing Sheep CAT Scan Images

Benefits of genetic superiority in residual feed intake in a large commercial feedlot.

SmartStretch™ technology. I. Improving the tenderness of sheep topsides (m. semimembranosus) using a meat stretching device.