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Journal of Animal Science Abstract - Animal Products

Modeling the relationship between scanned rump and 12th-rib fat in young temperate and tropical bovines: Model development and evaluation12


This article in JAS

  1. Vol. 88 No. 5, p. 1848-1859
    Received: Nov 04, 2009
    Accepted: Jan 19, 2010
    Published: December 4, 2014

    3 Corresponding author(s):

  1. B. J. Walmsley*†,
  2. M. L. Wolcott*‡ and
  3. M. J. McPhee 3
  1. Cooperative Research Centre for Beef Genetic Technologies,
    Beef Industry Centre of Excellence, Industry and Investment New South Wales, Armidale, New South Wales, Australia 2350; and
    Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales, Australia 2350



A decision support tool for predicting subcutaneous fat depths called BeefSpecs, based on the Davis growth model (DGM), has been developed by the Cooperative Research Centre for Beef Genetic Technologies. Currently, the DGM predicts 12th-rib fat thickness (RFT, mm). To allow predictions of fat thickness at the P8 rump (P8FT, mm) site, the standard carcass fat measurement in the Australian beef industry, a relationship was developed between ultrasound RFT and P8FT in steers and heifers from temperate (Angus, Hereford, Shorthorn, and Murray Grey) and tropical (Brahman, Belmont Red, and Santa Gertrudis) breed types. Model development involved fitting various combinations of sex, breed type (BrT), BW, age, and RFT to produce 6 models. The models were challenged with data from 3 independent data sets: 1) Angus steers from 2.4 generations of divergent selection for and against residual feed intake; 2) 2 tropically adapted genotypes [Brahman and tropically adapted composites (combinations of Belmont Red, Charbray, Santa Gertrudis, Senepol, and Brahman breeds)]; and 3) a study using sires from Charolais, Limousin, Belgian Blue, and Black and Red Wagyu breeds and 3 genetic lines of Angus to create divergence in progeny in terms of genetic potential for intramuscular fat percent and retail beef yield. When challenged with data from Angus cattle, the mean biases (MB, mm) for models A to F were −1.23, −0.56, −0.56, −0.02, 0.14, and 0.04, and the root mean square errors of predictions (mm) were 1.53, 0.97, 0.97, 0.92, 0.93, and 0.91, respectively. When challenged with data from Brahman cattle, MB were 0.04, −0.22, −0.14, 0.05, −0.11, and 0.02 and root mean square errors of predictions were 1.30, 1.29, 1.27, 1.23, 1.37, and 1.29, respectively. Generally, model accuracy indicated by MB tended to be less for model E, which contained age rather than BW as a covariate. Models B and C were generally robust when challenged with data from Angus, Brahman, and Tropical Composite cattle as well as crossbred cattle with temperate sires. Model D, which did not contain age, performed the most consistently and was selected for inclusion in the DGM: P8FT, mm = −3.6 (±0.14) + 1.3 (±0.13) × sex + 0.11 (±0.13) × BrT + 0.014 (±4.8E−4) × BW + 0.96 (±0.01) × RFT – 0.73 (±0.08) × sex × BrT − 3.8E−3 (±4.2E−4) × sex × BW − 0.09 (±0.01) × sex × RFT + 1.3E−3 (±3.7E−4) × BrT × BW + 0.24 (±0.01) × BrT × RFT (adjusted R2 = 0.86; SE = 0.013). Model D has been implemented in BeefSpecs to predict P8FT.

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