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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #349127
Title: 2017 Across-breed EPD Table and Improvements

Author
item Kuehn, Larry
item Thallman, Richard - Mark

Submitted to: World Wide Web
Publication Type: Popular Publication
Publication Acceptance Date: 12/28/2017
Publication Date: 1/4/2018
Citation: Kuehn, L.A., Thallman, R.M. 2018. 2017 Across-breed EPD Table and Improvements. World Wide Web. 2018-2 p. 1-4. Available: http://www.eBEEF.org.

Interpretive Summary:

Technical Abstract: Records of progeny of 18 breeds were used to estimate differences among the breeds for birth, weaning, and yearling weight and for maternal effects of weaning weight, among 15 of the 18 breeds for carcass marbling, ribeye area, fat depth, and carcass weight. The records were also used to estimate regression coefficients of progeny performance on breed association EPD. The regression coefficients represent the proportion of the differences in sire EPD that were exhibited in the progeny at USMARC and are used to adjust the breed differences estimated from USMARC data to the differences between animals in the breed born in 2015 (based on the 2015 breed average EPD). Using these adjusted breed differences, adjustment factors were calculated that can be added to breed association EPD to allow comparison of bulls from the 18 breeds (Angus, Beefmaster, Brahman, Brangus, Braunvieh, Charolais, Chiangus, Gelbvieh, Hereford, Limousin, Maine-Anjou, Red Angus, Salers, Santa Gertrudis, Shorthorn, Simmental, South Devon, and Tarentaise). This report details the calculations used for the annual update of the procedure first implemented in 1991. We have also changed the data used from USMARC to limit breed estimates to data produced since 1999 to decrease the influence of within-breed selection on the factors and estimates of breed differences. We also discuss current and future changes to the timing of the release of these factors and future plans to incorporate the factors into online decision support models.