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Feed Efficiency Genetic Projects

Feed Efficiency Genetic Projects. Terms. Gain/Feed = Feed Efficiency FE Feed/Gain = Feed Conversion FC: -FE Residual Feed Intake RFI Net Feed Intake NFI:-RFI.  Size at given age  Gain  Muscle  Fat: External and Intramuscular.

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Feed Efficiency Genetic Projects

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  1. Feed EfficiencyGenetic Projects

  2. Terms • Gain/Feed = Feed Efficiency FE • Feed/Gain = Feed Conversion FC: -FE • Residual Feed Intake RFI • Net Feed Intake NFI:-RFI

  3.  Size at given age  Gain  Muscle  Fat: External and Intramuscular Must measure individualintake Effects of Selection for FE or FC Dave Kirschten, Cornell University

  4. Observed DMI – Expected DMI Expected DMI given f(weight.75,ADG)  = Favorable Residual Feed Intake Dave Kirschten, Cornell University

  5.  Intake  Gain, Test Weight  Weaning Weight, Yearling Weight of Progeny  Lean Percent  Marbling  Feed Efficiency Must measureindividualintake Response to Selection for RFI Dave Kirschten, Cornell University

  6. Indicator Traits Useful when the ERT, is: 1 - difficult to measure, 2 - expensive to measure, or 3 - measured late in life. Option: not measure the trait but provide evaluations based on associated information

  7. Evaluations Weight Measurements EPD’s for Mature Size and Milk Genetic evaluation system Pedigree Use mature size and milk EPD’s  Cow Maintenance Requirement EPD’s - Evans et. al.

  8. Another Option Use data on related traits (indicators) to predict the EPD’s for the ERT.

  9. Evaluations Weight Traits Carcass Traits Genetic evaluation system EPD’s for Feed Efficiency Pedigree Herring and Bertrand

  10. Another Option Use data on related traits (indicators) to predict the phenotype for the ERT.

  11. Data Biological Model Predicted Phenotypes Evaluations e.g. Cornell Value Discovery System Data Genetic evaluation system EPD’s Pedigree

  12. Predicted Phenotypes The CVDS can be used for feedlot animals (carcass and feedlot data) as well as breeding animals (ultrasound and ranch data).

  13. Predicted Phenotypes Sire Slaughter progeny Seedstock progeny Opportunity for “large” volumes of data!

  14. Multiple Trait Approach Slaughter progeny Exactly the carcass / ultrasound model!! Both Seedstock progeny

  15. Project: Heritability Danny’s group has been working with the Jorgenson herd on predicting the Dry Matter Requirements (PDMR) of bulls on test. The PDMR has been obtained on 747 performance tested bulls during those three years and these bulls were themselves progeny of 84 sires. Heritability: PDMR = .32 ADG = .36

  16. Project: Heritability Collaborative project with breed associations to collect data on individual bulls on performance tests. Data from Circle A ranch in Missouri. Objective: Estimate the heritability of PDMR and the genetic correlations between PDMR and other traits.

  17. Group to Texas A&M for individual feeding, intense data collection and evaluation. Group kept at King Ranch and pen fed. Data collected to meet the requirements of the CVDS Project: Texas A&M, King Ranch, NBCEC, Texas CF, and NCBA checkoff dollars CG of Calves produced at the King Ranch

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