Literature DB >> 28464090

Genetic variance and covariance and breed differences for feed intake and average daily gain to improve feed efficiency in growing cattle.

K J Retallick, J M Bormann, R L Weaber, M D MacNeil, H L Bradford, H C Freetly, K E Hales, D W Moser, W M Snelling, R M Thallman, L A Kuehn.   

Abstract

Feed costs are a major economic expense in finishing and developing cattle; however, collection of feed intake data is costly. Examining relationships among measures of growth and intake, including breed differences, could facilitate selection for efficient cattle. Objectives of this study were to estimate genetic parameters for growth and intake traits and compare indices for feed efficiency to accelerate selection response. On-test ADFI and on-test ADG (TESTADG) and postweaning ADG (PWADG) records for 5,606 finishing steers and growing heifers were collected at the U.S. Meat Animal Research Center in Clay Center, NE. On-test ADFI and ADG data were recorded over testing periods that ranged from 62 to 148 d. Individual quadratic regressions were fitted for BW on time, and TESTADG was predicted from the resulting equations. We included PWADG in the model to improve estimates of growth and intake parameters; PWADG was derived by dividing gain from weaning weight to yearling weight by the number of days between the weights. Genetic parameters were estimated using multiple-trait REML animal models with TESTADG, ADFI, and PWADG for both sexes as dependent variables. Fixed contemporary groups were cohorts of calves simultaneously tested, and covariates included age on test, age of dam, direct and maternal heterosis, and breed composition. Genetic correlations (SE) between steer TESTADG and ADFI, PWADG and ADFI, and TESTADG and PWADG were 0.33 (0.10), 0.59 (0.06), and 0.50 (0.09), respectively, and corresponding estimates for heifers were 0.66 (0.073), 0.77 (0.05), and 0.88 (0.05), respectively. Indices combining EBV for ADFI with EBV for ADG were developed and evaluated. Greater improvement in feed efficiency can be expected using an unrestricted index versus a restricted index. Heterosis significantly affected each trait contributing to greater ADFI and TESTADG. Breed additive effects were estimated for ADFI, TESTADG, and the efficiency indices.

Entities:  

Mesh:

Year:  2017        PMID: 28464090     DOI: 10.2527/jas.2016.1260

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  8 in total

1.  Test duration for water intake, ADG, and DMI in beef cattle.

Authors:  Cashley M Ahlberg; Kristi Allwardt; Ashley Broocks; Kelsey Bruno; Levi McPhillips; Alexandra Taylor; Clint R Krehbiel; Michelle Calvo-Lorenzo; Chris J Richards; Sara E Place; Udaya DeSilva; Deborah L VanOverbeke; Raluca G Mateescu; Larry A Kuehn; Robert L Weaber; Jennifer M Bormann; Megan M Rolf
Journal:  J Anim Sci       Date:  2018-07-28       Impact factor: 3.159

2.  Reducing the period of data collection for intake and gain to improve response to selection for feed efficiency in beef cattle.

Authors:  Richard Mark Thallman; Larry A Kuehn; Warren M Snelling; Kelli J Retallick; Jennifer M Bormann; Harvey C Freetly; Kristen E Hales; Gary L Bennett; Robert L Weaber; Daniel W Moser; Michael D MacNeil
Journal:  J Anim Sci       Date:  2018-04-03       Impact factor: 3.159

3.  Characterization of water intake and water efficiency in beef cattle1,2.

Authors:  Cashley M Ahlberg; Kristi Allwardt; Ashley Broocks; Kelsey Bruno; Alexandra Taylor; Levi Mcphillips; Clint R Krehbiel; Michelle Calvo-Lorenzo; Chris J Richards; Sara E Place; Udaya Desilva; Deborah L Vanoverbeke; Raluca G Mateescu; Larry A Kuehn; Robert Weaber; Jennifer Bormann; Megan M Rolf
Journal:  J Anim Sci       Date:  2019-12-17       Impact factor: 3.159

4.  Cecal microbiota of feedlot cattle fed a four-species Bacillus supplement.

Authors:  Luke K Fuerniss; Kelly K Kreikemeier; Lynn D Reed; Matt D Cravey; Bradley J Johnson
Journal:  J Anim Sci       Date:  2022-10-01       Impact factor: 3.338

5.  Impacts of Heifer Post-Weaning Intake Classification on Performance Measurements of Lactating and Non-Lactating Two-, Five-, and Eight-Year-Old Angus Beef Females.

Authors:  Krista R Wellnitz; Cory T Parsons; Julia M Dafoe; Darrin L Boss; Samuel A Wyffels; Timothy DelCurto; Megan L Van Emon
Journal:  Animals (Basel)       Date:  2022-06-30       Impact factor: 3.231

6.  Heritability and genetic correlations of feed intake, body weight gain, residual gain, and residual feed intake of beef cattle as heifers and cows.

Authors:  Harvey C Freetly; Larry A Kuehn; Richard M Thallman; Warren M Snelling
Journal:  J Anim Sci       Date:  2020-01-01       Impact factor: 3.159

7.  Non-invasive metabolomics biomarkers of production efficiency and beef carcass quality traits.

Authors:  Virginia M Artegoitia; J W Newman; A P Foote; S D Shackelford; D A King; T L Wheeler; R M Lewis; H C Freetly
Journal:  Sci Rep       Date:  2022-01-07       Impact factor: 4.996

Review 8.  Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq.

Authors:  Brittney N Keel; Amanda K Lindholm-Perry
Journal:  Front Genet       Date:  2022-09-19       Impact factor: 4.772

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.