Literature DB >> 17526662

Genetic and phenotypic relationships of feed intake and measures of efficiency with growth and carcass merit of beef cattle.

J D Nkrumah1, J A Basarab, Z Wang, C Li, M A Price, E K Okine, D H Crews, S S Moore.   

Abstract

Feed intake and efficiency of growth are economically important traits of beef cattle. This study determined the relationships of daily DMI, feed:gain ratio [F:G, which is the reciprocal of the efficiency of gain (G:F) and therefore increases as the efficiency of gain decreases and vice versa, residual feed intake (RFI), and partial efficiency of growth (efficiency of ADG, PEG) with growth and carcass merit of beef cattle. Residual feed intake was calculated from phenotypic regression (RFIp) or genetic regression (RFIg) of ADG and metabolic BW on DMI. An F1 half-sib pedigree file containing 28 sires, 321 dams, and 464 progeny produced from crosses between Alberta Hybrid cows and Angus, Charolais, or Alberta Hybrid bulls was used. Families averaged 20 progeny per sire (range = 3 to 56). Performance, ultrasound, and DMI data was available on all progeny, of which 381 had carcass data. Phenotypic and genetic parameters were obtained using SAS and ASREML software, respectively. Differences in RFIp and RFIg, respectively, between the most and least efficient steers (i.e., steers with the lowest PEG) were 5.59 and 6.84 kg of DM/d. Heritabilities for DMI, F:G, PEG, RFIp, and RFIg were 0.54 +/- 0.15, 0.41 +/- 0.15, 0.56 +/- 0.16, 0.21 +/- 0.12, and 0.42 +/- 0.15, respectively. The genetic (r = 0.92) and phenotypic (r = 0.97) correlations between RFIp and RFIg indicated that the 2 indices are very similar. Both indices of RFI were favorably correlated phenotypically (P < 0.001) and genetically with DMI, F:G, and PEG. Residual feed intake was tendentiously genetically correlated with ADG (r = 0.46 +/- 0.45) and metabolic BW (r = 0.27 +/- 0.33), albeit with high SE. Genetically, RFIg was independent of ADG and BW but showed a phenotypic correlation with ADG (r = -0.21; P < 0.05). Daily DMI was correlated genetically (r = 0.28) and phenotypically (r = 0.30) with F:G. Both DMI and F:G were strongly correlated with ADG (r > 0.50), but only DMI had strong genetic (r = 0.87 +/- 0.10) and phenotypic (r = 0.65) correlations with metabolic BW. Generally, the phenotypic and genetic correlations of RFI with carcass merit were not different from zero, except genetic correlations of RFI with ultrasound and carcass LM area and carcass lean yield and phenotypic correlations of RFI with backfat thickness (P < 0.01). Daily DMI had moderate to high phenotypic (P < 0.01) and genetic correlations with all the ultrasound and carcass traits. Depending on how RFI technology is applied, adjustment for body composition in addition to growth may be required to minimize the potential for correlated responses to selection in cattle.

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Year:  2007        PMID: 17526662     DOI: 10.2527/jas.2006-767

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


  26 in total

1.  Associations between residual feed intake and apparent nutrient digestibility, in vitro methane-producing activity, and volatile fatty acid concentrations in growing beef cattle1.

Authors:  Jocelyn R Johnson; Gordon E Carstens; Wimberly K Krueger; Phillip A Lancaster; Erin G Brown; Luis O Tedeschi; Robin C Anderson; Kristen A Johnson; Arieh Brosh
Journal:  J Anim Sci       Date:  2019-07-30       Impact factor: 3.159

2.  Genetic parameters and genome-wide association study regarding feed efficiency and slaughter traits in Charolais cows.

Authors:  Pauline Martin; Sébastien Taussat; Aurélie Vinet; Daniel Krauss; David Maupetit; Gilles Renand
Journal:  J Anim Sci       Date:  2019-09-03       Impact factor: 3.159

3.  Relationship between feed efficiency and slaughter traits of French Charolais bulls.

Authors:  Sébastien Taussat; Romain Saintilan; Daniel Krauss; David Maupetit; Marie-Noëlle Fouilloux; Gilles Renand
Journal:  J Anim Sci       Date:  2019-05-30       Impact factor: 3.159

4.  Impact of selection for residual feed intake on production traits and behavior of mule ducks.

Authors:  L Drouilhet; R Monteville; C Molette; M Lague; A Cornuez; L Canario; E Ricard; H Gilbert
Journal:  Poult Sci       Date:  2016-06-22       Impact factor: 3.352

5.  Life cycle efficiency of beef production: IX. Relationship between residual feed intake of heifers and cow efficiency ratios based on harvest, carcass, and wholesale cut weight outputs.

Authors:  M E Davis; P A Lancaster; J J Rutledge; L V Cundiff
Journal:  J Anim Sci       Date:  2018-03-06       Impact factor: 3.159

6.  Genome-wide association study for feed efficiency traits using SNP and haplotype models.

Authors:  Kashly R Schweer; Stephen D Kachman; Larry A Kuehn; Harvey C Freetly; John E Pollak; Matthew L Spangler
Journal:  J Anim Sci       Date:  2018-06-04       Impact factor: 3.159

7.  Liver transcriptome profiling of beef steers with divergent growth rate, feed intake, or metabolic body weight phenotypes1.

Authors:  Robert Mukiibi; Michael Vinsky; Kate Keogh; Carolyn Fitzsimmons; Paul Stothard; Sinéad M Waters; Changxi Li
Journal:  J Anim Sci       Date:  2019-11-04       Impact factor: 3.159

8.  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

9.  Genome-wide association analysis for feed efficiency in Angus cattle.

Authors:  M M Rolf; J F Taylor; R D Schnabel; S D McKay; M C McClure; S L Northcutt; M S Kerley; R L Weaber
Journal:  Anim Genet       Date:  2011-10-24       Impact factor: 3.169

10.  ImmuneDEX: a strategy for the genetic improvement of immune competence in Australian Angus cattle.

Authors:  Antonio Reverter; Brad C Hine; Laercio Porto-Neto; Yutao Li; Christian J Duff; Sonja Dominik; Aaron B Ingham
Journal:  J Anim Sci       Date:  2021-03-01       Impact factor: 3.159

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