Literature DB >> 16110437

Genetics of efficient feed utilization and national cattle evaluation: a review.

D H Denny Crews1.   

Abstract

Selection for the wide range of traits for which most beef breed associations calculate expected progeny differences focus on increasing the outputs of the production system, thereby increasing the genetic potential of cattle for reproductive rates, weights, growth rates, and end-product yield. Feed costs, however, represent a large proportion of the variable cost of beef production and genetic improvement programs for reducing input costs should include traits related to feed utilization. Feed conversion ratio, defined as feed inputs per unit output, is a traditional measure of efficiency that has significant phenotypic and genetic correlations with feed intake, growth rate, and mature size. One limitation is that favorable decreases in feed to gain either directly or due to correlated response to increasing growth rate do not necessarily relate to improvement in efficiency of feed utilization. Residual feed intake is defined as the difference between actual feed intake and that predicted on the basis of requirements for maintenance of body weight and production. Phenotypic independence of residual feed intake with growth rate, body weight, and other energy depots can be forced. However, genetic associations may remain when a phenotypic prediction approach is used. Heritability estimates for phenotypic residual feed intake have been moderate, ranging from 0.26 to 0.43. Genetic correlations of phenotypic residual feed intake with feed intake have been large and positive, suggesting that improvement would produce a correlated response of decreased feed intake. Residual feed intake estimated by genetic regression results in a zero genetic correlation with its predictors, which reduces concerns over long-term antagonistic responses such as increased mature size and maintenance requirements. The genetic regression approach requires knowledge of genetic covariances of feed intake with weight and production traits. Cost of individual feed intake measurements on potential replacements must be considered in implementation of national cattle evaluations for efficiency of feed utilization. These costs need to be compared to expected, and, if possible, realized rates of genetic change and the associated reduction in feed input requirements.

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Year:  2005        PMID: 16110437

Source DB:  PubMed          Journal:  Genet Mol Res        ISSN: 1676-5680


  22 in total

1.  Residual feed intake as a feed efficiency selection tool and its relationship with feed intake, performance and nutrient utilization in Murrah buffalo calves.

Authors:  Bisitha Kattiparambil Subhashchandra Bose; Shivlal Singh Kundu; Nguyen Thi Be Tho; Vijay Kumar Sharma; Umesh Balaji Sontakke
Journal:  Trop Anim Health Prod       Date:  2014-02-23       Impact factor: 1.559

2.  Correlation of particular bacterial PCR-denaturing gradient gel electrophoresis patterns with bovine ruminal fermentation parameters and feed efficiency traits.

Authors:  Emma Hernandez-Sanabria; Le Luo Guan; Laksiri A Goonewardene; Meiju Li; Denis F Mujibi; Paul Stothard; Stephen S Moore; Monica C Leon-Quintero
Journal:  Appl Environ Microbiol       Date:  2010-08-13       Impact factor: 4.792

3.  Rumen methanogenic genotypes differ in abundance according to host residual feed intake phenotype and diet type.

Authors:  Ciara A Carberry; Sinéad M Waters; Sinead M Waters; David A Kenny; Christopher J Creevey
Journal:  Appl Environ Microbiol       Date:  2013-11-08       Impact factor: 4.792

4.  Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle.

Authors:  Ciara A Carberry; David A Kenny; Sukkyan Han; Matthew S McCabe; Sinead M Waters
Journal:  Appl Environ Microbiol       Date:  2012-05-04       Impact factor: 4.792

5.  Greenhouse Gas Emissions from Calf- and Yearling-Fed Beef Production Systems, With and Without the Use of Growth Promotants.

Authors:  John Basarab; Vern Baron; Óscar López-Campos; Jennifer Aalhus; Karen Haugen-Kozyra; Erasmus Okine
Journal:  Animals (Basel)       Date:  2012-04-16       Impact factor: 2.752

6.  QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies.

Authors:  Mahdi Saatchi; Jonathan E Beever; Jared E Decker; Dan B Faulkner; Harvey C Freetly; Stephanie L Hansen; Helen Yampara-Iquise; Kristen A Johnson; Stephen D Kachman; Monty S Kerley; JaeWoo Kim; Daniel D Loy; Elisa Marques; Holly L Neibergs; E John Pollak; Robert D Schnabel; Christopher M Seabury; Daniel W Shike; Warren M Snelling; Matthew L Spangler; Robert L Weaber; Dorian J Garrick; Jeremy F Taylor
Journal:  BMC Genomics       Date:  2014-11-20       Impact factor: 3.969

7.  Systems biology analysis merging phenotype, metabolomic and genomic data identifies Non-SMC Condensin I Complex, Subunit G (NCAPG) and cellular maintenance processes as major contributors to genetic variability in bovine feed efficiency.

Authors:  Philipp Widmann; Antonio Reverter; Rosemarie Weikard; Karsten Suhre; Harald M Hammon; Elke Albrecht; Christa Kuehn
Journal:  PLoS One       Date:  2015-04-15       Impact factor: 3.240

8.  Quantitative analysis of ruminal methanogenic microbial populations in beef cattle divergent in phenotypic residual feed intake (RFI) offered contrasting diets.

Authors:  Ciara A Carberry; David A Kenny; Alan K Kelly; Sinéad M Waters
Journal:  J Anim Sci Biotechnol       Date:  2014-08-22

9.  Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production.

Authors:  J A Basarab; K A Beauchemin; V S Baron; K H Ominski; L L Guan; S P Miller; J J Crowley
Journal:  Animal       Date:  2013-06       Impact factor: 3.240

10.  Single nucleotide polymorphisms and haplotypes associated with feed efficiency in beef cattle.

Authors:  Nick Vl Serão; Dianelys González-Peña; Jonathan E Beever; Dan B Faulkner; Bruce R Southey; Sandra L Rodriguez-Zas
Journal:  BMC Genet       Date:  2013-09-25       Impact factor: 2.797

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