Genetic variability in the feeding behavior of crossbred growing cattle and associations with performance and feed efficiency.
correlation
genetic parameters
heritability
residual feed intake
Journal
Journal of animal science
ISSN: 1525-3163
Titre abrégé: J Anim Sci
Pays: United States
ID NLM: 8003002
Informations de publication
Date de publication:
01 Nov 2021
01 Nov 2021
Historique:
received:
26
05
2021
accepted:
19
10
2021
pubmed:
22
10
2021
medline:
20
11
2021
entrez:
21
10
2021
Statut:
ppublish
Résumé
The objectives of the present study were to estimate genetic parameters for several feeding behavior traits in growing cattle, as well as the genetic associations among and between feeding behavior and both performance and feed efficiency traits. An additional objective was to investigate the use of feeding behavior traits as predictors of genetic merit for feed intake. Feed intake and live-weight data on 6,088 growing cattle were used of which 4,672 had ultrasound data and 1,548 had feeding behavior data. Feeding behavior traits were defined based on individual feed events or meal events (where individual feed events were grouped into meals). Univariate and bivariate animal linear mixed models were used to estimate (co)variance components. Heritability estimates (± SE) for the feeding behavior traits ranged from 0.19 ± 0.08 for meals per day to 0.61 ± 0.10 for feeding time per day. The coefficient of genetic variation per trait varied from 5% for meals per day to 22% for the duration of each feed event. Genetically heavier cattle, those with a higher daily energy intake (MEI), or those that grew faster had a faster feeding rate, as well as a greater energy intake per feed event and per meal. Better daily feed efficiency (i.e., lower residual energy intake) was genetically associated with both a shorter feeding time per day and shorter meal time per day. In a validation population of 321 steers and heifers, the ability of estimated breeding values (EBV) for MEI to predict (adjusted) phenotypic MEI was demonstrated; EBVs for MEI were estimated using multi-trait models with different sets of predictor traits such as liveweight and/or feeding behaviors. The correlation (± SE) between phenotypic MEI and EBV for MEI marginally improved (P < 0.001) from 0.64 ± 0.03 to 0.68 ± 0.03 when feeding behavior phenotypes from the validation population were included in a genetic evaluation that already included phenotypic mid-test metabolic live-weight from the validation population. This is one of the largest studies demonstrating that significant exploitable genetic variation exists in the feeding behavior of young crossbred growing cattle; such feeding behavior traits are also genetically correlated with several performance and feed efficiency metrics. Nonetheless, there was only a marginal benefit to the inclusion of time-related feeding behavior phenotypes in a genetic evaluation for MEI to improve the precision of the EBVs for this trait.
Identifiants
pubmed: 34673943
pii: 6407711
doi: 10.1093/jas/skab303
pmc: PMC8679004
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : U.S. Department of Agriculture
Organisme : Food and the Marine Ireland Research Stimulus Fund
ID : 17/S/235
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science.
Références
Animal. 2018 Dec;12(s2):s321-s335
pubmed: 30139392
J Anim Sci. 2019 Mar 1;97(3):1158-1170
pubmed: 30590611
J Anim Sci. 2007 Oct;85(10):2382-90
pubmed: 17591713
Front Vet Sci. 2018 Dec 05;5:305
pubmed: 30568940
J Anim Sci. 2012 Jan;90(1):109-15
pubmed: 21890504
J Theor Biol. 2001 Dec 7;213(3):413-25
pubmed: 11735288
J Anim Sci. 2013 Jul;91(7):3088-104
pubmed: 23658330
Proc Nutr Soc. 2001 Feb;60(1):115-25
pubmed: 11310416
Anim Behav. 1999 Apr;57(4):807-817
pubmed: 10202089
J Anim Breed Genet. 2019 May;136(3):174-182
pubmed: 30945778
Annu Rev Anim Biosci. 2019 Feb 15;7:403-425
pubmed: 30485756
J Anim Sci. 2016 Oct;94(10):4109-4119
pubmed: 27898879
J Anim Sci. 2007 Feb;85(2):322-31
pubmed: 17040944
J R Soc Interface. 2007 Feb 22;4(12):65-72
pubmed: 17015286
J Anim Sci. 2019 Nov 4;97(11):4405-4417
pubmed: 31593986
J Dairy Sci. 2013 Apr;96(4):2654-2656
pubmed: 23462165
Animal. 2018 Sep;12(9):1815-1826
pubmed: 29779496
J Anim Sci. 2010 Mar;88(3):885-94
pubmed: 19966161
J Dairy Sci. 2014;97(1):537-42
pubmed: 24239085
J Anim Sci. 2014 Mar;92(3):974-83
pubmed: 24492561
J Anim Sci. 2020 Nov 1;98(11):
pubmed: 33125460
J Anim Sci. 2011 Nov;89(11):3401-9
pubmed: 21642495
J Anim Sci. 2020 Jul 1;98(7):
pubmed: 32658252
J Dairy Sci. 2003 Mar;86(3):1036-44
pubmed: 12703641