Literature DB >> 29549845

Prediction of beef carcass and meat quality traits from factors characterising the rearing management system applied during the whole life of heifers.

J Soulat1, B Picard1, S Léger2, V Monteils3.   

Abstract

In this study, four prediction models were developed by logistic regression using individual data from 96 heifers. Carcass and sensory rectus abdominis quality clusters were identified then predicted using the rearing factors data. The obtained models from rearing factors applied during the fattening period were compared to those characterising the heifers' whole life. The highest prediction power of carcass and meat quality clusters were obtained from the models considering the whole life, with success rates of 62.8% and 54.9%, respectively. Rearing factors applied during both pre-weaning and fattening periods influenced carcass and meat quality. According to models, carcass traits were improved when heifer's mother was older for first calving, calves ingested concentrates during pasture preceding weaning and heifers were slaughtered older. Meat traits were improved by the genetic of heifers' parents (i.e., calving ease and early muscularity) and when heifers were slaughtered older. A management of carcass and meat quality traits is possible at different periods of the heifers' life.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Carcass composition; Logistic regression; Meat sensory properties; Rearing practices; Rearing surveys

Mesh:

Year:  2018        PMID: 29549845     DOI: 10.1016/j.meatsci.2018.03.009

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  3 in total

1.  Characterization of Four Rearing Managements and Their Influence on Carcass and Meat Qualities in Charolais Heifers.

Authors:  Julien Soulat; Brigitte Picard; Cécile Bord; Valérie Monteils
Journal:  Foods       Date:  2022-04-27

2.  Integrating the RFID identification system for Charolaise breeding bulls with 3D imaging for virtual archive creation.

Authors:  Maria Grazia Cappai; Filippo Gambella; Davide Piccirilli; Nicola Graziano Rubiu; Corrado Dimauro; Antonio Luigi Pazzona; Walter Pinna
Journal:  PeerJ Comput Sci       Date:  2019-03-04

3.  Aggregation of Omic Data and Secretome Prediction Enable the Discovery of Candidate Plasma Biomarkers for Beef Tenderness.

Authors:  Sabrina Boudon; Joelle Henry-Berger; Isabelle Cassar-Malek
Journal:  Int J Mol Sci       Date:  2020-01-19       Impact factor: 5.923

  3 in total

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