Investigating potential causal relationships among carcass and meat quality traits using structural equation model in Nellore cattle

Tiago Bresolin, Tiago Luciano Passafaro, Camila Urbano Braz, Anderson Antonio Carvalho Alves, Roberto Carvalheiro, Luiz Artur Loyola Chardulo, Guilherme Jordão de Magalhães Rosa, Lucia Galvão de Albuquerque

Research output: Contribution to journalArticlepeer-review

Abstract

The objective of this study was to investigate potential causal relationships among hot carcass weight (HCW), longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), and marbling score (MB) traits in Nellore cattle using structural equation models (SEM). The SEM fitted comprises the following links between traits: WBSF → LMA, WBSF → HCW, HCW → LMA, BF → HCW, and BF → MB, where the arrows indicate the causal direction between traits, with structural coefficients posterior means (posterior standard deviation) equal to −0.29 cm2/kg (0.09), 0.43 kg/kg (0.29), 0.10 cm2/kg (0.006), 1.92 kg/mm (0.28), and 0.03 score-grade/mm (0.006), respectively. The final SEM revealed some important putative causal relationships among the traits studied here. The implied causal effects suggest that interventions on meat tenderness and fat content would affect overall growth and muscle deposition. Knowledge regarding potential causal relationships inferred among the traits studied here can have important implications for the genetic selection and management of Nellore cattle for improvement of carcass and meat quality.

Original languageEnglish (US)
Article number108771
JournalMeat Science
Volume187
DOIs
StatePublished - May 2022
Externally publishedYes

Keywords

  • Beef cattle
  • Causal effect
  • Inductive causation
  • Structural coefficients

ASJC Scopus subject areas

  • Food Science

Fingerprint

Dive into the research topics of 'Investigating potential causal relationships among carcass and meat quality traits using structural equation model in Nellore cattle'. Together they form a unique fingerprint.

Cite this