Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Analysis of beef cattle longitudinal data applying a nonlinear model.

S Forni1, M Piles, A Blasco

  • 1Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, SP, 14884900, Brazil.

Journal of Animal Science
|July 24, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Reaction norm models for the evaluation of reproductive and productive performance in Nellore cattle.

Tropical animal health and production·2026
Same author

Incorporating genomic and transcriptomic effects in joint linear and non-linear structural models for predicting complex traits in pigs.

Animal : an international journal of animal bioscience·2026
Same author

Genomic Analysis of Inbreeding Depression on Productive Traits in Rubia Galega Beef Cattle Breed.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2025
Same author

Genetic Parameters of Methane Emission, Feed Efficiency, Feeding Behaviour, and Growth Traits in Beef Cattle.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2025
Same author

The gut microbiota as a predictor of feed efficiency and feeding behavior in Iberian pigs.

Journal of animal science·2025
Same author

Improving imputation accuracy across tropically adapted beef cattle: an application for Brahman and Nellore using whole-genome sequencing data.

Animal : an international journal of animal bioscience·2025

This study evaluated Nelore beef cattle growth using the Von Bertalanffy function, revealing significant additive genetic variance for mature body weight (BW). Parameter "a" (adult BW) can control BW increases during growth rate selection.

Area of Science:

  • Animal Breeding and Genetics
  • Quantitative Genetics
  • Beef Cattle Production

Background:

  • Understanding cattle growth is crucial for optimizing production efficiency.
  • The Von Bertalanffy function is a standard model for describing animal growth trajectories.
  • Bayesian methods offer robust parameter estimation, especially with complex genetic models.

Purpose of the Study:

  • To estimate Nelore beef cattle growth curve parameters using a nested Bayesian approach.
  • To quantify genetic and environmental influences on growth curve parameters.
  • To assess the potential of growth curve parameters as selection criteria.

Main Methods:

  • Application of the Von Bertalanffy growth function within a hierarchical Bayesian framework.

Related Experiment Videos

  • Utilized Gibbs sampling and Metropolis-Hastings algorithms for posterior distribution estimation.
  • Analyzed a large dataset of 145,961 body weight records from 15,386 Nelore animals.

    • Main Results:

    • Estimated growth curve parameters, including significant additive genetic variance for mature body weight (parameter 'a').
    • Identified a large positive genetic correlation between mature body weight and parameter 'k' (curve slope).
    • Maternal environmental effects were found to influence growth through to maturity.

    Conclusions:

    • Mature body weight (parameter 'a') is a viable selection criterion to manage adult BW increases when selecting for growth rate.
    • Selection for growth rate is expected to increase adult BW without drastically altering growth curve shape.
    • Incorporating adult BW into selection indices can enhance overall beef cattle production efficiency.