Digital illustration of meat quails in a geometric structure with growth data points.

Unlocking Growth: How Quail Farming Can Optimize Meat Production

Nico Varela in Tech & Innovation August 2025 • 4 min read.

"A deep dive into using advanced modeling techniques to revolutionize quail meat yield and genetic selection"

For decades, farmers and agricultural scientists have sought innovative methods to enhance meat production efficiency. Accurately predicting and optimizing animal growth is pivotal in this endeavor. Growth curves, which graphically represent an animal's growth trajectory over time, serve as invaluable tools. These curves, defined by a few biologically interpretable parameters, facilitate the comparison of individuals or entire populations for critical traits such as growth rate, maturation rate, and mature size.

Traditionally, nonlinear models have been employed to correlate weight and age data, proving adept at describing growth patterns across various animal species. In poultry farming, the Gompertz, Logistic, and von Bertalanffy models are widely used due to their precision and relative simplicity. These models help farmers and scientists understand the interplay between various growth parameters, facilitating more informed breeding and management decisions.

However, the traditional methods for estimating genetic parameters of growth curves often involve a two-step process. This approach can lead to inaccuracies as it fails to account for the errors from the initial parameter estimation when assessing (co)variance components and predicting genetic values. Addressing these limitations requires more advanced analytical tools. This is where Bayesian hierarchical models come into play, offering a more integrated and precise method for optimizing meat production.

Bayesian Hierarchical Models: A New Approach to Quail Farming

Digital illustration of meat quails in a geometric structure with growth data points.

A recent study investigated the use of Bayesian hierarchical models to compare different nonlinear functions for describing the growth curve of European quails. This innovative approach allowed for the estimation of growth curve parameters, (co)variance components, and the genetic and systematic factors influencing the curve, all within a unified framework. The key advantage of this model lies in its ability to perform joint estimation, enhancing accuracy and reliability.

The study meticulously analyzed a dataset comprising 45,965 records from 6,838 meat quails, selected over 15 successive generations for higher body weight at 42 days of age. The quails were weighed at birth and then weekly until 42 days old. Three distinct models—Gompertz, Logistic, and von Bertalanffy—were fitted in the initial stage to assess their respective capabilities in describing the growth patterns.

The model's effectiveness was gauged through multiple criteria:

Deviance Information Criterion (DIC)
Mean Square Error (MSE)
Gelfand's check function (E(g|y-r))

The Gompertz function emerged as the superior fit, exhibiting a lower DIC and improved adjustment across different ages. Subsequent analyses focused on this model to estimate heritability, genetic correlations, and the impact of systematic effects such as sex and generation on the growth parameters. The results indicated moderate heritability for the parameters A (adult weight), b (integration constant), and k (growth rate), suggesting that genetic selection can effectively improve these traits. The study also revealed genetic correlations between these parameters, offering insights into how selection for one trait might influence others.

Optimizing Quail Growth: Implications for Future Farming

This research underscores the potential of Bayesian hierarchical models to revolutionize quail farming. By providing a more accurate and nuanced understanding of growth dynamics and genetic parameters, farmers can make better-informed breeding and management decisions. This, in turn, leads to enhanced meat production efficiency and improved profitability. As the demand for sustainable and efficient farming practices grows, methodologies like these will play an increasingly crucial role in shaping the future of agriculture.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.3382/ps/pey548, Alternate LINK

Title: Bayesian Hierarchical Model For Comparison Of Different Nonlinear Function And Genetic Parameter Estimates Of Meat Quails

Subject: Animal Science and Zoology

Journal: Poultry Science

Publisher: Elsevier BV

Authors: Ariane Gonçalves Gotuzzo, Miriam Piles, Raquel Pillon Della-Flora, Jerusa Martins Germano, Janaina Scaglioni Reis, Darilene Ursula Tyska, Nelson José Laurino Dionello

Published: 2019-04-01

Everything You Need To Know

What are growth curves and why are they important in optimizing quail meat production?

Growth curves are graphical representations of an animal's growth over time. They are defined by parameters like growth rate, maturation rate, and mature size. These curves allow for comparison between individual animals or populations, providing insights into critical growth-related traits. Understanding these curves enables more informed decisions about breeding and management strategies.

What are the limitations of traditional methods for estimating genetic parameters of growth curves in meat quails?

Traditional methods often use a two-step process to estimate genetic parameters of growth curves. This involves initial parameter estimation followed by assessment of (co)variance components and prediction of genetic values. A key limitation is that this approach doesn't account for errors from the initial parameter estimation, leading to potential inaccuracies in subsequent analyses.

How do Bayesian hierarchical models improve the accuracy of growth analysis in quail farming?

Bayesian hierarchical models offer a unified framework for estimating growth curve parameters, (co)variance components, and genetic/systematic factors. The advantage lies in joint estimation, enhancing accuracy and reliability by simultaneously considering all factors influencing growth. This allows for a more precise understanding of growth dynamics and genetic parameters in quail farming.

What criteria were used to assess the effectiveness of different growth models, such as Gompertz, Logistic, and von Bertalanffy, and which one performed best?

The study used the Deviance Information Criterion (DIC), Mean Square Error (MSE), and Gelfand's check function (E(g|y-r)) to evaluate the effectiveness of the Gompertz, Logistic, and von Bertalanffy models. These criteria helped determine how well each model fit the observed data, with the Gompertz function emerging as the superior fit due to its lower DIC and better adjustment across different ages.

What did the study reveal about the heritability and genetic correlations of growth parameters in European quails, and how can this information be used?

The heritability estimates for parameters A (adult weight), b (integration constant), and k (growth rate) were moderate, indicating that genetic selection can effectively improve these traits in European quails. Furthermore, the study revealed genetic correlations between these parameters, providing insights into how selecting for one trait may influence others. This knowledge allows for more targeted breeding strategies.