Modelling growth patterns of three guinea fowl varieties using non-linear models

Area of Science:

• Animal Science
• Quantitative Biology
• Poultry Genetics

Background:

• Growth is a key economic trait in poultry, influencing profitability.
• Understanding guinea fowl growth patterns is vital for optimizing management and breeding.
• Non-linear growth models are essential tools for evaluating animal development.

Purpose of the Study:

• To evaluate and compare the growth patterns of three guinea fowl varieties (pearl, lavender, white).
• To identify the most suitable non-linear growth model for describing guinea fowl development.
• To determine which variety possesses the most favorable growth characteristics for broiler production.

Main Methods:

• Fitted five non-linear models (Gompertz, Logistic, Richards, Lopez, Weibull) to body weight data from 291 guinea fowl (hatch to 168 days).
• Assessed model fit using Mean Square Error (MSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted R-squared.
• Calculated and compared growth parameters, including inflection point and absolute growth rate, across varieties.

Main Results:

• The Lopez model provided the best fit for guinea fowl growth patterns, while the Logistic model performed worst.
• Significant differences in growth parameters were observed among pearl, lavender, and white varieties.
• White guinea fowl showed a lower age at inflection point and higher weight at inflection point compared to other varieties.

Conclusions:

• The white guinea fowl variety demonstrates superior growth characteristics, making it more suitable for broiler production.
• The Lopez model is recommended for accurately describing guinea fowl growth dynamics.
• Growth parameter analysis provides valuable insights for selective breeding and management in the poultry industry.