Comparison of the Fit of the Richards Model to Broiler Chicken Growth Data With Gompertz and Logistic Models
DOI:
https://doi.org/10.61326/actanatsci.v6i2.387Keywords:
Broiler, Growth curve, Non-linear model, Richards model, Gompertz model, Logistic modelAbstract
This study comparatively evaluates three nonlinear regression models (Richards, Gompertz, and Logistic) commonly used to mathematically model the growth process of commercial broilers. The primary objective of the study was to determine the model that most accurately represents the growth curve by analyzing the fit of these models to live weight data. In the study, 360 Ross 308 hybrid male broiler chicks were monitored until 50 days of age, and weekly live weight measurements were taken. Parameter estimates for each model were evaluated based on statistical fit metrics, including root mean square error (RMSE), coefficient of determination (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results showed that the Richards model showed the highest fit across all criteria. The Gompertz model ranked second, and the Logistic model ranked third. However, all three models demonstrated high performance in explaining the growth process of broilers. These results highlight the importance of growth modeling as a key decision-support tool for determining optimal slaughter timing and feeding strategies, particularly in broiler production. The flexible structure of the Richards model makes it the most desirable option in terms of biological significance and statistical relevance.
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