Comparative analysis of non-linear growth model fit in chicken growth: a systematic review

Estimation growth curve is a critical aspect of chicken production, influencing management decisions and optimization of growth rates. The poultry industry struggles to make effective decisions on feed management, growth rate, and slaughter age, which are key factors influencing productivity and profitability. Thus, the aim of this systematic review was to identify the most suitable non-linear model for estimating growth curves in chickens. A literature search was conducted using population, exposure, and outcome (PEO) framework. A total number of 49 articles were included in the review following PRISMA guideline and were published between 1995 and 2024. Databases such as Google Scholar, ScienceDirect, PubMed, and Web of Science, with the combination of the following keywords: chickens OR 'Gallus gallus' OR hens OR cocks OR chicks, AND 'growth curve' OR 'growth patterns' OR 'growth parameters', AND 'non-linear models' OR 'non-linear mathematical models' OR 'non-linear statistical models' were used. The results showed that Logistic model was the most used (n = 44) non-linear model, followed by Gompertz model (n = 43) and the von Bertalanffy model (n = 31). Gompertz, and Weibull models provided the best fit for estimation of chicken growth curves, with high coefficients of determination (R² = 1.00). The Gompertz, and Weibull non-linear models are recommended for estimating chicken growth curves to support breed selection, optimize slaughter age and reduce cost.