Cross-sectional and longitudinal methods for describing the growth curve of Brahman females
Abstract
Growth curve modeling is a fundamental tool in animal production, allowing the analysis of the relationship between body weight and age through nonlinear statistical models. Traditionally, this curve has been obtained using the longitudinal method, which requires repeated measurements of the same individuals from birth to adulthood, making the process time-consuming and costly. As an alternative, the cross-sectional method uses single measurements from animals at different ages, reducing the time required for data collection. The present study aimed to evaluate the fit of different models to body weight data of Brahman females obtained by both longitudinal (n = 34) and cross-sectional (n = 219) methods, covering ages from birth to 65 months, select the most appropriate model, and compare the methods to assess the ability of the cross-sectional method to describe the growth curve of this population. In addition, critical points of the growth function were estimated to support decision-making in genetic improvement and productive management. The models were evaluated based on the coefficient of determination (R²), Akaike Information Criterion (AIC), and residual standard deviation, based on analyses performed in R software. The Gompertz model showed the best fit for the longitudinal data (R² = 0.9953), whereas the Logistic model provided the best fit for the cross-sectional data (R² = 0.9916). The results indicate that the cross-sectional method may represent a practical…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 219 participants or records.
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