Bayesian Multi-Model Comparison and Nonlinear Mixed Modelling of Growth Trajectories in Denizli Chickens

This study aimed to model the growth trajectories of Denizli chickens under different production systems and to identify the most appropriate nonlinear growth function within a Bayesian framework. A total of 156 birds were monitored weekly from hatch to 26 weeks of age under conventional cage, conventional floor, and enriched floor systems. Eight candidate nonlinear growth models were evaluated using Bayesian model comparison criteria, including leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC). Among the evaluated models, the Gompertz function showed the best predictive performance, with the lowest LOOIC (225.16) and superior predictive accuracy across fit statistics. The selected model was subsequently extended to a Bayesian nonlinear mixed modelling framework to evaluate the effects of sex and production system on growth dynamics while accounting for between-animal variability. Males exhibited substantially higher asymptotic weights than females, whereas females showed faster early growth and earlier stabilization. Birds reared under the conventional floor system, particularly males, exhibited the highest asymptotic growth potential and later inflection ages, indicating a more prolonged growth phase. In contrast, enriched systems appeared to have promoted greater variability in growth responses, possibly due to increased behavioral activity and energy expenditure. The findings demonstrated that production system and sex jointly influenced both the scale and timing of growth in Denizli chickens. Beyond statistical model comparison, the Bayesian nonlinear mixed modelling approach provided biologically meaningful information that could support breeding, housing, and management decisions for indigenous and dual-purpose poultry production systems.