AI Summary & Key Takeaways
AI InsightThe performance of the models was evaluated based on error metrics, fit coefficients, and information criteria, and the findings revealed significant differences between the models.
📌 3 Key Takeaways
Abstract
In this study, five different nonlinear growth models Gompertz, Logistic, Gamma, Brody, and Wilmink were compared using the average live weights of 32 Honamlı kids for 8 months of age. The performance of the models was evaluated based on error metrics, fit coefficients, and information criteria, and the findings revealed significant differences between the models. As a result of the overall evaluation, the logistic model (R2=0.987) was determined to be the model that best represents the growth process of Honamlı kids during their early and middle developmental stages, due to its low prediction error, high explanatory power, and superiority in terms of information criteria. The results show that the logistic growth model is an effective method for reliably predicting live weight gain in Honamlı kids and for explaining growth dynamics in a biologically significant way. This model demonstrates that it can be used as a decision support mechanism in planning selection programs, conducting early performance assessments, and developing breeding strategies. It is anticipated that future studies using datasets covering wider age ranges and including environmental factors will contribute to a more detailed understanding of growth characteristics in Honamlı goats.
Özet
In this study, five different nonlinear growth models Gompertz, Logistic, Gamma, Brody, and Wilmink were compared using the average live weights of 32 Honamlı kids for 8 months of age. The performance of the models was evaluated based on error metrics, fit coefficients, and information criteria, and the findings revealed significant differences between the models. As a result of the overall evaluation, the logistic model (R2=0.987) was determined to be the model that best represents the growth process of Honamlı kids during their early and middle developmental stages, due to its low prediction error, high explanatory power, and superiority in terms of information criteria. The results show that the logistic growth model is an effective method for reliably predicting live weight gain in Honamlı kids and for explaining growth dynamics in a biologically significant way. This model demonstrates that it can be used as a decision support mechanism in planning selection programs, conducting early performance assessments, and developing breeding strategies. It is anticipated that future studies using datasets covering wider age ranges and including environmental factors will contribute to a more detailed understanding of growth characteristics in Honamlı goats.