Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightHowever, runningthe analysis on large datasets will require significantly higher computational power than is ordinarily available.
📌 3 Maddede Bu Makale
Abstract
Differential equations and advanced statistical models have been used to predict growth phenomena. In the present study,general nonlinear growth functions such as von Bertalanffy, Gompertz, logistic, and Brody, along with hierarchical modeling wereapplied to investigate the phenotypic growth pattern of Iranian Lori-Bakhtiari sheep. Growth data from 1410 Lori- Bakhtiari lambswere used in the present study. The results showed that the Brody function outperformed the other three nonlinear growth functions.In addition, including hierarchical growth modeling results allowed the adoption of many random effect structures, suggesting thathierarchical growth modeling has a useful role in growth data modeling. This method provides an estimation of growth parametersbased on individual animals, improving individual growth selection. The results suggest this approach for growth modeling. Combiningthe strength of individual growth modeling with general growth modeling, e.g., von Bertalanffy, Gompertz, logistic, and Brody wouldbe deeply appealing in the future. In this regard, dealing with sheep growth phenomenon using pure mathematical models, i.e. greysystem theory models that could be new powerful prediction tools for breeders and experts, has not been done yet. However, runningthe analysis on large datasets will require significantly higher computational power than is ordinarily available.