Ability of non-linear mixed models to predict growth in laying hens

Luis Fernando Galeano-Vasco,Mario Fernando Cerón-Muñoz,William Narváez-Solarte

doi:10.1590/s1516-35982014001100003

Luis Fernando Galeano-Vasco, Mario Fernando Cerón-Muñoz + Show 1 more

Open Access

https://doi.org/10.1590/s1516-35982014001100003

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Abstract

In this study, the Von Bertalanffy, Richards, Gompertz, Brody, and Logistics non-linear mixed regression models were compared for their ability to estimate the growth curve in commercial laying hens. Data were obtained from 100 Lohmann LSL layers. The animals were identified and then weighed weekly from day 20 after hatch until they were 553 days of age. All the nonlinear models used were transformed into mixed models by the inclusion of random parameters. Accuracy of the models was determined by the Akaike and Bayesian information criteria (AIC and BIC, respectively), and the correlation values. According to AIC, BIC, and correlation values, the best fit for modeling the growth curve of the birds was obtained with Gompertz, followed by Richards, and then by Von Bertalanffy models. The Brody and Logistic models did not fit the data. The Gompertz nonlinear mixed model showed the best goodness of fit for the data set, and is considered the model of choice to describe and predict the growth curve of Lohmann LSL commercial layers at the production system of University of Antioquia.

Highlights

Growth can be defined as body weight gain or weight gain of body parts with age
According to Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and correlation values, the best fit for modeling the growth curve of the birds was obtained with Gompertz, followed by Richards, and by Von Bertalanffy models
The Gompertz and Richards models can be used to estimate bird weights for Lohmann LSL hens by projecting growth curves

Summary

Introduction

Growth can be defined as body weight gain or weight gain of body parts with age. This process is influenced by genetic and environmental conditions. The modeling of growth performance in laying hens is an elaborate process due to the use of parameters which are difficult to interpret from a biological perspective, and the difficulty to predict the events that are influenced by the variation of the observations in time (Aggrey, 2002; Aggrey, 2009; Galeano-Vasco et al, 2013)

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