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DOI: https://doi.org/10.5713/ab.20.0618    [Accepted] Published online January 1, 2021.
Nonlinear mixed models for characterization of growth trajectory of New Zealand rabbits raised in tropical climate
Vanusa Castro de Sousa1  , Daniel Biagiotti2  , José Lindenberg Rocha Sarmento3  , Luciano Silva Sena4  , Priscila Alves Barroso5  , Sued Felipe Lacerda Barjud1  , Marisa Karen de Sousa Almeida1  , Natanael Pereira da Silva Santos5,* 
1Graduate Program in Animal Science, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil
2Tecnhical College of Bom Jesus, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil
3Department of Animal Science, Federal University of Piauí, Teresina, PI 64049-550, Brazil
4PhD in Animal Science, Federal University of Piauí, Teresina, PI 64049-550, Brazil
5Department of Agronomy, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil
Correspondence:  Natanael Pereira da Silva Santos, Tel: +55-86-9-9941-5237, Email: natanael@ufpi.edu.br
Received: 2 September 2020   • Revised: 30 October 2020   • Accepted: 20 December 2020
Abstract
Objective
The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography.
Methods
Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piauí state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, Modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R2), mean squared error (MSE), percentage of convergence of each model (%C), mean absolute deviation of residuals (MAD), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects.
Results
The von Bertalanffy model was the best fitting model for BW according to the highest value of R2 (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51 g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic).
Conclusion
The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.
Keywords: Absolute Growth Rate; Longitudinal Data; Model Selection; Oryctolagus cuniculus; Random Effect
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