Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T20:22:18.296Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of non-linear mixed models for modelling the quail growth curve for meat and laying

Published online by Cambridge University Press:  21 March 2019

H. B. Santos Open the ORCID record for H. B. Santos [Opens in a new window] ,
D. A. Vieira Open the ORCID record for D. A. Vieira [Opens in a new window] ,
L. P. Souza ,
A. L. Santos ,
F. R. Santos  and
F. R. Araujo Neto Open the ORCID record for F. R. Araujo Neto [Opens in a new window]
H. B. Santos
Affiliation:
Instituto Federal Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, CEP 75901-970, Rio Verde, Goiás, Brazil
D. A. Vieira*
Affiliation:
Instituto Federal Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, CEP 75901-970, Rio Verde, Goiás, Brazil
L. P. Souza
Affiliation:
Instituto Federal Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, CEP 75901-970, Rio Verde, Goiás, Brazil
A. L. Santos
Affiliation:
Universidade Federal do Mato Grosso, Campus Rondonópolis, Rodovia Rondonópolis-Guiratinga, Km 06, MT 270, CEP 78735-901, Rondonópolis, Mato Grosso, Brazil
F. R. Santos
Affiliation:
Instituto Federal Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, CEP 75901-970, Rio Verde, Goiás, Brazil
F. R. Araujo Neto
Affiliation:
Instituto Federal Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, CEP 75901-970, Rio Verde, Goiás, Brazil
*
Author for correspondence: D. A. Vieira, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The objective of the current paper was to apply mixed models to adjust the growth curve of quail lines for meat and laying hens and present the rates of instantaneous, relative and absolute growth. A database was used with birth weight records up to the 148th day of female quail of the lines for meat and posture. The models evaluated were Brody, Von Bertalanffy, Logistic and Gompertz and the types of residues were constant, combined, proportional and exponential. The Gompertz model with the combined residue presented the best fit. Both strains present a high correlation between the parameters asymptotic weight (A) and average growth rate (k). The two strains presented a different growth profile. However, growth rates allow greater discernment of growth profiles. The meat line presented a higher growth rate (6.95 g/day) than the lineage for laying (3.65 g/day). The relative growth rate was higher for lineage for laying (0.15%) in relation to the lineage for meat (0.13%). The inflection point of both lines is on the first third of the growth curve (up to 15 days). All results suggest that changes in management or nutrition could optimize quail production.

Keywords

Coturnix coturnix coturnixCoturnix coturnix japonicamodel selectionSAEM algorithm
Type
Modelling Animal Systems Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 10 , December 2018 , pp. 1216 - 1221
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aggrey, SE (2003) Dynamics of relative growth rate in Japanese quail lines divergently selected for growth and their control. Growth, Development, and Aging 67, 47–54.Google Scholar
Aggrey, SE (2009) Logistic nonlinear mixed effects model for estimating growth parameters. Poultry Science 88, 276–280.Google Scholar
Albino, LFT and Barreto, SLT (2003) Criação de Codornas para Produção de Ovos e Carne. Viçosa, Brazil: Aprenda fácil.Google Scholar
Brody, S (1945) Bioenergetics and Growth. New York, USA: Reinhold Publishing Corporation.Google Scholar
Buzala, M and Janicki, B (2016) Effects of different growth rates in broiler breeder and layer hens on some productive traits. Poultry Science 95, 2151–2159.Google Scholar
Comets, E, Lavenu, A and Lavielle, M (2017) Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm. Journal of Statistical Software 80, 1–41.Google Scholar
Coyne, JM, Berry, DP, Mäntysaari, EA, Juga, J and McHugh, N (2015) Comparison of fixed effects and mixed model growth functions in modelling and predicting live weight in pigs. Livestock Science 177, 8–14.Google Scholar
Craig, BA and Schinckel, AP (2001) Nonlinear mixed effects model for swine growth. The Professional Animal Scientist 17, 256–260.Google Scholar
Demuner, LF, Suckeveris, D, Muñoz, JA, Caetano, VC, DE Lima, CG, DE Faria Filho, DE and DE Faria, DE (2017) Adjustment of growth models in broiler chickens. Pesquisa Agropecuária Brasileira 52, 1241–1252.Google Scholar
Firat, MZ, Karaman, E, Başar, EK and Narinc, D (2016) Bayesian analysis for the comparison of nonlinear regression model parameters: an application to the growth of Japanese quail. Revista Brasileira de Ciência Avícola 18, 19–26.Google Scholar
Gous, RM, Moran, ET Jr, Stilborn, HR, Bradford, GD and Emmans, GC (1999) Evaluation of the parameters needed to describe the overall growth, the chemical growth, and the growth of feathers and breast muscles of broilers. Poultry Science 78, 812–821.Google Scholar
Grieser, DDO, Marcato, SM, Furlan, AC, Zancanela, V, Ton, APS, Batista, E, Perine, TP, Pozza, PC and Sakomura, NK (2015) Comparison of growth curve parameters of organs and body components in meat-(Coturnix coturnix coturnix) and laying-type (Coturnix coturnix japonica) quail show interactions between gender and genotype. British Poultry Science 56, 6–14.Google Scholar
Grieser, DDO, Marcato, SM, Furlan, AC, Zancanela, V, Vesco, APD, Batista, E, Ton, APS and Perine, TP (2018) Estimation of growth parameters of body weight and body nutrient deposition in males and females of meat- and laying-type quail using the Gompertz model. Revista Brasileira de Zootecnia 47, e20170083. https://dx.doi.org/10.1590/rbz4720170083.Google Scholar
Hall, DB and Clutter, M (2004) Multivariate multilevel nonlinear mixed effects models for timber yield predictions. Biometrics 60, 16–24.Google Scholar
Karaman, E, Narinc, D, Firat, MZ and Aksoy, T (2013) Nonlinear mixed effects modeling of growth in Japanese quail. Poultry Science 92, 1942–1948.Google Scholar
Kass, RE and Raftery, AE (1995) Bayes factors. Journal of the American Statistical Association 90, 773–795.Google Scholar
Kizilkaya, K, Balcioglu, MS, Yolcu, HI, Karabag, K and Genc, IH (2006) Growth curve analysis using nonlinear mixed model in divergently selected Japanese quails. Archiv Fur Geflugelkunde 70, 181–186.Google Scholar
Knižetova, H, Hyanek, J, Kniže, B and Prochazkova, H (1991) Analysis of growth curves of fowl. II. Ducks. British Poultry Science 32, 1039–1053.Google Scholar
Koncagul, S and Cadirci, S (2009) Comparison of three non-linear models when data truncated at different lengths of growth period in Japanese quails. European Poultry Science 73, 7–12.Google Scholar
Kühn, E and Lavielle, M (2005) Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics & Data Analysis 49, 1020–1038.Google Scholar
Laird, AK (1965) Dynamics of relative growth. Growth 29, 249–263.Google Scholar
Lindstrom, MJ and Bates, DM (1990) Nonlinear mixed effects models for repeated measures data. Biometrics 46, 673–687.Google Scholar
Lopes, FB, Magnabosco, CDU, de Souza, FM, de Assis, AS and Brunes, LC (2016) Análises de dados longitudinais em bovinos Nelore Mocho por meio de modelos não lineares. Archivos de Zootecnia 65, 123–129.Google Scholar
Malhado, CHM, Rezende, MPG, Malhado, ACM, Azevedo, DMMR, de Souza, JC and Souza Carneiro, PL (2017) Comparison of nonlinear models to describe the growth curves of Jaffarabaddi, Mediterranean and Murrah buffaloes. Journal of Agricultural Science and Technology 19, 1485–1494.Google Scholar
Manjula, P, Park, HB, Seo, D, Choi, N, Jin, S, Ahn, SJ, Heo, KN, Kang, BS and Lee, JH (2018) Estimation of heritability and genetic correlation of body weight gain and growth curve parameters in Korean native chicken. Asian-Australasian Journal of Animal Sciences 31, 26–31.Google Scholar
Mignon-Grasteau, S, Beaumont, C, Le Bihan-Duval, E, Poivey, JP, De Rochambeau, H and Ricard, FH (1999) Genetic parameters of growth curve parameters in male and female chickens. British Poultry Science 40, 44–51.Google Scholar
Mohammed, FA (2015) Comparison of three nonlinear functions for describing chicken growth curves. Scientia Agriculturae 9, 120–123.Google Scholar
Mota, LFM, Alcântara, DC, Abreu, LRA, Costa, LS, Pires, AV, Bonafé, CM, Silva, MA and Pinheiro, SRF (2015) Crescimento de codornas de diferentes grupos genéticos por meio de modelos não lineares. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 67, 1372–1380.Google Scholar
Narinç, D, Aksoy, T and Karaman, E (2010) Genetic parameters of growth curve parameters and weekly body weights in Japanese quails (Coturnix coturnix japonica). Journal of Animal and Veterinary Advances 9, 501–507.Google Scholar
Narinç, D, Narinç, NÖ and Aygün, A (2017) Growth curve analyses in poultry science. World's Poultry Science Journal 73, 395–408.Google Scholar
Nelder, JA (1961) The fitting of a generalization of the logistic curve. Biometrics 17, 89–110.Google Scholar
Oliveira, HND, Lôbo, RB and Pereira, CS (2000) Comparação de modelos não lineares para descrever o crescimento de fêmeas da raça Guzerá. Pesquisa Agropecuária Brasileira 35, 1843–1851.Google Scholar
Rossi, RM, Grieser, DO, Conselvan, VA and Marcato, SM (2017) Growth curves in meat-type and laying quail: a Bayesian perspective. Semina: Ciências Agrárias 38(suppl. 1), 2743–2754.Google Scholar
Santos, ALD (2008) Desempenho, crescimento, qualidade do ovo, composição corporal e características reprodutivas e ósseas de poedeiras submetidas a diferentes programas nutricionais. Tese de Doutorado, Universidade de São Paulo, São Paulo, Brazil.Google Scholar
Schinckel, AP and Craig, BA (2002) Evaluation of alternative nonlinear mixed effects models of swine growth. The Professional Animal Scientist 18, 219–226.Google Scholar
Schwarz, G (1978) Estimating the dimension of a model. The Annals of Statistics 6, 461–464.Google Scholar
Škrobánek, P, Hrbatá, M, Baranovská, M and Juráni, M (2004) Growth of Japanese quail chicks in simulated weightlessness. Acta Veterinaria Brno 73, 157–164.Google Scholar
Von Bertalanffy, L (1957) Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32, 217–231.Google Scholar