Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Zootecnia (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982018000100543 |
Resumo: | ABSTRACT The objective of this study was to determine the adjustment quality of non-linear models and estimate the growth parameters and body chemical composition of a meat-type quail strain (Coturnix coturnix coturnix) and two laying quail strains (Coturnix coturnix japonica), designated yellow and red. The study used 1500 quail, not sexed, distributed in a completely randomized design with three treatments and five repetitions. The experimental period was from 1-42 days of age. The birds were raised in a conventional system and fed ad libitum with a diet formulated to meet their nutritional requirements. Quail were weighed weekly, and a representative sample was slaughtered to evaluate their body chemical composition. The adjustment quality of the models was evaluated by means of the residual mean square (RMS), regression residue squares sum (SSRR), and number of iterations required for convergence. In evaluating the adjustment quality for the body weight of the three strains, the Gompertz, Logistic, and Von Bertalanffy models gave good fit, with Gompertz providing the best adjustment among them. For body composition, the Gompertz and Logistic models were the best, with Gompertz showing a slight superiority. Gompertz is the best model for describing growth curves and body chemical composition of body weight, protein, water, and ash in meat-type quail. In addition, it is the best model for describing growth curves and body chemical composition of body weight of yellow laying quail and of body weight, protein, and ash in red laying quail. Logistic was the best model for describing growth curves and body chemical composition of water, protein, and ash in yellow laying quail, and of water in red laying quail. |
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Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quailbody chemical depositionGompertz curveiterationsresidual mean squaresquare sum of the regression residualABSTRACT The objective of this study was to determine the adjustment quality of non-linear models and estimate the growth parameters and body chemical composition of a meat-type quail strain (Coturnix coturnix coturnix) and two laying quail strains (Coturnix coturnix japonica), designated yellow and red. The study used 1500 quail, not sexed, distributed in a completely randomized design with three treatments and five repetitions. The experimental period was from 1-42 days of age. The birds were raised in a conventional system and fed ad libitum with a diet formulated to meet their nutritional requirements. Quail were weighed weekly, and a representative sample was slaughtered to evaluate their body chemical composition. The adjustment quality of the models was evaluated by means of the residual mean square (RMS), regression residue squares sum (SSRR), and number of iterations required for convergence. In evaluating the adjustment quality for the body weight of the three strains, the Gompertz, Logistic, and Von Bertalanffy models gave good fit, with Gompertz providing the best adjustment among them. For body composition, the Gompertz and Logistic models were the best, with Gompertz showing a slight superiority. Gompertz is the best model for describing growth curves and body chemical composition of body weight, protein, water, and ash in meat-type quail. In addition, it is the best model for describing growth curves and body chemical composition of body weight of yellow laying quail and of body weight, protein, and ash in red laying quail. Logistic was the best model for describing growth curves and body chemical composition of water, protein, and ash in yellow laying quail, and of water in red laying quail.Sociedade Brasileira de Zootecnia2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982018000100543Revista Brasileira de Zootecnia v.47 2018reponame:Revista Brasileira de Zootecnia (Online)instname:Sociedade Brasileira de Zootecnia (SBZ)instacron:SBZ10.1590/rbz4720170244info:eu-repo/semantics/openAccessGrieser,Daiane de OliveiraMarcato,Simara MárciaFurlan,Antonio ClaudioZancanela,VittorGasparino,ElianeVesco,Ana Paula DelLima,Nayara Cristine FreitasPozza,Paulo Cesareng2018-11-26T00:00:00Zoai:scielo:S1516-35982018000100543Revistahttps://www.rbz.org.br/pt-br/https://old.scielo.br/oai/scielo-oai.php||bz@sbz.org.br|| secretariarbz@sbz.org.br1806-92901516-3598opendoar:2018-11-26T00:00Revista Brasileira de Zootecnia (Online) - Sociedade Brasileira de Zootecnia (SBZ)false |
dc.title.none.fl_str_mv |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
title |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
spellingShingle |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail Grieser,Daiane de Oliveira body chemical deposition Gompertz curve iterations residual mean square square sum of the regression residual |
title_short |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
title_full |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
title_fullStr |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
title_full_unstemmed |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
title_sort |
Adjustment of nonlinear models and growth parameters and body nutrient deposition in meat-type and laying quail |
author |
Grieser,Daiane de Oliveira |
author_facet |
Grieser,Daiane de Oliveira Marcato,Simara Márcia Furlan,Antonio Claudio Zancanela,Vittor Gasparino,Eliane Vesco,Ana Paula Del Lima,Nayara Cristine Freitas Pozza,Paulo Cesar |
author_role |
author |
author2 |
Marcato,Simara Márcia Furlan,Antonio Claudio Zancanela,Vittor Gasparino,Eliane Vesco,Ana Paula Del Lima,Nayara Cristine Freitas Pozza,Paulo Cesar |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Grieser,Daiane de Oliveira Marcato,Simara Márcia Furlan,Antonio Claudio Zancanela,Vittor Gasparino,Eliane Vesco,Ana Paula Del Lima,Nayara Cristine Freitas Pozza,Paulo Cesar |
dc.subject.por.fl_str_mv |
body chemical deposition Gompertz curve iterations residual mean square square sum of the regression residual |
topic |
body chemical deposition Gompertz curve iterations residual mean square square sum of the regression residual |
description |
ABSTRACT The objective of this study was to determine the adjustment quality of non-linear models and estimate the growth parameters and body chemical composition of a meat-type quail strain (Coturnix coturnix coturnix) and two laying quail strains (Coturnix coturnix japonica), designated yellow and red. The study used 1500 quail, not sexed, distributed in a completely randomized design with three treatments and five repetitions. The experimental period was from 1-42 days of age. The birds were raised in a conventional system and fed ad libitum with a diet formulated to meet their nutritional requirements. Quail were weighed weekly, and a representative sample was slaughtered to evaluate their body chemical composition. The adjustment quality of the models was evaluated by means of the residual mean square (RMS), regression residue squares sum (SSRR), and number of iterations required for convergence. In evaluating the adjustment quality for the body weight of the three strains, the Gompertz, Logistic, and Von Bertalanffy models gave good fit, with Gompertz providing the best adjustment among them. For body composition, the Gompertz and Logistic models were the best, with Gompertz showing a slight superiority. Gompertz is the best model for describing growth curves and body chemical composition of body weight, protein, water, and ash in meat-type quail. In addition, it is the best model for describing growth curves and body chemical composition of body weight of yellow laying quail and of body weight, protein, and ash in red laying quail. Logistic was the best model for describing growth curves and body chemical composition of water, protein, and ash in yellow laying quail, and of water in red laying quail. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982018000100543 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982018000100543 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/rbz4720170244 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Zootecnia |
publisher.none.fl_str_mv |
Sociedade Brasileira de Zootecnia |
dc.source.none.fl_str_mv |
Revista Brasileira de Zootecnia v.47 2018 reponame:Revista Brasileira de Zootecnia (Online) instname:Sociedade Brasileira de Zootecnia (SBZ) instacron:SBZ |
instname_str |
Sociedade Brasileira de Zootecnia (SBZ) |
instacron_str |
SBZ |
institution |
SBZ |
reponame_str |
Revista Brasileira de Zootecnia (Online) |
collection |
Revista Brasileira de Zootecnia (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Zootecnia (Online) - Sociedade Brasileira de Zootecnia (SBZ) |
repository.mail.fl_str_mv |
||bz@sbz.org.br|| secretariarbz@sbz.org.br |
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1750318153110388736 |