Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling
Different polynomial functions were tested for mean trajectory modeling with different residual variance structures. A total of 15,148 weight records of 3,115 Nellore Mocho cattle with ages between 1 and 660 days, raised in northern Brazil. First, the mean trajectory of cattle growth curve was fitte...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Universidade Estadual de Londrina
2020-03-01
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| Series: | Semina: Ciências Agrárias |
| Subjects: | |
| Online Access: | https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36531 |
| _version_ | 1797944593906401280 |
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| author | Diego Helcias Cavalcante Severino Cavalcante Sousa Júnior Luciano Pinheiro Silva Carlos Henrique Mendes Malhado Raimundo Martins Filho Danielle Maria Machado Ribeiro Azevêdo José Elivalto Guimarães Campelo |
| author_facet | Diego Helcias Cavalcante Severino Cavalcante Sousa Júnior Luciano Pinheiro Silva Carlos Henrique Mendes Malhado Raimundo Martins Filho Danielle Maria Machado Ribeiro Azevêdo José Elivalto Guimarães Campelo |
| author_sort | Diego Helcias Cavalcante |
| collection | DOAJ |
| description | Different polynomial functions were tested for mean trajectory modeling with different residual variance structures. A total of 15,148 weight records of 3,115 Nellore Mocho cattle with ages between 1 and 660 days, raised in northern Brazil. First, the mean trajectory of cattle growth curve was fitted by a fixed regression using orthogonal polynomials with orders ranging from two to seven. Analyses were performed using the least-squares method, disregarding animal and/ or maternal random effects. Then, the best model was evaluated using different residual variance structures and homogeneous and heterogeneous classes. We considered as fixed effects those of groups of contemporary and of dam age at birth (as linear and quadratic covariate). The random model part included animal and maternal effects (direct genetic and permanent environments). We concluded that the estimates of variance components and genetic parameters were affected by both fixed regression curve polynomial order and residual variance structure. Moreover, random regression model considering an order-four polynomial function with a fixed curve and six-class residual variance showed better fits. |
| first_indexed | 2024-04-10T20:42:11Z |
| format | Article |
| id | doaj.art-fddd3f1f5ddb4db185215e3cf8a2b794 |
| institution | Directory Open Access Journal |
| issn | 1676-546X 1679-0359 |
| language | English |
| last_indexed | 2024-04-10T20:42:11Z |
| publishDate | 2020-03-01 |
| publisher | Universidade Estadual de Londrina |
| record_format | Article |
| series | Semina: Ciências Agrárias |
| spelling | doaj.art-fddd3f1f5ddb4db185215e3cf8a2b7942023-01-24T19:41:04ZengUniversidade Estadual de LondrinaSemina: Ciências Agrárias1676-546X1679-03592020-03-0141210.5433/1679-0359.2020v41n2p545Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modelingDiego Helcias Cavalcante0Severino Cavalcante Sousa Júnior1Luciano Pinheiro Silva2Carlos Henrique Mendes Malhado3Raimundo Martins Filho4Danielle Maria Machado Ribeiro Azevêdo5José Elivalto Guimarães Campelo6Universidade Federal do PiauíUniversidade Federal do PiauíUniversidade Federal do CearáUniversidade Estadual do Sudoeste da BahiaUniversidade Federal do CaririEmpresa de Brasileira de Pesquisa AgropecuáriaUniversidade Federal do PiauíDifferent polynomial functions were tested for mean trajectory modeling with different residual variance structures. A total of 15,148 weight records of 3,115 Nellore Mocho cattle with ages between 1 and 660 days, raised in northern Brazil. First, the mean trajectory of cattle growth curve was fitted by a fixed regression using orthogonal polynomials with orders ranging from two to seven. Analyses were performed using the least-squares method, disregarding animal and/ or maternal random effects. Then, the best model was evaluated using different residual variance structures and homogeneous and heterogeneous classes. We considered as fixed effects those of groups of contemporary and of dam age at birth (as linear and quadratic covariate). The random model part included animal and maternal effects (direct genetic and permanent environments). We concluded that the estimates of variance components and genetic parameters were affected by both fixed regression curve polynomial order and residual variance structure. Moreover, random regression model considering an order-four polynomial function with a fixed curve and six-class residual variance showed better fits.https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36531Mean curveGenetic parametersLinear modelsRandom regressionResidual modeling. |
| spellingShingle | Diego Helcias Cavalcante Severino Cavalcante Sousa Júnior Luciano Pinheiro Silva Carlos Henrique Mendes Malhado Raimundo Martins Filho Danielle Maria Machado Ribeiro Azevêdo José Elivalto Guimarães Campelo Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling Semina: Ciências Agrárias Mean curve Genetic parameters Linear models Random regression Residual modeling. |
| title | Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling |
| title_full | Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling |
| title_fullStr | Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling |
| title_full_unstemmed | Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling |
| title_short | Fitting of fixed regression curves with different residual variance structures for Nellore cattle growth modeling |
| title_sort | fitting of fixed regression curves with different residual variance structures for nellore cattle growth modeling |
| topic | Mean curve Genetic parameters Linear models Random regression Residual modeling. |
| url | https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36531 |
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