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Registro Completo |
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
02/08/2022 |
Data da última atualização: |
10/11/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
ROCHA, R. de F. B.; OTTO, P. I.; SILVA, M. V. G. B.; MARTINS, M. F.; MACHADO, M. A.; VERONEZE, R.; LEANDRO, F. D.; PEREIRA, S. M.; GUIMARÃES, S. E. F.; PANETTO, J. C. do C. |
Afiliação: |
RENATA DE FATIMA BRETANHA ROCHA, Universidade Federal de Viçosa; PAMELA ITAJARA OTTO, Universidade Federal de Santa Maria; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; MARTA FONSECA MARTINS, CNPGL; MARCO ANTONIO MACHADO, CNPGL; RENATA VERONEZE, Universidade Federal de Viçosa; FELIPE DAMASCENO LEANDRO, Universidade José do Rosário Vellano - UNIFENAS; STELA NAETZOLD PEREIRA, Universidade Federal de Santa Maria; SIMONE ELIZA FACIONI GUIMARÃES, Universidade Federal de Viçosa; JOAO CLAUDIO DO CARMO PANETTO, CNPGL. |
Título: |
Repeatability and random regression models to estimate genetic parameters for oocyte and embryo production in the Gir breed. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Animal Production Science, v. 62, n. 17, p. 1661-1670, 2022. |
DOI: |
https://doi.org/10.1071/AN21588 |
Idioma: |
Inglês |
Conteúdo: |
CONTEXT - Greater production of oocytes and embryos from Gir donors contributes to greater fertility and genetic progress. AIMS - This study aimed to obtain genetic parameters for oocyte and embryo production in the Gir breed. METHODS - Repeatability and random regression models were applied to data consisting of 17 526 Ovum Pick Up observations from 1641 Gir donors from five different herds. Single and multi-trait analyses were carried out with the application of both models for the traits: number of viable oocytes, number of total oocytes and number of embryos, using the BLUPF90 family programs. Legendre polynomials of second order were used in the random regression model. KEY RESULTS - Considering the repeatability model, additive genetic variance ranged from 0.06 to 0.13 and permanent environment variance ranged from 0.05 to 0.08 for all evaluated traits. Residual variance ranged from 0.30 to 0.45. Heritability estimates were 0.10 for number of embryos, 0.24 for total oocytes, and 0.25 for viable oocytes. Repeatability estimates were moderate, ranging from 0.20 to 0.40, and genetic correlation estimates were always above 0.80. Phenotypic correlation was high only between viable and total oocytes (0.95), and moderate in the other cases. Random regression model results were consistent with those from the repeatability model. The heritability values remained similar throughout the donors? ages, with moderate values for viable and total oocytes, and low values for number of embryos. Genetic correlations among ages for each trait were moderate to high. Also, the genetic correlations between traits within each age were high, with values always above 0.7. Conclusions - Selection of Gir donors for total oocyte production at any time, between 1 and 16 years of age, might lead to an increase in the number of viable oocytes and embryos obtained, but it?s preferable at younger ages to hasten genetic progress. Repeatability models could be the best method, as they require less computational effort when compared to the random regression models and the parameter estimates do not vary substantially throughout different ages of the donor. IMPLICATIONS - The use of repeatability models to estimate genetic parameters of oocytes and embryos resulted in similar results compared to random regression models. MenosCONTEXT - Greater production of oocytes and embryos from Gir donors contributes to greater fertility and genetic progress. AIMS - This study aimed to obtain genetic parameters for oocyte and embryo production in the Gir breed. METHODS - Repeatability and random regression models were applied to data consisting of 17 526 Ovum Pick Up observations from 1641 Gir donors from five different herds. Single and multi-trait analyses were carried out with the application of both models for the traits: number of viable oocytes, number of total oocytes and number of embryos, using the BLUPF90 family programs. Legendre polynomials of second order were used in the random regression model. KEY RESULTS - Considering the repeatability model, additive genetic variance ranged from 0.06 to 0.13 and permanent environment variance ranged from 0.05 to 0.08 for all evaluated traits. Residual variance ranged from 0.30 to 0.45. Heritability estimates were 0.10 for number of embryos, 0.24 for total oocytes, and 0.25 for viable oocytes. Repeatability estimates were moderate, ranging from 0.20 to 0.40, and genetic correlation estimates were always above 0.80. Phenotypic correlation was high only between viable and total oocytes (0.95), and moderate in the other cases. Random regression model results were consistent with those from the repeatability model. The heritability values remained similar throughout the donors? ages, with moderate values for viable and total oocytes, and low values for number of ... Mostrar Tudo |
Palavras-Chave: |
Correlação fenotípica; Correlação genética; Herdabilidade; Ovum pick up; Repetibilidade. |
Thesagro: |
Gado Leiteiro; Gado Zebu; Ovulo; Reprodução Animal. |
Thesaurus Nal: |
Animal reproduction; Dairy cattle; Genetic correlation; Heritability; Phenotypic correlation; Repeatability; Zebu breeds. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
Marc: |
LEADER 03620naa a2200433 a 4500 001 2145139 005 2022-11-10 008 2022 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1071/AN21588$2DOI 100 1 $aROCHA, R. de F. B. 245 $aRepeatability and random regression models to estimate genetic parameters for oocyte and embryo production in the Gir breed.$h[electronic resource] 260 $c2022 520 $aCONTEXT - Greater production of oocytes and embryos from Gir donors contributes to greater fertility and genetic progress. AIMS - This study aimed to obtain genetic parameters for oocyte and embryo production in the Gir breed. METHODS - Repeatability and random regression models were applied to data consisting of 17 526 Ovum Pick Up observations from 1641 Gir donors from five different herds. Single and multi-trait analyses were carried out with the application of both models for the traits: number of viable oocytes, number of total oocytes and number of embryos, using the BLUPF90 family programs. Legendre polynomials of second order were used in the random regression model. KEY RESULTS - Considering the repeatability model, additive genetic variance ranged from 0.06 to 0.13 and permanent environment variance ranged from 0.05 to 0.08 for all evaluated traits. Residual variance ranged from 0.30 to 0.45. Heritability estimates were 0.10 for number of embryos, 0.24 for total oocytes, and 0.25 for viable oocytes. Repeatability estimates were moderate, ranging from 0.20 to 0.40, and genetic correlation estimates were always above 0.80. Phenotypic correlation was high only between viable and total oocytes (0.95), and moderate in the other cases. Random regression model results were consistent with those from the repeatability model. The heritability values remained similar throughout the donors? ages, with moderate values for viable and total oocytes, and low values for number of embryos. Genetic correlations among ages for each trait were moderate to high. Also, the genetic correlations between traits within each age were high, with values always above 0.7. Conclusions - Selection of Gir donors for total oocyte production at any time, between 1 and 16 years of age, might lead to an increase in the number of viable oocytes and embryos obtained, but it?s preferable at younger ages to hasten genetic progress. Repeatability models could be the best method, as they require less computational effort when compared to the random regression models and the parameter estimates do not vary substantially throughout different ages of the donor. IMPLICATIONS - The use of repeatability models to estimate genetic parameters of oocytes and embryos resulted in similar results compared to random regression models. 650 $aAnimal reproduction 650 $aDairy cattle 650 $aGenetic correlation 650 $aHeritability 650 $aPhenotypic correlation 650 $aRepeatability 650 $aZebu breeds 650 $aGado Leiteiro 650 $aGado Zebu 650 $aOvulo 650 $aReprodução Animal 653 $aCorrelação fenotípica 653 $aCorrelação genética 653 $aHerdabilidade 653 $aOvum pick up 653 $aRepetibilidade 700 1 $aOTTO, P. I. 700 1 $aSILVA, M. V. G. B. 700 1 $aMARTINS, M. F. 700 1 $aMACHADO, M. A. 700 1 $aVERONEZE, R. 700 1 $aLEANDRO, F. D. 700 1 $aPEREIRA, S. M. 700 1 $aGUIMARÃES, S. E. F. 700 1 $aPANETTO, J. C. do C. 773 $tAnimal Production Science$gv. 62, n. 17, p. 1661-1670, 2022.
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Embrapa Gado de Leite (CNPGL) |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Gado de Corte; Embrapa Mandioca e Fruticultura; Embrapa Milho e Sorgo; Embrapa Pantanal; Embrapa Territorial. |
Data corrente: |
19/09/2023 |
Data da última atualização: |
19/09/2023 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
TERNES, S.; MOURA, M. F.; SOUZA, K. X. S. de; VAZ, G. J.; OLIVEIRA, S. R. de M.; HIGA, R. H.; LIMA, H. P. de; TAKEMURA, C. M.; COELHO, E. A.; BARBOSA, F. F. L.; VISOLI, M. C.; MENEZES, G. R. de O.; SILVA, L. O. C. da; SANTOS, S. A.; MASSRUHÁ, S. M. F. S.; ABREU, U. G. P. de; SORIANO, B. M. A.; SALIS, S. M.; OLIVEIRA, M. D. de; TOMAS, W. M. |
Afiliação: |
SONIA TERNES, CNPTIA; MARIA FERNANDA MOURA, CNPTIA; KLEBER XAVIER SAMPAIO DE SOUZA, CNPTIA; GLAUBER JOSE VAZ, CNPTIA; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; ROBERTO HIROSHI HIGA, CNPTIA; HELANO POVOAS DE LIMA, CNPTIA; CELINA MAKI TAKEMURA, CNPM; ENILDA ALVES COELHO, CNPMS; FRANCISCO FERRAZ LARANJEIRA BARBOSA, CNPMF; MARCOS CEZAR VISOLI, CNPTIA; GILBERTO ROMEIRO DE OLIVEIRA MENEZES, CNPGC; LUIZ OTAVIO CAMPOS DA SILVA, CNPGC; SANDRA APARECIDA SANTOS, CPAP; SILVIA MARIA FONSECA S MASSRUHA, CNPTIA; URBANO GOMES PINTO DE ABREU, CPAP; BALBINA MARIA ARAUJO SORIANO, CPAP; SUZANA MARIA DE SALIS, CPAP; MARCIA DIVINA DE OLIVEIRA, CPAP; WALFRIDO MORAES TOMAS, CPAP. |
Título: |
Scientific computing in agriculture. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (ed.). Digital agriculture: research, development and innovation in production chains. Brasília, DF: Embrapa, 2023. cap. 5, p. 92-108. |
ISBN: |
978-65-89957-72-0 |
Idioma: |
Inglês |
Notas: |
Na publicação: Enilda Coelho, Suzana Maria Salis. |
Conteúdo: |
Introduction. Artificial intelligence: Automatic soil classification; SiBCS-based expert system; Intelligent soil classification system. Text mining in technical-scientific publications. Mathematical and statistical modeling: Modeling the citrus HLB dispersion dynamics; Genetic evaluation of livestock; Sustainable Pantanal Farm; The Sustainable Pantanal Farm software. Final considerations. |
Palavras-Chave: |
Agricultura digital; Aprendizado de máquina; Computação científica; Digital agriculture; Inteligência artificial; Machine learning; Mineração de textos; Modelagem matemática; Text mining; Transformação digital na agricultura. |
Thesagro: |
Agricultura; Análise Estatística. |
Thesaurus NAL: |
Agriculture; Artificial intelligence; Mathematical models; Statistical analysis. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1156747/1/Digital-agriculture-2023-cap5.pdf
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Marc: |
LEADER 02241naa a2200565 a 4500 001 2156747 005 2023-09-19 008 2023 bl uuuu u00u1 u #d 020 $a978-65-89957-72-0 100 1 $aTERNES, S. 245 $aScientific computing in agriculture.$h[electronic resource] 260 $c2023 500 $aNa publicação: Enilda Coelho, Suzana Maria Salis. 520 $aIntroduction. Artificial intelligence: Automatic soil classification; SiBCS-based expert system; Intelligent soil classification system. Text mining in technical-scientific publications. Mathematical and statistical modeling: Modeling the citrus HLB dispersion dynamics; Genetic evaluation of livestock; Sustainable Pantanal Farm; The Sustainable Pantanal Farm software. Final considerations. 650 $aAgriculture 650 $aArtificial intelligence 650 $aMathematical models 650 $aStatistical analysis 650 $aAgricultura 650 $aAnálise Estatística 653 $aAgricultura digital 653 $aAprendizado de máquina 653 $aComputação científica 653 $aDigital agriculture 653 $aInteligência artificial 653 $aMachine learning 653 $aMineração de textos 653 $aModelagem matemática 653 $aText mining 653 $aTransformação digital na agricultura 700 1 $aMOURA, M. F. 700 1 $aSOUZA, K. X. S. de 700 1 $aVAZ, G. J. 700 1 $aOLIVEIRA, S. R. de M. 700 1 $aHIGA, R. H. 700 1 $aLIMA, H. P. de 700 1 $aTAKEMURA, C. M. 700 1 $aCOELHO, E. A. 700 1 $aBARBOSA, F. F. L. 700 1 $aVISOLI, M. C. 700 1 $aMENEZES, G. R. de O. 700 1 $aSILVA, L. O. C. da 700 1 $aSANTOS, S. A. 700 1 $aMASSRUHÁ, S. M. F. S. 700 1 $aABREU, U. G. P. de 700 1 $aSORIANO, B. M. A. 700 1 $aSALIS, S. M. 700 1 $aOLIVEIRA, M. D. de 700 1 $aTOMAS, W. M. 773 $tIn: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (ed.). Digital agriculture: research, development and innovation in production chains. Brasília, DF: Embrapa, 2023. cap. 5, p. 92-108.
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