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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
24/06/2013 |
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Data da última atualização: |
15/05/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ESPASANDIN, A. C.; URIOSTE, J. I.; NAYA, H.; ALENCAR, M. M. de. |
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Afiliação: |
A. C. ESPASANDIN, UNESP; J. I. URIOSTE, College of Agriculture, Montevideo, Uruguay; H. NAYA, College of Agriculture, Montevideo, Uruguay; MAURICIO MELLO DE ALENCAR, CPPSE. |
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Título: |
Genotype X production environment interaction for weaning weight in Angus populations of Brazil and Uruguay. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Livestock Science, v. 151, n. 2-3, p. 264-270, feb. 2013. |
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DOI: |
10.1016/J.LIVSCI.2012.11.015 |
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Idioma: |
Inglês |
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Conteúdo: |
he objective of this study was to define production environments by grouping different environmental factors and, consequently, to assess genotype by production environment interactions on weaning weight (WW) in the Angus populations of Brazil and Uruguay. Climatic conditions were represented by monthly temperature means (°C), minimum and maximum temperatures in winter and summer respectively and accumulated rainfall (mm/year). Mode in month of birth and weaning, and calf weight (kg) and age (days) at weaning were used as indicators of management conditions of 33 and 161 herds in 13 and 34 regions in Uruguay and Brazil, respectively. Two approaches were developed: (a) a bi-character analysis of extreme sub-datasets within each environmental factor (bottom and top 33% of regions), (b) three different production environments (including farms from both countries) were defined in a cluster analysis using standardized environmental factors. To identify the variables that influenced the cluster formation, a discriminant analysis was previously carried out. Management (month, age and weight at weaning) and climatic factors (accumulated rainfalls and winter and summer temperatures) were the most important factors in the clustering of farms. Bi or trivariate analyses were performed to estimate heritability and genetic correlations for WW in extreme sub-datasets within environmental factor or between clusters, using MTDFREML software. Heritability estimates of WW in the first approach ranged from 0.27 to 0.54, and genetic correlations between top and bottom sub-datasets within environmental factors, from ?0.29 to 0.70. In the cluster approach, heritabilities were 0.58±0.04 for cluster 1, 0.31±0.01 for Cluster 2 and 0.40±0.02 for Cluster 3. Genetic correlations were 0.27±0.08, 0.32±0.09 and 0.33±0.09, between clusters 1 and 2, 1 and 3, and 2 and 3, respectively. Both approaches suggest the existence of genotype x environment interaction for weaning weight in Angus breed of Brazil and Uruguay. Menoshe objective of this study was to define production environments by grouping different environmental factors and, consequently, to assess genotype by production environment interactions on weaning weight (WW) in the Angus populations of Brazil and Uruguay. Climatic conditions were represented by monthly temperature means (°C), minimum and maximum temperatures in winter and summer respectively and accumulated rainfall (mm/year). Mode in month of birth and weaning, and calf weight (kg) and age (days) at weaning were used as indicators of management conditions of 33 and 161 herds in 13 and 34 regions in Uruguay and Brazil, respectively. Two approaches were developed: (a) a bi-character analysis of extreme sub-datasets within each environmental factor (bottom and top 33% of regions), (b) three different production environments (including farms from both countries) were defined in a cluster analysis using standardized environmental factors. To identify the variables that influenced the cluster formation, a discriminant analysis was previously carried out. Management (month, age and weight at weaning) and climatic factors (accumulated rainfalls and winter and summer temperatures) were the most important factors in the clustering of farms. Bi or trivariate analyses were performed to estimate heritability and genetic correlations for WW in extreme sub-datasets within environmental factor or between clusters, using MTDFREML software. Heritability estimates of WW in the first approach... Mostrar Tudo |
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Palavras-Chave: |
Cluster; Genotype x interaction. |
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Thesaurus Nal: |
beef cattle. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
null
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Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
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