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Registro Completo |
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Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
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Data corrente: |
10/02/1998 |
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Data da última atualização: |
27/12/2013 |
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Autoria: |
STEHLING, R. N.; SOUZA, H. M. de. |
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Título: |
Leite de cabra: aspectos nutricionais e mercado. |
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Ano de publicação: |
1987 |
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Fonte/Imprenta: |
Informe Agropecuario, v.13, n.146, p.54-55, 1987. |
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Idioma: |
Português |
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Conteúdo: |
No presente artigo, pretende-se mostrar alguns aspectos nutricionais do leite de cabra, este nobre alimento, que muito pode contribuir para resolver os problemas da subnutricao e, sobretudo, os de ma-nutricao em paises em desenvolvimento. |
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Palavras-Chave: |
Composicao; Composition; Goat. |
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Thesagro: |
Caprino; Leite de Cabra; Valor Nutritivo. |
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Thesaurus Nal: |
milk; nutritive value. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00809naa a2200229 a 4500
001 1518232
005 2013-12-27
008 1987 bl uuuu u00u1 u #d
100 1 $aSTEHLING, R. N.
245 $aLeite de cabra$baspectos nutricionais e mercado.
260 $c1987
520 $aNo presente artigo, pretende-se mostrar alguns aspectos nutricionais do leite de cabra, este nobre alimento, que muito pode contribuir para resolver os problemas da subnutricao e, sobretudo, os de ma-nutricao em paises em desenvolvimento.
650 $amilk
650 $anutritive value
650 $aCaprino
650 $aLeite de Cabra
650 $aValor Nutritivo
653 $aComposicao
653 $aComposition
653 $aGoat
700 1 $aSOUZA, H. M. de
773 $tInforme Agropecuario$gv.13, n.146, p.54-55, 1987.
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Registro original: |
Embrapa Caprinos e Ovinos (CNPC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
29/05/2022 |
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Data da última atualização: |
12/04/2024 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
CROSSA, J.; MONTESINOS-LÓPEZ, O. A.; PÉREZ-RODRÍGUEZ, P.; COSTA-NETO, G.; FRITSCHE-NETO, R.; ORTIZ, R.; MARTINI, J. W. R.; LILLEMO, M.; MONTESINOS-LÓPEZ, A.; JARQUIN, D.; BRESEGHELLO, F.; CUEVAS, J.; RINCENT, R. |
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Afiliação: |
JOSE CROSSA, CIMMYT; OSVAL ANTONIO MONTESINOS-LOPEZ, UNIVERSIDAD DE COLIMA, México; PAULINO PEREZ-RODRIGUEZ, COLEGIO DE POSTGRADUADOS, Montecillos-Mexico; GERMANO COSTA-NETO, ESALQ; ROBERTO FRITSCHE-NETO, ESALQ; RODOMIRO ORTIZ, SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES, Alnarp-Sweden; JOHANNES W. R. MARTINI, CIMMYT; MORTEN LILLEMO, NORWEGIAN UNIVERSITY OF LIFE SCIENCES, Norway; ABELARDO MONTESINOS-LOPEZ, CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS, Guanajuato-Mexico; DIEGO JARQUIN, UNIVERSITY OF NEBRASKA, Lincoln-NE; FLAVIO BRESEGHELLO, CNPAF; JAIME CUEVAS, UNIVERSIDAD DE QUINTANA ROO, Quintana Roo-Mexico; RENAUD RINCENT, INRAE, Clermont-Ferrand-France. |
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Título: |
Genome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: AHMADI, N.; BARTHOLOME, J. (ed.). Genomic prediction of complex traits: methods and protocols. New York: Humana Press, 2022. |
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Páginas: |
p. 245-283. |
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Série: |
(Methods in Molecular Biology). |
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ISBN: |
978-1-0716-2205-6 |
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DOI: |
https://doi.org/10.1007/978-1-0716-2205-6_9 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E. MenosGenomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and... Mostrar Tudo |
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Palavras-Chave: |
Genome-enabled prediction; Genomic selection; Models with G x E interaction. |
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Thesagro: |
Genótipo; Interação Genética; Melhoramento Genético Vegetal. |
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Thesaurus NAL: |
Genome; Genomics; Genotype-environment interaction; Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143533/1/cap9-2022.pdf
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Marc: |
LEADER 03084naa a2200433 a 4500
001 2143533
005 2024-04-12
008 2022 bl uuuu u00u1 u #d
020 $a978-1-0716-2205-6
024 7 $ahttps://doi.org/10.1007/978-1-0716-2205-6_9$2DOI
100 1 $aCROSSA, J.
245 $aGenome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction.$h[electronic resource]
260 $c2022
300 $ap. 245-283.
490 $a(Methods in Molecular Biology).
520 $aGenomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
650 $aGenome
650 $aGenomics
650 $aGenotype-environment interaction
650 $aPlant breeding
650 $aGenótipo
650 $aInteração Genética
650 $aMelhoramento Genético Vegetal
653 $aGenome-enabled prediction
653 $aGenomic selection
653 $aModels with G x E interaction
700 1 $aMONTESINOS-LÓPEZ, O. A.
700 1 $aPÉREZ-RODRÍGUEZ, P.
700 1 $aCOSTA-NETO, G.
700 1 $aFRITSCHE-NETO, R.
700 1 $aORTIZ, R.
700 1 $aMARTINI, J. W. R.
700 1 $aLILLEMO, M.
700 1 $aMONTESINOS-LÓPEZ, A.
700 1 $aJARQUIN, D.
700 1 $aBRESEGHELLO, F.
700 1 $aCUEVAS, J.
700 1 $aRINCENT, R.
773 $tIn: AHMADI, N.; BARTHOLOME, J. (ed.). Genomic prediction of complex traits: methods and protocols. New York: Humana Press, 2022.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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| Registros recuperados : 2 | |
| 1. |  | 5150187, PACKAGING, Cahners Publishing Co., New York-NY
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Agroindústria de Alimentos. |   |
| 2. | | 0904463, NOVOS CADERNOS NAEA, UFPA, Nucleo de Altos Estudos Amazonicos, Belem, PA
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amazônia Oriental. | |
| Registros recuperados : 2 | |
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