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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
21/02/2014 |
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Actualizado : |
10/10/2018 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
SALDAIN, N.E.; DEAMBROSI, E. |
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Afiliación : |
NÉSTOR ELIO SALDAIN CROCCE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ENRIQUE GERMAN DEAMBROSI CHURRUT, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Arroz (Oryza sativa L.) sembrado en agua y convencional con molinate para el control del arroz rojo (Oryza sp.). [Resumen]. |
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Fecha de publicación : |
2003 |
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Fuente / Imprenta : |
ln: Conferencia Internacional de Arroz de Clima Templado, 3., 2003, Punta del Este, Uruguay Resúmenes. Montevideo (Uruguay): ACA; INIA; GMA; FLAR, 2003. |
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Páginas : |
p. 73. |
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Idioma : |
Español |
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Notas : |
"Instituto Nacional de Investigación Agropecuaria, Uruguay (INIA); Asociación de Cultivadores de Arroz (ACA); Gremial de Molinos Arroceros (GMA); Fondo Latinoamericano de Arroz de Riego (FLAR)" |
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Thesagro : |
ARROZ; CONTROL QUIMICO; MALEZAS; ORYZA. |
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Asunto categoría : |
H60 Malezas y escardas |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11409/1/conf-clima-temp-3-p.73.pdf
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Marc : |
LEADER 00848naa a2200193 a 4500
001 1032224
005 2018-10-10
008 2003 bl uuuu u00u1 u #d
100 1 $aSALDAIN, N.E.
245 $aArroz (Oryza sativa L.) sembrado en agua y convencional con molinate para el control del arroz rojo (Oryza sp.). [Resumen].
260 $c2003
300 $ap. 73.
500 $a"Instituto Nacional de Investigación Agropecuaria, Uruguay (INIA); Asociación de Cultivadores de Arroz (ACA); Gremial de Molinos Arroceros (GMA); Fondo Latinoamericano de Arroz de Riego (FLAR)"
650 $aARROZ
650 $aCONTROL QUIMICO
650 $aMALEZAS
650 $aORYZA
700 1 $aDEAMBROSI, E.
773 $tln: Conferencia Internacional de Arroz de Clima Templado, 3., 2003, Punta del Este, Uruguay Resúmenes. Montevideo (Uruguay): ACA; INIA; GMA; FLAR, 2003.
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Registro original : |
INIA Treinta y Tres (TT) |
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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
11/05/2018 |
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Actualizado : |
28/05/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. |
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Afiliación : |
ELIANA MONTEVERDE, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA.; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIERREZ, Department of Agronomy, University of Wisconsin, WI, USA.; SUSAN MCCOUCH, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA. |
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Título : |
Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Crop Science, 2018, 58:1519-1530. |
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DOI : |
10.2135/cropsci2017.09.0564 |
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Idioma : |
Inglés |
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Notas : |
Article history: Accepted on May 09, 2018. Published online June 21, 2018. |
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Contenido : |
ABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs. MenosABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when pr... Presentar Todo |
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Palabras claves : |
GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE. |
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Thesagro : |
ARROZ; GENOTIPOS; RICE. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 02635naa a2200289 a 4500
001 1058574
005 2019-05-28
008 2018 bl uuuu u00u1 u #d
024 7 $a10.2135/cropsci2017.09.0564$2DOI
100 1 $aMONTEVERDE, E.
245 $aMultienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa).$h[electronic resource]
260 $c2018
500 $aArticle history: Accepted on May 09, 2018. Published online June 21, 2018.
520 $aABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs.
650 $aARROZ
650 $aGENOTIPOS
650 $aRICE
653 $aGENOTYPE X ENVIRONMENT INTERACTION
653 $aINTERACCIONES GENOTIPO-AMBIENTE
700 1 $aROSAS, J.E.
700 1 $aBLANCO, P.H.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aBONNECARRERE, V.
700 1 $aQUERO, G.
700 1 $aGUTIERREZ, L.
700 1 $aMCCOUCH, S.
773 $tCrop Science, 2018, 58:1519-1530.
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