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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha : |
03/11/2015 |
Actualizado : |
10/09/2018 |
Tipo de producción científica : |
Documentos |
Autor : |
DÍA DE CAMPO ARROZ, 2000, UNIDAD EXPERIMENTAL PASO DE LA LAGUNA, TREINTA Y TRES, UY. |
Título : |
Arroz: día de campo. Unidad Experimental Paso de la Laguna (UEPL). [Información presentada]. |
Fecha de publicación : |
2000 |
Fuente / Imprenta : |
Treinta y Tres (Uruguay): INIA, 2000 |
Páginas : |
35 p. |
Idioma : |
Español |
Notas : |
Día de campo de arroz correspondientes a la zafra 1999-2000 realizado el 15 de marzo de 2000. |
Palabras claves : |
INIA ZAPATA; MOMENTO DE INUNDACIÓN; UPAG. |
Thesagro : |
APLICACION DE ABONOS; ARROZ; ARROZ ROJO; CLIMATOLOGIA; DENSIDAD DE SIEMBRA; ENFERMEDADES DE LAS PLANTAS; ENSAYOS DE VARIEDADES; ESCARDA; FITOMEJORAMIENTO; FITOPATOLOGIA; HERBICIDAS; INIA TACUARI; MALEZAS; MANEJO DEL CULTIVO; RIEGO. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5104/1/2000-DC-ARROZ-PL-MARZO.pdf
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Marc : |
LEADER 01089nam a2200337 a 4500 001 1053775 005 2018-09-10 008 2000 bl uuuu u0uu1 u #d 100 1 $aDÍA DE CAMPO ARROZ, 2000, UNIDAD EXPERIMENTAL PASO DE LA LAGUNA, TREINTA Y TRES, UY. 245 $aArroz$bdía de campo. Unidad Experimental Paso de la Laguna (UEPL). [Información presentada].$h[electronic resource] 260 $aTreinta y Tres (Uruguay): INIA$c2000 300 $a35 p. 500 $aDía de campo de arroz correspondientes a la zafra 1999-2000 realizado el 15 de marzo de 2000. 650 $aAPLICACION DE ABONOS 650 $aARROZ 650 $aARROZ ROJO 650 $aCLIMATOLOGIA 650 $aDENSIDAD DE SIEMBRA 650 $aENFERMEDADES DE LAS PLANTAS 650 $aENSAYOS DE VARIEDADES 650 $aESCARDA 650 $aFITOMEJORAMIENTO 650 $aFITOPATOLOGIA 650 $aHERBICIDAS 650 $aINIA TACUARI 650 $aMALEZAS 650 $aMANEJO DEL CULTIVO 650 $aRIEGO 653 $aINIA ZAPATA 653 $aMOMENTO DE INUNDACIÓN 653 $aUPAG
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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. |
Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
11/05/2018 |
Actualizado : |
28/05/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. |
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. |
Título : |
Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Crop Science, 2018, 58:1519-1530. |
DOI : |
10.2135/cropsci2017.09.0564 |
Idioma : |
Inglés |
Notas : |
Article history: Accepted on May 09, 2018. Published online June 21, 2018. |
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 |
Palabras claves : |
GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE. |
Thesagro : |
ARROZ; GENOTIPOS; RICE. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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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