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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
21/02/2014 |
Actualizado : |
12/10/2018 |
Tipo de producción científica : |
Documentos |
Autor : |
MAESO, D.; MARTINEZ, A.; FEDERICI, M.; GONCALVEZ, L.; SILVERA, M.; CABRERA, D.; NUÑEZ, S.; WALASEK, W.; GIUNCHEDI, L. |
Afiliación : |
DIEGO CESAR MAESO TOZZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANABEL LEE MARTINEZ BENGOCHEA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA TERESA FEDERICI RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANA LUCIA GONCALVEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIANA SILVERA ORREGO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS DANILO CABRERA BOLOGNA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SATURNINO NUÑEZ BUA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WILMA GLADYS WALASEK UVIEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCIANO GIUNCHEDI. |
Título : |
El decaimiento del peral en Uruguay: generalidades y trabajos experimentales de INIA Las Brujas. |
Fecha de publicación : |
2012 |
Fuente / Imprenta : |
In: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Frutícola. Frutales de pepita. Seminario de actualización técnica. Canelones (UY): INIA Las Brujas, 2012. |
Páginas : |
p. 51-68. |
Serie : |
(INIA Serie Actividades de Difusión; 687) |
ISSN : |
1688-9258 |
Idioma : |
Español |
Contenido : |
El decaimiento del peral, ?pear decline? o ?moria del pero?, es una enfermedad que afecta al cultivo en casi todas las zonas productoras del mundo.
El agente causal es un fitoplasma (Candidatus phytoplasma pyri), organismo relacionado con las bacterias gram positivas (Eubacteria?Mollicute), sin pared celular (por lo que adopta diferentes formas, pleomorfo) que habita en el floema de las plantas afectadas. |
Thesagro : |
FRUTALES DE PEPITA; FRUTICULTURA; PERAL. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11478/1/sad-687-p.51-68.pdf
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Marc : |
LEADER 01350naa a2200289 a 4500 001 1012526 005 2018-10-12 008 2012 bl uuuu u00u1 u #d 022 $a1688-9258 100 1 $aMAESO, D. 245 $aEl decaimiento del peral en Uruguay$bgeneralidades y trabajos experimentales de INIA Las Brujas. 260 $c2012 300 $ap. 51-68. 490 $a(INIA Serie Actividades de Difusión; 687) 520 $aEl decaimiento del peral, ?pear decline? o ?moria del pero?, es una enfermedad que afecta al cultivo en casi todas las zonas productoras del mundo. El agente causal es un fitoplasma (Candidatus phytoplasma pyri), organismo relacionado con las bacterias gram positivas (Eubacteria?Mollicute), sin pared celular (por lo que adopta diferentes formas, pleomorfo) que habita en el floema de las plantas afectadas. 650 $aFRUTALES DE PEPITA 650 $aFRUTICULTURA 650 $aPERAL 700 1 $aMARTINEZ, A. 700 1 $aFEDERICI, M. 700 1 $aGONCALVEZ, L. 700 1 $aSILVERA, M. 700 1 $aCABRERA, D. 700 1 $aNUÑEZ, S. 700 1 $aWALASEK, W. 700 1 $aGIUNCHEDI, L. 773 $tIn: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Frutícola. Frutales de pepita. Seminario de actualización técnica. Canelones (UY): INIA Las Brujas, 2012.
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Registro original : |
INIA Las Brujas (LB) |
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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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