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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
10/05/2021 |
Actualizado : |
10/05/2021 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
ROMAN, L.; BANCHERO, G.; MORALES-PIÑEYRUA, J.; ACOSTA, Y.; MENDOZA, A.; PLA, M.; LA MANNA, A. |
Afiliación : |
LORENA CAROLINA ROMAN GAY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GEORGGET ELIZABETH BANCHERO HUNZIKER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JESSICA TATIANA MORALES PIÑEYRUA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; YAMANDU MARTIN ACOSTA AZPIROZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO FRANCISCO MENDOZA AGUIAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCELO PLA TEJERA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO FRANCISCO LA MANNA ALONSO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Efecto del acceso a sombra artificial durante el periodo seco de vacas lecheras.[Effect of access to artificial shade during the dry period in dairy cows]. |
Complemento del título : |
SP 52. |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
In: Congreso Argentino de Producción Animal, 38., 2015. Resúmenes. Santa Rosa, La Pampa, AR: ASAS/AAPA, 2015. |
Páginas : |
p. 134. |
Serie : |
(Revista Argentina de Producción Animal; 2015; 35; supl.1). |
Idioma : |
Español |
Contenido : |
Conclusiones: El acceso a sombra artificial durante los últimos 60 días de gestación no afecto la TR, el PV y la CC animal, mientras
que mostró efecto sobre la FR a las 13:30 y 17 horas. Se observó efecto sobre el consumo de alimento preparto sin
afectar el consumo posparto y el consumo de agua. |
Palabras claves : |
PERÍODO SECO. |
Thesagro : |
ESTRES TERMICO; LECHERÍA; VACA LECHERA. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15574/1/Revista-Argentina-de-Produccion-Animal-2015.v.35.supl.1.-SP-52.pdf
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Marc : |
LEADER 01182nam a2200253 a 4500 001 1062052 005 2021-05-10 008 2015 bl uuuu u01u1 u #d 100 1 $aROMAN, L. 245 $aEfecto del acceso a sombra artificial durante el periodo seco de vacas lecheras.[Effect of access to artificial shade during the dry period in dairy cows].$h[electronic resource] 260 $aIn: Congreso Argentino de Producción Animal, 38., 2015. Resúmenes. Santa Rosa, La Pampa, AR: ASAS/AAPA$c2015 300 $ap. 134. 490 $a(Revista Argentina de Producción Animal; 2015; 35; supl.1). 520 $aConclusiones: El acceso a sombra artificial durante los últimos 60 días de gestación no afecto la TR, el PV y la CC animal, mientras que mostró efecto sobre la FR a las 13:30 y 17 horas. Se observó efecto sobre el consumo de alimento preparto sin afectar el consumo posparto y el consumo de agua. 650 $aESTRES TERMICO 650 $aLECHERÍA 650 $aVACA LECHERA 653 $aPERÍODO SECO 700 1 $aBANCHERO, G. 700 1 $aMORALES-PIÑEYRUA, J. 700 1 $aACOSTA, Y. 700 1 $aMENDOZA, A. 700 1 $aPLA, M. 700 1 $aLA MANNA, A.
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Registro original : |
INIA La Estanzuela (LE) |
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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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