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 | Acceso al texto completo restringido a Biblioteca INIA Tacuarembó. Por información adicional contacte bibliotb@tb.inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
23/09/2020 |
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Tipo de producción científica : |
Documentos |
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Autor : |
MONTOSSI, F.; DE BARBIERI, I.; CIAPPESONI, G.; RAMOS, Z.; SOARES DE LIMA, J.M.; MEDEROS, A. |
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Afiliación : |
FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ZULLY MARGOT RAMOS ALVEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AMERICA ESTHER MEDEROS SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Tendencias en el Uruguay ovino y la creación y productos del Consorcio Regional de lana ultrafinas. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
Asociacción Argentina Criadores de Merino, Anuario 2012, p. 42-55. |
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Idioma : |
Español |
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Contenido : |
Los cambios globales observados en la producción ovina: Análisis de los casos de Nueva Zelanda y Australia. Recordando enfoques y propuestas del INIA. Algunas cifras para compartir sobre la aplicación de las tecnologías propuestas por INIA en el sub sector lanero. ¿Se puede producir sustentablemente lanas ultrafinas a cielo abierto en el Uruguay?: El nacimiento del CRILU. ¿Cómo nos organizamos? ¿Cómo nos financiamos? Dos años de vida del CRILU: Resumen de avances logrados. Reflexiones Finales. |
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Palabras claves : |
AGRONEGOCIOS; CRILU; LANAS. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 01148naa a2200217 a 4500
001 1027072
005 2020-09-23
008 2012 bl uuuu u00u1 u #d
100 1 $aMONTOSSI, F.
245 $aTendencias en el Uruguay ovino y la creación y productos del Consorcio Regional de lana ultrafinas.$h[electronic resource]
260 $c2012
520 $aLos cambios globales observados en la producción ovina: Análisis de los casos de Nueva Zelanda y Australia. Recordando enfoques y propuestas del INIA. Algunas cifras para compartir sobre la aplicación de las tecnologías propuestas por INIA en el sub sector lanero. ¿Se puede producir sustentablemente lanas ultrafinas a cielo abierto en el Uruguay?: El nacimiento del CRILU. ¿Cómo nos organizamos? ¿Cómo nos financiamos? Dos años de vida del CRILU: Resumen de avances logrados. Reflexiones Finales.
653 $aAGRONEGOCIOS
653 $aCRILU
653 $aLANAS
700 1 $aDE BARBIERI, I.
700 1 $aCIAPPESONI, G.
700 1 $aRAMOS, Z.
700 1 $aSOARES DE LIMA, J.M.
700 1 $aMEDEROS, A.
773 $tAsociacción Argentina Criadores de Merino, Anuario 2012, p. 42-55.
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Registro original : |
INIA Tacuarembó (TBO) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
12/08/2016 |
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Actualizado : |
02/01/2017 |
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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 : |
LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO, Universidad de la República (UdelaR)/ Facultad de Agronomía; PABLO GONZÁLEZ BARRIOS, Universidad de la República (UdelaR)/ Facultad de Agronomía; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCIA GUTIÉRREZ, Universidad de la República (UdelaR)/ Facultad de Agronomía. |
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Título : |
Modeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Crop Science, 2016, v. 56, p. 1-15. OPEN ACCESS. |
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DOI : |
http://dx.doi.org/10.2135/cropsci2015.04.0207 |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT.
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.
© 2016. Crop Science Society of America, Inc. MenosABSTRACT.
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ab... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; WHEAT. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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URL : |
http://dx.doi.org/10.2135/cropsci2015.04.0207
http://www.ainfo.inia.uy/digital/bitstream/item/5875/1/Lado-B.-2016.-Crop-Science.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/5876/1/Lado-B.-2016.-Crop-Science-supplement.pdf
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Marc : |
LEADER 02297naa a2200217 a 4500
001 1055260
005 2017-01-02
008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.2135/cropsci2015.04.0207$2DOI
100 1 $aLADO, B.
245 $aModeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program.$h[electronic resource]
260 $c2016
520 $aABSTRACT. Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context. © 2016. Crop Science Society of America, Inc.
650 $aTRIGO
653 $aGENOMIC SELECTION
653 $aWHEAT
700 1 $aGONZÁLEZ BARRIOS, P.
700 1 $aQUINCKE, M.
700 1 $aSILVA, P.
700 1 $aGUTIÉRREZ, L.
773 $tCrop Science, 2016$gv. 56, p. 1-15. OPEN ACCESS.
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