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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
12/08/2016 |
Actualizado : |
02/01/2017 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. |
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. |
Título : |
Modeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
Crop Science, 2016, v. 56, p. 1-15. OPEN ACCESS. |
DOI : |
http://dx.doi.org/10.2135/cropsci2015.04.0207 |
Idioma : |
Inglés |
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 |
Palabras claves : |
GENOMIC SELECTION; WHEAT. |
Thesagro : |
TRIGO. |
Asunto categoría : |
-- |
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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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
10/09/2020 |
Actualizado : |
10/09/2020 |
Tipo de producción científica : |
Informes Agroclimáticos |
Autor : |
INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); GRAS |
Afiliación : |
UNIDAD DE AGROCLIMA Y SISTEMAS DE INFORMACIÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Informe agroclimático 2020- Situación a Agosto. |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2020. |
Páginas : |
5 p. |
Serie : |
(Informe Agroclimático; Año 15, No.8) |
Idioma : |
Español |
Notas : |
Equipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García. |
Contenido : |
Contenido. Síntesis de la Situación Agroclimática de Agosto-- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia -- Índice de Vegetación (IVDN) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Índice de Bienestar Hídrico (IBH) -- Agua No Retenida (ANR) -- Perspectivas Climáticas Set-Oct-Nov elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Pronóstico de Don en trigo y condiciones favorables para el
desarrollo de fusariosis de espiga en trigo. Link directo: www.inia.uy/gras/Alertas-y-herramientas/Pron%C3%B3stico-DON-para-trigo- |
Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; AGTECH; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICAS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INFORME AGROCLIMÁTICO 2020; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; EVAPOTRANSPIRACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
Asunto categoría : |
P40 Meteorología y climatología |
URL : |
http://www.inia.uy/Publicaciones/Documentos%20compartidos/Informe%20agroclimatico%20INIA-GRAS%20Agosto%20de%202020.pdf
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Marc : |
LEADER 02938nam a2200817 a 4500 001 1061307 005 2020-09-10 008 2020 bl uuuu u0uu1 u #d 100 1 $aINIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA) 245 $aInforme agroclimático 2020- Situación a Agosto.$h[electronic resource] 260 $aMontevideo (Uruguay): INIA$c2020 300 $a5 p. 490 $a(Informe Agroclimático; Año 15, No.8) 500 $aEquipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García. 520 $aContenido. Síntesis de la Situación Agroclimática de Agosto-- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia -- Índice de Vegetación (IVDN) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Índice de Bienestar Hídrico (IBH) -- Agua No Retenida (ANR) -- Perspectivas Climáticas Set-Oct-Nov elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Pronóstico de Don en trigo y condiciones favorables para el desarrollo de fusariosis de espiga en trigo. Link directo: www.inia.uy/gras/Alertas-y-herramientas/Pron%C3%B3stico-DON-para-trigo- 650 $aAGROCLIMATOLOGIA 650 $aCAMBIO CLIMATICO 650 $aCLIMA 650 $aCLIMATOLOGIA 650 $aESTACIONES METEOROLOGICAS 650 $aESTRES HIDRICO 650 $aEVAPORACION 650 $aEVAPOTRANSPIRACION 650 $aHUMEDAD 650 $aHUMEDAD RELATIVA 650 $aLLUVIA 650 $aMETEOROLOGIA 650 $aPERSPECTIVAS 650 $aPLUVIOMETROS 650 $aPRONOSTICO DEL TIEMPO 650 $aSENSORES 650 $aSISTEMAS 650 $aSISTEMAS DE INFORMACION 650 $aSUELO 650 $aTEMPERATURA 650 $aTERMOMETROS 653 $aAGROCLIMA 653 $aAGROCLIMATOLOGÍA 653 $aAGTECH 653 $aBOLETIN AGROCLIMÁTICO 653 $aCARACTERIZACIÓN AGROCLIMÁTICA 653 $aDIRECCION VIENTO 653 $aESTACIONES AGROMETEOROLOGICAS 653 $aESTACIONES AUTOMATICAS 653 $aESTACIONES INIA 653 $aESTADO DEL TIEMPO 653 $aESTRÉS HÍDRICO 653 $aGRAFICAS AGROCLIMATICAS 653 $aGRAS 653 $aHELIOFANOGRAFO 653 $aINFORMACION SATELITAL 653 $aINFORME AGROCLIMÁTICO 2020 653 $aINUNDACIONES 653 $aLLUVIAS DIARIAS 653 $aMAXIMA 653 $aMEDIA 653 $aMINIMA 653 $aPANEL SOLAR 653 $aPERSPECTIVAS CLIMATICAS 653 $aPLUVIOMETRO 653 $aPRECIPITACION NACIONAL 653 $aPREVENCION HELADAS 653 $aPRONOSTICO 653 $aSENSOR 653 $aSIMETRICO 653 $aTANQUE A 653 $aTERMOCUPLAS 653 $aTERMOHIDROGRAFO 653 $aVARIABLES AGROCLIMATICAS 653 $aVELETA 700 1 $aGRAS
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