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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
20/06/2015 |
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Actualizado : |
20/06/2015 |
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Tipo de producción científica : |
Informes Agroclimáticos |
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Autor : |
GIMENEZ, A.; CASTAÑO, J.; FUREST, J.; CAL, A.; TISCORNIA, G.; SCHIAVI, C. |
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Afiliación : |
AGUSTIN EDUARDO GIMENEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE PEDRO CASTAÑO SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE MARIA FUREST CROCCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS IGNACIO SCHIAVI RAMPELBERG, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Informe Agroclimático 2014 - Situación a Febrero. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2014. |
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Páginas : |
4 p. |
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Idioma : |
Español |
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Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICOS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; 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. |
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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. |
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Asunto categoría : |
P40 Meteorología y climatología |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/4732/1/Inf.Agr.-febrero-2014.pdf
http://www.inia.uy/Publicaciones/Paginas/Informe-agroclimatico-2014---Situacion-Febrero.aspx
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Marc : |
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100 1 $aGIMENEZ, A.
245 $aInforme Agroclimático 2014 - Situación a Febrero.$h[electronic resource]
260 $aMontevideo (Uruguay): INIA$c2014
300 $a4 p.
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
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650 $aPRONOSTICO DEL TIEMPO
650 $aSENSORES
650 $aSISTEMAS
650 $aSISTEMAS DE INFORMACION
650 $aSUELO
650 $aTEMPERATURA
650 $aTERMOMETROS
653 $aAGROCLIMA
653 $aAGROCLIMATOLOGÍA
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
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653 $aMAXIMA
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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 $aCASTAÑO, J.
700 1 $aFUREST, J.
700 1 $aCAL, A.
700 1 $aTISCORNIA, G.
700 1 $aSCHIAVI, C.
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Registro original : |
INIA Las Brujas (LB) |
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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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