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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha : |
21/02/2014 |
Actualizado : |
29/11/2022 |
Tipo de producción científica : |
Poster |
Autor : |
SCAGLIA, G.; TERRA, J.A.; GARCÍA PRÉCHAC, F. |
Afiliación : |
GUILLERMO SCAGLIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
A comparison of four land use intensities for forage production in eastern Uruguay. Poster. |
Fecha de publicación : |
1998 |
Fuente / Imprenta : |
ln: The world conference on Animal Production, 8, 1998, Seoul, Korea Contributed papers: proceedings. Seoul (Korea): Seoul National University, 1998. |
Páginas : |
vol 2. p. 490-491 |
ISBN : |
89-950054-9-1 93520 y 89-950054-7-5 93520 |
Idioma : |
Inglés |
Thesagro : |
ANIMALES; PASTOREO; PRODUCCION INTENSIVA; SUPLEMENTACION; URUGUAY. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16886/1/Scaglia-Terra.pdf
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Marc : |
LEADER 00687nam a2200193 a 4500 001 1032271 005 2022-11-29 008 1998 bl uuuu u00u1 u #d 100 1 $aSCAGLIA, G. 245 $aA comparison of four land use intensities for forage production in eastern Uruguay. Poster.$h[electronic resource] 260 $aln: The world conference on Animal Production, 8, 1998, Seoul, Korea Contributed papers: proceedings. Seoul (Korea): Seoul National University$c1998 300 $avol 2. p. 490-491 650 $aANIMALES 650 $aPASTOREO 650 $aPRODUCCION INTENSIVA 650 $aSUPLEMENTACION 650 $aURUGUAY 700 1 $aTERRA, J.A. 700 1 $aGARCÍA PRÉCHAC, F.
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Registro original : |
INIA Treinta y Tres (TT) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
12/08/2016 |
Actualizado : |
02/01/2017 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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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