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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
04/01/2018 |
Actualizado : |
30/01/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
GONZALEZ-BARRIOS, P.; CASTRO, M.; PÉREZ, O.; VILARÓ, D.; GUTIÉRREZ, G. |
Afiliación : |
PABLO GONZALEZ-BARRIOS,; MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OSVALDO MARTIN PÉREZ GONZÁLEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DIEGO VILARÓ; LUCÍA GUTIÉRREZ. |
Título : |
Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency. |
Fecha de publicación : |
2017 |
Fuente / Imprenta : |
Spanish Journal of Agricultural Research, v.15. n.4, e0705, 2017. |
DOI : |
10.5424/sjar/2017154-11016 |
Idioma : |
Inglés |
Notas : |
Article history: Received: 06 Jan 2017, Accepted: 01 Dec 2017. |
Contenido : |
Abstract:
Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars. MenosAbstract:
Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and f... Presentar Todo |
Palabras claves : |
GENOTYPE BY ENVIRONMENT INTERACTION; MULTI-ENVIRONMENT TRIALS; NETWORK EFFICIENCY; SUNFLOWER; YIELD STABILITY. |
Thesagro : |
GIRASOL; INTERACCIÓN GENOTIPO AMBIENTE. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/8628/1/SJAR.2017.v.15.n.4.pdf
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Marc : |
LEADER 02709naa a2200277 a 4500 001 1057950 005 2020-01-30 008 2017 bl uuuu u00u1 u #d 024 7 $a10.5424/sjar/2017154-11016$2DOI 100 1 $aGONZALEZ-BARRIOS, P. 245 $aGenotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency.$h[electronic resource] 260 $c2017 500 $aArticle history: Received: 06 Jan 2017, Accepted: 01 Dec 2017. 520 $aAbstract: Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars. 650 $aGIRASOL 650 $aINTERACCIÓN GENOTIPO AMBIENTE 653 $aGENOTYPE BY ENVIRONMENT INTERACTION 653 $aMULTI-ENVIRONMENT TRIALS 653 $aNETWORK EFFICIENCY 653 $aSUNFLOWER 653 $aYIELD STABILITY 700 1 $aCASTRO, M. 700 1 $aPÉREZ, O. 700 1 $aVILARÓ, D. 700 1 $aGUTIÉRREZ, G. 773 $tSpanish Journal of Agricultural Research$gv.15. n.4, e0705, 2017.
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
15/09/2015 |
Actualizado : |
10/09/2018 |
Tipo de producción científica : |
Documentos |
Autor : |
DEAMBROSI, E.; BLANCO, P.H.; PÍRIZ, M.; PÉREZ DE VIDA, F.; ZORRILLA DE SAN MARTÍN, G.; ACEVEDO, A.; BLANCO, F.; ROEL, A.; MÉNDEZ, R. |
Afiliación : |
ENRIQUE GERMAN DEAMBROSI CHURRUT, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTÍN PÍRIZ CHIALANZA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GONZALO ROBERTO ZORRILLA DE SAN MARTÍN PEREYRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANTONIO MARIA ACEVEDO VEGA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO GUILLERMO BLANCO BORDÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALVARO ROEL DELLAZOPPA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RAMÓN FELIPE MÉNDEZ LARROSA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Arroz: día de campo. Unidad Experimental Paso de la Laguna (UEPL). [Información presentada]. |
Fecha de publicación : |
1992 |
Fuente / Imprenta : |
Treinta y Tres (Uruguay): INIA, 1992. |
Páginas : |
52 p. |
Idioma : |
Español |
Notas : |
Día de campo de arroz correspondientes a la zafra 1991-1992 realizado el 26 de marzo de 1992. |
Thesagro : |
APLICACION DE ABONOS; ARROZ; CLIMATOLOGIA; ENFERMEDADES DE LAS PLANTAS; ENSAYOS DE VARIEDADES; ESCARDA; FITOMEJORAMIENTO; FITOPATOLOGIA; MALEZAS; MANEJO DEL CULTIVO; RIEGO. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/4968/1/DC-AZ-1992.pdf
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Marc : |
LEADER 01053nam a2200349 a 4500 001 1053356 005 2018-09-10 008 1992 bl uuuu u0uu1 u #d 100 1 $aDEAMBROSI, E. 245 $aArroz$bdía de campo. Unidad Experimental Paso de la Laguna (UEPL). [Información presentada].$h[electronic resource] 260 $aTreinta y Tres (Uruguay): INIA$c1992 300 $a52 p. 500 $aDía de campo de arroz correspondientes a la zafra 1991-1992 realizado el 26 de marzo de 1992. 650 $aAPLICACION DE ABONOS 650 $aARROZ 650 $aCLIMATOLOGIA 650 $aENFERMEDADES DE LAS PLANTAS 650 $aENSAYOS DE VARIEDADES 650 $aESCARDA 650 $aFITOMEJORAMIENTO 650 $aFITOPATOLOGIA 650 $aMALEZAS 650 $aMANEJO DEL CULTIVO 650 $aRIEGO 700 1 $aBLANCO, P.H. 700 1 $aPÍRIZ, M. 700 1 $aPÉREZ DE VIDA, F. 700 1 $aZORRILLA DE SAN MARTÍN, G. 700 1 $aACEVEDO, A. 700 1 $aBLANCO, F. 700 1 $aROEL, A. 700 1 $aMÉNDEZ, R.
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