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Registros recuperados : 34 | |
1. | | PÉREZ, O. Efecto del déficit hídrico en cinco cultivares de trigo: tesis Mag. Sc. Montevideo (UY): UdelaR, Facultad de Agronomía, 2015. Tesis aprobada por el tribunal integrado por el Dr. Omar Borsani, Dr. Andrés Berger y Dr. Daniel Miralles el 1º de octubre de 2015. Autor: Osvaldo Pérez González. Directora Dra. Marina Castro y Co-director Ing. Agr. Luis Viega. Tesis de...Biblioteca(s): INIA La Estanzuela. |
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4. | | BAO, L.; PÉREZ, O. (Ed.). El gorgojo acuático del arroz Montevideo (UY): INIA, 2012. 34 p. (Serie FPTA-INIA; 38) Proyecto FPTA 228: Estudios biológicos de Oryzophagus oryzae como base para la implementación de buenas prácticas de manejo del cultivo de arroz en diferentes zonas de Uruguay. Período de Ejecución: Dic. 2006-Mar. 2010.
EQUIPO DE TRABAJO:...Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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13. | | BAO, L.; PÉREZ, O.; CANTOU, G.; ROEL, A.; BENTANCOURT, C. Efecto del momento de inundación sobre la abundancia del gorgojo acuático del arroz Oryzophagus oryzae. Paso Farías, Artigas, 27 de agosto, 2009; Tacuarembó, 28 de agosto, 2009. ln: INIA TACUAREMBO. PROGRAMA NACIONAL DE INVESTIGACIÓN EN PRODUCCIÓN DE ARROZ. Presentación resultados experimentales de arroz Zafra 2008/2009. Tacuarembó (Uruguay): INIA, 2009. p. 69-70 (INIA Serie Actividades de Difusión ; 585)Biblioteca(s): INIA Tacuarembó. |
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15. | | PÉREZ, O.; VIEGA, L.; GUTIÉRREZ, L.; CASTRO, M. Post-anthesis water deficit in spring wheat: effects on yield components and relative water content. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: resúmenes; posters. La Estanzuela, Colonia, UY: INIA, 2014. p. 41.Biblioteca(s): INIA La Estanzuela. |
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16. | | PÉREZ, O.; VIEGA, L.; GUTIERREZ, L.; CASTRO, M. Post-anthesis water deficit in spring wheat: effects on yield components and relative water content. [Poster]. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. P.130. (INIA Serie Técnica; 241).Biblioteca(s): INIA La Estanzuela. |
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19. | | CASTRO, M.; HOFFMAN, E.; PÉREZ, O.; VIEGA, L. Variabilidad en la respuesta al estrés hídrico de cultivares de trigo en Uruguay. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 78-86. (INIA Serie Técnica; 241).Biblioteca(s): INIA La Estanzuela. |
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20. | | CASTRO, M.; HOFFMAN, E.; PÉREZ, O.; VIEGA, L. Variabilidad en la respuesta al estrés hídrico de cultivares de trigo.[Presentación oral]. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro. presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014.Biblioteca(s): INIA La Estanzuela. |
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Registros recuperados : 34 | |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
04/01/2018 |
Actualizado : |
30/01/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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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