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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 Las Brujas. |
Fecha actual : |
21/02/2014 |
Actualizado : |
15/06/2021 |
Tipo de producción científica : |
Documentos |
Autor : |
GONZÁLEZ-ARCOS, M.; GIMÉNEZ, G.; BERRUETA, C.; LENZI, A. |
Afiliación : |
MATIAS GONZÁLEZ-ARCOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO GIMÉNEZ FRANQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA CECILIA BERRUETA MOREIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALBERTO RICARDO LENZI CEDREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Nuevas líneas de tomate para industria en validación productiva. |
Fecha de publicación : |
2010 |
Fuente / Imprenta : |
In: INIA Las Brujas; Programa Nacional Producción Hortícola. Resultados experimentales en el cultivo del tomate. Jornada de divulgación. Las Brujas, Canelones (Uruguay): INIA, 2010. |
Páginas : |
p. 35-37 |
Serie : |
(INIA Serie Actividades de Difusión ; 606) |
Idioma : |
Español |
Contenido : |
En el año 2002 INIA LB comenzó a trabajar en la evaluación de cultivares de tomate para industria tratando de identificar los mejores adaptados a las condiciones de
producción del sur del país. Este proceso fue complementado en el año 2005 con el desarrollo de un proyecto de mejoramiento genético local, que posibilitó la generación
de una diversidad de materiales que luego fueron seleccionados por características adaptativas en el mismo ambiente de producción. |
Thesagro : |
ENSAYO DE VARIEDADES; TOMATE. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/576/1/18429080610155354.pdf;ad606#page=41
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Marc : |
LEADER 01184naa a2200205 a 4500 001 1008820 005 2021-06-15 008 2010 bl uuuu u00u1 u #d 100 1 $aGONZÁLEZ-ARCOS, M. 245 $aNuevas líneas de tomate para industria en validación productiva. 260 $c2010 300 $ap. 35-37 490 $a(INIA Serie Actividades de Difusión ; 606) 520 $aEn el año 2002 INIA LB comenzó a trabajar en la evaluación de cultivares de tomate para industria tratando de identificar los mejores adaptados a las condiciones de producción del sur del país. Este proceso fue complementado en el año 2005 con el desarrollo de un proyecto de mejoramiento genético local, que posibilitó la generación de una diversidad de materiales que luego fueron seleccionados por características adaptativas en el mismo ambiente de producción. 650 $aENSAYO DE VARIEDADES 650 $aTOMATE 700 1 $aGIMÉNEZ, G. 700 1 $aBERRUETA, C. 700 1 $aLENZI, A. 773 $tIn: INIA Las Brujas; Programa Nacional Producción Hortícola. Resultados experimentales en el cultivo del tomate. Jornada de divulgación. Las Brujas, Canelones (Uruguay): INIA, 2010.
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INIA Las Brujas (LB) |
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