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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
04/01/2018 |
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Actualizado : |
30/01/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
GONZALEZ-BARRIOS, P.; CASTRO, M.; PÉREZ, O.; VILARÓ, D.; GUTIÉRREZ, G. |
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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. |
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Título : |
Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Spanish Journal of Agricultural Research, v.15. n.4, e0705, 2017. |
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DOI : |
10.5424/sjar/2017154-11016 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 06 Jan 2017, Accepted: 01 Dec 2017. |
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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 |
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Palabras claves : |
GENOTYPE BY ENVIRONMENT INTERACTION; MULTI-ENVIRONMENT TRIALS; NETWORK EFFICIENCY; SUNFLOWER; YIELD STABILITY. |
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Thesagro : |
GIRASOL; INTERACCIÓN GENOTIPO AMBIENTE. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/8628/1/SJAR.2017.v.15.n.4.pdf
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Marc : |
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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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| Registros recuperados : 5 | |
| 2. |  | CABRERA, A.; BERNÁ, L.; FRESIA, P.; SILVEIRA, C.S.; MACÍAS-RIOSECO, M.; PRITSCH,O.; RIET-CORREA, F.; GIANNITTI, F.; FRANCIA, M.E.; REBOLLO, C. Generación de nuevas herramientas para el control de Neospora caninum a partir de un enfoque epidemiológico y genómico. [Resumen] In: REDBIO; INIA (Instituto Nacional de Investigación Agropecuaria); REDBIO Argentina. X Encuentro Latinoamericano y del Caribe de Biotecnología Agropecuaria y XI Simposio Redbio Argentina. Libro de Resúmenes. Montevideo 12 - 15 Noviembre 2019. Montevideo (UY): INIA, 2019. p. 148. (INIA Serie Técnica; 253).| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA La Estanzuela. |
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| 3. | | COLOMBATTI OLIVIERI, M. A.; FRESIA, P.; GRAÑA, M.; CUERDA, M.X.; NAGEL, A.; ALVARADO PINEDO, F.; ROMANO, M. I.; CAIMI, K.; BERNÁ, L.; SANTANGELO, M. P. Genomic comparison of two strains of Mycobacterium avium subsp. paratuberculosis with contrasting pathogenic phenotype. Letter. Tuberculosis, 2023, volume 138, article 102299. doi: https://doi.org/10.1016/j.tube.2022.102299 Article history: Received 30 September 2022; Received in revised form 28 November 2022; Accepted 19 December 2022; Available online 21 December 2022. -- Correspondence authors: Berná, L.; Unidad de Biología Molecular, Institut...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 4. | | CABRERA, A.; FRESIA, P.; BERNÁ, L.; SILVEIRA, C.S.; MACÍAS-RIOSECO, M.; AREVALO, A.P.; CRISPO, M.; PRITSCH, O.; RIET-CORREA, F.; GIANNITTI, F.; GIANNITTI, F.; FRANCIA, M.E.; ROBELLO, C. Isolation and molecular characterization of four novel Neospora caninum strains. Genetics, Evolution, and Phylogeny - Short Communication. Parasitology Research, 1 December 2019, Volume 118, Issue 12, Pages 3535-3542. Doi: 10.1007/s00436-019-06474-9 Article history: Received: 11 April 2019 / Accepted: 24 September 2019 / Published online: 7 November 2019.
Funding Sponsor: Agencia Nacional de Investigación e Innovación (ANII).
Funding Text: This project was funded by grant...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 5. | | PRITSCH, C.; OBERTI, H.; VAIO, M.; BAZ, N.; MANGINO, M.; BERNÁ, L.; ROSSI, F.; RODRÍGUEZ DECUADRO, S.; QUEZADA, M.; GAIERO, P.; PESCE, M.; FILIPPI, C.; GARAYCOCHEA, S.; DINI, M.; GUTIÉRREZ GONZÁLEZ, J.; VARANI, A. Terra Incógnita: conociendo el genoma del guayabo del país. [Presentación oral]. Módulo 1. Recursos genéticos. Presentaciones Orales. In: Dini, M.; Speroni, G. (Eds.). Encuentro Nacional sobre Frutos Nativos, 11°. Universidad Tecnológica (UTEC), Durazno, Uruguay, 4 y 5 abril 2024, Libro de resúmenes. Canelones (UY): INIA, 2024. p.6. (Serie Actividades de Difusión; 804) Agradecimientos: A la Comisión Sectorial de Investigación Científica, Udelar por la financiación.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 5 | |
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