Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina
- Autores
- Curti, Ramiro Nestor; de la Vega, A. J.; Andrade, A. J.; Bramardi, Sergio Jorge; Bertero, Hector Daniel
- Año de publicación
- 2014
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The quinoa growing region of Northwest Argentina (NWA) shows a strong environmental variability, both seasonal and spatial. In consequence, the site-year combinations in which yield trials are established can complicate quinoa genotypic selection through strong genotype-by-environment interactions (G × E). The magnitude and nature of the genotype (G) and G × E interaction effects for grain yield, its physiological determinants and components, and days-to-flower exhibited by quinoa at NWA were examined in a multi-environment trial involving a reference set of 12 genotypes tested in six environments. The tested genotypes were selected based on their known contrasting relative performance to environments and different geographical origin. They represent three out of the four genotypic groups identified in previous studies. The G × E interaction to G component of variance was 3:1, 30:1 and 1.3:1 for grain yield, harvest index and grain number, respectively. Conversely, the G effect was large for biomass, grain weight and days-to-flower. Two-mode pattern analysis of the double-centred matrix for grain yield revealed four genotypic groups with different response pattern across environments. This clustering which separates genotypes from Highlands and Valleys showed a close correspondence with the genotypic groups previously proposed based on phenotypic and genetic characterization. On the other hand, a strong and repeatable negative association was observed between Highland and Valley sites, in terms of their G × E interaction effects. Phenological variation among genotypes in combination with environmental differences in the incidence of mildew or frost risk gave rise to significant crossover yield responses to site changes and determined specific adaptation to different ecological conditions. All yield components and determinants were involved in the genotype-specific yield responses. The genotypic variability observed for time to flowering determined the form of the G × E interactions observed for total above-ground biomass in Valley Environments, while in the Highland sites, harvest index made a significant contribution. On the other hand, grain number was the major component in grain yield determination, while grain weight showed a weak to strongly negative association with grain number across both types of environment. In this sense, the future breeding programs in NWA region should focus on these physiological attributes underlying grain yield variation among genotypes across groups of environments for faster genetic progress.
Fil: Curti, Ramiro Nestor. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; Argentina
Fil: de la Vega, A. J.. DuPont Pioneer; España
Fil: Andrade, A. J.. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Salta-Jujuy; Argentina
Fil: Bramardi, Sergio Jorge. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina. Universidad Nacional del Comahue. Facultad de Ciencis Agrarias; Argentina
Fil: Bertero, Hector Daniel. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina - Materia
-
CHENOPODIUM QUINOA WILLD
DOWNY MILDEW
GROUPS OF ENVIRONMENTS
G×E INTERACTION
QUINOA
YIELD COMPONENTS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/7102
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Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest ArgentinaCurti, Ramiro Nestorde la Vega, A. J.Andrade, A. J.Bramardi, Sergio JorgeBertero, Hector DanielCHENOPODIUM QUINOA WILLDDOWNY MILDEWGROUPS OF ENVIRONMENTSG×E INTERACTIONQUINOAYIELD COMPONENTShttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4The quinoa growing region of Northwest Argentina (NWA) shows a strong environmental variability, both seasonal and spatial. In consequence, the site-year combinations in which yield trials are established can complicate quinoa genotypic selection through strong genotype-by-environment interactions (G × E). The magnitude and nature of the genotype (G) and G × E interaction effects for grain yield, its physiological determinants and components, and days-to-flower exhibited by quinoa at NWA were examined in a multi-environment trial involving a reference set of 12 genotypes tested in six environments. The tested genotypes were selected based on their known contrasting relative performance to environments and different geographical origin. They represent three out of the four genotypic groups identified in previous studies. The G × E interaction to G component of variance was 3:1, 30:1 and 1.3:1 for grain yield, harvest index and grain number, respectively. Conversely, the G effect was large for biomass, grain weight and days-to-flower. Two-mode pattern analysis of the double-centred matrix for grain yield revealed four genotypic groups with different response pattern across environments. This clustering which separates genotypes from Highlands and Valleys showed a close correspondence with the genotypic groups previously proposed based on phenotypic and genetic characterization. On the other hand, a strong and repeatable negative association was observed between Highland and Valley sites, in terms of their G × E interaction effects. Phenological variation among genotypes in combination with environmental differences in the incidence of mildew or frost risk gave rise to significant crossover yield responses to site changes and determined specific adaptation to different ecological conditions. All yield components and determinants were involved in the genotype-specific yield responses. The genotypic variability observed for time to flowering determined the form of the G × E interactions observed for total above-ground biomass in Valley Environments, while in the Highland sites, harvest index made a significant contribution. On the other hand, grain number was the major component in grain yield determination, while grain weight showed a weak to strongly negative association with grain number across both types of environment. In this sense, the future breeding programs in NWA region should focus on these physiological attributes underlying grain yield variation among genotypes across groups of environments for faster genetic progress.Fil: Curti, Ramiro Nestor. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: de la Vega, A. J.. DuPont Pioneer; EspañaFil: Andrade, A. J.. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Salta-Jujuy; ArgentinaFil: Bramardi, Sergio Jorge. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina. Universidad Nacional del Comahue. Facultad de Ciencis Agrarias; ArgentinaFil: Bertero, Hector Daniel. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2014-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/7102Curti, Ramiro Nestor; de la Vega, A. J.; Andrade, A. J.; Bramardi, Sergio Jorge; Bertero, Hector Daniel; Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina; Elsevier Science; Field Crops Research; 166; 7-2014; 46-570378-4290enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378429014001567info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fcr.2014.06.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:18:17Zoai:ri.conicet.gov.ar:11336/7102instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:18:17.882CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
title |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
spellingShingle |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina Curti, Ramiro Nestor CHENOPODIUM QUINOA WILLD DOWNY MILDEW GROUPS OF ENVIRONMENTS G×E INTERACTION QUINOA YIELD COMPONENTS |
title_short |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
title_full |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
title_fullStr |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
title_full_unstemmed |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
title_sort |
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina |
dc.creator.none.fl_str_mv |
Curti, Ramiro Nestor de la Vega, A. J. Andrade, A. J. Bramardi, Sergio Jorge Bertero, Hector Daniel |
author |
Curti, Ramiro Nestor |
author_facet |
Curti, Ramiro Nestor de la Vega, A. J. Andrade, A. J. Bramardi, Sergio Jorge Bertero, Hector Daniel |
author_role |
author |
author2 |
de la Vega, A. J. Andrade, A. J. Bramardi, Sergio Jorge Bertero, Hector Daniel |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
CHENOPODIUM QUINOA WILLD DOWNY MILDEW GROUPS OF ENVIRONMENTS G×E INTERACTION QUINOA YIELD COMPONENTS |
topic |
CHENOPODIUM QUINOA WILLD DOWNY MILDEW GROUPS OF ENVIRONMENTS G×E INTERACTION QUINOA YIELD COMPONENTS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/4.1 https://purl.org/becyt/ford/4 |
dc.description.none.fl_txt_mv |
The quinoa growing region of Northwest Argentina (NWA) shows a strong environmental variability, both seasonal and spatial. In consequence, the site-year combinations in which yield trials are established can complicate quinoa genotypic selection through strong genotype-by-environment interactions (G × E). The magnitude and nature of the genotype (G) and G × E interaction effects for grain yield, its physiological determinants and components, and days-to-flower exhibited by quinoa at NWA were examined in a multi-environment trial involving a reference set of 12 genotypes tested in six environments. The tested genotypes were selected based on their known contrasting relative performance to environments and different geographical origin. They represent three out of the four genotypic groups identified in previous studies. The G × E interaction to G component of variance was 3:1, 30:1 and 1.3:1 for grain yield, harvest index and grain number, respectively. Conversely, the G effect was large for biomass, grain weight and days-to-flower. Two-mode pattern analysis of the double-centred matrix for grain yield revealed four genotypic groups with different response pattern across environments. This clustering which separates genotypes from Highlands and Valleys showed a close correspondence with the genotypic groups previously proposed based on phenotypic and genetic characterization. On the other hand, a strong and repeatable negative association was observed between Highland and Valley sites, in terms of their G × E interaction effects. Phenological variation among genotypes in combination with environmental differences in the incidence of mildew or frost risk gave rise to significant crossover yield responses to site changes and determined specific adaptation to different ecological conditions. All yield components and determinants were involved in the genotype-specific yield responses. The genotypic variability observed for time to flowering determined the form of the G × E interactions observed for total above-ground biomass in Valley Environments, while in the Highland sites, harvest index made a significant contribution. On the other hand, grain number was the major component in grain yield determination, while grain weight showed a weak to strongly negative association with grain number across both types of environment. In this sense, the future breeding programs in NWA region should focus on these physiological attributes underlying grain yield variation among genotypes across groups of environments for faster genetic progress. Fil: Curti, Ramiro Nestor. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; Argentina Fil: de la Vega, A. J.. DuPont Pioneer; España Fil: Andrade, A. J.. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Salta-Jujuy; Argentina Fil: Bramardi, Sergio Jorge. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina. Universidad Nacional del Comahue. Facultad de Ciencis Agrarias; Argentina Fil: Bertero, Hector Daniel. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Produccion Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
description |
The quinoa growing region of Northwest Argentina (NWA) shows a strong environmental variability, both seasonal and spatial. In consequence, the site-year combinations in which yield trials are established can complicate quinoa genotypic selection through strong genotype-by-environment interactions (G × E). The magnitude and nature of the genotype (G) and G × E interaction effects for grain yield, its physiological determinants and components, and days-to-flower exhibited by quinoa at NWA were examined in a multi-environment trial involving a reference set of 12 genotypes tested in six environments. The tested genotypes were selected based on their known contrasting relative performance to environments and different geographical origin. They represent three out of the four genotypic groups identified in previous studies. The G × E interaction to G component of variance was 3:1, 30:1 and 1.3:1 for grain yield, harvest index and grain number, respectively. Conversely, the G effect was large for biomass, grain weight and days-to-flower. Two-mode pattern analysis of the double-centred matrix for grain yield revealed four genotypic groups with different response pattern across environments. This clustering which separates genotypes from Highlands and Valleys showed a close correspondence with the genotypic groups previously proposed based on phenotypic and genetic characterization. On the other hand, a strong and repeatable negative association was observed between Highland and Valley sites, in terms of their G × E interaction effects. Phenological variation among genotypes in combination with environmental differences in the incidence of mildew or frost risk gave rise to significant crossover yield responses to site changes and determined specific adaptation to different ecological conditions. All yield components and determinants were involved in the genotype-specific yield responses. The genotypic variability observed for time to flowering determined the form of the G × E interactions observed for total above-ground biomass in Valley Environments, while in the Highland sites, harvest index made a significant contribution. On the other hand, grain number was the major component in grain yield determination, while grain weight showed a weak to strongly negative association with grain number across both types of environment. In this sense, the future breeding programs in NWA region should focus on these physiological attributes underlying grain yield variation among genotypes across groups of environments for faster genetic progress. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/7102 Curti, Ramiro Nestor; de la Vega, A. J.; Andrade, A. J.; Bramardi, Sergio Jorge; Bertero, Hector Daniel; Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina; Elsevier Science; Field Crops Research; 166; 7-2014; 46-57 0378-4290 |
url |
http://hdl.handle.net/11336/7102 |
identifier_str_mv |
Curti, Ramiro Nestor; de la Vega, A. J.; Andrade, A. J.; Bramardi, Sergio Jorge; Bertero, Hector Daniel; Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina; Elsevier Science; Field Crops Research; 166; 7-2014; 46-57 0378-4290 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378429014001567 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fcr.2014.06.011 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1842981000211070976 |
score |
12.993085 |