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The aim of this research was select the best combination of contrasting parents to develop a mapping population for drought tolerance, based on phenotypic and genotypic data. Phenotyping was conducted in a greenhouse during 16 days at vegetative stage under well-watered (WW) and water-deficit (WD) conditions. Traits evaluated were: gain of leaf area (GLA), total water use (TWU), net assimilation rate (NAR), water use efficiency (WUE) and transpiration [ver mas...]
dc.contributor.authorGrandon, Nancy Gabriela
dc.contributor.authorMartin, Eugenia Alejandra
dc.contributor.authorCicconi, Emmanuel
dc.contributor.authorDiaz, Carolina Del Pilar
dc.contributor.authorMamani, Eva Maria Celia
dc.contributor.authorMoreno, Maria Valeria
dc.date.accessioned2024-05-21T14:02:07Z
dc.date.available2024-05-21T14:02:07Z
dc.date.issued2021-12-20
dc.identifier.issn2693-5015 (online)
dc.identifier.otherhttps://doi.org/10.21203/rs.3.rs-1135239/v1
dc.identifier.urihttp://hdl.handle.net/20.500.12123/17825
dc.identifier.urihttps://www.researchsquare.com/article/rs-1135239/v1
dc.descriptionPreprint
dc.description.abstractThe aim of this research was select the best combination of contrasting parents to develop a mapping population for drought tolerance, based on phenotypic and genotypic data. Phenotyping was conducted in a greenhouse during 16 days at vegetative stage under well-watered (WW) and water-deficit (WD) conditions. Traits evaluated were: gain of leaf area (GLA), total water use (TWU), net assimilation rate (NAR), water use efficiency (WUE) and transpiration rate (TR) response to vapor pressure deficit (VPD) (slope and breakpoint). Genotyping was performed with 127 SSR markers and a cluster analyses was conducted. An important interaction was observed for NAR, WUE and breakpoint in the VPD response. Under WD conditions, all genotypes showed lower GLA and TWU, whereas NAR and WUE increased its values. All genotypes showed reduction of the slope and breakpoint in high VPD response on WD. PCA analysis explains the 80% of the total variability. PC1 discriminated HA89 and R419 due to a lower slope and higher breakpoint, while PC2 separated by water treatment based on the WUE and TWU values. Nighty nine SSR marker were amplified detecting 262 alleles. Cluster analyzes showed two main groups, one including HAR4 and B59 and the other one including five remaining genotypes. According to these results, only R419xHA64 and HA89xHAR4 had a greater genetic distance (1.08), besides a high polymorphism level between ILs (about 60%). Therefore, we conclude that these would be the best combination of contrasting parents to develop mapping populations for drought tolerance in sunflower.es_AR
dc.formatapplication/pdfeng
dc.language.isoenges_AR
dc.publisherResearch Squarees_AR
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/eng
dc.sourceResearch Square: v1. 2021es_AR
dc.subjectGirasoles_AR
dc.subjectSunflowerses_AR
dc.subjectDrought Tolerancees_AR
dc.subjectTolerancia a la sequiaes_AR
dc.subjectPhenotypinges_AR
dc.subjectHelianthus annuuses_AR
dc.subjectFenotipado
dc.subjectMarcadores Genéticos
dc.subjectGenetic Markerseng
dc.subject.otherSunflower Inbred Lineses_AR
dc.subject.otherLíneas endogámicas de girasoles_AR
dc.subject.otherSSR markerses_AR
dc.subject.otherMarcadores SSRes_AR
dc.subject.otherWater Use Efficiencyes_AR
dc.subject.otherEficiencia en el Uso del Aguaes_AR
dc.subject.otherVapor Pressure Deficit Responsees_AR
dc.subject.otherRespuesta al déficit de presión de vapores_AR
dc.titleSelection of Contrasting Parents for Drought Tolerance in Sunfower (Helianthus Annuus L.)es_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/acceptedVersioneng
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)eng
dc.description.origenEEA Manfredies_AR
dc.description.filFil: Grandón, Nancy Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Biotecnología en Cultivo; Argentinaes_AR
dc.description.filFil: Martìn, Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Campo Experimental Villarinoes_AR
dc.description.filFil: Dìaz, Carolina del Pilar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Manfredi. Grupo Economía Estadística; Argentinaes_AR
dc.description.filFil: Mamani, Eva Maria Celia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Biotecnología en Cultivo; Argentinaes_AR
dc.description.filFil: Moreno, María Valeria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Biotecnología en Cultivo; Argentinaes_AR
dc.description.filFil: Martin, Eugenia Alejandra. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Campo Experimental Villarino; Argentina
dc.description.filFil: Cicconi, Emanuel. Asesor Técnico. Argentina
dc.subtypecientifico


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