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Abstract
The presence of genotype-by-environment interactions (GE) remains a major issue for crop improvement. The aims of this work were: i) to compare the efficiency of parametric and non-parametric methods to test the presence of crossover (COI) and non-crossover GE (NCOI), ii) visual examination of the relationships between environments and genotypes tested, and iii) to test the effectiveness of dividing the peach season evaluations into mega-environments (ME) [ver mas...]
dc.contributor.authorAngelini, Julia
dc.contributor.authorFaviere, Gabriela Soledad
dc.contributor.authorBortolotto, Eugenia Belén
dc.contributor.authorArroyo, Luis Enrique
dc.contributor.authorValentini, Gabriel Hugo
dc.contributor.authorCervigni, Gerardo Domingo Lucio
dc.date.accessioned2019-05-28T13:05:36Z
dc.date.available2019-05-28T13:05:36Z
dc.date.issued2019
dc.identifier.issn0304-4238
dc.identifier.otherhttps://doi.org/10.1016/j.scienta.2019.03.024
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0304423819301980
dc.identifier.urihttp://hdl.handle.net/20.500.12123/5213
dc.description.abstractThe presence of genotype-by-environment interactions (GE) remains a major issue for crop improvement. The aims of this work were: i) to compare the efficiency of parametric and non-parametric methods to test the presence of crossover (COI) and non-crossover GE (NCOI), ii) visual examination of the relationships between environments and genotypes tested, and iii) to test the effectiveness of dividing the peach season evaluations into mega-environments (ME) using the biplot based on AMMI and SREG. Non-parametric ANOVA was more useful than the parametric approach because it can distinguish between the presence of COI and NCOI. Three test methods, suitable for investigating two-factor interactions, were used to show that interactions between genotypes and environment involve significant changes in rank order. The Yang test based on mixed model theory combined with interaction-wise error rate was the most sensitive to detect COI, while the Gail and Simon, as well as the Azzalini and Cox methods were conservative. Which-won-where pattern was followed with four and two ME were found with AMMI and SREG, respectively. Entries G16 (Hermosillo P), G21 (María Emilia N), G2 (84.351.029 N) and G8 (Cotogna del Berti P) showed specific adaptability to ME-1, ME-2, ME-3 and ME-4 generated by AMMI, respectively; while G28 (Sunprince P) exhibited specific adaptation to ME-1 and G16 in ME-2 which were created by SREG. Average environment coordination (AEC) view of the GGE biplot involving the seven environments identified G10 (Flameprince P) as the most stable and high-yielding genotype across environments, unlike G8 and G28, which showed only high yields. Results indicated that AMMI and GGE biplots are informative methods to explore stability and adaptation patterns of genotypes in practical plant breeding and in subsequent variety recommendations. In addition, finding ME helps identify the most suitable peach genotypes that can be recommended for areas within a specific ME in either one or more test locations.es_AR
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevier
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceScientia Horticulturae 252 : 298-309 (June 2019)es_AR
dc.subjectDuraznoes_AR
dc.subjectPeacheseng
dc.subjectAdaptaciónes_AR
dc.subjectAdaptationeng
dc.subjectRendimientoes_AR
dc.subjectYieldseng
dc.subjectInteracción Genotipo Ambientees_AR
dc.subjectGenotype Environment Interactioneng
dc.subjectAnálisis Multivariantees_AR
dc.subjectMultivariate Analysiseng
dc.subjectFitomejoramientoes_AR
dc.subjectPlant Breedingeng
dc.subjectMétodos Estadísticoses_AR
dc.subjectEstatistical Methodseng
dc.titleBiplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peaches_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenEEA San Pedroes_AR
dc.description.filFil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina. Universidad Nacional de Rosario; Argentinaes_AR
dc.description.filFil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina. Universidad Nacional de Rosario; Argentinaes_AR
dc.description.filFil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina. Universidad Nacional de Rosario; Argentinaes_AR
dc.description.filFil: Arroyo, Luis Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentinaes_AR
dc.description.filFil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentinaes_AR
dc.description.filFil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina. Universidad Nacional de Rosario; Argentinaes_AR
dc.subtypecientifico


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