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dc.contributor.author
Angelini, Julia  
dc.contributor.author
Faviere, Gabriela Soledad  
dc.contributor.author
Bortolotto, Eugenia Belén  
dc.contributor.author
Arroyo, Luis Enrique  
dc.contributor.author
Valentini, Gabriel Hugo  
dc.contributor.author
Cervigni, Gerardo Domingo Lucio  
dc.date.available
2021-09-06T20:01:31Z  
dc.date.issued
2019-06  
dc.identifier.citation
Angelini, Julia; Faviere, Gabriela Soledad; Bortolotto, Eugenia Belén; Arroyo, Luis Enrique; Valentini, Gabriel Hugo; et al.; Biplot pattern interaction analysis and statistical test for crossover and non-crossover genotype-by-environment interaction in peach; Elsevier Science; Scientia Horticulturae; 252; 6-2019; 298-309  
dc.identifier.issn
0304-4238  
dc.identifier.uri
http://hdl.handle.net/11336/139741  
dc.description.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) 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 ME2 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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
MULTI-ENVIRONMENTS TRIALS  
dc.subject
MULTIVARIATE METHODS  
dc.subject
PARAMETRIC AND NON-PARAMETRIC STATISTICS  
dc.subject
PEACH BREEDING  
dc.subject
PRUNUS PERSICA L.  
dc.subject.classification
Otras Ciencias Biológicas  
dc.subject.classification
Ciencias Biológicas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Biplot pattern interaction analysis and statistical test for crossover and non-crossover genotype-by-environment interaction in peach  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-11-25T17:36:16Z  
dc.journal.volume
252  
dc.journal.pagination
298-309  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina  
dc.description.fil
Fil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina  
dc.description.fil
Fil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina  
dc.description.fil
Fil: Arroyo, Luis Enrique. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria San Pedro; Argentina  
dc.description.fil
Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria San Pedro; Argentina  
dc.description.fil
Fil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina  
dc.journal.title
Scientia Horticulturae  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.scienta.2019.03.024  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0304423819301980