Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach
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 r...
| Autores principales: | , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2019
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| Materias: | |
| Acceso en línea: | https://www.sciencedirect.com/science/article/pii/S0304423819301980 http://hdl.handle.net/20.500.12123/5213 https://doi.org/10.1016/j.scienta.2019.03.024 |
| _version_ | 1855035458228060160 |
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| author | Angelini, Julia Faviere, Gabriela Soledad Bortolotto, Eugenia Belén Arroyo, Luis Enrique Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio |
| author_browse | Angelini, Julia Arroyo, Luis Enrique Bortolotto, Eugenia Belén Cervigni, Gerardo Domingo Lucio Faviere, Gabriela Soledad Valentini, Gabriel Hugo |
| author_facet | Angelini, Julia Faviere, Gabriela Soledad Bortolotto, Eugenia Belén Arroyo, Luis Enrique Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio |
| author_sort | Angelini, Julia |
| collection | INTA Digital |
| description | 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 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA5213 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA52132019-05-28T14:04:59Z Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach Angelini, Julia Faviere, Gabriela Soledad Bortolotto, Eugenia Belén Arroyo, Luis Enrique Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio Durazno Adaptación Rendimiento Interacción Genotipo Ambiente Análisis Multivariante Fitomejoramiento Métodos Estadísticos Peaches Adaptation Yields Genotype Environment Interaction Multivariate Analysis Plant Breeding Estatistical Methods 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 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. EEA San Pedro Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina. Universidad Nacional de Rosario; Argentina Fil: 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; Argentina Fil: 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; Argentina Fil: Arroyo, Luis Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentina Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentina Fil: 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; Argentina 2019-05-28T13:05:36Z 2019-05-28T13:05:36Z 2019 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.sciencedirect.com/science/article/pii/S0304423819301980 http://hdl.handle.net/20.500.12123/5213 0304-4238 https://doi.org/10.1016/j.scienta.2019.03.024 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Scientia Horticulturae 252 : 298-309 (June 2019) |
| spellingShingle | Durazno Adaptación Rendimiento Interacción Genotipo Ambiente Análisis Multivariante Fitomejoramiento Métodos Estadísticos Peaches Adaptation Yields Genotype Environment Interaction Multivariate Analysis Plant Breeding Estatistical Methods Angelini, Julia Faviere, Gabriela Soledad Bortolotto, Eugenia Belén Arroyo, Luis Enrique Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach |
| title | Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach |
| title_full | Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach |
| title_fullStr | Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach |
| title_full_unstemmed | Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach |
| title_short | Biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype-by-environment interaction in peach |
| title_sort | biplot pattern interaction analysis and statistical test for crossover and noncrossover genotype by environment interaction in peach |
| topic | Durazno Adaptación Rendimiento Interacción Genotipo Ambiente Análisis Multivariante Fitomejoramiento Métodos Estadísticos Peaches Adaptation Yields Genotype Environment Interaction Multivariate Analysis Plant Breeding Estatistical Methods |
| url | https://www.sciencedirect.com/science/article/pii/S0304423819301980 http://hdl.handle.net/20.500.12123/5213 https://doi.org/10.1016/j.scienta.2019.03.024 |
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