Identification of peach accessions stability and adaptability in non-balanced trials through years

Autores
Maulion, Evangelina; Arroyo, Luis Enrique; Daorden, Maria Elena; Valentini, Gabriel Hugo; Cervigni, Gerardo Domingo Lucio
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Identification of genotypes with acceptable yield and yield stability in different environments is an important issue in plant breeding. Genotype-by-environment interaction (GEI) can alter genotypes performances making the selection of superior material a tedious task for breeders. Consequently, it is necessary to assess the usefulness of different available methods and identify the most suitable for understanding GEI. The objectives of this work were to compare three methods to study genotype stability considering incomplete data sets: (i) Di Rienzo, Guzmán and Casanoves’ test (DGC), (ii) relative yield (RY) and (iii) Piepho’s method. In addition, AMMI (additive main effect and multiplicative interaction) analysis and eight AMMI stability measures SIPC, EV, ASV, Da, FP, B, FA and Za were computed to explore their advantages and disadvantages to select stable entries. The usefulness of the genotype selection index (GSI) and the rank-sum (RS) procedures to identify stable and high-yielding genotypes were evaluated and then compared with the superiority (P) and reliability indexes (I).The association between yield variation and climatic factors as frosts, chilling, heat, rainfall and the interactions among them were also analyzed. 29 peach entries were assessed in four to seven seasons in a completely randomized design with three replications. DGC and RY tests agreed on classifying Fireprince as a stable and high-yielding peach, RY classified 25 entries as stable, while Piepho’s method did not separate the tested genotypes as DGC and RY did. The results of AMMI indicated that 25.06% of total variability was justified by genotypes, 9.76% by environments and 58.97% by GEI. The first five interaction principal components could explain 94.82% of GEI and showed the efficiency of AMMI model to study and understand GEI. The AMMI parameters showed no association with fruit yield, therefore, they could be useful to indicate stable entries but they would not be appropriate to select stable and high-yielding genotypes. The EV and Za indicated static stability while ASV, SIPC, Da, FA and FP pointed out the dynamic stability concept. The performance of the best entries selected by GSI, RS, P and I procedures were not different, therefore, any of them can be used to select superior peach genotypes. Rainfall during endodormancy, rainfall from floral bud endo- to ecodormancy - and heat accumulation during fruit development period showed significant correlation with yield variation across seasons.
http://www.sciencedirect.com/science/article/pii/S0304423815303733?via%3Dihub
EEA San Pedro
Fil: Maulión, Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Arroyo, Luis Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentina
Fil: Daorden, Maria Elena. 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
Fuente
Scientia horticulturae 199 : 198-208. (February 2016)
Materia
Durazno
Genotipos
Interacción Genotipo Ambiente
Peaches
Genotypes
Genotype Environment Interaction
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
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spelling Identification of peach accessions stability and adaptability in non-balanced trials through yearsMaulion, EvangelinaArroyo, Luis EnriqueDaorden, Maria ElenaValentini, Gabriel HugoCervigni, Gerardo Domingo LucioDuraznoGenotiposInteracción Genotipo AmbientePeachesGenotypesGenotype Environment InteractionIdentification of genotypes with acceptable yield and yield stability in different environments is an important issue in plant breeding. Genotype-by-environment interaction (GEI) can alter genotypes performances making the selection of superior material a tedious task for breeders. Consequently, it is necessary to assess the usefulness of different available methods and identify the most suitable for understanding GEI. The objectives of this work were to compare three methods to study genotype stability considering incomplete data sets: (i) Di Rienzo, Guzmán and Casanoves’ test (DGC), (ii) relative yield (RY) and (iii) Piepho’s method. In addition, AMMI (additive main effect and multiplicative interaction) analysis and eight AMMI stability measures SIPC, EV, ASV, Da, FP, B, FA and Za were computed to explore their advantages and disadvantages to select stable entries. The usefulness of the genotype selection index (GSI) and the rank-sum (RS) procedures to identify stable and high-yielding genotypes were evaluated and then compared with the superiority (P) and reliability indexes (I).The association between yield variation and climatic factors as frosts, chilling, heat, rainfall and the interactions among them were also analyzed. 29 peach entries were assessed in four to seven seasons in a completely randomized design with three replications. DGC and RY tests agreed on classifying Fireprince as a stable and high-yielding peach, RY classified 25 entries as stable, while Piepho’s method did not separate the tested genotypes as DGC and RY did. The results of AMMI indicated that 25.06% of total variability was justified by genotypes, 9.76% by environments and 58.97% by GEI. The first five interaction principal components could explain 94.82% of GEI and showed the efficiency of AMMI model to study and understand GEI. The AMMI parameters showed no association with fruit yield, therefore, they could be useful to indicate stable entries but they would not be appropriate to select stable and high-yielding genotypes. The EV and Za indicated static stability while ASV, SIPC, Da, FA and FP pointed out the dynamic stability concept. The performance of the best entries selected by GSI, RS, P and I procedures were not different, therefore, any of them can be used to select superior peach genotypes. Rainfall during endodormancy, rainfall from floral bud endo- to ecodormancy - and heat accumulation during fruit development period showed significant correlation with yield variation across seasons.http://www.sciencedirect.com/science/article/pii/S0304423815303733?via%3DihubEEA San PedroFil: Maulión, Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Arroyo, Luis Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; ArgentinaFil: Daorden, Maria Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; ArgentinaFil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; ArgentinaFil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina2017-09-06T13:00:33Z2017-09-06T13:00:33Z2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/1143http://www.sciencedirect.com/science/article/pii/S0304423815303733?via%3Dihub0304-4238https://doi.org/10.1016/j.scienta.2015.12.048Scientia horticulturae 199 : 198-208. (February 2016)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-18T10:06:58Zoai:localhost:20.500.12123/1143instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-18 10:06:58.6INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Identification of peach accessions stability and adaptability in non-balanced trials through years
title Identification of peach accessions stability and adaptability in non-balanced trials through years
spellingShingle Identification of peach accessions stability and adaptability in non-balanced trials through years
Maulion, Evangelina
Durazno
Genotipos
Interacción Genotipo Ambiente
Peaches
Genotypes
Genotype Environment Interaction
title_short Identification of peach accessions stability and adaptability in non-balanced trials through years
title_full Identification of peach accessions stability and adaptability in non-balanced trials through years
title_fullStr Identification of peach accessions stability and adaptability in non-balanced trials through years
title_full_unstemmed Identification of peach accessions stability and adaptability in non-balanced trials through years
title_sort Identification of peach accessions stability and adaptability in non-balanced trials through years
dc.creator.none.fl_str_mv Maulion, Evangelina
Arroyo, Luis Enrique
Daorden, Maria Elena
Valentini, Gabriel Hugo
Cervigni, Gerardo Domingo Lucio
author Maulion, Evangelina
author_facet Maulion, Evangelina
Arroyo, Luis Enrique
Daorden, Maria Elena
Valentini, Gabriel Hugo
Cervigni, Gerardo Domingo Lucio
author_role author
author2 Arroyo, Luis Enrique
Daorden, Maria Elena
Valentini, Gabriel Hugo
Cervigni, Gerardo Domingo Lucio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Durazno
Genotipos
Interacción Genotipo Ambiente
Peaches
Genotypes
Genotype Environment Interaction
topic Durazno
Genotipos
Interacción Genotipo Ambiente
Peaches
Genotypes
Genotype Environment Interaction
dc.description.none.fl_txt_mv Identification of genotypes with acceptable yield and yield stability in different environments is an important issue in plant breeding. Genotype-by-environment interaction (GEI) can alter genotypes performances making the selection of superior material a tedious task for breeders. Consequently, it is necessary to assess the usefulness of different available methods and identify the most suitable for understanding GEI. The objectives of this work were to compare three methods to study genotype stability considering incomplete data sets: (i) Di Rienzo, Guzmán and Casanoves’ test (DGC), (ii) relative yield (RY) and (iii) Piepho’s method. In addition, AMMI (additive main effect and multiplicative interaction) analysis and eight AMMI stability measures SIPC, EV, ASV, Da, FP, B, FA and Za were computed to explore their advantages and disadvantages to select stable entries. The usefulness of the genotype selection index (GSI) and the rank-sum (RS) procedures to identify stable and high-yielding genotypes were evaluated and then compared with the superiority (P) and reliability indexes (I).The association between yield variation and climatic factors as frosts, chilling, heat, rainfall and the interactions among them were also analyzed. 29 peach entries were assessed in four to seven seasons in a completely randomized design with three replications. DGC and RY tests agreed on classifying Fireprince as a stable and high-yielding peach, RY classified 25 entries as stable, while Piepho’s method did not separate the tested genotypes as DGC and RY did. The results of AMMI indicated that 25.06% of total variability was justified by genotypes, 9.76% by environments and 58.97% by GEI. The first five interaction principal components could explain 94.82% of GEI and showed the efficiency of AMMI model to study and understand GEI. The AMMI parameters showed no association with fruit yield, therefore, they could be useful to indicate stable entries but they would not be appropriate to select stable and high-yielding genotypes. The EV and Za indicated static stability while ASV, SIPC, Da, FA and FP pointed out the dynamic stability concept. The performance of the best entries selected by GSI, RS, P and I procedures were not different, therefore, any of them can be used to select superior peach genotypes. Rainfall during endodormancy, rainfall from floral bud endo- to ecodormancy - and heat accumulation during fruit development period showed significant correlation with yield variation across seasons.
http://www.sciencedirect.com/science/article/pii/S0304423815303733?via%3Dihub
EEA San Pedro
Fil: Maulión, Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Arroyo, Luis Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentina
Fil: Daorden, Maria Elena. 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
description Identification of genotypes with acceptable yield and yield stability in different environments is an important issue in plant breeding. Genotype-by-environment interaction (GEI) can alter genotypes performances making the selection of superior material a tedious task for breeders. Consequently, it is necessary to assess the usefulness of different available methods and identify the most suitable for understanding GEI. The objectives of this work were to compare three methods to study genotype stability considering incomplete data sets: (i) Di Rienzo, Guzmán and Casanoves’ test (DGC), (ii) relative yield (RY) and (iii) Piepho’s method. In addition, AMMI (additive main effect and multiplicative interaction) analysis and eight AMMI stability measures SIPC, EV, ASV, Da, FP, B, FA and Za were computed to explore their advantages and disadvantages to select stable entries. The usefulness of the genotype selection index (GSI) and the rank-sum (RS) procedures to identify stable and high-yielding genotypes were evaluated and then compared with the superiority (P) and reliability indexes (I).The association between yield variation and climatic factors as frosts, chilling, heat, rainfall and the interactions among them were also analyzed. 29 peach entries were assessed in four to seven seasons in a completely randomized design with three replications. DGC and RY tests agreed on classifying Fireprince as a stable and high-yielding peach, RY classified 25 entries as stable, while Piepho’s method did not separate the tested genotypes as DGC and RY did. The results of AMMI indicated that 25.06% of total variability was justified by genotypes, 9.76% by environments and 58.97% by GEI. The first five interaction principal components could explain 94.82% of GEI and showed the efficiency of AMMI model to study and understand GEI. The AMMI parameters showed no association with fruit yield, therefore, they could be useful to indicate stable entries but they would not be appropriate to select stable and high-yielding genotypes. The EV and Za indicated static stability while ASV, SIPC, Da, FA and FP pointed out the dynamic stability concept. The performance of the best entries selected by GSI, RS, P and I procedures were not different, therefore, any of them can be used to select superior peach genotypes. Rainfall during endodormancy, rainfall from floral bud endo- to ecodormancy - and heat accumulation during fruit development period showed significant correlation with yield variation across seasons.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017-09-06T13:00:33Z
2017-09-06T13:00:33Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
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info:ar-repo/semantics/articulo
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status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/1143
http://www.sciencedirect.com/science/article/pii/S0304423815303733?via%3Dihub
0304-4238
https://doi.org/10.1016/j.scienta.2015.12.048
url http://hdl.handle.net/20.500.12123/1143
http://www.sciencedirect.com/science/article/pii/S0304423815303733?via%3Dihub
https://doi.org/10.1016/j.scienta.2015.12.048
identifier_str_mv 0304-4238
dc.language.none.fl_str_mv eng
language eng
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dc.source.none.fl_str_mv Scientia horticulturae 199 : 198-208. (February 2016)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
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