Cargando…
Yield of soybean genotypes identified through GGE biplot and path analysis
Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs),...
Autores principales: | , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555624/ https://www.ncbi.nlm.nih.gov/pubmed/36223386 http://dx.doi.org/10.1371/journal.pone.0274726 |
_version_ | 1784806899718291456 |
---|---|
author | Silva, Welder José dos Santos de Alcântara Neto, Francisco Al-Qahtani, Wahidah H. Okla, Mohammad K. Al-Hashimi, Abdulrahman Vieira, Paulo Fernando de Melo Jorge Gravina, Geraldo de Amaral Zuffo, Alan Mario Dutra, Alexson Filgueiras Carvalho, Leonardo Castelo Branco de Sousa, Ricardo Silva Pereira, Arthur Prudêncio de Araujo Leite, Wallace de Sousa da Silva Júnior, Gabriel Barbosa da Silva, Adriana Conceição Leite, Marcos Renan Lima Lustosa Sobrinho, Renato AbdElgawad, Hamada |
author_facet | Silva, Welder José dos Santos de Alcântara Neto, Francisco Al-Qahtani, Wahidah H. Okla, Mohammad K. Al-Hashimi, Abdulrahman Vieira, Paulo Fernando de Melo Jorge Gravina, Geraldo de Amaral Zuffo, Alan Mario Dutra, Alexson Filgueiras Carvalho, Leonardo Castelo Branco de Sousa, Ricardo Silva Pereira, Arthur Prudêncio de Araujo Leite, Wallace de Sousa da Silva Júnior, Gabriel Barbosa da Silva, Adriana Conceição Leite, Marcos Renan Lima Lustosa Sobrinho, Renato AbdElgawad, Hamada |
author_sort | Silva, Welder José dos Santos |
collection | PubMed |
description | Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs), through GGE biplot analysis that combines the main effects of the genotype (G) plus the genotype-by-environment (G×E) interaction. As well as, through path analysis, determine the direct and indirect influences of yield components on soybean grain yield, as a genotype selection strategy. Eight soybean genotypes from the breeding program of Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) were evaluated in field trials using a randomized block experimental design, in an 8 x 8 factorial scheme with four replications in eight different environments of the Cerrado of Northeastern Brazil during two crop seasons. Phenotypic performance data were measured for the number of days to flowering (NDF), height of first pod insertion (HPI), final plant height (FPH), number of days to maturity (NDM), mass of 100 grains (M100) and grain yield (GY). The results revealed that the variance due to genotype, environment, and G×E interaction was highly significant (P < 0.001) for all traits. The ST820RR, BRS 333RR, BRS SambaíbaRR, M9144RR and M9056RR genotypes exhibited the greatest GY stability in the environments studied. However, only the BRS 333RR genotype, followed by the M9144RR, was able to combine good productive performance with high yield stability. The study also revealed that the HPI and the NDM are traits that should be prioritized in the selection of soybean genotypes due to the direct and indirect effects on the GY. |
format | Online Article Text |
id | pubmed-9555624 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95556242022-10-13 Yield of soybean genotypes identified through GGE biplot and path analysis Silva, Welder José dos Santos de Alcântara Neto, Francisco Al-Qahtani, Wahidah H. Okla, Mohammad K. Al-Hashimi, Abdulrahman Vieira, Paulo Fernando de Melo Jorge Gravina, Geraldo de Amaral Zuffo, Alan Mario Dutra, Alexson Filgueiras Carvalho, Leonardo Castelo Branco de Sousa, Ricardo Silva Pereira, Arthur Prudêncio de Araujo Leite, Wallace de Sousa da Silva Júnior, Gabriel Barbosa da Silva, Adriana Conceição Leite, Marcos Renan Lima Lustosa Sobrinho, Renato AbdElgawad, Hamada PLoS One Research Article Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs), through GGE biplot analysis that combines the main effects of the genotype (G) plus the genotype-by-environment (G×E) interaction. As well as, through path analysis, determine the direct and indirect influences of yield components on soybean grain yield, as a genotype selection strategy. Eight soybean genotypes from the breeding program of Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) were evaluated in field trials using a randomized block experimental design, in an 8 x 8 factorial scheme with four replications in eight different environments of the Cerrado of Northeastern Brazil during two crop seasons. Phenotypic performance data were measured for the number of days to flowering (NDF), height of first pod insertion (HPI), final plant height (FPH), number of days to maturity (NDM), mass of 100 grains (M100) and grain yield (GY). The results revealed that the variance due to genotype, environment, and G×E interaction was highly significant (P < 0.001) for all traits. The ST820RR, BRS 333RR, BRS SambaíbaRR, M9144RR and M9056RR genotypes exhibited the greatest GY stability in the environments studied. However, only the BRS 333RR genotype, followed by the M9144RR, was able to combine good productive performance with high yield stability. The study also revealed that the HPI and the NDM are traits that should be prioritized in the selection of soybean genotypes due to the direct and indirect effects on the GY. Public Library of Science 2022-10-12 /pmc/articles/PMC9555624/ /pubmed/36223386 http://dx.doi.org/10.1371/journal.pone.0274726 Text en © 2022 Silva et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Silva, Welder José dos Santos de Alcântara Neto, Francisco Al-Qahtani, Wahidah H. Okla, Mohammad K. Al-Hashimi, Abdulrahman Vieira, Paulo Fernando de Melo Jorge Gravina, Geraldo de Amaral Zuffo, Alan Mario Dutra, Alexson Filgueiras Carvalho, Leonardo Castelo Branco de Sousa, Ricardo Silva Pereira, Arthur Prudêncio de Araujo Leite, Wallace de Sousa da Silva Júnior, Gabriel Barbosa da Silva, Adriana Conceição Leite, Marcos Renan Lima Lustosa Sobrinho, Renato AbdElgawad, Hamada Yield of soybean genotypes identified through GGE biplot and path analysis |
title | Yield of soybean genotypes identified through GGE biplot and path analysis |
title_full | Yield of soybean genotypes identified through GGE biplot and path analysis |
title_fullStr | Yield of soybean genotypes identified through GGE biplot and path analysis |
title_full_unstemmed | Yield of soybean genotypes identified through GGE biplot and path analysis |
title_short | Yield of soybean genotypes identified through GGE biplot and path analysis |
title_sort | yield of soybean genotypes identified through gge biplot and path analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555624/ https://www.ncbi.nlm.nih.gov/pubmed/36223386 http://dx.doi.org/10.1371/journal.pone.0274726 |
work_keys_str_mv | AT silvawelderjosedossantos yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT dealcantaranetofrancisco yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT alqahtaniwahidahh yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT oklamohammadk yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT alhashimiabdulrahman yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT vieirapaulofernandodemelojorge yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT gravinageraldodeamaral yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT zuffoalanmario yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT dutraalexsonfilgueiras yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT carvalholeonardocastelobranco yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT desousaricardosilva yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT pereiraarthurprudenciodearaujo yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT leitewallacedesousa yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT dasilvajuniorgabrielbarbosa yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT dasilvaadrianaconceicao yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT leitemarcosrenanlima yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT lustosasobrinhorenato yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis AT abdelgawadhamada yieldofsoybeangenotypesidentifiedthroughggebiplotandpathanalysis |