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AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments

This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repe...

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Autores principales: Wodebo, Kibreab Yosefe, Tolemariam, Taye, Demeke, Solomon, Garedew, Weyessa, Tesfaye, Tessema, Zeleke, Muluken, Gemiyu, Deribe, Bedeke, Worku, Wamatu, Jane, Sharma, Mamta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490153/
https://www.ncbi.nlm.nih.gov/pubmed/37687311
http://dx.doi.org/10.3390/plants12173064
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author Wodebo, Kibreab Yosefe
Tolemariam, Taye
Demeke, Solomon
Garedew, Weyessa
Tesfaye, Tessema
Zeleke, Muluken
Gemiyu, Deribe
Bedeke, Worku
Wamatu, Jane
Sharma, Mamta
author_facet Wodebo, Kibreab Yosefe
Tolemariam, Taye
Demeke, Solomon
Garedew, Weyessa
Tesfaye, Tessema
Zeleke, Muluken
Gemiyu, Deribe
Bedeke, Worku
Wamatu, Jane
Sharma, Mamta
author_sort Wodebo, Kibreab Yosefe
collection PubMed
description This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha(−1)). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype–environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype–environment interaction) for identifying high-yielding and stable oat genotypes were very similar.
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spelling pubmed-104901532023-09-09 AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments Wodebo, Kibreab Yosefe Tolemariam, Taye Demeke, Solomon Garedew, Weyessa Tesfaye, Tessema Zeleke, Muluken Gemiyu, Deribe Bedeke, Worku Wamatu, Jane Sharma, Mamta Plants (Basel) Article This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha(−1)). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype–environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype–environment interaction) for identifying high-yielding and stable oat genotypes were very similar. MDPI 2023-08-25 /pmc/articles/PMC10490153/ /pubmed/37687311 http://dx.doi.org/10.3390/plants12173064 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wodebo, Kibreab Yosefe
Tolemariam, Taye
Demeke, Solomon
Garedew, Weyessa
Tesfaye, Tessema
Zeleke, Muluken
Gemiyu, Deribe
Bedeke, Worku
Wamatu, Jane
Sharma, Mamta
AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments
title AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments
title_full AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments
title_fullStr AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments
title_full_unstemmed AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments
title_short AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments
title_sort ammi and gge biplot analyses for mega-environment identification and selection of some high-yielding oat (avena sativa l.) genotypes for multiple environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490153/
https://www.ncbi.nlm.nih.gov/pubmed/37687311
http://dx.doi.org/10.3390/plants12173064
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