Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Alemu, Gadisa and Delesa, Abebe and Duga, Ruth and Dabi, Alemu and Sime, Berhanu and Geleta, Negash and Zegaye, Habtemariam and Solomon, Tafesse and Zewdu, Demeke and Getamesay, Abebe and Asefa, Bayisa and Geleta, Bekele and Badebo, Ayele and Bayisa, Tilahun (2024) Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis. Asian Journal of Research in Agriculture and Forestry, 10 (4). pp. 150-165. ISSN 2581-7418

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Abstract

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.

Item Type: Article
Subjects: e-Archives > Agricultural and Food Science
Depositing User: Managing Editor
Date Deposited: 28 Oct 2024 06:44
Last Modified: 28 Oct 2024 06:44
URI: http://ebooks.abclibraries.com/id/eprint/2214

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