@article{10.11648/j.bio.20221003.15,
  author = {Berhanu Sime and Gudeta Nepir and Gadisa Alemu},
  title = {Analysis of Genotype by Environment Interaction for Agronomic Traits of Bread Wheat (Triticum aestivum L) Genotypes in Oromia, Ethiopia},
  journal = {American Journal of Bioscience and Bioengineering},
  volume = {10},
  number = {3},
  pages = {70-77},
  doi = {10.11648/j.bio.20221003.15},
  url = {https://doi.org/10.11648/j.bio.20221003.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.20221003.15},
  abstract = {Twenty five bread wheat genotypes were tested in 2019/20 cropping season across six environments viz Kulumsa, Bekoji, Assasa, Arsi-Robe, Debre-Zeit and Holeta in alpha lattice design replicated trice. The study cried out with objectives to determine the effect of genotype, environment, and GEI on agronomic traits and to identify stable genotype for specific adaptation. Data was collected for yield and component traits and subjected to different statistical procedures. ANOVA revealed highly significant differences (p < 0.01) among 25 genotypes for grain yield and other studied traits. Combined ANOVA depicted highly significant differences among environments. Genotype ETBW9089 ranked first in mean grain yield in four of the six environments. It showed highest mean grain yield of 9.03 t/ha at Kulumsa, and also showed highest yield (4.00 t/ha) in the lowest yielding environment, Holeta. The proportions total sum of squares for genotype, environment and GEI for grain yield were 5.34%, 84.25% and 10.40%, respectively. Having the largest proportion of sum of squares, the environment had the highest impact on genotype yield performance. The combined ANOVA obtained from AMMI model showed highly significant differences for environment, genotype and GEI. The combined results showed that bread wheat grain yield was significantly affected by the environment (p < 0.01) which explained 82.44% of the total variation, indicating that the environments were highly variable. While genotype and GEI captured 6.23% and 11.33% of the total sum of squares, respectively. The AMMI model demonstrated the presence of significant GEI. The first and second IPCA were highly significantly (p < 0.01) contributed for 88% of the GEI of which PC1 and PC2 accounted for 62.25% and 25.74%, respectively of the variations explained by GEI. Considering both ranks of ASV and grain yield using yield stability index (YSI), BW174466 followed by BW174463) and ETBW9094 were stable genotypes. The results of AMMI’s first four selection of genotypes per environments and GGE-biplot revealed that ETBW9089 is an ideal and promising genotype across most test environments. Moreover, Bekoji was the best discriminating environment to screen bread wheat genotypes. ETBW9089 genotype is suggested to be further evaluated for commercial release.},
 year = {2022}
}

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TY  - JOUR
T1  - Analysis of Genotype by Environment Interaction for Agronomic Traits of Bread Wheat (Triticum aestivum L) Genotypes in Oromia, Ethiopia
AU  - Berhanu Sime
AU  - Gudeta Nepir
AU  - Gadisa Alemu
Y1  - 2022/06/08
PY  - 2022
N1  - https://doi.org/10.11648/j.bio.20221003.15
DO  - 10.11648/j.bio.20221003.15
T2  - American Journal of Bioscience and Bioengineering
JF  - American Journal of Bioscience and Bioengineering
JO  - American Journal of Bioscience and Bioengineering
SP  - 70
EP  - 77
PB  - Science Publishing Group
SN  - 2328-5893
UR  - https://doi.org/10.11648/j.bio.20221003.15
AB  - Twenty five bread wheat genotypes were tested in 2019/20 cropping season across six environments viz Kulumsa, Bekoji, Assasa, Arsi-Robe, Debre-Zeit and Holeta in alpha lattice design replicated trice. The study cried out with objectives to determine the effect of genotype, environment, and GEI on agronomic traits and to identify stable genotype for specific adaptation. Data was collected for yield and component traits and subjected to different statistical procedures. ANOVA revealed highly significant differences (p < 0.01) among 25 genotypes for grain yield and other studied traits. Combined ANOVA depicted highly significant differences among environments. Genotype ETBW9089 ranked first in mean grain yield in four of the six environments. It showed highest mean grain yield of 9.03 t/ha at Kulumsa, and also showed highest yield (4.00 t/ha) in the lowest yielding environment, Holeta. The proportions total sum of squares for genotype, environment and GEI for grain yield were 5.34%, 84.25% and 10.40%, respectively. Having the largest proportion of sum of squares, the environment had the highest impact on genotype yield performance. The combined ANOVA obtained from AMMI model showed highly significant differences for environment, genotype and GEI. The combined results showed that bread wheat grain yield was significantly affected by the environment (p < 0.01) which explained 82.44% of the total variation, indicating that the environments were highly variable. While genotype and GEI captured 6.23% and 11.33% of the total sum of squares, respectively. The AMMI model demonstrated the presence of significant GEI. The first and second IPCA were highly significantly (p < 0.01) contributed for 88% of the GEI of which PC1 and PC2 accounted for 62.25% and 25.74%, respectively of the variations explained by GEI. Considering both ranks of ASV and grain yield using yield stability index (YSI), BW174466 followed by BW174463) and ETBW9094 were stable genotypes. The results of AMMI’s first four selection of genotypes per environments and GGE-biplot revealed that ETBW9089 is an ideal and promising genotype across most test environments. Moreover, Bekoji was the best discriminating environment to screen bread wheat genotypes. ETBW9089 genotype is suggested to be further evaluated for commercial release.
VL  - 10
IS  - 3
ER  - 

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