,
Gadisa Alemu,
Negash Geleta,
Alemu Dabi,
Berhanu Sime,
Habtemariam Zegaye,
Tafesse Solomon,
Demeke Zewdu,
Abebe Delesa,
Bayisa Asefa,
Abebe Getamesey,
Tamirat Negash,
Bekele Abeyo,
Ayele Badebo,
Yewubdar Sheweye
To predict bread wheat genetic potential across environments and adaption in low moisture stress wheat growing areas of Ethiopia. Multi-location trials were conducted in Ethiopia from 2020 to 2021 in main seasons. A total of advanced genotypes including the checks were arranged in randomized complete block design in a rectangular (row x column) array of plots with two replications. The results showed that, under the linear mixed model, the spatial and factor analytic models were efficient methods of data analysis for this study. By ranking average best linear unbiased prediction (BLUPs) within clusters, the 13 bread wheat environments were clustered into three mega environments (C1, C2, and C3) for the trait grain yield. This method used as a selection indicator, assisting in the selection of superior and adaptable types. The predicted performance of genotypes based on BLUP values averaged across correlated settings of C1 and C2, eliminating C3 due to low genetic correlation with the other trials and low genetic variation. Based on these clusters, the genotypes with the highest potential EBW192350 and EBW192369 were selected for a subsequent verification study that might potentially use them as a released variety. For genetic variance, the estimates for variance component parameters ranged from 0.069 to 2.896 and error variance, they ranged from 0.175 to 1.002. Therefore, increasing the application of this efficient analysis method will improve the selection of superior bread wheat varieties. The two genotypes can be further verified using national performance trials/ or verified in farmers’ fields for registration and commercialization.
| Published in | American Journal of Biological and Environmental Statistics (Volume 10, Issue 3) |
| DOI | 10.11648/j.ajbes.20241003.15 |
| Page(s) | 76-86 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Average Yield, BLUPs, Cluster, Factor Analytic, Genetic Variation, Spatial, Target Environment
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APA Style
Duga, R., Alemu, G., Geleta, N., Dabi, A., Sime, B., et al. (2024). Multi Environment Trials and Adaption of Advanced Bread Wheat (Triticum aestivum L.) Genotypes in Low Moisture Stress Areas of Ethiopia. American Journal of Biological and Environmental Statistics, 10(3), 76-86. https://doi.org/10.11648/j.ajbes.20241003.15
ACS Style
Duga, R.; Alemu, G.; Geleta, N.; Dabi, A.; Sime, B., et al. Multi Environment Trials and Adaption of Advanced Bread Wheat (Triticum aestivum L.) Genotypes in Low Moisture Stress Areas of Ethiopia. Am. J. Biol. Environ. Stat. 2024, 10(3), 76-86. doi: 10.11648/j.ajbes.20241003.15
AMA Style
Duga R, Alemu G, Geleta N, Dabi A, Sime B, et al. Multi Environment Trials and Adaption of Advanced Bread Wheat (Triticum aestivum L.) Genotypes in Low Moisture Stress Areas of Ethiopia. Am J Biol Environ Stat. 2024;10(3):76-86. doi: 10.11648/j.ajbes.20241003.15
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Download@article{10.11648/j.ajbes.20241003.15,
author = {Rut Duga and Gadisa Alemu and Negash Geleta and Alemu Dabi and Berhanu Sime and Habtemariam Zegaye and Tafesse Solomon and Demeke Zewdu and Abebe Delesa and Bayisa Asefa and Abebe Getamesey and Tamirat Negash and Bekele Abeyo and Ayele Badebo and Yewubdar Sheweye},
title = {Multi Environment Trials and Adaption of Advanced Bread Wheat (Triticum aestivum L.) Genotypes in Low Moisture Stress Areas of Ethiopia
},
journal = {American Journal of Biological and Environmental Statistics},
volume = {10},
number = {3},
pages = {76-86},
doi = {10.11648/j.ajbes.20241003.15},
url = {https://doi.org/10.11648/j.ajbes.20241003.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20241003.15},
abstract = {To predict bread wheat genetic potential across environments and adaption in low moisture stress wheat growing areas of Ethiopia. Multi-location trials were conducted in Ethiopia from 2020 to 2021 in main seasons. A total of advanced genotypes including the checks were arranged in randomized complete block design in a rectangular (row x column) array of plots with two replications. The results showed that, under the linear mixed model, the spatial and factor analytic models were efficient methods of data analysis for this study. By ranking average best linear unbiased prediction (BLUPs) within clusters, the 13 bread wheat environments were clustered into three mega environments (C1, C2, and C3) for the trait grain yield. This method used as a selection indicator, assisting in the selection of superior and adaptable types. The predicted performance of genotypes based on BLUP values averaged across correlated settings of C1 and C2, eliminating C3 due to low genetic correlation with the other trials and low genetic variation. Based on these clusters, the genotypes with the highest potential EBW192350 and EBW192369 were selected for a subsequent verification study that might potentially use them as a released variety. For genetic variance, the estimates for variance component parameters ranged from 0.069 to 2.896 and error variance, they ranged from 0.175 to 1.002. Therefore, increasing the application of this efficient analysis method will improve the selection of superior bread wheat varieties. The two genotypes can be further verified using national performance trials/ or verified in farmers’ fields for registration and commercialization.
},
year = {2024}
}
TY - JOUR T1 - Multi Environment Trials and Adaption of Advanced Bread Wheat (Triticum aestivum L.) Genotypes in Low Moisture Stress Areas of Ethiopia AU - Rut Duga AU - Gadisa Alemu AU - Negash Geleta AU - Alemu Dabi AU - Berhanu Sime AU - Habtemariam Zegaye AU - Tafesse Solomon AU - Demeke Zewdu AU - Abebe Delesa AU - Bayisa Asefa AU - Abebe Getamesey AU - Tamirat Negash AU - Bekele Abeyo AU - Ayele Badebo AU - Yewubdar Sheweye Y1 - 2024/09/26 PY - 2024 N1 - https://doi.org/10.11648/j.ajbes.20241003.15 DO - 10.11648/j.ajbes.20241003.15 T2 - American Journal of Biological and Environmental Statistics JF - American Journal of Biological and Environmental Statistics JO - American Journal of Biological and Environmental Statistics SP - 76 EP - 86 PB - Science Publishing Group SN - 2471-979X UR - https://doi.org/10.11648/j.ajbes.20241003.15 AB - To predict bread wheat genetic potential across environments and adaption in low moisture stress wheat growing areas of Ethiopia. Multi-location trials were conducted in Ethiopia from 2020 to 2021 in main seasons. A total of advanced genotypes including the checks were arranged in randomized complete block design in a rectangular (row x column) array of plots with two replications. The results showed that, under the linear mixed model, the spatial and factor analytic models were efficient methods of data analysis for this study. By ranking average best linear unbiased prediction (BLUPs) within clusters, the 13 bread wheat environments were clustered into three mega environments (C1, C2, and C3) for the trait grain yield. This method used as a selection indicator, assisting in the selection of superior and adaptable types. The predicted performance of genotypes based on BLUP values averaged across correlated settings of C1 and C2, eliminating C3 due to low genetic correlation with the other trials and low genetic variation. Based on these clusters, the genotypes with the highest potential EBW192350 and EBW192369 were selected for a subsequent verification study that might potentially use them as a released variety. For genetic variance, the estimates for variance component parameters ranged from 0.069 to 2.896 and error variance, they ranged from 0.175 to 1.002. Therefore, increasing the application of this efficient analysis method will improve the selection of superior bread wheat varieties. The two genotypes can be further verified using national performance trials/ or verified in farmers’ fields for registration and commercialization. VL - 10 IS - 3 ER -
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia
CIMMYT Ethiopia Office, Addis Ababa, Ethiopia
CIMMYT Ethiopia Office, Addis Ababa, Ethiopia
Dabra Zeit Agricultural Research Center, Ethiopia Institute of Agricultural Research, Bishoftu, Ethiopia
