Multi Environment Trials and Adaption of Advanced Bread Wheat (<i>Triticum aestivum </i>L.) Genotypes in Low Moisture Stress Areas of Ethiopia

Rut Duga; 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

doi:doi:10.11648/j.ajbes.20241003.15

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Multi Environment Trials and Adaption of Advanced Bread Wheat (Triticum aestivum L.) Genotypes in Low Moisture Stress Areas of Ethiopia

Rut Duga^*

, 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

Published in American Journal of Biological and Environmental Statistics (Volume 10, Issue 3)

Received: 2 July 2024 Accepted: 30 July 2024 Published: 26 September 2024

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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.

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

Keywords

Average Yield, BLUPs, Cluster, Factor Analytic, Genetic Variation, Spatial, Target Environment

References

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[15]	Piepho, H. P., Möhring, J., Melchinger, A. E. and Büchse, A., 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161(1-2), pp. 209-228. https://doi.org/10.1007/s10681-007-9449-8
[16]	Sharifi, P., Abbasian, A., Mohaddessi, A., 2021. Evaluation the mean performance and stability of rice genotypes by combining features of AMMI and BLUP techniques and selection based on multiple traits. Plant Genetic Researches, 7(2), pp. 163-180. http://pgr.lu.ac.ir/article-1-200-en.html
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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

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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

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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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@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}
}

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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  -

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Author Information

Rut Duga

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

Contact Email

http://orcid.org/0009-0000-4867-9890
Gadisa Alemu

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Negash Geleta

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Alemu Dabi

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Berhanu Sime

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Habtemariam Zegaye

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Tafesse Solomon

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Demeke Zewdu

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Abebe Delesa

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Bayisa Asefa

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Abebe Getamesey

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Tamirat Negash

Kulumsa Agricultural Research Center, Ethiopia Institute of Agricultural Research, Asella, Ethiopia

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Bekele Abeyo

CIMMYT Ethiopia Office, Addis Ababa, Ethiopia

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Ayele Badebo

CIMMYT Ethiopia Office, Addis Ababa, Ethiopia

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Yewubdar Sheweye

Dabra Zeit Agricultural Research Center, Ethiopia Institute of Agricultural Research, Bishoftu, Ethiopia

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Plain Text BibTeX RIS

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

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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

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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

Copy | 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}
}

Copy | Download

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  -

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