[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
About the Journal
Editorial Board
Aims& Scopes
History
Journal News
Journal metrics
Peer Review Process
Thematic index
Articles archive::
All Issues
Current Issue
For Authors::
Guide for authors
Publication Ethics
Commitment form
Conflict of Interest Form
Manuscript submission steps:
Style of the Manuscript
Submission Form
For Reviewers::
Reviewer Guide
Reviewers
Registration::
Registration Information
Registration Form
Contact us::
Contact Information
Contact us
Site Facilities::
Site map
Search contents
FAQ
Top 10 contents
Inform to friends
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 22, Issue 2 (Summer 2020 2020) ::
علوم زراعی 2020, 22(2): 183-197 Back to browse issues page
Evaluation of adaptability and seed yield stability of soybean (Glycine max L. Merril) promising lines using GGE biplot analysis
Hamid reza Babaei , , Nasrin Razmi , Samie Raeisi , Hosein Sabzi
Field and Horticultural Crops Sciences Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
Abstract:   (2652 Views)
Selection of adapted genotypes with high seed yield and yield stability is the goal of soybean breeding programs. To evaluate the adaptability and seed yield stability of soybean promising lines, 19 promising lines and cv. Williams as check were evaluated using randomized complete block design with four replications in four locations: Karaj, Gorgan, Moghan and Khoramabad in Iran during two growing seasons (2013 and 2014). GGE biplot analysis was employed to evaluate the adaptability and seed yield stability. Combined analysis of variance showed thatyear, location, genotype, year × location, year × genotype, location × genotype and genotype × location × year interaction effects were significant on studied traits. The contribution of year, location and genotype variance to total variance was 0.01, 0.60 and 0.02, respectively, indicating considerable contribution of location variance. The first two components of PC1 and PC2 explained overall 58% of total observed variation of genotype and genotype × environment (G + GE). In this study, three mega-environments were identified. The first mega-environment included: E2 (Karaj 2014), E5 (Moghan 2013) and E8 (Gorgan 2014) and G16 was the superior genotype in this mega-environment. The second mega-environment included: E3 (Khorramabad 2013) and E4 (Khorramabad 2014) and G8 was the superior genotype in this mega-environment. Third mega-environment consisted: E1 (Karaj 2013) and E7 (Gorgan 2013) and G17 was the superior genotype in this mega- environment. Biplot analysis showed that genotypes: G17 (L85-3059) with 2702 kg.ha-1 and G16 (L12/Chaleston × Mustang) with 2750 kg.ha-1 were highly adapted genotypes with high seed yield and yield stability. The E7 environment (Gorgan, 2013) was the most desirable environment in respect to its discriminating ability among soybean genotypes and the best representative of the target environments.
Keywords: Desirable environment, Desirable genotype, GGE biplot analysis and Genotype × environment interaction.
Full-Text [PDF 572 kb]   (877 Downloads)    
Type of Study: Scientific & Research | Subject: Special
Received: 2019/08/28 | Accepted: 2020/06/28 | Published: 2020/08/31
References
1. Akura, M., S.Taner, Y. Kaya. 2011. Evaluation of bread wheat genotypes under irrigated multi-environment conditions using GGE biplot analyses. Agriculture Journal. 98(1): 35-40.
2. Amira, J. O., D. K. Ojo, O. J. Ariyo, O. A. Oduwaye, and M. A. Ayo-Vaughan. 2013. Relative Discriminating Powers of GGE and AMMI Models in the selection of Tropical Soybean Genotypes. African Crop Science Journal. 21 (1): 67-73.
3. Alake, C. O. and O. J. Ariyo. 2012. Comparative Analysis of Genotype × Environment Interaction Techniques in West African Okra. Journal of Agricultural Science. 4(4): 135-150. [DOI:10.5539/jas.v4n4p135]
4. Atnaf, M., S. Kidane, S. Abadi, and Z. Fisha. 2013. GGE biplots to analyse soybean multi-environment yield trial data in north Western Ethiopia. Journal of Plant Breeding and Crop Science. 5: 245-254. [DOI:10.5897/JPBCS13.0403]
5. Basford, K. E., and M. Cooper. 1998. Genotype by environment interactions and some considerations of their implication for wheat breeding in Australia. Australian Journal of Agricultural Research. 49: 154-175. [DOI:10.1071/A97035]
6. Bhartiya, A., J. P. Aditya,K. S. Pushpendra, J. P. Purwar and A. Agarwal. 2017. AMMI & GGE biplot analysis of multi environment yield trial of soybean in North Western Himalayan state Uttarakhand of India. Legume Research Journal. 40 (2): 306-312. [DOI:10.18805/lr.v0iOF.3548]
7. Chaudhary, K. J. and J. Wu. 2012. Stability analysis for yeild and seed quality of soybean (Glycine max) across different environment in eastern South Dakota. Annual Conference on Applied Statistics in Agriculture [Online]. Available at http://newprairiepress.org/agstatconference/2012/proceedings/11 [DOI:10.4148/2475-7772.1033]
8. Eberhart, S. A. And W. A. Russel.1966. Stability parameters for comparing varieties. Crop Sci. 6: 36-40. [DOI:10.2135/cropsci1966.0011183X000600010011x]
9. Finlay, K. W. and G. N. Wilkinson. 1963. The analysis of adaptation in a plant breeding program. Australian Journal of Agricultural Research. 14: 742 -754. [DOI:10.1071/AR9630742]
10. Eskridge, K. M. 1996. Analysis of multi environment trial using the probability of outperforming a check. p. 273 -307. In : M. S. Kang and Guach, J., (eds. ) Genotype by Environment Interaction. CRC Press. [DOI:10.1201/9781420049374.ch10]
11. Gauch, H. G. J. and R. W. Zobel. 1996. AMMI analysis of yield trials. p. 85 - 122. In : M. S. Kang and Guach, J., (eds.) Genotype by environment interaction. CRC Press. [DOI:10.1201/9781420049374.ch4]
12. Gurmu, F., H. Mohammed, G. Alemaw. 2009. Genotype x Environment interactions and stability of soybean for grain yield and nutrition quality. African Crop Science Journal. 17: 87 - 99. [DOI:10.4314/acsj.v17i2.54202]
13. Kang, M. S. 1993. Simultaneous selection for yield and stability in crop performance Trials. Consequences for growers. Agronomy Journal. 85: 754 -757. [DOI:10.2134/agronj1993.00021962008500030042x]
14. Pacheco, R. M., J. B. Duarte, P. I. M. Souza, S. A. Silva, and J. Nunes. 2009. Key locations for soybean genotype assessment in Central Brazil. Pesquisa Agropecuária Brasileia. 44 ( 5): 478 - 486. [DOI:10.1590/S0100-204X2009000500007]
15. Payne, R.W., S. A. Harding,D. A. Murray, and D. M. Soutar. 2009. GenStat Release 12. Published by VSN International, 5 The Waterhouse, Waterhouse Street, Hemel Hempstead, Hertfordshire HP1 1ES, UK.
16. Silveira, D. A., L. F. Pricinotto, M. Nardino, C. A. Bahry, C. E. Cavenaghi Prete, L. Cruz. 2016. Determination of the adaptability and stability of soybean cultivars in different locations and at different sowing times in Parana state using the AMMI and Eberhart and Russel methods [Online]. Available at https://www.researchgate.net/publication/311849977 [DOI:10.5433/1679-0359.2016v37n6p3973]
17. Yan, W. 1999. The interconnectedness among the traits of wheat and its implication in breeding for higher yield. Cereal Crops. (1): 43 - 45.
18. Yan, W. 2000. Singular-value partitioning in biplot analysis of multi -environment trial data. Agronomy Journal. 94: 990 - 996. [DOI:10.2134/agronj2002.0990]
19. Yan, W. and I. Rajcan. 2002. Biplot analysis of sites and trait relations of soybean in Ontario. Crop Science. 42: 11-20. [DOI:10.2135/cropsci2002.1100]
20. Yan, W. and M. S. Kang. 2003. GGE biplot analysis : A graphical tool for breeders, Geneticists and agronomists. CRC Press. [DOI:10.1201/9781420040371]
21. Yan, W., M. S. Kang, B. Ma, S. woods, and P. L. Cornelius. 2007. GGE biplot vs. AMMI analysis of genotype by environment data. Crop Science. 47: 643 - 655. [DOI:10.2135/cropsci2006.06.0374]
22. Yates, F. and W. G. Cochran. 1956. The analysis of experiments. Journal of Agronomic Science. 14: 742 -754.
23. Akmal, C. M. Gunarsih and M. Y. Samaullah. 2014. Adaptation and stability of aromatic rice lines in North Sumatera (in Indonesian). Food Crop Res. J. 33 (1): 9-16 [DOI:10.21082/jpptp.v33n1.2014.p9-16]
24. Alake, C. O. and O. J. Ariyo. 2012. Comparative analysis of genotype × environment interaction techniques in West African okra. J. Agric. Sci. 4(4): 135-150. [DOI:10.5539/jas.v4n4p135]
25. Amira, J. O., D. K. Ojo, O. J. Ariyo, O. A. Oduwaye and M. A. Ayo-Vaughan. 2013. Relative discriminating powers of GGE and AMMI models in the selection of tropical soybean genotypes. Afr. Crop Sci. J. 21 (1): 67-73.
26. Atnaf, M., S. Kidane, S. Abadi and Z. Fisha. 2013. GGE biplots to analyse soybean multi-environment yield trial data in north Western Ethiopia. J. Plant Breed. Crop Sci. 5: 245-254. [DOI:10.5897/JPBCS13.0403]
27. Babaei, H. R., M. Bagheri and N. Razmi. 2016. Study on adaptability of new soybean pure lines. Registered Final Report of Research Project (Registeration number: 55194 - 5.3.2019). Agricultural Research, Education and Extension Organization (AREEO). (In Persian with English abstract).
28. Basford, K. E. and M. Cooper. 1998. Genotype by environment interactions and some considerations of their implication for wheat breeding in Australia. Aust. J. Agric. Res. 49: 154-175. [DOI:10.1071/A97035]
29. Bhartiya, A., J. P. Aditya, K. S. Pushpendra, J. P. Purwar and A. Agarwal. 2017. AMMI & GGE biplot analysis of multi environment yield trial of soybean in North Western Himalayan state Uttarakhand of India. Legume Res. J. 40 (2): 306-312. [DOI:10.18805/lr.v0iOF.3548]
30. Chaudhary, K. J. and J. Wu. 2012. Stability analysis for yield and seed quality of soybean (Glycine max) across different environment in eastern South Dakota. Annual Conference on Applied Statistics in Agriculture [Online]. Available at http://newprairiepress.org/agstatconference/2012/proceedings/11 [DOI:10.4148/2475-7772.1033]
31. Crossa, J., P. L. Cornelius and W. Yan. 2002. Biplots of linear-bilinear models for studying crossover genotype × environment interaction. Crop Sci. 42 (2): 619- 633. [DOI:10.2135/cropsci2002.6190]
32. Eberhart, S. A. And W. A. Russel.1966. Stability parameters for comparing varieties. Crop Sci. 6: 36-40. [DOI:10.2135/cropsci1966.0011183X000600010011x]
33. Eskridge, K. M. 1996. Analysis of multi environment trial using the probability of outperforming a check. p. 273 -307. In: M. S. Kang and Guach, J., (Eds.) Genotype by Environment Interaction. CRC Press. [DOI:10.1201/9781420049374.ch10]
34. Finlay, K. W. and G. N. Wilkinson. 1963. The analysis of adaptation in a plant breeding program. Aust. J. Agric. Res. 14: 742 -754. [DOI:10.1071/AR9630742]
35. Jandong E. A., M. I. Uguru and B. C. Oyiga. 2011. Determination of yield stability of seven soybean (Glycine max) genotypes across diverse soil pH levels using GGE biplot analysis. J. Appl. Bio. Sci. 43: 2924- 2941.
36. Kang, M. S. 1993. Simultaneous selection for yield and stability in crop performance Trials. Consequences for growers. Agron. J. 85: 754 -757. [DOI:10.2134/agronj1993.00021962008500030042x]
37. Karakus, M. and U. A. Yildirim. 2019. GGE biplot analysis of genotype × environment interaction in soybean grown as a second crop. Turk. J. Field Crops. 24(2): 145-154.
38. Kocaturk, M, P. Cubuku, A. T. Goksoy, M. Sincik, E. Ilke, A. Kadiroglu, Y. Vurarak, Y. Sahin, M.
39. Mohammadi, R., M. Armion, H. Esmaeilzadeh, M. Eskandari. 2013. Analysis of genotype × environment interaction for grain yield in rainfed durum wheat. Iran. J. Dryland Agric. J. 4 (1): 1-16 (in Persian with English abstract).
40. Makinde S.C. O. and O. J. Ariyo. 2011. Analysis of genotype× environment interaction of groundnut (Arachis hypogaea L.). Malaysian J. Appl. Biol. 40 (2): 19-26.
41. Payne, R. W., S. A. Harding, D. A. Murray and D. M. Soutar. 2009. GenStat Release 12. Published by VSN International, 5 The Waterhouse, Waterhouse Street, Hemel Hempstead, Hertfordshire HP1 1ES, UK.
42. Pourdad, S. S. and M. Jamshid-Mogaddam .2013. Study on genotype × environment interaction through GGE Biplot for seed yield in spring rapeseed (Brassica Napus L.) in rainfed condition. J. Crop Breed. 12(5): 1- 14 (in Persian with English abstract).
43. Ramos, J. E. U., R. L. Brogin, V. P. C. Godinho, F. J. E. Botelho, F. D. Tardin and P. E. Teodoro. 2017. Identification of soybean genotypes with high stability for the Brazilian macro-region 402 via biplot analysis. Genet. Mol. Res. 16(3): 1-10. [DOI:10.4238/gmr16039786]
44. Silveira, D. A., L. F. Pricinotto, M. Nardino, C. A. Bahry, C. E. Cavenaghi Prete and L. Cruz. 2016. Determination of the adaptability and stability of soybean cultivars in different locations and at different sowing times in Parana state using the AMMI and Eberhart and Russel methods [Online]. Available at https://www.researchgate.net/publication/311849977 [DOI:10.5433/1679-0359.2016v37n6p3973]
45. Van Euwijk F. A., A. Elgersma. 1993. Incorporating environmental information in an analysis of G × E interaction for seed yield in perennial ryegrass. Heredity, 70: 447-457. [DOI:10.1038/hdy.1993.66]
46. Yan, W. 1999. The interconnectedness among the traits of wheat and its implication in breeding for higher yield. Cereal Crops. (1): 43 - 45.
47. Yan, W. 2000. Singular-value partitioning in biplot analysis of multi -environment trial data. Agron. J. 94: 990 - 996. [DOI:10.2134/agronj2002.0990]
48. Yan, W. and I. Rajcan. 2002. Biplot analysis of sites and trait relations of soybean in Ontario. Crop Sci. 42: 11-20. [DOI:10.2135/cropsci2002.1100]
49. Yan, W. and M. S. Kang. 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists. CRC Press. [DOI:10.1201/9781420040371]
50. Yan, W., M. S. Kang, B. Ma, S. woods and P. L. Cornelius. 2007. GGE biplot vs. AMMI analysis of genotype by environment data. Crop Sci. 47: 643 - 655. [DOI:10.2135/cropsci2006.06.0374]
51. Yang, R., J. Crossa, P. L. Cornelius and J. Burgueno. 2009. Biplot analysis of GEI effect. Crop Sci. 49: 1564-1576. [DOI:10.2135/cropsci2008.11.0665]
52. Yan, W. and N. A. Tinker. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 86: 623-645. [DOI:10.4141/P05-169]
53. Zhang, M., M. S. Kang, P. F. Reese Jr and H. L. Bhardwaj. 2006. Soybean cultivar evaluation via GGE biplot analysis. J. New Seeds. 7 (4): 37-50. [DOI:10.1300/J153v07n04_03]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA

XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Babaei H R, Razmi , N, Raeisi S, Sabzi H. Evaluation of adaptability and seed yield stability of soybean (Glycine max L. Merril) promising lines using GGE biplot analysis. علوم زراعی 2020; 22 (2) :183-197
URL: http://agrobreedjournal.ir/article-1-1027-en.html
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 22, Issue 2 (Summer 2020 2020) Back to browse issues page
نشریه علوم زراعی ایران Iranian Journal of Crop Sciences
Persian site map - English site map - Created in 0.05 seconds with 36 queries by YEKTAWEB 4645