Evaluation of Stability of Rapeseed (Brassica napus L.) Winter Genotypes Using Multivariate Statistical Methods

Document Type : Research Paper

Authors

1 Msc. Graduated student, Depatment of Genetic and plant breeding, Imam Khomeini international university, Qazvin, Iran

2 Assistant Professor. Depatment of Genetic and plant breeding, Imam Khomeini international university, Qazvin, Iran

3 Associate professor, Department Oil seeds research, Seed and plant improvement institute, Karaj, Iran

4 PhD student, Depatment of Genetic and plant breeding, Imam Khomeini international university, Qazvin, Iran

Abstract

Introduction
Canola (Brassica napus L.) is one of the most important edible oil seed after soybean (Glycin max L.). Canola is self-pollinated plant but it will be cross-pollinated in presence of insects nearly to 30% that use for hybrid variety with interested heterosis. A total of genotype, environment and interaction of these two factor effects resulte in a genotype yield value. The genotype × environment interaction reduce selection efficient of a genotype. So, The evaluation of genotype × environment interaction result in suitable variety selection. It is used different statistical nonparametric and parametric uni and multivariate methods to evaluate interaction of genotype × environment which one assess a specific aspect of genotypes yield. AMMI, GGE biplot and PCA are the common methods to evaluate interaction effects. In common, the aim of compatibility is gaining varieties with high yield in different environments but in specific concept, it means detection of varieties which have high yield in specific environments.
   
Materials and methods
This research was conducted with aim to study of stability in 9 winter hybrid lines of canola which evaluated in primary yield experiment in cold and mild environments of country with 4 controls varieties, Ahmadi, Nima, Ocapi and Nafis and revealed their superiorities. The experiment was done in complete block design with three replications in six enviroments include Karaj, Esfahan, Khoy, Kermanshah, Hamedan and Zarghan during 2013-2014 and 2014- 2015. The cultivation was done according to common method in each environment and the genotypes were considered as constant factor. It was used of AMMI and GGE biplot models for selection of high yield genotypes and varieties with specific and common adaptability using ‌‌R-project software.       
 
Results and discussion
The results of combined analysis of variance for 12 environments revealed that the effect of environment, interaction of year × environment, genotype, interaction of genotype × year, interaction of genotype × environment and interaction of genotype × year × environment were 22.8%, 45.5%, 2.9%, 1.35%, 7.02% and 6.54%, respectively which the highest one was the changes resulted from year × environment effect. The significant effect of genotype × environment means to different response of genotypes to various environment in means of years and so, we can recognize compatible genotypes for specific environment. This effect resulted from changing in genotype ranking in different environments that show fluctuation of yield in the environments. In spite of significant genotype effect in combined analysis, the effect was not significant in separate variance analysis in each year which indicates the effect of year on genotypes reaction and means different stability of the genotypes. The interaction of genotype × environment were studied in detail by AMMI model. According to AMMI analysis, Okapi had the highest adaptability in experimental environments. BAL- 92, HW-92-4, BAL-90-3 and BAL- 92-1 had good adaptability in Kermanshah, Karaj and Hamedan, too. The genotypes BAL- 92- 3, BAL-92-11, HW-92-3 and Nafis were well compatible to Khoy and Nima and Ahmadi compatible to Zarghan. Also, GGE biplot model was used for more analysis. The results of the analysis revealed that Nafis had the lowest distance to ideal genotype and then, HW-92-1, BAL-92-6 and BAL-92-1 placed in next categories.
 
Conclusion
In present research, Nafis variety had the highest yield than to other experimental genotypes addition to suitable compatibility to all environments and then, HW-92-1, BAL-92-6 and BAL-92-1 showed more genetic potential for yield and compatibility than to others. It was found in the study that multivariate methods for compatibility studies were efficient due to simultaneous detection of different factors effects on a suitable genotype in canola. One of the results of the research was that the genotype effect was included a small part of variance change and the most of the change belong to environment factors. For the reason, GGE biplot was more functional method to delete environment effects in the results for compatibility studies in canola.

Keywords

Main Subjects

References

AMIRI, O. H., Rameeh, V., Faraji, A., Fanaei, H. R., Kazerani, N. K., & Rahmanpour, S. (2020). Evaluation of seed yield stability of spring rapeseed genotypes using GGE biplot analysis. Seed and Plant Journal, 36(2), 207- 222. (In Persian).  https://doi.org/10.22092/SPPI.2020.123205
Azizinia, S., & Mortazavian, M. M. (2015). A Yield Stability Survey in Winter Type Canola Using Univariate Methods and Genotypic Distribution Pattern. Isfahan University of Technology-Journal of Crop Production and Processing, 5(15), 57-68. (In Persian). https://doi.org/10.18869/acadpub.jcpp.5.15.57
Babaei, H. R., Razmi, N., Raeisi, S., & Sabzi, H. (2020). Evaluation of adaptability and seed yield stability of soybean (Glycine max L. Merril) promising lines using GGE biplot analysis. Iranian Journal of Crop Science, 22(2), 183-197. (In Persian). http://dx.doi.org/10.29252/abj.22.2.183.
Basafa, M., Taherian, M., & Beheshti, A. (2015). Stability analysis for forage yield in sorghum lines. Applied Field Crops Research, 28(107), 99-107. https://doi.org/10.22092/AJ.2015.105710
Bornhofen, E., Benin, G., Storck, L., Woyann, L. G., Duarte, T., Stoco, M. G., & Marchioro, S. V. (2017). Statistical methods to study adaptability and stability of wheat genotypes. Bragantia, 76, 1-10. https://doi.org/10.1590/1678-4499.557 
Dashtaki, M., Yazdansepas, A., Mirak, T. N., Ghannadha, M. R., Joukar, R., Islampour, M. R., & Ashouri, S. (2004). Stability of grain yield and harvest index in winter and facultative bread wheat (Triticum aestivum L.) genotypes. Seed Plant Journal, 20, 263-279. (In Persian).https://doi.org/10.22092/SPIJ.2017.110583
Dezfouli, P. M., Sedghi, M., Shariatpanahi, M. E., Niazian, M., & Alizadeh, B. (2019). Assessment of general and specific combining abilities in doubled haploid lines of rapeseed (Brassica napus L.). Industrial Crops and Products, 141, 111754. https://doi.org/10.1016/j.indcrop.2019.111754
Escobar, M., Berti, M., Matus, I., Tapia, M., & Johnson, B. (2011). Genotype× environment interaction in canola (Brassica napus L.) seed yield in Chile. Chilean Journal of Agricultural Research, 71(2), 175. https://doi.org/10.4067/S0718-58392011000200001
Gauch Jr, H. G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science, 46(4), 1488-1500. https://doi.org/10.2135/cropsci2005.07-0193
Hashemi, A., Nematzadeh, G.A., Oladi, M., Afkhami Ghadi, A., & Gholizadeh Ghara., A. (2018). Study of Rapeseed (Brassica napus) Promising Genotypes Adaptation in Different Regions of Mazandaran. Journal of crop breeding, 10(28), 119-124. http://dx.doi.org/10.29252/jcb.10.28.119
Jankowski, K. J., Załuski, D., & Sokólski, M. (2020). Canola-quality white mustard: Agronomic management and seed yield. Industrial crops and products, 145, 112138. https://doi.org/10.1016/j.indcrop.2020.112138
Kebede B, A., & Getahun, A. (2017). Adaptability and stability analysis of groundnut genotypes using AMMI model and GGE biplot. Journal of crop science and biotechnology, 20, 343-349. https://doi.org/10.1007/s12892-017-0061-0
Mohamed, N. E. (2013). Genotype by environment interactions for grain yield in bread wheat (Triticum aestivum L.). Journal of Plant Breeding and Crop Science, 5(7), 150-157. https://doi.org/10.5897/JPBCS2013.0390
Mohammadi, M., Karimizadeh, R., Hosseinpour, T., Ghojogh, H., Shahbazi, K., & Sharifi, P. (2017). Use of parametric and non-parametric methods for genotype× environment interaction analysis in bread wheat genotypes. Plant Genetic Researches, 4(2), 75-88 (In Persian). https://doi.org/10.29252/pgr.4.2.75
Mortazavıan, S. M., & Azızı-nıa, S. (2014). Nonparametric stability analysis in multi-environment trial of canola. Turkish Journal of Field Crops, 19(1), 108-117. https://doi.org/10.17557/tjfc.41390
Roy, D. (2000). Plant breeding: Analysis and exploitation of variation. Alpha Science Int'l Ltd.
Sabaghnia, N., Dehghani, H., & Sabaghpour, S. H. (2008). Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agronomy Journal, 100(3), 760-764. https://doi.org/10.2134/agronj2006.0282
Shadan, E., Zarrini, H. N., Alizadeh, B., Ranjbar, G., & Kiani, G. (2022). Evaluation of seed yield stability and compatibility in some winter rapeseed genotypes. Journal of Crop Breeding, 14(41), 97- 107 (In Persian). https://doi.org/10.52547/jcb.14.41.97
Sneller, C. H., & Dombek, D. (1995). Comparing soybean cultivar ranking and selection for yield with AMMI and full‐data performance estimates. Crop Science, 35(6), 1536-1541. https://doi.org/10.2135/cropsci1995.0011183X003500060003x
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., Armion, M., Mehraban, A., Hossein-Pour, T., & Dorii, M. (2017). GGE biplot and AMMI analysis of barley yield performance in Iran. Cereal Research Communications, 45, 500-511. https://doi.org/10.1556/0806.45.2017.019
Wu, W., Ma, B. L., & Whalen, J. K. (2018). Enhancing rapeseed tolerance to heat and drought stresses in a changing climate: perspectives for stress adaptation from root system architecture. Advances in agronomy, 151, 87-157. https://doi.org/10.1016/bs.agron.2018.05.002
Yan, W., Hunt, L. A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar evaluation and mega‐environment investigation based on the GGE biplot. Crop science, 40(3), 597-605. https://doi.org/10.2135/cropsci2000.403597x
Yan, W., & Kang, M. S. (2002). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC press https://doi.org/10.1201/9781420040371
Yan, W., & Tinker, N. A. (2005). An integrated biplot analysis system for displaying, interpreting, and exploring genotype× environment interaction. Crop Science, 45(3), 1004-1016. https://doi.org/10.2135/cropsci2004.0076
Yan, W., Kang, M. S., Ma, B., Woods, S., & Cornelius, P. L. (2007). GGE biplot vs. AMMI analysis of genotype‐by‐environment data. Crop science, 47(2), 643-653. https://doi.org/10.2135/cropsci2006.06.0374
Zobel, R. W., Wright, M. J., & Gauch Jr, H. G. (1988). Statistical analysis of a yield trial. Agronomy journal, 80(3), 388-393. https://doi.org/10.2134/agronj1988.00021962008000030002x
Volume 16, Issue 1
August 2024
Pages 65-76

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