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بررسی پایداری عملکرد دانه ژنوتیپ‌های سویا (Glycine max L.) از طریق تجزیه GGE بای‌پلات | ||
| پژوهش های کاربردی زراعی | ||
| دوره 34، شماره 1 - شماره پیاپی 130، خرداد 1400، صفحه 39-54 اصل مقاله (642.69 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/aj.2021.128787.1448 | ||
| نویسندگان | ||
| حمید رضا بابائی* 1؛ نسرین رزمی2؛ حسین سبزی3 | ||
| 1مرکز تحقیقات کشاورزی و منابع طبیعی خراسان رضوی- مشهد- ایران | ||
| 2استادیار پژوهش بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران | ||
| 33- کارشناس مرکز تحقیقات کشاورزی و منابع طبیعی لرستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، خرم‌آباد، ایران | ||
| چکیده | ||
| گزینش ژنوتیپ‌های مطلوب با عملکرد و پایداری بالا هدف نهائی اغلب برنامه‌های اصلاحی سویا است. این تحقیق با هدف بررسی سازگاری و پایداری عملکرد دانه 20 لاین خالص سویا همراه با رقم شاهد ویلیامز (21 ژنوتیپ) طی دو سال زراعی (1395- 1394) در مناطق کرج، مغان و خرم‌آباد انجام گردید. در مناطق آزمایشی از طرح بلوک‌های کامل تصادفی با سه تکرار و جهت تعیین سازگاری و پایداری عملکرد از روش تجزیه GGE بای‌پلات استفاده شد. تجزیه واریانس مرکب بیانگر اثرات معنی‌دار ژنوتیپ و ژنوتیپ × محیط در سطح احتمال 1 درصد بود. مولفه‌های اصلی اول و دوم تجزیه GGE بای پلات به ترتیب 47 % و 28 % از واریانس اثر متقابل ژنوتیپ × محیط و ژنوتیپ (G X E + G) را بخود اختصاص دادند براساس معیار بای‌پلات ژنوتیپ G6 (L12/Williams x Katool) با عملکرد 3514 کیلوگرم در هکتار در کوتاه‌ترین فاصله از ژنوتیپ ایده‌آل قرار گرفت و به‌عنوان مطلوب‌ترین ژنوتیپ شناخته شد و پس از آن ژنوتیپ G4 (L12/Williams x Katool) با عملکرد 3522 کیلوگرم در هکتار در جایگاه دوم قرار گرفت. در این بررسی همچنین یک محیط کلان مشخص گردید که شامل دو محیط E1 (کرج 1394) و E3 (مغان 1394) بود. همچنین محیط E4 (مغان 1395) نزدیک‌ترین محیط به محیط ایده‌آل و از حیث توان تمایز بین ژنوتیپ‌ها و نمایندگی محیط هدف موثرترین محیط بود و پس از آن از حیث مطلوبیت محیط‌های E2 (کرج 1395)، E1 (کرج 1394)، E3 (مغان 1394) و E5 (خرم‌آباد 1394) به ترتیب در رتبه‌های بعدی قرار گرفتند. | ||
| کلیدواژه‌ها | ||
| اثر متقابل ژنوتیپ × محیط؛ ژنوتیپ؛ مطلوب؛ ژنوتیپ ایده‌آل؛ محیط کلان؛ محیط مطلوب | ||
| عنوان مقاله [English] | ||
| Study on grain yield stability of soybean genotypes [Glycine max (L.) Merril ] through GGE biplot analysis | ||
| نویسندگان [English] | ||
| Hamid reza Babaei1؛ Nasrin Razmi2؛ Hossein Sabzi3 | ||
| 1Agricultural and Natural Resources Research Center of Khorasan Razavi Pravince. Mashhad. Iran. | ||
| 22- Research Assistant of Professor of Horticulture Crops Research Department of Center of Agricultural Research and Natural Resources Ardabil Province (Parsabad Moghan), Agricultural Research,Education and Extension Organization (AREEO),, Parsabad | ||
| 33- Research Expert of Center of Agricultural Research and Natural Resources Lorestan Province . Agricultural Research,Education and Extension Organization (AREEO), Khoramabad Iran | ||
| چکیده [English] | ||
| Introduction: The main goal of the mostly soybean breeding programs is selection of desirable genotypes with high yield and stability. Genotype × environment interactions for quantitative traits such as grain yield cause genotypes to have not similar relative yields in different environments. In many statistical methods that have been used to determine yield stability and adaptability of cultivars, some basic assumptions of stability analysis such as nonlinear response of genotype and environment and dependence of environmental index on mean of genotypes are not true (Basford and cooper, 1998). Using multivariate methods such as principal components and GGE biplot method, genotype x environment interactions can be analyzed and their component values estimated ( Pacheco et al., 2009). Many breeders believe that selection of genotypes based on G or GE alone is not sufficient, and it is advisable to study these two effects together done by method such as GGE biplot . The aim of this study was to evaluate the grain yield and adaptability stability of 20 soybean pure lines in three soybean cultivation region and selection the best pureline as new crop cultivar in the territory. Material and methods: In order to evaluate the grain yield and stability of 20 soybean pure lines along with Williams control (20 genotypes) during two year (2014-2015) in regions : Karaj (1321m altitude, 35.5 N & 51.1 E), Moghan ((45 m altitude, 39.3 N & 47.2 E), Khorramabad (1155 m altitude, 33.3 N & 48.2 E), farming operations and necessary fertilization were carried out uniformly in all three regions. A randomized complete block design with 3 replications was used in the locations. Each block consisted of 21 plots and each plot consisted of 4 rows of 4 m with intervals of 60 cm. Simple and composite analysis of variance on grain yield was performed to estimate main effects of genotype, environment and genotype × environment interaction and to determine adaptability and stability of grain yield of the genotypes used GGE Biplot analysis methods by Genstate Ver.12 Software . Results and discussion: Combined analysis of variance showed significant effects of environment, genotype, genotype x environment interaction, at 1% level probeblity (p ≤ 0.01). The first and second major components (PCA1 & PCA2) of GGE biplot analysis accounted for 47% and 28% of the variance of genotype × environment and genotype (G X E + G) interaction, respectively.. Based on GGE biplot criterion G6 (L12/Williams x Katool) genotype with yield of 3514 kg/ha had shortest distance from ideal genotype and was selected as the most desirable genotype and after that G4 (L12/Williams x Katool) with 3522 kg/ha in terms of desirability was ranked second. In this study one mega environment consisting of two environments E1 (Karaj 1394) and E3 (Moghan 2015) was identified. Also E4 environment (Moghan 2016) was the nearest to ideal environment and was recognized as the most effective environment in terms of discriminating ability and representativeness and then E2 (Karaj 1395), E1 (Karaj 1394), E3 (Moghan 1394) and E5 (Khorramabad 1394) environments were favorably ranked next. Conclusion: The first and second major components (PCA1 & PCA2) of GGE biplot analysis accounted for 47% and 28% of the variance of genotype × environment and genotype (G x E + G) interaction, respectively. G6 genotype (L12/Williams x Katool) with yield of 3514 kg/ha was identified as the most desirable genotype and then G4 (L12/Williams x Katool) with 3522 kg/ha in terms of desirability was ranked second. In this study one mega environment consisting of two environments E1(Karaj 1394) and E3 (Moghan 2015) was identified. Also E4 (Moghan 2016) was recognized as the most desirable environment in terms of discriminating ability and representativeness. Key words: Desirable environment , desirable genotype, Genotype x Envionment interaction, ideal genotype, and mega environment 1. Basford, K. E., and Cooper, M.1998. Genotype by environment interactions and some considerations of their implication for wheat breeding in Australia. Australian Journal of Agricultural Research. 49: 154–175. 2. Pacheco, R. M., Duarte, J. B., Souza,P. I. M., Silva, S. A. and Nunes. J. 2009. Key locations for soybean genotype assessment in Central Brazil. Pesquisa Agropecuaria Brasileia. 44 ( 5): 478 - 486. | ||
| کلیدواژه‌ها [English] | ||
| Desirable environment, desirable genotype, Genotype x Envionment interaction, ideal genotype, and mega environment | ||
| مراجع | ||
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Akcura, M., Taner, S., and Kaya, Y. 2011. Evaluation of bread wheat genotypes under irrigated multi-environment conditions using GGE biplot analyses. Agriculture Journal, 98(1): 35-40. Amira, J. O., Ojo, D. K., Ariyo, O. A., and Ayo-Vaughan, M. A. 2013. Relative discriminating powers of GGE & AMMI models in the selection of Tropical Soybean genotypes. African Crop Science Journal, 21 (1): 67-73. Alake, C. O., and Ariyo, O. J. 2012. Comparative analysis of genotype × environment interaction techniques in West African Okra. Journal of Agricultural Science, 4(4): 135-150. Atnaf, M., Kidane, S., Abadi, S., and Fisha, Z. 2013. GGE biplots to analyse soybean multi-environment yield trial data in north Western Ethiopia. Journal of Plant Breeding & Crop Science, 5(12): 245-254. Basford, K. E., and Cooper, M. 1998. Genotype by environment interactions & some considerations of their implication for wheat breeding in Australia. Australian Journal of Agricultural Research, 49:154–175. Bhartiya, A., Aditya, J. P., Pushpendra, K. S., Purwar, J. P., and Agarwal, A. 2017. AMMI & GGE biplot analysis of multi environment yield trial of soybean in North Western Himalayan state Uttarakh& of India. Legume Research Journal, 40(2): 306-312. Chaudhary, K. J., and Wu, J. 2012. Stability analysis for yeild & 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 Cochran, W. G. 1941. The distribution of the largest of a set of estimated variances as a fraction of their total. Annals of Human Genetics, 11(1): 47-52. Eberhart, S. A., and Russel, W. E. 1966. Stability parameters for comparing varieties. Crop Science, 6: 36-40. Finlay, K. W., and Wilkinson, G. N. 1963. The analysis of adaptation in a plant breeding program. Australian Journal of Agricultural Research, 14: 742 –754. Eskridge, K. M. 1996. Analysis of multi environment trial using the probability of outperforming a check. pp. 273 -307. In : M. S. Kang and Guach, J., (eds.) Genotype by Environment Interaction. CRC Press. London. UK. Gurmu, F., Mohammed, H., and Alemaw, G. 2009. Genotype x Environment interactions & stability of soybean for grain yield & nutrition quality. African Crop Science Journal, 17: 87- 99. Hartley, H. O. 1950. The maximum f-ratio as a short-cut test for heterogeneity of variance. Biometrika, 37: 308-312 Kang, M. S. 1993. Simultaneous selection for yield & stability in crop performance trials. Consequences for growers. Agronomy Journal, 85: 754 -757. Pacheco, R. M., Duarte, J. B., Souza, P. I. M., Silva, S. A. and Nunes, J. 2009. Key locations for soybean genotype assessment in Central Brazil. Pesquisa Agropecuaria Brasileia, 44 (5): 478- 486. Payne, R.W., Harding, S. A., Murray, D. A., and Soutar, D. M. 2009. GenStat Release 12. Published by VSN International, 5 The Waterhouse, Waterhouse Street, Hemel Hempstead, Hertfordshire HP1 1ES, UK. Poordad,S. S., and Jamshid-Mogaddam, M., 2013. Study on genotype × environment interaction through GGE biplot for seed yield in spring rapeseed (Brassica Napus L.) in rain-fed condition. Journal of Crop Breeding, 12(5): 1-14 (in persian with english abstract). Silveira, D. A., Pricinotto, L. F., Nardino, M., Bahry, C. A., Cavenaghi Prete, C. E., and Cruz, L. 2016. Determination of the adaptability & stability of soybean cultivars in different locations & at different sowing times in Parana state using the AMMI & Eberhart & Russel methods [Online]. Available at https://www.researchgate.net/publication/311849977 Yan, W. 1993. The interconnectedness among the traits of wheat & its implication in breeding for higher yield. Cereal Crops, 1993 (1): 43 - 45. Yan, W. 2000. Singular-value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal, 94: 990 - 996. Yan, W., and Rajcan, I. 2002. Biplot analysis of sites & trait relations of soybean in Ontario. Crop Science, 42: 11-20. Yan, W. and Kang, M. S. 2003. GGE biplot analysis: A graphical tool for breeders, Geneticists & agronomists. CRC Press.London.UK. Yan, W., Kang, M. S., Ma, B., Woods, S., and Cornelius, P. L. 2007. GGE biplot vs. AMMI analysis of genotype by environment data. Crop Science, 47: 643 - 655. Yates, F., and Cochran, W. G. 1956. The analysis of experiments. Journal of Agronomic Science, 14: 742 -754. | ||
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