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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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