نوع مقاله : پژوهشی
نویسندگان
1 گروه اصلاح نباتات و بیوتکنولوژی، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
2 گروه تولیدات گیاهی، دانشکده کشاورزی و منابع طبیعی، دانشگاه گنبد کاووس، گنبد کاووس، ایران
3 پژوهشکده کشاورزی هستهای، پژوهشگاه علوم و فنون هستهای، کرج، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Background and objectives
Rice is one of the most important crops in Iran and all over the world, and Drought stress is a significant limiting factor in producing this crop. Breeding for drought tolerance could be an effective method to improve and sustain yield in drought-prone regions. Inducing mutation is a suitable method for increasing the genetic variation of crops. The current research aimed was carried out to identify and classify tolerant and sensitive mutant lines of rice to drought stress.
Materials and methods
Parent lines of the ninth generation resulting from a cross of Ahlemi-Tarom (relatively drought sensitive) and Sepidroud (relatively drought tolerance) were obtained. In 2015, to improve genetic diversity in drought tolerance, an irradiation with gamma-ray (250 grays) was conducted on 300 ninth generation lines at Nuclear Science and Technology Research Institute in Karaj. Using a primary screening in M1 based on important agronomic and breeding traits, ninety-six mutant lines were selected. The present experiment was conducted on 96 mutant lines of rice (M2) in two environments, i.e. non-stress (flooding) and drought stress conditions, on the research farm of Gonbad Kavous University in 2016 using a randomized complete block design with three replications. Through applying yield for each genotype and yield mean for all genotypes under stress and non-stress conditions, seventeen drought tolerance indexes were calculated. The indexes under study included TOL, MP, GMP, HM, YSI, YI, SSI, STI, ATI, DI, K1STI, K2STI, RDI, RDY, SSPI, SPI and SNPI. To determine the best indicators for identifying high yielding genotypes under different moisture conditions, correlations of indices with yield in stress and non-stress conditions were employed. To identify the relationship between the indices, multivariate analyses including a principal component analysis, a cluster analysis, and a discriminant function analysis were used. The discriminant function analysis was used to determine the number of significant groups in cluster analysis and dendrogram cutting points. In order to select the best genotypes and indicators for determination of tolerant and sensitive genotypes to drought stress, a biplot was drawn.
Results
Results of the correlation analysis between drought tolerance indices and grain yield showed that GMP, HM, STI, MP, and RDY were the best indices for identifying high yield of genotypes under flooding and drought stress conditions. By examining the correlations of indices with grain yield (stress and non-stress conditions) and their coefficient of variations, it was determined that the STI index, due to its significant correlation in both environments and its high coefficient of variation, can be introduced as an index which justifies the greatest variation under different moisture conditions. The principal component analysis showed that three principal and independent factors explained 99.93 percent of total variance in all data. The first, second, and third factors with 84.47, 14.12, and 1.33 percent of the variance respectively were named as drought sensitive, drought tolerance and yield potential. The cluster analysis using the WARD method and Euclidean distance led to grouping mutant lines of rice. The discriminant function analysis showed that there were four significant groups in the cluster analysis. The first to fourth groups had 7, 25, 17 and 47 genotypes, respectively. The first and third cluster genotypes were identified as tolerant and sensitive to drought stress, respectively.
Conclusion
Genotypes No. 94 was selected as the best mutant line in terms of yield and tolerance to drought stress among plant sources in the present study. The biplot drawn based on the first two components introduced SNPI and SSPI indices as indexes for identifying tolerant and sensitive drought stress genotypes, respectively.
کلیدواژه‌ها [English]
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