Paper Details


Screening of rice germplasm for drought stress tolerance using multivariate analysis

Mst Tanjina Shahanaj Turin, Md. Arifuzzaman and Mohammad Abul Kalam Azad

Drought stress reduces rice yields around the globe. For this, an investigation was carried out with rice landraces and cultivars to identify drought tolerant genotypes under drought stress conditions using multivariate statistical analysis. The experiment was conducted with 24 genotypes composed of 20 land races and four cultivars during October – March of 2016-17 season using strip-plot design with three replications under control and rainfed drought blocks. The results of the analysis of variance revealed significant differences among the genotypes, treatments and genotype × treatment interactions for most of the characters studied. Comparing the mean values, control blocks produced superior performances than drought blocks for most of the traits except proline content. Based on the genetic distance, all the 24 genotypes were grouped into three different clusters. In both control and drought conditions, the cluster II contained some common genotypes viz. Kalamanik and Burikatari with the highest number of relative means for number of tillers per hill, chlorophyll content, shoot fresh weight, shoot dry weight, root fresh weight and yield per plant. Principal component analysis revealed maximum variation by first three components. The biplot analysis indicated the maximum proximity of genotypes BRRI Dhan56, BRRI Dhan66 and BRRI Dhan71 with root traits and yield per plant. Drought susceptibility indices (DSI) indicated the superiority in the BRRI dhan66. Considering overall superior performances, the genotypes BRRI dhan71, BRRI dhan57, BRRI dhan56, Kalamanik, Burikatari, Noyantara and Honumanjata might be useful for novel sources of drought tolerant genes and genotypes in future drought tolerance breeding.

To Cite this article: Turin, M.T.S., M. Arifuzzaman and M.A.K. Azad. 2021. Screening of rice germplasm for drought stress tolerance using multivariate analysis. Pak. J. Bot., 53(2): DOI:

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