Study the relationship between agronomic and morphological traits in the promising rice lines and their grouping by multivariate statistical methods

Subject Areas : Agroecology Journal

Saeid Bakhshipour ¹ , Ali Gazanchian ² , Ali Mohaddesi ³ , Majid Nahvi ⁴ , Roghayeh Razeghi-Jadid ⁵

1 - M.Sc. Agricultural Biotechnology Research Institute of Iran (ABRII), North region, Rasht, Iran
2 - Faculty Member, Central of Agricultural and Natural Resources Research of Khorasan, Mashhad, Iran
3 - Faculty Member, Iran Rice Research Institute, Tonekabon
4 - M.Sc. Research Institute of Rice Research Institute, Rasht, Iran
5 - Faculty Member, Islamic Azad University, Tonekabon Branch, Tonekabon, Iran

Received: 2011-02-07 Accepted : 2011-12-12 Published : 2012-01-21

Keywords: Cluster analysis, Genetic variation, Path analysis, Grain yield, Rice,

Abstract :

A field experiment was conducted to determine effective traits on grain yield in seven promising rice lines at Iran Rice Research Institute, Deputy of Mazandaran (Amol). The experimental design was a randomized complete block with four replications. Grain yield showed positively significant genotypic and phenotypic correlations with plant height, panicle length, panicle weight, number of filled grains and flag leaf area. Regression analysis showed that the most effective characters related to grain yield were panicle weight, total number of tillers and number of filled grains. Path analysis showed that panicle weight and number of filled grains had direct effects on grain yield. Cluster analysis using Ward's minimum variance method with Euclidean distance for agronomic traits classified genotypes into three groups. The second group included line 2 and was considered as the best agronomic group containing the highest yield, plant height, filled grain, panicle length, flag leaf area and panicle weight among other groups. In general, the results showed that the improvement of yield in rice was achieved by increasing of panicle weight and number of filled grains in the breeding programs.

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