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Morphometric Analysis of Upland Rice Phenotypes in Lowland Condition

By: Aldrin Y. Cantila, Sailila E. Abdula, Jenalyn L. Balos

Key Words: Clustering, Diversity coefficient, Morphometric analysis, Principal component analysis, Upland rice.

J. Bio. Env. Sci. 10(4), 62-69, April 2017.

Abstract

Knowledge about upland characters expressed in an unfavorable environment will direct to appropriate utilization of upland rice varieties (URVs) for breeding and improvement. Morphometric analysis of 55 URVs in lowland condition was done using different statistical parameters such as basic statistics (standard deviation, coefficient of variation and ANOVA), Shannon-Weaver diversity coefficient (H’), principal component analysis (PCA) and clustering analysis based on 14 characters. H’ values were ranged from 0.69 (flag leaf width, FLW) to 0.95 (grain yield, GY), indicating a medium to high diversity characters. PCA captured84.78% variation for six principal components (PC), retained using proportion of variance and eigenvalues >1.0.Grain length (GL), grain width (GW) and grain size ratio (GSR) formed PC1 and days to 50% flowering (DF), days to maturity (DM) and thousand grain weight (TGW)formed PC2. PCA found that grain attributes (GL, GW, GSR and TGW) followed by DF and DM were highly affected. Clustering analysis grouped varieties into four. The results therefore could be used especially on deciding what URV is to be utilized for any rice breeding program in lowland condition.

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Morphometric Analysis of Upland Rice Phenotypes in Lowland Condition

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Aldrin Y. Cantila, Sailila E. Abdula, Jenalyn L. Balos. 2017. Morphometric Analysis of Upland Rice Phenotypes in Lowland Condition. J. Bio. Env. Sci. 10(4), 62-69.
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