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Statistical analysis of phenotypic traits of rice (Oryza sativa L.) related to grain yield under neck blast disease

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Abstract

Investigating the traits of rice in conditions of blast disease and its relationship with grain yield can help to identify suitable strategies for selecting high-yielding cultivars, especially in areas prevalent with the disease. One hundred and twenty-one rice cultivars and lines (genotypes) selected from the collection of the Rice Research Institute of Iran were evaluated for the traits related to neck blast disease, including number of filled and unfilled grains per panicle, neck blast infection type, 1000-grain weight, number of productive panicles per plant, and grain yield. Mean square of genotypes was highly significant (P < 0.01) for all traits, and most of the traits exhibited a high heritability with low level of phenotypic variations in each genotype, indicating the small environmental effect in this study. Grain yield in infected plants correlated significantly with number of filled and unfilled grains per panicle and with 1000-grain weight. Through a multiple regression analysis, three traits including number of filled grains per panicle, number of productive panicles per plant and 1000-grain weight were entered to the model in a stepwise method, which justified 86% of grain yield variances in infected plants. Also direct and indirect effects of each trait were evaluated by path analysis. Three main and independent factors identified with factor analysis explained 72.86% of the changes in the all data. The cluster analysis for classification of genotypes according the traits revealed six main clusters, in which five genotypes were identified as resistant genotypes. The results of principle component analysis also confirmed the results of cluster analysis.

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Acknowledgements

The authors are thankful to University of Guilan for financial supports and Rice Research Institute of Iran for providing research farm and rice seeds used in this study. This work was also partially supported by the USDA NIFA (Hatch Project #: LAB94203), the Louisiana State University Agricultural Center, and the Louisiana Rice Research Board.

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Correspondence to Babak Rabiei or Jong Hyun Ham.

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Zarbafi, S.S., Rabiei, B., Ebadi, A.A. et al. Statistical analysis of phenotypic traits of rice (Oryza sativa L.) related to grain yield under neck blast disease. J Plant Dis Prot 126, 293–306 (2019). https://doi.org/10.1007/s41348-019-00230-y

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