Journal of Plant Diseases and Protection

, Volume 126, Issue 4, pp 293–306 | Cite as

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

  • Seyedeh Soheila Zarbafi
  • Babak RabieiEmail author
  • Ali Akbar Ebadi
  • Jong Hyun HamEmail author
Original Article


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.


Iranian landrace rice Magnaporthe oryzae Number of filled grains Number of productive panicles Path analysis Thausand grain weight 



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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Albayrak S, Tongel O (2006) Path analysis of yield and yield-related traits of common vetch (Vicia sativa L.) under different rainfall conditions. OMU Zir Fak Dergisi 21:27–32Google Scholar
  2. Anbanandan V, Saravanan K, Sabesan T (2009) Variability, heritability and genetic advance in rice (Oryza sativa L.). Int J Plant Sci 4:61–63Google Scholar
  3. Ariyo OJ, Akenova ME, Fatokun CA (1987) Plant character correlations and path analysis of pod yield in Okra (Abelmoschus esculentus). Euphytica 36:677–686. CrossRefGoogle Scholar
  4. Bhatt GM (1973) Significance of path coefficient analysis in determining nature of character association. Euphytica 22:338–343. CrossRefGoogle Scholar
  5. Bonman JM, Estrada BA, Bandong JM (1989) Leaf and neck blast resistance in tropical lowland rice cultivars. Plant Dis 73:388–390. CrossRefGoogle Scholar
  6. Couch BC, Kohn LM (2002) A multilocus gene genealogy concordant with host preference indicates segregation of a new species, Magnaporthe oryzae, from M. grisea. Mycologia 94:683–693CrossRefGoogle Scholar
  7. Divya B, Robin S, Biswas A, John Joel A (2015) Genetics of association among yield and blast resistance traits in rice (Oryza sativa). Indian J Agric Sci 85:354–360Google Scholar
  8. Dofing SM, Knight CW (1992) Alternative model for path-analysis of small-grain yield. Crop Sci 32:487–489. CrossRefGoogle Scholar
  9. Ekka RE, Sarawgi AK, Kanwar RR (2011) Correlation and path analysis in traditional rice accessions of Chhattisgarh. J Rice Res 4:11–17Google Scholar
  10. Emani C, Jiang Y, Miro B, Hall TC, Kohli A (2008) Transgenic cereals and forage grasses. In: Kole C, Hall TC (eds) Compendium of transgenic crop plants. Wiley, New York, pp 1–47Google Scholar
  11. Hammer O, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4:1–9Google Scholar
  12. Immanuel SC, Pothiraj N, Thiyagarajan K, Bharathi M, Rabindran R (2011) Genetic parameters of variability, correlation and path coefficient studies for grain yield and other yield attributes among rice blast disease resistant genotypes of rice (Oryza sativa L.). Afr J Biotechnol 10:3322–3334. CrossRefGoogle Scholar
  13. International Rice Research Institute (1996) Standard evaluation system (SES) for rice. International Rice Research Institute, ManilaGoogle Scholar
  14. Inukai T, Nelson RJ, Zeigler RS, Sarkarung S, Mackill DJ, Bonman JM, Takamure I, Kinoshita T (1994) Allelism of blast resistance genes in near-isogenic lines of rice. Phytopathology 84:1278–1283. CrossRefGoogle Scholar
  15. Jobson JD (2012) Applied multivariate data analysis: volume II: categorical and multivariate methods. Springer, BerlinGoogle Scholar
  16. Lee FN, Cartwright RD, Jia Y, Correll JC (2009) Field resistance expressed when the Pi-ta gene is compromised by Magnaporthe oryzae. In: Wang GL, Valent B (eds) Advances in genetics, genomics and control of rice blast disease. Springer, Amsterdam, pp 281–289CrossRefGoogle Scholar
  17. Maji AT, Shaibu AA (2012) Application of principal component analysis for rice germplasm characterization and evaluation. J Plant Breed Crop Sci 4:87–93. Google Scholar
  18. Majumder DAN, Hassan L, Rahim MA, Kabir MA (2012) Correlation and path coefficient analysis of Mango (Mangifera indica L.). Bangladesh J Agric Res 37:493–503. CrossRefGoogle Scholar
  19. Ou SH (1985) Rice diseases. Commonwealth Mycological Institute, KewGoogle Scholar
  20. Qudsia H, Riaz A, Akhtar M (2017) Evaluation of rice germplasm for resistance against Pyricularia oryzae the cause of rice leaf blast. Asian Res J Agric 4:1–6. CrossRefGoogle Scholar
  21. Romesburg C (2004) Cluster analysis for researchers. Lulu Press, MorrisvilleGoogle Scholar
  22. Sabesan T, Suresh R, Saravanan K (2009) Genetic variability and correlation for yield and grain quality characters of rice grown in coastal saline low land of Tamilnadu. Electron J Plant Breed 1:56–59Google Scholar
  23. Sarker MM, Hassan L, Islam MM, Rashid MM, Seraj S (2014) Correlation and path coefficient analysis of some exotic early maturing rice (Oryza sativa L.) lines. J Biosci Agric Res 1:1–7CrossRefGoogle Scholar
  24. Seiler GJ, Stafford RE (1985) Factor analysis of component of yield in guar. Crop Sci 25:905–908. CrossRefGoogle Scholar
  25. Singh SP, Joshi AB (1966) Line × tester analysis in relation to breeding for yield in linseed. Indian J Genet Plant Breed 26:177–194Google Scholar
  26. Sirohi SPS, Yadav R, Meenakshi A (2007) Assaying genetic divergence for morpho-physiological traits in lentil (Lens culinaris L. Medik.). Plant Arch 7:331–333Google Scholar
  27. Teng PS, Revilla IM (1996) Technical issues using crop-loss data for research prioritization. In: Evenson RE, Herdt RW, Hossain M (eds) Rice research in Asia: progress and priorities. CAB International, Wallingford, pp 261–275Google Scholar
  28. Teng PS, Klein-Gebbinck HW, Pinnschmidt H (1991) An analysis of the blast pathosystem to guide modeling and forecasting. In: Teng PS (ed) Rice blast modeling and forecasting. IRRI, Los Banos, pp 1–30Google Scholar

Copyright information

© Deutsche Phytomedizinische Gesellschaft 2019

Authors and Affiliations

  1. 1.Department of Agronomy and Plant Breeding, Faculty of Agricultural SciencesUniversity of GuilanRashtIran
  2. 2.Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO)RashtIran
  3. 3.Department of Plant Pathology and Crop PhysiologyLouisiana State University Agricultural CenterBaton RougeUSA

Personalised recommendations