Skip to main content
SpringerLink
Log in

Statistical Analysis of Grain Yield in Iranian Cultivars of Barley (Hordeum vulgare)

  • Full-Length Research Article
  • Published:
Agricultural Research Aims and scope Submit manuscript

Abstract

To determine the most important traits influencing grain yield of barley, 10 cultivars were evaluated in three field experiments in randomized block design with three replicates during 2013–2014 growing season. In each experiment, 10 yield component traits were evaluated. Following a combined analysis of variance, multivariate statistical analyses such as simple correlation coefficients, factor analysis, stepwise analysis, path analysis and cluster analysis were administered on combined means. The correlation coefficient analysis showed significant positive correlation of grain yield with grain filling period, biological yield, harvest index, number of seed/spike, number of seed/m2, 1000-seed weight and number of spikes/m2. Factor analysis divided the 11 measured variables into five factors. Factor 1 was affected by grain yield and grain filling period. Factor 2 was affected by number of spikes/m2 and number of seed. Factor 3 was associated with days to hardening and days to maturity. Factor 4 was influenced by number of seed/spike, number of spikes/m2 and 1000-seed weight. Fifth factor was strongly affected by biological yield and plant height. In stepwise regression analysis for grain yield as independent variable, two traits include biological yield and harvest index entered to regression model in four steps with R-square = 0.98. Regression coefficient of both entered variables was positive and significant at 1% probability level. The path coefficient analysis based on grain yield, as a dependent variable, implicated that biological yield had the highest positive direct effect on grain yield. Highest positive indirect effect on grain yield was related to harvest index through biological yield. Cluster analysis using Ward’s method divide 10 investigated cultivars into three clusters. Cluster 1 specified to cultivars with the highest plant height, number of seed/spike and 1000-seed weight. Cluster 2 was specified to lines with higher amount of grain yield, biological yield and number of seeds/m2. Two control cultivars include Sina and Nosrat were located to cluster 3. Finally, based on GGE biplot analysis, Sina, which has higher yield and more relative stability, can be suggested as superior than the other cultivars.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Bloom AJ, Chapin FSI, Mooney HA (1985) Resource limitation in plants-an economic analogy. Annu Rev Ecol Syst 16:363–392

    Article  Google Scholar 

  2. Cary NC (2004) SAS Institute. The SAS system for windows. Release 9.1. Cary, SAS Institute, p 654

    Google Scholar 

  3. Dofing SM, Knight CW (1992) Alternative model for path analysis of small-grain yield. Crop Sci 32:487–489

    Article  Google Scholar 

  4. El-Deeb AA, Mohamed NA (1999) Factor and cluster analysis for some quantitative characters in sesame (Sesamum indicum L.). In: The annual conference ISSR, Cairo University, 4–6 December, vol 34, Part (II)

  5. Fraser J, Eaton GW (1983) Application of yield component analysis to crop research. Field Crop 39:787–797

    Google Scholar 

  6. Güler M, Sait Adak M, Ulukan H (2001) Determining relationships among yield and some yield components using path coefficient analysis in chickpea (Cicer arietinum L.). Eur J Agron 14:161–166

    Article  Google Scholar 

  7. Hailu A, Alamerew S, Nigussie M, Assefa E (2016) Correlation and path coefficient analysis of yield and yield associated traits in barley (Hordeum vulgare l.) germplasm. Adv Crop Sci Technol 4:216. https://doi.org/10.4172/2329-8863.1000216

    Article  Google Scholar 

  8. Högy P, Poll C, Marhan S, Kandeler E, Fangmeier A (2013) Impacts of temperature increase and change in precipitation pattern on crop yield and yield quality of barley. Food Chem 136:1470–1477

    Article  CAS  PubMed  Google Scholar 

  9. Jaynes DB, Kaspar TC, Colvin TS, James DE (2003) Cluster analysis of spatial temporal corn yield pattern in an Iowa field. Agron J 95(3):574–586

    Article  Google Scholar 

  10. Jobson JD (1992) Applied multivariate data analysis. Vol II: categorical and multivariate methods. Springer, New York, p 768

    Book  Google Scholar 

  11. Leilah AA, Al-Khateeb SA (2005) Statistical analysis of wheat yield under drought conditions. J Arid Environ 61:483–496

    Article  Google Scholar 

  12. Mansouri SA, Solati Njafabadi M (2004) Study and systemic analysis on yield and yield components association for sesame (Sesame indicum L.) breeding. Seed Plant Improv J 20(2):149–165 (In Farsi-Abstract in English)

    Google Scholar 

  13. Mehrdelan M, Marjani A, Reihani M, Ragbar T, Masoumi K (2013) Reviewing changes of yield relationship with yield components of promising genotypes of rainfed barley by path analysis. Int J Farm Allied Sci 2(S):1226–1232

    Google Scholar 

  14. Milomirkaa M, Paunović A, Đurović D, Knežević D (2005) Correlation and path coefficient analysis for yield and yield components in winter barley. Acta Agric Serbica 20:3–9

    Google Scholar 

  15. Mohsin T, Khan N, Naqvi FN (2009) Heritability, phenotypic correlation and path coefficient studies for some agronomic characters in synthetic elite lines of wheat. J Food Agric Environ 7:278–282

    Google Scholar 

  16. Niazian M, Rajabi A, Amiri R, Orazizadeh MR, Sharifi H (2012) Surveying the relations among traits affecting root yield and sugar content in o-type lines of sugar beet for winter sowing. Plant Prod 35(2):115–135 (In Farsi-Abstract in English)

    Google Scholar 

  17. Niazi-Fard A, Nouri F, Nouri A, Yoosefi B, Moradi A, Zareei A (2012) Investigation of the relationship between grain yield and yield components under normal and terminal drought stress conditions in advanced barley lines (Hordeum vulgare L.) using path analysis in Kermanshah province. Int J Agric Crop Sci 4(24):1885–1887

    Google Scholar 

  18. Paunovic MMA, Djurovic D, Knezevic D (2005) Correlations and ‘path’ coefficient analysis for yield and yield components in winter barley. Acta Agric Serbica 10(20):3–9

    Google Scholar 

  19. Sadras VO (2007) Evolutionary aspects of the trade-off between seed size and number in crops. Field Crops Res 100:125–138

    Article  Google Scholar 

  20. Sadras VO, Denison RF (2009) Do plant parts compete for resources? An evolutionary perspective. New Phytol 183:565–574

    Article  PubMed  Google Scholar 

  21. Sadras VO, Slafer GA (2012) Environmental modulation of yield components in cereals: heritability’s reveal a hierarchy of phenotypic plasticity’s. Field Crops Res 127:215–224

    Article  Google Scholar 

  22. Seyed Aghamiri S, Mostafavi K, Mohammadi A (2012) Investigation of the relationship between grain yield and yield components in barley varieties and new hybrids using multivariate statistical methods. Iran J Field Crops Res 10(2):421–427 (In Farsi, Abstract in English)

    Google Scholar 

  23. Tofiq SE, Hama Amin TM, Sheikh Abdulla SM, Abdulkhaleq DZ (2015) Correlation and path coefficient analysis of grain yield and yield components in some barley genotypes created by full diallel analysis in Sulaimani region for F2 generation. Int J Plant Anim Environ Sci 5(4):76–79

    CAS  Google Scholar 

  24. Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86:623–645

    Article  Google Scholar 

  25. Yan W, Tinker NA (2005) An integrated biplot analysis system for displaying, interpreting, and exploring genotype-by- environment interactions. Crop Sci 45:1004–1016

    Article  Google Scholar 

  26. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton

    Google Scholar 

  27. Zeng L, Shannon MC, Grieve CM (2002) Evaluation of salt tolerance in rice genotypes by multiple agronomic parameters. Euphytica 127(2):235–245

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agronomy and Plant Breeding, Agricultural College of Sarayan, University of Birjand, P. O. Box: 331, Sarayan, Iran

    Seyyed Hamid Reza Ramazani

  2. Kohgiluyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Yasuj, Iran

    Moslem Abdipour

Authors
  1. Seyyed Hamid Reza Ramazani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moslem Abdipour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyyed Hamid Reza Ramazani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramazani, S.H.R., Abdipour, M. Statistical Analysis of Grain Yield in Iranian Cultivars of Barley (Hordeum vulgare). Agric Res 8, 239–246 (2019). https://doi.org/10.1007/s40003-018-0360-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40003-018-0360-4

Keywords

Search

Navigation