Assessing genetic diversity and selection of effective traits on yield of Chickpea lines using multivariate statistical methods

Document Type : Original Article


1 Agriculture Department, Payame Noor University,Tehran, Iran,

2 Agronomy and Plant Breeding Department, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran


Chickpea contains a high level of protein and plays very important role in the food security cycle in the worldwide, especially in the developing countries such as Iran which is one of the most important Asian countries in terms of chickpea production. Indeed, Iranian chickpea is planted in 33 countries and its area of cultivation in Iran is approximately 650000 hectare (Ahmed Khan, 2009). Low grain yield is the most basic problem of chickpea in Iran and plant breeding is a economic and permanent solution for solving this problem. Genetic diversity, heritability and intensive selection are three effective factors for responding to selection of traits. As a consequence, multivariate statistical methods are conventional and suitable for identifying genetic diversity in plant breeding programmes; principal component analysis, factor analysis, cluster analysis and discriminate function are the most important multivariate methods generally used for different plants. Many researchers have used multivariate statistical methods in rice (Gholipour and Mohammad-Salehi, 2003), potato (Rabie et al., 2008), wheat (Zaki Zadeh et al., 2010), chickpea (Jeena and Arora, 2002), and Identifying the most effective genotypes and traits in chickpea and assessing the genotype diversity were the main objectives of the current research.
Material and methods:
In order to assess genetic diversity and identify the most important and effective traits of grain yield in 19 chickpea genotypes, the current experiment was conducted based on a randomized complete block design with three replications under well water conditions. The experiment was carried out in the Bu-Ali Sina University research field growing season and laboratory of Payame Noor University. The traits of chlorophyll index, plant height, branch number per plant, pod number per plant, seed number per plant, seed number per pod, 100-kernel weight, economic yield per unit area, biological yield per unit area and harvest index were measured. After trait measurement, principal component analysis (PCA), factor analysis (FA), cluster analysis (CA) and discriminate function analysis (DFA) were carried out in order to reach the aims of the research.
Results and discussion:
The results showed that the maximum coefficient of variation belonged to the traits of pod number per plant and harvest index, while the trait of 100-kernel weight had the minimum coefficient of variation. The results of principal component analysis showed that the three first components explained 68.9 percent of the total variance of the traits. The first and second components were known as the “grain yield” and “harvest index” components, respectively. In addition, factor analysis identified the three factors of “grain yield, “harvest index” and “plant vigour”. Cluster analysis based on the WARD method grouped the genotypes into four clusters. Meanwhile, discriminate function analysis confirmed the cluster analysis groups. The results showed a high genetic diversity among the lines. Lines number 12 and 18 were recognized as generally the best and the worst genotypes. In addition, the traits of pod number per plant, 100-kernel weight and harvest index had maximum effect on grain yield, while the trait of branch number per plant had a negative effect on grain yield in the current research.
According to the results obtained in the research, multivariate statistical methods are suitable and efficient methods for data reduction and indirect selection of grain yield that could successfully separate the efficient genotypes and the traits. The main objective was to assess the genetic diversity of 19 chickpea lines for their application in the new plant breeding programmes. According to the total results, the traits of pod number per plant were recognized as the most suitable traits for indirect grain yield selection. Line number 12, as a suitable line, had a maximum amount of the traits of pod number per plant, 100-kernel weight and harvest, while line number 18 (an unsuitable line) included the maximum amount of the trait of branch number per plant.


  1. Ahmed Khan, T., 2009. Application of univariate and multivariate techniques in evaluation of chickpea (Cicer aretinum L.) genotypes. MS.c. Thesis. Dharwad University of Agriculture Sciences, India.
  2. Chegamirza, S.H., Chegamirza, K. and Mohammadi, R., 2011. Study of genetic variation in cultivars and landraces of chickpea based on agronomic traits in dryland conditions. Journal of Agricultural Sciences Rainfed Iran. 1(1), 108-119.
  3. Dargahi, H.R., 2006. Assessing genetic diversity of some white bean lines in Iran by multivariate statistical methods. In Proceedings 9th International Genetic Congress of Iran, 20th – 22th, Tehran, Iran. p. 25.
  4. Falconer, D S., 1989. Introduction to Quantitative Genetics”. (3rd edition). Longman, New York.
  5. Farshadfar, A., 1997. The Application of Quantitative Genetics in Plant Breeding. Kermanshah Razi University Press, Kermanshah, Iran.
  6. Farshadfar, A., 2005. Principles and Multivariate Statistical Methods. Kermanshah Taq-e Bostan Publications, Kermanshah, Iran.
  7. Food and Agricultural Organization of the United Nations 2012. FAOSTAT. Available online at
  8. Food and Agricultural Organization of the United Nations 2014. Statistical Database. Rome, Italy, Available online at
  9. Fayyas, F. and Talebi, R., 2011. Determine the relationship between yield and some yield components of chickpea (Cicer aretinum L.) using path analysis. Iran Agricultural Research. 7(1), 131-141 (In Persian with English abstract).
  10. Fazeli, F. and Cheghamirza, K., 2011. Genetic diversity of chickpea (Cicer arietinum L.) Iran based on agronomic traits and markers RAPD. Journal of Plant and Seed Breeding. 27 (4), 555-579 (In Persian with English abstract).
  11. Ghafoor, A. and Arshad, M., 2008. Multivariate analyses for quantitative traits to determine genetic diversity of blackgram (Vigna mungo L. Hepper) germplasm. Pakistan Journal of Botany. 40 (6), 2307-2313.
  12. Gholipour, M., and Mohammad-Salehi, M.S., 2003. Factor analysis and causality in different rice genotypes. Seed and Plant Journal. 19 (1), 76-86 (In Persian with English abstract).
  13. Jafari, A.A., Ziaee-Nasab, M., Hesamzade, M. and Madah-Arefi, H., 2004. Evaluation of genetic diversity in populations of red clover seed yield (Trifolium pretense L.) using multivariate statistical analysis. Journal of Genetic Research and Plant Breeding of Pasture and Forest. 1(12),91-109 (In Persian with English abstract).
  14. Jeena, A.S. and Arora, P.P., 2002. Multivariate techniques in chickpea. Agriculture Science. 22(1), 57-58.
  15. Jomova k, Benkova M, Zakova M, Gregova E, Kraic J., 2005. Clustering of chickpea (Cicer arietinum L.) accessions. Genetic Resources and Crop Evolution. 52, 1039-1084.
  16. Kanouni, H., Farayedi, Y., Sabaghpour, S.H. and Saeid, A., 2016. Assessment of genotype×environment interaction effect onseed yield of chickpea (Cicer arietinum L.) lines under rained winter planting conditions. Iranian Journal of Crop Sciences. 18(1), 63 -75 (In Persian with English abstract).
  17. Kanouni, G., Bekele, E., Assefa, F., Imtiaz, M., Debele, T., Dagne, K. and Getu, E., 2012. Phenotypic diversity for symbio-agronomic characters in Ethiopian chickpea (Cicer aretinum L.) germplasm accession. African Journal of Biotechnology. 33 (63), 12634-12651.
  18. Mardi, M., Talei, A. and Omidi, M., 2003. Genetic diversity and identify its components in Desi-chickpea. Journal of Agricultural Sciences of Iran. 34 (2), 345-351 (In Persian with English abstract).
  19. Moghadam, M., Mohammadi-Shooty, S.A. and Aghaie Sarbarze, M., 1994. Introduction to multivariate methods. (Translation), leading of science publisher.
  20. Mohammadi, M., Ghanadha, M.R. and Talei, A., 2002. Genetic diversity of indigenous wheat lines using multivariate statistical Iran. Seed and Plant Journal. 18 (3), 328-347 (In Persian with English abstract).
  21. Moosavi, S.S., Abdollahi, M.R, Ghanbari, F, and Kanouni, H., 2015. Detection and selection of effective traits on grain yield in chickpea (Cicer arietinum L.) under normal moisture conditions. Plant Production. 39 (1), 119-131 (In Persian with English abstract).
  22. Moosavi, S.S., Kian-ersi, F. and Abdollahi, M.R., 2013. Application of multivariate statistical methods in identifying effective traits on bread wheat grain yield under moisture stress condition. Cereal Research. 3(2), 119-130 (In Persian with English abstract).
  23. Moshtaghi, N., Bagheri, A.R., Jalali-Javaran, M. and Ghareh-Yazi, B., 2006. Multiple shoots straight five chickpea (Cicer aretinum L.) in vitro. Agricultural research: water, soil and agricultural plants. 6 (4), 62-49 (In Persian with English abstract).
  24. Nematzadeh, G.h. and Kiyani, G.h., 2010. Plant breeding; classical methods. First volume. Mazandaran University Press.153 pp. (In Persian with English abstract).
  25. Rabie, K., Khodambashi, M. and Rezai, S., 2008. Identification of traits potatoes using multivariate statistical methods in terms of tension and stress. Journal of Science and Technology of Agriculture and Natural Resources. 46(12), 131-140 (In Persian with English abstract).
  26. Ray chadhury. P., Tanveer, H. and Dixit, G.P., 2007. Identification and detection of genetic relatedness among important varieties of pea (Pisum sativum L.) grown in India. Genetica. 130, 183- 191.
  27. Saman, S., Mozaffari, M.J., Vaezi, S.h., Abbasi-Moghadam, A. and Mostafaei, H., 2012. Evaluation of genetic diversity in germplasm characterization of pods and seeds of lentils.Iranian Journal of Crop Sciences. 14(2), 171-182 (In Persian with English abstract).
  28. Sharma, B.D. and Hore, D.K., 1993. Multivariate analysis of divergence in upland rice. Indian journal of Agriculture Science. 63, 515-517.
  29. Toker, C., 2003. Evaluation of yield criteria with phenotypic correlations and factor analysis in chickpea. Soil and Plant Sciences. 54, 45-48.
  30. Toker, C. and Cagirgan, M.I., 2004.The use of phenotypic correlations and factor analysis in determining characters for grain yield selection in chickpea (Cicer arietinum L.). Hereditas.140, 226-228.
  31. Ward, J.H., 1983. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association. 58, 236-244. Available online at,
  32. Zabet, M. and Hassan zadeh, A., 2011. Determine the traits of mung bean (Vigna radiate L.) using multivariate statistical methods in stress and non-stress conditions. Iranian Journal of Beans. 1(2), 87-98 (In Persian with English abstract).