Document Type : Original Article


1 Ph.D. Member, Agricultural and Natural Resources Research Center of Qazvin, Qazvin, Iran

2 Professor, Faculty of Agriculture, Razi University, Kermanshah,

3 Associated Professor, Imam Khomeini International University, Qazvin, Iran


Objective: Yield components and genetic contribution have the most important in final yield and breeding programs of crop plants. For this purpose, 20 varieties of grapevines with Russia origin were evaluated in Urmia and Takestan research station (under full irrigation and drought stress). Methods: Twenty grapevine genotypes were evaluated in Urmia and Takestan research station (under full irrigation and drought stress) in randomized complete blocks design with three replications and three plants in each plot. Number of cluster per plant, Number of berry per cluster, berry weight and yield of each plants were recorded. Compound and logarithmic analysis of variance, variance of genetic components and environmental interactions were presented by multiplicative three environmental and genotypic elements. Results: Results indicated that number of cluster per plant had the highest genetic contribution in final yield and also had the most sensitivity and variation in different environments. Direct effect of number of cluster per plant in final yield was higher than other studied traits. V3 value was higher than V2 and V2was higher than V1, therefore sequence of manifestation of yield components were number of cluster per plant, number of berry per cluster and berry weight, respectively. Environmental components of interactions were indicated that absolute value of r1 was higher than r2 and r3. Conclusion: These results indicated that number of cluster per plant has higher sensitivity than the other main yield components in different environments.


Main Subjects

Fanizza, G., Lamaj, F., Costantini, L., Chaabane, R., Grando, M.S. (2005). QTL analysis for fruit yield components in table grapes (Vitis vinifera). Theor. Appl. Genet, 111:658-665.
FAO, 2009. Statistical database. 
Farshadfar, E. (1999). Path analysis of genotype and environment interactions in wheat chromosome substitution lines. Iran agricultural science journal, 30:
Farshadfar, E. (2010). New discussions in biometrical genetics. Kermansha Islamic Azad University Press II,1174- 1219.
Farshadfar, E., Mahtabi, E., Jowkar, M.M. (2013). Evaluation of genotype × environment interaction in chickpea genotypes using path analysis. International journal of Advanced Biological and Biomedical Research,
1: 583-590.
Farshadfar, E., Rasoli, V., Mohammadi, R., Veisi, Z. (2012). Path analysis of phenotypic stability and drought tolerance in bread wheat (Triticum aestivum L.). Int. J.Plant Breed., 6:106-112.
Huhn, M. (1979). Beitrage zur erfassung der phanotypischen stabilitat. I. Vorschlag einiger auf Ranginformationnen beruhenden stabilitatsparjameter. EDV Medizin Biol., 10: 112-117.
Sparnaaij, L.D., Bos, I. (1993). Component analysis of complex characters in plant breeding. Euphytica, 70: 225-235.
Tai, G.C.C. (1975). Analysis of genotype environment interactions based on the method of path coefficient analysis. Canadian Journal of Genetics and Cytology, 17: 141-149.
Tai, G.C.C. (1979). Analysis of genotype environment interaction of potato yield. Crop Sci., 19: 434 - 438.
Tai, G.C.C., Levy, D., Coleman, W.K. (1994). Path analysis of genotype-environment interaction of potatoes exposed
to increasing warm climate. Euphytica, 75: 49-61.