Articles
RELATIONSHIP AMONG QUANTITATIVE AND QUALITATIVE CHARACTERS IN 90 GRAPEVINE (VITIS VINIFERA) CULTIVARS
Article number
640_33
Pages
275 – 282
Language
English
Abstract
In order to study genetic diversity, 90 cultivars of Vitis vinifera were evaluated for 23 characters.
Vines were planted in a density of 2m x 3m (vine x row), trained to bilateral cordon and spur pruned.
The experiment was conducted in a randomized complete block design with three two-vine replications.
Characteristics related to the bunch (weight, number, size, width, length), berry (number, size, width, length, weight and density), seed (length, weight, number / berry), composition (soluble solids, titratable acidity, time of maturity) and vine yield were evaluated.
The phenotypic coefficients of variation and variance analysis were highly significant (p < 1%), indicating a huge variation among cultivars.
The results of phenotypic correlation, multiple regression and path analysis showed that bunch number, bunch weight, berry number and berry weight had the greatest effect on yield.
Factor analysis showed that seven factors with eigenvalues >1 contributed 81% of total variation.
Cluster analysis was also run on the basis of full characters using the Ward method and cultivars grouped to four subclusters.
Vines were planted in a density of 2m x 3m (vine x row), trained to bilateral cordon and spur pruned.
The experiment was conducted in a randomized complete block design with three two-vine replications.
Characteristics related to the bunch (weight, number, size, width, length), berry (number, size, width, length, weight and density), seed (length, weight, number / berry), composition (soluble solids, titratable acidity, time of maturity) and vine yield were evaluated.
The phenotypic coefficients of variation and variance analysis were highly significant (p < 1%), indicating a huge variation among cultivars.
The results of phenotypic correlation, multiple regression and path analysis showed that bunch number, bunch weight, berry number and berry weight had the greatest effect on yield.
Factor analysis showed that seven factors with eigenvalues >1 contributed 81% of total variation.
Cluster analysis was also run on the basis of full characters using the Ward method and cultivars grouped to four subclusters.
Authors
R. Fatahi, A. Ebadi, A. Vezvaei, Z. Zamani, M.R. Ghanadha
Keywords
Genetic diversity, grape breeding, gene pool, factor analysis, multiple regressions, cluster analysis
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