Title of article
A Comparative Study of Genetic Diversity, Heritability and Inter-relationships of Tree and Nut Attributes between Prunus scoparia and P. elaeagnifolia using Multivariate Statistical Analysis
Author/Authors
Ansari, Akram College of Agriculture - Shiraz University , Gharaghani, Ali College of Agriculture - Shiraz University
Pages
14
From page
137
To page
150
Abstract
By applying multivariate statistical analysis, this research aimed to estimate the heritability
and find relationships between the vegetative and reproductive characteristics of Prunus
scoparia and Prunus elaeagnifolia. Twenty genotypes of each species were selected randomly from cultivated populations and twenty-two traits including the tree, leaf, flower, nut and kernel attributes were measured. Results showed that there were high levels of genotypic and phenotypic variations among the genotypes of both species. Many of the measurements pertaining to the leaf, flower, nut and kernel, showed very high heritability (H2 >90%) in both species, whilst some traits such as shoot diameter in P. scoparia and kernel moisture in both species had very lower heritability (H2
<50%). Generally, the heritability of measured traits in P. elaeagnifolia were higher than those of P. scoparia, especially for economically important traits including yield (H2
= 94 and H2 = 54.61, respectively in P. elaeagnifolia and P. scoparia), nut weight (H2
= 97.83 and H2 = 85.39.61, respectively in P. elaeagnifolia and P. scoparia) and oil percentage (H2 = 75.55 and H2 = 61.43, respectively in P. elaeagnifolia and P. scoparia). Stepwise regression analysis revealed that the most influential factors on yield of P. scoparia, were the fruit set, flower diameter and leaf length, whilst for the P. elaeagnifolia, the yield was mostly determined by fruit set and leaf area. The high genetic diversity and heritability of the studied traits, indicates high genetic potential of this germplasm to be utilized in future breeding programs.
Keywords
Wild almond , Breeding , Stepwise regression , Cluster analysis , Bi-plot
Journal title
Astroparticle Physics
Serial Year
2019
Record number
2475296
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