Vol. 34 No. 4 (2020): Advances in Horticultural Science
Articles

Morphological and biochemical classification of Iranian Mango germplasm collection by multivariate analysis: Implications for breeding

D. Samsampour
Department of Horticulture Science, Biotechnology & Molecular Genetic Section, University of Hormozgan, Bandar Abbas, Iran
H. Kazemzadeh-Beneh
Department of Horticulture Science, Biotechnology and Molecular Genetic Section, University of Hormozgan, Bandar Abass
G.-R. Damizadeh
Agriculture and Natural Resources Research Center of Hormozgan, Bandar Abbas, Iran
Z. Mirzaei
Department of Horticulture Science, Biotechnology and Molecular Genetic Section, University of Hormozgan, Bandar Abbas, Iran
Published October 9, 2020
Keywords
  • cluster grouping,
  • factor analysis,
  • genetic variability,
  • mango fruit,
  • principal component
How to Cite
Samsampour , D., Kazemzadeh-Beneh, H., Damizadeh, G.-R., & Mirzaei, Z. (2020). Morphological and biochemical classification of Iranian Mango germplasm collection by multivariate analysis: Implications for breeding. Advances in Horticultural Science, 34(4), 381-395. https://doi.org/10.13128/ahsc-9005

Abstract

This study was conducted at southern Iran with the aim to evaluate the phenotypic diversity of 84 mango cultivars by using morphological and biochemical traits. Two other industrial cultivars ‘Longra’ and ‘Senderi’ used as control of the study. Descriptive results indicated that the value of both quantitative and qualitative variables all the cultivars were lower than the ‘Senderi’. The variability among cultivars was highly significant in the measured traits. The fruit weight, fruit length, stone weight, stone length, fruit beak, dry matter, and petiole length showed highly discriminating power. Pearson’s correlation analysis revealed high variability due to the existence of significant positive and negative correlations among traits. Agglomerative hierarchical clustering confirmed remarkable variation in the studied germplasm and identified three major clusters with several sub­clusters. The 80.20, 70.11, and 100% of the quantitative variability was explained by principal component analysis (PCA), factor analysis (FA), and liner discriminant function (LDF) where fruit descriptors contributed most of the total variation, respectively. However, multivariate analysis proved that fruit related characters were most powerful to differentiate cultivars. The cultivars displayed distinct grouping by FA and LDF compared to PCA. The results revealed that the Iranian mango germplasm has a high potential for specific breeding project regarding fruit size and quality that should be further completed by a molecular marker analysis.

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