Multivariate Clustering of Grapes Cultivars for Productivity and Quality Enhancement under Rainfed Conditions
Multivariate Clustering of Grapes Cultivars for Productivity and Quality Enhancement under Rainfed Conditions
Naveeda Anjum1, Muhammad Aqeel Feroze1, Rizwan Rafique2,3*, Monis Hussain Shah4, Tehseen Ashraf5, Muhammad Asim6, Bushra Zulfiqar6 and Muhammad Shahid Iqbal7
ABSTRACT
Grape yield and quality traits are interlinked and understanding their interactions is highly critical. This challenge is more obvious under rain fed conditions which impose physical constraints for normal plant growth. The present study was designed to find interactions between yield and quality characters of grapes cultivars. Therefore, several grapevine cultivars like ‘Kings Ruby’, ‘Flame Seedless’, ‘Perlette’, ‘Superior’, ‘Early White’, ‘Vitro Black’ and ‘Sultanina C’ were tested in rain fed area of Punjab. Data of two seasons were collected by a randomized complete block design. Experiment and results were analyzed through correlation, principal component analysis (PCA), and a cluster analysis (CA). Grapes varieties indicated a distinct cluster grouping in terms of yield and quality characteristics. ‘Superior’ with a closely associated group of CVs; ‘Sultanina C’, ‘Kings Ruby’ and ‘Flame Seedless’ are highly suitable for district Chakwal in Punjab whereas‘ Perlette’ and ‘Early White’ didn’t show suitability for this region. The results also indicated that the berry size is positively correlated with berry weight and yield (group-I), cluster weight and TSS (group-II) and number of clusters/plant and titratable acidity (TA) (group-III). However, TSS and TA are negatively correlated to each other in a multivariate analysis. In most of the cases the annual pattern for grapevine yield and quality traits were almost similar. The findings of current study provide a better understanding of grapes yield and quality interactions.
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