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Research Article Open access CC BY 4.0

Comparative Performance Response of Ten Economically Important Garlic Cultivars in Alluvial Soils of Punjab

Jitendra Wayade, K. Arun Kumar, J. Jayanthi

International Journal of Plant & Soil Science · pp. 816–827 · Published 26 Oct 2022

10.9734/ijpss/2022/v34i232493

Abstract

All the varieties are not performed equally in all the agroclimatic and soil conditions. A field experiment was conducted at the Agricultural farm of the Lovely Professional University, Punjab to evaluate the growth and yield performance of garlic cultivars in alluvial soil of Punjab. T Ten cultivars viz., Fewari, G-50, G-323, G-189, G-1, Parvati-2, G-389, G-384, G-282, and G-404 were considered as experimental treatments, The experiment was arranged in a randomized complete block designand replicated thrice. Growth, yield attributes and yield were significantly varied among the varieties.. Garlic productivity has been declining and more improved breeding lines are required to enhance the productivity status. The selected cultivars were prominent among farmers and identifying the variation among germplasms may aid in genetic improvement among garlic cultivars. Plant height after 30 days of sowing shows significant difference among cultivars; the pseudo-stem length of G-389 cultivar recorded higher values as compared to others. Yield parameter like length of bulb, bulb width and clove length were recorded higher for Fewari and Parvati-2 cultivars.  The cluster analysis based on growth and yield parameters segregates into two major clusters, viz., Fewari in one major cluster and the second cluster is segregated into further sub-clusters; sub-cluster 1 in second major cluster segregates G1, Parvati-2, G-189, and G-389 cultivars; however the sub-cluster 2 in second major cluster segregates G-323, G-282, G-384, and G-50 cultivars. Principal component analysis (PCA) show the close similarity between G-389 and G-189 cultivars and Fewari cultivars show high variation as compared to all other cultivars.

Garlic genetic variability cluster analysis principal component analysis

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