Morphological distinctiveness between Solanum aethiopicum Shum group and its progenitor
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Date
2017-06
Authors
Sseremba, Godfrey
Tongoona, Pangirayi
Yaw Eleblu, John Savior
Yirenkyi Danquah, Eric
Kabod, Nahamya Pamela
Balyejusa Kizito, Elizabeth
Journal Title
Journal ISSN
Volume Title
Publisher
Academic Journals
Abstract
Use of morphological markers offers an alternative in germplasm discrimination of research-neglected
crop species. A collection of 25 accessions including five wild progenitors was evaluated in screen
house to identify the morphological difference between Solanum aethiopicum Shum and Solanum
anguivi. An Unweighted Pair Group Method with Arithmetic mean hierarchical clustering revealed
presence of moderate structure with a cophenetic correlation coefficient of 0.73. Five distinct clusters
were produced; the progenitor accessions for the S. aethiopicum Shum were grouped in their own
cluster. The Richness, Shannon-Weaver and Simpson indices were also different among qualitative
variable categories. A ‘prcomp’ function based Principal component analysis (PCA) in R on quantitative
variables indicated that days to germination and emergence, cotyledonous leaf length, cotyledonous
leaf width, shoot biomass, plant height, petiole length, days to first flowering opening, plant width, plant
branching, and number of leaves per plant are the major drivers of variability in the study accessions.
Further, results from canonical discriminant analysis to discern between the S. aethiopicum and its
progenitor accession groups showed that the days to germination and emergence provide the best
separation; with the former emerging earlier than the latter. The mean values for flowering time, leaves
per plant, number of branches per plant and plant height were more favorable for the Shum than its wild
progenitor accessions. The study revealed that morphological markers are useful in distinguishing
between the S. aethiopicum Shum and its progenitor accessions.
Description
Keywords
African indigenous vegetable species, Genetic diversity, Reordered hierarchical clustering, Principal component analysis (PCA), Linear discriminant analysis