Vol. 11, Issue 12 (2022)

Author(s):
Kaveri Chawan, P Ravishankar, S Ramesh and T Onkarappa

Abstract:
In India, soybean is the most important protein-rich oilseed crop; it has a 40% protein content and 20% oil content. The use of genetic resources in breeding programmes will be managed and accelerated by core collection. The Standard Stratified Clustering (SSC) approach was used in the current study to develop core sets of soybean from a base collection of 2000 accessions based on data on 13 qualitative and 7 quantitative traits. The SSC approach with a combination of two core sizes (10% and 15%), two sampling strategies (proportional and logarithmic), and two allocation strategies (random and preferred) were used to develop eight core sets. Similarity of classes on the basis of qualitative traits of 8 core sets with the base collection was examined using Chi-square test, Shannon-Weaver diversity index, and ‘class coverage’ statistics. Univariate statistics, based on quantitative traits, such as mean and variance and multivariate statistics, standardized mean difference (SMD %), coincidence ratio (CR %), variance difference (VD %), and variable rate (VR %) were also used to assess the representativeness of core sets. Core set of 15% size developed using logarithmic sampling with preferred allocation strategy retained higher CR%, VD%, and VR% based on quantitative traits.