Multivariate Dissection of Phenotypic Variation and Genetic Divergence in Advanced Groundnut Lines under Rainy Season
Wahid Husen M. Peerzade *
Department of Seed Science and Technology, SRTC, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, India and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
Jagan Mohan Rao
Department of Genetics and Plant Breeding, College of Agriculture, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, India.
Sunita Choudhary
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
Manish Pandey
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
M. Pallavi
Agricultural Research Station, Adilabad, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
Groundnut (Arachis hypogaea L.) is an important oilseed and legume crop for global food security, yet its genetic advancement remains constrained by a narrow genetic base. Improving pod yield and kernel quality by systematically exploiting available genetic variability is a primary objective in groundnut breeding. In this study, we evaluated 35 groundnut accessions, comprising 30 advanced breeding lines (ICGV series) and five commercial checks during the rainy season of 2024 using an Augmented Randomised Block Design (Augmented RBD) at the Seed Research and Technology Centre (SRTC), PJTAU. A total of 11 quantitative morphological and agronomic traits were recorded and subjected to multivariate analysis. Hierarchical cluster analysis using Euclidean distance and Ward’s minimum variance method (Ward.D2) partitioned the genotypes into four distinct clusters. Cluster II was the largest (n = 22), comprised lines with a low average yield (2,589.30 kg ha-1) and seed weight, whereas Cluster III (n = 2) exhibited the maximum plant height (56.29 cm) and 100-seed weight (58.80 g), but fewer pods per plant (8.60), suggesting a trade-off between seed size and pod proliferation. Cluster IV (n = 3, including K6, JCG 2141, and G5) demonstrated superior agronomic performance, combining early maturity (110 days) with the highest pod yield (5,215.28 kg ha-1). The maximum inter-cluster distance occurred between Clusters II and III (6.32), followed by Clusters III and IV (6.10), highlighting substantial genetic divergence. Principal component analysis (PCA) identified four principal components (λ > 1.0) accounting for 76.81% of the total phenotypic variation, with PC1 (33.74%) dominated by 100-seed weight, pod wall thickness, kernel diameter, and pod yield. Crossing genotypes from Cluster IV with those from Cluster III offers the potential to combine large kernel traits with early maturity and high pod yield, facilitating transgressive segregation in future groundnut improvement programmes. Furthermore, targeted hybridisation between the high-yielding, early-maturing lines of Cluster IV and the bold-seeded lines of Cluster III may help to overcome the physiological trade-off between kernel mass and pod density, establishing a strategic foundation for future high-yielding groundnut cultivars.
Keywords: Groundnut, Arachis hypogaea L., advanced breeding lines, phenotypic diversity, hierarchical clustering, principal component analysis, Ward's method, pod yield, 100-seed weight, parental selection