Evaluation of Trait Relationships in Urdbean [Vigna mungo L. Hepper] Genotypes Using Principal Component Analysis

Abstract

Blackgram [Vigna mungo (L.) Hepper] is a nutritionally rich pulse crop widely cultivated under rainfed conditions, where drought stress severely limits its productivity across seasons. Therefore, identification of high-yielding, drought-tolerant genotypes through efficient statistical tools like Principal Component Analysis (PCA) is essential for genetic improvement and sustainable production. The present investigation was undertaken to identify promising high-yielding urdbean genotypes suitable for cultivation across Kharif seasons by evaluating 96 genotypes along with two checks. The field experiment was conducted at Soybean Farm, College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Jabalpur, Madhya Pradesh, India under natural field conditions with Randomized Complete Block Design in Kharif 2023 and 2024. The pooled data across seasons were analysed using Principal Component Analysis (PCA) to select superior genotypes with maximum yield potential. In Pooled analysis PCA was applied to thirteen quantitative traits, and among the thirteen principal components (PCs), only three had eigenvalues greater than 1.00, revealing the essential features of the dataset and showed about 64.68% of variability among the traits studied. Rotated component matrix revealed that the PC1 which accounted for the highest variability (34.77%) was mostly related with traits such as days to maturity, plant height, number of primary branches per plant, number of pod clusters per plant, number of pods per plant and pod length.