1Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (U.P.)-211007
2Division of Oilseed, Department of Agriculture and Farmers’ Welfare, New Delhi-110012
3Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (U.P.)-211007
4Department of Genetics and Plant Breeding, Sri Karan Narendra Agriculture University, Jobner, (Rajasthan)-303329
5Department of Genetics and Plant Breeding, Rajasthan College of Agriculture, MPUAT, Udaipur (Rajasthan)-313004
6Department of Genetics and Plant Breeding, B. A. College of Agriculture, Anand Agricultural University, Anand (Gujarat) - 388110
7Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Hisar, (Haryana)-125004
*E-Mail: vishnoi.ruchi25@gmail.com
Online Published on 02 May, 2025.
Variability is the prerequisite for selection of superior traits and parents. Multivariate analysis acts as an aid to the process of selection indices. Therefore, twenty-two farmers’ varieties of pea (Pisum sativum var. arvense L.) of Vindhyan zone, Uttar Pradesh were studied at Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (U.P.). The varieties were grown in randomized block design (RBD) during Rabi, 2019-20 and Rabi, 2020-21. The data obtained on 11 traits for the two consecutive years were pooled for principal component (PC) analyses. The result revealed that the four principal components (PC1, PC2, PC3 and PC4) had eigen value greater than unity. They contributed a total of 78.68% of variance. Days to maturity, pod length, pod width, number of effective nodes and yield per plant exhibited maximum variability for the characters, which can be exploited to a greater extent. Days to 50% flowering, days to maturity and harvest index had positive correlation with yield per plant. Number of seeds per pod, number of pods per plant and pod length exhibited negative association with yield per plant. Hence, selection of these traits could be useful to increase the productivity in farmers’ pea varieties. To the farmers’ varieties viz., PMKK-232 and PKKK-227 could be selected in PC1 for future breeding programme.
Pea, PCA, Farmers’ varieties, Multivariate analysis
