Journal of Food Legumes
SCOPUS
  • Year: 2014
  • Volume: 27
  • Issue: 4

Multivariate analysis of yield and lodging traits in a diverse collection of pea (Pisum sativum L.)

  • Author:
  • Shubhra Kujur, AK Singh, CP Srivastava
  • Total Page Count: 4
  • Page Number: 293 to 296

Department of Genetics and Plant breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221 005 (India)

*E-mail: cpsgenetics@gmail.com

Online published on 10 February, 2015.

Abstract

Lodging resistance is one of the important selection criteria in pea breeding. Evaluation of 191 diverse pea genotypes was done for lodging resistance and yield traits through multivariate analysis to identify some key traits influencing lodging resistance and thereby yield potential in pea. A wide range of variation for all the seven quantitative traits viz., plant height (PH), pods per plant (PPP), test weight (TW), yield per plant (YPP), lodging score (LS), stem diameter (SD) and linear stem density (LSD) was observed in present study. Estimates of correlation coefficients showed significant positive correlations between different traits viz., PPP with PH, YPP and TW, PH with LS and LSD with SD, whereas, significant negative associations were observed between PH with SD and LSD. Principal component analysis revealed significant variation among traits with the first three principal components explaining 68% of the total variation. SD, PH, LSD and TW are important traits determining most of the variation. Cluster analysis clearly separated lodging resistant and susceptible genotypes in two distinct groups. Most of the lodging resistant genotypes were shorter in plant height than susceptible genotypes. Stepwise regression analysis revealed that plant height and stem diameter is the best predictor for lodging. The present study revealed that, short statured genotypes with greater lodging resistance can be used as parents in pea breeding programmes.

Keywords

Linear stem density, Lodging, Pea, Principal component analysis (PCA), Regression, Yield