Genetic divergence studies of fodder yield and quality attributing characteristics in promising maize (Zea mays L.) composites Singh Devinder Pal*, Jain Mohit, Goyal Meenakshi, Sandhu Surinder Punjab Agricultural University, Ludhiana-141004, India *Corresponding author e-mail: devinderpal301@pau.edu
Online Published on 4 January, 2024. Abstract The objective of the current study was to assess the extent of genetic variation among twenty-seven maize varieties using morpho-agronomic data based on Principal Component Analysis (PCA) and to measure the genetic distance among these genotypes using hierarchical cluster analysis. Twenty-seven composites were grown in a randomised complete block design with three replications for two years. The experimental material was assessed for 15 morpho-agronomic traits. Green fodder yield depends on various other traits such as plant height, number of leaves, leaf length and fodder quality traits such as crude protein, acid detergent fibre and neutral detergent fibre. This study showed a positive correlation of green fodder yield with various such traits. A very high positive correlation was noticed between number of leaves and ear height (0.723) and between crude protein and in vitro dry matter digestibility (0.823). However, crude protein showed a significant negative correlation with acid detergent fibre (-0.739) and neutral detergent fibre (-0.678). Five principal components had more than one eigen value, contributing 75% variability among genotypes. PC1 contributes 25.1% followed by PC2 with 19.9%, PC3 with 12.5%, PC4 with 9.7% and PC5 with 7.8%. The scree plot revealed that the experimental material could be divided into five clusters. The genotypes under cluster five could be used to improve green fodder yield. The minimum intra-cluster distance observed for cluster 1 was 48.008, and the maximum inter-cluster distance observed between clusters 2 and 5 was 259.45. The different groups obtained could be useful for deriving the inbred lines with diverse features, which could be used in various maize breeding programmes. Top Keywords Cluster analysis, Correlation, Principal Component Analysis, Zea mays. Top |