<div style="text-align: justify;">The aim of the study was to evaluate the variation among some Pisum genotypes using principal component analysis technique. The plant material covering wild pea accessions (Pisum fulvum L., P. abyssinicum L., P. sativum var. elatius), local varieties (P. sativum var. sativum L. and P. sativum var. arvense L.) and two commercial varieties were evaluated for 50 morphological traits. According to results, the first eleven principal component with Eigenvalues &gt;1 contributed % 96.95 of the variability. The percentages of cumulative variation accounted for by each of the four principal component are 33.14%, 48.72%, 58.65%, and 66.88% respectively. Based on the principal coordinate analysis, five major groups were formed. In general, the diagram represented an obvious division between taxonomic groups except for P. sativum var. sativum and P. sativum var. arvense accessions.</div>

Determination of morphological variability of different pisum genotypes using principal component analysis

Background: A breeder&rsquo;s success in rice breeding depends on identifying and using genetic diversity at genotypic level. This involves evaluating different traits to select parent genotypes with high genetic variability. The purpose of this study is to analyze genetic diversity and assess yield and yield attributes in the collected diverse rice germplasm. 
 
Methods: The study aimed to assess genetic diversity and perform multivariate analysis of 16 grain yield and yield attributes among 50 rice genotypes. The experiment was conducted using Randomized Block Design with three replications at P.G. Research Farm, Ranadevi, MSSSoA, CUTM, Paralakhemundi, Odisha, during Kharif season of 2022-23. 
 
Result: Cluster analysis categorized genotypes into six distinct clusters with Cluster I containing the most genotypes (40), followed by Cluster II (4) and Cluster V (3), while the rest had one genotype each. Cluster V exhibited the highest intra-cluster distance followed by Clusters II and I. The most significant inter-cluster distance was observed between Clusters V and II and then between Clusters VI and V, indicating substantial genetic divergence among these clusters. Grain yield plant-1 contributed the most to total genetic divergence followed by biological yield plant-1, 1000 grain weight and plant height. Five of 11 PCs analyzed had eigenvalues exceeding 1.0, accounting for 84.72% of the total cumulative variation. Based on per se performance, genotypes such as NLR 28523, NLR 9674, Khandagiri, Tulaipanji and Barsha emerged as genetically diverse, offering promise for breeding programs.

Determination of morphological variability of different pisum genotypes using principal component analysis

Genetic Divergence in Rice (Oryza sativa L.) Germplasms based on Agro-morphological Traits using Multivariate Analysis