Integrating principal component and regression analyses for genetic diversity and trait evaluation in oat genotypes

Abstract

Oat holds significant importance in global agriculture and nutrition due to their adaptability and versatility. In the present study, Principal Component Analysis (PCA) and Regression analysis were carried out to identify the cause and effect relationship among various traits. PCA on 13 yield attributes revealed five main components contributing to 80.75% cumulative variance. PC1, associated with green fodder yield, dry matter yield, tillers per plant and seed yield was a prominent contributor. PC2 was influenced largely by days to 50% flowering and days to maturity. Biplot analysis identified two distinct trait groups. Multiple regression analysis revealed tillers per plant, test weight and number of spikelets as significant predictors of seed yield. The findings offer insights into genetic association among traits in oat by uncovering the quantitative relationships among them and to identify patterns of genetic variation among different oat genotypes. The analysis of individual trait regression graphs enhances understanding of trait contributions to seed yield. This study advances oat improvement strategies for enhanced crop productivity and resilience.

Keywords

• oat
• principal component
• regression analysis
• yield