Multivariate analysis for improving selection of yield and related traits in rice (Oryza sativa L.)

Rice (Oryza sativa L.) serves as a staple food for nearly half of the world population, with yield being critical traits for both consumers and food security. In this study, 45 diverse rice genotypes, including standard checks, were evaluated for yield and related agronomic traits over the 2023 and 2024 growing seasons. The experiment was carried out at lovely professional university, Jalandhar, Punjab, using a randomized complete block design (RCBD) with three replications. Rice genotypes such as Goyadi (G35), Kalamati (G39), AAU DR-1 (G16), IR 167 1662 (G40) and Mala Gauri (G26) showed the maximum potential for achieving superior seed yield per plant (SYP). These genotypes consistently exhibited robust average results in various traits related to yield. In terms of specific traits, IR 82635-B-B-75-2 (G41), G39, CO-51 (G42) and Kanchan (G14) were identified for earliest Days to 50 % flowering (DFF), while IR 167 1662 (G40), Khajur (G25), Asamiya Dhan (G15), CO-51 (G42) and IR 82635-B-B-75-2 (G41) were the earliest to reach maturity. For reduced Plant height (PH), genotypes Fara Dhan (G37), Kani Dhan (G36), Tama Koni (G34), Khajur (G25) and Bhushu (G11) stood out. The promising performance of these genotypes across multiple yield-contributing traits suggests they can serve as valuable male parents in hybridization programs. High estimates of phenotypic coefficient of variation (PCV) and Genotypic coefficient of variation (GCV) coefficients of variation remained observed for both Harvest index (HI) and Seed yield per plant (SYP), indicating significant genetic variability with minimal environmental influence. Cluster analysis revealed that Cluster II had the minimum intra-cluster distance (ICD), suggesting greater homogeneity among its genotypes, whereas Cluster IV ICD. The maximum genetic divergence was found among Clusters IV and VII, while the least was between Clusters IV and VI. Traits such as PH and grains/panicle (GPP) contributed most to the total genetic divergence. Principal Component Analysis showed that PC1 was largely influenced by traits like 1000 seeds weight (TSW), SYP and DFF, while PC2, PC3 and PC4 contributed significantly to yield enhancement, supporting their use in selection for yield improvement.