Evaluating Panicle Architecture and Yield Characteristics in Medium Duration Rice Genotypes through Correlation and Principal Component Analysis

Abstract

Panicle architecture is a crucial factor that significantly improves economic grain yield. The panicle exhibits considerable variation in its structure, including the number of branches, the length and weight of the panicle, and arrangement of the grains. These traits are not only vital for the plants reproductive efficiency but also for optimizing yield potential. This study focuses on assessing variation in panicle architecture among 31 different medium duration rice genotypes by employing principal component analysis and correlation analysis. This study aimed to dissect the complex relationship between different panicle characteristics. The findings revealed that the first three principal components together explained 60.3% of the total variability. In the first principal component (PC1), the traits SF, PBL, YPH, and HI had the highest positive eigenvalues, indicating their significant impact on overall genotype variations. The second principal component (PC2) was mainly influenced by TSP and SBN, while the third principal component (PC3) was driven by PW and SBL. These results suggest that grain yield is significantly affected by the traits SF, PBL, YPH, HI, TSP, SBN, PW, and SBL. The correlation analysis revealed that the number of secondary branches showed a positive association with unfilled spikelet. Spikelet fertility exhibited a strong positive correlation with yield hectare-1 and harvest index, highlighting its significance in overall yield improvement. Therefore, these components are crucial for differentiating rice genotypes with higher grain yield potential. By evaluating correlation and principal component analysis, we aimed to identify traits that can be targeted for crop improvement initiatives. The findings will contribute to the development of rice varieties that are better suited to specific growing conditions and have enhanced yield potential. This approach will enable the identification of key panicle architecture and yield and yield traits that optimize resource allocation and improve grain filling efficiency, ultimately leading to rice varieties with higher productivity and adaptability.