Fig 1.
Monthly average weather data of 2022WS (T. Aman) and 2023DS (Boro).
Fig 2.
Boxplot displaying quartile distribution of nine yield attributing traits in both seasons.
DFF- Days to 50% flowering; DTM- Days to Maturity; PH- Plant Height (cm); ET- Effective Tillers per Hill; PL- Panicle Length (cm); SF- Spikelet’s Fertility (%); LWR- Grain Length Width Ratio; TGW- Thousand Grain Weight (g); GYTH- Grain Yield (t/ha); WS- Wet Season; DS- Dry Season. *** represents 0.001 significant level.
Table 1.
Variance components and heritability values of different traits of 2022WS & 2023DS.
Fig 3.
Pearson correlation coefficient of nine quantitative traits in rice.
Table 2.
Eigenvalues, % variance and cumulative eigenvalues of Rice genotypes.
Fig 4.
K-means clustering plot of A) 2022WS (T. Aman) and B) 2023DS (Boro).
Table 3.
Distances between cluster centroids based on K-means values of 216 rice genotypes for nine agronomic traits.
Table 4.
Factorial loadings, communalities, uniqueness, and selection gains (SG) based on the multi-trait genotype–ideotype distance index (MGIDI).
Table 5.
The multi-trait genotype-ideotype distance index for Rice genotypes.
Fig 5.
Rice accession rankings showing selected accessions using the multi-trait genotype– ideotype index (MGIDI).
Fig 6.
The strengths and weaknesses of the selected genotypes are shown as the proportion of each factor on the computed multi-trait genotype-ideotype index (MGIDI).
The smaller the proportion explained by a factor (closer to the external edge), the closer the traits within that factor are to the ideotype. The black broken circle at the center shows the theoretical value if all the factors contributed equally.
Fig 7.
Histogram showing the frequency distribution of 200 rice genotypes for Genomic estimated breeding values (GEBV) on yield.
Table 6.
List of genotypes having positive GEBVs (>0.5).