Genetic variability between rice genotypes
The grand mean, genotypic and phenotypic coefficient of variability, broad-sense heritability and genetic advance as percentages of the mean are illustrated in Table 4. Low estimates of phenotypic and genotypic variances were recorded for all the studied traits except head rice % and grain elongation traits. The environmental variance was very low for all traits especially for grain shape and grain yield. The phenotypic coefficient of variability (PCV) was higher than the GCV for all the characters studied. PCV was ranged between 2.572 and hulling to 19.956 for grain elongation. PCV and GCV were lower than (10 %) for grain length, grain shape, hulling percentage, milling percentage, amylose content and grain yield (t/ha). Otherwise, high estimates of both PCV and GCV were detected for grain elongation followed by gelatinization temperature and head rice. Negligible values of the difference between PCV and GCV were recorded for grain length, grain shape, hulling, milling, head rice recovery, amylose, grain elongation and grain yield. In general, PCV values were higher than GCV for various characters studied. Among the desirable traits, low GCV and PCV were observed for grain length. High estimates of broad-sense heritability were noted for grain length, grain shape, hulling, milling, head rice, amylose content, grain elongation and grain yield. Their estimated values were ranged between 71.25% for grain length and 99.55% for both grain elongation and grain yield traits, while almost moderate estimates were exhibited for gelatinization temperature (67.78 %). However, high estimates of expected genetic advances were found for grain elongation (40.924 %), head rice recovery (27.336 %) and gelatinization temperature (25.603 %). Grain elongation, gelatinization temperature and head rice had shown high PCV and GCV along with high to moderate heritability (h2) and genetic advance. The additive gene action governed the above-mentioned three traits. High heritability with high genetic advance as percent mean was observed for all the grain quality traits except for hulling percent and milling percent.
Genotypes Performance under different NPK treatments
Data pointed out that there were highly significant differences among different genotypes in the studied characteristics under different NPK levels. Grain yield, grain quality and cooking quality traits were affected significantly by NPK treatments. The studied characteristics were increased gradually by increasing NPK levels from 0 up to the full dose of recommended doses of NPK fertilizers. The results revealed that the GZ10590-1-3-3-2 rice genotype produced the highest grain yield followed by IET1444, while Nerica1 came in the last rank and gave the lowest value in this aspect (Fig. 1). Grain length was higher in IRAT170 which came in the first rank and recorded the highest value of grain length while Giza178 recorded the shortest grains (Fig. 2). Grain shape was higher in IRAT170 and came in the first rank followed by Giza182 (Fig. 3). Milyang 109 produced the lowest values of grain shape. Giza177 recorded the highest values of hulling percentage followed by Giza179. Korea1, Nerica1 and Milyang109 came in the last rank and recorded the lowest hulling percentage and recorded nearly the same value of hulling percentage (Fig. 4). The highest percentage of milling was observed by the rice genotypes Giza177, GZ10991-5-18-5-1, GZ10333-9-1-1-3 and GZ10101-5-1-1-1. While the lowest value of milling rice was observed with Nerica1(Fig. 5). GZ10333-9-1-1-3 recorded the highest values of head rice while GZ10598-9-1-5-5 recorded the lowest value of head rice under this study (Fig. 6). Amylose content was high in Nerica 1, it came in the first rank and recorded the highest value of amylose content, while, GZ10598-9-1-5-5 recorded the lowest value of amylose content (Fig. 7). The highest value of grain elongation was recorded by IET1444. While IRAT170 and GZ10590-1-3-3-2 recorded the lowest grain elongation (Fig. 8). Gelatinization temperature (GT) in IET1444 came in the first rank and recorded the highest value while IRAT170 and GZ10590-1-3-3-2 recorded the lowest values (Fig. 9).
Principal component analyses
Principal component analysis (PCA) is used to generalize the variation in different explaining factors. Our PCA was done for different grain quality traits of the twenty rice genotypes. Eigenvalues and a fraction of variation in each principal component are shown in Table 5. PC1 explained 37.6 % of total variations observed among the genotypes while it was 28.0% for PC2. The total diversity was explained by nine principal components (Table 5). The results showed that only the first three principal components jointly accounted for 80.9% of the total variation among the genotypes, while these three exhibited eigenvalues above unity. However, the large fraction of the divergence (88.2% variation) was explained by the first four components. Hulling was correlated with Milling, in addition, gelatinization temperature was closely associated with grain elongation, as well as grain shape was found to be correlated with grain length. All Egyptian genotypes and promising lines were features with the high yielding ability and high milling and hulling relearn. IET 1444 was greater in gelatinization temperature and grain elongation compared with other rice genotypes. Both Nerica 1 and IRAT 170 were unique to grain length and grain shape traits (Fig. 10). The Principle components for grain yield and grain quality traits in twenty rice genotypes and their distribution of priority in resulting PCA were reported in Tables 6 and 7, respectively. The results demonstrated that grain length, milling and amylose content were classified in PC1, while, head rice, gelatinization temperature and grain elongation were located in PC2. On the other hand, grain yield and hulling were distributed in PC3.
Clustering analysis was performed to look for similarities between rice accessions and assess the possibility of hybridization. Mean performance of different clusters for the characters manifested that genotypes with maximum grain yield, high milling output, good grain shape and low amylose content were accumulated in cluster I, whereas genotypes with maximum hulling were grouped in cluster II. Moreover, genotypes that took minimum head rice were clubbed into cluster III. Whereas low yielding genotypes with soft gelatinization temperature and long grain elongation were grouped into cluster IV (Table 8).
Among twenty rice genotypes, cluster analysis resulted in four clusters following Ward’s method (Fig. 11). Genotypes from the same origin or taxonomic type were found to be clustered together in the cluster results. Four distinct clusters emerged from the diversity analysis. Clusters I (7 genotypes) and III (7 genotypes) had the most genotypes (70%) while Cluster IV (one genotype) had the highest mean values for the qualities being evaluated. In cluster I, out of 7 rice genotypes, 5 of them were Egyptian japonica type, with high yielding, shortest grains, bold grain shape, highest milling and head rice recovery and low amylose content, namely, Giza 177, Sakha 107, Gz 10101-5-1-1-1, Gz 10598 -9-1-1-5-5 and Gz10590 – 1-1-3-9-1, while the other two genotypes Giza 178 and Korea 1 were (indica/japonica) type. With regarding cluster III, 7 genotypes were aggregated, out of them, 4 genotypes were Egyptian japonica type, namely, Sakha 108, Sakha 109, Gz 10333-9-1-1-3 and Gz 10590-1-3-3-2 with high hulling. However, the other three genotypes were exotic, namely Milyang 109 (Indica/japonica type), IRAT 170 (Indica type) and Nerica 1 (Indica type). On the other hand, 5 rice genotypes were grouped into cluster II, 0ut 0f them, 2 genotypes, Giza 179 and Suweon 375 were (Indica/Japonica) type and 2 genotypes, Fukunishiki and Gz 10991-5-18-5-1 were (japonica type) as well as only one genotype Giza 182 was (Indica type). Finally, only one rice genotype namely, IET 1444 tolerant to water stress and suitable to cultivate under drought conditions (Indica type) was located in cluster IV. The distribution of 20 rice genotypes in different clusters is illustrated in (Table 9) and the dissimilarity matrix according to Euclidean square based on studied traits in the tested rice genotypes were observed in Table 10.