3.1. Stem hardness variations among the genotypes
The ANOVA given in shows in Tab. 1. that genotypic difference in stem hardness was highly significant for traits like bending (BL and BU), and compression CU (p > 0.05). Basic descriptive statistics (mean, standard deviation, minimum, maximum and coefficient of variance) of all the genotypes for morphological, yield and fiber traits were studied (Additional file 1: Table S1) It was observed that maximum coefficient of variance (26.67% & 20.40) was recorded in bending (BU & BL) respectively, which mean and SD was (0.08 & 0.19) and (0.02 & 0.04) followed by acupuncture upper and lower (17.81 &15.76) compression upper and lower (14.96 & 13.45), seed index (14) and fiber length (14) with a mean and SD of (0.07, 0.08, 0.54, 0.82, 12.30 & 30.39) and (0.01 ,0.01,0.08,0.11,1.43 & 1.82) respectively. The traits like fiber elongation (1.47) and fiber strength (3.43) have comparably less coefficient of variance among the genotypes. A similar result was found in 2017 data.
The results showed (Additional file 1: Table S2) the variation among different varieties. Based on the bending trait values all 237 accessions of cotton were differentiated into two groups, higher stem hardness (HSH) because those varieties that have a higher value of bending trait and lower stem hardness (LSH) because those varieties have lower values of bending traits. In Table S2. Only mentioned in detail are six HSH genotypes and six LSH genotypes.
3.2. Principal component analysis
The principal component analysis was performed to only the Principal Component (PCs) with an eigenvalue higher than 1 according to the Kaiser (1960) criterion. Thus PC1, PC2, PC3, PC4, PC5 and PC6 (Tab. 2) were selected as these represented 23%, 14%, 12%, 8%, 8% and 6% of progeny variation, respectively, and accounted for 73% of the overall diversity. PC7, PC8 and PC9 variances represented a cumulative percentage of 78%, 82% and 86% respectively. Table 2 summarizes the PCs and the eigenvectors, which were estimated on the average of twenty variables. All twenty traits contributed to the total variation in PC1, but Fiber length (FL), uniformity percentage (UP), fiber elongation (FE), Lint index (LI), Bending lower (BL) and bending upper (BU) have contributed more in PC1. PC1 is a weighted average of these characters indicating that fiber quality traits have significant importance for this component. On the other hand, other traits are less important to PC1. While in PC2, all variables are significantly contributing the main contributors to variation were bending upper (BU), acupuncture upper (AU), compression upper (CU), days to flowering (FD), lint percentage (LP) and fiber weight per boll (FEPB) So Yield traits have more contribution in PC2. For PC3, bending lower (BL), compression lower (CL), compression Upper (CU) bending upper (BU), Maturity (M) and Spinning consistency index (SCI) is the most important trait, while multiple traits contributed to the other PCs in varying proportions and the same trend as found in the 2017 study result.
3.3. Stem hardness correlation with fiber quality traits
The result of the 2018 correlation of stem hardness indicated that bending lower (BL) has a positive association with fiber length, micronaire value, uniformity percentage, fiber elongation, spinning consistency index, and days to flowering (Fig. 1). Bending upper (BU) was a positive correlation with FL, UP, SCI, and FD. Compression lower (CL) was a positive correlation with FL, M, SCI, and FD, while compression upper (CU) was a positive correlation with LU, SCI, and FD. The same result was found in the 2017 correlation of stem hardness with fiber-related traits (Additional file 2: Figure S2).
3.4. Stem hardness correlation with yield-related traits and morphological traits
In Fig. 2, the 2018 correlation result showed that Bending lower (BL) was highly positively associated with PH, GP, LP, and FWPB, while bending upper (BU) was in positive association with PH、 GP, and LI. Acupuncture lower (AL) showed a positive correlation with PH. Compression (CL and CU) has a positive correlation with PH, GP, BW, and LI. We also found the same correlation trend of all traits in 2017 data (Additional file 2: Figure S3).