ANOVA and descriptive statistics
The analysis of variance results indicated significant (P<0.05) effects of the environment (drought stress treatment), genotype and environment x genotype interactions for the most of the studied physio-agronomic traits (Table 2). The drought stress treatment (environment) significantly affected SPAD (leaf chlorophyll value), RLWC (relative leaf water contents), MSI (membrane stability index), PC (proline content), KPS (kernels per spike), TKW (thousand kernel weight) and GY (grain yield). The ANOVA results also showed that there is significant variability among the studied genotypes with regard to physio-agronomic traits under both normal and drought condition. Similarly, the effect of the environment x genotype interactions was also significant for SPAD, RLWC, PC, TKW and GY, however, MSI and KPS were not significantly influenced (Table 2).
Table 2
Mean square and Analysis of variance (ANOVA) for the physio-agronomic traits of the 50 synthetic back-derived wheat lines evaluated for drought tolerance at reproductive stage.
Source of variation | DF | Physio-agronomic traits |
SPAD | RLWC | MSI | PC | KPS | TKW | GY |
Environment | 1 | 13101** | 24604.1** | 41550.9** | 2927.44** | 16995.2** | 9185.33** | 140660** |
Genotype | 49 | 60.0** | 124.7** | 12.4 ns | 3.49** | 11.6 ns | 10.81** | 362** |
Environment x Genotype | 49 | 64.2** | 125.4** | 20.4 ns | 3.76** | 13.4 ns | 10.37** | 334** |
Error | 198 | 3.7 | 10.8 | 18.4 | 0.64 | 9.8 | 1.75 | 42 |
*, ** indicates significance at P<0.05, P<0.01 respectively and ns = non-significant |
Abbreviations: SPAD, leaf chlorophyll value; RLWC, relative leaf water contents; MSI, membrane stability index; PC, proline content; KPS, kernels per spike; TKW, thousand kernel weight; GY, grain yield; |
The basic statistical summary of means with standard deviation, range, relative traits change and coefficient of genetic variance for the physio-agronomic traits of the 50 synthetic back-derived wheat lines evaluated for drought tolerance at reproductive stage are provided in the Table 3.
Table 3
Mean values, range, relative traits change and coefficient of genetic variance for the physiological and agronomic traits of the 50 synthetic back-derived wheat lines evaluated for drought tolerance at reproductive stage.
Physio-agronomic traits | Normal | Drought stress | RTC | CGV |
Mean ± SD | Range | Mean ± SD | Range |
SPAD value | 50.4 ± 2.1 | 46.8 - 53.2 | 37.2 ± 6.4 | 29.5 - 50.1 | 0.26 | 23.3 |
RLWC (%) | 81.6 ± 2.3 | 76.5 - 84.7 | 63.5 ± 9.5 | 44.3 - 79.3 | 0.22 | 30.6 |
MSI (%) | 80.5 ± 2.2 | 76.6 - 85.0 | 58.1 ± 5.5 | 44.3 - 65.3 | 0.28 | 22.8 |
PC (µmol per g FW) | 2.30 ± 0.6 | 0.5 - 3.9 | 8.5 ± 1.6 | 4.4 - 12.4 | -2.70 | 9.30 |
KPS (no.) | 48.6 ± 3.9 | 43.0 - 57.0 | 33.5 ± 2.3 | 30.0 - 40.0 | 0.31 | 18.2 |
TKW (g) | 34.7 ± 1.2 | 33.0 - 37.0 | 23.6 ± 2.8 | 19.0 -30.0 | 0.32 | 22.7 |
GY (g/pot) | 50.0 ± 1.9 | 43.5 - 53.4 | 36.6 ± 1.4 | 28.6 - 45.0 | 0.27 | 23.5 |
Abbreviations: SPAD, leaf chlorophyll value; RLWC, relative leaf water contents; MSI, membrane stability index; PC, proline content; KPS, kernels per spike; TKW, thousand kernel weight; GY, grain yield; RTC, Relative traits change; CGV, coefficient of genetic variance. |
The relative traits change (RTC) values of physio-agronomic traits were calculated as relative difference of their mean values under control and drought conditions. The RTC of measured traits, were recorded in the pattern, i.e., GY (0.37) > TKW (0.32) > RLWC (0.28) > MSI (0.26) > SPAD (0.23) > KPS (0.21). Nevertheless, the PC showed inverse RTC value (-2.70) as it is increased under drought stress environment in comparison to other measured traits. The coefficient of genetic variation (CGV) of physio-agronomic traits of the 50 synthetic back-derived wheat lines, tested under normal and drought stressed environments, was ranged from 9.30 to 30.6% (Table 3). The genetic variation of some traits i.e. MSI, RLWC, TKW and GY was >20%, while genetic variation of SPAD and KPS was ranged between 10% and 20%. The genetic variation coefficient for PC was <10% (Table 3). These results indicated high genetic variation within the studied synthetic back-derived wheat lines under both normal and drought stress condition.
The drought stress had higher reducing effects on pooled means of grain yields of all 50 wheat lines. The grain yields of genotypes are ranged from 43.5 to 53.4 under normal conditions with a mean value of 50.0 (Table 2). The fifteen 15 SBL showed higher while 20 lines showed lower GY than check varieties (cultivated commercial varieties). However, under drought stressed condition, The GY of the genotypes is ranged from 28.6 to 45.3 with a mean value of 36.6. The 35 SBL indicated higher and 10 SBL showed lower yield as compared to commercial checks (Fig. 1). The results showed considerable potential of synthetic back-derived wheat lines for genetic improvement and could be useful sources of drought tolerance.
Correlations of physio-agronomic traits
Correlation coefficients (r) describing the level of correlations among observed physiological and agronomic parameters are summarized in Table 4. The grain yield, under normal growing conditions, showed a positive and significant correlation (r = 0.35*) with TKW, while correlation with physiological parameters like SPAD, RWC, MSI was weak positive and non-significant (P>0.05). The grain yield, under drought stress condition, showed a strong positive (r > 0.7) and significant (P<0.05) correlations with TKW, KPS, SPAD, RLWC, MSI and Proline.
Table 4
Correlation coefficients (Pearson’s) among agronomic and physiological traits of 50 synthetic backcross derived (SBL) wheat lines evaluated under drought stressed and normal condition.
| | Normal |
| Traits | GY | KPS | TKW | SPAD | PC | MSI | RLWC |
Drought | GY | 1 | 0.045 | 0.351* | 0.133 | -0.217 | 0.090 | 0.065 |
KPS | 0.872** | 1 | 0.061 | -0.042 | -0.214 | 0.100 | -0.183 |
TKW | 0.882** | 0.884** | 1 | -0.340* | 0.074 | -0.035 | 0.040 |
SPAD | 0.908** | 0.890** | 0.889** | 1 | -0.076 | -0.147 | -0.129 |
PC | 0.785** | 0.777** | 0.870** | 0.843** | 1 | -0.130 | 0.020 |
MSI | 0.883** | 0.849** | 0.839** | 0.877** | 0.790** | 1 | 0.263 |
RWC | 0.864** | 0.840** | 0.849** | 0.842** | 0.821** | 0.868** | 1 |
*, ** indicates significance at P<0.05, P<0.01 respectively |
Abbreviations: SPAD, leaf chlorophyll value; RLWC, relative leaf water contents; MSI, membrane stability index; PC, proline content; KPS, kernels per spike; TKW, thousand kernel weight; GY, grain yield; |
TKW did not show any strong correlation with measured physiological traits under normal conditions, however, it showed a strong positive correlation (r >0.7**) with SPAD, MSI, RLWC and PC under drought stressed environment.
Similarly, KPS did not show any significant correlation with recorded agronomic and physiological parameters under normal conditions, but showed strong positive correlations (r > 0.7**) with all physio-agronomic traits under drought stress.
Principal Component Analysis (Pca)
The fraction of the overall variance expounded by different principal components and their associations with variable traits is shown in the rotated component matrix (Table 5). Two principal components were contributing 91.74% of the total variation noticed under drought stress treatment. Nevertheless, the first principal component was most important with a cumulative contribution of 88.06% to the total variation. All physio-agronomic variables like SPAD, MSI, RLWC, PC, KPS, TKW and GY had high positive loading into the first principle component. Under normal growing environment, three principal components were important, recording for 73.66% of the total variation. SPAD, MSI and TKW had high positive loading into the 1st, while, KPS and GY had high positive loading into the 2nd principal component. However, high positive loading of the Proline and RLWC was noticed into the 3rd principal component.
Table 5
Rotated component matrix of agronomic and physiological traits of 50 synthetic backcross derived (SBL) wheat lines evaluated under drought stressed and normal conditions.
| Drought | Normal |
Traits | PC-1 | PC-2 | PC-3 | PC-1 | PC-2 | PC-3 |
SPAD | 0.955 | -0.024 | -0.158 | 0.519 | -0.143 | 0.166 |
PC | 0.898 | 0.421 | 0.038 | 0.094 | -0.554 | 0.229 |
MSI | 0.934 | -0.159 | 0.168 | 0.445 | 0.083 | 0.540 |
RWC | 0.932 | -0.024 | 0.292 | -0.426 | -0.396 | -0.214 |
KPS | 0.947 | -0.123 | -0.134 | -0.249 | 0.599 | 0.260 |
TKW | 0.954 | 0.098 | -0.134 | 0.432 | -0.103 | -0.512 |
GY | 0.948 | -0.170 | -0.061 | 0.306 | 0.373 | -0.503 |
Eigenvalue | 6.165 | 0.258 | 0.180 | 1.684 | 1.363 | 1.205 |
Total variance (%) | 88.065 | 3.682 | 2.565 | 24.064 | 19.475 | 17.217 |
Cumulative variance (%) | 88.065 | 91.747 | 94.312 | 24.064 | 43.539 | 60.756 |
Abbreviations: SPAD, leaf chlorophyll value; RLWC, relative leaf water contents; MSI, membrane stability index; PC, proline content; PC-1, principal component 1; PC-2, principal component 2; PC-3, principal component 3; KPS, kernels per spike; TKW, thousand kernel weight; GY, grain yield |
The biplot analysis (Fig. 2) illustrates the associations between the different variables and genotypes with respective principal components for the normal (Fig. 2; a) and drought stress conditions (Fig. 2; b). The acute angles (<90°) between dimension vectors in the similar direction showed positive correlation of the variable traits in terms of describing genotypes. The genotypes outrivaling in a specific trait were plotted nearer to the vector length. The angles between dimension vectors of GY and other yield related traits like KPS, TKW, RLWC, MSI, PC and SPAD showed positive correlation (<90°) under drought stress (Fig. 2; b). However, under normal conditions, GY was only positively correlated with TKW, KPS, SPAD and MSI (Fig. 2; a). Moreover, PCA biplot analysis helped grouping tested wheat lines according to their similarities in yield contributing traits correspondences under drought stress. Most of the better performing genotypes under drought stress environment (tolerant) were concentrated in the positive side of the first principal component (Fig. 2; b). These genotypes excelling in overall productivity over check varieties was contributed mostly by high TKW, RLWC and optimum values for other physio-agronomic traits as well, nevertheless, the genotypes were more scattered on the both sides of the two principal components under normal condition (Fig. 2; a).
Cluster analysis and heat map
The heatmap and cluster analysis of the mean values of physio-agronomic traits of the wheat genotypes under stress and normal conditions was performed. The heatmap was created based on the standardized Z-score values and the hierarchical clustering was performed using ward’s method. The similar z-score colour and the distance between clusters show the similarity of the genotypes (Fig. 3). The heatmap and ward’s hierarchical clustering classified SBLs into three distinct clusters, i.e., cluster 1 with negative Z-score (dark blue colour) was comprised of 21 SBLs which showed less mean yield attributes and recognized as drought sensitive lines i.e. SBLs 25, 23, 19, 28, 34, 39, 41, 46, 47, 08, 31, 43, 07, 42, 14, 45, 48, 33, 49, 18 and 20. The group 2, comprised of 17 SBLs with strong positive Z-score (dark yellow colour) for yield contributing traits is considered as drought tolerant group. It include SBLs 21, 05, 02, 37, 09, 32, 40, 06, 16, 12, 17, 44, 50, 04, 30, 22 and 26. The 3rd group (13 SB lines) showed weak positive Z-score (light yellow colour) and accepted as moderate drought tolerant group (Fig. 3).