Response of nine triticale genotypes to different salt concentrations at the germination and early seedling stages

Plant genotypes and characteristics

Nine triticale genotypes were used in the current study, and their names and characteristics are listed in Table 1. “Zhongsi 1084”, ‘Gannong No. 2’, ‘Gannong No. 4’ “Shida 1” and lines “C6, C16, C23, C25, C36” were bred by the College of Grassland Science, Gansu Agricultural University, China, using the traditional sexual hybridization techniques and a pedigree selection method (Ramadan et al., 2023).

Study location

The experiment was conducted at Gansu Agricultural University, P. R. China. 36°5′26″ north, 103°41′41″ east.

Germination conditions

The seeds of the studied genotypes were sterilized using sodium hypochlorite (1%) for 30 min and washed thrice using distilled water. Next, 50 seeds of each genotype were germinated on Whatman No. 1 filter paper in 9-cm Petri dishes under the following six NaCl concentrations: control, 40 mM, 80 mM, 120 mM, 160 mM, and 200 mM. The seeds were allowed to germinate in an incubator at 20 ± 1 °C under a 16/8 − h dark/light cycle for 7 d (Warham, Butler & Sutton, 1995); they were irrigated and washed twice daily using their corresponding treatment solution, and the filter papers were changed once every 2 d to prevent salt accumulation. After 2 d of planting, the germinated seeds were counted; the seeds were considered to have been germinated when the emerging radicle was one mm in length. Germination percentage was evaluated every 24 h for 5 d.

Analysis of different germination and growth parameters

After 7 d of planting, shoot length (SL; cm), root length (RL; cm), shoot fresh weight (SFW; mg), root fresh weight (RFW; mg), shoot dry weight (SDW; mg), root dry weight (RDW; mg), and root/shoot dry weight ratio (RSR) were measured. Dry weight was measured after drying the roots or shoots at 70 °C for 72 h in an oven.

Germination traits were calculated as follows: (1)${\displaystyle \mathbf{Germination}\mathbf{rate}\left(\mathbf{GR}\right)=\sum _{i=1}^n{S}_i/D_i\text{(Maguire, 1962)}.}$

S_i is the germinated seeds per total seeds, D_i represents seed numbers until nthday, and n is the number of counting. (2)${\displaystyle \mathbf{Germination}\mathbf{vigor}\mathbf{index}\left(\mathbf{GV\; I}\right)=\sum _{i=1}^k{n}_i/t_i\text{(Maguire, 1962)}.}$

n_i is the percentage of seeds germinated on the nth day, and t_i is the number of days counted from the start of the experiment (i) to the last day on which the seeds germinated (k). Higher values represent a more rapid rate of germination. (3)${\displaystyle \mathbf{Germination}\mathbf{percentage}\left(\mathbf{GP}\text{\%}\right)=\left(\text{Seeds germinated/Total seeds}\right)\times 100\text{(Manmathan \& Lapitan, 2013)}.}$ (4)${\displaystyle \mathbf{Mean}\mathbf{daily}\mathbf{germination}\left(\mathbf{MDG}\right)=\text{Final germination percentage}/\text{number of days to final germination}}$ (5)${\displaystyle \mathbf{Mean}\mathbf{germination}\mathbf{time}\left(\mathbf{MGT}\right)=\sum \left(T_i{N}_i\right)/\sum N_i\text{(Kankarla et al., 2020)}.}$

N_i is the number of the newly germinated seeds in times of T_i (6)${\displaystyle \text{The}\mathbf{energy}\mathbf{of}\mathbf{germination}\left(\mathbf{GE}\right)=\text{Percentage of the germinated seeds 4 d after planting}/\text{Total number of seeds tested}\text{(Ruan, Xue \& Tylkowska, 2002)}.}$ (7)${\displaystyle \mathbf{Relative}\mathbf{salt}\mathbf{injury}\left(\mathbf{RSI}\right)=\left(\text{Germination percentage of the control}-\text{Germination}\text{percentage of the treatment}\right)/\text{Germination percentage of the control}}$ (8)${\displaystyle \mathbf{Seedling}\mathbf{vigor}\mathbf{index}\left(\mathbf{SV\; I}\right)=\left(\text{Average shoot length}+\text{Average root length}\right)\times \text{Germination percentage}\text{(Abdul-Baki \& Anderson, 1973)}.}$

Salinity stress tolerance

As a quantitative measure, stress indices can quantify the stress responses of a crop. They are easier to use and interpret than raw data. Many indices of abiotic stress tolerance have been proposed (Table 2) for estimating abiotic stress tolerant genotypes using a mathematical equation that describes the relationship between growth under stress and control conditions. The abiotic stress indices are classified into two types: The first type contains indices with maximum values indicating high-stress tolerance, whereas the other type includes other indices with minimum values indicating high-stress tolerance. Using these indices, the tolerant and sensitive genotypes and their stability can be identified (Parvaze & Ahmad, 2018).

Statistical analysis

The experiment was performed as per a factorial, completely randomized design (CRD) (where Factor-1 was genotype including nine levels, and Factor-2 was salt stress treatments including six levels) with three replicates and 50 seeds in each replicate. Two-way analysis of variance (ANOVA) was used for data analysis using SAS statistical software, version 9.2 (SAS Institute, Cary, NC, USA). The means were compared using Duncan’s multiple range test (P < 0.05), and correlation coefficient was calculated using SPSS version 16. Principal component analysis (PCA) was performed using the statistical package PAST (Hammer, Harper & Ryan, 2001) to visualize the differences in various stress-related traits among the nine genotypes.

To categorize the genotypes under both control and salinity stress treatments, cluster analysis was performed using R software version 4.1.0, 2021 (R Core Team, 2021). Euclidian metric as a distance measure was used to measure dissimilarity among the genotypes, and Ward Jr.’s (1963) algorithm was applied for grouping the genotypes.

Before conducting the analysis, the data were standardized due to their different scale by subtracting the mean from each value and dividing the obtained value by the standard deviation. The cubic cluster criterion (Milligan & Cooper, 1985) was used to ensure whether clusters existed. Fuzzy C-means as a soft clustering algorithm (Bezdek, 1973; Bezdek, 1981) was used to detect if overlapping existed between clusters. PCA is a multi-variable statistical analysis that reduces the dimensions of high-dimension data, and fewer eigenvectors explain the multivariate data (Shlens, 2005).

Genotypes differentially responded to salinity stress

To study genotype specific responses to salt stress treatment, Analysis of variance (ANOVA) were performed. Highly significant mean squares due to the genotypes and treatments, and genotypes × treatments were detected for all studied traits except for MGT, where the mean square was non-significant for genotypes and significant for the interaction between treatments and genotypes (Table 3). This result indicated high variation among the studied genotypes under different salt stress treatments.

Mean performance of different genotypes

Analysis of germination traits (Table 4) revealed that the triticale genotype Zhongsi 1084 had the highest mean GR, GVI, GP%, MDG and GE. In contrast, the genotypes Gannong No.2 and Shida No. 1 exhibited the lowest mean of GR, GVI, GP% and MDG. There was no significant difference between the two cultivars. All genotypes exhibited the highest MGT except for line C6. The lowest RSI was observed for Zhongsi 1084, whereas the highest RSI was observed in case of Shida No.1. The SVI of genotypes Zhongsi 1084 and C6 was 39% and 18.1%, respectively. This was higher than the overall mean SVI, whereas that of Gannong No.2 was 41% less than this mean.

Analysis of seedling traits revealed that the mean SL of C6 and Zhongsi 1084 was 12.4% and 9.1% higher than the overall mean of SL, respectively. Whereas the means SL of Gannong No. 2 and C16 were 16.7% and 6.1% lower than the overall mean of SL, respectively. Moreover, the mean RL of Zhongsi 1084, C6, and C23 was the highest. They recorded 17.8%, 16.2%, and 11.3% higher than the overall mean value, respectively. The mean RL of genotypes C36 and Gannong No. 2 was the lowest i.e., 16.8% and 12.4% lesser than the overall mean value, respectively. The highest RSRs were observed for genotypes C23 and Gannong No. 2, whereas the lowest ratio was observed for genotype C36. The highest increase in SFW compared with the overall mean SFW was observed for genotypes C6 (15.9%) and Gannong No. 4 (12.1%). Whereas the highest decrease was observed in Gannong No. 2 (13%), C16 (10%), and C25 (9.7%). For RFW, the highest mean values were exhibited by C6 and Gannong No. 4. They recorded 32.1% and 20.4% more than the general mean, respectively. Meanwhile, genotypes C25, C16 and C36 exhibited the lowest mean values i.e., they were 13.1%, 11.7%, and 11% lower than the overall mean, respectively. Genotype C6 had the highest mean SDW i.e., 12.9% higher than the general mean. Meanwhile, both genotypes Gannong No. 2 and C16 had the lowest mean SDW values, exhibiting 13.1% and 8.2% decreases compared to the general mean, respectively. Moreover, both genotypes C6 and Gannong No. 4 had the highest mean RDW values. They were 24.5% and 20.5% higher than the general mean. The mean RDW values of genotypes C23, C25 and Gannong No. 2 were 13.5%, 12.6%, and 11.1% was lower than the general mean.

Differential effect of salt treatments on germination

The GR of different triticale genotypes under varying salt concentrations were 1.42–4.4% (Table 5). The highest GR was observed in the control and 40 mM NaCl treated groups. GR gradually also reduced with increasing NaCl concentrations. GR reduced by 41% and 67.9% in the 80 mM and 200 mM NaCl treated groups, respectively. GVI was significantly different under different salinity levels, whereas mean values of different treatments were 10.28–33.54. The highest value was observed in the 40 mM NaCl treated group i.e., the lowest value was observed in the 200 mM NaCl group. Moreover, significant differences were not observed between the control and 40 mM NaCl groups. The highest reduction in GVI was observed, where 60%, 62%, and 69.2 were observed under 120, 160, and 200 mM NaCl treatments, respectively. However, the significant differences were not observed in GP% between control and 40 mM NaCl treated groups. In contrast, significant differences in GP% were observed as NaCl concentration increased from 80 mM to 200 mM and the GP% reduced by 39.8% in the 80 mM NaCl group. The highest GP% (88.04%) was observed for the 40 mM NaCl treated group, whereas the lowest GP% (28.29%) was observed in the 200 mM NaCl treated group. The highest MDG was observed in both control and 40 mM NaCl groups (12.50 and 12.58, respectively) and the lowest value was observed in the 200 mM NaCl treated group. The reduction % in MDG increased from 39.8% to 67.4% as NaCl concentration increased from 80 mM to 200 mM. The number of days required for germination increased from 2.48 day in the control group to 4.09 day in the 120 mM NaCl treated group. In groups treated with NaCl concentration >120 mM, the number of days for germination gradually decreased with increasing NaCl concentrations. However, significant differences were not observed among 40, 80, 160, and 200 mM NaCl groups. GE decreased from 48.76% in the control group to 35.96% in the 120 mM NaCl group. However, in groups with NaCl concentration > 120 mM, GE gradually increased and it was 51.35% at 200 mM NaCl. However, significant differences in GE were not observed between the control, and 160 mM and 200 mM NaCl treated groups. The RSI was negative in the 40 mM NaCl treated group and increased significantly with increasing salt concentration. It increased from 39.82% under 80 mM NaCl treatment to 67.44% under 200 mM NaCl treatment. SVI decreased with increasing salt concentrations, where SVI was reduced by 27.2% in the 40 mM NaCl treated group and by 95.6% in the 200 mM NaCl treated group.

Both SL and RL reduced significantly with increasing salt stress (Table 5). The highest mean values were observed in the control group, whereas the lowest mean values were recorded in the 200 mM NaCl group. Mean SL varied from 9.83 cm to 1.77 cm, and the reduction in SL ranged from 27.2% (40 mM NaCl) to 82% (200 mM NaCl). Mean RL varied from 6.57 cm to 0.48 cm, and the reduction in RL ranged from 32.4% (40 mM NaCl) to 92.7% (200 mM NaCl). RSR gradually decreased from 0.67 in the control group to 0.3 in the 200 mM NaCl treated group; however, significant differences were not observed between the 120 and 160 mM NaCl treated groups. In the 200 mM NaCl group, >50% reduction in RSR was observed compared to that of the control group. SFW and SDW were significantly affected by salt stress. Compared to that of the control group, reduction in SFW and SDW was 13.6–75.4% and 10.3–68.1% under increased NaCl concentration from 40 mM to 200 mM. Moreover, RFW and RDW were significantly reduced by salinity, where increasing NaCl concentration increased from 40 mM to 200 mM reduced the RFW and RDW values by18.4–69% and 14.5–55.6%, respectively.

Interaction effects

The mean performance of the different triticale genotypes under salt stress (Figs. 1A, 1B, 2A and 2B). The highest GR, GVI, and GP% were observed for Zhongsi 1084 under 40–200 mM NaCl treatments, whereas the lowest values were observed for Shida No.1 under 80–200 mM NaCl treatments. Zhongsi 1084 exhibited the best MDG under 40–200 mM NaCl treatments, whereas Shida No. 1 was the most affected under high salt concentrations (120–200 mM NaCl). MGT was 2.01–3.41, 2.7–3.31, 2.58–3.96, 3.23–4.59, 2.68–3.53 and 2.59–3.37 days for control, and 40 mM, 80 mM, 120 mM, 160 mM, and 200 mM NaCl treated groups, respectively. The lowest number of days under control and 120 mM NaCl treatments was observed in genotype C6. Gannong No. 4 exhibited the best GE under control treatment (55.97%), Zhongsi 1084 under 40 and 120 mM NaCl treatments (52.84 and 48.03%, respectively), C6 under 80 mM treatment (46.06%) and Shida No. 1 under 160 mM and 200 mM NaCl treatments (56.5 and 57.5%, respectively). RSI increased with increasing salt concentrations. The lowest percentage of injury was observed in Zhongsi 1084 i.e., 10.23, 24.18, 25.36 and 38% under 80, 120, 160, and 200 mM NaCl treatments, respectively. Meanwhile, the highest percentage of injury was observed in Shida No. 1 (57.57, 82.17, 87.38, and 87.21% under 80, 120, 160, and 200 mM NaCl treatments, respectively). For SVI, the most desirable genotypes were Zhongsi 1084 and Gannong No. 4 under control treatment; Zhongsi 1084 and C6 under 40 mM, 120, and 200 mM NaCl treatments; Zhongsi 1084 and C23 under 80 mM NaCl treatments; and both Zhongsi 1084 and C25 under 160 mM NaCl treatments. In contrast, Shida No. 1 was the most affected genotype under high salt concentrations.

Furthermore, Zhongsi 1084 had the highest mean SL under control and 40 mM NaCl treatments, but the lowest mean SL under 160 and 200 mM NaCl treatments. C6 had the mean highest SL under 80 and 120 mM NaCl treatments. C16 also had the highest mean SL under 160 and 200 mM NaCl treatments. The lowest mean SL under control and 40 mM, 80 mM, and 120 mM NaCl treatments were exhibited by Gannong No. 2. The mean RL was 8.57–5.13 and 5.45–3.61 cm for control and 40 mM NaCl groups, respectively. It gradually decreased to 0.97–0.62 and 0.61–0.29 cm for 160 mM and 200 mM NaCl treated groups, respectively. The mean RSR decreased with increasing salt concentrations. The mean ratios ranged from 0.79 to 0.53 under control and from 0.42 to 0.20 under 200 mM NaCl treatment. Shida No. 1 had the highest RSRs under 160 and 200 mM NaCl treatments, whereas C16 had the lowest ratios. Both C6 and Shida No. 1 were the best genotypes as per their mean SFW under 0, 40 and 80 mM NaCl treatments, whereas both Gannong No. 4 and C6 were the best genotypes under 120, 160, 200 mM NaCl treatments as per their mean SFW. C6 and Gannong No. 4 were the best genotypes as per mean RFW under 0-120 mM NaCl treatments, whereas C6 and Gannong No. 2 were the best under 160 and 200 mM NaCl treatments. Furthermore, the highest mean SDW was observed for Shida No. 1 and C6 under 0–80 mM NaCl treatments, for C6 under 120 mM and 160 mM NaCl treatments and for Gannong No. 4 under 200 mM NaCl treatment. In contrast, the lowest mean SDW under high salt concentrations was exhibited by Zhongsi 1084 and C36. Gannong No. 4 and C6 were the most desirable genotypes under 0-120 mM salt treatments for RDW. It was also reported that different salinity concentrations caused considerable effects on GP%, GR, total dry weight, and all seedling traits in all studied genotypes. Similar results for the interaction between salt stress and genotypes have been reported by Kandil et al. (2012).

Phenotypic correlation

Phenotypic correlation coefficients among the studied traits (Table 6). The highest positive correlation (r = 1.00) was observed between GP% and MDG. High significant positive correlations were observed among GR, GVI, GP%, MDG, SVI, and RL. Significant positive correlations were also observed among RL, SFW, RFW and RDW. SVI was significantly positively correlated with RL. GVI was significantly positively correlated with GE and SL. Significant positive correlations were observed between GE, SVI, and SL, and between SL and RL. Positive but non-significant correlations were observed between germination traits GR, GVI, GP%, MGT, GE and SVI and seedling traits RSR, SFW, RFW, SDW and RDW. In contrast, highly significant negative correlations were observed between RSI and GR, GVI, GP%, and MDG. Significant negative correlations were also observed between MGT and RDW and between RSI and both SVI and RL.

PCA

In the current study, PCA classified the nine genotypes into four clusters based on their mean performance under different NaCl treatments (Fig. 3). The first cluster was found in the 1st quadrant, which included triticale genotypes C6 and Gannong No. 4. Both genotypes scored the highest values for the seedling traits SFW, RFW, SDW, RDW and high values for RL, SVI, MDG and GVI. The second cluster was found in the 2nd quadrant and included the genotypes Zhongsi 1084, C23, and C25. These genotypes had high mean GR, GVI, GP%, MDG, SVI, SL and RL and low RSI. The third cluster was found in the 3rd quadrant and included Gannong No. 2 and C16 genotypes, whereas the fourth cluster was found in the 4th quadrant and included both Shida No. 1 and C36. The genotypes in the third and the fourth clusters had the lowest mean GR, GVI, GP%, MDG, SVI, and RL. These results suggested considerable variability for salt tolerance in the studied triticale genotypes.

Tables 7 and 8 reveal that Gannong No. 4 was the most tolerant genotype with an average rank (AR) equal to 2.12 (Fig. 4). However, Zhongsi 1084 was the least tolerant genotype (AR = 8.04). Both Gannong No. 2 and C25 were moderately tolerant as their ARs were 4.29 and 4.62, respectively. Higher AR suggested the lower tolerance of the genotype (Table 8).

Cluster analysis

SFW and RFW were used to construct a distance matrix and to generate a tanglegram exhibiting dissimilarity among all genotypes under control and the treatment with the highest salt concentration (200 mM) (Fig. 5). The fuzzy C-means method elucidated that low overlap existed between clusters, thus hard clustering methods were applied to construct the tanglegram (Fig. 5). Six hard clustering methods were compared using an agglomerative coefficient to choose the most accurate method for clustering the data, which were average, generalized average, single, weighted, complete, and Ward.

The valued of agglomerative coefficients were 0.76, 0.81, 0.53, 0.77, 0.85, and 0.88 respectively, under control treatment, whereas under 200 mM NaCl treatment, they were 0.68, 0.72, 0.55, 0.73, 0.77, and 0.81 respectively. These results reveal that Ward’s method had the highest coefficient compared to those of the other five methods under control and 200 mM NaCl treatments. Therefore, Ward’s method was chosen to conduct cluster analysis. To identify the optimum number of clusters in the data, 30 internal validation indices were selected and screened (Charrad et al., 2014). As shown in Fig. 5, all genotypes were separated into two clusters under control and 200 mM NaCl treatment groups (Table 9). The structure of the clusters changed markedly when the genotypes were subjected to 200 mM NaCl treatment except for genotypes Gannong No. 4 and C6, which migrated from cluster 1 under control to cluster 2 under the saline treatment because they were more tolerant than the other members of their cluster.

Heatmaps elucidate the relationship between the genotypes and the studied traits based on standardized (scaled) data using a color scale under control and 200 mM NaCl treatments (Figs. 6 and 7). Before drawing the heatmap, the data were standardized by subtracting the mean from each value and dividing the obtained value by the standard deviation. Genotype C6 had the highest mean SFW and SDW in the control group, whereas genotype Gannong No. 4 had the highest mean SFW and SDW under the highest salinity treatment (200 mM). These results demonstrated that Gannong No. 4 was the most tolerant genotype. The lowest mean SFW and SDW under control treatment were observed in C16, whereas Zhongsi 1084 exhibited the lowest mean SFW and C26 had the lowest mean SDW under 200 mM NaCl treatment. Moreover, GP% of genotypes Gannong No. 4 and Gannong No. 2 was the highest and the lowest, respectively, under control treatment.

In contrast, the genotypes Zhongsi 1084 and Shida No. 1 were the highest and the lowest, respectively, under 200 mM. The genotype Zhongsi 1084 had higher values of germination traits under the highest salinity treatment. However, it had the lowest mean SFW, RFW, SL, and RSI. Gannong No. 4 had higher values of germination traits under control treatment. The heatmap does not reveal any association between germination traits and the tolerance indices of the genotypes, except for MGT, which was negatively associated with the tolerance of the genotypes.