Evaluating salt tolerance in soybean core collection: germination response under salinity stress

Evaluating salt tolerance in soybean core collection: germination response under salinity stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating salt tolerance in soybean core collection: germination response under salinity stress Aditya Gobade, Arathi S, Shreyash Gijare, Deepak Pawar, Abhinandan Patil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4558107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 8 You are reading this latest preprint version Abstract High levels of soil salinity inhibit the growth of legumes such as soybeans, significantly reducing their productivity. This research aimed to assess the salt tolerance of soybean genotypes by evaluating seed germination at varying salt concentrations (100 mM, 150 mM, and 200 mM NaCl) from two seed source locations. A total of 198 soybean genotypes were analyzed post-germination using ten quantitative traits: germination percentage, seedling fresh weight, seedling dry weight, seedling length, shoot length, root length, seedling vigor index-1, seedling vigor index-2, seedling water content, and salt tolerance. Analysis of Variance (ANOVA) results indicated significant differences among treatments across both locations. Principal Component Analysis revealed that certain quantitative traits were more prominent at different salt concentrations, confirming varied responses to salt stress. Correlation analysis demonstrated a positive relationship between germination percentages and growth parameters such as fresh weight, dry weight, and vigor index. The study observed a decline in all quantitative traits as salt concentration increased, highlighting the stress experienced by plants during germination and growth under high salinity conditions. Using K-means clustering, the 198 genotypes were categorized into tolerant, moderately tolerant, moderately susceptible, and susceptible groups. This clustering helped identify genotypes exhibiting high tolerance (≥ 80% germination at 200 mM NaCl) and high susceptibility (≤ 40% germination at 100 mM NaCl) consistently across both seed source locations. Consequently, seven salt-tolerant genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173) and three salt-sensitive genotypes (HIMSO 1563, EC 391181, and EC 241920) were identified, providing new insights into soybean cultivation under saline conditions. Glycine max Soybean Salinity Salt stress Germination Salt-tolerant Salt-sensitive. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The genus Glycine of the Fabaceae family comprises wild soybean ( Glycine soja ), semi-wild soybean ( Glycine gracilis ), and cultivated soybean ( Glycine max (L.) Merr.). Cultivated soybean, known as the “golden miracle bean”, is a rich source of protein and oil. It is widely used as food, feed, and biofuel and is one of India's important oilseed cash crops (Kumar et al. 2022). Traits like seed oil content, yield, and protein significantly contribute to the economic value of soybeans. Major soybean-producing countries include India, the USA, Argentina, and Brazil. In India, soybeans contribute 42% to total oilseed production and 22% to total oil production ( https://iisrindore.icar.gov.in/readmore.html ). However, the genetic gain rate of soybean yield in Brazil and Argentina is higher compared to the USA, China, Canada, and India (Umburanas et al. 2022). Although Glycine max was domesticated from wild soybean varieties approximately 6000–9000 years ago, it exhibits lower genetic diversity than its wild relatives. Genetic bottlenecks and artificial selection may have contributed to this reduced diversity and potential loss of beneficial genes necessary for environmental adaptation. Wild soybeans ( Glycine soja ) possess many beneficial alleles that can be introduced to domesticated soybeans to improve their adaptation to changing environments (Zhuang et al. 2022). In the current era of climate change, two significant abiotic stresses, drought and salinity (pH 7 to 8.5), profoundly impact crop plants' germination, growth, and yield. These conditions limit the biological, molecular, and physiological systems of plants. Salinity induces the generation of Reactive Oxygen Species (ROS) in plants, such as hydrogen peroxide (H 2 O 2 ), superoxide radical (O 2 − ), and hydroxyl radical (OH − ). This leads to protein degradation, lipid peroxidation, enzyme inactivation, alteration in gene expression, antioxidant deprivation, and disruption of metabolic pathways (Choudhury et al. 2013; Begum et al. 2022; Alizadeh et al. 2024). Sodium chloride (NaCl) -induced salinity causes both osmotic and ionic stress, with osmotic stress occurring quickly and ionic stress resulting from the accumulation of sodium (Na + ) and chloride (Cl − ) ions (Cho et al. 2021). Soybean is a salt-sensitive glycophyte, which can impair seed germination, seedling growth, and overall plant development (Rasheed et al. 2022). Germination is a critical phase in a plant’s life cycle, and its successful completion under saline conditions is essential for crop establishment, as higher salt levels can inhibit water absorption by seeds. In comparison, lower levels induce seed dormancy (Zuffo et al. 2020). Evaluating the germination response of soybean genotypes under salinity stress is a crucial step in identifying and developing salt-tolerant varieties. Salt tolerance varies at different developmental stages (Guan et al. 2023). Understanding the mechanisms underlying salinity tolerance during germination can provide insights into breeding strategies and genetic modifications to enhance salt tolerance. Much of the agricultural land has become saline due to irrigation with saline water and poor soil management, which necessitates the development of salt-tolerant soybean genotypes to sustain and enhance crop production in affected areas (Xu et al. 2011). Wild soybeans are more salt-tolerant than domesticated varieties (Cao et al. 2016; Hou et al. 2022). As salt tolerance is a polygenic trait, Quantitative Trait Loci (QTL) or genes from wild soybeans can be introduced into cultivated soybeans to improve their salt tolerance. This approach can also facilitate marker-assisted selection in breeding salt-tolerant soybeans, which is crucial for developing genotypes that can germinate and survive under various stress conditions (Begum et al. 2022). Identifying genomic regions responsible for salinity tolerance and using QTL mapping are essential steps in developing salt-tolerant soybean genotypes (Rasheed et al. 2022). Previous research has demonstrated variability in salinity tolerance among soybean cultivars. For instance, Essa (2002) used three soybean cultivars- Lee, Coquitt, and Clark 63- to determine the effect of soil salinity on seed germination, plant growth, and leaf mineral content under natural conditions. The results revealed a reduction in germination percentage with increasing salinity levels, with Lee being the most tolerant and Clark 63 the most susceptible cultivar. Similarly, research on D8 and D140 cultivars indicated a reduction in plant height, number of leaves, leaf area index, and shoot and root biomass after 100 millimolar (mM) NaCl treatment, identifying D140 as having better salt tolerance at 100 mM NaCl concentration (Linh et al. 2021). Many of the earlier research studies have examined the salinity tolerance of soybean genotypes during the germination stage. However, some of these studies only included a small number of genotypes, while others utilized low amounts of NaCl. For example, Rahman et al. (2021) assessed the performance of only 20 different types of soybean plants at a concentration of 50 mM NaCl. In contrast, Anwar et al. (2016) examined 30 different types of soybean plants under a concentration of 75 mM NaCl. In India, similar screening experiments utilized low salt concentrations and fewer genotypes (Singh et al. 2019). Although significant efforts have been made, there is still a considerable opportunity to uncover donor genes or genetic resources from numerous soybean accessions that have the potential to aid in the development of salt-tolerant cultivars. Key salt-tolerant genes have been discovered, aiding our understanding of the mechanisms behind salt tolerance. Specifically, GmSALT3 , GmCHX , and GsERD15B were identified as salt-tolerance genes during the seedling stage. Additionally, GmCDF1 was found to be active during both germination and the seedling stages (Guan et al. 2023). To identify the specific regions of the soybean genome that are linked to salt tolerance during the germination stage, QTL mapping was conducted using a recombinant inbred population of Kefeng No. 1 and Nannong 1138–2 and the identified QTLs were found to be located on chromosome 8 (Leung et al. 2023). This research investigates the germination response of various soybean genotypes under salinity stress. Specifically, the impact of salinity on the seed germination of 198 soybean genotypes at NaCl concentrations of 100 mM, 150 mM, and 200 mM was assessed. By examining the effects of different salinity levels on germination rates, seedling vigor, and other related parameters, we seek to identify genotypes that exhibit superior tolerance to salinity during the germination stage. By identifying genotypes that exhibit superior tolerance to salinity during the germination stage, this study will contribute to the broader efforts of improving soybean resilience to salinity, ensuring stable production and food security in regions affected by soil salinization. Materials and methods Plant materials and experimental design We used 198 diverse soybean genotypes from the core collection to evaluate their salt tolerance during germination. These accession seeds were multiplied at two different locations during the regular Kharif (July-Nov) season of 2023 under irrigated conditions with standard agronomic practices: -Experiment 1 (E1): Hol farm (8.5204° N, 73.8567° E) and Experiment 2 (E2): Soangaon farm (17.6444° N, 73.9910° E). Both locations were chosen to assess the impact of different environmental conditions on the germination of soybeans under salinity stress. These soybean accessions belonged to different maturity groups - Early Maturing (EM), Mid-Maturing (MM), Mid-Late (ML), Promising Collection (PC), and Farmer’s Collection (FC). The list of the 198 genotypes taken for the study is provided in Supplemental Table S1 . The experiments were performed in a completely randomized design with three replicates per treatment. Salinity Treatments NaCl concentrations were selected to create varying levels of salinity stress treatments and to monitor the reaction of soybean seed germination. We used plastic trays containing different quantities of Sodium Chloride (NaCl): (a) 0 mM NaCl (Control), (b) 100 mM NaCl, (c) 150 mM NaCl, and (d) 200 mM NaCl. Distilled water was used as the control to compare with salt-stressed conditions. Seed preparation and germination conditions From each accession, 72 healthy and uniform seeds were randomly chosen from E1 and E2 locations. The seeds were surface-sterilized using 0.2% Sodium hypochlorite solution, Hi-AR™/ACS grade, 4% (w/v), provided by HiMedia (Catalog number: AS102-12) for one minute and washed three times with distilled water. The sterilized seeds were then placed in a uniform pattern on moist germination papers. The germination papers used were made of high-quality brown absorbent paper exclusively for seed germination examinations. It has a standard size of 45×28cm with an essential weight of 125 g/m 2 . Its parameters include a bursting strength of 25 kg/m 2 , a capillary rise rate of 38 mm/minute, a neutral pH of 7.0, and a maximum ash content of 0.49%, ensuring ideal circumstances for accurate germination tests. Then, each germination paper was rolled around 3 − 4 cm along the left and bottom edges. It was then firmly rolled from the bottom to the top, with each roll neatly labeled according to the treatment and genotype. These rolls were then placed vertically in respective plastic trays for germination. The trays were labeled to indicate the treatment conditions and then put in the polyhouse at 25°C for germination. All the trays were replenished with fresh water and NaCl salt concentrations every three days to maintain consistency. In the rolled germination papers, each accession seed was placed in plastic trays containing the respective NaCl solutions, ensuring that each treatment was consistent across replicates and locations. Seed germination and seedling vigor assessment under salinity stress Seed germination observations were made on the eighth day of germination. Seeds were categorized based on germination patterns into hard, germinated, diseased, and rotten seeds (Singh, 2019). The final data recorded ten quantitative traits like germination percentage (GP, %), seedling fresh weight (FW, g), seedling dry weight (DW, g), seedling length (SLE, cm), shoot length (SL, cm), root length (RL, cm), seedling vigor index-1 (VI1), seedling vigor index-2 (VI2), seedling water content (SW, %), and salt tolerance (ST, %) of the 198 soybean genotypes grown at different NaCl levels (Control, 100 mM, 150 mM, and 200 mM) in experiments E1 and E2. The germination percentage was determined on the eighth day using the following formula: Germination Percentage (%) = (Number of germinated seeds) / (Total number of seeds) × 100 (Kumar et al. 2019). We categorized mean seed germination into four categories based on their response to salinity stress. The first category, tolerant, includes genotypes with a germination percentage greater than 80%, indicating high tolerance to salinity stress and robust growth even under saline conditions. The second category, moderately tolerant, comprises genotypes with a 60–80% germination percentage. These genotypes show moderate tolerance to salinity stress, with slightly lower germination rates than the tolerant group but still maintaining relatively good growth. The third category, moderately susceptible, encompasses genotypes with a germination percentage between 40% and 60%, suggesting a mild impact of salinity on their growth. Lastly, the susceptible category includes genotypes with a germination percentage below 40%, where salinity stress significantly impacts their growth, resulting in poor germination rates and reduced overall growth under saline conditions (Mannan et al. 2012; Wu et al. 2019). We compared the mean germination percentage below 40% in the 100 mM NaCl treatment to identify the salt-sensitive genotypes common in E1 and E2. For the salt-tolerant genotypes, we looked for a mean germination percentage above 80% in the 200 mM NaCl treatment, which was common in E1 and E2. We evaluated the seedling vigor Index-1 using the formula (Seedling length × Germination percentage) / 100 to obtain comprehensive insights into the seedlings' growth potential and treatment effects. Additionally, Vigor Index-2 was calculated using the formula (Seedling dry weight × Germination percentage) / 100, focusing on biomass accumulation. To determine dry weight, the seedlings were dried in an oven at 80°C for 24 hours after the eighth day of germination (Kharb et al. 1994). The percentage of Seedling Water Content was determined in a seedling by subtracting the Dry Weight from the Fresh Weight, dividing by the Fresh Weight, and multiplying by 100 (Pavli et al. 2021). Salt Tolerance (%) is computed by dividing the germination in treated seedlings by the germination in control seedlings, multiplying the result by 100 (El Sabagh et al. 2015). Statistical Analysis Mean data were analyzed using one-way analysis of variance (ANOVA), followed by Tukey's post-hoc test at a confidence level of > 95%. The study was conducted using an online web statistical calculator: https://astatsa.com/OneWay_Anova_with_TukeyHSD . Boxplots of the GP of seeds in experiments E1 and E2 at different salt concentrations were generated using the online application called BoxPlotR, a web tool for creating box plots ( http://shiny.chemgrid.org/boxplotr/ ) (Spitzer et al., 2014). A heat map for the GP of all 198 seeds in experiments E1 and E2 at different salinity levels was created using the online tool Heatmapper (Babicki et al., 2016) ( http://www.heatmapper.ca/expression/ ). Heat maps for the GP of salt-sensitive and salt-tolerant genotypes were created using the ‘Heat map’ package of the R program. Principal component analysis (PCA), K-means clustering, and creation of the correlation matrix for the quantitative characteristics of seeds in experiments E1 and E2 were performed using the trial version of JMP 17 (SAS Institute Inc., 2023). Results Seed germination performance across salinity gradients The average data for germination and the other nine growth parameters exhibited higher means in the control group compared to the NaCl-treated groups (refer to Table 1 , Supplemental Tables S2 and S3). Notably, the germination process of seeds placed in the control group progressed more rapidly than those subjected to salt treatments. This observation is further illustrated in Fig. 1 , depicting the seed germination on the fifth and eighth days after sowing. Table 1 Descriptive Statistics for different quantitative traits of 198 soybean genotypes grown at different NaCl salinity levels in E1 and E2. Experiments Treatment Statistical parameters Traits Germination Percentage (%) Fresh Weight (g) Dry Weight (g) Seedling Length (cm) Shoot Length (cm) Root Length (cm) Vigor Index-1 Vigor Index-2 Seedling Water Content Salt Tolerance E1 Control Range 0-100 0-7.03 0-0.71 0-23.2 0-17.08 0-10.89 0-23.2 0-0.71 0-100 - Mean ± SE 62.77 ± 2.48 2.06 ± 0.09 0.21 ± 0.01 11.63 ± 0.43 7.79 ± 0.29 3.84 ± 0.17 8.89 ± 0.41 0.20 ± 0.01 64.81 ± 2.05 - 100mM Range 0-100 0-4.77 0-0.5 0-14.56 0-9.22 0-38.85 0-43.67 0-0.5 0-100 0-100 Mean ± SE 59.37 ± 2.42 1.62 ± 0.07 0.16 ± 0.01 5.73 ± 0.25 3.67 ± 0.16 22.44 ± 0.85 13.55 ± 0.68 0.15 ± 0.01 57.94 ± 2.01 78.85 ± 2.18 150mM Range 0-100 0-4.53 0-0.47 0-10.44 0-6.46 0-3.99 0-31.33 0-0.47 0-98.78 0-100 Mean ± SE 83.84 ± 2.37 1.48 ± 0.07 0.14 ± 0.01 3.26 ± 0.17 2.14 ± 0.10 1.12 ± 0.08 6.94 ± 0.42 0.13 ± 0.01 54.94 ± 2.13 71.48 ± 2.18 200mM Range 0-100 0-3.83 0-0.43 0-5.26 0-4.22 0-2.03 0-12.68 0-0.43 0-100 0-114 Mean ± SE 48.51 ± 2.35 1.07 ± 0.07 0.09 ± 0.01 1.62 ± 1.36 1.32 ± 0.08 0.30 ± 0.03 3.39 ± 0.23 0.09 ± 0.01 43.68 ± 2.26 64.88 ± 2.36 E2 Control Range 0-100 0–4 0–1 0–15 0–6 0–20 0–23 0–1 0-100 - Mean ± SE 82 ± 1.59 2 ± 0.05 0 ± 0.01 8 ± 0.23 3 ± 0.10 11 ± 0.33 8 ± 0.34 0.15 ± 0.01 22.31 ± 1.59 - 100mM Range 0-100 0–3 0 0–10 0–5 0–15 0–12 0–1 0-100 0-100 Mean ± SE 62 ± 2.65 1 ± 0.04 0 ± 0.01 4 ± 0.13 2 ± 0.08 6 ± 0.21 4 ± 0.19 0 ± 0.01 81 ± 1.37 90 ± 1.49 150mM Range 0-100 0–2 0–1 0–5 0–2 0–7 0–7 0–1 0-100 0-100 Mean ± SE 56 ± 2.4 1 ± 0.03 0 ± 0.01 3 ± 0.08 1 ± 0.04 3 ± 0.11 2 ± 0.11 0 ± 0.01 61 ± 1.41 88 ± 1.52 200mM Range 0-100 0–2 0 0–4 0–1 0–4 0–5 0–1 0–78 0-100 Mean ± SE 73 ± 1.69 1 ± 0.03 0 ± 0.01 0 ± 0.06 0 ± 0.01 0 ± 0.06 1 ± 0.08 0 ± 0.01 45 ± 1.09 82 ± 1.67 The mean seed germination percentage was generally higher in the E2 location compared to the E1 location, except for the 150 mM treatment in E2. In both locations, seed germination ranged from 0 to 100 percent. However, the mean values for FW and DW, SLE, SL, and vigor index 1 and 2 were higher in the E1 location than in E2. Additionally, we observed that RL was higher in E2 for both the control and the 150 mM treatment. Furthermore, the SW was higher in E2 for all three treatments except for the control. Moreover, the mean ST was higher in E2 than in E1 (Table 1 ). Table 1 provides comprehensive information on the response of soybean genotypes to different levels of salinity stress and insights into their growth parameters, performance, and tolerance levels under various conditions. For E1, a one-way ANOVA with a post-hoc Tukey's HSD test revealed a p-value lower than 0.05, suggesting that one or more treatment pairs differed significantly. The treatment pairs of control vs 150 mM and control vs 200 mM had a p-value of less than 0.05, while the treatment pair control vs 100 mM was insignificant (Supplemental Table S2 ). For E2, a one-way ANOVA with a post-hoc Tukey's HSD test revealed a p-value lower than 0.05, strongly suggesting that one or more treatment pairs are significantly different. Supplemental Table S3 indicated that the treatment pairs of control vs 100 mM, control vs 150 mM, and control vs 200 mM have a p-value less than 0.05. The experiments demonstrated that salinity stress negatively affects soybean seedlings' germination and early growth. The impact varies depending on the salinity level, with higher concentrations generally leading to reduced growth metrics such as GP, FW, DW, SLE, SL, and RL. While E1 showed some resilience at 150 mM NaCl regarding GP, most growth parameters decreased at higher salinity levels in both experiments. ST decreased with increasing NaCl concentration, highlighting the sensitivity of soybean seedlings to salinity stress. This discovery emphasizes the susceptibility of soybean plants to high amounts of salt, underscoring the significance of developing salt tolerance in soybean breeding programs and agricultural methods. PCA and Cluster Analysis under salinity conditions PCA was used to analyze the variation in salt tolerance among 198 soybean core collection genotypes. Figure 2 shows the PCA biplot and K-means clustering for E1 and E2 under 100 mM and 200 mM NaCl concentrations. Black dots represent samples, and red arrows represent variable loadings on the principal components (PCs). The estimation of the contribution of each PC to the total variance is determined by the eigenvectors associated with each PC. K-means non-hierarchical cluster analysis classified the 198 genotypes into four groups based on salt tolerance (tolerant, moderately tolerant, moderately susceptible, and susceptible), and is shown in Table 2 . As indicated in Fig. 2 , cluster one is red, two is green, three is blue, and four is light brown. Clusters varied by salinity level in E1 and E2. Table 2 K-means clustering of the genotypes. Experiment NaCl concentration Cluster 1 (Red color) Cluster 2 (Green color) Cluster 3 (Blue color) Cluster 4 (Light brown color) Figure no. E1 100mM ++ + - -- Figure 2 (b) 200mM ++ + - -- Figure 2 (d) E2 100mM + - ++ -- Figure 2 (f) 200mM ++ + -- - Figure 2 (h) Note: ++ is tolerant, + is moderately tolerant, - is moderately susceptible and -- is susceptible In E1, the first two components explained 77.7% and 73.5% of the variation for 100 mM and 200 mM treatments, respectively. For 100 mM (Fig. 2 (a)), Component 1 accounted for 63.7%, with SLE, VI1, and ST having strong positive influences. At 100 mM NaCl concentration (Fig. 2 (b)), the genotypes were classified into Cluster 3 (71 genotypes), Cluster 1 (66), Cluster 4 ( 38 ), and Cluster 2 ( 23 ). For 200 mM (Fig. 2 (c)), Component 1 accounted for 61.9%, with GP, VI2, and DW having strong influences. At 200 mM NaCl concentration (Fig. 2 (d)), the genotypes were classified into Cluster 2 (77 genotypes), Cluster 4 (65), Cluster 1 ( 36 ), and Cluster 3 ( 20 ). In E2, the first two components explained about 75% (100 mM) and 70.6% (200 mM) of the variation, with VI2 ST and SW strongly influencing Component 1 at 100 mM, and SL, RL, SW, FW, and SLE at 200 mM. For 100mM, Component 1 accounted for 61.8%, the second component for 13.2% (Fig. 2 (e)), and the genotypes were clustered into Cluster 3 (103 genotypes), Cluster 1 (44), Cluster 2 ( 40 ), and Cluster 4 ( 11 ) (Fig. 2 (f)). The first and second components accounted for 57.1% and 13.5%, respectively, at 200 mM (Fig. 2 (g)). A noticeable spread of genotypes (data points) along Component 2 indicated varied responses to the 200 mM treatment. The genotypes were clustered into Cluster 3 (83 genotypes), Cluster 4 (82), Cluster 2 ( 31 ), and Cluster 1 ( 2 ) (Fig. 2 (h)). In both E1 and E2, the influence of specific traits varied with salt concentration, indicating different dominant traits under different conditions. Genotypes were more tightly clustered at 100 mM, showing consistent responses, while 200 mM treatments showed more dispersion, indicating increased variability. From Supplemental Table S6, it can be found that all the variables in PC1 have a positive correlation between them at both 100 mM and 200 mM NaCl concentrations in both E1 and E2. The negative values in PC2 are not significant. The PCA results reveal that specific traits are crucial in distinguishing genotypes under varying salt concentrations. In both experiments, PCA showed that the eigenvalues and the percentage of variance explained by GP, FW, and DW significantly contributed to the variability captured by PC1 and PC2, with each trait contributing over 9%. This underscores these traits as dominant factors in the overall variability. Additionally, the eigenvectors provided insight into how each variable contributes to these factors (Fig. 2 ; Supplemental Table S6). Cluster positions shifted with increasing salinity, reflecting changes in tolerance levels of the genotypes. When tested at the same concentrations, the differences between E1 and E2 emphasized the influence of experimental conditions on the clustering patterns and, hence, the salinity tolerance. Common genotypes identified as salt-sensitive and salt-tolerant at both 100 mM and 200 mM NaCl concentrations are listed in Tables 5 and 6. Heat map of Germination Percentage under salinity conditions A heat map was created to understand the GP pattern of the soybean seeds across different salt–stress environments and in a controlled environment. The heat map in Fig. 3 (a) helped visualize the variations in the germination percentage of seeds between the two experiments and under the conditions applied in each experiment. Blue indicates a lower germination percentage, while orange and yellow indicate higher percentages. In E1, gradient colors can be observed in E1-C (0 mM/control) and E1-100 (100 mM NaCl), depicting a range of variations in the GP within the control and treatment (100 mM) groups. At the same time, the 150 mM salt treatment group has a higher germination percentage compared to the 200 mM treated group, which has a lower germination percentage. In E2, gradients of the yellow color can be observed in E2-C (0 mM/control), E2-100 (100 mM NaCl), and E2-200 (200 mM NaCl) treated groups, revealing the range of variations in the GP within the groups. When comparing the GP of seeds treated with 150 mM NaCl to the other treatment groups and the control group, it was seen that most of the seeds in those groups had a higher germination percentage. However, only half of the seed sets in the 150 mM group exhibited high germination percentages. Correlation matrix and box plot under saline conditions A Correlation Matrix was constructed using the ten quantitative traits at 100 mM and 200 mM in both E1 and E2 (Supplemental Fig. 1 in supplemental document S5), with (a) and (b) representing the traits at 100 mM NaCl concentration in E1 and E2, respectively and (c) and (d) representing the traits at 200 mM NaCl concentration in E1 and E2, respectively. The different colors indicate the strength of the correlation: closer to red indicates a strong positive correlation, and closer to blue indicates a strong negative correlation. When comparing the quantitative traits of the seeds germinated in E1 (a) and E2 (c) at 100 mM NaCl concentration, it can be seen that GP, FW, DW, SLE, SL, RL, VI1, and VI2 were positively correlated with each other. ST had a mild positive correlation with all the traits other than VI1 in E1. DW had a mild negative correlation with SLE. Also, SLE, SL, and RL have a mild negative correlation. At 200 mM NaCl concentration, all traits were negatively correlated with ST, except VI1. All other traits had a strong positive correlation in E1 (b). In E2 (d), GP, FW, DW, and SL had a negative correlation. Similarly, SL was negatively correlated with RL, VI1, VI2, SW, and ST. All other traits had mostly positive correlations with each other. It can be seen from Supplemental Fig. 1 that across all conditions, GP, FW, and DW showed strong positive correlations with each other. This indicates that a higher germination percentage can be associated with better growth metrics (FW and DW). Vigor index (VI1 and VI2) consistently showed strong positive correlations with GP and FW, making it a reliable indicator of seedling health. Though the correlation tends to weaken when salinity increases from 100 mM to 200 mM, E2 showed a better correlation than E1. The figure also reveals that some key growth parameters were closely linked across different salinity levels and experimental conditions. Strong positive correlations suggested that improvement in one parameter often accompanies improvements in others, highlighting the interconnected nature of seedling growth metrics. Under higher salinity, correlations weaken, indicating the impact of salinity stress. The germination percentages of seeds at different salt concentrations in E1 and E2 were compared using a boxplot (Supplemental Fig. 2 in supplemental document S5). In the figure, the y-axis represents the range of GP, while the x-axis represents the GP of the seeds at E1 - control (E1-C), 100 mM NaCl concentration (E1-100), 150 mM NaCl concentration (E1-150) and 200 mM NaCl concentration (E1-200) – and E2 - control (E2-C), 100 mM NaCl concentration (E2-100), 150 mM NaCl concentration (E2-150) and 200 mM NaCl concentration (E2-200). It was also revealed that E1-C and E2-C (control conditions) have high germination percentages, indicating good germination without salinity stress. It can also be seen that E2 consistently showed higher median germination percentages and narrower IQR (Inter-Quartile Range) when compared to E1 across all NaCl concentrations. This indicates that seeds under E2 conditions are more tolerant to salinity stress than those under E1. As NaCl concentration increases, GP generally decreases, with E1 showing a more significant decline than E2, as is evident from the drop in median germination. Identification of salt-sensitive genotypes at a salt (NaCl) stress of 100 mM Common soybean genotypes from E1 and E2 were classified into four categories (tolerant, moderately tolerant, moderately susceptible, and susceptible) based on their germination response to ≤ 40% for 100 mM NaCl stress (Table 3 ) common to E1 and E2. HIMSO 1563, EC 391181, and EC 241920 were categorized as susceptible due to their poor growth and reduced germination under saline conditions. This classification helps identify genotypes that may benefit from additional breeding efforts and management strategies to improve their salt tolerance and overall performance in saline environments. The heat map in Fig. 3 (b) illustrates the germination of these genotypes on the eight days after sowing under various conditions: control, 100 mM, 150 mM, and 200 mM NaCl in E1 and E2. Table 3 Categorization of 40 soybean genotypes based on Germination Percentage at 100 mM NaCl solution. GP > 80% 60–80% 40–60% < 40% Tolerance category Tolerant Moderately tolerant Moderately Susceptible Susceptible Genotypes PLSO 23 MACS 985 EC 14426 PK 1029 MACS 1168 EC 7951 IC 202 CAT II47 MACS 1037 MACS 1281 JS 9560 HILL IC 33776 IC 13050 EC 100800 JS 9971 MACS 96 MACS 199 JS 75 1 EC 100022 JS 72280 JS 8021 EC 100027 MACS 37 MACS 136 G 118 EC 251470 IC 9451 NRC 147 MACS 57 PUNJAB 1 VLC 86 KDS 992 EC 14477 JS 72 451 MACS 472 TS 213 (PRIVATE) HIMSO 1563 EC 391181 EC 241920 Total 22 11 4 3 Identification of salt-tolerant genotypes at a salt (NaCl) stress of 200 mM Soybean genotypes from E1 and E2 were similarly classified into four categories based on their response to 200 mM NaCl stress (Table 4 ). Seven genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173) were identified as tolerant, demonstrating robust growth and germination percentages ≥ 80% at 200 mM NaCl, indicating their high tolerance to salinity stress. The heat map in Fig. 3 (c) depicts the germination of these genotypes on the eight days after sowing under different salt conditions: control, 100 mM, 150 mM, and 200 mM NaCl in E1 and E2. Table 4 Categorization of 28 soybean genotypes based on Germination Percentage at 200mM NaCl solution. GP > 80% 60–80% 40–60% < 40% Tolerance category Tolerant Moderately tolerant Moderately Susceptible Susceptible Genotypes MACS 708 KALITUR MACS 1037 IC 13050 MACS 1010 PK 1029 MACS 173 EC 100027 MACS 37 MACS 136 G 118 EC 251470 IC 9451 NRC 147 MACS 57 PUNJAB 1 VLC 86 KDS 992 EC 14477 PLSO 39 JS 72 451 MACS 472 EC 95807 TS 213 (PRIVATE) MACS NRC 1667 EC 18207 IC 13048 HIMSO 1563 Total 7 11 6 4 Discussion The present study evaluated salt tolerance in a soybean core collection by assessing germination response under salinity stress. Our findings revealed significant variability in salt tolerance among the 198 soybean accessions, with some lines exhibiting robust germination rates despite high salinity levels. This study identified three salt-sensitive (HIMSO 1563, EC 391181, and EC 241920) and seven salt-tolerant genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173). These results align with previous studies highlighting genetic diversity in soybean salt tolerance and suggest the potential for breeding programs to develop salt-tolerant varieties. The observed variability underscores the importance of identifying and utilizing salt-tolerant genotypes to enhance soybean productivity in saline-prone regions. This study provides a foundation for further research into the genetic and physiological mechanisms underlying salt tolerance in soybeans, offering practical insights for improving crop resilience in challenging environments. Effect of Salinity on Seed Germination and Early Growth The study revealed the detrimental effects of salinity stress on seed germination and early growth parameters of soybean genotypes. The slower germination progression in NaCl-treated groups, when compared to the control group, underscores the inhibitory impact of salinity on seedling establishment. This finding aligns with previous research, indicating that higher salt concentrations in the soil can impact water uptake by seeds, thus delaying their germination and reducing seedling vigor (Zuffo et al. 2020; Açıkbaş et al. 2023). Variation in Seed Germination and Growth Metrics between Locations Seeds under saline conditions has potential salt tolerance at germination stage, but does not guarantee that salinity stress will not impact other life cycle phases (Miransari 2016; Zuffo et al. 2020; Guan et al. 2023). In soybeans, salinity affects the seed germination and post-germination stages (Alizadeh et al. 2024). The toxic effect of NaCl primarily causes poor seed germination (Khaje-Hosseini et al. 2003). Our ANOVA analysis revealed that all ten traits exhibited significant differences in E1 and E2. While mean GP was generally higher in E2, growth metrics like FW, DW, and SLE were higher in E1. This variation could be attributed to differences in soil composition, environmental conditions, or genetic factors between the two locations. Additionally, it is crucial to ensure consistency and account for environmental impacts when conducting seed germination experiments under salinity stress, particularly for salinity QTL or gene mapping at the germination stage. Therefore, using multiple sources for a single set of seeds is important to verify the reliability of the results and mitigate the influence of external variables on germination outcomes. Salt Tolerance among Soybean Genotypes PCA and cluster analysis identified the substantial variation in the quantitative traits among the 198 genotypes studied. It also helped identify key variables for further analysis or interventions to understand the mechanisms behind responses to different salt concentrations. Understanding the patterns in PCA and K-means clustering can aid in categorizing the genotypes based on their responses to salinity (tolerant, moderately tolerant, moderately susceptible, and susceptible), which is crucial for developing new strategies for breeding programs aimed at developing salt-tolerant soybean varieties. Crop productivity can be increased in saline soils by identifying genotypes with varying degrees of salt tolerance, thus supporting targeted breeding efforts. Impact of Salinity on Seedling Growth Metrics The experiments demonstrated that salinity stress negatively affects soybean seedlings’ germination and early growth, as higher salt concentrations lead to a significant reduction in GP, FW, DW, SLE, SL, and RL. These findings align with prior research documenting similar growth parameter declines under salinity stress. Previous studies have also reported a reduction in the seedlings' height, fresh weight (FW), and dry weight (DW), similar to our findings. (Hosseini et al. 2002; Amirjani 2010). The results presented in this work confirm previous findings (Essa 2002; Datta et al. 2006; Pavli et al. 2021; Begum et al. 2022; Alizadeh et al. 2024). The observed decrease in salt tolerance with increasing NaCl concentrations highlights the sensitivity of soybean seedlings to salinity stress. These findings highlight the importance of developing salt-tolerant varieties to lessen the unfavorable effects of salinity stress on crop yield and quality. Correlation between Growth parameters A strong positive correlation between GP, FW, and DW indicated that higher germination rates are associated with better growth parameters. The strong correlation of the vigor index (VI1 and VI2) with GP and FW, positions it as a dependable predictor of seedling health, consistent with previous findings that link higher vigor indices to faster seedling emergence (Ebone et al. 2020). However, correlations weakened as the salinity levels increased, highlighting the impact of salinity stress on seedling growth. Understanding these correlations provides insights into the interconnected nature of seedlings' growth metrics and can help develop breeding strategies to improve overall crop performance under saline conditions. Comparison of GP between Locations Comparison of GP between E1 and E2 revealed a difference in tolerance to salinity stress, with E2 consistently showing higher median germination percentages across all three NaCl concentrations. These findings suggested that the seeds under E2 conditions were more tolerant to salinity stress than those under E1, thus indicating the difference in the environmental conditions between the locations. Understanding these differences is essential for optimizing soybean cultivation practices in different geographical regions. Boxplot and heat maps effectively visualized the negative impact of salinity, showing a consistent decrease in germination percentage and growth parameters across E1 and E2. These visual tools validated prior findings on the negative effects of higher salinity levels on soybean growth (Hosseini et al. 2002; Amirjani 2010). Identification of Salt-Sensitive and Salt-Tolerant Genotypes In previous studies conducted in India, the soybean varieties CoSoy-2, DS-40, PalamSoy, and Pusa-16 were identified as salt-tolerant, whereas Co-1, GujaratSoy-1, and NRC-2 were found to be salt-sensitive (Kondetti et al. 2012). Additionally, research has identified cultivars such as Lee, BB52, Lee68, JWS156-1, S100, Nannong1138-2, PI483463, Wenfeng 7, Jindou 33, Tiefeng 8, and Dare as salt-tolerant, while Coquitt, Clark 63, Kefeng No. 1, Jackson, N23232, Kwangan, Daepung, and Tachiyutaka were recognized as salt-sensitive (An et al. 2002; Essa 2002; Yu and Liu 2003; Phang et al. 2008; Zeng et al. 2019; Moon et al. 2023; Guan et al. 2023). Prior research studies investigated the salinity tolerance of soybean genotypes at the germination stage. However, these studies were limited in scope as they either utilized a limited number of genotypes or employed low concentrations of NaCl. We assessed the salinity tolerance of 198 genotypes collected at different salt concentrations from two locations. A better understanding of plants' physiological and biological processes under salinity stress can accelerate the implementation of strategies to increase agricultural production by introducing salt-tolerant or salt-resistant plants (Alizadeh et al. 2024). The classification of soybean genotypes into salt-sensitive and salt-tolerant categories, based on their responses to 100 mM and 200 mM NaCl stress, provides valuable insights for breeding efforts to improve salt tolerance in these crops. Identifying genotypes with high salinity tolerance, like MACS 708, KALITUR, and MACS 1037, facilitates the development of salt-tolerant varieties that can thrive in saline environments. This classification helps prioritize such genotypes for further breeding efforts and management strategies to enhance slat tolerance and overall crop performance in saline soils. Conclusion The current study elucidated the detrimental effects of salinity stress on 198 soybean genotypes' germination and early growth under different salt concentrations. A significant variation in salt tolerance was demonstrated among different soybean genotypes. We observed that the seed germination percentage and other quantitative traits decreased as the salt content increased in both source seeds. The varieties MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173 exhibited salt tolerance, whereas HIMSO 1563, EC 391181 and EC 241920 displayed salt sensitivity. The salt-tolerant genotypes could germinate even at a concentration as high as 200 mM, but the salt-sensitive genotypes could not germinate even at the lowest concentration of 100 mM. These findings offer invaluable insights for breeding endeavors to cultivate salt-tolerant soybean varieties, which are crucial for sustainable crop production in saline environments. Declarations Acknowledgements The authors extend their sincere gratitude to Dr. Prashant Dhakephalkar (Director, Agharkar Research Institute), Dr. Manoj D. Oak (Head of the Department, Department of Genetics and Plant Breeding), Dr. Ravindra Patil, Mr. Santosh Jaybhay, Dr. Suresha P. G., Mr. V. D. Surve, Mrs. Anuja Deshpande, Mr. Bhanudas Idhol, Mr. B. N. Waghmare, and Mr. Dattatraya Salunkhe from the Department of Genetics and Plant Breeding, Agharkar Research Institute, for their generous provision of resources and facilities essential for the successful execution of this study. Author contributions The conceptualization of the experimental design and layout was done by Abhinandan Patil. Deepak Pawar did the seed threshing and packet preparation in locations E1 and E2. Data recording was done by Aditya Gobade. Shreyash Gijare assisted in phenotyping and data recording. Analysis and the first draft of the manuscript was prepared by Arathi. S. Conceived the concept, designed the analysis and critical revision of the manuscript was done by Abhinandan Patil. All authors read and approved the final manuscript. Funding This work is funded by the Ramalingaswami Re-entry Fellowship (BT/RLF/Re-entry/01/2021) Department of Biotechnology (DBT), Government of India. Data Availability All data supporting the findings of this study are available within the paper and its supplementary materials published online. Competing interests The authors declare no conflict of interest. Ethical Approval The authors declare that this article does not contain any research involving animals or human participants performed by any of the authors. References Açıkbaş, S., Özyazıcı, M. A., Bıçakçı, E., & Özyazıcı, G. (2023). Germination and Seedling Development Performances of Some Soybean (Glycine max (L.) Merrill) Cultivars Under Salinity Stress. Turkish Journal of Range and Forage Science, 4 (2), 108–118. https://doi.org/10.51801/turkjrfs.1387963 Alizadeh, M. M., Gerami, M., Majidian, P., & Ghorbani, H. R. (2024). The potential application of biochar and salicylic acid to alleviate salt stress in soybean (Glycine max L.). Heliyon. https://doi.org/10.1016/j.heliyon.2024.e26677 Amirjani, M. R. (2010). Effect of salinity stress on growth, mineral composition, proline content, antioxidant enzymes of soybean. American Journal of Plant Physiology, 5 (6), 350–360. An, P., Inanaga, S., Cohen, Y., Kafkafi, U., & Sugimoto, Y. (2002). Salt tolerance in two soybean cultivars. Journal of plant nutrition, 25 (3), 407–423. https://doi.org/10.1081/PLN-120003373 Anwar, J., Subhani, G. M., Hussain, M., Ahmad, J., Hussain, M., & Munir, M. (2016). Screening soybean genotypes for salinity tolerance at the germination stage. Pakistan Journal of Agricultural Sciences, 53 (1), 165–170. Babicki, S., Arndt, D., Marcu, A., Liang, Y., Grant, J. R., Maciejewski, A., & Wishart, D. S. (2016). Heat mapper: web-enabled heat mapping for all. Nucleic acids research, 44 (W1), W147-W153. https://doi.org/10.1093/nar/gkw419 Begum, N., Hasanuzzaman, M., Li, Y., Akhtar, K., Zhang, C., & Zhao, T. (2022). Seed germination behavior, growth, physiology, and antioxidant metabolism of four contrasting cultivars under combined drought and salinity in soybean. Antioxidants, 11 (3), 498. https://doi.org/10.3390/antiox11030498 Cao, L., Yu, Y., DuanMu, H., Chen, C., Duan, X., Zhu, P., … Ding, X. (2016). A novel Glycine soja homeodomain-leucine zipper (HD-Zip) I gene, Gshdz4, positively regulates bicarbonate tolerance and responds to osmotic stress in Arabidopsis. BMC plant biology, 16 , 1–14. https://doi.org/10.1186/s12870-016-0872-7 Choudhury, S., Panda, P., Sahoo, L., & Panda, S. K. (2013). Reactive oxygen species signaling in plants under abiotic stress. Plant signaling & behavior, 8 (4), e23681. https://doi.org/10.4161/psb.23681 Cho, K. H., Kim, M. Y., Kwon, H., Yang, X., & Lee, S. H. (2021). Novel QTL identification and candidate gene analysis for enhancing salt tolerance in soybean (Glycine max (L.) Merr.). Plant Science, 313 , 111085. https://doi.org/10.1016/j.plantsci.2021.111085 Datta, A. K., Hossain, M. A., Robin ABMAHK, R. S., & Hossain, M. A. (2006). Screening of soybean genotypes for salinity tolerance at seedling stage. Bangladesh J Crop Sci, 17 (1), 155–161. Ebone, L. A., Caverzan, A., Tagliari, A., Chiomento, J. L. T., Silveira, D. C., & Chavarria, G. (2020). Soybean seed vigor: uniformity and growth as key factors to improve yield. Agronomy, 10 (4), 545. https://doi.org/10.3390/agronomy10040545 El Sabagh, A., Abd Elhamid Omar, H. S., & Barutçular, C. (2015) Physiological performance of soybean germination and seedling growth under salinity stress. DUFED 4(1), 6–15. Essa, T. A. (2002). Effect of salinity stress on growth and nutrient composition of three soybean ( Glycine max L. Merrill) cultivars. Journal of Agronomy and Crop science, 188 (2), 86–93. https://doi.org/10.1046/j.1439-037X.2002.00537.x Guan, R. X., Guo, X. Y., Qu, Y., Zhang, Z. W., Bao, L. G., Ye, R. Y., … Qiu, L. J. (2023). Salt Tolerance in Soybeans: Focus on Screening Methods and Genetics. Plants, 13 (1), 97. https://doi.org/10.3390%2Fplants13010097 Hosseini, M. K., Powell, A. A., & Bingham, I. J. (2002). Comparison of the seed germination and early seedling growth of soybean in saline conditions. Seed Science Research, 12 (3), 165–172. https://doi.org/10.1079/SSR2002108 Hou, Z., Li, Y., Kong, F., Dong, L., & Cheng, Q. (2022). Genome-wide analysis of DREB genes identifies a novel salt tolerance gene in wild soybean (Glycine soja). Frontiers in Plant Science, 13 , 821647. https://doi.org/10.3389/fpls.2022.821647 https://iisrindore.icar.gov.in/readmore.html Khajeh-Hosseini, M., Powell, A. A., & Bingham, I. J. (2003). The interaction between salinity stress and seed vigor during germination of soyabean seeds. Seed Science and technology, 31 (3), 715–725. https://doi.org/10.15258/sst.2003.31.3.20 Kharb, R. P. S., Lather, B. P. S., & Deswal, D. P. (1994). Prediction of field emergence through heritability and genetic advance of vigor parameters. Seed Science and Technology, 22(3): 461–466. Kondetti, P., Jawali, N., Apte, S. K., & Shitole, M. G. (2012). Salt tolerance in Indian soybean (Glycine max (L.) Merill) varieties at germination and early seedling growth. Annals of Biological Research, 3 (3), 1489–1498. Kumar, S. J., Susmita, C., Sripathy, K. V., Agarwal, D. K., Pal, G., Singh, A. N., … Simal-Gandara, J. (2022). Molecular characterization and genetic diversity studies of Indian soybean ( Glycine max (L.) Merr.) cultivars using SSR markers. Molecular Biology Reports, 1–12. https://doi.org/10.1007/s11033-021-07030-4 Kumari, S., Lal, S. K., Sreenivasa, V., Rajendran, A., Singh, S. K., Singh, K. P., … Xu, D. (2019). Screening and identification of sources of salinity tolerance at seed germination stage in Indian soybean genotypes. International Journal of Current Microbiology and Applied Sciences, 8 , 3006–3013. https://doi.org/10.20546/ijcmas.2019.804.346 Leung, H. S., Chan, L. Y., Law, C. H., Li, M. W., & Lam, H. M. (2023). Twenty years of mining salt tolerance genes in soybean. Molecular Breeding, 43 (6), 45. https://doi.org/10.1007/s11032-023-01383-3 Linh, N. T., Cham, L. T. T., & Thang, V. N. (2021). Effects of salinity stress on the growth, physiology, and yield of soybean ( Glycine max (L.) Merrill). https://doi.org/10.31817/vjas.2021.4.2.05 Mannan, M. A., Karim, M. A., Haque, M. M., Khaliq, Q. A., Higuchi, H., & Nawata, E. (2012). Response of soybean to salinity: I. Genotypic variations in salt tolerance at the vegetative stage. Tropical Agriculture and Development, 56 (4), 117–122. https://doi.org/10.11248/jsta.56.117 Miransari, M. (2016). Soybean production and salinity stress. In Abiotic and Biotic Stresses in Soybean Production (pp. 157–176). Academic Press. https://doi.org/10.1016/B978-0-12-801536-0.00007-4 Moon, C. Y., Kang, B. H., Kim, W. J., Chowdhury, S., Kang, S., Lee, J. D., … Ha, B. K. (2023). Morpho-physiological and genetic characteristics of a salt-tolerant mutant line in soybean (Glycine max L.). Theoretical and Applied Genetics, 136 (7), 166. https://doi.org/10.1007/s00122-023-04408-9 Pavli, O. I., Foti, C., Skoufogianni, G., Karastergiou, G., Panagou, A., & Khah, E. M. (2021). Effect of salinity on seed germination and seedling development of soybean genotypes. intervals , 3 , 7th. https://doi.org/10.19080/IJESNR.2021.27.556210 Phang, T. H., Shao, G., & Lam, H. M. (2008). Salt tolerance in soybean. Journal of Integrative Plant Biology, 50 (10), 1196–1212. Rahman, M. A., Islam, M. M., Akter, S., Islam, M. M., Hasanuzzaman, M., & Ahsan, M. A. (2021). Screening of soybean genotypes for salinity tolerance at the germination stage. Journal of Crop Science and Biotechnology, 24 (4), 289–294. Rasheed, A., Raza, A., Jie, H., Mahmood, A., Ma, Y., Zhao, L., … Jie, Y. (2022). Molecular tools and their applications in developing salt-tolerant soybean (Glycine max L.)cultivars. Bioengineering , 9 (10), 495. https://doi.org/10.3390/bioengineering9100495 Singh, R. K., Sharma, V., Tyagi, S., & Gupta, S. (2019). Evaluation of soybean genotypes for salinity tolerance at the germination and seedling stage. Indian Journal of Agricultural Sciences, 89 (7), 1153–1157. Spitzer, M., Wildenhain, J., Rappsilber, J., & Tyers, M. (2014). BoxPlotR: a web tool for generation of box plots. Nature methods, 11 (2), 121–122. https://doi.org/10.1038/nmeth.2811 Umburanas, R. C., Kawakami, J., Ainsworth, E. A., Favarin, J. L., Anderle, L. Z., Dourado-Neto, D., & Reichardt, K. (2022). Changes in soybean cultivars released over the past 50 years in southern Brazil. Scientific Reports, 12 (1), 508. https://doi.org/10.1038/s41598-021-04043-8 Wu, H., Guo, J., Wang, C., Li, K., Zhang, X., Yang, Z., … Wang, B. (2019). An effective screening method and a reliable screening trait for salt tolerance of Brassica napus at the germination stage. Frontiers in Plant Science , 10 , 530. doi: 10.3389/fpls.2019.00530 Xu, X. Y., Fan, R., Zheng, R., Li, C. M., & Yu, D. Y. (2011). Proteomic analysis of seed germination under salt stress in soybeans. Journal of Zhejiang University Science B, 12 (7), 507–517. Yu BingJun, Y. B., & Liu YouLiang, L. Y. (2003). Effects of salt stress on the metabolism of active oxygen in seedlings of annual halophyte Glycine soja . Zeng, A., Chen, P., Korth, K. L., Ping, J., Thomas, J., Wu, C., … Ma, J. (2019). RNA sequencing analysis of salt tolerance in soybean (Glycine max). Genomics , 111 (4), 629–635. Zhuang, Y., Li, X., Hu, J., Xu, R., & Zhang, D. (2022). Expanding the gene pool for soybean improvement with its wild relatives. Abiotech, 3 (2), 115–125. https://doi.org/10.1007/s42994-022-00072-7 Zuffo, A. M., Steiner, F., Aguilera, J. G., Teodoro, P. E., Teodoro, L. P. R., & Busch, A. (2020). Multi-trait stability index: A tool for simultaneous selection of soya bean genotypes in drought and saline stress. Journal of Agronomy and Crop Science, 206 (6), 815–822. https://doi.org/10.1111/jac.12409 Additional Declarations No competing interests reported. Supplementary Files SupplementalTableS1.xlsx Supplemental Table S1: Soybean core collection genotypes used for the study. SupplementalTableS2.xlsx Supplemental Table S2: Mean data of the ten quantitative traits at 0mM, 100mM, 150mM, and 200mM NaCl at E1. SupplementalTableS3.xlsx Supplemental Table S3: Mean data of the ten quantitative traits at 0mM, 100mM, 150mM, and 200mM NaCl at E2. SupplementalTableS4S5S6.xlsx Supplemental Table S4: ANOVA analysis of germination percentage in E1 at 0mM, 100mM, 150mM, and 200mM NaCl. Supplemental Table S5: ANOVA analysis of germination percentage in E2 at 0mM, 100mM, 150mM, and 200mM NaCl. Supplemental Table S6: Eigenvalues and eigenvectors of the quantitative traits at 100mM and 200mM NaCl at E1 and E2 SupplementalDocumentS7.docx Supplemental Document S7: Supplemental figures 1 (correlation matrix) and 2 (boxplot). Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 23 Jun, 2024 Reviews received at journal 22 Jun, 2024 Reviewers agreed at journal 13 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers invited by journal 10 Jun, 2024 Editor assigned by journal 10 Jun, 2024 Submission checks completed at journal 10 Jun, 2024 First submitted to journal 10 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4558107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317848771,"identity":"d9bfc6a8-1874-450f-bcff-f4782356c9a7","order_by":0,"name":"Aditya Gobade","email":"","orcid":"","institution":"Rajarshri Shahu Mahavidyalaya (Autonomous)","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Gobade","suffix":""},{"id":317848773,"identity":"86a9ef98-cd32-4894-998f-4293561957cb","order_by":1,"name":"Arathi S","email":"","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Arathi","middleName":"","lastName":"S","suffix":""},{"id":317848775,"identity":"e1060a60-e0cf-42e0-a63d-971f354ee1ad","order_by":2,"name":"Shreyash Gijare","email":"","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shreyash","middleName":"","lastName":"Gijare","suffix":""},{"id":317848776,"identity":"e4245346-4403-4438-bdae-a955cbc24db6","order_by":3,"name":"Deepak Pawar","email":"","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Pawar","suffix":""},{"id":317848777,"identity":"12f794ae-bfa2-4a27-9282-b5552fe0a0aa","order_by":4,"name":"Abhinandan Patil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYLCChAIbBgZmEjQwNiQYpJGqhcHgMAnq+Wc3P3/wwOC8vHk77zHpggoGOfP+Bfi1SNw5Zgh02G3DOYf50qRnnGEwlrnxgIA1NxLAWhhnMPOYSfO2MSTOkDiAX4f8jfSPQC3n7CFa/hGhxeBGDsiWA4kQLQ1ALfwN+LUY3jlTOCPBIDl5BjNfsjXPMQljCQkCXpG73b7h448KO9sZ/GcP3uapsZGT4CfgMAaEmTxQrkQCaVqAgKAto2AUjIJRMNIAAL6DPpSFJM6sAAAAAElFTkSuQmCC","orcid":"","institution":"Agharkar Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Abhinandan","middleName":"","lastName":"Patil","suffix":""}],"badges":[],"createdAt":"2024-06-10 12:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4558107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4558107/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02081-5","type":"published","date":"2024-08-02T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59126437,"identity":"69bd75cd-8414-409c-b6f4-91048e7f7a91","added_by":"auto","created_at":"2024-06-26 15:43:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4074599,"visible":true,"origin":"","legend":"\u003cp\u003eRollerd germination papers placed in different concentrations of NaCl solution (a) on 5th DAS (day after sowing) and (b) on 8th DAS (day after sowing).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/04b1cdf5edd4a19443f93d2d.png"},{"id":59125997,"identity":"a81c18d2-d7c1-4794-8b5c-551ebd3c366d","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82289,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot and K-means non-hierarchical cluster analysis of the ten quantitative traits at 100mM and 200mM NaCl in E1 and E2 on 8th DAS (day after sowing).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/7fb6e363ff09c43d05b99f1a.png"},{"id":59125999,"identity":"63ddb46e-d7c4-4b81-94a1-85f2879127fa","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":373826,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map showing Germination percentage at all salt concentrations in E1 and E2: (a) heat map of 198 genotypes, (b) heat map of the salt-sensitive genotypes, (c) heat map of the salt-tolerant genotypes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/16e0027d4881c6591bfc86aa.png"},{"id":59126005,"identity":"5dd240fc-49d8-4af4-9646-28e05b41973f","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5867408,"visible":true,"origin":"","legend":"\u003cp\u003eSalt-sensitive genotypes EC 241920, EC 391181 and HIMSO 1563 at different NaCl concentrations at five (a, c) and eight (b, d) days after sowing (DAS) in E1 and E2.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/db918a76fae969c817557384.png"},{"id":59126438,"identity":"3e075052-1904-43c9-b654-cf7d9b5c878a","added_by":"auto","created_at":"2024-06-26 15:43:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":958171,"visible":true,"origin":"","legend":"\u003cp\u003eSalt-tolerant genotypes IC 13050, KALITUR, MACS 173, MACS 708, MACS 1010, MACS 1037 and PK 1029 at different NaCl concentrations at five (a, c) and eight (b, d) days after sowing (DAS) in E1 and E2.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/485b653bd9654f9c9cd90ff0.png"},{"id":61793431,"identity":"0a7768ff-fb57-4273-83a3-cca213eb1f7f","added_by":"auto","created_at":"2024-08-05 16:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15519707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/e4eb9774-8643-470d-90cf-5161d3f7dfe0.pdf"},{"id":59126436,"identity":"81b736e0-b779-40c7-9a79-0cd77f76eb6b","added_by":"auto","created_at":"2024-06-26 15:43:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15326,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S1: Soybean core collection genotypes used for the study.\u003c/p\u003e","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/201260bb64d39e16c62156bf.xlsx"},{"id":59126001,"identity":"aab6a52e-dbf0-4dc0-9334-292174f8701f","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":83118,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S2: Mean data of the ten quantitative traits at 0mM, 100mM, 150mM, and 200mM NaCl at E1.\u003c/p\u003e","description":"","filename":"SupplementalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/affdb1f1957f31d1fb3cc0d1.xlsx"},{"id":59126000,"identity":"b5667e57-25ce-43bd-9996-e0ec7900e91c","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":86059,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S3: Mean data of the ten quantitative traits at 0mM, 100mM, 150mM, and 200mM NaCl at E2.\u003c/p\u003e","description":"","filename":"SupplementalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/9ad4d717b99089a766c49fd6.xlsx"},{"id":59126003,"identity":"e914ff87-c441-4433-b603-4b6d4a2e3278","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13497,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S4: ANOVA analysis of germination percentage in E1 at 0mM, 100mM, 150mM, and 200mM NaCl.\u003c/p\u003e\n\u003cp\u003eSupplemental Table S5: ANOVA analysis of germination percentage in E2 at 0mM, 100mM, 150mM, and 200mM NaCl.\u003c/p\u003e\n\u003cp\u003eSupplemental Table S6: Eigenvalues and eigenvectors of the quantitative traits at 100mM and 200mM NaCl at E1 and E2\u003c/p\u003e","description":"","filename":"SupplementalTableS4S5S6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/01053734ee0781b1c94c35f4.xlsx"},{"id":59126006,"identity":"828579b7-b8fb-4958-b18b-f77e4da5ff8f","added_by":"auto","created_at":"2024-06-26 15:35:47","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":209565,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Document S7: Supplemental figures 1 (correlation matrix) and 2 (boxplot).\u003c/p\u003e","description":"","filename":"SupplementalDocumentS7.docx","url":"https://assets-eu.researchsquare.com/files/rs-4558107/v1/51daefdde65c902595cb2c9b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating salt tolerance in soybean core collection: germination response under salinity stress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe genus \u003cem\u003eGlycine\u003c/em\u003e of the Fabaceae family comprises wild soybean (\u003cem\u003eGlycine soja\u003c/em\u003e), semi-wild soybean (\u003cem\u003eGlycine gracilis\u003c/em\u003e), and cultivated soybean (\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.). Cultivated soybean, known as the \u0026ldquo;golden miracle bean\u0026rdquo;, is a rich source of protein and oil. It is widely used as food, feed, and biofuel and is one of India's important oilseed cash crops (Kumar et al. 2022). Traits like seed oil content, yield, and protein significantly contribute to the economic value of soybeans. Major soybean-producing countries include India, the USA, Argentina, and Brazil. In India, soybeans contribute 42% to total oilseed production and 22% to total oil production (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iisrindore.icar.gov.in/readmore.html\u003c/span\u003e\u003cspan address=\"https://iisrindore.icar.gov.in/readmore.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). However, the genetic gain rate of soybean yield in Brazil and Argentina is higher compared to the USA, China, Canada, and India (Umburanas et al. 2022). Although \u003cem\u003eGlycine max\u003c/em\u003e was domesticated from wild soybean varieties approximately 6000\u0026ndash;9000 years ago, it exhibits lower genetic diversity than its wild relatives. Genetic bottlenecks and artificial selection may have contributed to this reduced diversity and potential loss of beneficial genes necessary for environmental adaptation. Wild soybeans (\u003cem\u003eGlycine soja\u003c/em\u003e) possess many beneficial alleles that can be introduced to domesticated soybeans to improve their adaptation to changing environments (Zhuang et al. 2022).\u003c/p\u003e \u003cp\u003eIn the current era of climate change, two significant abiotic stresses, drought and salinity (pH 7 to 8.5), profoundly impact crop plants' germination, growth, and yield. These conditions limit the biological, molecular, and physiological systems of plants. Salinity induces the generation of Reactive Oxygen Species (ROS) in plants, such as hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), superoxide radical (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), and hydroxyl radical (OH\u003csup\u003e\u0026minus;\u003c/sup\u003e). This leads to protein degradation, lipid peroxidation, enzyme inactivation, alteration in gene expression, antioxidant deprivation, and disruption of metabolic pathways (Choudhury et al. 2013; Begum et al. 2022; Alizadeh et al. 2024). Sodium chloride (NaCl) -induced salinity causes both osmotic and ionic stress, with osmotic stress occurring quickly and ionic stress resulting from the accumulation of sodium (Na\u003csup\u003e+\u003c/sup\u003e) and chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) ions (Cho et al. 2021).\u003c/p\u003e \u003cp\u003eSoybean is a salt-sensitive glycophyte, which can impair seed germination, seedling growth, and overall plant development (Rasheed et al. 2022). Germination is a critical phase in a plant\u0026rsquo;s life cycle, and its successful completion under saline conditions is essential for crop establishment, as higher salt levels can inhibit water absorption by seeds. In comparison, lower levels induce seed dormancy (Zuffo et al. 2020). Evaluating the germination response of soybean genotypes under salinity stress is a crucial step in identifying and developing salt-tolerant varieties. Salt tolerance varies at different developmental stages (Guan et al. 2023). Understanding the mechanisms underlying salinity tolerance during germination can provide insights into breeding strategies and genetic modifications to enhance salt tolerance.\u003c/p\u003e \u003cp\u003eMuch of the agricultural land has become saline due to irrigation with saline water and poor soil management, which necessitates the development of salt-tolerant soybean genotypes to sustain and enhance crop production in affected areas (Xu et al. 2011). Wild soybeans are more salt-tolerant than domesticated varieties (Cao et al. 2016; Hou et al. 2022). As salt tolerance is a polygenic trait, Quantitative Trait Loci (QTL) or genes from wild soybeans can be introduced into cultivated soybeans to improve their salt tolerance. This approach can also facilitate marker-assisted selection in breeding salt-tolerant soybeans, which is crucial for developing genotypes that can germinate and survive under various stress conditions (Begum et al. 2022). Identifying genomic regions responsible for salinity tolerance and using QTL mapping are essential steps in developing salt-tolerant soybean genotypes (Rasheed et al. 2022).\u003c/p\u003e \u003cp\u003ePrevious research has demonstrated variability in salinity tolerance among soybean cultivars. For instance, Essa (2002) used three soybean cultivars- Lee, Coquitt, and Clark 63- to determine the effect of soil salinity on seed germination, plant growth, and leaf mineral content under natural conditions. The results revealed a reduction in germination percentage with increasing salinity levels, with Lee being the most tolerant and Clark 63 the most susceptible cultivar. Similarly, research on D8 and D140 cultivars indicated a reduction in plant height, number of leaves, leaf area index, and shoot and root biomass after 100 millimolar (mM) NaCl treatment, identifying D140 as having better salt tolerance at 100 mM NaCl concentration (Linh et al. 2021). Many of the earlier research studies have examined the salinity tolerance of soybean genotypes during the germination stage. However, some of these studies only included a small number of genotypes, while others utilized low amounts of NaCl. For example, Rahman et al. (2021) assessed the performance of only 20 different types of soybean plants at a concentration of 50 mM NaCl. In contrast, Anwar et al. (2016) examined 30 different types of soybean plants under a concentration of 75 mM NaCl. In India, similar screening experiments utilized low salt concentrations and fewer genotypes (Singh et al. 2019). Although significant efforts have been made, there is still a considerable opportunity to uncover donor genes or genetic resources from numerous soybean accessions that have the potential to aid in the development of salt-tolerant cultivars.\u003c/p\u003e \u003cp\u003eKey salt-tolerant genes have been discovered, aiding our understanding of the mechanisms behind salt tolerance. Specifically, \u003cem\u003eGmSALT3\u003c/em\u003e, \u003cem\u003eGmCHX\u003c/em\u003e, and \u003cem\u003eGsERD15B\u003c/em\u003e were identified as salt-tolerance genes during the seedling stage. Additionally, \u003cem\u003eGmCDF1\u003c/em\u003e was found to be active during both germination and the seedling stages (Guan et al. 2023). To identify the specific regions of the soybean genome that are linked to salt tolerance during the germination stage, QTL mapping was conducted using a recombinant inbred population of Kefeng No. 1 and Nannong 1138\u0026ndash;2 and the identified QTLs were found to be located on chromosome 8 (Leung et al. 2023).\u003c/p\u003e \u003cp\u003eThis research investigates the germination response of various soybean genotypes under salinity stress. Specifically, the impact of salinity on the seed germination of 198 soybean genotypes at NaCl concentrations of 100 mM, 150 mM, and 200 mM was assessed. By examining the effects of different salinity levels on germination rates, seedling vigor, and other related parameters, we seek to identify genotypes that exhibit superior tolerance to salinity during the germination stage. By identifying genotypes that exhibit superior tolerance to salinity during the germination stage, this study will contribute to the broader efforts of improving soybean resilience to salinity, ensuring stable production and food security in regions affected by soil salinization.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and experimental design\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe used 198 diverse soybean genotypes from the core collection to evaluate their salt tolerance during germination. These accession seeds were multiplied at two different locations during the regular \u003cem\u003eKharif\u003c/em\u003e (July-Nov) season of 2023 under irrigated conditions with standard agronomic practices: -Experiment 1 (E1): Hol farm (8.5204\u0026deg; N, 73.8567\u0026deg; E) and Experiment 2 (E2): Soangaon farm (17.6444\u0026deg; N, 73.9910\u0026deg; E). Both locations were chosen to assess the impact of different environmental conditions on the germination of soybeans under salinity stress. These soybean accessions belonged to different maturity groups - Early Maturing (EM), Mid-Maturing (MM), Mid-Late (ML), Promising Collection (PC), and Farmer\u0026rsquo;s Collection (FC). The list of the 198 genotypes taken for the study is provided in Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The experiments were performed in a completely randomized design with three replicates per treatment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSalinity Treatments\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNaCl concentrations were selected to create varying levels of salinity stress treatments and to monitor the reaction of soybean seed germination. We used plastic trays containing different quantities of Sodium Chloride (NaCl): (a) 0 mM NaCl (Control), (b) 100 mM NaCl, (c) 150 mM NaCl, and (d) 200 mM NaCl. Distilled water was used as the control to compare with salt-stressed conditions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSeed preparation and germination conditions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom each accession, 72 healthy and uniform seeds were randomly chosen from E1 and E2 locations. The seeds were surface-sterilized using 0.2% Sodium hypochlorite solution, Hi-AR\u0026trade;/ACS grade, 4% (w/v), provided by HiMedia (Catalog number: AS102-12) for one minute and washed three times with distilled water. The sterilized seeds were then placed in a uniform pattern on moist germination papers. The germination papers used were made of high-quality brown absorbent paper exclusively for seed germination examinations. It has a standard size of 45\u0026times;28cm with an essential weight of 125 g/m\u003csup\u003e2\u003c/sup\u003e. Its parameters include a bursting strength of 25 kg/m\u003csup\u003e2\u003c/sup\u003e, a capillary rise rate of 38 mm/minute, a neutral pH of 7.0, and a maximum ash content of 0.49%, ensuring ideal circumstances for accurate germination tests. Then, each germination paper was rolled around 3\u0026thinsp;\u0026minus;\u0026thinsp;4 cm along the left and bottom edges. It was then firmly rolled from the bottom to the top, with each roll neatly labeled according to the treatment and genotype. These rolls were then placed vertically in respective plastic trays for germination. The trays were labeled to indicate the treatment conditions and then put in the polyhouse at 25\u0026deg;C for germination. All the trays were replenished with fresh water and NaCl salt concentrations every three days to maintain consistency. In the rolled germination papers, each accession seed was placed in plastic trays containing the respective NaCl solutions, ensuring that each treatment was consistent across replicates and locations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSeed germination and seedling vigor assessment under salinity stress\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSeed germination observations were made on the eighth day of germination. Seeds were categorized based on germination patterns into hard, germinated, diseased, and rotten seeds (Singh, 2019). The final data recorded ten quantitative traits like germination percentage (GP, %), seedling fresh weight (FW, g), seedling dry weight (DW, g), seedling length (SLE, cm), shoot length (SL, cm), root length (RL, cm), seedling vigor index-1 (VI1), seedling vigor index-2 (VI2), seedling water content (SW, %), and salt tolerance (ST, %) of the 198 soybean genotypes grown at different NaCl levels (Control, 100 mM, 150 mM, and 200 mM) in experiments E1 and E2.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe germination percentage was determined on the eighth day using the following formula: Germination Percentage (%) = (Number of germinated seeds) / (Total number of seeds) \u0026times; 100 (Kumar et al. 2019). We categorized mean seed germination into four categories based on their response to salinity stress. The first category, tolerant, includes genotypes with a germination percentage greater than 80%, indicating high tolerance to salinity stress and robust growth even under saline conditions. The second category, moderately tolerant, comprises genotypes with a 60\u0026ndash;80% germination percentage. These genotypes show moderate tolerance to salinity stress, with slightly lower germination rates than the tolerant group but still maintaining relatively good growth. The third category, moderately susceptible, encompasses genotypes with a germination percentage between 40% and 60%, suggesting a mild impact of salinity on their growth. Lastly, the susceptible category includes genotypes with a germination percentage below 40%, where salinity stress significantly impacts their growth, resulting in poor germination rates and reduced overall growth under saline conditions (Mannan et al. 2012; Wu et al. 2019).\u003c/p\u003e \u003cp\u003eWe compared the mean germination percentage below 40% in the 100 mM NaCl treatment to identify the salt-sensitive genotypes common in E1 and E2. For the salt-tolerant genotypes, we looked for a mean germination percentage above 80% in the 200 mM NaCl treatment, which was common in E1 and E2.\u003c/p\u003e \u003cp\u003eWe evaluated the seedling vigor Index-1 using the formula (Seedling length \u0026times; Germination percentage) / 100 to obtain comprehensive insights into the seedlings' growth potential and treatment effects. Additionally, Vigor Index-2 was calculated using the formula (Seedling dry weight \u0026times; Germination percentage) / 100, focusing on biomass accumulation. To determine dry weight, the seedlings were dried in an oven at 80\u0026deg;C for 24 hours after the eighth day of germination (Kharb et al. 1994). The percentage of Seedling Water Content was determined in a seedling by subtracting the Dry Weight from the Fresh Weight, dividing by the Fresh Weight, and multiplying by 100 (Pavli et al. 2021). Salt Tolerance (%) is computed by dividing the germination in treated seedlings by the germination in control seedlings, multiplying the result by 100 (El Sabagh et al. 2015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eMean data were analyzed using one-way analysis of variance (ANOVA), followed by Tukey's post-hoc test at a confidence level of \u0026gt;\u0026thinsp;95%. The study was conducted using an online web statistical calculator: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://astatsa.com/OneWay_Anova_with_TukeyHSD\u003c/span\u003e\u003cspan address=\"https://astatsa.com/OneWay_Anova_with_TukeyHSD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Boxplots of the GP of seeds in experiments E1 and E2 at different salt concentrations were generated using the online application called BoxPlotR, a web tool for creating box plots (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://shiny.chemgrid.org/boxplotr/\u003c/span\u003e\u003cspan address=\"http://shiny.chemgrid.org/boxplotr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Spitzer et al., 2014). A heat map for the GP of all 198 seeds in experiments E1 and E2 at different salinity levels was created using the online tool Heatmapper (Babicki et al., 2016) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.heatmapper.ca/expression/\u003c/span\u003e\u003cspan address=\"http://www.heatmapper.ca/expression/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Heat maps for the GP of salt-sensitive and salt-tolerant genotypes were created using the \u0026lsquo;Heat map\u0026rsquo; package of the R program. Principal component analysis (PCA), K-means clustering, and creation of the correlation matrix for the quantitative characteristics of seeds in experiments E1 and E2 were performed using the trial version of JMP 17 (SAS Institute Inc., 2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSeed germination performance across salinity gradients\u003c/h2\u003e \u003cp\u003eThe average data for germination and the other nine growth parameters exhibited higher means in the control group compared to the NaCl-treated groups (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplemental Tables S2 and S3). Notably, the germination process of seeds placed in the control group progressed more rapidly than those subjected to salt treatments. This observation is further illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e, depicting the seed germination on the fifth and eighth days after sowing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for different quantitative traits of 198 soybean genotypes grown at different NaCl salinity levels in E1 and E2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistical parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c13\" namest=\"c4\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGermination Percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFresh Weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDry Weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeedling Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShoot Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRoot Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVigor Index-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVigor Index-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSeedling Water Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSalt Tolerance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0-7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0-23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0-17.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0-10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e100mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0-4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0-14.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0-9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0-38.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-43.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e57.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e78.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e150mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0-4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0-10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0-6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0-3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-31.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-98.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e71.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e200mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0-3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0-5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0-4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-12.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0-114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e43.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e64.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e100mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e150mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e200mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u0026ndash;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean seed germination percentage was generally higher in the E2 location compared to the E1 location, except for the 150 mM treatment in E2. In both locations, seed germination ranged from 0 to 100 percent. However, the mean values for FW and DW, SLE, SL, and vigor index 1 and 2 were higher in the E1 location than in E2. Additionally, we observed that RL was higher in E2 for both the control and the 150 mM treatment. Furthermore, the SW was higher in E2 for all three treatments except for the control. Moreover, the mean ST was higher in E2 than in E1 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides comprehensive information on the response of soybean genotypes to different levels of salinity stress and insights into their growth parameters, performance, and tolerance levels under various conditions.\u003c/p\u003e \u003cp\u003eFor E1, a one-way ANOVA with a post-hoc Tukey's HSD test revealed a p-value lower than 0.05, suggesting that one or more treatment pairs differed significantly. The treatment pairs of control vs 150 mM and control vs 200 mM had a p-value of less than 0.05, while the treatment pair control vs 100 mM was insignificant (Supplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For E2, a one-way ANOVA with a post-hoc Tukey's HSD test revealed a p-value lower than 0.05, strongly suggesting that one or more treatment pairs are significantly different. Supplemental Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e indicated that the treatment pairs of control vs 100 mM, control vs 150 mM, and control vs 200 mM have a p-value less than 0.05.\u003c/p\u003e \u003cp\u003eThe experiments demonstrated that salinity stress negatively affects soybean seedlings' germination and early growth. The impact varies depending on the salinity level, with higher concentrations generally leading to reduced growth metrics such as GP, FW, DW, SLE, SL, and RL. While E1 showed some resilience at 150 mM NaCl regarding GP, most growth parameters decreased at higher salinity levels in both experiments. ST decreased with increasing NaCl concentration, highlighting the sensitivity of soybean seedlings to salinity stress. This discovery emphasizes the susceptibility of soybean plants to high amounts of salt, underscoring the significance of developing salt tolerance in soybean breeding programs and agricultural methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePCA and Cluster Analysis under salinity conditions\u003c/h2\u003e \u003cp\u003ePCA was used to analyze the variation in salt tolerance among 198 soybean core collection genotypes. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the PCA biplot and K-means clustering for E1 and E2 under 100 mM and 200 mM NaCl concentrations. Black dots represent samples, and red arrows represent variable loadings on the principal components (PCs). The estimation of the contribution of each PC to the total variance is determined by the eigenvectors associated with each PC. K-means non-hierarchical cluster analysis classified the 198 genotypes into four groups based on salt tolerance (tolerant, moderately tolerant, moderately susceptible, and susceptible), and is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e, cluster one is red, two is green, three is blue, and four is light brown. Clusters varied by salinity level in E1 and E2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eK-means clustering of the genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaCl concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003cp\u003e(Red color)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003cp\u003e2 (Green color)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCluster 3\u003c/p\u003e \u003cp\u003e(Blue color)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCluster 4\u003c/p\u003e \u003cp\u003e(Light brown color)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFigure no.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (b)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e200mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (d)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (f)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e200mM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (h)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ++ is tolerant, + is moderately tolerant, - is moderately susceptible and -- is susceptible\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn E1, the first two components explained 77.7% and 73.5% of the variation for 100 mM and 200 mM treatments, respectively. For 100 mM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a)), Component 1 accounted for 63.7%, with SLE, VI1, and ST having strong positive influences. At 100 mM NaCl concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (b)), the genotypes were classified into Cluster 3 (71 genotypes), Cluster 1 (66), Cluster 4 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and Cluster 2 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). For 200 mM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (c)), Component 1 accounted for 61.9%, with GP, VI2, and DW having strong influences. At 200 mM NaCl concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (d)), the genotypes were classified into Cluster 2 (77 genotypes), Cluster 4 (65), Cluster 1 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and Cluster 3 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn E2, the first two components explained about 75% (100 mM) and 70.6% (200 mM) of the variation, with VI2 ST and SW strongly influencing Component 1 at 100 mM, and SL, RL, SW, FW, and SLE at 200 mM. For 100mM, Component 1 accounted for 61.8%, the second component for 13.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (e)), and the genotypes were clustered into Cluster 3 (103 genotypes), Cluster 1 (44), Cluster 2 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), and Cluster 4 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (f)). The first and second components accounted for 57.1% and 13.5%, respectively, at 200 mM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (g)). A noticeable spread of genotypes (data points) along Component 2 indicated varied responses to the 200 mM treatment. The genotypes were clustered into Cluster 3 (83 genotypes), Cluster 4 (82), Cluster 2 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and Cluster 1 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e (h)).\u003c/p\u003e \u003cp\u003eIn both E1 and E2, the influence of specific traits varied with salt concentration, indicating different dominant traits under different conditions. Genotypes were more tightly clustered at 100 mM, showing consistent responses, while 200 mM treatments showed more dispersion, indicating increased variability. From Supplemental Table S6, it can be found that all the variables in PC1 have a positive correlation between them at both 100 mM and 200 mM NaCl concentrations in both E1 and E2. The negative values in PC2 are not significant.\u003c/p\u003e \u003cp\u003eThe PCA results reveal that specific traits are crucial in distinguishing genotypes under varying salt concentrations. In both experiments, PCA showed that the eigenvalues and the percentage of variance explained by GP, FW, and DW significantly contributed to the variability captured by PC1 and PC2, with each trait contributing over 9%. This underscores these traits as dominant factors in the overall variability. Additionally, the eigenvectors provided insight into how each variable contributes to these factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplemental Table S6).\u003c/p\u003e \u003cp\u003eCluster positions shifted with increasing salinity, reflecting changes in tolerance levels of the genotypes. When tested at the same concentrations, the differences between E1 and E2 emphasized the influence of experimental conditions on the clustering patterns and, hence, the salinity tolerance. Common genotypes identified as salt-sensitive and salt-tolerant at both 100 mM and 200 mM NaCl concentrations are listed in Tables\u0026nbsp;5 and 6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHeat map of Germination Percentage under salinity conditions\u003c/h2\u003e \u003cp\u003eA heat map was created to understand the GP pattern of the soybean seeds across different salt\u0026ndash;stress environments and in a controlled environment. The heat map in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e (a) helped visualize the variations in the germination percentage of seeds between the two experiments and under the conditions applied in each experiment. Blue indicates a lower germination percentage, while orange and yellow indicate higher percentages. In E1, gradient colors can be observed in E1-C (0 mM/control) and E1-100 (100 mM NaCl), depicting a range of variations in the GP within the control and treatment (100 mM) groups. At the same time, the 150 mM salt treatment group has a higher germination percentage compared to the 200 mM treated group, which has a lower germination percentage.\u003c/p\u003e \u003cp\u003eIn E2, gradients of the yellow color can be observed in E2-C (0 mM/control), E2-100 (100 mM NaCl), and E2-200 (200 mM NaCl) treated groups, revealing the range of variations in the GP within the groups. When comparing the GP of seeds treated with 150 mM NaCl to the other treatment groups and the control group, it was seen that most of the seeds in those groups had a higher germination percentage. However, only half of the seed sets in the 150 mM group exhibited high germination percentages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation matrix and box plot under saline conditions\u003c/h2\u003e \u003cp\u003eA Correlation Matrix was constructed using the ten quantitative traits at 100 mM and 200 mM in both E1 and E2 (Supplemental Fig.\u0026nbsp;1 in supplemental document S5), with (a) and (b) representing the traits at 100 mM NaCl concentration in E1 and E2, respectively and (c) and (d) representing the traits at 200 mM NaCl concentration in E1 and E2, respectively. The different colors indicate the strength of the correlation: closer to red indicates a strong positive correlation, and closer to blue indicates a strong negative correlation.\u003c/p\u003e \u003cp\u003eWhen comparing the quantitative traits of the seeds germinated in E1 (a) and E2 (c) at 100 mM NaCl concentration, it can be seen that GP, FW, DW, SLE, SL, RL, VI1, and VI2 were positively correlated with each other. ST had a mild positive correlation with all the traits other than VI1 in E1. DW had a mild negative correlation with SLE. Also, SLE, SL, and RL have a mild negative correlation. At 200 mM NaCl concentration, all traits were negatively correlated with ST, except VI1. All other traits had a strong positive correlation in E1 (b). In E2 (d), GP, FW, DW, and SL had a negative correlation. Similarly, SL was negatively correlated with RL, VI1, VI2, SW, and ST. All other traits had mostly positive correlations with each other. It can be seen from Supplemental Fig.\u0026nbsp;1 that across all conditions, GP, FW, and DW showed strong positive correlations with each other. This indicates that a higher germination percentage can be associated with better growth metrics (FW and DW). Vigor index (VI1 and VI2) consistently showed strong positive correlations with GP and FW, making it a reliable indicator of seedling health. Though the correlation tends to weaken when salinity increases from 100 mM to 200 mM, E2 showed a better correlation than E1. The figure also reveals that some key growth parameters were closely linked across different salinity levels and experimental conditions. Strong positive correlations suggested that improvement in one parameter often accompanies improvements in others, highlighting the interconnected nature of seedling growth metrics. Under higher salinity, correlations weaken, indicating the impact of salinity stress.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe germination percentages of seeds at different salt concentrations in E1 and E2 were compared using a boxplot (Supplemental Fig.\u0026nbsp;2 in supplemental document S5). In the figure, the y-axis represents the range of GP, while the x-axis represents the GP of the seeds at E1 - control (E1-C), 100 mM NaCl concentration (E1-100), 150 mM NaCl concentration (E1-150) and 200 mM NaCl concentration (E1-200) \u0026ndash; and E2 - control (E2-C), 100 mM NaCl concentration (E2-100), 150 mM NaCl concentration (E2-150) and 200 mM NaCl concentration (E2-200). It was also revealed that E1-C and E2-C (control conditions) have high germination percentages, indicating good germination without salinity stress. It can also be seen that E2 consistently showed higher median germination percentages and narrower IQR (Inter-Quartile Range) when compared to E1 across all NaCl concentrations. This indicates that seeds under E2 conditions are more tolerant to salinity stress than those under E1. As NaCl concentration increases, GP generally decreases, with E1 showing a more significant decline than E2, as is evident from the drop in median germination.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of salt-sensitive genotypes at a salt (NaCl) stress of 100 mM\u003c/h2\u003e \u003cp\u003eCommon soybean genotypes from E1 and E2 were classified into four categories (tolerant, moderately tolerant, moderately susceptible, and susceptible) based on their germination response to \u0026le;\u0026thinsp;40% for 100 mM NaCl stress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) common to E1 and E2. HIMSO 1563, EC 391181, and EC 241920 were categorized as susceptible due to their poor growth and reduced germination under saline conditions. This classification helps identify genotypes that may benefit from additional breeding efforts and management strategies to improve their salt tolerance and overall performance in saline environments. The heat map in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b) illustrates the germination of these genotypes on the eight days after sowing under various conditions: control, 100 mM, 150 mM, and 200 mM NaCl in E1 and E2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCategorization of 40 soybean genotypes based on Germination Percentage at 100 mM NaCl solution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;80%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026ndash;60%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTolerance category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTolerant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately tolerant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately Susceptible\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSusceptible\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLSO 23\u003c/p\u003e \u003cp\u003eMACS 985\u003c/p\u003e \u003cp\u003eEC 14426\u003c/p\u003e \u003cp\u003ePK 1029\u003c/p\u003e \u003cp\u003eMACS 1168\u003c/p\u003e \u003cp\u003eEC 7951\u003c/p\u003e \u003cp\u003eIC 202\u003c/p\u003e \u003cp\u003eCAT II47\u003c/p\u003e \u003cp\u003eMACS 1037\u003c/p\u003e \u003cp\u003eMACS 1281\u003c/p\u003e \u003cp\u003eJS 9560\u003c/p\u003e \u003cp\u003eHILL\u003c/p\u003e \u003cp\u003eIC 33776\u003c/p\u003e \u003cp\u003eIC 13050\u003c/p\u003e \u003cp\u003eEC 100800\u003c/p\u003e \u003cp\u003eJS 9971\u003c/p\u003e \u003cp\u003eMACS 96\u003c/p\u003e \u003cp\u003eMACS 199\u003c/p\u003e \u003cp\u003eJS 75 1\u003c/p\u003e \u003cp\u003eEC 100022\u003c/p\u003e \u003cp\u003eJS 72280\u003c/p\u003e \u003cp\u003eJS 8021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC 100027\u003c/p\u003e \u003cp\u003eMACS 37\u003c/p\u003e \u003cp\u003eMACS 136\u003c/p\u003e \u003cp\u003eG 118\u003c/p\u003e \u003cp\u003eEC 251470\u003c/p\u003e \u003cp\u003eIC 9451\u003c/p\u003e \u003cp\u003eNRC 147\u003c/p\u003e \u003cp\u003eMACS 57\u003c/p\u003e \u003cp\u003ePUNJAB 1\u003c/p\u003e \u003cp\u003eVLC 86\u003c/p\u003e \u003cp\u003eKDS 992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEC 14477\u003c/p\u003e \u003cp\u003eJS 72 451\u003c/p\u003e \u003cp\u003eMACS 472\u003c/p\u003e \u003cp\u003eTS 213 (PRIVATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHIMSO 1563\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eEC 391181\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eEC 241920\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of salt-tolerant genotypes at a salt (NaCl) stress of 200 mM\u003c/h2\u003e \u003cp\u003eSoybean genotypes from E1 and E2 were similarly classified into four categories based on their response to 200 mM NaCl stress (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Seven genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173) were identified as tolerant, demonstrating robust growth and germination percentages\u0026thinsp;\u0026ge;\u0026thinsp;80% at 200 mM NaCl, indicating their high tolerance to salinity stress. The heat map in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c) depicts the germination of these genotypes on the eight days after sowing under different salt conditions: control, 100 mM, 150 mM, and 200 mM NaCl in E1 and E2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCategorization of 28 soybean genotypes based on Germination Percentage at 200mM NaCl solution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;80%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026ndash;60%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTolerance category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTolerant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately tolerant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately Susceptible\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSusceptible\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMACS 708\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eKALITUR\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMACS 1037\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eIC 13050\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMACS 1010\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePK 1029\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMACS 173\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC 100027\u003c/p\u003e \u003cp\u003eMACS 37\u003c/p\u003e \u003cp\u003eMACS 136\u003c/p\u003e \u003cp\u003eG 118\u003c/p\u003e \u003cp\u003eEC 251470\u003c/p\u003e \u003cp\u003eIC 9451\u003c/p\u003e \u003cp\u003eNRC 147\u003c/p\u003e \u003cp\u003eMACS 57\u003c/p\u003e \u003cp\u003ePUNJAB 1\u003c/p\u003e \u003cp\u003eVLC 86\u003c/p\u003e \u003cp\u003eKDS 992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEC 14477\u003c/p\u003e \u003cp\u003ePLSO 39\u003c/p\u003e \u003cp\u003eJS 72 451\u003c/p\u003e \u003cp\u003eMACS 472\u003c/p\u003e \u003cp\u003eEC 95807\u003c/p\u003e \u003cp\u003eTS 213 (PRIVATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMACS NRC 1667\u003c/p\u003e \u003cp\u003eEC 18207\u003c/p\u003e \u003cp\u003eIC 13048\u003c/p\u003e \u003cp\u003eHIMSO 1563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study evaluated salt tolerance in a soybean core collection by assessing germination response under salinity stress. Our findings revealed significant variability in salt tolerance among the 198 soybean accessions, with some lines exhibiting robust germination rates despite high salinity levels. This study identified three salt-sensitive (HIMSO 1563, EC 391181, and EC 241920) and seven salt-tolerant genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173). These results align with previous studies highlighting genetic diversity in soybean salt tolerance and suggest the potential for breeding programs to develop salt-tolerant varieties. The observed variability underscores the importance of identifying and utilizing salt-tolerant genotypes to enhance soybean productivity in saline-prone regions. This study provides a foundation for further research into the genetic and physiological mechanisms underlying salt tolerance in soybeans, offering practical insights for improving crop resilience in challenging environments.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffect of Salinity on Seed Germination and Early Growth\u003c/h2\u003e \u003cp\u003eThe study revealed the detrimental effects of salinity stress on seed germination and early growth parameters of soybean genotypes. The slower germination progression in NaCl-treated groups, when compared to the control group, underscores the inhibitory impact of salinity on seedling establishment. This finding aligns with previous research, indicating that higher salt concentrations in the soil can impact water uptake by seeds, thus delaying their germination and reducing seedling vigor (Zuffo et al. 2020; A\u0026ccedil;ıkbaş et al. 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVariation in Seed Germination and Growth Metrics between Locations\u003c/h2\u003e \u003cp\u003eSeeds under saline conditions has potential salt tolerance at germination stage, but does not guarantee that salinity stress will not impact other life cycle phases (Miransari 2016; Zuffo et al. 2020; Guan et al. 2023). In soybeans, salinity affects the seed germination and post-germination stages (Alizadeh et al. 2024). The toxic effect of NaCl primarily causes poor seed germination (Khaje-Hosseini et al. 2003). Our ANOVA analysis revealed that all ten traits exhibited significant differences in E1 and E2. While mean GP was generally higher in E2, growth metrics like FW, DW, and SLE were higher in E1. This variation could be attributed to differences in soil composition, environmental conditions, or genetic factors between the two locations. Additionally, it is crucial to ensure consistency and account for environmental impacts when conducting seed germination experiments under salinity stress, particularly for salinity QTL or gene mapping at the germination stage. Therefore, using multiple sources for a single set of seeds is important to verify the reliability of the results and mitigate the influence of external variables on germination outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSalt Tolerance among Soybean Genotypes\u003c/h2\u003e \u003cp\u003ePCA and cluster analysis identified the substantial variation in the quantitative traits among the 198 genotypes studied. It also helped identify key variables for further analysis or interventions to understand the mechanisms behind responses to different salt concentrations. Understanding the patterns in PCA and K-means clustering can aid in categorizing the genotypes based on their responses to salinity (tolerant, moderately tolerant, moderately susceptible, and susceptible), which is crucial for developing new strategies for breeding programs aimed at developing salt-tolerant soybean varieties. Crop productivity can be increased in saline soils by identifying genotypes with varying degrees of salt tolerance, thus supporting targeted breeding efforts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Salinity on Seedling Growth Metrics\u003c/h2\u003e \u003cp\u003eThe experiments demonstrated that salinity stress negatively affects soybean seedlings\u0026rsquo; germination and early growth, as higher salt concentrations lead to a significant reduction in GP, FW, DW, SLE, SL, and RL. These findings align with prior research documenting similar growth parameter declines under salinity stress. Previous studies have also reported a reduction in the seedlings' height, fresh weight (FW), and dry weight (DW), similar to our findings. (Hosseini et al. 2002; Amirjani 2010). The results presented in this work confirm previous findings (Essa 2002; Datta et al. 2006; Pavli et al. 2021; Begum et al. 2022; Alizadeh et al. 2024). The observed decrease in salt tolerance with increasing NaCl concentrations highlights the sensitivity of soybean seedlings to salinity stress. These findings highlight the importance of developing salt-tolerant varieties to lessen the unfavorable effects of salinity stress on crop yield and quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between Growth parameters\u003c/h2\u003e \u003cp\u003eA strong positive correlation between GP, FW, and DW indicated that higher germination rates are associated with better growth parameters. The strong correlation of the vigor index (VI1 and VI2) with GP and FW, positions it as a dependable predictor of seedling health, consistent with previous findings that link higher vigor indices to faster seedling emergence (Ebone et al. 2020). However, correlations weakened as the salinity levels increased, highlighting the impact of salinity stress on seedling growth. Understanding these correlations provides insights into the interconnected nature of seedlings' growth metrics and can help develop breeding strategies to improve overall crop performance under saline conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eComparison of GP between Locations\u003c/h2\u003e \u003cp\u003eComparison of GP between E1 and E2 revealed a difference in tolerance to salinity stress, with E2 consistently showing higher median germination percentages across all three NaCl concentrations. These findings suggested that the seeds under E2 conditions were more tolerant to salinity stress than those under E1, thus indicating the difference in the environmental conditions between the locations. Understanding these differences is essential for optimizing soybean cultivation practices in different geographical regions. Boxplot and heat maps effectively visualized the negative impact of salinity, showing a consistent decrease in germination percentage and growth parameters across E1 and E2. These visual tools validated prior findings on the negative effects of higher salinity levels on soybean growth (Hosseini et al. 2002; Amirjani 2010).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Salt-Sensitive and Salt-Tolerant Genotypes\u003c/h2\u003e \u003cp\u003eIn previous studies conducted in India, the soybean varieties CoSoy-2, DS-40, PalamSoy, and Pusa-16 were identified as salt-tolerant, whereas Co-1, GujaratSoy-1, and NRC-2 were found to be salt-sensitive (Kondetti et al. 2012). Additionally, research has identified cultivars such as Lee, BB52, Lee68, JWS156-1, S100, Nannong1138-2, PI483463, Wenfeng 7, Jindou 33, Tiefeng 8, and Dare as salt-tolerant, while Coquitt, Clark 63, Kefeng No. 1, Jackson, N23232, Kwangan, Daepung, and Tachiyutaka were recognized as salt-sensitive (An et al. 2002; Essa 2002; Yu and Liu 2003; Phang et al. 2008; Zeng et al. 2019; Moon et al. 2023; Guan et al. 2023).\u003c/p\u003e \u003cp\u003ePrior research studies investigated the salinity tolerance of soybean genotypes at the germination stage. However, these studies were limited in scope as they either utilized a limited number of genotypes or employed low concentrations of NaCl. We assessed the salinity tolerance of 198 genotypes collected at different salt concentrations from two locations.\u003c/p\u003e \u003cp\u003eA better understanding of plants' physiological and biological processes under salinity stress can accelerate the implementation of strategies to increase agricultural production by introducing salt-tolerant or salt-resistant plants (Alizadeh et al. 2024). The classification of soybean genotypes into salt-sensitive and salt-tolerant categories, based on their responses to 100 mM and 200 mM NaCl stress, provides valuable insights for breeding efforts to improve salt tolerance in these crops. Identifying genotypes with high salinity tolerance, like MACS 708, KALITUR, and MACS 1037, facilitates the development of salt-tolerant varieties that can thrive in saline environments. This classification helps prioritize such genotypes for further breeding efforts and management strategies to enhance slat tolerance and overall crop performance in saline soils.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study elucidated the detrimental effects of salinity stress on 198 soybean genotypes' germination and early growth under different salt concentrations. A significant variation in salt tolerance was demonstrated among different soybean genotypes. We observed that the seed germination percentage and other quantitative traits decreased as the salt content increased in both source seeds. The varieties MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173 exhibited salt tolerance, whereas HIMSO 1563, EC 391181 and EC 241920 displayed salt sensitivity. The salt-tolerant genotypes could germinate even at a concentration as high as 200 mM, but the salt-sensitive genotypes could not germinate even at the lowest concentration of 100 mM. These findings offer invaluable insights for breeding endeavors to cultivate salt-tolerant soybean varieties, which are crucial for sustainable crop production in saline environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere gratitude to Dr. Prashant Dhakephalkar (Director, Agharkar Research Institute), Dr. Manoj D. Oak (Head of the Department, Department of Genetics and Plant Breeding), Dr. Ravindra Patil, Mr. Santosh Jaybhay, Dr. Suresha P. G., Mr. V. D. Surve, Mrs. Anuja Deshpande, Mr. Bhanudas Idhol, Mr. B. N. Waghmare, and Mr. Dattatraya Salunkhe from the Department of Genetics and Plant Breeding, Agharkar Research Institute, for their generous provision of resources and facilities essential for the successful execution of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptualization of the experimental design and layout was done by Abhinandan Patil. Deepak Pawar did the seed threshing and packet preparation in locations E1 and E2. Data\u0026nbsp;recording\u0026nbsp;was done by Aditya Gobade.\u0026nbsp;Shreyash Gijare assisted in phenotyping and data recording.\u0026nbsp;Analysis and the first draft of the manuscript was prepared by Arathi. S. Conceived the concept, designed the analysis and critical revision of the manuscript was done by Abhinandan Patil. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is funded by the Ramalingaswami Re-entry Fellowship (BT/RLF/Re-entry/01/2021) Department of Biotechnology (DBT), Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All data supporting the findings of this study are available within the paper and its supplementary materials published online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this article does not contain any research involving animals or human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eA\u0026ccedil;ıkbaş, S., \u0026Ouml;zyazıcı, M. A., Bı\u0026ccedil;ak\u0026ccedil;ı, E., \u0026amp; \u0026Ouml;zyazıcı, G. (2023). Germination and Seedling Development Performances of Some Soybean (Glycine max (L.) Merrill) Cultivars Under Salinity Stress. Turkish Journal of Range and Forage Science, \u003cem\u003e4\u003c/em\u003e(2), 108\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.51801/turkjrfs.1387963\u003c/span\u003e\u003cspan address=\"10.51801/turkjrfs.1387963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlizadeh, M. M., Gerami, M., Majidian, P., \u0026amp; Ghorbani, H. R. (2024). The potential application of biochar and salicylic acid to alleviate salt stress in soybean (Glycine max L.). Heliyon. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e26677\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e26677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmirjani, M. R. (2010). Effect of salinity stress on growth, mineral composition, proline content, antioxidant enzymes of soybean. American Journal of Plant Physiology, \u003cem\u003e5\u003c/em\u003e(6), 350\u0026ndash;360.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn, P., Inanaga, S., Cohen, Y., Kafkafi, U., \u0026amp; Sugimoto, Y. (2002). Salt tolerance in two soybean cultivars. Journal of plant nutrition, \u003cem\u003e25\u003c/em\u003e(3), 407\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1081/PLN-120003373\u003c/span\u003e\u003cspan address=\"10.1081/PLN-120003373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnwar, J., Subhani, G. M., Hussain, M., Ahmad, J., Hussain, M., \u0026amp; Munir, M. (2016). Screening soybean genotypes for salinity tolerance at the germination stage. Pakistan Journal of Agricultural Sciences, \u003cem\u003e53\u003c/em\u003e(1), 165\u0026ndash;170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabicki, S., Arndt, D., Marcu, A., Liang, Y., Grant, J. R., Maciejewski, A., \u0026amp; Wishart, D. S. (2016). Heat mapper: web-enabled heat mapping for all. Nucleic acids research, \u003cem\u003e44\u003c/em\u003e(W1), W147-W153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkw419\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkw419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegum, N., Hasanuzzaman, M., Li, Y., Akhtar, K., Zhang, C., \u0026amp; Zhao, T. (2022). Seed germination behavior, growth, physiology, and antioxidant metabolism of four contrasting cultivars under combined drought and salinity in soybean. Antioxidants, \u003cem\u003e11\u003c/em\u003e(3), 498. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox11030498\u003c/span\u003e\u003cspan address=\"10.3390/antiox11030498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, L., Yu, Y., DuanMu, H., Chen, C., Duan, X., Zhu, P., \u0026hellip; Ding, X. (2016). A novel Glycine soja homeodomain-leucine zipper (HD-Zip) I gene, Gshdz4, positively regulates bicarbonate tolerance and responds to osmotic stress in Arabidopsis. BMC plant biology, \u003cem\u003e16\u003c/em\u003e, 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-016-0872-7\u003c/span\u003e\u003cspan address=\"10.1186/s12870-016-0872-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury, S., Panda, P., Sahoo, L., \u0026amp; Panda, S. K. (2013). Reactive oxygen species signaling in plants under abiotic stress. Plant signaling \u0026amp; behavior, \u003cem\u003e8\u003c/em\u003e(4), e23681. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4161/psb.23681\u003c/span\u003e\u003cspan address=\"10.4161/psb.23681\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho, K. H., Kim, M. Y., Kwon, H., Yang, X., \u0026amp; Lee, S. H. (2021). Novel QTL identification and candidate gene analysis for enhancing salt tolerance in soybean (Glycine max (L.) Merr.). Plant Science, \u003cem\u003e313\u003c/em\u003e, 111085. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plantsci.2021.111085\u003c/span\u003e\u003cspan address=\"10.1016/j.plantsci.2021.111085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDatta, A. K., Hossain, M. A., Robin ABMAHK, R. S., \u0026amp; Hossain, M. A. (2006). Screening of soybean genotypes for salinity tolerance at seedling stage. Bangladesh J Crop Sci, \u003cem\u003e17\u003c/em\u003e(1), 155\u0026ndash;161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbone, L. A., Caverzan, A., Tagliari, A., Chiomento, J. L. T., Silveira, D. C., \u0026amp; Chavarria, G. (2020). Soybean seed vigor: uniformity and growth as key factors to improve yield. Agronomy, \u003cem\u003e10\u003c/em\u003e(4), 545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy10040545\u003c/span\u003e\u003cspan address=\"10.3390/agronomy10040545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Sabagh, A., Abd Elhamid Omar, H. S., \u0026amp; Barut\u0026ccedil;ular, C. (2015) Physiological performance of soybean germination and seedling growth under salinity stress. DUFED 4(1), 6\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEssa, T. A. (2002). Effect of salinity stress on growth and nutrient composition of three soybean (\u003cem\u003eGlycine max\u003c/em\u003e L. Merrill) cultivars. Journal of Agronomy and Crop science, \u003cem\u003e188\u003c/em\u003e(2), 86\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1439-037X.2002.00537.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1439-037X.2002.00537.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan, R. X., Guo, X. Y., Qu, Y., Zhang, Z. W., Bao, L. G., Ye, R. Y., \u0026hellip; Qiu, L. J. (2023). Salt Tolerance in Soybeans: Focus on Screening Methods and Genetics. Plants, \u003cem\u003e13\u003c/em\u003e(1), 97. https://doi.org/10.3390%2Fplants13010097\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosseini, M. K., Powell, A. A., \u0026amp; Bingham, I. J. (2002). Comparison of the seed germination and early seedling growth of soybean in saline conditions. Seed Science Research, \u003cem\u003e12\u003c/em\u003e(3), 165\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1079/SSR2002108\u003c/span\u003e\u003cspan address=\"10.1079/SSR2002108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou, Z., Li, Y., Kong, F., Dong, L., \u0026amp; Cheng, Q. (2022). Genome-wide analysis of DREB genes identifies a novel salt tolerance gene in wild soybean (Glycine soja). Frontiers in Plant Science, \u003cem\u003e13\u003c/em\u003e, 821647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.821647\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.821647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iisrindore.icar.gov.in/readmore.html\u003c/span\u003e\u003cspan address=\"https://iisrindore.icar.gov.in/readmore.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhajeh-Hosseini, M., Powell, A. A., \u0026amp; Bingham, I. J. (2003). The interaction between salinity stress and seed vigor during germination of soyabean seeds. Seed Science and technology, \u003cem\u003e31\u003c/em\u003e(3), 715\u0026ndash;725. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15258/sst.2003.31.3.20\u003c/span\u003e\u003cspan address=\"10.15258/sst.2003.31.3.20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKharb, R. P. S., Lather, B. P. S., \u0026amp; Deswal, D. P. (1994). Prediction of field emergence through heritability and genetic advance of vigor parameters. Seed Science and Technology, 22(3): 461\u0026ndash;466.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondetti, P., Jawali, N., Apte, S. K., \u0026amp; Shitole, M. G. (2012). Salt tolerance in Indian soybean (Glycine max (L.) Merill) varieties at germination and early seedling growth. Annals of Biological Research, \u003cem\u003e3\u003c/em\u003e(3), 1489\u0026ndash;1498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, S. J., Susmita, C., Sripathy, K. V., Agarwal, D. K., Pal, G., Singh, A. N., \u0026hellip; Simal-Gandara, J. (2022). Molecular characterization and genetic diversity studies of Indian soybean (\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.) cultivars using SSR markers. Molecular Biology Reports, 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11033-021-07030-4\u003c/span\u003e\u003cspan address=\"10.1007/s11033-021-07030-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumari, S., Lal, S. K., Sreenivasa, V., Rajendran, A., Singh, S. K., Singh, K. P., \u0026hellip; Xu, D. (2019). Screening and identification of sources of salinity tolerance at seed germination stage in Indian soybean genotypes. International Journal of Current Microbiology and Applied Sciences, \u003cem\u003e8\u003c/em\u003e, 3006\u0026ndash;3013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20546/ijcmas.2019.804.346\u003c/span\u003e\u003cspan address=\"10.20546/ijcmas.2019.804.346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung, H. S., Chan, L. Y., Law, C. H., Li, M. W., \u0026amp; Lam, H. M. (2023). Twenty years of mining salt tolerance genes in soybean. Molecular Breeding, \u003cem\u003e43\u003c/em\u003e(6), 45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-023-01383-3\u003c/span\u003e\u003cspan address=\"10.1007/s11032-023-01383-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinh, N. T., Cham, L. T. T., \u0026amp; Thang, V. N. (2021). Effects of salinity stress on the growth, physiology, and yield of soybean (\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merrill). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31817/vjas.2021.4.2.05\u003c/span\u003e\u003cspan address=\"10.31817/vjas.2021.4.2.05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMannan, M. A., Karim, M. A., Haque, M. M., Khaliq, Q. A., Higuchi, H., \u0026amp; Nawata, E. (2012). Response of soybean to salinity: I. Genotypic variations in salt tolerance at the vegetative stage. Tropical Agriculture and Development, \u003cem\u003e56\u003c/em\u003e(4), 117\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11248/jsta.56.117\u003c/span\u003e\u003cspan address=\"10.11248/jsta.56.117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiransari, M. (2016). Soybean production and salinity stress. In \u003cem\u003eAbiotic and Biotic Stresses in Soybean Production\u003c/em\u003e (pp. 157\u0026ndash;176). Academic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-801536-0.00007-4\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-801536-0.00007-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon, C. Y., Kang, B. H., Kim, W. J., Chowdhury, S., Kang, S., Lee, J. D., \u0026hellip; Ha, B. K. (2023). Morpho-physiological and genetic characteristics of a salt-tolerant mutant line in soybean (Glycine max L.). Theoretical and Applied Genetics, \u003cem\u003e136\u003c/em\u003e(7), 166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-023-04408-9\u003c/span\u003e\u003cspan address=\"10.1007/s00122-023-04408-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavli, O. I., Foti, C., Skoufogianni, G., Karastergiou, G., Panagou, A., \u0026amp; Khah, E. M. (2021). Effect of salinity on seed germination and seedling development of soybean genotypes. \u003cem\u003eintervals\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 7th. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.19080/IJESNR.2021.27.556210\u003c/span\u003e\u003cspan address=\"10.19080/IJESNR.2021.27.556210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhang, T. H., Shao, G., \u0026amp; Lam, H. M. (2008). Salt tolerance in soybean. Journal of Integrative Plant Biology, \u003cem\u003e50\u003c/em\u003e(10), 1196\u0026ndash;1212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman, M. A., Islam, M. M., Akter, S., Islam, M. M., Hasanuzzaman, M., \u0026amp; Ahsan, M. A. (2021). Screening of soybean genotypes for salinity tolerance at the germination stage. Journal of Crop Science and Biotechnology, \u003cem\u003e24\u003c/em\u003e(4), 289\u0026ndash;294.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasheed, A., Raza, A., Jie, H., Mahmood, A., Ma, Y., Zhao, L., \u0026hellip; Jie, Y. (2022). Molecular tools and their applications in developing salt-tolerant soybean (Glycine max L.)cultivars. \u003cem\u003eBioengineering\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(10), 495. https://doi.org/10.3390/bioengineering9100495\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh, R. K., Sharma, V., Tyagi, S., \u0026amp; Gupta, S. (2019). Evaluation of soybean genotypes for salinity tolerance at the germination and seedling stage. Indian Journal of Agricultural Sciences, \u003cem\u003e89\u003c/em\u003e(7), 1153\u0026ndash;1157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpitzer, M., Wildenhain, J., Rappsilber, J., \u0026amp; Tyers, M. (2014). BoxPlotR: a web tool for generation of box plots. Nature methods, \u003cem\u003e11\u003c/em\u003e(2), 121\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nmeth.2811\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.2811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmburanas, R. C., Kawakami, J., Ainsworth, E. A., Favarin, J. L., Anderle, L. Z., Dourado-Neto, D., \u0026amp; Reichardt, K. (2022). Changes in soybean cultivars released over the past 50 years in southern Brazil. Scientific Reports, \u003cem\u003e12\u003c/em\u003e(1), 508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-04043-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-04043-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, H., Guo, J., Wang, C., Li, K., Zhang, X., Yang, Z., \u0026hellip; Wang, B. (2019). An effective screening method and a reliable screening trait for salt tolerance of Brassica napus at the germination stage. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 530. doi: 10.3389/fpls.2019.00530\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, X. Y., Fan, R., Zheng, R., Li, C. M., \u0026amp; Yu, D. Y. (2011). Proteomic analysis of seed germination under salt stress in soybeans. Journal of Zhejiang University Science B, \u003cem\u003e12\u003c/em\u003e(7), 507\u0026ndash;517.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu BingJun, Y. B., \u0026amp; Liu YouLiang, L. Y. (2003). Effects of salt stress on the metabolism of active oxygen in seedlings of annual halophyte \u003cem\u003eGlycine soja\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng, A., Chen, P., Korth, K. L., Ping, J., Thomas, J., Wu, C., \u0026hellip; Ma, J. (2019). RNA sequencing analysis of salt tolerance in soybean (Glycine max). \u003cem\u003eGenomics\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(4), 629\u0026ndash;635.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang, Y., Li, X., Hu, J., Xu, R., \u0026amp; Zhang, D. (2022). Expanding the gene pool for soybean improvement with its wild relatives. Abiotech, \u003cem\u003e3\u003c/em\u003e(2), 115\u0026ndash;125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42994-022-00072-7\u003c/span\u003e\u003cspan address=\"10.1007/s42994-022-00072-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuffo, A. M., Steiner, F., Aguilera, J. G., Teodoro, P. E., Teodoro, L. P. R., \u0026amp; Busch, A. (2020). Multi-trait stability index: A tool for simultaneous selection of soya bean genotypes in drought and saline stress. Journal of Agronomy and Crop Science, \u003cem\u003e206\u003c/em\u003e(6), 815\u0026ndash;822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jac.12409\u003c/span\u003e\u003cspan address=\"10.1111/jac.12409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Glycine max, Soybean, Salinity, Salt stress, Germination, Salt-tolerant, Salt-sensitive.","lastPublishedDoi":"10.21203/rs.3.rs-4558107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4558107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh levels of soil salinity inhibit the growth of legumes such as soybeans, significantly reducing their productivity. This research aimed to assess the salt tolerance of soybean genotypes by evaluating seed germination at varying salt concentrations (100 mM, 150 mM, and 200 mM NaCl) from two seed source locations. A total of 198 soybean genotypes were analyzed post-germination using ten quantitative traits: germination percentage, seedling fresh weight, seedling dry weight, seedling length, shoot length, root length, seedling vigor index-1, seedling vigor index-2, seedling water content, and salt tolerance. Analysis of Variance (ANOVA) results indicated significant differences among treatments across both locations. Principal Component Analysis revealed that certain quantitative traits were more prominent at different salt concentrations, confirming varied responses to salt stress. Correlation analysis demonstrated a positive relationship between germination percentages and growth parameters such as fresh weight, dry weight, and vigor index. The study observed a decline in all quantitative traits as salt concentration increased, highlighting the stress experienced by plants during germination and growth under high salinity conditions. Using K-means clustering, the 198 genotypes were categorized into tolerant, moderately tolerant, moderately susceptible, and susceptible groups. This clustering helped identify genotypes exhibiting high tolerance (\u0026ge;\u0026thinsp;80% germination at 200 mM NaCl) and high susceptibility (\u0026le;\u0026thinsp;40% germination at 100 mM NaCl) consistently across both seed source locations. Consequently, seven salt-tolerant genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173) and three salt-sensitive genotypes (HIMSO 1563, EC 391181, and EC 241920) were identified, providing new insights into soybean cultivation under saline conditions.\u003c/p\u003e","manuscriptTitle":"Evaluating salt tolerance in soybean core collection: germination response under salinity stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 15:35:42","doi":"10.21203/rs.3.rs-4558107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-23T09:52:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-22T18:33:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209065077642108108748086310268426713022","date":"2024-06-13T11:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224828138940573648518212559833567705442","date":"2024-06-12T14:41:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-10T20:21:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-10T16:13:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-10T16:12:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-06-10T12:42:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"032ae8a9-dfa5-4247-a7ea-b827e9ad1e51","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:01:11+00:00","versionOfRecord":{"articleIdentity":"rs-4558107","link":"https://doi.org/10.1007/s10722-024-02081-5","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2024-08-02 15:57:12","publishedOnDateReadable":"August 2nd, 2024"},"versionCreatedAt":"2024-06-26 15:35:42","video":"","vorDoi":"10.1007/s10722-024-02081-5","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02081-5","workflowStages":[]},"version":"v1","identity":"rs-4558107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4558107","identity":"rs-4558107","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}