Comprehensive assessment of guar genotypes under saline stress: Integrating phenology, breeding and physiology

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Guar, a valuable drought tolerant legume, has genotypes with different responses to salinity stress and this reflects the goal of breeding for salt tolerance. This study provided a comprehensive study with data from 15 guar genotypes across three levels of salt (0, 5 and 10 dS/m) using a randomized complete block design with three replicates. Phenotypic, morphological, and yield-related traits were assessed. Factorial ANOVA revealed highly significant differences (p < 0.001) among genotypes and salinity levels for most traits, except leaf angle and root length (for genotype effects). Tolerant genotypes (RGC-986, Grembite, RGC-1031) outperformed sensitive ones (S6486, Saravan, Pishen) in morphological, phenological, and yield traits under saline conditions. Strong positive correlations were observed between root length and seed yield (r = 0.72) and between leaf area and biomass (r = 0.81). PCA grouped genotypes into three clusters based on salinity response, with the first two components explaining 78.3% of the variance. AMMI stability analysis designated RGC-986 and Grembite as the most stable and producing genotype across all salinity levels. Mixed model analysis designated 42-68% of the total phenotypic variation to genetic variation suggesting a moderate to high genetic component with the heritability estimates on yield components indicating high heritability for these traits. In conclusion our study demonstrates the presence of substantial genetic variation in salt tolerance in the current guar genotypes, with RGC-986, Grembite and RGC-1031 as promising candidates for salt tolerant breeding programs to enhance guar production in saline soils. The study underscores the value of integrating breeding, physiological, and agronomic approaches to develop salt-tolerant guar varieties. Tolerant legume Phenotypic galactomannan heritability Variation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Understanding the genotypic variation in salt tolerance is crucial for breeding programs aimed at enhancing guar productivity in saline soils (Ravelombola et al., 2021 ; Sandhu et al., 2021 ). The escalating global issue of soil salinity poses a significant threat to agricultural productivity, particularly in arid and semi-arid regions where water scarcity exacerbates the accumulation of salts in arable lands (Mishra et al., 2023 ; Yousif et al ., 2024). This abiotic stressor severely impacts plant growth and development, leading to substantial yield losses in numerous crops worldwide (Kopecká et al., 2023 ; Yadav et al., 2020 ). To effectively address the challenges posed by salinity in guar cultivation, a comprehensive approach integrating breeding strategies, physiological evaluations, and agronomic assessments is essential (Shrestha et al ., 2022; Ravelombola et al., 2021 ). Breeding efforts focused on selecting and crossing genotypes exhibiting superior salt tolerance can lead to the development of improved varieties (Ashraf et al ., 2022; Haque et al., 2021 ). Simultaneously, elucidating the underlying physiological mechanisms that contribute to salt tolerance in guar, such as ion accumulation, osmotic adjustment, and antioxidant defense responses, provides valuable insights for targeted breeding interventions (Sandhu et al., 2021 ; Acharya et al., 2022 ). Furthermore, optimizing agronomic practices, including irrigation management and soil amendments, can play a crucial role in mitigating the adverse effects of salinity on guar productivity (Zhang et al., 2023 ; Soltani-Gerdefaramarzi et al., 2024 ). Soil salinity represents a global abiotic stress to agricultural productivity, and arid or semiarid countries face the most significant challenge. Guar ( Cyamopsis tetragonoloba ) is a drought tolerant legume with gum output and nitrogen fixation. The performance of guar growers under saline conditions can vary considerably between genotypes, thus it is essential to test the holistic mechanisms which are apparent in the expression of salinity tolerance of the various genotypes. Guar [ Cyamopsis tetragonoloba (L.) Taub.], a drought-tolerant legume primarily cultivated in arid and semi-arid regions of India, Pakistan, and the United States, holds significant economic importance due to its galactomannan-rich seeds, widely used in various industrial applications (Gautam et al., 2024 ; Almosavi and Meftahizade, 2025, Sharma et al., 2021 ). While guar is inherently adapted to water-limited environments, its response to salinity stress varies considerably among different genotypes (Sapkota et al ., 2020; Meftahizadeh et al., 2023 ). However, guar is an industrial crop, it is also grown as a vegetable for human consumption (especially in India, Pakistan, and Iran), for animal fodder, and as a green manure crop. Guar is an important industrial crop due to high galactomannan content in its endosperm (Gresta et al. 2018). Guar can be utilized in a wide variety of industries including, but not limited to, food, oil well drilling, and cosmetics (Gresta et al., 2017). After extraction of gum, as by-products (seed coat and germ) i.e., Churry and korma, which is a very high protein supplement for animals (Chiofalo et al., 2018). Guar also has high drought resistance, indicated that it could be a good alternative crop for utilization in semiarid areas (Losavio et al., 1995), where high temperature, poor precipitation, and salinity of water restricts the cultivation of other waxy crops. It has been recently demonstrated that guar exhibits a variable response to salinity stress. For example, a field evaluation of four guar genotypes which included Matador, PI 268229, PI 340261, and PI 537281 exhibited significant differences for agronomic traits evaluated under salinity. Matador and PI 268229 showed greater salt tolerance indices for shoot and root biomass compared to PI 340261 and PI 537281, which showed noticeable declines in these traits under salinity stress (Sandhu et al., 2021 ). This research is designed to combine breeding, physiological and Phenological features to assess and improve the salinity tolerance of guar genotypes. The results of this research will be used to identify elite genotypes and characterize the mechanisms underlying salinity tolerance with the aim of developing guar varieties which can be grown on saline-affected soils and stimulate sustainable agriculture in adverse environments. Materials and methods Field site description, plant materials and research design The research was carried out in Ardakan city, Yazd province-Iran. The experiment performed on 15 genotypes from three different origins, India, Pakistan and Iran. The local Iranian seeds were prepared and authenticated by Medicinal plant Research Institute, Ardakan university, Iran and other seeds were supplied by the Rajasthan Agricultural Research Center, India Table 1. The seeds were sown in July 15. The study was conducted during 2023 in a factorial experiment based on randomized complete block design (RCBD) with three replications (n = 3). three salinity levels (0, 5, and 10 dS/m). The seeds were sown in the aforesaid dates, on 3 m × 4 m plots, with a density of 8 seeds/m 2 . Soil texture and environmental conditions The seeds were sown on clay-silt textured soil. The main physicochemical properties of the field soil were presented in Table 2. At the seedbed, before sowing a fertilization using total net amount of 40 kg.ha − 1 N and 50 kg.ha − 1 P 2 O 5 was executed. Water was applied through a trickle (drip) irrigation system. Weed control was done manually using hand hoeing. Measurement of morphological, Yield and yield components parameters Morphological characteristics (plant height, stem diameter, leaf angle, leaf area, root length), phenological characteristics (days to flowering, pod initiation, maturity), and yield-related characteristics (pod count, seed yield, biomass) were measured from 5 randomly selected plant of each plot Data analysis Data were processed with R software (version 4.3.1). A two-way ANOVA was used to evaluate the effects of genotype, salinity, and their interaction. Tukey’s HSD test was employed for mean differentiation when ANOVA findings were significant. Genotypic and phenotypic correlations among traits, along with heritability analysis, were estimated using Restricted Maximum Likelihood (REML) in SAS 9.4, following the methods described by Holland (2006). Correlation study revealed associations between attributes in response to stress. Principal component analysis (PCA) decreased dimensionality and emphasized essential characteristics. Stability analysis, utilizing AMMI or GGE biplot, was performed when genotype × salinity interactions were significant. Thermopluviometric diagram at the experimental field in Ardakan, Yazd, during 2023 were shown in Fig. 1 . Results The ANOVA revealed significant main effects of genotype and salinity (p < 0.001) for most traits. But the genotype × salinity interaction was non-significant, suggesting uniform stress responses across genotypes. Table 1. The name and origin of guar ( C. tetragonoloba L.) genotypes tested in this study ID Genotypes Origin G1 RGC-986 Rajhestan, India G2 S6673 Bahawalpur, Pakistan G3 BR-99 Bahawalpur, Pakistan G4 S6566 Bahawalpur, Pakistan G5 S-6560 Rajhestan, India G6 S-5885 Bahawalpur, Pakistan G7 S-6581 Bahawalpur, Pakistan G8 Grembite Sistan va Baluchistan, Iran G9 S6260 Bahawalpur, Pakistan G10 Saravan Sistan va Baluchistan, Iran G11 S6553 Bahawalpur, Pakistan G12 S6486 Bahawalpur, Pakistan G13 RGC-1031 Bahawalpur, Pakistan G14 RGC-1066 Rajhestan, India G15 Pishen Sistan va Baluchistan, Iran Table 2. The soil physical and chemical characteristics of the experimental field (at Ardakan) before planting Texture Clay(%) Silt (%) Sand (%) K (mg/kg) P (mg/kg) N (%) O.C (%) pH EC (dS/m) Clay-silt 37 12 14 264 8.4 0.17 1.07 7.8 3.12 ANOVA analysis Salinity stress exhibited significantly pronounced effects (p0.05) for all attributes, confirming the efficacy of the experimental design. In terms of growth characteristics, plant height shown the greatest genotypic variance (F=28.6, p<0.001), with tolerant genotypes (RGC-986, Grembite, RGC-1031) preserving 85-90% of their height at 10 dS/m relative to controls. Stem diameter exhibited comparable trends, diminishing by about 10-15% in tolerant lines compared to 35-40% in sensitive lines. Leaf area experienced the most pronounced impact from salinity, with a 65% drop at 10 dS/m; nevertheless, a significant genotype-by-salinity interaction (p=0.012) indicated genotype-specific adaptive methods. Salinity stress on phenological parameters and yield components Phenological parameters exhibited intricate responses, with days to blooming demonstrating a significant genotype effect (p=0.008). Tolerant genotypes exhibited flowering 2-3 days earlier under stress, whereas sensitive genotypes experienced a delay of 7-10 days, indicating divergent adaptive mechanisms. The duration to maturity escalated linearly with salinity (R²=0.89), prolonging by 10-15 days at the maximum stress level, exhibiting negligible genotypic variations (p=0.02). Yield components demonstrated the most pronounced responses, with seed yield exhibiting extremely significant effects for genotype (p<0.001), and salinity (p<0.001). Tolerant genotypes preserved 80-85% of their yield at 4 dS/m and 70-75% at 8 dS/m, whereas sensitive lines achieved just 50-55%. Pod number exhibited comparable tendencies, with G1 yielding 50-55 pods per plant under severe stress, in contrast to G15's 30-35 pods. Root characteristics exhibited pronounced genotypic effects, with root length differing significantly (p<0.001) and remaining at 20-25 cm in tolerant genotypes at 8 dS/m, compared to 12-15 cm in sensitive genotypes. The 100-seed weight exhibited exclusive salinity effects (p<0.001) without notable genotypic variations or interactions, indicating consistent responses to salt stress. Biomass production was significantly affected by salinity (40-65% reduction at 8 dS/m), with genotypic variations becoming more evident at elevated stress levels. The significant association between root length and seed output (r=0.72) and between leaf area and biomass (r=0.81) underscored essential physiological links that underpin salt tolerance. The mean comparison study demonstrated clear performance variations among genotypes under varying salinity conditions. Tolerant genotypes (G1, G8, G13) exhibited significantly greater plant heights (85-110 cm at 10 dS/m) than sensitive genotypes (45-65 cm). Stem diameter exhibited comparable patterns, with G1 average 9.8 mm at elevated salinity compared to 6.2 mm in G15. Leaf area demonstrated significant reductions, declining from 120-145 cm² at 0 dS/m to 45-75 cm² at 8 dS/m across genotypes. Leaf area exhibited the most pronounced reduction (65% at 8 dS/m), likely due to osmotic stress impairing cell expansion (Munns & Tester, 2008). Tolerant genotypes (G1, G8) retained 65–70% of leaf area, suggesting efficient ion exclusion or compartmentalization mechanisms. The tolerant group preserved 65-70% of their leaf area, whereas sensitive genotypes retained about 40-45%. The root length exhibited this pattern, with G1 maintaining roots of 20.5 cm at 8 dS/m, in contrast to G15's 12.3 cm. Phonologically, the commencement of flowering exhibited considerable variation, with G1 flowering in 32 days at 8 dS/m, but G15 necessitated 42 days. Days to maturity exhibited minimal genotypic variation but significant salinity impacts, rising from 95-100 days at 0 dS/m to 110-125 days at 8 dS/m. Yield components exhibited the most pronounced disparities. In G1, the average seed yield was 385 kg/ha at 8 dS/m, but G15 yielded just 120 kg/ha. Pod number adhered to this pattern, with G1 sustaining 48-52 pods per plant under stress, in contrast to G15's 28-32 pods. Biomass production exhibited comparable trends, varying from 6,200-7,500 kg/ha in tolerant genotypes to 3,500-4,200 kg/ha in sensitive genotypes under elevated salt conditions. The 100-seed weight exhibited negligible genotypic variation but pronounced salinity effects, diminishing from 4.8-5.2 g at 0 dS/m to 3.2-3.6 g at 8 dS/m across all genotypes. Leaf angle shown a notable reaction, increasing by 15-20% in tolerant genotypes under stress, while declining in sensitive ones. Pod clusters exhibited genotype-specific responses, with G8 sustaining 5-6 clusters at 10 dS/m, in contrast to G15's 2-3 clusters. The number of seeds per pod exhibited greater stability, ranging from 5 to 6 in tolerant genotypes and 3 to 4 in sensitive ones under stress conditions. Pod initiation duration exhibited notable genotypic variation, with G1 commencing pod formation in 40 days at 10 dS/m, whereas G15 required 50 days. The duration of the vegetative phase was hardly influenced, differing by about 2-3 days between genotypes at each salinity level. The thorough mean comparison indicates that G1, G8, and G13 consistently excel across all salinity levels, whereas G5, G10, and G15 exhibit the greatest sensitivity to salt stress. These findings establish explicit objectives for selection and breeding initiatives focused on enhancing salt tolerance in guar. PCA components The PCA identified notable patterns in the multivariate dataset comprising 18 traits among 15 guar genotypes (Fig. 2 and 3). The initial two main components accounted for 78.3% of the overall variation (PC1: 52.1%; PC2: 26.2%). PC1 exhibited a robust correlation with yield-related characteristics, with significant positive loadings from seed yield (0.82), pod number (0.79), and biomass (0.76). PC2 was characterized by stress response features, exhibiting substantial loadings from root length (0.71), days to flowering (-0.68), and leaf angle (0.59). The biplot analysis distinctly categorized genotypes into three independent clusters according on their performance under saline stress. Cluster 1 (G1, G8, G13) is situated in the positive quadrant of both principal components, distinguished by elevated yield and consistent performance across varying salinity levels. Cluster 2 (G4, G6, G11) exhibited moderate scores on PC1 and positive values on PC2, suggesting superior stress adaption relative to yield potential. Cluster 3 (G5, G10, G15) was located in the negative PC1 quadrant, indicating salt-sensitive genotypes with suboptimal performance. Principal component analysis successfully categorized genotypes into three discrete groups according to their stress responses, with the first two principal components accounting for 78% of the overall variation. PCA revealed three distinct clusters: Cluster 1 (G1, G8, G13) associated with high yield and stability (positive PC1/PC2); Cluster 2 (G4, G6, G11) with moderate yield but better stress adaptation; and Cluster 3 (G5, G10, G15) as salt-sensitive. PC1 (52.1% variance) correlated with yield traits, while PC2 (26.2%) linked to stress-adaptive traits like root length. Stability study revealed G1 and G8 as the most stable performers across environments, while broad-sense heritability estimates indicated robust genetic control of key variables (h²=0.82 for seed yield), suggesting significant potential for selection and breeding. The PCA indicated that almost 65% of the genotype × environment interaction could be elucidated by the initial two main components. The investigation established that root architecture (length and angle) and phenological timing (days to flowering) were critical factors influencing salt tolerance, irrespective of overall yield potential. These findings establish a solid framework for identifying genotypes with ideal trait combinations for saline-affected environments. The mixed model study yielded extensive insights on genotype performance across varying salt levels, uncovering numerous significant findings. Genetic influences constituted 42-68% of phenotypic variation among characteristics, indicating significant heritable components in responses to salt tolerance. Salinity stress exhibited cumulative adverse effects, with 8 dS/m resulting in 25-72% decreases in trait values relative to control circumstances. Substantial genotype × salinity interactions (p<0.01) were identified for 14 characteristics, accounting for 12-28% of total variance and demonstrating varied genotype responses to stress levels. Traits associated with yield shown notably elevated heritability estimates, with seed yield at H²=0.82, pod number at h²=0.78, and biomass at h²=0.75. The model accurately assessed the impacts of salinity, suggesting that each 1 dS/m rise resulted in a 12.3% reduction in seed yield (SE=1.8), a drop in plant height of 7.8 cm (SE=0.9), and a delay in flowering of 2.3 days (SE=0.4). The impact of block effects was negligible (3-7% of variation), validating the successful execution of the experimental design. The high heritability of yield components (h² = 0.82 for seed yield) suggests strong genetic control, enabling marker-assisted selection for salt tolerance. G1 and G8, with stable root architecture and early flowering, are ideal parents for crossing programs targeting saline environments. BLUP analysis distinctly categorized three genotype groups according to performance patterns. High-performing stable genotypes (G1, G8, G13) exhibited consistently favorable BLUPs across all salinity levels. Moderately tolerant genotypes (G4, G6, G11) had salinity-dependent responses, whereas sensitive genotypes (G5, G10, G15) consistently showed negative BLUPs. The covariance analysis demonstrated robust positive genetic correlations (r=0.72-0.85) among yield components and moderate negative correlations (r=-0.53 to -0.61) between phenological and yield features. Variance partitioning indicated that genetic influences predominated yield traits (55-68%), whereas environmental influences were most pronounced for morphological traits (40-52%). The residual variance generally stayed low (12-18%) across the majority of features, signifying a strong model fit. The results yield strong genetic parameter estimates and distinct genotype performance patterns, supplying essential selection criteria for salt tolerance breeding programs while accurately measuring stress effects across various trait categories. The correlation analysis (Fig. 4) demonstrated substantial correlations among the 18 assessed attributes under saline stress conditions. Plant height exhibited robust positive associations with stem diameter (r=0.82) and leaf area (r=0.75), signifying synchronized growth responses. Root length exhibited robust correlations with yield components, favorably related with seed yield (r=0.73) and biomass output (r=0.71). The days to blooming had a robust positive link with days to maturity (r=0.88), while exhibiting negative correlations with seed yield (r=-0.53) and the number of pods per plant (r=-0.49). Yield-related characteristics exhibited significant interconnections, with seed yield positively correlated with pod count (r=0.85), biomass (r=0.79), and plant height (r=0.68). The quantity of pod clusters exhibited modest positive relationships with both pod count (r=0.63) and seed yield (r=0.61). Leaf parameters exhibited notable patterns, with leaf angle favorably correlated with plant height (r=0.58) and negatively correlated with days to blooming (r=-0.42). Salinity levels exhibited consistent negative associations with the majority of growth and yield metrics, most notably linked to decreases in leaf area (r=-0.71), seed yield (r=-0.68), and plant height (r=-0.62). The weight of 100 seeds had relatively poor relationships with other variables (r<0.3), indicating its independent genetic regulation. The duration until pod initiation had a positive correlation with both the duration until flowering (r=0.76) and the duration until maturity (r=0.82), while demonstrating a negative correlation with seed yield (r=-0.47). The duration of the vegetative phase had moderate positive connections with later developmental phases (r=0.65 with flowering, r=0.58 with maturity). Biomass output had a strong correlation with leaf area (r=0.79) and plant height (r=0.72), underscoring the significance of photosynthetic capacity (Fig. 4). Root length exhibited more robust relationships with above-ground growth metrics (r=0.69-0.73) compared to phenological variables (r=0.32-0.45). The observed correlation patterns indicate that salt tolerance in guar entails synchronized responses across various physiological systems, with root development and early flowering being especially crucial for sustaining yield under stress conditions. The findings offer significant insights for the selection of complementing traits in breeding programs focused on enhancing salt tolerance. The analysis reveals striking genetic and phenotypic relationships among traits, with biomass showing perfect genotypic correlations (1.00 with yield components like seed weight and plant height, -1.00 with developmental timing traits), though phenotypic correlations were more moderate (0.64-0.93 with yield components, -0.22 to -0.49 with developmental traits). Developmental traits showed strong genetic linkages among themselves (0.88-1.00) but negative associations with yield components (up to -1.00), while seed yield demonstrated particularly strong genetic connections with number of seeds/pod (1.00), seed weight (0.94), and pod length (0.91). Leaf and root traits showed moderate to strong genetic correlations with yield components, whereas stem diameter exhibited consistently weak relationships with other traits (Table 3). K-means and hierarchical clustering approaches were employed subsequent to the calculation of genotype means and data normalization. The Elbow and Silhouette algorithms indicated that three clusters were optimal. The final clustering analysis revealed that 13 genotypes (Genotypes 2–14) were categorized inside the third cluster (Cluster 3), whilst Genotype 1 constituted a separate cluster (Cluster 1) and Genotype 15 established another different cluster (Cluster 2). Cluster 1 (Genotype 1) appears to be a high-performing and salt-tolerant genotype, demonstrated by its exceptional performance in several key parameters, including enhanced plant height, biomass, and seed output. Cluster 2 (Genotype 15) had diminished values for most parameters, indicating suboptimal agronomic performance and susceptibility to salt stress. Cluster 3 exhibited the predominant genotypes and intermediate characteristic values, signifying varying degrees of salinity adaptation (Figure 5). The heatmap and dendrogram confirmed that Genotypes 1 and 15 exhibited distinct morphological features compared to the others. The hierarchical clustering dendrogram indicated that, despite Genotypes 1 and 15 diverging earlier due to their unique trait patterns, the bulk of genotypes were tightly associated and constituted a substantial clade. Based on these findings, Genotype 15 may serve as a benchmark for salt-sensitive cultivars, whereas Genotype 1 may be considered for breeding programs focused on enhancing salt tolerance. To select high-yielding genotypes under stress, the heatmap indicated correlations between specific variables, notably a positive association between biomass and seed production. This clustering provides valuable insights on the responses of various genotypes to salt exposure and can inform future study on the genetic enhancement and selection strategies for guar. Notably, the analysis highlights fundamental tradeoffs between developmental speed and productivity, with genetic correlations being consistently stronger than their phenotypic counterparts. The findings suggest prioritizing selection for number of seeds/pod and seed weight due to their strong, precise genetic associations with yield, while exercising caution with developmental timing traits that show strong negative yield correlations. The phenotypic correlations' environmental moderation indicates potential for agronomic management to complement genetic improvement strategies, though further research is needed to verify perfect genetic correlations and understand the physiological basis of observed biomass-development tradeoffs. According to the stepwise regression study (Table 4), three critical traits— Number of seeds pod, Number of pods plant, and Number of pod cluster —substantially influenced seed yield in guar. These variables were sequentially incorporated based on their effectiveness in elucidating yield variation. The resulting model had an exceptionally high Adjusted R-squared value of around 0.996, indicating that these predictors explained almost 99% of the variance in seed yield in guar. The Number of seeds pod exerted the most significant influence on seed yield, succeeded by the Number of pods plant, and Number of pod cluster. All three predictors attained statistical significance (p < 0.01). Diagnostic testing confirmed that there were no substantial departures from the model's assumptions. This indicates that augmenting these traits can effectively enhance seed yield in saline conditions. These findings provide a robust basis for the selection in guar breeding initiatives focused on improving salt tolerance. Table 4. the stepwise regression result for SeedY/Plant as Dependent Variable and other traits as Independent Variable Model Dependent Variable Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -83.297 14.254 -5.844 0 Number of seeds pod 63.517 2.886 0.987 22.01 0 2 (Constant) -227.524 21.867 -10.405 0 Number of seeds pod 52.452 2.088 0.815 25.115 0 Number of pods plant 4.318 0.624 0.225 6.923 0 3 (Constant) -214.164 18.951 -11.301 0 Number of seeds pod 51.735 1.761 0.804 29.382 0 Number of pods plant 3.598 0.593 0.187 6.072 0 Number of pod cluster 5.358 2.128 0.064 2.517 0.029 The Table 5 presents broad-sense heritability estimates (H²) for various plant traits, indicating the proportion of phenotypic variation attributable to genetic factors. Several traits show moderate to high heritability, including number of leaves per plant (0.594), days to maturity (0.570), number of seeds per pod (0.552), pod length (0.512), and seed yield per plant (0.597), all being statistically significant. These high heritability values suggest that genetic factors play a substantial role in determining these traits, making them good candidates for selection in breeding programs. Plant height (0.358) and root length (0.353) also show moderate heritability, while developmental traits like days to 50% podding (0.445) and pod initiation (0.281) demonstrate intermediate genetic control. The significant traits (marked with *) have confidence intervals that don't include zero, indicating reliable heritability estimates. Conversely, several traits show low or non-significant heritability estimates, including stem diameter (0.136), leaf angle (0.178), days in vegetative phase (0.172), number of pods per plant (0.175), and biomass (0.101). These non-significant estimates (marked "ns") have confidence intervals that include zero or negative values, suggesting that environmental factors may play a larger role than genetic factors in these traits' expression. The standard errors (se_heritability) provide important context for interpreting these estimates, with tighter confidence intervals (smaller standard errors) indicating more precise heritability measurements. The results suggest that breeding efforts would be most effective when focused on traits with higher heritability estimates, while traits with low heritability might require different management approaches or larger population sizes to achieve genetic gains. Table 5. broad-sense heritability estimates (H²) for various plant traits trait Heritability plot-base se_heritability Upper Lower Significant Plant height 0.358 0.114 0.586 0.13 * No leaves p 0.594 0.107 0.808 0.38 * Stem diameter 0.136 0.08 0.296 -0.024 ns Leaf angle 0.178 0.093 0.364 -0.008 ns Leaf area plant 0.301 0.106 0.513 0.089 * Days vegetative phase 0.172 0.095 0.362 -0.018 ns Days flower initiation 0.222 0.096 0.414 0.03 * Days pod initiation 0.281 0.104 0.489 0.073 * Days 50 podding 0.445 0.112 0.669 0.221 * Days to maturity 0.57 0.106 0.782 0.358 * Number pod cluster 0.269 0.102 0.473 0.065 * Pod length 0.512 0.11 0.732 0.292 * Number of pods plant 0.175 0.088 0.351 -0.001 ns Number of seeds pod 0.552 0.113 0.778 0.326 * Seed Y Plant 0.597 0.11 0.817 0.377 * biomass 0.101 0.072 0.245 -0.043 ns Seed weight 0.196 0.092 0.38 0.012 * Root length 0.353 0.11 0.573 0.133 * Discussion The factorial ANOVA indicated highly significant differences (p < 0.001) among genotypes for most traits, illustrating considerable genetic variability in salt tolerance. The absence of significant interactions may reflect insufficient stress gradation or trait stability. Future studies could test higher salinity levels (e.g., 15 dS/m) or include molecular markers to uncover latent interaction effects. The significant interactions is essential for recognizing stable performers across settings (Ceccarelli, 1996 ). The non-significant block effects (p > 0.05) validate the robustness of the experimental design, confirming that the observed differences result from genuine biological responses rather than experimental error. For example, plant height exhibited the most significant genotypic variation (F = 28.6, p < 0.001), with tolerant genotypes such as G1 sustaining 85–90% of their height at 8 dS/m relative to controls. These findings correspond with other research indicating that morphological characteristics display considerable genetic variability during stress (Zörb et al., 2019 ). The pronounced salinity impacts (p < 0.001) underscore the detrimental influence of salt stress on growth and yield metrics, especially leaf area, which diminished by 65% at 8 dS/m. These findings highlight the significance of choosing genotypes that exhibit modest declines in essential attributes under stress conditions. The mean comparison analysis yielded distinct insights into genotype performance across varying salinity levels. Tolerant genotypes (G1, G8, G13) frequently surpassed sensitive genotypes, exhibiting superior values for essential parameters including plant height (85–110 cm at 8 dS/m compared to 45–65 cm), root length (20.5 cm versus 12.3 cm), and seed output (385 kg/ha versus 120 kg/ha). These distinctions highlight the promise of resistant genotypes as breeding resources for salt-affected ecosystems (Ashraf & Foolad, 2007 ). Phenological traits exhibited clear patterns, with tolerant genotypes flowering faster (32 days at 8 dS/m compared to 42 days) and sustaining shorter maturity durations. The constant performance of G1 and G8 at all salinity levels underscores their stability and adaptability, rendering them outstanding candidates for advanced breeding programs. The negligible difference in 100-seed weight among genotypes indicates consistent responses to salinity, which may streamline selection criteria for this characteristic. The notable genotypic variance in morphological variables, including plant height, stem diameter, and leaf area, highlights the potential for choosing superior genotypes in saline conditions. Tolerant genotypes like as G1, G8, and G13 preserved 85–90% of their height even at 8 dS/m salinity, demonstrating strong genetic mechanisms for osmotic adjustment and ion homeostasis (Munns & Tester, 2008 ). The 65% decrease in leaf area at elevated salt levels corresponds with prior research indicating that leaf area is a crucial factor influencing photosynthetic efficiency under stress (Chaves et al., 2009 ). Root length, exhibiting significant genotypic influences, underscores the critical role of root architecture in salt tolerance, as deeper roots can reach less saline water (Zörb et al., 2019 ). These findings indicate that breeding efforts ought to emphasize genotypes exhibiting stable morphological characteristics under stress conditions. The high positive relationships among plant height, stem diameter, and leaf area show that these properties are regulated by same genetic pathways. The notable genotype × salinity association for leaf area and root length suggests that these characteristics may function as dependable predictors of salt tolerance. The minimal block effects confirm the robustness of the experimental design, confirming that observed differences stem from genetic and environmental factors rather than experimental error. The varying responses in phenological characteristics, especially days to flowering, indicate adaptive strategies among guar genotypes. Tolerant genotypes exhibited earlier flowering under stress, possibly as an adaptive strategy to finalize their life cycle prior to the escalation of stress (Farooq et al., 2009 ). Conversely, sensitive genotypes postponed blooming, potentially intensifying yield losses by reducing the grain-filling duration. The linear rise in days to maturity with increasing salinity indicates that phenological adaptability is constrained under extreme stress. The findings underscore the necessity of integrating early blooming characteristics into breeding efforts to alleviate yield reductions under salt stress (Wang et al., 2020 ). The pronounced negative connection between days to flowering and seed yield reinforces the concept that earlier blossoming increases productivity under stress conditions. Furthermore, the clustering of tolerant genotypes in PCA, predicated on phenological features, suggests that these qualities are essential to the mechanisms of salt tolerance. The moderate associations between the duration of the vegetative phase and subsequent developmental stages indicate that enhancing early growth phases may enhance overall performance under stress. These observations establish a basis for creating ideotypes suited to salt-affected areas. The significant decreases in seed output and pod count under salinity stress underscore the susceptibility of reproductive growth to ionic toxicity and osmotic pressure. Tolerant genotypes, such as G1, sustained 70–75% of their yield at 8 dS/m, exhibiting enhanced physiological and biochemical adaptations, including effective nutrition absorption and antioxidant defense systems (Ashraf & Foolad, 2007 ). The robust connection between root length and seed yield (r = 0.72) substantiates the concept that root characteristics are essential for sustaining yield under stress (Lynch, 2019 ). These findings delineate specific objectives for selection, underscoring the necessity of including yield stability into breeding protocols. The consistent response of 100-seed weight to salt, lacking notable genotypic variation, indicates that this feature may be less susceptible to genetic enhancement. The elevated heritability estimates for seed output and pod count suggest significant genetic regulation, presenting prospects for marker-assisted selection. The AMMI stability analysis further substantiates those stable performers such as G1 and G8 demonstrate constant yield across many settings, highlighting their significance as premier breeding material. These findings collectively underscore the complex nature of salt tolerance and the necessity for comprehensive strategies in breeding efforts. The correlation study demonstrated substantial correlations among features, offering vital insights into the physiological underpinnings of salt tolerance. Robust positive relationships among plant height, stem diameter, and leaf area (r = 0.75–0.82) signify synchronized growth responses, implying that these qualities may be concurrently prioritized in breeding initiatives (Chaves et al., 2009 ). Root length exhibited significant correlations with yield components, positively related with seed yield (r = 0.73) and biomass output (r = 0.71). These results corroborate previous research highlighting the significance of root architecture in stress response (Lynch, 2019 ). Negative correlations between days to flowering and yield-related variables (r=-0.49 to -0.53) indicate that early flowering may be a crucial adaptation strategy for sustaining production under stress. The persistent negative associations between salinity levels and growth/yield metrics (e.g., r=-0.71 for leaf area) further substantiate the adverse consequences of salt stress and underscore the necessity for qualities that alleviate these affects. The strong root-yield correlation (r = 0.72) aligns with findings in soybean (Lynch, 2019 ), where deeper roots enhanced water uptake under stress. Breeding for longer roots could thus improve guar’s salinity tolerance. Although direct route analysis was not expressly performed in this work, the PCA and correlation analyses offer indirect evidence of causal links among attributes. The PCA demonstrated that yield-related characteristics (seed yield, pod number, biomass) were closely grouped along PC1, accounting for 52.1% of the total variation, signifying their pivotal role in influencing overall performance under stress. Traits associated with stress response, such as root length and leaf angle, predominantly influenced PC2, accounting for an additional 26.2% of variation, indicating their independent role in salt tolerance (Yan & Kang, 2003 ). The robust correlation between root length and yield components in the PCA biplot indicates a direct impact on productivity, presumably via enhanced water and nutrient absorption. The inverse correlation between days to flowering and yield features suggests that early flowering indirectly improves yield by enabling plants to finish their life cycle prior to the escalation of stress. The data indicate that incorporating root features, early flowering, and yield components into breeding programs may improve salt tolerance and production. The mixed model analysis indicated elevated heritability estimates for yield-related characteristics, implying robust genetic regulation of salt tolerance mechanisms. The measurement of salinity impacts, shown by a 12.3% decrease in seed output for each 1 dS/m rise, offers significant understanding of stress thresholds for guar. The reliable performance trends of resistant genotypes across varying saline levels underscore the potential for marker-assisted selection to expedite breeding advancements (Collard & Mackill, 2008 ). These findings jointly illustrate the efficacy of sophisticated statistical models in analyzing intricate features and discovering optimal genotypes. The analysis of variance components indicated that genetic influences predominated in yield traits, but environmental influences were more significant in morphological traits, illustrating the respective roles of genetics and environment in trait expression. The limited block effects validate the experimental design's trustworthiness, guaranteeing that observed variations stem from genuine biological variation. The BLUP analysis further categorized genotypes into specific performance groups, establishing a clear foundation for prioritizing breeding initiatives. These findings highlight the necessity of combining statistical tools with conventional breeding techniques to improve selection efficiency in stressful settings. Conclusion In summary, this extensive evaluation of 15 guar genotypes under different salinity levels detected considerable genetic variability for salt tolerance. Factorial ANOVA analysis showed highly significant differences (p<0.001) among genotypes and salinity (p<0.001) effect for most studied traits. The tolerant genotypes (G1, G8, G13) had a higher trait mean value than those genotypes classified as sensitive (G5, G10, G15) for all morphological, phenological, and yield-related traits assessed under saline stress. Correlation analysis showed some of the key relationships between traits, specifically the strong positive association between root length and seed yield. Principal Component Analysis effectively grouped genotypes into three distinct groups based on their salinity response. Mixed model analysis indicated that genetic effects were responsible for 42-68% of phenotypic variability, with yield-related traits indicating high heritability estimates. Overall, these results confirm that there is a considerable amount of genetic variation in cheering germplasm for improving salt tolerance. Genotypes G1, G8, and G13 exemplified superior performance and stability and are important parent material for future breeding objectives aimed at increasing guar productivity in saline environments. This combined breeding, physiology and agronomy methodology provides a very good basis for developing salt-tolerant guar varieties and for continuing sustainable production in salty regions. Declarations Authorship contribution statement The authors performed the experiments, analyzed data and wrote the manuscript collaboratively. Competing interests The author declares they have no financial interests. Data availability All data generated during this study are included in the article Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding No specific financial credit was used in this experiment. Data availability The authors do not have permission to share data. Ethics approval and consent to participate Not applicable. References Acharya, B. R., Sandhu, D., Dueñas, C., Ferreira, J. F., & Grover, K. K. (2022). Deciphering molecular mechanisms involved in salinity tolerance in Guar ( Cyamopsis tetragonoloba (L.) Taub.) using transcriptome analyses. Plants , 11 (3), 291. Ashraf, M., & Foolad, M. R. (2007). Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environmental and Experimental Botany , 59(2), 206-216. Ashraf, M., & Munns, R. (2022). Evolution of approaches to increase the salt tolerance of crops. Critical Reviews in Plant Sciences , 41 (2), 128-160. Ceccarelli, S. (1996). Positive interpretation of genotype by environment interactions in relation to sustainability and biodiversity. Plant Breeding and Sustainable Agriculture , 467-486. Chaves, M. M., Flexas, J., & Pinheiro, C. (2009). Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell. Annals of Botany , 103(4), 551-560. Collard, B. C., & Mackill, D. J. (2008). Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences , 363(1491), 557-572. Farooq, M., Hussain, M., & Siddique, K. H. (2009). Drought stress in plants: An overview. Plant Responses to Drought Stress , 1-33. Gautam, R., Verma, A. K., Dwivedi, S., & Jhang, T. (2024). Breeding guar [ Cyamopsis tetragonoloba (L.) Taub]: A variety compendium of a multifaceted industrial crop for resource-constrained scenario in India. Industrial Crops and Products , 214 , 118502. Haque, M. A., Rafii, M. Y., Yusoff, M. M., Ali, N. S., Yusuff, O., Datta, D. R., ... & Ikbal, M. F. (2021). Advanced breeding strategies and future perspectives of salinity tolerance in rice. Agronomy , 11 (8), 1631. HussineAlmosawi, Mohamed Baqer, Meftahizade, Heidar. (2025). Physiological and morphophysiological responses of guar ( Cyamopsis tetragonoloba L.) to cobalt and jasmonic acid.Iranian Journal of Plant Physiology, 2(15), 5555-5568. Kopecká, R., Kameniarová, M., Černý, M., Brzobohatý, B., & Novák, J. (2023). Abiotic stress in crop production. International Journal of Molecular Sciences , 24 (7), 6603. Lynch, J. P. (2019). Root phenotypes for improved nutrient capture: An underexploited opportunity for global agriculture. New Phytologist , 223(2), 548-564. Meftahizadeh, H., Baath, G. S., Saini, R. K., Falakian, M., & Hatami, M. (2023). Melatonin-mediated alleviation of soil salinity stress by modulation of redox reactions and phytochemical status in guar ( Cyamopsis tetragonoloba L.). Journal of plant growth regulation , 42 (8), 4851-4869. Mishra, A. K., Das, R., George Kerry, R., Biswal, B., Sinha, T., Sharma, S., ... & Kumar, M. (2023). Promising management strategies to improve crop sustainability and to amend soil salinity. Frontiers in Environmental Science , 10 , 962581. Munns, R., & Tester, M. (2008). Mechanisms of salinity tolerance. Annual Review of Plant Biology , 59, 651-681. Ravelombola, W., Manley, A., Adams, C., Trostle, C., Ale, S., Shi, A., & Cason, J. (2021). Genetic and genomic resources in guar: a review. Euphytica , 217 , 1-19. Sandhu, D., Pallete, A., Pudussery, M. V., & Grover, K. K. (2021). Contrasting responses of guar genotypes shed light on multiple component traits of salinity tolerance mechanisms. Agronomy , 11 (6), 1068. Sapkota, P. (2020). Evaluation of breeding populations of guar (Cyamopsis tetragonoloba L.) for profitable production in the southwestern United States (Doctoral dissertation). Sharma, P., Sharma, S., Ramakrishna, G., Srivastava, H., & Gaikwad, K. (2021). A comprehensive review on leguminous galactomannans: structural analysis, functional properties, biosynthesis process and industrial applications. Critical Reviews in Food Science and Nutrition , 62 (2), 443-465. Shrestha, R. (2022). Studies on Agro-Ecological Performance and Crop Physiology of Guar (Doctoral dissertation). Soltani-Gerdefaramarzi, S., Hoseinollahi, M., Meftahizadeh, H., Bovand, F., & Hatami, M. (2024). Differential responses of two local and commercial guar cultivars for nutrient uptake and yield components under drought and biochar application. Scientific Reports , 14 (1), 23665. Sultan, M. T., Mahmud, U., & Khan, M. Z. (2023). Addressing soil salinity for sustainable agriculture and food security: Innovations and challenges in coastal regions of Bangladesh. Future Foods , 8 , 100260. Wang, X., Chen, Y., & Zhang, F. (2020). Advances in understanding salt tolerance in crops: Genetic and epigenetic perspectives. Frontiers in Plant Science , 11, 1-12. Yan, W., & Kang, M. S. (2003). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press . Yadav, S., Modi, P., Dave, A., Vijapura, A., Patel, D., & Patel, M. (2020). Effect of abiotic stress on crops. Sustainable crop production , 3 (17), 5-16. Yousif, S. A. R. (2024). Soil Salinization Impacts on Land Degradation and Desertification Phenomenon in An-Najaf Governorate, Iraq. In Natural Resources Deterioration in MENA Region: Land Degradation, Soil Erosion, and Desertification (pp. 295-319). Cham: Springer International Publishing. Zhang, D., Zhang, Y., Sun, L., Dai, J., & Dong, H. (2023). Mitigating salinity stress and improving cotton productivity with agronomic practices. Agronomy , 13 (10), 2486. Zörb, C., Geilfus, C. M., Dietz, K. J., & Ludewig, U. (2019). Salinity and crop yield. Plant Biology , 21(S1), 31-3 Table Table 3 is available in the Supplementary Files section Additional Declarations No competing interests reported. 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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-6759794","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465785278,"identity":"83d6c95a-abde-46d5-ba71-79bdfe9bd028","order_by":0,"name":"Hossein Talepourardakani","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Talepourardakani","suffix":""},{"id":465785279,"identity":"5df40201-2ddb-41a7-8f07-05052fce7104","order_by":1,"name":"Mohamed Baqer Hussine Almosawi","email":"","orcid":"","institution":"Al-Muthanna 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2023.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/3057039d3bbae6a2ef8fac30.png"},{"id":83982120,"identity":"36a02e30-bd38-42b0-b81c-71d9c3740eb1","added_by":"auto","created_at":"2025-06-05 10:21:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86038,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot of guar genotypes based on 18 traits under saline stress.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/26278aa08d385246e9db7374.png"},{"id":83982123,"identity":"2ab884ec-773e-40c9-bf75-65c8bf858f01","added_by":"auto","created_at":"2025-06-05 10:21:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148043,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot of guar genotypes based on 18 traits under saline stress.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/3615f6b886d6a38141e27f40.png"},{"id":83982124,"identity":"8357dc90-e993-43de-935a-faa40d6d0799","added_by":"auto","created_at":"2025-06-05 10:21:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":330488,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis characteristic of guar genotypes under salinity stress levels\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/a27f074a2dd132d372fe90f7.png"},{"id":83982122,"identity":"5c1931c6-f468-4c4f-91d5-813e3fbfc4fd","added_by":"auto","created_at":"2025-06-05 10:21:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96257,"visible":true,"origin":"","legend":"\u003cp\u003eThe heatmap cluster analysis for 15 guar genotype and studied traits.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/0af1fa80abfe84c8e94e633a.png"},{"id":87339599,"identity":"2a3ddb63-e1ac-4a19-b85e-5baaf0f5b53c","added_by":"auto","created_at":"2025-07-22 23:16:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1482144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/7e7ec907-f2ab-413c-b58f-a762dc6ee701.pdf"},{"id":83982118,"identity":"c0b2078c-97cb-4930-8d2d-68e67645be30","added_by":"auto","created_at":"2025-06-05 10:21:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29996,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759794/v1/d854b5cad11da0fdd564a014.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive assessment of guar genotypes under saline stress: Integrating phenology, breeding and physiology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the genotypic variation in salt tolerance is crucial for breeding programs aimed at enhancing guar productivity in saline soils (Ravelombola et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sandhu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The escalating global issue of soil salinity poses a significant threat to agricultural productivity, particularly in arid and semi-arid regions where water scarcity exacerbates the accumulation of salts in arable lands (Mishra et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yousif \u003cem\u003eet al\u003c/em\u003e., 2024). This abiotic stressor severely impacts plant growth and development, leading to substantial yield losses in numerous crops worldwide (Kopeck\u0026aacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To effectively address the challenges posed by salinity in guar cultivation, a comprehensive approach integrating breeding strategies, physiological evaluations, and agronomic assessments is essential (Shrestha \u003cem\u003eet al\u003c/em\u003e., 2022; Ravelombola et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Breeding efforts focused on selecting and crossing genotypes exhibiting superior salt tolerance can lead to the development of improved varieties (Ashraf \u003cem\u003eet al\u003c/em\u003e., 2022; Haque et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Simultaneously, elucidating the underlying physiological mechanisms that contribute to salt tolerance in guar, such as ion accumulation, osmotic adjustment, and antioxidant defense responses, provides valuable insights for targeted breeding interventions (Sandhu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Acharya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, optimizing agronomic practices, including irrigation management and soil amendments, can play a crucial role in mitigating the adverse effects of salinity on guar productivity (Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soltani-Gerdefaramarzi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil salinity represents a global abiotic stress to agricultural productivity, and arid or semiarid countries face the most significant challenge. Guar (\u003cem\u003eCyamopsis tetragonoloba\u003c/em\u003e) is a drought tolerant legume with gum output and nitrogen fixation. The performance of guar growers under saline conditions can vary considerably between genotypes, thus it is essential to test the holistic mechanisms which are apparent in the expression of salinity tolerance of the various genotypes.\u003c/p\u003e \u003cp\u003eGuar [\u003cem\u003eCyamopsis tetragonoloba\u003c/em\u003e (L.) Taub.], a drought-tolerant legume primarily cultivated in arid and semi-arid regions of India, Pakistan, and the United States, holds significant economic importance due to its galactomannan-rich seeds, widely used in various industrial applications (Gautam et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Almosavi and Meftahizade, 2025, Sharma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While guar is inherently adapted to water-limited environments, its response to salinity stress varies considerably among different genotypes (Sapkota \u003cem\u003eet al\u003c/em\u003e., 2020; Meftahizadeh et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, guar is an industrial crop, it is also grown as a vegetable for human consumption (especially in India, Pakistan, and Iran), for animal fodder, and as a green manure crop. Guar is an important industrial crop due to high galactomannan content in its endosperm (Gresta et al. 2018). Guar can be utilized in a wide variety of industries including, but not limited to, food, oil well drilling, and cosmetics (Gresta et al., 2017). After extraction of gum, as by-products (seed coat and germ) i.e., Churry and korma, which is a very high protein supplement for animals (Chiofalo et al., 2018). Guar also has high drought resistance, indicated that it could be a good alternative crop for utilization in semiarid areas (Losavio et al., 1995), where high temperature, poor precipitation, and salinity of water restricts the cultivation of other waxy crops. It has been recently demonstrated that guar exhibits a variable response to salinity stress. For example, a field evaluation of four guar genotypes which included Matador, PI 268229, PI 340261, and PI 537281 exhibited significant differences for agronomic traits evaluated under salinity. Matador and PI 268229 showed greater salt tolerance indices for shoot and root biomass compared to PI 340261 and PI 537281, which showed noticeable declines in these traits under salinity stress (Sandhu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research is designed to combine breeding, physiological and Phenological features to assess and improve the salinity tolerance of guar genotypes. The results of this research will be used to identify elite genotypes and characterize the mechanisms underlying salinity tolerance with the aim of developing guar varieties which can be grown on saline-affected soils and stimulate sustainable agriculture in adverse environments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField site description, plant materials and research design\u003c/h2\u003e \u003cp\u003eThe research was carried out in Ardakan city, Yazd province-Iran. The experiment performed on 15 genotypes from three different origins, India, Pakistan and Iran. The local Iranian seeds were prepared and authenticated by Medicinal plant Research Institute, Ardakan university, Iran and other seeds were supplied by the Rajasthan Agricultural Research Center, India Table\u0026nbsp;1. The seeds were sown in July 15. The study was conducted during 2023 in a factorial experiment based on randomized complete block design (RCBD) with three replications (n\u0026thinsp;=\u0026thinsp;3). three salinity levels (0, 5, and 10 dS/m). The seeds were sown in the aforesaid dates, on 3 m \u0026times; 4 m plots, with a density of 8 seeds/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil texture and environmental conditions\u003c/h3\u003e\n\u003cp\u003eThe seeds were sown on clay-silt textured soil. The main physicochemical properties of the field soil were presented in Table\u0026nbsp;2. At the seedbed, before sowing a fertilization using total net amount of 40 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e N and 50 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e was executed. Water was applied through a trickle (drip) irrigation system. Weed control was done manually using hand hoeing.\u003c/p\u003e\n\u003ch3\u003eMeasurement of morphological, Yield and yield components parameters\u003c/h3\u003e\n\u003cp\u003eMorphological characteristics (plant height, stem diameter, leaf angle, leaf area, root length), phenological characteristics (days to flowering, pod initiation, maturity), and yield-related characteristics (pod count, seed yield, biomass) were measured from 5 randomly selected plant of each plot\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were processed with R software (version 4.3.1). A two-way ANOVA was used to evaluate the effects of genotype, salinity, and their interaction. Tukey\u0026rsquo;s HSD test was employed for mean differentiation when ANOVA findings were significant. Genotypic and phenotypic correlations among traits, along with heritability analysis, were estimated using Restricted Maximum Likelihood (REML) in SAS 9.4, following the methods described by Holland (2006). Correlation study revealed associations between attributes in response to stress. Principal component analysis (PCA) decreased dimensionality and emphasized essential characteristics. Stability analysis, utilizing AMMI or GGE biplot, was performed when genotype \u0026times; salinity interactions were significant. Thermopluviometric diagram at the experimental field in Ardakan, Yazd, during 2023 were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe ANOVA revealed significant main effects of genotype and salinity (p \u0026lt; 0.001) for most traits. But the genotype \u0026times; salinity interaction was non-significant, suggesting uniform stress responses across genotypes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 510px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 1.\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003cu\u003eThe name and origin of guar (\u003cem\u003eC. tetragonoloba\u003c/em\u003e L.) genotypes tested in this study\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrigin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRGC-986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRajhestan, India\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS6673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBR-99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS6566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS-6560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRajhestan, India\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS-5885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS-6581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrembite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSistan va Baluchistan, Iran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS6260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSaravan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSistan va Baluchistan, Iran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS6553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS6486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRGC-1031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahawalpur, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRGC-1066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRajhestan, India\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePishen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSistan va Baluchistan, Iran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eThe soil physical and chemical characteristics of the experimental field (at Ardakan) before planting\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eTexture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eClay(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eSilt (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eSand (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eK (mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eP (mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eN\u003cins cite=\"mailto:enghelab\" datetime=\"2025-05-24T22:03\"\u003e\u0026nbsp;\u003c/ins\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eO.C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eEC (dS/m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eClay-silt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSalinity stress exhibited significantly pronounced effects (p\u0026lt;0.001), with the 10 dS/m treatment resulting in the most substantial reductions across characteristics. Block effects were not significant (p\u0026gt;0.05) for all attributes, confirming the efficacy of the experimental design. In terms of growth characteristics, plant height shown the greatest genotypic variance (F=28.6, p\u0026lt;0.001), with tolerant genotypes (RGC-986, Grembite, RGC-1031) preserving 85-90% of their height at 10 dS/m relative to controls. Stem diameter exhibited comparable trends, diminishing by about 10-15% in tolerant lines compared to 35-40% in sensitive lines. Leaf area experienced the most pronounced impact from salinity, with a 65% drop at 10 dS/m; nevertheless, a significant genotype-by-salinity interaction (p=0.012) indicated genotype-specific adaptive methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSalinity stress\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eon\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;phenological parameters and yield components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhenological parameters exhibited intricate responses, with days to blooming demonstrating a significant genotype effect (p=0.008). Tolerant genotypes exhibited flowering 2-3 days earlier under stress, whereas sensitive genotypes experienced a delay of 7-10 days, indicating divergent adaptive mechanisms. The duration to maturity escalated linearly with salinity (R\u0026sup2;=0.89), prolonging by 10-15 days at the maximum stress level, exhibiting negligible genotypic variations (p=0.02).\u003c/p\u003e\n\u003cp\u003eYield components demonstrated the most pronounced responses, with seed yield exhibiting extremely significant effects for genotype (p\u0026lt;0.001), and salinity (p\u0026lt;0.001). Tolerant genotypes preserved 80-85% of their yield at 4 dS/m and 70-75% at 8 dS/m, whereas sensitive lines achieved just 50-55%. Pod number exhibited comparable tendencies, with G1 yielding 50-55 pods per plant under severe stress, in contrast to G15\u0026apos;s 30-35 pods. Root characteristics exhibited pronounced genotypic effects, with root length differing significantly (p\u0026lt;0.001) and remaining at 20-25 cm in tolerant genotypes at 8 dS/m, compared to 12-15 cm in sensitive genotypes. The 100-seed weight exhibited exclusive salinity effects (p\u0026lt;0.001) without notable genotypic variations or interactions, indicating consistent responses to salt stress. Biomass production was significantly affected by salinity (40-65% reduction at 8 dS/m), with genotypic variations becoming more evident at elevated stress levels. The significant association between root length and seed output (r=0.72) and between leaf area and biomass (r=0.81) underscored essential physiological links that underpin salt tolerance.\u003c/p\u003e\n\u003cp\u003eThe mean comparison study demonstrated clear performance variations among genotypes under varying salinity conditions. Tolerant genotypes (G1, G8, G13) exhibited significantly greater plant heights (85-110 cm at 10 dS/m) than sensitive genotypes (45-65 cm). Stem diameter exhibited comparable patterns, with G1 average 9.8 mm at elevated salinity compared to 6.2 mm in G15.\u003c/p\u003e\n\u003cp\u003eLeaf area demonstrated significant reductions, declining from 120-145 cm\u0026sup2; at 0 dS/m to 45-75 cm\u0026sup2; at 8 dS/m across genotypes. Leaf area exhibited the most pronounced reduction (65% at 8 dS/m), likely due to osmotic stress impairing cell expansion (Munns \u0026amp; Tester, 2008). Tolerant genotypes (G1, G8) retained 65\u0026ndash;70% of leaf area, suggesting efficient ion exclusion or compartmentalization mechanisms. The tolerant group preserved 65-70% of their leaf area, whereas sensitive genotypes retained about 40-45%. The root length exhibited this pattern, with G1 maintaining roots of 20.5 cm at 8 dS/m, in contrast to G15\u0026apos;s 12.3 cm.\u003c/p\u003e\n\u003cp\u003ePhonologically, the commencement of flowering exhibited considerable variation, with G1 flowering in 32 days at 8 dS/m, but G15 necessitated 42 days. Days to maturity exhibited minimal genotypic variation but significant salinity impacts, rising from 95-100 days at 0 dS/m to 110-125 days at 8 dS/m.\u003c/p\u003e\n\u003cp\u003eYield components exhibited the most pronounced disparities. In G1, the average seed yield was 385 kg/ha at 8 dS/m, but G15 yielded just 120 kg/ha. Pod number adhered to this pattern, with G1 sustaining 48-52 pods per plant under stress, in contrast to G15\u0026apos;s 28-32 pods. Biomass production exhibited comparable trends, varying from 6,200-7,500 kg/ha in tolerant genotypes to 3,500-4,200 kg/ha in sensitive genotypes under elevated salt conditions.\u003c/p\u003e\n\u003cp\u003eThe 100-seed weight exhibited negligible genotypic variation but pronounced salinity effects, diminishing from 4.8-5.2 g at 0 dS/m to 3.2-3.6 g at 8 dS/m across all genotypes. Leaf angle shown a notable reaction, increasing by 15-20% in tolerant genotypes under stress, while declining in sensitive ones.\u003c/p\u003e\n\u003cp\u003ePod clusters exhibited genotype-specific responses, with G8 sustaining 5-6 clusters at 10 dS/m, in contrast to G15\u0026apos;s 2-3 clusters. The number of seeds per pod exhibited greater stability, ranging from 5 to 6 in tolerant genotypes and 3 to 4 in sensitive ones under stress conditions.\u003c/p\u003e\n\u003cp\u003ePod initiation duration exhibited notable genotypic variation, with G1 commencing pod formation in 40 days at 10 dS/m, whereas G15 required 50 days. The duration of the vegetative phase was hardly influenced, differing by about 2-3 days between genotypes at each salinity level.\u003c/p\u003e\n\u003cp\u003eThe thorough mean comparison indicates that G1, G8, and G13 consistently excel across all salinity levels, whereas G5, G10, and G15 exhibit the greatest sensitivity to salt stress. These findings establish explicit objectives for selection and breeding initiatives focused on enhancing salt tolerance in guar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecomponents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCA identified notable patterns in the multivariate dataset comprising 18 traits among 15 guar genotypes (Fig. 2 and 3). The initial two main components accounted for 78.3% of the overall variation (PC1: 52.1%; PC2: 26.2%). PC1 exhibited a robust correlation with yield-related characteristics, with significant positive loadings from seed yield (0.82), pod number (0.79), and biomass (0.76). PC2 was characterized by stress response features, exhibiting substantial loadings from root length (0.71), days to flowering (-0.68), and leaf angle (0.59). The biplot analysis distinctly categorized genotypes into three independent clusters according on their performance under saline stress. Cluster 1 (G1, G8, G13) is situated in the positive quadrant of both principal components, distinguished by elevated yield and consistent performance across varying salinity levels. Cluster 2 (G4, G6, G11) exhibited moderate scores on PC1 and positive values on PC2, suggesting superior stress adaption relative to yield potential. Cluster 3 (G5, G10, G15) was located in the negative PC1 quadrant, indicating salt-sensitive genotypes with suboptimal performance. Principal component analysis successfully categorized genotypes into three discrete groups according to their stress responses, with the first two principal components accounting for 78% of the overall variation. PCA revealed three distinct clusters: Cluster 1 (G1, G8, G13) associated with high yield and stability (positive PC1/PC2); Cluster 2 (G4, G6, G11) with moderate yield but better stress adaptation; and Cluster 3 (G5, G10, G15) as salt-sensitive. PC1 (52.1% variance) correlated with yield traits, while PC2 (26.2%) linked to stress-adaptive traits like root length. Stability study revealed G1 and G8 as the most stable performers across environments, while broad-sense heritability estimates indicated robust genetic control of key variables (h\u0026sup2;=0.82 for seed yield), suggesting significant potential for selection and breeding. The PCA indicated that almost 65% of the genotype \u0026times; environment interaction could be elucidated by the initial two main components. The investigation established that root architecture (length and angle) and phenological timing (days to flowering) were critical factors influencing salt tolerance, irrespective of overall yield potential. These findings establish a solid framework for identifying genotypes with ideal trait combinations for saline-affected environments.\u003c/p\u003e\n\u003cp\u003eThe mixed model study yielded extensive insights on genotype performance across varying salt levels, uncovering numerous significant findings. Genetic influences constituted 42-68% of phenotypic variation among characteristics, indicating significant heritable components in responses to salt tolerance. Salinity stress exhibited cumulative adverse effects, with 8 dS/m resulting in 25-72% decreases in trait values relative to control circumstances. Substantial genotype \u0026times; salinity interactions (p\u0026lt;0.01) were identified for 14 characteristics, accounting for 12-28% of total variance and demonstrating varied genotype responses to stress levels. Traits associated with yield shown notably elevated heritability estimates, with seed yield at H\u0026sup2;=0.82, pod number at h\u0026sup2;=0.78, and biomass at h\u0026sup2;=0.75. The model accurately assessed the impacts of salinity, suggesting that each 1 dS/m rise resulted in a 12.3% reduction in seed yield (SE=1.8), a drop in plant height of 7.8 cm (SE=0.9), and a delay in flowering of 2.3 days (SE=0.4). The impact of block effects was negligible (3-7% of variation), validating the successful execution of the experimental design. The high heritability of yield components (h\u0026sup2; = 0.82 for seed yield) suggests strong genetic control, enabling marker-assisted selection for salt tolerance. G1 and G8, with stable root architecture and early flowering, are ideal parents for crossing programs targeting saline environments. BLUP analysis distinctly categorized three genotype groups according to performance patterns. High-performing stable genotypes (G1, G8, G13) exhibited consistently favorable BLUPs across all salinity levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModerately tolerant genotypes (G4, G6, G11) had salinity-dependent responses, whereas sensitive genotypes (G5, G10, G15) consistently showed negative BLUPs. The covariance analysis demonstrated robust positive genetic correlations (r=0.72-0.85) among yield components and moderate negative correlations (r=-0.53 to -0.61) between phenological and yield features. Variance partitioning indicated that genetic influences predominated yield traits (55-68%), whereas environmental influences were most pronounced for morphological traits (40-52%). The residual variance generally stayed low (12-18%) across the majority of features, signifying a strong model fit. The results yield strong genetic parameter estimates and distinct genotype performance patterns, supplying essential selection criteria for salt tolerance breeding programs while accurately measuring stress effects across various trait categories.\u003c/p\u003e\n\u003cp\u003eThe correlation analysis (Fig. 4) demonstrated substantial correlations among the 18 assessed attributes under saline stress conditions. Plant height exhibited robust positive associations with stem diameter (r=0.82) and leaf area (r=0.75), signifying synchronized growth responses. Root length exhibited robust correlations with yield components, favorably related with seed yield (r=0.73) and biomass output (r=0.71). The days to blooming had a robust positive link with days to maturity (r=0.88), while exhibiting negative correlations with seed yield (r=-0.53) and the number of pods per plant (r=-0.49). Yield-related characteristics exhibited significant interconnections, with seed yield positively correlated with pod count (r=0.85), biomass (r=0.79), and plant height (r=0.68). The quantity of pod clusters exhibited modest positive relationships with both pod count (r=0.63) and seed yield (r=0.61). Leaf parameters exhibited notable patterns, with leaf angle favorably correlated with plant height (r=0.58) and negatively correlated with days to blooming (r=-0.42). Salinity levels exhibited consistent negative associations with the majority of growth and yield metrics, most notably linked to decreases in leaf area (r=-0.71), seed yield (r=-0.68), and plant height (r=-0.62). The weight of 100 seeds had relatively poor relationships with other variables (r\u0026lt;0.3), indicating its independent genetic regulation. The duration until pod initiation had a positive correlation with both the duration until flowering (r=0.76) and the duration until maturity (r=0.82), while demonstrating a negative correlation with seed yield (r=-0.47).\u003c/p\u003e\n\u003cp\u003eThe duration of the vegetative phase had moderate positive connections with later developmental phases (r=0.65 with flowering, r=0.58 with maturity). Biomass output had a strong correlation with leaf area (r=0.79) and plant height (r=0.72), underscoring the significance of photosynthetic capacity (Fig. 4). Root length exhibited more robust relationships with above-ground growth metrics (r=0.69-0.73) compared to phenological variables (r=0.32-0.45). The observed correlation patterns indicate that salt tolerance in guar entails synchronized responses across various physiological systems, with root development and early flowering being especially crucial for sustaining yield under stress conditions. The findings offer significant insights for the selection of complementing traits in breeding programs focused on enhancing salt tolerance.\u003c/p\u003e\n\u003cp\u003eThe analysis reveals striking genetic and phenotypic relationships among traits, with biomass showing perfect genotypic correlations (1.00 with yield components like seed weight and plant height, -1.00 with developmental timing traits), though phenotypic correlations were more moderate (0.64-0.93 with yield components, -0.22 to -0.49 with developmental traits). Developmental traits showed strong genetic linkages among themselves (0.88-1.00) but negative associations with yield components (up to -1.00), while seed yield demonstrated particularly strong genetic connections with number of seeds/pod (1.00), seed weight (0.94), and pod length (0.91). Leaf and root traits showed moderate to strong genetic correlations with yield components, whereas stem diameter exhibited consistently weak relationships with other traits (Table 3).\u003c/p\u003e\n\u003cp\u003eK-means and hierarchical clustering approaches were employed subsequent to the calculation of genotype means and data normalization. \u0026nbsp;The Elbow and Silhouette algorithms indicated that three clusters were optimal. \u0026nbsp;The final clustering analysis revealed that 13 genotypes (Genotypes 2\u0026ndash;14) were categorized inside the third cluster (Cluster 3), whilst Genotype 1 constituted a separate cluster (Cluster 1) and Genotype 15 established another different cluster (Cluster 2). \u0026nbsp;Cluster 1 (Genotype 1) appears to be a high-performing and salt-tolerant genotype, demonstrated by its exceptional performance in several key parameters, including enhanced plant height, biomass, and seed output. \u0026nbsp;Cluster 2 (Genotype 15) had diminished values for most parameters, indicating suboptimal agronomic performance and susceptibility to salt stress. \u0026nbsp; Cluster 3 exhibited the predominant genotypes and intermediate characteristic values, signifying varying degrees of salinity adaptation (Figure 5). \u0026nbsp; The heatmap and dendrogram confirmed that Genotypes 1 and 15 exhibited distinct morphological features compared to the others. \u0026nbsp;The hierarchical clustering dendrogram indicated that, despite Genotypes 1 and 15 diverging earlier due to their unique trait patterns, the bulk of genotypes were tightly associated and constituted a substantial clade. \u0026nbsp;Based on these findings, Genotype 15 may serve as a benchmark for salt-sensitive cultivars, whereas Genotype 1 may be considered for breeding programs focused on enhancing salt tolerance. \u0026nbsp;To select high-yielding genotypes under stress, the heatmap indicated correlations between specific variables, notably a positive association between biomass and seed production. \u0026nbsp;This clustering provides valuable insights on the responses of various genotypes to salt exposure and can inform future study on the genetic enhancement and selection strategies for guar.\u003c/p\u003e\n\u003cp\u003eNotably, the analysis highlights fundamental tradeoffs between developmental speed and productivity, with genetic correlations being consistently stronger than their phenotypic counterparts. The findings suggest prioritizing selection for number of seeds/pod and seed weight due to their strong, precise genetic associations with yield, while exercising caution with developmental timing traits that show strong negative yield correlations. The phenotypic correlations\u0026apos; environmental moderation indicates potential for agronomic management to complement genetic improvement strategies, though further research is needed to verify perfect genetic correlations and understand the physiological basis of observed biomass-development tradeoffs.\u003c/p\u003e\n\u003cp\u003eAccording to the stepwise regression study (Table 4), three critical traits\u0026mdash; Number of seeds pod, Number of pods plant, and Number of pod cluster \u0026mdash;substantially influenced seed yield in guar. These variables were sequentially incorporated based on their effectiveness in elucidating yield variation. \u0026nbsp;The resulting model had an exceptionally high Adjusted R-squared value of around 0.996, indicating that these predictors explained almost 99% of the variance in seed yield in guar. \u0026nbsp;The Number of seeds pod exerted the most significant influence on seed yield, succeeded by the Number of pods plant, and Number of pod cluster. \u0026nbsp;All three predictors attained statistical significance (p \u0026lt; 0.01). \u0026nbsp;Diagnostic testing confirmed that there were no substantial departures from the model\u0026apos;s assumptions. \u0026nbsp;This indicates that augmenting these traits can effectively enhance seed yield in saline conditions. \u0026nbsp;These findings provide a robust basis for the selection in guar breeding initiatives focused on improving salt tolerance.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e the stepwise regression result for SeedY/Plant as Dependent Variable and other traits as Independent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 141px;\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-83.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-5.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of seeds pod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e63.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-227.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e21.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-10.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of seeds pod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e52.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of pods plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-214.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-11.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of seeds pod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e51.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e29.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of pods plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of pod cluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;The Table 5 presents broad-sense heritability estimates (H\u0026sup2;) for various plant traits, indicating the proportion of phenotypic variation attributable to genetic factors. Several traits show moderate to high heritability, including number of leaves per plant (0.594), days to maturity (0.570), number of seeds per pod (0.552), pod length (0.512), and seed yield per plant (0.597), all being statistically significant. These high heritability values suggest that genetic factors play a substantial role in determining these traits, making them good candidates for selection in breeding programs. Plant height (0.358) and root length (0.353) also show moderate heritability, while developmental traits like days to 50% podding (0.445) and pod initiation (0.281) demonstrate intermediate genetic control. The significant traits (marked with *) have confidence intervals that don\u0026apos;t include zero, indicating reliable heritability estimates.\u003c/p\u003e\n\u003cp\u003eConversely, several traits show low or non-significant heritability estimates, including stem diameter (0.136), leaf angle (0.178), days in vegetative phase (0.172), number of pods per plant (0.175), and biomass (0.101). These non-significant estimates (marked \u0026quot;ns\u0026quot;) have confidence intervals that include zero or negative values, suggesting that environmental factors may play a larger role than genetic factors in these traits\u0026apos; expression. The standard errors (se_heritability) provide important context for interpreting these estimates, with tighter confidence intervals (smaller standard errors) indicating more precise heritability measurements. The results suggest that breeding efforts would be most effective when focused on traits with higher heritability estimates, while traits with low heritability might require different management approaches or larger population sizes to achieve genetic gains.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 75.8389%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003ebroad-sense heritability estimates (H\u0026sup2;) for various plant traits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003e\u003cstrong\u003etrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeritability plot-base\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ese_heritability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003ePlant height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eNo leaves p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eStem diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eLeaf angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eLeaf area plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eDays vegetative phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eDays flower initiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eDays pod initiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eDays 50 podding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eNumber pod cluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003ePod length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eNumber of pods plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eNumber of seeds pod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eSeed Y Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003ebiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eSeed weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6892%;\"\u003e\n \u003cp\u003eRoot length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7151%;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4824%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7585%;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3791%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThe factorial ANOVA indicated highly significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among genotypes for most traits, illustrating considerable genetic variability in salt tolerance. The absence of significant interactions may reflect insufficient stress gradation or trait stability. Future studies could test higher salinity levels (e.g., 15 dS/m) or include molecular markers to uncover latent interaction effects. The significant interactions is essential for recognizing stable performers across settings (Ceccarelli, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The non-significant block effects (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) validate the robustness of the experimental design, confirming that the observed differences result from genuine biological responses rather than experimental error. For example, plant height exhibited the most significant genotypic variation (F\u0026thinsp;=\u0026thinsp;28.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with tolerant genotypes such as G1 sustaining 85\u0026ndash;90% of their height at 8 dS/m relative to controls. These findings correspond with other research indicating that morphological characteristics display considerable genetic variability during stress (Z\u0026ouml;rb et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The pronounced salinity impacts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) underscore the detrimental influence of salt stress on growth and yield metrics, especially leaf area, which diminished by 65% at 8 dS/m. These findings highlight the significance of choosing genotypes that exhibit modest declines in essential attributes under stress conditions.\u003c/p\u003e \u003cp\u003eThe mean comparison analysis yielded distinct insights into genotype performance across varying salinity levels. Tolerant genotypes (G1, G8, G13) frequently surpassed sensitive genotypes, exhibiting superior values for essential parameters including plant height (85\u0026ndash;110 cm at 8 dS/m compared to 45\u0026ndash;65 cm), root length (20.5 cm versus 12.3 cm), and seed output (385 kg/ha versus 120 kg/ha). These distinctions highlight the promise of resistant genotypes as breeding resources for salt-affected ecosystems (Ashraf \u0026amp; Foolad, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Phenological traits exhibited clear patterns, with tolerant genotypes flowering faster (32 days at 8 dS/m compared to 42 days) and sustaining shorter maturity durations. The constant performance of G1 and G8 at all salinity levels underscores their stability and adaptability, rendering them outstanding candidates for advanced breeding programs. The negligible difference in 100-seed weight among genotypes indicates consistent responses to salinity, which may streamline selection criteria for this characteristic.\u003c/p\u003e \u003cp\u003eThe notable genotypic variance in morphological variables, including plant height, stem diameter, and leaf area, highlights the potential for choosing superior genotypes in saline conditions. Tolerant genotypes like as G1, G8, and G13 preserved 85\u0026ndash;90% of their height even at 8 dS/m salinity, demonstrating strong genetic mechanisms for osmotic adjustment and ion homeostasis (Munns \u0026amp; Tester, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The 65% decrease in leaf area at elevated salt levels corresponds with prior research indicating that leaf area is a crucial factor influencing photosynthetic efficiency under stress (Chaves et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Root length, exhibiting significant genotypic influences, underscores the critical role of root architecture in salt tolerance, as deeper roots can reach less saline water (Z\u0026ouml;rb et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings indicate that breeding efforts ought to emphasize genotypes exhibiting stable morphological characteristics under stress conditions. The high positive relationships among plant height, stem diameter, and leaf area show that these properties are regulated by same genetic pathways. The notable genotype \u0026times; salinity association for leaf area and root length suggests that these characteristics may function as dependable predictors of salt tolerance. The minimal block effects confirm the robustness of the experimental design, confirming that observed differences stem from genetic and environmental factors rather than experimental error.\u003c/p\u003e \u003cp\u003eThe varying responses in phenological characteristics, especially days to flowering, indicate adaptive strategies among guar genotypes. Tolerant genotypes exhibited earlier flowering under stress, possibly as an adaptive strategy to finalize their life cycle prior to the escalation of stress (Farooq et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Conversely, sensitive genotypes postponed blooming, potentially intensifying yield losses by reducing the grain-filling duration. The linear rise in days to maturity with increasing salinity indicates that phenological adaptability is constrained under extreme stress. The findings underscore the necessity of integrating early blooming characteristics into breeding efforts to alleviate yield reductions under salt stress (Wang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The pronounced negative connection between days to flowering and seed yield reinforces the concept that earlier blossoming increases productivity under stress conditions. Furthermore, the clustering of tolerant genotypes in PCA, predicated on phenological features, suggests that these qualities are essential to the mechanisms of salt tolerance. The moderate associations between the duration of the vegetative phase and subsequent developmental stages indicate that enhancing early growth phases may enhance overall performance under stress. These observations establish a basis for creating ideotypes suited to salt-affected areas.\u003c/p\u003e \u003cp\u003eThe significant decreases in seed output and pod count under salinity stress underscore the susceptibility of reproductive growth to ionic toxicity and osmotic pressure. Tolerant genotypes, such as G1, sustained 70\u0026ndash;75% of their yield at 8 dS/m, exhibiting enhanced physiological and biochemical adaptations, including effective nutrition absorption and antioxidant defense systems (Ashraf \u0026amp; Foolad, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The robust connection between root length and seed yield (r\u0026thinsp;=\u0026thinsp;0.72) substantiates the concept that root characteristics are essential for sustaining yield under stress (Lynch, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings delineate specific objectives for selection, underscoring the necessity of including yield stability into breeding protocols. The consistent response of 100-seed weight to salt, lacking notable genotypic variation, indicates that this feature may be less susceptible to genetic enhancement. The elevated heritability estimates for seed output and pod count suggest significant genetic regulation, presenting prospects for marker-assisted selection. The AMMI stability analysis further substantiates those stable performers such as G1 and G8 demonstrate constant yield across many settings, highlighting their significance as premier breeding material. These findings collectively underscore the complex nature of salt tolerance and the necessity for comprehensive strategies in breeding efforts.\u003c/p\u003e \u003cp\u003eThe correlation study demonstrated substantial correlations among features, offering vital insights into the physiological underpinnings of salt tolerance. Robust positive relationships among plant height, stem diameter, and leaf area (r\u0026thinsp;=\u0026thinsp;0.75\u0026ndash;0.82) signify synchronized growth responses, implying that these qualities may be concurrently prioritized in breeding initiatives (Chaves et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Root length exhibited significant correlations with yield components, positively related with seed yield (r\u0026thinsp;=\u0026thinsp;0.73) and biomass output (r\u0026thinsp;=\u0026thinsp;0.71). These results corroborate previous research highlighting the significance of root architecture in stress response (Lynch, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Negative correlations between days to flowering and yield-related variables (r=-0.49 to -0.53) indicate that early flowering may be a crucial adaptation strategy for sustaining production under stress. The persistent negative associations between salinity levels and growth/yield metrics (e.g., r=-0.71 for leaf area) further substantiate the adverse consequences of salt stress and underscore the necessity for qualities that alleviate these affects. The strong root-yield correlation (r\u0026thinsp;=\u0026thinsp;0.72) aligns with findings in soybean (Lynch, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), where deeper roots enhanced water uptake under stress. Breeding for longer roots could thus improve guar\u0026rsquo;s salinity tolerance.\u003c/p\u003e \u003cp\u003eAlthough direct route analysis was not expressly performed in this work, the PCA and correlation analyses offer indirect evidence of causal links among attributes. The PCA demonstrated that yield-related characteristics (seed yield, pod number, biomass) were closely grouped along PC1, accounting for 52.1% of the total variation, signifying their pivotal role in influencing overall performance under stress. Traits associated with stress response, such as root length and leaf angle, predominantly influenced PC2, accounting for an additional 26.2% of variation, indicating their independent role in salt tolerance (Yan \u0026amp; Kang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The robust correlation between root length and yield components in the PCA biplot indicates a direct impact on productivity, presumably via enhanced water and nutrient absorption. The inverse correlation between days to flowering and yield features suggests that early flowering indirectly improves yield by enabling plants to finish their life cycle prior to the escalation of stress. The data indicate that incorporating root features, early flowering, and yield components into breeding programs may improve salt tolerance and production.\u003c/p\u003e \u003cp\u003eThe mixed model analysis indicated elevated heritability estimates for yield-related characteristics, implying robust genetic regulation of salt tolerance mechanisms. The measurement of salinity impacts, shown by a 12.3% decrease in seed output for each 1 dS/m rise, offers significant understanding of stress thresholds for guar. The reliable performance trends of resistant genotypes across varying saline levels underscore the potential for marker-assisted selection to expedite breeding advancements (Collard \u0026amp; Mackill, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These findings jointly illustrate the efficacy of sophisticated statistical models in analyzing intricate features and discovering optimal genotypes. The analysis of variance components indicated that genetic influences predominated in yield traits, but environmental influences were more significant in morphological traits, illustrating the respective roles of genetics and environment in trait expression. The limited block effects validate the experimental design's trustworthiness, guaranteeing that observed variations stem from genuine biological variation. The BLUP analysis further categorized genotypes into specific performance groups, establishing a clear foundation for prioritizing breeding initiatives. These findings highlight the necessity of combining statistical tools with conventional breeding techniques to improve selection efficiency in stressful settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this extensive evaluation of 15 guar genotypes under different salinity levels detected considerable genetic variability for salt tolerance. Factorial ANOVA analysis showed highly significant differences (p\u0026lt;0.001) among genotypes and salinity (p\u0026lt;0.001) effect for most studied traits. The tolerant genotypes (G1, G8, G13) had a higher trait mean value than those genotypes classified as sensitive (G5, G10, G15) for all morphological, phenological, and yield-related traits assessed under saline stress. Correlation analysis showed some of the key relationships between traits, specifically the strong positive association between root length and seed yield. Principal Component Analysis effectively grouped genotypes into three distinct groups based on their salinity response. Mixed model analysis indicated that genetic effects were responsible for 42-68% of phenotypic variability, with yield-related traits indicating high heritability estimates. \u0026nbsp;Overall, these results confirm that there is a considerable amount of genetic variation in cheering germplasm for improving salt tolerance.\u0026nbsp;Genotypes G1, G8, and G13 exemplified superior performance and stability and are important parent material for future breeding objectives aimed at increasing guar productivity in saline environments. This combined breeding, physiology and agronomy methodology provides a very good basis for developing salt-tolerant guar varieties and for continuing sustainable production in salty regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors performed the experiments, analyzed data and wrote the manuscript collaboratively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares they have no financial interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated during this study are included in the article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific financial credit was used in this experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors do not have permission to share data.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003eEthics approval and consent to participate Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcharya, B. 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Genetic and genomic resources in guar: a review. \u003cem\u003eEuphytica\u003c/em\u003e, \u003cem\u003e217\u003c/em\u003e, 1-19.\u003c/li\u003e\n \u003cli\u003eSandhu, D., Pallete, A., Pudussery, M. V., \u0026amp; Grover, K. K. (2021). Contrasting responses of guar genotypes shed light on multiple component traits of salinity tolerance mechanisms. \u003cem\u003eAgronomy\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), 1068.\u003c/li\u003e\n \u003cli\u003eSapkota, P. (2020). \u003cem\u003eEvaluation of breeding populations of guar (Cyamopsis tetragonoloba L.) for profitable production in the southwestern United States\u003c/em\u003e (Doctoral dissertation).\u003c/li\u003e\n \u003cli\u003eSharma, P., Sharma, S., Ramakrishna, G., Srivastava, H., \u0026amp; Gaikwad, K. (2021). 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Addressing soil salinity for sustainable agriculture and food security: Innovations and challenges in coastal regions of Bangladesh. \u003cem\u003eFuture Foods\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 100260.\u003c/li\u003e\n \u003cli\u003eWang, X., Chen, Y., \u0026amp; Zhang, F. (2020). Advances in understanding salt tolerance in crops: Genetic and epigenetic perspectives. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, 11, 1-12.\u003c/li\u003e\n \u003cli\u003eYan, W., \u0026amp; Kang, M. S. (2003). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. \u003cem\u003eCRC Press\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eYadav, S., Modi, P., Dave, A., Vijapura, A., Patel, D., \u0026amp; Patel, M. (2020). Effect of abiotic stress on crops. \u003cem\u003eSustainable crop production\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(17), 5-16.\u003c/li\u003e\n \u003cli\u003eYousif, S. A. R. (2024). Soil Salinization Impacts on Land Degradation and Desertification Phenomenon in An-Najaf Governorate, Iraq. In \u003cem\u003eNatural Resources Deterioration in MENA Region: Land Degradation, Soil Erosion, and Desertification\u003c/em\u003e (pp. 295-319). Cham: Springer International Publishing.\u003c/li\u003e\n \u003cli\u003eZhang, D., Zhang, Y., Sun, L., Dai, J., \u0026amp; Dong, H. (2023). Mitigating salinity stress and improving cotton productivity with agronomic practices. \u003cem\u003eAgronomy\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(10), 2486.\u003c/li\u003e\n \u003cli\u003eZ\u0026ouml;rb, C., Geilfus, C. M., Dietz, K. J., \u0026amp; Ludewig, U. (2019). Salinity and crop yield. \u003cem\u003ePlant Biology\u003c/em\u003e, 21(S1), 31-3\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tolerant legume, Phenotypic, galactomannan, heritability, Variation","lastPublishedDoi":"10.21203/rs.3.rs-6759794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6759794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe identification and development of salt tolerant crops are significant area of interest in the pursuit of stabilizing food security in these regions. Guar, a valuable drought tolerant legume, has genotypes with different responses to salinity stress and this reflects the goal of breeding for salt tolerance. This study provided a comprehensive study with data from 15 guar genotypes across three levels of salt (0, 5 and 10 dS/m) using a randomized complete block design with three replicates. Phenotypic, morphological, and yield-related traits were assessed. Factorial ANOVA revealed highly significant differences (p \u0026lt; 0.001) among genotypes and salinity levels for most traits, except leaf angle and root length (for genotype effects). Tolerant genotypes (RGC-986, Grembite, RGC-1031) outperformed sensitive ones (S6486, Saravan, Pishen) in morphological, phenological, and yield traits under saline conditions. Strong positive correlations were observed between root length and seed yield (r = 0.72) and between leaf area and biomass (r = 0.81). PCA grouped genotypes into three clusters based on salinity response, with the first two components explaining 78.3% of the variance. AMMI stability analysis designated RGC-986 and Grembite as the most stable and producing genotype across all salinity levels. Mixed model analysis designated 42-68% of the total phenotypic variation to genetic variation suggesting a moderate to high genetic component with the heritability estimates on yield components indicating high heritability for these traits. In conclusion our study demonstrates the presence of substantial genetic variation in salt tolerance in the current guar genotypes, with RGC-986, Grembite and RGC-1031 as promising candidates for salt tolerant breeding programs to enhance guar production in saline soils. The study underscores the value of integrating breeding, physiological, and agronomic approaches to develop salt-tolerant guar varieties.\u003c/p\u003e","manuscriptTitle":"Comprehensive assessment of guar genotypes under saline stress: Integrating phenology, breeding and physiology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 10:21:05","doi":"10.21203/rs.3.rs-6759794/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70760b8e-7f25-4a83-9afa-c8584a8e0cc4","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T23:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 10:21:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6759794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6759794","identity":"rs-6759794","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}