Multivariate analysis of phenotypic traits and trait associations for identifying elite lentil (Lens culinaris Medik.) genotypes using STI and MFV indices for breeding in salt-affected soil

Multivariate analysis of phenotypic traits and trait associations for identifying elite lentil (Lens culinaris Medik.) genotypes using STI and MFV indices for breeding in salt-affected soil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivariate analysis of phenotypic traits and trait associations for identifying elite lentil (Lens culinaris Medik.) genotypes using STI and MFV indices for breeding in salt-affected soil Vijayata Singh, Jogendra Singh, Ravi Kiran KT, Kailash Prajapat, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7176739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims This study aimed to identify superior lentil genotypes with tolerance to both salinity and alkalinity stress through field phenotyping and robust trait selection indices. Methods A total of 500 lentil genotypes (317 indigenous, 183 exotic) were evaluated under natural field conditions—control, salinity (ECe ~7 dS/m), and alkalinity (pH ~9.3)—at the ICAR-CSSRI, Karnal. Data on seven agro morphological traits were analyzed via ANOVA, correlation, regression, and PCA. Two indices—the stress tolerance index (STI) and membership function value (MFV)—were employed to identify salt-tolerant genotypes on the basis of yield and multivariate trait performance. Results Highly significant variation was observed among the genotypes across the treatments. Compared with salinity, alkalinity stress had a more pronounced effect on key traits such as seed yield and pod number. Regression and PCA highlighted pods per plant, test weight, and plant height as key yield determinants. STI was more effective under salinity, whereas both STI and MFV performed equally well under alkalinity. Six genotypes (IC267104, IC248956, IC268241, IC267658, IC267657, and IC268240) consistently ranked highly under both stress types across indices. This study provides a comprehensive field-based evaluation of the salinity and alkalinity tolerance of lentil germplasm via integrated selection indices. The identified genotypes offer valuable resources for breeding programs targeting salt-affected soils and can serve as parents for mapping populations aimed at dissecting the genetic basis of stress tolerance in lentils. Lens culinaris salt tolerance stress tolerance index (STI) membership function value (MFV) saline and alkaline soils trait-based selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Soil salinity and alkalinity are among the most important abiotic stress factors limiting agricultural productivity worldwide. Together, salt-affected soils cover an estimated 932.2 million hectares, with approximately 351.2-million-hectare saline and 581 million hectares alkaline or alkaline in nature (FAO, 2022). These challenges reduce the availability of arable land and threaten food security, especially in arid and semiarid regions where irrigation practices and poor drainage further worsen soil degradation (Rengasamy, 2010 ). Salt stress impacts plant physiology through osmotic stress, ion toxicity (notably Na⁺ and Cl⁻), nutritional imbalance, and oxidative damage, thereby hindering growth, reproductive development, and yield (Munns & Tester, 2008 ; Grattan & Grieve, 1999 ). Lentil ( Lens culinaris Medik. ), an important cool-season legume, plays a significant role in human nutrition and sustainable agriculture because of its high protein content, nitrogen-fixing ability, and relatively low water requirement. The lentil ( Lens culinaris ), a protein-rich legume, is a vital dietary component in regions with predominantly vegetarian or low-meat diets. However, salinity stress negatively affects its germination and early seedling development (Foti et al., 2019 ). When cultivated mainly in arid and semiarid regions, lentil shows moderate sensitivity to salinity, with a 50% yield reduction reported at an electrical conductivity of approximately 6.0 dS/m (Singh et al., 2017 a). This sensitivity limits its cultivation in saline and alkaline soils, especially in South Asia, which accounts for more than 60% of the world's lentil area and production (FAOSTAT, 2023). Since there are limited prospects for expansion in suitable environments, future yield improvements should focus on enhancing tolerance to abiotic stresses and utilizing marginal lands (Singh et al., 2017 ; Dissanayake et al., 2020 ). Breeding lentil for increased salt tolerance has proven to be particularly challenging because of several interrelated factors. A primary obstacle is the narrow genetic base of a crop, which severely limits the genetic variation essential for identifying and selecting beneficial traits and has the lowest probability of introgressing desirable traits from wild relatives into elite cultivars (Pratap et al., 2017 ). This limited diversity constrains breeders’ ability to develop new, resilient varieties that can thrive in saline environments. Moreover, the response of lentils to salt stress is characterized by a complex interplay of physiological and morphological adaptations. Plants may employ osmoregulation to maintain water balance while also working to manage ion toxicity through mechanisms that prevent sodium accumulation in crucial tissues. Morphologically, lentils may alter their root architecture, promoting deeper root systems to access moisture while simultaneously adjusting their leaf structure to reduce water loss. Overall, the multifaceted nature of these responses poses significant challenges for conventional breeding techniques, where the intricate balance of traits must be carefully navigated to enhance salt tolerance effectively. Understanding and unraveling these complexities is essential for improving crop resilience in increasingly saline agricultural landscapes. Furthermore, earlier studies were limited in scope, often involving fewer than 300 genotypes and were conducted under controlled or artificially induced stress conditions (Singh et al., 2017 ; Singh et al., 2018 ; Dissanayake et al., 2020 ). There remains a critical need to phenotype diverse lentil germplasms under natural field conditions to capture realistic stress responses and to identify stable, high-performing genotypes for direct use or incorporation into breeding pipelines. The present study fills this gap by providing a comprehensive characterization of 500 lentil genotypes, including 317 indigenous and 183 exotic accessions, grown under control, saline, and alkaline field conditions. Such extensive evaluation across natural environmental conditions is uncommon in lentil research. Two reliable indices, the stress tolerance index (STI) (Fernandez, 1992) and membership function value (MFV), a fuzzy logic-based selection tool (Zhang et al., 2021 ; Gholizadeh et al., 2022 ), were employed to thoroughly assess genotypic performance under stress. While the STI is a proven metric for evaluating yield stability across stress and nonstress scenarios, MFV offers a multivariate assessment that combines responses from all traits, reducing the risk of biased selection on the basis of a single trait. The objectives of this study were to (i) assess phenotypic variability and trait relationships under normal, saline, and alkaline conditions; (ii) identify traits contributing to salt tolerance via multivariate analysis; and (iii) select superior genotypes with STIs and MFVs for breeding programs aimed at salt-affected soils. These findings provide a strong foundation for the development of lentil cultivars adapted to saline and alkaline environments and offer insights into the complex genetic and phenotypic factors underlying salt tolerance in this species. Materials and methods Plant material The present study included 500 lentil genotypes (Table S1 ) , along with three controls. The test genotypes consisted of 317 indigenous collections and 183 exotic collections. All these genotypes were obtained from the ICAR-National Bureau of Plant Genetic Resources in New Delhi, India. The checks include PDL1, PSL9, and IPL 526. PDL1 and PSL9 are salt-tolerant lentil varieties released in 2019 and were developed collaboratively by the ICAR-Central Soil Salinity Research Institute and the ICAR-Indian Agricultural Research Institute. PDL1 is a small-seeded type with potential yields of 2.5-3.0 tons per hectare and 1.1–1.6 tons per hectare under normal and salt stress conditions, respectively. PSL9 is a bold-seeded variety with a yield potential of 2.0-2.5 tons per hectare under normal conditions and 1.1–1.5 tons per hectare under salt stress. These two varieties serve as tolerance checks. IPL526 is a bold-seeded lentil variety released in 2018 from the ICAR-Indian Institute of Pulses Research in Kanpur, Uttar Pradesh, India. This variety is not suitable for salt stress conditions and was used as a negative control. Field experiments for screening lentil genotypes for salt tolerance During the post monsoon season of 2023–24, 500 lentil genotypes were evaluated under three different stress conditions: control (ECe < 4 dS/m, pH ~ 7.5), salinity (ECe ~ 7 dS/m, pH ~ 7.5), and alkalinity (ECe < 4 dS/m, pH ~ 9.3). These treatments were applied in three separate experiments carried out under natural field conditions at ICAR-CSSRI, Karnal. A consistent experimental design and plot size were maintained across all the experiments, and an augmented randomized complete block design was used to assess the genotypes and controls. The site was divided into six blocks, each subdivided into 86 plots. Within these blocks, 83 test entries and three controls were randomly assigned, except in the final block, which included 85 test entries and a total of 88 plots. The genotypes were sown in early November 2023 in single rows, each 2 m long, with 50 cm inter-row spacing. All recommended agronomic practices were strictly followed to ensure a healthy crop, regardless of the treatment. Conditions were closely monitored through periodic sampling to measure the EC and pH levels, maintaining the stress conditions in the saline and alkaline treatments. Estimation of phenotypic attributes Data on grain yield and its contributing traits were recorded at key growth stages of the crop. Specifically, the date when approximately 50% of the plants of each genotype flowered was noted. This information, combined with the sowing date, was used to calculate the number of days to 50% flowering. Similarly, the date of maturity for each genotype was recorded and converted into days to maturity. These two traits were documented for each plot. At physiological maturity, five randomly selected plants per genotype were tagged and sampled for further trait analysis. The total number of branches and pods per plant was counted for each selected plant, and the averages were calculated. Pods were then harvested from each plant, and seeds were extracted. The seeds from each plant were pooled and weighed to determine the yield per plant. Additionally, 100 seeds were counted per plant to measure the weight of each sample. Both the seed yield and test weight were averaged over the five plants. Statistical analysis A preliminary augmented RCB analysis of variance was conducted to evaluate the significance of various sources of variation for each trait, with a focus on test entries and checks. Additionally, the adjusted means of each genotype for each trait were calculated for use in subsequent analyses. Pearson correlation coefficients among traits were calculated separately for different treatments and tested for significance at the 1% level to identify true associations. Multiple regression analysis was performed with seed yield as the dependent variable and the other traits as independent variables to identify traits highly important for yield formation under each treatment. These traits can serve as proxies for selecting promising genotypes under stress conditions. To explore the contribution of each trait to the total variation within each treatment, principal component analysis (PCA) was conducted. Biplots generated via the first two principal components (PC1 and PC2) were used for interpretation. To select promising genotypes for salinity and alkalinity treatments, two indices were calculated: the stress tolerance index (salinity tolerance index and alkalinity tolerance index for the respective treatments) and the mean membership function value (MFV). The stress tolerance index (STI) for seed yield was calculated via the formula provided by Fernandez (1992). $$\:STI=\:\frac{{(Y}_{s}\left)\:\right({Y}_{t})}{{Y}_{a}^{2}}$$ where Y s and Y t are the mean seed yields of a genotype under stress and normal conditions, respectively, and Y a is the average seed yield across all genotypes under normal conditions. To calculate the mean MFV, a fuzzy comprehensive evaluation parameter, the following parameter was initially calculated for each trait separately. $$\:TI=\:\frac{{Y}_{s}}{{Y}_{t}}$$ where TI indicates the tolerance index and Y s and Y t are the mean trait values of a genotype under stress and normal conditions, respectively. The stress tolerance membership function value was calculated as follows. $$\:MFV=\:\frac{Y-\:{Y}_{min}}{{Y}_{max}-{Y}_{min}}$$ where Y is the TI value of a specific genotype for a trait and Y min and Y max are the minimum and maximum TI values, respectively, for that trait across all genotypes. These MFV values therefore ranged from 0–1 and were averaged across traits. Correlation coefficients were also calculated among the STI and MFV for various traits to examine the relationships between these indices. The top 10 genotypes were selected separately for each stress condition (saline or alkaline) and each index (STI or MFV). Additionally, the mean trait value of the selected genotypes across the entire population was estimated to identify the best top 10 genotypes. In this process, the selection differential (SD) was calculated by subtracting the trait value of the entire population from the mean trait value of the top 10 genotypes, and the selection differential percentage (SD%) was determined by dividing this difference by the mean trait value of the population, expressed as a percentage. Results Analysis of variance and mean performance of the genotypes A preliminary analysis of variance (ANOVA) based on the augmented RCB design revealed significant differences among treatments and genotypes. Phenotypic screening of lentil germplasm was conducted under three conditions: control, salinity stress (ECe 7 dS/m), and alkalinity stress (pH 9.3). Drone imagery captured the field conditions for each treatment: (a) control, (b) salinity, and (c) alkalinity (Fig. 1). This analysis was performed separately for the control, saline, and alkaline treatments, as shown in Table 1. The results indicated that most traits highly significantly differed (at the 1% level of probability) for all sources of variation, except for the block (adjusted) source for the trait number of branches per plant, which was significant at the 5% level. Regardless of the treatment, the block (unadjusted) source contributed more to the total variance (mean sum of squares) than the other sources did. Among the test entries and checks, the test entries contributed more variance. The error variance was minimal for most traits across all the treatments, except for the number of pods per plant. Among the treatments, especially for the different genotypes, greater variance was observed under stress conditions than under the control conditions, except for the seed yield per plant, where the opposite trend was observed. The means and ranges of various traits estimated under the control and two stress treatments are shown in Table 2. Compared with that under normal conditions, a significant decrease in the average performance of genotypes (including both test entries and checks) under stress conditions was detected. In general, the reduction was more noticeable under salinity stress than alkalinity stress for traits such as days to 50% flowering and maturity, plant height, and number of branches per plant. Conversely, for traits such as the number of pods per plant, test weight, and seed yield per plant, the reduction was greater under alkalinity stress than under salinity stress. Notably, plants flowered and matured earlier under alkaline conditions than under saline and control conditions. The smallest reduction under stress was observed for days to maturity under alkalinity stress (7.17%), whereas the greatest reduction was recorded for the seed yield per plant (86.805%). Across both test entries and checks, the mean values for all traits were greater for the checks, regardless of the trait. Compared with those under normal conditions, the ranges for most traits under stress conditions shifted toward lower values. However, for days to 50% flowering and days to maturity, the range expanded in both directions under stress. Low to moderate coefficients of variation (CVs) were observed for most traits, with the highest (26.12%) seed yield per plant under alkaline stress and the lowest (8.85%) number of days to 50% flowering and maturity under normal conditions. Table 1 Analysis of variance for agronomic traits was conducted on more than 500 lentil genotypes and three controls tested under normal, saline, and alkaline environments in an augmented randomized complete block (RCB) design. The experimental setup included 500 genotypes plus 3 checks across three environments: normal, saline, and alkaline soil conditions. Sources of variation include Block (Unadjusted): the raw block effect; Treatment (Adjusted): treatment effect after correction; Block (Adjusted): the adjusted block effect accounting for treatments; Treatment (Unadjusted): the raw treatment effect; Checks: the effect of control check varieties; Genotypes: the effect of test entries; Checks vs. Genotypes: the comparison between checks and test entries; Error: residual variance. The agronomic traits evaluated included the following: DFF—Days to 50% flowering; DTM—Days to maturity; PH—Plant height (cm); NBPP—Number of branches per plant; NPPP—Number of pods per plant; TW—Test weight (g), usually 100-seed weight; and SYPP—Seed yield per plant (g). For each trait and environment, ANOVA was performed, and the mean squares for each source of variation are reported. Higher mean squares with significance (marked by * or **) indicate significant variability contributed by that factor. For DFF under normal conditions, genotypes presented significant variation (1328.40) at the 1% level (**), indicating meaningful diversity among test entries. Similarly, under saline and alkaline conditions, all traits—including SYPP, NPPP, and TW—exhibited significant genotypic variation, highlighting the potential for selection. Traits Environment Source of variation Block (Unadjusted) Treatment (adjusted) Block (adjusted) Treatment (unadjusted) Checks Genotypes Checks vs Genotypes Error DFF Normal 12294.44** 1213.20** 9.45** 1335.56** 131.66** 1328.40** 7317.96** 0.007 Saline 22581.63** 2460.36** 10.36** 2685.17** 27.18** 2608.64** 46189.99** 0.001 Alkaline 45699.39** 2235.27** 9.63** 2690.35** 25.09** 2661.31** 22511.25** 0.001 DTM Normal 23395.38** 2253.66** 17.82** 2486.50** 18.81** 2471.99** 14661.50** 0.001 Saline 36719.39** 3983.83** 17.81** 4349.39** 25.09** 4205.57** 84764.99** 0.001 Alkaline 80168.43** 4030.83** 20.07** 4829.12** 14.63** 4736.57** 60642.31** 0.001 PH (cm) Normal 1943.54** 239.05** 2.08** 258.39** 655.41** 250.21** 3546.51** 0.034 Saline 2273.19** 260.04** 0.92** 282.67** 18.34** 277.13** 3577.74** 0.001 Alkaline 4082.30** 198.86** 1.06** 239.51** 140.27** 232.67** 3846.96** 0.007 NBPP Normal 115.82** 9.65** 0.07* 10.80** 35.10** 10.35** 186.73** 0.002 Saline 47.77** 7.24** 0.02* 7.71** 0.69** 7.55** 102.61** 0.001 Alkaline 92.81** 7.61** 0.03* 8.53** 0.69** 8.39** 93.09** 0.001 NPPP Normal 46299.84** 5134.67** 35.67** 5595.47** 4987.58** 5519.03** 44955.14** 0.254 Saline 58384.37** 7856.11** 3.92** 8437.59** 2469.12** 8461.90** 8246.54** 0.128 Alkaline 8015.19** 1881.02** 0.52** 1960.84** 46.25** 1967.99** 2222.61** 0.002 TW (g) Normal 2.57** 0.44** 0.01* 0.47** 2.35** 0.42** 20.84** 0.001 Saline 4.83** 0.58** 0.01* 0.63** 0.55** 0.57** 33.90** 0.001 Alkaline 7.32** 0.46** 0.01* 0.52** 0.31** 0.49** 20.17** 0.001 SYPP (g) Normal 313.39** 38.91** 0.29** 42.03** 14.31** 40.54** 842.65** 0.001 Saline 296.89** 31.91** 0.03* 34.87** 32.75** 34.90** 22.80** 0.002 Alkaline 30.67** 5.45** 0.01* 5.75** 0.25** 5.78** 2.06** 0.001 Degrees of freedom 5 502 5 502 2 499 1 10 *: Significant at the 5% probability level; **: Significant at the 1% probability level; DFF: Days to 50% flowering; DTM: Days to maturity; PH: Plant height; NBPP: Number of branches per plant; NPPP: Number of pods per plant; TW: Test weight; SYPP: Seed yield per plant. Table 2. Mean performance, range, and variability of test entries and checks for key agronomic traits in lentils under normal, saline, and alkaline environments. The values include the mean, range (with genotype identifiers), standard error (SE±), and number of test genotypes that surpass the best check. Compared with that under normal conditions, the combined genotype mean under stress is accompanied by a percentage reduction. C.V. (%) indicates the coefficient of variation, reflecting trait variability across genotypes. Trait abbreviations: DFF – Days to 50% flowering, DTM – Days to maturity, PH – Plant height, NBPP – Number of branches per plant, NPPP – Number of pods per plant, TW – Test weight, SYPP – Seed yield per plant. The performance of 500 lentil genotypes and 3 controls under normal, saline, and alkaline conditions revealed significant variation across all evaluated traits. Compared with those under normal conditions, the number of days to 50% flowering (DFF) and maturity (DTM) were delayed under stress, with a 14.33% reduction in the mean DFF under salinity and 9.46% under alkalinity. Yield-related traits such as the seed yield per plant (SYPP) decreased by 66.19% and 86.80% under saline and alkaline conditions, respectively. Despite these reductions, several test genotypes outperformed the best checks: 237 genotypes flowered earlier than did the controls under salinity, and 134 genotypes exceeded the best check for SYPP under alkalinity. Notably, the number of pods per plant (NPPP) and branches per plant (NBPP) showed high variability, with the C.V. exceeding 25% under alkaline stress, suggesting ample scope for selection. The test genotypes demonstrated superior resilience in traits such as DFF, SYPP, and NBPP, indicating their breeding potential under salt-affected conditions. Genotypes such as IC268245, IC158668, and IC610426 presented exceptional yield performance under stress. Traits Environment Mean Range Number of genotypes surpassing the best check C.V. (%) Test entries Checks Genotypes combined # SE± Test entries Checks DFF Normal 81.42 101.23 91.325 1.58 83 (IC547035)-108 (IC412932) 95 (IPL526)-109 (PDL1) 416 8.85 Saline 50.48 106.00 78.24 (14.33%) 2.24 90 (EC223231)-118 (IC447858) 102 (IPL526)-111 (PSL9) 237 18.16 Alkaline 63.12 102.25 82.685 (9.46%) 2.25 98 (IC430348)-120 (IC573471) 98 (IPL526)-107.37 (PSL9) 293 15.14 DTM Normal 111.02 139.06 125.04 2.16 118 (IC268237)- 138 (EC267602) 134 (PSL9)-144 (PDL1) 416 8.85 Saline 64.19 139.06 101.625 (18.73%) 2.86 116 (EC2232231)-140 (IC266800) 134 (IPL526)-145.26 (PSL9) 237 18.14 Alkaline 84.57 147.58 116.075 (7.17%) 3.01 133 (EC78447)-151 (ILL5371) 143 (PSL9)-153.68 (PDL1) 293 15.12 PH (cm) Normal 33.7 47.49 40.595 0.70 24 (IC241253)-55 (IC244072) 34.68 (IPL526) -55.79 (PDL 1) 1 9.40 Saline 15.9 31.58 23.74 (41.52%) 0.73 15 (IC267116) – 50 (NC62518) 29 (IPL 526-34.03 (PSL9) 102 18.69 Alkaline 18.42 33.97 26.195 (35.47%) 0.67 19 (IC382687)-46 (EC223150) 29.00 (PSL9)- 40.35 (PDL 1) 18 15.55 NBPP Normal 5.58 8.75 7.165 0.14 3 (IC274062)-12 (EC78476) 6.00 (IPL526)-11.23 (PSL9) 25 11.61 Saline 2.5 5.11 3.805 (46.89%) 0.12 2 (IC341355)-10 (IC244070) 4.67 (PDL1)-5.61 (PSL9) 81 19.77 Alkaline 2.98 5.45 4.215 (41.17%) 0.13 1 (EC267705)- 10 (IC268241) 5 (PSL9)-5.96 (PDL1) 123 17.88 NPPP Normal 147.68 196.78 172.23 3.24 45 (ILL1665)-300 (PL639, IC267665 and IC267667) 162 (IPL526)- 229.12 (PSL9) 42 9.98 Saline 77.59 65.21 71.4 (58.54%) 3.98 15 (IC29945)-310 (EC78475) 43.67 (IPL526)-87.82 (PSL9) 211 20.99 Alkaline 32.41 23.97 28.19 (83.63%) 1.92 4 (EC255550)- 226 (IC424863) 20.33 (PSL9)-26.67 (IPL526) 205 25.01 TW (g) Normal 1.25 2.31 1.78 0.03 0.83 (IC332103)-3.65 (EC266630) 1.85 (PDL1)-3.12 (IPL526) 2 10.80 Saline 0.73 2.17 1.45 (18.54%) 0.04 0.61 (IC267529)-2.57 (IC565303) 1.84 (PDL1)-2.56 (IPL526) 1 19.17 Alkaline 0.81 1.92 1.365 (23.31%) 0.03 0.70 (IC544561)-2.72 (IC586784) 1.67 (PSL9)-2.22 (IPL526) 9 16.30 SYPP (g) Normal 11.19 17.59 14.39 0.28 2.64 (IC241253)-35.16 (IC158668) 15.60 (PDL1)-19.58 (IPL526) 33 11.75 Saline 3.83 5.90 4.865 (66.19%) 0.26 0.14 (IC267529)-22.89 (IC610426) 4.30 (IPL526)-8.84 (PSL 9) 105 24.30 Alkaline 1.65 2.15 1.9 (86.80%) 0.10 0.10 (EC267630)- 12.08 (IC268245) 1.90 (IPL526)-2.42 (PSL9) 134 26.12 DFF: Days to 50% flowering; DTM: Days to maturity; PH: Plant height; NBPP: Number of branches per plant; NPPP: Number of pods per plant; TW: Test weight; SYPP: Seed yield per plant; C.V.: Coefficient of variation. # Numbers in parentheses indicate the percent reduction in stress (saline & alkaline) compared with normal conditions. Association analysis among various attributes Pearson correlation coefficients were calculated for various traits under different treatments: normal, saline, and alkaline. Under normal conditions, the seed yield per plant was highly significantly (p < 0.01) positively correlated with the test weight, followed by the number of pods per plant, plant height, and number of branches per plant (Fig. 2a) . Notably, a significant positive correlation (p < 0.01) was also found between the number of days with 50% flowering and the seed yield per plant. Additionally, plant height was significantly positively correlated (p < 0.01) with the number of branches per plant, the number of pods per plant, and the test weight. Overall, all the traits presented positive correlations with one another, regardless of statistical significance. Under stress conditions, these relationships changed considerably. For example, under salinity, the number of pods per plant and the seed yield per plant were significantly negatively correlated (p < 0.01) with the number of days to 50% flowering and maturity (Fig. 2b). Under alkaline conditions, the trait correlations resembled those observed under normal conditions. However, most of these correlations were not significant, except for the strong positive relationship between the seed yield per plant and the number of pods per plant (Fig. 2c). Notable associations under alkalinity included a positive correlation between the number of branches per plant and both the number of pods per plant and the seed yield per plant. To further explore the importance of each trait in contributing to seed yield, multiple regression analysis was conducted (Table 3). Under both control and salinity conditions, the number of pods per plant, test weight, and plant height were significantly associated with seed yield. However, under alkaline conditions, only the number of pods per plant had a significant effect on seed yield. Notably, the highest R² and adjusted R² values were observed for alkalinity, followed by the control, and the lowest values were observed under salinity. Additionally, to assess how each trait contributed to the overall variation, a principal component analysis (PCA) was performed. Under control conditions, the first two principal components (PC1 and PC2) accounted for approximately 47.9% of the total variation (Fig. 3a) . Among the traits, seed yield per plant and test weight contributed the most to the variation, whereas days to 50% flowering and maturity contributed the least. In contrast, under salinity conditions, PC1 and PC2 explained 59.8% of the total variance, with days to 50% flowering and maturity contributing the most and the test weight the least (Fig. 3b). The PCA under alkaline conditions revealed a pattern similar to that under the control and saline conditions. Here, PC1 and PC2 explained approximately 54.6% of the total variance, with the highest contributions from seed yield per plant (similar to the control) and the number of pods per plant, whereas plant height contributed the least (Fig. 3c). The relationships between various traits, as indicated by the angles between their vectors in the PCA, closely matched the estimated correlation coefficients. Table 3 Multiple regression statistics were calculated using seed yield per plant (SYPP) as the dependent variable and all other traits as independent variables, which were assessed separately under the control (normal), salinity, and alkalinity treatments. Column groups show traits as independent agronomic predictors; estimates are regression coefficients indicating the strength of the effect on seed yield per plant and the standard error of the estimate. The P value indicates the statistical significance of the predictor’s effect; lower values (<0.05) suggest a significant contribution. R-Squared (R²) represents the proportion of variance in seed yield explained by the model, along with accuracy metrics (per environment). The adjusted R-square accounts for the number of predictors, and the residual standard error (RSE) reflects the standard deviation of the residuals; lower values indicate a better fit. Key interpretations: The control environment model explained 54% of the variation in seed yield (R² = 0.54). The significant predictors (p < 0.05) included plant height (PH): positive effect (estimate = 0.07); number of pods per plant (NPP): strong positive effect (estimate = 0.05); test weight (TW): highly positive effect (estimate = 5.83); and days to flowering (DFF): near-significant positive trend (p = 0.08). In the saline environment, the model explained 34% of the variation (R² = 0.34), with significant predictors—PH, NPP, and TW—all with p < 0.05—positively influencing seed yield. The alkalinity environment model explained 77% of the variation in seed yield (R² = 0.77), indicating high predictive power. Only NPP is a significant positive predictor (p = 0.00). Other traits, including TW, pH, and NBPP, were not significantly different under alkaline stress. Trait Control Salinity Alkalinity Estimate Std. Error p value Estimate Std. Error p value Estimate Std. Error p value Intercept -10.66 5.16 0.04 6.51 6.41 0.31 4.91 2.92 0.09 DFF 0.08 0.04 0.08 -0.13 0.08 0.12 -0.01 0.02 0.67 DTM -0.04 0.03 0.20 0.00 0.08 0.98 -0.03 0.02 0.18 PH 0.07 0.03 0.01 0.18 0.05 0.00 0.01 0.02 0.64 NBPP 0.04 0.07 0.59 0.01 0.19 0.96 -0.01 0.04 0.90 NPP 0.05 0.00 0.00 0.02 0.01 0.00 0.05 0.00 0.00 TW 5.83 0.38 0.00 3.16 0.83 0.00 0.22 0.21 0.30 Model accuracy parameters R_Squared 0.54 0.34 0.77 Adjusted R_Squared 0.53 0.33 0.77 RSE 3.03 3.82 1.16 Selection of promising lentil genotypes To identify promising entries under each stress treatment, two indices were calculated: the stress tolerance index (STI) based on seed yield and the mean membership function value (MFV) across all traits. The STI values ranged from 0.01 to 4.90, with a mean of 0.99 under salinity, whereas under alkalinity, they varied from 0.01 to 1.44, with a mean of 0.20. Similarly, the MFV values ranged from 0.15 to 0.57 under salinity, with a mean of 0.36, and from 0.15 to 0.64 under alkalinity, with a mean of 0.32, as shown in Table 4. On the basis of these values, the top 10 entries were selected for each index and stress condition. Although different genotypes proved promising for each combination, some genotypes were common across two or more combinations. For example, IC267104 was superior in both STI under salinity and alkalinity; IC248956 was superior for MFV (salinity) and STI (alkalinity); IC268241 was superior for MFV under both salinity and alkalinity; and IC267658 was superior for STI (alkalinity) and MFV (alkalinity). Additionally, two genotypes, IC267657 and IC268240, showed promise in three combinations: MFV (salinity and alkalinity) and STI (alkalinity). Table 4 Summary statistics and top-performing genotypes based on the stress tolerance index (STIy) and mean membership function value (MFV) under salinity and alkalinity stress conditions. STIy (stress tolerance index for yield): A numerical index that assesses the relative yield performance of genotypes under stress conditions compared with optimal (nonstress) conditions. Higher STIy values indicate better yield stability and tolerance under stress. MFV (mean membership function value): A fuzzy logic-based combined score derived from multiple physiological and agronomic traits. It ranks genotypes by integrating performance across traits, making it a reliable indicator for identifying stress-resilient genotypes with overall superior adaptation. Descriptive statistics (mean, minimum, maximum, and standard deviation) for both STIy and MFV provide an overview of variation among genotypes. The top 10 genotypes with the highest STIy and MFV scores are listed, representing the most promising candidates for salinity and alkalinity tolerance breeding. These accessions showed exceptional performance either in yield stability (STIy) or in integrated trait expression (MFV). High-ranking genotypes, such as IC586784, IC342716, IC248956, IC267657, and IC268240, demonstrate significant potential for improving stress tolerance in breeding programs. The consistent performance of these strains under stress conditions highlights them as elite donor lines for the genetic enhancement of salt- and alkali-tolerant cultivars in Lentil. Salinity Alkalinity STI y MFV STI y MFV Mean 0.99 0.36 0.20 0.32 Minimum 0.01 0.15 0.01 0.15 Maximum 4.90 0.57 1.44 0.64 S.D. 0.77 0.08 0.22 0.01 Top 10 genotypes IC586784 IC342716 EC223202 IC248965 IC342721 IC248966 IC610426 IC565304 EC223231 IC267104 IC241609 IC248956 IC260853 IC267083 IC267097 IC267657 IC267659 IC268240 IC268241 IC335474 IC267104 IC248963 IC614827 IC248956 IC267074 IC268240 IC267658 IC267657 IC267076 IC248964 IC248956 IC267657 IC267658 IC267664 IC267666 IC268232 IC268236 IC268240 IC268241 IC268243 A correlation analysis was conducted between the MFV values of each trait, the mean MFV, and the STI of the seed yield for each stress treatment individually. Under saline stress, the STI was significantly (p<0.01) positively correlated with the MFV of the seed yield per plant, followed by the MFV of the plant height and the mean MFV (Fig. 4a) . Similarly, the mean MFV exhibited a significant positive relationship with the MFV values of each trait except for the MFV from days to 50% flowering. Among these factors, the strongest correlation was with the MFV of the number of branches per plant. Under alkalinity stress, the STI also demonstrated a significant (p<0.01) positive relationship with the MFV of the seed yield per plant, followed by the MFV of the number of pods per plant, the mean MFV, and the MFV of the number of branches per plant (Fig. 4b) . Unlike salinity, the mean MFV under alkalinity stress was significantly positively associated with the MFV of all individual traits, with the strongest relationship observed with the MFV of the seed yield per plant, closely followed by the MFV of the number of pods per plant and the number of branches per plant. We also examined the selection dividends achieved for each trait after choosing genotypes for stress conditions via STI and MFV, the results of which are shown in Table 5. Under salinity, selection based on the STI offered the greatest benefit for the seed yield per plant (214.49% of SD%), followed by the number of pods per plant (174.79% of SD%), whereas the lowest benefit was for the test weight (9.66% of SD%). These selection gains slightly decreased when selection was based on the mean MFV. Nonetheless, the greatest advantage was still for the number of pods per plant (170.31% of SD%), with a seed yield of 105.55%. The smallest gain was for the test weight (0.2% of the standard deviation (SD)). Therefore, selection on the basis of the STI resulted in greater gains than selection on the MFV for salinity. Similar patterns appeared for alkalinity, with the highest selection dividends for seed yield per plant and the number of pods per plant in both the STI (334% and 400% of SD%, respectively) and MFV (332% and 371% of SD%, respectively), whereas the test weight had the least gain. Notably, selection on the basis of the STI yielded zero to negative gains for the test weight. Unlike salinity, the selection of superior genotypes via STI and MFV provided similar gains under alkalinity. Thus, for alkalinity stress, either parameter can be used to select promising genotypes. Table 5 Selection gains for different traits after choosing genotypes on the basis of the stress tolerance index for yield (STIy) and mean field value (MFV) in saline and alkaline environments. Column descriptions (for each trait under STIy and MFV): Xo shows the original population mean (before selection); Xs shows the selected population mean (after selection); SD shows the selection differential (Xs − Xo), indicating improvement; and SD% shows the selection differential as a percentage of the original mean = (SD/Xo) × 100. The interpretation of SYPP (seed yield per plant) revealed the greatest genetic gain: salinity (STIy): +10.44 g (214.49% gain); alkalinity (STIy): +6.34 g (333.84% gain). The interpretation of the NPP (number of pods per plant) also revealed very high gains: alkalinity (STIy): +112.81 pods (400.18% increase); salinity (STIy): +124.80 pods (174.79% increase). The interpretation of TW (test weight) indicated minimal or even negative gain, e.g., -0.29% under alkaline STIy selection. The interpretation of NBPP and pH showed moderate to high percentage gains, especially under MFV-based selection in alkalinity. Selection on the basis of STIy and MFV results in considerable genetic gains in yield-related traits (SYPP and NPP), especially under stress conditions. STIy seems slightly more effective under stress for identifying genotypes with greater potential yield improvements. Trait Salinity Alkalinity STI y MFV STI y MFV Xo Xs SD SD% Xo Xs SD SD% Xo Xs SD SD% Xo Xs SD SD% DFF 78.24 98.67 20.43 26.11 78.24 102.75 24.51 31.33 82.69 107.20 24.52 29.65 82.69 105.10 22.42 27.11 DTM 101.63 128.20 26.58 26.15 101.63 132.50 30.88 30.38 116.08 141.10 25.03 21.56 116.08 141.30 25.23 21.73 PH 23.74 36.13 12.39 52.19 23.74 36.13 12.39 52.19 26.20 31.00 4.81 18.34 26.20 31.00 4.81 18.34 NBPP 3.81 6.00 2.20 57.69 3.81 6.38 2.58 67.67 4.22 6.90 2.69 63.70 4.22 7.80 3.59 85.05 NPP 71.40 196.20 124.80 174.79 71.40 193.00 121.60 170.31 28.19 141.00 112.81 400.18 28.19 132.90 104.71 371.44 TW 1.45 1.59 0.14 9.66 1.45 1.65 0.20 13.79 1.37 1.36 0.00 -0.29 1.37 1.41 0.04 3.30 SYPP 4.87 15.30 10.44 214.49 4.87 10.00 5.14 105.55 1.90 8.24 6.34 333.84 1.90 8.20 6.30 331.58 Discussion Like many pulses, lentil exhibit a marked sensitivity to soil salinity, posing significant challenges for their cultivation. As pulses are a critical source of plant-based protein, which is essential for human nutrition, expanding their cultivation is imperative to meet the dietary needs of a growing global population. This expansion will increasingly necessitate the utilization of marginal lands, including those affected by salinity and alkalinity. The development of salt-tolerant lentil varieties is thus a strategic approach, both economically and environmentally, to bring these degraded lands under productive cultivation. A fundamental step in this endeavor is the screening of genetic resources to assess the extent of genetic variation available for breeding purposes. In this study, we evaluated the salt stress tolerance—encompassing both salinity and alkalinity—of 500 diverse lentil genotypes, comprising two-thirds indigenous and one-third exotic accessions. This study represents a pioneering effort, as it is the first to evaluate an extensive number of lentil genotypes under varying salt stress conditions. Prior studies have been limited in scope, assessing 162 (Singh et al., 2017 ), 276 (Dissanayake et al., 2020 ), and 285 (Singh et al., 2018 ) genotypes, respectively. Notably, a significant portion of the genotypes in our study were of indigenous origin, further distinguishing them from those identified in previous studies. An initial analysis of variance indicated that the block (unadjusted) source contributed significantly to the total variation, a finding that is consistent with the natural field conditions under which both salinity and alkalinity treatments were applied. Field-based phenotyping of lentil, as used in our study, aligns with methodologies reported in earlier research (Singh et al., 2017 , 2018 ). The impact of salinity and alkalinity stress on various agronomic traits was significant, with alkalinity having a more pronounced effect on key yield-related traits than does salinity. This suggests that lentil is more sensitive to alkalinity, supporting findings in other crops, such as rice (Lv et al., 2013 ) and oats (Bai et al., 2018 ). The complexity of alkalinity stress, which disrupts the soil structure and reduces hydraulic conductivity, leads to nutrient deficiencies, likely explains this increased sensitivity. In particular, the seed yield per plant and the number of pods per plant substantially decreased. The reduction under salinity stress was less severe in our study, whereas the opposite trend was observed for alkalinity stress (Singh et al., 2017 ; Singh et al., 2018 ). These differences may result from variations in the experimental location (Karnal in our study versus Aagra for salinity and Kanpur & Lucknow for alkalinity (Singh et al., 2017 )) and the method of stress application (natural saline field in our study versus artificially induced salinity in Singh et al., 2017 )). Conversely, mung bean has been reported to suffer greater yield reductions at similar salinity levels (Sehrawat et al., 2015 ; Hasan et al., 2017 ), whereas black grams have relatively greater tolerance than green grams (Hasan et al., 2017 ). A notable observation in this study was the reduction in the crop growth period under both salinity and alkalinity stress, as evidenced by the decrease in the mean number of days to 50% flowering and maturity. This phenomenon can be attributed to accelerated senescence caused by stress, increased abscisic acid accumulation, which triggers an earlier transition to the reproductive stage, and nutrient imbalances, which disrupt normal growth processes (Grattan and Grieve, 1999 ; Munns and Tester, 2008 ; Zhang and Jia, 2010 ). Interestingly, this pattern contrasts with findings in wheat, where salt stress has been shown to delay flowering (Sharma et al., 2011 ). The test weight also significantly decreased, by approximately 20%, under both salinity and alkalinity stress. This decline is likely due to impaired photosynthesis, leading to insufficient photosynthates for seed filling (Zeng and Shannon, 2000 ). Such decreases in test weight under salt stress have been consistently reported across various crops. Furthermore, plant height substantially decreased under both types of salt stress, primarily due to decreased levels of gibberellin, a key growth regulator involved in stem elongation, under salt stress conditions (Achard and Genschik, 2009 ). Additionally, the accumulation of reactive oxygen species (ROS) under salt stress disproportionately affects actively growing tissues, such as the shoot apical meristem, resulting in stunted growth and reduced plant height (Mittler, 2002 ). The reduction in plant height under salt stress is strongly linked to a simultaneous decrease in the number of branches per plant, ultimately leading to fewer pods. This relationship is supported by the significant positive correlations between plant height, branch number, and pod number per plant found in this study. Moreover, across all the treatments, both the number of branches and the number of pods per plant were significantly positively associated with the seed yield per plant. Therefore, increasing seed yield, despite its potentially low heritability, could be effectively achieved through indirect selection for these traits. Among them, the number of pods per plant stands out as a critical determinant of seed yield, regardless of treatment (as determined by multiple regression analysis). This conclusion is reinforced by the similar percentage reductions in pod number and seed yield per plant under both saline and alkaline stress. However, the R2 values were moderate for both stresses, indicating that other traits, such as nodule formation, should also be considered in future studies. The decrease in pod number under salinity stress can be attributed to flower abortion and reduced pod set, likely due to insufficient photosynthates necessary for flower development into pods (Vadez et al., 2007 ; Ashraf and Harris, 2004 ). A reduction in pod number per plant has also been reported in fenugreek (Soughir et al., 2012) and Brassica juncea (Wani et al., 2013 ). Furthermore, the decrease in the number of branches per plant is due mainly to hormonal imbalances caused by salt stress. Specifically, reductions in cytokinins and increases in abscisic acid (ABA) can inhibit lateral bud outgrowth, resulting in fewer branches (Albacete et al., 2008 ). Additionally, plants under salt stress tend to prioritize energy for survival mechanisms, such as the synthesis of osmoprotectants and antioxidants, rather than for growth (Zhu et al., 2001). Principal component analysis (PCA) revealed that the seed yield per plant was the main contributor to the total variance under both the control and alkaline conditions, whereas the number of days to flowering and maturity were the primary factors influencing salinity stress. This pattern can be attributed to several factors, including hormonal imbalances, where increased levels of abscisic acid (ABA) accelerate flowering, and reduced levels of gibberellic acid (GA), which results in decreased plant height and delayed flowering (Achard and Genschik, 2009 ). These effects are further influenced by genotype-specific responses to salinity. The goal of screening experiments is to find superior genotypes for breeding programs, requiring reliable parameters to avoid selecting false positives or escapes. Tolerant genotypes should be evaluated under both control and stress conditions, with a focus on traits that significantly impact seed yield. In this study, two key parameters were used: the stress tolerance index (STI), which is based on the seed yield per plant, and the membership function value (MFV), which combines all measured traits. STI is a well-established metric for selecting genotypes resilient to abiotic stress, as it considers performance under both normal and stress conditions and has been previously used in lentils for salinity tolerance (Kumawat et al., 2017 ; Pandey and Sengar, 2020 ). This study introduces MFV to lentil research for the first time, providing a comprehensive index that assesses genotypic performance across all traits under both conditions. MFV has gained popularity in recent studies for selecting salt-tolerant genotypes (Choudhary et al., 2021 ; Zhang et al., 2021 ; Gholizadeh et al., 2022 ; Mathankumar et al., 2023 ; Gyanagoudar et al., 2024 ). By using both indices, this research identified promising genotypes for salinity and alkalinity tolerance. While different genotypes excelled under various indices and stress conditions, certain genotypes, including IC267104, IC248956, IC268241, IC267658, IC267657, and IC268240, consistently performed well across multiple criteria. This consistency is likely due to the shared impact of Na + ions in both salinity and alkalinity stress environments, despite differences in soil chemistry. This may also be because the STI is based only on seed yield, whereas the mean MFV considers all traits. This is supported by moderate yet significant correlations observed under both stresses between the STI and the mean MFV. This finding aligns with earlier research in oats, where three genotypes showed tolerance to both salinity and alkalinity (Bai et al., 2018 ). Interestingly, although exotic material was also screened in this study, the superior genotypes were mainly indigenous, except for EC223202 and EC223231, which is understandable since the experiment was conducted in India, a region better suited for indigenous collections than exotic ones. Additionally, to determine which set of genotypes will yield maximum benefits, especially for grain production, we estimated selection differentials—another unique feature of this study. On the basis of these results, selection on the basis of the STI provided good gains under salinity, whereas neither of the indices were suitable under alkalinity. Conclusion This research is pioneering in lentils because of several factors, including the wide diversity of the genotypes analyzed and the rigorous selection criteria used. Using the STI and MFV indices, the genotypes IC267104, IC248956, IC268241, IC267658, IC267657, and IC268240 were identified as promising genotypes for salinity and alkalinity tolerance. While different genotypes perform well under various indices and stress conditions, these genotypes consistently perform well across multiple criteria, likely because of the shared influence of Na + ions in both salinity and alkalinity stress environments, despite differences in soil chemistry. The selected genotypes demonstrated significant superiority over the other genotypes, as shown by the selection differences. These genotypes have potential for further breeding programs focused on developing salt-tolerant lentil varieties or could be used to create biparental or multiparental mapping populations for identifying genomic regions linked to salt tolerance. Additionally, these genotypes provide valuable resources for future functional genomics studies. Declarations Acknowledgments The authors are thankful to the Indian Council of Agricultural Research (ICAR), India, for support in carrying out the work through “CRP on Agrobiodiversity for Pulses and Oilseeds: Lentil Component” , Ministry of Agriculture and Farmers Welfare, Government of India. Competing interests None declared. Author contributions VS and JS involved in conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, and original draft. RKT performed data curation and Writing-Original Draft. KT, SKS, AKY and PGG provided germplasm with visualization. AK, KP, SP, VS: contributed equally to this work. References Achard P, Genschik P. (2009). Releasing the brakes of plant growth: how GAs shut down DELLA proteins. Journal of Experimental Botany 60(4): 1085-1092, https://doi.org/10.1093/jxb/ern301 Albacete A, Ghanem M E, Martínez-Andújar C, Acosta M, Sánchez-Bravo J, Martínez V, Pérez-Alfocea F. (2008). Hormonal changes in relation to biomass partitioning and shoot growth impairment in salinized tomato ( Solanum lycopersicum L.) plants. Journal of Experimental Botany 59(15): 4119-4131, https://doi.org/10.1093/jxb/ern251 Ashraf MPJC, Harris PJ. (2004). Potential biochemical indicators of salinity tolerance in plants. Plant science 166(1):3-16, https://doi.org/10.1016/j.plantsci.2003.10.024 Bai J, Yan W, Wang Y, Yin Q, Liu J, Wight C, Ma B. 2018. Screening Oat Genotypes for Tolerance to Salinity and Alkalinity. Frontier in Plant Science 2-9: 1302, https://doi.org/10.3389/fpls.2018.01302 Choudhary A, Kaur N, Sharma A, Kumar A. (2021). Evaluation and screening of elite wheat germplasm for salinity stress at the seedling phase. Physiologia plantarum , 173(4): 2207-2215, https://doi.org/10.1111/ppl.13571 Dissanayake IAJK, Ranwala SMW, Perera SSN. (2020). Germination and seedling growth responses of Sri Lankan grown Sesame/Thala ( Sesamum indicum L.) for simulated drought conditions. Journal of the National Science Foundation of Sri Lanka , 47(4), https://doi.org/10.4038/jnsfsr.v47i4.9686 Foti K, Wang D, Appel LJ, Selvin E. (2019). Hypertension awareness, treatment, and control in US adults: trends in the hypertension control cascade by population subgroup (National Health and Nutrition Examination Survey, 1999–2016). American Journal of Epidemiology , 188(12): 2165-2174, https://doi.org/10.1093/aje/kwz177 Gholizadeh A, Dehghani H, Akbarpour O, Amini A, Sadeghi K, Hanifei M, Sharifi-Zagheh A. (2022). Assessment of Iranian wheat germplasm for salinity tolerance using analysis of the membership function value of salinity tolerance (MFVS). Journal of Crop Science and Biotechnology 25(5): 611-619, https://doi.org/10.1007/s12892-022-00156-2 Grattan SR, Grieve C M. (1999). Salinity–mineral nutrient relations in horticultural crops. Scientia Horticulturae 78(1-4): 127-157, https://doi.org/10.1016/S0304-4238(98)00192-7 Gyanagoudar, H.S., Hatiya, S.T., Guhey, A., Dharmappa, P.M. and Seetharamaiah, S.K., (2024). A comprehensive approach for evaluating salinity stress tolerance in brinjal (Solanum melongena L.) germplasm using membership function value. Physiologia Plantarum , 176 (2), p.e14239, https://doi.org/10.1111/ppl.14239 Hasan M.K, El-Sabagh A, Sikdar MS, Alam MJ, Ratnasekera D, Barutcular C, Abdelaal KA, Islam MS. (2017). Comparative adaptable agronomic traits of blackgram and mungbean for saline lands. Plant Archives 17:589-593, http://ir.lib.ruh.ac.lk/xmlui/handle/iruor/9046 Kumawat KR, Gothwal DK and Singh D. (2017). Salinity tolerance of lentil genotypes based on stress tolerance indices. Journal of Pharmacognosy and Phytochemistry , 6(4):1368-1372. Lv BS, Li XW, Ma HY, Sun Y, Wei LX, Jiang CJ, Liang ZW. (2013). Differences in growth and physiology of rice in response to different saline‐alkaline stress factors. Agronomy Journal 105(4): 1119-1128, https://doi.org/10.2134/agronj2013.0017 Mathankumar P, Geetha S, Saraswathi R, Raveendran M, Vijayalakshmi D, Suresh R. (2023). Development and validation of a membership function-based evaluation system for salinity screening in rice. Electronic Journal of Plant Breeding 14(4): 1412-1420. Mittler R. (2002). Oxidative stress, antioxidants, and stress tolerance. Trends in Plant Science 7(9): 405-410, https://doi.org/10.1016/S1360-1385(02)02312-9 Munns, R. and Tester M. (2008). Mechanisms of salinity tolerance. Annual Review of Plant Biology 59:651-681, https://doi.org/10.1146/annurev.arplant.59.032607.092911 Pandey, A.K. and Sengar, R.S., (2020). Effect of salt stress on salt tolerant indices of morpho-physiological traits and yield attributes of lentil ( Lens culinaris Medik.). Int. J. Chem. Stud , 8 , pp.2292-2301, https://doi.org/10.22271/chemi.2020.v8.i1ah.8609 Pratap A, Chaturvedi SK, Tomar R, Rajan N, Malviya N, Thudi, Singh NP. (2017). Marker-assisted introgression of resistance to fusarium wilt race 2 in Pusa 256, an elite cultivar of desi chickpea. Molecular Genetics and Genomics , 292(6): 1237-1245, https://doi.org/10.1007/s00438-017-1343-z Rengasamy P. (2010). Soil processes affecting crop production in salt-affected soils. Functional Plant Biology 37(7): 613-620, https://doi.org/10.1071/FP09249 Sehrawat N, Yadav M, Bhat KV, Sairam RK, Jaiwal PK.. (2015). Effect of salinity stress on mungbean [ Vigna radiata (L.) Wilczek] during consecutive summer and spring seasons. Journal of Agricultural Sciences, Belgrade 60(1) :23-32, https://doi.org/10.2298/JAS1501023S Sharma PC, Sehgal D, Singh D, Singh, G, Yadav R.S. (2011). A major terminal drought tolerance QTL of pearl millet is also associated with reduced salt uptake and enhanced growth under salt stress. Molecular Breeding 27:207-222, https://doi.org/10.1007/s11032-010-9423-3 Singh D, Singh CK, Kumari S, Singh Tomar RS, Karwa S, Singh R. (2017). Discerning morpho-anatomical, physiological and molecular multiformity in cultivated and wild genotypes of lentil with reconciliation to salinity stress. PLoS ONE 12(5): e0177465, https://doi.org/10.1371/journal.pone.0190462 Singh D, Singh CK, Singh YP, Singh V, Singh R, Tomar RSS, Sharma PC. (2018). Evaluation of cultivated and wild genotypes of Lens species under alkalinity stress and their molecular collocation using microsatellite markers. PloS one 13(8):e0199933, https://doi.org/10.1371/journal.pone.0199933 Vadez V, Krishnamurthy L, Upadhyaya HD. (2007). Large variation in salinity tolerance in chickpea is explained by differences in sensitivity at the reproductive stage. Field Crops Research 104(1-3): 123-129, https://doi.org/10.1016/j.fcr.2007.05.014 Wani, AS, Ahmad A, Hayat S, Fariduddin, Q. (2013). Salt-induced modulation in growth, photosynthesis and antioxidant system in two varieties of Brassica juncea . Saudi journal of biological sciences 20(2): 183-193, https://doi.org/10.1016/j.sjbs.2013.01.006 Zeng L, Shannon MC. (2000). Salinity effects on seedling growth and yield components of rice. Crop Science 40(4): 996-1003, https://doi.org/10.2135/cropsci2000.404996x Zhang J, Jia W. (2010). Role of ABA in integrating plant responses to drought and salt stresses. Field Crops Research , 97(1): 111-119, https://doi.org/10.1016/j.fcr.2005.08.018 Zhang R, Hussain S, Wang Y, Liu Y, Li Q, Chen Y, Wei H, Gao P, Dai Q. (2021). Comprehensive evaluation of salt tolerance in rice ( Oryza sativa L.) germplasm at the germination stage. Agronomy 11(8):1569, https://doi.org/10.3390/agronomy11081569 Zhu, J. K. (2001). Plant salt tolerance. Trends in Plant Science 6(2):66-71, https://doi.org/10.1016/S1360-1385(00)01838-0 Supplementary Files GermplasmPassportdata.xlsx Supplementary Information The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Supporting information is available in Table S1 (germplasm passport data). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7176739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490542908,"identity":"c2d0e041-d189-48ac-b79a-f63246adc708","order_by":0,"name":"Vijayata Singh","email":"data:image/png;base64,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","orcid":"","institution":"Central Soil Salinity Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Vijayata","middleName":"","lastName":"Singh","suffix":""},{"id":490542909,"identity":"b2f8ca62-e971-4fd8-8b8b-69480824815a","order_by":1,"name":"Jogendra Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jogendra","middleName":"","lastName":"Singh","suffix":""},{"id":490542910,"identity":"f4b239de-0c4f-47f4-9ed6-571c7d59be25","order_by":2,"name":"Ravi Kiran KT","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ravi","middleName":"Kiran","lastName":"KT","suffix":""},{"id":490542911,"identity":"da3962d4-53fb-4bfb-a49f-146d3133f9b5","order_by":3,"name":"Kailash Prajapat","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kailash","middleName":"","lastName":"Prajapat","suffix":""},{"id":490542912,"identity":"566862e7-6162-4a03-a89c-db46dc5c0537","order_by":4,"name":"Kuldeep Tripathi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kuldeep","middleName":"","lastName":"Tripathi","suffix":""},{"id":490542913,"identity":"a6ae64d0-cc25-45a8-9175-f5ad68911168","order_by":5,"name":"Padmavati Ganpat Gore","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Padmavati","middleName":"Ganpat","lastName":"Gore","suffix":""},{"id":490542914,"identity":"b9d694a2-6416-4b8a-9b90-a6315ef415a4","order_by":6,"name":"Sushil Pandey","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sushil","middleName":"","lastName":"Pandey","suffix":""},{"id":490542915,"identity":"a41439b0-c00e-45b7-8d80-5bdce3ee08e8","order_by":7,"name":"Vivek Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Singh","suffix":""},{"id":490542916,"identity":"974accda-3ea7-4ecb-ad37-15b9d231919e","order_by":8,"name":"Ankit Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Kumar","suffix":""},{"id":490542917,"identity":"8d0484d5-fb55-4dcf-b72c-37430a549592","order_by":9,"name":"Satish Kumar Sanwal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Satish","middleName":"Kumar","lastName":"Sanwal","suffix":""},{"id":490542918,"identity":"21c84e18-b16e-4ea2-bf6e-a31892be338c","order_by":10,"name":"Ashutosh Kumar Yadav","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ashutosh","middleName":"Kumar","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2025-07-21 11:14:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7176739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7176739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87725803,"identity":"fc6aa9f4-e75c-47ea-b6c9-a3f35cb98c0c","added_by":"auto","created_at":"2025-07-28 10:39:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":803243,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic screening of Lentil germplasm. Drone image of the Lentil field: \u003cstrong\u003e(a)\u003c/strong\u003e control, \u003cstrong\u003e(b)\u003c/strong\u003e salinity (ECe 7 dS/m, \u003cstrong\u003e(c)\u003c/strong\u003e alkalinity (pH 2 9.3).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7176739/v1/071d1b5b7229335b4a28f490.png"},{"id":87725823,"identity":"a6f9ccf0-cf6a-42b0-9800-f3c1c2da65d7","added_by":"auto","created_at":"2025-07-28 10:39:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147870,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficients for the agronomic traits of lentil under three different soil conditions. The traits included DFF (days to 50% flowering), DTM (days to maturity), PH (plant height), NBPP (number of branches per plant), NPPP (number of pods per plant), TW (test weight, i.e., 100-seed weight), and SYPP (seed yield per plant). Significant positive correlations are represented by filled circles, with the strength of the correlation indicated by the circle size and color intensity. Nonsignificant correlations (p \u0026gt; 0.05) are marked with an “×”. \u003cstrong\u003e(a)\u003c/strong\u003e Under normal (nonsaline, nonalkaline) conditions, DFF and pH had a moderate positive correlation (r = 0.30), PH had a strong correlation with NBPP (r = 0.32) and a moderate correlation with SYPP (r = 0.27), TW and SYPP had the strongest correlation (r = 0.53), and NPPP also correlated moderately with SYPP (r = 0.49). DFF and DTM had a weak correlation (r = 0.13), with DTM showing no other significant correlations. Traits such as TW, NPPP, and PH are key contributors to yield. \u003cstrong\u003e(b)\u003c/strong\u003e Under saline conditions (ECe ~7 dS/m), DFF and SYPP and DTM and SYPP presented significant negative correlations, indicating that early flowering/maturity enhances yield. NPPP and SYPP were strongly correlated; PH, NBPP, and TW had mostly weak or nonsignificant associations with SYPP. Early phenology and pod number should be prioritized during breeding under salinity stress. \u003cstrong\u003e(c)\u003c/strong\u003e Under alkaline conditions (pH ~9.3; ECe \u0026lt; 4 dS/m), SYPP and NPPP were strongly positively correlated; NBPP was also moderately correlated with both NPPP and SYPP. Other traits, such as DFF, DTM, TW, and PH, presented mostly nonsignificant correlations. Emphasis should be placed on branching and pod traits rather than phenology under alkaline stress.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7176739/v1/09e7eb54276449da730f904c.png"},{"id":87725787,"identity":"a1663a52-0fb5-4752-b543-2cf9302a62c7","added_by":"auto","created_at":"2025-07-28 10:39:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). \u003c/strong\u003ePrincipal component analysis (PCA) biplot of agronomic traits of lentil under normal soil conditions (nonsaline, nonalkaline). Dim1 and Dim2 explain 30.3% and 17.6% of the total variation, respectively. The traits analyzed included days to 50% flowering (DFF), days to maturity (DTM), plant height (PH), number of branches per plant (NBPP), number of pods per plant (NPPP), test weight (TW), and seed yield per plant (SYPP). SYPP and TW have the greatest contributions to the principal components, especially Dim1, followed by NPPP. Traits such as DFF, DTM, and PH contribute weakly and cluster near the origin. These findings suggest that SYPP, TW, and NPPP are key traits for genotype selection under normal conditions. (b). Principal component analysis (PCA) biplot of the agronomic traits of lentils under salinity stress conditions (ECe ~7 dS/m). Dim1 and Dim2 explain 30.3% and 17.6% of the total variation, respectively. The traits include DFF, DTM, PH, NBPP, NPPP, TW, and SYPP. Similar to normal conditions, SYPP and TW contribute most significantly to Dim1 and Dim2, whereas NPPP also notably contributes to Dim1. DFF, DTM, and PH remain weak contributors. The PCA results indicate that SYPP, TW, and NPPP are still important for genotype differentiation under saline conditions. (c). Principal component analysis (PCA) biplot of the agronomic traits of lentil under alkalinity stress conditions (pH ~9.3; ECe \u0026lt; 4 dS/m). Dim1 and Dim2 explain 32.5% and 22.1% of the total variation, respectively. SYPP, NPPP, and NBPP are the main contributors to Dim1, suggesting that yield under alkalinity is more influenced by plant architecture traits than by phenological traits. DFF and DTM contribute more to Dim2 but are weakly associated with yield. This highlights the importance of branching and pod development for yield performance in alkaline soils\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7176739/v1/000a7cb5766a72588032c1bb.png"},{"id":87725762,"identity":"b754323c-9a34-4a37-a899-e0256e91c167","added_by":"auto","created_at":"2025-07-28 10:39:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":259291,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise Pearson correlation matrix showing the relationships between morpho-agronomic traits and yield indices under the mean field value (MFV), mean multienvironment field value (MMFV), and stress tolerance index (STI). The color bar on the right indicates the strength of the correlation, ranging from - 1 (deep red) to + 1 (dark gray). Red/light orange represent negative correlations; gray/black denote positive correlations; and white cells with an X signify nonsignificant (insignificant) correlations. (a) Numerical values in each cell display the Pearson correlation coefficient between two traits. MFV _ DFF (daysto 50% flowering) and MFV _ DTM (days to maturity) have coefficients of 0. 0.49, indicating a moderately positive correlation. MFV _ DFF and MFV _ SYPP have coefficients of 0. 34, indicating a moderate negative correlation. Traits such as seed yield per plant (MFV _ SYPP) and plant height (MFV _ PH) were positively correlated (r = 0. 49) with STI and MMFV, suggesting their contribution under stress. Early flowering and maturity (lower DFF and DTM) are negatively correlated with the STI (r = 0. 32), implying that earlier genotypes may perform better under stress conditions. Cross-marked (X) correlations with r = 0. 28 indicate weak or insignificant relationships, aiding in trait selection refinement. (b) MFV _ SYPP shows strong, significant positive correlations with: MFV _ NPPP (r = 0. 79); MFV _ NBPP (r = 0. 43); MFV _ Mean (r = 0. 68); and ATI (r = 0. 67). MFV _ NPPP is highly correlated with MFV_ Mean (r = 0. 67) and ATI (r = 0. 65). MFV _ TW also correlates with MFV _ SYPP (r = 0. 38) and MFV_ Mean (r = 0. 38). Moderate correlations are observed between the MFV _ DTM and MFV _ Mean (r = 0. 41), ATI (r = 0. 41), along with MFV _ PH and MFV_ Mean (r = 0. 41). MFV_DFF demonstrated weak or non-significant correlations with most traits, except for a weak negative relationship with MFV_SYPP (r = -0. 25) and a weakpositive relationship with MFV _ Mean (r = 0. 28). MFV _ DTM has a weak negative correlation with MFV _ SYPP (r = -0. 24). Seed yield per plant (MFV _ SYPP) is strongly influenced by the number of pods per plant (MFV _ NPPP) and the number of branches per plant (MFV _ NBPP). High ATI scores are associated with genotypes with high MFV _ SYPP, MFV _ NPPP, and MFV _ NBPP values, indicating that these traits contribute to superior performance under abiotic stress. Traits such as MFV _ DFF and MFV _ DTM have relatively low or no significant contribution to the stress tolerance index (ATI), suggesting that they are less effective for selection under stress.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7176739/v1/ec9efa324429a5efc8da6d4c.png"},{"id":92732833,"identity":"117766d3-aa2d-4e75-8e49-1f7da2cb44f4","added_by":"auto","created_at":"2025-10-03 16:08:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2558024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7176739/v1/d847b8a7-794a-456b-9924-3c8689a4c4d7.pdf"},{"id":87725789,"identity":"598dc275-09ff-4f86-b98e-cf07a5ffcb59","added_by":"auto","created_at":"2025-07-28 10:39:04","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003cstrong\u003e \u003c/strong\u003eSupporting information\u003cstrong\u003e \u003c/strong\u003eis available in \u003cstrong\u003eTable S1 \u003c/strong\u003e(germplasm passport data).\u003c/p\u003e","description":"","filename":"GermplasmPassportdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7176739/v1/3c360b5c0c1b7993d34f71e2.xlsx"}],"financialInterests":"","formattedTitle":"Multivariate analysis of phenotypic traits and trait associations for identifying elite lentil (Lens culinaris Medik.) genotypes using STI and MFV indices for breeding in salt-affected soil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil salinity and alkalinity are among the most important abiotic stress factors limiting agricultural productivity worldwide. Together, salt-affected soils cover an estimated 932.2\u0026nbsp;million hectares, with approximately 351.2-million-hectare saline and 581\u0026nbsp;million hectares alkaline or alkaline in nature (FAO, 2022). These challenges reduce the availability of arable land and threaten food security, especially in arid and semiarid regions where irrigation practices and poor drainage further worsen soil degradation (Rengasamy, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Salt stress impacts plant physiology through osmotic stress, ion toxicity (notably Na⁺ and Cl⁻), nutritional imbalance, and oxidative damage, thereby hindering growth, reproductive development, and yield (Munns \u0026amp; Tester, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Grattan \u0026amp; Grieve, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medik. ), an important cool-season legume, plays a significant role in human nutrition and sustainable agriculture because of its high protein content, nitrogen-fixing ability, and relatively low water requirement. The lentil (\u003cem\u003eLens culinaris\u003c/em\u003e), a protein-rich legume, is a vital dietary component in regions with predominantly vegetarian or low-meat diets. However, salinity stress negatively affects its germination and early seedling development (Foti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When cultivated mainly in arid and semiarid regions, lentil shows moderate sensitivity to salinity, with a 50% yield reduction reported at an electrical conductivity of approximately 6.0 dS/m (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003ea). This sensitivity limits its cultivation in saline and alkaline soils, especially in South Asia, which accounts for more than 60% of the world's lentil area and production (FAOSTAT, 2023). Since there are limited prospects for expansion in suitable environments, future yield improvements should focus on enhancing tolerance to abiotic stresses and utilizing marginal lands (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dissanayake et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBreeding lentil for increased salt tolerance has proven to be particularly challenging because of several interrelated factors. A primary obstacle is the narrow genetic base of a crop, which severely limits the genetic variation essential for identifying and selecting beneficial traits and has the lowest probability of introgressing desirable traits from wild relatives into elite cultivars (Pratap et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This limited diversity constrains breeders\u0026rsquo; ability to develop new, resilient varieties that can thrive in saline environments. Moreover, the response of lentils to salt stress is characterized by a complex interplay of physiological and morphological adaptations. Plants may employ osmoregulation to maintain water balance while also working to manage ion toxicity through mechanisms that prevent sodium accumulation in crucial tissues. Morphologically, lentils may alter their root architecture, promoting deeper root systems to access moisture while simultaneously adjusting their leaf structure to reduce water loss. Overall, the multifaceted nature of these responses poses significant challenges for conventional breeding techniques, where the intricate balance of traits must be carefully navigated to enhance salt tolerance effectively. Understanding and unraveling these complexities is essential for improving crop resilience in increasingly saline agricultural landscapes.\u003c/p\u003e\u003cp\u003eFurthermore, earlier studies were limited in scope, often involving fewer than 300 genotypes and were conducted under controlled or artificially induced stress conditions (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dissanayake et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There remains a critical need to phenotype diverse lentil germplasms under natural field conditions to capture realistic stress responses and to identify stable, high-performing genotypes for direct use or incorporation into breeding pipelines.\u003c/p\u003e\u003cp\u003eThe present study fills this gap by providing a comprehensive characterization of 500 lentil genotypes, including 317 indigenous and 183 exotic accessions, grown under control, saline, and alkaline field conditions. Such extensive evaluation across natural environmental conditions is uncommon in lentil research. Two reliable indices, the stress tolerance index (STI) (Fernandez, 1992) and membership function value (MFV), a fuzzy logic-based selection tool (Zhang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gholizadeh et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), were employed to thoroughly assess genotypic performance under stress. While the STI is a proven metric for evaluating yield stability across stress and nonstress scenarios, MFV offers a multivariate assessment that combines responses from all traits, reducing the risk of biased selection on the basis of a single trait.\u003c/p\u003e\u003cp\u003eThe objectives of this study were to (i) assess phenotypic variability and trait relationships under normal, saline, and alkaline conditions; (ii) identify traits contributing to salt tolerance via multivariate analysis; and (iii) select superior genotypes with STIs and MFVs for breeding programs aimed at salt-affected soils. These findings provide a strong foundation for the development of lentil cultivars adapted to saline and alkaline environments and offer insights into the complex genetic and phenotypic factors underlying salt tolerance in this species.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003ePlant material\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe present study included 500 lentil genotypes \u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e, along with three controls. The test genotypes consisted of 317 indigenous collections and 183 exotic collections. All these genotypes were obtained from the ICAR-National Bureau of Plant Genetic Resources in New Delhi, India. The checks include PDL1, PSL9, and IPL 526. PDL1 and PSL9 are salt-tolerant lentil varieties released in 2019 and were developed collaboratively by the ICAR-Central Soil Salinity Research Institute and the ICAR-Indian Agricultural Research Institute. PDL1 is a small-seeded type with potential yields of 2.5-3.0 tons per hectare and 1.1\u0026ndash;1.6 tons per hectare under normal and salt stress conditions, respectively. PSL9 is a bold-seeded variety with a yield potential of 2.0-2.5 tons per hectare under normal conditions and 1.1\u0026ndash;1.5 tons per hectare under salt stress. These two varieties serve as tolerance checks. IPL526 is a bold-seeded lentil variety released in 2018 from the ICAR-Indian Institute of Pulses Research in Kanpur, Uttar Pradesh, India. This variety is not suitable for salt stress conditions and was used as a negative control.\u003c/p\u003e\u003cp\u003e\u003cem\u003eField experiments for screening lentil genotypes for salt tolerance\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDuring the post monsoon season of 2023\u0026ndash;24, 500 lentil genotypes were evaluated under three different stress conditions: control (ECe\u0026thinsp;\u0026lt;\u0026thinsp;4 dS/m, pH\u0026thinsp;~\u0026thinsp;7.5), salinity (ECe\u0026thinsp;~\u0026thinsp;7 dS/m, pH\u0026thinsp;~\u0026thinsp;7.5), and alkalinity (ECe\u0026thinsp;\u0026lt;\u0026thinsp;4 dS/m, pH\u0026thinsp;~\u0026thinsp;9.3). These treatments were applied in three separate experiments carried out under natural field conditions at ICAR-CSSRI, Karnal. A consistent experimental design and plot size were maintained across all the experiments, and an augmented randomized complete block design was used to assess the genotypes and controls. The site was divided into six blocks, each subdivided into 86 plots. Within these blocks, 83 test entries and three controls were randomly assigned, except in the final block, which included 85 test entries and a total of 88 plots. The genotypes were sown in early November 2023 in single rows, each 2 m long, with 50 cm inter-row spacing. All recommended agronomic practices were strictly followed to ensure a healthy crop, regardless of the treatment. Conditions were closely monitored through periodic sampling to measure the EC and pH levels, maintaining the stress conditions in the saline and alkaline treatments.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEstimation of phenotypic attributes\u003c/em\u003e\u003c/p\u003e\u003cp\u003eData on grain yield and its contributing traits were recorded at key growth stages of the crop. Specifically, the date when approximately 50% of the plants of each genotype flowered was noted. This information, combined with the sowing date, was used to calculate the number of days to 50% flowering. Similarly, the date of maturity for each genotype was recorded and converted into days to maturity. These two traits were documented for each plot. At physiological maturity, five randomly selected plants per genotype were tagged and sampled for further trait analysis. The total number of branches and pods per plant was counted for each selected plant, and the averages were calculated. Pods were then harvested from each plant, and seeds were extracted. The seeds from each plant were pooled and weighed to determine the yield per plant. Additionally, 100 seeds were counted per plant to measure the weight of each sample. Both the seed yield and test weight were averaged over the five plants.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eA preliminary augmented RCB analysis of variance was conducted to evaluate the significance of various sources of variation for each trait, with a focus on test entries and checks. Additionally, the adjusted means of each genotype for each trait were calculated for use in subsequent analyses. Pearson correlation coefficients among traits were calculated separately for different treatments and tested for significance at the 1% level to identify true associations. Multiple regression analysis was performed with seed yield as the dependent variable and the other traits as independent variables to identify traits highly important for yield formation under each treatment. These traits can serve as proxies for selecting promising genotypes under stress conditions. To explore the contribution of each trait to the total variation within each treatment, principal component analysis (PCA) was conducted. Biplots generated via the first two principal components (PC1 and PC2) were used for interpretation. To select promising genotypes for salinity and alkalinity treatments, two indices were calculated: the stress tolerance index (salinity tolerance index and alkalinity tolerance index for the respective treatments) and the mean membership function value (MFV). The stress tolerance index (STI) for seed yield was calculated via the formula provided by Fernandez (1992).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:STI=\\:\\frac{{(Y}_{s}\\left)\\:\\right({Y}_{t})}{{Y}_{a}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere Y\u003csub\u003es\u003c/sub\u003e and Y\u003csub\u003et\u003c/sub\u003e are the mean seed yields of a genotype under stress and normal conditions, respectively, and Y\u003csub\u003ea\u003c/sub\u003e is the average seed yield across all genotypes under normal conditions. To calculate the mean MFV, a fuzzy comprehensive evaluation parameter, the following parameter was initially calculated for each trait separately.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:TI=\\:\\frac{{Y}_{s}}{{Y}_{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere TI indicates the tolerance index and Y\u003csub\u003es\u003c/sub\u003e and Y\u003csub\u003et\u003c/sub\u003e are the mean trait values of a genotype under stress and normal conditions, respectively. The stress tolerance membership function value was calculated as follows.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:MFV=\\:\\frac{Y-\\:{Y}_{min}}{{Y}_{max}-{Y}_{min}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere Y is the TI value of a specific genotype for a trait and Y\u003csub\u003emin\u003c/sub\u003e and Y\u003csub\u003emax\u003c/sub\u003e are the minimum and maximum TI values, respectively, for that trait across all genotypes. These MFV values therefore ranged from 0\u0026ndash;1 and were averaged across traits. Correlation coefficients were also calculated among the STI and MFV for various traits to examine the relationships between these indices. The top 10 genotypes were selected separately for each stress condition (saline or alkaline) and each index (STI or MFV). Additionally, the mean trait value of the selected genotypes across the entire population was estimated to identify the best top 10 genotypes. In this process, the selection differential (SD) was calculated by subtracting the trait value of the entire population from the mean trait value of the top 10 genotypes, and the selection differential percentage (SD%) was determined by dividing this difference by the mean trait value of the population, expressed as a percentage.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnalysis of variance and mean performance of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egenotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA preliminary analysis of variance (ANOVA) based on the augmented RCB design revealed significant differences among treatments and genotypes. Phenotypic screening of lentil germplasm was conducted under three conditions: control, salinity stress (ECe 7 dS/m), and alkalinity stress (pH 9.3). Drone imagery captured the field conditions for each treatment: (a) control, (b) salinity, and (c) alkalinity \u003cstrong\u003e(Fig. 1).\u003c/strong\u003e This analysis was performed separately for the control, saline, and alkaline treatments, as shown in Table 1. The results indicated that most traits highly significantly differed (at the 1% level of probability) for all sources of variation, except for the block (adjusted) source for the trait number of branches per plant, which was significant at the 5% level. Regardless of the treatment, the block (unadjusted) source contributed more to the total variance (mean sum of squares) than the other sources did. Among the test entries and checks, the test entries contributed more variance. The error variance was minimal for most traits across all the treatments, except for the number of pods per plant. Among the treatments, especially for the different genotypes, greater variance was observed under stress conditions than under the control conditions, except for the seed yield per plant, where the opposite trend was observed.\u003c/p\u003e\n\u003cp\u003eThe means and ranges of various traits estimated under the control and two stress treatments are shown in \u003cstrong\u003eTable 2.\u003c/strong\u003e Compared with that under normal conditions, a significant decrease in the average performance of genotypes (including both test entries and checks) under stress conditions was detected. In general, the reduction was more noticeable under salinity stress than alkalinity stress for traits such as days to 50% flowering and maturity, plant height, and number of branches per plant. Conversely, for traits such as the number of pods per plant, test weight, and seed yield per plant, the reduction was greater under alkalinity stress than under salinity stress. Notably, plants flowered and matured earlier under alkaline conditions than under saline and control conditions. The smallest reduction under stress was observed for days to maturity under alkalinity stress (7.17%), whereas the greatest reduction was recorded for the seed yield per plant (86.805%). Across both test entries and checks, the mean values for all traits were greater for the checks, regardless of the trait. Compared with those under normal conditions, the ranges for most traits under stress conditions shifted toward lower values. However, for days to 50% flowering and days to maturity, the range expanded in both directions under stress. Low to moderate coefficients of variation (CVs) were observed for most traits, with the highest (26.12%) seed yield per plant under alkaline stress and the lowest (8.85%) number of days to 50% flowering and maturity under normal conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eAnalysis of variance for agronomic traits was conducted on more than 500 lentil genotypes and three controls tested under normal, saline, and alkaline environments in an augmented randomized complete block (RCB) design. The experimental setup included 500 genotypes plus 3 checks across three environments: normal, saline, and alkaline soil conditions. Sources of variation include Block (Unadjusted): the raw block effect; Treatment (Adjusted): treatment effect after correction; Block (Adjusted): the adjusted block effect accounting for treatments; Treatment (Unadjusted): the raw treatment effect; Checks: the effect of control check varieties; Genotypes: the effect of test entries; Checks vs. Genotypes: the comparison between checks and test entries; Error: residual variance. The agronomic traits evaluated included the following: DFF\u0026mdash;Days to 50% flowering; DTM\u0026mdash;Days to maturity; PH\u0026mdash;Plant height (cm); NBPP\u0026mdash;Number of branches per plant; NPPP\u0026mdash;Number of pods per plant; TW\u0026mdash;Test weight (g), usually 100-seed weight; and SYPP\u0026mdash;Seed yield per plant (g). For each trait and environment, ANOVA was performed, and the mean squares for each source of variation are reported. Higher mean squares with significance (marked by * or **) indicate significant variability contributed by that factor. For DFF under normal conditions, genotypes presented significant variation (1328.40) at the 1% level (**), indicating meaningful diversity among test entries. Similarly, under saline and alkaline conditions, all traits\u0026mdash;including SYPP, NPPP, and TW\u0026mdash;exhibited significant genotypic variation, highlighting the potential for selection.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 655px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBlock (Unadjusted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003cp\u003e(adjusted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eBlock\u003c/p\u003e\n \u003cp\u003e(adjusted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003cp\u003e(unadjusted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eChecks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eChecks vs Genotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eDFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e12294.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1213.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e9.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1335.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e131.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1328.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7317.96**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e22581.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2460.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2685.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e27.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2608.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e46189.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e45699.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2235.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e9.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2690.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2661.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e22511.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eDTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e23395.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2253.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e17.82**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2486.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e18.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2471.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e14661.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e36719.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3983.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e17.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4349.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4205.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e84764.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e80168.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4030.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20.07**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4829.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e14.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4736.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e60642.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ePH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1943.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e239.05**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2.08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e258.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e655.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e250.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3546.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2273.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e260.04**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e282.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e18.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e277.13**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3577.74**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e4082.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e198.86**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.06**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e239.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e140.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e232.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3846.96**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eNBPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e115.82**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e10.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e35.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e186.73**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e47.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e7.71**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e102.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e92.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e93.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eNPPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e46299.84**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5134.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e35.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5595.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4987.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5519.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e44955.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e58384.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7856.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8437.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2469.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8461.90**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8246.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e8015.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1881.02**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1960.84**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e46.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1967.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2222.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eTW (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.42**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20.84**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e4.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e33.90**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e7.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSYPP (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e313.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e42.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e14.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e40.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e842.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e296.89**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e34.87**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e32.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e34.90**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e22.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e30.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.78**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.06**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*: Significant at the 5% probability level; **: Significant at the 1% probability level; DFF: Days to 50% flowering; DTM: Days to maturity; PH: Plant height; NBPP: Number of branches per plant; NPPP: Number of pods per plant; TW: Test weight; SYPP: Seed yield per plant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Mean performance, range, and variability of test entries and checks for key agronomic traits in lentils under normal, saline, and alkaline environments. The values include the mean, range (with genotype identifiers), standard error (SE\u0026plusmn;), and number of test genotypes that surpass the best check. Compared with that under normal conditions, the combined genotype mean under stress is accompanied by a percentage reduction. C.V. (%) indicates the coefficient of variation, reflecting trait variability across genotypes. Trait abbreviations: DFF \u0026ndash; Days to 50% flowering, DTM \u0026ndash; Days to maturity, PH \u0026ndash; Plant height, NBPP \u0026ndash; Number of branches per plant, NPPP \u0026ndash; Number of pods per plant, TW \u0026ndash; Test weight, SYPP \u0026ndash; Seed yield per plant. The performance of 500 lentil genotypes and 3 controls under normal, saline, and alkaline conditions revealed significant variation across all evaluated traits. Compared with those under normal conditions, the number of days to 50% flowering (DFF) and maturity (DTM) were delayed under stress, with a 14.33% reduction in the mean DFF under salinity and 9.46% under alkalinity. Yield-related traits such as the seed yield per plant (SYPP) decreased by 66.19% and 86.80% under saline and alkaline conditions, respectively. Despite these reductions, several test genotypes outperformed the best checks: 237 genotypes flowered earlier than did the controls under salinity, and 134 genotypes exceeded the best check for SYPP under alkalinity. Notably, the number of pods per plant (NPPP) and branches per plant (NBPP) showed high variability, with the C.V. exceeding 25% under alkaline stress, suggesting ample scope for selection. The test genotypes demonstrated superior resilience in traits such as DFF, SYPP, and NBPP, indicating their breeding potential under salt-affected conditions. Genotypes such as IC268245, IC158668, and IC610426 presented exceptional yield performance under stress.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"880\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of genotypes surpassing the best check\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTest entries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eChecks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eGenotypes combined\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eSE\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTest entries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eChecks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e81.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e101.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e91.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e83 (IC547035)-108 (IC412932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e95 (IPL526)-109 (PDL1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e50.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e106.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e78.24 (14.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e90 (EC223231)-118 (IC447858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;102 (IPL526)-111 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e63.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e102.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e82.685 (9.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e98 (IC430348)-120 (IC573471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e98 (IPL526)-107.37 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e111.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e139.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e125.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e118 (IC268237)- 138 (EC267602)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;134 (PSL9)-144 (PDL1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e64.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e139.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e101.625 (18.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e116 (EC2232231)-140 (IC266800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e134 (IPL526)-145.26 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e84.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e147.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e116.075 (7.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e133 (EC78447)-151 (ILL5371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e143 (PSL9)-153.68 (PDL1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003ePH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e47.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e40.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e24 (IC241253)-55 (IC244072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e34.68 (IPL526) -55.79 (PDL 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e31.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e23.74 (41.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e15 (IC267116) \u0026ndash; 50 (NC62518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e29 (IPL\u0026nbsp;526-34.03 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e18.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e33.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e26.195 (35.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e19 (IC382687)-46 (EC223150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e29.00 (PSL9)- 40.35 (PDL\u0026nbsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eNBPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e7.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3 (IC274062)-12 (EC78476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.00 (IPL526)-11.23 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.805 (46.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2 (IC341355)-10 (IC244070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.67 (PDL1)-5.61 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.215 (41.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (EC267705)- 10 (IC268241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5 (PSL9)-5.96 (PDL1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eNPPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e147.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e196.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e172.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e45 (ILL1665)-300 (PL639, IC267665 and IC267667)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e162 (IPL526)- 229.12 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e77.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e65.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e71.4 (58.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e15 (IC29945)-310 (EC78475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e43.67 (IPL526)-87.82 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e32.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e23.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e28.19 (83.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4 (EC255550)- 226 (IC424863)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e20.33 (PSL9)-26.67 (IPL526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e25.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eTW (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.83 (IC332103)-3.65 (EC266630)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.85 (PDL1)-3.12 (IPL526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.45 (18.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.61 (IC267529)-2.57 (IC565303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.84 (PDL1)-2.56 (IPL526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.365 (23.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.70 (IC544561)-2.72 (IC586784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.67 (PSL9)-2.22 (IPL526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eSYPP (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e11.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e17.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e14.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2.64 (IC241253)-35.16 (IC158668)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e15.60 (PDL1)-19.58 (IPL526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.865 (66.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.14 (IC267529)-22.89 (IC610426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;4.30 (IPL526)-8.84 (PSL 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAlkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.9 (86.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.10 (EC267630)- 12.08 (IC268245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.90 (IPL526)-2.42 (PSL9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDFF: Days to 50% flowering; DTM: Days to maturity; PH: Plant height; NBPP: Number of branches per plant; NPPP: Number of pods per plant; TW: Test weight; SYPP: Seed yield per plant; C.V.: Coefficient of variation. \u003csup\u003e#\u003c/sup\u003e Numbers in parentheses indicate the percent reduction in stress (saline \u0026amp; alkaline) compared with normal conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation analysis among various attributes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation coefficients were calculated for various traits under different treatments: normal, saline, and alkaline. Under normal conditions, the seed yield per plant was highly significantly (p \u0026lt; 0.01) positively correlated with the test weight, followed by the number of pods per plant, plant height, and number of branches per plant \u003cstrong\u003e(Fig. 2a)\u003c/strong\u003e. Notably, a significant positive correlation (p \u0026lt; 0.01) was also found between the number of days with 50% flowering and the seed yield per plant. Additionally, plant height was significantly positively correlated (p \u0026lt; 0.01) with the number of branches per plant, the number of pods per plant, and the test weight. Overall, all the traits presented positive correlations with one another, regardless of statistical significance. Under stress conditions, these relationships changed considerably. For example, under salinity, the number of pods per plant and the seed yield per plant were significantly negatively correlated (p \u0026lt; 0.01) with the number of days to 50% flowering and maturity \u003cstrong\u003e(Fig. 2b).\u003c/strong\u003e Under alkaline conditions, the trait correlations resembled those observed under normal conditions. However, most of these correlations were not significant, except for the strong positive relationship between the seed yield per plant and the number of pods per plant \u003cstrong\u003e(Fig. 2c).\u003c/strong\u003e Notable associations under alkalinity included a positive correlation between the number of branches per plant and both the number of pods per plant and the seed yield per plant.\u003c/p\u003e\n\u003cp\u003eTo further explore the importance of each trait in contributing to seed yield, multiple regression analysis was conducted \u003cstrong\u003e(Table 3).\u003c/strong\u003e Under both control and salinity conditions, the number of pods per plant, test weight, and plant height were significantly associated with seed yield. However, under alkaline conditions, only the number of pods per plant had a significant effect on seed yield. Notably, the highest R\u0026sup2; and adjusted R\u0026sup2; values were observed for alkalinity, followed by the control, and the lowest values were observed under salinity. Additionally, to assess how each trait contributed to the overall variation, a principal component analysis (PCA) was performed. Under control conditions, the first two principal components (PC1 and PC2) accounted for approximately 47.9% of the total variation \u003cstrong\u003e(Fig. 3a)\u003c/strong\u003e. Among the traits, seed yield per plant and test weight contributed the most to the variation, whereas days to 50% flowering and maturity contributed the least. In contrast, under salinity conditions, PC1 and PC2 explained 59.8% of the total variance, with days to 50% flowering and maturity contributing the most and the test weight the least \u003cstrong\u003e(Fig. 3b).\u003c/strong\u003e The PCA under alkaline conditions revealed a pattern similar to that under the control and saline conditions. Here, PC1 and PC2 explained approximately 54.6% of the total variance, with the highest contributions from seed yield per plant (similar to the control) and the number of pods per plant, whereas plant height contributed the least \u003cstrong\u003e(Fig. 3c).\u003c/strong\u003e The relationships between various traits, as indicated by the angles between their vectors in the PCA, closely matched the estimated correlation coefficients.\u003c/p\u003e\n\u003cp\u003eTable 3 Multiple regression statistics were calculated using seed yield per plant (SYPP) as the dependent variable and all other traits as independent variables, which were assessed separately under the control (normal), salinity, and alkalinity treatments. Column groups show traits as independent agronomic predictors; estimates are regression coefficients indicating the strength of the effect on seed yield per plant and the standard error of the estimate. The P value indicates the statistical significance of the predictor\u0026rsquo;s effect; lower values (\u0026lt;0.05) suggest a significant contribution. R-Squared (R\u0026sup2;) represents the proportion of variance in seed yield explained by the model, along with accuracy metrics (per environment). The adjusted R-square accounts for the number of predictors, and the residual standard error (RSE) reflects the standard deviation of the residuals; lower values indicate a better fit. Key interpretations: The control environment model explained 54% of the variation in seed yield (R\u0026sup2; = 0.54). The significant predictors (p \u0026lt; 0.05) included plant height (PH): positive effect (estimate = 0.07); number of pods per plant (NPP): strong positive effect (estimate = 0.05); test weight (TW): highly positive effect (estimate = 5.83); and days to flowering (DFF): near-significant positive trend (p = 0.08). In the saline environment, the model explained 34% of the variation (R\u0026sup2; = 0.34), with significant predictors\u0026mdash;PH, NPP, and TW\u0026mdash;all with p \u0026lt; 0.05\u0026mdash;positively influencing seed yield. The alkalinity environment model explained 77% of the variation in seed yield (R\u0026sup2; = 0.77), indicating high predictive power. Only NPP is a significant positive predictor (p = 0.00). Other traits, including TW, pH, and NBPP, were not significantly different under alkaline stress.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalinity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlkalinity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eDFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eDTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eNBPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eNPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel accuracy parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eR_Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eAdjusted R_Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eRSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of promising lentil genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify promising entries under each stress treatment, two indices were calculated: the stress tolerance index (STI) based on seed yield and the mean membership function value (MFV) across all traits. The STI values ranged from 0.01 to 4.90, with a mean of 0.99 under salinity, whereas under alkalinity, they varied from 0.01 to 1.44, with a mean of 0.20. Similarly, the MFV values ranged from 0.15 to 0.57 under salinity, with a mean of 0.36, and from 0.15 to 0.64 under alkalinity, with a mean of 0.32, as shown in Table 4. On the basis of these values, the top 10 entries were selected for each index and stress condition. Although different genotypes proved promising for each combination, some genotypes were common across two or more combinations. For example, IC267104 was superior in both STI under salinity and alkalinity; IC248956 was superior for MFV (salinity) and STI (alkalinity); IC268241 was superior for MFV under both salinity and alkalinity; and IC267658 was superior for STI (alkalinity) and MFV (alkalinity). Additionally, two genotypes, IC267657 and IC268240, showed promise in three combinations: MFV (salinity and alkalinity) and STI (alkalinity).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eSummary statistics and top-performing genotypes based on the stress tolerance index (STIy) and mean membership function value (MFV) under salinity and alkalinity stress conditions. STIy (stress tolerance index for yield): A numerical index that assesses the relative yield performance of genotypes under stress conditions compared with optimal (nonstress) conditions. Higher STIy values indicate better yield stability and tolerance under stress. MFV (mean membership function value): A fuzzy logic-based combined score derived from multiple physiological and agronomic traits. It ranks genotypes by integrating performance across traits, making it a reliable indicator for identifying stress-resilient genotypes with overall superior adaptation. Descriptive statistics (mean, minimum, maximum, and standard deviation) for both STIy and MFV provide an overview of variation among genotypes. The top 10 genotypes with the highest STIy and MFV scores are listed, representing the most promising candidates for salinity and alkalinity tolerance breeding. These accessions showed exceptional performance either in yield stability (STIy) or in integrated trait expression (MFV). High-ranking genotypes, such as IC586784, IC342716, IC248956, IC267657, and IC268240, demonstrate significant potential for improving stress tolerance in breeding programs. The consistent performance of these strains under stress conditions highlights them as elite donor lines for the genetic enhancement of salt- and alkali-tolerant cultivars in Lentil.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"92%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalinity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlkalinity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTI\u003csub\u003ey\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMFV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTI\u003csub\u003ey\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMFV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop 10 genotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eIC586784\u003c/p\u003e\n \u003cp\u003eIC342716\u003c/p\u003e\n \u003cp\u003eEC223202\u003c/p\u003e\n \u003cp\u003eIC248965\u003c/p\u003e\n \u003cp\u003eIC342721\u003c/p\u003e\n \u003cp\u003eIC248966\u003c/p\u003e\n \u003cp\u003eIC610426\u003c/p\u003e\n \u003cp\u003eIC565304\u003c/p\u003e\n \u003cp\u003eEC223231\u003c/p\u003e\n \u003cp\u003eIC267104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eIC241609\u003c/p\u003e\n \u003cp\u003eIC248956\u003c/p\u003e\n \u003cp\u003eIC260853\u003c/p\u003e\n \u003cp\u003eIC267083\u003c/p\u003e\n \u003cp\u003eIC267097\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIC267657\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIC267659\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIC268240\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIC268241\u003c/p\u003e\n \u003cp\u003eIC335474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eIC267104\u003c/p\u003e\n \u003cp\u003eIC248963\u003c/p\u003e\n \u003cp\u003eIC614827\u003c/p\u003e\n \u003cp\u003eIC248956\u003c/p\u003e\n \u003cp\u003eIC267074\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIC268240\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIC267658\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIC267657\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIC267076\u003c/p\u003e\n \u003cp\u003eIC248964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eIC248956\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIC267657\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIC267658\u003c/p\u003e\n \u003cp\u003eIC267664\u003c/p\u003e\n \u003cp\u003eIC267666\u003c/p\u003e\n \u003cp\u003eIC268232\u003c/p\u003e\n \u003cp\u003eIC268236\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIC268240\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIC268241\u003c/p\u003e\n \u003cp\u003eIC268243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA correlation analysis was conducted between the MFV values of each trait, the mean MFV, and the STI of the seed yield for each stress treatment individually. Under saline stress, the STI was significantly (p\u0026lt;0.01) positively correlated with the MFV of the seed yield per plant, followed by the MFV of the plant height and the mean MFV \u003cstrong\u003e(Fig. 4a)\u003c/strong\u003e. Similarly, the mean MFV exhibited a significant positive relationship with the MFV values of each trait except for the MFV from days to 50% flowering. Among these factors, the strongest correlation was with the MFV of the number of branches per plant. Under alkalinity stress, the STI also demonstrated a significant (p\u0026lt;0.01) positive relationship with the MFV of the seed yield per plant, followed by the MFV of the number of pods per plant, the mean MFV, and the MFV of the number of branches per plant \u003cstrong\u003e(Fig. 4b)\u003c/strong\u003e. Unlike salinity, the mean MFV under alkalinity stress was significantly positively associated with the MFV of all individual traits, with the strongest relationship observed with the MFV of the seed yield per plant, closely followed by the MFV of the number of pods per plant and the number of branches per plant.\u003c/p\u003e\n\u003cp\u003eWe also examined the selection dividends achieved for each trait after choosing genotypes for stress conditions via STI and MFV, the results of which are shown in Table 5. Under salinity, selection based on the STI offered the greatest benefit for the seed yield per plant (214.49% of SD%), followed by the number of pods per plant (174.79% of SD%), whereas the lowest benefit was for the test weight (9.66% of SD%). These selection gains slightly decreased when selection was based on the mean MFV. Nonetheless, the greatest advantage was still for the number of pods per plant (170.31% of SD%), with a seed yield of 105.55%. The smallest gain was for the test weight (0.2% of the standard deviation (SD)). Therefore, selection on the basis of the STI resulted in greater gains than selection on the MFV for salinity. Similar patterns appeared for alkalinity, with the highest selection dividends for seed yield per plant and the number of pods per plant in both the STI (334% and 400% of SD%, respectively) and MFV (332% and 371% of SD%, respectively), whereas the test weight had the least gain. Notably, selection on the basis of the STI yielded zero to negative gains for the test weight. Unlike salinity, the selection of superior genotypes via STI and MFV provided similar gains under alkalinity. Thus, for alkalinity stress, either parameter can be used to select promising genotypes.\u003c/p\u003e\n\u003cp\u003eTable 5 Selection gains for different traits after choosing genotypes on the basis of the stress tolerance index for yield (STIy) and mean field value (MFV) in saline and alkaline environments. Column descriptions (for each trait under STIy and MFV): Xo shows the original population mean (before selection); Xs shows the selected population mean (after selection); SD shows the selection differential (Xs \u0026minus; Xo), indicating improvement; and SD% shows the selection differential as a percentage of the original mean = (SD/Xo) \u0026times; 100. The interpretation of SYPP (seed yield per plant) revealed the greatest genetic gain: salinity (STIy): +10.44 g (214.49% gain); alkalinity (STIy): +6.34 g (333.84% gain). The interpretation of the NPP (number of pods per plant) also revealed very high gains: alkalinity (STIy): +112.81 pods (400.18% increase); salinity (STIy): +124.80 pods (174.79% increase). The interpretation of TW (test weight) indicated minimal or even negative gain, e.g., -0.29% under alkaline STIy selection. The interpretation of NBPP and pH showed moderate to high percentage gains, especially under MFV-based selection in alkalinity. Selection on the basis of STIy and MFV results in considerable genetic gains in yield-related traits (SYPP and NPP), especially under stress conditions. STIy seems slightly more effective under stress for identifying genotypes with greater potential yield improvements.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"924\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 438px;\"\u003e\n \u003cp\u003eSalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 436px;\"\u003e\n \u003cp\u003eAlkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eSTI\u003csub\u003ey\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eMFV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eSTI\u003csub\u003ey\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eMFV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eXo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eXs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eXo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eXs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eXo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eXs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eXo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eXs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSD%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eDFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e78.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e98.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e20.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e78.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e102.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e24.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e31.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e82.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e107.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e24.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e29.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e82.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e105.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e22.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e27.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eDTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e101.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e128.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e26.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e26.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e101.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e132.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e30.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e30.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e116.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e141.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e25.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e21.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e116.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e141.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e25.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e21.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e23.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e36.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e52.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e23.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e36.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e52.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e26.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e31.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e18.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e26.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e31.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e18.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eNBPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e57.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e67.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e63.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e85.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eNPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e71.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e196.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e124.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e174.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e71.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e193.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e121.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e170.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e28.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e141.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e112.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e400.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e28.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e132.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e104.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e371.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e9.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e13.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eSYPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e10.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e214.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e105.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e333.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e331.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eLike many pulses, lentil exhibit a marked sensitivity to soil salinity, posing significant challenges for their cultivation. As pulses are a critical source of plant-based protein, which is essential for human nutrition, expanding their cultivation is imperative to meet the dietary needs of a growing global population. This expansion will increasingly necessitate the utilization of marginal lands, including those affected by salinity and alkalinity. The development of salt-tolerant lentil varieties is thus a strategic approach, both economically and environmentally, to bring these degraded lands under productive cultivation. A fundamental step in this endeavor is the screening of genetic resources to assess the extent of genetic variation available for breeding purposes. In this study, we evaluated the salt stress tolerance\u0026mdash;encompassing both salinity and alkalinity\u0026mdash;of 500 diverse lentil genotypes, comprising two-thirds indigenous and one-third exotic accessions. This study represents a pioneering effort, as it is the first to evaluate an extensive number of lentil genotypes under varying salt stress conditions. Prior studies have been limited in scope, assessing 162 (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), 276 (Dissanayake et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and 285 (Singh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) genotypes, respectively.\u003c/p\u003e\u003cp\u003eNotably, a significant portion of the genotypes in our study were of indigenous origin, further distinguishing them from those identified in previous studies. An initial analysis of variance indicated that the block (unadjusted) source contributed significantly to the total variation, a finding that is consistent with the natural field conditions under which both salinity and alkalinity treatments were applied. Field-based phenotyping of lentil, as used in our study, aligns with methodologies reported in earlier research (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The impact of salinity and alkalinity stress on various agronomic traits was significant, with alkalinity having a more pronounced effect on key yield-related traits than does salinity. This suggests that lentil is more sensitive to alkalinity, supporting findings in other crops, such as rice (Lv et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and oats (Bai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The complexity of alkalinity stress, which disrupts the soil structure and reduces hydraulic conductivity, leads to nutrient deficiencies, likely explains this increased sensitivity. In particular, the seed yield per plant and the number of pods per plant substantially decreased. The reduction under salinity stress was less severe in our study, whereas the opposite trend was observed for alkalinity stress (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These differences may result from variations in the experimental location (Karnal in our study versus Aagra for salinity and Kanpur \u0026amp; Lucknow for alkalinity (Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)) and the method of stress application (natural saline field in our study versus artificially induced salinity in Singh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). Conversely, mung bean has been reported to suffer greater yield reductions at similar salinity levels (Sehrawat et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hasan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas black grams have relatively greater tolerance than green grams (Hasan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA notable observation in this study was the reduction in the crop growth period under both salinity and alkalinity stress, as evidenced by the decrease in the mean number of days to 50% flowering and maturity. This phenomenon can be attributed to accelerated senescence caused by stress, increased abscisic acid accumulation, which triggers an earlier transition to the reproductive stage, and nutrient imbalances, which disrupt normal growth processes (Grattan and Grieve, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Munns and Tester, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhang and Jia, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Interestingly, this pattern contrasts with findings in wheat, where salt stress has been shown to delay flowering (Sharma et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The test weight also significantly decreased, by approximately 20%, under both salinity and alkalinity stress. This decline is likely due to impaired photosynthesis, leading to insufficient photosynthates for seed filling (Zeng and Shannon, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Such decreases in test weight under salt stress have been consistently reported across various crops. Furthermore, plant height substantially decreased under both types of salt stress, primarily due to decreased levels of gibberellin, a key growth regulator involved in stem elongation, under salt stress conditions (Achard and Genschik, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, the accumulation of reactive oxygen species (ROS) under salt stress disproportionately affects actively growing tissues, such as the shoot apical meristem, resulting in stunted growth and reduced plant height (Mittler, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The reduction in plant height under salt stress is strongly linked to a simultaneous decrease in the number of branches per plant, ultimately leading to fewer pods. This relationship is supported by the significant positive correlations between plant height, branch number, and pod number per plant found in this study. Moreover, across all the treatments, both the number of branches and the number of pods per plant were significantly positively associated with the seed yield per plant. Therefore, increasing seed yield, despite its potentially low heritability, could be effectively achieved through indirect selection for these traits. Among them, the number of pods per plant stands out as a critical determinant of seed yield, regardless of treatment (as determined by multiple regression analysis). This conclusion is reinforced by the similar percentage reductions in pod number and seed yield per plant under both saline and alkaline stress. However, the R2 values were moderate for both stresses, indicating that other traits, such as nodule formation, should also be considered in future studies. The decrease in pod number under salinity stress can be attributed to flower abortion and reduced pod set, likely due to insufficient photosynthates necessary for flower development into pods (Vadez et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ashraf and Harris, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A reduction in pod number per plant has also been reported in fenugreek (Soughir et al., 2012) and \u003cem\u003eBrassica juncea\u003c/em\u003e (Wani et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, the decrease in the number of branches per plant is due mainly to hormonal imbalances caused by salt stress. Specifically, reductions in cytokinins and increases in abscisic acid (ABA) can inhibit lateral bud outgrowth, resulting in fewer branches (Albacete et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additionally, plants under salt stress tend to prioritize energy for survival mechanisms, such as the synthesis of osmoprotectants and antioxidants, rather than for growth (Zhu et al., 2001).\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) revealed that the seed yield per plant was the main contributor to the total variance under both the control and alkaline conditions, whereas the number of days to flowering and maturity were the primary factors influencing salinity stress. This pattern can be attributed to several factors, including hormonal imbalances, where increased levels of abscisic acid (ABA) accelerate flowering, and reduced levels of gibberellic acid (GA), which results in decreased plant height and delayed flowering (Achard and Genschik, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These effects are further influenced by genotype-specific responses to salinity. The goal of screening experiments is to find superior genotypes for breeding programs, requiring reliable parameters to avoid selecting false positives or escapes. Tolerant genotypes should be evaluated under both control and stress conditions, with a focus on traits that significantly impact seed yield. In this study, two key parameters were used: the stress tolerance index (STI), which is based on the seed yield per plant, and the membership function value (MFV), which combines all measured traits. STI is a well-established metric for selecting genotypes resilient to abiotic stress, as it considers performance under both normal and stress conditions and has been previously used in lentils for salinity tolerance (Kumawat et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pandey and Sengar, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study introduces MFV to lentil research for the first time, providing a comprehensive index that assesses genotypic performance across all traits under both conditions. MFV has gained popularity in recent studies for selecting salt-tolerant genotypes (Choudhary et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gholizadeh et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mathankumar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gyanagoudar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By using both indices, this research identified promising genotypes for salinity and alkalinity tolerance. While different genotypes excelled under various indices and stress conditions, certain genotypes, including IC267104, IC248956, IC268241, IC267658, IC267657, and IC268240, consistently performed well across multiple criteria. This consistency is likely due to the shared impact of Na\u0026thinsp;+\u0026thinsp;ions in both salinity and alkalinity stress environments, despite differences in soil chemistry. This may also be because the STI is based only on seed yield, whereas the mean MFV considers all traits. This is supported by moderate yet significant correlations observed under both stresses between the STI and the mean MFV. This finding aligns with earlier research in oats, where three genotypes showed tolerance to both salinity and alkalinity (Bai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Interestingly, although exotic material was also screened in this study, the superior genotypes were mainly indigenous, except for EC223202 and EC223231, which is understandable since the experiment was conducted in India, a region better suited for indigenous collections than exotic ones. Additionally, to determine which set of genotypes will yield maximum benefits, especially for grain production, we estimated selection differentials\u0026mdash;another unique feature of this study. On the basis of these results, selection on the basis of the STI provided good gains under salinity, whereas neither of the indices were suitable under alkalinity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research is pioneering in lentils because of several factors, including the wide diversity of the genotypes analyzed and the rigorous selection criteria used. Using the STI and MFV indices, the genotypes IC267104, IC248956, IC268241, IC267658, IC267657, and IC268240 were identified as promising genotypes for salinity and alkalinity tolerance. While different genotypes perform well under various indices and stress conditions, these genotypes consistently perform well across multiple criteria, likely because of the shared influence of Na\u003csup\u003e+\u003c/sup\u003e ions in both salinity and alkalinity stress environments, despite differences in soil chemistry. The selected genotypes demonstrated significant superiority over the other genotypes, as shown by the selection differences. These genotypes have potential for further breeding programs focused on developing salt-tolerant lentil varieties or could be used to create biparental or multiparental mapping populations for identifying genomic regions linked to salt tolerance. Additionally, these genotypes provide valuable resources for future functional genomics studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Indian Council of Agricultural Research (ICAR), India, for support in carrying out the work through \u003cstrong\u003e“CRP on Agrobiodiversity for Pulses and Oilseeds: Lentil Component”\u003c/strong\u003e, Ministry of Agriculture and Farmers Welfare, Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVS and JS involved in conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, and original draft. RKT performed data curation and Writing-Original Draft. KT, \u0026nbsp;SKS, AKY and PGG provided germplasm with visualization. AK, KP, SP, VS: contributed equally to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAchard P, Genschik P. (2009). Releasing the brakes of plant growth: how GAs shut down DELLA proteins. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e 60(4): 1085-1092, https://doi.org/10.1093/jxb/ern301\u003c/li\u003e\n \u003cli\u003eAlbacete A, Ghanem M E, Mart\u0026iacute;nez-And\u0026uacute;jar C, Acosta M, S\u0026aacute;nchez-Bravo J, Mart\u0026iacute;nez V, P\u0026eacute;rez-Alfocea F. (2008). Hormonal changes in relation to biomass partitioning and shoot growth impairment in salinized tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e L.) plants. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e 59(15): 4119-4131, https://doi.org/10.1093/jxb/ern251\u003c/li\u003e\n \u003cli\u003eAshraf MPJC, Harris PJ. (2004). Potential biochemical indicators of salinity tolerance in plants. \u003cem\u003ePlant science\u003c/em\u003e 166(1):3-16, https://doi.org/10.1016/j.plantsci.2003.10.024\u003c/li\u003e\n \u003cli\u003eBai J, Yan W, Wang Y, Yin Q, Liu J, Wight C, Ma B. 2018. Screening Oat Genotypes for Tolerance to Salinity and Alkalinity. \u003cem\u003eFrontier in Plant Science\u003c/em\u003e 2-9: 1302, https://doi.org/10.3389/fpls.2018.01302\u003c/li\u003e\n \u003cli\u003eChoudhary A, Kaur N, Sharma A, Kumar A. (2021). Evaluation and screening of elite wheat germplasm for salinity stress at the seedling phase. \u003cem\u003ePhysiologia plantarum\u003c/em\u003e, 173(4): 2207-2215, https://doi.org/10.1111/ppl.13571\u003c/li\u003e\n \u003cli\u003eDissanayake IAJK, Ranwala SMW, Perera SSN. (2020). Germination and seedling growth responses of Sri Lankan grown Sesame/Thala (\u003cem\u003eSesamum indicum\u003c/em\u003e L.) for simulated drought conditions. \u003cem\u003eJournal of the National Science Foundation of Sri Lanka\u003c/em\u003e, 47(4), https://doi.org/10.4038/jnsfsr.v47i4.9686\u003c/li\u003e\n \u003cli\u003eFoti K, Wang D, Appel LJ, Selvin E. (2019). Hypertension awareness, treatment, and control in US adults: trends in the hypertension control cascade by population subgroup (National Health and Nutrition Examination Survey, 1999\u0026ndash;2016). \u003cem\u003eAmerican Journal of Epidemiology\u003c/em\u003e, 188(12): 2165-2174, https://doi.org/10.1093/aje/kwz177\u003c/li\u003e\n \u003cli\u003eGholizadeh A, Dehghani H, Akbarpour O, Amini A, Sadeghi K, Hanifei M, Sharifi-Zagheh A. (2022). Assessment of Iranian wheat germplasm for salinity tolerance using analysis of the membership function value of salinity tolerance (MFVS). \u003cem\u003eJournal of Crop Science and Biotechnology\u003c/em\u003e 25(5): 611-619, https://doi.org/10.1007/s12892-022-00156-2\u003c/li\u003e\n \u003cli\u003eGrattan SR, Grieve C M. (1999). Salinity\u0026ndash;mineral nutrient relations in horticultural crops. \u003cem\u003eScientia Horticulturae\u003c/em\u003e 78(1-4): 127-157, https://doi.org/10.1016/S0304-4238(98)00192-7\u003c/li\u003e\n \u003cli\u003eGyanagoudar, H.S., Hatiya, S.T., Guhey, A., Dharmappa, P.M. and Seetharamaiah, S.K., (2024). A comprehensive approach for evaluating salinity stress tolerance in brinjal (Solanum melongena L.) germplasm using membership function value. \u003cem\u003ePhysiologia Plantarum\u003c/em\u003e, \u003cem\u003e176\u003c/em\u003e(2), p.e14239, https://doi.org/10.1111/ppl.14239\u003c/li\u003e\n \u003cli\u003eHasan M.K, El-Sabagh A, Sikdar MS, Alam MJ, Ratnasekera D, Barutcular C, Abdelaal KA, Islam MS. (2017). Comparative adaptable agronomic traits of blackgram and mungbean for saline lands. \u003cem\u003ePlant Archives\u003c/em\u003e 17:589-593, http://ir.lib.ruh.ac.lk/xmlui/handle/iruor/9046\u003c/li\u003e\n \u003cli\u003eKumawat KR, Gothwal DK and Singh D. (2017). Salinity tolerance of lentil genotypes based on stress tolerance indices. \u003cem\u003eJournal of Pharmacognosy and Phytochemistry\u003c/em\u003e, 6(4):1368-1372.\u003c/li\u003e\n \u003cli\u003eLv BS, Li XW, Ma HY, Sun Y, Wei LX, Jiang CJ, Liang ZW. (2013). Differences in growth and physiology of rice in response to different saline‐alkaline stress factors. \u003cem\u003eAgronomy Journal\u003c/em\u003e 105(4): 1119-1128, https://doi.org/10.2134/agronj2013.0017\u003c/li\u003e\n \u003cli\u003eMathankumar P, Geetha S, Saraswathi R, Raveendran M, Vijayalakshmi D, Suresh R. (2023). Development and validation of a membership function-based evaluation system for salinity screening in rice. \u003cem\u003eElectronic Journal of Plant Breeding\u003c/em\u003e 14(4): 1412-1420.\u003c/li\u003e\n \u003cli\u003eMittler R. (2002). Oxidative stress, antioxidants, and stress tolerance. \u003cem\u003eTrends in Plant Science\u003c/em\u003e 7(9): 405-410, https://doi.org/10.1016/S1360-1385(02)02312-9\u003c/li\u003e\n \u003cli\u003eMunns, R. and Tester M. (2008). Mechanisms of salinity tolerance. \u003cem\u003eAnnual Review of Plant Biology\u003c/em\u003e 59:651-681, https://doi.org/10.1146/annurev.arplant.59.032607.092911\u003c/li\u003e\n \u003cli\u003ePandey, A.K. and Sengar, R.S., (2020). Effect of salt stress on salt tolerant indices of morpho-physiological traits and yield attributes of lentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medik.). \u003cem\u003eInt. J. Chem. Stud\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, pp.2292-2301, https://doi.org/10.22271/chemi.2020.v8.i1ah.8609\u003c/li\u003e\n \u003cli\u003ePratap A, Chaturvedi SK, Tomar R, Rajan N, Malviya N, Thudi, Singh NP. (2017). Marker-assisted introgression of resistance to fusarium wilt race 2 in Pusa 256, an elite cultivar of desi chickpea. \u003cem\u003eMolecular Genetics and Genomics\u003c/em\u003e, 292(6): 1237-1245, https://doi.org/10.1007/s00438-017-1343-z\u003c/li\u003e\n \u003cli\u003eRengasamy P. (2010). Soil processes affecting crop production in salt-affected soils. \u003cem\u003eFunctional Plant Biology\u003c/em\u003e 37(7): 613-620, https://doi.org/10.1071/FP09249\u003c/li\u003e\n \u003cli\u003eSehrawat N, Yadav M, Bhat KV, Sairam RK, Jaiwal PK.. (2015). Effect of salinity stress on mungbean [\u003cem\u003eVigna radiata\u003c/em\u003e (L.) Wilczek] during consecutive summer and spring seasons. \u003cem\u003eJournal of Agricultural Sciences, Belgrade\u003c/em\u003e 60(1) :23-32, https://doi.org/10.2298/JAS1501023S\u003c/li\u003e\n \u003cli\u003eSharma PC, Sehgal D, Singh D, Singh, G, Yadav R.S. (2011). A major terminal drought tolerance QTL of pearl millet is also associated with reduced salt uptake and enhanced growth under salt stress. \u003cem\u003eMolecular Breeding\u003c/em\u003e 27:207-222, https://doi.org/10.1007/s11032-010-9423-3\u003c/li\u003e\n \u003cli\u003eSingh D, Singh CK, Kumari S, Singh Tomar RS, Karwa S, Singh R. (2017). Discerning morpho-anatomical, physiological and molecular multiformity in cultivated and wild genotypes of lentil with reconciliation to salinity stress. PLoS ONE 12(5): e0177465, https://doi.org/10.1371/journal.pone.0190462\u003c/li\u003e\n \u003cli\u003eSingh D, Singh CK, Singh YP, Singh V, Singh R, Tomar RSS, Sharma PC. (2018). Evaluation of cultivated and wild genotypes of Lens species under alkalinity stress and their molecular collocation using microsatellite markers. \u003cem\u003ePloS one\u003c/em\u003e 13(8):e0199933, https://doi.org/10.1371/journal.pone.0199933\u003c/li\u003e\n \u003cli\u003eVadez V, Krishnamurthy L, Upadhyaya HD. (2007). Large variation in salinity tolerance in chickpea is explained by differences in sensitivity at the reproductive stage. \u003cem\u003eField Crops Research\u003c/em\u003e 104(1-3): 123-129, https://doi.org/10.1016/j.fcr.2007.05.014\u003c/li\u003e\n \u003cli\u003eWani, AS, Ahmad A, Hayat S, Fariduddin, Q. (2013). Salt-induced modulation in growth, photosynthesis and antioxidant system in two varieties of \u003cem\u003eBrassica juncea\u003c/em\u003e. \u003cem\u003eSaudi journal of biological sciences\u003c/em\u003e 20(2): 183-193, https://doi.org/10.1016/j.sjbs.2013.01.006\u003c/li\u003e\n \u003cli\u003eZeng L, Shannon MC. (2000). Salinity effects on seedling growth and yield components of rice. \u003cem\u003eCrop Science\u003c/em\u003e 40(4): 996-1003, https://doi.org/10.2135/cropsci2000.404996x\u003c/li\u003e\n \u003cli\u003eZhang J, Jia W. (2010). Role of ABA in integrating plant responses to drought and salt stresses. \u003cem\u003eField Crops Research\u003c/em\u003e, 97(1): 111-119, https://doi.org/10.1016/j.fcr.2005.08.018\u003c/li\u003e\n \u003cli\u003eZhang R, Hussain S, Wang Y, Liu Y, Li Q, Chen Y, Wei H, Gao P, Dai Q. (2021). Comprehensive evaluation of salt tolerance in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) germplasm at the germination stage. \u003cem\u003eAgronomy\u003c/em\u003e 11(8):1569, https://doi.org/10.3390/agronomy11081569\u003c/li\u003e\n \u003cli\u003eZhu, J. K. (2001). Plant salt tolerance. \u003cem\u003eTrends in Plant Science\u003c/em\u003e 6(2):66-71, https://doi.org/10.1016/S1360-1385(00)01838-0\u003c/li\u003e\n\u003c/ol\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":"Lens culinaris, salt tolerance, stress tolerance index (STI), membership function value (MFV), saline and alkaline soils, trait-based selection","lastPublishedDoi":"10.21203/rs.3.rs-7176739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7176739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAims\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to identify superior lentil genotypes with tolerance to both salinity and alkalinity stress through field phenotyping and robust trait selection indices.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 500 lentil genotypes (317 indigenous, 183 exotic) were evaluated under natural field conditions—control, salinity (ECe ~7 dS/m), and alkalinity (pH ~9.3)—at the ICAR-CSSRI, Karnal. Data on seven agro morphological traits were analyzed \u003cem\u003evia\u003c/em\u003e ANOVA, correlation, regression, and PCA. Two indices—the stress tolerance index (STI) and membership function value (MFV)—were employed to identify salt-tolerant genotypes on the basis of yield and multivariate trait performance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHighly significant variation was observed among the genotypes across the treatments. Compared with salinity, alkalinity stress had a more pronounced effect on key traits such as seed yield and pod number. Regression and PCA highlighted pods per plant, test weight, and plant height as key yield determinants. STI was more effective under salinity, whereas both STI and MFV performed equally well under alkalinity. Six genotypes (IC267104, IC248956, IC268241, IC267658, IC267657, and IC268240) consistently ranked highly under both stress types across indices.\u003cstrong\u003e \u003c/strong\u003eThis study provides a comprehensive field-based evaluation of the salinity and alkalinity tolerance of lentil germplasm via integrated selection indices. The identified genotypes offer valuable resources for breeding programs targeting salt-affected soils and can serve as parents for mapping populations aimed at dissecting the genetic basis of stress tolerance in lentils.\u003c/p\u003e","manuscriptTitle":"Multivariate analysis of phenotypic traits and trait associations for identifying elite lentil (Lens culinaris Medik.) genotypes using STI and MFV indices for breeding in salt-affected soil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 10:38:34","doi":"10.21203/rs.3.rs-7176739/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":"241c71d5-26e2-47b4-a0c4-fb443231f300","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-03T16:00:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 10:38:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7176739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7176739","identity":"rs-7176739","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}