Phenotypic diversity and Principal component analysis to associate physiological traits contributing to grain yield under moisture stress in durum wheat germplasm

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B. Chaya, G. Uday, G. M. Vijeth, H. B. Sharada, U. V. Mummigatti, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7230605/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 Drought stress is a major abiotic constraint affecting the productivity of durum wheat ( Triticum turgidum subsp. durum ), particularly in arid and semi-arid regions. Our study evaluated 225 durum wheat germplasm lines (GDP), with five standard checks under contrasting moisture regimes to dissect physiological, morphological, and seedling traits associated with drought tolerance. The results showed significant variability for key traits, including chlorophyll content (SPAD), membrane stability index (MSI), canopy temperature (CT), root-to-shoot ratio (RSR), and grain yield. Drought stress led to pronounced reductions in grain yield (26.8%), seedling vigor (up to 57.3%), and membrane stability (24.8%), while increasing canopy temperature and RSR. Correlation analysis revealed strong positive associations between grain yield and SPAD, MSI, and seedling vigor under stress, while PCA identified these traits as major contributors to drought adaptation. Hierarchical clustering and principal component analysis enabled the identification of divergent genotypic groups and high-performing lines, notably UASDWG_246, UASDWG_216 and UASDWG_299, which consistently exhibited superior performance across both seedling and reproductive stages. These results highlight the value of integrating multistage phenotyping and multivariate analyses for improving drought resilience in durum wheat breeding programs. Durum wheat Drought Stress tolerance index Principal Component Analysis Hierarchical clustering Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Wheat ( Triticum spp. ) is a globally significant cereal crop that serves as a staple food for more than one-third of the world’s population. Among the cultivated species, durum wheat ( Triticum turgidum subsp. durum ) is predominantly grown in arid and semi-arid regions and is highly valued for its use in pasta, couscous, and other semolina-based products. However, the lack of water significantly lowers the productivity of durum wheat, particularly in the Mediterranean and rainfed regions, where drought often occurs during the grain-filling and reproductive stages (Sall et al, 2019). The increasing frequency and intensity of droughts due to climate change also threaten global wheat production and food security. Therefore, improving drought tolerance in durum wheat is a key goal in modern breeding. All phenological stages of wheat development are negatively impacted by drought stress, but the extent of damage differs based on the developmental stage and severity of the stress (Djanaguiraman et al, 2020 ). In arid and semi-arid areas, the combination of water shortage and high temperatures leads to a significant loss of yield during the grain-filling stage (Shah et al, 2022 ). Physiological disruptions like reduced photosynthetic efficiency, lower stomatal conductance, decreased chlorophyll content, compromised membrane integrity, and altered water relations greatly contribute to losses in productivity under stress conditions (Sehgal et al, 2018 ). Drought stress also shortens the life cycle and disrupts reproductive development, leading to pollen abortion, anther indehiscence, and a decrease in both grain size and number. Other effects include accelerated senescence, disrupted osmolytes balance, smaller flag leaf area, shorter grain filling duration, and less biomass directed to grains, which together reduce the harvest index and overall crop performance (Pandey et al, 2022 ). At the cellular level, drought stress leads to the production and buildup of reactive oxygen species (ROS) such as hydrogen peroxide, hydroxyl radicals, and superoxide ions. These ROS cause oxidative damage to cell membranes, disrupt electron transport in chloroplasts and mitochondria, and affect photosynthesis (Ahmadizadeh 2013 ). While plants have antioxidative defense mechanisms to detoxify ROS, their effectiveness varies with genotype and the intensity and timing of the water deficit (Ahmed et al. 2019 ).Understanding physiological and morphological responses to drought is critical for identifying resilient genotypes. Plant-based physiological traits, such as canopy temperature, chlorophyll content (SPAD value), membrane stability index (MSI), and relative water content (RWC), are increasingly used as indirect selection indices in drought breeding programs (Sallam et al., 2019 ). Drought-tolerant plants often exhibit traits like smaller leaf area, higher root biomass, and increased accumulation of osmolytes (e.g., proline, soluble sugars) that support water retention and stress mitigation (Abid, 2016 ). The stress tolerance index (STI) is frequently used to evaluate genotype performance under both optimal and stress conditions, with higher STI values indicating superior tolerance (Mohammadijoo et al., 2015). It was hypothesized that the physiological and biochemical traits under investigation would exhibit differential responses under stress and non-stress conditions, and evaluating their relative performance would provide valuable insights for future breeding programs aimed at enhancing wheat tolerance to moisture stress. The utility of genetic diversity lies not only in its existence but in its effective exploitation. Advances in molecular breeding, genomics, and functional genomics have enhanced our ability to dissect complex traits and target specific genomic regions associated with adaptive responses (Tuberosa and Pozniak, 2014 ). The establishment of the Global Durum Wheat Panel (GDP) is a milestone in this direction. Initiated by the international durum wheat community in Bologna, Italy, in 2015 under the Wheat Initiative, the GDP comprises 1,011 genotypes drawn from over 2,500 tetraploid wheat accessions, including modern cultivars, landraces, and primitive tetraploid wheat (Kabbaj et al., 2017 ). This panel captures approximately 94–97% of the genetic diversity within durum wheat and serves as a global reference set for genomics-assisted breeding and pre-breeding efforts. In this context, the present study utilized a wide germplasm collection to evaluate phenotypic diversity under moisture stress conditions with specific objectives (i) assess the responsiveness of physiological traits contributing to drought adaptation; (ii) analyse the association of these traits with grain yield under stress; and (iii) apply Principal Component Analysis (PCA) and Hierarchical clustering analysis to identify key traits and genotypes exhibiting superior drought tolerance. The findings from this study aim to facilitate the selection of climate-resilient durum wheat germplasm and contribute to the genetic improvement of yield stability under water-limited environments and utilize them as donors and development of Multi-parent Advanced Generation Inter-Cross (MAGIC) populations. Materials and methods Experimental location and planting material The study was conducted at the All India Coordinated Research Project on Wheat, University of Agricultural Sciences, Dharwad, situated in the peninsular zone of India. Experimental material consists of 225 durum wheat germplasm that were collected from CIMMYT and ICARDA. The germplasm was initially screened for grouping them based on days to flowering and genetic purification was done by single ear head selection during 2021-22 and 2022-23. The germplasm lines were evaluated for reproductive stage stress for two seasons (Rabi 2023-24 and 2024-25). The two season data were pooled for combined statistical analysis using a test for homogeneity (Battles test). Drought evaluation under field conditions A total of two hundred and twenty wheat germplasm collections and five popular checks were evaluated using an Augmented block design (Unreplicated entries + replicated checks). The experiment included two environmental setups: (i) Non-stress (periodically irrigated during CRI stage and booting stage) and (ii) Stress (withhold of Irrigation after booting stage till harvest). Critical Moisture stress was observed between 65 to 90 days after sowing (DAS) corresponding to the flowering, grain-filling and Physiological maturity stages of the crop. Each germplasm line was grown in two rows of 3 m length with 20 cm × 5 cm row and plant spacing. The control plots were watered with furrow irrigation, whereas the stress plots did not get receive irrigation during the stress period. The same standard agronomic practices were used to manage both sets and they were also protected against biotic stresses. A 10 m buffer zone was maintained to prevent moisture seepage between treatments. The morphological and yield traits like plant height, days to 50 per cent flowering, spike length, number of grains/spikes; 1000-grain weight and grain yield were recorded following standard procedures. The observations on physiological parameters were recorded at two time intervals during the moisture stress period (flowering/grain filling stage). The first set of measurements, including SPAD I and canopy temperature I was taken 10 days after the initiation of stress. The second set, comprising SPAD II, canopy temperature II and membrane stability index, was recorded 20 days after the onset of stress. The randomly selected five plants were tagged and observations were recorded on the same plant for all the phenotypic observations. The grain yield data were recorded for each germplasm line for both environments and used to calculate the stress tolerance indices. Stress Tolerance Index STI = [( Y p ) × ( Y s )] / ( Ῡ p ). 2 (Fernandez 1992)In these formulas, Y s , Y p and Ῡ p were yield under drought stress, yield under non-stress for each genotype and yield mean in non-stress conditions for all genotypes, respectively. Drought evaluation for seedling growth traits The experiment was conducted in nursery plastic trays filled with a mixture of Sieved soil, cocopeat and vermicompost in a 2:1:0.5 ratio. Two moisture treatments were applied: a control at 100% field capacity and moisture stress at 50% field capacity. Watering occurred every other day by weighing the trays to keep the moisture levels consistent. To create different levels of moisture stress varying amounts of irrigation water were applied: 85 ml per tray for the control and 425 ml per tray for the stress treatment. Moisture stress began three days after the seedlings were established. For each germplasm line, observations were recorded on three randomly selected plants, and the mean was calculated for all traits. Germination percentage was assessed following ISTA rules (Anon, 2013), with count taken on the seventh day. Root length and shoot length were measured on the 12th day after sowing (DAS), and the root-to-shoot ratio was calculated. Coleoptile length Coleoptile lengths were measured as the distance from the scutellum to the tip of the coleoptile, as described by Rebetzke et al. ( 2004 ) on 12th DAS. Seedling vigour index I was determined as given by Abdul-Baki and Anderson ( 1973 ), while seedling vigour index II (SVI-II) was computed by multiplying germination percentage with seedling dry weight. Seedling vigour index (SVI) = Germination (%) x [Root length + Shoot length] Seedling vigour index (II) = Germination (%) x Seedling dry weight (g) Analysis of phenotypic data The data analysis was conducted using an augmented design based on the methodology proposed by Fisher and Yates ( 1963 ). Variability among and within the germplasm lines was assessed through the F-test at both 1% and 5% significance levels. Variance analysis and the estimation of related parameters were performed using the R-statistic software ( R version 4.3.3 https://www.R-project.org ). Pearson’s correlation coefficient, as described by Hall (2015), was applied in R to examine the relationships among traits. To reduce the dimensionality of the dataset while preserving essential information, principal component analysis (PCA) was carried out using the R packages ggplot2 , tidyr , dplyr and FactoMineR . Furthermore, hierarchical clustering was conducted in R Studio with the aid of ggplot2 , cluster , factoextra and stats packages to identify patterns and groupings among the genotypes. Results Analysis of variance and phenotypic expression of yield and stress-responsive traits under contrasting moisture conditions Analysis of variance ( ANOVA) revealed significant genotypic variation for most morpho-physiological and yield traits under non-stress, except SPAD II and canopy temperature II. Under stress, all traits showed substantial differences under different moisture regimes except canopy temperature I (Table S1) . Standard checks also exhibited significant variation across most traits. Seedling traits showed highly significant differences among genotypes and checks under both moisture regimes (Table S2 ). Box plot analysis of eleven morpho-physiological and yield traits in 220 durum wheat genotypes revealed substantial differences in trait performance under control and moisture stress conditions (Fig. 1a) . SPAD I and SPAD II showed a drop in mean values from 49.8 to 45.3 and 50.2 to 46.5, respectively, under moisture stress. Canopy Temperature I increased from 21.8°C to 25.6°C, while Canopy Temperature II rose from 25.3°C to 31.6°C under stress. The Membrane Stability Index fell significantly from 78.2–61.3%. Plant Height decreased from 81.5 cm in the control to 73.2 cm under stress, and Spike Length declined from 74 cm to 62 cm. The Number of Seeds per Spike dropped from 48.6 to 43.2, while 1000 Seed Weight fell from 46.7 g to 43.9 g. Grain Yield reduced significantly from 0.51 kg to 0.36 kg. Days to 50% Flowering shifted earlier, with the mean days decreasing from 68.9 to 63.5. Moisture stress also significantly affected all seedling traits in durum wheat, as seen in the comparative boxplots (Fig. 1b) . Germination Percentage fell from 93.2–49.4% under drought. Coleoptile Length decreased from 60 cm to 43 cm, and both Seedling Vigour I and Seedling Vigour II dropped significantly, from 3426 to 1673 and 4344 to 1854, respectively, under moisture stress. The Root-to-Shoot Ratio increased from 0.5 to 0.7. Phenotypic correlation analysis between yield components and stress-responsive traits Phenotypic correlations among traits often arise due to genetic linkage, pleiotropy, or coordinated developmental processes. Pearson’s correlation analysis provides insights into the direction and strength of associations between trait pairs, facilitating simultaneous genetic improvement through correlated response to selection. In the present study, phenotypic correlation coefficients were estimated for morpho-physiological, yield, and seedling traits in two hundred twenty durum wheat germplasm lines under non-stress and stress conditions (Table 1a and 1b). The analysis revealed environment-specific patterns of trait interrelationships, highlighting variable degrees of trait association under contrasting moisture regimes. Under both conditions, SPAD I and SPAD II exhibited a highly significant positive correlation. Under moisture stress, Grain yield (GY) showed strong positive correlations with SPAD I (0.92), SPAD II (0.75), and membrane stability (0.95). Conversely, canopy temperature I was negatively correlated with GY (–0.16) and SPAD I (–0.12) under stress and also CT2 with GY (–0.21). GY also correlated positively with plant height, number of seeds per spike and thousand seed weight. Notably, days to 50% flowering (DF) showed a strong negative correlation with GY (–0.33 stress; − 0.35 non-stress) and SPAD. Germination percentage was strongly correlated with seedling vigour I (0.92 stress; 0.74 control), seedling vigour II (0.92 stress; 0.54 control). It showed a non-significant association with coleoptile length under non stress, but a positive correlation under stress (0.62). In contrast, the root-to-shoot ratio showed negative correlations with all traits under non stress conditions, whereas it showed positive correlations with all traits under stress conditions. Table 1 a: Phenotypic association among the morpho-physiological and yield traits under non-stress conditions (Lower half) and Stress condition (Upper half) SP1 SP2 CT1 CT2 MS PH SL NSS SW GY DF SP1 1 0.847*** -0.128* -0.233*** 0.883*** 0.351*** -0.009 0.421*** 0.008 0.927*** -0.333*** SP2 0.985*** 1 -0.105 -0.175** 0.722*** 0.273*** -0.016 0.346*** 0.004 0.751*** -0.268*** CT1 -0.146* -0.125* 1 0.372*** -0.129* -0.061 -0.099 -0.18** -0.186** -0.127* -0.066 CT2 -0.05 -0.046 0.074 1 -0.212*** -0.1 0.019 -0.092 -0.21*** -0.217*** -0.001 MS 0.736*** 0.756*** -0.091 -0.02 1 0.337*** 0.006 0.419*** 0.034 0.954*** -0.304*** PH 0.287*** 0.278*** -0.186** -0.013 0.309*** 1 0.034 0.344*** 0.15* 0.379*** -0.276*** SL -0.171** -0.205*** -0.167** -0.091 -0.161** 0.068 1 -0.024 0.072 0.025 0.211*** NSS 0.425*** 0.429*** -0.158* -0.024 0.455*** 0.062 -0.052 1 -0.102 0.456*** -0.482*** SW 0.197** 0.214*** 0.094 0.086 0.259*** 0.094 -0.252*** -0.015 1 0.039 0.113 GY 0.889*** 0.874*** -0.169** -0.107 0.855*** 0.325*** -0.145* 0.484*** 0.267*** 1 -0.334*** DF -0.295*** -0.348*** -0.144* -0.083 -0.387*** 0.034 0.305*** -0.285*** -0.284*** -0.351*** 1 * - P = < 0.05; ** P = < 0.01; *** P = < 0.001 (PH = Plant height, DF = Days to 50% flowering, SP 1 = SPAD I, SP 2 = SPAD II, CT 1 = Canopy temperature I, CT 2 = Canopy temperature II, MS = Membrane stability, SL = Spike length, NSS = Number of Seeds per spike, SW = Thousand grain weight, GY = Grain yield) Table 1 b: Phenotypic association among the seedling traits under non-stress condition (Lower half) and Stress condition (Upper half) Germination percentage Coleoptile length Seedling vigour I Seedling vigour II Root to shoot ratio Germination percentage 1 0.627*** 0.238*** 0.91*** 0.912*** Coleoptile length 0.073 1 0.038 0.707*** 0.706*** Seedling vigour I 0.676*** 0.314*** 1 0.162* 0.158* Seedling vigour II 0.479*** 0.199** 0.699*** 1 0.987*** Root to shoot ratio -0.152* -0.376*** -0.254*** -0.215*** 1 * - P = < 0.05; ** P = < 0.01; *** P = < 0.001 Principal component analysis under stress conditions Principal Component Analysis (PCA) was conducted to explore trait variability among durum wheat genotypes under moisture stress, revealing that the first three principal components (PC 1 , PC 2 , and PC 3 ) cumulatively explained 66.12% of the total variation, with PC1 alone accounting for 40.98%, followed by PC 2 (14.56%) and PC 3 (10.58%). PC 1 was primarily influenced by grain yield (GY), SPAD I (SP1), SPAD II (SP2), membrane stability (MS), and number of seeds per spike (NSS), all contributing negatively, while PC 2 was largely driven by canopy temperature traits (CT 1 , CT 2 ), seed weight (SW), and days to 50% flowering (DF). PC 3 showed a significant association with spike length (SL) and CT traits. Grain yield exhibited strong positive correlations with SPAD I (0.96), SPAD II (0.93), and membrane stability (0.92), while days to 50% flowering (-0.47), canopy temperature I (-0.61), and canopy temperature II (-0.56) showed negative associations with the principal components (Fig. 2a). The scree plot demonstrated a sharp decline in eigenvalues after PC1 and PC2, indicating that most of the meaningful variation was captured within the first two axes ( Fig. S1 ). The biplot and genotype distribution plots (Fig. 2b) further revealed clear groupings of genotypes, enabling effective discrimination based on multivariate trait performance under drought stress conditions. PCA of seedling traits in durum wheat under moisture stress at the seedling stage revealed that the first two components explained 90.1% of the total variation, with PC 1 (70.1%) capturing most of the variation. (Fig. 3a). Among the traits, Root to shoot ratio contributes strongly to PC 1 and indicates its key role in drought adaptation through enhanced root development. Coleoptile length, seedling vigour index I, seedling vigour index II and germination percentage also contributed significantly, with SVI, SVII, and GP showing strong positive correlation, reflecting their shared influence on seedling vigour. CL exhibited a distinct pattern, likely associated with early emergence under stress. The near-orthogonal orientation of RSR to shoot-related traits suggests a low correlation between root and shoot vigour traits. The individual PCA plot ( Fig. 3b) revealed clear differentiation among genotypes, resulting in the formation of four distinct clusters. Genotypes 122 and 165 appeared as outliers with extreme positions along the principal components, indicating unique physiological trait combinations. Hierarchical Clustering of Genotypes under Moisture Stress Cluster analysis of 220 durum wheat genotypes under moisture stress conditions resulted in the formation of three distinct clusters: Cluster 1 comprised 94 genotypes, Cluster 2 had 46 genotypes and Cluster 3 included 80 genotypes (Singh et al. 2020). The intra-cluster D² values were relatively low and comparable across clusters, indicating high genetic similarity and compactness within each cluster (Table 2a). Among the inter-cluster distances, the greatest separation was observed between Cluster 1 and Cluster 2 (6.024), reflecting maximum phenotypic divergence between these groups. In contrast, Cluster 3 showed moderate distances with both Cluster 1 (3.243) and Cluster 2 (4.382), suggesting that it harbors genotypes with intermediate or overlapping trait expressions. The dendrogram (Fig. 4a) visually supported these findings, revealing distinct branching patterns that correspond to morpho-physiological and yield trait diversity under stress conditions. Hierarchical cluster analysis grouped the 206 durum wheat genotypes into four distinct clusters based on seedling traits under moisture stress, as illustrated in the dendrogram (Fig. 4b ) . Cluster 1 contained the largest number of genotypes (82), followed by Cluster 2 with 73 genotypes, Cluster 3 with 40 genotypes, and Cluster 4 with the fewest, comprising 11 genotypes. The intra-cluster D² values, which indicate the genetic homogeneity within clusters, ranged from 2.771 (Cluster 1) to 2.917 (Cluster 2) (Table 2b), suggesting moderate variability among genotypes within each cluster. The inter-cluster D² distances, representing genetic divergence between clusters, showed the highest value between Cluster 1 and Cluster 3 (4.547), followed by Cluster 1 and Cluster 4 (3.936), and Cluster 2 and Cluster 4 (2.572). The minimum inter-cluster distance was observed between Cluster 2 and Cluster 3 (2.260), suggesting these two groups shared more similarities in seedling traits under stress. Table 2 a: Intra and inter-cluster D² values in durum wheat germplasm lines under stress conditions for field condition Cluster. 1 Cluster. 2 Cluster. 3 Cluster. 1 4.471 Cluster. 2 6.024 4.491 Cluster. 3 3.243 4.382 4.513 Table 2 b: Intra and inter-cluster D² values in durum wheat germplasm lines under stress condition (seedling traits) Cluster. 1 Cluster. 2 Cluster. 3 Cluster. 4 Cluster. 1 2.771 Cluster. 2 2.387 2.917 Cluster. 3 4.547 2.260 2.849 Cluster. 4 3.936 2.572 2.533 2.840 Discussion In the context of ongoing climate change, breeding for drought-tolerant wheat cultivars requires substantially more focus and funding. Drought stress poses a significant threat to wheat productivity, exerting adverse effects across all developmental stages. The complexity of drought tolerance arises from the fact that the effect of the stress is dependent on the developmental stage of the plant, and the intensity and duration of the stress (Tarawneh et al., 2019 ). The present study revealed that there is great genetic diversity in durum wheat elite genotypes for drought stress tolerance, which is a prerequisite for effective selection, supported by continuous phenotypic variation across traits, suggesting polygenic inheritance. Genotypes responded differently to moisture stress at both seedling and reproductive stages, confirming wide genetic variability. The findings of the present study underscore the multifaceted impacts of drought stress on wheat morphology, physiology, and yield-related traits, emphasizing the complex interplay of factors governing drought tolerance. Drought stress markedly impaired plant performance, with a substantial reduction in grain yield (26.81%) (Poudel et al., 2020) primarily associated with cumulative declines in spike length, number of grains per spike and thousand grain weight ( Table S3 ). These reductions stem from impaired reproductive processes under water-deficit conditions, such as decreased pollen viability and ovule development, ultimately restricting grain set and reproductive success (Rawtiya & Kasal, 2021 ). This observation aligns with prior findings (Giunta et al., 1993 ; Butler et al., 2005 ), which reported that drought stress during stem elongation and grain filling reduces tiller survival and spike formation, leading to lower productivity. The decline in plant height is due to the dehydration of protoplasm that ultimately reduces cell division, cell expansion, and loss of cell turgidity (Salam et al., 2022 ), Furthermore, photosynthetic efficiency was adversely affected, as evidenced by declines in SPAD I and SPAD II values by 2.94% and 6.97%, respectively, indicating a decrease in chlorophyll content due to chlorophyllase activation and ROS-mediated damage to chloroplast structures (Nikolaeva et al., 2010 ; Mafakheri et al., 2010 ). A corresponding 24.84% reduction in membrane stability index further revealed cellular damage and compromised membrane integrity, which negatively influence dry matter accumulation and assimilate translocation essential for grain development. Drought stress also altered canopy temperature dynamics, with Canopy Temperature I and II exhibiting increments of 10.18% and 21.02%, respectively, reflecting decreased stomatal conductance and diminished transpirational cooling (Banerjee et al., 2020 ). Hence, canopy temperature is used as a proxy for water status and transpiration efficiency. Additionally, a reduction in days to 50% flowering under drought conditions suggests an adaptive early flowering response, which can be advantageous for drought escape (Aslam et al., 2015 ; Chowdhury et al., 2021 ). Correlation analysis reinforced these physiological observations, revealing strong positive associations between grain yield and SPAD I, SPAD II, and MSI, underscoring the pivotal roles of chlorophyll retention and cellular stability in yield maintenance. In contrast, the negative correlation of days to 50% flowering and canopy temperature with yield and chlorophyll content suggests that early phenology and efficient transpirational cooling are advantageous for drought escape and improved performance under terminal drought conditions (Bapela et al. 2022 ; Bayisa et al. 2019 ). The detection of traits associated with drought tolerance at the seedling stage is crucial for enhancing crop adaptation and boosting grain yield in regions where early-season drought is a major limiting factor (Lin et al., 2019; Thabet & Alqudah, 2019). Under drought conditions, wheat germination percentage was reduced by 50.5%, primarily due to decreased water potential limiting water uptake and delaying germination (Abdoli & Saedi, 2012 ; Ahmad et al., 2022 ). Seedling vigor index declined by 53–59% attributed to restricted water availability, impaired root and shoot growth, nutrient deficiency, and oxidative stress (La & Sylvestris, 1990 ). Under water stress conditions, reduced cell wall extensibility inhibits cell elongation, leading to a 11.4% reduction in coleoptile length (Khadka et al. 2020 ). In response to limited surface moisture, plants enhanced root proliferation, resulting in a 40% increase in the root-to-shoot ratio to access deeper soil moisture (Kou et al., 2022 ). Under non-stress conditions, trait correlations were generally weak to moderate, with stronger associations observed among shoot-related traits. Coleoptile length showed a moderate to strong positive correlation with seedling vigour due to its role in promoting faster emergence and early shoot growth (Wei et al, 2022 ; Chloupek et al., 2010 ). However, its relationship with germination percentage and root-to-shoot ratio (RSR) was weak or negative, as adequate moisture favours shoot growth over root development. Seedling vigour showed a positive correlation with germination percentage, reflecting the uniform growth of metabolically active seeds, but it demonstrated a negative correlation with RSR, revealing that there is a preference for shoot investment in favourable conditions. Under drought stress, the correlations of traits not only have become stronger but also more positive, indicating their significance in the process of stress adaptation. Long coleoptiles were found to be related with better germination and seedling vigour, which may be due to the improved internal water status and better establishment. The positive correlation with RSR implies that the genotypes that have better growth potential also invest more in roots under stress (Anjum et al, 2017 ). On the other hand, high germination was associated with increased seedling vigour and moderate root allocation, whereas vigorous seedlings showed a strong positive correlation with RSR, reflecting a drought-induced shift toward root-biased growth to enhance water uptake and ensure early survival (Sánchez-Bermúdez et al, 2022 ; Sharada et al. 2022 ). Principal component analysis is a powerful statistical method for representing highly correlated data that significantly draws positive feedback by eliminating the dimensionality of variables. The biplot showed strong positive associations among SP1, SP2, MS, and GY, which were inversely related to CT1 and CT2—indicating that genotypes with higher chlorophyll content and membrane stability tended to maintain lower canopy temperatures and higher yield under stress. The PCA-based distribution of genotypes under moisture stress exhibited clear divergence along PC1 and PC2, reflecting differential adaptive strategies among genotypes. Genotypes positioned toward the negative side of PC1 were associated with higher values for SPAD I, SPAD II, membrane stability, and grain yield, signifying better physiological resilience to drought through maintenance of photosynthetic apparatus and cellular integrity (Ahmed et al., 2020 ). Those on the positive PC2 axis showed elevated canopy temperatures, indicating reduced transpirational cooling and greater stress impact. Furthermore, genotypic dispersion along PC3 highlighted variation in reproductive traits such as spike length, seed number per spike, and flowering time, suggesting the role of phenological plasticity and reproductive efficiency in drought adaptation. The PCA loading pattern emphasizes the physiological and morphological coordination among variables, where traits like high membrane stability, SPAD values, and grain yield clustered together, reflecting a robust drought tolerance mechanism (Donga et al. 2022). The PCA biplot for seedling traits highlights RSR as the dominant trait contributing to PC1, while CL, SVII, and SVI exhibit moderate but relevant influence along both dimensions. RSR displayed the longest vector aligned closely with the positive side of PC1, signifying that it is the most influential trait contributing to variability among genotypes. This suggests that genotypes with higher RSR values may be better adapted to moisture stress conditions due to enhanced root development and water absorption capacity. The grouping and alignment of SVI, SVII, and GP vectors indicate a strong positive correlation among these traits, implying that vigorous seedlings with higher germination rates tend to share similar trait performance under moisture stress. CL was moderately separated from the seedling vigour traits, indicating a distinct influence, likely tied to early emergence and establishment capabilities in stress conditions. The nearly orthogonal position of RSR relative to SVI, SVII, and GP suggests a relatively low correlation between root allocation and shoot-based seedling vigour traits. This distinction underscores the importance of combining both root-related and shoot-related physiological traits to comprehensively assess drought adaptation potential. The clustering pattern under field-induced drought stress offers valuable insights into genotypic divergence and trait-based groupings among durum wheat lines (Uzair et al., 2022 ). Cluster 2 exhibited the highest inter-cluster distance from Cluster 1, indicating a significant genetic difference between these groups. This implies that genotypes in Cluster 2, which are linked to beneficial traits like higher grain yield, thousand grain weight, membrane stability, and chlorophyll content, are genetically different from those in Cluster 1. The genotypes in Cluster 1 tend to have traits that are more susceptible to stress, including higher canopy temperature and lower chlorophyll content. The moderate distance between Cluster 2 and Cluster 3, along with the low distance between Cluster 1 and Cluster 3, suggests that genotypes in Cluster 3 have intermediate or overlapping characteristics, likely showing moderate ability to stress tolerance. Notably, the intra-cluster D² values were relatively low across all clusters, ranging from 4.471 to 4.513. This indicates genetic similarity among genotypes within each group. Therefore, hybridizing genotypes from Cluster 1 and Cluster 2 could lead to maximum heterosis and transgressive segregation, as they are the most divergent pools. The clustering pattern showed significant diversity in seedling trait expression among durum wheat genotypes under drought stress, with Cluster 1 displaying moderate variability and reflecting intermediate trait performance. Cluster 2, having the highest intra-cluster variability, comprised genotypes with high values for RSR and SVII, which indicate broader adaptive responses. Cluster 3 showed moderate variation and had similarities with Cluster 2, suggesting limited diversity if crossed. In contrast, Cluster 4, while the smallest group, demonstrated distinct trait expression, especially for RSR and coleoptile length. This highlights its potential as a source of unique traits for drought adaptation. The most significant genetic divergence was found between Cluster 1 and Cluster 3, followed by Cluster 1 and Cluster 4, suggesting their suitability as parents for hybridization to improve genetic variability and drought resilience. Cluster 2, comprising genotypes exhibiting favorable morpho-physiological and yield-related traits under drought stress at the reproductive stage, shows substantial overlap with genotypes previously classified under Cluster 2 based on their performance under seedling-stage drought stress ( Table 3 ). This convergence indicates a consistency in drought tolerance mechanisms across developmental stages, suggesting that these genotypes possess inherent genetic resilience that manifests from early growth through to maturity. This reinforces the predictive value of seedling-stage screening for identifying genotypes with long-term drought adaptability, and highlights the potential of these genotypes as stable performers under water-limited environments. Conclusion This study elucidates the significant phenotypic variability among durum wheat germplasm lines for drought-responsive traits, emphasizing the significance of integrative phenotyping across seedling and reproductive stages for precise identification of drought-tolerant genotypes. Significant genotype × environment interactions that were observed for physiological, morphological, and yield-related traits demonstrate that the drought adaptation has a polygenic and stage-specific nature. The drought-resistant traits such as chlorophyll retention, membrane stability index, early flowering, and increased root-to-shoot allocation were highlighted as crucial factors for maintaining productivity under moisture stress. The principal component analysis of phenotypic data showed that SPAD, MSI, seedling vigour and grain yield are the primary factors contributing to total phenotypic variation, while hierarchical clustering delineated genotypic groups based on multivariate performance. The genotypes UASDWG_246, UASDWG_216 and UASDWG_299 consistently exhibited superior performance across developmental stages, indicating stable physiological resilience. Seedling-stage screening's being consistent with reproductive-stage performance, confirms its predictive capacity for long-term drought adaptability. These findings provide a robust framework for trait-based selection in durum wheat. Furthermore, the identified genotypes serve as promising donor parents for breeding programs aiming to enhance drought resilience and yield stability. Table 3 Top performing drought tolerant durum wheat germplasm lines based on Spike and Seedling traits along with their yield Germplasm lines Stress susceptibility indices Spike Characters Seedling Characters YS YP STI Grains/spike Spike length Coleoptile length Seedling vigor Root to shoot ratio NS S NS S NS S NS S NS S UASDWG_246 0.575 0.556 1.832 64.6 64.2 6.33 6.17 6.8 5.9 5500 3221 0.387 0.529 UASDWG_216 0.491 0.655 1.84 60.6 56.8 8.95 8.83 4.8 2.6 4000 512 0.424 0.455 UASDWG_248 0.429 0.584 1.44 46.6 46 7.17 7.33 6.0 4.2 4633 1888 0.434 0.551 UASDWG_168 0.415 0.564 1.34 49.2 39 6.67 6.83 6.8 5.4 4460 698 0.384 0.320 UASDWG_259 0.41 0.557 1.31 60.4 52.6 5.83 8.17 6.2 5.3 5333 3307 0.418 0.588 UASDWG_299 0.461 0.554 1.46 52.2 40 8.83 7 5.0 5.2 2400 2381 0.363 0.715 UASDWG_175 0.414 0.531 1.26 44 43 6.17 6.17 6.9 3.1 4240 412 0.362 0.300 UASDWG_30 0.423 0.527 1.28 49.9 49.4 6.83 5.83 5.6 5.3 3767 3321 0.432 0.428 UASDWG_85 0.408 0.522 1.22 48.8 50 6.67 6.17 4.7 4.1 4033 2216 0.532 0.494 UASDWG_75 0.379 0.513 1.11 68.4 66.2 6.83 6.17 6.3 4.6 5850 3995 0.358 1.105 UASDWG_12 0.404 0.494 1.14 53.4 51.6 6.33 6.33 5.3 2.8 5233 1024 0.426 0.661 SSI = Stress susceptibility index, SI = stress tolerance index, YS = yield under stress (kg/plot), YP = yield under control (kg/plot), S = stress, NS = non-stress Declarations Author Contribution U.G and C.G.B has formualted research, finalized the objective and conducted the researchC.G.B, U.G and V.G.M drafted research article, statiscial interpretation of the data and prepared the tables/figuresS.H.B, S.LB, M.U.V and N.P drafting of the research article and proof reading for the grammitical errorsA.G, shared the part of the germplam line and suggested for the startergic conduct of research with diffrent methodology. Acknowledgment The financial support of DST New Delhi, for awarding INSPIRE fellowship is gratefully acknowledged. References Abdoli M, Saedi M (2012) Effects of water deficiency stress during seed growth on yield and its components, germination and seedling growth parameters of some wheat cultivars. Int J Agric Crop Sci 4:1110–1118 Abdul-Baki A, Anderson JD (1973) Vigor determination in soybean seed by multiple criteria 1. Crop Sci 13(6):630–633 Abid M (2016) Improved tolerance to post-anthesis drought stress by pre-drought priming at vegetative stages in drought-tolerant and-sensitive wheat cultivars. Plant Physiol Biochem 106:218–227. https://doi.org/10.1016/j.plaphy.2016.05.003 Ahmad A, Aslam Z, Javed T, Hussain S, Raza A, Shabbir R, Mora-Poblete F, Saeed T, Zulfiqar F, Ali MM, Nawaz M, Rafiq M, Osman HS, Albaqami M, Ahmed MAA, Tauseef M (2022) Screening of wheat (Triticum aestivum L.) genotypes for drought tolerance through agronomic and physiological response. 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Aust J Agric Res 55:733–743. https://doi.org/10.1071/ar04037 Salam A, Ali A, Afridi MS, Ali S (2022) Agrobiodiversity: Effect of drought stress on the eco-physiology and morphology of wheat. In: Agrobiodiversity. Springer, Cham, pp 23–23. https://doi.org/10.1007/978-3-030-73943-0 Sallam A, Alqudah AM, Dawood MF, Baenziger PS, Börner A (2019) Drought stress tolerance in wheat and barley: advances in physiology, breeding and genetics research. Int J Mol Sci 20:3137. https://doi.org/10.3390/ijms20133137 Sánchez-Bermúdez M, Del Pozo JC, Pernas M (2022) Effects of combined abiotic stresses related to climate change on root growth in crops. Front Plant Sci 13:918537 Sehgal A, Sita K, Siddique KHM, Kumar R, Bhogireddy S, Varshney RK, et al (2018) Drought or/and heat-stress effects on seed filling in food crops: impacts on functional biochemistry, seed yields, and nutritional quality. Front Plant Sci 9:1705. https://doi.org/10.3389/fpls.2018.01705 Shah SMDM, Shabbir G, Malik SI, Raja NI, Shah ZH, Rauf M, et al (2022) Delineation of physiological, agronomic and genetic responses of different wheat genotypes under drought condition. Agronomy 12(5):1056. https://doi.org/10.3390/agronomy12051056 Sharada HB, Uday G, Priyanka K, Gopalareddy K, Shamarao (2022) Root characterization and identification of drought tolerant dicoccum wheat germplasm lines using stress tolerance index (STI). JCR 14(Spl2):1–7 Singh M, Shali V, Kumar L (2020) Diversity analysis of wheat genotypes using SSR molecular markers. Int J Curr Microbiol App Sci 9(10):1413–1423 Tarawneh RA, Szira F, Monostori I, Behrens A, Alqudah AM, Thumm S, Lohwasser U, Röder MS, Börner A, Nagel M (2019) Genetic analysis of drought response of wheat following either chemical desiccation or the use of a rain-out shelter. J Appl Genet 60:137–146. https://doi.org/10.1007/s13353-019-00494-y Tuberosa R, Pozniak C (2014) Durum wheat genomics comes of age: introduction to the special issue on durum wheat genomics. Mol Breed 34:1527–1530 Uzair M, Ali M, Fiaz S, Attia K, Khan N, Al-Doss AA, et al (2022) The characterization of wheat genotypes for salinity tolerance using morpho-physiological indices under hydroponic conditions. Saudi J Biol Sci 29(6):103299. https://doi.org/10.1016/j.sjbs.2022.103299 Wei N, Zhang S, Liu Y, Wang J, Wu B, Zhao J, Qiao L, Zheng X, Wang J, Zheng J (2022) Genome-wide association study of coleoptile length with Shanxi wheat. Front Plant Sci 13:1016551 Additional Declarations No competing interests reported. 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Graphical Representation of Seedling Traits Plasticity in Durum Wheat Under Normal and Water-Stressed Conditions Using Box Plots\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7230605/v1/0b41ac54a05163f4d6a31877.png"},{"id":87901778,"identity":"094ab771-ec74-4c56-b5b0-67b58a406898","added_by":"auto","created_at":"2025-07-30 08:21:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":430823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. Dimensional Reduction of Morpho- physiological and Yield Traits under Water Deficit Using PCA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePH = Plant height, DF = Days to 50% flowering, SP 1 = SPAD I, SP 2 = SPAD II, CT 1 = Canopy temperature I, CT 2 = Canopy temperature II, MS = Membrane stability, SL = Spike length, NSS = Number of Seeds per spike, SW = Thousand grain weight, GY = Grain yield\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. PCA based clustering of germplasm lines evaluated under moisture stress condition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7230605/v1/1507c8eb91a31a20daac4711.png"},{"id":87903198,"identity":"e5ae1b9f-0296-45cd-8124-d62da200cc1b","added_by":"auto","created_at":"2025-07-30 08:29:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. Dimensional Reduction of Seedling Traits under Water Deficit Using PCA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(GP= Germination percentage, CL= Coleoptile length, SVI= Seedling vigour I, SVII= Seedling vigour II, RSR= Root to shoot ratio)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. PCA based clustering of germplasm lines evaluated under moisture stress during seedling stage\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7230605/v1/4c71f478d8ef0a62ef75dd71.png"},{"id":87903642,"identity":"d2c56e74-55b9-4d47-81a8-8c5b4c1a6137","added_by":"auto","created_at":"2025-07-30 08:37:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":681059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. Dendrogram Depicting Hierarchical Clustering of durum wheat genotypes based on morpho, physiological and yield traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Dendrogram Depicting Hierarchical Clustering of durum wheat genotypes based on seedling traits\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7230605/v1/d6c03d8c693a94250d4d39d5.png"},{"id":88304696,"identity":"dabec689-5941-4432-8bf4-f4f9fd95be29","added_by":"auto","created_at":"2025-08-05 05:32:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3696560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7230605/v1/25406dd9-d180-4d1d-bfe0-beb11701bcda.pdf"},{"id":87900859,"identity":"d65f4ba4-2db6-44e4-8c82-f3afd6b484db","added_by":"auto","created_at":"2025-07-30 08:13:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":99302,"visible":true,"origin":"","legend":"","description":"","filename":"ChayaSupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-7230605/v1/97ea3a0388e44548e8bea025.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phenotypic diversity and Principal component analysis to associate physiological traits contributing to grain yield under moisture stress in durum wheat germplasm","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum spp.\u003c/em\u003e) is a globally significant cereal crop that serves as a staple food for more than one-third of the world\u0026rsquo;s population. Among the cultivated species, durum wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e subsp. \u003cem\u003edurum\u003c/em\u003e) is predominantly grown in arid and semi-arid regions and is highly valued for its use in pasta, couscous, and other semolina-based products. However, the lack of water significantly lowers the productivity of durum wheat, particularly in the Mediterranean and rainfed regions, where drought often occurs during the grain-filling and reproductive stages (Sall et al, 2019). The increasing frequency and intensity of droughts due to climate change also threaten global wheat production and food security. Therefore, improving drought tolerance in durum wheat is a key goal in modern breeding. All phenological stages of wheat development are negatively impacted by drought stress, but the extent of damage differs based on the developmental stage and severity of the stress (Djanaguiraman et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In arid and semi-arid areas, the combination of water shortage and high temperatures leads to a significant loss of yield during the grain-filling stage (Shah et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Physiological disruptions like reduced photosynthetic efficiency, lower stomatal conductance, decreased chlorophyll content, compromised membrane integrity, and altered water relations greatly contribute to losses in productivity under stress conditions (Sehgal et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Drought stress also shortens the life cycle and disrupts reproductive development, leading to pollen abortion, anther indehiscence, and a decrease in both grain size and number. Other effects include accelerated senescence, disrupted osmolytes balance, smaller flag leaf area, shorter grain filling duration, and less biomass directed to grains, which together reduce the harvest index and overall crop performance (Pandey et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the cellular level, drought stress leads to the production and buildup of reactive oxygen species (ROS) such as hydrogen peroxide, hydroxyl radicals, and superoxide ions. These ROS cause oxidative damage to cell membranes, disrupt electron transport in chloroplasts and mitochondria, and affect photosynthesis (Ahmadizadeh \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While plants have antioxidative defense mechanisms to detoxify ROS, their effectiveness varies with genotype and the intensity and timing of the water deficit (Ahmed et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Understanding physiological and morphological responses to drought is critical for identifying resilient genotypes. Plant-based physiological traits, such as canopy temperature, chlorophyll content (SPAD value), membrane stability index (MSI), and relative water content (RWC), are increasingly used as indirect selection indices in drought breeding programs (Sallam et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Drought-tolerant plants often exhibit traits like smaller leaf area, higher root biomass, and increased accumulation of osmolytes (e.g., proline, soluble sugars) that support water retention and stress mitigation (Abid, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The stress tolerance index (STI) is frequently used to evaluate genotype performance under both optimal and stress conditions, with higher STI values indicating superior tolerance (Mohammadijoo et al., 2015). It was hypothesized that the physiological and biochemical traits under investigation would exhibit differential responses under stress and non-stress conditions, and evaluating their relative performance would provide valuable insights for future breeding programs aimed at enhancing wheat tolerance to moisture stress. The utility of genetic diversity lies not only in its existence but in its effective exploitation. Advances in molecular breeding, genomics, and functional genomics have enhanced our ability to dissect complex traits and target specific genomic regions associated with adaptive responses (Tuberosa and Pozniak, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The establishment of the Global Durum Wheat Panel (GDP) is a milestone in this direction. Initiated by the international durum wheat community in Bologna, Italy, in 2015 under the Wheat Initiative, the GDP comprises 1,011 genotypes drawn from over 2,500 tetraploid wheat accessions, including modern cultivars, landraces, and primitive tetraploid wheat (Kabbaj et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This panel captures approximately 94\u0026ndash;97% of the genetic diversity within durum wheat and serves as a global reference set for genomics-assisted breeding and pre-breeding efforts. In this context, the present study utilized a wide germplasm collection to evaluate phenotypic diversity under moisture stress conditions with specific objectives (i) assess the responsiveness of physiological traits contributing to drought adaptation; (ii) analyse the association of these traits with grain yield under stress; and (iii) apply Principal Component Analysis (PCA) and Hierarchical clustering analysis to identify key traits and genotypes exhibiting superior drought tolerance. The findings from this study aim to facilitate the selection of climate-resilient durum wheat germplasm and contribute to the genetic improvement of yield stability under water-limited environments and utilize them as donors and development of Multi-parent Advanced Generation Inter-Cross (MAGIC) populations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eExperimental location and planting material\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study was conducted at the All India Coordinated Research Project on Wheat, University of Agricultural Sciences, Dharwad, situated in the peninsular zone of India. Experimental material consists of 225 durum wheat germplasm that were collected from CIMMYT and ICARDA. The germplasm was initially screened for grouping them based on days to flowering and genetic purification was done by single ear head selection during 2021-22 and 2022-23. The germplasm lines were evaluated for reproductive stage stress for two seasons (Rabi 2023-24 and 2024-25). The two season data were pooled for combined statistical analysis using a test for homogeneity (Battles test).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDrought evaluation under field conditions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of two hundred and twenty wheat germplasm collections and five popular checks were evaluated using an Augmented block design (Unreplicated entries\u0026thinsp;+\u0026thinsp;replicated checks). The experiment included two environmental setups: (i) Non-stress (periodically irrigated during CRI stage and booting stage) and (ii) Stress (withhold of Irrigation after booting stage till harvest). Critical Moisture stress was observed between 65 to 90 days after sowing (DAS) corresponding to the flowering, grain-filling and Physiological maturity stages of the crop. Each germplasm line was grown in two rows of 3 m length with 20 cm \u0026times; 5 cm row and plant spacing. The control plots were watered with furrow irrigation, whereas the stress plots did not get receive irrigation during the stress period. The same standard agronomic practices were used to manage both sets and they were also protected against biotic stresses. A 10 m buffer zone was maintained to prevent moisture seepage between treatments. The morphological and yield traits like plant height, days to 50 per cent flowering, spike length, number of grains/spikes; 1000-grain weight and grain yield were recorded following standard procedures. The observations on physiological parameters were recorded at two time intervals during the moisture stress period (flowering/grain filling stage). The first set of measurements, including SPAD I and canopy temperature I was taken 10 days after the initiation of stress. The second set, comprising SPAD II, canopy temperature II and membrane stability index, was recorded 20 days after the onset of stress. The randomly selected five plants were tagged and observations were recorded on the same plant for all the phenotypic observations. The grain yield data were recorded for each germplasm line for both environments and used to calculate the stress tolerance indices.\u003c/p\u003e\u003cp\u003eStress Tolerance Index STI = [(\u003cem\u003eY\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e) \u0026times; (\u003cem\u003eY\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e)] / (\u003cem\u003eῩ\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e).\u003csup\u003e2\u003c/sup\u003e (Fernandez 1992)In these formulas, \u003cem\u003eY\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e, \u003cem\u003eY\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e and \u003cem\u003eῩ\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e were yield under drought stress, yield under non-stress for each genotype and yield mean in non-stress conditions for all genotypes, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDrought evaluation for seedling growth traits\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe experiment was conducted in nursery plastic trays filled with a mixture of Sieved soil, cocopeat and vermicompost in a 2:1:0.5 ratio. Two moisture treatments were applied: a control at 100% field capacity and moisture stress at 50% field capacity. Watering occurred every other day by weighing the trays to keep the moisture levels consistent. To create different levels of moisture stress varying amounts of irrigation water were applied: 85 ml per tray for the control and 425 ml per tray for the stress treatment. Moisture stress began three days after the seedlings were established. For each germplasm line, observations were recorded on three randomly selected plants, and the mean was calculated for all traits. Germination percentage was assessed following ISTA rules (Anon, 2013), with count taken on the seventh day. Root length and shoot length were measured on the 12th day after sowing (DAS), and the root-to-shoot ratio was calculated. Coleoptile length Coleoptile lengths were measured as the distance from the scutellum to the tip of the coleoptile, as described by Rebetzke et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) on 12th DAS. Seedling vigour index I was determined as given by Abdul-Baki and Anderson (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), while seedling vigour index II (SVI-II) was computed by multiplying germination percentage with seedling dry weight.\u003c/p\u003e\u003cp\u003eSeedling vigour index (SVI)\u0026thinsp;=\u0026thinsp;Germination (%) x [Root length\u0026thinsp;+\u0026thinsp;Shoot length]\u003c/p\u003e\u003cp\u003eSeedling vigour index (II)\u0026thinsp;=\u0026thinsp;Germination (%) x Seedling dry weight (g)\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of phenotypic data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data analysis was conducted using an augmented design based on the methodology proposed by Fisher and Yates (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). Variability among and within the germplasm lines was assessed through the F-test at both 1% and 5% significance levels. Variance analysis and the estimation of related parameters were performed using the R-statistic software (\u003cem\u003eR version 4.3.3\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e Pearson\u0026rsquo;s correlation coefficient, as described by Hall (2015), was applied in R to examine the relationships among traits. To reduce the dimensionality of the dataset while preserving essential information, principal component analysis (PCA) was carried out using the R packages \u003cb\u003eggplot2\u003c/b\u003e, \u003cb\u003etidyr\u003c/b\u003e, \u003cb\u003edplyr\u003c/b\u003e and \u003cb\u003eFactoMineR\u003c/b\u003e. Furthermore, hierarchical clustering was conducted in R Studio with the aid of \u003cb\u003eggplot2\u003c/b\u003e, \u003cb\u003ecluster\u003c/b\u003e, \u003cb\u003efactoextra\u003c/b\u003e and \u003cb\u003estats\u003c/b\u003e packages to identify patterns and groupings among the genotypes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnalysis of variance and phenotypic expression of yield and stress-responsive traits under contrasting moisture conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of variance \u003cstrong\u003e(\u003c/strong\u003eANOVA) revealed significant genotypic variation for most morpho-physiological and yield traits under non-stress, except SPAD II and canopy temperature II. Under stress, all traits showed substantial differences under different moisture regimes except canopy temperature I \u003cstrong\u003e(Table S1)\u003c/strong\u003e. Standard checks also exhibited significant variation across most traits. Seedling traits showed highly significant differences among genotypes and checks under both moisture regimes \u003cstrong\u003e(Table S2\u003c/strong\u003e). Box plot analysis of eleven morpho-physiological and yield traits in 220 durum wheat genotypes revealed substantial differences in trait performance under control and moisture stress conditions \u003cstrong\u003e(Fig.\u0026nbsp;1a)\u003c/strong\u003e. SPAD I and SPAD II showed a drop in mean values from 49.8 to 45.3 and 50.2 to 46.5, respectively, under moisture stress. Canopy Temperature I increased from 21.8°C to 25.6°C, while Canopy Temperature II rose from 25.3°C to 31.6°C under stress. The Membrane Stability Index fell significantly from 78.2–61.3%. Plant Height decreased from 81.5 cm in the control to 73.2 cm under stress, and Spike Length declined from 74 cm to 62 cm. The Number of Seeds per Spike dropped from 48.6 to 43.2, while 1000 Seed Weight fell from 46.7 g to 43.9 g. Grain Yield reduced significantly from 0.51 kg to 0.36 kg. Days to 50% Flowering shifted earlier, with the mean days decreasing from 68.9 to 63.5. Moisture stress also significantly affected all seedling traits in durum wheat, as seen in the comparative boxplots \u003cstrong\u003e(Fig.\u0026nbsp;1b)\u003c/strong\u003e. Germination Percentage fell from 93.2–49.4% under drought. Coleoptile Length decreased from 60 cm to 43 cm, and both Seedling Vigour I and Seedling Vigour II dropped significantly, from 3426 to 1673 and 4344 to 1854, respectively, under moisture stress. The Root-to-Shoot Ratio increased from 0.5 to 0.7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic correlation analysis between yield components and stress-responsive traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhenotypic correlations among traits often arise due to genetic linkage, pleiotropy, or coordinated developmental processes. Pearson’s correlation analysis provides insights into the direction and strength of associations between trait pairs, facilitating simultaneous genetic improvement through correlated response to selection. In the present study, phenotypic correlation coefficients were estimated for morpho-physiological, yield, and seedling traits in two hundred twenty durum wheat germplasm lines under non-stress and stress conditions (Table 1a and 1b). The analysis revealed environment-specific patterns of trait interrelationships, highlighting variable degrees of trait association under contrasting moisture regimes. Under both conditions, SPAD I and SPAD II exhibited a highly significant positive correlation. Under moisture stress, Grain yield (GY) showed strong positive correlations with SPAD I (0.92), SPAD II (0.75), and membrane stability (0.95). Conversely, canopy temperature I was negatively correlated with GY (–0.16) and SPAD I (–0.12) under stress and also CT2 with GY (–0.21). GY also correlated positively with plant height, number of seeds per spike and thousand seed weight. Notably, days to 50% flowering (DF) showed a strong negative correlation with GY (–0.33 stress; − 0.35 non-stress) and SPAD. Germination percentage was strongly correlated with seedling vigour I (0.92 stress; 0.74 control), seedling vigour II (0.92 stress; 0.54 control). It showed a non-significant association with coleoptile length under non stress, but a positive correlation under stress (0.62). In contrast, the root-to-shoot ratio showed negative correlations with all traits under non stress conditions, whereas it showed positive correlations with all traits under stress conditions.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ea: Phenotypic association among the morpho-physiological and yield traits under non-stress conditions (Lower half) and Stress condition (Upper half)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCT1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCT2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.847***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.233***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.883***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.351***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.333***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.175**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.268***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.146*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.125*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.129*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.186**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.127*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.212***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.217***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.736***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.419***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.954***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.304***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.287***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.278***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.186**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.309***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.344***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.171**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.205***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.167**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.161**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.158*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.455***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.482***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.252***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.889***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.874***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.169**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.145*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.484***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.334***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.295***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.348***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.144*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.387***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.285***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.284***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.351***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e* - P = \u0026lt; 0.05; ** P = \u0026lt; 0.01; *** P = \u0026lt; 0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e(PH = Plant height, DF = Days to 50% flowering, SP 1 = SPAD I, SP 2 = SPAD II, CT 1 = Canopy temperature I, CT 2 = Canopy temperature II, MS = Membrane stability, SL = Spike length, NSS = Number of Seeds per spike, SW = Thousand grain weight, GY = Grain yield)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eb: Phenotypic association among the seedling traits under non-stress condition (Lower half) and Stress condition (Upper half)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGermination percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eColeoptile length\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeedling vigour I\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeedling vigour II\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRoot to shoot ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGermination percentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.912***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eColeoptile length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.706***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeedling vigour I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeedling vigour II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.479***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.699***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoot to shoot ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.152*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.376***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.254***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.215***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e* - P = \u0026lt; 0.05; ** P = \u0026lt; 0.01; *** P = \u0026lt; 0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal component analysis under stress conditions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) was conducted to explore trait variability among durum wheat genotypes under moisture stress, revealing that the first three principal components (PC\u003csub\u003e1\u003c/sub\u003e, PC\u003csub\u003e2\u003c/sub\u003e, and PC\u003csub\u003e3\u003c/sub\u003e) cumulatively explained 66.12% of the total variation, with PC1 alone accounting for 40.98%, followed by PC\u003csub\u003e2\u003c/sub\u003e (14.56%) and PC\u003csub\u003e3\u003c/sub\u003e (10.58%). PC\u003csub\u003e1\u003c/sub\u003e was primarily influenced by grain yield (GY), SPAD I (SP1), SPAD II (SP2), membrane stability (MS), and number of seeds per spike (NSS), all contributing negatively, while PC\u003csub\u003e2\u003c/sub\u003e was largely driven by canopy temperature traits (CT\u003csub\u003e1\u003c/sub\u003e, CT\u003csub\u003e2\u003c/sub\u003e), seed weight (SW), and days to 50% flowering (DF). PC\u003csub\u003e3\u003c/sub\u003e showed a significant association with spike length (SL) and CT traits. Grain yield exhibited strong positive correlations with SPAD I (0.96), SPAD II (0.93), and membrane stability (0.92), while days to 50% flowering (-0.47), canopy temperature I (-0.61), and canopy temperature II (-0.56) showed negative associations with the principal components (Fig. 2a). The scree plot demonstrated a sharp decline in eigenvalues after PC1 and PC2, indicating that most of the meaningful variation was captured within the first two axes (\u003cstrong\u003eFig. S1\u003c/strong\u003e). The biplot and genotype distribution plots (Fig. 2b) further revealed clear groupings of genotypes, enabling effective discrimination based on multivariate trait performance under drought stress conditions. PCA of seedling traits in durum wheat under moisture stress at the seedling stage revealed that the first two components explained 90.1% of the total variation, with PC 1 (70.1%) capturing most of the variation. (Fig. 3a). Among the traits, Root to shoot ratio contributes strongly to PC\u003csub\u003e1\u003c/sub\u003e and indicates its key role in drought adaptation through enhanced root development. Coleoptile length, seedling vigour index I, seedling vigour index II and germination percentage also contributed significantly, with SVI, SVII, and GP showing strong positive correlation, reflecting their shared influence on seedling vigour. CL exhibited a distinct pattern, likely associated with early emergence under stress. The near-orthogonal orientation of RSR to shoot-related traits suggests a low correlation between root and shoot vigour traits. The individual PCA plot \u003cstrong\u003e(\u003c/strong\u003eFig. 3b) revealed clear differentiation among genotypes, resulting in the formation of four distinct clusters. Genotypes 122 and 165 appeared as outliers with extreme positions along the principal components, indicating unique physiological trait combinations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHierarchical Clustering of Genotypes under Moisture Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster analysis of 220 durum wheat genotypes under moisture stress conditions resulted in the formation of three distinct clusters: Cluster 1 comprised 94 genotypes, Cluster 2 had 46 genotypes and Cluster 3 included 80 genotypes (Singh et al. 2020). The intra-cluster D² values were relatively low and comparable across clusters, indicating high genetic similarity and compactness within each cluster (Table 2a). Among the inter-cluster distances, the greatest separation was observed between Cluster 1 and Cluster 2 (6.024), reflecting maximum phenotypic divergence between these groups. In contrast, Cluster 3 showed moderate distances with both Cluster 1 (3.243) and Cluster 2 (4.382), suggesting that it harbors genotypes with intermediate or overlapping trait expressions. The dendrogram (Fig. 4a) visually supported these findings, revealing distinct branching patterns that correspond to morpho-physiological and yield trait diversity under stress conditions. Hierarchical cluster analysis grouped the 206 durum wheat genotypes into four distinct clusters based on seedling traits under moisture stress, as illustrated in the dendrogram (Fig. 4b\u003cstrong\u003e)\u003c/strong\u003e. Cluster 1 contained the largest number of genotypes (82), followed by Cluster 2 with 73 genotypes, Cluster 3 with 40 genotypes, and Cluster 4 with the fewest, comprising 11 genotypes. The intra-cluster D² values, which indicate the genetic homogeneity within clusters, ranged from 2.771 (Cluster 1) to 2.917 (Cluster 2) (Table 2b), suggesting moderate variability among genotypes within each cluster. The inter-cluster D² distances, representing genetic divergence between clusters, showed the highest value between Cluster 1 and Cluster 3 (4.547), followed by Cluster 1 and Cluster 4 (3.936), and Cluster 2 and Cluster 4 (2.572). The minimum inter-cluster distance was observed between Cluster 2 and Cluster 3 (2.260), suggesting these two groups shared more similarities in seedling traits under stress.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ea: Intra and inter-cluster D² values in durum wheat germplasm lines under stress conditions for field condition\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eb: Intra and inter-cluster D² values in durum wheat germplasm lines under stress condition (seedling traits)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster. 4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster. 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the context of ongoing climate change, breeding for drought-tolerant wheat cultivars requires substantially more focus and funding. Drought stress poses a significant threat to wheat productivity, exerting adverse effects across all developmental stages. The complexity of drought tolerance arises from the fact that the effect of the stress is dependent on the developmental stage of the plant, and the intensity and duration of the stress (Tarawneh et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The present study revealed that there is great genetic diversity in durum wheat elite genotypes for drought stress tolerance, which is a prerequisite for effective selection, supported by continuous phenotypic variation across traits, suggesting polygenic inheritance. Genotypes responded differently to moisture stress at both seedling and reproductive stages, confirming wide genetic variability. The findings of the present study underscore the multifaceted impacts of drought stress on wheat morphology, physiology, and yield-related traits, emphasizing the complex interplay of factors governing drought tolerance. Drought stress markedly impaired plant performance, with a substantial reduction in grain yield (26.81%) (Poudel et al., 2020) primarily associated with cumulative declines in spike length, number of grains per spike and thousand grain weight (\u003cb\u003eTable S3\u003c/b\u003e). These reductions stem from impaired reproductive processes under water-deficit conditions, such as decreased pollen viability and ovule development, ultimately restricting grain set and reproductive success (Rawtiya \u0026amp; Kasal, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This observation aligns with prior findings (Giunta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Butler et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which reported that drought stress during stem elongation and grain filling reduces tiller survival and spike formation, leading to lower productivity. The decline in plant height is due to the dehydration of protoplasm that ultimately reduces cell division, cell expansion, and loss of cell turgidity (Salam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Furthermore, photosynthetic efficiency was adversely affected, as evidenced by declines in SPAD I and SPAD II values by 2.94% and 6.97%, respectively, indicating a decrease in chlorophyll content due to chlorophyllase activation and ROS-mediated damage to chloroplast structures (Nikolaeva et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mafakheri et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A corresponding 24.84% reduction in membrane stability index further revealed cellular damage and compromised membrane integrity, which negatively influence dry matter accumulation and assimilate translocation essential for grain development. Drought stress also altered canopy temperature dynamics, with Canopy Temperature I and II exhibiting increments of 10.18% and 21.02%, respectively, reflecting decreased stomatal conductance and diminished transpirational cooling (Banerjee et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, canopy temperature is used as a proxy for water status and transpiration efficiency. Additionally, a reduction in days to 50% flowering under drought conditions suggests an adaptive early flowering response, which can be advantageous for drought escape (Aslam et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Correlation analysis reinforced these physiological observations, revealing strong positive associations between grain yield and SPAD I, SPAD II, and MSI, underscoring the pivotal roles of chlorophyll retention and cellular stability in yield maintenance. In contrast, the negative correlation of days to 50% flowering and canopy temperature with yield and chlorophyll content suggests that early phenology and efficient transpirational cooling are advantageous for drought escape and improved performance under terminal drought conditions (Bapela et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bayisa et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The detection of traits associated with drought tolerance at the seedling stage is crucial for enhancing crop adaptation and boosting grain yield in regions where early-season drought is a major limiting factor (Lin et al., 2019; Thabet \u0026amp; Alqudah, 2019). Under drought conditions, wheat germination percentage was reduced by 50.5%, primarily due to decreased water potential limiting water uptake and delaying germination (Abdoli \u0026amp; Saedi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Seedling vigor index declined by 53\u0026ndash;59% attributed to restricted water availability, impaired root and shoot growth, nutrient deficiency, and oxidative stress (La \u0026amp; Sylvestris, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Under water stress conditions, reduced cell wall extensibility inhibits cell elongation, leading to a 11.4% reduction in coleoptile length (Khadka et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In response to limited surface moisture, plants enhanced root proliferation, resulting in a 40% increase in the root-to-shoot ratio to access deeper soil moisture (Kou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Under non-stress conditions, trait correlations were generally weak to moderate, with stronger associations observed among shoot-related traits. Coleoptile length showed a moderate to strong positive correlation with seedling vigour due to its role in promoting faster emergence and early shoot growth (Wei et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chloupek et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, its relationship with germination percentage and root-to-shoot ratio (RSR) was weak or negative, as adequate moisture favours shoot growth over root development. Seedling vigour showed a positive correlation with germination percentage, reflecting the uniform growth of metabolically active seeds, but it demonstrated a negative correlation with RSR, revealing that there is a preference for shoot investment in favourable conditions. Under drought stress, the correlations of traits not only have become stronger but also more positive, indicating their significance in the process of stress adaptation. Long coleoptiles were found to be related with better germination and seedling vigour, which may be due to the improved internal water status and better establishment. The positive correlation with RSR implies that the genotypes that have better growth potential also invest more in roots under stress (Anjum et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). On the other hand, high germination was associated with increased seedling vigour and moderate root allocation, whereas vigorous seedlings showed a strong positive correlation with RSR, reflecting a drought-induced shift toward root-biased growth to enhance water uptake and ensure early survival (S\u0026aacute;nchez-Berm\u0026uacute;dez et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sharada et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Principal component analysis is a powerful statistical method for representing highly correlated data that significantly draws positive feedback by eliminating the dimensionality of variables. The biplot showed strong positive associations among SP1, SP2, MS, and GY, which were inversely related to CT1 and CT2\u0026mdash;indicating that genotypes with higher chlorophyll content and membrane stability tended to maintain lower canopy temperatures and higher yield under stress. The PCA-based distribution of genotypes under moisture stress exhibited clear divergence along PC1 and PC2, reflecting differential adaptive strategies among genotypes. Genotypes positioned toward the negative side of PC1 were associated with higher values for SPAD I, SPAD II, membrane stability, and grain yield, signifying better physiological resilience to drought through maintenance of photosynthetic apparatus and cellular integrity (Ahmed et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Those on the positive PC2 axis showed elevated canopy temperatures, indicating reduced transpirational cooling and greater stress impact. Furthermore, genotypic dispersion along PC3 highlighted variation in reproductive traits such as spike length, seed number per spike, and flowering time, suggesting the role of phenological plasticity and reproductive efficiency in drought adaptation. The PCA loading pattern emphasizes the physiological and morphological coordination among variables, where traits like high membrane stability, SPAD values, and grain yield clustered together, reflecting a robust drought tolerance mechanism (Donga et al. 2022). The PCA biplot for seedling traits highlights RSR as the dominant trait contributing to PC1, while CL, SVII, and SVI exhibit moderate but relevant influence along both dimensions. RSR displayed the longest vector aligned closely with the positive side of PC1, signifying that it is the most influential trait contributing to variability among genotypes. This suggests that genotypes with higher RSR values may be better adapted to moisture stress conditions due to enhanced root development and water absorption capacity. The grouping and alignment of SVI, SVII, and GP vectors indicate a strong positive correlation among these traits, implying that vigorous seedlings with higher germination rates tend to share similar trait performance under moisture stress. CL was moderately separated from the seedling vigour traits, indicating a distinct influence, likely tied to early emergence and establishment capabilities in stress conditions. The nearly orthogonal position of RSR relative to SVI, SVII, and GP suggests a relatively low correlation between root allocation and shoot-based seedling vigour traits. This distinction underscores the importance of combining both root-related and shoot-related physiological traits to comprehensively assess drought adaptation potential. The clustering pattern under field-induced drought stress offers valuable insights into genotypic divergence and trait-based groupings among durum wheat lines (Uzair et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cluster 2 exhibited the highest inter-cluster distance from Cluster 1, indicating a significant genetic difference between these groups. This implies that genotypes in Cluster 2, which are linked to beneficial traits like higher grain yield, thousand grain weight, membrane stability, and chlorophyll content, are genetically different from those in Cluster 1. The genotypes in Cluster 1 tend to have traits that are more susceptible to stress, including higher canopy temperature and lower chlorophyll content. The moderate distance between Cluster 2 and Cluster 3, along with the low distance between Cluster 1 and Cluster 3, suggests that genotypes in Cluster 3 have intermediate or overlapping characteristics, likely showing moderate ability to stress tolerance. Notably, the intra-cluster D\u0026sup2; values were relatively low across all clusters, ranging from 4.471 to 4.513. This indicates genetic similarity among genotypes within each group. Therefore, hybridizing genotypes from Cluster 1 and Cluster 2 could lead to maximum heterosis and transgressive segregation, as they are the most divergent pools. The clustering pattern showed significant diversity in seedling trait expression among durum wheat genotypes under drought stress, with Cluster 1 displaying moderate variability and reflecting intermediate trait performance. Cluster 2, having the highest intra-cluster variability, comprised genotypes with high values for RSR and SVII, which indicate broader adaptive responses. Cluster 3 showed moderate variation and had similarities with Cluster 2, suggesting limited diversity if crossed. In contrast, Cluster 4, while the smallest group, demonstrated distinct trait expression, especially for RSR and coleoptile length. This highlights its potential as a source of unique traits for drought adaptation. The most significant genetic divergence was found between Cluster 1 and Cluster 3, followed by Cluster 1 and Cluster 4, suggesting their suitability as parents for hybridization to improve genetic variability and drought resilience. Cluster 2, comprising genotypes exhibiting favorable morpho-physiological and yield-related traits under drought stress at the reproductive stage, shows substantial overlap with genotypes previously classified under Cluster 2 based on their performance under seedling-stage drought stress \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This convergence indicates a consistency in drought tolerance mechanisms across developmental stages, suggesting that these genotypes possess inherent genetic resilience that manifests from early growth through to maturity. This reinforces the predictive value of seedling-stage screening for identifying genotypes with long-term drought adaptability, and highlights the potential of these genotypes as stable performers under water-limited environments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates the significant phenotypic variability among durum wheat germplasm lines for drought-responsive traits, emphasizing the significance of integrative phenotyping across seedling and reproductive stages for precise identification of drought-tolerant genotypes. Significant genotype \u0026times; environment interactions that were observed for physiological, morphological, and yield-related traits demonstrate that the drought adaptation has a polygenic and stage-specific nature. The drought-resistant traits such as chlorophyll retention, membrane stability index, early flowering, and increased root-to-shoot allocation were highlighted as crucial factors for maintaining productivity under moisture stress. The principal component analysis of phenotypic data showed that SPAD, MSI, seedling vigour and grain yield are the primary factors contributing to total phenotypic variation, while hierarchical clustering delineated genotypic groups based on multivariate performance. The genotypes UASDWG_246, UASDWG_216 and UASDWG_299 consistently exhibited superior performance across developmental stages, indicating stable physiological resilience. Seedling-stage screening's being consistent with reproductive-stage performance, confirms its predictive capacity for long-term drought adaptability. These findings provide a robust framework for trait-based selection in durum wheat. Furthermore, the identified genotypes serve as promising donor parents for breeding programs aiming to enhance drought resilience and yield stability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop performing drought tolerant durum wheat germplasm lines based on Spike and Seedling traits along with their yield\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGermplasm lines\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eStress susceptibility indices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eSpike Characters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e\u003cp\u003eSeedling Characters\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eYS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eYP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSTI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eGrains/spike\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eSpike length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eColeoptile length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eSeedling vigor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003eRoot to shoot ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e3221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e3307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e3321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e3995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUASDWG_12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSSI\u0026thinsp;=\u0026thinsp;Stress susceptibility index, SI\u0026thinsp;=\u0026thinsp;stress tolerance index, YS\u0026thinsp;=\u0026thinsp;yield under stress (kg/plot), YP\u0026thinsp;=\u0026thinsp;yield under control (kg/plot), S\u0026thinsp;=\u0026thinsp;stress, NS\u0026thinsp;=\u0026thinsp;non-stress\u003c/b\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eU.G and C.G.B has formualted research, finalized the objective and conducted the researchC.G.B, U.G and V.G.M drafted research article, statiscial interpretation of the data and prepared the tables/figuresS.H.B, S.LB, M.U.V and N.P drafting of the research article and proof reading for the grammitical errorsA.G, shared the part of the germplam line and suggested for the startergic conduct of research with diffrent methodology.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe financial support of DST New Delhi, for awarding INSPIRE fellowship is gratefully acknowledged.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdoli M, Saedi M (2012) Effects of water deficiency stress during seed growth on yield and its 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Front Plant Sci 13:1016551\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Durum wheat, Drought, Stress tolerance index, Principal Component Analysis, Hierarchical clustering","lastPublishedDoi":"10.21203/rs.3.rs-7230605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7230605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought stress is a major abiotic constraint affecting the productivity of durum wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e subsp. \u003cem\u003edurum\u003c/em\u003e), particularly in arid and semi-arid regions. Our study evaluated 225 durum wheat germplasm lines (GDP), with five standard checks under contrasting moisture regimes to dissect physiological, morphological, and seedling traits associated with drought tolerance. The results showed significant variability for key traits, including chlorophyll content (SPAD), membrane stability index (MSI), canopy temperature (CT), root-to-shoot ratio (RSR), and grain yield. Drought stress led to pronounced reductions in grain yield (26.8%), seedling vigor (up to 57.3%), and membrane stability (24.8%), while increasing canopy temperature and RSR. Correlation analysis revealed strong positive associations between grain yield and SPAD, MSI, and seedling vigor under stress, while PCA identified these traits as major contributors to drought adaptation. Hierarchical clustering and principal component analysis enabled the identification of divergent genotypic groups and high-performing lines, notably UASDWG_246, UASDWG_216 and UASDWG_299, which consistently exhibited superior performance across both seedling and reproductive stages. These results highlight the value of integrating multistage phenotyping and multivariate analyses for improving drought resilience in durum wheat breeding programs.\u003c/p\u003e","manuscriptTitle":"Phenotypic diversity and Principal component analysis to associate physiological traits contributing to grain yield under moisture stress in durum wheat germplasm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 08:13:40","doi":"10.21203/rs.3.rs-7230605/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":"56552294-825e-44a7-9f0e-0a179fac2da1","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T05:24:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 08:13:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7230605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7230605","identity":"rs-7230605","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}