Microsoft Word - 11365 NSB Mankar 2023.06.21.docx Received: 20 Oct 2022. Received in revised form: 20 May 2023. Accepted: 14 Jun 2023. Published online: 21 Jun 2023. From Volume 13, Issue 1, 2021, Notulae Scientia Biologicae journal uses article numbers in place of the traditional method of continuous pagination through the volume. The journal will continue to appear quarterly, as before, with four annual numbers. SHSTSHSTSHSTSHST Horticulture and ForestryHorticulture and ForestryHorticulture and ForestryHorticulture and Forestry Society of TransylvaniaSociety of TransylvaniaSociety of TransylvaniaSociety of Transylvania Mankar GD et al. (2023) Notulae Scientia BiologicaeNotulae Scientia BiologicaeNotulae Scientia BiologicaeNotulae Scientia Biologicae Volume 15, Issue 2, Article number 11365 DOI:10.15835/nsb15211365 Research ArticleResearch ArticleResearch ArticleResearch Article.... NSBNSBNSBNSB Notulae Scientia Notulae Scientia Notulae Scientia Notulae Scientia BiologicaeBiologicaeBiologicaeBiologicae Multivariate analysis of the salinityMultivariate analysis of the salinityMultivariate analysis of the salinityMultivariate analysis of the salinity----induced alterations in morphology, induced alterations in morphology, induced alterations in morphology, induced alterations in morphology, physiology, physiology, physiology, physiology, nodulation, and yield in twonodulation, and yield in twonodulation, and yield in twonodulation, and yield in two contrasting mungbean varietiescontrasting mungbean varietiescontrasting mungbean varietiescontrasting mungbean varieties Ganesh D. MANKAR1, Uttam R. WAYASE1, Deepak B. SHELKE2, Kiran B. RASKAR1, Pankaj S. MUNDADA3, Mahendra L. AHIRE4, Tukaram D. NIKAM5, Rajkumar B. BARMUKH1* 1Post Graduate Research Centre, Modern College of Arts, Science and Commerce (Autonomous), Shivajinagar, Pune - 5, affiliated to Savitribai Phule Pune University, Pune - 7, MS, India; gdmbotany@gmail.com; botanica5050@gmail.com; kiranraskar1488@gmail.com; barmukhbotany@moderncollegepune.edu.in (*corresponding author) 2Department of Botany, Amruteshwar Arts, Commerce and Science College, Vinzar, Velhe, Pune - 4112213, MS, India; dpk.shelke1@gmail.com 3Yashavantrao Chavan Institute of Science, Department of Biotechnology, Satara 415001, MS, India; mundada.pankaj77@gmail.com 4Yashavantrao Chavan Institute of Science, Department of Botany, Satara 415001, MS, India; mlahire@gmail.com 5Savitribai Phule Pune University, Department of Botany, Pune 411007, MS, India; tdnikam@unipune.ac.in AbstractAbstractAbstractAbstract Changes were estimated in the morphology, physiology, photosynthesis, nodulation, and yield in two mungbean varieties ‘PKV AKM 12-28’ and ‘VBN (Gg)3’ under salt stress (0, 75, 100, and 125 mM NaCl) for 15, 30, and 45 days. Multivariate modelling was used to analyse results to explore complex data and to visualize time and concentration-dependent modulations. Principal component analysis showed modulations in morpho-physiological attributes such as shoot length, root length, the number of secondary branches, shoot fresh weight, root fresh weight, shoot dry weight, root dry weight and leaf area; photosynthetic attributes such as chlorophyll ‘a’, chlorophyll ‘b’, total chlorophyll, total carotene and total anthocyanine content; nodulation attributes such as nodules per plant, size of the nodule, and fresh weight per nodule, and yield attributes such as number of pods per plant, fresh weight per pod, and seed characteristics such as the number of seeds per pod and fresh weight per 1000 seeds are key traits affected by salt stress and can be used as indicators. Discriminant analysis identiFed modulations in morpho-physiological attributes such as root length, leaf area, root fresh weight, shoot fresh weight, shoot dry weight, shoot length and photosynthetic attributes such as chlorophyll-a content, and mean nodule weight as discriminating variables at different salt concentrations. Besides, it identified modulations in morpho-physiological attributes such as root length, root fresh weight, photosynthetic attributes such as total anthocyanin content and total chlorophyll content, nodulation attribute such nodule size and nodule weight, and yield attributes such as pod number and number of seeds per pod are discriminating variables at various durations of salt stress. Principal component analysis and discriminant analysis identiFed ‘PKV-AKM 12-28’ as salt-tolerant and ‘VBN (Gg)3’ as salt-susceptible varieties. Multiple correlation analysis identified significant correlations among morphological, physiological, photosynthetic, nodulation and yield parameters. https://www.notulaebiologicae.ro/index.php/nsb/index Mankar GD et al. (2023). Not Sci Biol 15(2):11365 2 Keywords:Keywords:Keywords:Keywords: mungbean; nodulation; physiological; salt stress; yield Abbreviations:Abbreviations:Abbreviations:Abbreviations: Chla: Chlorophyll ‘a’ content, Chlb: Chlorophyll ‘b’ content, DPT: day post salt treatment, FW-1000: fresh weight per 1000 seeds, FWP: fresh weight per pod, HCA: hierarchical cluster analysis, , LA: leaf area, MCA: multiple correlation analysis, NN: number of nodules per plant, NP: number of pods per plant, NS: size of the nodule, NSP: number of seeds per pod, NW: fresh weight per nodule, PCA: principal component analysis, RDW: Root dry weight, RFW: root fresh weight, RL: root length, RTWC%: Root tissue water content, SB: secondary branches, SDW: shoot dry weight, SFW: shoot fresh weight, SL: shoot length, STI: salt tolerance index, STWC%: shoot tissue water content, TAC: total anthocyanin content, TCC: total carotene content, TChl: Total chlorophyll content IntroductionIntroductionIntroductionIntroduction Mungbean, Vigna radiata (L.) R. Wilczek (Fabaceae) is one of the valuable dietary pulse crops playing a vital role in fulfilling the food requirements of the ever-increasing population in the world and especially in India (Ram and Singh, 1993). In India, 3.72 million hectares of agricultural land is under mungbean cultivation, giving 1.56 million tons of production (Ali and Gupta, 2012). Mungbean is used as food, fodder, and green manure and also in cosmetics and pharmaceuticals industries (Tang et al., 2014). It is an important source of proteins, vitamins, antioxidants, and minerals (Randhir et al., 2004) and has a significant role in ensuring the nutrition security of developing countries such as India (Dhingra et al., 1991). Soil salinity is one of the significant abiotic environmental stress factors responsible for limiting agricultural productivity in many regions of the world (Panta et al., 2014). Salinity has already affected 20– 50% of agricultural land, and it is increasing by nearly 10% annually (Xu et al., 2011; Shrivastava and Kumar, 2015). FAO (2015) reported that 800 million hectares of land and 32 million hectares of agricultural land are under salinity worldwide. Saline soils are dominated mainly by Na+ and Cl- ions; both are toxic to plants and are considered the most important ions (Hasegawa, 2013). Salinity limits plant growth and development by disrupting the osmotic and ionic balance in the form of water stress, nutritional stress, oxidative stress, and ion toxicity (Arif et al., 2020). Salinity affects the shoot and root length, fresh and dry biomass, and leaf area in most crops (Sarabi et al., 2016; Raza et al., 2017; Shelke et al., 2017). Moreover, it adversely affects many essential cellular and metabolic processes like photosynthesis (Hamani et al., 2020). Furthermore, reduction in photosynthetic capacity under salinity depends on salinity type, duration of treatment, species, and plant age (Sultana et al., 1999; Steduto et al., 2000; Hester et al., 2001; Koyro, 2006). Salinity also limits plants' growth and development, which ultimately affects yield parameters such as pod and seed characters in most of the crops (Ahmed, 2009). Also, salinity limits the plant productivity in legumes by hampering the nodulation process by affecting nodule number, nodule size, and fresh nodule mass (Elahi et al., 2004). The effect of NaCl stress in plants has been studied generally through morphological and physiological responses in plants and conventional visual perception of their variations or changes (Elahi et al., 2004; Ghosh et al., 2015; Sehrawat et al., 2015; Muchate et al., 2016; Shelke et al., 2017; Rahneshan et al., 2018). However, interpretations and conclusions based on conventional approaches are less conclusive because of the complicated nature of morphological and physiological responses and their interrelationships. Moreover, traditional data analysis can extract only quantitative data characteristics and does not interpret conceptual descriptions of dependencies among data variables and the underlying reasons (Michalski and Kaufman, 1997). Multivariate analysis tools such as principal component analysis (PCA), discriminant analysis (DA), Pearson’s multiple correlation analysis (MCA) enables the accurate analysis and interpretation of vast and complex datasets. These tools adequately analyse and interpret complex interrelationships among parameters in Mankar GD et al. (2023). Not Sci Biol 15(2):11365 3 environmental, biological, chemical, and ecotoxicological studies (Mujunen et al., 1996; Simeonov et al., 2003; Singh et al., 2004; Sinha et al., 2009a, 2009b; Shelke et al., 2017). In the present study, the effects of salt stress levels and exposure time on morpho-physiological, photosynthetic, nodulation, and yield parameters in previously screened salt-tolerant ‘PKU-AKM 12-28’ and susceptible ‘VBN (Gg)3’ mungbean varieties were evaluated. Furthermore, the effect of these changes on yield under salinity was also evaluated. Materials and MethodsMaterials and MethodsMaterials and MethodsMaterials and Methods Plant materials, growth, and salt treatment Certified and disease-free seeds of mungbean [Vigna radiata (L.) R. Wilczec] varieties ‘PKU-AKM 12- 28’ and ‘VBN (Gg)3’ were procured from Pulses Research Unit, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, and National Pulses Research Centre, Tamil Nadu Agricultural University, respectively. Plants were grown in the Botanical Garden of the Modern College of Arts, Science, and Commerce, Shivajinagar, Pune-5. The potting mixture was prepared from the sandy clay loam soil collected from Charholi village in Pune district (MS). Plants were grown in non-perforated 35 cm × 20 cm plastic pots. Each pot contained a 15 kg soil and farmyard manure in a 3:1 ratio. Fifteen seeds were sown in each pot. Thinning of plants was done fifteen days after sowing to maintain six plants per pot. The salt stress was given to fifteen-day seedlings through Hoagland nutrient medium (Hoagland and Arnon, 1950) containing 0.75, 100, and 125 mM NaCl (equivalent to 0.3, 7, 8, and 9 dsm-2 EC, respectively). To maintain the desired EC of the potting mixture, 300 ml respective salt solution was added to each pot on every alternate day until the experiments were concluded. Each treatment was replicated in three pots. The following data was collected/analyses were performed on two plants per pot on the 15th, 30th, and 45th day after the salt stress treatments began. Morpho-physiological growth analysis The data was collected on the plant morphological parameters that included shoot length (SL), root length (RL), the number of secondary branches (SB), shoot fresh weight (SFW), root fresh weight (RFW), shoot dry weight (SDW), root dry weight (RDW), shoot tissue water content (STWC%), root tissue water content (RTWC%), and leaf area (LA) (Shelke et al., 2017). Photosynthetic pigments analysis Chlorophyll content was estimated by Arnon’s (1949) method. The carotenoid content was estimated by Maclachlan and Zalik’s (1963) method. The anthocyanins were estimated by Mancinelli’s (1984) method. Nodulation parameters analysis After harvesting the pods, plants were uprooted. The number of nodules per plant (NN), size of the nodule (NS), and fresh weight per nodule (NW) were measured (Elahi et al., 2004). Yield parameters analysis After completion of the plant’s life cycle, plant pod characteristics such as the number of pods per plant (NP), fresh weight per pod (FWP), and seed characteristics such as the number of seeds per pod (NSP) and fresh weight per 1000 seeds (FW-1000) were measured (Ahmed, 2009). Mankar GD et al. (2023). Not Sci Biol 15(2):11365 4 Statistical analyses All the experiments were performed with three replicates in a completely randomized block design (CRD). The data were presented as a mean ± standard deviation (Table 1a-1d). For multivariate modelling, the morpho-physiological dataset consisted of 12 variables, photosynthetic datasets consisted of 5 variables, the nodulation dataset consisted of 3 variables, and the yield dataset consisted of 4 variables. These data sets were subjected to multivariate modelling through principal component analysis (PCA) in the PAST statistical package (Hammer et al., 2001). The discriminant analysis (DA) was performed in Statistica V 10.0 software using the standard, forward stepwise and, backward stepwise modes (Sinha et al., 2009a). The correlations between morpho-physiological and photosynthetic, nodulation, and yield parameters of the NaCl stressed plants at different salt stress levels and durations were determined using Pearson’s correlation method in SPPS V 20 software (Chunthaburee et al., 2015; Shelke et al., 2017). Results Results Results Results Effect of salt stress on morpho-physiological parameters The variations in the morpho-physiological parameters in ‘PKU-AKM 12-28’ and ‘VBN (Gg)3’ at different concentrations and exposure of NaCl stress are presented in Table 1a. The analysis of variance (ANOVA) showed a differential response of mungbean varieties to NaCl. At 15 DPT (day post salt treatment), increase in salt concentration significantly reduced plants SL, RL, LA, SB, SFW, RFW, RDW, SDW, STWC and RTWC. Noteworthy similar trends were observed for 30 and 45 DPT. At 15 DPT in ‘VBN(Gg)3’ SL (18%, 22% and 25%), RL (14%, 18% and 30%), LA (33%, 42% and 49%), SB (0%, 5% and 33%), SFW (2%, 23% and 31%), RFW (11%, 40% and 66%), SDW (42%, 56% and 81%), RDW (27%, 40% and 61%), STWC (11%, 12% and 21%), and RTWC (8%, 0% and 9%), were significantly reduced under 75, 100 and 125 mM NaCl respectively compared to control. While, at 30 DPT SL (11%, 23%, and 27%), RL (11%, 14%, and 40%), LA (17%, 20% and 32%), SB (0%, 13% and 13%), SFW (24%, 25% and 41%), RFW (20%, 33% and 44%), SDW (12%, 58% and 67%), RDW (27%, 43% and 61%), STWC (5%, 13% and 13%), and RTWC (3%, 5% and 8%) were decreased under 75, 100 and 125 mM NaCl respectively. Further 45 DPT, the salt’s effect was more pronounced resulting in reduced SL (22%, 28%, and 37%), RL (11%, 17%, and 39%), LA (39%, 33% and 52%), SB (23%, 28% and 37%), SFW (12%, 27% and 32%), RFW (23%, 32% and 42%), SDW (39%, 62% and 73%) RDW (19%, 71% and 72%), STWC (21%, 32% and 40%), and RTWC (5%, 46% and 42%) under 75, 100 and 125 mM NaCl respectively. Effect of salt stress on photosynthetic parameters Photosynthetic pigment content was affected in both the varieties with increasing salinity and exposure duration (Table 1b). At 15 DPT in ‘VBN(Gg)3’, Chl ‘a’ (26%, 28% and 45%), Chl ‘b’ (33%, 40% and 30%), TChl (28%, 32% and 40%), TCC (56%, 35% and 42%), TAC (2%, 6% and 9%), was significantly reduced under 75, 100 and 125 mM NaCl respectively compared to control. While, at 30 DPT even more reduction in Chl ‘a’ (24%, 52% and 60%), Chl ‘b’ (25%, 44% and 64%), TChl (24%, 49% and 59%), TCC (26%, 35% and 57%), TAC (25%, 36% and 41%) was observed under 75, 100 and 125 mM NaCl. At 45 DPT, the salt’s effect was more pronounced and profound reduction in Chl ‘a’ (49%, 72% and 74%), Chl ‘b’ (55%, 69% and 79%), TChl (51%, 71% and 75%), TCC (57%, 69% and 89%), TAC (44%, 59% and 69%) was observed under 75, 100 and 125 mM NaCl. Effect of salt stress on nodulation parameters Increasing salinity and exposure duration also affected nodulation parameters in both varieties (Table 1c). At 15 DPT in ‘VBN(Gg)3’ NN (52%, 76% and 100%), NS (20%, 37% and 100%), NW (46%, 61% and Mankar GD et al. (2023). Not Sci Biol 15(2):11365 5 100%) was reduced significantly under 75, 100 and 125 mM NaCl respectively compared to control. While, at 30 DPT, further reduction in NN (28%, 48% and 66%), NS (36%, 58% and 100%), NW (49%, 59% and 100%) was observed. After 45 DPT, the effect was more pronounced and severe reduction in NN (43%, 64% and 87%), NS (20%, 51% and 65%), NW (30%, 47% and 70%) was observed under 75, 100 and 125 mM NaCl stress. However, both genotypes differed significantly for nodulation parameters under 75, 100 and 125 mM NaCl at 15, 30 and 45 DPT compared with their controls. The ‘PKU-AKM 12-28’ was less harmed for these parameters than ‘VBN(Gg)3’ at all salt concentrations and exposure durations. Thus, though both genotypes differed significantly in their responses on morpho-physiological, photosynthetic, and nodulation parameters at all salt concentrations and exposure durations, these parameters were less affected in ‘PKU-AKM 12-28’ than ‘VBN(Gg)3’. Mankar GD et al. (2023). Not Sci Biol 15(2):11365 6 Table 1a.Table 1a.Table 1a.Table 1a. Effect of NaCl stress on morpho-physiological traits in Vigna radiata varieties ‘PKU AKM 12-28’ and ‘VBN (Gg)3’ Variety NaCl (mM) Exposure duration (days) Coding Shoot length (cm) Root length (cm) Number of Secondary branches Leaf area (cm2) Shoot fresh Weight (gm) Root fresh Weight (gm) Shoot dry Weight (gm) Root dry Weight (gm) Shoot tissue water content (%) Root tissue water content (%) PKU AKM 12-28 0 15 PC0E1 34.60 ± 2.28 23.10 ± 2.61 4.00 ± 0.00 33.94 ± 4.66 6.79 ± 0.59 0.414 ±0.050 1.419 ± 0.126 0.158 ± 0.019 79.09 ± 0.93 61.11 ± 8.80 30 PC0E2 37.20 ± 1.56 28.12 ± 2.37 5.20 ± 1.10 40.34 ± 9.95 8.59 ± 0.88 1.104 ±0.229 2.111 ± 0.202 0.250 ± 0.032 75.08 ± 4.72 76.67 ± 5.45 45 PC0E3 43.93 ± 2.11 31.40 ± 3.86 7.20 ± 0.45 62.34 ± 4.06 9.61 ± 0.92 1.689 ± 0.262 3.650 ± 0.243 0.710 ± 0.027 61.83 ± 3.34 57.33 ± 5.15 75 15 PC1E1 34.76 ± 2.93 28.82 ± 2.00 4.00 ± 0.00 28.97 ± 6.20 6.42 ± 0.89 0.733 ± 0.045 1.287 ± 0.121 0.179 ± 0.021 79.90 ± 2.83 75.52 ± 2.84 30 PC1E2 36.92 ± 2.40 32.16 ± 1.26 4.80 ± 0.45 42.87 ± 6.95 9.26 ± 1.03 1.496 ± 0.153 2.101 ± 0.221 0.430 ± 0.013 77.10 ± 3.49 71.02 ± 3.00 45 PC1E3 41.22 ± 1.26 34.26 ± 2.06 6.40 ± 0.55 49.45 ± 3.06 9.48 ± 0.76 1.787 ± 0.254 3.151 ± 0.156 0.957 ± 0.029 66.59 ± 3.17 45.54 ± 8.41 100 15 PC2E1 33.28 ± 3.25 18.72 ± 2.72 4.40 ± 0.55 28.35 ± 6.48 5.57 ± 1.15 0.486 ± 0.994 1.147 ± 0.292 0.146 ± 0.018 78.14 ± 9.14 69.53 ± 5.96 30 PC2E2 36.54 ± 1.40 26.72 ± 1.16 4.40 ± 0.55 36.06 ± 1.92 7.74 ± 1.77 0.994 ± 0.197 1.183 ±0.197 0.309 ± 0.011 84.24 ± 3.62 68.01 ± 6.25 45 PC2E3 39.06 ± 1.39 27.26 ± 1.47 5.20 ± 0.45 42.24 ± 3.68 8.51 ± 0.87 1.527 ± 0.271 3.064 ± 0.137 0.545 ± 0.025 63.67 ± 4.49 63.14 ± 8.18 125 15 PC3E1 31.32 ± 0.31 19.90 ± 3.63 3.75 ± 0.50 23.71 ± 2.82 4.49 ± 0.78 0.324 ± 0.089 0.995 ± 0.189 0.137 ± 0.035 77.27 ± 6.08 54.42 ±19.73 30 PC3E2 35.66 ± 1.59 21.78 ± 1.20 4.20 ± 0.45 32.11 ± 1.96 6.40 ± 1.20 0.756 ± 0.120 1.295 ± 0.119 0.154 ± 0.012 79.17 ± 4.38 79.12 ± 4.73 45 PC3E3 35.54 ± 1.09 23.26 ± 2.98 5.40 ± 0.55 37.49 ± 1.76 7.69 ± 0.53 1.287 ± 0.173 1.607 ± 0.278 0.274 ± 0.026 78.90 ± 4.75 78.38 ± 3.57 VBN (Gg)3 0 15 VC0E1 30.54 ± 1.96 21.20 ± 2.63 3.60 ± 0.55 31.18 ± 2.12 5.80 ± 1.08 0.443 ± 0.061 1.296 ± 0.146 0.146 ± 0.018 77.30 ± 2.76 66.91 ± 3.50 30 VC0E2 34.40 ± 2.90 26.16 ± 1.07 4.60 ± 0.55 37.00 ± 3.56 8.73 ± 0.85 0.969 ± 0.100 1.980 ± 0.178 0.234 ± 0.017 77.02 ± 4.18 75.62 ± 3.61 45 VC0E3 43.92 ± 1.65 28.90 ± 2.14 7.00 ± 0.00 54.99 ± 4.38 9.39 ± 1.72 1.560 ± 0.110 3.554 ± 0.237 0.695 ± 0.019 60.68 ±10.46 55.23 ± 4.07 75 15 VC1E1 24.88 ± 1.04 18.20 ± 1.80 3.60 ± 0.55 21.01 ± 5.61 5.64 ± 1.38 0.394 ± 0.056 0.746 ± 0.061 0.107 ± 0.011 86.14 ± 3.53 72.64 ± 3.05 30 VC1E2 30.54 ± 2.59 23.20 ± 4.82 4.60 ± 0.55 30.65 ± 3.40 6.62 ± 1.03 0.774 ± 0.101 1.747 ± 0.160 0.169 ± 0.014 72.97 ± 5.88 77.92 ± 2.24 45 VC1E3 34.26 ± 0.89 25.64 ± 2.27 5.40 ± 0.55 33.29 ± 2.00 8.25 ± 0.59 1.207 ± 0.191 2.176 ± 0.312 0.562 ± 0.035 73.56 ± 4.04 52.32 ± 9.60 100 15 VC2E1 23.99 ± 1.07 17.30 ± 2.10 3.40 ± 0.55 17.95 ± 1.78 4.46 ± 0.83 0.264 ± 0.035 0.572 ± 0.041 0.087 ± 0.008 86.68 ± 3.51 66.92 ± 3.00 30 VC2E2 26.62 ± 1.61 22.48 ± 2.57 4.00 ± 0.00 29.69 ± 3.20 6.52 ± 0.62 0.644 ± 0.075 0.834 ± 0.071 0.132 ± 0.012 87.07 ± 1.86 79.16 ± 3.41 45 VC2E3 31.72 ± 1.10 23.90 ± 1.70 5.00 ± 0.00 36.92 ± 2.22 6.85 ± 0.92 1.053 ± 0.148 1.339 ± 0.148 0.199 ± 0.018 80.09 ± 3.89 80.86 ± 2.95 125 15 VC3E1 22.94 ± 1.26 14.88 ± 1.54 2.40 ± 0.55 15.89 ± 2.62 3.98 ± 0.82 0.148 ± 0.026 0.240 ± 0.044 0.057 ± 0.013 93.79 ± 1.43 60.58 ± 12.78 30 VC3E2 25.18 ± 1.31 15.56 ± 0.99 4.00 ± 0.00 25.28 ± 2.66 5.18 ± 0.71 0.543 ± 0.120 0.647 ± 0.094 0.092 ± 0.012 87.39 ± 1.86 82.11 ± 5.96 45 VC3E3 27.56 ± 1.34 17.66 ± 2.49 4.40 ± 0.55 26.27 ± 3.19 6.38 ± 0.59 0.899 ± 0.177 0.948 ± 0.111 0.191 ± 0.021 85.12± 1.10 78.31 ± 3.76 Mankar GD et al. (2023). Not Sci Biol 15(2):11365 7 Table 1b.Table 1b.Table 1b.Table 1b. Effects of NaCl stress on photosynthetic pigments in Vigna radiata varieties ‘PKU AKM 12-28’ and ‘VBN (Gg)3’ The data represented mean ± standard deviation Variety NaCl (mM) Exposure Duration (days) Coding Chlorophyll ‘a’ content (µg/gm FW) Chlorophyll ‘b’ content (µg/gm FW) Total Chlorophyll content (µg/gm FW) Total caroteonoid content (µg/gm FW) Total anthocyanine content (µg/gm FW) PKU AKM 12-28 0 15 PC0E1 25.62 ± 0.38 13.19 ± 0.32 39.43 ± 0.53 9.67 ± 0.28 0.479 ± 0.011 30 PC0E2 32.70 ± 0.61 17.02 ± 0.47 50.51 ± 1.10 13.12 ± 0.33 0.843 ± 0.056 45 PC0E3 36.98 ± 2.62 15.99 ± 2.43 53.85 ± 4.91 20.77 ± 0.34 1.383 ± 0.046 75 15 PC1E1 25.54 ± 0.69 13.20 ± 0.83 39.36 ± 1.54 9.15 ± 0.42 0.568 ± 0.010 30 PC1E2 28.73 ± 0.61 14.35 ± 1.00 43.76 ± 1.62 12.56 ± 0.44 0.635 ± 0.037 45 PC1E3 25.06 ± 0.64 11.51 ± 1.08 37.17 ± 1.74 14.52 ± 0.34 0.980 ± 0.065 100 15 PC2E1 26.53 ± 0.73 14.08 ± 0.27 41.25 ± 1.01 7.59 ± 0.37 0.424 ± 0.010 30 PC2E2 26.56 ± 0.69 14.24 ± 0.63 41.45 ± 1.15 11.95 ± 0.50 0.732 ± 0.041 45 PC2E3 19.33 ± 0.72 9.94 ± 0.63 29.74 ± 1.26 9.76 ± 0.41 0.676 ± 0.035 125 15 PC3E1 19.93 ± 0.30 9.11 ± 0.96 29.51 ± 1.20 7.27 ± 0.39 0.458 ± 0.028 30 PC3E2 24.86 ± 0.47 13.87 ± 0.50 39.32 ± 0.82 7.66 ± 0.43 0.660 ± 0.045 45 PC3E3 17.68 ± 0.46 8.19 ± 1.19 26.29 ± 1.64 6.72 ± 0.35 0.738 ± 0.027 VBN (Gg)3 0 15 VC0E1 24.25 ± 0.35 12.97 ± 0.74 37.80 ± 1.03 9.43 ± 0.49 0.464 ± 0.036 30 VC0E2 29.41 ± 0.64 16.92 ± 0.63 47.04 ± 1.08 11.76 ± 0.41 0.781 ± 0.039 45 VC0E3 36.08 ± 1.58 15.64 ± 1.24 52.59 ± 2.86 18.77 ± 0.57 1.143 ± 0.035 75 15 VC1E1 18.03 ± 0.70 8.67 ± 0.90 27.13 ± 1.61 4.14 ± 0.25 0.474 ± 0.016 30 VC1E2 22.32 ± 0.57 12.72 ± 1.37 35.58 ± 1.95 8.69 ± 0.57 0.585 ± 0.026 45 VC1E3 18.27 ± 0.74 6.98 ± 0.88 25.68 ± 1.64 8.02 ± 0.50 0.641 ± 0.049 100 15 VC2E1 17.55 ± 0.32 7.71 ± 0.70 25.68 ± 1.03 6.08 ± 0.51 0.490 ± 0.036 30 VC2E2 14.00 ± 0.19 9.41 ± 0.72 23.75 ± 0.91 7.68 ± 0.25 0.495 ± 0.031 45 VC2E3 10.09 ± 0.72 4.78 ± 0.54 15.12 ± 1.13 5.71 ± 0.36 0.466 ± 0.026 125 15 VC3E1 13.28 ± 0.98 9.04 ± 2.42 22.64 ± 1.77 5.49 ± 0.22 0.421 ± 0.026 30 VC3E2 12.96 ± 0.40 6.00 ± 0.64 19.28 ± 0.40 5.04 ± 0.30 0.463 ± 0.019 45 VC3E3 9.46 ± 0.46 3.26 ± 0.41 12.94 ± 0.74 2.04 ± 1.45 0.356 ± 0.020 Mankar GD et al. (2023). Not Sci Biol 15(2):11365 8 Table 1c.Table 1c.Table 1c.Table 1c. Effect of NaCl stress on nodulation traits in Vigna radiata varieties ‘PKU AKM 12-28’ and ‘VBN (Gg)3’ The data represented mean ± standard deviation Table 1d.Table 1d.Table 1d.Table 1d. Effect of NaCl stress on pod and seed traits in Vigna radiata varieties ‘PKU AKM 12-28’ and ‘VBN (Gg)3’ at 45 days after salt treatment The data represented mean ± standard deviation Variety NaCl (mM) Exposure Duration (days) Codings Number of nodules/ Plant Size of nodule (mm) Fresh weight / Nodule (mg) PKU AKM 12-28 0 15 PC0E1 11.00 ± 1.22 2.65 ± 0.32 12.41 ± 1.84 30 PC0E2 14.20 ± 0.84 3.11± 0.54 15.79 ± 0.94 45 PC0E3 25.80 ± 1.30 5.28 ± 0.18 18.24 ± 0.72 75 15 PC1E1 11.00 ± 2.45 2.22 ± 0.25 10.94 ± 1.63 30 PC1E2 12.00 ± 2.24 2.94 ± 0.33 13.22 ± 0.99 45 PC1E3 18.60 ± 2.07 4.34 ± 0.25 16.29 ± 0.89 100 15 PC2E1 9.40 ± 1.82 1.53 ± 0.28 7.40 ± 0.71 30 PC2E2 11.60 ± 2.07 2.68 ± 0.05 10.91 ± 0.57 45 PC2E3 15.80 ± 0.84 3.53 ± 0.28 14.69 ± 1.14 125 15 PC3E1 5.40 ± 0.55 1.23 ± 0.29 6.15 ± 0.16 30 PC3E2 11.00 ± 1.58 1.85 ± 0.27 8.67 ± 0.46 45 PC3E3 12.20 ± 0.84 2.62 ± 0.34 11.71 ± 1.29 VBN (Gg)3 0 15 VC0E1 9.20 ± 0.45 2.17 ± 0.18 13.30 ± 1.93 30 VC0E2 11.20 ± 1.30 2.79 ± 0.20 14.09 ± 0.81 45 VC0E3 22.80 ± 0.84 4.89 ± 0.35 17.82 ± 0.72 75 15 VC1E1 4.40 ± 0.55 1.73 ± 0.14 7.11 ± 0.77 30 VC1E2 8.00 ± 0.71 1.78 ± 0.26 7.16 ± 1.30 45 VC1E3 13.00 ± 0.71 3.91 ± 0.44 12.53 ± 0.78 100 15 VC2E1 2.20 ± 0.45 1.37 ± 0.10 5.24 ± 0.56 30 VC2E2 5.80 ± 1.30 1.16 ± 0.29 5.71 ± 0.97 45 VC2E3 8.20 ± 0.84 2.38 ± 0.60 9.44 ± 0.71 125 15 VC3E1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 30 VC3E2 3.80 ± 0.45 0.00 ± 0.00 0.00 ± 0.00 45 VC3E3 3.00 ± 1.00 1.72 ± 0.21 5.25 ± 0.76 Variety NaCl (mM) Coding Number of pods/Plant Fresh weight /Pod (gm) Number of seeds/Pod Fresh weight of 1000 seeds (gm) PKU AKM 12-28 0 PC1 4.00±0.71 1.21±0.24 11.20±0.84 58.83±7.73 75 PC2 3.20±0.45 1.16±0.10 10.80±0.45 58.60±10.33 100 PC3 2.80±0.45 0.70±0.06 11.20±0.84 59.02±6.35 125 PC4 2.00±0.00 0.41±0.17 7.00±1.58 50.67±7.69 VBN (Gg)3 0 VC1 3.80±0.45 0.97±0.07 5.62±2.00 52.12±8.21 75 VC2 2.40±0.55 0.44±0.13 6.02±0.55 37.80±6.39 100 VC3 1.40±0.55 0.28±0.02 4.29±1.00 31.23±4.88 125 VC4 1.20±0.45 0.04±0.01 0.00±0.00 0.00±0.00 Mankar GD et al. (2023). Not Sci Biol 15(2):11365 9 Effect of salt stress on yield parameters In ‘PKU-AKM 12-28’ at 45 DPT of 75, 100, and 125 mM NaCl stress (Table 1d), NP was reduced by 20, 30, and 50% respectively, whereas in ‘VBN (Gg)3’, these reductions were by 36.84, 63.16, and 68.42% respectively. At 75 mM NaCl, FWP was drastically decreased in VBN (Gg)3 by 54.34%. As the salt concentration increased to 125 mM NaCl, FWP was reduced by 65.72% and 95.46% in ‘PKU-AKM 12-28’ and ‘VBN (Gg)3’, respectively. Seed parameters such as NSP and FW-1000 were affected differently in these two mungbean varieties under salinity. At 75 mM NaCl, SPP and SW-1000 were decreased significantly in ‘VBN (Gg)3’ (16 and 22.47% respectively). As the salt concentration increased to 125 mM NaCl, SPP, and SW-1000 were decreased by 37.50% and 13.86%, respectively in ‘PKU-AKM 12-28’. Moreover, seed formation did not occur in ‘VBN (Gg)3’ at 125 mM NaCl. Thus, ‘PKU-AKM 12-28’ was less affected by salt stress than ‘VBN(Gg)3’. Principal component analysis PCA was performed on the normalized dataset to evaluate: a) the plant-NaCl interactions, b) differences in responses on parameters under NaCl stress levels, and c) dependence among variables and the factors that inSuence them. PCA of the entire morphophysiological data set (Table 1a) produced ten PCs, with the first two PCs explaining about 90.69% of the total variance in the morphophysiological data set. The loadings and scores of the Frst two PCs (PC1 vs. PC2) are presented in Figure 1. The Frst two PCs represent maximum variance and reSect the main groupings in the data set. PC1 accounting for 80.87% of the total variance was positively correlated (loading > 90%) with SL, RL, SB, LA, SFW, RFW, SDW, and RDW. PC2 accounting for 9.82% of the total variance was positively correlated (loading > 86%) with RTWC. The scores show a visible differentiation between the tissue responses at different salinity levels and exposure time in both varieties. Scores of the PC1 in PKU-AKM 12-28 were higher than those of ‘VBN(Gg)3’. The score in PC1 was highest in ‘PKU-AKM 12-28’ for 75 mM NaCl exposure for 45 days. On the contrary, it was least in ‘VBN (Gg)3’ exposed to all NaCl concentrations for 45 days. It indicates ‘PKU-AKM 12-28’ has a higher tolerance level than ‘VBN (Gg)3’ under salinity. Thus, differences in changes in the morphological variables under all salt concentrations and exposure durations suggest intrinsic differences in two mungbean varieties. Figure 1.Figure 1.Figure 1.Figure 1. PCA scores and loadings of the Frst two PCs obtained from the morpho-physiological dataset of mungbean varieties Mankar GD et al. (2023). Not Sci Biol 15(2):11365 10 PCA of the photosynthetic dataset (Table 1b) gave five PCs, with the first two PCs explaining 98.03% of the total variance. Figure 2 shows the photosynthetic data sets' loadings and scores of the Frst two PCs (PC1 vs. PC2). The PC1 (86.76% variance) showed high positive loadings (>90%) on Chl-a, Chl-b, TChl, and TCC and positive loadings (>83%) on TAC. On the other hand, the second PC accounted for 11.27% of the total variance. The corresponding scores in the plot, along with the loadings, suggest that at all NaCl concentrations for a short exposure (E1, 15 days), Chl-a, Chl-b, TChl, and TCC were affected and reduced by a greater extent in ‘VBN (Gg)3’ as compared to ‘PKU-AKM 12-28’. However, both varieties showed increased TAC at 75 mM NaCl and decreased at 125 mM NaCl. Moderate exposure (E2, 30 days) brought significant changes in Chl-a, Chl-b, TChl, and TCC. These parameters were more affected in ‘VBN (Gg)3’ than ‘PKU-AKM 12-28’. Likewise, the TChl was more affected in ‘VBN (Gg)3’ at 125 mM NaCl at moderate exposure (E2). Exposure for a longer duration (E3, 45 days) and higher salt concentration was associated with a remarkable reduction in all photosynthetic parameters in ‘VBN (Gg)3’ than ‘PKU-AKM 12-28’. Hence, scores of ‘VBN (Gg)3’ (VC1E3, VC2E3, VC3E3) lay more on the negative side as compared to scores of ‘PKU-AKM 12-28’ (PC1E3, PC2E3, PC3E3) for PC1. Moreover, the scores of ‘VBN (Gg)3’ were more negative compared to ‘PKU-AKM 12-28’ at all salt concentrations and exposure durations. It indicates that ‘PKU-AKM 12-28’ has a higher salinity tolerance than ‘VBN (Gg)3’. Figure 2.Figure 2.Figure 2.Figure 2. PCA scores and loadings of the Frst two PCs obtained from the photosynthetic dataset of mungbean varieties PCA of nodulation dataset (Table 1c) yielded three PCs, with the first two PCs explaining 98.26% of the total variance. The loadings and scores of the Frst two PCs (PC1 vs. PC2) of the nodulation data set are given in (Figure 3). It may be noted that PC1 (95.11% variance) showed high positive loadings (>97%) on all nodulation parameters studied in the present investigation. On the other hand, the second PC accounted for 3.15% of the total variance. The corresponding scores in combination with the loadings suggested that at short exposure (E1), at 75 mM NaCl, NN and NW was drastically decreased in ‘VBN (Gg)3’ compared to ‘PKU- AKM 12-28’. Moreover, at 125 mM, nodules were not formed in ‘VBN (Gg)3’ at a short exposure period. Hence, scores of ‘VBN (Gg)3’ (VC1E1, VC2E1, VC3E1) lie more on the negative side compared to scores of ‘PKU-AKM 12-28’ (PC1E1, PC2E1, PC3E1) for PC1. However, the moderate exposure (E2) brought significant changes in NN, NS, and NW. The NS and NW were negligible in ‘VBN (Gg)3’ compared to ‘PKU- AKM 12-28’. Exposure to higher duration (E3) and higher salt concentration was associated with remarkable changes in NN, NS, and NW, which was more reduced in ‘VBN (Gg)3’ than ‘PKU-AKM 12-28’. Thus, ‘PKU- Mankar GD et al. (2023). Not Sci Biol 15(2):11365 11 AKM 12-28’ showed a higher tolerance level than VBN (Gg)3 under saline conditions, resulting in better nodulation. Figure 3.Figure 3.Figure 3.Figure 3. PCA scores and loadings of the Frst two PCs obtained for the nodulation dataset of mungbean varieties At 45 days exposure time (E3), PCA was performed on the normalized yield dataset (Table 1d) that gave four PCs, with the first two PCs explaining about 97.01% of the total variance in the yield data set. The loadings and scores of the Frst two PCs (PC1 vs. PC2) of yield data set given in (Figure 4) The PC1 (87.48%) showed high positive loadings (>91%) on all yield parameters studied in the present investigation. On the other hand, the second PC accounted for 9.53% of the total variance. The corresponding scores in combination with the loadings suggested that at more prolonged exposure (E3), salinity influenced all yield parameters as salt concentration increased. However, at 75 and 100 mM NaCl, all yield parameters were significantly reduced in ‘VBN (Gg)3’ than ‘PKU-AKM 12-28’. Hence, the scores of (VC3 and VC2) and (PC2 and PC3) are present at negative and positive quadrant with respect to PC1. In contrast, a higher salt concentration (125 mM) was associated with a remarkable reduction in all yield parameters in ‘VBN (Gg)3’. Hence, the score (VC4) is present in the negative quadrant with respect to PC1 and PC2. Thus, for yield output, ‘PKU-AKM 12-28’ has a higher salinity tolerance than ‘VBN (Gg)3’. Mankar GD et al. (2023). Not Sci Biol 15(2):11365 12 Figure 4.Figure 4.Figure 4.Figure 4. PCA scores and loadings of the Frst two PCs obtained for the yield dataset of mungbean varieties Discriminant analysis Variations in morpho-physiological, photosynthetic, nodulation, and yield parameters at different NaCl concentrations in mungbean varieties were further investigated through discriminant analysis (DA). Discriminant analysis at different NaCl concentrations The category variables (Y) were four concentrations of NaCl to which mungbean varieties were exposed. Standard, forward and backward stepwise modes of DA were performed, and the discriminant functions (DFs) (Table 2a) and classification matrices were obtained (Table S1a). For morphophysiological parameters, the standard and forward stepwise DA constructed DFs that included all ten parameters and six parameters, respectively, and rendered the corresponding CMs assigning 91.66% and 83.83% cases correctly. DA with forward stepwise mode includes variables obtained from more significant to insignificant changes. In contrast, backward stepwise mode removes the beginning with less significant changes until no considerable changes are observed. Forward stepwise DA showed RL, LA, RFW, SFW, SDW, and SL. Thus, the DA results suggest that RL, LA, RFW, SFW, SDW, and SL are the most signiFcant parameters to differentiate among the four sets of the plant responses observed at the four NaCl-induced stress levels. Both CA and DA identified morphological parameters that significantly changed under different salt stress levels. The box and whisker plots of selected parameters (forward step mode) showing different responses identified by DA are presented in (Figure S1a). All the chosen parameters showed observable variations in morpho-physiological characters at different NaCl concentrations. The RL and RFW showed a similar variation pattern that increased at 75 mM NaCl and again decreased at 100 and 125 mM NaCl. LA, SFW, SDW, and SL also showed a similar pattern of variation. For photosynthetic parameters, the standard and forward stepwise DA modes constructed DFs (Table 2b) that included all five and only one parameters, respectively, and rendered the corresponding CMs (Table S1b), assigning 62.50% and 50% cases correctly. These results suggest that Chla is a signiFcant parameter to discriminate the four sets of the plant responses corresponding to the four NaCl concentrations. Figure S1b shows box and whisker plots of the selected parameters showing responses at different NaCl concentrations identified by DA. Chl ‘a’ showed significant variation under different salinity levels. For nodulation parameters, the standard and forward stepwise DA modes constructed DFs (Table 2c), including all three, and one parameter, respectively, and rendered the corresponding CMs (Table S1c), assigning 50% and 58.33% cases correctly. Thus, the DA results suggest that the NW variable is a signiFcant parameter to discriminate between the four sets of the plant responses corresponding to different NaCl concentrations. As identified by DA, box and whisker plots of selected parameters showing different responses Mankar GD et al. (2023). Not Sci Biol 15(2):11365 13 to NaCl concentration are given in (Figure S1c). NW showed variations in nodulation under salinity at different NaCl concentrations. For yield parameters, the standard and forward stepwise DA modes constructed DFs (Table 2d), including all four, and two parameters, respectively, and rendered the corresponding CMs (Table S1d), assigning 87.50% and 75% cases correctly. Thus, the DA results suggest that NP and NSP variables are signiFcant parameters to discriminate between the four sets of the plant responses corresponding to different NaCl concentrations. As identified by DA, box and whisker plots of selected parameters showing different NaCl concentration responses are presented in (Figure S1d). NP and NSP showed variations in nodulation changes under salinity at different NaCl concentrations. Table 2aTable 2aTable 2aTable 2a. ClassiFcation functions for discriminant analysis (DA) of NaCl stress levels and morpho- physiological parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficient a C0 C1 C2 C3 SL 24.09 24.31 25.47 26.84 RL 4.06 5.25 3.38 1.98 SB 95.04 95.70 92.42 90.80 LA -11.30 -12.90 -12.29 -12.48 SFW -75.71 -82.88 -87.28 -93.62 RFW -363.18 -325.42 -296.43 -261.47 SDW 711.44 692.20 690.40 691.55 RDW -223.60 -168.99 -196.43 -211.50 STWC 54.47 53.54 53.99 54.67 RTWC 4.27 4.68 4.44 4.32 Constant -2902.09 -2804.36 -2804.61 -2837.42 Forward DA mode RL 1.051 1.817 0.554 -0.429 LA 2.537 1.136 1.429 1.125 RFW -198.965 -144.388 -128.863 -100.970 SFW 40.750 31.071 28.189 23.523 SDW 19.767 13.535 5.836 -0.906 SL -0.580 -0.062 0.811 1.788 Constant -146.082 -98.446 -87.125 -72.126 Backward DA mode No variables in the model a Discriminant function coefficient for different concentrations of NaCl Table 2b.Table 2b.Table 2b.Table 2b. ClassiFcation functions for discriminant analysis (DA) of NaCl stress levels and photosynthetic parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficient a C0 C1 C2 C3 Chl-a -156.233 -25.4380 -223.567 44.4849 Chl-b -153.414 -25.3860 -218.584 43.5800 Chl-T 153.607 25.6656 218.931 -42.9237 TCC -1.034 -1.4601 -1.180 -2.1963 TAC 14.550 18.6754 16.735 28.7089 Constant -18.310 -11.7295 -9.381 -9.6374 Forward DA mode Mankar GD et al. (2023). Not Sci Biol 15(2):11365 14 Chl-a 1.0141 0.7560 0.62507 0.53799 Constant -17.0231 -10.0771 -7.32761 -5.78752 Backward DA mode No variables in the model aDiscriminant function coefficient for different concentrations of NaCl Table 2c.Table 2c.Table 2c.Table 2c. ClassiFcation functions for discriminant analysis (DA) of NaCl stress levels and nodulation parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficients a C0 C1 C2 C3 NN -0.2851 -0.47787 -0.29071 -0.02407 NS -2.2465 0.52571 -0.50152 -0.90021 NW 2.1207 1.30428 1.17356 0.67328 Constant -11.4343 -6.76885 -4.79501 -2.54175 Forward DA mode NW 1.1444 0.83974 0.66667 0.39683 Constant -10.1268 -6.09233 -4.35242 -2.43724 Backward DA mode No variables in the model aDiscriminant function coefficient for different concentrations of NaCl Table 2d.Table 2d.Table 2d.Table 2d. ClassiFcation functions for discriminant analysis (DA) of NaCl stress levels and yield parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficients a C0 C1 C2 C3 NP 41.9003 26.3729 18.4692 19.5117 FWP -6.7078 -8.3712 -14.2649 -13.9229 NSP -4.0862 -2.0917 -0.9653 -1.2877 FWS-1000 -0.1388 -0.0944 -0.0283 -0.0428 Constant -58.4046 -23.8890 -12.9079 -12.6333 Forward DA mode NP 38.9620 23.5019 14.9170 15.8907 NSP -4.7786 -2.7054 -1.5894 -1.9453 Constant -57.2681 -22.9129 -10.8943 -10.6946 Backward DA mode No variables in the model aDiscriminant function coefficient for different concentrations of NaCl Discriminant analysis at different salt exposure periods The effect of the duration of salt stress in the mungbean varieties was also analysed through discriminant analysis. The category variables (Y) were the three exposure durations (E1, E2, and E3). For morphophysiological parameters, the standard and forward stepwise DA modes constructed DFs (Table 3a) that included all ten parameters of standard DA mode, three parameters of forward DA mode, and two parameters backward DA mode and rendered the corresponding CMs assigning 100%, 95.83%, and 87.50% cases correctly (Table S2a). Backward stepwise DA showed that the two variables RL and RFW followed RFW, Mankar GD et al. (2023). Not Sci Biol 15(2):11365 15 RL and, RTWC in the forward stepwise DA. The box whisker plots of selected parameters showing responses at three durations of salt stress are given in Figure S2a. The RL and RFW showed observable variations in the plants exposed to NaCl stress for different durations. These results suggest higher salt stress tolerance in ‘PKU- AKM 12-28’ than ‘VBN (Gg)3’. For photosynthetic parameters, the standard, forward, and backward stepwise DA modes constructed DFs, including all 5, 3, and 2 parameters, respectively (Table 3b) and rendered the corresponding CMs (Table S2b), assigning 87.50%, 87.50%, and 79.16% cases correctly. Thus, the DA results revealed that TAC and Chl-T (Figure S2b) variables are the most critical parameters to distinguish the plant’s responses at different salt stress durations For nodulation parameters, DA modes constructed DFs, including all 3 and 2 parameters, respectively (Table 3c), and rendered the corresponding CMs (Table S2c), assigning 70.83% and 70.83% cases correctly. Thus, the DA results revealed that NS and NW (Figure S2c) variables are the most critical parameters to discriminate between the three sets of exposure duration. Table 3aTable 3aTable 3aTable 3a. ClassiFcation function for discriminant analysis (DA) of NaCl stress exposure duration and morpho-physiological parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficient a E1 E2 E3 SL 27.82 27.24 26.61 RL 14.59 13.26 11.85 SB 76.85 78.42 85.09 LA -16.73 -16.59 -16.69 SFW -106.03 -102.05 -103.13 RFW -412.56 -383.93 -336.05 SDW 679.07 668.86 663.07 RDW 60.77 66.44 39.23 STWC 53.60 53.08 52.76 RTWC 7.25 7.45 6.98 Constant -2896.19 -2863.60 -2803.71 Forward DA mode RFW -52.9685 -24.8113 7.9188 RL 4.7909 3.7403 2.007 RTWC 1.2531 1.4998 1.3714 Constant -80.3519 -92.8168 -76.9834 Backard DA mode RL 4.0180 2.8152 1.1612 RFW -60.4579 -33.7754 -0.2775 Constant -29.6968 -20.2488 -16.3136 aDiscriminant function coefficient for different Exposure time to NaCl Mankar GD et al. (2023). Not Sci Biol 15(2):11365 16 Table 3b.Table 3b.Table 3b.Table 3b. ClassiFcation function for discriminant analysis (DA) of NaCl stress exposure duration and photosynthetic parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficient a E1 E2 E3 TCC -2.0453 -2.150 -1.7515 TAC 15.5846 28.852 43.5428 Chl-T 77.6689 98.227 89.2248 Chl-b -76.3725 -95.990 -89.2785 Chl-a -79.1170 -100.831 -91.1556 Constant -8.8371 -11.723 -10.9816 Forward DA mode TAC -3.98361 8.32545 26.84189 Chl-b 1.07203 2.03613 -0.19708 Chl-a -0.05132 -0.75577 -0.21431 Constant -5.50456 -8.05605 -8.55061 Backward DA mode TAC -5.89820 3.58689 26.82064 Chl-T 0.33578 0.21606 -0.20508 Constant -5.22112 -6.32339 -8.55060 aDiscriminant function coefficient for different Exposure time to NaCl Table 3c.Table 3c.Table 3c.Table 3c. ClassiFcation function for discriminant analysis (DA) of NaCl stress exposure duration and nodultion parameters in the mungbean varieties under salinity Standard DA mode Linear discriminant functions for groups Coefficients a E1 E2 E3 NN -0.22221 0.03228 -0.27848 NS 0.18621 -0.12711 7.62617 NW 0.54314 0.42797 -0.75725 Constant -2.64157 -3.14643 -7.67020 Forward DA mode NS -0.68209 -0.00097 6.53798 NW 0.50589 0.43338 -0.80393 Constant -2.52641 -3.14400 -7.48934 Backward DA mode No variables in the model aDiscriminant function coefficient for different Exposure time to NaCl Multiple correlations Variations in the Morphophysiological, photosynthetic and nodulation parameters in mungbean varieties exposed to different levels and durations of NaCl stress were evaluated through Pearson’s correlation (Table S3a and Figure 5). Variation in SL was positively correlated with LA (r=0.91**), SFW (r=0.85**) and SDW (r=0.89**) while variation in RL was positively correlated with SB (r=0.75**), SFW (r=0.89**). The SB showed a positive correlation (r=0.90**) with LA, RFW, and SDW. LA was positively correlated with SFW (r=0.90**) and SDW (r=0.93**). Furthermore, SFW was positively correlated with RFW (r=0.93**). Very high positive correlations were observed among variations in Chl-a, Chl-b, and TChl. Variation in TCC was positively correlated with Chl-a (r=0.88**), Chl-b (r=0.76**) and TChl (r=0.85**). The variation in TAC was positively correlated (r=0.90**) with TCC. The Photosynthetic parameters significantly correlated with SL, Mankar GD et al. (2023). Not Sci Biol 15(2):11365 17 RL, SB, LA, SFW, RFW, SDW, and RDW. More than (r=0.90**) strong positive correlation was observed in variation of NN, NS, and NW under salinity. NN, NS, and NW were positively correlated with all morphological and photosynthetic parameters except RTWC and STWC, which are negatively correlated. Figure 5.Figure 5.Figure 5.Figure 5. Pearson’s correlation among variations in the morpho-physiological, photosynthetic and nodulation parameters in mungbean plants exposed to different concentrations NaCl for different durations. (Correlations significant at p<0.05 are boxed.) At 45 days (E3) exposure time (Table S3b and Figure 6), the variations in NP and FWP were correlated significantly positively with each other by more than (r=0.90**) under salt stress. While variation in NSP and SW-1000 was significantly positively correlated with each other by more than (r=0.90**) under salt stress. Variations in seed parameters NP and FWP were significantly positively correlated with seed parameters by more than 72 and 80% under salt stress, respectively. Moreover, more than (r=0.90**) positive correlation was observed in NN, NS, NW, NP and FWP. Variation in NSP showed positive correlation with variation in NN (r=0.75*), NS (r=0.68), NW (r=0.80*), NP (r=0.71*) and FWP (r=0.80*) under salinity. Moreover, variation in FW-1000 was positively correlated with NN (r=0.83**), NS (r=0.75*), NW (r=0.90**). Changes in all pod and seed parameters showed a significant positive correlation with changes in morphological, photosynthetic, and nodule parameters except RTWC and STWC, which were negatively correlated under salinity. Thus, a decrease in morpho-physiological, photosynthetic, and nodulation parameters directly affect yield parameters. Mankar GD et al. (2023). Not Sci Biol 15(2):11365 18 Figure 6. Figure 6. Figure 6. Figure 6. Pearson’s correlation among variations in the morpho-physiological, photosynthetic and nodulation and yield parameters in mungbean plants exposed to different concentrations NaCl at 45 days after salt treatment. Correlation is significant at p<0.05 are boxed. DiscussionDiscussionDiscussionDiscussion Soil salinity limits the plant’s growth and development through osmotic and ionic stress and reduces productivity (Arif et al., 2020). Mungbean [Vigna radiata (L.) R. Wilczek], one of India's economically important and significant dietary crop plants is susceptible to salt stress (Ghosh et al., 2015). Its production in the last decades was influenced mainly by its susceptibility to different biotic and abiotic stresses at various stages of growth, including the soil salinity (Sehrawat et al., 2015). The salinity equivalent to about 50 mM NaCl can reduce the yield by more than 60% (Abd-Alla et al., 1998). It is also suspected that increasing soil salinity will result in ∼50% loss of arable land by the mid-21st century (Hasanuzzaman et al., 2012). Soil salinity mainly alters various morphological, photosynthetic, nodulation, and yield parameters, the extent of which varies with severity and duration of stress, and ultimately limits crop productivity (Elahi et al., 2004; Munns 2005; Ahmed 2009; Shelke et al., 2017). Differential responses to salinity at the varietal level were observed for morphology, photosynthesis, nodulation, and yield (Rao et al., 2002; Singla and Garg, 2005; Kumar and Singh, 2012; Chunthaburee et al., 2015; Sarabi et al., 2016; Shelke et al., 2017). Therefore, the present study was aimed to investigate morpho-physiological, photosynthetic, nodulation, and yield modulation in two mungbean varieties, ‘PKV AKM 12-28’ and ‘VBN (Gg)3’, under various salt regimes and exposure durations. Soil salinity has significantly affected the morphological parameters such as shoot and root length, total biomass, plant height, and leaf growth in many crops (Dolatabadian et al., 2011; Morales et al., 2012). In the present investigation, modulations in SL, RL, SB, SFW, RFW, SDW, RDW, STWC%, RTWC%, and LA with the increasing NaCl concentrations of 75, 100, and 125 mM were observed. Principal component analysis (PCA) of the morphological dataset suggests that the parameters with significant loadings in the first principal component (PC1) are related to plant stress and may be considered ‘stress factor.’ A close association of SL, RL, SB, LA, SFW, RFW, SDW, and RDW showed a nearly 90% positive correlation with PC1, which indicates a reduction in these parameters under salinity. PCA finds structure in a multivariate dataset, identifies the most relevant parameters, uncovers the variance of a large dataset of inter-correlated variables and transform them Mankar GD et al. (2023). Not Sci Biol 15(2):11365 19 into a smaller set of (uncorrelated) independent variables (principal components), and can discriminate samples of diverse biological groups (Singh et al., 2004; Sinha et al., 2009b; 2009a; Chunthaburee et al., 2015; Shelke et al., 2017). A simultaneous interpretation of the scores and loadings suggests significant changes in morphological parameters at all levels of salt stress. However, the plant’s defense system is suppressed at higher salt concentrations and more prolonged exposures. It was observed that ‘VBN(Gg)3’ is more affected under salinity stress than ‘PKU-AKM 12-28’. The reduction SL, RL, SB, LA, SFW, RFW affects the absorption and transport of water and nutrient allocation from root to shoot, ultimately affecting plants' growth and development (Satti and Lopez, 1994; Sherif et al., 2007). Our results are in line with those of (Kamrul et al., 2018) and (Rahman et al., 2016). The DA is used to identify the variables which discriminate between two or more naturally occurring groups. It constructs a discriminant function (DF) for each group by analysing raw data (Singh et al., 2004; Sinha et al., 2009a; 2009b). In the present investigation, the DA results indicate RL, LA, RFW, SFW, SDW, and SL to be the most significant parameters to discriminate the four sets of the plant responses corresponding to four concentrations of NaCl. In contrast, the RL and RFW were the most critical parameters to distinguish among the three levels of stress exposure durations. Salinity impairs the synthesis of plant pigments (Taïbi et al., 2016). Also, it reduces photosynthesis either due to a reduction in green pigments or inhibition of their synthesis (Najar et al., 2019). In PCA, a highly positive correlation of Chl-a, Chl-b, Chl-T, TCC, and TAC loadings to PC1 indicate that these photosynthetic parameters are closely related and affected under the increasing concentration of NaCl. The distribution pattern of scores and loading plots suggests a remarkable reduction in the photosynthetic parameters during moderate and more prolonged salt stress exposures. During the moderate exposure duration (30 days), significant induction in the defense mechanism was observed, worsened by prolonged exposure of 45 days. These results suggest an impaired stress defense mechanism with increasing levels of salt stress. These results align with those in P. vulgaris (Turan et al., 2007) and Vigna subterranean (Taffouo et al., 2010). An increase in the anthocyanin content was observed in the present investigation at a low salinity level. It could induce an active protective response under saline stress (Chutipaijit et al., 2009). Salinity also affected carotenoid contents in both varieties. These results corroborate with reports in P. vulgaris (Gadallah, 1999) and maize and wheat genotypes (Singh et al., 2008). The DA results indicate Chl-a to be the most significant attribute to discriminate the four sets of the plant responses corresponding to four concentrations of NaCl stress. These results also reveal the TAC and Chl-T as the most critical parameter to discriminate the three levels of stress exposure durations. In the present study, the photosynthetic pigments viz., chlorophyll a, b, and total chlorophyll, carotenoid, and anthocyanins decreased with increasing salinity. A more significant reduction was observed in ‘VBN (Gg)3’ as compared to ‘PKU AKM 12-28’. A reduction in nodulation parameters was observed with increasing salinity. Leguminous plants can fix atmospheric nitrogen through symbiotic association with soil bacteria (Rhizobium spp.) which form nodules on the roots of these plants. It is essential to understand the optimum conditions required for nitrogen fixation to provide full benefits to the plant (Kijne et al., 1995). Legume-Rhizobium symbiosis may get affected under salinity by inhibiting the bacterial infection process, reducing the survival of rhizobia, disrupting nodule development and function, or decreasing plant growth (Singleton and Bohlool, 1984). In the PCA analysis of the nodulation dataset, a close association of NN, NS, and NW was observed in PC1, which is indicative of a decrease in nodule number, nodule size, and nodule weight under NaCl stress. Scores indicated that salinity significantly affects plants at moderate and highest salinity and exposure time. However, it was affected more in ‘VBN (Gg)3’ than ‘PKU-AKM 12-28’. These results corroborate earlier reports in other plant species. (Elsheikh and Wood, 1995) reported adverse effects of salinity on growth and nodulation in soybean and that nodulation was more sensitive than plant growth under saline conditions. A significant reduction was observed in the nodule number, nodule size, and nodule biomass under salinity in soybean (Singleton and Bohlool, 1984), Sesbania sesban (Mahmood et al., 2008). Mirza and Tariq (1993a,1993b) also reported adversely Mankar GD et al. (2023). Not Sci Biol 15(2):11365 20 affected nodulation of Cicer arietinum and Trifolium alexandrinum under salt stress. The DA results in the present investigation indicate NW to be the most significant parameters to discriminate between the four sets of the plant responses corresponding to four concentrations of NaCl stress. These results also reveal that NS and NW are the most critical parameters to discriminate the three levels of stress exposure durations. Our results revealed a decrease in yield (pod and seed) parameters as salt concentration increased. Salinity adversely affects the economic yield of the crop (Sarin, 1975). Reduced pod number may be one of the main parameters to measure the quantitative yield. PCA revealed that studied yield parameters had a close association with each other and showed more than 90% positive correlation with PC1 under salinity, thus indicating that all these parameters are significantly reduced under salinity. Furthermore, nodulation in ‘VBN (Gg)3’ was affected more than ‘PKV-AKM 12-28’. Gill (1979) observed similar results in barley yield under salinity. Reduction in yield under salinity has been reported in many crops such as rice, cotton, bean, barley, and wheat (Keating and Fisher, 1985). Reduced dry matter and grain yield were reported in sorghum cultivars by Maas et al. (1986). Elahi et al. (2004) also reported a reduction in pod number and pod fresh mass under increasing salinity levels in mungbean. The DA results indicate NP and NSP to be the most significant parameters to discriminate between the four sets of the plant responses corresponding to four concentrations of NaCl stress. Furthermore, MCA revealed a significant positive correlation between photosynthetic and growth parameters. Taiz and Zeiger (1998) have suggested that plant growth depends on photosynthesis. Therefore, environmental stresses affecting photosynthesis, in turn, reduce growth. Fisarakis et al. (2001) remarked that a decline in photosynthesis under salinity inhibits vegetative growth. Significant Positive correlations were observed among photosynthetic and yield parameters. Less green leaves, leaf expansion, production, and senescence result in less photosynthetic activity, which may be why the yield is reduced under salinity (Ahmed, 2009). There was observed a positive correlation between nodulation and yield parameters. It indicates that the effects of salinity on nodulation can ultimately reduce yield and production. Our result corroborates with the observations of Rao et al. (2002), who suggested that the grain production in legumes is reduced because of their low salt tolerance in combination with the high sensitivity of the symbiotic nitrogen fixation process under stress. Applying a multivariate modelling technique to analyse the effects of salt stress on morpho- physiological, photosynthetic nodulation, and yield parameters in two mungbean varieties thus demonstrated the grouping of variables and their interrelationship. This technique also identified significant differences in variables responsible for differential behaviour. It also identified significant parameters responsible for differential behaviour. Such an interpretation is not possible by using conventional methods. ConclusionsConclusionsConclusionsConclusions These analytical tools revealed differential patterns for morpho-physiological, photosynthetic, nodulation, and yield changes in the mungbean varieties ‘PKU-AKM 12-28’ and ‘VBN(Gg)3’. These tools extracted the patterns of variations in significant morpho-physiological, photosynthetic, nodulation, and yield parameters and their inter-relationships under NaCl stress. The multivariate modelling approach identified morpho-physiological (RL, LA, RFW, SFW, SDW, SL, RL, RFW) and photosynthetic (Chl-a, Chl-T, and TAC), nodulation (NN, NS, and NW), and yield (NP, FWP, NSP and FW-1000) as critical parameters that can discriminate tolerant varieties. This analysis also identified positive correlations among yield and other morpho-physiological, photosynthetic, nodulation parameters. Moreover, it also revealed the level of salt tolerance in selected mungbean varieties and confirmed ‘PKU-AKM 12-28’ as salt-tolerant and ‘VBN(Gg)3’ as salt susceptible variety. This multivariate modelling approach can be used to understand the complex datasets on nodulation and yield, their interrelationships, and visualization of relationships among other variables. Mankar GD et al. (2023). Not Sci Biol 15(2):11365 21 Authors’ ContributionsAuthors’ ContributionsAuthors’ ContributionsAuthors’ Contributions GDM: Performed the experiments, collected and analysed the data and prepared draft manuscript, URW: Analysed data and prepared draft manuscript, KBR: Performed the experiments and collected data, DBS: Analysed data and prepared draft manuscript, PSM and MLA: Analysed data and prepared draft manuscript, TDN: Designed the experiments and RBB: Designed the experiments, analysed the data, and finalized the manuscript. All authors read and approved the final manuscript. Ethical approvalEthical approvalEthical approvalEthical approval (for researches involving animals or humans) Not applicable. AcknowledgementsAcknowledgementsAcknowledgementsAcknowledgements We are grateful to Dr. Neeta M. Patil, Head, Department of Botany, and Principal Dr. R. S. Zunjarrao, for providing the required research infrastructure. This work was supported by Council of Scientific and Industrial Research, New Delhi, MS, India, by awarding the CSIR-SRF fellowship to Mr. Ganesh Mankar (Grant number- 08/658(0001)/2017-EMR-I). Conflict of InterestsConflict of InterestsConflict of InterestsConflict of Interests The authors declare that there are no conflicts of interest related to this article. ReferencesReferencesReferencesReferences Abd-Alla MH, Vuong TD, Harper JE (1998). Genotypic differences in dinitrogen fixation response to NaCl stress in intact and grafted soybean. Crop Science 38:72-77. https://doi.org/10.2135/cropsci1998.0011183X003800010013x. Ahmed S (2009). Effect of soil salinity on the yield and yield components of mungbean. Pakistan Journal of Botany 41:263-268. Ali M, Gupta S (2012). Carrying capacity of Indian agriculture: pulse crops. Current Science 102:874-881. Arif Y, Singh P, Siddiqui H, Bajguz A, Hayat S (2020). Salinity induced physiological and biochemical changes in plants: An omic approach towards salt stress tolerance. Plant Physiology and Biochemistry 156:64-77. https://doi.org/10.1016/j.plaphy.2020.08.042 Arnon DI (1949). Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiology 24:1- 15. https://doi.org/10.1104/pp.24.1.1 Chunthaburee S, Dongsansuk A, Sanitchon J, Pattanagul W, Theerakulpisut P (2015). Physiological and biochemical parameters for evaluation and clustering of rice cultivars differing in salt tolerance at seedling stage. Saudi Journal of Biological Sciences 23:467-477. https://doi.org/10.1016/j.sjbs.2015.05.013 Chutipaijit S, Cha-Um S, Sompornpailin K (2009). Differential accumulations of proline and flavonoids in indica rice varieties against salinity. Pakistan Journal of Botany 41:2497-2506. Dhingra KK, Dhillon MS, Grewal DS, Sharma K (1991). Performance of maize and mungbean intercropping in different planting patterns and row orientations. Indian Journal of Agronomy 36:207-212. Dolatabadian A, Modarres SS, Ghanati F (2011). Effect of salinity on growth, xylem structure and anatomical characteristics of soybean. Notulea Scientia Biologicae 3:41-45. https://doi.org/10.15835/nsb315627 Mankar GD et al. (2023). Not Sci Biol 15(2):11365 22 Elahi NN, Mustafa S, Mirza JI (2004). Growth and nodulation of mungbean (Vigna radiata [L.] Wilczek) as affected by sodium chloride. Journal of Research 15:139-143. Elsheikh EAE, Wood M (1995) Nodulation and N2 fixation by soybean inoculated with salt-tolerant rhizobia or salt- sensitive bradyrhizobia in saline soil. Soil Biology and Biochemistry 27:657-661. https://doi.org/10.1016/0038- 0717(95)98645-5 FAO (2015). FAOSTAT. Retrieved 2021 March 30 from: Agric database http//apps.fao.org. Fisarakis I, Chartzoulakis K, Stavrakas D (2001). Response of Sultana vines (V. vinifera L.) on six rootstocks to NaCl salinity exposure and recovery. Agricultural Water Management 51:13-27. https://doi.org/10.1016/S0378- 3774(01)00115-9 Gadallah MAA (1999). Effects of proline and glycinebetaine on Vicia faba responses to salt stress. Biologia Plantarum 42:249-257. https://doi.org/10.1023/A:1002164719609 Ghosh S, Mitra S, Paul A (2015). Physiochemical studies of sodium chloride on mungbean (Vigna radiata L. Wilczek) and its possible recovery with spermine and gibberellic acid. The Scientific World Journal 858016. https://doi.org/10.1155/2015/858016. Gill KS (1979). Effect of soil salinity on grain filling and grain development in barley. Biologia Plantarum 21:241-244. https://doi.org/10.1007/BF02902204 Hamani AKM, Wang G, Soothar MK, Shen X, Gao Y, Qiu R, Mehmood F (2020). Responses of leaf gas exchange attributes, photosynthetic pigments and antioxidant enzymes in NaCl-stressed cotton (Gossypium hirsutum L.) seedlings to exogenous glycine betaine and salicylic acid. BMC Plant Biology 20:1-14. https://doi.org/10.1186/s12870-020-02624-9. Hammer Ø, Harper DAT, Ryan PD (2001). PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica 4:4-9. https://palaeo-electronica.org/2001_1/past/past.pdf Hasanuzzaman M, Hossain MA, da Silva JAT, Fujita M (2012). Plant response and tolerance to abiotic oxidative stress: antioxidant defense is a key factor, In Crop stress and its management: perspectives and strategies, Springer, Dordrecht pp 261-315. https://doi.org/10.1007/978-94-007-2220-0_8 Hasegawa PM (2013). Sodium (Na+) homeostasis and salt tolerance of plants. Environmental and Experimental Botany 92:19-31. https://doi.org/10.1016/j.envexpbot.2013.03.001. Hester MW, Mendelssohn IA, McKee KL (2001). Species and population variation to salinity stress in Panicum hemitomon, Spartina patens, and Spartina alterniflora: Morphological and physiological constraints. Environmental and Experimental Botany 46:277-297. https://doi.org/10.1016/S0098-8472(01)00100-9 Hoagland DR, Arnon DI (1950). The water-culture method for growing plants without soil. Circular. California agricultural Experiment Station, 347. (2nd edit). Kamrul HM, Islam MS, Islam MR., Ismaan HN, Sabagh AEL (2018). Germination and early seedling growth of mungbean (Vigna radiata L.) as influenced by salinity. Azarian Journal of Agriculture 5:79-59. Keating BA, Fisher MJ (1985). Comparative tolerance of tropical grain legumes to salinity. Australian Journal of Agricultural Research 36:373-383. https://doi.org/10.1071/AR9850373 Kijne JW, Stacey G, Burris RH, Evans HJ (1992). Biological nitrogen fixation. In: Stacey G, Burris RH, Evans HJ (Eds). Chapman Hall, London pp 349-398. Koyro HW (2006). Effect of salinity on growth, photosynthesis, water relations and solute composition of the potential cash crop halophyte Plantago coronopus (L.). Environmental and Experimental Botany 56:136-146. https://doi.org/10.1016/j.envexpbot.2005.02.001 Kumar RA, Singh MP, Kumar S (2012). Effect of salinity on germination, growth, yield and yield attributes of wheat. International Journal of Scientific & Technology Research 1:19-23. Maas EV, Poss JA, Hoffman GJ (1986). Salinity sensitivity of sorghum at three growth stages. Irrigation Science 7:1-11. https://doi.org/10.1007/BF00255690 Maclachlan S, Zalik S (1963). Plastid Structure, chlorophyll concentration, and free amino acid composition of a chlorophyll mutant of barley. Canadian Journal of Botany 41:1053-1062. https://doi.org/10.1139/b63-088 Mahmood A, Athar M, Qadri R, Mahmood N (2008). Effect of NaCl salinity on growth, nodulation and total nitrogen content in Sesbania sesban. Agriculturae Conspectus Scientificus 73:137-141. Mankar GD et al. (2023). Not Sci Biol 15(2):11365 23 Mancinelli AL (1984). Photoregulation of anthocyanin synthesis. Plant Physiology 75:447-453. https://doi.org/10.1104/pp.75.2.447 Michalski R, Kaufman K (1997). Data mining and knowledge discovery: A review of issues and a multi-strategy approach. Mach Learn Data Min Methods 1-42. Mirza JI, Tariq R (1993). Effect of salinity on growth, seed yield and nodulation of Cicer Arietinum. Pakistan Journal of Botany 25:47-50. Mirza JI, Tariq R (1993). The growth and nodulation of Trifolium alexandrinum as affected by salinity. Biologia Plantarum 35:289-292. Morales SG, Trejo-Téllez LI, Merino FCG, Caldana C, Espinosa-Victoria D, Cabrera BEH (2012). Growth, photosynthetic activity, and potassium and sodium concentration in rice plants under salt stress. Acta Scientiarum Agronomy 34:317-324. Muchate NS, Nikalje GC, Rajurkar NS, Suprasanna P, Nikam TD (2016). Physiological responses of the halophyte Sesuvium portulacastrum to salt stress and their relevance for saline soil bio-reclamation. Flora 224:96-105. https://doi.org/10.1016/j.flora.2016.07.009 Mujunen SP, Minkkinen P, Holmbom B, Oikari A (1996). PCA and PLS methods applied to ecotoxicological data: ecobalance project. Journal of Chemometrics 10:411-424. https://doi.org/10.1002/(SICI)1099- 128X(199609)10:5/6<411::AID-CEM441>3.0.CO;2-7 Munns R (2005). Genes and salt tolerance: Bringing them together. New Phytologist 167:645-663. https://doi.org/10.1111/j.1469-8137.2005.01487.x. Najar R, Aydi S, Sassi-Aydi S, Zarai A, Abdelly C (2019). Effect of salt stress on photosynthesis and chlorophyll fluorescence in Medicago truncatula. Plant Biosystems 153:88-97. https://doi.org/10.1080/11263504.2018.1461701. Panta S, Flowers T, Lane P, Doyle R, Haros G, Shabala S (2014). Halophyte agriculture: Success stories. Environmental and Experimental Botany 107:71-83. https://doi.org/10.1016/j.envexpbot.2014.05.006 Rahman MM, Habib MA, Sikdar MSI, Md S, Islam MS (2016). Evaluation of mungbean genotypes for salt tolerance at seedling stage and alleviation of saline stress by gypsum. Fundamental and Applied Agriculture 1:39-43. Rahneshan Z, Nasibi F, Moghadam AA (2018). Effects of salinity stress on some growth, physiological, biochemical parameters and nutrients in two pistachio rootstocks. Journal of Plant Interaction 13:73-82. https://doi.org/10.1080/17429145.2018.1424355 Ram HH, Singh HG (1993). Rapeseed-Mustard. Crop Breeding and Genetics. Kalyani Publ New Delhi. Randhir R, Lin YT, Shetty K (2004). Stimulation of phenolics, antioxidant and antimicrobial activities in dark germinated mung bean sprouts in response to peptide and phytochemical elicitors. Process Biochemistry 39:637-646. https://doi.org/10.1016/S0032-9592(03)00197-3 Rao DLN, Giller KE, Yeo AR, Flowers TJ (2002). The effects of salinity and sodicity upon nodulation and nitrogen fixation in chickpea (Cicer arietinum). Annals of Botany 89:563-570. https://doi.org/10.1093/aob/mcf097 Raza MA, Saeed A, Munir H, Ziaf K, Shakeel A, Saeed N, ... Rehman F (2017). Screening of tomato genotypes for salinity tolerance based on early growth attributes and leaf inorganic osmolytes. Archives of Agronomy and Soil Science 63:501-512. https://doi.org/10.1080/03650340.2016.1224856. Sarabi B, Bolandnazar S, Ghaderi N, Tabatabaei SJ (2016) Multivariate analysis as a tool for studying the effects of salinity in different melon landraces at germination stage. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 44:264- 271. https://doi.org/10.15835/nbha44110234. Sarin MN, YCJ, KSG (1975). Salt tolerance of wheat and barley varieties. Proc Symposium. New Developments in the field of salt affected soils. IS.sS Carro 647-652. Satti SME, Lopez M (1994). Effect of increasing potassium levels for alleviating sodium chloride stress on the growth and yield of tomato. Communications in Soil Science and Plant Analysis 25:2807-2823. https://doi.org/10.1080/00103629409369227 Sehrawat N, Yadav M, Bhat K, Sairam R, Jaiwal P (2015). Effect of salinity stress on mungbean [Vigna radiata (L.) Wilczek] during consecutive summer and spring seasons. Journal of Agricultural Sciences, Belgrade 60:23-32. https://doi.org/10.2298/JAS1501023S Mankar GD et al. (2023). Not Sci Biol 15(2):11365 24 Shelke DB, Pandey M, Nikalje GC, Zaware BN, Suprasanna P, Nikam TD (2017). Salt responsive physiological, photosynthetic and biochemical attributes at early seedling stage for screening soybean genotypes. Plant Physiology and Biochemistry 118:519-528. https://doi.org/10.1016/j.plaphy.2017.07.013 Sherif FK, Raslan MM, El-Sammak FZ (2007). Effect of Gamma radiation on some morphological and biochemical characters of Tagetes erecta grown in saline soil. Alexandria Science Exchange Journal 28:54-67. 10.21608/ASEJAIQJSAE.2007.1851 Shrivastava P, Kumar R (2015). Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi Journal of Biological Sciences 22:123-131. https://doi.org/10.1016/j.sjbs.2014.12.001. Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, ... Kouimtzis T (2003). Assessment of the surface water quality in Northern Greece. Water Research 37:4119-4124. https://doi.org/10.1016/S0043- 1354(03)00398-1 Singh AK, Singh RA, Kumar S (2008). Influence of salinity on seedling growth and metabolism in maize genotypes. Indian Journal of Plant Physiology 13:95-99. Singh KP, Malik A, Mohan D, Sinha S (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) - A case study. Water Research 38:3980-3992. https://doi.org/10.1016/j.watres.2004.06.011 Singla R, Garg N (2005). Influence of salinity on growth and yield attributes in chickpea cultivars. Turkish Journal of Agriculture and Forestry 29:231-235. Singleton PW, Bohlool BB (1984). Effect of salinity on nodule formation by soybean. Plant Physiology 74:72-76. https://doi.org/10.1104/pp.74.1.72 Sinha S, Basant A, Malik A, Singh KP (2009a). Iron-induced oxidative stress in a macrophyte: A chemometric approach. Ecotoxicology and Environmental Safety 72:585-595. https://doi.org/10.1016/j.ecoenv.2008.04.017 Sinha S, Basant A, Malik A, Singh KP (2009b). Multivariate modelling of chromium-induced oxidative stress and biochemical changes in plants of Pistia stratiotes L. Ecotoxicology 18:555-566. https://doi.org/10.1007/s10646- 009-0313-6 Steduto P, Albrizio R, Giorio P, Sorrentino G (2000). Gas-exchange response and stomatal and non-stomatal limitations to carbon assimilation of sunflower under salinity. Environmental and Experimental Botany 44:243-255. https://doi.org/10.1016/S0098-8472(00)00071-X Sultana N, Ikeda T, Itoh R (1999). Effect of NaCl salinity on photosynthesis and dry matter accumulation in developing rice grains. Environmental and Experimental Botany 42:211-220. https://doi.org/10.1016/S0098- 8472(99)00035-0 Taffouo VD, Wamba OF, Youmbi E, Nono GV, Akoa A (2010). Growth, yield, water status and ionic distribution response of three bambara groundnut (Vigna subterranea (L.) Verdc.) landraces grown under saline conditions. International Journal of Botany 6:53-58. Taïbi K, Taïbi F, Ait Abderrahim L, Ennajah A, Belkhodja M, Mulet JM (2016). Effect of salt stress on growth, chlorophyll content, lipid peroxidation and antioxidant defence systems in Phaseolus vulgaris L. South African Journal of Botany 105:306-312. https://doi.org/10.1016/j.sajb.2016.03.011. Taiz L, Zeiger E (1998). Plant physiology. 2nd edn. Sinauer Associates Publishers, Sunderland, Massachusetts. Tang D, Dong Y, Ren H, Li L, He C (2014). A review of phytochemistry, metabolite changes, and medicinal uses of the common food mung bean and its sprouts (Vigna radiata). Chemistry Central Journal 8:1-9. https://doi.org/10.1186/1752-153X-8-4 Turan MA, Turkmen N, Taban N (2007). Effect of NaCl on stomatal resistance and proline, chlorophyll, Na, Cl and K concentrations of lentil plants. Journal of Agronomy 6:378-381. Walsh KB (1995). Physiology of the legume nodule and its response to stress. Soil Biology and Biochemistry 27:637-655. https://doi.org/10.1016/0038-0717(95)98644-4 Xu FJ, Jin CW, Liu WJ, Zhang YS, Lin XY (2011). Pretreatment with H2O2 alleviates aluminum-induced oxidative stress in wheat seedlings. Journal of Integrative Plant Biology 53:44-53. https://doi.org/10.1111/j.1744- 7909.2010.01008.x. Mankar GD et al. (2023). Not Sci Biol 15(2):11365 25 The journal offers free, immediate, and unrestricted access to peer-reviewed research and scholarly work. 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