INTRODUCTION Zea mays L. is a staple food possessing the highest yield potential among the cereals and it is known as queen of cereals. It plays an important role in livelihood of millions of poor farmers. Since it is a short-day plant with C4 type of photosynthesis; the crop has very efficient utilization of solar radiation. However, it is very sensitive to excess or deficit soil moisture. Most of the world maize area is grown under rainfed conditions and it is more susceptible to drought than other cereals (Hall et al., 1981). At a cellular level, drought signals promote stomatal closure to save water, stimulate the production of stress-protectant metabolites, upregulate the antioxidant system, and deploy peroxidase enzymes to prevent acute cellular damage and loss of membrane integrity (Gupta et al., 2020; Liu et al. 2021; Choudhary et al., 2021a; Choudhary et al., 2021b; Choudhary et al., 2021c; Mishra et al., 2021 a; Mishra et al., 2021 b; Mishra et al., 2021 c; Sharma et al., 2021). Crop yield is vulnerable when drought conditions occur during the reproductive phase of plant growth. Although grain yield (GY) when plants subjected to water stress is the final target trait employed to assess the degree of drought tolerance, correlated traits, such as ASI and kernel number per row (KNR), are considered to have a higher heritability and thus may be more suitable as target traits for improving maize drought resistance (Monneveux et al., 2008; Jia et al., 2020; Liu et al., 2021). About 67% of the total maize production in the developing countries emanates from low and lower middle-incomenations. Moisture stress is one of the major constraints in maize productivity and it is very common in the areas, where, maize is predominantly grown under rainfed condition. Drought at any stage of crop improvement affects production, but maximum damage is inflicted when it occurs during flowering stage. Effect of drought stress includes delayed silking and female sterility caused by embryo abortion (Moss and Downey, 1971, Aslam et al., 2013; Bibhu et al., 2020) resulting huge reduction in grain yield. It was estimated that annual yield loss due to drought may be close to 24 million tones and it is equivalent to 17 % of a normal year’s production in a developing world. Due to increased demand of maize, there should be an intensive effort should be made to increase maize production in the changing climatic conditions, particularly related to water stress, salinity, extreme temperature regimes etc. Selection for best performing genotypes under stress condition is the primary objective for any crop improvement programme (Thiry et al., 2016; Wattoo et al., 2018; Makwana et al., 2021; Mishra et al., 2021d; Mishra et al., 2021e; Mishra et al., 2021f). Assessment of germplasm line (s) against abiotic stresses like heat and drought under natural field conditions is tough owing to uncertain environmental conditions like rainfall and humidity (Zafar et al., 2017; Wattoo et al., 2018; Choudhary et al., 2021a; Choudhary et al., 2021b). The task turns into more challenging when dealing with huge number of germplasm lines owing to necessity of large area, labor, resources and long time period. On the other hand, its rapid evaluation can be done at early growth stages under controlled environmental conditions based on many growth and physiological parameters (Zafar et al., 2017; Wattoo et al., 2018). An array of morphological, physiological and biological features of higher plants show adaptability in response to water stress. Existence of variability for drought tolerance in different plant species has been reported (Frova et al., 1999; Guttieri et al., 2004; Wattoo et al., 2018). Numerous growth related morpho-physiological traits have been potentially employed for evaluating genotypes of different crops against water stress (Taiz and Zeiger, 2006; Ali et al., 2009; Chohan et al., 2012; Javed, 2012; Wattoo et al., 2018; Rajpoot et al., 2020; Choudhary et al., 2021a; Choudhary et al., 2021b; Mishra et al., 2021a; Mishra et al., 2021b). Present study was conducted with the objectives to screen genotype (s) on the basis of various morpho-physiological parameters related to drought tolerance which can perform well under limited water conditions. MATERIALS AND METHODS A. Plant material and growth conditions The seeds of 12 maize inbred lines were acquired from Sam Higgonbottom Agriculture Science and Technology University, Prayagraj, U.P., India. A crossing programme initiated with 12 maize inbred lines following half diallel analysis (Jinks and Hayman, 1954) to raise 66 F1hybrids. These 66 hybrids along with two checks (drought tolerant HKI1105 and drought susceptible HKI1128) and parents total 80 entries were included in this study (Table 1) and were evaluated at Research Farm, Department of Genetics &Plant Breeding, College of Agriculture, Gwalior, M.P. during Rabi season, 2019-20. The experiment was conducted under irrigated and partial irrigated condition and laid out in a randomized block design (RBD) with two replications. Each genotype was sown in 2 rows of 4 meters with a spacing of 60 cm between rows and 20 cm between plant to plant. Drought stress was imposed from 10 days before flowering by with-holding irrigation. The irrigation was resumed when soil moisture reaches temporary wilting point. The characters like Turgid weight (TW), Relative water content (RWC), Saturation water deficit (SWD) and Membrane Stability Index (MSI) were calculated for samples collected from a single competitive plant per replication per genotypes and grain yield/ plant (GY/P) was calculated on plot basis(g). Relative water content (RWC) is considered one of the important physiological parameters to assess the water content in plants during stressed and control condition and calculated by the formula: RWC = (FW — DW/ TW—DW) × 100 where, Fresh weight of plant (g), DW = Dry weight of plant (g), TW = Turgid weight of plant (g). Turgid weight (TW) was determined after floating of plants on water overnight at room temperature. Saturation water deficit (SWD) (%) was calculated after subtracting the value of RWC from 100. Membrane stability index (MSI) was estimated taking two sets of 200 mgs of leaf sample in test tube containing 10 ml of double distilled water. One set was heated at 40°C for 30 min in a water bath to record the electrical conductivity (C1) of the solution by an electrical conductivity meter. Second set was subjected to 100°C on a boiling water bath for 15 min to measure its conductivity (C2) as explained above. Membrane Stability Index was calculated by formulae MSI = [1 — {C1/C2}] × 100 as proposed by Razzaq et al. (2013). Where, Cl = electrical conductivity of water containing the leaf sample in set one and C2 = electrical conductivity of water containing the leaf sample in set two. The cobs from randomly selected five plants were dried, shelled, cleaned and weight of grains was recorded and expressed as grams and considered as grain yield per plant (g). RESULT AND DISCUSSION Drought stress encourages an assortment of morpho-physiological changes in plants in order that plants are capable to broaden tolerance mechanisms. Drought tolerance is the result of abundant morphological, anatomical and physiological parameters which interrelate with continuation of growth and developmental progressions. Relative aptitude for drought tolerance of maize genotypes may be arbitrated by guesstimating numerous morpho-physiological traits for instance fresh weight, dry weight, turgid weight, relative water content, saturation water deficit and membrane stability index. Water saturation deficit, relative water content (RWC) and leaf water loss are the main physiological measures that maneuver plant water relations and used to evaluate drought tolerance in plants (Kachre, 2017;Kachre et al., 2019; Mishra et al., 2021a). A. Morpho-physiological characterization of genotypes under irrigated and partial irrigated conditions Among all 80 maize genotypes under irrigated condition grain yield per plant ranged between 42.11g to 113.73 g with a mean value of 71.06g. Ten genotypes viz., IL1 × IL7, IL11 × IL12, IL3 × IL10, IL6 × IL7, IL7 × IL8, IL7 × IL12, IL4 × IL6, IL2 × IL7, IL6 × IL12 and IL3 × IL11 showed better response in respect to morpho-physiological traits along with grain yield (Table 2). Turgid weight ranged from 0.16 to 0.26 with an average worth of 0.21. Relative water content varied between 48.82% to 89.44% with a mean value of 72.88. While saturation water deficit arrayed between 10.57 to 51.19 with a mean worth of 27.12. Whereas, membrane stability index fluctuated between 1.78 to 58.91 with a mean value of 25.78. Under partial irrigated condition grain yield per plant arrayed between 40.33g to 105.17g with a mean worth of 68.08g.Ten genotypes viz., IL11 × IL12, IL1 × IL7, IL7 × IL8, IL3 × IL10, IL6 × IL7, IL7 × IL12, IL2 × IL7, IL6 × IL12, IL4 × IL6 and IL3 × IL11 displayed better response in respect to different morpho-physiological parameters along with grain yield (Table 2), Turgid weight ranged from 0.16 to 0.26 with an average worth of 0.21. Relative water content varied between 48.36% to 90.17% with a mean value of 72.33%, while saturation water deficit arrayed between 9.83 to 51.65 with an average value of 27.67 and membrane stability index varied between 1.34 to 78.56 with a mean worth of 31.57. Based on these findings, it is clear that the drought tolerant genotypes giving more yield under drought condition. Similar results were also reported by Homayoun (2011); Kachre et al. (2019); Mishra et al. (2021 a). Among all 80-maize genotypes under irrigated and partial irrigated conditions, ten genotypes viz., IL11 × IL12, IL1 × IL7, IL7 × IL8, IL3 × IL10, IL6 × IL7, IL7 × IL12, IL2 × IL7, IL6 × IL12, IL4 × IL6 and IL3 × IL11 exhibited better response in terms of all morpho-physiological parameters along with grain yield. The inter se correlations of grain yield per plant (GY/P) with an individual morpho-physiological trait e.g., turgid weight, relative water content, saturation water deficit and membrane stability index have been presented in (Table 4 and 6). Saturation water deficit revealed significant and positive association with grain yield indicating favorable response towards yield. But turgid weight, relative water content and membrane stability index exhibited significant and negative correlation with grain yield. Similar results were alsoaddressed by Good et al. (1993); Magorokosho et al. (2003); Kachre et al. (2019); Mishra et al. (2021a); Sharma et al. (2021). B. Correlation Coefficient (Under Irrigated Condition) At the phenotypic level. The details of the phenotypic correlation coefficients between grain yield and its attributing characters are presented in the Table 3. Turgid weight displayed significant and positive correlation with SDW (0.0835) while significant and negative correlation with RWC (-0.0835) tracked by MSI (-0.1504) and GY/P (g) (-0.0724). Relative water content showed significant and positive correlation with GY/P (g) (0.0138) while significant and negative correlation with SWD (-1.0002) pursued by MSI (-0.0119). Saturation water deficit exhibited significant and positive correlation with MSI (0.0119) whereas significant and negative correlation with GY/P (g) (-0.0138). MSI displayed significant and negative correlation with GY/P (g) (-0.1638). Whilst grain yield per gram showed significant and positive correlation with RWC (0.0138). Whereas it displayed negative correlation with TW (-0.0724), SDW (-0.0138) and MSI (-0.1638). At the genotypic level. Correlation coefficients between grain yield and its accrediting parameters have been described in the Table 3. Turgid weight showed significant and positive correlation with SDW (0.0823) while significant and negative correlation with RWC (-0.0823) tracked by MSI (-0.1503) and GY/P (g) (-0.0727). Relative water content revealed significant and positive correlation with GY/P (g) (0.0142) whereas significant and negative correlation with SWD (-1.0002) trailed by MSI (-0.0122). Saturation water deficit exhibited significant and positive correlation with MSI (0.0122) whilst significant and negative correlation with GY/P (g) (-0.0142). Membrane stability indexshowed significant and negative correlation with GY/P (g) (-0.1641). GY/P (g) showed significant and positive correlation with RWC (0.0142). Whereas it had negative correlation with TW (-0.0727), SDW (-0.0142) and MSI (-0.1641). C. Phenotypic path coefficient analysis Phenotypic path coefficient analysis revealed higher R2 (0.0365) value and residual effect (0.9816) indicating exploitation of phenotypic variation in response to irrigated condition. The phenotypic direct and indirect effects of various traits on yield per plant are presented in Table 4. Phenotypic path coefficient analysis revealed that RWC (0.0034) had the highest positive and direct effect on SY/P (g) followed by SWD (0.0002). However, maximum negative and direct effect on seed yield per plant was documented through MSI (-0.1787) and TW (-0.0989). The indirect effects of turgid weight showed a positive and indirect effect on grain yield per plant via RWC (0.0083), tracked by MSI (0.0149) and negative and indirect effect on yield per plant via SWD (-0.0083). Relative water content displayed a positive and indirect effect on seed yield per plant via MSI (0.0003), negative and indirect effect on yield per plant via SWD (-0.0034) pursued by TW (-0.0003). Saturation water deficit displayed positive and indirect effect on seed yield per plant via RWC(0.0004) trailed by TW (0.0001) and MSI (0.0001). Membrane stability index demonstrated positive and indirect effect on seed yield per plant via TW (0.0269) tracked by RWC (0.0021) and it showed negative and indirect effect on seed yield per plant via SWD (-0.0021). D. Correlation Coefficient (Under Partial Irrigated Condition) At the phenotypic level. The details of the phenotypic correlation coefficients between grain yield and its attributing characters are presented in the Table 5. Turgid weight displayed significant and positive correlation with SDW (0.0701) while significant and negative correlation with RWC (-0.0701) tracked by MSI (-0.0109) and GY/P (g) (-0.069). Relative water content displayed significant and positive correlation with MSI (0.1319) followed by GY/P (g) (0.0232) whilst significant and negative correlation with SWD (-1.0002). Saturation water deficit showed significant and negative correlation with MSI (-0.1319) tracked by GY/P (g) (-0.0232). Membrane stability index exhibited significant and negative correlation with GY/P (g) (-0.0871). However, Grain yield per plant in gram displayed significant and positive correlation with RWC (0.0232), whereas it exhibited negative correlation with TW (-0.069), SDW (-0.0232) and MSI (-0.0871). At the genotypic level. Correlation coefficients between grain yield and its ascribing characters has been designated in the Table 5. Turgid weight exhibited significant and positive correlation with SDW (0.0689) while significant and negative correlation with RWC (-0.0689) followed by MSI (-0.011) and GY/P (g) (-0.0694). Relative water content displayed significant and positive correlation with MSI (0.1323) tracked by GY/P (g) (0.0237) whilst significant and negative correlation with SWD (-1.0002). Saturation water deficit showed significant and negative correlation with MSI (-0.1323) perused by GY/P (g) (-0.0237). Membrane stability index displayed significant and negative correlation with GY/P (g) (-0.0873). Whilst grain yield per plant in gram showed significant and positive correlation with RWC (0.0237). Whereas it exhibited negative correlation with TW (-0.094), SDW (-0.0237) and MSI (-0.0873). Phenotypic path coefficient analysis. Phenotypic path coefficient analysis discovered higher R2 (0.0134) value and residual effect (0.9933) demon starting exploitation of phenotypic variation in response to under partial irrigated condition. The phenotypic direct and indirect effects of diverse parameters on yield per plant are presented in Table 6. Phenotypic path coefficient analysis exposed that RWC (0.0306) had the highest positive and direct effect on SY/P (g) followed by SWD (0.0001). However, maximum negative and direct effect on seed yield per plant was documented through MSI (-0.0919) and TW (-0.0678). The indirect effects of turgid weight exhibited a positive and indirect effect on grain yield per plant via RWC (0.0048), tracked by MSI (0.0007) and negative and indirect effect on yield per plant via SWD (-0.0048). Relative water content displayed a positive and indirect effect on seed yield per plant via MSI (0.004) and negative and indirect effect on yield per plant via SWD (-0.0306) trailed by TW (-0.0021). Saturation water deficit showed positive and indirect effect on seed yield per plant via RWC (0.0002) tracked by TW (0.0003) and MSI (0.0002). Membrane stability index exhibited positive and indirect effect on seed yield per plant via SWD (0.0121) tracked by TW (0.001) and it showed negative and indirect effect on seed yield per plant via RWC (-0.0121). It was evidenced that water scarcity unpleasantly disturbs plant growth and designate more fluctuations in dry weight of maize. As the stomata close in response to low water supply, there is low CO2 fixation. Apart from reducing cell division and enlargement, water stress is testified to be restrictive to almost all aspects of cellular metabolism. The result in decrease in dry matter production and yield is apparent in present investigation which is harmony to research of Kachare (2017). Sharifa et al. (2015) and Mishra et al. (2021a) also recognized decreased fresh weights with drought stress in soybean genotypes. In many other studies, osmotic stress also instigated a significant decrease in fresh weight of soybean genotypes (Hamayun et al. 2010; Sepanlo et al., 2014; Kachare et al., 2019; Mishra et al., 2021b). RWC is considered as a protruding physiological trait to envisage tolerance against drought stress. Drought stress causes water loss within the plant and results in relative water content (RWC) reduction. This parameter is one of the most persistent and broadly employed indicator for defining both the sensitivity and the tolerance to water deficit in plants (Rampino et al., 2012). Computation of RWC enables in the approximation of the metabolic action in leaf tissues which is then considered as an integrated measure of plant water standing. In the present investigation, RWC steadily diminished with susceptible genotypes in comparison to tolerant genotypes. The reducing tendency of relative water content for all the genotypes may be accredited towards the reduction in external water potential (Datta et al., 2011). All the genotypes displayed significant variations in RWC which advised that diverse cultivars have different threshold levels to retain the water status (Datta et al., 2011). In the current investigation, RWC was higher in those genotypes may be drought tolerant as anticipated by Hossain et al. (2014); Sepanlo et al. (2014); Kachare (2017); Mishra et al. (2021b). In this research, all the susceptible genotypes displayed significant higher values of SWD, nevertheless, tolerant genotypes revealed lower value of SWD as compared to susceptible one. Souza et al. (2013); Kachare et al. (2019); Mishra et al. (2021a) addressed analogous trend in SWD for the soybean genotypes. On the basis of these findings, it is clear that the drought tolerant genotypes giving more yield under drought condition with increased value of turgid weight relative water content, saturation water deficit and membrane stability index. Similar results were also reported by Homayoun (2011). Under both conditions, correlation studies of grain yield per plant showed significant positive correlation with relative water contents (RWC) while negative correlation was evident with turgid weight, saturation water deficit, and Membrane stability index (MSI). Similar results also reported by Good et al. (1993); Magorokosho et al. (2003). Furthermore, correlation analysis along with other indices was proved to be a useful approach for rapid and cost-efficient screening of large number of genotypes against drought stress condition.