Comparison of Maize Genotypes Using Drought-Tolerance Indices and Graphical Analysis under Normal and Humidity Stress Conditions

This study aimed to identify drought-tolerant genotypes and to evaluate and compare the response of genotypes under normal conditions and humidity stress. The experiment was conducted in a Randomized Complete Block Design (RCBD) on 12 commercial single cross hybrids of maize (Zea mays L.) with three replications in two separate experiments under normal and stress conditions. GT biplot was used to compare genotypes under normal conditions and humidity stress. Based on the polygon diagrams’ graphical analysis, KSC206, KSC704, KSC705 and KSC706 genotypes were identified as desirable hybrids. The ranking diagram of genotypes based on ideal genotype also showed that the KSC704 genotype had high desirability in all evaluated traits in normal and stress conditions. TOL, MP, HARM, GMP, SSI and STI indices were used to identify drought-tolerant genotypes, and the genotypes were ranked based on this index. Based on this, KSC260, SC302 and KSC400 hybrids were selected as resistant hybrids. Based on the correlation analysis between drought-tolerance indices, a positive correlation was observed between MP, GMP, HARM and STI indices. Based on the analysis of the PCA on the indices, the first and second principal components were given the titles of grain yield tolerance component under humidity stress conditions and grain yield stability component under normal humidity conditions, respectively. KSC704 was superior to other hybrids in terms of grain yield under normal conditions and stress, and the KSC260 hybrid was identified as a tolerant hybrid in terms of all studied traits under drought stress.


Introduction
Corn (Zea mays L.) is an annual monocotyledonous plant of the cereal family [1]. After wheat and rice, it is the third most important crop among cereals [2]. Environmental stress is one of the most important factors in reducing the yield and production of crops. To increase the yield of these products, dealing with the effects of stress is considered one of the useful methods [3]. Among abiotic stresses, drought stress is one of the biggest environmental constraints that reduces and limits crop production [4]. Drought stress is one of the most important constraints on agricultural production in most developing countries located in arid and semi-arid regions of the world. Drought stress is one of the

Analysis of Variance and Mean Comparison
Analysis of variance in terms of traits was performed on the tested hybrids. Under normal humidity conditions, different hybrids had significant differences in all traits except plant height, number of rows per ear, grain length and grain thickness. Under stress conditions, genotypes showed significant differences in all traits except plant height, the number of grains per row, grain width and grain thickness. In both normal and stress conditions, the highest percentage of coefficient of variation was related to the grain thickness trait, and the lowest was related to the ear length trait (p ≤ 0.01) ( Table 1). The significant difference between different genotypes in maize yield and yield-dependent traits indicates genetic diversity and the possibility of selection for genotype tolerant to drought stress [21]. Comparing the mean of Duncan method genotypes under normal and stress conditions showed that KSC707, SC301 and KSC704 genotypes had better performance than other hybrids in all evaluated traits, respectively. Additionally, the DC307 genotype under normal conditions and the KSC400 genotype under stress conditions were identified as hybrids with low yield and rank (Table 2), Mousavi et al. [22]. In their experiments under normal humidity conditions, the KSC704 genotype had the highest yield compared to other hybrids.

Analysis of Correlations between Traits
The correlation coefficient matrix for normal humidity conditions also showed the number of grains per row with the trait of ear width, the number of rows per ear with the number of grains per ear, the grain width trait with the ear width, and the grain length trait with the ear length; the trait Grain thickness had a positive and significant correlation with grain width and grain yield with grain length and 1000-grain weight. There was also a significant negative correlation between ear width, number of grains per row and grain width-plant height, grain thickness-ear length, grain yield-ear width, grain length and grain thickness-number of rows per ear. The results of correlation coefficients under stress conditions also showed a positive and significant correlation between ear length, grain length and 1000-grain weight-plant height, ear width, number of seeds per row and number of rows per ear-ear length, grain width, grain thickness, 1000-grain weight and grain yieldear width, grain thickness-number of rows per ear, grain length and grain yield-grain thickness, 1000-grain weight-grain length and grain thickness, and grain yield-1000-grain weight. Additionally, a negative and significant correlation was observed between the number of rows per ear with the ear's width, the grain thickness with the grain length and the trait of 1000-grain weight with the grain width (Table 3). Refiq et al. The authors of [23] reported a significant positive correlation between 1000-grain weight and grain yield in the plot. To investigate the correlation of the studied traits, a graphical analysis of the correlation between the traits was used (Figure 1). In this cosine biplot diagram, the angle between the trait vectors indicates the intensity of the correlation between the traits. Suppose the angle between the vectors is less than 90 degrees. In that case, the correlation between the vectors is equal to +1. If the angle between the vectors of the attributes is 90 degrees, the correlation between the vectors of the attributes is zero. If the angle between the vectors is 180 degrees, the correlation is −1 [24]. Based on the graph obtained under normal conditions ( Figure 1A); the number of grains per row and ear width together; the number of rows per ear and ear length together; the plant height traits with grain length; and finally, the grain yield traits, grain length, grain thickness and 1000-grain weight showed a positive and significant correlation. The 180-degree angle between the plant height and ear width vectors showed a significant negative correlation between these two traits. Based on the graph obtained under stress conditions ( Figure 1B), ear width; grain thickness; ear length; 1000-grain weight together and grain width; ear length and number of rows per ear together; and, finally, grain yield trait with the number of grain per row had a positive correlation. A negative correlation was observed between plant height and 1000-grain weight and grain yield with grain width, Farajzadeh et al. [25]. In the study of grain yield and yield components of 22 maize genotypes, a positive and significant correlation was observed in the number of grains per row, the number of grains per ear, and ear length with grain yield [21]. Mousavi et al. also reported a significant positive correlation between grain yield traits and many grains per row [26].
Plants 2022, 11, x FOR PEER REVIEW 4 of 15 a significant negative correlation between ear width, number of grains per row and grain width-plant height, grain thickness-ear length, grain yield-ear width, grain length and grain thickness-number of rows per ear. The results of correlation coefficients under stress conditions also showed a positive and significant correlation between ear length, grain length and 1000-grain weight-plant height, ear width, number of seeds per row and number of rows per ear-ear length, grain width, grain thickness, 1000-grain weight and grain yield-ear width, grain thickness-number of rows per ear, grain length and grain yieldgrain thickness, 1000-grain weight-grain length and grain thickness, and grain yield-1000grain weight. Additionally, a negative and significant correlation was observed between the number of rows per ear with the ear's width, the grain thickness with the grain length and the trait of 1000-grain weight with the grain width (Table 3). Refiq et al. The authors of [23] reported a significant positive correlation between 1000-grain weight and grain yield in the plot. To investigate the correlation of the studied traits, a graphical analysis of the correlation between the traits was used ( Figure 1). In this cosine biplot diagram, the angle between the trait vectors indicates the intensity of the correlation between the traits. Suppose the angle between the vectors is less than 90 degrees. In that case, the correlation between the vectors is equal to +1. If the angle between the vectors of the attributes is 90 degrees, the correlation between the vectors of the attributes is zero. If the angle between the vectors is 180 degrees, the correlation is −1 [24]. Based on the graph obtained under normal conditions ( Figure 1A); the number of grains per row and ear width together; the number of rows per ear and ear length together; the plant height traits with grain length; and finally, the grain yield traits, grain length, grain thickness and 1000-grain weight showed a positive and significant correlation. The 180-degree angle between the plant height and ear width vectors showed a significant negative correlation between these two traits. Based on the graph obtained under stress conditions ( Figure 1B), ear width; grain thickness; ear length; 1000-grain weight together and grain width; ear length and number of rows per ear together; and, finally, grain yield trait with the number of grain per row had a positive correlation. A negative correlation was observed between plant height and 1000-grain weight and grain yield with grain width, Farajzadeh et al. [25]. In the study of grain yield and yield components of 22 maize genotypes, a positive and significant correlation was observed in the number of grains per row, the number of grains per ear, and ear length with grain yield [21]. Mousavi et al. also reported a significant positive correlation between grain yield traits and many grains per row [26].

Ranking and Grouping of Genotypes in Terms of Traits
A polygon diagram identifies the best genotypes among the studied traits. This diagram is drawn by connecting the genotypes farthest from the origin so that the other genotypes fit into this polygon. In each section, genotypes with higher yield and desirability with specific traits are separated by lines [27,28]. The authors of [29] used this type of graph for their studies on rapeseed cultivars and maize cultivars [29]. Based on the polygon diagram obtained under normal humidity conditions ( Figure 2A), KSC260, KSC704, KSC707, SC647, KSC705, KSC706, SC301 and SC604 hybrids had the longest distance from the origin of the diagram. They were placed at the vertex of the polygon. Titles of desirable hybrids were identified in terms of traits. In each section, KSC260 hybrid in terms of the number of grains per row and ear width, SC647 hybrid in terms of grain width, KSC705 hybrid in terms of a number of rows per ear, KSC706 genotype in terms of plant height, and SC604 and DC370 genotypes in terms of traits grain yield and grain thickness were identified as more favorable hybrids than other genotypes ( Figure 2A). The diagram obtained under stress identified KSC704, DC370, KSC260, KSC400, KSC706 and KSC705 genotypes as more favorable genotypes than other genotypes. In each section, DC370 and SC301 genotypes were identified in terms of numbers of grain per row and KSC705 hybrid in terms of ear length and number of rows per ear as high-performance genotypes in these traits ( Figure 2B). Considering the comparison of normal and stress conditions, it can be concluded that based on this diagram, KSC260, KSC704, KSC705 and KSC706 genotypes are identified as desirable hybrids in both conditions. In terms of adjective, the number of rows per ear shows good stability and performance.

Ranking of Genotypes Based on Ideal Genotype
According to the genotype-ranking diagram, the ideal genotype ( Figure 3) is connected to the mean point from the origin of the coordinates of the linear graph and continues to both sides. In this form, the best point is the center of the concentric circle, which is marked with an arrow, and other genotypes are ranked according to this point. Based on the diagram obtained under normal moisture conditions ( Figure 3A), KSC260 and KSC704 genotypes were preferred to other hybrids. KSC706 and KSC705 genotypes were also identified as unfavorable genotypes. The order of genotypes from the best hybrid to the most unfavorable hybrid is as follows: In the diagram obtained under stress conditions, KSC704 and KSC707 hybrids were identified as desirable hybrids and KDC260, KSC400 and KSC706 genotypes were based on the ideal genotype unfavorable hybrids ( Figure 3B). The order of genotypes from the best genotype to the most unfavorable genotype in stress conditions is as follows:

Ranking of Genotypes Based on Ideal Genotype
According to the genotype-ranking diagram, the ideal genotype ( Figure 3) is connected to the mean point from the origin of the coordinates of the linear graph and continues to both sides. In this form, the best point is the center of the concentric circle, which is marked with an arrow, and other genotypes are ranked according to this point. Based on the diagram obtained under normal moisture conditions ( Figure 3A), KSC260 and KSC704 genotypes were preferred to other hybrids. KSC706 and KSC705 genotypes were also identified as unfavorable genotypes. The order of genotypes from the best hybrid to the most unfavorable hybrid is as follows:

Ranking of Genotypes Based on Ideal Genotype
According to the genotype-ranking diagram, the ideal genotype ( Figure 3) is connected to the mean point from the origin of the coordinates of the linear graph and continues to both sides. In this form, the best point is the center of the concentric circle, which is marked with an arrow, and other genotypes are ranked according to this point. Based on the diagram obtained under normal moisture conditions ( Figure 3A), KSC260 and KSC704 genotypes were preferred to other hybrids. KSC706 and KSC705 genotypes were also identified as unfavorable genotypes. The order of genotypes from the best hybrid to the most unfavorable hybrid is as follows: In the diagram obtained under stress conditions, KSC704 and KSC707 hybrids were identified as desirable hybrids and KDC260, KSC400 and KSC706 genotypes were based on the ideal genotype unfavorable hybrids ( Figure 3B). The order of genotypes from the best genotype to the most unfavorable genotype in stress conditions is as follows:  In the diagram obtained under stress conditions, KSC704 and KSC707 hybrids were identified as desirable hybrids and KDC260, KSC400 and KSC706 genotypes were based on the ideal genotype unfavorable hybrids ( Figure 3B). The order of genotypes from the best genotype to the most unfavorable genotype in stress conditions is as follows: KSC704 > KSC707 > KSC705 > SC647 > SC604 > SC301 > DC370 > KSC703 > SC302 > KSC260 > KSC400 > KSC706.

Grouping of Hybrids
The genotype grouping diagram evaluates hybrids based on stability and yield in different traits and groups the genotypes based on the traits (Figure 4). Based on the grouping diagram under normal humidity conditions, four groups were formed regarding yield and desirability in all traits. The first group included KSC260, KSC704, KSC707 and SC302 genotypes; the second group included DC370 and SC604 genotypes; the third group included SC301 KSC400 KSC706 genotypes; and the fourth group included KSC703 and KSC705 genotypes. The SC647 genotype was not grouped ( Figure 4A). Under stress conditions, the grouping diagram classified the genotypes into four groups. The first group included KSC707, SC301 and SC604 genotypes; the second group included KSC260, KSC400, SC604 and KSC707 genotypes. In these two groups, two hybrids, KSC707 and SC604, were common between these groups. KSC706 and SC302 genotypes were in the third group, and KSC703 and KSC705 were in the fourth group. In this diagram, DC370, SC647 and KSC704 genotypes were not in any group ( Figure 4B). By examining the graphs of normal and stress conditions, KSC703 and KSC705 genotypes were in the same group in both conditions, indicating the stability of these two genotypes in terms of the studied traits under stress.

Grouping of Hybrids
The genotype grouping diagram evaluates hybrids based on stability and yield in different traits and groups the genotypes based on the traits (Figure 4). Based on the grouping diagram under normal humidity conditions, four groups were formed regarding yield and desirability in all traits. The first group included KSC260, KSC704, KSC707 and SC302 genotypes; the second group included DC370 and SC604 genotypes; the third group included SC301 KSC400 KSC706 genotypes; and the fourth group included KSC703 and KSC705 genotypes. The SC647 genotype was not grouped ( Figure 4A). Under stress conditions, the grouping diagram classified the genotypes into four groups. The first group included KSC707, SC301 and SC604 genotypes; the second group included KSC260, KSC400, SC604 and KSC707 genotypes. In these two groups, two hybrids, KSC707 and SC604, were common between these groups. KSC706 and SC302 genotypes were in the third group, and KSC703 and KSC705 were in the fourth group. In this diagram, DC370, SC647 and KSC704 genotypes were not in any group ( Figure 4B). By examining the graphs of normal and stress conditions, KSC703 and KSC705 genotypes were in the same group in both conditions, indicating the stability of these two genotypes in terms of the studied traits under stress.

The Centred Scatter Plot
This diagram is a two-dimensional graph used to compare genotypes in two different positions or compare different positions and test environments in two genotypes. This diagonal linear diagram is divided into two parts and shows compatible and stable genotypes in each environment. According to Figure 5, which shows the genotypes in terms of all the traits evaluated in the experiment under normal conditions and moisture stress, KSC400 and SC302 hybrids are among the hybrids that have good performance in all traits under normal conditions. KSC707, KSC703 and DC370 are also hybrids that have better performance under drought stress conditions. The rest of the genotypes were identified as stable intermediate hybrids in both conditions due to their proximity to the line separating the normal and stress positions.

The Centred Scatter Plot
This diagram is a two-dimensional graph used to compare genotypes in two different positions or compare different positions and test environments in two genotypes. This diagonal linear diagram is divided into two parts and shows compatible and stable genotypes in each environment. According to Figure 5, which shows the genotypes in terms of all the traits evaluated in the experiment under normal conditions and moisture stress, KSC400 and SC302 hybrids are among the hybrids that have good performance in all traits under normal conditions. KSC707, KSC703 and DC370 are also hybrids that have better performance under drought stress conditions. The rest of the genotypes were identified as stable intermediate hybrids in both conditions due to their proximity to the line separating the normal and stress positions.

Evaluation of Drought Stress Using Drought-Tolerance Indices
Drought-tolerance indices were analyzed to evaluate the evaluated hybrids under drought stress conditions ( Table 4). The highest and lowest mean yields under normal conditions of humidity and stress did not belong to a specific genotype, so the use of stress tolerance and sensitivity indices is effective in evaluating genotypes. According to the Drought-Tolerance Index (TOL), which is obtained from the difference in the performance of each genotype under normal and stress conditions, tolerant hybrids are considered to be less than this index [30]. Based on this index, the KSC260 genotype was the most resistant hybrid with 1.38, and in the second and third ranks were DC370 (1.42) and KSC400 (1.53) hybrids, respectively. The highest TOL index was related to the SC302 genotype (2.34). Based on the mean productivity index (MP), genotypes are tolerated that have a higher value of this index [7]. Based on this index, KSC260 (6.95), SC302 (6.83) and KSC400 (6.63) genotypes as tolerant genotypes and KSC704 (5.245) and SC647 (5.635) hybrids as sensitive hybrids were identified. Based on the Harmonic Mean (HARM), the genotype with the highest index value was identified as the resistant genotype. Based on this, KSC260 (6.881), SC302 (629.6) and KSC400 (6.546) hybrids were identified as resistant hybrids, and KSC704 (0.512) and SC647 (5.525) genotypes were identified as susceptible hybrids. Based on the Geometric Mean Performance Index (GMP), tolerant genotypes accounted for more of this index. Accordingly, KDC260 (6.91), SC302 (6.72) and KSC400 (6.59) hybrids were identified as resistant hybrids, and KSC704 (5.12) and SC647 (5.58) genotypes were identified as susceptible hybrids. According to the Stress Sensitivity Index (SSI), which is mostly used to remove sensitive genotypes, any genotype with higher values of this index is more sensitive to stress [10]. Accordingly, hybrids of KDC260 (0.71), DC370 (0.81) and KSC400 (0.82) as the most resistant hybrids and genotypes SC302 (1.6) and KSC704 (1.38) as susceptible genotypes were identified. According to the stress tolerance index (STI), the higher the value of this index, the more tolerance of the genotype, based on the genotypes KSC260 (17.3), SC302 (16) and KSC400 (15.9) as resistant genotypes, and KSC704 (10.4) and SC647 (12.2) hybrids were identified as susceptible hybrids. Based on the results obtained from Table 4, it can be concluded that based on droughttolerance indices on hybrids studied in this experiment, KSC260, SC302 and KSC400 hy-

Evaluation of Drought Stress Using Drought-Tolerance Indices
Drought-tolerance indices were analyzed to evaluate the evaluated hybrids under drought stress conditions ( Table 4). The highest and lowest mean yields under normal conditions of humidity and stress did not belong to a specific genotype, so the use of stress tolerance and sensitivity indices is effective in evaluating genotypes. According to the Drought-Tolerance Index (TOL), which is obtained from the difference in the performance of each genotype under normal and stress conditions, tolerant hybrids are considered to be less than this index [30]. Based on this index, the KSC260 genotype was the most resistant hybrid with 1.38, and in the second and third ranks were DC370 (1.42) and KSC400 (1.53) hybrids, respectively. The highest TOL index was related to the SC302 genotype (2.34). Based on the mean productivity index (MP), genotypes are tolerated that have a higher value of this index [7]. Based on this index, KSC260 (6.95), SC302 (6.83) and KSC400 (6.63) genotypes as tolerant genotypes and KSC704 (5.245) and SC647 (5.635) hybrids as sensitive hybrids were identified. Based on the Harmonic Mean (HARM), the genotype with the highest index value was identified as the resistant genotype. Based on this, KSC260 (6.881), SC302 (629.6) and KSC400 (6.546) hybrids were identified as resistant hybrids, and KSC704 (0.512) and SC647 (5.525) genotypes were identified as susceptible hybrids. Based on the Geometric Mean Performance Index (GMP), tolerant genotypes accounted for more of this index. Accordingly, KDC260 (6.91), SC302 (6.72) and KSC400 (6.59) hybrids were identified as resistant hybrids, and KSC704 (5.12) and SC647 (5.58) genotypes were identified as susceptible hybrids. According to the Stress Sensitivity Index (SSI), which is mostly used to remove sensitive genotypes, any genotype with higher values of this index is more sensitive to stress [10]. Accordingly, hybrids of KDC260 (0.71), DC370 (0.81) and KSC400 (0.82) as the most resistant hybrids and genotypes SC302 (1.6) and KSC704 (1.38) as susceptible genotypes were identified. According to the stress tolerance index (STI), the higher the value of this index, the more tolerance of the genotype, based on the genotypes KSC260 (17.3), SC302 (16) and KSC400 (15.9) as resistant genotypes, and KSC704 (10.4) and SC647 (12.2) hybrids were identified as susceptible hybrids. Based on the results obtained from Table 4, it can be concluded that based on drought-tolerance indices on hybrids studied in this experiment, KSC260, SC302 and KSC400 hybrids are drought-tolerant hybrids. KSC704 and SC647 genotypes were identified as susceptible hybrids (Table 4). Table 5 also shows the selected hybrids based on drought-tolerance indices.

Correlation of Drought-Tolerance Indices
Correlation coefficients based on data obtained from grain yield under normal humidity and stress conditions with drought-tolerant indices showed that TOL, MP, HARM and SSI indices with average grain yield under normal humidity conditions (Yp); index GMP with mean grain yield under stress (Ys); MP, HARM and SSI indices with TOL index; HARM and SSI indices with MP index; and SSI index with HARM index had a positive and significant correlation at the probability level of 0.01. (Table 6). Additionally, based on the correlation diagram drawn between the data obtained from the average grain yield under normal conditions of moisture (Yp) and moisture stress (Ys) as well as drought-tolerance indices, it can be concluded that between MP GMP, there is a significant positive correlation between HARM, STI, Yp and Ys. According to the 90-degree angle between the vectors of MP and TOL, the correlation was estimated to be zero ( Figure 6). Many researchers have reported a significant positive correlation between Yp and Ys, suggesting that high-yielding genotypes under normal conditions can perform well under stress conditions [13,30].

Polygon Diagram
Based on the obtained polygon diagram in terms of drought-tolerance indices ( Figure  7), SC302, KSC260, DC370, SC647 and KSC704 genotypes were identified as more favorable hybrids than other evaluated hybrids. Additionally,, in each section, the KSC260 genotype was more desirable than other genotypes in MP, GMP, STI, HARM and Ys indices. The KSC704 genotype was superior to other genotypes in the SSI index. In his study on wheat genotypes, Karaman used this type of graph to investigate the response of different genotypes to drought-tolerance indices [31].

Polygon Diagram
Based on the obtained polygon diagram in terms of drought-tolerance indices (Figure 7), SC302, KSC260, DC370, SC647 and KSC704 genotypes were identified as more favorable hybrids than other evaluated hybrids. Additionally, in each section, the KSC260 genotype was more desirable than other genotypes in MP, GMP, STI, HARM and Ys indices. The KSC704 genotype was superior to other genotypes in the SSI index. In his study on wheat genotypes, Karaman used this type of graph to investigate the response of different genotypes to drought-tolerance indices [31].

Principal Components Analysis in Drought-Tolerance Indices
After analyzing drought-tolerance indices and mean grain yield under normal conditions and moisture stress in the studied hybrids, based on principal component analysis, the most changes were expressed in the first two components, and more than 99% of the data variance by the two components was justified ( Table 7). The first component accounted for more than 78% of the data variance in this analysis. This component showed a high correlation with the average performance under water stress (Ys), MP, HARM, GMP and STI indices. A negative correlation was identified with TOL and SSI indices. Hence, under stress conditions, the first component was named the grain yield tolerance component. The second component explained more than 20% of the data variance. A positive correlation was observed with the mean grain yield under normal conditions (Yp), and the highest correlation was with TOL and SSI indices. This component negatively correlated with the average grain yield under moisture stress (Ys) and was named the grain yield stability component under normal moisture conditions. In their study, Ali and El-Sadek evaluated drought-tolerance indices using the analysis of principal components under stress and non-stress conditions. As a result, the first two components comprised more than 98% of the total changes related to the index for drought tolerance [32].

Materials and Methods
In this experiment, the effect of drought stress on grain yield and morphological characteristics and yield components, as well as a comparison of 12 commercial single cross hybrids (Table 8)

Principal Components Analysis in Drought-Tolerance Indices
After analyzing drought-tolerance indices and mean grain yield under normal conditions and moisture stress in the studied hybrids, based on principal component analysis, the most changes were expressed in the first two components, and more than 99% of the data variance by the two components was justified ( Table 7). The first component accounted for more than 78% of the data variance in this analysis. This component showed a high correlation with the average performance under water stress (Ys), MP, HARM, GMP and STI indices. A negative correlation was identified with TOL and SSI indices. Hence, under stress conditions, the first component was named the grain yield tolerance component. The second component explained more than 20% of the data variance. A positive correlation was observed with the mean grain yield under normal conditions (Yp), and the highest correlation was with TOL and SSI indices. This component negatively correlated with the average grain yield under moisture stress (Ys) and was named the grain yield stability component under normal moisture conditions. In their study, Ali and El-Sadek evaluated drought-tolerance indices using the analysis of principal components under stress and non-stress conditions. As a result, the first two components comprised more than 98% of the total changes related to the index for drought tolerance [32].

Materials and Methods
In this experiment, the effect of drought stress on grain yield and morphological characteristics and yield components, as well as a comparison of 12 commercial single cross hybrids (Table 8) under normal conditions and humidity stress in a randomized complete block design (RCBD) in three replications in the research field Islamic Azad University, Karaj Branch, was examined. Karaj region has a longitude of '54 • 50 E' and latitude of '55 • 35 N', is 1312 m above sea level and has an average annual rainfall of 247.3 mm. A separate experiment was considered for each environmental condition (normal and drought stress). Specifications of each experimental plot were planted, including four lines with a length of 2 m and planting lines with a distance of 75 cm. Planting, holding and harvesting operations were performed accurately under normal conditions and humidity stress. It was determined based on soil sampling and 50% (normal irrigation), and stress was applied to apply irrigation stress. Sampling and taking notes were performed from the two middle rows and the plant height pre-harvest and other post-harvest traits. The studied traits include plant height (PH), ear length (EL), ear diameter (ED), number of seeds per row (NGR), number of rows per ear (NRE), grain width (GW), grain length (GL), grain thickness (GT), 1000-grain weight (TWG) and grain yield (YLD). (Table 8). The soil characteristics of the cultivated area are presented in Table 9.   25 22 To calculate drought-tolerance indices from tolerance index (TOL), mean productivity (MP), harmonic mean (HARM), geometric mean productivity (GMP), stress sensitivity index (SSI) and stress tolerance index (STI), the following formulas were used: In these equations, Yp is the average yield under normal moisture conditions,Ӯp is the average yield of all genotypes under normal moisture conditions, Ys is the average yield under moisture stress conditions andӮp is the average yield all genotypes under drought stress conditions.
For studying the genotype × trait interaction, Yan and Rajcan [16] method was used as below (Equation (7)): where α ij : the average amount of genotype i for every trait j, β j : the average amount of all the genotypes for the traits, and σ j : standard deviation of the trait j in the average genotypes. ε ij : the amount of genotype i remained in the trait j, λ n : certain amount for the main element (PC n ), ξ i : the amount of PC n for the genotype i, and η jn : the amount of PC n for the genotype j. SAS.v9.2 software was used in the statistical analyzes, which included analysis of variance, comparison of means by Duncan method, correlation coefficients between traits and drought-tolerance indices, and principal components analysis (PCA). Excel software was also used to analyze drought-tolerance indices, and Genstat.v12 software was used to analyze correlation graphically, polygon diagrams, rank genotypes based on ideal genotype, the grouping of genotypes, and Centered Scatter Plot.

Conclusions
KSC260, SC302 and KSC400 hybrids were identified as drought-tolerant hybrids, and KSC704 and SC647 genotypes were identified as susceptible hybrids based on droughttolerance indices for the hybrids studied in this experiment. KSC260, KSC704, KSC705 and KSC706 genotypes are identified as desirable hybrids in both conditions. It can be concluded based on this diagram that the number of rows per ear shows good stability and performance in terms of adjectives. Based on the correlation coefficients of droughttolerance indices, mean grain yield under normal moisture conditions (Yp) with TOL, MP, HARM and SSI indices and mean grain yield under humidity stress (Ys) with GMP index had a positive and significant correlation. The principal components (PCA) analysis on drought-tolerance indices also showed that the first two components explained more than 99% of the variance. The first component was the grain yield tolerance component under stress conditions, and the second component was the grain yield stability component under conditions. Finally, it can be concluded that the KSC704 hybrid as a hybrid was superior to other studied hybrids in terms of grain yield under normal conditions and stress and the KSC260 hybrid was superior as a hybrid in terms of all studied traits in drought stress.

Conflicts of Interest:
The authors declare that they have no conflict of interest.