Drought is one of the most important problems in crop production in the world, especially in arid and semi-arid regions of the world such as Iran, and more than any other environmental factor, it limits plant growth and reduces crop production. Drought stress causes the cessation of root and aerial growth, as well as a reduction in leaf area, and as a result, reduces plant growth and development. In addition, drought stress can lead to the production of oxygen free radicals in plant cells and the occurrence of oxidative stress. In numerous studies, the negative effects of drought stress on the abundance and composition of soil microbial populations, which play a key role in crop production, especially in sustainable systems, have been proven. Achieving sustainable agriculture requires optimal use of available water resources, and one of the solutions to increase the efficiency of water use of agricultural products in these systems is the use of ecological inputs such as super-absorbent hydrogels. In pepper, the effects of different irrigation levels (100, 80, 60, and 40 percent of water requirement) on traits such as the number and weight of fruit per plant, root weight and volume, fruit yield, and water use efficiency were investigated and reported that the highest and lowest values ​​of the studied traits were obtained in the treatments of 100 and 40 percent of water requirement, respectively. In another study, the shoot to root ratio of thyme species decreased under the influence of drought stress, but the amount of volatile essential oil compounds and the amount of thymol in the plant increased under these conditions. A decrease in grain yield, thousand-grain weight, harvest index, and plant height under drought conditions has also been reported in different sorghum hybrids. Superabsorbent polymers are organic compounds and are synthetically made from potassium polyacrylate and polyacrylamide copolymers. They can rapidly absorb and retain water up to several times their volume, increase the water retention capacity of the soil, and ultimately improve plant growth by reducing the effects of drought stress. These materials are odorless, colorless, and non-polluting to soil, water, and plant tissue with neutral acidity and have the ability to absorb water 300 to 400 times their weight. Today, superabsorbent polymers are widely used in agriculture, and their role in reducing the severity of drought stress and plant mortality, as well as increasing crop production, has been proven in numerous studies. In one study, the effect of different levels of superabsorbent on corn yield and yield components was investigated and reported that with increasing amounts of superabsorbent consumption, corn grain and forage yield increased significantly compared to the control treatment. In another study, after investigating the effect of different amounts of superabsorbent moisture and humic acid under deficit irrigation conditions in corn, it was reported that the highest grain yield belonged to the treatment of 120 kg/ha of superabsorbent moisture, eight kg/ha of humic acid, and an irrigation area of ​​300 cubic meters/ha. In two types of loamy and sandy soil textures, the effect of superabsorbent moisture on the growth characteristics of Artemisia vulgaris plant under drought stress conditions was investigated and it was reported that superabsorbent in both soil textures and especially in sandy soil led to an improvement in plant height, shoot dry weight, root dry weight, and root length and volume compared to the control through the expansion of the root system. The use of superabsorbent in spinach fields prevented excessive increases in soil acidity and electrical conductivity, and as a result, increased water and nutrient absorption, leading to improved plant growth characteristics. In another study, the rate of water penetration into the depths of sandy soil decreased from 12.4 to 84.75 percent under the application of superabsorbent moisture. In another study, the agroecological characteristics of basil were investigated under the influence of the application of superabsorbent moisture, humic acid and irrigation cycles and it was reported that in a 5-day irrigation cycle, levels of zero, 40 and 80 kg/ha of superabsorbent led to an increase of 13, 50 and 17% in dry matter yield, respectively, compared to a 10-day irrigation cycle. Today, in order to analyze phenomena correctly and accurately, statistics and statistical techniques are widely used in various sciences. The statistical technique of principal component analysis (PCA) 1 is a statistical method that is often used to examine a group of correlated variables. The most important applications of this method can be found in the analysis of multiple indicators, measuring and recognizing complex structures, indexing and reducing the dimensions of data. In fact, in this method, a smaller number of factors called principal components are selected from among the primary factors, so that some less important information is eliminated. The first extracted component takes into account the largest amount of data dispersion from the entire data set. This means that the first component is correlated with at least a number of variables. The second extracted component has two important properties, first, it takes into account the largest amount of data set that is not accounted for by the first component, and second, it is not correlated with the first component. In other words, regardless of the previous component, each component explains less variance as it moves from the initial component to the final component. That is, the first principal component always explains the largest amount of variance and the last component explains the least variance, so that not much information is lost by removing the final components.
Given the insufficient access to water in most parts of the country and the importance of increasing the water use efficiency of various crops, as well as the limited information on the use of multivariate statistical techniques to more accurately examine the variables involved in the water use process, this study aimed to determine the main components in the water use efficiency of beans, sesame, and corn as three important crop species in response to the application of increased amounts of superabsorbent hydrogel.

Materials and methods

This research was conducted in the 2015-2016 crop year at the research farm of the Faculty of Agriculture of Ferdowsi University of Mashhad, located 10 km east of Mashhad (28°59′E longitude, 15°36′N latitude, and 985 m above sea level) on a land area of ​​approximately 250 m2 in split plots in a randomized complete block design with three replications for three plants: bean, sesame, and corn. Two irrigation levels of 50 and 100 percent of the water requirement of the studied plants were used in the main plots, and 80 kg/ha of super absorbent hydrogel and no application were used in the subplots. The dimensions of the main plots were 3 × 6 m and the dimensions of the subplots were 3 × 3 m. The experimental design map and the arrangement of treatments in the farm plots are given in Table 1. Sesame seeds (Esfarayen mass), Corn (Single Cross 704) and beans (Darakhshan) were planted on May 10, 2016, in rows spaced 50 cm apart and with densities of 50, 7, and 20 plants per square meter, respectively, in the corresponding plots and immediately irrigated by the leakage method. The selected amount of super absorbent was selected based on the results of some previous studies (Jahan et al., 2016; Jahan et al., 2015; Jahan et al., 2013.) In order to calculate the water requirement of sesame, corn and beans in Mashhad conditions, OPTIWAT software was used (Alizadeh et al., 2007). With information on the length of the growing season of beans, sesame and corn, data on daily evapotranspiration and an irrigation interval of 7 days, the volume of water required per irrigation for beans, sesame and corn in the 100% water requirement treatment was calculated as 300, 200 and 400 cubic meters per hectare, respectively, and for 50% water requirement, it was determined as 150, 100 and 200 cubic meters per hectare, respectively, and these values ​​were considered the same for the entire growing season for each plant and in each irrigation session. Before the experiment, soil samples were taken from a depth of zero to 30 cm and sent to the laboratory to determine the physical and chemical properties (Table 2). In order to prepare the land with an emphasis on minimum tillage, only disking operations were performed and all subsequent steps were performed by the worker. And it was done with the help of a shovel. Then the superabsorbent quantities were spread evenly on the surface of the desired plots and immediately entered into the soil by a shovel.
The characteristics of the superabsorbent used in the experiment are given in Table 3.

In order to avoid mixing the water of the plots with each other, a separate irrigation pipe was considered for each replication and each plot. To achieve appropriate density, thinning was performed after the plant reached the 4-leaf stage. After thinning, irrigation was performed every 7 days and its amount was recorded and controlled by a meter depending on the experimental treatment. Considering the use of the pipe and meter and proper leveling of the land, the irrigation efficiency was evaluated as desirable. In order to control weeds, three times of manual weeding were performed at 15, 30 and 45 days after planting, respectively. No chemical herbicides, pesticides or fungicides were used during land preparation and during the growth period. The depth of bean root development was 75 and 80 cm. In order to calculate the crop growth rate (CGR) of some growth indices, destructive sampling was carried out from 30 days after emergence, every 15 days, by removing the effects of 0 square meters per side and randomly from the area of ​​25 experimental plots, and traits such as plant height, leaf area and dry weight of aerial parts were measured. To determine the leaf area index (Lea), a leaf area measuring device (Area Meter, Delta T, Co. Ltd, UK) was used. The crop growth rate during the growing season was calculated by equation 1:

In this equation, the time interval t1-t and 2 (g) are the initial and secondary weights, respectively. (day) Different samplings were carried out at the end of the growing season, with the beginning of the ripening and yellowing stage of the plants, after removing the marginal effect, the plants present in one square meter of each plot were randomly harvested and the grain yield and dry matter yield of the studied plants were determined. At the end of the harvesting operation, the nitrogen, phosphorus, and soil content of the experimental plots were measured. The pH and EC of the soil nitrogen were determined according to the AOAC Official Kjeldahl method and using Method 968.06 (4.2.04). The soil phosphorus content was determined by the Semi-Automated Distillation Unit spectrophotometric method (Horwitz and Latimer, 2005). To calculate the productivity, the irrigation water consumption (WUE) (kg Seed.m-3 Water) was used: Equation 2.

In this equation, the rainfall (mm) and irrigation water (m3.ha- ) are standardized to the principal components of the different stages as follows: (Moghadam et al., 2009) so that the variables Xp, X2, X1 have mean zero and variance one. The matrix is ​​calculated. If the first stage is performed, the covariance of the covariance matrix C will be equal to the correlation matrix. The eigenvectors λ, a and λ2, λ, λ corresponding to the 1st eigenvalues ​​of the ith component are calculated. Therefore, the coefficients a and λ2, λ are displayed and the variance of the main ai is removed with the components that only show a small part of the data variation. Data analysis was performed using principal components analysis of variance (ANOVA) and plotting of graphs using Minitab Ver. 17 and SAS Ver. 9.4 software. Means were compared using Duncan’s multiple range test at a probability level of 5%.

Results and discussion of the effect of irrigation levels and super absorbents on water use efficiency

The water use efficiency of all three plants, beans, sesame, and corn, was affected by irrigation levels and superabsorbents (Table 4). In beans, sesame, and corn, when 50% of the water requirement was met, water use efficiency was 32, 43, and 28% higher, respectively, than when 100% of the water requirement was met (Table 5). The use of superabsorbents increased the water use efficiency of beans, sesame, and corn by 46, 29, and 43%, respectively (Table 5). In all three plants studied, the highest water use efficiency was achieved when only 50% of the plant’s water requirement was met and superabsorbents were used at the same time (Table 6). Superabsorbents probably improve soil physical properties and structure, reduce soil bulk density (Abedi-Koupai et al., 2008), increase nutrient use efficiency, and increase seed germination and emergence (Eneji et al., 2013), reducing plant water requirements (Xie et al., 2011) and reducing evaporation from the soil surface (Nykanen et al., 2011) have led to improved water use efficiency of crops. In a study, while investigating the effect of different levels of superabsorbent on corn yield and yield components, it was reported that with increasing amounts of superabsorbent consumption, corn grain and forage yield increased significantly compared to the control treatment (Karimi and Naderi, 2007). In another study, after investigating the effect of different amounts of superabsorbent moisture and humic acid under irrigation deficiency conditions in corn, it was reported that the highest grain yield belonged to the treatment of 120 kg/ha superabsorbent moisture, eight kg/ha humic acid and an irrigation area of ​​300 cubic meters/ha (Jahan et al., 2016).

The correlation coefficients between the studied traits, as seen in Table 7, had a positive and significant correlation between bean grain yield and dry matter yield (** = 0.86 r), grain weight per plant (** = 0.95 r), plant height (** = 0.93 r), leaf area index (** = 0.82 r), and crop growth rate (** = 0.86 r). On the other hand, considering the positive correlation between grain yield and nitrogen content (** = 0.80 r) and soil phosphorus (** = 0.77 r) (Table 7), it seems that improving grain yield is not far-fetched when using inputs that increase the amount or availability of these elements in the soil. The positive correlation between dry matter yield and traits such as plant height (** = 0.92 r), leaf area index (** = 0.85 r) and crop growth rate Crop growth (r = 0.86**) indicated the effective role of the aforementioned traits in improving biological performance (Table 7). According to the experimental results, increasing plant height, crop growth rate, and soil phosphorus content were effective in improving water use efficiency of beans (Table 7). Seed yield and dry matter in sesame had a positive and significant correlation with the traits of seed weight per plant, plant height, crop growth rate, phosphorus content, and soil pH (Table 7). In other words, to achieve maximum grain yield and dry matter, inputs can be used that are effective in improving the aforementioned traits. The positive correlation of water use efficiency with plant height, leaf area index and soil nitrogen content indicates the importance and role of these traits in increasing water use efficiency (Table 7). In corn, soil nitrogen and phosphorus content played a determining role in grain yield and dry matter (Table 7). In addition, it seems that by performing agronomic operations, including the use of eco-friendly inputs such as super-absorbent hydrogel and the role of these inputs in increasing plant height, leaf area index and crop growth rate, it is possible to benefit from optimal production (Table 7). Water use efficiency in all three studied plants improved with increasing plant height (Table 7). It seems that with increasing plant height, the amount of shading of the plants on the ground increased and as a result, water loss was prevented. In beans and corn, increasing crop growth rate led to improved water use efficiency. Also, in sesame and corn, factors increasing leaf area index caused water use efficiency in these plants to increase (Table 7.) It seems that the studied plants, when they have a larger leaf area, have the ability to send more photosynthetic materials to the roots, which probably leads to the growth and development of their roots and, as a result, increased water use efficiency. The positive and significant correlation of the amount of soil nutrients nitrogen and phosphorus with the water use efficiency of the studied plants (Table 7) also indicates the great importance of these elements, especially in conditions of water limitations.
Decomposition into principal components and determination of the effective component on water use efficiency The eigenvalues ​​1 and the corresponding variance 2 of the principal components in the water use efficiency of beans, sesame and corn in response to the application of increased amounts of super absorbent moisture are shown in Tables 8 and 9. Accordingly, the variables in all three plants were decomposed into two components. In beans, sesame and corn, the first component explained 73, 60 and 80 percent of the variance in the variables, respectively.
The cumulative variance of the second component for beans, sesame and corn was 89, 91 and 95 percent of the total variance, respectively (Tables 8 and 9). As can be seen in Figure 1 In beans, the variables of grain yield, dry matter yield, grain weight per plant, plant height, crop growth rate, nitrogen, phosphorus, and soil pH were placed on the first component, and the variables of leaf area index, soil salinity, and water use efficiency were placed on the second component. The dendrogram from cluster analysis 3 (Figure 2) largely confirmed the results of the principal component analysis. In sesame, the variables grain yield, dry matter yield, grain weight per plant, plant height, crop growth rate, and soil phosphorus and pH had the highest loading on the first component, and the variables leaf area index, soil nitrogen, soil salinity, and water use efficiency had the highest loading on the second component (Figure 3). The dendrogram from cluster analysis (Figure 4) confirmed the results of the principal component analysis. In corn, the principal component analysis resulted in the variables grain yield, dry matter yield, grain weight per plant, plant height, leaf area index, crop growth rate, nitrogen, and soil pH having the highest loading on the first component, and the variables soil phosphorus, soil salinity, and water use efficiency having the highest loading on the second component (Figure 5). The dendrogram from cluster analysis (Figure 6) shows the results of the principal component analysis Confirmed the original.

Comparing the variance of the principal components (specific values) with the variance of the primary variables shows the relative importance of the principal components. After standardizing the primary variables, their mean and variance become zero and one, respectively. Therefore, the variance of the first principal component in beans is 8.04 times that of the primary variables. Principal component analysis of the data on beans indicates that 73% of the total variance explained by the components is related to the first component (Table 8). Since the variables of grain yield, dry matter yield, grain weight per plant, plant height, crop growth rate, and nitrogen, phosphorus, and soil pH had the highest load on the first component (Figure 1 and Table 9), and also, considering that the variables in each component have a high correlation with each other (Moghadam et al. 2009), it seems that agronomic management in order to improve the aforementioned traits leads to: It will increase grain yield. According to the ecophysiological principles and foundations of crop plants, the first component that reflects the inherent characteristics of the plant’s spatial arrangement (high loading of plant height variables on this component) can be called the spatial arrangement component and the second component that reflects the characteristics related to water use efficiency (high loading of the water use efficiency variable on this component) can be called the water use efficiency component.

The correlation of grain yield with the first component in sesame and corn was 0.34 and 0.33, respectively (Tables 8 and 9). The variables of grain yield, dry matter yield, grain weight per plant, plant height, crop growth rate, and phosphorus and soil pH in sesame had the highest loading on the first component (Table 9), therefore, this component can be an indicator of crop yield. Soil nitrogen and soil salinity had the highest weight on the second component, therefore, this component seems to be descriptive of soil conditions.
In corn, the variables soil phosphorus, soil salinity, leaf area index and water use efficiency had the highest weights with a negative sign on the second component (Table 9). As previously stated, the variables within each component have a high correlation with each other, therefore, it seems that any change in the amount of soil phosphorus, soil salinity and leaf area index will cause a change in water use efficiency. In the first component, important variables such as grain yield, dry matter yield and soil nitrogen had the highest weights (Table 9), therefore, it seems that this component can be an indicator of crop yield. In contrast, the second component, considering the variables that have a higher load on it than the first component (phosphorus and soil salinity), can reflect soil characteristics. Comparison of the eigenvectors related to the first components of beans, sesame and corn, (Equations 3, 4 and 5) revealed that the coefficients of the yield variables Grain, dry matter yield, grain weight per plant, plant height, leaf area index, soil phosphorus content, soil nitrogen content (in beans and corn), and crop growth rate were positive and had significant values. Therefore, any management to improve dry matter yield, grain weight per plant, plant height, leaf area index, soil phosphorus and nitrogen content, and crop growth rate will lead to improvement in grain yield and ultimately improve water use efficiency.

( 3 )
PC1Bean=0.33(SY)+0.32(BY)+0.33(SW)+0.34(H)+0.28(LAI)+0.32(CGR)+0.29(Soil N)+0.30(Soil P)-0.15(EC)+0.32(pH)+0.23(WUE)
( 4 )
PC1Sesame=0.34(SY)+0.33(BY)+0.35(SW)+0.33(H)+0.26(LAI)+0.35(CGR)+0.05(Soil N)+0.36(Soil P)-0.21(EC)+0.36(pH)+0.14(WUE)
( 5 )
PC1Corn=0.33(SY)+0.33(BY)+0.32(SW)+0.33(H)+0.32(LAI)+0.33(CGR)+0.33(Soil N)+0.24(Soil P)-0.08(EC)+0.33(pH)+0.24(WUE)

A significance level of 5% was considered for each equation. In a study, wheat (Triticum aestivum L.) yield was predicted using soil characteristics using principal component analysis and reported that the majority of the variability in the field was caused by fertility factors and the resulting regression models explained 57% of the total yield variability (Ayubi et al., 2009). In another study, morphological and phenological traits in a number of lettuce (Lathyrus sativus) genotypes were analyzed into principal components and reported that the first and second components explained 69.1% of the total variance changes (Daneshgilvai et al., 2011). The results of principal component analysis in identifying rapeseed (Brassica napus L.) cultivars for drought tolerance indicated that the variables under study were decomposed into two principal components, germination capacity and seedling growth, and LiKord and Okapi cultivars were identified as drought tolerant cultivars. Drought was identified (Majidi, 2012). It has been proven that drought stress has severe negative effects, especially in the three stages of flower emergence and formation, pollination and fertilization, and grain formation. Drought stress prevents the emergence of flower stem cells. Stress in the pollination and fertilization stages reduces the number of seeds due to pollen grain shedding, and stress-induced stigma wilting prevents pollen tube growth. In the grain filling stage, stressed plants, instead of allocating photosynthetic materials to the grain, use nutrients to cope with the stress, and as a result, grain weight and, subsequently, grain yield are affected by the negative effects of drought stress. Therefore, the performance of most of the studied plants under conditions of 100% water requirement seems reasonable. The results of Pandey and Maranvill’s (2000) research on the application of moisture stress at different stages of corn growth showed that applying moisture stress reduced grain yield, number of grains per ear, hundred-grain weight, stem diameter, and plant height. Superabsorbents improve the quantitative and qualitative characteristics of various products by improving the physical properties and structure of the soil, reducing the apparent specific gravity of the soil (Abedi-Koupai et al., 2008), increasing the efficiency of nutrient use, increasing seed germination and emergence (Eneji et al., 2013), reducing plant water requirements (Xie et al., 2011), and reducing evaporation from the soil surface (Nykanen et al., 2011). These materials are odorless, colorless, and do not pollute the soil, water, or plant tissue. They are also completely safe and non-toxic, and ultimately decompose in the soil into carbon dioxide, water, ammonia, and potassium ions (Nazarli et al., 2010). In a study, while investigating the effect of different levels of superabsorbent on corn yield and yield components, it was reported that with increasing the amount of superabsorbent used, corn grain and forage yield increased significantly compared to the control treatment (Karimi and Naderi, 2007). In another study, after investigating the effect of different amounts of superabsorbent moisture and humic acid under irrigation deficiency conditions in corn, it was reported that the highest grain yield belonged to the treatment of 120 kg/ha of superabsorbent moisture, eight kg/ha of humic acid and an irrigation area of ​​300 cubic meters/ha (Jahan et al., 2016). It was also reported that the use of superabsorbent, while reducing drought stress in corn, led to a 16% increase in the yield of this plant (Khadem et al., 2011).

Overall conclusion

In general, the results of the experiment showed that the use of super absorbent hydrogel was effective in improving the water use efficiency of all three bean, sesame and corn plants, but in this respect the greatest effect was observed in corn plants. It seems that considering that in terms of morphology, the size of corn plants is larger than bean and sesame plants, the issue of optimal use of water resources and root development and maintaining moisture in the root environment is of great importance in this plant. The results of the principal component analysis showed that in all three plant species studied, the variables of soil salinity and water use efficiency were in the second component, and since the variables within each component have a high correlation with each other, it seems that paying attention to the factors effective in reducing soil salinity will increase water use efficiency.
In all three crops, the placement of grain yield, with variables such as plant height and crop growth rate in one component, indicates that to achieve maximum production, environmentally friendly inputs such as super absorbents can be used, which increase plant height, leaf area index, soil phosphorus availability, crop growth rate, and ultimately increase water use efficiency. The average volume of water saved as a result of using super absorbents under conditions of providing only 50% of the water requirement for all three crops is 450 cubic meters per hectare per irrigation.

If the growth period for all three crop species is considered to be 90 days and the irrigation interval is 7 days, the total volume of water saved will be 5850 cubic meters per hectare, which, considering the area under cultivation of crops and the current conditions of increasing water shortage, the amount of water saved can be very significant. Designing and implementing research focused on estimating and comparing the economic and ecological benefits of reducing irrigation water consumption and increasing water consumption efficiency as a result of the use of superabsorbents can justify the relatively high cost of this eco-friendly input and pave the way for its widespread use by farmers and producers in the agriculture and horticulture sector.