Introduction

Water stress is a type of abiotic stress that profoundly affects agricultural productivity. It is a major threat to global food security (Shao et al. 2008; Chaudhry and Sidhu 2022). As water resources become increasingly scarce due to climate change and other environmental factors, understanding plant responses to water stress is crucial to developing strategies to mitigate its adverse effects (Seleiman et al. 2021). Nitrogen (N) is an essential nutrient that plays an important role in various physiological processes and stress responses in plants, making it essential for plant growth and development (Fageria et al. 2011; Dubey et al. 2021). Investigating the interactions between water stress and nitrogen fertilizer application is essential for sustainable agricultural practices and improving crop resilience under water-limited conditions (Ullah et al. 2019).

Barley (Hordeum vulgare L.) is the fourth most important cereal crop in the world and is the most important crop in many regions of the world. It exhibits remarkable adaptability to diverse environmental conditions, including water stress (El-Hashash and El-Absy 2019). However, the extent of drought tolerance varies among different barley cultivars, emphasizing the significance of selecting drought tolerant varieties and optimizing nitrogen fertilizer management to maximize crop yields and ensure food security (Barati et al. 2015). Barley is divided into two distinct categories based on the number of rows of flowers in its spike: two-row barley, characterized by central florets that yield kernels and sterile lateral florets; and six-row barley, which features a spike with three spikelets notched on each side, with each notch containing a small individual flower that matures into a kernel (Vaezi et al. 2017). The higher sugar content in two-row barley makes it the preferred choice for malt production; whereas, the increased protein content in six-row barley renders it more suitable for animal feed. Nevertheless, Heuze and colleagues have reported that two-row and awnless varieties exhibit superior nutritive quality as forage (Fatemi et al. 2022).

Understanding the underlying mechanisms governing plant responses to water stress and nitrogen availability is crucial for developing effective strategies to enhance stress tolerance and improve crop productivity. Water stress triggers a complex array of physiological, biochemical, and molecular responses in plants, including stomatal closure, adjustments in photosynthetic efficiency, osmotic regulation, and activation of antioxidant defense systems (Chaves et al. 2009). Nitrogen availability influences plant metabolism, nutrient uptake, and assimilation, so affecting various aspects of plant growth and stress tolerance mechanisms (Marschner 2012).

While several studies have investigated the effects of water stress and nitrogen fertilization on different crop species, limit information is available regarding the specific responses of two-row and six-row barley cultivars to these factors. Two-row and six-row barley cultivars have different spike form, grain yield and other agro-morphological traits, which can create a potential difference in their response to water stress and nitrogen availability. Hence, exploring the varietal differences and understand the mechanisms employed by these cultivars under stress conditions are necessary for breeding programs and agriculture (Hackett 2016).

This study aims to investigate the impact of water stress and nitrogen fertilizer application on the performance and stress response mechanisms of two-rowed and six-rowed barley cultivars, through a comprehensive assessment of agro-morphological, physiological and biochemical traits, and clarify the intricate mechanisms underlying drought tolerance and nitrogen utilization efficiency in these cultivars.

Materials and methods

Plant material, experimental site and design

This study evaluated ten barley genotypes, including five two-row genotypes (Behrokh, CITO, M318, M936, M311) and five six-row genotypes (Goharan, Nosrat, Yusef, M413, Mahtab) (Table 1). The study was conducted at Kabutarabad Research Farm of Isfahan Agricultural and Natural Resources Research and Education Center (32° 8′ 30ʺ N, 51° 8′ 21ʺ E; 1630 m altitude) Isfahan, Iran, over 2 years.

Table 1 - Specifications and codes of evaluated barley genotype
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Precrops, soil tillage, and seedbed preparation

The experimental site was left fallow the previous year. Primary tillage involved plowing followed by disc harrowing to prepare the seedbed. Soil health was monitored regularly, and soil samples were analyzed for physical and chemical properties before planting. Soil samples were taken randomly from 0–30 to 30–60 cm depths before planting to determine their physical and chemical characteristics. The soil analysis results are shown in Table 2.

Table 2 Physical and chemical properties of soil at the experimental site
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Fertilizer use

In both years, two nitrogen levels were included: no nitrogen application (N0) and 69 kg N ha–1 from urea fertilizer (N1). Urea fertilizer was applied via fertigation along with irrigation at three specific growth stages according to the Zadoks et al. (1974): stage 13 (seedling growth), stage 31 (stem elongation), and stage 51 (heading). At each of these stages, 23 kg ha–1 of nitrogen was administered.

Phytosanitary conditions

Throughout the growing season, manual weeding was conducted to manage weed pressure. No herbicides or pesticides were applied during land preparation or the plant’s growing season to ensure the focus remained on the effects of the nitrogen treatments and irrigation levels. Phytosanitary conditions were closely monitored throughout the study period. No significant disease or pest infestations were observed, and any minor infestations were managed manually to avoid chemical interference. Soil and plant health were maintained to ensure the accuracy and reliability of the experimental results.

Experimental design

Two experiments were carried out over two years. In both years, treatments were arranged as split–split plots in a randomized complete block design with three replications. Treatments included two irrigation levels (irrigation after 50% and 80% depletion of the available soil water as control levels and water deficit stress) as main plots, two levels of nitrogen (non-use nitrogen (N0) and 69 kg N ha–1, from urea fertilizer (N1)) as the subplots, and ten barley genotypes (two-row and six-row) as the sub-subplots.

Seeds were sown with a density of 350 seeds m2 using a cereal row planting machine (Wintersteiger Plotman) on October 22, 2021, and October 18, 2022. Each subplot consisted of 6 rows, 6 m in length, spaced 20 cm apart.

Irrigation treatments

From planting to establishment, all plots were irrigated based on the crop water need (based on 40% soil water depletion) in order to avoid plant mortalities. Water stress was applied at the tillering stage, according to the maximum allowable depletion (MAD) of soil available water (SAW) (i.e., between ‒ 0.03 and ‒ 1.5 MPa) (Kramer and Boyer 1995). From planting to the tillering stage (i.e., plant full establishment), irrigation was done in both environments at the same time and to the same extent, based on crop water need (50% soil water depletion as the control) water stress treatment (80% depletion of SAW) was initiated after this stage. Soil–water potential based on depletion of SAW was determined using a soil moisture release curve. The following equations were used to the irrigation treatments:

$${\text{SAW}} = (\theta_{{{\text{fc}}}} - \theta {\text{PWP}}) \times D_{{{\text{rz}}}}$$
$$V_{{{\text{irrig}}}} = {\text{SAW}} \times {\text{MAD}}$$

θfc is the volume basis soil–water content at field capacity (%), θPWP is the volume basis soil–water content on wilting point (%), Drz is the depth of the root zone (cm), Virrig is the volume of irrigation water and MAD is soil moisture depletion (50% and 80% of ASW). The drip irrigation tape system had an approximate application rate of 1.3 L/h for each dripper and water volume was measured using the irrigation meter. The distance between two drip irrigation tapes was 30 cm and each tape were irrigated two rows of crops.

Measurement of the biological traits

Sampling was carried out at the time of emergence of the ear from the leaves and the plants were harvested at the time of physiological maturity.

Leaf area index (LAI)

The leaf area index was measured after the appearance of the flag leaf by using a digital leaf area meter (Model GA-5, OSK Company, Tokyo, Japan). For this purpose, 5 plants were randomly chosen from each plot, and their leaf areas were meticulously measured. The average leaf area of the five selected plants was calculated. Subsequently, the leaf area index was computed for each plot by utilizing the average leaf area and considering the plant density per square meter.

Relative water content (RWC) and electrolyte leakage index (ELI)

Relative leaf water content was determined at the heading stage by measuring the fresh weight (FW), turgid weight (TW), and dry weight (DW) of leaf samples according to the method of Barrs and Weatherley (1962). The relative leaf water content was calculated using the formula:

$${\text{Relative water content}} = \left[ {\left( {{\text{FW}} - {\text{DW}}} \right)/\left( {{\text{TW}} - {\text{DW}}} \right)} \right]\; \times \;{1}00$$

Ion leakage, an indicator of cell membrane integrity, was measured by assessing the electrical conductivity of leaf samples. Leaf discs were immersed in deionized water, and the initial electrical conductivity (EC1) was measured. Subsequently, the leaf discs were autoclaved, and the final electrical conductivity (EC2) was measured. The percentage of ion leakage was calculated using the formula:

$${\text{Electrolyte leakage index}} = \left( {{\text{EC1}}/{\text{EC2}}} \right)\; \times \;{1}00$$

Malondialdehyde (MDA) and hydrogen peroxide (H2 O 2) concentration

Malondialdehyde content was measured using the method of Du and Bramlage (1992). Leaf samples (0.2 g) were pulverized with liquid nitrogen, and 2 ml of 0.1% trichloroacetic acid (TCA) was added. After centrifugation, 0.5 ml of the supernatant from each sample was mixed with 1 ml of 20% trichloroacetic acid containing 0.5% thiobarbituric acid (TBA) in another microtube, followed by a second centrifugation. The malondialdehyde value was calculated using the provided equation (extinction coefficient (ɛ) = 155):

$${\text{Malondialdehyde}} = \left( {{\text{A532}} - {\text{A6}}00/{155}} \right)\; \times \;\left( {{\text{mount of reaction mixture}}/{\text{leaf tissue}}} \right)$$

To measure hydrogen peroxide content, a 200 mg sample of leaf tissue was homogenized in 2 mL of 0.1% (w/v) TCA (Trichloroacetic acid) solution. Subsequently, the homogenate was centrifuged at 10,000 g for 4 min at 48 °C. From the resulting supernatant, 0.5 mL was collected and mixed with 0.5 mL of potassium phosphate buffer (pH 7.0) and 1 mL of 1 M KI (Potassium Iodide). The mixture was then kept in the dark for 1 h to allow for a reaction to occur. Following the incubation period, the absorption was measured at 390 nm using a spectrophotometer (Velikova et al. 2000). The quantification of hydrogen peroxide content was achieved by establishing a standard curve for reference and comparison.

Antioxidant enzyme activities

Antioxidant enzyme activities were assessed in barley leaves using the method described by Bradford (1976). In brief, 0.1 g of fresh leaves were weighed from each treatment and pulverized with liquid nitrogen. Subsequently, 1 ml of extraction buffer, comprising Tris, EDTA, Triton, and DTT in specific proportions, was added to the pulverized leaves, and the mixture was transferred to a 1.5 ml microtube. The microtubes were then centrifuged in the Eppendorf 5810 R refrigerated centrifuge at 12,000 rpm and 4 °C for 30 min. The resulting supernatants were collected for further analysis of antioxidant enzyme activities.

Guaiacol peroxidase activity (GPX)

The determination of GPX activity in leaves was carried out following Chance and Maehly method (1955) using a U-1800 spectrophotometer. The increase in absorbance at 470 nm was monitored every 30 s for a duration of 2 min. The assay buffer consisted of 3 mL of Na-phosphate buffer (50 mM, pH 7), 4.51 mL of hydrogen peroxide, 3.35 mL of guaiacol, and 50 μL of enzyme extract (pH 7.8).

Ascorbate peroxidase activity (APX)

APX activity was assessed using the modified method proposed by Nakano and Asada (1981). To initiate the reaction, 3 ml of 50 mM potassium phosphate buffer (containing Na2HPO4 and NaH2PO4 at specific ratios with pH = 7), 1 mL of 5 mM APX, and 4.45 μl of 0.5 mM hydrogen peroxide were combined with 50 μl of plant extract. The APX activity was determined by monitoring the decrease in absorbance at 290 nm for 2 min using the HITACHI U-1800 spectrophotometer.

Catalase peroxidase activity (CPX)

CPX activity was determined using the modified method proposed by Aebi (1974). To conduct the assay, 3 ml of 50 mM potassium phosphate buffer (containing Na2HPO4 and Na2HPO4 at specific ratios with pH = 7) and 4.45 μl of 15 mM hydrogen peroxide were combined with 50 μl of plant extract. The CPX activity was evaluated by measuring the decrease in absorbance at 240 nm for 2 min using the HITACHI U-1800 spectrophotometer.

DPPH (2,2-diphenyl−1-picrylhydrazyl) radical scavenging assay

The activity of free radical inhibition of DPPH was measured using Aebi’s method (Aebi 1974). Initially, 0.1 g of plant leaf was pulverized with iquid nitrogen in a 1.5 ml microtube, and 500 μl of 95% ethanol was added. The samples were then vortexed, refrigerated at 4 °C for 4 h, and subsequently centrifuged for 5 min at 3000 rpm using an Eppendorf 5810 R refrigerated centrifuge. Next, 20 μl of the clear supernatant was transferred to new microtubes and mixed with 800 μl of ethanol-soluble DPPH (0.5 mM). After 30 min of storage in dark conditions, the absorbance was measured at 517 nm using a Unico 2100 spectrophotometer. A DPPH solution was employed as a control (blank). Finally, the concentration of DPPH was determined using the calibration equation drawn from ascorbic acid standards.

Agro-morphological traits

Grain yield (GY), shoot dry weight (ShDW) and harvest index (HI)

After manually harvesting plants at the maturity stage from a 1 m2 area in each plot, the samples were oven-dried for 72 h at 70 °C. Subsequently, grain yield (GY) and shoot dry weight (ShDW) were calculated. Additionally, the harvest index (HI) was determined using the following equation:

$${\text{HI}} = \left( {{\text{GY}}/\left( {{\text{BY}}} \right)} \right) \times {1}00$$

where HI represents the harvest index in percentage (%), GY is the economic yield based on the grain yield (kg) reported at 12.5% moisture content, and BY is the biomass yield (kg) includes grain yield and shoot dry weight.

Combination of significant indices (CSI) and yield stability index (YSI)

To select for high yield genotypes under normal and water stress conditions, yield stability index and combination of significant indices (CSI) were calculated using the following equations according to (Sabouri et al. 2022):

$${\text{Yield stability index}} = {\text{Ys}}/{\text{Yp}}$$
$${\text{CSI}}_{i} \; = \;\frac{1}{2} \, [(r_{{{\text{YP}}.{\text{MP}}}} \; \times \;{\text{MP}}_{i} )\; + \;(r_{{{\text{YP}}.{\text{GMP}}}} \; \times \;{\text{GMP}}_{i} )\; + \;(r_{{{\text{YP}}.{\text{HM}}}} \; \times \;{\text{HM}}_{i} )\; + \;(r_{{{\text{YP}}.{\text{STI}}}} \; \times \;{\text{STI}}_{i} )\; + \;(r_{{{\text{YS}}.{\text{MP}}}} \; \times \;{\text{MP}}_{i} )\; + \;{226}(r_{{{\text{YS}}.{\text{GMP}}}} \; \times \;{\text{GMP}}_{i} )\; + \;(r_{{{\text{YS}}.{\text{HM}}}} \; \times \;{\text{HM}}_{i} )\; + \;(r_{{{\text{YS}}.{\text{STI}}}} \; \times \;{\text{STI}}_{i} )]$$

where i is genotype, r is correlation coefficients between index and seed yield, MP is mean productivity, HM is harmonic mean, GMP is geometric mean productivity, STI is stress tolerance index, Ys is the grain yield of an experimental genotype under water stress and. Yp is the seed yield of an experimental genotype under normal conditions.

Statistical analyses

First, the normality of the data was checked, and combined analysis of variance of data was performed in a split–split plot arrangement using a randomized complete block design. Analysis of variance was performed using the general linear model (GLM) procedure of SAS (ver. 9.4; SAS Institute Inc. Cary, NC, USA) and least significant difference test (LSD) was then used at the 5% probability level for the comparison of treatment means. Principal component analysis (PCA) was performed, and biplot and heatmapping drawings were prepared to identify the interrelationships among the genotypes and measured traits using the Stat Graphics centurion XVIII (http://www.statgraphics.com) and JMP (ver.16), respectively. Also, Graph Pad Prism (ver.9) software was then used to draw the histograms.

Results

Univariate data analysis

The results of variance analysis of studied traits in two years, two moisture environments (normal and water stress) and two nitrogen levels (use and non-use) are reported in Tables 3 and S1. The ANOVA results (Table 3) showed that the effect of year was significant (p < 0.01) for all traits except electrolyte leakage index, the effect of nitrogen was significant (p < 0.01) for all traits except leaf area index, hydrogen peroxide and electrolyte leakage index, and was significant (p < 0.05) for harvest index. In addition, the effect of irrigation and genotypes were significant (p < 0.01) for all the traits.

Table 3 Analysis of variance (ANOVA) for agro- physiological characteristics and tolerance indices of barley genotypes under different irrigation regime and nitrogen levels
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Interaction effects of irrigation and different genotypes

The effects of water stress on all the studied traits were affected by barley genotypes (Table 4). Water stress caused a significant decrease in grain yield, shoot dry weight, harvest index, leaf area index and relative water content in all genotypes that averagely this decrease in six-row barley genotypes was higher than two-row ones. Also, the genotype Mahtab (six-row) in terms of grain yield, shoot dry weight and leaf area index (− 52.9%, − 31.5% and − 26.9%, respectively) and genotype M413 (six-row) in terms of harvest index and relative water content showed the greatest decrease (− 25.2% and − 17.7%, respectively) in water stress. On the other hand, water stress increased electrolyte leakage index, malondialdehyde, hydrogen peroxide, antioxidant enzymes and DPPH in all barley genotypes that this increase was higher in terms of electrolyte leakage index, malondialdehyde and hydrogen peroxide in six-row genotypes and in terms of antioxidant enzymes and DPPH in two-row genotypes (Table 4).

Table 4 The interaction effects of irrigation regime and genotype on agro-physiological traits
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Compared to the two-row genotypes, the six-row genotypes showed the highest values for leaf area index, electrolyte leakage index, malondialdehyde and hydrogen peroxide under both normal and water stress conditions and also, the highest values for grain yield, shoot dry weight, harvest index and relative water content exclusively under normal irrigation conditions. However, the two-row genotypes exhibited the highest levels of grain yield, shoot dry weight, harvest index and relative water content under water stress condition, and the highest values of antioxidant enzymes, and DPPH under both normal and water stress conditions (Table 4). Under normal irrigation, the highest grain yield and shoot dry weight were observed in the Mahtab (six-row), the highest harvest index, relative water content, electrolyte leakage index and malondialdehyde in the M413 (six-row), the highest leaf area index and hydrogen peroxide in M936 (two-row), the highest guaiacol peroxidase, ascorbate peroxidase and DPPH in CITO (two-row) and also, the highest catalase peroxidase were observed in the M318 (two-row) (Table 4). Under water stress, the highest grain yield and shoot dry weight were observed in M936 (two-row), the highest harvest index and leaf area index in Behrokh (two-row) and Goharan (six-row), respectively, the highest value of relative water content and DPPH in CITO (two-row), the highest value of electrolyte leakage index and hydrogen peroxide in Yusef (six-row) and also, the highest value of all antioxidant enzymes in M318 (two-row) (Table 4).

Interaction effects of nitrogen and different genotypes

Use nitrogen increased grain yield and shoot dry weight and decreased malondialdehyde, DPPH and CSI in all genotypes, but caused different reactions in different genotypes in terms of hydrogen peroxide, guaiacol peroxidase and yield stability index. The highest changes compared to use nitrogen were observed in grain yield, malondialdehyde and DPPH in six-row genotypes and in the shoot dry weight, hydrogen peroxide and guaiacol peroxidase in two-row genotypes.

In both nitrogen levels, the highest amount of grain yield, shoot dry weight, malondialdehyde and hydrogen peroxide were observed in six-row genotypes and the highest guaiacol peroxidase, DPPH, CSI and yield stability index were observed in two-row genotypes. Under non-use nitrogen conditions, the highest grain yield, shoot dry weight, malondialdehyde and hydrogen peroxide were observed in the Goharan, Mahtab, M413 and Yusef genotypes, respectively, and the highest guaiacol peroxidase, DPPH, CSI and yield stability index were observed in M318, CITO, M936 and Behrokh genotypes, respectively (Table 5). On the other hand, under use nitrogen conditions, the highest grain yield and shoot dry weight were observed in Mahtab, the highest malondialdehyde and hydrogen peroxide were observed in M413 and Goharan, respectively, and the highest guaiacol peroxidase, DPPH, CSI and yield stability index were observed in M318 (Table 5).

Table 5 Interaction effects of nitrogen levels and genotype on agro-physiological traits
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Interaction of irrigation and nitrogen levels

According to Figs. 1 and 2, water stress caused decrease in grain yield, shoot dry weight, harvest index, leaf area index, relative water content and increase in electrolyte leakage index, malondialdehyde, hydrogen peroxide, guaiacol peroxidase, ascorbate peroxidase and DPPH in both use and not-use nitrogen conditions in both groups of two and six-row genotypes.

Fig. 1
figure 1

Interaction effects of irrigation regime and genotype on traits. A same letter above columns for each trait (diagram) indicates that values are not statistically different (p > 0.05). Values are mean±standard error. N0, non-use of nitrogen; N1, use of nitrogen; GY, grain yield (kg ha–1); ShDW, shoot dry weight (g plant–1); HI, harvest index (%); LAI, leaf area index; RWC, relative water content (%); ELI, electrolyte leakage index (%)

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Fig. 2
figure 2

Interaction effects of irrigation regime and genotype on traits. A same letter above columns for each trait (diagram) indicates that values are not statistically different (p > 0.05). Values are mean±standard error. N0, non-use of nitrogen; N1, use of nitrogen, MDA, malondialdehyde (nmol g−1FW), H2O2; hydrogen peroxide (µmol g−1 FW); GPX, guaiacol peroxidase (units mg–1 protein); APX, ascorbate peroxidase specific activity (units mg–1 protein); CPX, catalase peroxidase specific activity (units mg–1 protein); DPPH, 2,2-diphenyl–1-picrylhydrazyl scavenging assay (mg mL–1)

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Under the normal irrigation conditions, the use nitrogen increased grain yield, shoot dry weight, leaf area index, relative water content and decreased electrolyte leakage index, malondialdehyde, hydrogen peroxide, all antioxidant enzymes and DPPH in both groups of two and six-row genotypes, and it had a no significant effect on harvest index. But under water stress conditions, the use nitrogen fertilizer decreased shoot dry weight, leaf area index, relative water content, DPPH and increased electrolyte leakage index, malondialdehyde, hydrogen peroxide, and all antioxidant enzymes in both two and six-row groups and decreased grain yield in two-row genotypes and had no significant effect on harvest index (Figs. 1, 2).

In normal irrigation conditions, the group of two-row genotypes had lower values of grain yield, shoot dry weight, harvest index, electrolyte leakage index, malondialdehyde and higher values of guaiacol peroxidase and DPPH in both use and non-use nitrogen conditions compared to the six-row. Also, the group of two-row genotypes compared to the six-row had lower values of hydrogen peroxide and catalase peroxidase and higher values of ascorbate peroxidase in non-use nitrogen condition. In contrast, in the water stress condition, the two-row group has lower values of harvest index, electrolyte leakage index, malondialdehyde and hydrogen peroxide and higher values of all antioxidant enzymes and DPPH in both use and non-use nitrogen conditions compared to the six-row ones. Also, the group of two-row genotypes had higher values of grain yield and shoot dry weight in non-use nitrogen (Figs. 1, 2).

Correlation between irrigation condition and nitrogen levels (use, non-use)

Under normal irrigation conditions, when nitrogen fertilizer was not used, there was a positive and significant correlation between grain yield, harvest index, electrolyte leakage index, malondialdehyde, and hydrogen peroxide. Conversely, there was a negative and significant correlation between grain yield and the enzymes guaiacol peroxidase and ascorbate peroxidase. However, when nitrogen fertilizer was used, a positive and significant correlation was observed between grain yield, harvest index, electrolyte leakage index, and malondialdehyde; whereas, a negative and significant correlation was found between grain yield and DPPH (Table 6).

Table 6 Correlation coefficients among the measured traits in barley genotypes under normal irrigation (use of nitrogen above diagonal and non-use of nitrogen below diagonal) conditions
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Additionally, under water stress conditions, when nitrogen fertilizer was not used, there was a positive and significant correlation between grain yield, leaf area index, ascorbate peroxidase, catalase peroxidase, DPPH, and CSI; whereas, a negative and significant correlation was observed between grain yield and malondialdehyde and hydrogen peroxide. However, when nitrogen fertilizer was used, a positive and significant correlation was observed between grain yield, ascorbate peroxidase, catalase peroxidase, DPPH, and CSI, and a negative and significant correlation between grain yield and electrolyte leakage index (Table 7).

Table 7 Correlation coefficients among the measured traits in barley genotypes under water stress (use of nitrogen above diagonal and non-use of nitrogen below diagonal) conditions
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Multivariate data analysis

To reduce the complexity of large datasets and uncover patterns, relationships, and trends that may not be apparent through univariate analysis, multivariate data analysis techniques such as principal component analysis (PCA) and heatmap analysis were employed.

PCA helps in identifying the most significant variables by transforming the data into a set of orthogonal components, thus simplifying the dataset while retaining most of the variability. Heatmapping provides a visual representation of data, highlighting correlations and making it easier to spot areas of interest or concern. Together, these techniques enhance the interpretability and insight of complex data.

Principal component analysis

Based on the univariate data analysis, it was found that the studied two and six-row barley genotypes were significantly affected by water stress and nitrogen. Therefore, the interactions effects between genotypes and nitrogen fertilizer under normal and water stress conditions were prepared using biplot analysis (Figs. 3, 4). Under normal moisture conditions the first two components explained 80% of the total variance and under water stress 69.1%. Under the normal moisture condition the first principal component (PC1) had a positively correlated with grain yield, shoot dry weight, leaf area index and relative water content and negatively correlated with all antioxidant enzymes and DPPH. The second component (PC2) positively correlates with harvest index, malondialdehyde, hydrogen peroxide and electrolyte leakage index. Therefore, PC1 was considered as "yield component" and PC2 only as a "harvest index component" (Fig. 3). The biplot diagram showed that the studied genotypes were divided into 4 groups, which shows the difference between two-row and six-row genotypes, as well as their reactions to use nitrogen fertilizer. In general, the use nitrogen made the position of the genotypes more positive in terms of PC1 and decreased the amount of enzymatic and non-enzymatic antioxidants and increased yield in all genotypes. Also, the biplot showed that under normal moisture conditions, the use nitrogen had a same effect on the two-row and six-row barley genotypes in such a way that under both nitrogen levels, the six-row genotypes were on the positive side of PC2 (Fig. 3). In order to select genotypes with higher yield, the Goharan and Mahtab genotypes (Six-row) had the highest in both levels of use and non-use nitrogen (Fig. 3).

Fig. 3
figure 3

Principal component analysis of the relationship among the studied traits and the distribution of barley genotypes relative to these traits under normal moisture conditions and two nitrogen level. N0, non-use of nitrogen; N1, use of nitrogen; GY, grain yield (kg ha–1); ShDW, shoot dry weight (g plant–1); HI, harvest index (%); LAI, leaf area index; RWC, relative water content (%); ELI, electrolyte leakage index (%); MDA, malondialdehyde (nmol g–1FW); H2O2, hydrogen peroxide (µmol g–1FW); GPX, guaiacol peroxidase (units mg–1 protein); APX, ascorbate peroxidase specific activity (units mg–1 protein); CPX, catalase peroxidase specific activity (units mg–1 protein); DPPH, 2,2-diphenyl–1-picrylhydrazyl scavenging assay (mg mL–1)

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Fig. 4
figure 4

Principal component analysis of the relationship among the studied traits and the distribution of barley genotypes relative to these traits under water stress conditions and two nitrogen level. N0, non-use of nitrogen; N1, use of nitrogen; GY, grain yield (kg ha–1); ShDW, shoot dry weight (g plant–1); HI, harvest index (%); LAI, leaf area index; RWC, relative water content (%); ELI, electrolyte leakage index (%); MDA, malondialdehyde (nmol g–1FW); H2O2, hydrogen peroxide (µmol g–1FW); GPX, guaiacol peroxidase (units mg–1 protein); APX, ascorbate peroxidase specific activity (units mg–1 protein); CPX, catalase peroxidase specific activity (units mg–1 protein); DPPH, 2,2-diphenyl–1-picrylhydrazyl scavenging assay (mg mL–1), CSI, combination of significant indices; YSI, yield stability index

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Under water stress condition, PC1 explained 40.2% of the total variance and was mainly positive associated with grain yield, DPPH and CSI and negative coefficients with malondialdehyde, hydrogen peroxide and electrolyte leakage index (Fig. 4). Also, PC2 explained 28.9% and had a positive associated with shoot dry weight and leaf area index and negative associated with all antioxidant enzymes. PC1 represents the grain yield and stress resistance and PC2 represents the shoot dry weight (Fig. 4). The biplot chart showed that, like normal irrigation condition, in water stress condition, the treatment compounds were divided into 4 groups, and all the treatments of use nitrogen were completely separated from the non-use nitrogen. The use nitrogen fertilizer in the condition of water stress, caused the position of all genotypes to become negative for PC1, which means an increase in the effect of water stress and, as a result, a decrease in yield and stress resistance of the genotypes. Also, the use nitrogen decreased the position of all genotypes in PC2, which means a decrease in the shoot dry weight. The reaction of the genotypes was different in both use and not-use nitrogen, so that in both nitrogen level, two-row genotypes were more positive than six-row genotypes for PC1 and more negative than six-row genotypes for PC2. In not-use nitrogen condition the genotype CITO (two-row) and in use nitrogen condition the genotype M318 (two-row) had the highest grain yield (Fig. 4).

Heatmapping analysis

The heatmapping graph under normal moisture conditions showed that the genotypes were placed in four separate groups. The first group included Behrokh-N0, M936-N0, M311-N0, M318-N0 and CITO-N0 (two-row genotypes on non-use nitrogen), the second group included Mahtab-N0, Goharan-N0, Nosrat-N0, M413-N0 and Yusef-N0 (six-row genotypes on non-use nitrogen), the third group included Behrokh-N1, Yusef-N1, M318-N1, M311-N1, M936-N1 and CITO-N1 (two-row genotypes and one six-row genotype on use nitrogen) and fourth group included Mahtab-N1, Goharan-N1, Nosrat-N1 and M413-N1 (four six-row genotypes on use nitrogen). It also showed that the measured traits were also placed in three separate groups. The first group includes grain yield, harvest index, relative water content, shoot dry weight and leaf area index, the second group malondialdehyde, electrolyte leakage index and hydrogen peroxide and the third group all antioxidant enzymes and DPPH traits. According to the heatmapping graph, the first group of genotypes had the lowest amount of traits of group one and the highest amount the traits of group three; while, the opposite of this situation was seen in the fourth group of genotypes. In addition, the genotypes of the second group had high amount the traits of the second group, and the genotypes of the third group had low amount the traits of the second and third group (Fig. 5). The heatmapping graph also shows, the use nitrogen increased the traits of groups one and decreased the traits of groups three and four in both groups of two-row and six-row genotypes. Also, the results of heatmapping under normal moisture conditions showed that the highest values of yield and yield component were observed in six-row barley genotypes at both levels of nitrogen use and non-use (Fig. 5). Under water stress, the heatmapping diagram showed that the genotypes were placed in four separate groups. The first group included M311-N0, CITO-N0, M936-N0, Behrokh-N0 and M318-N0 (All two-row genotypes on non-use nitrogen), the second group included Behrokh-N1, M936-N1, CITO-N1, M311-N1 and M318-N1 (All two-row genotypes on use nitrogen), the third group included M413-N0, Mahtab-N0, Yusef-N0, Nosrat-N0 and Goharan-N1 (All six-row genotypes on non-use nitrogen) and fourth group included Mahtab-N1, M413-N1, Yusef-N1, Goharan-N1 and Nosrat-N1 (All six-row genotypes on use nitrogen). It also showed that the measured traits were placed in four separate groups. The first group included grain yield, CSI, DPPH and yield stability index traits, the second group included shoot dry weight, leaf area index and relative water content, the third group included guaiacol peroxidase. ascorbate peroxidase and catalase peroxidase and the fourth group included other measured traits (Fig. 6). The genotypes of the first group had higher values in terms of the traits of the first and second groups and lower values in traits of the fourth group; while, the opposite of this situation was seen in the fourth group of genotypes. The genotypes of the second group had low values in the traits of the second group and high values in the traits of the third group; while, the opposite of this situation was seen in the third group of genotypes. These changes show that the use nitrogen fertilizer under water stress conditions cause an increase in water stress damages, an increase in enzymatic and non-enzymatic antioxidants, and as a result, a decrease in resistance to stress and a decrease in the yield of both groups of six-row and two-row genotypes. The results of heatmapping under water stress conditions also showed that the highest values for yield, yield component, water stress resistance and yield stability were observed in two-row barley genotypes at both levels of nitrogen use and non-use (Fig. 6).

Fig. 5
figure 5

Heatmapping diagram related to the studied traits of barley genotypes under normal conditions and two nitrogen level. The dark blue color indicates the lowest value of the characters, and the dark red color indicates the highest value. N0, non-use of nitrogen; N1, use of nitrogen; GY, grain yield (kg ha–1); ShDW, shoot dry weight (g plant–1); HI, harvest index (%); LAI, leaf area index; RWC, relative water content (%); ELI, electrolyte leakage index (%); MDA, malondialdehyde (nmol g–1FW); H2O2, hydrogen peroxide (µmol g–1FW); GPX, guaiacol peroxidase (units mg–1 protein); APX, ascorbate peroxidase specific activity (units mg–1 protein); CPX, catalase peroxidase specific activity (units mg–1 protein); DPPH, 2,2-diphenyl–1-picrylhydrazyl scavenging assay (mg mL–1)

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Fig. 6
figure 6

Heatmapping diagram related to the studied traits of barley genotypes under water stress conditions and two nitrogen level. The dark blue color indicates the lowest value of the characters, and the dark red color indicates the highest value. N0, non-use of nitrogen; N1, use of nitrogen; GY, grain yield (kg ha–1); ShDW, shoot dry weight (g plant–1); HI, harvest index (%); LAI, leaf area index; RWC, relative water content (%); ELI, electrolyte leakage index (%); MDA, malondialdehyde (nmol g–1FW); H2O2, hydrogen peroxide (µmol g–1FW); GPX, guaiacol peroxidase (units mg–1 protein); APX, ascorbate peroxidase specific activity (units mg–1 protein); CPX, catalase peroxidase specific activity (units mg–1 protein); DPPH, 2,2-diphenyl–1-picrylhydrazyl scavenging assay (mg mL–1); CSI, combination of significant indices; YSI, yield stability index

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Discussion

Water stress exerts a profound influence on plant growth and productivity by diminishing the availability of water for critical physiological processes, including photosynthesis, transpiration, and nutrient uptake (Bardehji et al. 2021). This deficit in water resources curtails a plant’s capacity to synthesize energy-rich molecules like adenosine triphosphate (ATP), which are essential for growth, ultimately resulting in decreased grain yield and shoot dry weight (Farooq et al. 2009). Furthermore, water stress prompts the closure of stomata as a water conservation measure, which further restricts the intake of carbon dioxide required for photosynthesis (Noori 2023). In addition, water stress initiates a cascade of physiological responses aimed at adapting to adverse conditions. Elevated levels of electrolyte leakage index, malondialdehyde, and hydrogen peroxide signify disruptions in cellular membranes and the accumulation of reactive oxygen species (ROS). These ROS originate from metabolic imbalances triggered by water stress and can cause oxidative damage to cellular components, including lipids, proteins, and DNA (Razi and Muneer 2021). In response to heightened ROS levels induced by water stress, plants activate their antioxidant defense mechanisms. Key antioxidant enzymes such as guaiacol peroxidase, ascorbate peroxidase, and catalase peroxidase play a pivotal role in neutralizing ROS and safeguarding cellular structures. The augmentation in the activity of these enzymes reflects the plant’s endeavor to counteract oxidative stress and forestall further harm to cellular components (Rajput et al. 2021). Furthermore, the elevation in DPPH radical scavenging activity in response to water stress denotes the plant’s adaptive response to combat oxidative stress. DPPH is a free radical capable of inflicting cellular damage, and an increase in its scavenging activity suggests the plant’s enhanced capability to ameliorate oxidative damage by neutralizing free radicals (Ahanger and Agarwal 2017ref).

The differences observed in grain yield, shoot dry weight, harvest index, and relative water content between six-row and two-row barley genotypes under varying conditions are likely attributed to genetic variations in their drought tolerance mechanisms. Six-row genotypes seem to possess genetic attributes that confer greater resilience and productivity under typical irrigation conditions (Bardehji et al. 2023). The differences in electrolyte leakage index, malondialdehyde, and hydrogen peroxide levels between these two genotypic groups imply distinct responses to oxidative stress. Six-row genotypes may exhibit genetic traits rendering them more vulnerable to oxidative damage during water stress conditions; whereas, two-row genotypes demonstrate better resistance to such stress. The distinctions in all antioxidant enzymes, and DPPH levels between the two genotypic groups indicate that they harbor distinct antioxidant defense systems. Two-row genotypes might possess genetic traits enabling them to activate antioxidant enzymes more efficiently, thereby enhancing their ability to mitigate oxidative stress (Fernando et al. 2021).

Nitrogen is an indispensable nutrient for plant growth, serving as a major constituent of chlorophyll and amino acids, so the enhancement in grain yield and shoot dry weight in response to nitrogen fertilizer application arises from improved nitrogen availability, which augments photosynthesis, protein synthesis, and overall plant growth (Shrivastav et al. 2020). Nitrogen fertilizer contributes to a reduction in oxidative stress by positively impacting cellular processes, likely promoting the production of antioxidants and bolstering the plant’s capacity to scavenge free radicals. This, in turn, leads to lower levels of malondialdehyde, increased DPPH radical scavenging activity, and improved CSI (Movludi et al. 2014). The varying impact of nitrogen on hydrogen peroxide, guaiacol peroxidase, and yield stability index across different genotypes can be attributed to genetic disparities and differences in nitrogen utilization efficiency.

Nitrogen promote growth in normal moisture conditions, but in moisture stress conditions, it increases the stress by increasing water demand and intensifying oxidative stress, thus leading to a decrease in shoot dry weight, leaf area index, relative water content and DPPH inhibition activity. The positive effect of nitrogen under normal irrigation conditions can be explained by its role in stimulating plant growth and increasing physiological processes, which increases grain yield, shoot dry weight, leaf area index and relative water content (Zhang et al. 2021). The observed discrepancies in genotypic responses to water stress and nitrogen supplementation are reflective of genetic variations in stress tolerance and nutrient utilization efficiency. Two-row genotypes exhibit superior performance under water stress conditions, likely due to genetic traits that enable them to withstand oxidative stress more effectively and maintain higher antioxidant enzyme activity.

Under normal irrigation conditions, the biplot analysis reveals that nitrogen fertilizer application consistently promote increased crop yield but simultaneously reduces both enzymatic and non-enzymatic antioxidants. This observation suggests that under normal conditions, nitrogen fertilizer primarily enhances crop yield rather than reinforcing antioxidative defense mechanisms (Lian et al. 2023). The diminished levels of antioxidants may imply that genotypes rely less on these defense mechanisms under use nitrogen conditions. Furthermore, the biplot analysis showed that the effect of nitrogen fertilizer is similar for both two-row and six-row barley genotypes, indicating that, irrespective of their genetic distinctions, both genotypic types respond comparably to nitrogen fertilization in terms of yield augmentation and antioxidant levels.

Under water stress conditions, like to normal irrigation, the biplot analysis segregates treatment combinations into four distinct groups. However, the response to nitrogen fertilizer and water stress in this context diverges significantly from that under normal moisture conditions. Under water stress, the application of nitrogen fertilizer in conjunction with water stress exacerbates the impact of water stress, resulting in diminished crop yield and reduced stress resistance among genotypes. This suggests that nitrogen fertilizer, typically a growth enhancer, can itself become a stressor when water availability is restricted, which is consistent with the findings of Bardehji et al. (2021). Additionally, the biplot analysis highlights that genotypes exhibit differential responses under both nitrogen use and non-use conditions in the presence of water stress, underscoring the pivotal role of genotypic variability in how barley responds to the combined stresses of water scarcity and nitrogen availability.

The heatmap observed that under normal moisture conditions, genotypes are effectively divided into four distinct groups based on their response to N application and genotype type. This classification underscores the significant roles played by both genotype and nitrogen in determining the characteristics and performance of barley crops. The first group, comprising two-row genotypes without nitrogen application, displays a unique trait profile characterized by lower values for traits associated with stress tolerance and higher values for traits indicative of metabolic activity and antioxidant capacity. Conversely, the fourth group, consisting of six-row genotypes with nitrogen use, exhibits the opposite pattern, suggesting a potential trade-off between yield and stress resistance. Traits were classified into three distinct groups that traits such as grain yield, harvest index, relative water content, shoot dry weight, and leaf area index cluster together, indicating their interdependency in response to environmental conditions and nitrogen application. Conversely, malondialdehyde, electrolyte leakage index, and hydrogen peroxide formed a separate group, reflecting their roles as indicators of oxidative stress. Lastly, all antioxidant enzymes, and DPPH traits clustered together, indicating their potential involvement in antioxidant defense mechanisms. The impact of nitrogen use on these groupings is evident. Nitrogen application generally led to an increase in traits associated with metabolic activity and yield components (group one) but a decrease in traits linked to stress resistance (groups three and four). This suggests that while nitrogen fertilization can enhance productivity, it may come at the cost of reduced stress tolerance, a factor that should be carefully considered in agricultural practices. Transitioning to water stress conditions, the categorization of genotypes and traits follows a different pattern compared to normal moisture conditions. The formation of four genotype groups is largely influenced by the presence or absence of nitrogen under water stress. Traits such as grain yield, CSI, DPPH and yield stability index formed a distinct group, which emphasizes their significance in evaluating water stress resistance and yield stability.

Two-row genotypes without nitrogen use (group one) displayed higher values for traits associated with stress resistance but lower values for traits linked to yield and yield stability. In contrast, six-row genotypes with nitrogen use (group four) demonstrated the inverse trend. The impact of nitrogen application under water stress conditions revealed a concerning trend; while, nitrogen use may enhance productivity under normal moisture conditions, it appears to exacerbate the negative effects of water stress. This is evident through increased water stress damage, elevated levels of enzymatic and non-enzymatic antioxidants (group three), and a corresponding decrease in stress resistance. Additionally, both two-row and six-row genotypes exhibited decreased yield under water stress conditions when nitrogen is applied.

The results strongly supported that under normal moisture conditions, two-row barley genotypes exhibited superior yields and yield component values, making them a suitable choice for maximizing productivity. However, under water stress conditions, these genotypes showed improved stress resistance and yield stability, highlighting their suitability for water-stressed regions.

Conclusion

This study delves in the relationship between moisture stress and nitrogen fertilizer application in two-row and six-row barley genotypes. In addition, two-row barley genotypes demonstrate heightened stress resilience, particularly when subjected to water stress conditions, showcasing their adaptive advantages in arid or water-limited agricultural regions. In contrast, six-row barley genotypes excel in parameters related to yield components when moisture levels are optimal. The deleterious impact of water stress is evident in the substantial reduction in grain yield and the concomitant initiation of oxidative stress, leading to the activation of intricate antioxidant defense mechanisms within the plant. However, significantly, nitrogen fertilization increases yield-related traits under normal moisture conditions, while ameliorating stress-induced damage and simultaneously reducing stress tolerance. This delicate balance between increasing crop yield and maintaining resilience to stress highlights the critical importance of appropriate genotyping and nitrogen fertilizer management. Together, these factors contribute to sustainable agriculture under conditions of water scarcity and other environmental stresses.