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Synergistic effect of indole‒3‒acetic acid and nitrogen on yield, sugar profile, and nitrogen utilization of salt-stressed sugar beet crop

BMC Plant Biology volume 25, Article number: 632 (2025) Cite this article

Abstract

Purpose

Salt stress often reduces plant efficiency in nutrient utilization, particularly nitrogen (N), leading to physiological disorders, primarily those related to phytohormones. Hence, the current study assessed the combined effect of indole-3-acetic acid (IAA) and N in inducing salt stress tolerance in sugar beet.

Methods

Using a split-plot in randomized complete block design replicated thrice, the effect of three IAA levels (0, 150, and 300 mg L− 1, denoted IAA0, IAA150 and IAA300, respectively) and three N fertilization rates (240, 290, and 340 kg N ha− 1, abbreviated as N240, N290 and N340, respectively) on sugar beet’s growth, nutritional status, and quality and sugar quality in saline soil was explored.

Results

Findings exhibited that IAA300 × N340 was the best combination for enhancing root diameter, leaf fresh weight, and leaf area index. Ionic homeostasis, expressed as the leaf K⁺/Na⁺ and Ca²⁺/Na⁺ ratios, reached its highest values with N340 (1.21 and 0.51, respectively), exceeding those observed with N240 and N290. The IAA0 or IAA150 × N340 gave the highest juice sodium content (34.0 and 33.8 mmol kg⁻¹, respectively), while N240 across all IAA treatments recorded the lowest ones. The IAA300 × N340 was the most effective practice for enhancing yields and N use efficiency in sugar beet, resulting in the highest root yield (97.6 t ha⁻¹), pure sugar yield (14.50 t ha⁻¹), and N use efficiency (0.342 kg root kg⁻¹ N), significantly outperforming other IAA × N interactions.

Conclusion

In conclusion, progressive increases in IAA and N caused the enhancements sugar beet growth, yield, and related quality, since IAA at 300 mg L− 1 plus N at 340 kg N ha− 1 had the favorable synergism in this respect.

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Introduction

Marginal agricultural lands, particularly saline soils, often suffer from severe nutrient deficiencies, limiting crop growth and productivity [1, 2]. Various soil characteristics, including acidity, water status, nutrient cycling, and salinity, significantly affect nutrient bioavailability and homeostasis [3,4,5]. Among these factors, soil salinity is a critical threat to global food security, as it imposes abiotic stress on crops, leading to reduced final yield and quality of different crops [6, 7].

Nitrogen (N) as an essential macronutrient had the potential to alleviate the unfavorable impacts of abiotic stresses [8,9,10]. The N is significant mineral nutrient under abiotic stress situations, owing to the essentiality of organic nitrogenous complexes accumulation in plant [11]. In this respect, N is regarded as the basal constituent of all amino acids, proteins, and other nitrogenous osmolytes that assist in preserving plants from abiotic stress hazards [12]. Osmo-protectants related to N such as soluble proteins play a substantial influence in plant stresses tolerance. Proline amino acid acts as a macromolecular agent capable of safeguarding the integrity of protein that enhances the activities of different enzymes [13]. However, in addition to exclusive water uptake and ion toxicity, deficiency of nutrients such as N is one of the critical issues of soil salinity [14]. Accordingly, owing to the low availability of N in saline soils, impairment in plant growth and productivity is expected. Therefore, to save better plant growth in saline soils, exogenous supply of N via fertilization is recommended [15]. However, the excessive or insufficient N dramatically decreased the yield of sugar beet [16] and negatively affected sugar content and quality of roots [17, 18].

Plants respond to abiotic stress by accumulating osmoprotectants such as free amino acids and proline, which play a crucial role in stress tolerance and cellular homeostasis [19,20,21]. However, under such conditions, the endogenous levels of indole-3-acetic acid (IAA), a key phytohormone, tend to decline [22]. The IAA is a vital signaling molecule in higher plants, regulating various physiological processes, including cell elongation and overall plant growth [23]. Beyond its role in growth regulation, IAA also contributes to stress adaptation by enhancing plant resilience to environmental challenges [24]. Notably, studies suggest that the detrimental effects of nutritional stress on N metabolism can be alleviated through the exogenous IAA application, improving plant N use efficiency and stress tolerance [25]. In sugar beets, IAA has been reported to influence physiological traits and improve stress tolerance, including enhancing root growth and nutrient uptake efficiency under adverse conditions [22]. Additionally, exogenous IAA application has been linked to improved N metabolism, leading to enhanced yield and quality parameters in sugar beet cultivars [26].

However, available information regarding the synergistic effect between IAA and N on sugar beet physiology, yield and quality under salt stress is scarce. Therefore, the current research was performed to outstand the potential of IAA plus N in improving yield, sugar quality and N utilization of sugar beet grown in saline soil. This study hypothesizes that the artificial supply of IAA and N can restore the nutritional and physiological homeostasis of sugar beet subjected to salt stress.

Materials and methods

Experimental site attributes

A field experiment was carried out during the 2022/23 and 2023/24 winter seasons at College of Agriculture Research Farm (latitudes 32° 42’ N and longitude: 29° 75’ E), Fayoum University, Fayoum, Egypt. The experimental soil is a saline loamy sand, classified by the Soil Survey Staff USDA as Typic Torripsamments, siliceous and hyperthermic. Representative soil samples were collected before planting in each season from the soil surface layer (taken from 0.0 to 0.5 m). The main physical and chemical properties of the soil and main climate features of the study area during the sugar beet (Beta vulgaris L.) growth period (from October to May) are illustrated in Table 1; Fig. 1, respectively.

Table 1 Main physical and chemical properties of the tested soil at a 0–50 cm depth before planting and and irrigation water (average for 2022/23 and 2023/24)
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Fig. 1
figure 1

Monthly average temperature, pan evaporation, and precipitation of the experimental location during the sugar beet growing period for 2022/23 and 2023/24 cropping seasons

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Experimental design and sugar beet cultivation

The experiment was set up using a split-plot in randomized block design with three replications. Uniform and healthy multi-germ seeds of sugar beet (cv. BTS–301, Germany) were provided from the Sugar Crops Research Institute (SCRI), Agriculture Research Center, Egypt. The sugar beet seeds were sown on October 10, 2022/23, and October 12, 2023/24. Seeds were placed in drills on one ridge flank 0.2 m apart. Each experimental unit had 5 ridges, each 0.6 m and 3.5 m wide and length. Prior to sowing and while land was prepared, ordinary superphosphate, 110 kg P2O5 ha− 1, was added and mixed with soil. At 35 days from sowing (DFS), the excess sprouted plantlets were carefully reduced to retain one plant per drill, followed by top-dressing of potassium sulfate, 60 kg K2O ha− 1 and then undergo normal irrigation.

Treatments comprised three IAA (Sigma–Aldrich, USA, purity > 99.0%) concentrations at 0 (distilled water as a check; IAA0), 150 (IAA150), and 300 (IAA300) mg L− 1 were uniformly arranged in the main plots. Each IAA-applied concentration was sprayed thrice at 45, 60, and 75 days after planting (DAP) to a runoff early in the morning using a motorized pressurized backpack sprayer with a 20-L capacity. These ages align with specific sugar beet phenological development stages based on BBCH scale, a standardized codification system established by the Biologische Bundesanstalt, Bundessortenamt, and CHemische Industrie in Germany: BBCH 30/31 (10–20% ground coverage by leaves), BBCH 31/32 (20–30% ground coverage by leaves), and BBCH 32/33 (30–40% ground coverage by leaves) as reported by Meier [27]. Different IAA concentrations were prepared in distilled water, adding Tween®-20 (0.01%, v/v) as a surfactant agent to each treatment concentration to guarantee optimum penetration into leaf tissue. On basis the recommended N fertilization rate for sugar beet grown under newly reclaimed non-saline sandy soil conditions is 240 kg N ha− 1, three N fertilizer rates (i.e., 240 (N240), 290 (N290), and 340 (N340) kg ha− 1) as ammonium nitrate (33.5% N) were symmetrically top-dressed in the sub plots in three equal portions 30, 45, and 60 DFS. Due to the salt stress conditions in our study, we applied higher than recommended N rates (290 and 340 kg N ha− 1) based on the fact that N use efficiency is anticipated to decrease in saline soils. The traditional surface furrow irrigation system was used in the experimental field with irrigation freshwater having a 0.46 dS m− 1 salinity level. In this situation, the average amounts of irrigation water applied were 6316.9 m3 ha− 1 in the 2022/23 season, and 6190.2 m3 ha− 1 in the 2023/24 season, with following the local best practices for sugar beet cultivation under saline conditions in Egypt.

Metrics

Growth attributes

At 95 DFS, the relative concentration of leaf chlorophyll (soil plant analysis development; SPADchlorophyll value) for six sugar beet plants, each with three completely expanded leaves, from each experimental plot was assesses via a SPAD-502 chlorophyll meter (Konica Minolta, Inc., Tokyo, Japan). Leaf area (cm² plant⁻¹) was measured for all leaves of each of the six selected sugar beet plants using a Planix 7 digital planometer (Tamaya Technics Inc., Tokyo, Japan). The leaf area index was determined by Eq. 1.

$$\:\text{L}\text{e}\text{a}\text{f}\:\text{a}\text{r}\text{e}\text{a}\:\text{i}\text{n}\text{d}\text{e}\text{x}=\left[\frac{\text{l}\text{e}\text{a}\text{f}\:\text{a}\text{r}\text{e}\text{a}\:\left({\text{c}\text{m}}^{2}{\text{p}\text{l}\text{a}\text{n}\text{t}}^{-1}\right)}{\text{g}\text{r}\text{o}\text{u}\text{n}\text{d}\:\text{a}\text{r}\text{e}\text{a}\:\left({\text{c}\text{m}}^{2}{\text{p}\text{l}\text{a}\text{n}\text{t}}^{-1}\right)}\right]$$
(1)

At root maturation (215 DFS), sugar beet plants were randomly sampling and gathered from each experimental plot. Further, the plant samples were submitted to the laboratory, and roots were washed. After that, root diameter was measured using a digital vernier caliper at the broadest root point. Also, the fresh weights of root and leaf per plant were recorded.

Beet juice quality

Unbiased sample of six roots per treatment was taken to assess the juice quality traits at the Delta sugar beet factory located at Kafr El-Sheikh province, Egypt, as explained by McGinnis [28] and AOAC [29]. Sugar (i.e., polarity%) of beet roots was determined as described by McGinnis [28]. In this method, a clarified juice sample from the beet roots is prepared and then analyzed using a using ATAGO AP-300 digital automatic sugar polarimeter (Tokyo, Japan) to measure optical rotation, which is directly related to the sample’s sugar content (polarity). The soluble non-sucrose impurities, e.g., sodium (Na) and potassium (K) in mmol kg− 1 root were determined following a flame photometry method, while alpha-amino-N (α-AN) was measured using the ninhydrin and hydrindantin method [30]. The percentages of pure sugar (Eq. 2), loss sucrose (Eq. 3), and juice purity (Eq. 4) were calculated according to Harvey and Dotton [31] as follows:

$$\begin{aligned}\text{P}\text{u}\text{r}\text{e}\:\text{s}\text{u}\text{g}\text{a}\text{r}\:\left(\%\right)&=\text{s}\text{u}\text{g}\text{e}\text{r}\left(\%\right)\\&\quad-\left[\begin{aligned}&0.343\left(\text{K}+\text{N}\text{a}\right)\cr&\quad+0.094\:{\alpha\:}\cr&\quad-\text{A}\text{N}+0.29\end{aligned}\right]\end{aligned}$$
(2)
$$\:\text{L}\text{o}\text{s}\text{s}\:\text{s}\text{u}\text{c}\text{r}\text{o}\text{s}\text{e}\left(\% \right)=\text{s}\text{u}\text{g}\text{e}\text{r}\left(\%\right)-\text{p}\text{u}\text{r}\text{e}\:\text{s}\text{u}\text{g}\text{a}\text{r}\left(\% \right)$$
(3)
$$\:\text{J}\text{u}\text{i}\text{c}\text{e}\:\text{p}\text{u}\text{r}\text{i}\text{t}\text{y}\left(\% \right)=\left[\frac{\text{p}\text{u}\text{r}\text{e}\:\text{s}\text{u}\text{g}\text{e}\text{r}\left( \% \right)}{\text{s}\text{u}\text{g}\text{e}\text{r}\left(\% \right)}\right]\times\:100$$
(4)

Mineral content in sugar beet leaves

After being ground, a 200 mg dried powder sample of sugar beet leaves (taken at 95 DFS) was digested with 10 mL H₂SO₄-HClO4 mixture (3:1, v/v) in a digestive vessel by heating up to 300 ºC until the digestion mixture was colorless. Each sample was diluted to 100 ml with distilled water in a volumetric flask. Using Gerhardt’s micro Kjeldahl device, the total N concentration in mg g− 1 leaf dry weight (LDW) was determined following the Kjeldahl procedure [29]. The total phosphorus (P) concentration (mg g− 1 LDW) was determined following the standard molybdenum blue spectrophotometric procedure [32]. The total concentration of cationic elements (i.e., potassium; K+ sodium; Na+, and calcium; Ca2+ in mg g− 1 LDW) in sugar beet leaves was determined using an Agilent Spectra-55 AA flame atomic absorption spectroscopy (CA, USA) according to Johnson’s [33] method.

Sugar beet yields and nitrogen-use efficiency

Sugar beet plants from each experimental plot were harvested, cleaned, and topped. The weight of their roots, along with the root weight of ten plants sampled earlier, was measured and then converted to root yield ha− 1. Pure sugar yield ha− 1 was estimated by multiplying root yield by pure sugar content. The N use efficiency based on root yield [R-NUE = root yield (kg)/total N fertilizer applied (kg)] was calculated (kg roots kg N⁻¹) according to Moll et al. [34].

Statistical analysis

Once the homogeneity of variances (for the data of both seasons) via Levene’s test (p > 0.05) was verified, the dataset for each tested variable/attribute met this statistical assumption, justifying a combined ANOVA). Herein, the data were statistically analyzed by Genstat software package (VSN International Ltd., Oxford, UK). For separating means, Duncan’s multiple range test was used at p ˂ 0.05 and p ˂ 0.01 levels of probability. Furthermore, the R (version 4.0.2) and IBM SPSS (version 25, SPSS Inc., Chicago, IL, USA) statistical software programs were utilized for the PCA biplot-building, Pearson’s correlation heat-map, and automatic linear modeling.

Result

Sugar beet growth

The differences in all studied growth traits of sugar beet due to application of IAA and N and their interaction were significant (p ˂ 0.05), except root fresh weight and SPADchlorophyll value with the interaction (Table 2). Progressive increase in IAA dose showed increases in the growth traits. Thus, IAA300 was the effective practice recording 5.3, 15.9, 29.5, 5.2, and 22.1% increases in root diameter, root fresh weight, leaf fresh weight, SPADchlorophyll value and leaf area index, respectively, compared to IAA0 (control treatment). The highest application rate of N, i.e., 340 kg N ha− 1 (N340) possessed the maximum increases in all sugar beet growth traits surpassing (p ˂ 0.05) the lower N rates (N240 and N290) by approximately 16.6 and 9.0%, 45.6 and 17.5%, 78.0 and 34.8%, 16.2 and 6.7%, and 87.3 and 45.9% for root diameter, root fresh weight, leaf fresh weight, SPADchlorophyll value and leaf area index, respectively. The interaction effect between IAA and N rate revealed that IAA300 × N340 was the best combination for enhancing root diameter, leaf fresh weight, and leaf area index. Under different IAA levels, the highest N rate showed remarkable values greater than the lower N rate.

Table 2 Root diameter, root fresh weight, leaf fresh weight, relative chlorophyll content (SPADchlorophyll value), and leaf area index of sugar beet plant as influenced by indole‒3‒acetic acid (IAA) and nitrogen (N) rate (data average for 2022/23 and 2023/24 cropping seasons)
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Mineral content in sugar beet leaves

As shown in Table 3, leaf mineral contents and ionic homeostasis significantly (p ˂ 0.05) influenced by the main effect of IAA and N rate, while the interaction was not significant. Concerning IAA, IAA300 recorded the maximum values of leaf mineral contents and ionic homeostasis surpassing the other IAA levels, except IAA150 for leaf P content. The increases in leaf N content, leaf P content, leaf K+ content, leaf K+/Na+ ratio and leaf Ca2+/Na+ ratio due to IAA300 application amounted to 10.0, 10.9, 2.1, 6.9 and 10.5%, respectively compared to the control treatment.

Table 3 Leaf mineral (nitrogen; N, phosphorus; P, and potassium; K+) contents and ionic homeostasis (K+/Na+ and Ca2+/Na+ ratios) of sugar beet plant as influenced by indole‒3‒acetic acid (IAA) and nitrogen (N) rate (data average for 2022/23 and 2023/24 cropping seasons)
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Application of N340 or N290 possessed the highest leaf mineral contents exceeding application of N240 by about 1.33 and 1.20 times for leaf N content; 1.37 and 1.28 for leaf P content and 1.20 and 1.14 for leaf K+ content, respectively. Ionic homeostasis expressed in leaf K+/Na+ ratio and leaf Ca2+/Na+ ratio exhibited the maximum values with N340 higher than each of N240 and N290.

Sugar content and juice non-sucrose impurities

By supplying sugar beet plants with IAA300, the maximum values of sugar content and non-sucrose impurities were recorded (Table 4). However, the differences among IAA0, IAA150 or AA300 for Na content and α-AN content were not significant (p > 0.05). The greatest values of sugar content and K content (with N240) as well as Na content α-AN content (with N340) were observed. Among the non-sucrose impurities, only Na content significantly (p ˂ 0.05) affected by the interaction between IAA and N rate. IAA0 or IAA150 × N340 gave the maximum values, while all IAA levels × IAA150 exhibited the lowest ones.

Table 4 Sugar content and juice non-sucrose impurities (sodium; Na, potassium; K, and alpha-amino-nitrogen; α-AN) of sugar beet as influenced by indole‒3‒acetic acid (IAA) and nitrogen (N) rate (data average for 2022/23 and 2023/24 cropping seasons)
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Juice purity, yield, and nitrogen use efficiency based on root yield (R-NUE)

The IAA level had significant effect (p ˂ 0.05) on root yield, pure sugar yield and R-NUE of sugar beet as well as insignificant (p > 0.05) effect on loss sugar content and juice purity (Table 5). In this respect, IAA300 treatment showed values of root yield, pure sugar yield and R-NUE greater than IAA0 and IAA150, which reached 1.15 and 1.10, 1.18 and 1.09, and 1.14 and 1.08 times, respectively.

Table 5 Loss sucrose content, juice purity, root and pure sugar yields, and nitrogen (N) use efficiency based on root yield (R-NUE) of sugar beet as influenced by indole‒3‒acetic acid (IAA) and nitrogen (N) rate (data average for 2022/23 and 2023/24 cropping seasons)
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The N fertilization levels had insignificant (p > 0.05) effect on loss sugar content and significant (p ˂ 0.05) effects on juice purity, root yield, pure sugar yield and R-NUE of sugar beet. The maximum values of Juice purity and R-NUE were obtained with application of N240. While N340 was the effective treatment for enhancing root yield and pure sugar yield.

Insignificant (p > 0.05) effect on loss sugar content and significant (p ˂ 0.05) effects on juice purity, root yield, pure sugar yield and R-NUE of sugar beet were gained due to the interaction between IAA and N rate (Table 5). The highest tested level of both IAA and N (IAA300 × N340) was the superior practice for improving root yield, pure sugar yield and R-NUE of sugar beet.

Linear dimensionality reduction (LDR) technique and correlogram for pairwise correlations

A thorough summary of the relationships among treatments and traits was explored by the principal component analysis (PCA) as a LDR statistical technique which quantified 91% of the variability in the dataset (Fig. 2a). Of the variability 80.1% was explained by the first principal component (Dim1) and 10.9% by the second (Dim2). Sugar-related traits including sugar content, juice purity and juice K were the ones that Dim1 predominantly captured variation in suggesting that they had a major influence on overall treatment distinction. Alternatively, Dim2 explained differences in growth-related traits like SPADchlorophyll, root yield and, and pure sugar yield.

Fig. 2
figure 2

Principal component (PCA) analysis (a) for applied indole‒3‒acetic acid (IAA) × nitrogen (N) treatments and studied attributes. Pearson’s correlation coefficients, among the morpho-physiological attributes, yield, and sugar quality-related traits of sugar beet are revealed in the correlogram (b). The circle’s size correspond significance level, while * refers to a significant (p ˂ 0.05) correlation. IAA0, IAA150 and IAA300: spraying of IAA at rates of 0, 150 and 300 mg L− 1, respectively; N240, N290 and N340: soil application of N at rates of 240, 290 and 340 kg N ha− 1, respectively. RD: root diameter, RFW and LFW: root and leaf fresh weight, respectively, SPAD: soil plant analysis development, LAI: leaf area index, Leaf N/P/K+: leaf nitrogen/phosphorus/potassium, respectively, Ca2+ and Na+: calcium and sodium, respectively, α-AN: alpha-amino-N, LSC: loss sucrose content, Juice purity, PSY: pure sugar yield, NUE: N use efficiency based on root yield. Each black dot in sub-figure (a) refers to a IAA × N treatment. Values based on average of 2022/23 and 2023/24 cropping seasons

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Treatment performance varied significantly according to the PCA dimensions. The treatment IAA300 × N240 showed potential as the most effective trait to improve sugar quality attributes because of its strong correlation with sugar content, juice purity and juice K. Similarly, IAA300 × N340 showed a strong correlation with root yield, pure sugar yield and SPADchlorophyll, suggesting that it can be used to increase biomass production and vegetative growth. Other treatments like IAA0 × N240 and IAA150 × N240 were situated close to the PCA plots origin showed moderate and non-specialized performance across traits. Juice Na notably showed an inverse relationship with characteristics related to sugar, suggesting that high juice Na levels could have a detrimental effect on juice sugar content and purity. The PCA provided essential insights into treatment selection for desired agricultural productions by clearly illustrating the differences between treatments optimized for biomass output and those optimized for sugar production.

Pearson’s pairwise correlation analysis exhibited significant relationships between sugar beet agronomic traits in our study (Fig. 2b). Root yield showed robust positive correlations with root diameter, root fresh weight, leaf fresh weight, leaf area index, and leaf Ca²⁺/Na⁺ ratio and moderate to strong positive correlations with SPADchlorophyll, leaf N, P, and K+/Na+ ratio, suggesting that these characteristics positively influence root productivity. Conversely, root yield was negatively associated with sugar content and juice K, indicating potential trade-offs. Pure sugar yield exhibited robust positive associations with root diameter, root fresh weight, leaf fresh weight, leaf area index, leaf N, P, and K+/Na+ ratio, highlighting its importance in supporting root growth while negatively correlated with sugar content, juice Na, and juice purity. The R-NUE showed moderate positive correlations with pure sugar yield, suggesting that efficient nutrient use is associated with enhanced photosynthetic performance, although weak correlations were found with several leaf nutrient traits.

Automatic linear modeling (ALM)

The IBM SPSS’s ALM is commonly used for automated predictor selection and model fitting. It allows IBM SPSS to evaluate potential variables systematically to select the best-fitting linear regression model. Results indicate that the forward stepwise regression (FSR) model explicates the relationship between pure sugar yield as a target attribute and its explanatory predictors in this study (Fig. 3). Adjusted R2 is 0.937 for the outputted FSR model in Fig. 3 denotes that 93.7% of the variations in pure sugar yield are interpreted by variations in leaf fresh weight, leaf N, juice α-AN, sugar content, leaf P, K+, and K+/Na+ ratio (pure sugar yield = 4.757ns + 7.199** leaf fresh weight + 0.338** leaf N − 0.373** juice α-AN + 0.309** sugar content − 0.210* leaf K+ + 1.627* leaf K+/Na+ - 1.005ns leaf P). These results highlight the complex interplay of morpho-physio-biochemical traits in determining pure sugar yield, underscoring the critical roles of leaf fresh weight and IAA × N treatment dynamics in optimizing salt-stressed sugar beet productivity under semi-arid conditions.

Fig. 3
figure 3

The automated linear modeling diagram of influential attributes (e.g., LFW: leaf fresh weight, leaf N/P/K+: leaf nitrogen/phosphorus/potassium, Na+: sodium, and α-AN: alpha-amino-N) in sugar beet’s pure sugar yield (PSY) by data of indole‒3‒acetic acid (IAA) × nitrogen (N) interaction (data pooled across 2022/23 and 2023/24 cropping seasons) under saline soil (saturated electrical conductivity = 6.98 dS m− 1) conditions. AdjR2, Coeff, and sig. stand for adjusted determination coefficient, estimate coefficient, and significance, respectively. * and ** indicate significance levels at p ˂ 0.05 and p ˂ 0.01, respectively; ns denotes no significance

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Discussion

Notably, the exogenous supply of diversified IAA concentrations enhanced the salt tolerance of sugar beet by improving its physiological and biochemical resilience. Additionally, it mitigated the hazards of salinity by reducing ion toxicity and oxidative stress, thereby protecting plant health. This was clarified well with high concentrations of IAA (300 mg L− 1) which increased root diameter, root fresh weight, leaf fresh weight, SPADchlorophyll value and leaf area index. Promotion of plant growth with alleviation of stress deleterious impact was obtained by IAA application [35–36]. In this respect, in common, abiotic stress and specifically, salinity found to deteriorate the leaf pigments and cause continual atonality in the plant photosystems and reduce gaseous exchange resulting in decreasing plant growth [6, 37,38,39]. Normally, reactive oxygen species (ROS) are generated with abiotic stresses. Since ROS are malicious molecules, the disintegration rate of vital cell constituents increased [40]. Overaccumulation of ROS occurs under stresses owing to reduction in light absorption and electron transport, causing photooxidation and deactivation in photosystem apparatus [41]. Additionally, exposure to unfavorable conditions during growth stage causes imbalance in plant physiology, hence reduction in yield potential [42,43,44]. However, plants respond to the stress by accumulating various osmolytes and activating defensive enzymes for ROS scavenging [45, 46]. Enzymatic antioxidants, i.e., catalase, super oxide dismutase and glutathione peroxidase [47, 48], and non-enzymatic antioxidants, i.e., glutathione, phenolic compound, flavonoid, tocopherol, etc. had a strong potential to relieve the concentrations of H2O2 and O2– in stressed plant cell, hence quenching the ROS injuries [49, 50]. As demonstrated in the present study, the application of IAA, particularly at higher concentrations, significantly improved root and leaf growth as well as leaf pigments in salt-stressed sugar beet plants. These findings align with those of Ben Massoud et al. [51], who reported that IAA enhances antioxidant defense mechanisms while mitigating lipid peroxidation damage. However, while our results also indicate an increase in proline accumulation, as noted in previous studies [52], we observed a more pronounced improvement in growth parameters compared to some reports. This variation may be attributed to differences in plant species, salt stress levels, or the concentration and mode of IAA application [53, 54]. Additionally, IAA’s role in promoting ethylene biosynthesis and root elongation, as previously reported [55], was consistent with our findings, though our study further suggests a stronger interaction between IAA and ABA under salinity stress, which may contribute to enhanced root system regulation [56]. Moreover, while IAA has been shown to increase dry weight and total chlorophyll content in various crops [57], the magnitude of these effects in our study suggests that sugar beet may exhibit particularly high sensitivity to auxin under salt stress conditions. These observations underscore the importance of species-specific responses and environmental conditions in shaping the physiological outcomes of IAA application.

Concerning the leaf nutrient content in sugar beet, our results demonstrated the significant role of IAA in enhancing nutrient absorption under salt stress. While previous studies have reported that salt stress disrupts nutrient uptake and metabolism [58,59,60], our findings align with those of Abdel Latef et al. [36], which also observed increased K+, Ca²⁺, and Mg²⁺ ion contents following auxin application. However, unlike Gong et al. [26], which primarily attributed the improvement in N metabolism to enhanced enzymatic activity, our study suggests that IAA-mediated root elongation and capillary root development also play a crucial role in increasing nutrient availability to aerial plant parts. This discrepancy may be due to differences in experimental conditions, including plant species, auxin concentration, and stress severity. Further comparative analysis with studies [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61] indicates that IAA not only enhances nitrate assimilation but also modulates key enzymes such as nitrate reductase and glutamate dehydrogenase, supporting more efficient N utilization under saline conditions. Thus, our findings provide additional insights into the mechanisms by which auxins mitigate salt-induced nutrient imbalances [62, 63].

In the present study, the application of IAA led to a significant enhancement in CO₂ assimilation, likely due to its role in increasing photosynthetic enzyme activity, which aligns with previous findings [64, 65]. However, while earlier research has demonstrated improvements in root growth, plant pigments, and nutrient content under saline conditions [56], our study further confirms that these effects translate into enhanced root yield and quality traits, particularly under varying salinity levels. Compared to the findings of Qotob et al. [66], who reported improvements in sucrose content and total soluble solids in sugar beet following the foliar application of plant growth regulators, our results indicate that IAA specifically enhances N use efficiency, which may contribute to overall plant performance. The observed differences could be attributed to variations in crop species, experimental conditions, or the specific mode of action of IAA compared to other growth regulators. These findings highlight the potential of IAA in mitigating salinity stress, emphasizing its role in improving physiological and biochemical attributes in stressed plants.

Our results confirm that increasing N fertilizer levels positively influenced sugar beet growth and quality, aligning with previous studies that reported enhancements in chlorophyll content, photosynthetic efficiency, and assimilate translocation [67,68,69]. In agreement with Wang et al. [69] and Yan et al. [70], we observed that higher N rates improved the photosynthetic rate and dry matter accumulation but led to a decline in R-NUE. However, unlike some previous findings [71–72], where excessive N primarily led to nitrate accumulation in the soil, our results suggest that it also significantly altered nutrient partitioning in sugar beet tissues. Notably, while some studies [73] emphasized that limited N supply inhibits root expansion and reduces sugar content and yield, our findings suggest that moderate N application optimizes sugar yield, increasing it by approximately 46% with a slight improvement (0.2%) in sugar content [74]. Additionally, our results provide new insights into nutrient distribution, as N application enhanced the uptake of Na+, K+, Ca²⁺, and Mg²⁺, with K⁺ and Ca²⁺ accumulating in the leaves and petioles, while Na⁺ and Mg²⁺ were primarily found in the taproot [70]. These differences in nutrient partitioning might be attributed to variations in soil properties, climate conditions, or differences in sugar beet cultivars used in different studies. Overall, our findings emphasize the importance of optimized N management for maximizing sugar beet productivity while minimizing negative environmental impacts.

It is worth mentioning that the most affected sugar beet traits by the interaction between IAA level and N rate were root diameter, leaf fresh weight, leaf area index, root yield, and pure sugar yield. Previous studies have shown similar results, with amino acids playing a crucial role in regulating plant growth and development, particularly in influencing cell division, differentiation, and acting as antioxidants in the photosynthesis system [75]. However, some differences may arise in the response of plant traits due to variations in experimental conditions, such as the plant species, environmental factors, and nutrient availability. For instance, L-tryptophan, a key metabolite and precursor of auxins like IAA, has been reported to positively influence plant growth by enhancing auxin biosynthesis [76, 77]. The impact of tryptophan on osmolarity regulation, ion transport, and stomatal control has also been noted [78]. In comparison to earlier studies, our findings emphasize a stronger interaction between nitrogen levels and tryptophan in promoting sugar beet growth, which could be attributed to the specific N rate and IAA level combinations tested in our study. The integration of N and amino acids in improving plant growth and productivity has been widely reported, with enhanced nutrient utilization and increased chlorophyll biosynthesis often resulting in better crop yields [79,80,81]. However, the degree of improvement may vary depending on the concentration and timing of nutrient applications, highlighting the need for further research to optimize these interactions.

Overall, this study highlights the critical role of N levels and IAA interactions in enhancing sugar beet growth and productivity. The findings provide valuable insights into the physio-biochemical mechanisms underlying these effects, particularly the role of amino acids such as tryptophan in auxin biosynthesis and plant stress responses. However, this study has certain limitations, including the focus on a specific range of N and IAA levels, which may not fully capture the broader spectrum of interactions under different environmental conditions. Additionally, while our results demonstrate significant improvements in key agronomic traits, further research is needed to explore the long-term effects of these treatments across multiple growing seasons and soil types. Future studies should also investigate the molecular pathways involved in these interactions to gain a deeper understanding of their regulatory mechanisms. Expanding this research to different crops could further validate the general applicability of these findings in sustainable agricultural practices.

Conclusions

The synergistic effect of indole‒3‒acetic acid and nitrogen was more pronounced in root diameter, leaf fresh weight, leaf area index, root yield and pure sugar yield of sugar beet. Combination of higher levels of indole‒3‒acetic acid (300 mg L− 1) and nitrogen (340 kg N ha− 1) showed the distinctive enhancements in most tested traits of sugar beet grown under salt-affected soil. Hence, further research should be performed to test the influence progressive levels, specifically on sugar beet physiology and sugar profile.

Data availability

The datasets used and/or analyzed during the current investigation are available from the corresponding author on reasonable request.

References

  1. Shaaban A, Abdou NM, Abd El–Mageed TA, Semida WM, Abd El Tawwab AR, Mohamed GF, Mohamed MS, El–Saadony MT, El-Tarabily KA, AbuQamar SF, Hemida KA. Foliar fertilization with potassium silicate enhances water productivity and drought resilience in sugar beet. Field Crops Res. 2025;326:109840. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.fcr.2025.109840.

    Article  Google Scholar 

  2. Shaaban A, Abd El-Mageed TA, Abd El-Momen WR, Saudy HS, Al-Elwany OAAI. The integrated application of phosphorous and zinc affects the physiological status, yield and quality of Canola grown in phosphorus-suffered deficiency saline soil. Gesun Pflanz. 2023;75:1813–21. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-023-00843-2.

    Article  CAS  Google Scholar 

  3. Saudy HS, El-Metwally IM, Abd El-Samad GA. Physio–biochemical and nutrient constituents of peanut plants under Bentazone herbicide for broad–leaved weed control and water regimes in dry land areas. J Arid Land. 2020a;12(4):630–39. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s40333-020-0020-y.

    Article  Google Scholar 

  4. Alghamdi SA, Alharby HF, Abdelfattah MA, Mohamed IA, Hakeem KR, Rady MM, Shaaban A. Spirulina platensis-inoculated humified compost boosts rhizosphere soil hydro-physico-chemical properties and Atriplex nummularia forage yield and quality in an arid saline calcareous soil. J Soil Sci Plant Nutr. 2023;23:2215–36. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s42729-023-01174-x.

    Article  CAS  Google Scholar 

  5. Saudy HS, El-Bially MA, Hashem FA, Shahin MG, El–Gabry YA. The changes in yield response factor, water use efficiency, and physiology of sunflower owing to ascorbic and citric acids application under mild deficit irrigation. Gesun Pflanz. 2023;75:899–09. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-022-00736-w.

    Article  CAS  Google Scholar 

  6. Mekdad AAA, Shaaban A, Rady MM, Ali EF, Hassan FAS. Integrated application of K and Zn as an avenue to promote sugar beet yield, industrial sugar quality, and K-use efficiency in a salty semi-arid agro-ecosystem. Agronomy. 2021;11(4):780. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/agronomy11040780.

    Article  CAS  Google Scholar 

  7. Saudy HS, Salem EMM, Abd El–Momen WR. Effect of potassium silicate and irrigation on grain nutrient uptake and water use efficiency of wheat under calcareous soils. Gesunde Pflanz. 2023;75:647–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-022-00729-9.

    Article  CAS  Google Scholar 

  8. Saudy HS, Mubarak M. Mitigating the detrimental impacts of nitrogen deficit and fenoxaprop-p-ethyl herbicide on wheat using silicon. Commun Soil Sci Plant Anal. 2015;46:913–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/00103624.2015.1011753.

    Article  CAS  Google Scholar 

  9. Khan A, Tan DKY, Afridi MZ, Luo H, Tung SA, Ajab M, et al. Nitrogen fertility and abiotic stresses management in cotton crop: A review. Environ Sci Pollut Res. 2017;24:14551–66. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11356-017-8920-x.

    Article  CAS  Google Scholar 

  10. Saudy HS, El-Metwally IM. Effect of irrigation, nitrogen sources and Metribuzin on performance of maize and its weeds. Commun Soil Sci Plant Anal. 2023;54:22–31. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/00103624.2022.2109659.

    Article  CAS  Google Scholar 

  11. Abd-Elrahman S, Saudy HS, Abd El–Fattah DA, Hashem FA. Effect of irrigation water and organic fertilizer on reducing nitrate accumulation and boosting lettuce productivity. J Soil Sci Plant Nutr. 2022;22:2144–55. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s42729-022-00799-8.

    Article  CAS  Google Scholar 

  12. Singh M, Singh VP, Prasad SM. Responses of photosynthesis, nitrogen and proline metabolism to salinity stress in Solanum lycopersicum under different levels of nitrogen supplementation. Plant Physiol Biochem. 2016;109:72–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.plaphy.2016.08.021.

    Article  PubMed  CAS  Google Scholar 

  13. Murmu K, Murmu S, Kundu CK, Bera PS. Exogenous proline and glycine betaine in plants under stress tolerance. Int J Curr Microbiol App Sci. 2017;6:901–13. https://doiorg.publicaciones.saludcastillayleon.es/10.20546/ijcmas.2017.609.109.

    Article  CAS  Google Scholar 

  14. Shrivastava P, Kumar R. Soil salinity: a serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J Biol Sci. 2015;22:123–31. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.sjbs.2014.12.001.

    Article  PubMed  CAS  Google Scholar 

  15. El-Ramady H, Alshaal T, Elhawat N, Ghazi A, Elsakhawy T, Omara AE, et al. Plant nutrients and their roles under saline soil conditions. In: Hasanuzzaman, et al. editors. Plant nutrients and abiotic stress tolerance. Springer Nature Singapore Pte Ltd; 2018. pp. 297–324. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/978-981-10-9044-8_13.

  16. Mekdad AAA, Rady MM. Response of Beta vulgaris L. to nitrogen and micronutrients in dry environment. Plant Soil Environ. 2016;62:23–9. https://doiorg.publicaciones.saludcastillayleon.es/10.17221/631/2015-PSE.

    Article  CAS  Google Scholar 

  17. Tsialtas JT, Maslaris N. Effect of N fertilization rate on sugar yield and non-sugar impurities of sugar beets (Beta vulgaris) grown under mediterranean conditions. J Agron Crop Sci. 2005;191:330–39. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1439-037X.2005.00161.x.

    Article  Google Scholar 

  18. Sheha AM, Abou El-Enin MM, El-Hashash EF, Rady MM, El-Serafy RS, Shaaban A. The productivity and overall benefits of faba bean-sugar beet intercropping systems interacted with foliar-applied nutrients. J Plant Nutr. 2023;46:1683–700. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/01904167.2022.2093747.

    Article  CAS  Google Scholar 

  19. Saudy HS, El-Bially MA, El-Metwally IM, Shahin MG. Physio–biochemical and agronomic response of ascorbic acid–treated sunflower (Helianthus annuus) grown at different sowing dates and under various irrigation regimes. Gesunde Pflanz. 2021;73:169–79. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-020-00535-1.

    Article  CAS  Google Scholar 

  20. El-Metwally IM, Geries L, Saudy HS. Interactive effect of soil mulching and irrigation regime on yield, irrigation water use efficiency and weeds of trickle–irrigated onion. Archiv Agron Soil Sci. 2022a;68:1103–16. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/03650340.2020.1869723.

    Article  CAS  Google Scholar 

  21. Shahin MG, Saudy HS, El-Bially ME, Abd El-Momen WR, El-Gabry YA, Abd El-Samad GA, et al. Physiological and agronomic responses and nutrient uptake of soybean genotypes cultivated under various sowing dates. J Soil Sci Plant Nutr. 2023;23:5145–58. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s42729-023-01389-y.

    Article  CAS  Google Scholar 

  22. Karagöz H, Çakmakçi R, Hosseinpour A, Kodaz S. Alleviation of water stress and promotion of the growth of sugar beet (Beta vulgaris L.) plants by multi-traits rhizobacteria. Appl Ecol Environ Res. 2018;16:6801–13. https://doiorg.publicaciones.saludcastillayleon.es/10.15666/AEER/1605_68016813.

    Article  Google Scholar 

  23. El-Sayed SR, Amany A, Hellal F. Drought stress mitigation by application of algae extract on peanut grown under sandy soil conditions. Asian J Plant Sci. 2020;19:230–39. https://doiorg.publicaciones.saludcastillayleon.es/10.3923/ajps.2020.230.239.

    Article  Google Scholar 

  24. Ahmed EA, Farrag AA, Kheder AA, Shaaban A. Effect of Phytoplasma associated with Sesame phyllody on ultrastructural modification, physio-biochemical traits, productivity and oil quality. Plants. 2022;11(4):477. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/plants11040477.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Agami RA. Pre-soaking in indole-3-acetic acid or spermidine enhances copper tolerance in wheat seedlings. S Afr J Bot. 2016;104:167–74. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.sajb.2015.10.003.

    Article  CAS  Google Scholar 

  26. Gong Q, Li Z, Wang L, Dai T, Kang Q, Niu D. Exogenous of indole-3-acetic acid application alleviates copper toxicity in spinach seedlings by enhancing antioxidant systems and nitrogen metabolism. Toxics. 2019;8:1. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/toxics8010001.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Meier U. Growth stages of mono- and dicotyledonous plants. BBCH monograph. Quedlinburg: Julius Kühn-Institut (JKI); 2018.

    Google Scholar 

  28. McGinnis RA. Chemistry of the beet and processing materials. In: McGinnis, R.A, editor, Beet-Sugar Technology, 3rd ed. Beet sugar develop. Found., Denver; 1982. pp. 25–63.

  29. AOAC. Association of Official Agriculture Chemists, Official Method of Analysis: Association of Analytical Chemists. 19th Ed, Washington DC. 2012. pp. 121–30.

  30. Carruthers A, Oldfield JFT, Teague HJ. Assessment of beet quality. In: The 15th Annual Technical Conference, Nottingham, U.K: British Sugar Corp. 1962.

  31. Harvey GW, Dotton JV. Root quality and processing. In: Cooke DA, Scott RK, editors. The sugar beet crop science into practice. London: Chapman & Hall; 1993. pp. 571–17.

    Chapter  Google Scholar 

  32. Jackson M. Soil chemical analysis Prentice. New Delhi: Hall of India Private Limited; 1967. p. 498.

    Google Scholar 

  33. Johnson CM, Ulrich A. Analytical methods for use in plant analysis. Calif Agric Exp Stat Bull. 1959;767:25–78.

    Google Scholar 

  34. Moll RH, Kamprath EJ, Jackson WA. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron J. 1982;74:562–4. https://doiorg.publicaciones.saludcastillayleon.es/10.2134/agronj1982.00021962007400030037x.

    Article  Google Scholar 

  35. Egamberdieva D. Alleviation of salt stress by plant growth regulators and IAA producing bacteria in wheat. Acta Physiol Plant. 2009;31:861–64. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11738-009-0297-0.

    Article  CAS  Google Scholar 

  36. Abdel Latef AA, Tahjib-Ul-Arif M, Rhaman MS. Exogenous auxin-mediated salt stress alleviation in faba bean (Vicia faba L). Agronomy. 2021;11:547. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/agronomy11030547.

    Article  CAS  Google Scholar 

  37. El-Bially MA, Saudy HS, El-Metwally IM, Shahin MG. Sunflower response to application of L–ascorbate under thermal stress associated with different sowing dates. Gesun Pflanz. 2022a;74:87–96. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-021-00590-2.

    Article  CAS  Google Scholar 

  38. El Sherbiny HA, El-Hashash EF, Abou El-Enin MM, Nofal RS, Abd El-Mageed TA, Bleih EM, El-Saadony MT, El-Tarabily KA, Shaaban A. Exogenously applied Salicylic acid boosts morpho-physiological traits, yield, and water productivity of lowland rice under normal and deficit irrigation. Agronomy. 2022;12:1860. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/agronomy12081860.

    Article  CAS  Google Scholar 

  39. Makhlouf BSI, Khalil SRA, Saudy HS. Efficacy of humic acids and Chitosan for enhancing yield and sugar quality of sugar beet under moderate and severe drought. J Soil Sci Plant Nutr. 2022;22:1676–91. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s42729-022-00762-7.

    Article  CAS  Google Scholar 

  40. Endale HT, Tesfaye W, Mengstie TA. ROS induced lipid peroxidation and their role in ferroptosis. Front Cell Dev Biol. 2023;11:1226044. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fcell.2023.1226044.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dalal VK, Tripathy BC. Water-stress induced downsizing of light harvesting antenna complex protects developing rice seedlings from photo-oxidative damage. Sci Rep. 2018;8:5955. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-017-14419-4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. El-Bially MA, Saudy HS, El-Metwally IM, Shahin MG. Efficacy of ascorbic acid as a cofactor for alleviating water deficit impacts and enhancing sunflower yield and irrigation water–use efficiency. Agric Water Manage. 2018;208:132–39. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.agwat.2018.06.016.

    Article  Google Scholar 

  43. El-Metwally IM, Saudy HS. Interactional impacts of drought and weed stresses on nutritional status of seeds and water use efficiency of peanut plants grown in arid conditions. Gesun Pflanz. 2021;73(407):16. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-021-00557-3.

  44. El-Metwally IM, Saudy HS, Elewa TA. Natural plant by-products and mulching materials to suppress weeds and improve sugar beet (Beta vulgaris L.) yield and quality. J Soil Sci Plant Nutr. 2022c;22:5217–30. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s42729-022-00997-4.

    Article  CAS  Google Scholar 

  45. Snezhkina AV, Kudryavtseva AV, Kardymon OL, Savvateeva MV, Melnikova NV, Krasnov GS, et al. ROS generation and antioxidant defense systems in normal and malignant cells. Oxid Med Cell Longev. 2019;5:6175804. https://doiorg.publicaciones.saludcastillayleon.es/10.1155/2019/6175804.

    Article  CAS  Google Scholar 

  46. Ramadan KMA, El-Beltagi HS, Abd El-Mageed TAA, Saudy HS, Al-Otaibi HH, Mahmoud MAA. The changes in various physio-biochemical parameters and yield traits of faba bean due to humic acid plus 6-benzylaminopurine application under deficit irrigation. Agronomy. 2023;13:1227. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/agronomy13051227.

    Article  CAS  Google Scholar 

  47. Rucinska-Sobkowiak R, Pukacki PM. Antioxidative defense system in lupin roots exposed to increasing concentrations of lead. Acta Physiol Plant. 2006;28:357–64. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11738-006-0032-z.

    Article  CAS  Google Scholar 

  48. El-Metwally IM, Sadak MS, Saudy HS. Stimulation effects of glutamic and 5-Aminolevulinic acids on photosynthetic pigments, physio-biochemical constituents, antioxidant activity, and yield of peanut. Gesun Pflanz. 2022b;74:915–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-022-00663-w.

    Article  CAS  Google Scholar 

  49. Upadhyay R, Panda SK. Zinc reduces copper toxicity induced oxidative stress by promoting antioxidant defense in freshly grown aquatic duckweed Spirodela polyrhiza L. J Hazard Mater. 2010;175:1081–84. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jhazmat.2009.10.016.

    Article  PubMed  CAS  Google Scholar 

  50. El-Metwally IM, Saudy HS, Abdelhamid MT. Efficacy of benzyladenine for compensating the reduction in soybean productivity under low water supply. Italian J Agromet. 2021;2:81–90. https://doiorg.publicaciones.saludcastillayleon.es/10.36253/ijam-872.

    Article  Google Scholar 

  51. Ben Massoud M, Karmous I, El Ferjani E, Chaoui A. Alleviation of copper toxicity in germinating pea seeds by IAA, GA3, Ca and citric acid. J Plant Interact. 2017;13:21–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/17429145.2017.1410733.

    Article  CAS  Google Scholar 

  52. Choudharya SP, Bhardwajb R, Guptac BD, Duttc P, Guptac RK, Biondid S, et al. Epibrassinolide induces changes in indole-3-acetic acid, abscisic acid and polyamine concentrations and enhances antioxidant potential of radish seedlings under copper stress. Physiol Plant. 2010;140:280–96. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1399-3054.2010.01403.x.

    Article  CAS  Google Scholar 

  53. Abas L, Benjamins R, Malenica N, Paciorek T, Wi´sniewska J, Moulinier-Anzola JC, et al. Intrecellular trafficking and proteolysis of the Arabidopsis auxin-efflux facilitator PIN2 are involved root gravitropism. Nat Cell Biol. 2006;8:249–56. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/ncb1369.

    Article  PubMed  CAS  Google Scholar 

  54. Serrano A, Kuhn N, Restovic F, Meyer-Regueiro C, Madariaga M, Arce-Johnson P. The glucose-related decrease in Polar auxin transport during ripening and its possible role in grapevine berry coloring. J Plant Growth Regul. 2023;42:365–75. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00344-021-10553-6.

    Article  CAS  Google Scholar 

  55. Abts W, Vandenbussche B, De Proft MP, Van de Poel B. The role of auxin-ethylene crosstalk in orchestrating primary root elongation in sugar beet. Front Plant Sci. 2017;8:444. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fpls.2017.00444.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Ashraf MY, Azhar N, Hussain M. Indole acetic acid (IAA) induced changes in growth, relative water contents and gas exchange attributes of barley (Hordeum vulgare L.) grown under water stress conditions. Plant Growth Regul. 2006;50:85–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10725-006-9130-6.

    Article  CAS  Google Scholar 

  57. Zhang Y, Li Y, Hassan MJ, Li Z, Peng Y. Indole-3-acetic acid improves drought tolerance of white clover via activating auxin, abscisic acid and jasmonic acid related genes and inhibiting senescence genes. BMC Plant Biol. 2020;20:150. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12870-020-02354-y.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Saudy HS, El-Metwally IM. Nutrient utilization indices of NPK and drought management in groundnut under sandy soil conditions. Commun Soil Sci Plant Anal. 2019;50:1821–28. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/00103624.2019.1635147.

    Article  CAS  Google Scholar 

  59. Salem EMM, Kenawey MKM, Saudy HS, Mubarak M. Influence of silicon forms on nutrient accumulation and grain yield of wheat under water deficit conditions. Gesun Pflanz. 2022;74:539–48. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10343-022-00629-y.

    Article  CAS  Google Scholar 

  60. Shaaban A, Mahfouz H, Megawer EA, Saudy HS. Physiological changes and nutritional value of forage clitoria grown in arid agro-ecosystem as influenced by plant density and water deficit. J Soil Sci Plant Nutr. 2023;23:3735–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s42729-023-01294-4.

    Article  CAS  Google Scholar 

  61. Gajewska E, Sklodowska M. Nickel-induced changes in nitrogen metabolism in wheat shoots. Plant Physiol. 2009;166:1034–44. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jplph.2008.12.004.

    Article  CAS  Google Scholar 

  62. Meuwly P, Pilet P. Local treatment with indole-3-acetic acid induces differential growth responses in Zea mays L. root. Planta. 1991;185:58–64. https://www.jstor.org/stable/23381357.

    Article  PubMed  CAS  Google Scholar 

  63. Edelmann HG. Plant root development: is the classical theory for auxin-regulated root growth false? Protoplasma. 2022;259:823–32. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00709-021-01697-z.

    Article  PubMed  CAS  Google Scholar 

  64. Jiang H, Xu D. The cause of the difference in leaf net photosynthesis rate between two soybean cultivars. Photosynthetica. 2001;39:453–59. https://doiorg.publicaciones.saludcastillayleon.es/10.1023/A:1015150931227.

    Article  CAS  Google Scholar 

  65. Pandey DM, Goswami CL, Kumar B. Physiological effects of plant hormones in cotton under drought. Biol Plant. 2003;47:535–40. https://doiorg.publicaciones.saludcastillayleon.es/10.1023/B:BIOP.0000041058.65442.41.

    Article  CAS  Google Scholar 

  66. Qotob MA, Nasef MA, Elhakim HKA, Shaker OG, Habashya NR, Abdelhamid IA. Integrated effect of plant growth regulators with Boron sources on some biological parameters of sugar beet. Egypt J Agron. 2019;38:419–36. https://doiorg.publicaciones.saludcastillayleon.es/10.1101/839068.

    Article  Google Scholar 

  67. Saudy HS. Chlorophyll meter as a tool for forecasting wheat nitrogen requirements after application of herbicides. Archiv Agron Soil Sci. 2014;60:1077–90. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/03650340.2013.866226.

    Article  CAS  Google Scholar 

  68. Saudy HS, Abd El–Momen WR, El-Khouly NS. Diversified nitrogen rates influence nitrogen agronomic efficiency and seed yield response index of Sesame (Sesamum indicum, L.) cultivars. Commun Soil Sci Plant Anal. 2018;49:2387–95. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/00103624.2018.1510949.

    Article  CAS  Google Scholar 

  69. Wang N, Fu F, Wang H, Wang P, He S, Shao H, et al. Effects of irrigation and nitrogen on chlorophyll content, dry matter and nitrogen accumulation in sugar beet (Beta vulgaris L). Sci Rep. 2021;11:16651. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-021-95792-z.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Yan F, Zhang F, Fan J, Hou X, Bai W, Liu X, et al. Optimization of irrigation and nitrogen fertilization increases Ash salt accumulation and ions absorption of drip-fertigated sugar beet in saline-alkali soils. Field Crops Res. 2021;271:108247. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.fcr.2021.108247.

    Article  Google Scholar 

  71. Nangia V, Gowda PH, Mulla DJ. Effects of changes in N-fertilizer management on water quality trends at the watershed scale. Agric Water Manage. 2010;97:1855–60. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.agwat.2010.06.023.

    Article  Google Scholar 

  72. Ziadi N, B´elanger G, Classens A. Relationship between soil nitrate accumulation and in-season corn N nutrition indicators. Can J Plant Sci. 2012;92:331–39. https://doiorg.publicaciones.saludcastillayleon.es/10.4141/cjps2011-086.

    Article  CAS  Google Scholar 

  73. Shaw B, Tomas TH, Cooke DT. Response of sugar beet (Beta vulgaris L.) to drought and nutrient defciency stress. Plant Growth Regul. 2002;37:77–83. https://doiorg.publicaciones.saludcastillayleon.es/10.1023/A:1020381513976.

    Article  CAS  Google Scholar 

  74. Bagherzadeh A, Kalat SMN, Hajian J. Effects of residual wheat straw and nitrogen fertilizer on yield and quality of sugar beet in a semi-arid region. Sugar Tech. 2014;16:189–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12355-013-0253-6.

    Article  Google Scholar 

  75. Ibrahim HE. Effect of Tryptophan, ascorbic acids and super max different rates on vegetative growth and flowering Spathiphyllum Wallsi L. J Plant Prod. 2016;7:813–20. https://doiorg.publicaciones.saludcastillayleon.es/10.21608/jpp.2016.46177.

    Article  Google Scholar 

  76. Xu X, Ouyang H, Kuzyakov Y, Richter A, Wanek W. Significance of organic nitrogen acquisition for dominant plant species in an alpine meadow on the Tibet plateau, China. Plant Soil. 2006;285:221–31. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11104-006-9007-5.

    Article  CAS  Google Scholar 

  77. Mustafa A, Imran M, Ashraf M, Mahmood K. Perspectives of using L-tryptophan for improving productivity of agricultural crops: A review. Pedosphere. 2018;28:16–34. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1002-0160(18)60002-5.

    Article  CAS  Google Scholar 

  78. Rai VK. Role of amino acid in plant responses to stresses. Biol Plantar J. 2002;45:481–87. https://doiorg.publicaciones.saludcastillayleon.es/10.1023/A:1022308229759.

    Article  CAS  Google Scholar 

  79. Khan S, Yu H, Li Q, Gao Y, Sallam BN, Wang H, et al. Exogenous application of amino acids improves the growth and yield of lettuce by enhancing photosynthetic assimilation and nutrient availability. Agronomy. 2019;9:266. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/agronomy9050266.

    Article  CAS  Google Scholar 

  80. Hammad SAR, Ali OAM. Physiological and biochemical studies on drought tolerance of wheat plants by application of amino acids and yeast extract. Ann Agric Sci. 2014;59:133–45.

    Article  Google Scholar 

  81. Gondek K, Mierzwa-Hersztek M. Effect of soil-applied L-tryptophan on the amount of biomass and nitrogen and sulfur utilization by maize. Agronony. 2021;11:2582. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/agronomy11122582.

    Article  CAS  Google Scholar 

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Funding

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Authors and Affiliations

  1. Agronomy Department, Faculty of Agriculture, Fayoum University, Fayoum, 63514, Egypt

    Ahmed Shaaban, Mohamed A. M. Eid & Ali A. A. Mekdad

  2. Agronomy Department, Faculty of Agriculture, Ain Shams University, Hadayek Shoubra, P.O. Box 68, Cairo, 11241, Egypt

    Hani S. Saudy

  3. Soil, Water and Environment Research Institute, Agricultural Research Center, Giza, 12112, Egypt

    Sammar F. Zahran

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  1. Ahmed Shaaban
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Contributions

A.S., M.A.M.E., S.F.Z., A.A.A.M. designed the experiments; A.S., H.S.S., M.A.M.E., A.A.A.M. performed the experiments; A.S., H.S.S., M.A.M.E., A.A.A.M. analyzed the data and wrote the manuscript; A.S., H.S.S., S.F.Z., A.A.A.M. finalized the article. Whole authors agree with the article contents and with its submission.

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Correspondence to Ahmed Shaaban.

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Shaaban, A., Saudy, H.S., Eid, M.A.M. et al. Synergistic effect of indole‒3‒acetic acid and nitrogen on yield, sugar profile, and nitrogen utilization of salt-stressed sugar beet crop. BMC Plant Biol 25, 632 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12870-025-06531-9

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Keywords

BMC Plant Biology

ISSN: 1471-2229

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