Effect of Biostimulants on Drought Tolerance of Greenhouse-Grown Tomato

Abstract

The use of biostimulants is one of the recognized strategies for mitigating the adverse effects of drought on crops. In a greenhouse tomato experiment, the effect of two biostimulants in combination with three levels of drought was investigated. Specifically, the doses of 150 mL and 1000 g ha⁻¹ of a plant-derived polyhydroxy acids extract (B1) and a Sargassum seaweed extract (B2), respectively, were studied in combination with drought levels of 85, 63.75, and 42.5% of field capacity. Four applications were performed during key growth stages. The effects were comprehensively investigated by assessing agronomic and physiological traits of the plants at three defined time points during the experimental period. Furthermore, organoleptic characteristics, bioactive compounds, antioxidant activity in the fruits, and overall yield components were evaluated. Drought stress provoked a consistent negative impact on several physiological traits, such as stomatal conductance (up to −58.3%), net photosynthesis (up to −47.9%), and quantum yield. A comparable impact was observed on agronomic traits, such as plant height, stem thickness, and number of leaves, with reductions of up to 13.6%. Both biostimulants’ applications enhanced certain physiological features across all irrigation levels, including net photosynthesis by up to 44.3% and chlorophyll content index by up to 33.4%, while B2 further increased intrinsic water use efficiency by up to 42.9% compared to the respective controls. However, this trend was not reflected in the evaluated post-harvest parameters, such as fruit yield, fruit number, fruit weight, and quality indices. These findings suggest that biostimulants may have a supporting role in physiological responses under drought stress but have limited effects on fruit production. Future research should focus on optimizing the formulation, dosage, and timing of biostimulant applications, as these factors may be critical to enhancing plant tolerance to drought stress and improving fruit yield responses.

1. Introduction

Tomato (Solanum lycopersicum L.) is one of the most important and widely grown vegetable crops in the Mediterranean region and in many regions worldwide. According to FAOSTAT, tomatoes represent the second most extensively cultivated and highest-producing horticultural crop worldwide, surpassed only by potatoes, with the total cultivated area being estimated at 5.2 million hectares and a total yield production of approximately 185 million tons [1]. Tomato fruits possess high water content ranging from 93 to 95%, and they exhibit a high nutraceutical profile with notable concentrations of carotenoids, polyphenols (mainly phenolic acids and flavonoids), soluble sugars, citric and malic acid, minerals [2], and vitamins C and K [3]. The synergistic effect of the lipophilic and the hydrophilic fraction enhances the physiological properties of tomato fruits, including anti-inflammatory, antiallergenic, antimicrobial, vasodilating, anti-thrombotic, cardioprotective, and antioxidant effects.

Despite its nutritional and economic importance, tomato cultivation is often challenged by various environmental stresses, particularly abiotic factors such as drought, salinity, and temperature extremes. Among these, drought stress is especially critical in arid and semi-arid regions, with the Mediterranean basin being recognized as a climate hotspot [4,5]. The lack or the irregularity of precipitation, along with water scarcity, intensifies the restrictions in production, mainly in vegetables, due to high yield components and irrigation requirements compared with other crops [6,7]. Exposure to drought stress generally causes morphological, physiological, and biochemical modifications in plants, including a decrease in the shoot–root ratio, a reduction in photosynthetic rate and relative water content, proline accumulation, hormonal imbalance, and the overproduction of antioxidant enzymes such as catalase and superoxide dismutase [8,9]. Tomato is particularly sensitive to drought stress, especially during the early reproductive stage, notably during flowering and fruit set. The severity of the drought period, as well as the inherent mechanism of tomato for drought alleviation, defines the changes in morphological and physiological parameters [10]. Throughout the reproductive stage, tomato plants exhibit reduced leaf area, flower shedding, smaller fruits, puffiness, fruit splitting, and calcium deficiency-related disorders such as blossom-end rot and poor seed viability [7,11].

To mitigate these effects and enhance plant tolerance under suboptimal conditions, the use of biostimulants, along with other methods such as breeding and genetic engineering, is considered a promising approach [12]. Biostimulants act in addition to fertilizers, with the aim of optimizing their efficiency and reducing the nutrient application rates [13,14]. In this context, the European Green Deal provided by the European Commission in 2019 aims to make agriculture more sustainable by reducing agrochemical use and promoting biostimulants as an alternative [15]. In the latest European regulation, the term biostimulant refers to organic or inorganic products containing bioactive substances and/or microorganisms, which, when applied to the plant or rhizosphere, stimulate the growth and productivity of the plant by improving the absorption and assimilation efficiency of nutrients, tolerance to abiotic stresses and/or quality of the product, regardless of their nutrient content [16].

Indeed, biostimulants improve plant productivity due to new or emerging properties of the complex of the constituents and not only by the presence of the essential nutrients or growth regulators. However, the positive effects from biostimulant application are not always consistent or profound, as they depend on multiple factors, including environmental conditions at the time of application, the cultivated plant species, the cultural system (e.g., open-field or greenhouse), the plant’s developmental stage at the time of application, the origin of the biostimulant, and its specific formulation. Notably, these interactions can modulate the extent of the biostimulant’s effectiveness under drought stress, leading to variable outcomes in plant growth and productivity [17,18]. Understanding these complex dynamics is essential for optimizing their application and maximizing their agronomic benefits. Among biostimulants, seaweed and plant extracts used in the present study provide a complex of polysaccharides, pycoccoloides (fucoidan, alginate), polyhydroxylic acids, amino acids, and hormones [19,20]. These compounds improve crop performance under unfavorable conditions by regulating primary and secondary metabolism through phytohormone synthesis and regulation, nutrient uptake and translocation, the stimulation of enzymes involved in carbon and nitrogen assimilation, and soil amendment by enhancing water–air conditions and promoting the growth of beneficial organisms [21,22].

Although biostimulants may improve physiological parameters, their effectiveness on fruit productivity and quality under moderate or severe drought stress conditions in protected cultivation remains uncertain. For this reason, the aim of the current study was to investigate whether two biostimulants can induce plant resistance under three levels of drought stress in greenhouse tomato cultivation. The biostimulant used was a commercial product of plant-derived polyhydroxy acids (1% w/w, ‘BioElite’, biostimulant 1) and a commercial product of Sargassum seaweed extract, rich in organic matter, plant hormones, amino acids, alginic acid, and vitamins (‘Alga 600’, biostimulant 2).

2. Materials and Methods

2.1. Plant Material, Greenhouse Experiment, and Drought Stress Treatment

The experiment was conducted in a 0.8 ha polyethylene greenhouse located at the Institute of Plant Breeding and Genetic Resources (IPBGR), Thermi-Thessaloniki (40°32′08.7″ N, 23°00′06.4″ E). Tomato (Solanum lycopersicum L.) seedlings, cv. ‘Optima’, were produced in seed trials filled with a 3:1 peat–perlite substrate medium during late March−April 2023. During the intervening period between seedbeds and stress initiation, the young seedlings received full irrigation. One hundred and thirty−five seedlings at the stage of 4–6 true leaves were transplanted in 6 L pots previously filled with a 3:1 mixture of peat–perlite in early May 2023 in the polyethylene greenhouse.

A two-factor split-plot arrangement of treatments was employed in a randomized complete block design with 3 replicates, each one comprising 45 plants. The drought level (Factor A) was the main plot, and the biostimulant treatments (Factor B) were the subplots. Each experimental subplot consisted of 5 plants. Specifically, drought stress treatments comprised non-drought stress (ND), mild drought stress (MDS), and severe drought stress (SDS), achieved by drip irrigation at 85%, 63.75%, and 42.5% of field capacity (FC), respectively. The three biostimulant treatments consisted of (i) application of 150 mL ha⁻¹ of ‘BioElite’ (Agrichem Co., Yatala, QLD, Australia) (B1), (ii) application of 1000 g ha⁻¹ of ‘Alga 600′ (Leili Marine Bioindustry Inc., Beijing, China) (B2), and (iii) no biostimulant added—control (Control). Biostimulants were foliar applied 4 times during trial duration, viz. at the growth stage of 4–6 leaves, before blooming, and at fruiting of the 1st and 3rd inflorescence. Control plants were sprayed with distilled water. For foliar application, a 16 L high-performance agricultural sprayer was used and calibrated to deliver up to 400 L ha⁻¹ (up to the point of runoff) at 250 kPa pressure. In a preliminary laboratory experiment using pots filled with a 3:1 peat–perlite mixture, the FC was determined gravimetrically by saturating the substrate and allowing free drainage for 24 h. This procedure yielded a volumetric soil water content (VSWC) of 0.470 m³ m⁻³ at 100% FC, as measured using a soil moisture sensor (5TE, Decagon Devices, Pullman, WA, USA) coupled to a ProCheck (Decagon Devices, Pullman, WA, USA) readout device [23]. To impose the different stress levels during the greenhouse experiment, a drip irrigation system was employed with a flow of 2 L h⁻¹. For the ND treatment, irrigation was applied to maintain soil moisture at approximately 85% FC—an irrigation considered optimal for greenhouse tomato cultivation, corresponding to a target VSWC of 0.400 m³ m⁻³. Based on calibration trials, irrigation volumes of 2.0, 1.5, and 1.0 L per pot were applied daily to achieve the target VSWC levels for the ND, MDS, and SDS treatments, respectively (equivalent to irrigation durations of 60, 45, and 30 min).

Each block occupied approximately an area of 50 m², while the plant spacing was 1.0 m on the row and 1.0 m between rows. The first and the fifth plants in each combined factor of each block were guard plants.

The NPK chemical fertilization was performed with 200 kg·ha⁻¹. A full fertilization program was applied manually by adding 3 g of 17–12–17 NPK (12–10–30 after fertilizer every 3rd day in order to supply 15 g N to each plant during the whole trial duration). All the other cultural practices were kept according to a typical low-input sustainable tomato cultivation system. Plants were grown under weed-free conditions by regular hand weeding. Air temperature and relative humidity were recorded continuously at 1 h intervals during experimentation by Hobo U23 Pro v2 data loggers (Onset Computer Corporation, Barnstable, MA, USA). The weekly relative humidity (%) and the mean, minimum, and maximum temperature recorded are given in Figure 1.

2.2. Evaluated Features

2.2.1. Plant Growth and Physiology Assessments

Plant measurements included (1) plant height (PH) (cm); (2) stem thickness (ST) (mm); (3) number of leaves (LN); (4) number of inflorescences (IN); (5) relative water content % (RWC); (6) the gas exchange measurements of net photosynthesis (Anet) (mol m⁻² s⁻¹), stomatal conductance (Gs) (mol CO₂ m⁻² s⁻¹), and transpiration rate (E) (mmol H₂O m⁻² s⁻¹); (7) ‘intrinsic’ water use efficiency (_iWUE); (8) quantum yield (QY); (9) chlorophyll content index (CCI); and (10) leaf electrolyte leakage (LEL). Measurements were carried out three times during experimentation, namely on 20, 35, and 55 days after the transplantation (DAT), except for IN and LEL, which were measured two times, at 35 and 55 DAT. Note that due to the many different measurements, these were taken ±1 day around the mentioned time.

Analytically, ST was measured with an electronic caliper at the point just above the pot surface.

RWC was assayed according to the procedure described by Jungklang et al. [24]. For this purpose, leaf disks 2.5 cm in diameter were cut from the apical leaflet of the younger fully expanded leaf. Fresh weight (FW) was immediately measured on a 4-digit analytical balance, and then the disks were immersed in distilled water for 4 h at room temperature. The turgid samples were carefully wiped dry, and then the turgid weight (TW) was determined. The samples’ dry weight (DW) was obtained after 24 h of oven drying at 68 °C. RWC was determined using the following equation: (FW − DW)/(TW − DW) × 100.

The gas exchange parameters Anet, E, and Gs were assessed using the portable photosynthesis system LCi-SD (ADC Bioscientific Ltd., Hoddesdon, UK) according to Kadoglidou et al. [25]. The photosynthetically active radiation (PAR) incident on the leaf surface was approximately 1000 ± mol photon m⁻² s⁻¹, the CO₂ concentration inside the chamber was 420 ± 10 μmol mol⁻¹, and the water reference as partial pressure was 27.5 ± 3 mbar. Finally, the intrinsic water use efficiency (_iWUE) was calculated as the quotient of the ratio A/Gs according to Liang et al. [26].

The QY was measured on light-adapted leaves using the FluorPen FP100 (Photon Systems Instruments, Brno, Czech Republic). For this purpose, six measurements of QY were taken of each subplot, according to the procedure described by Kadoglidou et al. [27]. The CCI was assessed with a portable Chlorophyll Content Meter (CCM-200, Opti-Sciences, Tyngsboro, MA, USA), and six measurements were obtained for each subplot.

All the abovementioned physiology assessments were evaluated on the second fully developed leaf from the top of tomatoes and were conducted in time intervals approximately 2 h after sunrise to avoid the photoinhibition phenomenon.

In the middle of July 2023, three fully grown leaves counted from the apex of tomatoes per treatment were taken per replicate and were scanned and analyzed using WinRHIZO Pro software (Regent Instruments Inc., Ste-Foy, QC, Canada) for the determination of leaf surface area.

The LEL was determined as previously described by Kadoglidou et al. [25] in leaf samples collected at 35 and 55 DAT using the electrical conductivity meter 712 CondutoMeter (Metrohm, Herisau, Switzerland).

2.2.2. Fruit Yield and Quality Assessments

Ripe tomato fruits were harvested weekly per plot to determine total yield per plant, fruit number per plant, and mean fruit weight. In total, six harvests were conducted, and every time, fruits from the greenhouse were transferred to the Laboratory of Chemistry and Technology of the IPGRB for further analysis.

From each harvest, the physicochemical parameters of pH and total solid sugars (TSS or °brix) were determined in representative samples of fruits from each treatment and each replicate. Tomato pH was measured with a portable pH meter (MW802, Milwaukee Instruments Inc., Rocky Mount, NC, USA), and TSS was measured in the fruit juice using a digital handheld refractometer (DR201-95 Krüss Optronic, Hamburg, Germany).

Three representative fruits per experimental plot and per harvest were cut into thin slices (about 3 cm wide) and considered as one sample. Subsequently, samples were freeze-dried and then ground in a laboratory mill according to Kadoglidou et al. [28]. Lyophilized powdered samples were stored at −20 °C until analysis of their bioactive compounds and antioxidant activity.

2.2.3. Sample Extraction, Fruit Bioactive Compound Assessments, and Antioxidant Activity

• Sample extraction

A quantity of 200 mg of powdered freeze-dried samples was placed into glass vials containing 10 mL of a methanol–water mixture (70:30, v/v), following the extraction procedure previously described by Kadoglidou et al. [28]. Each sample was extracted and analyzed in triplicate.

• Total Phenolic Content (TPC)

The TPC analysis was conducted using the Folin–Ciocalteu method described by Singleton et al. [29], with slight modifications. In brief, 0.2 mL of the sample extract was placed in a test tube, and then 0.8 mL of the Folin–Ciocalteu reagent was added. A methanol–water mixture (70:30, v/v) served as the blank. After a 2 min incubation, 2 mL of a 7.5% (w/v) sodium carbonate solution was added, and the final volume was adjusted to 10 mL with distilled water. The mixture was left to stand in the dark for 60 min before measuring absorbance at 725 nm, using gallic acid (GA) as the standard. Results were expressed as mg of GA equivalents (GAE) per g of sample on a dry weight basis (mg GAE g⁻¹ dw).

• Total Flavonoid Content (TFC)

The TFC of the sample extracts was determined using the AlCl₃ reagent method based on the Bao et al. [30] protocol, with slight modifications. In short, a quantity of 0.3 mL of the extract was mixed with 2 mL of double-distilled water in a test tube, and then 0.225 mL of 5% NaNO₂ was added. After 5 min, 0.225 mL of 10% AlCl₃·6H₂O solution was added, and the mixture was left to stand for another 5 min. Then, 0.750 mL of 2 M NaOH was added, and the reaction solution was thoroughly mixed and incubated in the dark for 15 min. The absorbance was measured at 510 nm using catechin (CAT) as the standard, with methanol serving as the blank. Results were expressed as mg of catechin equivalents (CATE) per g of sample on a dry weight basis (mg CATE g⁻¹ dw).

• Antioxidant activity

Three different techniques were applied to assess the antioxidant activity of the tomato fruits: (i) the radical scavenging activity of extracts against the 2,2-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) radical cation, which was evaluated according to the protocol of Re et al. [31], with appropriate adjustments described by Ntinas et al. [32]; (ii) the 2,2-diphenylpicrylhydrazyl scavenging assay (DPPH) that was carried out according to Yen and Chen [33], with slight modifications described by Kadoglidou et al. [28]; and (iii) the ferric reducing antioxidant power assay (FRAP) that was performed following the procedure of Benzie and Strain [34], as later modified by Kadoglidou et al. [35]. The results obtained from all assays of antioxidant activity were expressed in mg of trolox equivalents (TE) per g of dry weight (mg TE g⁻¹ dw).

2.3. Statistical Analysis

A multivariate analysis of variance (MANOVA) was applied to test the 3 × 3 (three drought levels × three biostimulant applications) variables as a group with the repeated measure (sampling time) being the repeated variable by using the computer software MSTAT-C version 1.41, experimental model no. 12 (Michigan State University, East Lansing, MI, USA). Plant growth and physiology assessments were recorded with 3 repeated measures (20, 35, and 55 DAT), except IN and LEL with 2 repeated measures, the yield components had 6 repeated measures (1st-6th harvest), and pH and TSS had 3. Since the bioactive compounds and the antioxidant activity were estimated after the 5th harvest, there was no combined-over-experimental-time statistical analysis. Instead, these variables were subjected to an ANOVA of a two-factor randomized complete block design, with factor B (biostimulant application) being a split plot on factor A (drought level) (experimental model no.9 in MSTAT program). Tukey’s honestly significant difference test was used to compare the obtained means, with a significance level set at p ≤ 0.05.

3. Results and Discussion

3.1. Effects of Biostimulant Treatments on Tomato Growth and Physiology Under Drought Stress

MANOVA showed that the majority of growth and physiology assessments were significantly affected by drought stress, biostimulant application, drought stress × biostimulant interaction, and repeated measures (R) (

Table 1. Analysis of variance (degrees of freedom and mean squares) for PH, ST, LN, RWC, E, Gs, Anet, _iWUE, QY, and CCI, as affected by drought stress and biostimulant application. All abbreviations of evaluated parameters are explained in the Section 2.

		Mean Squares
Source of Variance	df 2	PH	ST	LN	RWC	E	Gs	Anet	iWUE	QY	CCI
Drought Stress (A)	2	616.213 ***	27.522 ***	20.783 ***	107.111 **	11.720 ***	0.252 ***	308.267 ***	2426.111 ***	0.008 ***	184.922 ns
Biostimulant (B)	2	25.383 ns	0.079 ns	1.062 ns	0.929 ns	1.792 **	0.021 **	42.175 ***	2623.597 ***	0.003 ***	1795.123 ***
A × B	4	18.114 ns	0.478 ns	0.386 ns	29.138 ns	0.334 ns	0.004 ns	5.585 ns	187.580 ns	0.001 ns	6.491 ns
Error (a)	16	30.310	0.649	0.707	16.153	0.269	0.003	1.977	182.461	0.001	80.522
Repeated Measures (R)	2	33,498.402 ***	96.499 ***	998.799 ***	67.438 ns	26.196 ***	0.164	776.342 ***	4644.685 ***	0.025 ***	7051.345 ***
A × R	4	30.967 ns	13.550 ***	2.127 **	18.668 ns	6.080 ***	0.009 ***	39.457 ***	610.989 *	0.001 ns	70.280 ns
B × R	4	10.671 ns	0.347 ns	1.254 *	38.340 ns	0.125 ns	0.005 **	1.839 ns	92.010 ns	0.001 ns	137.311 **
A × B × R	8	24.553 ns	0.274 ns	0.069 ns	16.192 ns	0.394 ns	0.001 ns	2.280 ns	65.457 ns	0.001 ns	15.001
Error (b)	36	14.265	0.324	0.367	21.429	0.232	0.002	2.558	210.243	0.001	27.280
CV 1 (%)		4.98	5.66	4.07	5.53	9.07	17.48	11.68	21.35	2.76	8.58

¹ CV, coefficient of variance; ² df, degrees of freedom; * significant at the 0.05 probability level; ** significant at the 0.01 probability level; *** significant at the 0.001 probability level; ns, not significant.

and

Table 2. Analysis of variance (degrees of freedom and mean squares) for IN, LEL, pH, °brix, yield plant⁻¹, fruit number plant⁻¹, and mean fruit weight as affected by drought stress and biostimulant application. All abbreviations of evaluated parameters are explained in the Section 2.

		Mean Squares
Source of Variance	df 2	IN	LEL	pH	°Brix	df	Yield Plant−1	Fruit Number Plant−1	Mean Fruit Weight
Drought Stress (A)	2	0.778 *	77.278 **	0.009 ns	1.292 *	2	4,712,219.614 ***	57.265 **	21,686.056 **
Biostimulant (B)	2	0.002 ns	115.343 ***	0.032 ns	0.510 ns	2	69,535.247 ns	1.396 ns	531.429 ns
A × B	4	0.116 ns	19.175 ns	0.004 ns	0.068 ns	4	51,786.231 ns	2.610 ns	2581.156 ns
Error (a)	16	0.192	10.188	0.022	0.345	16	428,976.273	5.410	2841.926
Rep. Measures (R)	1	89.063 ***	56,194.985 ***	0.001 ns	0.980 ns	5	8,197,620.232 ***	263.670 ***	69,112.120 ***
A × R	2	0.403 *	100.509 **	0.023 ns	0.518 ns	10	879528.970 *	16.668 *	6838.995 *
B × R	2	0.040 ns	185.702 ***	0.001 ns	0.221 ns	10	191,615.620 ns	11.365 ns	1131.876 ns
A × B × R	4	0.036 ns	30.305 ns	0.013 ns	1.532 ns	20	200,952.422 ns	7.299 ns	1229.603 ns
Error (b)	18	0.102	11.582	0.034	4.932	90	368,658.576	8.256	3263.665
CV 1 (%)		9.97	6.96	4.81	11.56		13.06	15.69	14.09

¹ CV, coefficient of variance; ² df, degrees of freedom; * significant at the 0.05 probability level; ** significant at the 0.01 probability level; *** significant at the 0.001 probability level; ns, not significant.

). The effect of repeated measures was highly statistically significant, as was expected, since different vegetative stages distinctly affect the growth and physiology traits of tomato. However, the interaction A × B × R had indicated no significance, and for this reason, the results for these parameters are presented pooled over time.

Drought stress caused a negative effect, proportional to its level, on growth and physiology traits like PH, ST, LN, IN, LEL, and RWC (Figure 2a–f). In most cases, decreases were statistically significant compared to the unstressed control in the SDS treatment. Analytically, the PH under all SDS treatments was 7.2–11.4% lower than the respective one of the ND-Control (Figure 2a). None of the biostimulant treatments caused any difference in the PH in any drought stress level, which was also observed in tomato plants by Sudiro et al. [36]. The above observations are in agreement with previous findings that demonstrate the negative effects of drought in most of the aforementioned traits, emphasizing that it can lead to a decrease in the plant size, number of leaves, stem elongation, and, generally, crop yields [37,38,39,40]. This reduction in plant growth can probably be attributed to changes in the endogenous hormone system of the plants, disrupting its balance [41]. This state of water deficit can also lead to reduced nutrient uptake, as it prevents the nutrients from moving from the soil into the plant [42].

Similarly, the ST under all SDS treatments was significantly lower than that of the unstressed control by 10.4–12.1%, whilst the application of biostimulants did not affect ST at all three levels of drought stress (Figure 2b). Likewise, tomato plants treated with SDS had an 8.3–13.6% lower LN than the corresponding unstressed control, whereas slight increases up to 6.1%—even if statistically non-significant—were noted from both biostimulant applications in comparison to the relative SDS control (Figure 2c). The same trend with ST was observed for IN results, even though decreases were statistically non-significant (Figure 2d). The above results are in agreement with Top et al. [43], who observed no significant changes in the traits of interest after biostimulant application. In contrast to our findings, tomato seedlings treated with another commercial biostimulant with algae extracts presented an increase in stem thickness, plant height, and number of leaves, as supported by Moncada et al. [44]. This may have been observed due to its content in natural plant growth hormones that are known to stimulate cambium activity and cell division.

Concerning LEL results, on the other hand, plants subjected to both mild and severe drought stresses presented almost the same LEL with the respective unstressed controls (Figure 2e). This could be attributed to the stimulating effect of drought stress, triggering a low level of reactive oxygen species (ROS) as an adaptive response to LEL [45]. Furthermore, the biostimulants applied herein did not enhance or reduce the LEL in any drought stress level. An exception was the case of SDS-B2 treatment, with 5.8% and 18.7% reduced LEL compared to the SDS-Control and ND-Control, respectively, indicating that B2 might be involved in suppressing the disruption of cell membrane stability induced by drought. Generally, both LEL and RWC, which are mentioned below, are commonly used as indicators of abiotic stress. According to relevant literature, LEL usually increases as the stress conditions increase [24,46,47,48,49,50]. Previous studies showed that algae-based biostimulants decreased LEL under drought conditions [49,50]. When the integrity of plant plasma membranes is compromised, as happens under drought, a leakage of potassium ions from the plant cells begins. Under moderate to low stress levels, these potassium ions act by stimulating catabolic processes to save energy for the adaptation and repair processes of the plant. Under more severe conditions, though, the cumulative concentration of reactive oxygen species leads to cell death and the leakage of cellular electrolytes [47]. This increase in the permeability of stressed tissues can be explained by changes in the plant’s membrane and tonoplast structures, assuming that the membrane proteins responsible for potassium and sodium ion transport are destroyed due to water stress [46].

The RWC ranged from 79.7% in SDS-B2 treatment up to 87.0% in ND-B2 treatment (Figure 2f). Generally, higher RWC values were recorded in ND treatments and lower ones in SDS, regardless of the biostimulant application, indicating that the severe drought stress with a 50% water deficit led to a nearly 5% reduction in RWC. This is justified by the fact that water stress leads to lower stomatal conductance, which, consequently, results in lower RWC. A similar trend was also reported by Bantis et al. [51], who mentioned that RWC was not affected by the different water stress levels, although the 50% water reduction resulted in significantly lower values without being affected by the application of biostimulants.

The photosynthetic apparatus sufficiently reflects the effect of drought on tomato plants due to a plethora of metabolic and energetic imbalances induced by stress. According to relevant literature, the most pronounced effects of drought stress that decrease photosynthetic activity are the limitations of CO₂ diffusion into the mesophyll due to the stomatal closure, as well as decreased enzymatic activity (Rubisco) for carbon assimilation and the excessive production of reactive oxygen species [52]. Indeed, in the current study, the transpiration rate (E) ranged between 6.17 mmol H₂O m⁻² s⁻¹ in MDS-B1 to 4.29 mmol H₂O m⁻² s⁻¹ in SDS-Control (Figure 3a). Under ND and MDS treatments, neither of the biostimulants affected the E. However, during severe drought stress treatments, the E was reduced, especially in the control, with a 15.9% reduction in comparison to the ND-control, and this was expected since the lowest transpiration under drought forces the plants to withstand water deficiency. Furthermore, in the case of SDS, B1 caused a 21.5% increase in E compared to the relative SDS-Control, meaning, apparently, a positive effect of B1 application. This effect on water regulation is most likely due to the presence of plant-derived polyhydroxylic acids in the B1 formulation, which enhance cellular hydration and root water uptake. These findings are in accordance with Shabbir et al. [53].

In terms of stomatal conductance (Gs), the results showed that the more severe the drought stress, the more pronounced the observed reduction in Gs (Figure 3b). Precisely, the Gs ranged from 0.15 to 0.39 mol CO₂ m⁻² s⁻¹. Drought stress was the main factor that affected Gs herein, since both biostimulants’ B1 and B2 treatments demonstrated similar results to the respective controls under MDS and SDS. The B1 application in SDS increased—even though it was non-significant—by up to 32.1% of the Gs in relation to the relative control. Water content is closely related to stomatal aperture, as it appears that changes to water status ultimately reflect the fluctuations in stomatal conductance, which affects the transport of carbon dioxide to the chloroplasts, altering the photosynthetic rate and eventually plant growth and development [54]. In the present study, the B1 biostimulant contributed rather similarly to the respective control response of stomatal conductance, indicating a lack of enhancement in stomatal responsiveness and in facilitating CO₂ diffusion. A different trend is observed in Covașă et al. [55] with the application of the biostimulant ‘Razormin’ that resulted in higher values in both tomato varieties (281.85 mol m⁻² s⁻¹ for Buzau 4 and 288.12 mol m⁻² s⁻¹ for Buzau 22) compared to the control plants, having a stimulating effect on the stomata opening and promoting gas exchange and transpiration. On the contrary, Francesca et al. [56] found similar results with the current study, reporting that the drought stress tomato plants treated with ’Cyco Flow’, a plant extract-derived biostimulant, had similar CO₂ values as the untreated drought stress tomato plants. The positive effect of the abovementioned biostimulant was profound only in fully irrigated tomato plants.

A similar pattern for the net photosynthesis (Anet) was observed, which varied from 8.06 to 17.93 mol m⁻² s⁻¹ (Figure 3c). Specifically, in non-drought treatments, B2 managed to increase the Anet to 15.9%, whereas in MDS and SDS treatments, B2 increased the Anet to 12.2% and 27.7% in comparison to the respective controls. B1 caused a 13.5% and 44.3% increase in Anet, respectively, in MDS and SDS in comparison to the corresponding controls. These specific observed differences, especially on Anet, indicated that both B1 and B2 seem to enhance Anet under drought stress. These results are partially in agreement with Ahmed et al. [50], who found that the combined soil application of A. nodosum and potassium caused a significant increase in net photosynthetic rate (189%), stomatal conductance (500%), and transpiration rate (186%) under drought stress. Similarly, Della Lucia et al. [57] noticed enhanced photosynthetic efficiency and a greater level of water content through all developmental stages imposed by biostimulants under water stress that improved metabolic activity of the plants and promoted cell enlargement, preventing ROS from damaging pollen viability, with a beneficial effect on fruit development.

Results related to the responses on _iWUE showed that values for B2 of all drought levels were 26.4–42.9% higher than the respective controls, although the same was not true for B1 (Figure 3d). It has been observed that plant-derived protein hydrolysates (similar to B1) enhance the _iWUE, mainly in the case where they are applied by root drench and not by foliar spraying, like in the current study [58]. Under drought stress, the avoidance mechanism of stomatal closure is activated, resulting in a reduction in the mesophyll diffusion of ambient CO₂ and, consequently, in the intercellular CO₂. This condition leads to an increase in _iWUE, as it is estimated based on the A/Gs ratio, as well as on the (Ca–Ci)/1.6 ratio, with Ca and Ci being the ambient and the intercellular CO₂ [26]. Generally, the rather higher values of _iWUE observed on drought treatments could be partially attributed to both water deficiency and biostimulant B2 application. This was also observed in tomato plants treated with algae-based biostimulants (similar to B2) by Moncada et al. [44].

The results obtained from QY estimation revealed the negative effect of drought on photosynthetic energy conversion in photosystem II since QY was up to 4.4% lower in MDS and SDS controls in comparison to the unstressed control (Figure 3e). However, both biostimulant applications showed a significantly higher QY than the respective controls. Regarding QY, we can hypothesize that both biostimulants applied herein may contribute to the protection of PSII from oxidative damage, possibly due to their antioxidant compounds and phytohormones, which help maintain electron transport efficiency. Also, it should be mentioned that water stress was consistently maintained throughout the study, with no recovery phase observed following the addition of water. In the literature, QY is reported to be a good drought indicator [59,60]. Sudiro et al. [36] found that at the start of imposing drought stress in tomato seedlings, stressed plants had slightly increased the electron transfer efficiency (Ψο) and quantum yield of electron transfer (φEo) compared to the well-watered plants. Treated plants showed a statistically significant lower φDo compared to the untreated plants in both stress and non-stress conditions, which is opposite to the present study. Additionally, 4 days after the plants’ recovery, no differences were observed among treatments in the photosynthetic parameters, except for Ψο, which was higher in the plants treated with biostimulants, similar to the findings of the present study. This possible impact of both biostimulants on the stabilization of the PSII and the mitigation of oxidative stress can be regarded as a form of eustress. This mechanism enables plants to allocate their resources more efficiently towards other photosynthetic parameters, promoting leaf formation and increasing biomass accumulation [51]. Similarly, higher values of CCI were detected in the leaves of plants treated with B1 and B2 in both MDS and SDS treatments (Figure 3f). It is noteworthy that in non-drought plants, B1 and B2 increased the CCI by 22.1% and by 28.3%, respectively, compared to the corresponding control. In the case of MDS and SDS, the CCI was higher, up to 29.6% and 33.4%, respectively, due to B1 or B2 application, which may reflect a contribution to chlorophyll stability under stress conditions. In the same way as our research, there are studies that demonstrate the detrimental effects of drought on chlorophyll content and photosynthetic efficiency, and they found that biostimulants (similar to the ones that were used in this research) seem to have an enhancing effect on the chlorophyll content [10,41,58]. The CCI is directly correlated to the plant nitrogen status, so the higher CCI values indicate that plants receive sufficient nutrition from the soil. Similar results for biostimulants containing algae have been previously reported with respect to nitrogen content [61]; the enhanced CCI values in both biostimulant treatments could be partially attributed to the action of their amino acid content [58].

Leaf area results obtained from WinRHIZO analysis revealed that tomato under MDS or SDS had significantly smaller leaves (Figure 4 and Figure 5). Nevertheless, tomatoes subjected to any drought level managed to enhance their leaf area under B1 and B2 applications. In particular, B1 promoted the leaf area in ND, MDS, and SDS treatments by 35.3%, 38.1%, and 40.9%, respectively, compared to the respective controls. A more profound effect of B2 was observed since leaf area in the ND, MDS, and SDS treatments under a B2 effect increased by 83.3%, 73.4%, and 55.5%, respectively. Control plants under MDS and SDS water stress exhibited significantly smaller leaves compared to non-drought conditions. Leaf area increases in tomato plants treated with plant-derived protein hydrosylates (similar to B1) were observed by Choi et al. [58]. Similarly, in tomato plants treated with an algae-based biostimulant (similar to B2), comparable conclusions were reported by Ahmed et al. [50]. The above results suggest that the type of biostimulants applied may help the plants better control their water content and preserve the cell turgor pressure, resulting in the enhancement of the leaf area. It is well known that a reduction in leaf area is considered a drought-avoidance strategy, minimizing transpiration and conserving water at the expense of total photosynthetic surface area [62]. However, the application of both biostimulants in the current study increased leaf area, even under water-limiting conditions, probably by enhancing cellular osmotic potential and promoting turgor maintenance, metabolic activity, and cell expansion [63,64]. This could be attributed to the presence of polyhydroxylic acids; argininic acid; and plant hormones, e.g., auxins and cytokinins, derived from seaweed compounds. These findings are in accordance with Hernández-Herrera et al. [19] and Gedeon et al. [65], who found that the application of biostimulants had a positive effect on total leaf area, number of leaves, and the expansion of the third leaf of tomato seedlings under salinity stress.

Considering all the above on the observed effects of the applied biostimulants on tomato growth and physiology under varying water stress levels, it can be stated that specific growth traits like leaf area and physiological traits like Anet, QY, CCI, and _iWUE seem to be enhanced by the applied biostimulants. However, their precise mode of action remains unclear, warranting further research on additional biostimulant products, wider dosages, timing of application, and/or other studied cultivars.

3.2. Effects of Biostimulant Treatments on Fruit Yield and Quality Indices Under Drought Stress

MANOVA showed that fruit yield traits were, in most cases, significantly affected by drought stress, repeated measures (harvest time), and drought stress × repeated measures interaction (Table 2). The effect of biostimulants on tomato yield components was not statistically significant. Furthermore, the interaction A × B × R had indicated no significance, and for this reason, the results for these parameters are presented pooled over time.

The total fruit yield ranged from 3215.14 to 6965.0 g per plant (Figure 6a). Drought resulted in a 27.1% and 53.7% reduction in yield at mild and severe levels of stress, respectively. Both B1 and B2 applications caused slight, though not statistically significant, yield increases, namely 21.4% and 11.3% for MDS and 5.5% and 7.5% for SDS treatment, respectively, in comparison to the respective controls, potentially indicating a counterbalancing effect against the detrimental impact of moderate and severe drought conditions. Τhese results are in partial agreement with Patanè et.al. [66], who found that the effects of biostimulants did not vary across cultivar, irrigation level, or year, despite a yield increase exceeding 50% being recorded in the first year of the experiment. In the same direction, according to Top et al. [43], although no differences were observed in the overall yield, plants treated with some biostimulants (ASCO-N2) under deficit irrigation exhibited a slight tendency towards higher yield-related indices, such as weight and number of fruits. Notably, biostimulant application did not enhance the overall yield per plant, even in the well-watering treatments, suggesting that their primary role is in stress mitigation rather than yield enhancement under optimal conditions. That concept is in accordance with Di Mola et al. [67], proposing that the fundamental principle of biostimulant performance is their stimulating effect in order to improve one or more characteristics of the plant or the plant rhizosphere. In the current work, the main factor that affected fruit yield was drought stress level and, although the application of biostimulants enhanced Anet, QY, CCI, _iWUE, and total plant leaf area (in terms of leaf size, rather than leaf number), the effects on the corresponding fruit yield were limited throughout. This observation may indicate that an increase in leaf size, which most probably resulted in increased photosynthetic rates, is not enough to significantly enhance fruit yield in the tomato cultivar ‘Optima’ studied herein. Moreover, the lack of biostimulant effectiveness in increasing total tomato yield may be attributed to the dominant impact of water deficit, the dosage, and the method of biostimulant application, or the specific response of the tomato variety used. Therefore, these variables should be carefully examined in future studies to determine whether different formulations, dosages, application methods, or genetic backgrounds might yield different results.

However, B1 enhanced the early yield (first harvest) and the fifth harvest in ND unstressed controls, as well as the third and fourth harvest under MDS (Figure A1). Similarly, B2 enhanced the fourth yield in ND unstressed controls, the second, third, and fifth yield under MDS, and the fourth yield under SDS (Figure A1). Thus, while biostimulant application did not significantly enhance total yield, it appeared to affect yield distribution by supporting early and mid-season harvests under both MDS and SDS conditions. Similar findings were reported by Colla et al. [68], who observed that biostimulant application positively affected the early and total marketable yields of tomato fruits, in agreement with the outcomes of the present study. Although no clear conclusion can be drawn from the current data on the mechanistic involvement of the applied biostimulants on the fruit yield components of the tomato cultivar ‘Optima’, the above-observed effect is of particular interest when it comes to market-oriented production, where early yield improvements can enhance profitability.

Severe drought stress treatment caused a 35.4% reduction in the fruit number per plant (Figure 6b). Generally, the fruit number per plant varied between 27.7 and 38.8 under both ND and MDS, whereas it was between 22.8 and 26.7 under SDS. The applied biostimulants did not affect the fruit number per plant in all three drought levels. The mean fruit weight remained stable regardless of the ND and MDS levels and regardless of the biostimulant application (Figure 6c). In detail, the mean fruit weight ranged between 175.6 and 189.0 g under ND and MDS, whereas it varied from 126.9 to 150.9 g under SDS treatments. A reduction of 25.3% in fruit weight was noticed in the SDS-control in comparison to the unstressed control. Ntanasi et al. [69] supported the positive effect of biostimulants on the fruit number and mean fruit weight of two local tomato landraces, regardless of the presence/absence of abiotic stress (salinity). However, the combined effect of biostimulant application and stress level did not result in statistically significant differences in fruit number for both landraces. Nevertheless, the observed stability in fruit set supports the hypothesis that biostimulants help alleviate the adverse effects of drought stress, possibly due to their osmoprotective properties and phytohormonal composition.

The MANOVA indicated that neither drought stress nor biostimulant treatments affect the pH determined in tomato fruits, resulting in the presentation pooled over time (Table 2 and

Table 3. Values of pH, °brix, TPC, TFC, ABTS, DPPH, and FRAP (means ± SE) determined in tomato fruits as affected by different drought levels (non-drought stress, ND; mild drought stress, MDS; severe drought stress, SDS) and biostimulant treatments (Control, B1, B2). Within each column, the means followed by the same letter are not statistically significantly different at p < 0.05, according to Tukey’s honestly significance difference test. All abbreviations of evaluated parameters are explained in the Section 2.

Drought Level	Biostimulant Treatment	pH ± SE	°Brix ± SE	TPC (mg GAE 100g−1) ± SE	TFC (mg CE 100g−1) ± SE	ABTS (mg TE g−1) ± SE	DPPH (mg TE g−1) ± SE	FRAP (mg TE g−1) ± SE
ND	C	3.79 ± 0.03 a	4.36 ± 0.17 ab	435.32 ± 13 a	41.47 ± 6.05 a	7.32 ± 2.18 a	4.35 ± 0.69 a	6.23 ± 0.60 ab
	Β1	3.88 ± 0.06 a	4.33 ± 0.15 b	445.38 ± 42 a	38.04± 7.97 a	6.44 ± 0.55 a	4.36 ± 0.27 a	6.88 ± 0.64 ab
	Β2	3.84 ± 0.04 a	4.17 ± 0.08 b	444.33 ± 23 a	31.70 ± 7.90 a	6.94 ± 0.30 a	4.90 ± 0.29 a	6.79 ± 1.45 ab
MDS	C	3.86 ± 0.05 a	4.48 ± 0.20 ab	429.95 ± 42 a	39.74 ± 3.98 a	6.82 ± 0.29 a	4.56 ± 0.54 a	6.03 ± 0.65 ab
	Β1	3.89 ± 0.06 a	4.66 ± 0.08 ab	458.79 ± 29 a	45.49 ± 5.18 a	6.52 ± 0.57 a	4.38 ± 0.43 a	6.15 ± 0.50 ab
	Β2	3.89 ± 0.07 a	4.30 ± 0.20 b	478.49 ± 49 a	42.04 ± 6.23 a	6.98 ± 1.14 a	4.86 ± 0.69 a	7.41 ± 0.41 a
SDS	C	3.79 ± 0.03 a	4.74 ± 0.29 ab	483.76 ± 34 a	47.21 ± 1.73 a	6.95 ± 0.96 a	4.43 ± 0.67 a	4.44 ± 0.63b
	Β1	3.90 ± 0.04 a	5.10 ± 0.25 a	427.75 ± 30 a	36.87 ± 4.57 a	6.58 ± 0.35 a	4.44 ± 0.26 a	5.91 ± 1.58 ab
	Β2	3.91 ± 0.11 a	4.60 ± 0.29 ab	459.78 ± 19 a	43.21 ± 2.62 a	7.04 ± 0.33 a	4.80 ± 0.13 a	8.35 ± 0.67 a
Tukey’s HSD		0.0548	0.2497	20.220	3.691	0.5562	0.2961	0.4950

). In fact, the pH remained stable in all combined factors, ranging from 3.79 to 3.91 (Table 3). Shabbir et al. [53] found that under different irrigation regimes and gradient emitter densities, the pH values remained stable, which is in accordance with the abovementioned results of the present study. The total solid sugars (TSS or °brix) of fruits affected by drought level (Table 2) ranged from 4.17 to 5.10 °brix (Table 3). Precisely, TSS presented minimum values under ND-B1, ND-B2, and MDS-B2 and a maximum value at SDS-B1 (5.10 °brix). Similarly, Liava et al. [5] and Bai et al. [70] found that deficit irrigation led to increased soluble solids and reducing sugars. This finding could be associated with a concentration effect and the lower fruit moisture content under water stress, as well as with enhancing the accumulation of sugars as an adaptation to stress conditions. At the same time, the observed stability of the pH under different biostimulant treatments may indicate a distinct mechanistic pathway of sugar metabolism in the fruit or the balance between sugars and acidity. Concerning the neutral effect of biostimulant application, regardless of the irrigation level, this could be attributed to the altered transport and partitioning of sugars for alleviating drought stress. These results are in agreement with Distefano et al. [71] and Goñi et al. [10], who suggested that the presence of biostimulants enhanced the accumulation of lipophilic antioxidants in cherry tomatoes or substrates for cellular respiration or osmolytes at the expense of sugar accumulation as biochemical or physicochemical adjustments towards drought stress. The maximum value of TSS under SDS-B1 indicates that, under severe stress conditions, the accumulation of soluble sugars is profound. This is in accordance with Iacuzzi et al. [72], who found in a two-year study that TSS increased significantly with the application of beet extract biostimulants in the tomato landrace ‘’Pizzutelo delle valli ericine’’ under arid conditions.

3.3. Effect of Biostimulant Treatments on Bioactive Compounds and on Antioxidant Activity of Tomato Fruits Under Drought Stress

According to the statistical analysis of the results, neither drought stress nor biostimulant treatments affected the bioactive compounds TPC and TFC, as well as the antioxidant activity of tomato fruits determined by the ABTS, DPPH, and FRAP assays (

Table 4. Analysis of variance (degrees of freedom and mean squares) for TPC, TFC, ABTS, DPPH, and FRAP assay data, as affected by drought stress and biostimulant application. All abbreviations of evaluated parameters are explained in the Section 2.

		Mean Squares
Source of Variance	df 2	TPC	TFC	ABTS	DPPH	FRAP
Drought Stress (A)	2	656.261 ns	86.117 ns	0.037 ns	0.008 *	0.388 ns
Error (a)	4	1197.809	5.544	0.679	0.225	1.116
Biostimulant (B)	2	664.675 ns	34.636 ns	0.736 ns	0.562 ns	8.685 ns
A × B	4	1792.176 ns	72.845 ns	0.097 ns	0.019 ns	2.564 ns
Error (b)	12	1227.002	40.865	0.928	0.263	0.735
CV 1 (%)		7.76	15.73	14.07	11.25	13.27

¹ CV, coefficient of variance; ² df, degrees of freedom; ns, not significant. * significant at the 0.05 probability level.

). Indeed, TPC remained stable at around 451.51 mg GAE 100 g⁻¹ dw, ranging between 427.75 for SDS-B1 and 483.76 for SDS-Control (Table 3). Similarly, the TFC of tomato fruits averaged about 40.64 mg GAE 100 g⁻¹ dw, presenting the minimum value of 31.70 for ND-B2 and the maximum of 47.21 mg CE 100 g⁻¹ dw for SDS-Control (Table 3). Finally, the antioxidant activity of tomato fruits determined by ABTS and DPPH assays ranged around 6.84 and 4.56 mg TE g⁻¹ dw, respectively, regardless of drought stress and biostimulant (Table 3). The only difference among antioxidant activity determined by the FRAP assay was observed between MDS-B2, with 7.41 TE g⁻¹ dw, and SDS-B2, with 8.35 TE g⁻¹ dw (Table 3). The unaltered antioxidant activity aligns with the stable TPC and TFC levels, reinforcing the hypothesis that drought stress did not induce a significant oxidative stress response in tomato fruits. This stability also suggests that the phenolic metabolism in tomato fruits is relatively resilient to moderate and severe water deficit, perhaps due to intrinsic regulation mechanisms that maintain fruit quality. These findings are in accordance with Gil-Ortiz et al. [18], who found no significant differences for TPC and TFC, even in water-deficit plants, compared to the irrigated controls. Additionally, no clear pattern was observed regarding the effect of biostimulants.

4. Conclusions

Our results highlight that biostimulant application may generally contribute to improved key physiological traits in tomato plants. Specifically, the net photosynthetic rate (Anet) and intrinsic water use efficiency (_iWUE) increased by up to 44.3% and 42.9% compared to the corresponding untreated control. These maximum increases were noticed in the case of SDS-B1 and ND-B2 treatments, respectively. The chlorophyll content index (CCI) increased by 19.1–33.4% across all biostimulant and drought treatments, both compared to the untreated ND-control and their respective drought-level controls.

Regarding the agronomic features, all SDS-treated plants were 7.2–11.4% shorter than the untreated ND-control. No biostimulant treatment managed to overcome the negative effect of drought stress on PH, ST, or LN. In particular, the LN was reduced by 13.6% in the SDS control and 8.3–9.3% in the B1 and B2 treatments under SDS, relative to the untreated ND-control. The LEL decreased by 7.1% in the ND and up to 18.7% in the SDS under B2 treatment compared to the untreated ND-control, though no consistent effect was observed relative to drought-specific controls. The RWC in the leaves remained unaffected by both drought and biostimulants.

The fruit yield, the fruit number, and the fruit weight were affected only by drought stress, particularly under SDS. Specifically, the fruit yield decreased by up to 53.7% across all biostimulant treatments under SDS. Similarly, the fruit number per plant dropped by up to 41.4% under SDS compared to the untreated ND-control. A similar trend was observed for the fruit weight since it was 25.3–28.3% lower in both the control and B1 treatment under SDS, whereas the B2 treatment showed no difference compared to other treatments. However, biostimulants may support early and mid-season harvests under both MDS and SDS conditions.

With respect to the organoleptic characteristics in the fruits, we concluded that the TSS ranged from 4.17 to 5.10, and the pH was between 3.79 and 3.91; they remained stable, regardless of drought or biostimulant treatments. Finally, our results demonstrated that neither drought stress nor biostimulant treatments affected the bioactive compounds TPC and TFC, nor the antioxidant activity of tomato fruits measured by the ABTS, DPPH, and FRAP assays.

Overall, the biostimulants applied herein showed potential to partially improve drought tolerance in tomatoes by enhancing specific physiological parameters. However, they had minimal or no impact on yield and quality traits. There is no doubt that the present study strengthens the future prospect of further research into optimizing biostimulants with regard to the investigation of formulations, dosages, the timing of application, and their effects under varying drought levels or other abiotic stresses.