Foliar Application of Ca-Based Fertilizers (Conventional vs. Nanofertilizers): Effects on Fruit Traits, Seed Quality Parameters and Initial Plant Growth of Tomato Genotypes

Abstract

This study evaluated the effects of foliar-applied calcium-based fertilizers, including a conventional fertilizer (T1) and a nanofertilizer containing Ca, Si, B, and Fe (T2), on fruit traits, seed quality, and early seedling growth of seven determinate tomato genotypes. Field-grown plants were monitored for fruit traits, while seeds underwent germination tests and seedling growth assessments under controlled laboratory conditions. Factorial ANOVA showed significant effects of genotype, treatment, and their interaction on fruit weight, width, germination energy, final germination, seedling vigor index, and initial plant growth, indicating genotype-specific responses. Treatment T2 significantly increased fruit weight and width, germination energy, final germination, seedling vigor, root length, and biomass compared to T1 and control (T0), while shoot elongation rate remained unaffected. Total soluble solids decreased under both treatments, but fruit length, pericarp thickness, and locule number were mainly genetically determined. Principal Component Analysis highlighted differentiation among treatments and correlations among key traits. The enhanced performance under T2 likely results from the synergistic effects of Ca, Si, B, and Fe, improving nutrient uptake and physiological activity. These findings suggest that foliar nanofertilizer application is a promising approach to optimize tomato yield and seedling performance.

1. Introduction

Tomato (Solanum lycopersicum L.), a member of the Solanaceae family, ranks as the second most important vegetable crop worldwide, following potato (Solanum tuberosum L.) [1]. This species holds exceptional economic significance and is intensively cultivated across the globe. It has beneficial effects on human health due to the high content of potassium and antioxidants, such as ascorbic acid, vitamin A, lycopene, and tocopherol [2]. Over the past several decades, tomato has exhibited a consistent rise in yield and global production, thereby reinforcing its contribution to human nutrition. It is one of the most commonly consumed vegetable species worldwide, with an average annual consumption of 20.9 kg per person, while in some Mediterranean countries this consumption exceeds 50 kg per person per year [3]. Considering the aforementioned aspects, it is clear why tomato production ranks highest among vegetable species. In 2022, the global area under tomato cultivation totaled 4,917,735 ha, representing 8.44% of the total vegetable growing area and placing tomatoes second in terms of cultivated area. Global tomato production reached 186,107,972 t, accounting for 15.86% of overall vegetable production, establishing tomato as the leading vegetable species by production volume. Regarding average yield, tomatoes ranked third among vegetables, with an average of 37.84 t/ha [4].The rapid growth of the world’s population imposes demands for increased food supply. Enhancing the yield of cultivated crops is essential, and the development of advanced agricultural strategies is critical for sustaining crop productivity [5]. High-quality seed and vigorous initial plant growth are key factors for maximizing yield. To address evolving agricultural demands, researchers are pioneering innovative, efficient and environmentally friendly production systems by advancing technologies that improve seed germination parameters and initial plant growth [6].

Numerous studies highlight the positive impact of foliar application of nutrients, especially calcium (Ca) compounds, on plant growth, yield and fruit quality of tomato [7,8,9], as well as on its tolerance to abiotic stress [10,11].

Ca plays an indispensable role as a signaling agent [12], and is also crucial for the cross-linkage of pectic substances in the inner regions of the cell wall [13]. Through these processes, Ca is crucial for plant stress responses, growth, and cell wall remodeling, and also serves as a structural component of plant tissues. As a highly biologically active ion, Ca concentration and transport must be tightly regulated. If the concentration of Ca in tissues is high, it can lead to cell toxicity, excessive rigidity of cell walls and developmental abnormalities. When Ca supply is low or its transport is impaired, local Ca deficiencies occur. This may result in membrane disruption and/or cell wall damage, and in fruits, it is commonly associated with physiological disorders such as blossom end rot [14].

Ca deficiency in tomato leads to a reduction in leaf size and necrosis of young leaves, while in extreme cases, it can cause significant yield losses [15]. In addition to the aforementioned blossom-end rot, insufficient Ca uptake increases the incidence of tomato fruit cracking [16]. Furthermore, Ca plays a significant role not only in plant structural and physiological functions but also in enhancing tolerance to biotic and abiotic stresses. Various studies have demonstrated its significant contribution to the plant disease tolerance system, highlighting its positive effects on disease suppression [17,18]. In addition, Ca application has been shown to improve salinity tolerance in tomato plants, contributing to better growth, productivity and fruit quality under saline conditions [11,19].

Calcium has been shown to positively influence various agronomic and physiological traits in tomato, and several studies have addressed genotype-specific responses to its application. Mazumder et al. [9] reported that there was no significant interaction between genotype and calcium treatment with respect to fruit number and yield in tomato. Furthermore, Islam et al. [11] demonstrated positive effects of different calcium treatments in two distinct tomato genotypes, indicating that the response to calcium may vary depending on the genetic background.

When Ca is absorbed from the soil through the roots, it is transported to the leaves and shoots via the xylem. This mass flow of Ca bypasses the young leaves and fruits, as it is primarily directed towards the mature, established leaves, which is why Ca deficiencies are often observed in the new leaves and fruits [20]. Once Ca reaches the mature tissue, its remobilization to the young leaves and fruits via the phloem is highly limited. Therefore, the most efficient method for increasing Ca content in plant tissue is direct contact with Ca, as Ca is an immobile element that cannot easily translocate from one part of the plant to another [5]. In recent years, nanotechnology has gained prominence in agricultural research, offering innovative approaches to enhance nutrient efficiency, stress tolerance, and overall crop performance. By manipulating matter at the atomic and molecular level, nanotechnology provides unique physicochemical properties that can enhance agricultural productivity [21].

Among the most promising uses of nanotechnology in agriculture is its role in improving crop fertilization practices [22]. Nanofertilizers are defined as materials ranging in size from 1 to 100 nanometers, composed of macro- and micronutrients, designed to deliver essential nutrients to plants in a more efficient manner [23]. Conventional fertilizers, in contrast, suffer from low nutrient use efficiency, typically ranging between 20% and 50%. As a result, higher application rates are required to meet plant nutritional demands, which in turn increases production costs [24]. Moreover, the use of conventional fertilizers is associated with negative environmental impacts and significant pollution of aquatic ecosystems [25]. New physicochemical properties of nanomaterials, such as catalytic reactivity, large surface area, size and shape, have the potential to solve problems in primary production and increase productivity in agriculture [26]. The key advantages of nanofertilizers include: (i) improved nutrient absorption and utilization with minimal losses, (ii) reduced environmental contamination through decreased nutrient leaching, (iii) higher diffusion rates and solubility compared to conventional fertilizers, (iv) controlled and sustained release of nutrients, preventing rapid depletion, (v) lower application rates owing to reduced losses and improved nutrient uptake efficiency, (vi) improved soil fertility and the development of a more favorable environment for soil microorganisms [27].

Tantawy et al. [28] reported that treatment with nano-Ca fertilizer had a significantly superior effect compared to conventional Ca fertilizer in alleviating the negative effects of salinity on various growth parameters and tomato yield. Moreover, previous studies have demonstrated that the application of nanoformulations containing various micro- and macroelements exerts beneficial effects on tomato growth, development, and productivity. Rahman et al. [29] demonstrated that the application of mixed nanofertilizers significantly improved tomato plant growth parameters—including plant height, stem diameter, root length, and dry root biomass—as well as fruit weight, fruit diameter, and overall yield, compared to conventional fertilizer treatment. Similarly, Roushan et al. [30] also noted that tomato plants treated with 100% NPK + 25% ZnSO₄ applied conventionally and 75% applied as foliar nano—zinc achieved the highest fruit yield (135.57 t/ha), fruit weight (88.407 g), total soluble solids (5.36°Brix), and ascorbic acid content (26.4 mg/100 g).

Although scientific evidence indicates the positive effects of Ca nanoparticles on tomato growth, development, and yield, there is still a lack of information regarding the impact of foliar application of these particles during the growing season on seed quality and initial plant growth in tomato seed production. This study aimed to evaluate the effects of foliar application of two types of Ca-based fertilizers—conventional and nano—on maternal plants of seven tomato genotypes, and to determine their influence on fruit traits, seed quality, and early plant growth, as well as to explore potential interactions among fruit traits, seed germination, germination-related parameters, and initial plant growth traits.

2. Materials and Methods

2.1. Plant Material

The study included seven determinate tomato genotypes, comprising two commercial varieties, Alparac (S49) and Knjaz (S50), and five F₉ generation homozygous breeding lines: N4, N7, N9, N14, and N16. The evaluated breeding lines were developed through controlled crosses among different homozygous tomato cultivars. All genotypes used in this study were obtained from the tomato genetic collection of the Institute of Field and Vegetable Crops, National Institute of the Republic of Serbia, Novi Sad, Serbia (IFVCNS).

Seedlings were produced in a greenhouse under controlled conditions at 22 °C and 60% relative humidity. For the production of seedlings, the seeds of each genotype were sown in wooden boxes filled with Pindstrup PLUS substrate (Pindstrup Mosebrug A/S, Ryomgaard, Denmark). Seedlings were transplanted at the stage of well-developed cotyledon into new boxes filled with the same substrate at a distance of 8 × 8 cm. Transplantation of 40-day-old seedlings at the stage of 6 leaves was carried out on 10 May 2023.

2.2. Field Trial

The field experiment was conducted during 2023 on chernozem soil at the Rimski Šančevi experimental site (N 45°19′, E 19°50′), Department of Vegetable and Alternative Crops, IFVCNS, under drip irrigation, as a single-season trial designed to provide material for subsequent laboratory analyses. The chernozem soil had following properties: pH 7.98, CaCO₃ 53 g/kg, organic matter 24 g/kg, and total N 1.9 g/kg. In terms of the content of macroelements, it is classified as having medium content of P₂O₅ (118 mg/kg) and K₂O (192 mg/kg). The experiment was set up according to a randomized complete block design with three replications. The basic plot consisted of 10 tomato plants per genotype and treatment, with a spacing of 50 cm between plants within a row, 70 cm between rows, following standard farmer practices, and 1 m between plots.

The tested fertilizers included: T0—control, pure water treatment; T1—conventional fertilizer YaraLiva–Calcinit (Yara Suomi OI, Espoo, Finland) containing 15.5% of total nitrogen (N) and 26.3% of calcium oxide (CaO), equivalent to approximately 18.7% elemental Ca; T2—nanofertilizer Nanoplant Ca-Si (JSC “ECO-Vlit”, Trakai, Lithuania), which contains nanoparticles of: Ca–0.5% (as Ca(BO₂)₂), silicon (Si)—0.05% (as Fe₂SiO₄), boron (B)—0.1% (as Ca(BO₂)₂) and iron (Fe)—0.1% (as Fe₂SiO₄).

Fertilizers were applied foliarly during the growing season, three times: before flowering, during flowering, and at the fruiting stage. Recommended doses were used as per manufacturer instructions: 2 g·L⁻¹ for T1 and 2 mL·L⁻¹ for T2. The fertilizers differed in form, composition, and nutrient content. T1 supplied a significantly higher amount of calcium, while T2 included additional micronutrients (Si, B, Fe). These differences were considered in the interpretation of treatment effects.

During the field experiment, the highest average daily temperature was recorded during July (24.7 °C), which was 2.3 °C higher than the multi-year average (1991–2020). The lowest temperature was measured in May (17.2 °C), corresponding to the long-term monthly average. The highest precipitation was recorded in May (124.8 mm), exceeding the long-term average by 48.5 mm, while the lowest amount was recorded in June (35.4 mm), which was 51.3 mm below the average.

2.3. Tomato Fruit Characterization

Fruit harvesting was conducted on three separate dates—4, 15, and 24 August—according to the natural ripening progression of the tomato genotypes. In each harvest, five fully red, physiologically mature fruits were randomly collected from different plants within each replication (plot), per genotype and treatment, to ensure sample representativeness. These five fruits were pooled to form a composite sample per replication. The same trained person performed the sampling throughout the experiment to minimize variation and ensure consistency. In total, 15 fruits (5 fruits × 3 harvests) per genotype and treatment were used for analysis. Fruit analyses were performed immediately after each harvest. The following fruit characteristics were analyzed: fruit weight, fruit length, fruit width, pericarp thickness, number of locules, and total soluble solids (°Brix).

Fruit weight was determined using a digital industrial precision scale DECX10 (COLO LabExperts, Polje ob Sotli, Slovenia) and expressed in grams, while fruit length, fruit width (expressed in centimeters) and pericarp thickness (expressed in millimeters) were measured with a caliper. The number of locules was assessed by examining the fruit’s cross-section, and total soluble solids were quantified using a digital refractometer DR 6000/1 (A.KRÜSS Optronic GmbH, Hamburg, Germany).

2.4. Seed Extraction

During the harvest, ripe red fruits were randomly selected for seed extraction. The selected fruits were washed and mashed by hand, whereby the seeds with the mucous membrane, the juice and the skins were separated. The seeds were then placed in plastic containers for fermentation for 24 h at room temperature. The containers were filled up to 3/4 of their height due to increased pulp volume during fermentation. As fermentation progresses, the complex pectins from the mucous membrane break down into the simple ones and, as such, they were separated from the seed. After fermentation, the seeds were thoroughly washed with distilled water to remove any remaining husks, decomposed mucilaginous coating, and empty seeds. Once washed, they were dried for three days at room temperature, spread in a thin layer on glass plates and occasionally mixed to prevent the formation of seed lumps [31]. Upon drying, seeds of each genotype from each treatment were packed in separate paper bags, labeled with the corresponding code, and stored in a climate chamber MC 30 E (Frigotehna, Skopje, Republic of North Macedonia) at a constant temperature of 5 °C.

2.5. The Germination Test

The experiment was conducted at the Laboratory for Seed Testing, Institute of Field and Vegetable Crops, National Institute of the Republic of Serbia. The experiment was set up in a completely randomized design (CRD) with three replications, using a total of 63 Petri dishes (7 genotypes × 3 treatments × 3 replications). The sample consisted of three replication with 100 seeds per replicate. The seeds were sown in glass Petri dishes with a diameter of 15 cm, on a double layer of filter paper soaked in sterile distilled water. All samples were incubated in a germination chamber (Conviron CMP 4030, Winnipeg, MB, Canada) for fourteen days under a temperature regime of 20 °C for 8 h and 30 °C for 16 h [32].

2.5.1. Assessment of Seed Germination and Germination-Related Parameters

Germination energy, i.e., first count of germination, defined as the percentage (%) of normal seedling with a well-developed essential structure, was determined five days after sowing [32]. Final germination, defined as the percentage (%) of seedlings with a healthy and well-developed root and shoot system, and abnormal seedlings, defined as the percentage (%) of seedlings that do not show the potential for further development into satisfactory plants when grown in good-quality soil, under favorable conditions of moisture, temperature, and light, were determined fourteen days after sowing [32].

The Seedling Vigor Index (SVI) was calculated using the formula [33]:

SVI = SL × FG

where SL stands for shoot length (cm), while FG stands for final germination (%).

All measurements were performed in triplicate.

2.5.2. Assessment of Seedling Growth Parameters and Biomass Accumulation

For assessment of plant growth and growth-related parameters, 25 seeds of each tomato genotype per replicate were placed on moistened filter paper and incubated in a germination chamber under same optimal conditions as described in the germination test. Shoot length and root length of 10 normal seedlings per replication were determined using a ruler on the same days as germination energy (5th day) and final germination (14th day). Fresh seedling weight was determined on the day of final germination (14th day) using an analytical balance (Kern 770-13, KERN Sohn GmbH, Ballingen, Germany). Afterwards, the seedlings were oven-dried (Heraeus instruments, Hanau, Germany) at 80 °C for 24 h to determine the seedlings dry weight.

Shoot elongation rate (SER) (mm day⁻¹) and Root elongation rate (RER) (mm day⁻¹) were calculated using the following formulas [34]:

$$\mathit{SER}={\displaystyle \frac{SLE-SLS}{TE-TS}}$$

$$\mathit{RER}={\displaystyle \frac{RLE-RLS}{TE-TS}}$$

where SLS, SLE is shoot length (mm) at the beginning (5th day) and at the end (14th day) of the measurement period, respectively; RLS, RLE—root length (mm) at the beginning (5th day) and at the end (14th day) of the measurement period, respectively; TE − TS—time duration (days) between two measurements.

Root/shoot ratio (R/S RATIO) were calculated using following formula:

$$RS\mathit{RATIO}={\displaystyle \frac{RLE}{SLE}}$$

where RLE is root length (mm) at the end (14th day) of the measurement period; SLE—shoot length (mm) at the end (14th day) of the measurement period.

All determinations were performed in triplicate.

2.6. Statistical Data Analysis

To determine the effect of genotype and treatment and their interaction, the obtained results were processed using a factorial Analysis of Variance (ANOVA), after which the significance between mean values were determined according to the Tukey’s HSD Test (p ≤ 0.05). Principal Component Analysis (PCA) was used to assess the interrelationships between the examined traits, genotypes and applied fertilizers as well as to determine their contribution to the total variability. ANOVA and Tukey’s HSD test were performed using Statistica^® 14 by TIBCO Software Inc. software (Palo Alto, CA, USA), while the Principal Component Analysis (PCA) was performed in Jamovi 2.3.24 software (Sydney, Australia), within the Factor module without rotation, while the PCA biplot was created in the snowCluster module. To better understand the effects of the applied fertilizers (T1, T2), average values of the monitored parameters in each treatment were compared with those of the control (T0) using Relative Change Analysis (RC), according to the following formula [35]:

$$\mathit{Relative} \mathit{change} (\%)={\displaystyle \frac{TREATMENT}{CONTROL}}\times 100-100$$

3. Results

3.1. Characteristics of Tomato Fruit

The effect of genotype, treatment and their interaction on the examined fruit traits, along with the mean values for these traits across the analyzed genotypes and treatments, are summarized in

Table 1. Analysis of variance (ANOVA) and mean values of the observed fruit traits in tomato for the analyzed genotypes and treatments.

Trait	Fruit Weight (g)	Fruit Length (cm)	Fruit Width (cm)	Number of Locules	Pericarp Thickness (mm)	Total Soluble Solids (°Brix)
G (p)	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.230 ns	0.000 ***
S49	97.89 e	6.22 a	5.19 e	3.61 b	6.67 a	4.44 c
S50	152.23 cd	5.58 b	6.79 cd	6.25 a	5.09 a	5.87 a
N4	140.60 d	5.77 b	6.48 d	5.94 a	5.55 a	5.00 bc
N7	180.94 ab	5.81 b	7.28 ab	7.53 a	6.78 a	5.94 a
N9	153.93 cd	5.67 b	7.05 bc	6.25 a	5.58 a	4.82 bc
N14	204.34 a	6.32 a	7.57 a	6.97 a	6.79 a	5.08 b
N16	167.42 bc	5.78 b	6.96 bc	6.67 a	5.81 a	6.03 a
T (p)	0.003 **	0.103 ns	0.000 ***	0.768 ns	0.402 ns	0.048 *
T0	145.40 b	5.85 a	6.51 b	6.02 a	5.68 a	5.51 a
T1	161.73 a	5.96 a	6.88 a	6.31 a	6.42 a	5.18 b
T2	163.16 a	5.83 a	6.90 a	6.19 a	6.02 a	5.25 b
RC T1 (%)	+10.23	+1.88	+5.68	+4.82	+13.03	−5.99
RC T2 (%)	+12.22	−0.34	+5.99	+2.82	+5.99	−4.72
G × T(p)	0.016 *	0.074 ns	0.002 **	0.830 ns	0.302 ns	0.217 ns

* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, ns—non-significant. G (p): factor genotype; T (p): factor treatment (T0, T1 and T2); G × T: interaction between factor genotype and factor treatment. Data are presented as mean values on n = 45 (15 fruits per genotype × 3 treatments). Differences between genotypes and treatments were determined using Tukey’s HSD test (p ≤ 0.05). Mean values for the same trait that share the same letters are not significantly different. T0—control; T1—conventional fertilizer YaraLiva–Calcinit; T2—nanofertilizer Nanoplant Ca-Si; RC (%)—Relative change indicates the variation in comparison to the corresponding control (T0).

. The obtained results clearly indicate that factor genotype had a significant effect on all the tested traits of the fruit, except pericarp thickness. Factor treatment, on the other hand, had a significant effect on fruit weight, fruit width and total soluble solids, while fruit length, pericarp thickness and number of locules, largely remained stable regardless of applied fertilization fertilizers. In addition, only fruit weight and width were significantly influenced by the interaction between genotype and treatment (Table 1).

The applied foliar treatments exhibited different effects across the evaluated fruit traits and tomato genotypes (Table 1 and Figure 1). A significant genotype × treatment interaction was observed for both fruit weight (p = 0.016) and fruit width (p = 0.002), indicating that the effects of the treatments were not uniform across genotypes. The highest average fruit weight (204.34 g) was recorded in genotype N14, whereas the lowest (97.89 g) was observed in S49. On average, T1 and T2 increased fruit weight by 10.23% and 12.22%, respectively, compared to the control (T0) (Table 1); however, the magnitude of these effects varied among genotypes (Figure 1). Similarly, fruit width showed a significant treatment effect and genotype interaction, with an overall increase of 5.99% under T2 (Table 1). Genotype N14 again exhibited the highest average fruit width (7.57 cm), while S49 showed the lowest (5.19 cm). No significant treatment effects were observed for fruit length, the number of locules, or pericarp thickness. For total soluble solids, a slight but significant treatment effect was detected (p = 0.048), with mean decreases of 5.99% and 4.72% observed under T1 and T2, respectively (Table 1).

Detailed insights into the genotype-specific responses to foliar treatments on fruit traits are presented in Figure 1. Consistent with the ANOVA results (Table 1), significant genotype × treatment interactions were detected for both fruit weight and fruit width, indicating that the effects of treatments varied among genotypes. Fruit weight differed notably across genotypes and treatments (Figure 1). For example, genotype N14 exhibited the highest fruit weight under T2 (232.22 g), representing a 40.87% increase compared to the control (T0), which highlights the genotype-specific positive response to nanofertilizer application. Fruit width exhibited a similar variable pattern. The highest value of this trait (8.11 cm) was recorded in N14 under T2, while changes in other genotypes were negligible, confirming the presence of genotype-dependent treatment effects. For the number of locules and pericarp thickness, no significant treatment effects were detected, although some numerical variation was observed among genotypes. Finally, total soluble solids showed some variation among genotypes and treatments, with most genotypes exhibiting slight, non-significant reductions under both treatments relative to the control (T0) (Figure 1).

3.2. Seed Germination and Germination-Related Parameters

The obtained results clearly demonstrate that factors: genotype, treatment, and their interaction had a significant effect on seed germination and germination-related parameters (

Table 2. Analysis of variance (ANOVA) and mean values of the observed seed germination and germination related parameters for the analyzed genotypes and treatments.

Traits	Germination Energy (%)	Final Germination (%)	Abnormal Seedlings (%)	Seedling Vigor Index
G (p)	0.000 ***	0.000 ***	0.000 ***	0.000 ***
S49	72.33 bc	83.33 d	3.89 ab	1001.5 d
S50	68.44 de	89.33 b	2.78 bc	1011.8 cd
N4	66.33 e	88.56 b	3.22 abc	1115.9 a
N7	71.22 cd	90.11 b	2.44 bc	1129.6 a
N9	75.11 b	86.11 c	2.11 bc	994.5 d
N14	66.67 e	82.22 d	4.89 a	1059.8 bc
N16	85.44 a	92.56 a	1.56 c	1101.5 ab
T (p)	0.000 ***	0.000 ***	0.515 ns	0.000 ***
T0	72.00 b	86.67 b	3.24 a	1016.0 b
T1	68.19 c	87.00 b	2.86 a	1003.8 b
T2	76.48 a	88.71 a	2.86 a	1157.9 a
RC T1 (%)	−5.29	+0.38	−11.73	−1.20
RC T2 (%)	+6.22	+2.35	−11.73	+13.97
G × T (p)	0.000 ***	0.000 ***	0.217 ns	0.000 ***

*** p ≤ 0.001, ns—non-significant. G (p): factor genotype; T (p): factor treatment (T0, T1 and T2); G × T: interaction between factor genotype and factor treatment. Data are presented as mean values (n = 3; 100 seeds per replicate for germination energy, final germination, and abnormal seedlings; 10 seedlings per replicate for seedling vigor index). Differences between genotypes and treatments were determined using Tukey’s HSD test (p ≤ 0.05). Mean values for the same trait that share the same letters are not significantly different. T0—control; T1—conventional fertilizer YaraLiva–Calcinit; T2—nanofertilizer Nanoplant Ca-Si; RC (%)—Relative change indicates the variation in comparison to the corresponding control (T0).

). An exception was observed in the case of abnormal seedlings, where only the effect of genotype factor was significant, while neither the treatment nor the G × T interaction showed a significant influence. These findings suggest that the occurrence of abnormal seedlings is primarily determined by genetic factors, with minimal or no influence of the applied foliar treatments (Table 2).

The significant genotype × treatment interactions detected for germination energy, final germination, and seedling vigor index (p < 0.001) (Table 2), indicate that the effects of foliar treatments on these parameters differed among genotypes (Figure 2). On average, the application of T2 was associated with higher germination energy (+6.22%), final germination (+2.35%), and seedling vigor index (+13.97%) compared to the control (T0) (Table 2). However, these effects were not consistent across genotypes: in some genotypes, T2 led to improvements, while in others no increase or even a decrease was observed, reflecting the significant interaction detected by ANOVA. In contrast, T1 slightly reduced germination energy (−5.29%) compared to the control, with no significant effects on final germination or seedling vigor index at the overall level. Although the proportion of abnormal seedlings did not differ significantly among treatments (p = 0.515), numerically lower values were observed under T1 and T2 relative to T0. At the genotype level, N16 exhibited the highest mean values for germination energy and final germination (85.44% and 92.56%, respectively), as well as the lowest percentage of abnormal seedlings (1.56%). Conversely, genotype N14 showed the lowest values for these parameters (66.67%, 82.22%, and 4.89%, respectively) (Table 2).

The effects of foliar treatments on seed germination and germination-related parameters varied among tomato genotypes, reflecting a clear genotype-dependent response (Figure 2). T2 significantly increased germination energy in N4 and N7, but decreased or had no effect in S49, S50, N9, N14 and N16. In addition, T1 increased germination energy only in N4 and N7, decreased it in S49, N9, N14, and N16, and had no significant effect in S50. Final germination also varied depending on the genotype × treatment interaction. Both T1 and T2 enhanced final germination in N7, whereas T2 was effective in S50. The highest final germination (94%) was recorded under T2 in S50, N16, and N7; however, the magnitude of the response differed among genotypes. Seedling vigor was enhanced by T2 in S49, S50, N7, and N9, whereas T1 had a positive effect only in N7 compared to T0 (Figure 2).

3.3. Initial Plant Growth

The results indicate that factors: genotype, treatment, and their interaction, significantly influenced the examined traits, highlighting that the response of initial tomato plant growth is strongly dependent on both the genetic background and the applied foliar treatments (

Table 3. Analysis of variance (ANOVA) and mean values of the observed initial plant growth traits for the analyzed genotypes and treatments.

Traits	Shoot Length (mm)	Root Length (mm)	Shoot Elongation Rate (mm day−1)	Root Elongation Rate (mm day−1)	Fresh Seedling Weight (g)	Dry Seedlings Weight (g)	R/S Ratio
G (p)	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***
S49	45.29 bc	74.54 bc	2.66 b	1.98 c	0.35 c	0.017 b	1.64 a
S50	41.71 d	71.22 c	2.68 b	1.60 c	0.29 d	0.014 c	1.71 a
N4	46.94 ab	79.16 a	2.60 b	1.68 c	0.40 a	0.022 a	1.67 a
N7	45.93 bc	79.21 a	3.25 ab	2.13 bc	0.30 d	0.015 c	1.73 a
N9	49.23 a	66.34 d	3.68 a	3.04 a	0.37 b	0.017 b	1.34 b
N14	46.72 ab	82.30 a	3.07 ab	2.84 ab	0.40 a	0.017 b	1.76 a
N16	43.67 cd	75.37 b	3.01 ab	2.12 bc	0.34 c	0.014 c	1.73 a
T (p)	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***
T0	45.12 b	71.98 b	3.55 a	2.05 b	0.34 b	0.016 b	1.60 b
T1	44.50 b	70.81 b	2.77 b	1.81 b	0.34 b	0.016 b	1.60 b
T2	47.31 a	83.56 a	2.65 b	2.74 a	0.37 a	0.017 a	1.77 a
RC T1 (%)	−1.37	−1.63	−21.97	−11.71	0.00	0.00	0.00
RC T2 (%)	+4.85	+16.09	−25.35	+33.66	+8.82	+6.25	+10.63
G × T (p)	0.042 *	0.000 ***	0.001 ***	0.000 ***	0.000 ***	0.001 ***	0.000 ***

* p ≤ 0.05, *** p ≤ 0.001. G (p): factor genotype; T (p): factor treatment (T0, T1 and T2); G × T: interaction between factor genotype and factor treatment. Data are presented as mean values (n = 3; 10 seedlings per replicate). Differences between genotypes and treatments were determined using Tukey’s HSD test (p ≤ 0.05). Mean values for the same trait that share the same letters are not significantly different. T0—control; T1—conventional fertilizer YaraLiva–Calcinit; T2—nanofertilizer Nanoplant Ca-Si; RC (%)—Relative change indicates the variation in comparison to the corresponding control (T0).

).

According to Table 3, treatment T2 had a significant positive effect at the overall level on shoot length, root length, root elongation rate, fresh seedling weight, dry seedling weight, and root-to-shoot ratio, whereas a decrease compared to the control (−25.35%) was observed for shoot elongation rate. No significant effects of treatment T1 were observed on the evaluated traits relative to the control across the entire dataset. On average, the most pronounced improvements relative to the control were recorded in root length (+16.09%) and root elongation rate (+33.66%) under T2 (Table 3). Nonetheless, the extent and direction of treatment effects differed among genotypes (Figure 3), indicating genotype-specific responses to the applied foliar treatments (Table 3).

A more detailed genotype-level analysis revealed contrasting responses to the applied treatments (Figure 3). For shoot length, the maximum value (51.27 mm) was recorded in genotype N9 under T2, whereas the lowest was observed in S50 under T1. While T2 increased shoot length in several genotypes (e.g., S49, N14), some genotypes showed little or no response. Regarding root length, genotypes S49, S50, N7, and N9 responded positively to T2 compared to the control, whereas T1 caused a reduction in N4 and showed no significant effect in the remaining genotypes. With respect to shoot elongation rate, both treatments generally produced lower values than the control across most genotypes, except for N4 (T1 and T2) and S50 (T1), where slight but non-significant increases were recorded. The effects of the treatments on root elongation rate varied among genotypes. Regarding this trait, the most pronounced positive responses to T2 were observed in genotypes N4 and N9, whereas T1 did not produce any significant changes compared to the control in any of the genotypes. Fresh and dry seedling weights also displayed genotype-dependent responses. For instance, T2 significantly increased fresh seedling weight by 42.48% in genotype N7 compared to the control, whereas in genotype N4, the same treatment resulted in a 15.53% decrease. In contrast, T1 did not induce any significant changes in fresh seedling weight. For dry weight, significant increases were observed in N7 under both T1 and T2, and in N16 under T2. Finally, the root-to-shoot ratio also exhibited variability among genotypes and treatments, with genotype N9 under treatment T2 showing the highest increase (+39.66%) compared to the control (Figure 3).

3.4. PCA

Principal Component Analysis (PCA) was performed to extract a reduced set of linear combinations (principal components) from the original variables while retaining the maximum amount of information. This multivariate approach enabled a clearer understanding of the relationships among the evaluated traits, genotypes, and applied treatments, as well as their relative contributions to total variability. The first two principal components (PC1 and PC2) explained 23.62% and 22.10% of the total variability, respectively, accounting together for 45.72%. Although these two components captured the largest share of the variation, they did not dominate the overall variability, indicating that other variables also played an important role. In fact, the first six principal components cumulatively explained 85.97% of the total variability. Subsequent analyses therefore focused primarily on PC1 and PC2, as their contribution was considerably greater than that of the remaining components. Traits such as the number of locules, total soluble solids, and final germination contributed strongly to PC1, highlighting their importance in explaining variation along this axis. Root length and seedling vigor index were most positively associated with PC2, whereas abnormal seedlings, fruit length, and dry seedling weight were positioned in the opposite direction, suggesting a negative relationship with both PC1 and PC2. In PC3, which accounted for 14.54% of the total variability, fruit weight and width exerted the strongest positive influence, while final germination showed the most pronounced negative effect (

Table 4. Factor coordinates of the variables based on correlations, eigenvalues, and variance in first six significant Principal Components (PC).

Trait	PC1	PC2	PC3	PC4	PC5	PC6
FW	0.529	0.488	0.633
FL	−0.572	0.334		−0.421
FWI	0.599	0.375	0.673
NL	0.755		0.506
PT		0.319		−0.303		0.838
TSS	0.880
EG	0.360		−0.475	0.356	0.343	0.349
FG	0.704		−0.548
AS	−0.553	0.386		−0.403
SL		0.525		0.627
RL		0.913
FSW	−0.468	0.556		0.431
DSW	−0.525	0.437		0.450	−0.423
SER		−0.360	0.469	0.390	0.464
RER		0.387			0.759
SVI	0.352	0.775	−0.428
R/S RATIO		0.669	−0.387	−0.477
Eigenvalue	4.02	3.76	2.47	1.93	1.38	1.06
% Total variance	23.62	22.10	14.54	11.33	8.14	6.24
Cumulative variance %	23.62	45.72	60.26	71.59	79.73	85.97

Values less than 0.3 are not shown for the traits. Only the values of the principal components greater than the Eigenvalue of 1 are displayed. The tested traits are denoted by the following abbreviations: fruit weight (FW), fruit length (FL), fruit width (FWI), number of locules (NL), pericarp thickness (PT), total soluble solids (TSS), germination energy (EG), final germination (FG), abnormal seedlings (AS), shoot length (SL), root length (RL), fresh seedling weight (FSW), dry seedling weight (DSW), shoot elongation rate (SER), root elongation rate (RER), seedling vigor index (SVI), root/shoot ratio (R/S RATIO).

).

Given that the number of locules, total soluble solids, final germination, root length, and seedling vigor index had the highest loading coefficients among the first two principal components (Table 4), these five traits had the greatest influence on the grouping of genotypes and treatments. The close alignment of the vectors for fruit weight, fruit width, and locule number indicated a positive correlation among these traits, suggesting that an increase in one is likely accompanied by increases in the others. In contrast, traits such as fruit length and the number of abnormal seedlings showed negative correlations with these variables, reflecting opposing trends in their values.

The graphical representation (Figure 4) highlights the clear differences between the applied treatments (T1, T2) and the control (T0). Genotypes treated with T2 were distinctly separated above 0 on the PC2 axis, except for genotype N16, which was positioned below 0 on the PC2 axis, in proximity to the total soluble solids vector. This indicates a distinct response of genotype N16 to T2 treatment, particularly in relation to total soluble solids (Figure 1). In contrast, most genotypes treated with T1, as well as the control (T0), were positioned below 0 on the PC2 axis. Notable exceptions included genotype N4, which was placed above 0 on the PC2 axis, near the DSW vector (Figure 4), consistent with the average value of this trait observed in the T0 treatment of genotype N4 (Table 3).

4. Discussion

Ca intake is important for ensuring normal tomato fruit development [13], given that Ca, as an essential component of the cell wall, plays a significant role in cell division and elongation, stabilization of membrane structures, enhancement of nutrient uptake, and activation of various metabolic processes [5,36,37]. Numerous studies have demonstrated the effectiveness of foliar Ca application as a method for preventing physiological disorders caused by its deficiency in tomato [5,38,39]. Ca deficiency directly impacts tomato fruit development [11], emphasizing the critical role of Ca in plant nutrition. In this context, our results indicate that foliar application of Ca-based fertilizers, particularly in nanoform, to maternal plants enhanced certain fruit traits and, via seeds, improved seed germination parameters, and early seedling development in tomato. Importantly, the observed differential responses among genotypes to these treatments underscore a significant genotype × treatment interaction effect.

Both Ca-based fertilizer treatments resulted in a significant increase in fruit weight and width compared to the control. A clear increase in these parameters was observed in most of the evaluated genotypes, further confirming previous findings regarding the importance of Ca in improving fruit morphological traits [10,11,40]. However, it is important to emphasize that these two traits can be significantly influenced by genotype, treatment, and their interaction, as observed in our study. Similar findings have been reported in previous studies. Chaudhary et al. [41], who investigated the effects of organic and inorganic fertilizers, highlighted that both genotype and fertilizer treatment significantly affected fruit width, whereas their interaction did not show a significant influence on this trait. Moreover, Ayodele et al. [42], demonstrated that all three factors—genotype, treatment, and their interaction significantly influenced fruit weight. Furthermore, the nanofertilizer (T2) exerted a more pronounced effect on fruit weight and width compared to conventional fertilizer (T1). This effect may be partially explained by the presence of additional elements in the nanoformulation, such as Si, Fe, and B. Si has been shown to improve nutrient uptake [12], which may contribute to fruit development. Wang et al. [43] also reported that foliar application of Si increased fruit diameter and weight compared to untreated plants. A similar increase in fruit weight has been observed following foliar Fe application [44], as Fe enhances photosynthesis and carbohydrate synthesis, thereby supporting fruit development [45]. B is also essential for fruit set, fruit development, and hormonal activity in tomato, and it plays a key role in Ca absorption and utilization [46]. On the other hand, fruit length and pericarp thickness did not show significant differences between treatments, suggesting that these traits are predominantly influenced by genetic factors [47,48]. A similar trend was observed for locule number. Total soluble solids content represents one of the most important quality traits in fleshy vegetables, and it can vary under the influence of different environmental factors [48]. This study’s results demonstrate that both genotype and treatment factors significantly influenced total soluble solids content. These findings are supported by the work of Oko-Ibom and Asiegbu [49]. A slight reduction was observed at the overall level under both conventional and nanofertilizer treatments compared to the control. Similarly, Sajid et al. [50] reported that the highest dry matter content was observed in untreated plants, while plants treated with calcium showed a noticeable reduction in dry matter content. This phenomenon may be explained by the more intensive conversion of starch into sugars, which precedes physiological fruit ripening. Indeed, Ca-deficient fruits tend to ripen more rapidly, a process that can be regulated by maintaining optimal Ca levels in fruit tissues.

Although certain data show the effect of foliar application of fertilizers on seed production in vegetable species, such as tomato, cucumber and cauliflower [51,52,53], the effect of foliar application of Ca fertilizers in tomato seed production remains insufficiently investigated, particularly in the context of seed germination and initial plant growth parameters. Rapid and uniform germination, combined with high seed vigor and favorable environmental conditions, is essential for achieving consistent seedling emergence, which contributes to improved crop yields and product quality [54,55].

In this study, at the general level, nanofertilizer treatment exerted a significant positive effect on germination parameters, including germination energy, final germination percentage, and seedling vigor index. The application of Si nanoparticles can shorten the germination period, thereby enhancing germination energy [6,56]. Improved germination of seeds obtained from plants treated with nanoparticles may be explained by faster and more intense water uptake as well as increased activity of hydrolytic enzymes such as α-amylase, β-amylase, and proteases. Enhanced activity of these enzymes provides greater and more efficient availability of glucose and amino acids, which are essential for seedling growth [52,57]. Wang et al. [58] demonstrated that Si nanoparticles improve final germination under salt stress conditions. However, the response of genotypes to the applied treatments in terms of germination energy, final germination, and seedling vigor index was specific to each genotype, highlighting the significant genotype × treatment interaction. This finding is consistent with the results of Geshnizjani et al. [59], who, while investigating the effects of different fertilizers on tomato mother plants, reported a significant G × T interaction in the final germination of seeds obtained from those plants. Besides good final germination, the seedling vigor index is an important indicator of seed quality and a prerequisite for successful crop production [60]. In this study, nanofertilizer treatment exhibited the most pronounced effect on the seedling vigor index, which is consistent with previous research [56,61,62]. However, our results indicated significant differences among genotypes, treatments, and their interaction. In a study on different tomato seed priming treatments, Akram et al. [63] reported that both treatment and genotype factors had a significant effect on the seedling vigor index, whereas their interaction was not significant.

At a general level, nanofertilizer treatment exhibited a significant positive effect on all early seedling growth parameters, with the exception of shoot elongation rate. In contrast, conventional fertilizer treatments mostly had either negative effects on these parameters or produced values comparable to the untreated control. Similarly to the observations for seed germination parameters, significant differences were detected among genotypes, treatments, and their interactions for initial plant growth traits. This underscores the critical importance of aligning agrotechnical practices with the genetic potential of the plants to optimize early developmental outcomes.

Furthermore, genotype-specific analysis revealed a more pronounced response to nanofertilizer treatment in certain genotypes, emphasizing the relevance of genotype-dependent effects during early developmental stages. Greater shoot length enhances a seedling’s ability to capture light and develop photosynthetic organs, making it a key indicator of early growth and seedling quality [64]. Abbas et al. [65] showed that nano-Ca at certain concentrations promotes seedling length more effectively than conventional Ca fertilizer, which aligns with our findings, as T2 exhibited a significantly higher value than the control at the general level. Notably, both factors (G, T) and their interaction (G × T) had a significant effect on this trait, and, consistent with our results, Ali et al. [66] reported genotype-dependent responses of tomato seedlings to different nutrient media. Since Ca is an important cellular component involved in regulating both shoot and root growth [50], its role is particularly critical during early ontogenetic stages. Root length represents an important trait in tomato, especially during early growth phases. Increased root length can enhance nutrient uptake efficiency, particularly phosphorus (P) under conditions of limited availability, which is crucial for early tomato development given that P contributes to improved root establishment. Additionally, during drought periods, longer roots, especially in deeper soil layers, allow for more efficient water uptake [67]. The results confirm that seedlings derived from seeds of plants treated with nanofertilizer had significantly longer roots compared to those from control plants and plants treated with conventional Ca(NO₃)₂, both at the general level and in 4 of the 7 tested genotypes. However, this trait was also significantly influenced by genotype, treatment, and their interaction. This effect can be explained by the fact that the nanoformulation, in addition to Ca and Si, also contains Fe and B, which likely contributed to improved nutrition and physiological activity of the maternal plants, thereby enhancing seed quality and vigor. The higher Si content in seeds may have played a key role in increasing root length, as Si nutrition increases root mass and volume, as well as total and adsorptive root surface area [68]. Moreover, the nanoformulation itself may positively affect root length in tomato seedlings [13], as nano-Ca is more readily absorbed and translocated in plants compared to conventional forms [69]. These results were further supported by the analysis of root elongation rate, where nanofertilizer treatment demonstrated the most significant effect, with an overall increase in root elongation rate of 33.66%. However, this trait also showed a genotype-specific response to the applied treatments. For example, the nanofertilizer treatment had a significantly positive effect on genotypes N4 and N9, while it did not have a significant impact on the other genotypes, nor did the conventional fertilizer treatment.

Biomass accumulation is a crucial factor in achieving high tomato yield [70]. A genotype-specific response to the applied treatments was observed for both fresh and dry seedling biomass, consistent with previous studies [71]. Moreover, a significant effect of the treatment factor and the G × E interaction was observed for these traits. The nanofertilizer increased fresh seedling weight in several genotypes, unlike the conventional fertilizer, highlighting the genotype-specific responses to the applied treatments. Our results align with previous studies reporting the positive effects of nanofertilizers, particularly Si, on both fresh and dry biomass of tomato seedlings [6,56,72]. In addition to Si and Ca, which are essential for plant tissue development [21], the nanofertilizer also contained B and Fe, and such combined formulations have been shown to positively influence seedling dry biomass [73].

The PCA provided insights into the complex relationships between fertilizer treatments and the evaluated traits, effectively reducing data complexity and identifying the main factors driving variation in fruit characteristics, germination, and initial growth in tomato genotypes. The first two principal components (PC1 and PC2) accounted for 45.72% of the total variability, with the biplot clearly differentiating fertilizer treatments from the control. Notably, genotypes treated with nanofertilizer were distinctly separated on the positive side of the PC2 axis. Furthermore, PCA revealed significant positive correlations between fruit weight and width, indicating synergistic improvements under the applied treatments, consistent with previous findings [74]. Conversely, the negative correlation between fruit length and width implies potential trade-offs between these traits.

5. Conclusions

Foliar application of Ca-based nanofertilizer (T2) significantly improved tomato fruit weight and width, germination energy, final germination, seedling vigor index, and initial plant growth traits—except for shoot elongation rate—compared to conventional fertilizer (T1) and the control (T0). Genotype, treatment, and their interaction were found to play a critical role in determining the magnitude and direction of these effects, highlighting the importance of genotype-specific responses. Both treatments were associated with a general reduction in total soluble solids compared to the control, with this trait being significantly influenced by genotype and treatment. Traits such as fruit length, pericarp thickness, and number of locules were primarily determined by genetic background, whereas fruit weight, width, and seedling vigor exhibited clear genotype × treatment interactions.

The superior performance of the nanofertilizer is likely related not only to Ca, but also to the presence of Si, Fe, and B, which may enhance nutrient uptake, physiological activity, and overall seedling vigor. These elements appear to play a key role in improving fruit traits, seed quality, and initial plant growth, suggesting that their combined effects should be carefully investigated in future studies.

Overall, nanofertilizer application represents a promising strategy to enhance tomato productivity and seedling performance, provided that genotype-specific responses are taken into account.