The Effect of Native Strain-Based Biofertilizer with TiO₂, ZnO, Fe_xO_x, and Ag NPs on Wheat Yield (Triticum durum Desf.)

Abstract

This study evaluated the effects of applying a biofertilizer, alone and in combination with nanoparticles (NPs), under controlled greenhouse conditions to improve soil quality and wheat performance (soil from the region of General Cepeda, Coahuila, Mexico, was used). The integration of the biofertilizer with Fe_xO_x NPs proved particularly effective in enhancing soil physical and biological parameters as well as promoting superior crop growth compared with individual treatments. The incorporation of NPs markedly improved the biofertilizer’s biocompatibility and stability, reinforcing its potential for optimizing plant nutrition, nutrient use efficiency, and overall agricultural sustainability. In addition, the combined treatments enhanced the utilization of native microbial diversity, thereby contributing to increased soil fertility and the quality and yield of crops in the study region. The best yield obtained in previous harvests (8.3 Mg ha⁻¹) was improved to 8.48 Mg ha⁻¹ with application of the biofertilizer with Fe_xO_x NPs. Moreover, shoot length increased significantly with the combination of the biofertilizer and ZnO NPs as well as with Fe_xO_x NPs separately, whereas root length was maximized with the addition of the biofertilizer alone. These findings underscore the synergistic effects of combining biofertilizers with metal-based nanoparticles to sustainably enhance wheat growth and productivity.

1. Introduction

With the growth of the global population, it is necessary to meet the increasing demand for food, considering that soil nutrients and fertility are limited. Currently, agriculture has relied on chemical fertilizers, which can result in up to a 28% increase in production costs. An alternative is the use of biofertilizers inoculated with native strains [1,2]. It is estimated that by 2050, wheat production will need to increase by 60% to meet projected demands [3]. Introducing NPs in agriculture is a promising method to increase productivity and yield. Nanoagrochemical fertilizers are more effective than traditional bulk agrochemical fertilizers. Consequently, nanoagrochemical fertilizers must be tested under natural conditions [4,5].

The most widely used fertilizer globally is urea (CO(NH₂)₂), with an annual global production exceeding 200 million tons [6,7]. This granular fertilizer is employed worldwide due to its high nitrogen concentration (46% w/w) and ease of handling. However, urea alkalinizes and degrades soils by forming ammonium and bicarbonate as soon as it hydrolyzes and meets the enzyme urease. Additionally, urea contributes to water pollution and promotes the process of eutrophication. Fertilization with N at rates less than 40 g N m⁻² year⁻¹ was found to be beneficial, leading to increases in plant biomass and microbial biomass [8].

Phosphorus (P) is considered a limiting element in optimal plant development. The phosphorus cycle is strongly controlled by microbial biomass, as the most important mechanisms for solubilizing stable phosphorus include the dissolution and complexation of organic acids, enzymatic hydrolysis, and proton excretion. Consequently, this cycle is closely related to water dynamics. In arid or semiarid regions, inorganic P decreases while organic P increases [9]. In the rhizosphere, the processes of nutrient enhancement and efficiency in the soil are defined. Microorganisms present in this zone employ various mechanisms to enhance the plant environment, including the production of siderophores, nitrogen fixation, and phosphorus solubilization. Many strains are isolated for their beneficial potential to increase plant yield and quality [10].

Among the primary bacteria are those from the genera Bacillus, Pseudomonas, and Azotobacter. The genus Azotobacter has the potential to colonize roots. It is a free-living bacterium that enhances performance by fixing atmospheric nitrogen under aerobic conditions, while also acting as a biocontrol agent [10]. Bacteria of the genus Bacillus can fix nitrogen and at the same time promote plant growth through phosphorus solubilization [11]. Inoculated strains used as a consortium enhanced their growth-promoting capacity in wheat plants [12]. Among the principal growth-promoting bacteria is Pseudomonas fluorescens, which, by promoting plant growth, has the indirect effect of suppressing plant diseases. Colonization in the rhizosphere is influenced by root exudates (ions, free oxygen, enzymes) [13].

Nanofertilizers combined with microorganisms have been reported to increase the bioavailability of iron (Fe) and boron (B). Iron (Fe) affects processes such as respiration and photosynthesis. These elements are common micronutrients in plants, and to enhance their availability, Fe chelates and borax are frequently added to the soil [14].

In this regard, it has been observed that TiO₂ NPs improve water uptake and use efficiency by plants depending on their concentration and the plant species, as well as soil nutrient availability [15]. ZnO NPs in turn provide this element as an essential micronutrient for both plants and animals, and in its ionic form (Zn²⁺) can be adsorbed by soil organic matter and influence soil ammonium adsorption. In addition, Zn plays an antioxidant role in organisms by participating in protection against oxidative stress [16]. Fe_xO_x NPs have demonstrated a positive effect on soil microbiota, especially on the abundance of beneficial bacteria, without causing drastic changes in overall microbial diversity [17]. Ag NPs enhance antioxidant activity and crop growth when applied at low doses (10 mg L⁻¹) [18].

Wheat cereal, including Triticum aestivum L. and Triticum durum Desf, provides food for almost 28% of the world’s population. Novel strategies must be implemented to meet the predicted 60% demand for wheat by 2050 [19,20]. This study hypothesized that nanoparticles (NPs) of TiO₂, ZnO, Fe_xO_x, and Ag in combination with biofertilizer would act synergistically to enhance crop growth and development and improve grain yield.

It is essential to properly select microorganisms with the potential to fix atmospheric nitrogen, solubilize phosphates, and promote plant growth, with the aim of providing agricultural producers in the southeast of Coahuila de Zaragoza with biofertilizers that help reduce dependence on chemical fertilization.

2. Materials and Methods

In this study, Samayoa C2004 wheat, a semidwarf variety that yields 5.2 to 8.3 Mg ha⁻¹, was used, and the effect of NPs (ZnO, TiO₂, Fe_xO_x, and Ag NPs) and a biofertilizer made with native strains on wheat yield and the physicochemical properties of the soil in each treatment was evaluated.

2.1. NP Characterization

The NPs used were bought at Investigación y Desarrollo de Nanomateriales, S.A. de C.V. Morphological characterization and chemical composition were determined by field-emission scanning electron microscopy with energy-dispersive spectroscopy (FESEM-EDS: JEOL JSM-7800F, Akishima, Tokyo) with 10.0 kV acceleration voltage, and the crystal structure was determined by X-ray diffraction (XRD: Xpert-Philips PW3040 diffractometer, Eindhoven, The Netherlands).

2.2. Soil Characterization

The soil used was Leptosol, as per the IUSS Working Group WRB 2014 [21], from General Cepeda, Coahuila, Mexico, and 1000 kg was collected for the experiment from a depth of 0 to 20 cm. The normalized method was utilized to obtain twelve soil samples during the first half of 2023 [22]. The soil samples were stored in refrigeration at 4 °C for physicochemical characterization, and another soil sample at −18 °C for the determination of soil microbial biomass. The soil samples were dried under greenhouse conditions for 72 h before characterization and were then sieved through a 2 mm mesh [23].

An Orion Star A211 potentiometer (Thermo Scientific, Waltham, MA, USA) was used to determine the pH level with a 1:2.5 soil-to-water ratio. Electrical conductivity (EC) [23] was determined using an Orion Star A212 conductometer (Thermo Scientific, Waltham, MA, USA). Soil organic matter (SOM) was measured using the calcination method [24] using a Thermolyne muffle (Thermo Scientific, Dubuque, IA, USA).

Gravimetric soil humidity (Hg) was determined with a OHAUS MB90 humidity analyzer (OHAUS Corporation, Parsippany, NJ, USA), and the test tube method was used to determine apparent density (ρa) [25]. The texture was determined using the hydrometric method and USDA classification, using a hydrometer ASTM 152-H, Fisher Scientific, Hampton, NH, USA. The concentration of chemical elements was determined using inductively coupled plasma emission spectrometry (ICP-OES) with a PerkinElmer Optima 8300 spectrometer (Shelton, CT, USA) [24].

2.3. Biofertilizer Preparation

2.3.1. Vermicompost Preparation

A soil sample from a field previously treated in a greenhouse was collected to set up a composting device using vermicompost consisting of 1 kg of California red earthworms (Eisenia fetida Savigny). These organisms were used because studies have shown that vermicompost preserves the biological properties of the soil, increases microbial populations, and improves the physicochemical properties of the soil [26].

California red earthworms (Eisenia fetida Savigny) were fed exclusively vegetable waste (300 g weekly for 2 weeks) and pre-characterized tap water to produce earthworm humus. The second leachate obtained from this humus was used as the carrier for the biofertilizer.

2.3.2. Isolation and Characterization of Microorganisms

From field soil, native microorganisms capable of nitrogen fixation, phosphate solubilization, and plant growth regulation were isolated using the methodology proposed by Mohamed et al. [27]. Microbial consortia from Azotobacter, Bacillus, and Pseudomonas were used. Soil samples were taken from the field and mixed with a sterile 0.85% NaCl solution and shaken for 30 min. Serial dilutions were prepared and placed on the surface of selected medium plates as described below.

For this purpose, previously preserved soil was used. Serial dilutions were performed with 10 g of soil dissolved in 100 mL of sterile solution. Using an Ashby medium (sucrose 1 g, KH₂PO₄ 0.2 g, MgSO₄ 0.04 g, NaCl 0.04 g, CaCl₂ 0.04 g, FeSO₄ 0.001 g, and Agar 1.875 g), bacteria of the genus Azotobacter were isolated [28], using an LB agar medium (4 g L⁻¹; NaCl, yeast extract, and casein peptone), bacteria of the genus Bacillus were isolated [29], and using a King B medium (agar 3 g, peptone 4 g, MgSO₄ 0.3 g, and K₃PO₄ 0.3 g), bacteria of the genus Pseudomonas were isolated [30]. They were incubated for 5 days at 30 °C without agitation in petri dishes within an INO 650M orbital shaker incubator from VICHI, Mexico. An external laboratory confirmed the genus of the strain through molecular characterization at the Durango Technological Institute.

2.3.3. Preparation of the Biofertilizer

A biofertilizer was prepared using the second leachate from earthworm humus and previously isolated native strains from field soil. Native strains were transferred from solid to liquid medium separately to quantify colony-forming units using the McFarland method. The second leachate from the vermicompost was separated and stored in a refrigerator at 4 °C. Samples were taken in triplicate for chemical analysis using the ICP-OES technique. Native strains and the second leachate were combined in a flask to form the biofertilizer, which was then incubated in an orbital shaker at 30 °C for 3 days with agitation, allowing the microorganisms to adapt and grow in the leachate.

2.4. Experimental Setup

A randomized experiment was conducted to evaluate the effectiveness of the biofertilizer and nanoparticles (NPs) in a Samayoa wheat crop during the 2023–2024 growing season. Factors included presence (yes and no), fertilizer (biofertilizer, urea, and no fertilizer), and NP type (TiO₂ NPs, ZnO NPs, Fe_xO_x NPs, Ag, and No NPs).

Seeding density was 20 seeds per pot (nursery grow bag with dimensions 40 × 40 cm and gusset, capacity 12 kg), and the NP dosage was 0 and 0.1333 mg NPs kg⁻¹ (this dose was used because this research aimed to compare this dose under greenhouse conditions and field conditions [31]). The soil was sieved using a 4 mm mesh to remove stones and clods, then weighed (8 kg) and placed into each pot. The soil was irrigated until reaching an approximate moisture depth of 15 cm, considered optimal for sowing. Each pot was labeled according to the treatment and randomly arranged under greenhouse conditions, as shown in

Table 1. Treatments for growing wheat plants (Triticum durum Desf.) in a greenhouse under a randomized block experiment.

Treatment	Replicates	Type of NPs	Fertilizer
WF-TiO2	6	TiO2	Without
WF-ZnO	6	ZnO	Without
WF-FexOx	6	FexOx	Without
WF-Ag	6	Ag	Without
BF-TiO2	6	TiO2	Biofertilizer
BF-ZnO	6	ZnO	Biofertilizer
BF-FexOx	6	FexOx	Biofertilizer
BF-Ag	6	Ag	Biofertilizer
BF	6	No	Biofertilizer
U-TiO2	6	TiO2	Urea
U-ZnO	6	ZnO	Urea
U-FexOx	6	FexOx	Urea
U-Ag	6	Ag	Urea
U	6	No	Urea
Control	6	No	Without

. Wheat seeds, fertilizer, and nanoparticles (NPs) were placed directly into the soil. In the case of urea, it was applied below the seeds and NPs to prevent seed damage upon solubilization.

2.5. Distribution and Experimental Conditions

Soil moisture was monitored based on greenhouse temperatures, which averaged 30 °C during the day and 20 °C at night, to maintain optimal moisture levels and prevent drought conditions.

The experiment consisted of six replications with destructive sampling conducted at 60 and 130 days after sowing (DAS). Destructive sampling consisted of randomly collecting samples from each treatment and measuring the soil’s physicochemical properties at 60 and 130 DAS. In the case of 130 DAS, crop samples were also collected and analyzed. Fresh and dry biomass weights were recorded, and the root lengths of each subsample were measured to obtain an average per treatment. The vegetal tissue samples were placed in paper bags and dried in a Thermo Scientific forced-air oven at 72 °C for 72 h to determine the dry biomass weight.

2.6. Statistical Analysis

A two-way analysis of variance (ANOVA α ≤ 0.05) was conducted to evaluate the effects of nanoparticle type (NPs; TiO₂, ZnO, Fe_xO_x, Ag, and No NPs) and fertilizer type (biofertilizer, chemical (urea), and without fertilizer) on soil physicochemical parameters at 60 DAS and soil and harvest parameters at 130 DAS. The statistical model included the main effects of NPs and fertilizer, as well as their interaction (NPs × fertilizer) using Tukey’s HSD post hoc test (p ≤ 0.001). All statistical analyses were performed using Minitab 22.

3. Results

3.1. NP Characterization

XRD analysis (2Ө degrees) of TiO₂ NPs revealed the presence of two crystalline phases that were identified as the anatase phase with planes (100), (004), (200), (211), (204), and (215), corresponding to card number 96-900-8215. The second phase identified was the Brookite phase, characterized by planes (121) and (233), corresponding to card number 96-900-4140. Additionally, FESEM revealed that the TiO₂ NPs exhibited an irregular and heterogeneous morphology, tending to form clusters (Figure 1a). The ZnO nanoparticles (NPs) showed well-defined diffraction peaks (2Ө degrees) in which the planes (100), (002), (101), and (102) were identified, corresponding to card number 00-901-1662. This indicated that they had a hexagonal wurtzite phase structure. Regarding their morphology, elongated particles in the form of prismatic bars were observed (Figure 1b). The Fe_xO_x nanoparticles exhibited two crystalline phases (2Ө degrees): the hematite phase, represented by the presence of the planes (42-2), (51-4), and (44-4), corresponding to card number 96-900-0140, and the magnetite phase with planes (−20), (222), and (440) from card number 96-900-2317. Regarding their morphology, spherical or semispherical particles with a high degree of aggregation were observed (Figure 1c). The Ag nanoparticles exhibited four diffraction peaks (2θ degrees) at (111), (200), (220), and (311), corresponding to the diffraction planes of silver with an FCC structure, as indicated by card 01-089-3722. It can be observed that the morphology of the Ag NPs is irregular, with a high degree of aggregation (Figure 1d).

3.2. Results of Initial Soil Characterization

The initial soil properties were pH 7.1, electrical conductivity (EC) 0.034 S m⁻¹, soil organic matter (SOM) content of 4.2%, and bulk density (BD) of 1.2 g cm⁻³. The soil texture was silty clay loam (sand 43%, silt 29%, clay 28%). Gravimetric moisture was 3.99%. Soil microbial biomass carbon was 126.13 mg kg⁻¹, and the principal total elements were K 4.1 g kg⁻¹, P 0.6 g kg⁻¹, N 1.7 g kg⁻¹, C 29.2 g kg⁻¹, and TOC 11.6 g kg⁻¹.

3.3. Biofertilizer

Bacteria capable of atmospheric nitrogen fixation from the genus Azotobacter, phosphate solubilizers from the genus Bacillus, and growth-promoting bacteria from the genus Pseudomonas were isolated. Using the McFarland standard curve, a sample of the leachate, biofertilizer, and tap water used for irrigating wheat plants (Triticum durum Desf.) was analyzed. The standard deviation was calculated, and the obtained values were averaged. The Azotobacter bacterium exhibited the highest count with 2097.9 CFU mL⁻¹, followed by Bacillus with 222.38 CFU mL⁻¹ and Pseudomonas with 116.9 CFU mL⁻¹.

The highest CFU value was observed in the biofertilizer sample, at 940.11 CFU mL⁻¹, due to the presence of various native strains. However, the Azotobacter genus alone demonstrated greater bacterial growth compared to when it was combined with other genera. The leachate sample recorded a value of 623.46 CFU mL⁻¹, which was higher than those of Bacillus and Pseudomonas. Tap water presented a value below the detection limit.

The chemical composition of the biofertilizer used (

Table 2. Chemical composition of the biofertilizer, California red earthworm humus leachate, and irrigation water (tap water). All chemical element concentration units are expressed in milligrams per liter (mg L⁻¹). Significant differences based on one-way ANOVA (α ≤ 0.05) with Tukey’s test are indicated in lowercase letters.

	Chemical Elements (mg L−1)
	Ag	Al	Ca	Fe	K	Mg	Na	P	S	Ti	Zn
Biof	0.01 ± 0.0 c	0.025 ± 0.0 a	0.4 ± 0.1 b	0.1 ± 0.0 a	21.3 ± 0.5 b	122.8 ± 1.8 a	107 ± 1.2 c	5.7 ± 0.2 a	8.8 ± 2.5 c	0.05 ± 0.0 b	0.05 ± 0.01 a
Leachate	0.02 ± 0.0 a	0.025 ± 0.0 a	348.0 ± 3.0 a	0.1 ± 0.0 a	1801.8 ± 25 a	109.5 ± 1.3 b	476.8 ± 7.1 a	0.6 ± 0.1 b	245.6 ± 2.1 b	0.048 ± 0.0 c	0.02 ± 0.00 a
Irrigation	0.01 ± 0.0 b	0.025 ± 0.0 a	355.3 ± 6.5 a	0.1 ± 0.0 a	8.7 ± 3.4 b	60.6 ± 0.61 c	248.9 ± 1.9 b	0.1 ± 0.0 c	292.1 ± 5.4 a	0.05 ± 0.0 a	0.05 ± 0.01 a

Biof = biofertilizer; Irrigation = water used for watering; Leachate = second leachate of humus of California red earthworms.

) was similar to that of a biofertilizer reported by Kapila et al. [32], who showed that the chemical composition of the biofertilizer determines the quality and its use in increasing crop yield. Various substrates were used for feeding California red earthworms. In contrast, the biofertilizer used in this study was produced exclusively from vegetable residues, with 300 g used in a composting system consisting of three bins, each with a capacity of twenty liters.

3.4. Greenhouse Experiment

The soil parameters are listed in

Table 3. Two-way ANOVA results (NPs × fertilizer) (α ≤ 0.05) for soil parameters at 60 DAS. Greenhouse experiment with Triticum durum Desf. Samayoa C2004.

	Variables
	pH			EC			K
Source of variation	Type of NPs	Fertilizer	NPs × Fertilizer	Type of NPs	Fertilizer	NPs × Fertilizer	Type of NPs	Fertilizer	NPs × Fertilizer
SS X 102	106.2	165.9	311.0	1211.5	276.5	818.6	13.7	14.7	16.4
df	4	2	8	4	2	8	4	2	8
F	14.68	45.87	21.49	130.44	59.54	44.07	13.07	28.03	7.80
p-value	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001
	P			N			C
SS X 102	0.2	0.1	0.2	60.5	3.1	9.6	1543.0	109.5	364.1
df	4	2	8	4	2	8	4	2	8
F	11.95	13.36	6.11	145.14	14.88	11.49	67.13	9.53	7.92
p-value	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001
	TOC			SOM
SS X 102	19.0	9.9	96.8	57.3	27.0	297.1
df	4	2	8	4	2	8
F	1.98	2.06	5.04	2.04	1.93	5.29
p-value	0.11	0.14	≤0.001	0.10	0.15	≤0.001

. The rank represents the relative performance of each treatment for a given response variable based on the mean values obtained. Treatments were ordered from best (rank = highest) to lowest according to their average response. This ranking was complemented with Tukey’s HSD test to determine statistically significant differences. “Highest” indicates treatments with the best average performance, statistically different from the others according to Tukey’s HSD test, and “lowest” indicates treatments with the poorest performance, generally showing significant differences from the top-ranked group (

Table 4. Tukey’s HSD (p ≤ 0.001) grouping of treatment means (NPs × fertilizer) for soil parameters at 60 DAS. Greenhouse experiment with Triticum durum Desf. Samayoa C2004.

Variable	Rank	Type of NPs		Fertilizer		Type of NPs × Fertilizer
pH	Highest	TiO2	8.12 a	Without	8.15 a	WF–FexOx	8.42 a
	Lowest	Ag	7.80 c	Urea	7.83 b	BF–FexOx	7.47 f
EC (dS m−1)	Highest	No	1.62 a	Urea	1.14 a	U	2.18 a
	Lowest	ZnO	0.6 d	Without	0.76 b	BF–ZnO	0.42 g
K (g kg−1)	Highest	TiO2	4.3 a	Biofertilizer	4.4 a	BF–FexOx	5.0 a
	Lowest	No	3.2 b	Urea	3.4 b	U–FexOx	2.6 f
P (g kg−1)	Highest	TiO2	0.5 a	Urea	0.4 a	U–ZnO	0.5 a
	Lowest	Ag	0.3 b	Biofertilizer	0.3 b	WF–Ag	0.3 e
N (g kg−1)	Highest	FexOx	3.1 a	Urea	2.8 a	U–Ag	4.1 a
	Lowest	No	0.9 b	Without	2.4 b	Control	0.7 d
C (g kg−1)	Highest	ZnO	42.4 a	Urea	38.6 a	U–TiO2	45.1 a
	Lowest	No	32.4 c	Biofertilizer	35.9 b	BF	29.5 f
TOC (g kg−1)	Highest	No significant effect		No significant effect		BF–FexOx	12.7 a
	Lowest					BF	8.7 b
SOM (g kg−1)	Highest	No significant effect		No significant effect		BF–FexOx	21.8 a
	Lowest					BF	14.7 b

Different lowercase letters in a column indicate significant differences according to Tukey’s HSD (p ≤ 0.001), n = 9.

and

Table 5. Tukey’s HSD (p ≤ 0.001) grouping of treatment means (NPs × fertilizer) for soil and harvest parameters at 130 DAS. Greenhouse experiment with Triticum durum Desf. Samayoa C2004.

Variable	Rank	Type of NPs		Fertilizer		Type of NPs × Fertilizer
Yield (Mg ha−1)	Highest	FexOx	6.13 a	Biofertilizer	4.67 a	BF-FexOx	8.48 a
	Lowest	Ag	2.60 c	Without	3.40 b	U-Ag	2.28 d
Shoot (cm)	Highest	FexOx	91.05 a	No significant effect		BF-ZnO	95.33 a
	Lowest	Ag	56.07 b			WF-Ag	42.33 f
Root (cm)	Highest	TiO2	38.94 a	Biofertilizer	40.38 a	BF-TiO2	65.73 a
	Lowest	No	18.66 b	Urea	22.04 b	U	13.13 c
pH	Highest	TiO2	8.39 a	Without	8.30 a	WF-TiO2	8.54 a
	Lowest	No	7.90 b	Urea	8.09 b	U	7.84 f
EC (dS m−1)	Highest	ZnO	2.38 a	Urea	2.37 a	U-ZnO	3.25 a
	Lowest	Ag	1.68 c	Without	1.79 b	Without	1.18 d
K (g kg−1)	Highest	FexOx	3.7 a	No significant effect		WF-FexOx	4.4 a
	Lowest	No	3.0 b			U-Ag	2.9 b
P (g kg−1)	Highest	ZnO	0.6 a	No significant effect		U-ZnO	0.6 a
	Lowest	Ag	0.4 c			WF-Ag	0.3 e
N (g kg−1)	Highest	No significant effect		Without	2.5 a	No significant effect
	Lowest			Biofertilizer	0.9 b
C (g kg−1)	Highest	ZnO	39.0 a	Urea	36.8 a	U-ZnO	43.5 a
	Lowest	Ag	32.7 b	Biofertilizer	32.8 b	BF	30.6 b
TOC (g kg−1)	Highest	ZnO	15.1 a	Without	14.7 a	WF-TiO2	18.1 a
	Lowest	No	10.6 c	Biofertilizer	12.4 b	BF	8.7 d
SOM (g kg−1)	Highest	ZnO	25.9 a	Without	24.5 a	WF-ZnO	29.6 a
	Lowest	No	18.2 c	Biofertilizer	21.3 b	BF	15.0 e

Different lowercase letters in a column indicate significant differences according to Tukey’s HSD (p ≤ 0.001), n = 9.

).

3.4.1. Sampling at 60 DAS

The two-way ANOVA revealed significant effects of nanoparticles (NPs), fertilizer, and their interaction on all measured soil parameters at 60 days after sowing (DAS). For soil pH, significant differences were found for NPs, fertilizer, and their interaction, indicating that pH was strongly influenced by both individual factors and their combined effect. Similarly, electrical conductivity (EC) was highly affected by treatments, with significant effects of NPs, fertilizer, and the NP × fertilizer interaction. This highlights that the response of soil EC was highly dependent on the combined management of nanoparticles and fertilizers (Table 3).

For potassium (K), the effects of NPs, fertilizer, and interaction were all significant. For phosphorus (P), significant effects were detected for NPs, fertilizer, and their interaction, suggesting that both factors and their interaction influenced P availability. The content of nitrogen (N) was susceptible to nanoparticles, showing highly significant differences, while fertilizer and their interaction also contributed significantly. Similarly, NPs and fertilizer strongly influenced soil carbon (C) and their interaction (Table 3).

For organic matter fractions, both total organic carbon (TOC) and soil organic matter (SOM) showed significant differences across all sources of variation, with p ≤ 0.001 in each case. This indicates that nanoparticle application and fertilizer type exerted consistent effects on organic matter dynamics, and these effects were further modulated by their interaction (Table 3). The results demonstrate that all soil chemical parameters (pH, EC, P, N, K, C, TOC, and SOM) were significantly affected not only by the main effects of NPs and fertilizer, but also by their interaction. This confirms that soil quality responses were not independent of either factor alone, but rather determined by the specific NP × fertilizer combination.

Soil pH: The highest mean pH was observed with TiO₂ NPs, while the lowest pH was associated with Ag. Among fertilizer types, unfertilized pots maintained significantly higher pH compared with urea treatments. At the interaction level, WF–Fe_xO_x (without fertilizer + Fe_xO_x) recorded the highest pH, whereas BF–Fe_xO_x (biofertilizer + Fe_xO_x) showed one of the lowest values. Electrical conductivity (EC): The highest EC was detected in No NPs, whereas ZnO NPs resulted in the lowest. Urea fertilization enhanced EC compared to no fertilizer. The interaction confirmed that U treatments produced the highest EC values, while BF–ZnO remained the lowest (Table 4).

Potassium (K): The maximum K content was found with TiO₂, while No NPs had the lowest. Biofertilizer tended to increase K, whereas urea reduced it. The combination BF–Fe_xO_x yielded the highest interaction mean, whereas U–Fe_xO_x showed the lowest. Phosphorus (P): Treatments with TiO₂ NPs showed the highest P, significantly higher than Ag NPs. Urea enhanced P availability compared with biofertilizer. At the interaction level, U–ZnO yielded the maximum P value, while WF–Ag was the lowest. Nitrogen (N): The greatest N concentration was recorded with Fe_xO_x NPs, while the lowest was observed with no NPs. Urea fertilization enhanced N compared with unfertilized treatments (0.24%). At the interaction level, U–Ag produced the highest means, while the control remained the lowest (Table 4).

Carbon (C): The highest C values were observed with ZnO NPs, while no NPs recorded the lowest. Urea maintained higher C compared with biofertilizer. The interaction revealed that U–TiO₂ yielded the maximum C, whereas BF had the lowest. TOC and SOM: No significant differences among NPs were observed for total organic carbon (TOC) or soil organic matter (SOM). However, at the interaction level, BF–Fe_xO_x recorded the highest TOC and SOM, while biofertilizer alone showed the lowest (Table 4).

3.4.2. Sampling at 130 DAS (Harvest)

Two-way ANOVA showed significant yield effects for NPs, fertilizer, and their interaction. This indicates that yield was highly dependent not only on the individual factors but also on their combined application. Both NPs and fertilizer significantly influenced shoot length. A strong interaction confirmed that the effect of fertilizer on shoot growth was strongly modulated by the type of NP applied. In terms of root length, NPs and fertilizer exerted significant effects, with an additional significant interaction. This suggests that root development responded markedly to combined management with NPs and fertilizers (

Table 6. Two-way ANOVA results (NPs × fertilizer) (α ≤ 0.05) for soil and harvest parameters at 130 DAS. Greenhouse experiment with Triticum durum Desf. Samayoa C2004. Greenhouse experiment with Triticum durum Desf. Samayoa C2004.

	Variable
	Yield			Shoot			Root
Source of variation	Type of NPs	Fertilizer	NPs × Fertilizer	Type of NPs	Fertilizer	NPs × Fertilizer	Type of NPs	Fertilizer	NPs × Fertilizer
SS X 102	1,723,777.23	2803.04	4132.59	1,525,464.93	155,359.09	1,647,142.13	519,294.93	521,781.42	450,620.80
df	4	2	8	4	2	8	4	2	8
F	112.62	36.63	13.50	18.18	3.70	9.81	6.67	13.41	2.90
p-value	≤0.001	≤0.001	≤0.001	≤0.001	0.029	≤0.001	≤0.001	≤0.001	0.007
	pH			EC			K
SS X 102	271.45	90.74	60.81	581.4	548.8	499.0	4.7	0.2	15.4
df	4	2	8	4	2	8	4	2	8
F	41.65	27.85	4.67	15.96	30.12	6.85	3.91	0.25	6.37
p-value	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	0.006	0.780	≤0.001
	P			N			C
SS X 102	0.61	0.01	0.07	11.76	36.31	17.15	541.64	254.20	228.28
df	4	2	8	4	2	8	4	2	8
F	65.34	2.29	3.80	1.07	6.60	0.78	6.96	6.53	1.47
p-value	≤0.001	0.108	≤0.001	0.378	0.002	0.622	≤0.001	0.002	0.184
	TOC			SOM
SS X 102	239.96	100.82	146.36	603.05	183.83	335.95
df	4	2	8	4	2	8
F	13.45	11.30	4.10	24.21	14.76	6.74
p-value	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001

).

Soil pH: Highly significant differences were observed for NPs and fertilizer. The interaction demonstrated that the magnitude of pH changes depended on specific NP–fertilizer combinations. Electrical conductivity (EC): EC was significantly affected by NPs, fertilizer, and their interaction. This indicates a clear synergistic effect of NPs and fertilizers on soil salinity levels (Table 6).

Potassium (K): A significant effect of NPs was detected, while fertilizer alone was not significant. However, the interaction was highly significant, suggesting that the impact of fertilizer on K availability depended strongly on the NP applied. Nitrogen (N): NPs, fertilizer, and their interaction all had significant effects. These results highlight the robust role of NP–fertilizer combinations in regulating N availability at late growth stages. Carbon (C): Soil C content was influenced by NPs, but not significantly by fertilizer. However, the interaction was significant, indicating that C dynamics depended on specific NP–fertilizer treatments (Table 6).

TOC accumulation was sensitive to both nanoparticles and fertilizers, but the most important effect was the synergistic interaction. This suggests that organic C stabilization in soil depends strongly on matching the right NPs with the proper fertilization strategy. SOM dynamics were influenced even more strongly than TOC. The results confirm that nanoparticles can enhance or suppress SOM depending on the fertilizer type. The highest SOM was achieved under specific NP–fertilizer combinations, emphasizing the importance of integrative management practices for soil quality. This indicates that soil organic fractions are highly responsive to nanofertilizer strategies, with potential implications for long-term soil fertility and carbon sequestration (Table 6).The multiple comparison test (Tukey’s HSD, p ≤ 0.001) revealed clear differences among nanoparticles (NPs), fertilizer types, and their interactions in relation to crop performance and soil quality indicators.

Grain yield was maximized under Fe_xO_x NP treatments with biofertilizer. BF–FexOx showed the best yield, and the lowest yield was recorded with Ag NPs under urea fertilization. Shoot biomass showed a similar trend, where Fe_xO_x NPs and BF–ZnO promoted superior growth, while WF–Ag resulted in the poorest performance. Root biomass was highest with BF–TiO₂, confirming the strong positive effect of TiO₂ NPs under organic fertilization, while urea alone produced the lowest root development (Table 5).

Soil pH increased significantly under WF–TiO₂, while the lowest values were observed in the treatments without fertilizer and urea-only treatments. Electrical conductivity was maximized with U–ZnO. In comparison, the lowest EC was recorded without fertilization, suggesting a strong effect of ZnO NPs combined with N input (Table 5).

Potassium availability peaked in WF–Fe_xO_x, but was lowest under U–Ag. Phosphorus was maximized in U–ZnO, whereas WF–Ag had the lowest levels. Nitrogen did not exhibit significant NP effects at 99% CI, though fertilizer played a role: unfertilized soils maintained higher N compared to biofertilizer. Carbon concentration was significantly higher in U–ZnO, while Ag treatments and biofertilizer alone showed the lowest values (Table 5).

Total organic carbon (TOC) and soil organic matter (SOM) followed a similar pattern. WF–TiO₂ and ZnO NP treatments consistently produced the highest values. In contrast, without fertilizer and biofertilizer alone were classified as the lowest groups. These results highlight the ability of TiO₂ NPs and ZnO NPs, particularly under no fertilization, to enhance soil organic pools (Table 5).

4. Discussion

The observed growth with biofertilizer was influenced by the bacteria’s sensitivity to the medium and other contributing factors.

Overall, at 60 DAS, the Tukey test confirmed that TiO₂ and Fe_xO_x NPs combined with urea fertilizer tended to enhance nutrient availability (P, N, K, and C), while ZnO NP and Ag NP treatments under unfertilized or biofertilizer conditions generally showed lower values. Soil organic fractions (TOC and SOM) were less sensitive to individual NP effects, but certain NP × fertilizer combinations significantly modified their content.

ZnO NPs constitute a source of Zn as a micronutrient and have been shown to increase chlorophyll content, enhance the assimilation of Zn, B, and Cu, and promote the accumulation of K and Fe. In addition, they stimulate the activity of key enzymes such as nitrate reductase, phosphatase, and phytase (dose 2 g L⁻¹), which is consistent with this research, where the phosphate content was higher with this type of NP and likewise the SOM [33].

Similarly to this experiment, another study demonstrated that the application of Fe_xO_x NPs at doses of 1 to 10 mg kg⁻¹ of soil enhanced microbial metabolism by stimulating enzymatic activity, particularly at low and medium concentrations (0.1 and 1 mg kg⁻¹, respectively). Likewise, these NPs increased the nitrification potential and promoted bacterial abundance in winter wheat (Triticum aestivum L.) crop [34].

In this study, we observed a negative effect of Ag NPs, consistent with a previous study that reported doses higher than 1 mg Ag NPs kg⁻¹ of soil negatively affected the metabolism of soil microorganisms. That experiment was conducted in wheat crops [34].

The use of Bacillus subtilis in wheat crops (Triticum aestivum L.) has been implemented with a dual purpose: to act as a biological control agent against plant diseases and, at the same time, to enhance the absorption of phosphorus (P) and other nutrients. This effect is not limited solely to the solubilization of P, but also enhances its assimilation by the plant, as observed in this study, where biofertilizer increased shoot and root length and yield [35,36].

Studies conducted in China, one of the main producers of this cereal, demonstrated that the application of 20 kg ha⁻¹ of biofertilizers can reduce the need for synthetic fertilizers (NPK) by up to 50%, which in turn significantly lowers production costs [2].

As in this study, Fe_xO_x NPs have shown positive effects on wheat crops when used as foliar nanofertilizers. To counteract iron deficiency in calcareous soils with high pH, they have been applied at doses of 0.01 to 0.04% by weight based on 1000 g of wheat seed [37]. These NPs have shown benefits such as increased plant and root growth, higher biomass, and a greater concentration of photosynthetic pigments [38].

The contrasting performance of Fe_xO_x and ZnO nanoparticles reflects differences between greenhouse and field conditions. In greenhouses, Fe_xO_x NPs remain more stable, release Fe gradually, and interact more effectively with beneficial microbes under moderated pH and limited leaching, enhancing iron uptake and plant growth. In open-field conditions, however, soils are often more alkaline and subject to greater stress, where ZnO NPs provide a steadier supply of Zn²⁺, correct zinc deficiencies, and strengthen antioxidant defenses against drought and heat. Similar to our results, the literature indicates that Fe_xO_x NPs are more effective in controlled environments, while ZnO NPs perform better under field conditions, underscoring the need to match nanoparticle types to cultivation settings and soil properties [31].

The experiment demonstrated the potential of biofertilizers combined with nanoparticles (NPs), particularly Fe_xO_x, as a promising alternative to urea-based fertilizers. These findings are consistent with previous studies showing that Fe_xO_x NPs promote wheat growth, with optimal effects observed at doses below 50 mg·kg⁻¹. In particular, Fe_xO_x NPs have been reported to enhance shoot and spike development [39].

In this study, the treatments with biofertilizer yielded the best results at harvest time. The use of plant growth-promoting bacteria (PGPB) as biofertilizers combined with Fe_xO_x NPs enhanced the efficacy of the biofertilizer due to the synergy between them because NPs improve the physiological functions of both plants and bacteria [40]. Benedetti et al. [41] observed that these NPs increased colony growth by up to 14%/mg NPs. Additionally, they enhanced germination. Besides, in soils with low mineral content, the energy required by plants for their processes increases. Iron (Fe) is also essential for improving soil microorganism metabolism and enhancing the bioavailability of this mineral for plants.

The use of biofertilizers helped mitigate heat stress, particularly in areas where water easily evaporates, such as Pseudomonas colonies, which, being phosphate solubilizers, increase drought tolerance. According to Eliaspour et al. [40], if this genus alone is insufficient, Azotobacter can be used to counteract these effects. Their study found that the combination of Pseudomonas with mycorrhizae yielded the best results, followed by using the inoculum alone similar to the results of this study results.

The use of biological compounds as biofertilizers in conjunction with nanoparticles (NPs) enhances the biocompatibility, stability, and efficacy of the NPs. Microorganisms solubilize zinc by producing organic acids such as citric, lactic, acetic, or gluconic acids to lower the pH of the environment, as an acidic medium improves NP dissolution. According to Guardiola-Márquez et al. [42], some plants can acidify the soil through cation exchange, where Fe³⁺ is reduced to Fe²⁺ to facilitate root absorption through the cell membrane. Farid et al. [43] found that biofertilizers containing Bacillus can increase the soil’s potassium (K) content by forming acids, which promote plant assimilation and directly influence electrical conductivity (EC) and soil organic matter (SOM), regardless of whether the inoculum was used alone or supplemented with minerals.

This study’s results were in accord with a study of Guardiola-Márquez et al. [42], where it was observed that in alkaline soils, pH decreases the availability of Zn or Fe, even if these minerals are present in the soil and accessible to plants, and Furqan et al. [39], who noted that these NPs increase shoot and spike growth, leading to a higher number of grains per plant and consequently better yield.

Dimpka et al. [44] observed that applying NPs at lower doses than bulk formulations yields similar or better results at a lower cost due to reduced doses. The yields in that study, where urea was used in combination with ZnO NPs, demonstrated that, despite a lower loss of urea, wheat yield was better than with bulk combinations. In the present study, the yield with the combination U–Fe_xO_x was second best, and the combination of BF–Fe_xO_x was the best.

5. Conclusions

In this greenhouse study, the biofertilizer—particularly when combined with nanoparticles (NPs) such as Fe_xO_x and ZnO—proved to be highly effective compared with urea, leading to measurable improvements in multiple soil and crop parameters. For instance, while the expected maximum yield was 8.3 Mg ha⁻¹, this study achieved 8.48 Mg ha⁻¹ under combined treatments, while the control treatment yield was 3.40 Mg ha⁻¹. Stem length increased markedly with the application of the biofertilizer and ZnO NPs (95.33 cm), and root length also reached higher values with the biofertilizer alone (40.38 cm). The ability of Fe_xO_x and ZnO NPs to enhance the biocompatibility and stability of the biofertilizer highlights their strong potential to optimize soil nutrition, plant growth, and the accumulation of key elements such as K, P, and soil organic matter (SOM). These findings support the recommendation to integrate biofertilizers and Fe_xO_x/ZnO NPs into agricultural practices as an efficient, cost-effective strategy for improving crop performance and sustainability. Moreover, the observed synergistic effects between biofertilizers and metal-based NPs underscore the relevance of adopting innovative nanotechnologies to enhance resource use efficiency in semiarid cropping systems. Nevertheless, it is crucial to recognize the need for carefully designed, long-term field experiments to confirm the consistency, safety, and environmental implications of these technologies before their large-scale adoption. Overall, this study provides evidence that combining biofertilizers with selected nanoparticles can serve as a promising and sustainable alternative for future wheat production systems.