Effect of Application of Nitrogen Fertilizer, Microbial and Humic Substance-Based Biostimulants on Soil Microbiological Properties During Strawberry (Fragaria × ananassa Duch.) Cultivation

Abstract

Plant biostimulants have been the subject of intense interest in recent years. The aim of this study was to assess, during the years 2021–2022, the effect of mineral nitrogen (N) fertilizer, experimental (PGPB) and commercial (G) microbial biostimulants, and humic substance product (A) on the soil microbial communities, and yield of strawberries, under field conditions. Dehydrogenase activity was significantly affected by nitrogen fertilization, but an increase occurred in the treatment N+G. The treatments N+G, N+G+A, and N+PGPB+A increased FDA hydrolysis, and phosphatase activity. All plant biostimulants increased basal as well as substrate-induced respiration. Culturable bacteria (total counts, dormant forms, actinomycetes) were not clearly affected by treatment. Based on 16S rRNA analysis, bacterial community composition was different in N+PGPB+A and N+G+A treatments. The number of cultivable fungi was significantly lower in N+PGPB and N+PGPB+A treatments. The genus of fungi Pilidium, a potential phytopathogen of strawberries, was present in the second year, but in these treatments, it was absent. In the second year, strawberry yield was shown to be 95% higher in the N+PGPB+A treatment than in the control. Microbial biostimulants in combination with humic substances represent a potential solution in increasing strawberry production.

1. Introduction

The strawberry (Fragaria × ananassa Duch.) is one of the most popular horticulture crops belonging to the Rosaceae family, subfamily Rosoideae [1]. Strawberries are a rich source of a wide variety of nutritive compounds such as sugars, vitamins (especially vitamin C), and minerals (K, P, Ca, Fe), as well as non-nutritive, bioactive compounds such as flavonoids, anthocyanins, and phenolic acids, which contribute to their nutritional and health benefits [2,3]. The overall demand for strawberries has been steadily increasing, which is associated with a 20% increase in the area under cultivation worldwide over the last decade [4]. According to the Food and Agriculture Organization of the United Nations, the largest strawberry producers in the world in 2022 include China (148,015 ha), Poland (31,300 ha), Turkey (22,272 ha), USA (22,784 ha), and Egypt (19,236 ha) [5]. In Slovakia, strawberries are grown on about 240 ha producing 1240 tons per year [5]. The increased consumption of strawberries leads to the need to develop and implement interventions that will result in higher plant productivity while maintaining plant health in a sustainable manner.

Nutrient management is a critical factor influencing strawberry yield and quality. To improve soil quality and optimize plant growth, various organic fertilizers such as manures, organic mulches, composts, vermicomposts, and plant biostimulants are applied in horticulture [6]. These organic inputs have been shown to positively affect plant growth, root development, flowering, fruit set, yield, and quality of strawberry [3]. The use of organic fertilizers reduces the chemical inputs to the environment while enhancing soil and environmental health status [7].

Plant biostimulant is generally referred to as any substance or microorganism that, when applied to a plant or soil, stimulates biological and chemical processes in the plant and/or associated microorganisms. European Union regulation 2019/1009 [8] defines biostimulants as products that, independent of the nutrient content of the product, are able to improve one or more of the following properties of the plant or plant rhizosphere: (i) nutrient-use efficiency, (ii) tolerance to abiotic stress, (iii) quality characteristics, and (iv) availability of limited nutrients in the soil and rhizosphere [9]. Biostimulants are classified into several groups, including humic substances (e.g., humic and fulvic acids), protein hydrolysates, seaweed and plant extracts, chitosan and other biopolymers, beneficial elements (e.g., silicon, selenium), beneficial fungi (e.g., arbuscular mycorrhizal fungi, Trichoderma), and beneficial bacteria (e.g., Azotobacter, Azospirillum, Rhizobium) [10]. Humic substances are derived from the microbial decomposition and chemical degradation of organic matter in the soil [11]. These carbon-rich amendments, commonly derived from manure, compost, vermicompost, lignite, coal, and peat, are widely used in agriculture [12]. The main stimulatory effects associated with the promotion of strawberry growth by humic substances are increased nutrient utilization [13,14] and reduced incidence of plant diseases [15]. Beneficial microorganisms, such as plant growth-promoting rhizobacteria, enhance plant growth through mechanisms such as biological nitrogen fixation, phytohormone synthesis, and nutrient solubilization (e.g., phosphorus and potassium) [16]. Initially used in organic farming, plant biostimulants are now widely used in conventional and integrated farming systems, as well as in field and greenhouse cultivation. Their use covers a wide range of crops, including vegetables, fruit trees, berries, vines, ornamentals, cereals, and turf [10].

Recent studies have reported positive effects of biostimulants on plant growth, yield crop quality parameters, and stress tolerance [17,18,19]. However, limited information is available on their effects on soil quality, especially on soil microbiological parameters from the rhizosphere of strawberries. Soil microbiological properties, such as microbial biomass, respiration, enzymatic activity, and microbial community structure, are important indicators of soil fertility [20].

The aim of this study was to evaluate the effects of mineral nitrogen fertilizers and plant biostimulants based on plant growth-promoting microorganisms and humic substances on the abundance, activity, and structure of microbial communities as well as on the yield of strawberries grown in humic soils.

2. Materials and Methods

2.1. Experiment Design and Treatments

The field trials were carried out on the experimental site of the Slovak University of Agriculture (SUA) in Nitra (48°18′53″ N, 18°5′15″ E) during two growing seasons from 2021 to 2022. Soil type on the site is fluvisol with a high clay content (particles ˂ 0.01 mm 65%) and a pH value of 6.83. According to an agrochemical analysis of the soil performed before the trial was established, the soil properties were the following: soil organic carbon 1.84%, humus 3.17%, total nitrogen 0.25%, inorganic nitrogen 18.1 mg/kg, total phosphorus 85 mg/kg.

The trial was set up in a randomized block design with three replications (plots) per treatment. The area of each plot was 3.24 m² with 9 strawberry plants in a 0.6 × 0.6 m pattern. Experimental plots were arranged in a 7 × 3 layout where a single treatment appeared only once in either columns or rows. Frigo seedlings of strawberries (variety JOLY) were transplanted to soil in March 2021 and grown for two seasons.

Seven experimental treatments were analyzed. A control treatment (C) without any fertilization was prepared to evaluate the fertilization effect. Other treatments included the application of mineral nitrogen fertilizer solely (N) or in combination with commercial microbial biostimulant Groundfix (N+G), experimental microbial biostimulant (N+PGPB), humic substance-based biostimulant Agriful (N+A), and microbial and humic biostimulants together (N+G+A, N+PGPB+A). Timeplan of treatments and soil sampling is summarized in

Table 1. Timetable of interventions during strawberry cultivation in each treatment.

Activity/Application	Date	C	N	N+G	N+PGPB	N+A	N+G+A	N+PGPB+A
40 kg N	19 March 2021		•	•	•	•	•	•
Transplantation	26 March 2021	•	•	•	•	•	•	•
Agriful	26 March 2021					•	•	•
PGPB	26 March 2021				•			•
Grounfix	26 March 2021			•			•
Soil sampling	12 April 2021	•	•	•	•	•	•	•
Agriful	12 April 2021					•	•	•
40 kg N	30 April 2021		•	•	•	•	•	•
Agriful	5 May 2021					•	•	•
PGPB	5 May 2021				•			•
Grounfix	5 May 2021			•			•
Agriful	28 May 2021					•	•	•
Harvesting	4–18 June 2021	•	•	•	•	•	•	•
Soil sampling	8 July 2021	•	•	•	•	•	•	•
40 kg N	16 March 2022		•	•	•	•	•	•
Agriful	23 March 2022					•	•	•
PGPB	23 March 2022				•			•
Grounfix	23 March 2022			•			•
Soil sampling	8 April 2022	•	•	•	•	•	•	•
Agriful	6 April 2022					•	•	•
40 kg N	18 April 2022		•	•	•	•	•	•
Agriful	21 April 2022					•	•	•
PGPB	21 April 2022				•			•
Grounfix	21 April 2022			•			•
Agriful	4 May 2022					•	•	•
Harvesting	23 May–17 June 2022	•	•	•	•	•	•	•
Soil sampling	2 July 2022	•	•	•	•	•	•	•

• indicates that activity occurred on a selected date for a particular treatment.

.

Basic nitrogen fertilization was applied at a dose of 80 kg N per hectare per year in the form of DASA fertilizer divided into two applications. DASA is a granular fertilizer containing 26% N in the form of an ammonium sulfate and an ammonium nitrate. Groundfix is a commercial microbial biostimulant containing Bacillus subtilis, Bacillus megatherium var. phosphaticum, Azotobacter chroococcum, Enterobacter sp. and Paenibacillus polymyxa. The total number of living microorganisms is 1.0 × 10⁹ CFU/mL. This biostimulant also contains other beneficial microorganisms (Lactobacilli, enzyme-producing bacteria), vitamins, phytohormones, amino acids, and other physiologically active substances. The experimental microbial biostimulant PGPB was designed at the Institute of Biotechnology, SUA, in Nitra. It contains five bacterial strains with previously tested PGP properties [17,21] in a concentration of 1 × 10⁹ cells or spores/mL. The used microbial strains were identified as Bacillus pumilus, Bacillus arybhattai, Streptomyces puniceus, Streptomyces olivochromogenes, and Azotobacter sp. Agriful is a humic substance product containing humic acids 25% and fulvic acids 25%. The nitrogen content is 4.5%, phosphorus content (P₂O₅) 1.0%, potassium content 1.0% (K₂O) 1.0%, and organic matter 45.0%. Each of the biostimulants was suspended solely or in combination in a concentration of 1% in irrigation water. Then, 1 L of suspension per plant was applied directly to each strawberry plant crown as irrigation. On each application date, plants were irrigated with either clean irrigation water or water containing biostimulants or their mixture to ensure the same condition.

Soil cultivation and weed control were carried out by manual hoeing. During the vegetation, additional sprinkler irrigation was applied as required, based on climatic conditions and soil moisture status. Supplementary Table S1 shows the total rainfall and average air temperature on the experimental site in the years 2021 and 2022.

2.2. Soil Sampling and Analyses

Soil samples for analysis of microbiological parameters were collected on four dates April 2021, July 2021, April 2022, and July 2022 (Table 1). Soil was collected with a sterile core sampler from a depth of 0.00–0.15 m in the area of the root system of the strawberry plants. Five sub-samples were collected from a single plot and then mixed together. Soil from each experimental plot was analyzed separately; this means that all analyses were performed in three biological replications. The samples were stored at 4 °C until analysis. The actual soil moisture and maximum water capacity were determined by the method of Cassel and Nielson [22].

The plate dilution method was used for evaluation of microbial counts using various media. Plate count agar was used for total bacterial count and dormant forms (inoculum was heated at 80 °C for 10 min). Pochon’s medium was used for cultivation of actinomycetes and malt extract agar for microscopic filamentous fungi. Plates were cultivated at 30 °C for 72 h in case of total counts and dormant forms. Actinomycetes and microscopic fungi were cultivated at 25 °C for 7 days.

Three enzymatic activities (dehydrogenase activity, phosphatase activity, and hydrolysis of fluorescein diacetate) were measured in soil samples. Dehydrogenase activity (DHA) was determined using the assay of Casida Jr. et al. [23] as the reduction of triphenyltetrazolium chloride (TTC) to triphenylphormazan (TPF). The concentration of TPF was calculated according to the calibration curve and expressed as µg TPF per 1 g dry soil matter. Fluorescein diacetate hydrolysis (FDA) was measured as described by [24]. The concentration of released fluorescein was calculated according to the standard curve and expressed as µg FDA per 1 g dry soil matter per 3 h. Phosphatase activity (PA) was determined according to the Tabatabai and Bremner [25] method.

Microbial biomass carbon (Cmic) was determined following the fumigation extraction (FE) method [26]. Microbial respiration (basal and potential) was measured according to Alef and Nannipieri [27] as CO₂ sorption in a closed system on the 1 L jar for 24 h. Potential respiration was measured in the presence of glucose (2 g/kg soil). Results of basal and potential respiration were expressed as µg CO₂-C per g dry soil per hour. The metabolic quotient (qCO₂) was calculated as the amount of CO₂ mineralized per unit of microbial biomass carbon per hour (µg CO₂-C/mg Cmic/h) [28].

DNA was extracted from 0.25 g of soil using DNeasy^® PowerSoil^® kit (Qiagen, Hilden, Germany). The prokaryotic microbial community was analyzed using the V4 region of the 16S rRNA gene while ITS2 was used for assessing fungal diversity. Primer pairs 515F and 806R [29] and gITS7 and ITS4 [30] were used for prokariotic and fungi, respectively. PCR amplification was conducted using Q5 polymerase (New England Biolabs, Ipswich, MA, USA) according to manufacturer instructions. PCR products were controlled by gel electrophoresis, purified using AMPure XP reagent (Beckman Coulter, Brea, CA, USA), quantified by qubit HS DNA assay (Invitrogen, Waltham, MA, USA), and pooled together in an equimolar ratio. TruSeq LT PCR free kit (Illumina, San Diego, CA, USA) was used for sequencing library preparation. Sequencing was performed using MiSeq Reagent Kit v3 (600-cycle)on Illumina MiSeq sequencer (Illumina, San Diego, CA, USA).

2.3. Strawberry Yield

Strawberry fruits were collected five times during the harvesting season in 2021, and nine times in 2022. The total yield and average weight of a single fruit were calculated for each experimental plot.

2.4. Bioinformatics and Statistical Analysis

SEED2 environment ver. 2.1.4 [31] was used for demultiplexing of acquired sequences. Then, QIIME 2 pipeline version 2023.9 [32] using DADA2 algorithm [33] was used for denoising and ASV calling. Prokaryotic ASVs were identified using RDP classifier ver. 2.13 [34]. Fungal ITS2 region was extracted from ASVs by ITSextractor ver. 1.1.3 [35] and identified using the GenBank ITS fungal reference database. Then, diversity indices were calculated after rarefaction to the lowest sequence counts. ASVs were aligned using MAFFT ver. 7.487 [36] and weighted Unifrac ver. 1.8 [37] distances between samples were calculated. Beta diversity analysis including permutational analysis of variance PERMANOVA and non-metric multidimensional scaling (NMDS) was performed using Vegan package ver. 2.6-4 [38] in R environment, version 4.2.2 [39]. Before PERMANOVA, homogeneity was tested using betadisper in Vegan package. Differential abundance was assessed by LefSe ver. 1.16.0 [40]. For redundancy analysis (RDA), concatenated total sum scaled ASV tables of both prokaryotic and fungal communities were paired with all other measured variables, and significant variables were selected using backward selection.

Analysis of variance (ANOVA) followed by the Tukey test (significance level 0.05) was used to assess the effect of treatment and sampling on soil microorganism counts, enzymatic activities, respiration activities, and diversity indices strawberry yields. All statistical analyses were conducted in an R environment. Data used for ANOVA were checked for equivalence of variance using the Lavene test. ANOVA residuals were checked for normality using Shapiro–Wilk test. Prior ANOVA microbial counts were logarithmically transformed to ensure normal distribution.

3. Results

3.1. Soil Enzymatic Activity

Analysis of all three evaluated enzymatic activities (

Table 2. Effect of nitrogen fertilization and plant biostimulants application during strawberry cultivation on soil enzymatic activities.

Treatment	April 2021	July 2021	April 2022	July 2022	Average
Dehydrogenase activity in µg TPF/g soil/h
C	3.82 ± 0.07 1 a C 2	5.83 ± 0.22 c D	4.34 ± 0.41 a C	5.11 ± 0.07 b E	4.78 ± 0.82 C
N	1.82 ± 0.17 a A	3.75 ± 0.08 c B	2.08 ± 0.08 a A	2.98 ± 0.12 b AB	2.66 ± 0.80 A
N+G	3.51 ± 0.05 a C	8.35 ± 0.31 c E	3.30 ± 0.17 a B	4.07 ± 0.21 b D	4.81 ± 2.16 C
N+PGPB	2.00 ± 0.13 a A	4.96 ± 0.32 c C	2.58 ± 0.60 a AB	3.52 ± 0.09 b BC	3.27 ± 1.21 AB
N+A	1.76 ± 0.14 a A	3.51 ± 0.16 d B	2.43 ± 0.04 b A	2.84 ± 0.18 c A	2.64 ± 0.68 A
N+G+A	2.96 ± 0.23 a B	5.96 ± 0.15 c D	3.34 ± 0.18 ab B	3.86 ± 0.34 b CD	4.03 ± 1.23 BC
N+PGPB+A	2.14 ± 0.09 a A	2.72 ± 0.11 b A	2.37 ± 0.01 a A	3.47 ± 0.21 c BC	2.68 ± 0.54 A
Average	2.57 ± 0.82 a	5.01 ± 1.82 c	2.92 ± 0.78 a	3.69 ± 0.74 b
FDA hydrolysis in mg FDA/g soil/3 h
C	1.56 ± 0.16 a AB	1.52 ± 0.16 a AB	1.78 ± 0.07 a A	1.48 ± 0.10 a AB	1.59 ± 0.16 AB
N	1.32 ± 0.08 a A	1.25 ± 0.08 a A	1.69 ± 0.22 b A	1.29 ± 0.04 a A	1.39 ± 0.21 A
N+G	2.13 ± 0.09 c D	1.91 ± 0.08 ab B	2.03 ± 0.11 c AB	1.73 ± 0.12 a BC	1.95 ± 0.18 C
N+PGPB	1.42 ± 0.10 a AB	1.55 ± 0.12 a AB	1.56 ± 0.23 a A	1.29 ± 0.17 a A	1.46 ± 0.18 A
N+A	1.40 ± 0.08 a AB	1.36 ± 0.05 a A	1.59 ± 0.18 a A	1.30 ± 0.12 a A	1.42 ± 0.15 A
N+G+A	2.05 ± 0.23 a CD	1.96 ± 0.11 a B	1.93 ± 0.25 a AB	1.72 ± 0.02 a BC	1.92 ± 0.20 C
N+PGPB+A	1.76 ± 0.14 a BC	1.60 ± 0.34 a AB	2.36 ± 0.10 b B	1.84 ± 0.12 a C	1.89 ± 0.34 BC
Average	1.66 ± 0.33 ab	1.59 ± 0.28 ab	1.85 ± 0.31 b	1.52 ± 0.24 a
Phosphatase activityµg PNF/g soil/h
C	12.46 ± 0.45 c B	7.77 ± 0.35 a C	9.36 ± 0.46 b A	6.94 ± 0.24 a A	9.13 ± 2.23 AB
N	12.11 ± 0.19 d B	4.84 ± 0.60 a AB	8.52 ± 0.17 c A	7.30 ± 0.34 b A	8.20 ± 2.75 A
N+G	14.18 ± 0.42 c C	10.73 ± 1.05 b D	10.51 ± 0.21 b B	8.66 ± 0.20 a B	11.02 ± 2.14 C
N+PGPB	12.23 ± 0.67 c B	6.32 ± 0.09 a BC	8.96 ± 0.25 b A	6.77 ± 0.54 a A	8.57 ± 2.47 A
N+A	12.09 ± 0.20 c B	4.70 ± 0.29 a A	8.91 ± 0.64 b A	7.81 ± 0.69 b AB	8.38 ± 2.79 A
N+G+A	14.26 ± 0.34 c C	9.75 ± 0.56 a D	11.20 ± 0.44 b BC	8.83 ± 0.63 a B	11.01 ± 2.19 C
N+PGPB+A	10.15 ± 0.43 b A	9.70 ± 0.38 b D	11.74 ± 0.22 c C	8.77 ± 0.18 a B	10.09 ± 1.16 BC
Average	12.50 ± 1.38 c	7.69 ± 2.38 a	9.89 ± 1.23 b	7.87 ± 0.92 a

¹ Data are presented as mean ± standard deviation. ² Means followed by the same letters are not statistically different (ANOVA, Tukey test α = 0.05). Lowercase letters indicate differences between sampling dates (rows), and uppercase letters indicate statistically significant differences between treatments (columns).

) indicated that the treatment factors had a significant impact on the measured values. (p < 0.05). The addition of N-mineral fertilizer had a significant negative impact on dehydrogenase activity in all treatments in both experimental years with a 36 to 52% reduction in N treatments compared to the control (p < 0.05).

The addition of nitrogen-mineral fertilizer was found to have a significant (p < 0.05) negative impact on dehydrogenase activity with a reduction from 4.78 to 2.66 µg TPF/g soil/h in the N treatment in comparison to the control. The detrimental impact of nitrogen was similar when plant biostimulants, namely, PGPB and Agriful, were applied (p < 0.05). In April 2021, the effect was observed to be mitigated in the N+G (3.51) and N+G+A treatments (2.96). The positive impact of these treatments was observed to be consistent across all sampling periods. Moreover, the DHA levels were found to be even higher in the N+G treatment (8.35) than in the control (5.83) while the N+G+A treatment (5.96) was equivalent to the control in July 2021 (p < 0.05).

The addition of nitrogen in the form of mineral fertilizer did not affect FDA hydrolysis. The application of plant biostimulants was observed to have a positive effect on FDA hydrolysis, with the most pronounced results observed in April 2021 in the N+G (2.13 mg FDA/g soil/3 h) and N+G+A treatments (2.05) in comparison to control (1.56) or N (1.32) treatment (p < 0.05), as well as in the second cultivation season in the N+PGPB+A treatment (2.36 in April 2022 and 1.84 in July 2022).

The application of nitrogen fertilizer resulted in a negative impact (p < 0.05) on phosphatase activity in the N treatment (4.84 µg PNF/g soil/h) compared to the control (7.77), which was observed exclusively in July 2021. Once again, the N+G and N+G+A treatments were found to have a statistically significant (p < 0.05) effect on the activity. Furthermore, an increase was observed for the N+PGPB+A treatment from July 2021 to the end of the experiment.

All enzyme activities were affected by the soil sampling date, although the trend was not consistent across all activities. DHA was demonstrably lower for all treatments at the April sampling than at the post-harvest sampling. FDA hydrolysis was the least affected by sampling dates among all enzymatic activities. Significant differences (p < 0.05) between sampling dates were only found for N treatment and N+G treatment. The most pronounced effect was a demonstrable decrease in phosphatase activity after harvest.

3.2. Soil Respiration Activities

As illustrated in

Table 3. Effect of nitrogen fertilization and plant biostimulants application during strawberry cultivation on soil respiration activity and metabolic quotient.

Treatment	July 2021	July 2022	Average
Basal respiration in µg CO2-C/g soil/h
C	1.69 ± 0.08 1 a A 2	3.05 ± 0.04 b A	2.37 ± 0.75 A
N	2.23 ± 0.12 a B	3.19 ± 0.03 b AB	2.71 ± 0.53 B
N+G	2.28 ± 0.06 a B	3.57 ± 0.08 b D	2.93 ± 0.71 BC
N+PGPB	2.09 ± 0.11 a B	3.38 ± 0.04 b C	2.74 ± 0.71 B
N+A	2.57 ± 0.17 a C	3.29 ± 0.12 b BC	2.93 ± 0.42 BC
N+G+A	2.04 ± 0.05 a B	3.74 ± 0.03 b DE	2.89 ± 0.93 B
N+PGPB+A	2.66 ± 0.03 a C	3.78 ± 0.05 b E	3.22 ± 0.61 C
Average	2.22 ± 0.32 a	3.43 ± 0.27 b
Potential respiration in µg CO2-C/g soil/h
C	13.84 ± 0.27 a A	17.50 ± 0.14 b A	15.67 ± 2.01 A
N	18.61 ± 0.02 a D	20.36 ± 0.05 b CD	19.49 ± 0.96 C
N+G	14.91 ± 0.03 a B	20.17 ± 0.12 b BC	17.54 ± 2.88 B
N+PGPB	17.72 ± 0.71 a C	20.75 ± 0.07 b E	19.24 ± 1.72 C
N+A	18.91 ± 0.05 a D	20.50 ± 0.09 b D	19.70 ± 0.87 C
N+G+A	17.55 ± 0.19 a C	20.01 ± 0.06 b B	18.78 ± 1.35 BC
N+PGPB+A	16.99 ± 0.06 a C	21.60 ± 0.04 b F	19.29 ± 2.53 C
Average	16.93 ± 1.81 a	20.13 ± 1.21 b
Metabolic quotient in µg CO2-C/mg Cmic/h
C	2.35 ± 0.09 a A	3.45 ± 0.13 b AB	2.90 ± 0.61 A
N	4.33 ± 0.45 a AB	3.94 ± 0.11 a CD	4.14 ± 0.36 AB
N+G	4.72 ± 0.81 a AB	3.82 ± 0.28 a BC	4.27 ± 0.73 AB
N+PGPB	7.76 ± 1.60 b CD	3.28 ± 0.14 a A	5.52 ± 2.66 BC
N+A	8.94 ± 0.03 b D	4.30 ± 0.13 a D	6.62 ± 2.54 BC
N+G+A	5.42 ± 0.53 a BC	4.79 ± 0.00 a E	5.11 ± 0.48 ABC
N+PGPB+A	9.03 ± 1.63 b D	4.85 ± 0.15 a E	6.94 ± 2.51 C
Average	6.08 ± 2.55 b	4.06 ± 0.60 a

¹ Data are presented as mean ± standard deviation. ² Means followed by the same letters are not statistically different (ANOVA, Tukey test α = 0.05). Lowercase letters indicate differences between sampling dates (rows) and uppercase letters indicate statistically significant differences between treatments (columns).

, the addition of nitrogen and biostimulants led to an enhancement in the basal and potential respiration of microorganisms. It was observed that the application of nitrogen (N) without biostimulants resulted in a significant (p < 0.05) increase in basal respiration values (2.23 ± 0.12 µg CO₂-C/g soil/h), reaching 32% higher value than the control (1.69) in the first experimental year (based on two sampling periods). The application of nitrogen in conjunction with all plant biostimulants resulted in a significant (p < 0.05) enhanced respiration rate in comparison to the control (8 to 57%). The greatest increase was observed in both experimental years with the N+PGPB+A combination. In the second experimental year, the basal respiration rate was observed to be higher in all treatments (more than twice in treatments C, N+G, N+A, N+G+A) compared to the first year (p < 0.05).

Potential respiration reflects the capacity of microorganisms to degrade glucose. Application of nitrogen resulted in a demonstrable 34% increase in potential respiration, similar to basal respiration, compared to the control in the first experimental year. Of all the biostimulants, N+A treatment had the greatest effect on this parameter in the first experimental year with 18.91 ± 0.05 µg CO₂-C/g soil/h (up 37% compared to control), and N+PGPB+A in the second experimental year with 21.60 ± 0.04 µg CO₂-C/g soil/h (up 23% compared to control). Similarly, potential respiration was demonstrably higher in the second experimental year than in the first one (p < 0.05).

The soil of the experimental plot exhibited a high biomass of microorganisms (775 µg Cmic/g) at the beginning of the experiment. The addition of N and biostimulants resulted in a statistically demonstrable increase in qCO₂ compared to the control in treatments to which humic substance (N+A) was added and also in the combination of N+PGPB+A and N+G+A (p < 0.05). These results were confirmed in both experimental years (p< 0.05).

3.3. Numbers of Cultivable Microorganisms in Soil

The evaluated groups of cultivable microorganisms exhibited no clear influence from soil treatment; however, the effect of soil sampling was more prominent (

Table 4. Effect of nitrogen fertilization and plant biostimulants application during strawberry cultivation on the numbers of cultivable microorganisms in log CFU/g dry weight soil.

Treatment	April 2021	July 2021	April 2022	July 2022	Average
Total microbial count
C	6.05 ± 0.05 1 c A 2	6.23 ± 0.02 c B	5.43 ± 0.12 a AB	5.69 ± 0.07 b AB	5.85 ± 0.33 AB
N	6.13 ± 0.03 b A	7.30 ± 0.05 c C	5.43 ± 0.19 a AB	5.72 ± 0.15 a AB	6.15 ± 0.75 B
N+G	6.12 ± 0.07 bc A	5.93 ± 0.05 ab A	5.69 ± 0.13 a BC	6.23 ± 0.13 c C	5.99 ± 0.23 AB
N+PGPB	6.08 ± 0.06 bc A	6.35 ± 0.17 c B	5.65 ± 0.11 a BC	5.86 ± 0.10 ab ABC	5.99 ± 0.29 AB
N+A	6.06 ± 0.10 b A	6.21 ± 0.05 b B	5.26 ± 0.02 a A	5.58 ± 0.31 a A	5.78 ± 0.42 A
N+G+A	6.10 ± 0.08 b A	5.72 ± 0.03 a A	5.92 ± 0.06 b C	5.94 ± 0.10 b ABC	5.92 ± 0.15 AB
N+PGPB+A	6.07 ± 0.06 b A	6.15 ± 0.05 b B	5.34 ± 0.16 a AB	6.03 ± 0.11 b BC	5.90 ± 0.35 AB
Average	6.09 ± 0.06 bc	6.27 ± 0.48 c	5.53 ± 0.24 a	5.86 ± 0.25 b
Dormant forms count
C	6.09 ± 0.08 c AB	5.31 ± 0.09 a A	5.54 ± 0.05 b ABC	5.44 ± 0.07 ab AB	5.60 ± 0.32 A
N	6.26 ± 0.04 b C	5.63 ± 0.05 a ABC	5.60 ± 0.19 a BC	5.49 ± 0.15 a ABC	5.75 ± 0.33 ABC
N+G	6.36 ± 0.03 c C	5.85 ± 0.04 b BC	5.49 ± 0.15 a ABC	5.96 ± 0.11 b D	5.92 ± 0.33 C
N+PGPB	6.23 ± 0.05 c BC	6.05 ± 0.06 c C	5.35 ± 0.13 a AB	5.72 ± 0.10 b BCD	5.84 ± 0.36 BC
N+A	6.28 ± 0.04 c C	5.59 ± 0.24 b AB	5.21 ± 0.09 a A	5.43 ± 0.05 ab AB	5.63 ± 0.43 AB
N+G+A	6.04 ± 0.06 a A	5.72 ± 0.29 a ABC	5.72 ± 0.04 a C	5.80 ± 0.15 a CD	5.82 ± 0.20 BC
N+PGPB+A	6.06 ± 0.04 c A	5.71 ± 0.18 b ABC	5.31 ± 0.10 a AB	5.31 ± 0.14 a A	5.60 ± 0.35 A
Average	6.19 ± 0.12 c	5.70 ± 0.26 b	5.46 ± 0.20 a	5.59 ± 0.24 ab
Actinomycetes
C	4.57 ± 0.04 c B	4.35 ± 0.10 ab C	4.23 ± 0.12 a B	4.52 ± 0.05 bc A	4.42 ± 0.16 B
N	4.57 ± 0.09 b B	3.84 ± 0.18 a ABC	4.00 ± 0.17 a AB	4.80 ± 0.04 b C	4.30 ± 0.43 AB
N+G	4.72 ± 0.10 b B	3.46 ± 0.62 a AB	4.24 ± 0.06 ab B	4.61 ± 0.14 b ABC	4.26 ± 0.59 AB
N+PGPB	4.57 ± 0.07 b B	4.28 ± 0.08 a C	4.16 ± 0.06 a AB	4.57 ± 0.06 b AB	4.39 ± 0.19 B
N+A	4.32 ± 0.10 bc A	3.14 ± 0.17 a A	4.07 ± 0.12 b AB	4.67 ± 0.14 c ABC	4.05 ± 0.60 A
N+G+A	4.62 ± 0.07 b B	3.65 ± 0.27 a ABC	4.04 ± 0.14 a AB	4.49 ± 0.03 b A	4.20 ± 0.42 AB
N+PGPB+A	4.57 ± 0.06 c B	4.06 ± 0.00 b BC	3.87 ± 0.08 a A	4.77 ± 0.02 d BC	4.32 ± 0.39 AB
Average	4.56 ± 0.13 c	3.83 ± 0.48 a	4.09 ± 0.16 b	4.63 ± 0.13 c
Microscopic fungi
C	4.43 ± 0.05 ab A	4.48 ± 0.16 b BC	4.23 ± 0.07 a A	4.48 ± 0.02 b C	4.41 ± 0.13 A
N	4.02 ± 0.41 a A	4.21 ± 0.10 a ABC	5.73 ± 0.22 b B	4.20 ± 0.02 a A	4.54 ± 0.75 A
N+G	4.43 ± 0.09 ab A	4.57 ± 0.10 b C	4.16 ± 0.20 a A	4.67 ± 0.06 b D	4.46 ± 0.23 A
N+PGPB	4.50 ± 0.12 b A	3.87 ± 0.10 a A	4.42 ± 0.35 b A	4.29 ± 0.02 ab AB	4.27 ± 0.30 A
N+A	4.40 ± 0.14 ab A	4.07 ± 0.11 a A	4.33 ± 0.13 ab A	4.41 ± 0.13 b BC	4.30 ± 0.18 A
N+G+A	4.44 ± 0.13 a A	4.16 ± 0.21 a AB	4.45 ± 0.13 a A	4.51 ± 0.05 a CD	4.39 ± 0.19 A
N+PGPB+A	4.05 ± 0.36 a A	4.19 ± 0.13 a AB	4.33 ± 0.05 a A	4.20 ± 0.06 a A	4.19 ± 0.20 A
Average	4.32 ± 0.27 ab	4.22 ± 0.25 a	4.52 ± 0.54 b	4.39 ± 0.17 ab

¹ Data are presented as mean ± standard deviation. ² Means followed by the same letters are not statistically different (ANOVA, Tukey test α = 0.05). Lowercase letters indicate differences between sampling dates (rows) and uppercase letters indicate statistically significant differences between treatments (columns).

). In April 2021, no difference in total bacterial counts was observed between the treatments under investigation. The values ranged from 6.05 ± 0.05 log CFU/g d. m. soil in control to 6.13 ± 0.03 log CFU/g d. m. soil in N treatment. However, following the strawberry harvest (July 2021), the N+G and N+G+A treatments exhibited the lowest counts (5.93 ± 0.05 and 5.72 ± 0.03 log CFU/g d.m. soil, respectively) in comparison to the other treatments (p < 0.05). In contrast, in the second year, the N+G 6.23 ± 0.13 log CFU/g d. m. soil in July) and N+G+A (5.92 ± 0.06 log CFU/g d. m. soil in April) treatments exhibited statistically significantly higher (p < 0.05) total bacterial counts compared to the control (5.43 ± 0.12 log CFU/g d. m. soil in April 2022 and 5.69 ± 0.07 log CFU/g d. m. soil in July 2022).

The impact of the treatments on dormant bacterial forms exhibited variability across treatments. The N+PGPB+A treatment demonstrated the lowest total counts, statistically equivalent to the control, while the N+G treatment exhibited the statistically highest counts (except for April 2022. The highest number of dormant forms was identified in the initial sampling (April 2021), in comparison to subsequent samplings (p < 0.05).

The abundance of actinomycetes showed no clear effect of nitrogen and biostimulant treatments. In the first experimental year, the abundance of actinomycetes was significantly lower in the N+A treatment compared to the control (p < 0.05), but the difference evened out in the following year.

The microscopic filamentous fungi exhibited a relatively consistent population across the sampling period, with a slight increase observed in April 2022. The significantly highest fungal counts in this sampling were recorded in the N treatment (5.73 ± 0.22 log CFU/g d. m. soil). In July 2022, the lowest fungal counts were observed in the N (4.20 ± 0.02 log CFU/g d. m. soil), N+PGPB (4.29 ± 0.02 log CFU/g d. m. soil), and N+PGPB+A (4.20 ± 0.06 log CFU/g d. m. soil) treatments, while the remaining treatments demonstrated an increase in fungal abundance.

3.4. Microbial Communities in Soil

A total of 2,284,033 sequences of the 16S rRNA gene were collected for 84 samples. Following denoising, 3353 ASVs were identified. For fungi, a total of 2,159,936 sequences of the fungal ITS2 region were collected, comprising 2800 amplicon sequence variants (ASVs). The Shannon’s index of alpha diversity (

Table 5. Effect of nitrogen fertilization and plant biostimulants application during strawberry cultivation on Shannon’s diversity index (mean ± standard deviation).

Treatment	April 2021	July 2021	April 2022	July 2022	Average
Prokaryotic community
C	7.87 ± 0.19 1 bc B 2	6.95 ± 0.11 a B	8.06 ± 0.12 c C	7.33 ± 0.33 ab AB	7.55 ± 0.49 BC
N	8.05 ± 0.1 c B	7.57 ± 0.09 b CD	7.42 ± 0.18 b B	6.7 ± 0.12 a AB	7.44 ± 0.52 BC
N+G	7.68 ± 0.34 a AB	7.5 ± 0.12 a C	7.48 ± 0.07 a B	7.13 ± 0.89 a AB	7.45 ± 0.46 BC
N+PGPB	8.1 ± 0.11 b B	7.76 ± 0.19 b CD	8.06 ± 0.06 b C	6.9 ± 0.43 a AB	7.71 ± 0.55 C
N+A	7.59 ± 0.17 a AB	7.84 ± 0.05 ab D	8.03 ± 0.06 b C	7.46 ± 0.23 a B	7.73 ± 0.26 C
N+G+A	7.68 ± 0.22 b AB	6.39 ± 0.12 a A	6.58 ± 0.26 a A	6.29 ± 0.15 a A	6.74 ± 0.61 A
N+PGPB+A	7.15 ± 0.13 a A	7.58 ± 0.05 a CD	7.17 ± 0.27 a B	7.23 ± 0.15 a AB	7.28 ± 0.23 B
Average	7.73 ± 0.35 c	7.37 ± 0.50 b	7.54 ± 0.55 bc	7 ± 0.52 a
Fungal community
C	5.6 ± 0.51 c A	5.76 ± 0.37 c A	2.75 ± 0.49 b AB	1.07 ± 0.19 a AB	3.8 ± 1.84 A
N	5.63 ± 0.1 b A	5.65 ± 0.44 b A	2.76 ± 0.43 a A	2.22 ± 0.29 a AB	4.06 ± 1.59 A
N+G	5.53 ± 0.4 b A	5.73 ± 0.57 b A	3.55 ± 0.59 a B	4.24 ± 0.13 a C	4.76 ± 0.89 B
N+PGPB	5.41 ± 0.14 b A	5.78 ± 0.37 b A	4.89 ± 0.47 b C	4.32 ± 0.44 a C	5.1 ± 0.81 B
N+A	5.56 ± 1.14 ab A	5.76 ± 0.09 b A	3.37 ± 0.08 a AB	2.61 ± 0.12 a BC	4.33 ± 1.24 A
N+G+A	5.52 ± 0.15 b A	5.36 ± 0.10 b A	2.81 ± 0.12 a A	3.1 ± 0.07 a BC	4.2 ± 1.40 A
N+PGPB+A	5.46 ± 0.15 b A	5.58 ± 0.37 b A	5.64 ± 0.04 a C	4.48 ± 0.19 b C	5.29 ± 0.73 B
Average	5.53 ± 0.48 c	5.66 ± 0.2 d	3.68 ± 1.12 b	3.15 ± 10 a

¹ Data are presented as mean ± standard deviation. ² Means followed by the same letters are not statistically different (ANOVA, Tukey test α = 0.05). Lowercase letters indicate differences between sampling dates (rows) and uppercase letters indicate statistically significant differences between treatments (columns).

) for bacteria exhibited a range of 6.3 to 8.1, indicating significant alterations between sampling points and treatments. The lowest diversity was observed in the N+PGPB+A and N+G+A treatments. The Shannon’s index of fungal community demonstrated considerably higher variability, as illustrated in Table 5. The range remained stable at 5.36–5.78, with no discernible impact of treatment in 2021. A significant decline was observed in the 2022 samples, wherein the impact of the treatments became discernible. In April 2022, only the PGPB-containing treatments (N+PGPB and N+PGPB+A) demonstrated diversity values above four. In July 2022, the N+G treatment also exhibited values that were equivalent to those observed previously.

The results of the beta diversity analysis confirmed that both the bacterial and fungal communities were strongly dependent on the date of sampling. The NMDS scatterplots (Figure 1) illustrate that each sampling date is represented by a distinct cluster. The PERMANOVA analysis of the prokaryotic community confirmed that there were highly significant (p = 0.001) differences between the clusters, i.e., sampling dates, which collectively explained 27% of the total variability in Unifrac distances. The impact of the treatments was less pronounced (R² = 0.113), although still statistically significant (p = 0.001) with each treatment cluster within larger sampling dates clusters. Nevertheless, no significant differences were observed between the treatments in some pairs (see Supplementary Table S2). The most distant clusters were formed by the N+PGPB+A, which was significantly different from all other treatments (p values in the range of 0.001 to 0.039). Treatments N+G and N+G+A were different in comparison to control (p = 0.008 and 0.015, respectively).

Even though beta diversity analysis demonstrated the considerable impact of the sampling date on the fungal community, the 2021 samples formed clusters that are overlaid. The April 2022 samples were found to cluster according to the treatment, with the N+PGPB and N+PGPB+A treatments maintaining a fungal community similar to that observed in 2022, while the other treatments formed a separate cluster in the NMDS scatterplot. This caused longer distances between samples in the April 2022 sampling, and the homogeneity of sampling date clusters was thus significantly different (April 2022 vs. April 2021 and July 2021, p = 0.033 and 0.023, respectively). The effect of sampling explained 55% of the observed variability (p = 0.001), while only 9% of the variation could be attributed to the treatments (p = 0.001) with significant interactions (R²= 0.17; p = 0.001). However, it should be noted that the effect of sampling may be overestimated due to the significant difference in homogeneity as PERMANOVA may report increased differences in case of significant differences in homogeneity. The significant effect of the treatments was confirmed by the pairwise PERMANOVA comparison only between C and N treatment (p = 0.041), N and N+G (p = 0.044), N+G and N+PGPB (p = 0.043) N, and N+PGPB+A (p = 0.043) treatments, but the R2 values were low (see Supplementary Table S3).

The distribution of bacterial phyla is shown in the bar chart (Figure 2a), which demonstrates the strong effect of the sampling date. The most prevalent phylum, Actinomycetota, exhibited a significant (p < 0.001) decline in abundance, with a nearly threefold reduction observed between July 2022 and April 2022. Conversely, Bacteroidota and Planctomycetota exhibited significantly (p < 0.001) higher abundance in July 2022. The impact of the treatments is most evident in April 2022. The most prevalent changes are observed in the N+PGPB+A and N+G+A treatments.

The bar chart of fungal classes (Figure 2b) illustrates a notable increase in the prevalence of Leotiomycetes from 12–13% in 2021 to 46–58% in 2022 and an accompanied decline in the occurrence of Sordariomycetes from 43–27% in 2021 to 12–21% in 2022), Agaricomycetes (from 8% in 2021 to 2% in April 2022 and 0.004% in July), and Tremellomycetes (from 5% in 2021 to 3.2% in April 2022 and 0.007% in July).

Heatmap (Figure 3a) of the most common prokaryotic genera (or lowest possible taxonomic levels) shows the most prevalent groups were Acidobacteria GP6 (varied from 8 to 28% according to sample date and treatment), Nitrososphaera (2–16%), Gaiella (3–12%), and Tepidisphaera (3–13%). Once again, pronounced clustering according to the sampling date is evident on the heatmap. Variability in the abundance of genera between sampling dates and separately among treatments in each sampling date is shown in Supplementary Figures S3–S7. Among the other above-mentioned genera are those significantly affected by treatments according to their high linear discriminant analysis (LDA) score.

A heatmap of the most common fungal genera (Figure 3b) illustrates the rapid decline of fungal diversity in 2022. The principal reason for this discrepancy is the high prevalence of the Pilidium genus in 2022. In 2021, the abundance of Pilidium was below 5%, while other genera, including Fusarium (12–36%), Cladosporium (6–12%), Chaetoscypha (3–9%), and Alternaria (3–7%), were more prevalent. In 2022, however, the most common ASV of Pilidium constituted over 70% of the fungal community in some samples. The Pilidium genus is represented at a significantly lower level in the N+PGPB (18%) and N+PGPB+A (8%) treatments during the April 2022 sampling period. This trend persists in the July 2022 sampling (35 and 34%, respectively), where the Pilidium genus is less dominant in both treatments and the N+G treatment (35%). Additionally, an increased abundance in samples was recorded for Didimella (3–13% in 2022 and 2–3% in 2021) and Paraphoma (rise from less than 1% to 1–16% in 2022). Lefse LDA score dot plots for each sampling date are in Supplementary Figures S8–S12.

Redundancy analysis (RDA) of constrained data (Figure 4) shows basal respiration, potential respiration, and yield linked to microbiome composition in both years. In the year 2021, Dehydrogenase activity and FDA hydrolysis were also significant.

The strawberry yield (

Table 6. Effect of nitrogen fertilization and plant biostimulants application on the strawberry yield and the weight of a single fruit.

Treatment	2021	2022	Average
Total yield in t/ha
C	0.90 ± 0.19 1 a A 2	2.25 ± 0.17 b A	1.58 ± 0.76 A
N	0.86 ± 0.09 a A	2.56 ± 0.51 b A	1.71 ± 0.98 A
N+G	1.00 ± 0.10 a A	2.98 ± 0.46 b AB	1.99 ± 1.12 AB
N+PGPB	0.78 ± 0.13 a A	3.39 ± 0.46 b AB	2.09 ± 1.46 AB
N+A	0.90 ± 0.16 a A	3.07 ± 1.12 b AB	1.98 ± 1.39 AB
N+G+A	1.05 ± 0.13 a A	3.56 ± 0.54 b AB	2.31 ± 1.42 AB
N+PGPB+A	1.15 ± 0.42 a A	4.38 ± 0.66 b B	2.77 ± 1.83 B
Average	0.95 ± 0.21 a	3.17 ± 0.84 b
Average weight of a single fruit in g
C	8.81 ± 1.68 a A	7.68 ± 0.38 a A	8.25 ± 1.30 A
N	9.53 ± 1.70 a A	8.62 ± 0.74 a A	9.07 ± 1.28 A
N+G	10.20 ± 0.58 b A	8.12 ± 0.66 a A	9.16 ± 1.27 A
N+PGPB	8.08 ± 0.52 a A	8.93 ± 0.47 a A	8.50 ± 0.64 A
N+A	10.33 ± 0.77 a A	9.69 ± 1.97 a A	10.01 ± 1.38 A
N+G+A	10.05 ± 0.67 a A	10.26 ± 1.71 a A	10.16 ± 1.17 A
N+PGPB+A	8.70 ± 1.15 a A	9.47 ± 0.65 a A	9.09 ± 0.94 A
Average	9.39 ± 1.24 a	8.97 ± 1.26 a

¹ Data are presented as mean ± standard deviation. ² Means followed by the same letters are not statistically different (ANOVA, Tukey test α = 0.05). Lowercase letters indicate differences between sampling dates (rows) and uppercase letters indicate statistically significant differences between treatments (columns).

) in the experimental plot, expressed in t/ha, did not differ between treatments. In the second year of cultivation, the yield in the N+PGPB+A treatment was demonstrably higher than the yield in the control (95% higher) as well as in the N-only treatment (72% higher). The weight per fruit was not affected by the application of N and biostimulants.

4. Discussion

Soil microorganisms play a crucial role in the functioning of ecosystems, influencing biogeochemical cycles and, consequently, the availability of nutrients and soil fertility [41]. They exhibit high responsiveness and sensitivity to management practices, including mineral fertilization, pesticide application, and microbial products [42]. The activity and population dynamics of microorganisms in soil are also dependent on environmental factors and are highly variable over time. In our experiment, this variability was reflected in the different compositions of the microbial community and its activity at four dates. As a result, it was challenging to assess the effect of individual treatments, with the possibility that the effects of some treatments may have been masked by this variability.

The soil of our experimental plot was characterized by high soil organic carbon (SOC), humus, total nitrogen, and high activity of microorganisms. Higher SOC and humus content may provide sufficient substrate to support higher microbial biomass and thus higher enzyme production [43], which was confirmed in our experiment. Dehydrogenases play a significant role in the biological oxidation of soil organic matter by transferring hydrogen from organic substrates to inorganic acceptors [44]. Pukalchik et al. [45] reported that the activity of oxidative enzymes, which include DHA, usually decreases after the addition of humic products, which is also in agreement with our results. Latkovic et al. [46] also described the inhibition of dehydrogenase and proteinase activity as well as microbial biomass carbon in soil under wheat and maize in treatments with high rates of NPK nutrients. They also describe the stimulation of DHA and proteinase activity by low rates of NPK and bacterial inoculants (Enterobacter spp. strains and Klebsiella planticola). Also in our experiment, we confirmed that the negative effect of N on DHA was partially mitigated by the addition of a commercial biostimulant, even in combination with a humic substance. The reversal of the negative effect of N treatment can be explained by the fact that inoculation with microbial biostimulants will increase the colonization of biostimulant-derived microorganisms mainly to the rhizosphere region, leading to higher accumulation of available nutrients as a substrate for dehydrogenase activity [47]. FDA hydrolysis measures the activity of nonspecific esterases, proteases, and lipases involved in the decomposition of soil organic matter. Since more than 90% of the energy flux in the soil ecosystem is passed by microbial decomposers and is dominated by heterotrophic microorganisms, it is assumed that FDA hydrolysis reflects the overall microbial activity of the soil [20]. In our experiment, the application of a commercial stimulant alone, as well as with a humic substance, positively influenced FDA hydrolysis. The positive effect of biostimulant treatment on FDA hydrolysis in rice cultivation was reported by Buragohain et al. [48]. On the other hand, Soltaniband et al. [49] found that there was no increase in FDA hydrolysis when various algae, fungi, and bacteria-based plant biostimulants were applied in strawberry cultivation compared to control. Both plant biostimulants of microbial origin when combined with humic substances were shown to enhance phosphatase activity (treatments N+G+A and N+PGPB+A). In blueberry cultivation, a 43% increase in phosphatase activity occurred with the treatment of the microbial consortium Oiko bac 174 containing beneficial bacteria and fungi, and Biosolve (Oikos Chile Ltd.) containing humic substances [50].

The determination of CO₂ production is one of the effective methods used for assessing the overall microbial activity in soil [51], which records the catabolic degradation of native organic substances under aerobic conditions [52]. In general, basal respiration is high in the presence of an active microbial community and an abundance of readily mineralizable organic matter. The soil of the experimental trial met these requirements, and the limiting element for increased mineralization of organic matter was N. Therefore, the application of N alone but also N with all plant biostimulants promoted both basal and potential respiration compared to the control. The microbial metabolic quotient qCO₂ used as a so-called eco-physiological indicator provides information on the catabolic requirements of the microbial community for a certain short period [53]. In soils with a neutral soil reaction, qCO₂ values range from 0.5 to 2.0 µgCO₂-C/mg Cmic/h, according to Anderson and Domsch [54]. All of our determined qCO₂ data, for soils with the same pH, had qCO₂ values higher than those reported by these authors. The lowest mean qCO₂ value in the control was 2.90 ± 0.61 µgCO₂-C/mg Cmic/h. Anderson [55] points out that when qCO₂ values are higher than two, it indicates a less energy-efficient use of organic substrates by microbial biomass. In our experiment, the addition of N and biostimulants resulted in even further statistically demonstrable increases in qCO₂ to values greater than 4 and 9 in 2022 and 2021, respectively. N+A, N+G+A, and N+PGPB+A treatments contributed to the observed increase in both experimental years. The results showed that the added humic substances were not utilized by the microorganisms to support their biomass increase but for energy acquired by respiration. As the microorganisms were not exposed to substrate limitation their carbon utilization efficiency decreased [56].

Although the accuracy of culture methods in estimating microbial community size is affected by the relatively large fraction of nonculturable microorganisms, it represents a simple and comparable method of estimation [57].

Soils with a high stock of soil organic matter provide a favorable environment for the presence of an abundant microorganism community. Soils with a high organic matter content have sufficient nutrients and a naturally rich microbiome. In our opinion, this is the reason why the total number of microorganisms was not significantly affected by the addition of nitrogen, humic substances, and microbial biostimulant treatments compared to the control. In contrast to our results, the experiment by Hawrot-Paw et al. [58] confirms higher or equal bacterial counts than the control (135% of control values during strawberry fruiting toward the end of the research) when soil was inoculated with Rhizocell C-containing Bacillus amyloliquefaciens IT45 biofertilizer during strawberry fruiting.

The survival strategy of soil microorganisms is based on a dormant population, expending energy to maintain a metabolic alertness for the immediate use of any substrate that enters the soil [59]. We found a slightly increased abundance of dormant forms of bacteria in treatments with the application of microbial stimulants in the second year of cultivation. Both stimulants also contained bacteria belonging to the genus Bacillus, which are able to go dormant and necrotic in the case of fresh organic substrates [53] and transform into the active form.

Soil actinomycetes are producers of many secondary metabolites with antibiotic effects and some also significantly promote plant growth by phytostimulation through direct or indirect mechanisms [60]. In our experiment, we did not observe changes in actinomycete numbers caused by fertilizer or biostimulant application, even after the application of a formulation containing two different strains of actinomycetes. Hawrot-Paw et al. [58] described a decrease in actinobacteria after application of the Rhizocell C biopreparation compared to the control. In contrast, Bezuglova et al. [61] reported an increase in the number of ammonifying bacteria, cellulose-degrading fungi, and actinobacteria upon application of the humic preparation BIO-Don (2.24 g/l humic acid) in winter wheat cultivation.

For microscopic filamentous fungi, we found the smallest treatment-induced differences. Only in July 2022, compared to the control, was the count lower in the treatments N+PGPB and N+PGPB+A. The experimental microbial biostimulant contains microbial strains with proven antifungal activities; Bacillus arybhattai with antifungal activity against Leptosphaeria maculans [21], as well as Streptomyces puniceus and Streptomyces olivochromogenes with antifungal activity against Penicillium expansum, Alternaria tennuissima, and Leptosphaeria maculans [17], which may have reflected.

The experimental microbial biostimulant contains microbial strains with proven antifungal activity [17,21], which could have been reflected.

High-throughput sequencing helps to overcome problems with assessing non-cultivable microorganisms in soil. We used the most common metabarcoding method based on PCR amplification of phylogenetic markers [62]. Both prokaryotic and fungal communities changed between sampling dates. Temporal variability [63], together with the plant development stage [64], is a well-known factor affecting microbiome compositions. Fertilizing by N [65] or also S [66] was reported to change the soil and rhizosphere microbiome. However, we found fewer changes between the control and N treatment than after the application of biostimulants. Biostimulants based on plant growth-promoting microbial strains significantly affected the soil microbiome in sugarcane [64].

Overgrowth of potential phytopathogen causes a strong decrease in microbiome diversity in plants [67] but also in plant-associated soil. Pilidium concavum causes brown spot in strawberry leaves and fruits, although it can also live endophytically and saprophytically without directly damaging fruits. Its occurrence has been reported in Poland, Iran, China, the USA, and other countries [68]. We did not find apparent fruit decay caused by this potential phytopathogen. However, as it primarily develops on older leaves its proliferation may have happened in late 2021. Its abundance was significantly reduced in treatments where an experimental PGPB mixture was applied. Thảo et al. [69] analyzed the antifungal activity of bacterial strains against the fungus of the genus Pilidium, with the strongest activity confirmed for Bacillus subtilis. Our experimental PGPB mixture used Bacillus arybhattai with a previously confirmed ability to suppress fungal growth. PGPB possesses biocontrol mechanisms against phytopathogens such as the production of antibiotics, lytic enzymes (chitinases, lipases, proteases, etc.) attacking cell walls, and Fe³⁺ sequestering siderophores, and induces plant defenses by systemic resistance [16].

Mineral fertilizers affect yield directly by supplying nutrients to crop plants, whereas microbial plant biostimulants affect yield indirectly by improving soil biological properties, plant nutrient intake, or health status [19]. Despite there not being differences in strawberry fruit size due to the effect of N fertilizer and plant biostimulants applied, a positive effect on total yield was confirmed when the N+PGPB+A combination was applied. In comparison to the control, the yield increased by 95%. It is in congruence with Silva et al. [70] who found better plant growth properties and yield after inoculation by PGP bacterial mixture. The strawberry yield in our experiment suggests that the use of plant biostimulants is a suitable alternative to chemical fertilization, can improve the efficiency of mineral fertilizer, and ultimately increase strawberry production.

5. Conclusions

This study demonstrated that the combined application of mineral nitrogen fertilizers and microbial biostimulants, particularly in conjunction with humic substances, significantly influenced soil microbial activity and community composition during strawberry cultivation. However, the application of biostimulants did not have a consistent effect on all measured properties of soil microbiome during the whole experimental period. The effect of a particular treatment may be reduced by the high content of organic matter in the soil. The N+PGPB+A treatment enhanced enzymatic activities, such as phosphatase activity, and FDA hydrolysis, and probably reduced the prevalence of potentially phytopathogenic fungus Pilidium. Furthermore, this treatment led to the highest fruit yield in the second experimental year, outperforming other combinations and the control group. These findings highlight the potential of integrated biostimulant applications to sustainably improve strawberry production by fostering a healthier soil microbial ecosystem and the possible mitigation of phytopathogenic fungi. Future research should explore the long-term effects of these biostimulants under varying environmental conditions.