Biofertilizers Enhance Soil Fertility and Crop Yields Through Microbial Community Modulation

Abstract

Soil microorganisms play critical roles in mediating soil fertility. Exploring the effect of fertilization on soil microbial communities is of great importance to comprehend the sustainability of agriculture. However, the impacts of the application of different fertilization techniques on soil microbial communities remain ambiguous due to inconsistent findings across studies. In this study, we investigated changes in soil microbial communities under different fertilization techniques (chemical fertilizer (CK), organic fertilizer (OF), Bacillus-amended biofertilizer (BF), and Trichoderma-amended biofertilizer (MF)) and analyzed the link between soil fertility improvement and crop yield increase from a microbial perspective. Compared to the CK treatment, the BF and MF treatments increased corn yields by 16.07% and 12.98%, and soybean yields by 17.48% and 15.32%, respectively. BF tends to increase soil available phosphorus, whereas MF demonstrates a more pronounced enhancement in both available phosphorus and NH₄⁺-N contents. These differential effects were primarily linked to changes in the microbial community. Specifically, BF significantly enriched Bacillus, Rhodanobacter, Massilia, Mortierella, and Tetracladium, while the MF selectively increased the abundances of Burkholderia-Caballeronia-Paraburkholderia, Trichoderma, Penicillium, and Sistotrema. Co-occurrence network analysis revealed that biofertilizers enhanced microbial network stability and complexity compared to conventional fertilization techniques. Moreover, structural equation modeling (SEM) confirmed strong and positive relationships between crop yields and the abundances of specific probiotic microorganisms. These findings elucidate the mechanism-specific roles of biofertilizers in agricultural systems and provide novel insights for developing targeted biofertilizer formulations to advance sustainable agricultural practices.

1. Introduction

Soil microorganisms serve as fundamental drivers of ecosystem functioning through their mediation of three critical biogeochemical processes, including organic matter decomposition, nutrient cycling, and soil structure formation [1,2,3]. Their enzymatic activities facilitate the breakdown of complex organic compounds, releasing essential nutrients while simultaneously contributing to soil carbon sequestration [4]. In nutrient transformations, diverse microbial guilds participate in nitrogen fixation, nitrification, denitrification, and phosphorus solubilization, creating dynamic nutrients that support plant growth [5,6]. These microbial communities establish intricate relationships with plants through sophisticated root-exudate-mediated signaling networks [7,8]. Plants actively shape their rhizosphere microbiome by secreting specific carbon compounds that selectively recruit beneficial microorganisms [9]. In return, microbial communities influence plant physiology through the production of phytohormones, stress-alleviating metabolites, and nutrient-mobilizing enzymes [10]. This bidirectional exchange creates a self-regulating feedback loop that optimizes nutrient acquisition efficiency, enhances stress tolerance, and ultimately determines agricultural productivity [11]. The functional redundancy within soil microbial communities ensures ecosystem resilience, while their metabolic diversity enables adaptation to changing environmental conditions, making them indispensable components of sustainable agricultural systems [12].

Modern agricultural systems rely heavily on fertilization practices that significantly influence soil microbial communities through alterations in physicochemical properties [13]. While chemical inputs remain fundamental for crop production, their overuse leads to detrimental effects, including soil degradation, acidification, and reduced nutrient efficiency [14,15,16]. This has spurred interest in organic and biofertilizer alternatives that can maintain productivity while improving soil health through microbial community optimization [14,15,16].

Biofertilizers containing live microorganisms offer multiple benefits, including enhanced nutrient acquisition, disease suppression, and improved stress tolerance [17,18,19,20]. These plant-growth-promoting microorganisms (PGPM) function through diverse mechanisms, ranging from direct pathogen inhibition to rhizosphere modification [21,22]. PGPMs enhance plant performance through three interconnected physiological mechanisms that operate at different ecological scales. At the biochemical level, these beneficial microbes improve nutrient cycling efficiency by converting inaccessible nutrient forms into plant-available compounds through nitrogen fixation, phosphate solubilization, and organic matter mineralization [23,24]. These metabolic transformations occur through enzymatic processes that modify rhizosphere chemistry, creating localized nutrients around plant roots [25]. Simultaneously, PGPMs establish protective biological barriers against pathogens via multiple defense strategies. They synthesize antimicrobial metabolites that directly inhibit pathogen growth while competitively excluding harmful microorganisms through superior colonization of root surfaces and efficient resource utilization [26]. Furthermore, PGPMs prime plant immune systems by activating defense-related gene expression and phytohormone signaling pathways [27]. When plants face abiotic stress conditions, PGPMs mediate stress adaptation responses by producing osmoprotectants that maintain cellular turgor, regulating stress hormone levels, and stimulating root morphological changes that improve resource acquisition [28]. These coordinated mechanisms collectively enhance plant fitness while maintaining belowground ecosystem balance. Among the most effective PGPMs, Bacillus spp. and Trichoderma spp. have demonstrated particular promise due to their stress resilience, rapid growth cycles, and multifaceted plant-beneficial activities [29,30,31,32]. Bacillus spp. stimulate microbial activity and community restructuring [33], while Trichoderma spp. enhance nutrient availability and pathogen resistance through enzymatic activities and antibiotic production [34,35]. Both organisms significantly influence microbial diversity and function, leading to improved soil fertility and crop performance [36,37,38]. Together, they significantly enhance microbial diversity and functional redundancy in soil ecosystems, leading to more robust nutrient cycling networks, improved soil structure, and enhanced disease suppressiveness. Improvements in soil quality mediated by microorganisms directly translate into superior crop performance, including increased crop yield, improved stress resilience, and reduced dependence on chemical inputs [39]. The multifaceted nature of their beneficial interactions with plants and soil ecosystems positions these microorganisms as cornerstone components in sustainable agricultural practices.

A critical knowledge gap persists regarding the comparative effects of different fertilizer regimes on soil microbial ecology, although substantial progress has been made in understanding microbial processes in agroecosystems. Current research has largely focused on individual fertilizer types, leaving the systematic comparison of chemical fertilizer (CK), organic fertilizer (OF), Bacillus-amended biofertilizer (BF), and Trichoderma-amended biofertilizer (MF) underexplored, particularly in terms of their differential impacts on microbial community dynamics and ecosystem functioning. We hypothesize that biofertilizer application initiates a cascade of ecological changes in soil microbiomes, including the selective enrichment of beneficial microbial consortia and the restructure of microbial interaction networks. These microbial modifications may collectively drive improvements in both plant productivity and soil ecosystem services. To address these questions, we have employed an integrated approach combining high-throughput sequencing with structural equation modeling (SEM) to examine microbial community responses across four fertilization treatments. Our study specifically investigates the following: (1) the distinct patterns of microbial community restructuring induced by organic and biofertilizer applications; and (2) the role of microbial biodiversity as a mediator between fertilization and crop production. This comprehensive analysis provides novel insights into how different fertilizer regimes influence soil–plant–microbe interactions, offering valuable information for developing targeted soil management strategies in sustainable agriculture.

2. Materials and Methods

2.1. Experiment Design and Soil Sampling

The field experiment was implemented in 2022 at an agriculture station in Hailun City (46°58′ N, 126°14′ E), Heilongjiang Province, China. The main soil physicochemical properties observed before the experiment were as follows: pH 5.80 (1: 2.5 w/v soil/water), total nitrogen (TN) 2.01 g·kg⁻¹, total phosphorus (TP) 0.9 g·kg⁻¹, total potassium (TK) 23.8 g·kg⁻¹, total carbon (TC) 28.85 g·kg⁻¹, available phosphorus (AP) 63.33 mg·kg⁻¹, available potassium (AK) 173.0 mg·kg⁻¹, ammonium nitrogen (NH₄⁺-N) 33.0 mg·kg⁻¹, and nitrate nitrogen (NO₃⁻-N) 14.5 mg·kg⁻¹.

A split-plot experimental design was implemented, incorporating two main-plot cropping systems (corn and soybean) with four sub-plot fertilization treatments (six replicates per treatment), as follows: (1) CK: chemical fertilizer (corn, 75 kg·ha⁻¹ of urea, 150 kg·ha⁻¹ of diammonium phosphate, and 50 kg·ha⁻¹ of potassium sulfate; soybean, 50 kg·ha⁻¹ of urea, 150 kg·ha⁻¹ of diammonium phosphate, and 50 kg·ha⁻¹ of potassium sulfate); (2) OF: 30% chemical fertilizer reduction + organic matter fertilizer (26.8 kg·ha⁻¹); (3) BF: 30% chemical fertilizer reduction + Bacillus-amended biofertilizer (40.0 kg·ha⁻¹); and (4) MF: 30% chemical fertilizer reduction + Trichoderma-amended biofertilizer (40.0 kg·ha⁻¹). In our preceding studies, the biofertilizers produced by Bacillus and Trichoderma were utilized and characterized [40,41].

Composite soil samples were collected post-harvest from the 0–20 cm layer using a randomized five-point sampling method within each plot. Each blended soil sample was stored in a sterile bag and transferred to the laboratory in an icebox. Following the removal of visible roots, plant remnants, and stones, the samples were divided into two parts, one of which was stored at 4 °C for soil physicochemical properties assays, and the other was stored at −80 °C for DNA extraction.

2.2. Analysis of Soil Physicochemical Properties

Soil pH was determined from a soil–water suspension (1:2.5 weight/volume) using a pH meter. Soil total carbon (TC) and total nitrogen (TN) contents were measured using an elemental analyzer (VarioEL III, Elementar, Hanau, Germany) [42]. Soil total phosphorus (TP), available phosphorus (AP), ammonium nitrogen (NH₄⁺-N), and nitrate nitrogen (NO₃⁻-N) were assayed using a continuous flow analytical system (SKALAR, SANþþ, Breda, The Netherlands) [43,44]. Soil total potassium (TK) and available potassium (AK) were quantified using inductively coupled plasma-atomic emission spectrometry (ICPS-7500, Shimadzu, Kyoto, Japan) [45]. Soil available nitrogen (AN) was detected using the alkaline hydrolysis diffusion method. Total phosphorus (TP) was analyzed using a colorimetric method with sodium hydroxide, molybdenum, and antimony [46]. The available phosphorus (AP) was analyzed using a molybdenum-antimony-scandium-based colorimetry method after extraction with 0.5 M NaHCO₃.

2.3. DNA Extraction, PCR, and High-Throughput Sequencing

Soil total DNA was extracted from approximately 0.5 g of fresh soil using the Fast DNA ^® Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) and stored at −20 °C. The quality and quantity of soil DNA were examined using agarose 2% gel electrophoresis and a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The bacterial and fungal 16S rRNA genes were amplified with the primer pairs 515F (5′-GTG CCA GCM GCC GCG GTA A-3′)/907R (5′-CCG TCA ATT CCT TTG AGT TT-3′) and ITS1F (5′-CTT GGT CAT TTA GAG GAA GTA A-3′)/ITS2R (5′-GCT GCG TTC TTC ATC GAT GC-3′) [47,48]. All PCR products were further purified with the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). Paired-end sequencing was performed on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA) at Majorbio Bio-Pharm Technology (Shanghai, China).

To process the raw sequence data, we utilized the QIIME2 pipeline (version 2020.2) [49]. Raw sequences were analyzed using divisive amplicon denoising algorithm (DADA2) method to obtain the amplicon sequence variants (ASVs) following the recommended procedure [50]. The taxonomic assignment of ASVs for bacteria and fungi was performed using the SILVA database and UNITE database, respectively [51,52]. In total, 12,456 bacterial ASVs and 2071 fungal ASVs were rarefied to even sequence depths of 52,822 and 49,236, respectively. The potential plant pathogens were classified using the online application FUNGuild (https://github.com/UMNFuN/FUNGuild, accessed on 17 June 2025) [53]. We focused on the plant probiotics and pathogens associated with plant health and agroecosystem potential risks [54]. In addition, potentially beneficial bacteria and fungi with various ecological functions were reconfirmed in the published literature (Table S1).

2.4. Microbial Network Analysis

The co-occurrence patterns of microorganisms were explored by constructing separate networks. The ASVs present in all samples, with an average relative abundance > 0.01%, were retained for co-network construction. The topological properties of the network were calculated via Spearman correlation and significance p using the “WGCNA” packages. Subsequently, FDR correction was performed on the p-values, and only the nodes and edges with a correlation exceeding 0.6 and p less than 0.05 were retained and visualized using the Gephi software (version 0.10.1).

2.5. Structural Equation Modeling Construction

We used structural equation modeling (SEM) to evaluate the direct and indirect effects of major soil properties, potential plant probiotics, and pathogens on crop yield. The SEM analysis was performed using IBM SPSS AMOS software (version 21.0). The first step in SEM required establishing an a priori model based on the known effects of variables on the crop yield. We excluded the predictors of poor fitting to the model and then established unified structural equation modeling with the data from each treatment. The SEM fitness was examined on the basis of a nonsignificant chi-square test (p < 0.05), the goodness-of-fit index, and the root mean square error of approximation [55].

2.6. Statistical Analysis

One-way analysis of variance (ANOVA) and least significant difference (LSD) analysis were carried out at p < 0.05 to assess the interactive effects of soil type and fertilization on soil chemical properties, alpha diversity, and taxonomic relative abundance of the bacterial and fungal communities, which was implemented using IBM SPSS software (version 20.0). One-way ANOVA was then used to test the effects of fertilization on soil properties, alpha diversity, and taxonomic relative abundance of the bacterial and fungal communities in soils, respectively. Soil microbial alpha diversity (Shannon and Faith’s PD (phylogenetic distance)) was estimated based on the rarefied ASV table using the “vegan” package. Microbial beta diversity was indicated by the weighted UniFrac distance, and nonmetric multidimensional scaling (NMDS) ordination was performed to explore the differences in microbial communities between fertilization treatments and crops. The environmental variables used in the RDA and SEM analysis were preselected using the Mantel test and variance inflation factor (VIF). The RDA, Mantel test, and VIF analysis mentioned above were all conducted with the “vegan” package in R (version 3.4.3), and the significant differences were examined with the “envfit” function with 999 permutations. Meanwhile, the random forest model was employed to evaluate the importance of environmental variables in predicting the changes in bacterial and fungal community structures using the “rfPermute” package in the R environment.

3. Results

3.1. Effect of Different Fertilization Regimes on Soil Physicochemical Properties and Crop Yield

The fertilization regimes exerted significant impacts on the crop yield and soil physicochemical properties. The response of crop yields of corn and soybean were highest for the BF treatment and lowest for CK (Figure S1). Compared to the CK treatment, corn yields increased by 4.15%, 16.07%, and 12.98% under the OF treatment, BF treatment, and MF treatment, respectively. Similarly, compared to the CK, soybean yields increased by 5.04%, 17.48%, and 15.32%, respectively.

The soil physicochemical properties revealed treatment-specific modulation of nutrient dynamics (Table S2). In the corn cultivation systems, MF application significantly increased pH, AP, and NO₃⁻-N contents, and decreased TK and the C/N ratio compared to the CK treatment (p < 0.05). Under the BF treatment, the soil pH improved, and the AP, AK, NH₄⁺-N, TP, TC, and TN contents were higher than those observed under the other treatments. In the soybean cultivation systems, MF application significantly increased pH, AP, AK, NH₄⁺-N, TP, TC, and TN contents (p < 0.05). Under the BF treatment, the soil pH was increased, and the AP, AK, NH₄⁺-N, TP, TC, and TN contents were higher than those observed under the other treatments.

3.2. Effect of Different Fertilization Regimes on the Diversity of Microbial Communities

The α-diversity indices of the bacterial and fungal communities for each fertilization treatment were estimated using Shannon and Faith’s PD indices (Figure 1). In the corn cultivation systems, the MF treatment had a significantly (p < 0.05) higher bacterial alpha diversity and lower fungal alpha diversity than the other treatments. In the soybean cultivation systems, MF increased the alpha diversity of the bacterial and fungal communities. In addition, the α-diversity indices showed no significant differences among the BF and MF treatments (p < 0.05).

The compositional dissimilarity of soil bacterial and fungal communities was visualized using NMDS. The results were obviously divided into two major groups, that is, different kinds of crops (PEMANOVA, p < 0.05), whereas the microbial communities in the different fertilization treatments were grouped closely (Figure 2). Mantel test analysis implied that the pH, AP, and C/N ratio were significantly correlated with the bacterial and fungal community structures in the corn cultivation systems (p < 0.05), and the pH, AP, AK, TP, TC, and TN were significantly correlated with the bacterial and fungal community structures in the soybean cultivation systems (p < 0.05) (Table S3). Based on the Mantel test results and VIF screening, RDA revealed that NH₄⁺-N and the C/N ratio made the greatest contributions to the variations in both bacterial and fungal community structures in the corn cultivation systems, and AP and TP made the greatest contributions to the variations in both the bacterial and fungal community structures in the soybean cultivation systems (Figure 3, Table S4). Moreover, these observations were supported by the results of the random forest analysis (Figure S2).

3.3. Effect of Different Fertilization Regimes on Microbial Community Composition

The dominant taxa associated with the bacterial and fungal communities in soil varied with fertilizer type. At the phylum level, the dominant bacteria (relative abundance > 5%) in the soil samples of each treatment were mainly distributed among Proteobacteria, Acidobacteriota, Actinobacteriota, Chloroflexi, Bacteroidota, Gemmatimonadaota, Planctomycetota, Myxococcota, and Firmicutes (Figure S3a). At the genus level, the dominant genera shared among the different treatments mainly included Burkholderia-Caballeronia-Paraburkholderia, Bacillus, unclassified_f_Comamonadaceae, norank_f_norank_o_Gaiellales, Rhodanobacter, unclassified_f_Micrococcaeae, Massilia, Bradyhizobium, Streptomyces, unclassified_f_Enterobacteriaceae, etc. (Figure 4a). In the corn cultivation systems, the BF treatment evidently increased the relative abundances of Bacillus, Rhodanobacter, and Massilia, while the MF remarkedly increased the relative abundances of Burkholderia-Caballeronia-Paraburkholderia. In the soybean cultivation systems, the BF evidently enriched Bacillus, Rhodanobacter, Massilia, and Bradyhizobium, while the MF obviously increased the abundances of Rhodanobacter and Stretoptomyces. At the bacterial ASV level, the biofertilizer treatments distinctly increased the probiotics in both the corn and soybean cultivation systems, which belonged to Bacillus, Rhodanobacter, Massilia, and Burkholderia-Caballeronia-Paraburkholderia, among others (Table S5).

At the phylum level, the dominant fungi in each treatment were mainly affiliated with Ascomycota, Mortierellomycota, Basidiomycota, unclassified_k_Fungi, Glomeromycota, Chytridiomycota, Rozellomycota, Blastocladiomycota, Basidiobolomycota, and Zoopagomycota (Figure S3b). At the genus level, the dominant genera shared among the different treatments mainly included Mortierella, Tetracladium, Schizothecium, Trichoderma, unclassified_k_Fungi, Fusarium, Penicillium, Tausonia, Sistotrema, Clonostachys, etc. (Figure 4b). In the corn cultivation systems, the BF treatment obviously increased the relative abundances of Mortierella, Tetracladium, and Clonostachys, while the MF treatment remarkedly increased the relative abundances of Trichoderma, Sistotrema Penicillium, and Clonostachys. In the soybean cultivation systems, the BF treatment obviously increased the relative abundances of Mortierella, Teracladium, and Sistotrema, and the MF treatment evidently increased Trichoderma, Penicllium, and Scizothecium (Table S8). At the fungal ASV level, the biofertilizer treatments dramatically reduced the relative abundance of pathogens in both the corn and soybean cultivation systems, which belonged to Gibellulopsis, Neonectria, Cylindrocarpon, Drechslera, etc. (Table S6).

3.4. Co-Occurrence Network Analysis of Microbial Communities in Soils Under Different Fertilization Regimes

Microbial networks integrating bacterial and fungal communities were constructed to assess the effects of fertilization amendments of corn and soybean cultivation systems (Figure 5, Table S7). The numbers of nodes and edges were lower in the CK and OF treatments than those observed in the BF and MF treatments in both corn and soybean cultivation systems, and these two parameters were higher in the BF and MF treatments (Table S7).

The corresponding subnetworks of the potential plant probiotics and pathogens from each integrated microbial community network were further assessed (Figure 6). We found that BF and MF treatments could cause simpler probiotic and pathogen networks with apparently lower nodes and edges than those observed in the CK and OF treatments of both corn and soybean cultivation systems. Furthermore, the avgCC and avgK of the BF and MF treatments showed higher values than those observed in the CK and OF treatments, revealing that the probiotic–pathogen networks in the BF and MF treatments were more interconnected (Table S8). Additionally, the modularity values among the different treatments were higher than 0.4, revealing that the constructed network was modular.

3.5. Abiotic and Biotic Factors Influencing Crop Yield

SEM was used to evaluate whether the changes in soil properties induced by fertilization directly and/or indirectly affected probiotics and potential plant pathogens, which ultimately affected crop yield. The results showed that the model exhibited fit to the data (corn: χ²/df = 2.126, p = 0.392, comparative fit index (CFI) = 0.987, root mean square error of approximation (RMSEA) = 0.04; soybean: χ²/df = 1.536, p = 0.472, CFI = 0.962, RMSEA = 0.03), indicating that the model was highly consistent with the observation data (Figure 7a,c).

In the corn cultivation systems, the relative abundances of probiotics were the most influencing factor on crop yield, which had a significantly positive effect on crop yield (p < 0.05) (Figure 7a). Fertilization could also indirectly impact the crop yield by affecting the soil pH and NH₄⁺-N. However, the relative abundances of pathogens had a direct negative effect on crop yield. In the soybean cultivation systems, the relative abundances of pathogens were the most influencing factor on crop yield, which had a negative effect on crop yield (Figure 7c). Fertilization could also influence crop yield indirectly by altering soil pH and NH₄⁺-N levels. However, the relative abundances of probiotics had a significantly direct positive effect on crop yield (p < 0.001). The standardized total effect demonstrated that NH₄⁺-N exerted the strongest positive influence on crop yield in the corn cultivation systems, and fertilization exerted the strongest positive influence on crop yield in the soybean cultivation systems (Figure 7b,d). Collectively, these results indicated that fertilization played an important role in determining crop yields.

4. Discussion

4.1. Response of Soil Properties and Crop Yield to Different Fertilizer Treatments

The increasing adoption of organic amendments and biofertilizers as sustainable alternatives to chemical fertilizers reflects their dual capacity to maintain crop productivity while enhancing soil quality [56]. Our findings demonstrate that biofertilizer application (BF and MF treatments) significantly improved key soil parameters compared to chemical fertilizer (CK) in both corn and soybean cultivation systems (Table S1), with notable increases in soil pH, available phosphorus (AP), and ammonium nitrogen (NH₄⁺-N) content. These results corroborate previous studies documenting the beneficial effects of microbial inoculants on soil fertility [57,58].

Soil pH plays a pivotal role in regulating nutrient availability, directly and indirectly influencing soil fertility and quality. Both BF and MF treatments counteracted the acidification typically induced by chemical fertilizers, primarily through microbial regulation of nitrogen transformation processes. The microbial decomposition of organic matter releases ammonia (NH₃) that forms ammonium ions (NH₄⁺), whose subsequent hydrolysis consumes hydrogen ions (H⁺) and elevates soil pH [59,60]. Moreover, Bacillus spp. mediate dissimilatory nitrate reduction under anaerobic conditions, converting nitrate (NO₃⁻) to ammonium (NH₄⁺) while consuming protons (H⁺), thereby generating an alkalizing effect that elevates soil pH [61]. Simultaneously, Bacillus spp. and Trichoderma spp. secrete organic acids (e.g., citrate, oxalic acids), which chelate metal ions (e.g., Fe³⁺, Al³⁺) and stabilize pH through acid–base buffering [62]. In addition, Bacillus spp. and Trichoderma spp. solubilize insoluble phosphates into plant-available phosphorus while releasing hydroxide ions (OH⁻) that neutralize soil acidity, thereby elevating pH [61].

Fertilizer application, particularly organic amendments and biofertilizers, plays a pivotal role in determining crop yield outcomes by modulating soil nutrient cycling processes. The superior performance of BF and MF over OF can be attributed to more efficient nutrient mobilization mechanisms [63,64]. Bacillus spp. and Trichoderma spp. can accelerate phosphorus mineralization through secreting phosphatases, converting organic phosphorus to plant-available forms more rapidly than those in OF [65,66]. Similarly, Bacillus spp. and Trichoderma spp. can synergize with phosphate-solubilizing bacteria to enhance phosphorus solubilization efficiency [67]. Moreover, Bacillus spp. and Trichoderma spp. enhance nutrient acquisition through rhizosphere modification and competitive exclusion of deleterious microorganisms [36]. These microbial-mediated processes not only increase nutrient availability, but also stimulate plant growth through phytohormone production, ultimately leading to yield increases of 4.15–16.07% in corn and 5.04–17.48% in soybean compared to CK treatments (Figure 7).

4.2. Effects of Different Fertilizer Treatments on Soil Microbial Communities

Soil management practices, particularly fertilization regimes, exert profound influences on soil nutrient dynamics and microbial ecosystems by altering both nutrient inputs and microenvironmental conditions [68,69,70]. Our investigation revealed that different fertilization approaches significantly modified soil microbial community composition and structural organization. These findings align with contemporary agricultural paradigms emphasizing the critical role of beneficial soil microorganisms in achieving sustainable crop production [71].

Numerous studies have established that augmenting the functional capabilities of plant-associated, soil-beneficial microorganisms, or probiotics, is crucial for achieving reliable and sustainable crop production [72]. In response to this, biofertilizers have gained significant traction in agricultural practices. Their primary goal is to manipulate soil microbial communities, thereby enhancing plant health, optimizing nutrient uptake efficiency, and bolstering crop resilience against various stresses. The application of biofertilizers leads to the introduction of specific functional microbial strains. The impact of these strains on the ecosystem is multifaceted, inducing a series of ecological responses, including the activation of unique microbial taxa, the creation of new ecological niches, the induction of root-derived metabolites, and the initiation of competition with the native microbiota for resources. As a result, certain microbial populations are preferentially stimulated or inhibited, which in turn drives changes in the composition of the soil microbial community and modifies the ecosystem’s functional profiles [37,73]. Among the diverse plant-growth-promoting microorganisms, Bacillus spp. and Trichoderma spp. have been extensively investigated and are commonly used in the commercial production of biofertilizers [74]. Research has shown that biofertilizers amended with Trichoderma spp. and/or Bacillus spp. can significantly reshape the soil microbial community structure. By increasing the diversity and activity of beneficial microbes, these biofertilizers enhance soil fertility, accelerate nutrient cycling, and stimulate plant growth and development, ultimately leading to higher crop yields and improved crop quality [75]. Specifically, the application of Bacillus spp. and Trichoderma spp. can activate certain microbial groups, such as Burkholderia-Caballeronia-Paraburkholderia, Rhodanobacter, and Massilia (Figure 4). These microorganisms can form multispecies biofilms, which not only enhance plant disease resistance, but also promote plant growth [38]. Collectively, these findings provide strong evidence for the effectiveness of microbial community manipulation in safeguarding plant health.

Our results demonstrate treatment-specific microbial community restructuring (Figure 4). BF applications preferentially enriched bacterial genera, including Bacillus, Streptomyces, and Massilia, which contribute to carbon cycling through organic matter decomposition and enzymatic nutrient mobilization [76]. Conversely, MF treatments promoted fungal populations such as Burkholderia-Caballeronia-Paraburkholderia, Rhodanobacter, Stretoptomyces, Trichoderma, Sistotrema, and Penicillium, which facilitate organic matter breakdown, nutrient cycling, and soil aggregation through extracellular enzymatic activities [77,78]. These differential effects likely stem from distinct microbial interaction mechanisms, with Bacillus spp. primarily influencing bacterial communities through antimicrobial metabolite production (e.g., bacteriocins, lipopeptides) [73], while Trichoderma spp. engage in complex cross-kingdom interactions mediated by enzymatic secretions and volatile organic compounds [79,80].

4.3. Relationship Between Soil Quality Index, Soil Microbial Communities, and Crop Yield

The relationship between soil microbial communities and crop productivity is fundamentally mediated by key soil properties, particularly pH and nutrient availability. This finding is consistent with those of previous studies, which found that soil pH and TN content serve as primary drivers of microbial community composition in an organic-fertilizer-disturbed environment [77]. Our findings corroborate these observations, showing that biofertilizer application significantly altered soil pH, AP, and NH₄⁺-N contents. These changes in soil chemistry properties subsequently influence the composition and function of microbial communities, suggesting that fertilization-induced yield improvements may operate through modifications of soil physicochemical properties [81,82].

Structural equation modeling (SEM) analysis revealed distinct relationships between microbial populations and crop performance. The addition of biofertilizers exerts a direct or indirect influence on crop yields, thereby modifying the physicochemical properties of the soil and concomitantly increasing or suppressing the proliferation of beneficial and pathogenic bacteria (Figure 7). Probiotic microorganisms showed a positive correlation with yield, while pathogenic organisms exhibited a negative association, which was consistent with previous findings [79,80,83]. These differential responses likely reflect fundamental ecological differences between these microbial groups in terms of their metabolic capabilities and interactions with host plants.

Biofertilizer application consistently enhanced beneficial microbial populations while suppressing pathogenic organisms (Tables S5 and S6). This dual effect can be attributed to several complementary mechanisms. Above all, biofertilizer components such as Bacillus spp. and Trichoderma spp. directly inhibit pathogens through the production of antimicrobial compounds and lytic enzymes [80,84]. Additionally, these beneficial microorganisms competitively exclude pathogens by more efficiently utilizing available nutrients and occupying ecological niches [85]. Moreover, they stimulate plant immune responses, thereby enhancing systemic resistance to pathogen infection. Collectively, these mechanisms highlight the potential of biofertilizers as a sustainable agricultural practice for improving crop productivity while maintaining soil quality [86].

5. Conclusions

This study demonstrates that biofertilizer application effectively enhances soil nutrient availability and crop productivity through distinct microbial-mediated mechanisms. Both Bacillus-amended (BF) and Trichoderma-amended (MF) biofertilizers significantly improved soil key parameters, including pH and AP contents, while simultaneously increasing beneficial microorganisms. Bacillus-amended fertilizer exhibited particularly strong effects, primarily through its selective stimulation of beneficial bacterial taxa such as Bacillus, Rhodanobacter, and Massilia. In parallel, Trichoderma-amended fertilizer promoted fungal communities, including Trichoderma, Sistotrema, and Penicillum, which likely contributed to pH modulation through their metabolic activities. The differential but complementary actions of these microbial inoculants highlight their potential for integrated application, where their combined effects could be strategically harnessed to address specific soil limitations and crop requirements. Such microbial-based approaches represent a promising pathway toward sustainable intensification of agricultural production systems, capable of maintaining productivity while improving long-term soil quality.