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Article

Study on the Microbial Mechanism of Bacillus subtilis in Improving Drought Tolerance and Cotton Yield in Arid Areas

by
Peiqi Ren
,
Beibei Zhou
*,
Yanpeng Bi
,
Xiaopeng Chen
and
Shaoxiong Yao
State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi’an University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1932; https://doi.org/10.3390/agronomy15081932
Submission received: 4 July 2025 / Revised: 26 July 2025 / Accepted: 7 August 2025 / Published: 11 August 2025
(This article belongs to the Special Issue Crop Management in Water-Limited Cropping Systems)
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Abstract

Drought is a global issue that affects agricultural productivity and sustainable development. The application of Bacillus subtilis has significant potential in alleviating drought stress and increasing yield. However, it is not yet clear how Bacillus subtilis affects microbial populations, crop yield, and the biochemical characteristics of rhizosphere soil, as well as the interactions among these factors. In this study, cotton was used as the experimental crop, and different application rates of Bacillus subtilis (0 kg·ha−1 and 45 kg·ha−1 (B)) and drought stress levels (H represents conventional irrigation, 350 mm; L represents 80% of conventional irrigation, 280 mm) were set as three replicates per group. The changes in rhizosphere-soil-related variables, microbial community diversity, enzyme activity, and cotton yield were studied. Compared to the control, the available nitrogen content increased by 19.76–62.40%, and soil moisture increased by 2.48–7.72%. The activities of urease, sucrase, and alkaline phosphatase increased, malondialdehyde content decreased, the Soil Plant Analysis Development (SPAD) value increased, and cotton yield increased by 8.94–9.28%. According to the structural equation model, Bacillus subtilis can increase microbial community diversity and network complexity, improve soil nutrients and enzyme activity, and increase cotton yield. This study’s findings may offer a theoretical foundation for enhancing soil quality and raising agricultural yields in arid regions.

1. Introduction

Achieving sustainable development and guaranteeing food security depend on maintaining and boosting agricultural productivity. Drought is one of the world’s most devastating natural disasters [1,2], with a wide variety of consequences and a long duration that severely damages agricultural productivity and socioeconomic growth. Arid and semi-arid regions comprise 40% of the Earth’s geographical area and are projected to expand to 58% by the mid-21st century [3]. Approximately 2.82 million square kilometers of China, or 30.5% of the nation’s land area, experience drought each year [4]. Drought stress is regarded as one of the major issues that negatively affect plant growth, development, and production [5]. Drought affects the growth and productivity of numerous crops, including cotton [6,7,8].
Cotton is a commercial crop that is grown all over the world for its edible oil and fiber. Cotton, a plant that produces sugar, can withstand drought conditions a little better than other important crops [6,9,10]. Cotton has been cultivated extensively in Xinjiang in recent years because of its high biomass productivity and resistance to drought stress [11]. However, harsh drought conditions continue to impair the growth, productivity, and fiber quality of cotton [12]. Studies have revealed that during drought conditions, photosynthetic rate, stomatal conductance, and transpiration rate decreased [13,14], resulting in a 50% reduction in dry matter accumulation [15]. Poor soil quality brought on by drought significantly limits cotton’s ability to grow and yield, and it also exacerbates environmental issues associated to soil [16,17]. Therefore, in order to reduce the impact of drought stress and improve soil quality and cotton yield, an environmentally friendly enhancer is urgently needed.
Plant-growth-promoting rhizobacteria (PGPR) are a type of beneficial bacteria that stimulate plant growth via a variety of ways. Numerous helpful bacteria found in PGPR can enhance crop nutrient use, encourage soil nutrient cycling, and contribute to better soil health and productivity [18,19,20]. Bacillus subtilis (B. subtilis) is a typical plant growth-promoting bacterium, which is often used in soil improvement because of its safety and environmental protection [21,22]. B. subtilis produces tolerant spores in response to drought stress, which successfully raises the amounts of nutrients in the soil [23]. B. subtilis usually cooperates with rhizosphere nitrogen-fixing microorganisms to promote plant growth by dissolving phosphorus, secreting plant hormones, and inducing systemic resistance [24,25]. B. subtilis has the ability to convert available nutrients into inorganic forms and break down organic nutrients [26,27]. Crop nutrient absorption is guaranteed by the rise in soil nutrient content. B. subtilis has a special function in raising soil nutrient levels, encouraging crop nutrient accumulation, and boosting crop output under drought stress [28,29]. Additionally, by controlling the activity of stress-resistant enzymes in crop leaves and lowering the amount of malondialdehyde, B. subtilis can enhance the physiological and metabolic processes of crops [30].
Microorganisms in the soil are thought to be a good way to track changes in the health of the soil ecosystem. The microbial community and soil’s chemical and physical characteristics are improved by the application of B. subtilis. In order to preserve the advantage of the microbial community in soil, the application of B. subtilis can encourage the recovery of beneficial bacteria in soil as well as the production of antagonistic compounds that prevent the growth and spread of soil pathogens [31,32,33]. The relative abundance of some bacteria and fungi involved in fertilizer usage and degradation rose as a result of the introduction of exogenous microorganisms, which also markedly altered the variety and structure of bacterial and fungi communities [34,35].The application of B. subtilis influenced the relative abundance of soil bacterial and fungi communities in distinct ways, increasing the diversity of bacterial communities while inhibiting the diversity of fungi in the soil [36]. Furthermore, a number of investigations have demonstrated that microbial populations also affect soil enzyme activity. Microorganisms can speed up the breakdown of organic matter and the transformation of nutrients by producing enzymes through their own metabolism. These enzymes play a key role in maintaining soil fertility [37,38,39]. As a result, adding B. subtilis can boost soil enzyme activity and encourage nutrient release.
Clarifying the connection between rhizosphere microbial populations and soil nutrient availability, enzyme activity, and crop yield—as well as their reaction to B. subtilis application—was the aim of this study. In light of this, the following theories were confirmed by this study: (1) B. subtilis has a positive effect on soil quality and crop yield under drought conditions; (2) the application of B. subtilis could improve the community structure of bacteria and fungi in cotton rhizosphere; and (3) changes in microbial community structure can affect enzyme activity and nutrient availability, thereby affecting cotton yield.

2. Materials and Methods

2.1. Field Test

The experiment was conducted at the Alar City Water Conservancy Irrigation Test Station (81°17′56.52″ E, 40°32′36.90″ N, altitude 1015 m) of the First Division of Xinjiang Production and Construction Corps. The experimental area is located in the northern part of the Taklimakan Desert. It is a warm temperate extreme continental arid desert climate. The annual precipitation is 40.1–98.8 mm, the surface evaporation is strong, the annual evaporation is 1876.6–2558.9 mm, the groundwater level is below 3.5 m, and the test area is 100 m2. The soil samples in the test station were sandy loam (clay 2.62%, silt 41.33%, sand 56.05%) according to the international soil texture classification. The soil bulk density was measured by the ring knife method. The average pH of 0–100 cm soil was 8.6. The soil physical properties are shown in Table 1.
Cotton planting uses the machine-harvested cotton method. The test area’s irrigation method is drip irrigation under film. Cotton is planted using the approach of “one film, three pipes, and six rows”. Mechanical sowing occurs parallel to the drip irrigation belt. The drip irrigation belt is a φ16 inner-mounted patch drip irrigation system. The drip irrigation system has a flow rate of 2.0 L·h−1, a working pressure of 0.1 MPa, an automatic pressure regulating pump, and a differential pressure fertilization tank. The row spacing is 76 cm + 36 cm + 10 cm, and the cotton plant spacing is 10 cm (Figure 1). Xinjiang Tarim River Seed Industry Co., Ltd., cultivates cotton called “Ta he 2”.
The test was carried out after sowing, and the test was applied with 100 billion CFU·g−1 Bacillus subtilis wettable powder, which was cultivated by Shandong LuKang Biopesticides Co., Ltd. (Jinan High tech Development Zone, Jinan, China). The NCD-2 strain was used and made into wettable powder. The B. subtilis was dissolved into water the day before sowing, and the mixture was evenly sprayed onto the soil surface by spraying, and then the microbial agent was mixed with 0–20 cm soil of the tillage layer by mechanical rotary plowing. The amount of bacteria applied in this experiment was set to 0 kg·ha−1 and 45 kg·ha−1 according to the previous research results of the team [30]. The irrigation and fertilization system was developed utilizing local production expertise. The irrigation amounts were set at two levels: conventional irrigation H (350 mm) and deficit irrigation L (280 mm). Each treatment (BH, H, BL, L) was performed in triplicate, yielding a total of 12 plots, each measuring 3.3 m × 2 m × 3 m. During the complete growth period of cotton, urea (N46%, 450 kg·ha−1), potassium dihydrogen phosphate (P52%, 375 kg·ha−1), and potassium sulfate (K53.8%, 375 kg·ha−1) were utilized as nitrogen, phosphate, and potassium fertilizers, respectively. Conventional agricultural management practices, such as pest control and weeding, are consistent with regional cotton management techniques.

2.2. Soil and Plant Sampling

At the flowering and boll stage of cotton, three cotton plants with uniform growth and uniform growth were selected as a group in each plot, and samples were taken from the narrow rows adjacent to the direction of the vertical drip irrigation belt and the center of the cotton root (Figure 1). First, the soil sampler (Eijkelkamp 0110 SO, Giesbeek, The Netherlands) was cleaned and sterilized (using 75% alcohol fire sterilization), and the floating soil on the surface was removed. The soil layer of 0–20 cm in the main root zone of cotton was collected. Each sample was taken from three sampling locations and thoroughly blended after the obvious contaminants were eliminated. The drying method (105 °C, 8 h) was used to measure the mass water content in a portion of soil [30]. The Smartchem 450 Automatic Discontinuous Chemical Analyzer was used to determine the amounts of nitrogen and phosphorus were accessible in the soil (AMS Alliance, Guidonia, Italy) [40]. Soil-available potassium was determined with a flame photometer [40]. Soil organic carbon was determined by a potassium dichromate-concentrated sulfuric acid external heating method, and soil total nitrogen was determined by the Kjeldahl method. Part of the soil was stored at 4 °C, and the activity of soil enzymes (sucrase, urease, and alkaline phosphatase) was determined within one week [40]. Cotton rhizosphere soil sampling involves selecting three cotton plants with uniform growth from each plot as one group, with a total of three replicates. The complete underground roots of cotton were dug out with a disinfectant iron shovel (using 75% alcohol fire sterilization), and the loose soil attached to the roots was gently shaken off. The roots of the plants were placed in a vacuum sterile bag, and the sterile bag was gently flipped to make the soil (rhizosphere soil) attached to the root surface fall into the sterile bag, amounting to about 1–2 mm. The collected rhizosphere soil samples were placed in a 50 mL centrifuge tube and quickly frozen in an ultra-low-temperature refrigerator at −80 °C. The DNA extraction, PCR amplification, and purification of root-zone soil samples in this experiment were conducted by Shanghai Personal Biotechnology Co., Ltd. At the flowering and boll-forming stage of cotton, the leaves of three representative cotton plants were collected from each plot, and the content of malondialdehyde (MDA) in cotton leaves was measured [40]. The Soil Plant Analysis Development (SPAD) value of the third leaf of cotton at the flowering and boll-forming stage was measured with an SPAD-502 chlorophyll meter [23].

2.3. The Method for DNA Extraction, PCR Conditions, and the Platform/Technique Used for Sequencing

2.3.1. The Method for DNA Extraction

OMEGA Soil DNA Kit (D5635-02, Omega Bio-Tek, Norcross, GA, USA) was used to extract soil genomic DNA. The integrity of DNA molecules was verified by 0.8% agarose gel electrophoresis, and the concentration and purity of DNA were determined with a Nanodrop (NC2000, Thermo Scientific, Waltham City, MA, USA) ultramicro spectrophotometer.

2.3.2. PCR Amplification and Homogenization Treatment

Specific primers, 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), were designed for the V3–V4 region of the bacterial 16SrRNA gene hypervariable region, and a sample-specific 7–10-base barcoding sequence was introduced at the 5′ end of the forward primer. NEB Q5 High-Fidelity DNA Polymerase (M0491 L, NEB, Ipswich, MA, USA) was used for amplification. The reaction procedure was set as follows: initial denaturation at 98 °C for 5 min; 25 cycles (denaturation at 98 °C for 15 s, annealing at 55 °C for 30 s, extension at 72 °C for 30 s); the final extension at 72 °C for 5 min. The amplified products were separated by 2% agarose gel electrophoresis, and the target bands were cut and purified using an Axygen AxyPrep DNA Gel Extraction Kit (P7589, Invitrogen, Carlsbad, CA, USA). According to the sequence characteristics of the fungal ITS1 region, specific amplification primers, ITS5 (forward, 5′-GGAAGTAAAAGTCGTAACAAGG-3′) and ITS2 (reverse, 5′-GCTGCGTTCTTCATCGATGC-3′), were designed. The sample-specific 7–10-base barcode sequence was introduced at the 5′ end of the forward primer to achieve effective discrimination of different samples in the same sequencing library. The primer combination was used for PCR amplification of the target region and subsequent high-throughput sequencing analysis. The Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific, Carlsbad, CA, USA) was used for fluorescence quantitative analysis of the amplified products, and an FLx800 microplate reader (BioTek, Winooski, VT, USA) was used to determine the DNA concentration. Based on the sequencing depth requirements of each sample, the PCR products were mixed at an equal molar ratio.

2.3.3. Construction and Sequencing of Sequencing Library

The Illumina TruSeq Nano DNA LT Library Prep Kit (Illumina, San Diego, CA, USA) was used for end repair, adaptor ligation, and library amplification, in strict compliance with standardized manufacturer processes. A total of 1 μL library samples was taken, and the fragment distribution was detected using the Agilent 2100 Bioanalyzer system combined with the Agilent High Sensitivity DNA Kit (Agilent Technologies, Santa Clara, CA, USA). At the same time, the Quant-iT PicoGreen dsDNA Assay Kit was used to complete the accurate quantification of the library on the Promega QuantiFluor-ST fluorescence quantitative system. The qualified library was subjected to double-end sequencing on an Illumina NovaSeq (PE250) instrument(San Diego, CA, USA). All samples were sequenced by Illumina NovaSeq 6000 PE250 (San Diego, CA, USA), and a total of 1.2–1.8 × 106 raw reads were obtained. After Trimmomatic 0.39 quality control, the effective reads were 0.9–1.4 × 106 (average 1.1 × 106). All subsequent analyses were based on clean reads, and Good’s coverage was >0.97, indicating that the sequencing depth fully covered the microbial diversity of each sample.

2.4. Soil Chemistry and Enzyme Analysis

2.4.1. Analysis of Soil Indicators

After 30 min of shaking and filtering, the soil samples were extracted using a 2 mol/L KCl solution. An automatic intermittent chemical analyzer (Smartchem 450, AMSA lliance, Guidonia, France) was used to determine the amount of nitrogen accessible in the filtered extract (AN). After being extracted using a 0.5 mol/L NaHCO3 solution, the soil samples were agitated for half an hour and then filtered. The filtered extract was examined using an automatic intermittent chemical analyzer for soil-available phosphorus. The soil samples were extracted with 1 mol/L NH4Ac solution, and the available potassium was determined using a flame photometer [40]. Soil total nitrogen content was determined by Kjeldahl method. Soil organic carbon was determined by the potassium dichromate-concentrated sulfuric acid external heating method [41].

2.4.2. Determination of Soil Enzyme

The activity of sucrase was measured by the 3,5-dinitrosalicylic acid colorimetric method, and the color was compared with an ultraviolet spectrophotometer (UV-1800 Aoyi Instrument Co., Ltd. Minhang District, Shanghai, China) at a wavelength of 508 nm. The activity of sucrase was expressed by the number of milligrams of glucose produced in 1 g of soil [40]. Urease activity was measured by the phenol sodium–sodium hypochlorite colorimetric method. A UV spectrophotometer (UV-1800 Aoyi Instrument Co., Ltd. Minhang District, Shanghai, China) was used to compare color at a wavelength of 578 nm, and the soil urease activity was expressed as milligrams of NH3-N in 1 g of soil [40]. Alkaline phosphatase activity was measured by the disodium phenyl phosphate colorimetric method, using an ultraviolet spectrophotometer (UV-1800 Aoyi Instrument Co., Ltd. Minhang District, Shanghai, China) at a wavelength of 660 nm [40]. Phosphatase activity was expressed as the milligrams of phenols released from 1 g of soil [42].

2.4.3. Determination of Malondialdehyde and SPAD Value

Malondialdehyde content was determined by the Thio barbituric acid method [30], and the SPAD value represented the photosynthetic pigment content of leaves [43]. The SPAD value of the top three leaves was measured with the SPAD-502 handheld chlorophyll meter (Konica Minolta, Chiyoda-ku, Tokyo, Japan) at 10:00–12:00 in the morning on a sunny day during the flowering and boll stage of cotton. The measured value was the average value of 5 randomly selected leaves, and the veins of the leaves were avoided during the measurement.

2.5. Statistical Analysis

Using SPSS software v24.0 (IBM, Chicago, IL, USA), the effects of different treatments on cotton yield, soil-related variables, and bacterial and fungi diversity indexes were analyzed by ANOVA (one-way analysis of variance) and the Spearman rank correlation coefficient (p < 0.05). The variability of soil microbial community structure was studied by non-metric multidimensional scaling analysis (NMDS) (based on OTU abundance). The relationship between the soil microbial community and environmental factors was studied by redundancy analysis (RDA). The relative abundance chord diagram of soil microorganisms at the phylum level and the cluster analysis diagram of OTU species of soil bacteria and fungi were drawn by Origin v2022 b software. We used MicroCorps (https://www.bioincloud.tech accessed on 8 May 2025) to draw the NMDS of soil bacteria and fungi online, and used the genescloud tool to complete the RDA drawing of bacteria and fungi (https://www.genescloud.cn accessed on 8 May 2025). The correlation analysis between soil microbial community structure and soil biochemical properties was performed using the “ggcor” software package in R (V4.2.0) [34]. The importance of factors affecting yield was mapped using the “rfPermute” package in R (V4.2.0) [44]. The soil microbial symbiosis network was calculated using the “ggClusterNet” package in R (V4.2.0) [45] and visualized using the interactive platform Gephi (V0.10.1), and structural equation models were constructed using Smart PLS 4.0 software.

3. Results

3.1. Cotton Yield

The application of B. subtilis significantly increased the yield of cotton (Figure 2). The yield of the BH group was 9.28% higher than that of the H group, and the yield of the BL group was 8.94% higher than that of the L group, the yield of the L group was 10.13% lower than that of the H group, and the yield of H group was 2.13% higher than that of the BL group. It is worth noting that the BH treatment produced a significantly higher cotton yield of 6120.30 kg·ha−1, which was significantly higher than the yield obtained by other treatments (p < 0.05), indicating that the application of B. subtilis could increase cotton yield and resist the decline in yield caused by drought.

3.2. Physicochemical Properties of Rhizosphere Soil

The application of B. subtilis improved soil properties and physiological indicators (Figure 3). After the application of B. subtilis, the soil water content and soil nutrient content were improved. Compared with the H treatment, the soil water content after BH treatment was 2.48% higher than that after the H treatment, and the soil water content after the BL treatment was 7.72% higher than that after the L treatment. In terms of soil nutrients, the contents of available nitrogen, available potassium, available phosphorus, total nitrogen, and soil organic carbon after the BH treatment were 19.76%, 2.88%, 21.68%, 17.79% and 9.34% higher than those in the H treatment, respectively. The contents of available nitrogen, available potassium, available phosphorus, total nitrogen, and soil organic carbon after the BL treatment were 62.40%, 2.95%, 59.49%, 31.09%, and 5.65% higher than those after the L treatment, respectively. The enzyme activity of the soil treated with B. subtilis was higher than that of the soil without B. subtilis. The urease activity, sucrase activity, and alkaline phosphatase activity after the BH treatment were 26.69%, 7.25%, and 14.31% higher than those after the H treatment, respectively. The urease activity, sucrase activity, and alkaline phosphatase activity after the BL treatment increased by 10.11%, 4.95% and 20.76%, respectively, compared with the L treatment. In terms of plant physiology, the content of MDA in leaves decreased after the application of B. subtilis. The content of MDA in the BH treatment decreased by 4.35% compared with the H treatment, and the content of MDA in the BL treatment decreased by 5.72% compared with the L treatment; the application of B. subtilis increased the SPAD value of leaves. The SPAD value of the BH treatment was 4.96% higher than that of the H treatment, and the SPAD value of the BL treatment was 8.33% higher than that of the L treatment.

3.3. Changes in Soil Microbial Community

The bacterial and fungi communities’ Good’s coverage index was more than 98%, suggesting that the OTU sequencing findings accurately depicted the actual state of microorganisms in soil samples (Figure 4b,e). In comparison to the H and L groups without B. subtilis, the BH and BL groups had higher bacterial and fungi community species richness following B. subtilis administration. According to Bray–Curtis NMDS research, changes in the environment also had a major impact on the community structure of soil bacteria and fungi (Figure 5c,d).
We chose species with relative abundances above 1% as the dominating bacteria and species with relative abundances above 0.001% as the dominant fungi at the phylum level. The dominant bacteria of TOP10 were Proteobacteria (38.58–48.03%), Actinobacteria (12.75–18.15%), Bacteroidetes (6.78–12.71%), Gemmatimonadetes (6.35–10.08%), Acidobacteria (6.19–3.72%), Chloroflexi (6.82–3.66%), Firmicutes (5.82–2.7%), Patella (2.63–1.87%), Verrucomicrobia (2.53–1.61%), and Planktobacteria (1.26–0.78%). These dominant bacteria together accounted for more than 96% of the OTU. When using microbial agents, the relative abundance of soil bacteria at the phylum level was higher than when B. subtilis was not present (Figure 5a).
Ascomycota (77.32–95.62%), Basidiomycota (1.03–12.70%), Mortierellomycota (1.28–0.53%), Chytridiomycota (0.03–0.11%), Aphelidiomycota (0.008–0.057%), Glomeromycota (0.003–0.02%), Mucoromycota (0.002–0.003%), and Rozellomycota (0.001–0.002%) were the dominant phyla in TOP8. Compared with the treatment without B. subtilis, the relative abundance of soil bacteria at the phylum level increased after the treatment with microbial agents (Figure 5b).

3.4. Soil Microbial Synbiotic Network

The relationship between soil bacteria and fungi can be better explained by the synbiotic network and its topological features (Figure 6). Each processing is a co-occurrence network with a modular structure if its modularity index is larger than 0.4. Applying microbial agents could increase the complexity of microbial networks and the stability of external environmental interference, as evidenced by the fact that the treatment with microbial agents led to more edges and nodes than the treatment without microbial agents.

3.5. The Relationship Between Soil Microbial Community and Soil Physical and Chemical Properties

The association between soil biochemical properties and soil bacterial and fungi communities was examined using redundancy analysis (RDA) (Figure 7a,b). Of the entire variation in the organization of the soil bacterial and fungi communities, the first two RDA axes accounted for 83.38% and 99.84% of the variation, respectively. There was a negative correlation between MDA and the relative abundance of the dominant phylum of bacteria and a positive correlation with soil nutrients and enzyme activity (Figure 7a). The dominant phylum of fungi also showed similar results (Figure 7b). We employed Mantel test analysis to connect the two in order to investigate the impact of the microbial community on soil biochemical characteristics in more detail (Figure 7c). The results showed that bacterial community structure was significantly positively correlated with AN (p < 0.05). There was a significant negative correlation with MDA (p < 0.05) and a significant positive correlation with available phosphorus (p < 0.01). The factors influencing the yield were ranked and correlated using the random forest algorithm (Figure 7d). In terms of yield, AN had the biggest impact, followed by MDA, AK, AP, TN, SPAD, and SMC. Following Gemmatimonadota, Ascomycota, Firmicutes, and Acidobacteriota, Bacteroidota was the dominant bacterium that had the biggest impact on the yield.
The impact of B. subtilis on the organization of the microbial community, soil physical and chemical characteristics, soil nutrients, enzyme activities, cotton physiology, and development under water stress was examined using the partial least squares–structural equation model (PLS-SEM) (Figure 8).
The overall prediction accuracy of the model was high, as indicated by its goodness of fit of 0.693. The relationship between soil microbial diversity and soil moisture was significantly favorable (path coefficient = 0.807, p < 0.001). Enzyme activity (path coefficient = 0.531, p < 0.01) and soil nutrients (path coefficient = 0.651, p < 0.01) were significantly positively correlated with the increase in soil microbial diversity. Additionally, soil moisture considerably enhanced both soil nutrients (path coefficient = 0.786, p < 0.001) and enzyme activity (path coefficient = 0.692, p < 0.001), which in turn greatly increased enzyme activity. The yield and physiological parameters of cotton were significantly positively correlated with soil nutrients (path coefficient = 0.676, p < 0.001) and enzyme activity (path coefficient = 0.532, p < 0.05). It is evident that B. subtilis can reduce water stress by boosting the diversity of the soil microbial population, enriching the soil with nutrients and enzymes, and indirectly raising cotton production and physiological markers.

4. Discussion

4.1. Effects of B. subtilis on Cotton Yield and Soil Physical and Chemical Properties

Drought has a negative impact on soil nutrient availability and enzyme activity, which lowers crop yields and decreases agricultural output [46,47]. Following the B. subtilis application, the crop output increased dramatically and the soil condition improved (Figure 2 and Figure 3). The BH treatment produced the most cotton and had the greatest impact on soil improvement. This is because there are active Bacillus spores in the B. subtilis application. Bacillus generates pyruvate during dryness through the germination of spores and metabolic processes, and then transforms it into organic acids like acetic and lactic acid. These organic acids produce an acidic environment that can hasten the breakdown of insoluble potassium and phosphorus molecules. The secreted organic acids can also cooperate with alkaline phosphatase to promote the mineralization of organic phosphorus [48] and release available nutrients [49,50]; stimulate urease activity; accelerate the decomposition of urea into ammonium nitrogen; and alleviate the decrease in nitrogen availability caused by drought [51]. As sucrase activity rises, carbon sources break down more quickly and microbial activity is given energetic support [52,53].
In drought conditions, B. subtilis simultaneously produces extracellular polysaccharides that encourage soil aggregate formation and decrease water loss [54]. The extracellular polysaccharide generated can also slow down the rate at which water rises along capillaries, which lowers the soil surface layer’s evaporation loss, increases the soil’s ability to retain water, and lessens inefficient water evaporation [55,56]. Research indicates that the use of B. subtilis can increase soil drought resilience, generate hormone and antioxidant secretion, and encourage root growth and water absorption through the release of cytokinin and gibberellin [57]. Additionally, it can cause plants to manufacture superoxide dismutase and eliminate drought-induced reactive oxygen species [21].
Malondialdehyde can serve as an indirect measure of the extent of cell membrane damage by detecting membrane peroxidation during plant organ aging and stress [58]. The amount of MDA treated with B. subtilis was substantially lower than that of MDA treated without B. subtilis, ranging from 4.35 to 5.72% (p < 0.05) (Figure 3a). This is due to the fact that B. subtilis enhances cotton’s antioxidant system [59], regulates hormone balance [60], improves cell membrane stability [61], and lowers MDA content in cotton under drought stress, which reduces oxidative damage and increases plant resistance to drought.
Drought stress can cause plants’ chloroplast structure to be destroyed, speed up the breakdown of chlorophyll, and lower the SPAD value. By reducing chlorophyll degradation [57], improving nutrient absorption [62], regulating hormone levels [63], and enhancing antioxidant protection [64], among other mechanisms, B. subtilis can help cotton maintain a high photosynthetic capacity under drought stress, thereby increasing plant yield and drought resistance. B. subtilis improves soil quality and increases crop drought resistance in drought-prone environments. Cotton’s resistance to drought is increased by this control, which encourages plant development.

4.2. Responses of Cotton Rhizosphere Microbial Community to B. subtilis

The application of B. subtilis increased the diversity of bacterial and fungi communities in the soil (Figure 4). This could be due to the fact that microbial diversity is directly driven by the increase in rhizosphere soil nutrients following the application of microbial fertilizers [65,66], and that microbial diversity can significantly enhance the soil environment [19]. Microbial community structure and soil environmental parameters had a positive feedback relationship and enhanced synergistically after B. subtilis was applied. But according to certain research, the inclusion of B. subtilis enhanced the diversity and richness of bacterial communities, while decreasing those of fungi communities [56,67]. This is due to the fact that local environmental conditions, which frequently differ from study site to site, have a significant influence on the community structure of soil microorganisms.
Low soil nutrients and water content may be the primary limiting factors for microbial community alterations in arid environments [43,68]. The bacterial community structure in this investigation was closely associated with AN, AP, AK, and SOC (Figure 7a,c). SOC, AP, S-SC, and SMC are the primary variables influencing the structure of fungi communities in arid regions (Figure 7b,c). The findings demonstrated that the administration of B. subtilis raised the relative abundance of the majority of the soil’s dominating bacteria, and that the drought treatment considerably raised the relative abundance of Proteobacteria and Actinobacteriota following the application of B. subtilis (BL) (Figure 5a) [69]. B. subtilis treatment increased the relative abundance of Basidiomycota and decreased the relative abundance of other fungi. This is mainly due to the secretion of lipopeptide antibiotics [70] and volatile organic compounds [71] by B. subtilis, which inhibit the growth of fungi and bacteria [72] in soil. As a result of this inhibition, there were fewer microorganisms that were competing with Basidiomycetes, which allowed them to take up more niches in the rhizosphere soil and greatly increase their population. However, B. subtilis can also break down organic fertilizers into soluble carbon sources and nutrients like potassium, phosphate, and nitrogen. It can also release cellulase, protease, and other enzymes, giving basidiomycetes the substrates and microaerobic environment they need to break down lignin [73]. B. subtilis’s activities give it a lot of carbon and nitrogen sources, which helps it grow. Therefore, B. subtilis’s nitrogen fixation and phosphorus solubilization can improve soil fertility and indirectly support Basidiomycetes’ metabolic needs [74].
Under drought conditions, Bacillus subtilis enhanced the stability of soil aggregates, reduced water evaporation, and alleviated soil compaction caused by drought by secreting extracellular polysaccharides (EPSs) and forming biofilms [75]. At the same time, it increases the amount of organic matter and soil enzyme activity, improves the physical and chemical characteristics of the soil, and creates a stable microenvironment for the development and innovation of Basidiomycetes. B. subtilis stimulates root growth and secretes more root exudates (such as sugars and organic acids) [76], and it causes systemic resistance in plants (e.g., activation of jasmonic acid and salicylic acid signaling pathways) [77]. Beneficial bacteria like Basidiomycota may be drawn to these secretions specifically, creating a win–win synbiotic connection. By increasing the abundance of beneficial bacteria like Proteobacteria and Actinobacteria, which may form a functional complementary synbiotic network with basidiomycetes—for instance, Proteobacteria are involved in the nitrogen cycle [78], and Actinobacteria inhibit pathogens [79]—the application of B. subtilis changed the structure of the soil microbial community. Furthermore, Bacillus strains and basidiomycetes may have a mutually advantageous synbiotic relationship in which the metabolic byproducts or environmental conditions that Bacillus strains give aid in the growth and reproduction of basidiomycetes [80]. According to Yang et al. [81] and Feng et al. [82], B. subtilis was more selective toward fungi community members, which increased the effect of niche filtering on the fungi community. B. subtilis altering the structure of the rhizosphere microbial population, boosting soil enzyme activity, and improving water distribution.

4.3. B. subtilis Mainly Affects Soil Quality and Crop Yield by Changing Microbial Communities

By boosting soil microbial diversity and achieving the effective use of soil microorganisms, B. subtilis improved soil fertility and indirectly impacted enzyme activity, according to the structural equation model (Figure 8). Drought-tolerant Bacillus species found in B. subtilis can lessen the impact of drought on plants by preventing lipid peroxidation [83]. Bacillus species can alter the composition of the soil microbial community and boost soil microbial diversity [84,85,86]. Bacillus also produces antimicrobial substances (like peptide antibiotics) [87], which inhibit harmful microorganisms and encourage the growth of beneficial microorganisms [88]. By increasing these microbial metabolic processes, more enzymes (such urease and protease) can be produced, which can break down organic materials and nutritional components and release nutrients that plants can absorb [89,90,91].
We found a favorable correlation between the activities of catalase, sucrase, and urease and bacteria and fungi (Figure 7c). This is because there is less carbon available in soil during drought, and the rise in sucrase activity indicates that drought-tolerant microbes are more efficient at using basic carbon sources. By secreting sucrase, drought-tolerant microbes can adapt to arid conditions [92]. The amount of nutrients in the soil was positively connected with urease activity [93]. As a result, soil function and production improved and enzyme activity rose [94,95]. According to function, the most significant biological mechanism tying soil nutrient cycling and microbial community dynamics together is soil enzyme activity associated with microorganisms and plants. Microbial metabolic activity is directly reflected in soil enzyme activity, which has a major impact on the composition and functionality of microbial communities. The composition of the microbial community also controls the activity of the extracellular enzymes that microorganisms secrete to break down complex organic materials and obtain nutrients [96]. An important factor in soil nutrient cycling is soil enzyme activity, which can hasten organic matter breakdown and nutrient release, increasing soil fertility. The efficiency of soil nutrient cycling can be directly regulated by soil enzyme activity, which can also reflect the dynamic changes in microbial communities. This biological process is necessary to keep soil ecosystems healthy and productive [97]. Enzyme activity in soil has become the main mechanism linking nutrient cycling and microbial dynamics through a four-in-one network of substrate catalysis–microbial metabolism–plant interaction–environmental response. The control of plant nutrient uptake and environment by rhizosphere bacteria is essential to plant–environment interaction [98].
Certain microbial community functional microorganisms can enhance crop quality and production by boosting root nutrient uptake and making specific soil nutrients more accessible [81]. According to the findings of the random forest experiment, cotton yield was significantly impacted by Bacteroidota, Firmicutes, and Gemmatimonadota (Figure 7d). An essential microbial community in soil that participates in the nitrogen and organic carbon cycles is Bacteroidota. An increase in the relative abundance of Bacteroidota in soil can greatly enhance the soil’s capacity to cycle nutrients and encourage cotton development [99]. Firmicutes are crucial components of soil ecosystems, primarily contributing to the cycling of nutrients and the breakdown of organic materials. Research has indicated that a rise in the relative abundance of Firmicutes can greatly enhance the soil’s ability to retain water and absorb nitrogen, which in turn enhances the soil’s physical and chemical characteristics as well as the structure of its microbial community. The primary functions of soil are the decomposition of organic matter and the cycling of nutrients [100]. By increasing the relative abundance of Firmicutes, the soil’s ability to retain water and use nitrogen can be improved, which in turn can improve the soil’s physical and chemical characteristics as well as the microbial community. Gemmatimonadota is a type of microbe that can survive and has a significant role in environments with high salinity and drought because of its salt and drought tolerance traits. According to research, increasing the relative abundance of Gemmatimonadota can greatly strengthen the soil’s ability to withstand stress, which will help cotton grow more quickly during drought conditions [101]. B. subtilis reshapes the soil microbial community and activates the key enzyme system. This ensures that the ecosystem function is upgraded, the soil microbial community is reconstructed, and ultimately, the efficient circulation of nutrients, cotton resistance, and yield increase are achieved.

5. Conclusions

Applying B. subtilis can enhance soil quality, boost cotton yield, and considerably reduce soil drought stress. By controlling the richness of the rhizosphere soil microbial community structure, enhancing the intricacy of bacterial and fungi networks, boosting rhizosphere soil enzyme activity, and encouraging soil nutrient absorption, B. subtilis primarily increases soil quality and cotton yield. In the production of cotton, Bacteroidota, Firmicutes, and Gemmatimonadota might be important. The use of B. subtilis can greatly boost cotton productivity during drought circumstances. B. subtilis must be used sensibly, economically, and effectively to address the agricultural needs of enhancing soil health and raising crop output during drought circumstances.

Author Contributions

Data curation, P.R.; Writing—original draft, P.R.; Writing—review and editing, P.R.; Visualization, P.R.; Investigation, P.R.; Supervision, B.Z.; Funding acquisition, B.Z.; Project administration, B.Z.; Writing—review and editing, B.Z.; Resources, Y.B.; Supervision, Y.B.; Investigation, Y.B.; Validation, Y.B.; Supervision, X.C.; Resources, X.C.; Investigation, X.C.; Validation, X.C.; Supervision, S.Y.; Resources, S.Y.; Visualization, S.Y.; Software, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (52222903;41977007; 52409067).

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an on-going study. Requests to access the datasets should be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 15 01932 g001
Figure 1. Cotton planting layout diagram.
Figure 1. Cotton planting layout diagram.
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Figure 2. Effects of different treatments on cotton yield. The difference in lowercase letters between yields indicated that there was a significant difference between different amounts of bacteria (p < 0.05).
Figure 2. Effects of different treatments on cotton yield. The difference in lowercase letters between yields indicated that there was a significant difference between different amounts of bacteria (p < 0.05).
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Figure 3. (a) Effects of B. subtilis on physical and chemical properties of cotton. (b) Effects of B. subtilis on soil moisture and soil nutrients. (c) Effect of B. subtilis on soil enzyme activity. Soil water content (SMC), available nitrogen (AN), available phosphorus (AP), available potassium (AK), total nitrogen (TN), soil organic carbon (SOC), SPAD, malondialdehyde (MDA), soil urease (S-UE), soil invertase (S-SC), soil alkaline phosphatase (S-ALP). Lowercase letters indicate that there were significant differences between treatments (p < 0.05).
Figure 3. (a) Effects of B. subtilis on physical and chemical properties of cotton. (b) Effects of B. subtilis on soil moisture and soil nutrients. (c) Effect of B. subtilis on soil enzyme activity. Soil water content (SMC), available nitrogen (AN), available phosphorus (AP), available potassium (AK), total nitrogen (TN), soil organic carbon (SOC), SPAD, malondialdehyde (MDA), soil urease (S-UE), soil invertase (S-SC), soil alkaline phosphatase (S-ALP). Lowercase letters indicate that there were significant differences between treatments (p < 0.05).
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Figure 4. α diversity index of bacteria and fungi. (a) Bacterial Chao1 abundance index. (b) Bacterial Goods’ coverage index. (c) Bacterial Shannon index. (d) Fungi Chao1 abundance index. (e) Fungi Goods’ coverage index. (f) Fungi Shannon index.
Figure 4. α diversity index of bacteria and fungi. (a) Bacterial Chao1 abundance index. (b) Bacterial Goods’ coverage index. (c) Bacterial Shannon index. (d) Fungi Chao1 abundance index. (e) Fungi Goods’ coverage index. (f) Fungi Shannon index.
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Figure 5. At the phylum level: (a) the relative abundance of soil bacteria in the top 10 species, (b) the relative abundance of soil fungi in the top 8 species, (c) the Bray–Curtis NMDS analysis of rhizosphere bacterial community changes, and (d) the Bray–Curtis NMDS analysis of rhizosphere "phenol sodium–sodium hypochlorite " fungi community changes.
Figure 5. At the phylum level: (a) the relative abundance of soil bacteria in the top 10 species, (b) the relative abundance of soil fungi in the top 8 species, (c) the Bray–Curtis NMDS analysis of rhizosphere bacterial community changes, and (d) the Bray–Curtis NMDS analysis of rhizosphere "phenol sodium–sodium hypochlorite " fungi community changes.
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Figure 6. Synbiotic network and topological properties of soil bacterial and fungi communities at the phylum level. The blue and red edges indicate positive and negative correlations, respectively. The colors of the nodes represent their classification at the gate level. Strong correlation (Spearman r > 0.5) and significant correlation (p < 0.05). (a) represents the soil microbial symbiotic network of BH treatment, (b) represents the soil microbial symbiotic network of H treatment, (c) represents the soil microbial symbiotic network of BL treatment, (d) represents the soil microbial symbiotic network of L treatment. The number of nodes (e), the number of edges (f), the average degree (g), the number of positive edges (h), and the modularity index (i) are the five parameters of the co-occurrence network model. The red line in the figure represents treatment with H as the control group, and the green line represents treatment with L as the control group.
Figure 6. Synbiotic network and topological properties of soil bacterial and fungi communities at the phylum level. The blue and red edges indicate positive and negative correlations, respectively. The colors of the nodes represent their classification at the gate level. Strong correlation (Spearman r > 0.5) and significant correlation (p < 0.05). (a) represents the soil microbial symbiotic network of BH treatment, (b) represents the soil microbial symbiotic network of H treatment, (c) represents the soil microbial symbiotic network of BL treatment, (d) represents the soil microbial symbiotic network of L treatment. The number of nodes (e), the number of edges (f), the average degree (g), the number of positive edges (h), and the modularity index (i) are the five parameters of the co-occurrence network model. The red line in the figure represents treatment with H as the control group, and the green line represents treatment with L as the control group.
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Figure 7. (a) The relationship between RDA (top 10) of rhizosphere bacterial community composition and environmental factors, (b) the relationship between RDA (top 8) of rhizosphere fungi community composition and environmental factors, (c) Pearson correlation analysis between soil microbial dominant flora and soil biochemical properties at the phylum level of classification (** means p < 0.01, *** means p < 0.001). (d) The relative importance of soil microbial abundance and soil biochemical properties. According to the results of random forest analysis, the top six variables that contribute most to yield growth are shown. The results with statistical significance are marked (* means p < 0.05, ** means p < 0.01).
Figure 7. (a) The relationship between RDA (top 10) of rhizosphere bacterial community composition and environmental factors, (b) the relationship between RDA (top 8) of rhizosphere fungi community composition and environmental factors, (c) Pearson correlation analysis between soil microbial dominant flora and soil biochemical properties at the phylum level of classification (** means p < 0.01, *** means p < 0.001). (d) The relative importance of soil microbial abundance and soil biochemical properties. According to the results of random forest analysis, the top six variables that contribute most to yield growth are shown. The results with statistical significance are marked (* means p < 0.05, ** means p < 0.01).
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Figure 8. Structural equation model of the influence mechanism of B. subtilis on soil physical and chemical properties, soil nutrients, and cotton physiology and growth. Note: goodness of fit = 0.693. * means p < 0.05, ** means p < 0.01, *** means p < 0.001. In the external model, the number on the purple arrow is the external model load. In the internal model, the red arrow indicates a positive correlation. The number on the arrow line and the thickness of the arrow line represent the value of the path coefficient, and R2 represents the degree of interpretation of the model.
Figure 8. Structural equation model of the influence mechanism of B. subtilis on soil physical and chemical properties, soil nutrients, and cotton physiology and growth. Note: goodness of fit = 0.693. * means p < 0.05, ** means p < 0.01, *** means p < 0.001. In the external model, the number on the purple arrow is the external model load. In the internal model, the red arrow indicates a positive correlation. The number on the arrow line and the thickness of the arrow line represent the value of the path coefficient, and R2 represents the degree of interpretation of the model.
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Table 1. Main physical and chemical properties of soil in test area.
Table 1. Main physical and chemical properties of soil in test area.
Depth (cm)Bulk Density
(g·cm−3)		Field
Capacity (%)		Saturated Water Content (%)Total Nitrogen
Content (mg·kg−1)		Total Phosphorus Content (mg·kg−1)Rapidly Available Potassium (mg·kg−1)Organic Matter (g·kg−1)
0~201.6021.0024.000.390.53197.255.75
20~401.5524.0030.000.250.48192.574.71
40~601.5825.0033.000.170.42139.713.49
60~801.5925.0032.000.120.3990.192.37
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Ren, P.; Zhou, B.; Bi, Y.; Chen, X.; Yao, S. Study on the Microbial Mechanism of Bacillus subtilis in Improving Drought Tolerance and Cotton Yield in Arid Areas. Agronomy 2025, 15, 1932. https://doi.org/10.3390/agronomy15081932

AMA Style

Ren P, Zhou B, Bi Y, Chen X, Yao S. Study on the Microbial Mechanism of Bacillus subtilis in Improving Drought Tolerance and Cotton Yield in Arid Areas. Agronomy. 2025; 15(8):1932. https://doi.org/10.3390/agronomy15081932

Chicago/Turabian Style

Ren, Peiqi, Beibei Zhou, Yanpeng Bi, Xiaopeng Chen, and Shaoxiong Yao. 2025. "Study on the Microbial Mechanism of Bacillus subtilis in Improving Drought Tolerance and Cotton Yield in Arid Areas" Agronomy 15, no. 8: 1932. https://doi.org/10.3390/agronomy15081932

APA Style

Ren, P., Zhou, B., Bi, Y., Chen, X., & Yao, S. (2025). Study on the Microbial Mechanism of Bacillus subtilis in Improving Drought Tolerance and Cotton Yield in Arid Areas. Agronomy, 15(8), 1932. https://doi.org/10.3390/agronomy15081932

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