Effects of Microbial Fertilizer Application on Soil Ecology in Saline–Alkali Fields

Abstract

Microbial fertilizer is an environment-friendly fertilizer that can effectively improve the microecological environment of soil, playing an important role in the remediation of saline–alkali soil and promoting sustainable agricultural development. In this study, we examined the impact of microbial fertilizer application on saline–alkali field improvement over two years. The results indicated that, compared to NS0 and NS2 (the initial sowing period without microbial fertilizer addition), the soil pH and electrical conductivity (EC) levels significantly decreased by 4.1% and 8.49% and 60.56% and 39.66% for NS1 (after the first harvest) and NS3 (after the second harvest), respectively. Compared to NS0, the concentrations of Na⁺ and Cl⁻, among the eight major ions in the soil, decreased significantly by 87.23% and 80.91% in the second year, while Ca²⁺ increased significantly in NS1 and NS3, being 5.27 times and 2.46 times higher than before sowing. Comparing NS3 to NS0, the sodium adsorption ratio decreased by 87.04%. The activities of soil urease, alkaline phosphatase, and invertase in NS3 increased significantly by 90.18%, 45.67%, and 82.31% compared to those in NS0. In contrast, the activity of catalase decreased by 2.79% (p < 0.05). Alpha diversity analysis demonstrated that the Ace, Chao1, and Sobs indices for both bacteria and fungi were significantly higher at NS3 than before sowing, indicating the highest species richness at this stage. The Shannon index exhibited an ascending trend, and the difference in the Simpson index was not significant. After applying microbial fertilizer in the saline–alkali field, the number of bacterial and fungal operational taxonomic units (OTUs) significantly increased. In the bacteria, the proportion of Proteobacteria rose, while Actinobacteriota exhibited a significant reduction. Among fungi, the proportion of Ascomycota decreased and Basidiomycota increased. Principal component analysis (PCA) revealed distinct separation among treatments, indicating significant differences in microbial communities. Redundancy analysis (RDA) identified that the key physicochemical factors influencing bacterial community structure were available phosphorus (AP), electrical conductivity (EC), and pH, whereas for fungi, they were AP, available potassium (AK), and dissolved organic carbon (DOC). This research presents the effects of microbial fertilizer application on the improvement in a saline–alkali field over two years. It provides a scientific basis for the remediation of the saline–alkali field via microbe-induced changes in soil physicochemical properties, enzyme activity, microbial diversity, and community structure at different periods.

1. Introduction

Soil salinization has become a major obstacle to crop production, threatening the sustainable development of modern agriculture [1,2]. Saline–alkali soils are characterized by elevated levels of salinity and alkalinity, which make it difficult for crops to thrive. When salinity levels reach 8 dS·m⁻¹ or higher, crop emergence rates are severely hindered, and there is almost no harvest [3]. China ranks as the third-largest nation globally in terms of saline–alkali acreage, with approximately 9.9 × 10⁸ hm² affected, predominantly across 17 provinces, including regions in northeast, northwest, and north China, as well as coastal areas [4]. In northwest China, extensive areas of arable land experience significant salinization, leading to a decrease in arable land and reduced crop yields. It is crucial to identify the factors affecting the ecological environment of saline–alkali soils for sustainable agriculture [5]. Research by Van den Berg et al. highlighted that relying solely on natural succession for vegetation restoration was time-consuming and minimally effective in controlling soil salinity and alkalinity [6]. In contrast, Yang et al. [7] reported that artificial reclamation was more effective than the natural restoration of abandoned saline–alkali land, but the impact of such reclamation on soil microbial ecology was currently unclear.

Soil microorganisms play a crucial role in the process of restoring saline–alkali ecosystems. They can decompose organic matter in the soil, releasing nutrients required for plant growth, and break down complex organic substances into simple compounds that plants can directly absorb and utilize. At the same time, they promote the formation of soil aggregate structure and secrete organic acids, thereby improving soil structure and pH to restore saline–alkali land [8,9]. Moreover, microbial activity can increase the content of available calcium in the soil, enhance soil buffering capacity, and reduce the content of harmful ions, thus boosting the salt and alkali tolerance of crops [10]. Studies have found that applying microbial fertilizer in saline–alkali land can not only increase soil activity, improve soil organic matter content, and promote microbial decomposition, but also improve saline–alkali soil, reduce soil electrical conductivity (EC) and pH, facilitate soil nutrient utilization, promote desalination, and inhibit salt return [11].

Soil physicochemical properties, enzyme activity, and microbial diversity are critical indicators for assessing soil productivity. The physicochemical properties of soil primarily refer to nutrient content, organic matter content, moisture retention capacity, and pH, all of which directly influence soil fertility and the growth environment for plants [12]. Soil enzyme activities provide a rough estimate of the relative intensity of biochemical processes occurring under specific soil ecological conditions, and measuring specific enzyme activities can yield indirect insights into the transformation of certain substances within the soil [13]. Soil microbial diversity refers to the abundance of the species and number of microorganisms in the soil, often used to reflect the changes in soil nutrients and environmental quality [14]. Studies have demonstrated a strong correlation between soil biological activity and soil physicochemical properties. Investigating characteristics such as soil physicochemical properties, enzyme activity, and microbial diversity can significantly contribute to the improvement and restoration of saline–alkali soils, which is crucial for agricultural production [15,16]. Therefore, by applying microbial fertilizer to improve soil physicochemical properties and regulate the dynamic changes in soil microbial community, a foundation is laid for the development of new eco-friendly techniques for the improvement of saline–alkali soils.

The microbial fertilizer used in this study was jointly developed by our research group and Ningxia Wufeng Agricultural Technology Co., Ltd. (Yinchuan, China) Bacterial strains were isolated from saline–alkali soils, and their PGPR effects were tested in the bioassay using maize seedlings. Through multiple pot experiments, we obtained several strains that significantly improve maize growth under saline–alkali stress. The best strain was used to develop a microbial fertilizer in Ningxia Wufeng Agricultural Technology Co., Ltd. Field trials were conducted in Nuanquan Farm for two years to address the following issues: (1) Evaluating the fluctuations of soil physicochemical properties at different period intervals after the application of the microbial fertilizer; (2) Ascertaining the changes in soil enzyme activity, microbial diversity, and community composition throughout the enhancement process; and (3) Studying the relationship between soil physicochemical properties and microbial communities during this process. This study is intended to provide a theoretical basis for the application of a microbial fertilizer to improve saline–alkali fields and promote sustainable agricultural development.

2. Materials and Methods

2.1. Site Description, Microbial Fertilizer, and Field Trial Design

The site for field trials was about 3 hectares, located at Ningxia Nuanquan Farm (latitude N 38°45′, longitude E 106°11′, altitude 1065 m), at the east foot of the Helan Mountains, in the arid and semi-arid region of northwest China. The site had an average annual temperature of 9.0 °C, with precipitation averaging 180 mm per year, while evaporation exceeded 1500 mm, and precipitation occurred predominantly between June and September. The field was a barren land due to severe saline–alkali conditions and poor fertility.

The microbial strains used were isolated and screened in the initial phases by North Minzu University and Ningxia Wufeng Agricultural Technology Co., Ltd. These strains have been shown to improve the saline–alkali tolerance of maize seedlings. Ningxia Wufeng Agricultural Technology Co., Ltd. successfully developed a new microbial fertilizer “Jian Kang”, a powder formula, and “Chumiao Bao”, a liquid formula, using the best strain. At the same time, the liquid organic fertilizer was developed by using biogas slurry. The ingredients and contents of fertilizers are shown in

Table 1. The ingredient table for fertilizers.

Property	Liquid Organic Fertilizer	Microbial Fertilizer
Ingredients	Biogas slurry, a small amount of amino acids and humic acid.	Functional strain (effective viable bacteria count ≥ 1 × 107 cfu·mL−1), biogas slurry as carrier, a small amount of amino acids and humic acid.
pH	7.1	6.5
OM (g·kg−1)	24.5	28.6
DOC (g·kg−1)	14.2	16.5
N + P2O5 + K2O (g·kg−1)	≥26.0	≥18.0

.

The field trials were conducted for 2 years, from 2022 to 2023. Due to the land’s barren nature, liquid organic fertilizer was applied to this land at the rate of 15,000 kg per hectare and then the land was deeply plowed before sowing. The purpose of this application was to introduce organic nutrients into the soil. The blocks that only received liquid organic fertilizer were set as the control. The blocks that received the microbial fertilizer in addition to liquid organic fertilizer were set as the treatments. The treatments were carried out as follows:

(1)

Pre-treatment: In May, “Jian Kang” powder was mixed with the seeds at a ratio of 3 g per kilogram, resulting in a high concentration of a salt–alkaline-tolerant microbial community on the seed surface. After sowing, “Chumiao Bao” was administered by drip irrigation at an amount of 15–30 kg per hectare, establishing a medium-concentration microbial community in the soil around the seeds.

(2)

Mid-treatment: In mid-June, microbial liquid fertilizer (containing salt–alkaline-tolerant microbes at a concentration of 1 × 10⁷ cfu·mL⁻¹) was applied via drip irrigation at 150 kg per hectare along with 60 kg of compound fertilizer per hectare. In mid-July, microbial liquid fertilizer was applied at 300 kg per hectare, supplemented by 105 kg of compound fertilizer per hectare.

(3)

Post-treatment: At the beginning of August, 450 kg of microbial liquid fertilizer per hectare and 105 kg of compound fertilizer per hectare were applied using drip irrigation. In mid-August, 600 kg per hectare of microbial liquid fertilizer was applied, followed by 150 kg per hectare of compound fertilizer. Thus far, the field trials only used drip irrigation water, and the fertilization was completed.

2.2. Soil Samples

During corn cultivation in the saline–alkali field, soil samples covering the surface layer and reaching a depth of 20 cm were carefully collected on four different periods over two years. The “five-point sampling method” was used for these collections, with each designated area sampled three times to ensure accuracy. The collected soil samples were then divided into four different treatments: the first sampling, carried out in May 2022 before sowing, served as an original soil sample without microbial fertilizer (NS0); the second sampling took place after harvest by adding microbial fertilizer in October 2022 (NS1); the third sampling took place again before sowing without microbial fertilizer in May 2023 (NS2); and the last sampling occurred after harvest by adding microbial fertilizer in October 2023 (NS3). The samples were transported immediately to the laboratory and stored in a freezer at −20 °C. The soil samples collected from each were mixed and then filtered and sampled with a 0.25 mm, 1 mm, and 2 mm sieve, and each treatment was repeated three times. The indices of fresh soil samples and air-dried soil samples were tested.

2.3. Soil Physicochemical Properties and Enzyme Activities

Soil pH was determined using the potentiometric method with a soil-to-water ratio of 1:10. Electrical conductivity (EC) was measured using the electrode method. Organic matter (OM) was evaluated using the potassium dichromate oxidation titration method with external high-temperature heating. Alkaline nitrogen (AN) was quantified using the alkaline diffusion method. Available phosphorus (AP) was determined using the molybdenum–antimony colorimetric method. Available potassium (AK) was measured using the flame photometric method. The eight major ions were quantified using atomic absorption methods [17,18]. The sodium adsorption ratio (SAR) was used to assess the impact of sodium ions on soil structure and water retention capacity, calculated using the following formula [19]:

$$\mathrm{S}\mathrm{A}\mathrm{R}=\frac{{{\displaystyle \mathrm{N}\mathrm{a}}}^{+}}{\sqrt{\frac{\left({{\displaystyle \mathrm{C}\mathrm{a}}}^{2+}+{{\displaystyle \mathrm{M}\mathrm{g}}}^{2+}\right)}{2}}}$$

where Na⁺ is the concentration of sodium ions in the soil solution, Ca²⁺ is the concentration of calcium ions in the soil solution, and Mg²⁺ is the concentration of magnesium ions in the soil solution.

Soil urease activity was determined using the indophenol blue colorimetric method. Soil sucrase activity was evaluated using the 3,5-dinitrosalicylic acid colorimetry technique. Alkaline phosphatase activity was evaluated using the p-nitrophenyl phosphate disodium colorimetry method, and soil catalase activity was quantified using the potassium permanganate titration method [20,21,22].

2.4. Soil DNA Extraction, PCR Amplification, and Sequencing

The soil samples were collected and packaged and then entrusted to Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) for sequencing analysis. Total genomic DNA extraction of soil microbial communities was performed according to the E.Z.N.A.^® soil DNA kit (Omega Bio-tek, Norcross, GA, USA). The quality of the extracted genomic DNA was measured using 1% agar gel electrophoresis. DNA concentration and purity were determined using NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA) and sequenced using the Illumina Miseq PE300 platform (Illumina, San Diego, CA, USA). The bacterial PCR primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were used to amplify the V3–V4 region of the 16S rRNA gene [23]. The PCR program was performed at 95 °C for 5 min, followed by 25 cycles of 94 °C for 45 s, 50 °C for 30 s, and 72 °C for 30 s, with a final extension at 72 °C for 6 min. The fungal PCR primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) were used to amplify the ITS region of the ITS rRNA gene [24]. The PCR program was performed at 95 °C for 5 min, followed by 28 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension at 72 °C for 5 min. The PCR was repeated three times for each sample. The repeated PCR products were mixed, and 2% agarose gel was used for electrophoretic detection. The PCR products were obtained by cutting glue using the AxyPrepDNA gel recovery kit from AXYGEN (Majorbio Bio-Pharm Technology Co., Ltd., Shanghai, China, https://www.majorbio.com/, accessed on 30 November 2023) [25].

2.5. Library Construction

The purified PCR products were library-prepared using the NEXTFLEX^® Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, USA) and sequenced on the Illumina MiSeq PE300 platform. The raw sequencing data were subjected to quality control using Trimmomatic v0.39, followed by assembly of paired-end sequences using FLASH v1.2.11. UPARSE v7.1 was used to group the quality-controlled assembled sequences into operational taxonomic units (OTUs) based on 97% similarity and to remove chimeric sequences. Sequence counts for all samples were diluted to 20,000 (recommended for sequence dilution), and after dilution, the average sequence coverage (commodity coverage) for each sample remained at 99.09%. The RDP classifier was used to compare the OTU taxonomic classifications with the Silva 16S rRNA gene database (v138), ultimately obtaining taxonomic information for each OTU at the following levels: domain, kingdom, phylum, class, order, family, genus, and species [26,27,28].

2.6. Statistical Analysis

The initial data were collected using Microsoft Excel software. Significant differences in soil physicochemical properties were found, and Duncan’s test was used to determine statistical significance (p < 0.05). Mothur v1.30.1 was used to describe Venn diagrams of operational taxonomic units (OTUs) and calculate alpha diversity knowledge (Chao1, Shannon index, etc.) [29]. The Wilcoxon rank-sum test was used to analyze inter-group differences in alpha diversity. Principal component analysis (PCA) based on the Bray–Curtis distance algorithm was performed to assess the similarity of microbial community structures among samples, combined with PERMANOVA non-parametric testing to determine whether the differences in microbial community structures between sample groups were significant. The linear discriminant analysis (LDA) effect size (LEfSe) (http://huttenhower.sph.harvard.edu/LEfSe, accessed on 18 January 2024) was used to identify the significantly abundant taxa (phylum to genera) of bacteria among the different groups (LDA > 2, p < 0.05) [30]. Redundancy analysis (RDA) was conducted to investigate the influence of soil physicochemical properties on the structure of soil microbial communities [31].

3. Results

3.1. Effect of Microbial Fertilizer on Physicochemical Properties in Saline–Alkali Soil

The application of microbial fertilizer resulted in remarkable changes in the physicochemical properties of the saline–alkali field over various periods, as shown in

Table 2. Basic physicochemical properties of saline–alkali soil at different periods. Note: Different lowercase letters after a number indicate a significant difference (p < 0.05).

Property	NS0	NS1	NS2	NS3
pH	8.62 ± 0.1 a	8.27 ± 0.03 b	8.6 ± 0.13 a	7.87 ± 0.04 c
EC (µs·cm−1)	1269.33 ± 13.5 a	500.67 ± 25.32 d	1086 ± 54 b	655.33 ± 29.26 c
OM (g·kg−1)	2.61 ± 0.01 d	4.6 ± 0.03 a	2.96 ± 0.09 c	3.51 ± 0.12 b
DOC (mg·kg−1)	47.07 ± 1.64 b	72.59 ± 0.05 a	47.95 ± 0.19 b	33.52 ± 1.02 c
AN (mg·kg−1)	9.53 ± 0.25 c	19.37 ± 0.21 a	9.33 ± 0.5 c	11.73 ± 0.47 b
AP (mg·kg−1)	6.6 ± 0.26 c	8.03 ± 0.23 b	8.5 ± 0.3 b	10.23 ± 0.25 a
AK (mg·kg−1)	59.88 ± 0.33 d	137.79 ± 0.78 a	95.95 ± 2.34 b	66.99 ± 0.96 c
K+ (mg·kg−1)	10.62 ± 0.17 c	32.17 ± 0.08 a	13.04 ± 1.09 b	10.11 ± 0.51 c
Na+ (mg·kg−1)	2551.91 ± 28.07 b	5249.87 ± 14.55 a	982.68 ± 1.87 c	325.97 ± 8.92 d
Ca2+ (mg·kg−1)	83.33 ± 0.21 c	438.95 ± 0.81 a	40.39 ± 0.64 d	99.17 ± 0.27 b
Mg2+ (mg·kg−1)	61.65 ± 0.43 a	41.24 ± 0.26 b	21.24 ± 0.25 c	41.47 ± 0.17 b
SO42− (mg·kg−1)	122.4 ± 4.62 a	104.03 ± 4.13 b	66.37 ± 6.68 c	29 ± 2.42 d
CO32− (mg·kg−1)	0	0	0	0
HCO3− (mg·kg−1)	311.67 ± 31.12 a	297.1 ± 21.38 a	326.13 ± 6.12 a	240.87 ± 6.1 b
Cl− (mg·kg−1)	2330.57 ± 35.05 b	3936.47 ± 11.07 a	711.87 ± 15.54 c	444.97 ± 6.31 d

. Within two years after harvest (NS1 and NS3), there was a significant decrease in soil pH and EC compared to before sowing (NS0 and NS2), with pH decreasing by 4.1% and 8.49%, and EC decreasing by 60.56% and 39.66%. Eliminating the theoretical increase after fertilization, compared to NS0, the OM of NS3 significantly increased by 34.94%, and the DOC of NS1 significantly increased by 1.4 times, but decreased during the second harvest period. AN, AP, and AK all significantly increased by 23.08%, 55%, and 11.87%, respectively, compared to before the first year of sowing (p < 0.05). Among the eight major ions in the soil, except for the undetected CO₃²⁻, Mg²⁺, SO₄²⁻, and HCO₃⁻, all showed a downward trend. Compared to NS0, Na⁺ and Cl⁻ significantly decreased by 87.23% and 80.91% in NS3. It is noteworthy that Ca²⁺ significantly increased in both NS1 and NS3, being 5.27 times and 2.46 times that of NS0 and NS2, respectively. The sodium adsorption ratio (SAR) exhibited an increasing trend in the first year; however, it significantly decreased in the second year due to the application of microbial fertilizers, with NS3 showing a reduction of 87.04% compared to NS0 (p < 0.05). These results suggest that microbial fertilizer can reduce soil pH, electrical conductivity (EC), and sodium adsorption ratio (SAR), thereby promoting the improvement of soil structure, increasing the cation exchange capacity, and reducing the accumulation of Na⁺. Additionally, they enhance the content of beneficial substances such as organic matter (OM), available nitrogen (AN), available phosphorus (AP), and calcium ions (Ca²⁺), which are conducive to crop growth. This indicates a positive trend for the remediation of saline–alkali soils.

3.2. Effect of Microbial Fertilizer on Enzyme Activity in Saline–Alkali Soil

As shown in Figure 1, the application of microbial fertilizer significantly increased the activities of urease, alkaline phosphatase, and sucrose enzymes in saline–alkali soil after two years in comparison to the original soil before sowing in the first year. The order of activity was NS3 > NS2 > NS1 > NS0 for urease, NS3 > NS2 > NS1 > NS0 for alkaline phosphatase, and NS3 > NS1 > NS2 > NS0 for sucrose. The activities of these three enzymes were higher after the second-year harvest than at other times, with urease activity in NS3 increasing by 90.18% compared to NS0 (Figure 1a), alkaline phosphatase activity increasing by 45.67% (Figure 1b), and sucrose enzyme activity increasing by 82.31% (Figure 1c). In contrast, soil catalase activity revealed an opposite trend, with the ranking NS2 > NS0 > NS3 > NS1, and catalase activity at each harvest was lower than before sowing (Figure 1d) (p < 0.05). These results indicate that microbial fertilizer can increase the activities of soil enzymes beneficial to the improvement of saline–alkali soil, while reducing the activities of harmful soil enzymes, showing a positive correlation trend.

3.3. Alpha Diversity Analysis of Microbial Fertilizer on Soil Microbial Community in Saline–Alkali Soil

The Illumina MiSeq PE300 platform was used to sequence soil DNA from different periods, achieving over 98% coverage of the sample library. This indicated that the sequencing depth can cover the vast majority of microorganisms in the soil and accurately reflect the microbial community information in each sample.

Table 3. Alpha diversity index of microorganisms in saline–alkali soil at different periods. Note: Different lowercase letters after a number indicate a significant difference (p < 0.05).

Genus	Property	NS0	NS1	NS2	NS3
Bacteria	Shannon	6.07 ± 0.05 c	6.25 ± 0.05 b	6.63 ± 0.06 a	6.73 ± 0.07 a
	Simpson	0.01 ± 0 a	0.01 ± 0 a	0.01 ± 0 a	0.01 ± 0 a
	Ace	3276.64 ± 17.93 d	3595.35 ± 27.38 c	4082.68 ± 164.01 b	4286.01 ± 31.36 a
	Chao1	3244.13 ± 21.97 d	3523.77 ± 15.44 c	4013.57 ± 148.15 b	4198.77 ± 30.92 a
	Sobs	2841.33 ± 34.31 d	3115 ± 36.35 c	3534 ± 107.68 b	3785.67 ± 54.31 a
	Coverage	0.99 ± 0 a	0.99 ± 0 a	0.99 ± 0 a	0.99 ± 0 a
Fungi	Shannon	3.25 ± 0.07 c	3.86 ± 0.02 a	3.41 ± 0.05 bc	3.72 ± 0.37 ab
	Simpson	0.07 ± 0.01 a	0.04 ± 0 a	0.06 ± 0 a	0.06 ± 0.04 a
	Ace	156.69 ± 9.66 d	186.38 ± 4.94 c	223.48 ± 18.08 b	310.75 ± 20.16 a
	Chao1	156.5 ± 9.53 d	186.44 ± 5.59 c	223.46 ± 19.21 b	312.98 ± 18.1 a
	Sobs	156 ± 10.39 d	184.33 ± 4.73 c	220.33 ± 17.39 b	303.33 ± 17.21 a
	Coverage	1 ± 0 a	1 ± 0 a	1 ± 0 a	1 ± 0 a

shows the results of the alpha diversity indices for the sequenced soil samples. The Ace, Chao1, and Sobs indices represented species richness, while the Shannon and Simpson indices reflected species diversity. The Ace, Chao1, and Sobs indices of soil bacteria after two years of harvest were significantly higher than those before sowing, with NS3 showing the highest species richness and NS0 showing the lowest. The Shannon and Simpson indices of soil also showed significant variation at different periods, with the Shannon index showing an upward trend, while the Simpson index remained relatively stable (p < 0.05). Compared to the richness and diversity of the bacterial communities at different periods, the differences in the Simpson index for fungi were not significant. However, the Shannon, Ace, Chao1, and Sobs indices for soil fungi were significantly higher during the two-year harvest period than before sowing, with NS3 being the highest and NS0 the lowest (p < 0.05). This indicates that the application of microbial fertilizer can enhance the richness and diversity of both bacterial and fungal communities in saline–alkali soil.

3.4. Analysis of Community Structure and Composition of Microbial Fertilizer in Saline–Alkali Soil

3.4.1. The Number of Soil Bacteria and Fungi at Different Periods

The number of bacterial and fungal OTUs in the soil after two years of microbial fertilizer application is shown in Figure 2. In the Venn diagram for bacteria, a total of 8834 bacterial OTUs were identified from soil samples collected at four periods, with NS3 having the highest number of OTUs at 5414 and NS0 having the lowest at 2643. Analysis revealed that there were 1743 shared OTUs, representing 19.73% of the total. The unique OTUs for NS0, NS1, NS2, and NS3 were 638, 737, 783, and 1293, respectively, representing 7.22%, 8.34%, 8.86%, and 14.64%, with NS3 having the highest proportion and NS0 the lowest (Figure 2a). In the Venn diagram for fungi, a total of 896 fungal OTUs were identified from the same soil samples, with NS3 again having the highest number at 492 and NS0 the lowest at 266. Analysis demonstrated that there were 68 shared OTUs, representing 7.59% of the total. The unique OTUs for NS0, NS1, NS2, and NS3 were 102, 106, 127, and 231, respectively, representing 11.38%, 11.83%, 14.17%, and 25.78%, with NS3 having the highest proportion and NS0 the lowest (Figure 2b). The results indicate that the application of microbial fertilizer significantly increased the number of bacterial and fungal OTUs in the soil over two years, thereby enhancing the number of species, richness and diversity of microorganisms during the remediation process of saline–alkali soil.

3.4.2. Effects of the Horizontal Community Composition of Soil Bacteria and Fungi at Different Periods

During the remediation process, the community composition of soil bacteria at the phylum level displayed significant differences, with the top 10 bacterial phyla accounting for over 70% of the total relative abundance of the bacterial community. In particular, the relative abundance of Proteobacteria increased significantly during the two harvest periods NS1 and NS3. In contrast, the relative abundance of Actinobacteriota gradually decreased over the four periods. The proportion of Chloroflexi remained relatively stable throughout each period. Importantly, the relative abundance of Acidobacteriota continuously increased over the four time periods (Figure 3a). At the fungal phylum level, the dominant community was Ascomycota, which maintained a high relative abundance throughout all periods, only decreasing in NS3. Basidiomycota identified an increase in relative abundance in NS3, while Chytridiomycota appeared in the two harvest periods, NS1 and NS3, but was not detected in the two sowing periods, NS0 and NS2 (Figure 3b). The results indicate that the application of microbial fertilizer significantly influenced the bacterial communities of Proteobacteria and Acidobacteriota over the two years, contributing positively to the remediation of saline–alkali soil. Additionally, the emergence of the fungal community Chytridiomycota due to the microbial fertilizer application may also play a role in the remediation of saline–alkali soil.

3.4.3. Relationship Between Soil Samples and Species at Different Periods

At the phylum level in the bacterial circos, the dominant species among samples from the four periods were Actinobacteriota, Proteobacteria, and Chloroflexi. In the NS0 sample, Actinobacteriota had the highest proportion, ranging from 38% to 41%, followed by Proteobacteria with 25% and Chloroflexi with 9%. In NS1, both Proteobacteria and Actinobacteriota were around 30%, while the proportion of Chloroflexi increased. In NS2, Proteobacteria were 30%, Actinobacteriota were 27%, and Chloroflexi were 10%, with a decrease in both Actinobacteriota and Chloroflexi. In NS3, Proteobacteria were the most abundant at 35% to 38%, followed by Actinobacteriota at 19% and Chloroflexi at 10% to 13% (Figure 4a). At the phylum level in the fungal circos, Ascomycota were the dominant species in all four periods, followed by Basidiomycota. In NS0, Ascomycota had the highest proportion with 97%, which subsequently decreased in NS1, NS2, and NS3, reaching 87% in NS3. Basidiomycota identified a slight increase, rising from 2% in NS0 to over 6% in NS3 (Figure 4b). Overall, the application of microbial fertilizer significantly increased the proportion of Proteobacteria in the bacteria, while causing a significant decrease in Actinobacteriota, with minimal changes in Chloroflexi. In addition, it decreased the proportion of Ascomycota in fungi while increasing Basidiomycota, indicating that the application of microbial fertilizer altered the relationships between bacterial and fungal community species at different periods, which was beneficial for the remediation of saline–alkali soil.

3.5. Difference Analysis of Microbial Fertilizer on Microbial Community Structure in Saline–Alkali Soil

PCA was performed on the OTU data for bacteria and fungi, and the results are shown in Figure 5. In the PCA for both bacteria and fungi, the three samples from the same treatment were closely clustered, indicating good repeatability. The treatments from the four periods showed some distance from each other, indicating some differences between the samples. In the bacterial PCA, the contribution rates of PC1 and PC2 were 21.72% and 15.61%, respectively. NS2 and NS3 were clearly distinguished from NS0 and NS1 along PC1. In the PC2 analysis, NS0, NS1, NS2, and NS3 were all distinctly separated, indicating significant microbial differences between the groups. The distance between NS0 and NS1 along PC1 was not pronounced, suggesting a smaller difference (Figure 5a). For the fungal PCA, the contribution rates of PC1 and PC2 were 17.58% and 12.51%, respectively. In the PC1 analysis, NS3 was clearly distinguished from NS0, NS1, and NS2, indicating significant microbial differences among the groups. However, the differences between NS0, NS1, and NS2 were not pronounced, indicating smaller differences. In the PC1 analysis, NS0, NS1, NS2, and NS3 all showed some distance from each other, indicating some differences between the groups (Figure 5b). Therefore, whether for bacteria or fungi, the PCA indicates that there are distinct distances between treatments in both PC1 and PC2, indicating significant microbial differences between the groups. This suggests that the application of microbial fertilizer has a significant impact on the microbial diversity in saline–alkali soil remediation.

3.6. Correlation Analysis of Microbial Fertilizer on Physicochemical Properties and Microbial Communities in Saline–Alkali Soil

RDA was used to investigate the effect of the soil physicochemical properties on the microbial community structure of saline–alkali soil at different periods after the application of microbial fertilizer. In the RDA biotic community, the combination of RDA1 and RDA2 variables explained 91.27% of the variation in the bacterial community and 69.82% of the variation in the fungal community, as shown in Figure 6. It was found that AP, EC, and pH were the main factors influencing the bacterial community. The influence of different physicochemical factors on the bacterial community was ranked as follows: AP > EC > pH > OM > AN > DOC > AK. Before the application of microbial fertilizer (NS0), there was a significant positive correlation between soil EC and pH and a significant negative correlation with AP. However, after the application of microbial fertilizer (NS1), the results were reversed (Figure 6a). For the fungal community, soil AP, AK, and DOC were identified as the main influencing factors, with the influence of different physicochemical factors ranked as follows: AP > AK > DOC > pH > EC > AN > OM. Similarly to the bacterial community, before the application of microbial fertilizer (NS0), there was a significant positive correlation with soil EC and a significant negative correlation with AP. After the application of microbial fertilizer (NS1), these relationships were also reversed (Figure 6b). These results indicate that, after the application of microbial fertilizer, AP has a significant effect on soil microbial communities, which plays a crucial role in the improvement process of saline–alkali soil.

4. Discussion

4.1. Effects of Microbial Fertilizer on Physicochemical Properties and Enzyme Activities in Saline–Alkali Soil

With the application of microbial fertilizer to improve the saline–alkali field, changes occur in the internal soil environment, which subsequently affect the physicochemical properties of the saline–alkali soil [32]. Yang et al. [7] reported that reclamation not only significantly decreased soil pH and electrical conductivity (EC), but also increased soil nutrient content. Similarly, Zhang et al. [33] observed a decreasing trend in soil EC with the increased cultivation time. By applying microbial fertilizer, the microorganisms in the fertilizer and those enriched in the soil can secrete organic acids that help to regulate soil pH, reduce the inhibitory effects of saline–alkali soils on plants, and promote plant growth [34]. In this study, significant differences were found in the physicochemical properties of the saline–alkali field at Ningxia Nuanquan Farm after the improvement (Table 2). Soil pH and electrical conductivity (EC) showed a decreasing trend with the duration of improvement, leading to increases in organic matter (OM), available nitrogen (AN), and available phosphorus (AP). This indicates that high pH and EC can affect the availability of OM, AN, and AP to plants [35]. Research has demonstrated that the application of microbial agents can enhance the soil desalination rate, as beneficial microorganisms in saline–alkali soils can produce polysaccharides and mucilage during their activity, forming soil binders that affect aggregate structure, reduce bulk density, and decrease non-capillary pore space, thereby accelerating salt leaching and reducing soil salinity [36]. This is consistent with the results of this study, where the concentrations of three major salt ions—Mg²⁺, SO₄²⁻, and HCO₃⁻—displayed a decreasing trend. Na⁺ and Cl⁻ decreased significantly in the second year. However, Ca²⁺ exhibited a significant increase during both harvests. As demonstrated by Budamagunta et al. [10], microbial activity can increase the effective calcium content in the soil, enhance soil buffering capacity, reduce the concentration of harmful ions, and further improve the crop salt and alkali tolerance. Microbial fertilizers activate native soil microorganisms, and the constituent organic matter of these fertilizers provide additional nutrients, thereby increasing microbial populations.

Soil enzymes serve as a link between plants and nutrients, objectively reflecting soil fertility and indirectly influencing the cycling of nutrient elements [37]. Frankenberger et al. [38] found that the activities of alkaline phosphatase, sucrase, urease, and catalase in different soil samples were closely related to microbial respiration and biomass, indicating that these soil enzyme activities are the best predictors of microbial community activity and quality. In this study, the activities of urease, alkaline phosphatase, and sucrase in the soil at harvest were higher than those before sowing, after the application of microbial fertilizer. This may be the main reason that the application of microbial fertilizer accelerates the decomposition of organic matter in the soil, providing substrates for enzyme-catalyzed reactions [39]. Catalase can decompose hydrogen peroxide, which is toxic to plants, and is widely distributed in soils and organisms [40]. In this study, soil catalase activity revealed a downward trend at harvest, possibly due to changes in the soil microecological environment after the application of microbial fertilizer, which alleviated saline–alkali stress and reduced the level of hydrogen peroxide [41]. Overall, the application of microbial fertilizer improved the nutrient content and enzyme activity in saline–alkali soil, reduced some harmful physicochemical factors and ion content, and had a certain effect on improving saline–alkali soil.

4.2. Effects of Microbial Fertilizer on Microbial Diversity and Community Structure and Composition in Saline–Alkali Soil

Soil microbial diversity and relative abundance are important indicators of soil fertility. Increasing the number of beneficial microorganisms in the soil can effectively promote plant growth and development. The application of microbial fertilizer not only facilitates the formation of new microbial communities around plant roots but also improves the ecological environment of the soil [42]. The results indicated that with the application of microbial fertilizer to improve the saline–alkali field, the Ace, Chao1, and Sobs indices of soil bacteria and fungi at harvest over two years were significantly higher than those before sowing. Among the treatments, NS3 had the highest species richness while NS0 had the lowest. The Shannon and Simpson indices of soil samples at different periods also varied. This suggests that the application of microbial fertilizer can enhance the diversity and relative abundance of soil microorganisms in saline–alkali fields (Table 3). Research indicates that the use of microbial fertilizer significantly increases the diversity of soil microbial communities, promoting the proliferation of beneficial bacteria and thereby enhancing microbial activity [43,44]. Members of Ascomycota and Basidiomycota dominate the fungal communities in all soil samples [45]. The dominant bacterial phyla in agricultural soils are Proteobacteria, Acidobacteriota, Bacteroidota, and Actinobacteriota. The application of microbial inoculants alters the relative abundance of certain groups to some extent [46]. In this study, the highest proportion of bacterial and fungal OTUs was observed in the soil samples collected at harvest in the second year of improvement. The community composition at the phylum level identified different dynamic changes over time. The dominant bacterial phyla were Proteobacteria, Actinobacteriota, and Chloroflexi, which, together, accounted for over 70% of the total bacterial community’s relative abundance. The relative abundance of Proteobacteria increased significantly during both harvest periods, while that of Actinobacteriota decreased gradually during each sampling period. The effect on Chloroflexi was minimal, but notably, the relative abundance of Acidobacteriota increased continuously over the four periods, suggesting that it may be an important factor in reducing soil pH. The dominant fungal community was Ascomycota, which had the highest relative abundance in all periods, while Basidiomycota saw an increase in relative abundance at the second-year harvest. Chytridiomycota appeared in both harvest periods. The species diversity and richness of fungi at the phylum level were lower than those of bacteria. The results of this study indicate that the application of microbial fertilizer in the improvement of saline–alkali soils has a certain effect on Acidobacteriota in bacteria and the appearance of Chytridiomycota in fungi, which may play a crucial role in the improvement of saline–alkali soil.

4.3. Differences in Soil Microbial Community Structure and the Relationship to Soil Physicochemical Properties

The structure of soil microbial communities is closely related to the soil environment, and ameliorative practices can lead to changes in soil physicochemical properties, thereby altering the structure of microbial communities. Most researchers believe that the soil type is primarily determined by the composition of microbial communities or is influenced by complex soil–plant interactions [47,48]. Previous studies have also shown that microbial fertilizers can enhance soil fertility by altering the microbial community structure in the rhizosphere [49]. In their study on the response of soil microbial communities in maize fields to salt stress, Hou et al. [50] found a significant separation between different soil samples, indicating remarkable differences between the microbial communities of different soil samples. In this study, PCA was performed on the OTU data of bacteria and fungi, which showed that the contributions of PC1 and PC2 for bacteria were 21.72% and 15.61%, respectively, while for fungi, they were 17.58% and 12.51%. The analysis revealed a certain distance between the different treatments in the PC1 and PC2 plots, indicating significant microbial differences between the groups. This implies that the application of microbial fertilizer has a significant impact on soil microbial diversity in the improvement of the saline–alkali soil. Redundancy analysis (RDA) was used to describe the relationship between soil physicochemical properties and microbial community distribution. Previous studies have shown that environmental factors such as pH, SOC, AP, and enzyme activity determine soil microbial communities in different ecosystems [51,52]. The RDA in this study indicated that AP, EC, and pH were the main factors influencing the bacterial community structure after the improvement of a saline–alkali field, while AP, AK, and DOC were the key factors affecting the fungal community structure. This highlights the important role of these physicochemical factors in the changes to soil microbial communities after the application of microbial fertilizer, demonstrating their significant potential in the improvement of saline–alkali soil.

5. Conclusions

In this study, after two years of saline–alkali field improvement by microbial fertilizer, it was found that soil pH and electrical conductivity (EC) were significantly reduced. Eliminating the theoretical increase after fertilization, the contents of physicochemical factors such as organic matter (OM), dissolved organic carbon (DOC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) were increased. At harvest, the Ca²⁺ content increased significantly, while the levels of other ions showed a decreasing trend. A two-year harvesting period significantly increased the activities of soil urease, alkaline phosphatase, and sucrose synthase, while the activity of peroxidase decreased. In addition, the two-year improvement period significantly altered the diversity of microbial communities, significantly affecting the abundance and composition of bacterial and fungal communities in the saline–alkali field. The relative abundance of the dominant bacterial phyla, Proteobacteria and Chloroflexi, increased after improvement, while Actinobacteriota decreased. Acidobacteriota increased gradually over the four periods. The dominant fungal phylum, Ascomycota, displayed a reduced relative abundance during the improvement process, with the presence of Chytridiomycota observed during both harvest periods. Principal component analysis (PCA) of bacteria and fungi revealed significant differences in the microbial communities throughout the improvement process. Redundancy analysis (RDA) identified key physicochemical factors influencing soil bacterial community structure as AP, electrical conductivity (EC), and pH, while AP, AK, and DOC were significant for fungal community structure. This suggested that these factors contributed to the changes in soil microbial communities. Consequently, in agricultural production, microbial fertilizer could be used to regulate the stability of the soil–crop ecosystem, thereby mitigating the extent of saline–alkali stress. Subsequently, our team will continue to apply microbial fertilizer at different levels of saline–alkali soils to explore the saline–alkali tolerance of crops and to control the changes in the microecology of saline–alkali soils. Through more systematic experimental methods, we aim to provide effective scientific data and practical application value for the improvement of saline–alkali fields in northwest China.