1. Introduction
The stability and sustainability of crop production are particularly important when arable land is limited. The use of scientific and effective fertilization measures is one of the most important ways to improve crop yields, such as the implementation of inorganic–organic fertilization, bacterial agents, bio-organic fertilizers, or the preparation of bio-organic and inorganic compound fertilizers to partially or completely replace chemical fertilizers and other fertilizer application measures, which have a significant role in increasing crop yields, improving soil fertility, reducing crop pests and diseases, and improving environmental pollution [1]. Changes in microbial communities have potentially important implications for agricultural productivity. Soil microbial communities play a key role in regulating soil nutrient cycling and plant productivity and are important indicators of soil health. In agroecosystems, these microbial communities directly affect crop health and yield. Stability and diversity of soil microbial communities are essential for maintaining soil function and crop yields. Inter-root microbes can promote plant growth by improving soil structure, regulating plant hormone and nutrient levels, suppressing pathogens and pests, and increasing plant resistance to abiotic adversity. Changes in microbial communities affect agricultural productivity through a variety of pathways, including the promotion of nutrient cycling, enhancement of plant health, and improvement of crop yields. Therefore, rational management of soil and plant microbial communities is important for the development of sustainable agriculture. Organic fertilizers replacing part of chemical fertilizers can promote the green development of agriculture and reduce agricultural surface pollution [2]. The application of organic fertilizers can improve soil fertility, provide crop nutrition, and improve crop quality. Wen Yanchen et al. [3], through long-term observation of different fertilizer positioning tests, showed that a single application of organic fertilizers and organic and inorganic fertilizers with a single application of chemical fertilizers can effectively increase the soil nutrient content. Bacterial agents are rich in a certain number of active microorganisms, which can not only replace the use of chemical fertilizers and pesticides in agriculture but also supplement effective nutrients in the soil and increase the content of soil organic carbon, which is of far-reaching significance to the absorption and utilization of the soil [4,5]. Bacterial agents can promote plant growth, produce hormones to stimulate the root system, and enhance nutrient absorption. It can fix nitrogen in the atmosphere and reduce the use of chemical fertilizers, improve soil structure and enhance water and fertilizer retention, decompose organic carbon, and provide plant nutrients, thus replacing part of the chemical fertilizer to provide nutrients for the soil. Li Jingjing et al. [6] found that increased application of bacterial agents can promote crop growth, increase fruit quality, and improve soil nutrients. He Jia et al. [7] showed that the application of bacterial agents can significantly promote the growth and development of wolfberry plants and effectively improve soil organic carbon content. Li Xiang et al. [8] showed that organic fertilizer and fungicide can alleviate the decline of soil properties, imbalance of crop inter-root microbial structure, and yield decline caused by continuous cropping. Zheng et al. [9] showed that organic fertilizers and fungicides can increase soil organic carbon, available nitrogen dissolved nitrogen, and available phosphorus content, and improve crop quality. The study of the effects of different fertilization practices on the bacterial community structure of seed-producing maize fields can be widely applied in several fields of agriculture and environmental science. In terms of agricultural sustainability, fertilizer application strategies can be optimized and can increase crop yields while reducing negative environmental impacts. In soil health and fertility, a better understanding can be gained of how to manage soils to maintain and improve their productivity. In the area of agricultural surface pollution management, irrational fertilization can lead to nutrient loss and increase the risk of agricultural surface pollution. Studying the effects of different fertilization measures on soil bacterial communities can help develop strategies to reduce pollution. In terms of agricultural science and technology innovation, with the advancement of agricultural science and technology, new fertilization technologies and products are emerging. Studying how these new technologies affect soil microbial communities can promote agricultural science and technology innovation and application. In the area of soil microbial community structure stabilization and maintenance mechanism, the study of soil microbial community structure stabilization and maintenance mechanism can help us understand the sustainability of soil ecosystem service function and provide theoretical guidance for future regulation of soil microbiome to promote the stabilization of soil ecological function. In terms of soil structure and microbiome function, the structure and function of soil microbial communities are closely linked to the integrity of soil structure. Investigating how different fertilization practices affect the link between soil structure and microbiome function is essential for building climate-smart, resource-efficient, and resilient agroecosystems.
Bacteria are the most active and important components of soil microorganisms, and bacterial community composition and species diversity are the main indicators of soil ecological functions, and their metabolic functions are the main drivers of soil ecological processes [10]. Bacteria are capable of decomposing plant litter, degrading organic carbon, promoting humus formation, and improving soil structure and quality [11], and are commonly used to characterize soil ecological stability [12]. Soil bacteria are extremely sensitive to changes in the external environment [13], and changes in fertilizer application [14], application of microbial agents [15], and different field management [16] can lead to different degrees of changes in the structure of the bacterial community, thus affecting the stability of the soil ecosystem and ecological function. Organic fertilizers and bacterial agents can improve soil fertility, promote nutrient cycling, and increase biodiversity in an ecological sense. In the economic sense, they can increase yield, save costs, and improve fertilizer utilization. Fertilization of corn seed production fields not only helps to improve crop yield and economic efficiency, but also helps to maintain and improve the soil ecological environment, which is important for improving soil quality and achieving sustainable development of agriculture. The current research focuses on the effects of organic fertilizers and fungicides on crop yield and soil properties; under the joint action of the two, the changes in soil bacterial communities and their ecological functions have rarely been reported. Compared with the traditional fertilization methods of bottom fertilization and follow-up fertilization, the amount of fertilizer applied is large and the fertilizer utilization rate is low and is not conducive to crop growth and environmental protection. Here, with the use of the drip irrigation fertilization method, the fertilizer is applied to the soil in small quantities seven times, preventing the loss of fertilizer caused by leaching and surface runoff. This ensures that more fertilizer is absorbed by the crop, thus improving the fertilizer utilization rate.
Therefore, in this study, the structural characteristics and ecological functions of soil bacterial communities in seed corn fields were investigated with the help of high-throughput sequencing technology under two forms of organic fertilizers and fungicides. Organic fertilizers and bacterial agents are important for improving soil quality and sustainable development of agriculture, but current research focuses on the effects of organic fertilizers and fungicides on crop yield and soil properties, and the changes in soil bacterial communities and their ecological functions under the combined effects of the two are seldom reported. Therefore, in this study, the structural characteristics and ecological functions of soil bacterial communities in seed corn fields under two forms of organic fertilizers and fungicides were investigated with the help of high-throughput sequencing technology to provide a scientific basis for the improvement of soil quality and soil health and to determine suitable fertilization measures in seed corn fields.
2. Materials and Methods
2.1. Overview of the Study Area
The experiment started in 2021 and was conducted in the test field of Huangyang River Farm, Huangyang Town, Liangzhou District, Wuwei City, Gansu Province (37°43′41″ N, 102°57′46″ E). The trial occurred in the cold, temperate, arid climate zone, with an average elevation of 1660 m, average annual temperature of 7.2 °C, 2968 sunshine hours, ≥0 °C cumulative temperature of 3514 °C, ≥10 °C cumulative temperature of 2985 h, average annual precipitation of 156 mm, and average annual evaporation of more than 2400 mm. The previous crop was seed maize; the pre-existing soil properties of the trial site are shown in Table 1.
2.2. Experimental Design
The experimental treatment was the CK treatment control; M was a single application of liquid organic fertilizer, which was applied with irrigation at the seedling stage (4–6 leaves) and at the large trumpet stage, 10 kg/mu at a time. The BF treatment was a single fungicide application, with 1 kg/mu applied at the seedling stage (4–6 leaves). The main ingredient in the bacterial agent is the compound bacillus, which has Bacillus subtilis, Bacillus licheniformis, bacillus amyloliquefaciens, actinomycete, etc. Liquid organic fertilizer is a fermented liquid, the main ingredients of which are organic matter, potassium humate, and so on. The liquid organic fertilizer and fungicide were provided by Gansu Modern Agriculture Development Co., (Lanzhou, China); The M + BF treatment was applied with both liquid organic fertilizer and fungicide in the same way as above. Drip irrigation fertilization was applied a total of 7 times during the reproductive period of corn, consisting of 25.6 kg of special fertilizer and 18.4 kg of urea per mu.
2.3. Soil Sample Collection
In early September 2021, the soil in the 0–20 cm layer was collected by the “diagonal” five-point sampling method and the impurities in the soil samples (plant litter, roots, stones, etc.) were removed. The samples were sifted after drying naturally and used to determine soil pH, organic carbon, available phosphorus, available potassium, nitrogen, and other chemical indexes. In early September 2022, soil samples were collected, thoroughly mixed, and placed in sterilized self-sealing bags, kept on dry ice, and brought back to the laboratory quickly. The soil samples were divided into two parts, one sample was sieved through 2 mm and placed in a refrigerator at −80 °C for soil microbiological determination, and one was used for chemical determination (same as in 2021).
2.4. Measurement of Soil Physical and Chemical Indicators
An acidimeter (PB-10, Sartorius, Göttingen, Germany) was used to determine pH (water/soil ratio of 5:1), the sulfuric acid–potassium dichromate external heating method was used to determine organic carbon (SOC), the Kjeldahl nitrogen method was used to determine total nitrogen (TN), available nitrogen (AN) was determined by available nitrogen diffusion method, sodium bicarbonate leaching–aluminum antimony colorimetry was used to determine available phosphorus (AP), and ammonium acetate leaching–flame photometry was used to determine available potassium (AK). All of the above determinations are referenced in the Soil Agrochemical Analysis [17].
2.5. Bacterial 16S rRNA High-Throughput Sequencing
DNA extraction: Extraction was performed using the PowerSoil® DNA extraction kit (MoBio Laboratories, San Diego, CA, USA), and the concentration of the extracted genomic DNA was determined using 1% agarose gel electrophoresis, stained with ethidium bromide, and then detected on a gel imaging system.
PCR amplification: PCR amplification of the V4~V5 region of the bacterial 16S rRNA gene was performed using primers 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′-CCGT-CAATTCMTTTRAGTTT-3′) [18]. The amplification program was as follows: pre-denaturation at 95 °C for 2 min, 25 cycles of 95 °C (30 s), 55 °C (30 s), and 72 °C (30 s), and final extension at 72 °C for 5 min at the end of the cycle. PCR was performed using TransGenAP221-02: Trans Start Fast pfu DNA Polymerase; PCR instrument: ABIGeneAmp® Model 9700 (Thermo Fisher Scientific, Waltham, MA, USA). Each sample was run three times, and PCR products from the same sample were mixed and detected by 2% agarose gel electrophoresis, after which the amplicons were recovered. PCR products were cut and recovered using the Axy Prep DNA Gel Recovery Kit (AXYGEN, Union City, CA, USA), eluted with Tris-HCl, and detected by 2% agarose electrophoresis. The PCR products were detected and quantified by Quanti Fluor™-ST Blue Fluorescence Quantification System (Promega, Beijing, China) [19]. Finally, sequencing was performed with the help of a high-throughput sequencing platform (IlluminaMiseqPE300) from Shanghai Meiji Biomedical Technology Co., Shanghai, China.
2.6. PICRUSt Gene Function Prediction
The OTU abundance table was normalized by PICRUSt [20]. To obtain COG family information and KO information corresponding to OTUs, the abundance of each COG and KO abundance were calculated. Using the information in the COG database, the description information and function information of each COG were parsed to obtain the functional abundance spectrum. The information in the KEGG database was used to obtain KO, pathway, and EC information, and the abundance of each functional category was calculated according to the OTU abundance.
2.7. Data Processing
The data were analyzed and processed using SPSS 21.0 and Excel 2019 software, and the significance of differences was analyzed using one-way analysis of variance (ANOVA) and multiple comparisons method (Duncan). Correlation heatmap (Heatmap) and PICRUSt gene function prediction were carried out using the I-Sanger cloud platform of the Shanghai Meiji Company (Shanghai, China), redundancy analysis of soil bacterial communities (RDA) and principal component analysis (PCA) were carried out using CANOCO5.0 software, and graph modifications were performed using AI 2020 software.
3. Results
3.1. Analysis of Soil Chemical Properties Under Different Fertilization Measures
As shown in Table 2, in 2021, compared with CK, soil organic carbon and available phosphorus contents of different fertilization measures showed an increasing trend in which the organic carbon content of each fertilization treatment was significantly increased (p < 0.05). Available phosphorus contents of M, BF, and M + BF treatments were significantly higher than that of the control CK (p < 0.05). The total nitrogen content of the M + BF treatment was significantly increased (p < 0.05), whereas that of the M treatment was lower than CK (the difference was not significant). The quick-acting potassium content of the BF treatment was significantly increased, whereas that of the M treatment was significantly reduced (p < 0.05). The soil available nitrogen content of different fertilization measures showed an increasing trend, and the pH of the soil had a decreasing trend compared with CK, in which the M and BF treatments were significantly reduced compared with CK and there was no significant difference in the M + BF treatment compared with CK.
The patterns of change in soil chemical traits were similar between the cultivation measures in FY2022 and FY2021. Compared with CK, the organic carbon content of BF and M + BF treatments increased significantly (p < 0.05); the available phosphorus content of M, BF, and M + BF treatments increased significantly (p < 0.05); the total nitrogen content of M + BF treatments increased significantly (p < 0.05); and the quick-acting potassium content of BF treatment increased significantly, while that of M + BF treatment decreased significantly (p < 0.05). The pH values of different fertilization measures showed a decreasing trend, but there were no significant differences between treatments. The soil pH value tended to decrease compared to CK, but there were no significant differences between the treatments. Compared with 2021, the differences in pH values of BF and M + BF treatments were smaller. Soil organic carbon content showed an increasing trend, except for CK; compared with the previous year, soil total nitrogen only increased significantly in the M + BF treatment, while all other treatments showed a decreasing trend. The changes in the chemical soil traits of the various fertilizer measures in the years 2021 and 2022 were similar.
3.2. Soil Bacterial Community Diversity
The results of soil bacterial community diversity analysis are shown in Figure 1. The Sobs index (Figure 1A), Chao index (Figure 1B), and Shannon index (Figure 1D) showed that the M + BF treatment was significantly lower than CK (p < 0.05). The Sobs index and Shannon index of the M treatment were significantly higher than those of the BF and M + BF treatments, and the Chao index of the M + BF treatment was significantly lower than those of the M and BF treatments (p < 0.05). These indicate that the diversity and richness of bacterial communities were higher in the M treatment.
PCoA analysis of the distance matrix between samples from different treatments based on the Bray–Curtis algorithm (Figure 2) showed that the explained degree of difference in the composition of the samples in the first axis was 25.46%, and the explained degree of difference in the composition of the samples in the second axis was 20.08%. The soil samples in the M treatment were concentrated in quadrants 2 and 3, and the soil sample points for the CK, BF, and M + BF treatments were concentrated in quadrants 1, 2, and 4 and were clustered together, indicating that there was a similarity in the bacterial community structures of the CK, BF, and M + BF treatments and that the distance between the soil sample points of the M treatment and those of the CK, BF and M + BF treatments was greater, indicating that there was a significant difference in the soil bacterial community composition of the M treatment and those of the CK, BF, and M + BF treatment and the soil bacterial community composition was significantly different (p < 0.05). This indicates that the application of liquid organic fertilizer alone in the M treatment can significantly change the soil bacterial community structure.
3.3. Soil Bacterial Community Composition
In this study, 4603 OTUs were obtained by high-throughput sequencing of bacterial 16S rRNA genes, which were mainly distributed in 39 phyla, 123 phyla, 306 orders, 471 families, 794 genera, and 1624 species. Figure 3 shows the horizontal community composition of soil bacterial phyla, in which there were 11 species with relative abundance greater than 1%. Those with relative abundance greater than 1% were categorized as Others (3.24–3.58%), and there were fewer species with unclear classification (Unclassified), which only accounted for 1.03–1.16% of the total sequences. The clearly classified phyla Proteobacteria (20.14–25.30%), Actinobacteriota (18.21–20.47%), Actinobacteriota (13.55–22.00%), and Chloroflexi (14.24–17.59%) were all dominant. The relative abundance of the remaining classified bacterial taxa in descending order were Firmicutes (5.63–5.95%), Gemmatimonadota (4.18–5.23%), Myxococcota (2.27–2.79%), Bacteroidota (2.16–2.60%), Nitrospirota (1.20–1.65%), Methylomirabilota (1.01–1.43%), and Planctomycetes (0.77–1.41%). Compared with CK, the relative abundance of soil Proteobacteria, Actinobacteriota, Gemmatimonadota, and Bacteroidota increased in the M and BF treatments and decreased in the M + BF treatment, but there was no significant difference between treatments; the relative abundance of Planctomycetota gradually increased in the M, BF, and M + BF treatments, with a significant increase of 83.12% in the M + BF treatment.
Figure 4 shows the genus-level community composition of soil bacterial communities in different treatments, of which a total of 18 genera have been classified, with relative abundances greater than 1% for Bacillus (2.18–3.81%), RB41 (1.84–3.40%), Arthrobacter (2.14–2.69%), Sphingomonas (1.90–2.60%), Streptomyces (1.38–2.04%), Skermanella (1.19–1.73%), and Nitrospira (1.20–1.65%). Others were unlabeled and unnamed species. The dominant genera were Bacillus, RB41, Arthrobacter, and Sphingomonas. Compared with CK, the relative abundances of Bacillus and PaeniBacillus were significantly lower, and the relative abundance of Turicibacter was significantly higher in the M treatment. The relative abundance of Sphingomonas was significantly lower in the M and M + BF treatments, but significantly higher in the BF treatment. The relative abundance of Subgroup_10 was significantly lower in the BF and M + BF treatments, while the relative abundance of Skermanella was significantly higher in the BF treatment (p < 0.05).
3.4. Relationship Between Soil Bacterial Communities and Environmental Factors
To further reveal the main environmental factors affecting soil bacterial communities, a redundancy analysis was carried out with genus-level bacterial taxa as the response variable and soil chemical indicators as the explanatory variables (Figure 5). The results showed that the degrees of explanation of the bacterial community on the two sorting axes were 57.84% and 6.90%, respectively, and some of the bacterial taxa were more correlated with soil environmental factors: Streptomyces, Subgroup_10, Marmoricola, Nocardioides, Nitrospira, and Oceanobacillus were positively correlated with available potassium and available nitrogen; RB41 was positively correlated with available potassium, total nitrogen, and organic carbon content; and Turicibacillus and Arthrobacter were positively associated with pH. Soil available nitrogen dissolved nitrogen (p = 0.044) was the main environmental factor affecting soil bacterial communities of different fertilization measures.
3.5. Prediction of Soil Bacterial PICRUSt Function in Different Post-Crops
PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) is a bioinformatics tool that utilizes the sequences of tagged genes (e.g., 16S rRNA) in microbial communities to predict their functional composition. This approach analyzes the 16S rRNA sequences of microbes with known genomes to infer the functional profiles of genes from their common ancestors and, in turn, predict the functional profiles of genes from unsequenced species. PICRUSt predicts the metabolic functions of colonies by mapping the composition of colonies obtained from sequencing into a database. In this study, the data obtained from high-throughput sequencing were compared with the KEGG database (Kyoto Encyclopedia of genes and genomes) and further analyzed for PICRUSt function prediction (Table 3). The results showed that the soil bacterial community-level functions were mainly composed of six major categories: metabolism, genetic information processing, environmental information processing, cell transformation, organic systems, and human diseases, of which metabolism was the main functional component of bacteria, accounting for 78.45% to 78.94%. Compared with CK, the abundance of genes for metabolic function and genetic information processing decreased in the M and BF treatments and increased in the M + BF treatment; the abundance of genes for environmental information processing, cellular transformation, human disease, and organic system functions increased in the M and BF treatments and decreased in the M + BF treatment, but none of the treatments showed significant differences.
The prediction results of soil bacterial secondary function layers based on KEGG data (Figure 6) showed that the secondary functions of soil bacterial communities mainly consisted of 46 subfunctions, including global and overview pathways, signal transduction, exogenous substances degradation and metabolism, substance dependence, transport and catabolism, and environmental adaptation, of which global and overview pathways (40.49–40.83%), carbohydrate metabolism (9.30–9.41%), amino acid metabolism (8.07–8.10%), and energy metabolism (4.48–4.51%) were the major subfunctions of soil bacterial communities under different fertilization measures. Compared with CK, the abundance of functional genes for terpenoid and polyketide metabolism, endocrine system, excretory system, and immunity system was significantly higher, and the abundance of functional genes for the digestive system was significantly lower (p < 0.05) in soil bacterial communities in the M treatment, while there were no significant differences between all other treatments. The relative abundances of infectious disease/parasitism functional genes were significantly higher in BF and M + BF treatments than in the M treatment, and the abundance of material-dependent functional genes was significantly lower than in the M treatment (p < 0.05).
The results of the principal component analysis (PCA) of soil bacterial secondary functional diversity (Figure 7) showed that the explained degrees of functional differences in bacterial communities were 87.96% and 6.76% for PC1 and PC2 axes, respectively. CK functional genes were mainly concentrated in quadrants 1 and 3, M treatment functional genes were mainly concentrated in quadrant 4, BF treatment functional genes were mainly concentrated in quadrants 1, 3, and 4, and M + BF treatment functional genes were mainly concentrated in quadrants 2 and 3. Compared with CK, the M treatment group had the largest difference in functional genes, indicating that the M treatment with a single application of liquid organic fertilizer could significantly affect the expression of bacterial functional genes in the soil, which in turn resulted in the difference in the function of the bacterial community in the soil. There were some differences in the ecological functions of soil bacterial communities in the BF and M + BF treatments, indicating that different fertilization measures significantly affected the ecological functions of soil bacteria.
4. Discussion
4.1. Effects of Different Fertilization Measures on Soil Bacterial Community Structure
Energy flow and nutrient cycling in soil ecosystems as well as the abundance, community structure, and diversity of soil microorganisms influence crop utilization of soil nutrient resources and soil health [21]. However, key biological processes in soil, such as the formation of soil structure, mineralization of soil organic carbon, and formation of humus, are closely related to soil microbial activity and population, among others [21]. Soil bacteria have vigorous metabolism, rapid reproduction, and many species, and the number accounts for about 70–90% of total soil microorganisms, which is an important part of the quality of the soil microbiological environment and has an important impact on the growth of crops. The structure of the soil bacterial community is influenced by the crops grown, and the heterogeneity in root secretions of different plant types leads to changes in the environment of soil microorganisms and differences in bacterial composition and abundance [22]. In this study, it was found that the soil bacterial community composition was similar among the different fertilization practices and consisted of 11 taxa, mainly Actinobacteriota, Proteobacteria, Actinobacteriota, Chloroflexi, Firmicutes, Gemmatimonadota, Myxococcota, Bacteroidota, Nitrospirota, Methylomirabilota, and Planctomycetota, although the abundance of each taxon varied. Actinomycetes, Proteobacteria, Actinobacteriota, and Chloroflexi were the most abundant groups of soil bacteria under different fertilization practices, which was attributed to the strong environmental adaptability and relatively wide ecological range of Actinomycetes, Proteobacteria, and Actinobacteriota, which resulted in their wide distribution in different types of ecosystems (woodland, grassland, and soil, etc.), where most of them existed as dominant phyla [23]. The significant increase in the relative abundance of Planctomycetota may be attributed to the fact that the simultaneous application of organic fertilizers and bacterial fertilizers to the soil provided a richer substrate for the growth of the soil bacterial community, which promoted the heterogeneity of the soil resources and the growth and reproduction of Planctomycetota [24]. Meanwhile, even though the average relative abundance of Planctomycetota in soils with different fertilization measures was only 0.94%, the significant variation it showed suggests that this taxon also plays an important role in soil ecosystems in this study, although the specific role needs to be investigated further.
Sphingomonas is a dominant genus in soil bacterial communities under different fertilization measures. The study of Sphingomonas by Liu Hui et al. [25] found that Sphingomonas has an efficient metabolic regulation mechanism and gene regulation ability, and it is widely distributed in nature, encompassing the atmosphere, soil, water, plants, and even some unfavorable environments such as chemically polluted, high salinity, and nutrient-poor environments. Also, this study found that the relative abundance of soil Sphingomonas was significantly lower in the liquid organic fertilizer treatment alone and in the liquid organic fertilizer in combination with mycorrhizal application treatment, while it was significantly higher in the BF treatment. This coincided with the trend of soil fast-acting potassium content in this study, indicating that fast-acting potassium content is a limiting factor affecting the growth of Sphingomonas in soils with different fertilization measures.
At the phylum level, Proteobacteria, Actinobacteriota, Actinobacteriota, and Chloroflexi were the core flora. In this study, it was found that the relative abundance of soil Proteobacteria phylum and Actinobacteriota phylum increased in the M and BF treatments compared to CK, while it decreased in M + BF treatment, although there was no significant difference between treatments; the relative abundance of Planctomycetes phylum gradually increased in M, BF and M + BF treatments, with a significant increase of 83.12% in the M + BF treatment. Proteobacteria contribute to nitrogen cycling, organic matter decomposition, plant growth promotion, and pathogen suppression in soil. They improve nitrogen availability by enhancing nitrogen fixation and nitrification while breaking down organic matter, forming humus, promoting phytohormone production, improving plant health, and reducing disease. Actinobacteriota promote plant health and soil improvement by breaking down organic matter and producing antibiotics. They convert organic matter into readily absorbable forms, inhibit harmful microorganisms, and enhance crop growth and disease resistance while improving soil structure and fertility. This study noted that M and BF treatments promote the growth of Proteobacteria and Actinobacteriota, which play key roles in nitrogen cycling, organic matter decomposition, and plant growth promotion; therefore, a rational mix of microbial and organic fertilizers can improve soil fertility and crop yields. The M + BF treatment led to a decrease in the relative abundance of Proteobacteria and Actinobacteriota, which may be due to the antagonistic effects of some components in the fertilizer mix. Future fertilization strategies should consider the interactions between fertilizer components and avoid combinations that may lead to negative effects. Planctomycetes are involved in nitrogen cycling and organic matter decomposition, affect soil structure, and play a role in bioremediation. They enhance anaerobic ammonia oxidation, promote plant nutrient transformation, improve soil properties, and participate in pollutant degradation. The relative abundance of Planctomycetes increased in all treatments, suggesting that this group may be more sensitive to fertilization practices. The role of Planctomycetes in nitrogen cycling and organic matter decomposition should be considered in fertilization strategies to promote the growth of these flora in order to enhance soil biological functions.
4.2. Effects of Different Fertilization Measures on Soil Bacterial Ecological Functions
Bacteria are the most dominant class of soil microorganisms, and their community structure and soil ecological function are based on the change in functional gene abundance, which has a certain indication of soil environmental changes [26]. In this study, we used high-throughput sequencing technology to predict the PICRUSt function of soil bacterial communities treated with different fertilization measures and found that the core function of soil bacterial communities with different fertilization measures was metabolism. Metabolism provides energy for the bacteria through the uptake of carbohydrates, amino acids vitamins, etc., and is the core function of the bacterial community, which also coincides with the conclusions of many studies related to bacteria in soil ecosystems [27,28]. Guo Zhen et al. [29] found that the carbon sources absorbed and utilized by soil bacteria in soil with different compound ratios were mainly carbohydrates such as glucose, maltose, and starch, and it was shown in the study that the bacteria had a strong metabolism of carbohydrates. In an in-depth study of primary metabolic pathways in soil bacteria, we noted 46 different subfunctions, including cofactor and vitamin metabolism, nucleotide metabolism, degradation and metabolism of exogenous substances, metabolism of terpenoids and polyketides, as well as virulence issues in infectious diseases, cell growth and death, substance dependence, drug resistance (especially antitumor drugs), and cellular communities of eukaryotes. Of particular interest were significant changes in gene expression levels of eight subfunctions: endocrine system, terpenoid and polyketide metabolism, parasitism in infectious diseases, substance dependence, digestive system, excretory system, and immune disorders. Soil bacteria are capable of sensing changes in the external environment and responding rapidly, and this process releases some small molecules that enable them to adapt to environmental changes more quickly. When the accumulation of these signaling substances reaches a certain threshold, the expression of downstream functional genes is activated [30,31], which ultimately leads to the secretion of toxins, the production of antibiotics, and the formation of biofilm [30], which can be used to monitor changes in the surrounding environment [32]. The relative abundance of infectious disease/parasitism functional genes of soil bacterial communities in the BF (Gemmatimonadota) and M + BF (organic fertilizer + Gemmatimonadota) treatments was significantly higher than in the M (liquid organic fertilizer) treatment, and the abundance of substance-dependent functional genes in the M + BF treatment was significantly lower than that in the M treatment. This may be because the application of liquid organic fertilizer alone or liquid organic fertilizer in combination with Gemmatimonadota promoted or suppressed the expression of functional genes of substance-dependent, drug-resistant/antitumor drugs, infectious disease/parasitism, and so on, in soil bacterial communities. The abundance of functional genes for terpenoid and polyketide metabolism, and endocrine, excretory, and immunogenic systems was significantly higher in the M treatment soil bacterial community, while the abundance of functional genes for the digestive system was significantly lower. This suggests that liquid organic fertilizers can promote the activity of specific metabolic pathways in the soil, providing a molecular-level basis for future fertilization strategies. The relative abundance of infectious disease/parasitic functional genes was significantly higher in the BF and M + BF treatments than in the M treatment, while the abundance of substance-dependent functional genes was significantly lower in the M + BF treatment than in the M treatment. This suggests that different fertilization practices may affect the functional gene expression of soil bacterial communities, and future fertilization strategies should be tailored to meet the needs of crops and soil properties. The adjustment of fertilization strategies can finely regulate the functional gene expression of soil microorganisms, which in turn affects soil biochemical processes. Therefore, rationally designed fertilization programs can optimize the function of soil microbial communities, enhance soil ecosystem services, and improve the potential for biological control of plant diseases while reducing negative impacts on the environment.
5. Conclusions
In this study, we used high-throughput sequencing technology to deeply analyze soil bacteria under different fertilization practices and identified a total of 4603 OTUs covering 39 phyla, 123 orders, 306 orders, 471 families, 794 genera, and 1624 species. Among them, Proteobacteria, Actinobacteriota, Actinobacteriota, and Chloroflexi were the dominant phyla in the soil, while Bacillus, RB41, Arthrobacteria, and Sphingomonas were the main dominant genera. Through redundancy analysis (RDA), we found that available nitrogen was a key environmental factor influencing the structure of soil bacterial communities, suggesting that the effectiveness of soil nutrients plays a decisive role in shaping microbial communities. Further PICRUSt functional prediction analysis revealed the effects of different fertilization practices on the abundance of functional genes in the soil bacterial community. Specifically, the M-treated soil showed a significant increase in the abundance of functional genes for terpene and polyketide metabolism, the endocrine system, the excretory system, and the immunogenic system, while the abundance of functional genes for the digestive system was significantly reduced. This may imply that microbial fertilizers promoted the activity of some metabolic pathways in the soil while suppressing others. For the BF and M + BF-treated soils, we observed that the relative abundance of infectious disease/parasitic functional genes was significantly higher than that of the M treatment. In addition, the abundance of substance-dependent functional genes was significantly lower in the M + BF-treated soils than in the M treatment, which may reflect the complex effects of different fertilization practices on the expression of soil microbial functional genes.
From the gate-level analysis, we found that both single M and BF treatments were able to promote the growth of Proteobacteria and Actinobacteriota, resulting in a significant increase in the relative abundance of these groups. This suggests that the single fertilization measure may have provided favorable growth conditions for these bacteria. The opposite effect was observed for the M + BF treatment, i.e., a decrease in the relative abundance of Proteobacteria and Actinobacteriota phyla. This phenomenon may be attributed to the antagonistic effects of certain components of the fertilizer mixture on these phyla, thus inhibiting their growth. The mechanism of this interaction is not clear and further studies are needed to elucidate it. In addition, both M, BF, and M + BF treatments promoted the growth of Planctomycetes. This may imply that Planctomycetes is more sensitive to these fertilization measures, or these measures provide a more suitable growth environment for Planctomycetes. This study provides insights into the effects of different fertilization practices on the structure of soil bacterial communities and provides a scientific basis for developing more effective fertilization strategies. Future fertilization strategies for agricultural practices should consider the structure and function of microbial communities and the interactions between fertilizer components for sustainable management of soil fertility and maximization of crop yields. Optimization of fertilizer combinations, avoidance of antagonism, enhancement of soil function, changes in abundance of functional genes, and targeted fertilization should be taken into account in future fertilization strategies for agricultural practices.
Conceptualization, Y.Y. and B.D.; Formal Analysis, R.Z. and F.M.; Investigation, B.D. and Y.Z.; Methodology, J.J. and J.Z.; Resources, D.D., J.Q. and B.D.; Software, C.L.; Writing—original draft, Y.Y. and Z.Z.; Writing—review and editing, Y.Y., R.Z., B.D., F.M., Y.Z., J.J., J.Z., D.D., J.Q., C.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Diversity indexes of soil bacterial communities in different treatments. (A) is Sobs index; (B) is Chao index; (C) is Simpson index; (D) is Shannon index.
Enlarge this image.Figure 3. The relative abundances of soil bacterial communities under different treatments.
Enlarge this image.Figure 4. The relative abundances of soil bacterial communities in different crops.
Enlarge this image.Figure 5. Redundancy analysis of soil bacterial communities and environmental factors. AK is fast-acting potassium; AP is fast-acting phosphorus; AN is available nitrogen; TN is total nitrogen; SOC is organic carbon. The lengths of the arrows of the environmental factors represent the influence of the environmental factors on the species data (interpretation); the angles between the arrows of the environmental factors represent positive and negative correlations (acute angle: positive correlation; obtuse angle: negative correlation; right angle: no correlation); from the sample point to the arrow of the quantitative environmental factors to make a projection, the distance of the projected point from the origin represents the size of the relative influence of the environmental factors on the distribution of the sample community.
Enlarge this image.Figure 6. Heatmap of the secondary functional diversity of soil bacteria in different crops.
Soil properties of the test site.
| Treatment | pH | Organic Carbon (g·kg−1) | Effective Phosphorus (mg·kg−1) | Total Nitrogen (g·kg−1) | Quick-Acting Potassium (mg·kg−1) |
|---|---|---|---|---|---|
| CK | 8.49 | 10.8 | 18.9 | 0.63 | 201 |
| M | 8.43 | 10.9 | 19.2 | 0.57 | 235 |
| BF | 8.50 | 10.5 | 16.4 | 0.60 | 236 |
| M + BF | 8.50 | 10.7 | 16.4 | 0.61 | 231 |
Soil chemical properties of different treatments.
| Year | Treatment | Organic Carbon | Total Nitrogen (g·kg−1) | Available Nitrogen | Available Phosphorus | Available Potassium (mg·kg−1) | pH |
|---|---|---|---|---|---|---|---|
| 2021 | CK | 9.50 ± 0.03 d | 0.62 ± 0.01 ab | 54.04 ± 0.25 c | 17.61 ± 0.05 b | 238.33 ± 2.03 b | 8.49 ± 0.02 a |
| M | 11.03 ± 0.05 c | 0.60 ± 0.02 b | 59.54 ± 0.44 b | 20.51 ± 0.36 a | 243.67 ± 2.73 b | 8.44 ± 0.02 b | |
| BF | 11.21 ± 0.13 b | 0.62 ± 0.02 ab | 59.57 ± 1.13 b | 20.64 ± 0.29 a | 254.33 ± 2.96 a | 8.39 ± 0.01 c | |
| M + BF | 13.3 ± 0.07 a | 0.67 ± 0.02 a | 62.54 ± 0.63 a | 21.43 ± 0.33 a | 227.00 ± 1.15 c | 8.48 ± 0.03 a | |
| 2022 | CK | 8.90 ± 0.20 c | 0.61 ± 0.01 b | 59.38 ± 0.73 a | 17.17 ± 0.62 b | 250.67 ± 6.17 b | 8.62 ± 0.02 a |
| M | 11.05 ± 0.49 bc | 0.60 ± 0.01 b | 61.93 ± 3.92 a | 21.16 ± 1.30 a | 247.33 ± 1.86 b | 8.49 ± 0.02 a | |
| BF | 11.56 ± 0.67 b | 0.59 ± 0.04 b | 59.62 ± 1.87 a | 21.72 ± 0.94 a | 271.00 ± 3.51 a | 8.39 ± 0.09 a | |
| M + BF | 16.72 ± 0.11 a | 0.75 ± 0.01 a | 64.05 ± 0.49 a | 22.56 ± 0.90 a | 202.67 ± 2.73 c | 8.49 ± 0.01 a |
Different letters in the same column mean significant difference at 0.05 level.
The abundance of primary functional genes of soil bacteria under different treatments.
| Level 1 Function | Relative Abundance (%) | |||
|---|---|---|---|---|
| CK | M | BF | M + BF | |
| Metabolism | 78.67 a | 78.51 a | 78.45 a | 78.94 a |
| Genetic Information Processing | 6.86 a | 6.84 a | 6.72 a | 6.90 a |
| Environmental Information Processing | 5.12 a | 5.17 a | 5.24 a | 5.01 a |
| Cellular Processes | 4.26 a | 4.31 a | 4.36 a | 4.17 a |
| Human Diseases | 3.30 a | 3.36 a | 3.42 a | 3.21 a |
| Organismal Systems | 1.78 a | 1.81 a | 1.81 a | 1.76 a |
Data are mean ± standard deviation, n = 3. Letter “a” in the same column indicate significant differences between different fertilization treatments (p < 0.05).
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