1. Introduction
Soil micro-organisms play important roles in soil functions, ecosystem stability, and material cycling. They mainly participate in the formation of humus in soil, the decomposition and release of soil nutrients, and the conversion and cycling of energy [1]. In agricultural ecosystems, when functional microbacteria are limited by environmental changes, other microbial groups may exercise the same function to maintain the functional stability of the agricultural ecosystem [1,2]. Therefore, if community species are replaced in space or time due to environmental changes, the increase in soil microbial diversity can help to achieve functional complementarity between species, maintain soil functional diversity, and ensure ecosystem stability [3]. In addition, soil micro-organisms play important roles in the growth, development, and immunity of host plants [4,5]. When a host plant is under environmental pressure, soil micro-organisms can assist the plant in resisting stress and promoting its growth and development through synthesizing plant biomass, improving soil nutrient availability, and inhibiting the growth and reproduction of pathogenic micro-organisms [6,7]. Therefore, the improvement of the soil microbial diversity is of great significance for the sustainable development of agricultural ecology.
Soil micro-organisms, as an important component of agricultural ecosystems, are driven by the agricultural environment and host plant species and varieties [8]. Research has shown that different cultivation practices can lead to changes in the composition of soil microbial communities [9,10]. Continuous cropping is a common cultivation method that refers to the continuous cultivation of the same crop on the same cultivated land. Long-term continuous cropping can lead to an increase in the number of pathogenic micro-organisms and deterioration due to soil-borne pathogenic diseases, thereby affecting the stability of agricultural ecosystems [11]. In addition, long-term continuous cropping can lead to a decrease in soil carbon storage, a significant reduction in microbial availability of carbon sources, and a decrease in diversity [12]. To solve the problem of intensified soil-borne diseases and severe soil-quality degradation caused by long-term continuous cropping, crop rotation and fallow farming systems have been widely promoted and applied in many countries and regions, with significant ecological and socio-economic benefits [13].
Rotation fallow is an international practice that combines the planting system of rotation with the cultivation system of fallow, which solves problems, such as decreased soil fertility, deterioration of the ecological environment, and imbalanced grain production structure in farmland. Different crop types can induce the enrichment of specific micro-organisms through their nutrient preferences and root exudates [14,15]. Compared to continuous cropping, crop rotation can effectively regulate the dynamic balance of soil nutrients, improve the soil micro-environment in farmland, and, thus, affect soil microbial diversity. However, different crop rotations or fallow fields can result in differences in the composition and quantity of root exudates in the soil environment, leading to the recruitment of different soil microbial communities. The number of pathogenic bacteria, such as Phoma, Myrothecium, and Verticillium species, significantly decreased, and wheat–potato rotation with fallow soil occurred while crop yield and water use efficiency were significantly increased [16]. Corn–soybean rotation significantly increased the soil pH and nutrient content (e.g., N and P); increased the abundance of beneficial bacteria, such as Bradyrizobium sp., Gemmatimonas sp., and Mortierella sp.; improved soil fertility; and increased crop yield [17]. Therefore, in order to better understand the impact of rotation fallow modes on soil microbial community composition, this study aims to clarify key functional taxa and identify the main environmental variables driving microbial community changes, in order to obtain suitable rotation fallow modes for soil improvement, soil fertility cultivation, and crop yield increase in the arid area of the western foothills of the Greater Khingan Mountains. Spring wheat (Longmai 36) was taken as the research object, and five crop rotation fallow modes were considered. The changes in soil microbial diversity, microbial community composition, and abundance of beneficial micro-organisms under different rotation fallow modes were systematically analyzed, thus revealing the response mechanisms of soil micro-organisms to different rotation fallow modes.
2. Materials and Methods
2.1. Site Description and Experimental Design
This study was conducted as a long-term rotation fallow experiment at the Tenihe Soil Management and Ecological Restoration Scientific Observation and Experimental Station of the Inner Mongolia Autonomous Region Academy of Agricultural and Animal Husbandry Sciences (120°48′ E, 49°55′ N, altitude 650 m). This area is located in a middle temperate, semi-arid continental grassland climate zone, with an annual average temperature of 2.2 °C. The frost-free period is 90–105 days, and the annual average precipitation is 373–474 mm. The precipitation from June to September accounts for about 80% of the annual precipitation. The effective rainfall in the whole growth period of spring wheat in 2019 was 223.9 mm. The soil type of cultivated land is chernozem. The basic nutrient content of 0–20 cm of soil is shown in Table 1.
Long-term treatment was started in 2016, with the previous crop (2015) being rape. The experiment adopted a randomized block design (Figure 1), and a total of 5 treatment modes were set up: Wheat2016–Wheat2017– Wheat2018 (WWW); Wheat2016–Rape2017–Fallow2018 (WRF); Wheat2016–Potato2017–Fallow2018 (WPF); Wheat2016–Fallow2017–Rape2018 (WFR); and Wheat2016–Fallow2017–Potato2018 (WFP). The test material was spring wheat Longmai 36. Each processing method was set with 3 replicates, totaling 15 communities, with a community area of 9 square meters and a block spacing of 0.5 m. The experimental site was equipped with a 1.0 m bandwidth protection row. After land preparation (plowing, harrowing, and leveling), spring wheat sowing was carried out on 5 May 2019, with a sowing amount of 300 kg/hm−2 and a row spacing of 0.15 m. Urea: 60 kg/hm−2, diammonium phosphate: 180 kg/hm−2, potassium sulfate: 30 kg/hm−2 were applied as seed fertilizer before sowing, without topdressing in the later stage. Other management methods were the same as those in these fields.
2.2. Sample Collection
Bulk soil, rhizosphere, and endosphere samples were collected at the flowering stage of spring wheat. Referring to Pershina et al. [18], the specific approach was to use the “S” sampling method in the community, remove the topsoil at the sampling point, and collect soil samples from the 0–20 cm soil layer using a soil drill. Each treatment was repeated three times. After thoroughly and evenly sifting the soil, the sample was divided into two parts. One part was placed in a plastic bag and stored at 4 °C for the soil microbial biomass measurement; the other was screened with a 1 mm sterile mesh to remove impurities (spring wheat roots, leaves, etc.), then dried in the shade and stored in a sealed plastic bag containing desiccant for the soil physicochemical and biological properties’ determination. The specific determination method refers to Liu et al. [19]. We randomly selected 5 representative spring wheat plants in each community, shook the roots to mix the bulk soil and loose soil, and placed them in sterile tubes as bulk soil (BS) samples. We removed large pieces of soil from the roots and quickly transferred the wheat to the laboratory on ice. Then, we placed the wheat roots in sterile tubes, washed them with PBS buffer, and centrifuged them to obtain the rhizosphere (RS) sample. We washed the wheat roots clean with PBS buffer and sterile water, absorbed the surface moisture of the roots, cut them into small pieces with sterile scissors, mixed them well, and placed them in sterile tubes as endosphere (ER) samples (Figure 1). The samples of BS, RS, and ER were all frozen in liquid nitrogen and stored at −80 °C. A total of 45 samples was collected (treated with 5 rotation fallow modes) × 3 sample types (BS, RS, and ER) × 3 replicates and were used for bacterial diversity sequencing.
2.3. Determination of Soil Biological and Physicochemical Properties
The determination of the mass water content of soil (SMWC) involves the utilization of the drying and weighing method. Soil samples are collected and placed in an aluminum box with a known weight (g1) before being weighed (g2). Subsequently, they are transferred to an oven set at 105 °C and dried until a constant weight is achieved (g3). The soil moisture content is then calculated using the formula w = (g2 − g3)/(g3 − g1) × 100. Referring to Bai et al.‘s [20] analysis methods, the soil bulk density (SBd) and total soil porosity (TSP) were assessed using the ring knife method. Soil pH was measured utilizing the potentiometric method. The content of soil organic matter (SOM) was determined through the potassium dichromate dilution heat method, while the total nitrogen (TN) content was ascertained via the Kjeldahl digestion method. Total phosphorus (TP) content in the soil was determined by employing the HClO4-H2SO4 digestion method. Total potassium in the soil (TK) was quantified using an NaOH melt flame photometer (Jingke fp6410, Shanghai, China). Soil urease (SUE) activity was measured utilizing the indophenol blue colorimetric method. Soil alkaline phosphatase (SALP) detection employed sodium benzoate phosphate’s colorimetric approach, while the sucrase activity assessment utilized 3,5-dinitrosalicylic acid’s colorimetric technique. Catalase activity measurement involved potassium permanganate titration. The determination of soil microbial biomass carbon content (SMBC) utilized the chloroform fumigation extraction volumetric analysis methodology. Soil microbial biomass nitrogen (SMBN) was determined using chloroform fumigation extraction and the ninhydrin colorimetric method. Soil microbial biomass phosphorus (SMBP) was determined using the chloroform fumigation extraction total phosphorus determination method.
2.4. DNA Extraction, Amplification, and Construction of Illumina MiSeq Sequencing Library
DNA was extracted from soil samples using the Fast DNA® Spin Kit for Soil (Thomas Fisher Scientific, Waltham, MA, USA). The concentration and purity of DNA were detected by NanoDrop2000uv-Vis (Thermo Fisher Scientific, Waltham, MA, USA), and the quality of DNA was detected by 1% agarose gel electrophoresis. Place an appropriate amount of the sample into a centrifuge tube, dilute the sample to 1 ng with sterile water/µL, and store it at −80 °C for future use. Primers 515F(GTGCCAGCMGCCGCGGTAA)/806R (GGACTACHVGGGTWTCTAAT) were used to amplify the V4 variable region of the bacterial 16S rRNA gene [21]. All PCRs were carried out in 30 µL reactions with 15 µL of Phusion® High-Fidelity PCR Master Mix (Thermo Fisher Scientific, Waltham, MA, USA), 0.2 µM of forward and reverse primers, and about 10 ng of template DNA. Thermal cycling consisted of initial denaturation at 98 °C for 1 min, followed by 35 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 30 s. Finally, 72 °C for 5 min. The PCR product was extracted from 1.5% agarose gel and purified using the GeneJET Gel Extraction Kit (Thermo Fisher Scientific, Waltham, MA, USA), according to manufacturer’s instructions. The quality and content of DNA were tested using NanoDrop2000 (Thermo Fisher Scientific, Waltham, MA, USA). According to the standard protocols of Novo Genentech Co., LTD. (Beijing, China), sequencing libraries were generated using the Illumina TruSeq DNA PCR-Free Library Preparation Kit (Illumina, San Diego, CA, USA), following the manufacturer’s recommendations. Library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent Bioanalyzer 2100 system (Agilent Technologies Inc., Santa Clara, CA, USA). the library was sequenced on an Illumina NovaSeq platform. Raw data were uploaded to the NCBI SRA database (accession number: PRJNA1104604).
2.5. Data Processing Analysis
Fastp software (
The soil physical and chemical properties were statistically analyzed using Excel 2016 (Microsoft, Redmond, WA, USA), and a one-way analysis of variance (ANOVA) was performed on each variable using IBM SPSS Statistics 22.0 (IBMCorp., Amonk, NY, USA). The significant differences among the treatments were compared (p < 0.05), and the Spearman correlation between the variables was analyzed. Graphpad Prism 8 (Graphpad, La Jolla, CA, USA) was used for visualization. Analyze the correlation between environmental factors and dominant bacterial genera through redundancy analysis (RDA). We screened environmental factors at the operational taxonomic unit (OTU) level using the Mantel test and variance inflation factor (VIF). We calculated the ecosystem multifunctionality index (MFI) using the mean method based on key environmental variables [28]. Using the OmicStudio tool (
3. Results
3.1. Analysis of Soil Physicochemical Properties and Biological Properties
Soil physical and chemical properties are important indicators reflecting soil health, which are closely related to soil material circulation and energy flow. Through the analysis of soil physicochemical and biological properties under different rotation fallow modes, it was found that different rotation fallow modes had significant effects on the properties (ANOVA, p < 0.05). Compared to the continuous cropping mode of spring wheat (WWW), the spring wheat–fallow–potato mode (WFP), spring wheat–fallow–rape mode (WFR), and spring wheat–rape–fallow mode (WRF) increased soil mass water content (SMWC) by 6.88%, 4.31%, and 2.96%, respectively. Furthermore, the total potassium (TK) content in the soil increased by 25.57%, 13.36%, and 3.44%, and that under the spring wheat–potato–fallow mode (WPF) increased by 8.65%; except for WRF, the organic matter content (SOM) in the soil increased significantly under the other three modes, with WPF, WFP, and WFR leading to increases of 3.68%, 3.34%, and 3.34%, respectively. The soil pH value of the WWW spring wheat field was 7.54, significantly higher than the other four crop rotation fallow modes (Figure 2 and Figure S1). The activities of catalase (CAT) and alkaline phosphatase (SALP) in WWW soil were significantly increased, while the urease (SUE) activity, soil microbial biomass nitrogen (SMBN), and soil microbial biomass phosphorus (SMBP) content in WFP soil were significantly higher than those in the other four modes (ANOVA, p < 0.05).
3.2. Analysis of Soil Bacterial Diversity
Soil micro-organisms are an important component of the soil microenvironment. Rotation fallow changes the physical and chemical properties of soil, to some extent, and may also have a significant impact on soil microbial diversity. Therefore, we performed bacterial 16 S rRNA amplicon sequencing on 45 samples collected from three different locations (soil bulk, rhizosphere, and endosphere) under five crop rotation fallow modes. It was found that the rotation fallow mode had no significant effect on bacterial alpha diversity (Duncan, p = 0.148), while the ecological niche significantly affected bacterial alpha diversity (Duncan, p < 0.001). Moreover, under the same rotation fallow mode, Shannon and chao1 were found to be ordered as follows: Bulk soil > Rhizosphere > Endosphere (Figure 3A,B; Tables S1 and S2). The bacterial Shannon indices for soil bulk, rhizosphere, and endosphere were significantly increased under the WFR mode, compared with WWW (Duncan, p = 0.001).
Using principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and the rank-based Scheirer–Ray–Hare test, the bacterial community structure under different rotation fallow modes was analyzed. It was found that the bacterial community was significantly distinguished by the ecological niche (PC1 = 82.96%, H niche > H mode, p < 0.001; Table S2) and, compared with endosphere, bulk soil, and rhizosphere, the sample points were clustered and their community structure was similar (Figure 3C). In the same ecological niche, the bacterial communities under the five rotation fallow modes were significantly distinguished, with WWW located in a single quadrant, WPF and WRF located in the same quadrant, and WFR and WFP located in the same quadrant (Figure S2). These results indicate that a niche is the main factor driving bacterial community changes, while crop rotation fallow modes are secondary factors (Figure 3C). The bacterial communities under WPF and WRF rotations were similar, and the bacterial communities under WFR and WFP rotations were also similar. Therefore, crop rotation sequence may also be the reason for the differences in bacterial communities between samples.
3.3. Analysis of Differences in Soil Bacterial Community Composition
The species optimization annotation of bacterial operational taxonomic units (OTUs) was conducted at three different positions (BS, RS, and ER) under five rotation fallow modes at the phylum level. The top three bacterial phyla, in terms of relative abundance rankings, were found to be Proteobacteria (20.42–51.57%), Actinobacteria (7.57–27.94%), and Bacteroidetes (3.50–16.06%). The relative abundance of Proteobacteria showed BS > RS > ER in the WWW, WRF, and WPF modes, and RS > BS > ER in WFR and WFP modes (Figure 4A). In BS and ER samples, compared with WWW, the relative abundance of Firmicutes was increased by over 93.75% in the rotation fallow mode, while the relative abundance of Acidobacteria significantly decreased, with a decrease of no less than 25.89% (Table S3). Under the rotation fallow mode, the relative abundances of Bacteroidetes and Verrucomicrobia in BS and RS samples significantly increased, while ER significantly decreased. The results show that both the crop rotation fallow mode and ecological niche can affect the relative abundance of dominant bacteria. The relative abundances of Bacteroidetes, Firmicutes, and Verrucomicrobia were significantly increased under crop rotation fallow modes, while Actinobacteria tended to accumulate in wheat roots.
Linear discriminant analysis effect size (LEfSe) was used to analyze species with significant differences in three spatial locations under the five crop rotation fallow modes. The bacteria found to have significant differences were included in 5 phyla, 9 classes, 14 orders, 19 families, and 17 genera (Figure 4B). Under the WWW, WPF, WFR, and WFP modes, the BS biomarkers were Sphingomonas, Massilia, Segetibacter, and Clostridia, respectively. Under the WWW, WRF, and WFR modes, the RS biomarkers were Ruminococcus, Rubrobacteria, and Chthoniobacteraceae, respectively. In the WPF mode, the RS biomarkers were Massilia and Segetibacter, while, in the WFP mode, the RS biomarkers were Rhodanobacter and Pedobacter. There were 11 biomarkers in ER, of which only 1 was in the WWW mode, which was Glycomyces. WRF, WPF, and WFR all had two biomarkers each: Sphingomonas and Flavobacterium; Amycolatopsis and Niastella; and Massilia and Bacillus, respectively. There were four biomarkers in the WFP mode: Promicromonospora, Devosia, Phyllobacterium, and Neorhizobium (Figure S3). The results show that there are few species and a simple species composition in the WWW mode. In contrast, under the WFP mode, there were many species of bacteria with a rich species composition, with most bacteria having the ability to inhibit plant pathogenic micro-organisms, which may be more conducive to farmland ecological restoration and crop growth.
To further clarify the differences in bacterial community composition among the different crop rotation and fallow modes, we selected the top 100 species with total abundance at the genus classification level for microbial network construction (Spearman correlation, p < 0.01). There were significant differences in bacteria-related networks among the five rotation fallow modes (Figure 4C). Compared with the WWW mode, the rotation fallow modes exhibited a more complex and compact network structure, with a higher average degree, clustering coefficient (CC), and lower degree of modularity. In addition, the number and connectivity of negatively correlated bacteria in the WWW network were significantly lower than those under the crop rotation fallow modes (Figure 4C and Figure S4, Table S4). These results indicate that, compared to the WWW mode, bacterial interactions under rotation fallow modes are more aggregated and complex, while the long-term continuous cropping of spring wheat leads to decreases in negatively correlated bacteria and network stability.
3.4. Analysis of Functional Differences between Soil Dominant Bacteria
To clarify the metabolic and ecological functions of dominant bacteria enriched under the five modes, a functional annotation was performed on OTUs enriched in three ecological niches under the modes based on FAPROTAX. It was found that bacterial functional taxa were related to the ecological cycling of soil carbon, nitrogen, sulfur, and other elements, and were significantly influenced by the modes and bacterial ecological niches (Figure 5 and Figure S5). The main dominant functional taxa under the five modes and three niches were chemoheterotrophic and aerobic heterotrophic (Figure 5A). Compared with the WWW mode, the rotation fallow mode had a higher abundance of functional taxa related to cycling nitrogen, sulfur, arsenic, manganese, and other elements (Figure S5A), and nitrogen-related functional taxa (NRFT) were significantly enriched (Figure 5B). Meanwhile, the abundance of functional taxa of plant pathogens was significantly decreased. Among them, the functional classification of WFP-mode bacteria is rich and uniform, with significant advantages in the cycling of soil elements, such as carbon, nitrogen, sulfur, arsenic, and manganese. Compared with the WWW mode, the functional taxa under the rotation fallow modes in BS were relatively evenly distributed, especially in the WFP mode; in the RS and WWW modes, plant pathogens were significantly higher than in the rotation fallow modes (ANOVA, p < 0.05, Figure S5B). In ER, the main functional taxa were similar in composition under the five modes. The results show that different rotation fallow modes have a significant impact on the bacterial functions in BS and RS; meanwhile, the main functional taxa in ER were the result of host plant selection. Compared with the WWW mode, bacterial community functions under the crop rotation fallow modes were relatively complex, the bacterial community of plant pathogens was relatively low, and the abundance of nitrogen-cycling-related functional taxa was significantly increased.
3.5. Analysis of the Correlation between Dominant Bacteria and Environmental Factors
The differences in soil bacterial composition caused by the rotation fallow mode are closely related to soil properties (Figure 6 and Figure S6, Table 2). Through the Spearman correlation analysis of dominant bacterial phyla in three ecological niches (bulk soil, rhizosphere, and endosphere) under the five modes, it was found that Proteobacteria were significantly correlated with SMWC, TK, and total phosphorus content (TP) (p < 0.05). Actinobacteria were significantly positively correlated with TK and SMBN, while Bacteroides and Firmicutes were significantly negatively correlated with pH, SUE, and SALP. Acidobacteria were significantly positively correlated with pH, TN, CAT, SUE, and soil sucrase (SSC) (p < 0.05, Figure S6A). To further determine the key environmental factors affecting the composition of bacterial communities, we conducted RDA to analyze the correlations between environmental factors and dominant bacterial genera, and screened the environmental factors affecting the OTU levels of bacteria through the Mantel test and VIF (Table 2 and Table S5, Figure S6B). RDA analysis revealed a significant correlation between environmental factors and dominant bacterial genera (p < 0.05). The biomarker species (Figure 6) under different rotation fallow modes, such as Sphingomonas, Massilia, Segetibacter, Flavobacterium, Amycolatopsis, Bacillus, and Devosia, were significantly correlated with pH, SUE, CAT, SALP, TN, SMWC, TK, TP, SMWC, and SMBN (p < 0.05). In addition, the Mantel test revealed that BS and RS bacterial communities were more affected by soil properties, compared to ER (Table 2), and pH (BS: r = 0.445, p = 0.005; RS: r = 0.4765, p = 0.001), TN (BS: r = 0.4994, p = 0.001; RS: r = 0.3111, p = 0.041), TP (BS: r = 0.3625, p = 0.013), TK (BS: r = 0.2906, p = 0.018), SUE (BS: r = 0.4302, p = 0.002; RS: r = 0.4717, p = 0.002) CAT (BS: r = 0.2724, p = 0.025; RS: r = 0.5389, p = 0.001), and SSC (BS: r = 0.3715, p = 0.02; RS: r = 0.2992, p = 0.044) were significantly correlated with bacterial community structure. Meanwhile, the VIF results also demonstrate that pH, TK, SUE, and CAT, among other factors, are key influencing factors for the differences in sample and community distribution. In summary, our results demonstrate that the differences in soil bacterial composition caused by rotation fallow modes are closely related to soil characteristics. In particular, compared to ER, BS and RS bacterial communities are more susceptible to soil properties, and pH, TK, TN, SUE, and CAT are key environmental variables driving bacterial community distribution.
3.6. The Impact of Key Environmental Variables on the Complexity of Microbial Networks and Nitrogen-Related Functional Taxa
The complexity of bacterial networks and changes in nitrogen-cycling-related functional taxa under different rotation fallow modes were found to be significantly correlated with key environmental variables, which mainly affect the composition of bacterial communities (Figure 7). We analyzed the impact of key environmental variables on bacterial network complexity and nitrogen-cycling-related functional taxa through univariate linear regression. The results indicate that bacterial network complexity is significantly positively correlated with TN (R2 = 0.131, p < 0.001), TK (R2 = 0.156, p < 0.001), and MFI (R2 = 0.036, p < 0.001), and negatively correlated with pH (R2 = 0144, p = 0.011); meanwhile, NRFT is significantly positively correlated with TK (R2 = 0.116, p < 0.001) and negatively correlated with pH (R2 = 0.163, p = 0.006), TN (R2 = 0.199, p = 0.002), CAT (R2 = 0.184, p = 0.003), SUE (R2 = 0.199, p = 0.002), and MFI (R2 = 0.208, p = 0.002) (see Figure 7).
4. Discussion
Rotation fallow is an important agronomic practice known to improve productivity, reduce crop diseases, enhance soil fertility, and promote yield improvement [29,30,31]. During rotation, diverse crops are introduced into the cultivation area, and their unique growth characteristics and differentiated management methods often lead to changes in the soil microenvironment, which may alter the structure of soil microbial communities and cause differential responses in particular groups. The results of this study support this viewpoint, to some extent. Compared to the continuous cropping of spring wheat, the rotation of spring wheat with potato or rape was found to present changes in the soil environment and microbial community. However, the spring wheat–fallow–potato mode seems to have a more positive impact on soil nutrient maintenance and element cycling, especially nitrogen (Figure 5). Therefore, our results not only reveal the key mechanism of improving farmland quality through crop rotation and fallow, but also provide a suitable crop rotation and fallow model for studying the region.
4.1. Reasonable Rotation Fallow Has a Positive Impact on Soil Nutrient Maintenance and Soil Function
Soil health refers to the ability of soil, as an important living system, to function within its ecosystem and land-use boundaries [32]. Soil physicochemical and biological properties are the core and comprehensive manifestations of soil health. This study indicated that, compared with the WWW rotation, the rotation fallow modes significantly increased the contents of SMWC and TK, while the difference in SOM content was not significant. The changes in SMWC and TK contents are similar to the results of Zhang et al. [29] and Liu et al. [17], while the SOM content result is similar to that in the study of Bates et al. [33]. Rotation significantly changes soil biological characteristics and basal respiration, but TC is less sensitive to different cropping systems. Except for WFP, the TN and TP contents in the WWW rotation were significantly higher than those under other rotation fallow modes, indicating that different crop rotations and crop sequences have different effects on soil physical and chemical properties, and continuous cropping can also increase the content of certain nutrients in the soil. Liu et al. [17] found that long-term soybean rotation significantly increased soil TN and TP. Similarly, Chen et al. [34] investigated soil nutrients after 20 years of the continuous cropping of Achyranthes bidentata, and found that soil nutrients—including TC, TN, and TP—were significantly increased. We also found that the soil pH of WWW was significantly higher than that under the rotation fallow modes. This is consistent with Zhong et al. [35], who reported that the long-term continuous cultivation of bananas can lead to a significant increase in soil pH. However, Liu et al. [17] found that, compared with soybean continuous cropping, the soybean–corn rotation significantly increased soil pH, which differs from our results. It is speculated that this is due to the studied crop types, as well as different regions and planting years.
Soil enzyme activity and microbial biomass are closely related to soil function, as important biological traits. Agricultural techniques, such as crop rotation and continuous cropping, as well as crop types, can alter the soil environment and affect soil enzyme activity [36]. In this study, it was found that WFP significantly increased soil urease activity, soil microbial biomass nitrogen, and soil microbial biomass phosphorus content. Gonnety et al. [37] found that soil enzyme activity decreased significantly when corn was planted continuously for a long period of time, compared with that when Chromolaena odorata was fallow for 2 years, and it was believed that the difference was caused by different land-use modes. Borase et al. [38] found that crop rotation significantly affects soil enzyme activity and microbial biomass. Compared with the maize–wheat rotation, pigeonpea–wheat significantly increased SMBC and SMBN contents, as well as alkaline phosphatase activity. In this study, WWW significantly increased the activities of soil catalase and alkaline phosphatase (Figure 1). This differs from the research results of Liu et al. [39], who found that the continuous cultivation of American ginseng caused a decrease in soil alkaline phosphatase activity; the reason for this change is that changes in soil pH and salinity cause changes in the microbial community structure. In addition, we observed a highly significant correlation between soil pH and alkaline phosphatase and catalase (Figure 6A). Compared with the rotation fallow mode, soil pH significantly increased under the WWW rotation. Further research is needed to determine whether this is the reason for the increase in catalase and alkaline phosphatase under the WWW rotation. Malobane et al. [40] argued that crop rotation only affects acid phosphatase and, compared to sorghum—fallow—sorghum, sorghum—vetch—sorghum enhanced acid phosphatase activity. Yusuf et al. [41] found that the crop rotation sequence has a significant impact on soil microbial biomass carbon and nitrogen. Soybean crop rotation can significantly increase the soil nitrogen content and increase crop yield, mainly attributed to the growth characteristics of nitrogen-fixing bacteria, which are enriched in legume crops. In addition, the introduction of soil secretions or residues by plants themselves, as well as their preference for nutrients, can directly alter the soil environment and affect microbial content [42].
In summary, different rotation fallow modes can affect soil physicochemical and biological properties. The imbalance of soil nutrients caused by continuous cropping can be improved through crop rotation and fallow, while improper crop combinations or rotation sequences may be detrimental to the maintenance of soil nutrients. This is mainly attributed to the diversity of crop rotation and management methods, which together regulate the multifunctional aspects of soil [43]. Therefore, selecting a reasonable crop rotation and fallow system can have a positive impact on both soil nutrient maintenance and soil function.
4.2. Rotation Fallow Mode Can Affect the Diversity of Bacterial Communities, but Its Effect Is Weaker Than That of Ecological Niches
The soil microbial community is influenced by the type of host plant and its ecological niche [8]. The results of this study indicate that the rotation fallow mode does not significantly affect bacterial alpha diversity (Figure 2), while niche significantly affects bacterial alpha diversity, with the Shannon index and chao1 index both being in the order of Bulk soil = Rhizosphere > Endosphere. These research results are similar to those of previous studies [8,44,45,46]. The plant microbiome is largely formed by host selection, affected by factors such as the genotype and compartment niche. Host plants directly recruit the rhizosphere microbiome through plant signals and root exudates, and tend to select micro-organisms that can penetrate the root tissue to form endophytes. The diversity of the endosphere is usually lower than that of the rhizosphere and bulk soil. Similarly, based on the NMDS of the bacterial Bray–Curtis distance PCoA in different ecological niches and bacterial Bray–Curtis distance between rotation and fallow modes, we observed that ecological niches are the main driving factor of bacterial community structure, while rotation and fallow modes are secondary factors. In addition, in the analysis of dominant bacterial phyla in different spatial positions under the crop rotation and fallow modes, it was found that, compared to the dominant bacterial phyla in the rhizosphere and bulk soil, the distribution of endosphere bacterial phyla under different crop rotation and fallow modes was more uniform. This result can also be explained by the strong driving force of the niche in community construction, that is, the selection effect of the host plant (i.e., spring wheat) allows host-preferred bacteria and immune-resistant bacteria to parasitize within its roots. In summary, these results indicate that the contribution of ecological niches shaped by host plants to bacterial community construction is greater than that of crop rotation and fallow management strategies.
4.3. Reasonable Rotation Fallow Cultivation Is Beneficial for the Colonization and Growth of Beneficial Micro-organisms
The decomposition and release of soil nutrients, energy conversion, and material cycling are driven by soil micro-organisms and, thus, are closely related to community composition and metabolic functions [47]. The results of this study indicate that Proteobacteria, Actinobacteria, and Acidobacteria are dominant bacterial phyla under rotation fallow modes, similar to previous studies on the wheat microbiome [46,48,49]. This may be attributed to these bacterial phyla being the most common bacterial phyla in soil, which are widely present in various agricultural systems and soil types [50,51]. In particular, Bacteroidetes, Firmicutes, and Verrucomicrobia are eutrophic bacteria [52], while Acidobacteria are oligotrophic bacteria [53]. Compared with the continuous cropping mode, the relative abundance of Bacteroidetes, Firmicutes, and Verrucomicrobia was increased under the rotation fallow modes, while the relative abundance of Acidobacteria decreased (Figure 3), suggesting that the rotation fallow mode leads to a higher soil nutrient content, further confirming our results for soil nutrients under different modes. In addition, LEfSe analysis revealed that, compared with continuous cropping, the rotation fallow modes, especially WFP, significantly increased the types and numbers of main biomarkers (Figure 4), playing an important role in the cycling of soil carbon, nitrogen, phosphorus, sulfur, and other elements; ecological restoration; pathogen antagonism; and promotion of plant growth. For example, Massilia [54] can synthesize various secondary metabolites and enzymes, with functions such as phosphorus solubilization, phenanthrene degradation, and heavy metal tolerance. Clostridia [55] participates in the cycling of soil carbon-source substances, producing organic acids to inhibit the growth of soil pathogens; Phylobacterium [56] participates in soil phosphorus cycling, assisting crops in resisting abiotic stress; and Pedobacter [57] regulates wheat growth and development through synthesizing indole derivatives and thiobacilli to resist the wheat stripe rust pathogen. Flavobacteria [58] can be used as an effective biocontrol bacterial group for the prevention and control of tomato wilt disease. This supports the view that crop rotation fallow is an effective measure to overcome the obstacles associated with continuous cropping. However, although the rotation fallow mode has a rich variety of beneficial biomarker species, there are differences in the types and numbers of biomarker species between different modes, (e.g., WFP and WFR). The reason for this is that the residual effects (plant tissue or root exudates) of different crops in the rotation system can alter the soil microenvironment and stimulate the growth of specific micro-organisms; the effects of this stimulation may also affect the assembly of subsequent soil microbial communities [59,60]. Similarly, differences in crop cultivation can also lead to similar results. In addition to the legacy effects of crop cultivation, previous crop management strategies (e.g., cultivation, fertilization, and pesticides) may also have legacy effects that affect the microbial community composition of subsequent crops. As Kan et al. [61] found in the wheat–corn rotation system, compared to the tillage and stubble treatments, the no-tillage and stubble treatments of wheat straw led to higher organic carbon content input into the soil. The changes in carbon source types and contents are closely related to soil microbial composition [62]. It is worth noting that the contribution of legacy effects of previous crop management strategies to the microbial assembly of subsequent crops is not yet clear, although recent research reports have indicated that crop rotation has a greater impact on the soil microbial assembly than fertilization [63]. Overall, rotation fallow can affect microbial community structure through the legacy effects of crop and management practices, and, thus, selecting appropriate crop rotation and planting practices is beneficial for the growth of beneficial bacterial colonies.
4.4. Rotation Fallow Enhances the Complexity, Stability, and Relative Abundance of Nitrogen-Cycling Functional Bacterial Taxa in Bacterial Communities
As is well-known, soil microbial communities can regulate various ecosystem functions, and ecosystem processes often occur through complex interactions between microbial communities [64,65]. This study indicates that, compared with the WWW rotation, the rotation fallow modes exhibited a more complex and compact microbial network structure (Figure 5). Consistent with previous research findings, diversified crop rotation can enhance the complexity of underground microbial networks [43]. This is mainly attributed to the introduction of diverse crops that provide diverse litter and root exudates, thus directly or indirectly affecting the association of soil microbial communities [43,66]. Different host crops, such as potatoes and rape, recruit specific microbial groups through root exudates and block other groups. Diversified microbial environments can affect microbial community structure and alter microbial interactions [67,68]. In addition, diverse crop rotation systems can generate legacy effects and provide favorable niches for specific microbial communities through root action, leading to greater niche sharing and interactions among microbial communities [69].
The stability of microbial networks can characterize the ability of micro-organisms to resist external interference [70]. The results of this study reveal that, compared with the continuous cropping mode, crop rotation fallow is characterized by a lower degree of modularity, tending to form a large network module and an increase in negative correlation co-occurrence (Figure S4). Some studies have shown that environmental pressure makes the microbial community network unstable. In habitats with higher environmental pressure, microbial diversity is reduced, and positive correlation co-occurrence is predominant [71]. Our results indicate that the long-term continuous cropping of spring wheat may cause certain environmental stresses or nutrient limitations for micro-organisms. Micro-organisms can only survive by changing their survival strategies through niche differentiation, thus forming multiple smaller, positively correlated modules.
Changes in network complexity and stability affect the functional structure of microbial communities and the functional processes of ecosystems. Ecological network changes caused by crop rotation and fallow may affect the ability of ecosystems to provide stable ecological services. Through the FAPROTAX functional prediction, we found that the bacterial taxa related to soil nitrogen-cycling functions were significantly enriched during rotation fallow treatment compared to the WWW rotation (Figure 5), which is consistent with previous research results. Hao et al. [72] found that crop diversification (rotation or intercropping) significantly increased the abundance of soil nitrogen-cycling functional bacteria. The main reason for this phenomenon may also be attributed to the legacy effects of crops. Crop diversification increases both aboveground and underground biomass, and abundant organic matter provides resources for microbial growth. Surprisingly, soil TN content is not always positively correlated with soil microbial nitrogen-cycling-related taxa (Figure 6), and, compared with the WWW mode, other rotation fallow modes, except for WFP, presented significantly decreased levels of such groups. It can be speculated that the nitrogen-cycling taxa increased by rotation fallow may promote processes, such as nitrogen ammonification and nitrification in the input soil, which are beneficial for plant absorption and utilization. In addition, different microbial communities that mediate the same nitrogen-cycling process have different activities and environmental adaptability [73], and are influenced by variables such as crop type, planting methods, duration of diversification, and soil texture [72]. Further research is needed to investigate the impact of increased nitrogen-cycling functional taxa on soil nitrogen content.
4.5. Key Environmental Variables Affecting Microbial Community Assembly
Microbial community assembly is influenced by two ecological processes: deterministic and stochastic [74]. The results of this study indicate that the endosphere in spring wheat roots is mainly influenced by the selectivity of the host plant, while RS and BS are more susceptible to environmental factors, especially BS (Figure S6). This may imply that deterministic processes are the main ecological processes that affect the inter-niche microbial community structure in spring wheat soil areas, rather than random processes. In addition, as a unique ecological niche, the rhizosphere is influenced by both environmental and biological variables, which, to some extent, can weaken the effect of spatial heterogeneity on bacteria. In this study, rhizosphere bacteria were mainly influenced by pH and biological traits (CAT and SUE), while non-rhizosphere soil was also influenced by soil nutrients (e.g., TN, TP, and TK), in addition to pH and soil biological traits. This is similar to the results reported in previous research [75]. These results indicate that pH, TK, TN, SUE, and CAT play key roles in driving the distribution of bacterial communities. Studies have also indicated that SOC is another important factor affecting bacterial community assembly, which is significantly correlated with the soil aggregation structure and can provide nutritional resources and a favorable growth environment for micro-organisms [76]. In our study, soil organic matter content did not significantly affect bacterial community structure. This may be because the five rotation fallow modes did not cause significant differences in the soil organic matter content, which may be influenced by soil fertility conditions and planting cycles. Further research is needed to determine the specific reasons for these observations.
The main ecological functions of soil are nutrient cycling and a resistance to environmental stresses. We observed a positive correlation between bacterial network complexity and the soil multifunctionality index, which is consistent with previous studies [77,78]. The main reason for this is that complex communities have advantages in terms of resource utilization and information transmission, and bacteria with similar environments and resource preferences have strong interactions, making information transmission more efficient. In our study, TN, TP, and TK were significantly positively correlated with bacterial complexity (Figure 7), indicating that rich nutrition may help to enhance interactions within the microbiome. However, there was a negative correlation between NRFT and the soil ecological function index (MFI). Does this mean that a significant increase in certain specific microbial groups may affect the complexity of the microbial network and hinder soil ecological multifunctionality? We need to further explore this question in future research. In summary, compared to continuous cropping, rotation fallow tillage complicates the soil microbial network and increases the abundance of nitrogen-cycling-related microbial communities, which may enhance nitrogen availability, maintain nutrient cycling, and improve agricultural productivity.
In summary, we propose a model diagram of the impact mechanism of crop rotation and fallow mode on soil bacteria (Figure 8). The crop rotation fallow mode improved soil moisture and soil aeration, to a certain extent, through the “legacy effect” due to the rotation of different crops, thus promoting a neutral and stable soil pH, which, in turn, maintains enzyme activity in a benign state, thereby increasing soil microbial biomass, carbon, nitrogen, phosphorus, total nitrogen, phosphorus, potassium, and other nutrients, as well as promoting the efficient utilization of soil nutrients, which is conducive to crop growth. In addition, the beneficial bacteria left behind can also colonize in the soil, increase microbial diversity, promote the cycling of elements, such as C and N in the soil, and assist crops in resisting pathogenic hazards. And the well-growing crops can provide a sufficient environment for micro-organisms through root secretions and residues, enriching special micro-organisms, improving the abundance of beneficial micro-organisms, activating soil nutrients, and then realizing the combined effect of land enrichment.
5. Conclusions
Compared with the WWW mode, reasonable crop rotation fallow modes were found to be beneficial for conserving soil moisture, maintaining soil nutrient balance, and ensuring that the soil pH tends to be neutral. The ecological niche and rotation fallow mode both affected the relative abundance of dominant bacteria. The relative abundance of beneficial bacterial phyla, such as Bacteroidetes, Firmicutes, and Verrucomicrobia, was significantly increased under the rotation fallow mode, and the complexity and stability of bacterial networks and abundance of nitrogen-cycling related functional taxa were significantly improved. The differences in soil bacterial composition due to the rotation fallow mode were closely related to soil characteristics. Compared to ER, BS and RS bacterial communities were more susceptible to soil properties, and soil pH was a key environmental variable driving the distribution of bacterial communities. In summary, compared to continuous cropping, the rotation fallow mode complexity of the soil microbial network and abundance of nitrogen-cycling-related microbial communities increase, which may enhance nitrogen availability, maintain nutrient cycling, and improve agricultural productivity. In particular, the spring wheat–fallow–potato mode can be used as a suitable rotation fallow model for maintaining soil fertility in the western foothills of the Greater Khingan Mountains.
Conceptualization, Z.L. and X.Z. (Xiaoqing Zhao); methodology, X.Z. (Xiaoqing Zhao) and G.S.; software, G.S. and J.F.; validation, G.S., J.F., S.W., X.Z. (Xiangqian Zhang) and Z.L.; formal analysis, G.S., Y.C. and S.S.; investigation, G.S., J.F., S.W., X.Z. (Xiaoqing Zhao) and Z.L.; resources, G.S., J.F., J.W. (Jianguo Wang), L.Z., S.S. and J.W. (Jianhui Wu); data curation, G.S., J.F. and F.Z.; writing—original draft preparation, G.S.; writing—review and editing, G.S. and J.F.; visualization, G.S. and X.Z. (Xiaoqing Zhao); supervision, G.S.; project administration, X.Z. (Xiaoqing Zhao); funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.
The bacterial raw DNA sequences used in this study were deposited in the Sequence Read Achieve (SRA) of the NCBI database under the accession number PRJNA1104604 for open access.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Field experiments and experimental design. The lowercase letters on the bar chart indicate the significance of different rotation fallow modes at the p < 0.05 level.
Enlarge this image.Figure 2. Analysis of soil physical and chemical properties under different crop rotation fallow modes. The error bars on the bar chart are represented by standard deviation (SD), and the lowercase letters on the bar chart indicate the significance of different rotation fallow modes at the p < 0.05 level.
Enlarge this image.Figure 3. Diversity of bacterial communities in different root space positions under different crop rotation fallow modes. (A) Analysis of differences in bacterial Shannon index and Chao1 index between different rotation fallow modes in the same spatial position. The error bars on the bar chart are represented by standard deviation (SD), and the lowercase letters on the bar chart indicate the significance of different rotation fallow modes at the p < 0.05 level. (B) Analysis of differences in Shannon index and Chao1 index of bacteria at different spatial positions under the same rotation fallow modes. (C) Principal coordinate analysis (PCoA) of bacteria in different ecological niches. BS: bulk soil; RS: rhizosphere; ER: endosphere.
Enlarge this image.Figure 4. Differences in bacterial community composition among different crop rotation fallow modes. (A) Composition of bacterial communities in different crop rotation fallow modes. (B) LEfSe analyzes the evolutionary branching diagram, from the inner circle to the outer circle; the species at the phylum, class, order, family, and genus levels are in order, and the species names represented by the English letters in the figure are shown in the legend on the right. The red, green, blue, purple, and light-blue nodes in the branches are the microbial groups that play an important role in WFP, WFR, WPF, WRF, and WWW rotations, respectively, and the yellow nodes represent the microbial groups that do not play an important role in the five treatments. (C) Horizontal correlation network of bacterial genera in different crop rotation fallow modes. The circular nodes in the figure represent different genera, the node size represents abundance, the node color represents different bacterial phyla, the red line represents a positive correlation, and the green line represents a negative correlation.
Enlarge this image.Figure 5. Prediction of bacterial function in the root system of spring wheat under different crop rotation fallow modes. (A) The top 10 functional groups in terms of the abundance ranking for different crop rotation fallow modes. The horizontal axis represents rotation fallow modes, the vertical axis represents relative abundance, and colors represent functional taxa. (B) Nitrogen-cycling-related functional taxa in the top 100 functional taxa ranked by the abundance of different crop rotation fallow modes. The horizontal axis represents nitrogen-related functional taxa, the vertical axis represents relative abundance, and the color represents rotation fallow modes. BS: bulk soil; RS: rhizosphere; ER: endosphere.
Enlarge this image.Figure 6. Correlation analysis between environmental factors and root space (soil bulk, rhizosphere, and endosphere) bacteria in spring wheat. (A) Correlation analysis between environmental factors and bacterial alpha diversity and differences in bacterial communities. Bacterial diversity is represented by the Shannon index of bacteria, while differences in bacterial communities are represented by the beta diversity index based on Bray–Curtis index. The red line represents a positive correlation, while the blue line represents a negative correlation. The thickness of the line indicates the magnitude of the correlation coefficient, while the solid line indicates a significant correlation. SBd: soil bulk density, TSP: total soil porosity, and SMBC: soil microbial biomass carbon. (B) RDA analysis of the correlation between BS dominant bacteria and environmental variables; (C) RDA analysis of the correlation between RS dominant bacteria and environmental variables; (D) RDA analysis of the correlation between ER dominant bacteria and environmental variables. The red line represents environmental factors, and the blue line represents dominant bacteria.
Enlarge this image.Figure 7. Linear regression analysis of key environmental variables with the CC and NRFT. Clustering coefficient (CC) represents the complexity of the soil bacterial network; nitrogen-related functional taxa (NRFT) represents the predicted bacterial population with a nitrogen-cycling function.
Enlarge this image.Figure 8. Model diagram of the impact mechanism of the crop rotation fallow mode on soil bacteria. The gray modules in the figure represent the soil properties and bacterial community structure indicators detected in this study. The dashed module represents the inferred results based on this study. The red and white arrows indicate the up or down regulation indicators in the crop rotation fallow mode or continuous cropping mode. The left side refers to the continuous cropping mode, and the right side refers to the crop rotation fallow mode. The arrow pointing upward indicates the upregulation, and the arrow pointing downward indicates the downregulation. The blue arrow indicates the relationship between the soil, bacteria, and plants. The blue line represents the correlation between key environmental variables (pH and TN) and bacterial community structure based on the Mantel test analysis. The thickness of the line represents the magnitude of the correlation, and the correlation coefficient r value is expressed as the mean correlation coefficient between environmental variables and bulk soil and rhizosphere bacterial communities, **: p < 0.05. BS: bulk soil; RS: rhizosphere; ER: endosphere.
Basic physical and chemical properties of a 0~20 cm soil layer in the experimental land.
| Layer | Organic Matter | Available | Available | Available | Total Nitrogen | Total | pH |
|---|---|---|---|---|---|---|---|
| 0~20 | 57.82 | 37.26 | 22.25 | 195.83 | 2.39 | 0.60 | 6.80 |
Correlation between environmental factors and microbial communities analyzed by the mantel test.
| Niche | Variable | pH | TN | TP | TK | CAT | SUE | SSC |
|---|---|---|---|---|---|---|---|---|
| BS | r | 0.455 | 0.499 | 0.363 | 0.291 | 0.272 | 0.430 | 0.372 |
| P | 0.005 | 0.001 | 0.013 | 0.018 | 0.025 | 0.002 | 0.020 | |
| RS | r | 0.477 | 0.311 | 0.066 | 0.200 | 0.539 | 0.472 | 0.299 |
| P | 0.001 | 0.041 | 0.286 | 0.064 | 0.001 | 0.002 | 0.044 | |
| ER | r | 0.003 | 0.009 | 0.151 | 0.016 | 0.090 | 0.000 | 0.060 |
| P | 0.438 | 0.410 | 0.929 | 0.507 | 0.134 | 0.445 | 0.631 |
The bold font in the table represents the value of the correlation coefficient r between environmental variables and bacterial communities at the p < 0.05 level pH: soil pH value; TN: total nitrogen; TP: total phosphorus; TK: total potassium; CAT: catalase; SUE: urease; SSC: soil sucrase; BS: bulk soil; RS: rhizosphere; ER: endosphere.
Supplementary Materials
The following supporting information can be downloaded at:
References
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; Fang, Jing 1 ; Wei, Shuli 2 ; Cheng, Yuchen 3 ; Su, Shaofeng 3
; Zhang, Xiangqian 3 ; Wang, Jianguo 3 ; Zhang, Fan 1 ; Wu, Jianhui 1 ; Zhao, Lili 1 ; Zhao, Xiaoqing 2 ; Lu, Zhanyuan 2 1 Key Laboratory of Herbage & Endemic Crop Biotechnology, School of Life Science, Inner Mongolia University, Ministry of Education, Hohhot 010020, China;
2 Key Laboratory of Herbage & Endemic Crop Biotechnology, School of Life Science, Inner Mongolia University, Ministry of Education, Hohhot 010020, China;
3 Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Inner Mongolia Conservation Tillage Engineering Technology Research Center, Hohhot 010031, China