Next Article in Journal
Genome-Wide Identification and Characterization of Glutaredoxin Family Genes in Common Wheat
Previous Article in Journal
Potential Use of Plant Growth-Promoting Bacteria to Enhance Growth and Soil Fertility in Marginal Areas: Focus on the Apulia Region, Italy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 12
10.3390/agronomy13122984
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Response of Soil Bacterial Communities to Cropping Systems in Saline–Alkaline Soil in the Songnen Plain

by
Xiaoqian Liu
1,†,
Junnan Ding
2,†,
Jingyang Li
3,
Dan Zhu
1,
Bin Li
1,
Bohan Yan
1,
Lina Mao
1,
Guangyu Sun
4,
Lei Sun
1,* and
Xin Li
1,5,*
1
College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
2
Heilongjiang Province Key Laboratory of Cold Region Wetland Ecology and Environment Research, Harbin University, Harbin 150076, China
3
Heilongjiang Province Key Laboratory of Soil Environment and Plant Nutrition, Heilongjiang Academy of Agricultural Sciences, Harbin 150090, China
4
College of Life Science, Northeast Forestry University, Harbin 150040, China
5
School of Forestry, Northeast Forestry University, Harbin 150040, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(12), 2984; https://doi.org/10.3390/agronomy13122984
Submission received: 22 October 2023 / Revised: 27 November 2023 / Accepted: 28 November 2023 / Published: 3 December 2023
Browse Figures
Review Reports Versions Notes

Abstract

:
The high salt content in saline–alkaline land leads to insufficient nutrients, thereby reducing agricultural productivity. This has sparked widespread interest in improving saline–alkaline soil. In this investigation, 16S rRNA gene high-throughput sequencing was employed to examine the impacts of three cropping systems (monoculture, rotation, and mixture) on soil bacterial communities. It was found that cropping rotations and mixtures significantly increased soil bacterial α-diversity. Random forest analysis showed a significant linear relationship between AK and EC and bacterial α-diversity. In addition, principal coordinates analysis (PCoA) further confirmed the significant differences in β-diversity between different soil layers. Through co-occurrence network analysis, it was found that cropping rotations and mixtures increased the stability and complexity of co-occurrence networks. By calculating NST to analyze the assembly process of soil bacterial communities in different cropping systems, it was found that the assembly process of soil bacterial communities was dominated by a stochastic process. Functional prediction results showed that a large number of C, N, and S cycling microbes appeared in soil bacterial communities. Our study aims to establish a fresh perspective on the improvement and recovery of saline–alkaline soil.

1. Introduction

Saline–alkaline land is a kind of degraded soil with low agricultural productivity [1]. Worldwide, there are 11 million hectares of saline–alkaline soil, which makes up 25% of the total land area and is found in more than 100 different countries and regions [2]. Because of the excessive salt, only 25% of the world’s irrigated land is now producing as much crop yield [2,3]. Excessive salinity in soil not only impairs the soil’s respiration and microbial biomass [4] but also alters its microbial composition and diversity [5,6]. Additionally, saline–alkaline soil’s high salt concentration significantly restricts plant development and crop output, prompting a sharp drop in crop planting areas globally [7,8].
Studies have revealed a variety of salinity controls, including tillage, water leaching, chemical remediation, organic amendment, and phytoremediation [2,9,10,11]. Different cropping systems, such as monoculture, cropping rotations, and mixtures, are used in saline–alkaline soils to maximize yields and promote the sustainability of the soil environment [12]. The diversification of cropping systems can influence soil microbes due to variations in disturbance levels, in addition to differences in substrate quantity, quality, and availability when compared to traditional cropping systems. Moreover, the growth of diverse crops necessitates distinct inputs, such as varying fertilizer applications and herbicide usage, which also affect soil microbes [13]. Cropping rotations are a crucial management tool for raising crop yields, as has long been recognized [14,15], decreasing fertilizer needs [16], reducing pathogen accumulation, and enhancing the soil’s physical properties. According to Esmaeilzadeh-Salestani et al. [17], crop rotations change the carbon input, which changes microbial populations. Mixtures can potentially decrease the adverse effects of agricultural systems while maintaining crop productivity. This is achieved by optimizing resource utilization, reducing the risk of pest or disease outbreaks, and fostering biodiversity [18,19]. Additionally, compared to a single planting, a mixture significantly increases the populations of rhizosphere microbes and the microbial community [20].
Recent research investigations have suggested that the potential benefits of cropping rotations and mixtures over monoculture are due to an elevation in beneficial soil creatures [19,21]. Microorganisms are critical components of the soil [22]. Other research has shown that specific cropping systems, such as monoculture and crop rotations, are the primary factors contributing to alterations in microbial community [23]. These changes may be a direct result of root exudates impacting specific microbial groups or an indirect effect on soil chemical properties, which would change the make-up of the microbial community in the soil similarly [24]. For instance, the main environmental factor influencing bacterial diversity is soil pH [25], which also directly affects the existence and abundance of prominent bacterial species in different environments [26]. Soil organic carbon (OC) plays a crucial role in soil fitness and vigor, positively influencing crop yield and soil biological activity [27]. Increasing electrical conductivity (EC) levels are linked to a decline in diversity, and bacteria communities respond strongly to soil salt [28,29].
The focal points of this research are as follows: (1) the impacts of cropping systems and different soil layers on soil bacterial community diversity, bacterial community structure, and function; (2) the impacts of various soil layers and cropping systems on co-occurrence networks; and (3) the impacts of cropping systems on the bacteria community assembly process. Our knowledge of the benefits of planting strategies in managing soil microbial diversity and community stability is anticipated to be improved by this study.

2. Material and Methods

2.1. Experimental Site

This investigation was pursued at Sifangshan Farm, Zhaodong City, Heilongjiang Province (125°45′–126°30′ E, 46°12′–46°22′ N) starting from 2012. The area contains a flat topography and a single landform type. The soil in this area was identified as salic according to the World Soil Resources Reference Bank (IUSS Working Group WRB, 2014), and it belongs to the mid-temperate continental monsoon climate. The climate is characterized by high temperatures and rainy summers, as well as cold and dry winters. The annual average temperature is 2.4 °C, the greatest temperature is 39.0 °C, the lowest temperature is −37.5 °C, and the average accumulated temperature is between 2500 and 2700 °C. The annual average rainfall is 396 mm, each annual average evaporation is 1662 mm, and the annual average temperature is 2.4 °C. The pH of the soil in this location was as high as 10.50-10.00 before restoration, which indicated a natural alkali spot without any vegetation. The soil surface was white due to the high alkalinity.

2.2. Experimental Design and Sample Collection

There were three different cropping types: (1) CK, annual alfalfa monoculture; (2) CR, alfalfa–oat–tall wheatgrass cropping rotations that have been managed in this way for 13 years, starting with annual alfalfa in the first year, followed by oats in the second year and tall wheatgrass in the third year; (3) MC, an alfalfa–oat–tall wheatgrass mixture. Soil samples were gathered on 17 August 2017. With three replicates per treatment, five soil cores (0–15 cm layers and 15–30 cm layers) were randomly selected from each plot and homogenized into one sample, and an aggregate of 18 soil samples was obtained. After collection, the soil samples were placed in an ice box and brought back to the laboratory to be cleaned of plant roots, debris, and stones and mixed evenly after passing through a 2 mm soil sieve. To extract DNA, one portion was kept at −80 °C in the refrigerator [30], and the other component was air-dried at ambient temperature, to determine the chemical characteristics of the soil.

2.3. Determination of Soil Chemical Properties

The soil pH and electrical conductivity (EC) (soil H2O ratio: 1:5) were measured using electrical pH meters and conductivity meters, accordingly. Soil SOC was quantified using the K2Cr2O7-H2SO4 oxidation-reduction colorimetric method. The modified Kjeldahl method [31] was used for determining the total nitrogen (TN). With 0.5 mol L−1 NaHCO3 (pH 8.5) as an extraction solvent, available phosphorus (AP) was measured using a colorimetric technique and a molybdenum antimony reagent [32]. Utilizing flames photometry, the available potassium (AK) was extracted with NH4OAc [33], and the Mo-Sb colorimetric method was used to identify total soil phosphorus (TP) [34] after sulfuric acid digestion (Table S1).

2.4. Miseq Sequencing

From each mixed sample, 0.5 g of soil was weighed, and a Power Soil DNA Isolation Kit was used to extract the DNA (Mo Bio Laboratories, Inc., Carlsbad, CA, USA). DNA extraction quality was measured with 1% agarose gel electrophoresis. The extracted total DNA was mixed as the test sample, and the obtained DNA was detected via a Nanodrop 2000c. The V3–V4 region of the bacterial 16S rRNA gene was amplified using the primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGA CTACHVGGGTWTCTAAT-3′) [35]. The amplification procedure consisted of the following steps: predenaturation at 95 °C for 3 min; 27 cycles (denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s), and a final extension at 72 °C for 10 min (PCR instrument: ABI GeneAmp® 9700, SpectraLab Scientific Inc., Markham, ON, Canada). The amplification system was composed of a 20 μL PCR mixture, 4 μL of 5 × FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of primer (5 μM), 0.4 μL of FastPfu polymerase, and a 10 ng DNA template [36,37]. The PCR products were salvaged through gel cutting with the AxyPrep DNA Gel Recovery Kit (AXYGEN, One Riverfront Plaza Corning, NY 14831 USA) after the PCR products from all samples were combined and identified via 2% agarose gel electrophoresis. The QuantiFluorTM-ST Blue fluorescence quantification systems (Promega (Beijing) Biotech Co., Ltd., Beijing, China) were used to quantitatively identify PCR products, and the TruSeqTM DNA Sample Prep Kit was used to build the Miseq sequencing library, which was then sequenced using the PE300 method. At Annuoida Gene Technology Co., Ltd., in Beijing, China, the PCR products were purified, pooled in equimolar quantities, and paired-end sequenced (2 × 300) using the Illumina MiSeq platform.

2.5. Bioinformatics Analysis

QIIME Pipeline Version 1.8.0 was employed to remove raw sequences with lower quality (250 bp in length, ambiguous base “N”, and an average base quality score of 20) [38]. Mothur’s “chimera.chime” command was used to get rid of potential chimeras [39]. The sequences were sorted into numerous groups according to their similarity through the classification operation. During the analysis of metabarcoding data, reads were customarily clustered into operational taxonomic units (OTUs) using a threshold of 97% 16S rRNA sequence similarity. Using the “normalized. shared” command in Mothur [39], the OTU table was then rarefied (there were 839,366 sequences in 18 samples) to guarantee an even sampling depth. With the accession number PRJNA1002473, the initial sequences were submitted to the NCBI Sequence Read Archive (SRA).

2.6. Statistical Analysis

All experimental data obtained in the current research were statistically collated and analyzed using Microsoft Excel 2016. A two-factor analysis of variance (ANOVA) was performed using SPSS 23.0 software (IBM Crop, Chicago, IL, USA) to compare the impact of various cropping systems and different soil layers on the α-diversity of soil bacterial communities. By creating a random forest model, the dominant factors of the chemical parameters that predict microbial features were found. Utilizing linear regression analysis, the relationships between the bacterial diversity indices and the chemical characteristics of the soil were examined; Soil bacterial community β-diversity was analyzed and visualized using the “vegan” and “ggplot2” packages in R [40]. Additionally, Mantel a test analysis on the association between soil chemical characteristics and bacterial community composition was carried out using the “ecodist” and “vegan” packages of R.
A network of non-random co-occurrences was used to describe the intricate connections between the bacteria in the community. To analyze the affiliations between bacterial taxa, the OTUs with the highest 300 abundances were kept. The pair-wise correlation coefficient matrices and a significant correlation were computed using the “psych” package in R (Spearman coefficient, r > 0.06, p < 0.05) [41]. The interactive software Gephi (Version 0.10.1) was used to visualize the co-occurrence networks while their associated topological parameters were computed. To study the community structure inside the networks, we defined network modules utilizing the “cluster_fast_greedy” function in “igraph,” which employs a greedy optimization strategy for the modularity algorithm. The “vegan” package in R was used to assess the relative abundance of bacterial communities at the phylum level in each module, and “ggplot2” was used to display the results.
The R code from Burns AR et al. [42] was used to run the neutral model. The impact of both deterministic and stochastic processes was established through the utilization of the normalized stochasticity ratio (NST), which distinguishes between deterministic processes (NST < 0.5) and stochastic processes (NST > 0.5) [43]. FAPROTAX Statistics and data were analyzed with the Biozeron Cloud Platform (http://www.cloud.biomicroclass.com/CloudPlatform (accessed on 1 November 2022)).

3. Results

3.1. Bacterial Community Diversity

Eighteen soil samples yielded a total of 15,620 high-quality bacterial sequences, which were then combined into operational taxonomic units (OTUs, 97% similarity). The number of OTUs in the CK (0–15 cm) ranged from 1416 to 1506, 1382 to 1437 in the CK (15–30 cm), 1561 to 1730 in the MC (0–15 cm), 1458 to 1555 in the MC (15–30 cm), 1619 to 1710 in the CR (0–15 cm), and 1491 to 1702 in the CR (15–30 cm). Different cropping systems had different effects on bacterial diversity. The Ace index and Chao1 index were significantly affected by the cropping systems (p < 0.01) but not by the soil layers and their interaction (Table 1). The soil layers and cropping rotations had a significant effect on the Shannon index and Simpson index (p < 0.05) (Table 1). In comparison to mixtures and cropping rotations, monocultures had much lower alpha diversity (Figure 1a,c). The Shannon index was mostly driven by AK and EC, according to a random forest analysis (Figure 1b). According to linear regression analysis, AK had a significantly positive linear relationship with the Shannon index (R2 = 0.096, p < 0.001) and the Chao1 index (R2 = −0.051, p < 0.001), while EC had a significantly negative linear relationship with the Shannon index (R2 = 0.487, p < 0.001) (Figure 1a–c). PCoA was employed to analyze the changes in species abundance across different samples. According to the PERMANOVA test results (R2 = 0.6285, p = 0.001), significant differences were detected among various soil layers and cropping systems. Compared to the 0–15 cm soil layer, bacterial β-diversity was significantly increased in the 15–30 cm soil layer. Furthermore, cropping rotations significantly increased bacterial β-diversity when compared to monocultures (Figure 2).

3.2. Bacterial Community Composition

Based on Illumina platform analysis, the sequences were divided into 31 different bacterial phyla, 78 classes, 159 orders, 290 families, 506 genera, and 937 species in different cropping systems. The soil layers substantially influenced the relative abundance of the seven bacteria phyla (p < 0.05), while the cropping systems had a significant impact on the comparative abundance of the three bacterial phyla (p < 0.05) (Table S2). The comparative abundance of Bacteroidetes in the mixture and cropping rotations was greater than the monoculture in the 0–15 cm soil layers. The comparative abundance of Tectomicrobia in the monoculture was greater than the mixture and cropping rotations in the 15–30 cm soil layers, whereas Latescibacteria’s comparative abundance in the mixture was higher than that in the monoculture and cropping rotations. Under three different cropping systems, the abundance of Bacteroidetes and Proteobacteria in the 0–15 cm soil layers was significantly higher than that in the 15–30 cm soil layers, while the abundance of Chloroflexi, Gemmatimonadetes, and Latescibacteria at the 15–30 cm soil layers was significantly higher than that at the 0–15 cm soil layers. In addition, the Acidobacteria and Planctomycetes in the monoculture (0–15 cm soil layers) were significantly higher than those in the monoculture (15–30 cm soil layers), while those in the cropping rotations (15–30 cm soil layers) and mixture (15–30 cm soil layers) were significantly higher than those in the cropping rotations (0–15 cm soil layers) and mixture (0–15 cm soil layers) (Figure 3a). Moreover, EC was significantly correlated with Chloroflexi, Bacteroidetes, and Proteobacteria, and pH was significantly correlated with Actinobacteria (Figure 3c).
Further, we analyzed the abundant genera of bacterial communities and found that the soil layers significantly affected the relative abundance of six bacterial genera (p < 0.05), and the cropping systems significantly affected the relative abundance of four bacterial genera (p < 0.05) (Table S2). In two different soil layers, the relative abundance of the bacterial genus Rubrobacter in the monoculture was significantly higher than that in the mixture and cropping rotations. In addition, the Blastococcus and Pseudarthrobacter in the cropping rotations were significantly higher than those in the monoculture and mixture. However, the relative abundance of Roseiflexus in the monoculture (15–30 cm soil layers) was higher than that in the mixture (15–30 cm soil layers) and cropping rotations (15–30 cm soil layers). Under three different cropping systems, Sphingomonas, Blastococcus, and Bryobacter at 0–15 cm soil layers were significantly higher than those at 15–30 cm soil layers. In addition, RB41 in the monoculture (0–15 cm layers) was significantly higher than that in monoculture (15–30 cm layers), while cropping rotations (15–30 cm layers) and mixture (15–30 cm layers) were more significantly higher than that in the cropping rotations (0–15 cm layers) and mixture (0–15 cm layers). However, Pseudarthrobacter in the monoculture (15–30 cm layers) was significantly increased in comparison to that in the monoculture (0–15 cm layers), while cropping rotations (15–30 cm layers) and mixture (15–30 cm layers) were significantly reduced compared to that in the cropping rotations (0–15 cm layers) and mixture (0–15 cm layers) (Figure 3b). EC was significantly correlated with Sphingomonas and RB41. pH was significantly correlated with Rubrobcater, and TN was significantly correlated with Sphingomonas (Figure 3d).

3.3. Co-Occurrence Network Patterns of the Soil Bacterial Community

The bacterial community co-occurrence network in the monoculture mainly had four high-proportion modules (modules 1, 3, 4, and 5). Accounting for 22.67%, 20.33%, 20%, and 19% of the OTUs, respectively (Figure 4a), this co-occurrence network had lower connectivity than other co-occurrence networks (edges and density of 2562 and 0.057, respectively) (Table 2). Cropping rotations and mixtures had significant effects on the co-occurrence network structure of bacterial communities (Figure 4b,c). In contrast to the mixture, the co-occurrence network in cropping rotations was more densely connected, with more edges (9918 vs. 5542) and higher density (0.221 vs. 0.132) (Table 2). Compared to monoculture, cropping rotations and mixture networks had more structured communities in the network and node height clustering. (an average clustering coefficient of 0.532, 0.608, and 0.584, respectively) (Table 2). In addition, we found that OTUs were more clustered in cropping rotation networks than in the mixture networks. In addition to this, compared to monoculture, the relative abundance of Acidobacteria, Bacteroidetes, Firmicutes, and Gemmatimonadetes was increased via cropping rotations and mixture networks (Figure 4d–f). However, the mixture network raised the relative abundance of Proteobacteria, and the cropping rotation network reduced the comparative abundance of Actinobacteria (Figure 4d–f).

3.4. The Assembly Process of Soil Bacterial Communities

NST was calculated to analyze the assembly process of soil bacterial communities in different cropping systems. The NST values of the soil bacterial communities were all greater than 0.5, showing that stochastic processes predominated in all systems of cropping during the process of soil bacterial community construction (Figure 5a). To evaluate the different impacts of stochastic events on forming the microbial communities of every group, we employed a neutral community model (NCM) (Figure 5b–d). In general, at the OTU level, the bacterial community under the mixture (R2 = 0.581) and cropping rotations (R2 = 0.541) cropping systems was less affected by stochastics compared with monoculture (R2 = 0.627). However, the diffusion capacity of monoculture (Nm = 18,811) was higher than that of the mixture (Nm = 15,989) and cropping rotations (Nm = 16,146).

3.5. Function Prediction of the Soil Bacterial Community

The FAPROTAX function prediction results showed that the soil bacterial community contained 35 functions, mainly including C, N, and S cycle functions (Figure 6a). PCA analysis showed that different functional structures formed at different soil layers with no significant differences between cropping systems. Subsequent PERMANOVA analysis further confirmed significant differences between the soil layers (p < 0.001) (Figure 6b). There were significant differences in function involved in the C cycle between 0 and 15 cm (cropping rotations and the mixture) and 15–30 cm (cropping rotations and the mixture) (Figure 6c), and significant differences in function involved in the N cycle under cropping rotations compared with monoculture (Figure 6d). However, the function involved in the S cycle was not significant in the different soil layers and cropping types (Figure 6e). Among the functions involved in C cycling, the cropping rotations (0–15 cm soil layers) significantly increased the relative abundance of aerobic_chemoheterotrophy, intracellular_parasites, aromatic_compound_degradation, and cellulolysis compared to the cropping rotations (15–30 cm soil layers) (Figure S2a–d). The mixture (0–15 cm soil layers) significantly increased the relative abundance of aerobic_chemoheterotrophy, intracellular_parasites, cellulolysis, phototrophy, and photoautotrophy compared to the mixture (15–30 cm soil layers) (Figure S2a–f).

4. Discussion

4.1. Effects of Cropping Systems on Soil Bacteria Diversity

Soil microbes participate in the cycling of nutrients and are crucial for maintaining soil function and ecosystems’ sustainability [44]; particularly in agroecosystems, soil microorganisms play significant roles in terrestrial biological cycles and ecosystem functions [45]. Consequently, they must be taken into account when analyzing how cropping systems affect the restoration of saline–alkaline soil [46,47]. Previous results demonstrated that diverse cropping systems, such as crop rotations and mixtures, increased soil microbial diversity [48]. This is in concordance with our findings (Figure 1a,c). Cropping rotations and mixtures dramatically increased bacterial diversity compared to monoculture, which may be explained by the impact of plant variety and the planting sequence [48]. Compared with monoculture, diverse crops in cropping rotations and mixtures can alter the soil environment and promote the growth of microbial communities in the soil [49]. For instance, cropping rotations and mixtures enhance the quantity and variety of plant litter, which may result in a greater variety of microbial communities [50]. In addition, differing cropping systems’ residual effects on prior crops (such as lingering roots and bottom exudates) (e.g., cropping rotations and mixtures) may also influence the soil microbial community in subsequent cropping systems [51,52,53]. Furthermore, soil chemical properties may impact the structure and composition of soil microbial communities [54]. For instance, Song et al. [55] found that soil-available potassium (AK) was substantially correlated with bacterial and fungal communities, which is consistent with our findings that AK was significantly positively associated with soil bacterial diversity (Figure 1a,c). Thomas et al. [56] found that increased potassium in the soil is associated with residues that may come from residual roots and root secretions from previous crops. Decomposing litter may increase soil nutrients (AK, TN, TK, etc.), inhibit soil respiration, and thus affect soil microbial communities [57,58]. It is widely acknowledged that soil electrical conductivity (EC) can affect the number and abundance of soil bacteria [59,60]. The findings also revealed a substantial negative correlation between EC and the Shannon index of soil bacteria (Figure S1b), which may be because salinity mainly inhibited microbial proliferation through ionic toxicity and high osmotic pressure [61].

4.2. Effects of Cropping Systems on Soil Bacterial Community Composition

The main bacterial phyla identified in the measured soil were Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, Gemmatimonadetes, Bacteroidetes, Tectomicrobia, and Verrucomicrobia et al.; these phyla are described as common in soil bacteria [62]. Liu et al. [63] and Li et al. [64] also proved that Proteobacteria, Bacteroidetes, Acidobacteria, Chloroflexi, and Actinobacteria are the most abundant bacterial phyla. Our findings demonstrated that mixture and cropping rotations increased Bacteroidetes’s relative abundance in the 0–15 cm soil layers. At the same time, under the three different cropping systems, Bacteroidetes’s relative abundance declined as the soil layers increase (Figure 3a); this phenomenon has also been observed in other studies [65]. Generally speaking, the higher the diversity of soil microorganisms, the richer the soil nutrients [66,67]. Bacteroidetes prefer nutrient-rich environments [68,69]. The accumulation of Bacteroidetes can only be achieved under the conditions that the mixture and cropping rotations increase the diversity of soil microorganisms and that the diversity of microorganisms in the 0–15 cm soil layers is greater than that in the 15–30 cm soil layers (Figure 1a,c). In addition, Proteobacteria’s relative abundance fell as the soil layers’ depth increased (Figure 3a). This may be because the availability of C decreases as the soil layers increase [70], Proteobacteria, on the other hand, has been classified as a fast-growing replicating trophic bacterium that prefers a C-rich environment [71]. In addition, studies have found that EC is the primary variable influencing the diversity and composition of soil bacterial communities [72]. The relative abundance of Proteobacteria and Bacteroidetes will increase with the increase in EC, according to other studies [73]. This aligns with our research findings. The relationship between EC and Bacteroidetes, Proteobacteria, and Chloroflexi is significantly positive (Figure 3c). Many Proteobacteria and Bacteroidetes can use diverse organic materials as energy for low levels of nutrients to survive in situations with high salinity [74], and they may be resistant to high salinity [75]. However, Chowdhury et al. [76] showed that soil EC would affect the composition of the soil microbial community through osmotic potential. By elevating the osmotic pressure of water and then extracting water from microbial cells, EC would lead to the destruction and death of microorganisms, which would adversely affect soil microorganisms. This is contrary to our findings, which may be because EC has negative effects on soil microbial biomass and respiration, although these impacts are mitigated by increasing the soil’s organic matter [77]. Additionally, with increasing soil layers, Chloroflexi, Gemmatimonadetes, and Latescibacteria’s relative abundance increased (Figure 3a), as has been observed in other previous studies [78,79]. Chloroflexi is highly adaptable, making it resistant to environmental stresses (e.g., the ability to survive under conditions of low nutrient availability) [80,81]. With the increase of the soil layers, the availability of C also decreases [70], and the relative abundance of Chloroflexi increases. Additionally, the relative abundance of Gemmatimonadetes and Latescibacteria was found to be high in 15–30 cm soil layers. Previous studies have highlighted that Gemmatimonadetes are generalist species with a multifunctional metabolism, are able to adapt to a variety of nutrients, and are detected in subsoil [82,83].

4.3. Effects of Cropping Systems on the Soil Bacterial Community Co-Occurrence Network

Co-occurrence networks enable the visualization of dominant microbes and tightly interconnected microbial communities, which are distinctive and crucial for preserving the stability of microbial community structure and function in the environment [84]. They also provide insight into the ecological interactions between bacterial members [85,86]. Research has indicated that cropping rotations enhance the complexity of microbial networks [87], which is consistent with our findings. Compared with monoculture, the cropping rotations and mixture increased the edge, density, and average clustering coefficient of the co-occurrence network. The tighter and more complicated networks in cropping rotations and mixture can be attributed to crop diversity effects, which are often associated with litter input and exudations from different roots, which alter the associations of subsurface microbial communities [88,89]. The complexity of soil microbial networks has often been shown to ensure ecosystems’ function, for example, soil nutrient cycling [90,91] and resistance to environmental stress [92]. In addition, research has found that microbial communities dominated by stochastic processes can buffer the interference caused via external environmental changes and have a positive feedback effect on ecosystems [93]. However, our results show that the microbial communities under the three different cropping systems were dominated by stochastic processes (Figure 5a). The cropping rotations and mixture networks were more stable than the monoculture (Figure 4a–c). This could be because complex networks with stronger connections are more resistant to environmental disruptions than basic networks [94], resulting in higher stability in general for highly complex networks due to the buffering of the network [95]. Therefore, cropping rotations and mixtures are more resistant to environmental disturbance than monoculture [96]. The above results also indicate that the mixtures network increased the relative abundance of Proteobacteria. This is consistent with what Fierer et al. [97] found, which demonstrated that the relative abundance of nutrient-rich bacterial colonies, such as Proteobacteria, in soils with higher organic carbon content would increase (Table S1). In addition, other studies have found that Acidobacteria and Chloroflex are generally classified as slow-growing oligotrophic bacteria and thrive in soils with low available resources [98,99]. This is consistent with our results, which show that, compared with monoculture, cropping rotations decreased the relative abundance of Acidobacteria and Chloroflex. Moreover, the addition of crop species in crop rotation and mixed cropping can have legacy effects and create beneficial niches for particular microbial taxa through root interactions and the utilization of ecological niches. This leads to greater niche-sharing and interaction between microbial communities [89,100].

4.4. Effects of Cropping Systems on the Soil Bacterial Community Assembly Process

The process of microbial community assembly is a crucial aspect of microbial ecology [101,102,103]. On the other hand, early investigations were inclined towards spatial scales [101], long-term fertilization, [104] or environmental gradients [105] in the assembly of soil microbial communities, while there has been little research on the impact of the cropping systems. The diversity and composition of soil microbial communities are usually regulated via community assembly processes [106], and stochastic processes and deterministic processes are considered to be the principal ecological mechanisms that might simultaneously regulate the assemblage of communities [107]. Previous research has shown that deterministic processes occur under low soil nutrient conditions, while stochastic processes correspond to relatively high nutrient conditions [108,109,110]. This is consistent with our findings: this assembly process of bacterial communities under the three different cropping systems was mainly a stochastic process (Figure 5a). This may be because the differences in the quantity and quality of previous crop root exudates alter soil microbial community, which in turn affect the proportion of deterministic and stochastic processes in the assembly of soil microbial communities [100,111]. Other studies have shown that stochastic processes are more common in environments with higher nutrient availability [93], which may be because the abundance of nutrients reduces competition between microbial communities, causing stochastic processes to dominate [112]. However previous research has shown that the stochastic process increases exponentially as the nutrient state increases, and the deterministic process increases with the decrease of soil nutrient level [109]; this is inconsistent with our findings. Our results showed that the high salinity soil under monoculture was associated with low nutrient availability, while soil nutrients increased as the increase of bacterial diversity with the cropping rotations and mixture, and the influence of stochastic processes on the bacterial community gradually decreased with the cropping rotations and mixture compared with monoculture (Figure 5b,d). This may be due to differences in environmental adaptation and survival strategies [113].

4.5. Effects of Cropping Systems on Soil Bacterial Community Function Prediction

Environmental changes will have an impact on soil microbial community diversity, and these patterns will also have an impact on changes in soil C, N, and S [114]. Since soil microorganisms are essential for the N, S, and C cycles, it is important to understand their structure and function [115]. Some research results have shown that the main functions of bacterial communities analyzed via FAPROTAX are the N cycle, C cycle, and S cycle [116], which is consistent with our research results (Figure 6c and Figure S2). Additionally, the abundance of functional groups participating in the C cycle was higher in the cropping rotations (0–15 cm) and mixture (0–15 cm) than in monoculture (0–15 cm) (Figure 6c), which may be attributed to various environmental and nutritional factors under various land use patterns [117]. This may be because leftover roots and root exudates from past crops in crop rotations and mixtures can provide a variety of carbon sources for soil bacteria [51,52,53], increase soil bacteria’s ability to absorb and use carbon, and hence encourage the growth of C-cycle bacteria [117]; studies have also shown that cellulolysis is a key process affecting litter decomposition (Figure S2d) [118]. In addition, we found that the relative abundance of Sphingomona at the genus level was higher in the 0–15 cm soil layers than in the 15–30 cm soil layers (Figure 3b). According to studies that have shown increasing the relative abundance of Sphingomona can promote the process of carbon degradation and accelerate the mineralization of organic matter in soils, its ability to degrade organic matter is also enhanced, thus improving the C-cycle function [119]. Anoxic phototrophs can be classified as photoautotrophy or photoheterotrophy, depending on the carbon source. The functional abundance of photoautotrophy and photoheterotrophy was relatively reduced in the cropping rotations (15–30 cm) and mixture (15–30 cm) compared to the cropping rotations (0–15 cm) and mixture (0–15 cm), perhaps as a result of aerobic and anaerobic environments and aqueous conditions. According to certain studies, the amount of oxygen increases as the depth of the soil layers grows while the amount of water content. The spread of photoautotrophy function will grow when oxygen and water content changes [120], which aligns with the findings of this study (Figure S2e). Higher pH soils have been shown to benefit ammonia-oxidization bacteria (AOB) [121]. In other words, the relative abundance of AOB increases at higher pH values [122]; this is in line with our findings, which showed that cropping rotations reduced the pH value compared with monoculture (Table S1). Additionally, the relative abundance of AOB in cropping rotations (0–15 cm) was lower than that in monoculture (0–15 cm) (Figure 6d). Our study found that cropping rotations reduced the relative abundance of nitrogen fixation compared with monoculture (Figure 6a). This might be because alfalfa increases the nitrogen concentration of the soil by utilizing nitrogen fixation from the air into the soil through its root nodules [123]. A higher nitrogen concentration can improve alfalfa plants’ ability to adapt to environmental conditions by regulating their water status [124].

5. Conclusions

The study’s results showed that cropping rotations and mixtures increased the diversity of soil bacterial communities, and the composition of soil microbial communities also changed. Compared with monoculture, mixture and cropping rotations reduced the relative abundance of Tectomicrobia but increased the relative abundance of Bacteroides and Latescibacteria. Compared with the 0–15 cm soil layer, the relative abundance of Bacteroidetes and Proteobacteria decreased significantly at the 15–30 cm soil layer. The relative abundance of Chloroflexi, Gemmatimonadetes, and Latescibacteria was increased, suggesting that the cropping system could alter the relative abundance of dominant phyla and rapidly alter the community structure. Furthermore, cropping rotations and mixture increased the complexity of co-occurrence networks, indicating that the assembly process of bacterial communities was a stochastic process, and we found that the C-cycle function was relatively increased in the cropping rotations (0–15 cm) and mixtures (0–15 cm), while the cropping rotations reduced the N-cycle function compared to monoculture. In general, the diversity, composition, co-occurrence networks, stochastic process, and C- and N-cycle functions of soil bacterial communities were influenced by cropping rotations and mixtures.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13122984/s1. Figure S1: Linear regressions between bacterial Chao1 index (c), Shannon index (a and b) and important for the soil physicochemical properties. Figure S2: C cycle function bacteria in the different cropping system and different soil layer. Different case letters indicate significant differences (p < 0.05) based on two-way analysis of variance (ANOVA) as well as by LSD test for multiple comparisons. Lowercase letters the columns indicate different soil layer significance levels, capital letters indicate differences between different cropping system significant. Table S1: The chemical properties of soils in the different cropping system. Different lowercase letters indicate significant differences among different treatments according to one-way ANOVA with Duncan’s multiple range tests (p < 0.05). SOM: soil organic matter, TN: total nitrogen, TP: total phosphorus, AP: available phosphorus, AK: available potassium, EC: electrical conductivity. CK: monoculture; MC: mixture; CR: cropping rotation; Table S2: Two-way ANOVAs for the effects of soil layer (SL), cropping system (CS) and their interaction (SL × CS) on composition of soil bacterial community.

Author Contributions

Methodology, D.Z.; Software, B.L.; Validation, B.Y.; Formal analysis, L.M.; Investigation, J.D.; Resources, J.L. and G.S.; Data curation, X.L. (Xin Li); Writing—review & editing, X.L. (Xiaoqian Liu); Visualization, L.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Key Research and Development Program of China (2022YFD1500800), the Academic Backbone Support Project of the Northeast Agricultural University, and the National Natural Science Foundation of China (41701289).

Data Availability Statement

95% of our data is sequencing data that has been uploaded to NCBI and is open access. With the accession number PRJNA1002473, the initial sequences were submitted to the NCBI Sequence Read Archive (SRA).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhang, J.; Ding, Q.; Wang, Y.; He, M.; Jia, K. Soil quality assessment and constraint diagnosis of salinized farmland in the Yellow River irrigation area in northwestern China. Geoderma Reg. 2023, 34, e00684. [Google Scholar] [CrossRef]
  2. Mao, W.; Kang, S.; Wan, Y.; Sun, Y.; Li, X.; Wang, Y. Yellow River Sediment as a Soil Amendment for Amelioration of Saline Land in the Yellow River Delta. Land Degrad. Dev. 2014, 27, 1595–1602. [Google Scholar] [CrossRef]
  3. Wong, V.; Greene, R.; Dalal, R.; Murphy, B.W. Soil carbon dynamics in saline and sodic soils: A review. Soil Use Manag. 2009, 26, 2–11. [Google Scholar] [CrossRef]
  4. Yan, W.; Bai, R.; Wang, S.; Tian, X.; Li, Y.; Wang, S.; Yang, F.; Xiao, Y.; Lu, X.; Zhao, F. Antibiotic resistance genes are increased by combined exposure to sulfamethoxazole and naproxen but relieved by low-salinity. Environ. Int. 2020, 139, 105742. [Google Scholar] [CrossRef] [PubMed]
  5. Yang, C.; Sun, J. Soil Salinity Drives the Distribution Patterns and Ecological Functions of Fungi in Saline-alkaline Land in the Yellow River Delta, China. Front. Microbiol. 2020, 11, 594284. [Google Scholar] [CrossRef] [PubMed]
  6. Zhang, W.; Bahadur, A.; Sajjad, W.; Wu, X.; Zhang, G.; Liu, G.; Chen, T. Seasonal Variation in Fungal Community Composition Associated with Tamarix chinensis Roots in the Coastal Saline Soil of Bohai Bay, China. Microb. Ecol. 2021, 82, 652–665. [Google Scholar] [CrossRef] [PubMed]
  7. Shrivastava, P.; Kumar, R. Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J. Biol. Sci. 2015, 22, 123–131. [Google Scholar] [CrossRef]
  8. Zhao, S.; Zhang, Q.; Liu, M.; Zhou, H.; Ma, C.; Wang, P. Regulation of Plant Responses to Salt Stress. Int. J. Mol. Sci. 2021, 22, 4609. [Google Scholar] [CrossRef]
  9. Srivastava, P.; Gupta, M.; Shikha; Singh, N.; Tewari, S. Amelioration of Sodic Soil for Wheat Cultivation Using Bioaugmented Organic Soil Amendment. Land Degrad. Dev. 2014, 27, 1245–1254. [Google Scholar] [CrossRef]
  10. Mbarki, S.; Cerdà, A.; Zivcak, M.; Brestic, M.; Rabhi, M.; Mezni, M.; Jedidi, N.; Abdelly, C.; Pascual, J.A. Alfalfa crops amended with MSW compost can compensate the effect of salty water irrigation depending on the soil texture. Process Saf. Environ. Prot. 2018, 115, 8–16. [Google Scholar] [CrossRef]
  11. Li, K.-S.; Li, Q.; Geng, Y.; Liu, C. An evaluation of the effects of microstructural characteristics and frost heave on the remediation of saline-alkali soils in the Yellow River Delta, China. Land Degrad. Dev. 2020, 32, 1325–1337. [Google Scholar] [CrossRef]
  12. Cheng, Z.; Chen, Y.; Zhang, F. Effect of cropping systems after abandoned salinized farmland reclamation on soil bacterial communities in arid northwest China. Soil Tillage Res. 2019, 187, 204–213. [Google Scholar] [CrossRef]
  13. Williams, A.; Birt, H.W.G.; Raghavendra, A.; Dennis, P.G. Cropping Systems Diversification Influences Soil Microbial Diversity in Subtropical Dryland Farming Systems. Microb. Ecol. 2023, 85, 1473–1484. [Google Scholar] [CrossRef]
  14. Jarecki, M.; Grant, B.; Smith, W.; Deen, B.; Drury, C.; VanderZaag, A.; Qian, B.; Yang, J.; Wagner-Riddle, C. Long-term Trends in Corn Yields and Soil Carbon under Diversified Crop Rotations. J. Environ. Qual. 2018, 47, 635–643. [Google Scholar] [CrossRef] [PubMed]
  15. Agomoh, I.; Drury, C.; Phillips, L.; Reynolds, D.; Yang, X. Increasing crop diversity in wheat rotations increases yields but decreases soil health. Soil Sci. Soc. Am. J. 2020, 84, 170–181. [Google Scholar] [CrossRef]
  16. Stanger, T.F.; Lauer, J.G. Corn Grain Yield Response to Crop Rotation and Nitrogen over 35 Years. Agron. J. 2008, 100, 643–650. [Google Scholar] [CrossRef]
  17. Esmaeilzadeh-Salestani, K.; Bahram, M.; Ghanbari Moheb Seraj, R.; Gohar, D.; Tohidfar, M.; Eremeev, V.; Talgre, L.; Khaleghdoust, B.; Mirmajlessi, S.M.; Luik, A.; et al. Cropping systems with higher organic carbon promote soil microbial diversity. Agric. Ecosyst. Environ. 2021, 319, 107521. [Google Scholar] [CrossRef]
  18. Liu, C.L.C.; Kuchma, O.; Krutovsky, K.V. Mixed-species versus monocultures in plantation forestry: Development, benefits, ecosystems services and perspectives for the future. Glob. Ecol. Conserv. 2018, 15, e00419. [Google Scholar] [CrossRef]
  19. Ishaq, S.L.; Johnson, S.P.; Miller, Z.J.; Lehnhoff, E.A.; Olivo, S.; Yeoman, C.J.; Menalled, F.D. Impact of Cropping Systems, Soil Inoculum, and Plant Species Identity on Soil Bacterial Community Structure. Microb. Ecol. 2017, 73, 417–434. [Google Scholar] [CrossRef]
  20. Chen, G.; Hu, Y.-G.; Lin, Y.-C.; Lu, J.-S.; Wang, X.; Yuan, X.-L.; Zeng, Z.-H.; Zhu, B. Effects of oat mixed with common vetch on the microorganism populations in rhizosphere soil. Acta Prataculturae Sin. 2009, 18, 151–157. [Google Scholar]
  21. Xuan, D.T.; Guong, V.T.; Rosling, A.; Alström, S.; Chai, B.; Högberg, N. Different crop rotation systems as drivers of change in soil bacterial community structure and yield of rice, Oryza sativa. Biol. Fertil. Soils 2012, 48, 217–225. [Google Scholar] [CrossRef]
  22. Gorbacheva, M.A.; Melnikova, N.V.; Chechetkin, V.R.; Kravatsky, Y.V.; Tchurikov, N.A. DNA sequencing and metagenomics of cultivated and uncultivated chernozems in Russia. Geoderma Reg. 2018, 14, e00180. [Google Scholar] [CrossRef]
  23. Li, C.; Li, X.; Kong, W.; Wu, Y.; Wang, J. Effect of monoculture soybean on soil microbial community in the Northeast China. Plant Soil 2010, 330, 423–433. [Google Scholar] [CrossRef]
  24. Lienhard, P.; Terrat, S.; Prévost-Bouré, N.C.; Nowak, V.; Régnier, T.; Sayphoummie, S.; Panyasiri, K.; Tivet, F.; Mathieu, O.; Levêque, J.; et al. Pyrosequencing evidences the impact of cropping on soil bacterial and fungal diversity in Laos tropical grassland. Agron. Sustain. Dev. 2014, 34, 525–533. [Google Scholar] [CrossRef]
  25. Lladó, S.; López-Mondéjar, R.; Baldrian, P. Forest Soil Bacteria: Diversity, Involvement in Ecosystems Processes, and Response to Global Change. Microbiol. Mol. Biol. Rev. 2017, 81, e00063-16. [Google Scholar] [CrossRef] [PubMed]
  26. Yu, Z.; Liang, K.; Huang, G.; Wang, X.; Lin, M.; Chen, Y.; Zhou, Z. Soil Bacterial Community Shifts Are Driven by Soil Nutrient Availability along a Teak Plantation Chronosequence in Tropical Forests in China. Biology 2021, 10, 1329. [Google Scholar] [CrossRef] [PubMed]
  27. Lupi, A.; Steinbach, H.S.; Ciarlo, E.; Romaniuk, R.; Cosentino, V.R.N.; Rimski-Korsakov, H.; Alvarez, C.R. Organic carbon stored in soils under different land uses and soil textures in southeast Argentinean Mesopotamia. Geoderma Reg. 2021, 27, e00435. [Google Scholar] [CrossRef]
  28. Zhang, K.; Shi, Y.; Cui, X.; Yue, P.; Li, K.; Liu, X.; Tripathi, B.M.; Chu, H. Salinity Is a Key Determinant for Soil Microbial Communities in a Desert Ecosystems. mSystems 2019, 4, e00225-18. [Google Scholar] [CrossRef]
  29. Fang, J.; Deng, Y.; Che, R.; Han, C.; Zhong, W. Bacterial community composition in soils covered by different vegetation types in the Yancheng tidal marsh. Environ. Sci. Pollut. Res. 2020, 27, 21517–21532. [Google Scholar] [CrossRef]
  30. Alizadeh, H.; Kandula, D.R.W.; Hampton, J.G.; Stewart, A.; Leung, D.W.M.; Edwards, Y.; Smith, C. Urease producing microorganisms under dairy pasture management in soils across New Zealand. Geoderma Reg. 2017, 11, 78–85. [Google Scholar] [CrossRef]
  31. Bremner, J.M.; Shaw, K. Denitrification in soil. I. Methods of investigation. J. Agric. Sci. 1958, 51, 22–39. [Google Scholar] [CrossRef]
  32. Olsen, S.R. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate; US Department of Agriculture: Washington, DC, USA, 1954.
  33. Walker, J.M.; Barber, S.A. Absorption of potassium and rubidium from the soil by corn roots. Plant Soil 1962, 17, 243–259. [Google Scholar] [CrossRef]
  34. Wolf, A.M.A.; Baker, D.E. Comparisons of soil test phosphorus by Olsen, Bray P1, Mehlich I and Mehlich III methods. Commun. Soil Sci. Plant Anal. 1985, 16, 467–484. [Google Scholar] [CrossRef]
  35. Mori, H.; Maruyama, F.; Kato, H.; Toyoda, A.; Dozono, A.; Ohtsubo, Y.; Nagata, Y.; Fujiyama, A.; Tsuda, M.; Kurokawa, K. Design and Experimental Application of a Novel Non-Degenerate Universal Primer Set that Amplifies Prokaryotic 16S rRNA Genes with a Low Possibility to Amplify Eukaryotic rRNA Genes. DNA Res. 2013, 21, 217–227. [Google Scholar] [CrossRef] [PubMed]
  36. Zhou, Y.-J.; Li, J.-H.; Ross Friedman, C.; Wang, H.-F. Variation of Soil Bacterial Communities in a Chronosequence of Rubber Tree (Hevea brasiliensis) Plantations. Front. Plant Sci. 2017, 8, 849. [Google Scholar] [CrossRef] [PubMed]
  37. Zhou, X.; Liu, J.; Wu, F. Soil microbial communities in cucumber monoculture and rotation systems and their feedback effects on cucumber seedling growth. Plant Soil 2017, 415, 507–520. [Google Scholar] [CrossRef]
  38. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [PubMed]
  39. Schloss, P.D.; Westcott, S.L.; Ryabin, T.; Hall, J.R.; Hartmann, M.; Hollister, E.B.; Lesniewski, R.A.; Oakley, B.B.; Parks, D.H.; Robinson, C.J.; et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009, 75, 7537–7541. [Google Scholar] [CrossRef]
  40. Liu, W.; Qiu, K.; Xie, Y.; Huang, Y.; Wang, R.; Li, H.; Meng, W.; He, Y.; Li, Y.; Li, H.; et al. High-Throughput Absolute Quantification Sequencing Reveals that a Combination of Leguminous Shrubs Is Effective in Driving Soil Bacterial Diversity During the Process of Desertification Reversal. Microb. Ecol. 2023, 86, 1145–1163. [Google Scholar] [CrossRef]
  41. Mendes, L.W.; Raaijmakers, J.M.; de Hollander, M.; Mendes, R.; Tsai, S.M. Influence of resistance breeding in common bean on rhizosphere microbiome composition and function. ISME J. 2018, 12, 212–224. [Google Scholar] [CrossRef]
  42. Burns, A.R.; Stephens, W.Z.; Stagaman, K.; Wong, S.; Rawls, J.F.; Guillemin, K.; Bohannan, B.J.M. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016, 10, 655–664. [Google Scholar] [CrossRef] [PubMed]
  43. Ning, D.; Deng, Y.; Tiedje, J.M.; Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl. Acad. Sci. USA 2019, 116, 16892–16898. [Google Scholar] [CrossRef] [PubMed]
  44. Deepak, G.; Yogeshvari, K.; Harsha, N.; Rajababu, V. Microorganisms for Green Revolution Volume 2: Microbes for Sustainable Agro-Ecosystems; Department of Agricultural Microbiology, BA College of Agriculture Anand Agricultural University Anand: Gujarat, India, 2018. [Google Scholar]
  45. Song, B.; Li, Y.; Yang, L.; Shi, H.; Li, L.; Bai, W.; Zhao, Y. Soil Acidification Under Long-Term N Addition Decreases the Diversity of Soil Bacteria and Fungi and Changes Their Community Composition in a Semiarid Grassland. Microb. Ecol. 2023, 85, 221–231. [Google Scholar] [CrossRef] [PubMed]
  46. Luo, G.; Jin, T.; Zhang, H.; Peng, J.; Zuo, N.; Huang, Y.; Han, Y.; Tian, C.; Yang, Y.; Peng, K.; et al. Deciphering the diversity and functions of plastisphere bacterial communities in plastic-mulching croplands of subtropical China. J. Hazard. Mater. 2022, 422, 126865. [Google Scholar] [CrossRef] [PubMed]
  47. Luo, G.; Xue, C.; Jiang, Q.; Xiao, Y.; Zhang, F.; Guo, S.; Shen, Q.; Ling, N. Soil Carbon, Nitrogen, and Phosphorus Cycling Microbial Populations and Their Resistance to Global Change Depend on Soil C:N:P Stoichiometry. mSystems 2020, 5, e00162-20. [Google Scholar] [CrossRef] [PubMed]
  48. Woo, S.L.; De Filippis, F.; Zotti, M.; Vandenberg, A.; Hucl, P.; Bonanomi, G. Pea-Wheat Rotation Affects Soil Microbiota Diversity, Community Structure, and Soilborne Pathogens. Microorganisms 2022, 10, 370. [Google Scholar] [CrossRef] [PubMed]
  49. Venter, Z.S.; Jacobs, K.; Hawkins, H.-J. The impact of crop rotation on soil microbial diversity: A meta-analysis. Pedobiologia 2016, 59, 215–223. [Google Scholar] [CrossRef]
  50. Lavelle, P. Invertebrate biodiversity as bioindicators of sustainable landscapes. Plant Sci. 2001, 160, 1069–1070. [Google Scholar] [CrossRef]
  51. Benitez, M.-S.; Ewing, P.M.; Osborne, S.L.; Lehman, R.M. Rhizosphere microbial communities explain positive effects of diverse crop rotations on maize and soybean performance. Soil Biol. Biochem. 2021, 159, 108309. [Google Scholar] [CrossRef]
  52. Liu, B.; Arlotti, D.; Huyghebaert, B.; Tebbe, C.C. Disentangling the impact of contrasting agricultural management practices on soil microbial communities—Importance of rare bacterial community members. Soil Biol. Biochem. 2022, 166, 108573. [Google Scholar] [CrossRef]
  53. Kuerban, M.; Cong, W.-F.; Jing, J.; Bezemer, T.M. Microbial soil legacies of crops under different water and nitrogen levels determine succeeding crop performance. Plant Soil 2023, 485, 167–180. [Google Scholar] [CrossRef]
  54. Brockett, B.F.T.; Prescott, C.E.; Grayston, S.J. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil. Biol. Biochem. 2012, 44, 9–20. [Google Scholar] [CrossRef]
  55. Song, X.; Tao, B.; Guo, J.; Li, J.; Chen, G. Changes in the Microbial Community Structure and Soil Chemical Properties of Vertisols under Different Cropping Systems in Northern China. Front. Environ. Sci. 2018, 6, 132. [Google Scholar] [CrossRef]
  56. Thomas, G.A.; Dalal, R.C.; Standley, J. No-till effects on organic matter, pH, cation exchange capacity and nutrient distribution in a Luvisol in the semi-arid subtropics. Soil Tillage Res. 2007, 94, 295–304. [Google Scholar] [CrossRef]
  57. Xu, B.; Yang, G.; Lehmann, A.; Riedel, S.; Rillig, M.C. Effects of perfluoroalkyl and polyfluoroalkyl substances (PFAS) on soil structure and function. Soil Ecol. Lett. 2023, 5, 108–117. [Google Scholar] [CrossRef]
  58. Xu, R.; Tao, W.; Lin, H.; Huang, D.; Su, P.; Gao, P.; Sun, X.; Yang, Z.; Sun, W. Effects of Perfluorooctanoic Acid (PFOA) and Perfluorooctane Sulfonic Acid (PFOS) on Soil Microbial Community. Microb. Ecol. 2022, 83, 929–941. [Google Scholar] [CrossRef]
  59. Rath, K.M.; Fierer, N.; Murphy, D.V.; Rousk, J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 2019, 13, 836–846. [Google Scholar] [CrossRef]
  60. Wang, S.; Sun, L.; Ling, N.; Zhu, C.; Chi, F.; Li, W.; Hao, X.; Zhang, W.; Bian, J.; Chen, L.; et al. Exploring Soil Factors Determining Composition and Structure of the Bacterial Communities in Saline-alkaline Soils of Songnen Plain. Front. Microbiol. 2020, 10, 2902. [Google Scholar] [CrossRef]
  61. Yan, N.; Marschner, P.; Cao, W.; Zuo, C.; Qin, W. Influence of salinity and water content on soil microorganisms. Int. Soil Water Conserv. Res. 2015, 3, 316–323. [Google Scholar] [CrossRef]
  62. Zhou, X.; Wang, Z.; Jia, H.; Li, L.; Wu, F. Continuously Monocropped Jerusalem Artichoke Changed Soil Bacterial Community Composition and Ammonia-Oxidizing and Denitrifying Bacteria Abundances. Front. Microbiol. 2018, 9, 705. [Google Scholar] [CrossRef]
  63. Liu, J.; Shu, A.; Song, W.; Shi, W.; Li, M.; Zhang, W.; Li, Z.; Liu, G.; Yuan, F.; Zhang, S.; et al. Long-term organic fertilizer substitution increases rice yield by improving soil properties and regulating soil bacteria. Geoderma 2021, 404, 115287. [Google Scholar] [CrossRef]
  64. Li, Z.; Jiao, Y.; Yin, J.; Li, D.; Wang, B.; Zhang, K.; Zheng, X.; Hong, Y.; Zhang, H.; Xie, C.; et al. Productivity and quality of banana in response to chemical fertilizer reduction with bio-organic fertilizer: Insight into soil properties and microbial ecology. Agric. Ecosyst. Environ. 2021, 322, 107659. [Google Scholar] [CrossRef]
  65. Fischer, C.; Thürmer, A.; Wollherr, A.; Nacke, H.; Herold, N.; Schrumpf, M.; Gutknecht, J.; Wubet, T.; Buscot, F.; Daniel, R. Horizon-Specific Bacterial Community Composition of German Grassland Soils, as Revealed by Pyrosequencing-Based Analysis of 16S rRNA Genes. Appl. Environ. Microbiol. 2010, 76, 6751–6759. [Google Scholar]
  66. Zhang, L.; Huang, W.; Xiao, W.; Hu, D.; Shao, J.; Yao, B. Comparison of soil enzyme activity and microbial community structure between rapeseed-rice and rice-rice plantings. Int. J. Agric. Biol. 2018, 20, 1801–1808. [Google Scholar]
  67. Wagg, C.; Hautier, Y.; Pellkofer, S.; Banerjee, S.; Schmid, B.; van der Heijden, M.G. Diversity and asynchrony in soil microbial communities stabilizes ecosystems functioning. eLife 2021, 10, e62813. [Google Scholar] [CrossRef] [PubMed]
  68. Goldfarb, K.C.; Karaoz, U.; Hanson, C.A.; Santee, C.A.; Bradford, M.A.; Treseder, K.K.; Wallenstein, M.D.; Brodie, E.L. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front. Microbiol. 2011, 2, 94. [Google Scholar] [CrossRef]
  69. Griffith, J.C.; Lee, W.G.; Orlovich, D.A.; Summerfield, T.C. Contrasting bacterial communities in two indigenous Chionochloa (Poaceae) grassland soils in New Zealand. PLoS ONE 2017, 12, e0179652. [Google Scholar] [CrossRef]
  70. Fierer, N.; Allen, A.; Schimel, J.; Holden, P. Controls on microbial CO2 production: A comparison of surface and subsurface soil horizons. Glob. Chang. Biol. 2003, 9, 1322–1332. [Google Scholar] [CrossRef]
  71. Fierer, N.; Morse, J.L.; Berthrong, S.T.; Bernhardt, E.S.; Jackson, R.B. Environmental controls on the landscape-scale biogeography of stream bacterial communities. Ecology 2007, 88, 2162–2173. [Google Scholar] [CrossRef]
  72. Kim, J.M.; Roh, A.-S.; Choi, S.-C.; Kim, E.-J.; Choi, M.-T.; Ahn, B.-K.; Kim, S.-K.; Lee, Y.-H.; Joa, J.-H.; Kang, S.-S.; et al. Soil pH and electrical conductivity are key edaphic factors shaping bacterial communities of greenhouse soils in Korea. J. Microbiol. 2016, 54, 838–845. [Google Scholar] [CrossRef]
  73. Li, X.; Wang, A.; Wan, W.; Luo, X.; Zheng, L.; He, G.; Huang, D.; Chen, W.; Huang, Q. High Salinity Inhibits Soil Bacterial Community Mediating Nitrogen Cycling. Appl. Environ. Microbiol. 2021, 87, e0136621. [Google Scholar] [CrossRef]
  74. Li, X.; Meng, D.; Li, J.; Yin, H.; Liu, H.; Liu, X.; Cheng, C.; Xiao, Y.; Liu, Z.; Yan, M. Response of soil microbial communities and microbial interactions to long-term heavy metal contamination. Environ. Pollut. 2017, 231, 908–917. [Google Scholar] [CrossRef]
  75. Samaei-Nouroozi, A.; Rezaei, S.; Khoshnevis, N.; Doosti, M.; Hajihoseini, R.; Khoshayand, M.R.; Faramarzi, M.A. Medium-based optimization of an organic solvent-tolerant extracellular lipase from the isolated halophilic Alkalibacillus salilacus. Extremophiles 2015, 19, 933–947. [Google Scholar] [CrossRef] [PubMed]
  76. Chowdhury, N.; Marschner, P.; Burns, R.G. Soil microbial activity and community composition: Impact of changes in matric and osmotic potential. Soil Biol. Biochem. 2011, 43, 1229–1236. [Google Scholar] [CrossRef]
  77. Wichern, J.; Wichern, F.; Joergensen, R.G. Impact of salinity on soil microbial communities and the decomposition of maize in acidic soils. Geoderma 2006, 137, 100–108. [Google Scholar] [CrossRef]
  78. Hansel, C.M.; Fendorf, S.; Jardine, P.M.; Francis, C.A. Changes in bacterial and archaeal community structure and functional diversity along a geochemically variable soil profile. Appl. Environ. Microbiol. 2008, 74, 1620–1633. [Google Scholar] [CrossRef] [PubMed]
  79. Schlatter, D.C.; Kahl, K.; Carlson, B.; Huggins, D.R.; Paulitz, T. Soil acidification modifies soil depth-microbiome relationships in a no-till wheat cropping systems. Soil Biol. Biochem. 2020, 149, 107939. [Google Scholar] [CrossRef]
  80. Battistuzzi, F.U.; Hedges, S.B. A major clade of prokaryotes with ancient adaptations to life on land. Mol. Biol. Evol. 2009, 26, 335–343. [Google Scholar] [CrossRef]
  81. Trivedi, P.; Anderson, I.C.; Singh, B.K. Microbial modulators of soil carbon storage: Integrating genomic and metabolic knowledge for global prediction. Trends Microbiol. 2013, 21, 641–651. [Google Scholar] [CrossRef]
  82. Rime, T.; Hartmann, M.; Brunner, I.; Widmer, F.; Zeyer, J.; Frey, B. Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Mol. Ecol. 2015, 24, 1091–1108. [Google Scholar] [CrossRef]
  83. Mujakić, I.; Piwosz, K.; Koblížek, M. Phylum Gemmatimonadota and Its Role in the Environment. Microorganisms 2022, 10, 151. [Google Scholar] [CrossRef] [PubMed]
  84. Wang, Y.; Chu, L.; Ma, J.; Chi, G.; Lu, C.; Chen, X. Effects of multiple antibiotics residues in broiler manure on composting process. Sci. Total Environ. 2022, 817, 152808. [Google Scholar] [CrossRef] [PubMed]
  85. Faust, K.; Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 2012, 10, 538–550. [Google Scholar] [CrossRef]
  86. Banerjee, S.; Schlaeppi, K.; van der Heijden, M.G.A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 2018, 16, 567–576. [Google Scholar] [CrossRef] [PubMed]
  87. Yang, X.; Hu, H.-W.; Yang, G.-W.; Cui, Z.-L.; Chen, Y.-L. Crop rotational diversity enhances soil microbiome network complexity and multifunctionality. Geoderma 2023, 436, 116562. [Google Scholar] [CrossRef]
  88. Steinauer, K.; Chatzinotas, A.; Eisenhauer, N. Root exudate cocktails: The link between plant diversity and soil microorganisms? Ecol. Evol. 2016, 6, 7387–7396. [Google Scholar] [CrossRef]
  89. D’Acunto, L.; Andrade, J.F.; Poggio, S.L.; Semmartin, M. Diversifying crop rotation increased metabolic soil diversity and activity of the microbial community. Agric. Ecosyst. Environ. 2018, 257, 159–164. [Google Scholar] [CrossRef]
  90. Wagg, C.; Schlaeppi, K.; Banerjee, S.; Kuramae, E.E.; van der Heijden, M.G.A. Fungal-bacterial diversity and microbiome complexity predict ecosystems functioning. Nat. Commun. 2019, 10, 4841. [Google Scholar] [CrossRef]
  91. Qiu, L.; Zhang, Q.; Zhu, H.; Reich, P.B.; Banerjee, S.; van der Heijden, M.G.A.; Sadowsky, M.J.; Ishii, S.; Jia, X.; Shao, M.; et al. Erosion reduces soil microbial diversity, network complexity and multifunctionality. ISME J. 2021, 15, 2474–2489. [Google Scholar] [CrossRef]
  92. Griffiths, B.S.; Philippot, L. Insights into the resistance and resilience of the soil microbial community. FEMS Microbiol. Rev. 2013, 37, 112–129. [Google Scholar] [CrossRef]
  93. Chase, J.M. Stochastic community assembly causes higher biodiversity in more productive environments. Science 2010, 328, 1388–1391. [Google Scholar] [CrossRef] [PubMed]
  94. Santolini, M.; Barabási, A.-L. Predicting perturbation patterns from the topology of biological networks. Proc. Natl. Acad. Sci. USA 2018, 115, E6375–E6383. [Google Scholar] [CrossRef]
  95. Landi, P.; Minoarivelo, H.; Brännström, Å.; Hui, C.; Dieckmann, U. Complexity and Stability of Adaptive Ecological Networks: A Survey of the Theory in Community Ecology. In Systems Analysis Approach for Complex Global Challenges; Springer: Cham, Switzerland, 2018; pp. 209–248. [Google Scholar]
  96. Allison, S.D.; Martiny, J.B. Colloquium paper: Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. USA 2008, 105 (Suppl. S1), 11512–11519. [Google Scholar] [CrossRef] [PubMed]
  97. Fierer, N.; Bradford, M.A.; Jackson, R.B. Toward an ecological classification of soil bacteria. Ecology 2007, 88, 1354–1364. [Google Scholar] [CrossRef] [PubMed]
  98. Davis, K.E.; Sangwan, P.; Janssen, P.H. Acidobacteria, Rubrobacteridae and Chloroflexi are abundant among very slow-growing and mini-colony-forming soil bacteria. Environ. Microbiol. 2011, 13, 798–805. [Google Scholar] [CrossRef] [PubMed]
  99. Eo, J.; Park, K.-C. Long-term effects of imbalanced fertilization on the composition and diversity of soil bacterial community. Agric. Ecosyst. Environ. 2016, 231, 176–182. [Google Scholar] [CrossRef]
  100. Zhang, K.; Maltais-Landry, G.; Liao, H.-L. How soil biota regulate C cycling and soil C pools in diversified crop rotations. Soil Biol. Biochem. 2021, 156, 108219. [Google Scholar] [CrossRef]
  101. Shi, Y.; Li, Y.; Xiang, X.; Sun, R.; Yang, T.; He, D.; Zhang, K.; Ni, Y.; Zhu, Y.-G.; Adams, J.M.; et al. Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain. Microbiome 2018, 6, 27. [Google Scholar] [CrossRef]
  102. Zhao, J.; Gao, Q.; Zhou, J.; Wang, M.; Liang, Y.; Sun, B.; Chu, H.; Yang, Y. The scale dependence of fungal community distribution in paddy soil driven by stochastic and deterministic processes. Fungal Ecol. 2019, 42, 100856. [Google Scholar] [CrossRef]
  103. Jiao, S.; Yang, Y.; Xu, Y.; Zhang, J.; Lu, Y. Balance between community assembly processes mediates species coexistence in agricultural soil microbiomes across eastern China. ISME J. 2020, 14, 202–216. [Google Scholar] [CrossRef]
  104. Shi, Y.; Dang, K.; Dong, Y.; Feng, M.; Wang, B.; Li, J.; Chu, H. Soil fungal community assembly processes under long-term fertilization. Eur. J. Soil Sci. 2019, 71, 716–726. [Google Scholar] [CrossRef]
  105. Tripathi, B.M.; Stegen, J.C.; Kim, M.; Dong, K.; Adams, J.M.; Lee, Y.K. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 2018, 12, 1072–1083. [Google Scholar] [CrossRef] [PubMed]
  106. Leibold, M.A.; Chase, J.M.; Ernest, S.K. Community assembly and the functioning of ecosystems: How metacommunity processes alter ecosystems attributes. Ecology 2017, 98, 909–919. [Google Scholar] [CrossRef] [PubMed]
  107. Zhou, J.; Ning, D. Stochastic Community Assembly: Does It Matter in Microbial Ecology? Microbiol. Mol. Biol. Rev. 2017, 81, e00002-17. [Google Scholar] [CrossRef] [PubMed]
  108. Zhou, J.; Deng, Y.; Zhang, P.; Xue, K.; Liang, Y.; Van Nostrand, J.; Yang, Y.; He, Z.; Wu, L.; Stahl, D.; et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystems. Proc. Natl. Acad. Sci. USA 2014, 111, E836–E845. [Google Scholar] [CrossRef] [PubMed]
  109. Liu, Y.; Johnson, N.C.; Mao, L.; Shi, G.; Jiang, S.; Ma, X.; Du, G.; An, L.; Feng, H. Phylogenetic structure of arbuscular mycorrhizal community shifts in response to increasing soil fertility. Soil Biol. Biochem. 2015, 89, 196–205. [Google Scholar] [CrossRef]
  110. Feng, Y.; Chen, R.; Stegen, J.; Guo, Z.; Zhang, J.; Li, Z.; Lin, X. Two key features influencing community assembly processes at regional scale: Initial state and degree of change in environmental conditions. Mol. Ecol. 2018, 27, 5238–5251. [Google Scholar] [CrossRef]
  111. Doornbos, R.F.; van Loon, L.C.; Bakker, P.A.H.M. Impact of root exudates and plant defense signaling on bacterial communities in the rhizosphere. A review. Agron. Sustain. Dev. 2012, 32, 227–243. [Google Scholar] [CrossRef]
  112. Jiang, Y.; Song, H.; Lei, Y.; Korpelainen, H.; Li, C. Distinct co-occurrence patterns and driving forces of rare and abundant bacterial subcommunities following a glacial retreat in the eastern Tibetan Plateau. Biol. Fertil. Soils 2019, 55, 351–364. [Google Scholar] [CrossRef]
  113. Li, X.; Wan, W.; Zheng, L.; Wang, A.; Luo, X.; Huang, Q.; Chen, W. Community assembly mechanisms and co-occurrence patterns of nitrite-oxidizing bacteria communities in saline soils. Sci. Total Environ. 2021, 772, 145472. [Google Scholar] [CrossRef]
  114. Liang, S.; Deng, J.; Jiang, Y.; Wu, S.; Zhou, Y.; Zhu, W.-X. Functional Distribution of Bacterial Community under Different Land Use Patterns Based on FaProTax Function Prediction. Pol. J. Environ. Stud. 2019, 29, 1245–1261. [Google Scholar] [CrossRef] [PubMed]
  115. van der Heijden, M.G.; Bardgett, R.D.; van Straalen, N.M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 2008, 11, 296–310. [Google Scholar] [CrossRef] [PubMed]
  116. McKinley, K.R.; Wetzel, R.G. Photolithotrophy, photoheterotrophy, and chemoheterotrophy: Patterns of resource utilization on an annual and a diurnal basis within a pelagic microbial community. Microb. Ecol. 1979, 5, 1–15. [Google Scholar] [CrossRef] [PubMed]
  117. Huang, J.; Liu, X.; Liu, J.; Zhang, Z.; Zhang, W.; Qi, Y.; Li, W.; Chen, Y. Changes of soil bacterial community, network structure, and carbon, nitrogen and sulfur functional genes under different land use types. CATENA 2023, 231, 107385. [Google Scholar] [CrossRef]
  118. Phukongchai, W.; Kaewpradit, W.; Rasche, F. Inoculation of cellulolytic and ligninolytic microorganisms accelerates decomposition of high C/N and cellulose rich sugarcane straw in tropical sandy soils. Appl. Soil Ecol. 2022, 172, 104355. [Google Scholar] [CrossRef]
  119. Wang, C.; Yao, X.; Li, X.; Wang, Q.; Wang, J.; Zhu, L.; Wang, J. Effects of dibutyl phthalate on microbial community and the carbon cycle in salinized soil. J. Clean. Prod. 2023, 404, 136928. [Google Scholar] [CrossRef]
  120. Lee, D.Y.; Owens, M.S.; Crump, B.C.; Cornwell, J.C. Elevated microbial CO2 production and fixation in the oxic/anoxic interface of estuarine water columns during seasonal anoxia. Estuar. Coast. Shelf Sci. 2015, 164, 65–76. [Google Scholar] [CrossRef]
  121. Baggs, E.M.; Smales, C.L.; Bateman, E.J. Changing pH shifts the microbial sourceas well as the magnitude of N2O emission from soil. Biol. Fertil. Soils 2010, 46, 793–805. [Google Scholar] [CrossRef]
  122. Shu, X.; Daniell, T.J.; Hallett, P.D.; Baggs, E.M.; Griffiths, B.S. Soil pH moderates the resistance and resilience of C and N cycling to transient and persistent stress. Appl. Soil Ecol. 2023, 182, 104690. [Google Scholar] [CrossRef]
  123. Sun, H.; Peng, Q.; Guo, J.; Zhang, H.; Bai, J.; Mao, H. Effects of short-term soil exposure of different doses of ZnO nanoparticles on the soil environment and the growth and nitrogen fixation of alfalfa. Environ. Pollut. 2022, 309, 119817. [Google Scholar] [CrossRef]
  124. Sun, Y.; Yan, F.; Liu, F. Drying/rewetting cycles of the soil under alternate partial root-zone drying irrigation reduce carbon and nitrogen retention in the soil–plant systems of potato. Agric. Water Manag. 2013, 128, 85–91. [Google Scholar] [CrossRef]
Agronomy 13 02984 g001
Figure 1. Shannon index (a) and Chao1 index (c) of bacteria communities at two soil layers for three cropping systems; effects of soil variables predicted via random forest on the alpha diversity of the bacterial community (b,d). CK (monoculture), CR (cropping rotations), and MC (mixture). (* p < 0.05; ** p < 0.01); Capital letters indicate significant differences between different cropping systems.
Figure 1. Shannon index (a) and Chao1 index (c) of bacteria communities at two soil layers for three cropping systems; effects of soil variables predicted via random forest on the alpha diversity of the bacterial community (b,d). CK (monoculture), CR (cropping rotations), and MC (mixture). (* p < 0.05; ** p < 0.01); Capital letters indicate significant differences between different cropping systems.
Agronomy 13 02984 g001
Agronomy 13 02984 g002
Figure 2. Based on Bray–Curtis distances, the PCA bacteria community at the OTU level. CK (monoculture), CR (cropping rotations), and MC (mixture). Lowercase letters indicate significant differences between different treatments.
Figure 2. Based on Bray–Curtis distances, the PCA bacteria community at the OTU level. CK (monoculture), CR (cropping rotations), and MC (mixture). Lowercase letters indicate significant differences between different treatments.
Agronomy 13 02984 g002
Agronomy 13 02984 g003
Figure 3. Effect of different cropping systems on the relative abundance of the main bacterial communities in soil bacteria at the phylum (a) and genera (b) level; correlation analysis of the dominant bacterial phylum (c) and genus (d) found in microbial communities with soil physicochemical properties. CK15 (monoculture 0–15 cm), CK30 (monoculture 15–30 cm); CR15 (cropping rotations 0–15 cm), CR30 (cropping rotations 15–30 cm); MC15 (mixture 0–15 cm), MC30 (mixture 15–30 cm). * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 3. Effect of different cropping systems on the relative abundance of the main bacterial communities in soil bacteria at the phylum (a) and genera (b) level; correlation analysis of the dominant bacterial phylum (c) and genus (d) found in microbial communities with soil physicochemical properties. CK15 (monoculture 0–15 cm), CK30 (monoculture 15–30 cm); CR15 (cropping rotations 0–15 cm), CR30 (cropping rotations 15–30 cm); MC15 (mixture 0–15 cm), MC30 (mixture 15–30 cm). * p < 0.05; ** p < 0.01; *** p < 0.001.
Agronomy 13 02984 g003
Agronomy 13 02984 g004
Figure 4. Analysis of the co-occurrence network of bacteria in the soil of different cropping systems: (a) monoculture, (b) cropping rotations, and (c) mixture. Species composition under different modules in (d) CK, (e) CR, and (f) MC networks. CK (monoculture), CR (cropping rotations), and MC (mixture).
Figure 4. Analysis of the co-occurrence network of bacteria in the soil of different cropping systems: (a) monoculture, (b) cropping rotations, and (c) mixture. Species composition under different modules in (d) CK, (e) CR, and (f) MC networks. CK (monoculture), CR (cropping rotations), and MC (mixture).
Agronomy 13 02984 g004
Agronomy 13 02984 g005
Figure 5. Estimating the stochasticity of community assembly across different cropping systems and different soil layers. The NST in different microhabitats across three cropping systems at 0–15 cm and 15–30 cm soil layers: (a) fit of the neutral community model (NCM) of community assembly ((b) CK, (c) MC, and (d) CR). R2 represents the fitness of this model. Nm represents the quantification of dispersal between communities, which determines the correlation between occurrence frequency and regional relative abundance. The neutral-model predictions (lines) with corresponding 95% confidence intervals (dashed lines) are presented, as well as the actual distributions (points).
Figure 5. Estimating the stochasticity of community assembly across different cropping systems and different soil layers. The NST in different microhabitats across three cropping systems at 0–15 cm and 15–30 cm soil layers: (a) fit of the neutral community model (NCM) of community assembly ((b) CK, (c) MC, and (d) CR). R2 represents the fitness of this model. Nm represents the quantification of dispersal between communities, which determines the correlation between occurrence frequency and regional relative abundance. The neutral-model predictions (lines) with corresponding 95% confidence intervals (dashed lines) are presented, as well as the actual distributions (points).
Agronomy 13 02984 g005
Agronomy 13 02984 g006
Figure 6. Composition of bacteria functional groups (guilds) inferred via FAPROTAX: (a) based on Bary–Curtis distances, (b) the PCA of the bacteria functional groups, different colored circles represent different treatment groups. The differences in C (c), N (d), and S (e) cycle function among different cropping systems and soil layers. The asterisks above the columns indicate different soil layers’ significance levels (** p < 0.01). The capital letters indicate significant differences between the different cropping systems.
Figure 6. Composition of bacteria functional groups (guilds) inferred via FAPROTAX: (a) based on Bary–Curtis distances, (b) the PCA of the bacteria functional groups, different colored circles represent different treatment groups. The differences in C (c), N (d), and S (e) cycle function among different cropping systems and soil layers. The asterisks above the columns indicate different soil layers’ significance levels (** p < 0.01). The capital letters indicate significant differences between the different cropping systems.
Agronomy 13 02984 g006
Table 1. Analyzing the impacts (* p < 0.05; ** p < 0.01) of soil layers (SL), cropping systems (CS), and their interaction effect (SL × CS) on soil bacterial diversity using two-way ANOVAs.
Table 1. Analyzing the impacts (* p < 0.05; ** p < 0.01) of soil layers (SL), cropping systems (CS), and their interaction effect (SL × CS) on soil bacterial diversity using two-way ANOVAs.
VariablesSLCSSL*CS
FpFpFp
Ace0.5550.477.228<0.01 **0.8040.470
Chao10.4430.518.014<0.01 **0.8810.439
Shannon20.860<0.01 **10.190<0.01 **0.2830.758
Simpson12.344<0.01 **6.182<0.05 *0.1010.905
Table 2. Topological properties of co-occurrence networks. CK: monoculture; MC: mixture; CR: cropping rotations.
Table 2. Topological properties of co-occurrence networks. CK: monoculture; MC: mixture; CR: cropping rotations.
Topological PropertiesCKCRMC
Density0.0570.2210.132
Average degree17.0866.1239.613
Network diameter8.07.09.0
Average weighting15.21760.60635.744
Average clustering coefficients0.5320.6080.584
Average path length3.3372.3992.795
Edges256299185942
Modularization0.6210.120.16
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, X.; Ding, J.; Li, J.; Zhu, D.; Li, B.; Yan, B.; Mao, L.; Sun, G.; Sun, L.; Li, X. The Response of Soil Bacterial Communities to Cropping Systems in Saline–Alkaline Soil in the Songnen Plain. Agronomy 2023, 13, 2984. https://doi.org/10.3390/agronomy13122984

AMA Style

Liu X, Ding J, Li J, Zhu D, Li B, Yan B, Mao L, Sun G, Sun L, Li X. The Response of Soil Bacterial Communities to Cropping Systems in Saline–Alkaline Soil in the Songnen Plain. Agronomy. 2023; 13(12):2984. https://doi.org/10.3390/agronomy13122984

Chicago/Turabian Style

Liu, Xiaoqian, Junnan Ding, Jingyang Li, Dan Zhu, Bin Li, Bohan Yan, Lina Mao, Guangyu Sun, Lei Sun, and Xin Li. 2023. "The Response of Soil Bacterial Communities to Cropping Systems in Saline–Alkaline Soil in the Songnen Plain" Agronomy 13, no. 12: 2984. https://doi.org/10.3390/agronomy13122984

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop