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Article

The Effects of Long-Term Land Use Changes on Bacterial Community Structure and Soil Physicochemical Properties in the Northeast Mollisol Region of China

by
Xu Wang
1,
Qiang Chen
1,
Zhao Li
1,*,
Weiping Yin
1,2 and
Dalong Ma
1,3,*
1
College of Geographical Sciences, Harbin Normal University, Harbin 150025, China
2
Institute of Industrial Crops, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
3
Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin 150025, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1132; https://doi.org/10.3390/agronomy15051132
Submission received: 20 March 2025 / Revised: 24 April 2025 / Accepted: 3 May 2025 / Published: 5 May 2025
(This article belongs to the Section Farming Sustainability)
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Abstract

:
Soil microorganisms are essential for maintaining the function and health of agricultural ecosystems. However, the responses of microbial communities to long-term changes in land use have been insufficiently explored. Hence, based on a 15 years of field experiments in the northeast Mollisol region of China, we applied the Illumina high-throughput sequencing technology to study the effects of different land use types, including conventional tillage (CT), bare land (BL), no tillage (NT), natural vegetation restoration (NVR), and afforestation (AF), on bacterial communities along the soil profile (0–5 cm, 5–10 cm, 10–20 cm, and 20–30 cm) and co-occurrence networks and identified their relationships with soil physicochemical properties. The findings indicated that the land use type as well as soil depth affected the diversity and structure of bacterial communities significantly. There was no marked difference in the diversity of bacterial communities between CT and NT at different soil depths, except for a depth of 20–30 cm. In NT, NVR, and AF, the relative abundance of Actinomycetota and Firmicutes was higher than that in CT. Conversely, CT showed a remarkably higher abundance of Proteobacteria and Acidobacteriota than BL, NT, NVR, and AF. Compared with CT and BL, increased stability and complexity of the community co-occurrence networks was identified for NT, NVR, and AF. Additionally, the diversity and composition of bacterial communities were correlated with the soil’s total nitrogen (TN), pH as well as total organic carbon (TOC). Our study revealed the potential mechanism by which long-term land use changes affected the distribution of soil bacterial communities, which was of high importance for sustainable development of agriculture and optimal management of land resources.

1. Introduction

Over the past century, agricultural intensification has significantly increased food production, at the cost of substantial depletion of available land and a series of negative environmental impacts, including soil biodiversity loss, decreased soil fertility, increased greenhouse gas emissions, and extensive erosion and degradation [1,2]. As described by the Food and Agriculture Organization, over 30% of the global land is moderately to highly degraded, severely threatening social stability and food security. Such land degradation is often caused by unsustainable land use practices [3]. Globally, changes in agricultural land use practices are of great significance in addressing resource scarcity and soil health [4]. Conventional tillage (CT), due to vegetation removal and mechanical action, leads to soil structure damage, reduces soil’s organic matter content, and deteriorates water infiltration over time, resulting in unsustainable productivity of agricultural systems [5,6]. In contrast, no tillage (NT) is beneficial to enhance the quality and functions of soil and its carbon sequestration potential [7]. A number of studies have shown that NT practices contribute to crop residue decomposition and nutrient cycling, accumulating soil organic carbon and improving the soil aggregate structure while accompanied by reduced labor needs and costs [7,8,9]. Natural vegetation restoration (NVR) and afforestation (AF) are extensively used to improve soil biodiversity and properties and to restore the environmental sustainability functions of deteriorated soils [10]. Previous studies have indicated that NVR and AF displayed lower soil and water losses and higher ecosystem stability, owing to denser plant roots and greater vegetation cover than agricultural soils [11,12]. Furthermore, artificial afforestation of abandoned croplands has great potential to accumulate soil biomass and organic carbon [13]. Therefore, sustainable agricultural management practices and land uses are of great significance to prevent land degradation, improve soil fertility, as well as optimize ecosystem functions and services.
Soil microorganisms participate in organic matter decomposition and nutrient conversion and circulation processes, assisting in maintaining soil fertility. They have become sensitive biological indicators for estimating the health of the soil ecosystem [14]. In general, NT with straw mulching reduces soil disturbance and increases organic matter inputs, which enhances microbial diversity and abundance [15]. However, according to the studies conducted by Ibáñez et al. [16] and Wang et al. [17], the diversity and richness of soil microorganisms did not show significant differences between CT and NT. In addition, the conversion of grassland to intensively cultivated land decreased the soil microbial biomass by 35–49% and organic carbon content by 55% [18,19,20]. Afforestation of cropland results in greater microbial diversity due to the increased plant root density and litter biomass and reduces environmental disturbance compared with arable land [21]. There has been growing evidence suggesting that changes in land use can alter soil properties as well as affect microbial community structures and ecosystem stability, either positively or negatively [22,23].
Land use refers to all purposeful human land development and use activities [24]. Different land uses can cause differences in plant litter and crop residues, which reshape the micro-habitats where soil microorganisms survive and influence their development [25]. Meanwhile, root penetration depth and tillage depth are factors that can lead to a strong vertical heterogeneity of the soil profile, which is a vital driver of microbial succession [26]. Accordingly, soil depth should be considered when exploring species interaction patterns, particularly in studies concentrating on microbial communities and soil physicochemical properties, in order to better comprehend the complex microbial response mechanisms that take place in agricultural ecosystems. Co-occurrence network analysis can assist with investigating the adaptability and stability of a soil microbial community under different environmental conditions and identifying keystone species that are crucial for constructing the microbial community [27]. Recent studies have demonstrated that CT, NT, reduced tillage, strip tillage, and other management practices have distinct microbial networks [28,29]. Thus, clarifying how microbial communities are distributed along the soil profile in different land uses and identifying their driving factors can increase our understanding of microbial resilience to land use changes and will assist with improving agricultural sustainable land management strategies.
The Mollisol (black soil) region of Northeast China, one of the four major Mollisol regions worldwide, is a main contributor to the national food production [30]. However, long-term inappropriate management practices and excessive land use have caused soil erosion and degradation, reducing soil productivity and fertility over time [31]. The effects of land use changes on soil quality and agricultural systems usually require a long period to become obvious, while information on the microbial response patterns to long-term land use changes is still limited, hindering our comprehension concerning their roles in agricultural ecosystems. Consequently, based on a long-term (15 years) positioning experiment in the northeast Mollisol region of China, we examined soil bacterial communities under different land use types, including conventional tillage (CT), bare land (BL), no tillage (NT), natural vegetation restoration (NVR), and afforestation (AF). Specifically, the objectives of our study were to (1) reveal how land use changes determine the variation in soil bacterial communities with soil depth (0–5 cm, 5–10 cm, 10–20 cm, and 20–30 cm); (2) exhibit the influence on the bacterial co-occurrence networks caused by different land use types; and (3) identify the relationships between bacterial communities and soil physicochemical properties. We hypothesized that (1) long-term land use changes would significantly alter soil’s physicochemical properties, bacterial community structure, and co-occurrence networks, and (2) bacterial community diversity would decrease with increasing soil depths.

2. Materials and Methods

2.1. Study Area

The study was carried out at the Hailun Monitoring and Research Station of Soil and Water Conservation, which is run by the Northeast Institute of Geography and Agroecology of the Chinese Academy of Sciences (47°21′ N, 126°50′ E), located in the central distribution area of the northeast Mollisol region of China. This area is characterized by its Mollisol soil (USDA Soil Taxonomy, called black soil in the Chinese Soil Taxonomy) [6]. The region belongs to the temperate continental monsoon climate, with a mean annual precipitation of 530 mm (mainly in summer), a mean annual temperature of 1.5 °C, and a frost-free period lasting for 120 days on average for the typical semi-arid, rain-fed agricultural area [32].

2.2. Experimental Design and Soil Collection

The long-term (15 years) continuous treatment of the various land use types of the field experiment included five treatments: conventional tillage (CT), bare land (BL), no tillage (NT), natural vegetation restoration (NVR), and afforestation (AF) (Figure S1). Briefly, for CT, all surface biomass was manually removed following each autumn harvest, with sowing, rotary tillage, and weeding in May of the following year. For BL, experimental plots were built on previous croplands, and all the vegetation cover was removed manually at the beginning of each plant growth season to ensure that the surface remained consistently bare. For NT, the straw of the harvested material was crushed and covered the soil surface evenly. Except for harvesting, straw mulching, sowing, and weeding, any soil disturbance was avoided. For NVR, natural vegetation was allowed to grow back spontaneously on previous croplands without other land management practices. For AF, Populus L. had been planted on abandoned cropland in the 1980s, and the vegetation coverage was maintained at 75% during the experimental period. The CT, BL, NT, and NVR experimental plots (20 m × 4.5 m) were built in 2006 and were designed through a randomized complete block method with three replications. The AF field was located approximately 100 m from the position of the monitoring station and was randomly arranged with triplicate plots. Agricultural practice with soybean-maize rotation was applied in CT and NT, and fertilizer was applied with a dose per hm2 of 138 kg N, 152.2 kg P2O5, and 39.2 kg K2O for maize and 64 kg N, 152.2 kg P2O5, and 39.2 kg K2O for the cultivation of soybean. Acetochlor herbicides were used for weeding. The slope of these experimental plots was 5% in the east-west direction.
The soil samples described here were collected after the maize harvest of October 2021. Soil was collected at depths of 0–5, 5–10, 10–20, and 20–30 cm. At each depth in every plot, three soil cores were randomly collected with a sterile soil driller, and these were mixed, resulting in 60 composite samples in total (5 land use types × 4 soil depths × 3 plots). After removing coarse roots and gravel, the soil was transported in sterilized, air-tight plastic on ice. In the laboratory, one part of each sample was weighed, air-dried naturally, and sieved (2-mm sieve) for physicochemical property determination, while the remainder was frozen and stored (−80 °C) for high-throughput sequencing.

2.3. Physicochemical Determination of the Soil

The content of total organic carbon (TOC) was determined with a Multi N/C 3100 Analyzer (Jena, Germany), and the total nitrogen (TN) and total phosphorus (TP) contents were quantified with a Kjeldahl apparatus (Hanon K9860, Shanghai, China) and molybdenum antimony anti-colorimetry, respectively. The contents of ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) were determined as described previously [33]. The pH was measured with a pH meter using a soil-to-deionized-water ratio of 1:2.5 (Sartorius PB-10, Gottingen, Germany). The soil water content (SWC) was calculated by the decrease in weight following drying (105 °C, 24 h). The soil bulk density (SBD) was quantified with the cutting ring method according to standard practices [34].

2.4. Sequence Generation for Characterization of the Bacterial Communities

The total genomic DNA was extracted with a PowerSoil® DNA Isolation Kit (MOBIO, Carlsbad, CA, USA). After determining the DNA concentration via NanoDrop 2000 spectrophotometry (Thermo Fisher Scientific, Wilmington, DE, USA), amplification of bacterial 16S rRNA was achieved by PCR for the V3-V4 region with the primer pair 338F/806R. The triplicate PCR products were pooled and then checked via 2% agarose gel electrophoresis, purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and quantified by the QuantiFluorTM-ST (Promega Corporation, Madison, WI, USA) before being sent to Majorbio BioPharm Technology Co., Ltd. (Shanghai, China) for Illumina high-throughput sequencing.

2.5. Sequence Analysis and Statistics

Fastp software (v.0.19.6) was used to optimize the quality of the raw sequences, after which they were grouped into operational taxonomic units (OTUs) by UPARSE (v.11) using the standard settings. The RDP classifier (v.2.13) was applied based on the silva (v.138) bacterial 16S rRNA gene database to annotate the OTU sequences. For alpha diversity analysis, the Chao 1 and Shannon indices were determined from the sequences using Mothur (v.1.30.2). Two-way analysis of variance (ANOVA) tests were conducted using SPSS (v.20.0) software. Using Bray–Curtis distances, the beta diversity was characterized via principal coordinate analysis (PCoA) by means of QIIME (v.1.9.1) software. Permutational multivariate analysis of variance (PERMANOVA) was performed using the “vegan” package (v.2.5.5) in R (v.3.6.1). Tax4Fun (v.0.3.1) was employed to profile the potential functional pathways based on the sequence data. The “igraph” package (v.1.2.6) in R was used to produce co-occurrence networks, which were visualized with Gephi (v.0.9.2) software. Pearson correlation analysis was conducted using Origin 2021. Canoco (v.5.0) software was applied to perform redundancy analysis (RDA).

3. Results

3.1. Soil Physicochemical Properties

The characteristics of the collected soils were determined (Table S1). The contents of TN and TOC in the soil under NT and NVR were remarkably higher than that under CT, BL, and AF at 0–5 and 5–10 cm, except for the TN content of CT10 (p < 0.05). At the 10–20 and 20–30 cm depths, these contents were significantly higher under AF than CT, BL, and NVR, apart from the TN contents of CT20 and NVR20 (p < 0.05). Moreover, the TP content was notably lower under NT than BL at all investigated depths (p < 0.05). Likewise, the content of NH4+-N was higher under CT and NT than BL, AF, and NVR, and the highest NO3−-N content was observed under AF. The pH ranged from 5.93 to 6.86 and was lower under AF and BL than CT and NT (p < 0.05). Compared with the other land use types, the SWC under NT and NVR was markedly higher at 0–5 cm (p < 0.05), and it was higher under CT at the other three soil depths. At 0–5 cm and 5–10 cm, the SBD was markedly lower than that at 10–20 cm and 20–30 cm under BL and NVR (p < 0.05).

3.2. Characterization of the Soil Bacterial Community Diversity

The Chao 1 richness index is an indicator which reflects species richness, and the Shannon diversity index refers to an aggregated indicator of species diversity. In this study, the Chao 1 richness index was remarkably lower at 10–20 cm than at 5–10 cm under CT and BL and significantly lower at 20–30 cm than at 5–10 and 10–20 cm under AF and NVR (p < 0.05) (Figure 1a). Through the comparison of various depths within a treatment, the Chao 1 index of NT was not different among different soil depths (p > 0.05). With increased soil depths, the Shannon diversity index showed decreasing trends for NT, AF, and NVR and tended to decrease first and then increase under CT and BL (Figure 1b). Two-way ANOVA analysis confirmed that the land use type, soil depth, and their interaction had a highly significant impact on the Chao 1 and Shannon indices (p < 0.001).
PCoA was used to assess the heterogeneity of beta diversity on spatial scales (Figure 2). The two dimensions of the figure explained 45.31% (PC1) and 14.71% (PC2) of the total variance. The samples at depths of 0–5 and 5–10 cm were notably separated from other depths along the axis of PC1. Along the PC2 axis, the separation between AF and NVR and CT, BL, and NT was remarkable. The impact of land use type and soil depth on the bacterial community composition was significant according to this PERMANOVA analysis (p < 0.01).

3.3. Composition of the Soil Bacterial Communities

The dominant bacterial phyla were identified as Actinomycetota (29.17–45.69%), Proteobacteria (6.52–24.20%), Acidobacteriota (7.47–18.58%), and Chloroflexota (8.32–16.52%) among these five land use types (Figure 3a). Actinomycetota and Chloroflexota were more abundant under BL than in the other land use types, Proteobacteria and Acidobacteriota were more abundant under CT, and Firmicutes were more abundant under NT. Compared with other soil depths, BL30 showed a lower relative abundance of Actinomycetota, while NVR30 had a higher abundance, and Acidobacteriota and Chloroflexota displayed the opposite distribution. Except for AF10, the relative abundance of Proteobacteria was higher at depths of 0–5 and 5–10 cm than 10–20 and 20–30 cm. Further comparison of the bacterial community based on the top 30 abundant genera displayed extremely significant differences in KD4-96, Vicinamibacterales, MB-A2-108, Blastococcus, and SC-I-84 between samples (p < 0.01) (Figure 3b).

3.4. KEGG Metabolic Pathway Analysis

Tax4Fun functional prediction is a tool for predicting bacterial community function based on 16S rRNA data and the level 1 KEGG pathways, which are those pathways involved in the basic metabolic activities of microorganisms. In this study, the level 1 pathways we identified were metabolism, environmental information processing, and genetic information processing (Figure 4). Their highest abundance occurred under CT, AF, and CT and were 12.20%, 4.19%, and 2.20%, respectively. A total of 40 level 2 KEGG pathways were identified, of which the main functional pathways were membrane transport, carbohydrate metabolism, and amino acid metabolism, with their highest abundances being 2.67%, 2.57%, and 2.45% under AF, CT, and CT, respectively.

3.5. Co-Occurrence Network Analysis

The co-occurrence network analysis visualizes the co-occurrence of microorganisms and infers potential interactions between microorganisms by constructing a network graph. In this study, co-occurrence networks were created for the bacterial phyla for each land use type (Figure 5). This identified a higher number of positive links than negative links under CT, BL and NT, while NVR and AF showed the opposite trend (Table 1). The NT, NVR, and AF networks displayed a greater complexity, indicative of a higher stability than those for CT and BL, as identified by the higher total links, average degree, and network density, combined with a lower modularity. The keystone species detected in the five land use types belonged primarily to Actinomycetota, Proteobacteria, and Acidobacteriota.

3.6. Relationships Between Soil Bacterial Community and Physicochemical Properties

The proportion of the total variance explained by RDA was 71.79%, and the bacterial community composition was significantly influenced by the TN (F = 18.1, p = 0.002), pH (F = 8.8, p = 0.002), and TOC (F = 6.2, p = 0.002) (Figure 6a). According to the Pearson correlation analysis, the relative abundance of Actinomycetota was negatively related to the pH level and SWC (p < 0.05). Proteobacteria and the Chao 1 and Shannon indices positively correlated with the TOC and TN (p < 0.05). Meanwhile, Proteobacteria, Firmicutes, and the Shannon index showed extremely positive correlations with NH4+-N (p < 0.01), and Proteobacteria also positively correlated with NO3−-N (p < 0.05). Acidobacteriota and Chloroflexota had no significant correlations with the assessed soil physicochemical properties (p > 0.05).

4. Discussion

Agricultural land use changes are recognized as major drivers of ecosystem degradation and soil carbon and nitrogen loss, and conservation tillage is recognized to enhance the potential of the soil to sequester carbon [35]. In this study, we observed higher TOC and TN contents for NT and NVR only at the 0–5 and 5–10 cm depths than for CT, BL and AF, except for the TN content of CT10, which was consistent with a previous study [36]. NT with straw mulching can reduce soil physical disturbance and improve its structure while facilitating the build-up of organic matter in surface layers. In contrast, long-term CT practices weaken the soil aggregate’s stability and ultimately reduce the TOC content [26]. Differences in vegetation cover between NVR and BL directly affect the TOC and TN input into soil from aboveground plant parts and from roots. The studies of McLauchlan [37] and Chen et al. [38] suggest that higher carbon and nitrogen losses were observed in cropland compared with forest land, shrub land, and grazing land. In our study, we demonstrated that AF had higher TOC and TN contents than CT, BL, and NVR at depths of 10–20 and 20–30 cm, apart from the TN of CT20 and NVR20, mostly because the roots of trees reach deeper layers than those of herbaceous plants, creating channels that enable the movement of nutrients and root exudates into the subsurface [39]. Meanwhile, root exudates are able to attract rhizospheric microorganisms to drive TOC accumulation [40]. Some studies have shown that root exudates are a “labile” form of soil organic carbon, whereas the utilization and uptake by rhizospheric microorganisms cause the accumulation and stabilization of TOC in the form of microbial biomass residues in soil [41,42]. A previous study also described that conservation tillage accumulated organic matter at the surface instead of throughout the entire soil profile, altering only the distribution of organic carbon rather than enhancing the total content [43]. Therefore, to accurately assess the carbon sequestration benefits of conservation tillage and natural vegetation restoration, differences in organic matter in deeper soil profiles should be taken into consideration as well in the future. In addition, we found that the pH value was lower under BL than NT. BL without vegetation cover exacerbates soil acidification through significant nutrient loss due to leaching, whereas the straw mulching of NT releases an amount of alkaline elements during decomposition, thereby moderating the acidity and alkalinity in the soil [44,45].
Our results indicated a decline in bacterial community diversity that paralleled the decrease in TOC and TN content along the soil profile, which was in partial agreement with our hypothesis. A study by Naylor et al. [46] suggested that bacterial community diversity and its capacity to utilize various nutrients declined with the soil depth. Interestingly, no difference reaching statistical significance could be identified in bacterial community diversity between CT and NT for any of the investigated soil depths, except at 20–30 cm, after 15 years of experimental treatment. The outcomes of studies investigating the impact caused by different agricultural managements on bacterial community diversity are often contradictory; there are diverse results across experiments, such as increasing, decreasing, or unclear differences, as a result of variation in the local climate, soil type and vegetation cover, tillage systems and management practices, as well as the experimental duration [47,48,49]. The climate in the northeast Mollisol region of China is extremely cold and dry in winter and lasts a long time, which limits the ability of bacteria to multiply, and only some of the cold-tolerant bacteria are able to survive, which may be the reason why there are no significant differences in bacterial community diversity between different management practices [50]. Furthermore, substantial differences in the bacterial community level among different treatments were revealed by PCoA and PERMANOVA, with land use type and soil depth remarkably affecting the community structure, which supported our hypothesis. In this study, Actinomycetota, Proteobacteria, Acidobacteriota, and Chloroflexota were dominant among soil bacterial communities, which has generally been observed in agricultural and other ecosystems as well [51,52]. We detected a higher relative abundance of Actinomycetota and Chloroflexota and a lower abundance of Proteobacteria after cropland conversion to bare land. Actinomycetota are notably at an advantage in BL lacking vegetation cover and soil nutrient input, possibly because these bacteria have the potential to degrade diverse recalcitrant forms of carbon [53,54,55]. Proteobacteria participate in carbon compound metabolism and prefer to multiply in medium-to-alkaline soils, which explains why CT with a higher soil pH yielded a higher abundance while BL had a lower abundance [56]. Moreover, Proteobacteria abundance decreased with the soil depth, which is mainly owing to their copiotrophic preference, as the nutrient environment in deeper soil layers is poor [57]. Differences in bacterial communities between NT and CT were mainly in the higher relative proportion of Actinomycetota and Firmicutes, together with a lower Acidobacteriota abundance in NT. Firmicutes have the ability to secrete β-glucosidase and xylanases, which take part in the degradation of plant-derived cellulose and hemicellulose [58]. Compared with CT, the proportion of Firmicutes in NT was markedly higher, suggesting that these bacteria profit from their ability to decompose crop litter and residues. This is in line with published findings indicating that Firmicutes are more abundant under conservation tillage than conventional tillage [59]. Acidobacteriota are commonly considered to be oligotrophic and often occur in disturbed and low-nutrient environments. Their high abundance in CT can be attributed to the persistent soil disturbances related to mechanical action and chemical input brought about by conventional tillage, which considerably disrupts the soil’s physical structure and soil aggregates, creating an environment that is conductive for the growth of Acidobacteriota [60]. Qi et al. [61] found that Acidobacteriota could adapt to soil disturbances and flourish in nutrient-poor conditions.
Co-occurrence network analysis provides profound insights into the complex bacterial community responses to different environments by identifying potential interactions among bacterial species [62]. We found that the various long-term land use changes had strikingly changed the bacterial co-occurrence networks, which was consistent with our hypothesis. A series of topological properties showed that NT, AF, and NVR had greater complexity and stability than CT and BL. BL contained the fewest links and the lowest average degree and network density, which can be related to the resource-poor environment of bare land [63]. NT provided a more favorable habitat for microorganisms than CT, as NT treatment reduced mechanical disturbance of the soil and increased nutrient sources as a result of straw mulching. A previous study reported that the networks with a higher average degree were more robust to environmental perturbation, while conventional tillage reduced the average degree and complexity of the network, suggesting that NT resulted in greater community structure stability than CT [64]. Compared with cropland, grassland and forest land typically possess higher soil carbon inputs and denser plant roots, which support greater microbial diversity, greatly altering microbial community networks [63]. Our findings implied that straw mulching (NT) and vegetation restoration (NVR and AF) improved the stability of bacterial community networks, which is beneficial to enhancing the resistance of bacterial communities to disturbances and improves their defense against pathogen invasion. In addition, cooperative relationships were dominant in the networks of CT, BL and NT, owing to a higher number of positive links, whereas the networks of NVR and AF contained more negative links. This indicates that competitive or antagonistic interactions for substrate acquisition are enhanced, and more complex relationships among the bacteria occur, which is mainly due to the increased vegetation diversity, root exudates, and input of organic matter as a result of natural vegetation restoration and afforestation [39]. Keystone species not only play a critical role in microbial community composition and function but are also the indicators of environmental changes [65]. In this study, different land use types did not change the keystone species of soil bacterial communities (Actinomycetota, Proteobacteria, and Acidobacteriota) but affected their connection degree, which can be attributed to the differences in vegetation cover and soil physicochemical properties in different land uses, leading to micro-habitat heterogeneity of microorganisms.
The RDA ordination showed that the TN, pH, and TOC were the dominant factors shaping bacterial community. Soil pH has been confirmed as a crucial factor regulating the microbial community structure, whether at global or regional scales [66,67]. Meanwhile, the TOC can be the best predictor of soil microbial community structure in specific land use types at a given location [68]. No tillage not only reduces soil disturbance but, combined with straw mulching, it accumulates organic matter, which maintains the microbial diversity and stabilizes the community structure [15]. According to a study by Han et al. [69], bacterial communities showed significantly distinct responses to different agricultural management methods, which was caused by the differences in soil carbon and nitrogen substrates. In our study, for the NVR and AF treatments, their higher diversity in plant species and the type and amount of root exudates and plant litter released into the soil resulted in the original microbial communities, over time, being better adapted to these new substrates. One study by Machmuller et al. [70] was consistent with our results, as it described that the soil organic carbon content increased 6 years after cropland had been converted to grassland, thereby significantly affecting the microbial community structure. Moreover, our study also found that strong changes in soil physicochemical properties along the soil profile produced a strong microbial filter. Future studies should pay more attention to the distribution patterns of soil microbial communities at various spatial scales. The findings reported here facilitate a more comprehensive understanding of how land use changes influence microbial communities, particularly in the context of intensive agriculture.

5. Conclusions

The present study was conducted in order to compare the vertical differences in soil physicochemical properties and bacterial communities of various land use types through a 15-year field experiment. Our results supported the conclusion that the land use type and soil depth markedly affected the bacterial community structure and soil physicochemical properties. However, no significant differences were found in the bacterial community diversity between CT and NT at different soil depths, except for 20–30 cm. The co-occurrence networks of the bacterial communities from NT, NVR, and AF displayed greater complexity and enhanced stability compared with those obtained for CT and BL. TN, pH level, and TOC were the main driving factors behind the differences in bacterial community structure, and the diversity indices were significantly affected by the TOC and TN. Our findings contribute to a better understanding of how long-term land use changes affect soil ecosystem functions from a microbial perspective and provide valuable information for promoting sustainable development and resilient transitions in the northeast Mollisol region of China, as well as a theoretical basis for land use management and future planning in the agricultural sector.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15051132/s1. Figure S1: (a) Map of the sampling sites. Experimental designs of conventional tillage (CT), bare land (BL), no tillage (NT), natural vegetation restoration (NVR) and afforestation (AF) are (b), (c), (d), (e) and (f). (g) Layout of experimental plots; Table S1: Soil physicochemical properties of the different land use types.

Author Contributions

Conceptualization, Z.L. and D.M.; data curation, X.W. and Q.C.; investigation, Q.C. and W.Y.; writing—original draft, X.W.; writing—review and editing, Z.L. and D.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (42171127) and the Natural Science Foundation of Heilongjiang Province of China (PL2024D007).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Oliveira, E.M.; Wittwer, R.; Hartmann, M.; Keller, T.; Buchmann, N.; van der Heijden, M.G.A. Effects of conventional, organic and conservation agriculture on soil physical properties, root growth and microbial habitats in a long-term field experiment. Geoderma 2024, 447, 116927. [Google Scholar] [CrossRef]
  2. Zhu, Q.C.; de Vries, W.; Liu, X.J.; Hao, T.X.; Zeng, M.F.; Shen, J.B.; Zhang, F.S. Enhanced acidification in Chinese croplands as derived from element budgets in the period 1980–2010. Sci. Total Environ. 2018, 618, 1497–1505. [Google Scholar] [CrossRef] [PubMed]
  3. Smith, P.; Poch, R.M.; Lobb, D.A.; Bhattacharyya, R.; Alloush, G.; Eudoxie, G.D.; Anjos, L.H.C.; Castellano, M.; Ndzana, G.M.; Chenu, C.; et al. Status of the World’ s Soils. Annu. Rev. Environ. Resour. 2024, 49, 73–104. [Google Scholar] [CrossRef]
  4. Cárceles Rodríguez, B.; Durán-Zuazo, V.H.; Soriano Rodríguez, M.; García-Tejero, I.F.; Gálvez Ruiz, B.; Cuadros Tavira, S. Conservation Agriculture as a Sustainable System for Soil Health: A Review. Soil Syst. 2022, 6, 87. [Google Scholar] [CrossRef]
  5. Farhangi-Abriz, S.; Ghassemi-Golezani, K.; Torabian, S. A short-term study of soil microbial activities and soybean productivity under tillage systems with low soil organic matter. Appl. Soil Ecol. 2021, 168, 104122. [Google Scholar] [CrossRef]
  6. Li, M.; He, P.; Guo, X.L.; Zhang, X.Y.; Li, L.J. Fifteen-year no tillage of a Mollisol with residue retention indirectly affects topsoil bacterial community by altering soil properties. Soil Tillage Res. 2021, 205, 104804. [Google Scholar] [CrossRef]
  7. Chen, Q.; Zhang, X.Y.; Sun, L.; Ren, J.H.; Yuan, Y.R.; Zang, S.Y. Influence of Tillage on the Mollisols Physicochemical Properties, Seed Emergence and Yield of Maize in Northeast China. Agriculture 2021, 11, 939. [Google Scholar] [CrossRef]
  8. Kumar, S.; Rana, S.S.; Kumar, R.; Sharma, N. Effects of conservation tillage and weed management on soil microbial community and enzymatic activity. Bangladesh J. Bot. 2022, 51, 425–431. [Google Scholar] [CrossRef]
  9. Wang, C.L.; Ai, S.Z.; Chen, Q.; Li, J.Y.; Ding, J.H.; Yang, F. Effect of strip tillage widths on soil moisture, soil temperature and soil structure in northeast China. Front. Environ. Sci. 2024, 12, 1404971. [Google Scholar] [CrossRef]
  10. Neuenkamp, L.; de León, D.G.; Hamer, U.; Hölzel, N.; Mcgale, E.; Hannula, S.E. Comprehensive tools for ecological restoration of soils foster sustainable use and resilience of agricultural land. Commun. Biol. 2024, 7, 1577. [Google Scholar] [CrossRef]
  11. Kodesová, R.; Jirku, V.; Kodes, V.; Mühlhanselová, M.; Nikodem, A.; Zigová, A. Soil structure and soil hydraulic properties of Haplic Luvisol used as arable land and grassland. Soil Tillage Res. 2011, 111, 154–161. [Google Scholar] [CrossRef]
  12. Liu, Y.F.; Liu, Y.; Shi, Z.H.; López-Vicente, M.; Wu, G.L. Effectiveness of re-vegetated forest and grassland on soil erosion control in the semi-arid Loess Plateau. Catena 2020, 195, 104787. [Google Scholar] [CrossRef]
  13. Di, D.R.; Huang, G.W. Isotope analysis reveals differential impacts of artificial and natural afforestation on soil organic carbon dynamics in abandoned farmland. Plant Soil 2022, 471, 329–342. [Google Scholar] [CrossRef]
  14. Chen, S.M.; Yao, F.Y.; Mi, G.H.; Wang, L.C.; Wu, H.Y.; Wang, Y.J. Crop rotation increases root biomass and promotes the correlation of soil dissolved carbon with the microbial community in the rhizosphere. Front. Bioeng. Biotechnol. 2022, 10, 1081647. [Google Scholar] [CrossRef]
  15. Ma, L.; Song, D.X.; Liu, M.Z.; Li, Y.P.; Li, Y.A. Effects of earthworm activities on soil nutrients and microbial diversity under different tillage measures. Soil Tillage Res. 2022, 222, 105441. [Google Scholar] [CrossRef]
  16. Ibáñez, A.; Sombrero, A.; Santiago-Pajón, A.; Santiago-Calvo, Y.; Asensio-S-Manzanera, M.C. Effect of long-term conservation tillage management on microbial diversity under Mediterranean rainfed conditions. Soil Tillage Res. 2024, 236, 105923. [Google Scholar] [CrossRef]
  17. Wang, H.H.; Li, X.; Li, X.; Wang, J.; Li, X.Y.; Guo, Q.C.; Yu, Z.X.; Yang, T.T.; Zhang, H.W. Long-term no-tillage and different residue amounts alter soil microbial community composition and increase the risk of maize root rot in northeast China. Soil Tillage Res. 2020, 196, 104452. [Google Scholar] [CrossRef]
  18. Yu, Z.H.; Liu, J.J.; Liu, J.D.; Jin, J.; Liu, X.B.; Wang, G.H. Responses of ammonia-oxidizing bacterial communities to land-use and seasonal changes in Mollisols of Northeast China. Eur. J. Soil Biol. 2016, 74, 121–127. [Google Scholar] [CrossRef]
  19. Srivastava, R.; Mohapatra, M.; Latare, A. Impact of land use changes on soil quality and species diversity in the Vindhyan dry tropical region of India. J. Trop. Ecol. 2020, 36, 72–79. [Google Scholar] [CrossRef]
  20. Srivastava, R.; Bharti, V. Importance of Dalbergia sissoo for restoration of degraded land: A case study of Indian dry tropical vindhyan regions. Ecol. Eng. 2023, 194, 107021. [Google Scholar] [CrossRef]
  21. Xu, M.P.; Gao, D.X.; Fu, S.Y.; Lu, X.Q.; Wu, S.J.; Han, X.H.; Yang, G.H.; Feng, Y.Z. Long-term effects of vegetation and soil on the microbial communities following afforestation of farmland with Robinia pseudoacacia plantations. Geoderma 2020, 367, 114263. [Google Scholar] [CrossRef]
  22. Delelegn, Y.T.; Purahong, W.; Blazevic, A.; Yitaferu, B.; Wubet, T.; Göransson, H.; Godbold, D.L. Changes in land use alter soil quality and aggregate stability in the highlands of northern Ethiopia. Sci. Rep. 2017, 7, 13602. [Google Scholar] [CrossRef] [PubMed]
  23. Louisson, Z.; Hermans, S.M.; Buckley, H.L.; Case, B.S.; Taylor, M.; Curran-Cournane, F.; Lear, G. Land use modification causes slow, but predictable, change in soil microbial community composition and functional potential. Environ. Microbiome 2023, 18, 30. [Google Scholar] [CrossRef]
  24. Sun, Q.; Zhang, C.; Liu, M.; Zhang, Y.J. Land use and land cover change based on historical space-time model. Solid Earth 2016, 7, 1395–1403. [Google Scholar] [CrossRef]
  25. Kim, D.G.; Kirschbaum, M.U.F.; Eichler-Löbermann, B.; Gifford, R.M.; Liáng, L.L. The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis. Agric. Ecosyst. Environ. 2023, 348, 108402. [Google Scholar] [CrossRef]
  26. Li, N.; Yao, S.H.; You, M.Y.; Zhang, Y.L.; Qiao, Y.F.; Zou, W.X.; Han, X.Z.; Zhang, B. Contrasting development of soil microbial community structure under no-tilled perennial and tilled cropping during early pedogenesis of a Mollisol. Soil Biol. Biochem. 2014, 77, 221–232. [Google Scholar] [CrossRef]
  27. Ma, B.; Wang, H.Z.; Dsouza, M.; Lou, J.; He, Y.; Dai, Z.M.; Brookes, P.C.; Xu, J.M.; Gilbert, J.A. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016, 10, 1891–1901. [Google Scholar] [CrossRef]
  28. Li, K.K.; Hao, Z.H.; Chen, L.; Sha, Y.; Wang, E.T.; Sui, X.H.; Mi, G.H. Conservation strip-till modifies rhizosphere ammonia-oxidizing archaea and bacteria, increases nitrate accumulation and promotes maize growth at grain filling stage. Soil Tillage Res. 2023, 234, 105821. [Google Scholar] [CrossRef]
  29. Khan, M.H.; Liu, H.; Zhu, A.N.; Khan, M.H.; Hussain, S.; Cao, H. Conservation tillage practices affect soil microbial diversity and composition in experimental fields. Front. Microbiol. 2023, 14, 1227297. [Google Scholar] [CrossRef]
  30. Wang, Q.; Jia, S.X.; Liang, A.Z.; Chen, X.W.; Zhang, S.X.; Zhang, Y.; Mclaughlin, N.B.; Gao, Y.; Huang, D.D. Residue Return Effects Outweigh Tillage Effects on Soil Microbial Communities and Functional Genes in Black Soil Region of Northeast China. Chin. Geogr. Sci. 2023, 33, 679–692. [Google Scholar] [CrossRef]
  31. Hu, X.J.; Liu, J.J.; Wei, D.; Zhu, P.; Cui, X.; Zhou, B.K.; Chen, X.L.; Jin, J.; Liu, X.B.; Wang, G.H. Effects of over 30-year of different fertilization regimes on fungal community compositions in the black soils of northeast China. Agric. Ecosyst. Environ. 2017, 248, 113–122. [Google Scholar] [CrossRef]
  32. Tian, J.Y.; Wu, X.; Li, J.Y.; Guo, M.M.; Zhang, X.Y.; Chen, Q. Seasonal and Long-Term Variability in Soil Structure and Erodibility under Different Land-Use Patterns in the Mollisols Region of Northeast China. Agronomy 2023, 13, 449. [Google Scholar] [CrossRef]
  33. Fu, L.Y.; Xie, R.F.; Ma, D.L.; Zhang, M.; Liu, L. Variations in soil microbial community structure and extracellular enzymatic activities along a forest-wetland ecotone in high-latitude permafrost regions. Ecol. Evol. 2023, 13, e10205. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, D.; Chen, J.H.; Tang, Z.Y.; Zhang, Y.H. Effects of Soil Physical Properties on Soil Infiltration in Forest Ecosystems of Southeast China. Forests 2024, 15, 1470. [Google Scholar] [CrossRef]
  35. Marques, A.; Martins, I.S.; Kastner, T.; Plutzar, C.; Theurl, M.C.; Eisenmenger, N.; Huijbregts, M.A.J.; Wood, R.; Stadler, K.; Bruckner, M.; et al. Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol. 2019, 3, 628–637. [Google Scholar] [CrossRef]
  36. Zhou, Z.J.; Zeng, X.Z.; Chen, K.; Li, Z.; Guo, S.; Shangguan, Y.X.; Yu, H.; Tu, S.H.; Qin, Y.S. Long-term straw mulch effects on crop yields and soil organic carbon fractions at different depths under a no-till system on the Chengdu Plain, China. J. Soils Sediments 2019, 19, 2143–2152. [Google Scholar] [CrossRef]
  37. McLauchlan, K. The nature and longevity of agricultural impacts on soil carbon and nutrients: A review. Ecosystems 2006, 9, 1364–1382. [Google Scholar] [CrossRef]
  38. Chen, Q.; Zhang, J.Z.; Guo, M.M.; Zhang, X.Y.; Tian, J.Y.; Zhou, P.C.; Chen, Z.X. Comparison of the effects of five long-term land use and management practices on runoff, soil erosion, and nutrient loss under natural rainfall in the Mollisol region of Northeast China. Earth Surf. Process. Landf. 2024, 49, 1606–1620. [Google Scholar] [CrossRef]
  39. Chen, X.; Zhang, Z.M.; Han, X.Z.; Hao, X.X.; Lu, X.C.; Yan, J.; Biswas, A.; Dunfield, K.; Zou, W.X. Impacts of land-use changes on the variability of microbiomes in soil profiles. J. Sci. Food Agric. 2021, 101, 5056–5066. [Google Scholar] [CrossRef]
  40. Lange, M.; Eisenhauer, N.; Sierra, C.A.; Bessler, H.; Engels, C.; Griffiths, R.I.; Mellado-Vázquez, P.G.; Malik, A.A.; Roy, J.; Scheu, S.; et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 2015, 6, 6707. [Google Scholar] [CrossRef]
  41. Panchal, P.; Preece, C.; Peñuelas, J.; Giri, J. Soil carbon sequestration by root exudates. Trends Plant Sci. 2022, 27, 749–757. [Google Scholar] [CrossRef] [PubMed]
  42. Miltner, A.; Bombach, P.; Schmidt-Brücken, B.; Kästner, M. SOM genesis: Microbial biomass as a significant source. Biogeochemistry 2012, 111, 41–55. [Google Scholar] [CrossRef]
  43. Sarker, J.R.; Singh, B.P.; Cowie, A.L.; Fang, Y.Y.; Collins, D.; Badgery, W.; Dalal, R.C. Agricultural management practices impacted carbon and nutrient concentrations in soil aggregates, with minimal influence on aggregate stability and total carbon and nutrient stocks in contrasting soils. Soil Tillage Res. 2018, 178, 209–223. [Google Scholar] [CrossRef]
  44. Han, Y.; Yi, D.; Ye, Y.C.; Guo, X.; Liu, S.Y. Response of spatiotemporal variability in soil pH and associated influencing factors to land use change in a red soil hilly region in southern China. Catena 2022, 212, 106074. [Google Scholar] [CrossRef]
  45. Guo, Y.L.; Zhang, Z.X.; Liang, F.; Cao, W.C.; Wang, Y.J.; Chen, J.S.; Guo, J.H. Meta-analysis reveals that returning crop straw to arable land causes soil acidification at the global scale. Eur. J. Agron. 2025, 164, 127511. [Google Scholar] [CrossRef]
  46. Naylor, D.; McClure, R.; Jansson, J. Trends in Microbial Community Composition and Function by Soil Depth. Microorganisms 2022, 10, 540. [Google Scholar] [CrossRef]
  47. Bastida, F.; Eldridge, D.J.; García, C.; Png, G.K.; Bardgett, R.D.; Delgado-Baquerizo, M. Soil microbial diversity-biomass relationships are driven by soil carbon content across global biomes. ISME J. 2021, 15, 2081–2091. [Google Scholar] [CrossRef]
  48. Li, S.; Wu, F.Z. Diversity and Co-occurrence Patterns of Soil Bacterial and Fungal Communities in Seven Intercropping Systems. Front. Microbiol. 2018, 9, 1521. [Google Scholar] [CrossRef]
  49. Dong, W.Y.; Liu, E.K.; Yan, C.R.; Tian, J.; Zhang, H.H.; Zhang, Y.Q. Impact of no tillage vs. conventional tillage on the soil bacterial community structure in a winter wheat cropping succession in northern China. Eur. J. Soil Biol. 2017, 80, 35–42. [Google Scholar] [CrossRef]
  50. Wang, S.K.; Wang, R.; Vyzmal, J.; Hu, Y.K.; Li, W.; Wang, J.Z.; Lei, Y.R.; Zhai, X.J.; Zhao, X.S.; Li, J.; et al. Shifts of active microbial community structure and functions in constructed wetlands responded to continuous decreasing temperature in winter. Chemosphere 2023, 335, 139080. [Google Scholar] [CrossRef]
  51. Komorek, D.; Wyszkowska, J.; Borowik, A.; Zaborowska, M. Microbial Activity and Diversity in Soil Sown with Zea mays and Triticosecale. Agriculture 2024, 14, 1070. [Google Scholar] [CrossRef]
  52. Pechlivanis, N.; Karakatsoulis, G.; Kyritsis, K.; Tsagiopoulou, M.; Sgardelis, S.; Kappas, I.; Psomopoulos, F. Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity. Sci. Data 2024, 11, 672. [Google Scholar] [CrossRef] [PubMed]
  53. Liu, Z.X.; Gu, H.D.; Yao, Q.; Jiao, F.; Liu, J.J.; Jin, J.; Liu, X.B.; Wang, G.H. Microbial communities in the diagnostic horizons of agricultural Isohumosols in northeast China reflect their soil classification. Catena 2022, 216, 106430. [Google Scholar] [CrossRef]
  54. Liu, C.G.; Jin, Y.Q.; Hu, Y.N.; Tang, J.W.; Xiong, Q.L.; Xu, M.X.; Bibi, F.; Beng, K.C. Drivers of soil bacterial community structure and diversity in tropical agroforestry systems. Agric. Ecosyst. Environ. 2019, 278, 24–34. [Google Scholar] [CrossRef]
  55. Xiang, H.; Xu, J.J.; Yang, H.; Song, J.C.; Yu, X.J. Reduced Soil Quality but Increased Microbial Diversity in Cultivated Land Compared to Other Land-Use Types in the Longzhong Loess Plateau. Agriculture 2024, 14, 2106. [Google Scholar] [CrossRef]
  56. Hou, M.; Yu, H.; Wang, Y.; Ma, L.Q.; Zhao, X.R.; Chen, Y.M.; Jiao, X.G.; Sui, Y.Y. Bacterial Communities, Network Complexity, and Multifunctionality Affected by Soil Types in Northeastern China. Agronomy 2024, 14, 1297. [Google Scholar] [CrossRef]
  57. Qiu, J.M.; Cao, J.H.; Lan, G.Y.; Liang, Y.M.; Wang, H.; Li, Q. The Influence of Land Use Patterns on Soil Bacterial Community Structure in the Karst Graben Basin of Yunnan Province, China. Forests 2020, 11, 51. [Google Scholar] [CrossRef]
  58. Zhang, Q.L.; Lu, X.R.; Chen, G.S.; Luo, N.N.; Sun, J.; Li, X.J.; Ngozi, E.A. The priming effect patterns linked to the dominant bacterial keystone taxa during different straw tissues incorporation into Mollisols in Northeast China. Appl. Soil Ecol. 2024, 197, 105330. [Google Scholar] [CrossRef]
  59. Wang, Z.T.; Liu, L.; Chen, Q.; Wen, X.X.; Liao, Y.C. Conservation tillage increases soil bacterial diversity in the dryland of northern China. Agron. Sustain. Dev. 2016, 36, 28. [Google Scholar] [CrossRef]
  60. Chi, Y.K.; Song, S.Z.; Xiong, K.N. Effects of different grassland use patterns on soil bacterial communities in the karst desertification areas. Front. Microbiol. 2023, 14, 1208971. [Google Scholar] [CrossRef]
  61. Qi, X.N.; Chen, X.K.; Zhou, X.R.; Qian, Z.; Zhou, H.C.; Zhuang, G.Q.; Ma, A.Z. Effects of grazing in different intensities and 10-year grazing exclusion on soil bacterial community composition and interaction complexity in alpine grasslands of the Qinghai-Tibet Plateau. Land Degrad. Dev. 2024, 35, 2274–2283. [Google Scholar] [CrossRef]
  62. Ren, Z.; Zhang, C.; Li, X.; Ma, K.; Zhang, Z.; Feng, K.X.; Cui, B.S. Bacterial Communities Present Distinct Co-occurrence Networks in Sediment and Water of the Thermokarst Lakes in the Yellow River Source Area. Front. Microbiol. 2021, 12, 716732. [Google Scholar] [CrossRef]
  63. Zhang, Z.M.; Han, X.Z.; Pan, F.J.; Liu, H.; Yan, J.; Zou, W.X.; McLaughlin, N.B.; Hao, X.X. Land use alters diazotroph community structure by regulating bacterivores in Mollisols in Northeast China. Front. Microbiol. 2022, 13, 941170. [Google Scholar] [CrossRef]
  64. Zhao, Y.P.; Wang, Z.H.; Cai, K.; Wang, S.L.; Wright, A.L.; Jiang, X.J. Stability of nitrogen-cycling microbial communities and impact on microbial nitrogen function under different land use practices. Appl. Soil Ecol. 2024, 204, 105729. [Google Scholar] [CrossRef]
  65. Yang, Q.S.; Peng, J.; Ni, S.M.; Zhang, C.Y.; Wang, J.G.; Cai, C.F. Erosion and deposition significantly affect the microbial diversity, co-occurrence network, and multifunctionality in agricultural soils of Northeast China. J. Soils Sediments 2024, 24, 888–900. [Google Scholar] [CrossRef]
  66. Zhang, Z.M.; Han, X.Z.; Yan, J.; Zou, W.X.; Wang, E.T.; Lu, X.C.; Chen, X. Keystone Microbiomes Revealed by 14 Years of Field Restoration of the Degraded Agricultural Soil Under Distinct Vegetation Scenarios. Front. Microbiol. 2020, 11, 1915. [Google Scholar] [CrossRef] [PubMed]
  67. Shen, C.C.; Xiong, J.B.; Zhang, H.Y.; Feng, Y.Z.; Lin, X.G.; Li, X.Y.; Liang, W.J.; Chu, H.Y. Soil pH drives the spatial distribution of bacterial communities along elevation on Changbai Mountain. Soil Biol. Biochem. 2013, 57, 204–221. [Google Scholar] [CrossRef]
  68. Sul, W.J.; Asuming-Brempong, S.; Wang, Q.; Tourlousse, D.M.; Penton, C.R.; Deng, Y.; Rodrigues, J.L.M.; Adiku, S.G.K.; Jones, J.W.; Zhou, J.Z.; et al. Tropical agricultural land management influences on soil microbial communities through its effect on soil organic carbon. Soil Biol. Biochem. 2013, 65, 33–38. [Google Scholar] [CrossRef]
  69. Han, C.L.; Zhou, W.D.; Gu, Y.J.; Wang, J.Q.; Zhou, Y.F.; Xue, Y.Y.; Shi, Z.G.; Siddique, K.H.M. Effects of tillage regime on soil aggregate-associated carbon, enzyme activity, and microbial community structure in a semiarid agroecosystem. Plant Soil 2024, 498, 543–559. [Google Scholar] [CrossRef]
  70. Machmuller, M.B.; Kramer, M.G.; Cyle, T.K.; Hill, N.; Hancock, D.; Thompson, A. Emerging land use practices rapidly increase soil organic matter. Nat. Commun. 2015, 6, 6995. [Google Scholar] [CrossRef]
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Figure 1. Differences in (a) Chao 1 and (b) Shannon indices among different treatments. Statistical significance is indicated by different capital letters (p < 0.05) for comparisons among the land use types at a constant depth and by lowercase letters (p < 0.05) among depths for the same treatment.
Figure 1. Differences in (a) Chao 1 and (b) Shannon indices among different treatments. Statistical significance is indicated by different capital letters (p < 0.05) for comparisons among the land use types at a constant depth and by lowercase letters (p < 0.05) among depths for the same treatment.
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Figure 2. Principal coordinate analysis (PCoA) of the soil bacterial community composition.
Figure 2. Principal coordinate analysis (PCoA) of the soil bacterial community composition.
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Figure 3. (a) Composition of the bacterial communities at the phylum level. (b) Heatmap and hierarchical clustering of differences in relative abundance of bacteria at the genus level, with significance indicated by * (p < 0.05) and ** (p < 0.01).
Figure 3. (a) Composition of the bacterial communities at the phylum level. (b) Heatmap and hierarchical clustering of differences in relative abundance of bacteria at the genus level, with significance indicated by * (p < 0.05) and ** (p < 0.01).
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Figure 4. Cluster analysis showing the relative abundance of KEGG functional metabolic pathways in the soil bacterial communities.
Figure 4. Cluster analysis showing the relative abundance of KEGG functional metabolic pathways in the soil bacterial communities.
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Figure 5. The co-occurrence networks of five land use types of bacterial communities. The different colors of nodes indicate different phyla. Each node size indicates the number of connections (degree). Red lines are used for positive links and green lines for negative links.
Figure 5. The co-occurrence networks of five land use types of bacterial communities. The different colors of nodes indicate different phyla. Each node size indicates the number of connections (degree). Red lines are used for positive links and green lines for negative links.
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Figure 6. (a) Redundancy analysis (RDA) identifies relationships between soil physicochemical properties and bacterial community structure. (b) Correlation analysis among soil physicochemical properties, dominant bacterial phyla, and Chao 1 and Shannon indices, where * and ** indicate significance at p < 0.05 and p < 0.01, respectively.
Figure 6. (a) Redundancy analysis (RDA) identifies relationships between soil physicochemical properties and bacterial community structure. (b) Correlation analysis among soil physicochemical properties, dominant bacterial phyla, and Chao 1 and Shannon indices, where * and ** indicate significance at p < 0.05 and p < 0.01, respectively.
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Table 1. Bacterial network properties of different land use types.
Table 1. Bacterial network properties of different land use types.
Positive LinksNegative LinksTotal LinksAverage DegreeNetwork DensityModularity
CT107 (59.78%)72 (40.22%)1797.3060.1520.618
BL101 (75.94%)32 (24.06%)1335.3200.1090.749
NT123 (51.90%)114 (48.10%)23710.3040.2290.451
NVR124 (48.44%)132 (51.56%)25610.2400.2090.429
AF122 (48.61%)129 (51.39%)25110.6810.2320.484
Note: Positive links indicate significant positive correlations between microorganisms, usually reflecting cooperative relationships or mutualistic symbiosis. Negative links indicate significant negative correlations between microorganisms, usually reflecting competition or antagonism. Total links represent the total number of all links in the network, including positive and negative links. Average degree represents the average number of connections per node, with higher value indicating a higher strength of connections between each node. Network density represents the ratio of the number of actual connections to the number of possible connections in the network, with a higher value usually indicating a more complex network. Modularity represents the degree to which nodes in a network tend to form sub-clusters, with a higher value usually indicating a more stable network.
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Wang, X.; Chen, Q.; Li, Z.; Yin, W.; Ma, D. The Effects of Long-Term Land Use Changes on Bacterial Community Structure and Soil Physicochemical Properties in the Northeast Mollisol Region of China. Agronomy 2025, 15, 1132. https://doi.org/10.3390/agronomy15051132

AMA Style

Wang X, Chen Q, Li Z, Yin W, Ma D. The Effects of Long-Term Land Use Changes on Bacterial Community Structure and Soil Physicochemical Properties in the Northeast Mollisol Region of China. Agronomy. 2025; 15(5):1132. https://doi.org/10.3390/agronomy15051132

Chicago/Turabian Style

Wang, Xu, Qiang Chen, Zhao Li, Weiping Yin, and Dalong Ma. 2025. "The Effects of Long-Term Land Use Changes on Bacterial Community Structure and Soil Physicochemical Properties in the Northeast Mollisol Region of China" Agronomy 15, no. 5: 1132. https://doi.org/10.3390/agronomy15051132

APA Style

Wang, X., Chen, Q., Li, Z., Yin, W., & Ma, D. (2025). The Effects of Long-Term Land Use Changes on Bacterial Community Structure and Soil Physicochemical Properties in the Northeast Mollisol Region of China. Agronomy, 15(5), 1132. https://doi.org/10.3390/agronomy15051132

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