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Article

Response Mechanism of Soil Microbial Characteristics to Different Land-Use Types in China

by
Gang Ma
1,
Yantao Hu
2,3,
Yangyang Zhang
4,
Yaoguang Han
1,
Keyi Li
1,
Hongtao Jia
5 and
Xinping Zhu
6,7,*
1
College of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China
2
Shaanxi Land Engineering Construction Group Co., Ltd. Northwest Branch, Yulin 719054, China
3
College of Water Resources and Hydropower Engineering, Xian University of Technology, Xi’an 710048, China
4
Shaanxi Huanghe Guxian Water Conservancy Development Co., Ltd., Xi’an 710024, China
5
College of Life Sciences, Xinjiang Agricultural University, Urumqi 830052, China
6
College of Bioscience and Resources Environment, Beijing University of Agriculture, Beijing 102206, China
7
Key Laboratory for North China Urban Agriculture, Ministry of Agriculture and Rural Affairs, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(6), 1229; https://doi.org/10.3390/land14061229
Submission received: 22 April 2025 / Revised: 4 June 2025 / Accepted: 4 June 2025 / Published: 6 June 2025
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Abstract

Deciphering the responses of soil properties to land-use changes is of great importance for sustainable management in biogeochemically sensitive zones. This investigation examines the impacts of agricultural conversion on soil-microbial dynamics across four land-use patterns in western Xin-jiang, China: native grassland (NG), two-year paddy field (PF), one-year corn-rice rotation field (RF), and two-year sorghum field (SF). The results indicate that different land-use types significantly altered soil properties: NG retained superior soil structure, with significantly higher porosity and organic carbon (p < 0.05). Microbial communities differed distinctly across land uses. The relative abundance of Proteobacteria ranked SF > RF > PF > NG, contrasting with Bacteroidota trends. Non-metric multidimensional scaling (NMDS) revealed divergent structures of soil microbial communities under different land-use types. The results of correlation analysis and structural equation models (SEM) showed that land use could indirectly affect bacterial diversity through its influence on soil physicochemical properties, highlighting that land-use-driven shifts in bulk density, porosity, and carbon content critically shape microbial dynamics, particularly in bacteria. These results underscore the sensitivity of soil properties to land-use practices and offer actionable insights for optimizing soil quality and sustainability in vulnerable regions.

1. Introduction

As a fundamental element of terrestrial ecosystems, soil sustains diverse life forms by furnishing habitats and nutrient resources while simultaneously maintaining intricate biological communities [1,2]. Soil microbial communities are critical components of soil properties, driving nutrient cycling and energy transformation, and maintaining linkages between aboveground and belowground components of terrestrial ecosystems [3]. These communities directly participate in organic matter decomposition, nutrient transformation, and metabolic processes, playing vital roles in sustaining soil quality and ecological functions [4]. Although soil microbiota exhibits remarkable species and genetic diversity, they are more sensitive to environmental changes than are soil physicochemical properties [5] and are highly vulnerable to anthropogenic disturbances such as land-use modification [6,7]. Changes in soil properties can reshape bacterial ecology. Soil particle size affects soil porosity and aeration, and different microorganisms have different requirements for soil aeration, with more aerobic bacteria in sand and more anaerobic bacteria in clay [8,9]. Soil organic matter is an important source of energy and nutrition for soil microorganisms, and soils with higher organic matter content often have richer microbial communities [10]. Understanding interactions between microbial communities, environmental factors, and their responses to external pressures has become a key research focus [11,12,13]. Studies have shown that bacteria and fungi are the two most dominant groups in belowground microbial communities, exhibiting global-scale ecological niche differentiation in surface soils, linked to contrasting responses to precipitation and pH [14]. Advances in high-throughput sequencing (e.g., Illumina MiSeq) have revolutionized microbial ecology by enabling progress in identifying drivers, including microbe-soil interactions, which is critical for understanding land-use conversion mechanisms [15,16,17].
Land-use modification represents one of the most direct human interventions in changes in soil [18]. It enables resource development to meet socioeconomic demands [19]. Simultaneously, different land uses alter soil physicochemical properties and the structural and functional dynamics of microbial communities, reshaping microbe-soil interactions [20]. In paddy field cultivation, alternating flooding-drying regimes create cyclical shifts between dry and waterlogged conditions, modifying community structure by favoring aerobic and anaerobic taxa [21]. Conversely, woodlands and grasslands use vegetation to mitigate erosion and preserve soil structural integrity [22]. Ecologically fragile areas exhibit pronounced microbial divergence across land uses: wetland and grassland soils have higher diversity than woodland soils and agricultural systems [23]. Studies in temperate grasslands have revealed that converting steppe to winter wheat fields increases network complexity and variability, suggesting that agricultural intensification reduces ecological stability [24]. In conclusion, land-use patterns drive soil changes; identifying key drivers offers insights for optimizing land allocation in vulnerable ecosystems.
This investigation targets the arid agroecosystems of western Xinjiang—a region exhibiting pronounced edaphic vulnerability to anthropogenic disturbances. We systematically profiled edaphic microbiomes (0–30 cm depth) across four land management regimes (native grassland, two-year paddy field, one-year corn-rice rotation field, and two-year sorghum field) through Illumina MiSeq sequencing to resolve diversity and composition shifts in bacterial/fungal consortia. By integrating multivariate ordination (redundancy analysis, RDA), correlation analysis, and structural equation modeling (SEM), we aimed to elucidate three critical dimensions: (1) how shifts in land-use types affect soil physicochemical properties and microbial communities; (2) how land-use patterns mediate interactions between soil physicochemical properties and soil microbial communities; and (3) what dominant factors drive microbial community changes. These mechanistic insights advance precision land-use planning frameworks, offering empirically grounded strategies to optimize carbon sequestration potential and enhance agroecosystem resilience in fragile dryland environments.

2. Materials and Methods

2.1. Study Area

The investigated region is located in Yumai Township (39°8′1″ N, 75°58′48″ E), Aktao County, positioned within the Kizilsu Kirghiz Autonomous Prefecture of Xinjiang Uygur Autonomous Region, northwestern China (Figure 1). Characterized by a west-to-east topographic gradient, the region has a mean elevation of 1210 m. It experiences a temperate continental climate with intense solar irradiance, limited precipitation (annual average < 100 mm), and pronounced daily temperature variations (diurnal range > 15 °C). The mean annual temperature is 10.9 °C, with annual precipitation ranging from 70 to 120 mm. The dominant soil type in natural grassland is mountain chestnut soil. In agricultural zones, the initial soil types resemble natural grasslands, but under frequent anthropogenic disturbances, the soil types become more diverse, and exhibit concentric zonation centered on residential areas, where cultivation maturity decreases radially outward, forming a gradient from well-developed cultivated soils (mountain chestnut soil mixed with irrigated silty soil) near settlements to newly cultivated soils (mountain chestnut soil mixed with brown calcareous soil) and undisturbed desert soils (brown desert soil or saline soil) at the periphery. Land-use diversity is high, with the cultivated systems selected in this study including paddy fields, corn fields, sorghum fields, and corn-rice rotation systems, all located in new cultivated land. Native grassland vegetation is primarily composed of Artemisia frigida, Stipa sareptana, Carex turkestanica, and Krascheninnikovia ceratoides.

2.2. Experimental Design and Soil Sampling

In this study, four land-use types were selected. These included native grassland pasture (NG), paddy field for two years (PF), grain and paddy rotation field for one year (RF), and sorghum field for two years (SF). The initial soil conditions across these land uses were homogeneous, with clearly demarcated boundaries between adjacent plots. This design minimized confounding factors and ensured suitability for analyzing differences in soil-microbial communities under distinct land-use regimes.
Triplicate 1 m × 1 m quadrats were delineated per sampling site. Soil samples from each subplot were collected via a five-point sampling method. Subsamples (0–30 cm depth) were homogenized to form a composite sample (~500 g). Field-moist specimens underwent aseptic cryo-transport (sterile bags, ice-preserved) followed by bisection for divergent processing: one air-dried (<2 mm sieved to remove roots, stones, and macrofauna) for soil physicochemical analysis (including soil pH, bulk density, particle size distribution, soil total carbon content (TCC), total nitrogen content (TNC), total phosphorus content (TPC), soil organic carbon (SOC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK)), and the other stored at −80 °C for microbial community characterization [25].

2.3. Determination of Soil Physicochemical Properties

Soil pH was measured via the potentiometric method (PHS-3B pH meter). Soil bulk density and porosity were quantified using the core sampling method with 100 cm3 cutting rings. Soil particle size distribution was determined by the hydrometer method [13]. Particle size distribution was assessed through combined sieve-hydrometer analysis following sodium hexametaphosphate dispersion. Soil TCC and TNC were measured by dry combustion elemental analyzer (Vario EL III). Soil TKC was measured by flame photometry (FP). Soil TPC was analyzed through acid digestion (HClO4-HNO3) followed by molybdenum-antimony colorimetric detection, and SOC was determined via dichromate oxidation-volumetric method. Available nutrient quantification included: the alkali diffusion method for AN content; sodium bicarbonate extraction (0.5 mol/L NaHCO3) with molybdenum-antimony anti-colorimetry for AP content; and ammonium acetate extraction-flame photometry for AK content.

2.4. Determination and Analysis of Soil Microbial Community

Lyophilized soil samples (−80 °C storage) underwent genomic DNA extraction from 0.5 g aliquots using the FastDNA™ Spin Kit (Sigma-Aldrich Co., St. Louis, MO, USA) with mechanical homogenization via FastPrep-24® (6.5 m s−1, 3 × 30 s cycles). Target regions were amplified through multiplex PCR on a Bio-Rad S1000 Thermal Cycler: bacterial 16S rRNA V3-V4 hypervariable regions using barcoded primers 338F and 806R, alongside fungal ITS1 regions amplified with primers ITS1-F and ITS2-R. Post-amplification quality validation integrated 1% agarose gel electrophoresis and NanoDrop™ 8000 spectrophotometric quantification, followed by QIAquick® purification of amplicons. Triplicate technical replicates per sample were normalized to equimolar concentrations and sequenced on the Illumina NovaSeq platform (2 × 250 bp paired-end), ensuring library concentrations of 4 nM with <5% inter-sample variability.
High-throughput libraries were prepared via BMKCloud (China)-hosted Illumina NovaSeq 6000 system (Illumina Inc., San Diego, CA, USA) employing PE150 high-fidelity sequencing chemistry. Raw reads underwent adapter trimming and quality control (Q-score ≥ 20) via Trimmomatic v0.39, followed by paired-end merging with FLASH v1.2.11 to reconstruct full-length amplicons. Operational taxonomic units (OTUs) were clustered at 97% similarity using QIIME2’s UCLUST-based pipeline [13,17], with taxonomic annotation performed through BLAST (version 2.15.0) alignment against the SILVA 138 (16S rRNA) and UNITE v9.0 (ITS) reference databases. Phylum-level compositions visualized through stacked bar plots. Alpha-diversity indices (Ace, Chao1, Shannon-Wiener, Simpson) were calculated and compared across land-use types using one-way analysis of variance (ANOVA).

2.5. Statistical Analyses and Visualization

All data were preprocessed using Excel 2019 (Microsoft), IBM SPSS 24.0, Origin 2023 (OriginLab), and R 4.2.0 (R Core Team). A geospatial schematic of the study area was generated using ArcGIS Pro 3.1 (Esri). Microbial community composition was analyzed through Venn diagrams and non-metric multidimensional scaling (NMDS) [26], while land-use effects on soil parameters were assessed via one-way ANOVA with post-hoc LSD tests (α = 0.05). Multivariate analyses incorporated Pearson correlations and redundancy analysis (RDA) to quantify soil-microbial diversity relationships. Structural equation modeling (SEM) in AMOS 23.0 evaluated land-use impact pathways, employing principal component analysis (PCA)-derived composite indices (PC1 explaining 82.35–92.68% variance) to address multicollinearity among diversity metrics [27,28]. All visual outputs, including phylum-level composition bar plots and SEM path diagrams, were generated using Origin 2023 and ggplot2 in R 4.2.0 [29].

3. Results

3.1. Analysis of the Soil Physicochemical Properties in Different Land-Use Types

Soil physicochemical properties diverged significantly among land-use types (p < 0.05) (Table 1). These properties included soil pH, bulk density, porosity and the particle size distribution. Soil pH was highest in NG and lowest in PF. Compared with other land-use types (PF, RF and SF), NG exhibited significantly higher soil porosity but lower soil bulk density (p < 0.05). The soil bulk density of NG was 47.74% lower than that of PF and RF, and 0.63 g·cm−3 lower than SF. Soil porosity in NG was 52.06%, 67.26%, and 67.79% higher than that SF, RF, and PF, respectively. NG soils had lower sand content and higher silt content than cropland (Table 1).
Significant differences in soil TCC, SOC, ANC, and TPC were observed across land-use types (p < 0.05). In contrast, TNC, AP, TKC, and AK showed no significant variations (p > 0.05). NG exhibited the lowest soil TCC, which was 31.41% lower than SF, 25.19% lower than RF, and 18.19% lower than PF, respectively. However, NG demonstrated the highest SOC, with levels three times higher than SF and double those of RF and PF. Soil texture analysis revealed that SF had the highest sand content, which was 79.0% higher than NG, 42.1% higher than PF, and 43.1% higher than RF. However, SF had the lowest clay and silt content, which were 81.4% lower than NG, 75.0% lower than PF, and 77.4% lower than RF, respectively. Notably, silt content in PF showed no significant difference from NG and RF. In contrast, silt content in NG was 53.2% higher than RF and three times higher than SF (Table 2).

3.2. Changes of the Soil Microbial Community in Different Land-Use Types

A total of 800,265 high-quality bacterial sequences were obtained. These were distributed as follows: NG (199,065), PF (205,011), RF (202,581), and SF (193,608). Bacterial clustering at 97% similarity yielded 12,247 OTUs, with 13 core OTUs shared across all land uses. Unique OTU distribution showed land-use specificity: SF harbored the highest richness (4153 OTUs, 33.91%), followed by PF (3528 OTUs, 28.81%), RF (2426 OTUs, 19.81%), and NG (1199 OTUs, 9.79%) (Figure 2A). For fungi, 720,418 sequences were retained (NG: 178,408, PF: 174,649, RF: 178,915, SF: 188,446). Clustering at 97% similarity identified 2424 fungal OTUs, with 17 shared OTUs across all treatments. OTU counts per land use were NG (640), PF (649), RF (758), and SF (677), reflecting stronger niche differentiation than in bacterial communities (Figure 2B). NMDS revealed land-use dependent differentiation in microbial assemblage patterns, with distinct spatial separation observed between soil bacterial communities (Figure 2C) and fungal communities (Figure 2D).
Land management regimes exerted significant influence on bacterial taxonomic representation at the phylum level (Figure 3A). At the phylum level, the relative abundance of Proteobacteria under different land uses was in the order of SF > RF > PF > NG. The relative abundance of Bacteroidota had the opposite trend, and the relative abundance of Acidobacteria was in the order of SF > NG > RF > PF (Figure 3A). Microbial composition universally exhibited Ascomycota and Basidiomycota predominance among land-use regimes. Ascomycota, a widely distributed group, dominated all treatments (Figure 3B).
Land-use intensification differentially impacted bacterial α-diversity metrics, with significant alterations observed in Abundance-based Coverage Estimator (ACE), Shannon-Wiener, and Chao 1 indices (p < 0.05). However, soil fungal diversity remained stable across treatments, mirroring fungal communities’ resistance to land-use modifications. The ACE, Shannon-Wiener, and Chao 1 indices of soil bacteria exhibited hierarchical ranking across land-use regimes: SF > PF > RF > NG (p < 0.05). The ACE, Shannon-Wiener, and Chao 1 indices of bacteria in NG plots were significantly lower than those in SF and PF (Figure 4A–C).

3.3. Land-Use-Driven Interplay Between Soil Physicochemical Properties and Microbiota

Multivariate analyses delineated robust edaphic drivers of microbial diversity metrics (Figure 5). Soil bulk density and total C showed positive linkages with Shannon/Simpson metrics. Contrastingly, porosity and SOC demonstrated significant negative correlations with these bacterial indices. Particulate fractions revealed textural controls; sand content positively influenced ACE/Shannon/Chao1 sequences, while silt exerted suppressive effects (Figure 5A). Fungal communities displayed pH-dependent enhancement in Shannon/Simpson indices, contrasting with available potassium’s inhibitory role on ACE/Chao1 parameters (Figure 5B). Ordination patterns confirmed land-use stratification in bacterial consortia (RDA1: 27.16%; RDA2: 19.41%) (Figure 5C), while fungal assemblages maintained structural homogeneity across management regimes (Figure 5D).

3.4. Driving Factors of Soil Microbial Biodiversity in Different Land-Use Types

In order to discern the direct and indirect effects of environmental drivers on microbial biodiversity, structural equation modeling (SEM) was performed to conduct a more in-depth analysis. Both bacterial (GFI = 0.99, CFI = 0.98, RMSEA < 0.05) and fungal (GFI = 1, CFI = 1, RMSEA < 0.05) diversity showed excellent model fit; soil physicochemical factors explained 97% and 95% of the variations in bacterial and fungal diversity (Figure 6). Land-use-regulated TCC and bulk density emerged as primary bacterial determinants, while SOC exhibited inverse effects (Figure 6A). Fungal diversity was directly mediated by land-use-modulated TCC, bulk density, EC, and SOC (Figure 6B).

4. Discussion

4.1. Characteristics of the Soil Physicochemical Properties in Different Land-Use Types

Different land-use practices have a substantial influence on soil quality [30]. As an integrative measure of soil capacity to sustain plant productivity, regulate hydrological processes, and detoxify environmental pollutants, soil quality is inherently shaped by its physicochemical and biological attributes [31]. Global studies demonstrate that land-use decisions profoundly alter these attributes, with cascading effects on ecosystem services [32,33]. For example, conversion from paddy to upland systems elevates soil pH (+0.8–1.2), bulk density (+12–18%), and organic carbon stocks (+1.2–2.5 g kg⁻1), but reduces water-holding capacity (−15–22%), water retention (−18–30%) [34]. Studies indicate that physical properties are significantly affected by slope position and soil depth, and soil chemical properties (TCC, TNC, TKC, AN, AP, and AK) exhibit greater spatial variation than physical properties (soil particle size distribution and bulk density) [35]. Additionally, SOC varied across soil particle sizes among conservation tillage, plowing tillage, and native grassland ecosystems. Plowing was found to alleviate organic matter loss mediated by macroaggregates [36].
The results demonstrate distinct soil textural changes between native grassland and croplands. Native grassland exhibited higher silt content, and soil pH was higher than in the paddy field. In contrast, soil bulk density and sand content in native grassland were lower than those in cropland. This divergence could be attributed to intensive agricultural practices: recurrent tillage preferentially transports coarse particles (sand) to surface layers via mechanical sorting, while irrigation-induced compaction and chemical fertilization degrade soil structure, reducing porosity and electrical conductivity through colloidal dispersion and soluble salt leaching [37,38].

4.2. Characteristics of the Soil Microbial Community Structure in Different Land-Use Types

The conversion of grassland to cultivated land affects the structure of the soil microbial community [39]. In this study, metagenomic analysis revealed distinct divergence in both bacterial and fungal community structures between natural and agricultural systems. Notably, Proteobacteria were the dominant phylum of bacteria, with many species involved in soil material cycling, such as nitrogen fixation and organic matter decomposition [35,40,41]. Acidobacteria, typically oligotrophic K-strategists adapted to stable grassland ecosystems, showed a significant reduction under cultivation (p < 0.05), confirming their sensitivity to land-use shifts [41]. These phylum-level responses highlight functional trade-offs, with agricultural practices favoring rapid-growing taxa critical for nutrient cycling [40], while depleting stress-tolerant specialists essential for long-term soil resilience.
Numerous studies have demonstrated that the α-diversity of native fungal communities remains stable across different land-use types, whereas bacterial community α-diversity exhibits significant differences, typically being higher in croplands than in perennial grasslands [39,41]. This study also found that grassland-to-cropland conversion increased bacterial diversity, but did not affect fungal community diversity. These patterns may be attributed to crop rotation systems, where diversified root exudates and crop residues enhance soil nutrient availability, thereby promoting bacterial diversification [42]. However, our findings contradict those of Cao et al. [23], who reported higher bacterial diversity in natural grasslands than in cultivated lands. This discrepancy could stem from differences in crop types (continuous corn monoculture vs. diversified-rotations system) and sampling depths (0–20 cm vs. 0–30 cm). Previous research has confirmed that fungal community composition shows stronger stratification across soil horizons and depths compared with bacterial communities [43].

4.3. The Interaction Between Soil Physicochemical Properties and Soil Microbial Community Under Different Land-Use Types

Soil physicochemical properties are closely associated with microbial community composition and carbon dynamics [44,45]. Dominant bacterial groups in surface soils exhibit significant positive correlations with carbon metabolism intensity, while carbon metabolic capabilities decline with increasing soil depth [46,47]. Our results further revealed that bacterial community diversity indices positively correlate with total soil carbon content but negatively correlate with soil total organic carbon.
Soil fungal diversity is strongly influenced by soil properties, climate, and stand conditions [48]. The available literature indicates that pH value and water content are key determinants of microbial community structure and function [12,44]. Another research on nutrient addition found that dissolved organic carbon, available potassium, and nitrate-nitrogen were the main driving factors affecting the bacterial community structure [49]. Such effects may differ with changing land uses [50]. The Pearson correlation analysis in this study identified significant correlations between soil fungal community diversity and both pH and AK. These findings align with the existing literature indicating that soil pH and nutrient availability alter fungal community composition [41,51]. In addition, the excellent model fit statistics (GFI/CFI = 1, RMSEA < 0.05) suggest SEM models robustly capture the key relationships between land use, soil properties, and microbial communities in this system. The model results further confirmed that land use could drive microbial community restructuring by changing soil physiochemical properties, such as TCC, SOC and bulk density, and the slightly stronger explanatory power for bacterial versus fungal diversity may reflect bacterial faster response times to environmental changes compared to fungi. Ren et al. [52] also found soil organic carbon could affect bacterial and fungal community, and this linkage is largely modulated by land-use changes.

5. Conclusions

This study underscores the critical influence of land-use practices on soil in ecologically vulnerable regions. Agricultural intensification reshapes soil physicochemical properties and microbial community dynamics. Native grasslands, characterized by higher organic carbon accumulation and structural stability, sustain microbial communities with distinct functional profiles compared with cultivated systems. The interplay between soil properties (such as pH, porosity, and nutrient availability) and microbial composition highlights the importance of land management for soil health preservation. Conservation-tillage, diversified cropping systems, and native grassland preservation can mitigate soil degradation while supporting essential microbial functions. These approaches align with sustainable agriculture principles, fostering long-term ecological balance and resource efficiency in fragile arid landscapes. Ultimately, harmonizing agricultural productivity with soil conservation emerges as a cornerstone for achieving ecological and socioeconomic sustainability in dryland regions.

Author Contributions

Conceptualization, G.M., H.J. and X.Z.; Data curation, G.M., Y.Z., Y.H. (Yaoguang Han) and K.L.; Formal analysis, G.M., Y.Z., Y.H. (Yaoguang Han) and K.L.; Funding acquisition, X.Z.; Investigation, G.M.; Methodology, H.J. and X.Z.; Project administration, G.M., Y.H. (Yantao Hu) and X.Z.; Resources, H.J.; Supervision, X.Z.; Validation, Y.Z., Y.H. (Yaoguang Han) and K.L.; Visualization, G.M., Y.Z., Y.H. (Yaoguang Han) and K.L.; Writing—original draft, G.M.; Writing—review & editing, G.M., Y.H. (Yantao Hu) and X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 31960258.

Data Availability Statement

All links to input data are reported in the manuscript, and all output data are available upon re-quest to the authors.

Conflicts of Interest

Author Yantao Hu was employed by the company Shaanxi Land Engineering Construction Group Co., Ltd. Northwest Branch. Author Yangyang Zhang was employed by the company Shaanxi Huanghe Guxian Water Conservancy Development Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
NGnative grassland
PFtwo-year paddy field
RFone-year corn-rice rotation field
SFtwo-year sorghum field
OTUsoperational taxonomic unit counts
NMDSnonmetric multidimensional scaling
SEMsstructural equation models
TCCsoil total carbon content
TNCsoil total nitrogen content
TPCsoil total phosphorus content
SOCsoil organic carbon
ANsoil available nitrogen content
APsoil available phosphorus content
AKsoil available potassium content

References

  1. Adhikari, K.; Hartemink, A.E. Linking soils to ecosystem services—A global review. Geoderma 2016, 262, 101–111. [Google Scholar] [CrossRef]
  2. Yadav, A.N.; Kour, D.; Kaur, T.; Devi, R.; Yadav, A.; Dikilitas, M.; Abdel-Azeem, A.M.; Ahluwalia, A.S.; Saxena, A.K. Bio-diversity, and biotechnological contribution of beneficial soil microbiomes for nutrient cycling, plant growth improvement and nutrient uptake. Biocatal. Agric. Biotechnol. 2021, 33, 102009. [Google Scholar] [CrossRef]
  3. Philippot, L.; Chenu, C.; Kappler, A.; Rillig, M.C.; Fierer, N. The interplay between microbial communities and soil properties. Nat. Rev. Microbiol. 2024, 22, 226–239. [Google Scholar] [CrossRef]
  4. Wagg, C.; Schlaeppi, K.; Banerjee, S.; Kuramae, E.E.; van der Heijden, M.G.A. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 2019, 10, 4841. [Google Scholar] [CrossRef]
  5. Lavallee, J.M.; Chomel, M.; Alvarez Segura, N.; Baggs, E.M.; Johnson, D. Land management shapes drought responses of dominant soil microbial taxa across grasslands. Nat. Commun. 2024, 15, 29. [Google Scholar] [CrossRef]
  6. Liao, J.; Dou, Y.; Yang, X.; An, S. Soil microbial community and their functional genes during grassland restoration. J. Environ. Manag. 2023, 325, 116488. [Google Scholar] [CrossRef]
  7. Yang, P.; Yuan, M.; Qu, C.; Liu, Y.; Li, X. Metagenomic insight into the soil microbial functions across land uses. J. Soils Sediments 2024, 24, 3684–3693. [Google Scholar] [CrossRef]
  8. Perezrodriguez, J.; Maqueda, C.; Carretero, M.I. Effect of some clay minerals on the growing of sulphate-reducing bacteria in anaerobic reactors. Appl. Clay Sci. 1989, 4, 449–459. [Google Scholar] [CrossRef]
  9. Zhou, Y.; Chen, H.L.; Yao, J.; He, M.Y.; Si, Y.; Feng, L.; Wang, F.; Wang, G.; Martin, M.F. Influence of clay minerals on the Bacillus halophilus Y38 activity under anaerobic condition. Appl. Clay Sci. 2010, 50, 533–537. [Google Scholar] [CrossRef]
  10. Ding, J.J.; Zhang, Y.G.; Wang, M.M.; Sun, X.; Cong, J. Soil organic matter quantity and quality shape microbial community compositions of subtropical broadleaved forests. Mol. Ecol. 2015, 24, 5175–5185. [Google Scholar] [CrossRef] [PubMed]
  11. Lauber, C.L.; Ramirez, K.S.; Aanderud, Z.; Lennon, J.T.; Fierer, N. Temporal variability in soil microbial communities across land-use types. ISME J. 2013, 7, 1641–1650. [Google Scholar] [CrossRef] [PubMed]
  12. Liu, D.; Huang, Y.; An, S.; Sun, H.; Bhople, P. Soil physicochemical and microbial characteristics of contrasting land-use types along soil depth gradients. Catena 2018, 162, 345–353. [Google Scholar] [CrossRef]
  13. Huang, J.; Liu, X.; Liu, J.; Li, Z.; Jiang, L.; Tang, C. 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]
  14. Bahram, M.; Hildebrand, F.; Forslund, S.K.; Anderson, J.L.; Soudzilovskaia, N.A.; Bodegom, P.M.; Bengtsson-Palme, J.; Anslan, S.; Coelho, L.P.; Harend, H.; et al. Structure and function of the global topsoil microbiome. Nature 2018, 560, 233–237. [Google Scholar] [CrossRef] [PubMed]
  15. Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Huntley, J.; Fierer, N.; Owens, S.M.; Betley, J.; Fraser, L.; Bauer, M.; et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012, 6, 1621–1624. [Google Scholar] [CrossRef]
  16. Xiong, W.; Li, R.; Ren, Y.; Liu, C.; Zhao, Q.; Wu, H.; Jousset, A.; Shen, Q. Distinct roles for soil fungal and bacterial communities associated with the suppression of vanilla fusarium wilt disease. Soil Biol. Biochem. 2017, 107, 198–207. [Google Scholar] [CrossRef]
  17. Yao, Q.; Liu, J.; Yu, Z.; Li, Y.; Jin, J.; Liu, X.; Wang, G. Three years of biochar amendment alters soil physiochemical properties and fungal community composition in a black soil of northeast China. Soil Biol. Biochem. 2017, 110, 56–67. [Google Scholar] [CrossRef]
  18. Hasan, S.S.; Zhen, L.; Miah, M.G.; Ahamed, T.; Samie, A. Impact of land use change on ecosystem services: A review. Environ. Dev. 2020, 34, 100527. [Google Scholar] [CrossRef]
  19. Wall, D.H.; Bardgett, R.D. Soil Ecology and Ecosystem Services; Oxford University Press: Oxford, UK, 2013. [Google Scholar]
  20. Burst, M.; Chauchard, S.; Dambrine, E.; Dupouey, J.-L.; Amiaud, B. Distribution of soil properties along forest-grassland interfaces: Influence of permanent environmental factors or land-use after-effects? Agric. Ecosyst. Environ. 2020, 289, 106739. [Google Scholar] [CrossRef]
  21. Francioli, D.; Cid, G.; Kanukollu, S.; Ulrich, A.; Hajirezaei, M.-R.; Kolb, S. Flooding causes dramatic compositional shifts and depletion of putative beneficial bacteria on the spring wheat microbiota. Front. Microbiol. 2021, 12, 773116. [Google Scholar] [CrossRef]
  22. Liu, S.; Plaza, C.; Ochoa-Hueso, R.; Trivedi, C.; Wang, J.; Trivedi, P.; Zhou, G.; Piñeiro, J.; Martins, C.S.C.; Singh, B.K.; et al. Litter and soil biodiversity jointly drive ecosystem functions. Glob. Change Biol. 2023, 29, 6276–6285. [Google Scholar] [CrossRef] [PubMed]
  23. Cao, X.; Cui, Q.; Li, D.; Liu, Y.; Liu, K.; Li, Z. Characteristics of soil microbial community structure in different land use types of the Huanghe alluvial plain. Microorganisms 2025, 13, 273. [Google Scholar] [CrossRef] [PubMed]
  24. Qin, G.; He, W.; Sanders, C.J.; Zhang, J.; Zhou, J.; Wu, J.; Lu, Z.; Yu, M.; Li, Y.; Li, Y.; et al. Contributions of plant- and microbial-derived residuals to mangrove soil carbon stocks: Implications for blue carbon sequestration. Funct. Ecol. 2024, 38, 573–585. [Google Scholar] [CrossRef]
  25. Zhang, C.; Ndungu, C.N.; Feng, L.; Huang, J.; Ba, S.; Liu, W.; Cai, M. Plant diversity is more important than soil microbial diversity in explaining soil multifunctionality in Qinghai-Tibetan plateau wetlands. J. Environ. Manag. 2024, 365, 121509. [Google Scholar] [CrossRef]
  26. Yue, Y.; Lai, L.; Zhou, J.; Wang, G.; Zhu, Y.; Chen, Q.; Zheng, Y. Decoupled response of aboveground and belowground ecosystem multifunctionality to shrub encroachment in a semiarid grassland. J. Environ. Manag. 2025, 379, 124827. [Google Scholar] [CrossRef]
  27. He, M.; Fang, K.; Chen, L.; Feng, X.; Qin, S.; Kou, D.; He, H.; Liang, C.; Yang, Y. Depth-dependent drivers of soil microbial necromass carbon across Tibetan alpine grasslands. Glob. Change Biol. 2022, 28, 936–949. [Google Scholar] [CrossRef]
  28. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2024; Available online: https://www.r-project.org/ (accessed on 25 March 2025).
  29. Qiang, F.; Sheng, C.; Zhang, J.; Jiang, L.; Zhou, J. Soil quality variation under different land use types and its driving factors in beijing. Forests 2024, 15, 993. [Google Scholar] [CrossRef]
  30. Herrick, J.E. Soil quality: An indicator of sustainable land management? Appl. Soil Ecol. 2000, 15, 75–83. [Google Scholar] [CrossRef]
  31. Islam, K.R.; Weil, R.R. Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. Agric. Ecosyst. Environ. 2000, 79, 9–16. [Google Scholar] [CrossRef]
  32. Negasa, T.; Ketema, H.; Legesse, A.; Sisay, M.; Temesgen, H. Variation in soil properties under different land use types managed by smallholder farmers along the toposequence in southern Ethiopia. Geoderma 2017, 290, 40–50. [Google Scholar] [CrossRef]
  33. Nabi, F.; Yang, G.; Sajid, S.; Khan, A.; Ahmed, I.; Li, X. Linking soil microbial community with the changes in soil physicochemical properties in response to long-term agricultural land use change of different chronosequences and depth layers. Ecol. Indic. 2022, 145, 109727. [Google Scholar] [CrossRef]
  34. Chen, L.; Zhang, K.; Li, Y. Spatial variability of soil physicochemical properties under different land uses impacted by erosion on karst hillslopes in southwest China. Earth Surf. Process. Landf. 2024, 49, 2245–2259. [Google Scholar] [CrossRef]
  35. Rieder, Á.; Madarász, B.; Szabó, J.A.; Dyson, J.; Jakab, G. Soil organic matter alteration velocity due to land-use change: A case study under conservation agriculture. Sustainability 2018, 10, 943. [Google Scholar] [CrossRef]
  36. Liu, J.; Wang, Z.; Hu, F.; Li, H.; Zhang, Q.; Wang, K. Soil organic matter and silt contents determine soil particle surface electrochemical properties across a long-term natural restoration grassland. Catena 2020, 190, 104526. [Google Scholar] [CrossRef]
  37. Robinson, D.A.; Thomas, A.; Reinsch, S.; Mooney, S.J.; Whitmore, A.P. Analytical modelling of soil porosity and bulk density across the soil organic matter and land-use continuum. Sci. Rep. 2022, 12, 7085. [Google Scholar] [CrossRef]
  38. Romdhane, S.; Spor, A.; Banerjee, S.; Gervaix, J.; Karimi, B.; Philippot, L. Land-use intensification differentially affects bacterial, fungal and protist communities and decreases microbiome network complexity. Environ. Microbiome 2022, 17, 1. [Google Scholar] [CrossRef]
  39. Spain, A.M.; Krumholz, L.R.; Elshahed, M.S. Abundance, composition, diversity and novelty of soil proteobacteria. ISME J. 2009, 3, 992–1000. [Google Scholar] [CrossRef]
  40. Schneider, F.; Don, A.; Hennings, I.; Schmittmann, O.; Seidel, S.J. The effect of deep tillage on crop yield—What do we really know? Soil Tillage Res. 2017, 174, 193–204. [Google Scholar] [CrossRef]
  41. Mollier, S.; Kunstler, G.; Dupouey, J.-L.; Bergès, L.; Corcket, E. Forest management and former land use have no effect on soil fungal diversity in uneven-aged mountain high forests. Ann. For. Sci. 2024, 81, 2. [Google Scholar] [CrossRef]
  42. Sun, H.; Jiang, S.; Jiang, C.; Wu, C.; Gao, M.; Wang, Q. A review of root exudates and rhizosphere microbiome for crop production. Environ. Sci. Pollut. Res. 2021, 28, 54497–54510. [Google Scholar] [CrossRef]
  43. Taylor, D.L.; Sinsabaugh, R.L. Soil Microbiology, Ecology and Biochemistry. In Soil Microbiology, Ecology and Biochemistry; Elsevier: Amsterdam, The Netherlands, 2015; pp. 77–109. [Google Scholar]
  44. Banik, G.G.; Heath, C.A. Hybridoma growth and antibody production as a function of cell density and specific growth rate in perfusion culture. Biotechnol. Bioeng. 1995, 48, 289–300. [Google Scholar] [CrossRef] [PubMed]
  45. Kim, H.-S.; Lee, S.-H.; Jo, H.Y.; Finneran, K.T.; Kwon, M.J. Diversity and composition of soil Acidobacteria and Proteobacteria communities as a bacterial indicator of past land-use change from forest to farmland. Sci. Total Environ. 2021, 797, 148944. [Google Scholar] [CrossRef]
  46. Huang, R.; Crowther, T.W.; Sui, Y.; Sun, B.; Liang, Y. High stability and metabolic capacity of bacterial community promote the rapid reduction of easily decomposing carbon in soil. Commun. Biol. 2021, 4, 1376. [Google Scholar] [CrossRef]
  47. Zheng, B.; Xiao, Z.; Liu, J.; Zhu, Y.; Shuai, K.; Chen, X.; Liu, Y.; Hu, R.; Peng, G.; Li, J.; et al. Vertical differences in carbon metabolic diversity and dominant flora of soil bacterial communities in farmlands. Sci. Rep. 2024, 14, 9445. [Google Scholar] [CrossRef]
  48. Leclerc, L.; Calderón-Sanou, I.; Martinez-Almoyna, C.; Paillet, Y.; Thuiller, W.; Vincenot, L.; Kunstler, G. Beyond the role of climate and soil conditions: Living and dead trees matter for soil biodiversity in mountain forests. Soil Biol. Biochem. 2023, 187, 109194. [Google Scholar] [CrossRef]
  49. Yang, Q.; Zhang, M. Effect of bio-organic fertilizers partially substituting chemical fertilizers on labile organic carbon and bacterial community of citrus orchard soils. Plant Soil 2023, 483, 255–272. [Google Scholar] [CrossRef]
  50. Xiao, H.B.; Li, Z.W.; Dong, Y.T.; Chang, X.F.; Deng, L.; Huang, J.Q.; Nie, X.D.; Liu, C.; Liu, L.; Wang, D.Y. Changes in microbial communities and respiration following the revegetation of eroded soil. Agric. Ecosyst. Environ. 2017, 246, 30–37. [Google Scholar] [CrossRef]
  51. Abadie, J.; Avon, C.; Dupouey, J.L.; Lopez, J.M.; Tatoni, T.; Bergès, L. Land use legacies on forest understory vegetation and soils in the Mediterranean region: Should we use historical maps or in situ land use remnants? For. Ecol. Manag. 2018, 427, 17–25. [Google Scholar] [CrossRef]
  52. Ren, C.; Wang, T.; Xu, Y.; Deng, J.; Zhao, F.; Yang, G.; Ren, G. Differential soil microbial community responses to the linkage of soil organic carbon fractions with respiration across land-use changes. For. Ecol. Manag. 2018, 409, 170–178. [Google Scholar] [CrossRef]
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Figure 1. Location of the research area.
Figure 1. Location of the research area.
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Figure 2. Venn diagrams (A,B) and NMDS ordinations (C,D) revealed differences in soil bacterial and fungal composition among different land-use types. NG: natural grassland, PF: paddy field, RF: corn and rice rotation field, and SF: sorghum field.
Figure 2. Venn diagrams (A,B) and NMDS ordinations (C,D) revealed differences in soil bacterial and fungal composition among different land-use types. NG: natural grassland, PF: paddy field, RF: corn and rice rotation field, and SF: sorghum field.
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Figure 3. Relative abundances of bacterial (A) and fungal (B) taxonomic groups at the phylum level across the different land-use types. NG denotes natural grassland, PF denotes paddy field, RF denotes corn and rice rotation field and SF denotes sorghum field.
Figure 3. Relative abundances of bacterial (A) and fungal (B) taxonomic groups at the phylum level across the different land-use types. NG denotes natural grassland, PF denotes paddy field, RF denotes corn and rice rotation field and SF denotes sorghum field.
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Figure 4. The α-diversity index of bacterial (A–D) and fungal (E–H) communities under different land-use types. Significant level: *, p < 0.05; **, p < 0.01.
Figure 4. The α-diversity index of bacterial (A–D) and fungal (E–H) communities under different land-use types. Significant level: *, p < 0.05; **, p < 0.01.
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Figure 5. Correlation between soil physicochemical properties and soil bacterial (A) and fungal (B) community diversity, and the redundancy analysis (RDA) of soil bacterial communities (C) and fungal communities (D) associated with soil physicochemical properties under different land-use types. TCC: total carbon content; SOC: soil organic carbon; TNC: total nitrogen content; AN: available nitrogen content; AP: available phosphorus content; TKC: total potassium content; AK: available potassium content. * indicates a significant correlation between variables (α = 0.05).
Figure 5. Correlation between soil physicochemical properties and soil bacterial (A) and fungal (B) community diversity, and the redundancy analysis (RDA) of soil bacterial communities (C) and fungal communities (D) associated with soil physicochemical properties under different land-use types. TCC: total carbon content; SOC: soil organic carbon; TNC: total nitrogen content; AN: available nitrogen content; AP: available phosphorus content; TKC: total potassium content; AK: available potassium content. * indicates a significant correlation between variables (α = 0.05).
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Figure 6. Structural equation modeling and the standard total effects of interactions between land use, soil properties, and the diversity of bacterial (A,C) and fungal (B,D) communities. Arrow-adjacent numerals denote path coefficient magnitudes, with chromatic coding (green/red) distinguishing positive/negative directional effects. Line morphology conveys statistical significance: solid = validated associations (p < 0.05), dashed = non-significant linkages. BD: bulk density; TCC: total carbon content; SOC: soil organic carbon; AK: available potassium content.
Figure 6. Structural equation modeling and the standard total effects of interactions between land use, soil properties, and the diversity of bacterial (A,C) and fungal (B,D) communities. Arrow-adjacent numerals denote path coefficient magnitudes, with chromatic coding (green/red) distinguishing positive/negative directional effects. Line morphology conveys statistical significance: solid = validated associations (p < 0.05), dashed = non-significant linkages. BD: bulk density; TCC: total carbon content; SOC: soil organic carbon; AK: available potassium content.
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Table 1. Soil physicochemical properties in different land-use types.
Table 1. Soil physicochemical properties in different land-use types.
Soil PropertiesNGPFRFSF
pH7.87 ± 0.06 a7.41 ± 0.22 b7.62 ± 0.05 ab7.8 ± 0.09 ab
Bulk density (g·cm−3)0.81 ± 0.03 c1.55 ± 0.02 a1.55 ± 0.03 a1.44 ± 0.01 b
Soil porosity (%)69.43 ± 1.21 a41.38 ± 0.67 c41.51 ± 1.15 c45.66 ± 0.44 b
Sand content (%)49.94 ± 3.17 c62.91 ± 2.89 b62.47 ± 0.69 b89.4 ± 2.47 a
Clay content (%)28.28 ± 4 a21.09 ± 0.97 a23.31 ± 2.9 a5.27 ± 2.13 b
Silt content (%)21.78 ± 2.35 a16.00 ± 2.31 ab14.22 ± 2.22 b5.33 ± 1.54 c
Notes: Value are means ± standard deviation (n = 3); different letters indicate significant differences (p < 0.05). NG denotes natural grassland, PF denotes paddy field, RF denotes corn and rice rotation field, SF denotes sorghum field.
Table 2. Soil nutrition content in different land-use types.
Table 2. Soil nutrition content in different land-use types.
Soil Nutrient ContentNGPFRFSF
TCC (g·kg−1)12.86 ± 0.92 c15.72 ± 0.5 b17.19 ± 0.28 ab18.75 ± 0.28 a
SOC (g·kg−1)8.47 ± 0.23 a4.81 ± 0.3 b4.27 ± 0.46 b2.74 ± 0.13 c
TNC (g·kg−1)0.78 ± 0.061.53 ± 0.360.92 ± 0.151.08 ± 0.36
AN (mg·kg−1)47.91 ± 8.3 ab43.64 ± 9.27 a36.2 ± 3.64 ab18.11 ± 1.23 b
TPC (g·kg−1)1.66 ± 002 a1.72 ± 0.05 a1.6 ± 0.08 a1.31 ± 0.04 b
AP (mg·kg−1)24.91 ± 2.0727.51 ± 4.9230.9 ± 5.5620.52 ± 3.99
TKC (g·kg−1)0.27 ± 0.090.37 ± 0.150.3 ± 0.10.4 ± 0.21
AK (mg·kg−1)91.67 ± 11.7176 ± 18.18149.67 ± 45.8132.33 ± 30.66
Note: Value are means ± standard deviation (n = 3); different letters indicate significant differences (p < 0.05). TCC, TNC, TPC, and TKC denote the content of soil total carbon, nitrogen, phosphorus, and potassium, respectively. SOC, AN, AP, and AK denote the content of soil organic carbon, available nitrogen content, available phosphorus content, available potassium content, respectively.
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Ma, G.; Hu, Y.; Zhang, Y.; Han, Y.; Li, K.; Jia, H.; Zhu, X. Response Mechanism of Soil Microbial Characteristics to Different Land-Use Types in China. Land 2025, 14, 1229. https://doi.org/10.3390/land14061229

AMA Style

Ma G, Hu Y, Zhang Y, Han Y, Li K, Jia H, Zhu X. Response Mechanism of Soil Microbial Characteristics to Different Land-Use Types in China. Land. 2025; 14(6):1229. https://doi.org/10.3390/land14061229

Chicago/Turabian Style

Ma, Gang, Yantao Hu, Yangyang Zhang, Yaoguang Han, Keyi Li, Hongtao Jia, and Xinping Zhu. 2025. "Response Mechanism of Soil Microbial Characteristics to Different Land-Use Types in China" Land 14, no. 6: 1229. https://doi.org/10.3390/land14061229

APA Style

Ma, G., Hu, Y., Zhang, Y., Han, Y., Li, K., Jia, H., & Zhu, X. (2025). Response Mechanism of Soil Microbial Characteristics to Different Land-Use Types in China. Land, 14(6), 1229. https://doi.org/10.3390/land14061229

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