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Article

Tillage Effects on Bacterial Community Structure and Ecology in Seasonally Frozen Black Soils

by
Bin Liu
1,2,3,
Zhenjiang Si
3,
Yan Huang
1,2,
Yanling Sun
1,2,
Bai Wang
1,2,*,† and
An Ren
4,*,†
1
Heilongjiang Provincial Hydraulic Research Institute, Harbin 150080, China
2
Northeast Key Laboratory of Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Harbin 150078, China
3
School of Hydraulic and Electric Power, Heilongjiang University, Harbin 150080, China
4
Heilongjiang Academy of Land Reclamation Sciences, Harbin 150038, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(20), 2132; https://doi.org/10.3390/agriculture15202132
Submission received: 16 August 2025 / Revised: 26 September 2025 / Accepted: 11 October 2025 / Published: 14 October 2025
(This article belongs to the Section Agricultural Soils)
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Abstract

Against the backdrop of global climate change intensifying seasonal freeze–thaw cycles, deteriorating soil conditions in farmland within seasonal frost zones constrain agricultural sustainability. This study employed an in situ field experiment during seasonal freeze–thaw periods in the black soil zone of Northeast China to investigate the joint regulatory effects of seasonal freeze–thaw processes and tillage practices on multidimensional features of soil bacterial communities. Key results demonstrate that soil bacterial communities possess self-reorganization capacity. α-diversity exhibited cyclical fluctuations: an initial decline followed by a rebound, ultimately approaching pre-freeze–thaw levels. Significant compositional shifts occurred throughout this process, with the frozen period (FP) representing the phase of maximal differentiation. Actinomycetota and Acidobacteriota consistently dominated as the predominant phyla, collectively accounting for 33.4–49% of relative abundance. Bacterial co-occurrence networks underwent dynamic topological restructuring in response to freeze–thaw stress. Period-specific response patterns supported sustained soil ecological functionality. Furthermore, NCM and NST analyses revealed that stochastic processes dominated community assembly during freeze–thaw (NCM R2 > 0.75). Tillage practices modulated this stochastic–deterministic balance: no-tillage with straw mulching (NTS) shifted toward determinism (NST = 0.608 ± 0.224) during the thawed period (TP). Across the seasonal freeze–thaw process, soil temperature emerged as the primary driver of temporal community variations, while soil water content governed treatment-specific differences. This work provides a theoretical framework for exploring agricultural soil ecological evolution in seasonal frost zones.

1. Introduction

Against the backdrop of global climate change, the dynamic characteristics of seasonal freeze–thaw processes have undergone significant alterations [1,2,3]. These changes manifest as more frequent, longer-lasting, and more intense shifts in mid-to-high-latitude agricultural ecosystems. Within these systems, soil hydrothermal conditions fluctuate significantly due to freeze–thaw cycles, which affect soil structure and nutrient cycling. This, in turn, can induce soil microbial cell lysis and disrupt the stability and ecological functions of soil microbial communities [4]. Ultimately, freeze–thaw perturbations threaten soil moisture and productivity during the spring sowing season [5]. These challenges are particularly severe in the black soil region of Northeast China. Heilongjiang Province possesses 156 million mu of black soil farmland, accounting for 56.1% of the total black soil farmland area in Northeast China. As a vital part of China’s food production system [6,7], this region is subjected to the dual effects of seasonal freeze–thaw cycles and human-made disturbances. Climate change and long-term intensive farming have accelerated the degradation of soil conditions in the region, seriously affecting agricultural production processes and soil health ecosystems [6]. The National Black Soil Protection Project Implementation Plan (2021–2025) clearly proposes a technical model for cultivating a fertile tillage layer in drylands that includes straw burial, chopped-and-mixed incorporation, and less tillage or no-tillage combined with straw mulching that is returned to the field.
Soil microorganisms are essential for nutrient cycling and ecosystem multifunctionality, with soil bacteria constituting approximately 70% to 90% of this microbial community. Their dynamics profoundly influence the rate and direction of soil ecological processes [7]. The study of microbial ecology has advanced significantly with methodological progress, enabling high-throughput sequencing technologies to accurately characterize the composition and function of complex microbial communities in specific environments. This application of molecular methods provides a robust foundation for elucidating the true complexity of soil ecosystems [8]. Numerous studies have confirmed that freeze–thaw processes significantly impact soil bacterial communities by altering soil hydrothermal conditions and pore structure [9,10,11,12]. This impact is evident not only in the general reduction of species richness and evenness, i.e., decreased diversity [13], but more profoundly in the significant weakening of the complexity of interaction co-occurrence networks. Stresses generated by ice crystal growth during freezing disrupt soil pore structure, while low temperatures suppress microbial activity. Water redistribution and repeated phase changes during thawing collectively lead to alterations in soil bacterial biomass, driving community restructuring [10,14].
Concurrently, agricultural management practices, particularly different tillage measures, reshape the habitat for soil microorganisms by influencing soil physicochemical properties. This change directly affects microbial survival strategies, competitive relationships, and dispersal capabilities, thereby regulating the diversity, compositional structure, and co-occurrence networks of microbial communities. Studies have indicated that no-tillage reduces anthropogenic soil disturbance during cultivation, improves soil conditions, and exerts positive effects on soil microbial communities [15,16]. Some studies have found that straw incorporation measures significantly increase soil microbial biomass while regulating the complexity and interactions within co-occurrence networks [17]. However, some research suggests that straw incorporation may reduce the resistance of soil bacterial community composition to freeze–thaw stress, accompanied by a significant decline in diversity [18]. Consequently, consensus on how tillage practices influence soil microbial communities during seasonal freeze–thaw processes has not been reached.
Furthermore, current research is predominantly confined to laboratory-based freeze–thaw simulation experiments [19]. These artificial conditions often deviate from actual field scenarios in agricultural settings, resulting in a critical lack of multi-period field in situ experiments investigating soil microbial responses throughout the seasonal freeze–thaw cycle. Crucially, the construction of soil microbial communities is not determined by a single factor. In agricultural ecosystems within seasonal freeze–thaw zones, microbial communities are under the dual regulation of natural climatic rhythms (freeze–thaw cycles) and anthropogenic management activities (tillage practices). How this interaction jointly shapes the multidimensional characteristics of soil bacterial communities—including not only traditional species diversity indices but also the co-occurrence networks reflecting inter-species interactions and community stability, as well as the relative contributions of key ecological processes determining community composition patterns (community assembly mechanisms)—requires elucidation. Clarifying the multidimensional response mechanisms of soil bacterial community diversity, composition, co-occurrence network structure, and assembly processes under the combined regulation of seasonal freeze–thaw processes and different tillage patterns holds significant theoretical and practical importance for accurately assessing and enhancing the ecological functions and resilience of agricultural soils in seasonal frozen regions.
Against this background, we posit that conservation tillage practices can enhance the soil’s capacity to buffer against freeze–thaw disturbances. Furthermore, the physical disruption and physiological stress induced by ice crystal formation during soil freezing and thawing are likely to alter the habitat of soil bacteria and their patterns of interaction. Such pronounced changes in the micro-environment can reduce community stability, thereby increasing the influence of stochastic processes, such as ecological drift, on community assembly. Concurrently, variations in environmental factors, including moisture and temperature, serve as the primary pathways through which tillage practices and seasonal freeze–thaw cycles exert their effects.
Based on this content, this study focuses on the typical black soil region of Heilongjiang Province. The objective is to investigate the mechanisms by which different tillage practices influence soil bacterial community characteristics during seasonal freeze–thaw processes. This study proposes the following hypotheses: (1) Soil bacterial community diversity and species composition under different tillage practices exhibit differential responses to seasonal freeze–thaw processes. (2) Seasonal freeze–thaw action significantly affects the co-occurrence networks of soil bacterial communities. (3) Stochastic processes dominate the ecological assembly process of communities during seasonal freeze–thaw cycles. (4) Soil environmental factors drive differences in soil bacterial communities.
By testing these hypotheses, this research seeks not only to provide a practical basis for agricultural production in seasonal frozen regions but also to address the limitations of current laboratory simulation studies through field in situ experiments. In addition, analyzing the ecological assembly processes of bacterial communities provides a novel perspective for elucidating the interactive effects of tillage practices and seasonal freeze–thaw cycles.

2. Materials and Methods

2.1. Study Area Overview

This study was conducted at the Water Science and Technology Experimental Research Center of the Heilongjiang Provincial Hydraulic Research Institute (126°36′ E, 45°43′ N), located in Harbin City, Heilongjiang Province, Northeast China. The site experiences a temperate continental monsoon climate and is situated in a seasonal frozen soil region with distinct seasonal precipitation variation (Figure 1). The long-term average temperature at the experimental station ranges from −4 to 5 °C, with a frost-free period of 130–140 days and an average annual precipitation of 400–650 mm. The experimental area lies within the typical black soil belt of Northeast China. The soil is classified as a Mollisol (WRB 2022) with a silt loam texture in the 0–60 cm layer [20]. Experimental plots combining straw incorporation with different tillage practices were established in the study area starting in 2019. The experimental land is dryland where maize has been cultivated as the primary crop for many years. The basic soil properties prior to the experiment are presented in Table 1 below.

2.2. Experimental Design

The field experiment combined different tillage methods with maize straw incorporation, including a conventional tillage control treatment (Table 2). The treatments were the following: no-tillage without straw cover (No-Tillage 1–3 Years, NT1–3), no-tillage with straw mulching (NTS), straw chopped-and-mixed incorporation (SCM), straw burial incorporation (SBI), and conventional tillage without straw (CK). Details of the tillage and straw (residue) management for each treatment are listed in Table 2 below. Each treatment had three replicates, totaling 21 plots. The plot size was 2 m × 2.5 m. A locally approved, dominant spring maize variety was selected as the test crop, planted at a density of 63,000 plants per hectare. Spring maize sowing occurred around 5th May, and harvesting occurred around 1st October each year. Irrigation and fertilization during the maize growing season followed locally representative farming practices.

2.3. Sample Collection

In this study, temperature sensors were installed at different depths (0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–50 cm) along the soil profile within each plot. Soil profile temperatures were recorded hourly during the experimental period to assess changes in soil temperature throughout the seasonal freeze–thaw process.
In this study, the seasonal freeze–thaw process was divided into four stages: an unstable freezing period (UFP), a frozen period (FP), an unstable thawing period (UTP), and a thawed period (TP). The delineation and definition of these stages were based on changes in daily mean air temperature and daily soil temperature. The UFP was defined as the period from when soil temperature first dropped below 0 °C until the entire soil profile temperature fell below 0 °C. The FP was defined as the period when the entire soil profile temperature remained below 0 °C. The UTP was defined as the period from when soil temperature first rose above 0 °C until the entire soil profile temperature exceeded 0 °C. The TP was defined as the period when the entire soil profile temperature remained above 0 °C (Figure 2).
Soil samples were collected in mid-November 2024 (UFP), mid-January 2025 (FP), mid-March 2025 (UTP), and mid-May 2025 (TP). Soil samples were collected from each plot using a soil auger. Surface soil was removed to avoid exogenous contamination. Soil cores from a depth of 5–20 cm were collected and placed into sterile centrifuge tubes. The soil samples were divided into two portions: one portion was stored in a refrigerator at 4 °C for laboratory physical and chemical property analysis; the other portion of fresh soil samples was placed in sterile centrifuge tubes, stored in dry ice, and sent to Major Biobio-Pharm Technology (Shanghai, China) for sequencing. The soil water content was measured by drying. The content of nitrate nitrogen and ammonium nitrogen (Table S1) in soil was determined using a UV–visible spectrophotometer (Specord 200 Plus, Jena Analytical Instruments Co., Ltd., Jena, Germany).

2.4. DNA Extraction, Amplification, and Illumina Sequencing

Total genomic DNA of the microbial community was extracted using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions. The quality of the extracted genomic DNA was checked by 1% agarose gel electrophoresis. DNA concentration and purity were measured using a NanoDrop2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). Using the extracted DNA as a template, the V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified via PCR using barcoded forward primer 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [21]. Purified PCR products were used to construct libraries with the NEXTFLEX Rapid DNA-Seq Kit. Sequencing was performed on the Illumina Nextseq2000 platform (Shanghai Majorbio Bio-pharm Technology Co., Ltd., Shanghai, China). To minimize the impact of sequencing depth on subsequent alpha- and beta-diversity analyses, the sequence count for all samples was rarefied to an equal depth. After rarefaction, the average sequence coverage (Good’s coverage) for each sample still reached 99.09%. Amplicon sequence variants (ASVs) and the feature table were generated using the DADA2 [22] program within QIIME2 [23]. Taxonomic annotation of ASVs was performed using the RDP classifier against the SILVA 16S rRNA gene database (v138). The community composition of each sample was statistically analyzed at different taxonomic levels. The raw sequence data has been submitted to the NCBI Sequence Read Archive database with accession number PRJNA1334265.

2.5. Statistical Analysis

Alpha diversity indices of the soil bacterial community, including the Chao1 index and Shannon index, were calculated at the ASV level using QIIME2. One-way analysis of variance (ANOVA) followed by Fisher’s protected least significant difference (LSD) test were performed, with differences between groups indicated by shared letters. To assess and visualize the beta diversity, principal coordinate analysis (PCoA) based on the Bray–Curtis distance was conducted using R-3.3.1 with the vegan package to illustrate the influence of different treatments on soil bacterial community distribution across periods. Statistical significance of the differences in bacterial community composition among treatment groups was tested using analysis of similarities (ANOSIM) with 999 permutations [24]. The relative abundance of soil bacteria at the phylum level was calculated to analyze differences in community composition. Linear discriminant analysis effect size (LEfSe) (LDA score > 2, p < 0.05) was employed to identify bacterial taxa with significantly different abundances from phylum to genus level among different groups [25]. Gephi v0.9.2 (https://gephi.org) (accessed on 14 August 2025) was used to visualize the co-occurrence relationships in soil bacterial networks, and Python-3.9 was used to calculate network topological properties [26]. A neutral community model (NCM) was constructed using R-3.3.1 with the vegan package to determine the potential contribution of neutral processes to soil bacterial community assembly. Furthermore, based on a general zero model framework, a normalized stochasticity ratio (NST) index was established and calculated using the nst package in R-3.3.1, with 50% set as the threshold between determinism and stochasticity, to test the simulated communities [27,28]. This study utilized distance-based redundancy analysis (db-RDA), implemented with the vegan package in R-3.3.1, to examine the impact of soil physicochemical characteristics on the soil bacterial community [29].

3. Results

3.1. α- and β-Diversity of Freeze–Thaw Soil Bacterial Communities

To investigate the effects of tillage practices and seasonal freeze–thaw processes on soil bacterial community diversity, α-diversity indices were calculated during seasonal freeze–thaw processes. The distribution of phase fluctuations in soil bacterial community diversity under tillage practices and seasonal freeze–thaw processes indicated that freeze–thaw processes drove phase fluctuations in soil bacterial α-diversity (Figure 3).
During the FP, the Chao1 richness index significantly decreased across all treatments compared to the UFP by 25.6% (p < 0.01). It further decreased during the UTP (p < 0.01), reaching its lowest point in the seasonal freeze–thaw process. Recovery to varying degrees occurred during the TP compared to the UTP (p < 0.01). Although the CK, NTS, and SBI treatments had low Chao1 richness indices during the UTP, they recovered faster during the TP. The Chao1 indices of the CK, NTS, and SBI treatments during the TP essentially recovered to UFP levels (TP: 2507.2, 2616.76, 2768.96; UFP: 2494.99, 2641.67, 2706.93). The NT3 treatment did not fully recover to UFP levels but showed a small difference (4.3%). In contrast, the NT1, NT2, and SCM treatments still had large gaps (12.5%, 15.3%, and 14.6%) compared to the Chao1 index before the seasonal freeze–thaw process.
The Shannon index also showed a significant decreasing trend during the FP (p < 0.01). The difference between the UTP and FP was not significant (p > 0.05). Notably, during the FP, the Shannon index dropped to its lowest value in the seasonal freeze–thaw process for all treatments except SBI. Recovery occurred to varying degrees during the UTP. However, the Shannon index of the SBI treatment continued to decrease during the UTP, indicating that soil bacterial diversity was constrained during the UTP when straw was buried and returned to the field. During the TP, the Shannon index recovered by varying degrees across treatments, approaching UFP levels (p > 0.05).
Principal coordinate analysis (PCoA) based on the Bray–Curtis distance was used to analyze the distribution characteristics and evolutionary trends of soil bacterial communities during the seasonal freeze–thaw process (Figure 4).
During the UFP, there was a clustering trend among treatments, with SBI distinctly separated from CK, but no significant separation was observed between other treatments and CK. Upon entering the FP, the treatments became relatively dispersed, with SCM independently distributed in the second quadrant and significantly separated from other treatments. During the UTP and TP, sample points from all treatments were clustered again, and the distribution pattern became denser than in the previous two stages.
Cross-period analysis of the seasonal freeze–thaw process showed that the UFP was significantly separated from other periods, further confirmed by the cross-period ANOSIM test (R = 0.458, p = 0.001). There was no statistical separation among the remaining three periods. As the seasonal freeze–thaw process progressed from FP to UTP and UTP to TP, the Bray–Curtis distance between the sample points and the centroid of the UFP gradually decreased.

3.2. Composition and Differential Analysis of Freeze–Thaw Soil Bacterial Communities

Analysis of the soil microbial community composition under seven tillage treatments during the seasonal freeze–thaw process revealed that Actinomycetota and Acidobacteriota consistently remained the dominant phyla, collectively accounting for 33.4–49% of the relative abundance (Figure 5).
During the UFP, Chloroflexota was enriched in all three straw incorporation treatments (NTS, SCM, SBI), with the SBI treatment showing a 29.8% increase (p < 0.05) compared to CK. Significant treatment differentiation emerged during the FP—the abundance of Bacillota in the NT1 treatment reached 9705.3 (28.3%), significantly higher by 124.7% (p < 0.05) than CK (4320, 12.6%). The NTS treatment abundance was 9283.3 (27%), representing a 114.9% increase over CK, although not statistically significant. Short-term no-tillage promoted the proliferation of cold-tolerant bacterial groups. Conversely, the SCM treatment reduced Bacillota abundance to 2105 (6.1%), lower than CK. Bacteroidota was enriched in the NTS and SBI treatments; SBI showed a significant 91.1% increase (p < 0.05) compared to CK, while NTS increased by 54.5% compared to CK—though not significant, it represented a significant 196.8% increase (p < 0.05) compared to NT1 (pure no-tillage).
Nutrient-cycling bacterial groups dominated the thawing stage (UTP-TP)—during the UTP, the SCM treatment elevated Acidobacteriota to 9027.3 (26.3%), a 14.3% increase over CK (7901.3, 23.0%). The SBI treatment significantly increased Actinomycetota to 9105.3, a 49.3% rise (p < 0.05) compared to CK (6098, 17.8%). Long-term no-tillage (NT3) increased Pseudomonadota abundance during the UTP to 7785.7 (19.7%), higher than CK (5135, 15%), reflecting enhanced organic matter mineralization capacity.
Notably, Nitrospirota was generally suppressed during the UFP (0.6–1.2%), increased during the FP (0.9–2.2%), peaked during the UTP (1.6–2.4%), and slightly decreased but remained relatively high during the TP (1.3–1.9%). Abundances in the NT1, NT2, NT3, and NTS treatments (1.7%, 1.9%, 1.8%, and 1.8%, respectively) were higher than in CK (1.3%).
The linear discriminant analysis effect size (LEfSe) method was employed for hierarchical differential testing from phylum to genus level, with the LDA score measuring the effect size of differentially abundant taxa. Cross-period analysis (Figure 6) revealed 52 period-specific indicator taxa, predominantly enriched during the frozen stages: the FP contained 32 taxa (mainly Bacillota), the UFP had 16 (dominated by Actinomycetota and Chloroflexota), while the UTP and TP contained only 3 and 1 taxa respectively. This trend aligns with the results from period-specific analyses.
Period-specific analysis (Figure 7) confirmed maximal differentiation during the FP (65 differential taxa, p < 0.05), contrasting with declining numbers in subsequent periods (UFP: 25; UTP: 19; TP: 8, p < 0.05). Treatment-specific enrichments emerged across freeze–thaw stages.
During the UFP, conventional tillage (CK) significantly enriched Ramlibacter (Pseudomonadota), Lautropia (Pseudomonadota), Micromonospora (Actinomycetota), and AKYG587 (Planctomycetota), while long-term no-tillage (NT3) enriched the rare phylum MBNT15 and Thermodesulfobacteriota. Straw management further differentiated responses: no-tillage with straw mulching (NTS) promoted Negativicutes (Bacillota), whereas straw burial incorporation (SBI) enriched Syntrophobacter (Thermodesulfobacteriota).
In the FP, CK continued to enrich Thermodesulfobacteriota. The short-term no-tillage treatments NT1 and NT2 were characterized by Actinomycetota as indicator taxa, with NT1 specifically enriching Thermincola (Bacillota) and NT2 enriching Gaiella (Actinomycetota) and class Acidimicrobiia (Actinomycetota). NT3 uniquely enriched Halanaerobiaeota. Among the straw treatments, NTS specifically enriched Truepera (Deinococcota) and Myxococcota, while straw chopped-and-mixed incorporation (SCM) enriched Rubrobacter (Actinomycetota). SBI significantly increased Anaerolineae (Chloroflexota).
During the thawing stages, NT3 enriched Gemmatimonadota and Burkholderiales (Pseudomonadota) in the UTP, and NTS enriched Lachnospirales (Bacillota) in the TP.

3.3. Co-Occurrence Networks of Freeze–Thaw Soil Bacterial Communities

To investigate the impact of the freeze–thaw process on the ecological network of soil bacterial communities, co-occurrence networks were constructed for the four stages of the seasonal freeze–thaw process (Figure 8), focusing on changes in network topology and core bacterial phyla.
During the UFP, an initial interaction network formed for soil bacterial communities (143 nodes, 1127 edges), with a relatively low average degree (15.76) and network density (0.111). Under a moderate modularity level (0.38), Module 1 (accounting for 30.77%) was dominated by Actinomycetota. As the freezing process stabilized in the FP, network complexity rapidly increased. The number of edges (1611) and average degree (21.77) increased by 43.0% and 38.1% (p < 0.01), respectively, compared to the UFP. The modularity index remained stable (0.39), but the functional group distribution became more balanced, with Modules 1–6 accounting for 12–25%. Core modules were co-dominated by Bacillota and Pseudomonadota. Network density was higher (0.148), and notably, the small-world coefficient decreased from 3.33 to 3.03.
Upon entering the thawing stage in the UTP, rapid changes in the soil environment led to the collapse of the network structure. The number of edges (764) and average degree (11.32) sharply decreased by 52.6% and 48.0% (p < 0.01), respectively, compared to the FP. The small-world coefficient increased to 3.36, indicating high clustering alongside increased path lengths and low ecological network connectivity. Module centralization became significant, with Modules 1–3 accounting for 70.37%, where Pseudomonadota remained dominant. Modularity further increased (0.40), accompanied by the emergence of more modules (other modules: 2.96%), indicative of pioneer bacterial groups belonging to Chloroflexota, Actinomycetota, Pseudomonadota, and Bacillota. During the TP, the modularity index reached the highest level (0.46) across all freeze–thaw periods, with the top four modules accounting for 91.73%. Actinomycetota regained a dominant position, and the small-world coefficient peaked at 3.98.

3.4. Ecological Assembly Processes of Freeze–Thaw Soil Bacterial Communities

Neutral community model (NCM) analysis indicated (Figure 9) that stochastic processes dominated bacterial community assembly throughout the seasonal freeze–thaw process (R2 > 0.75), but their intensity exhibited significant stage specificity.
During the UFP, community assembly was strongly driven by stochastic dispersal (R2 = 0.8102, Nm = 1463.8), reflecting active microbial migration under physical disturbance during freezing initiation. As the freezing process stabilized in the FP, stochasticity significantly weakened (R2 = 0.7597, Nm = 743; ΔR2 = −0.0505, p < 0.01) and dispersal capacity decreased, indicating enhanced environmental filtering. Ice-melt disturbance during the UTP reactivated the dispersal process (R2 = 0.7904, Nm = 693.8). However, during the TP, communities maintained a moderate level of stochasticity (R2 = 0.765, Nm = 864.7), signifying a rebalancing of dispersal and selection. Stochastic processes contributed more to the bacterial community during the early UFP stage of the seasonal freeze–thaw process.
To further investigate the regulatory effects of different tillage practices on soil bacterial community assembly during the seasonal freeze–thaw process, normalized stochasticity ratio (NST) analysis was performed based on a null model framework (Figure 10). The results showed significant differences in bacterial community assembly pathways among tillage treatments (p < 0.01).
During the UFP, NST values for all treatments were greater than 0.5 (0.51–0.94), indicating that stochastic dispersal dominated community assembly, consistent with the NCM results. The SCM treatment maximized stochasticity during the FP (NST = 0.986 ± 0.01), significantly higher than CK (NST = 0.447 ± 0.222, p < 0.01), which tended towards deterministic processes in this period. When the seasonal freeze–thaw process entered the UTP, the NT3 treatment significantly strengthened deterministic processes (NST = 0.350 ± 0.087), representing a 54.3% reduction compared to CK (p < 0.01). Notably, during the TP, NST values were lower across treatments (0.608–0.756), indicating a general weakening of stochastic processes.
Among straw incorporation measures, the NTS treatment exhibited enhanced deterministic processes during the TP (NST = 0.608 ± 0.224), representing decreases of 18.9% and 13.1% (p < 0.01) compared to SCM (NST = 0.75 ± 0.07) and SBI (NST = 0.70 ± 0.02), respectively.

3.5. The Relationship Between Freeze–Thaw Soil Bacterial Communities and Environmental Factors

This study used db-RDA analysis, based on Bray–Curtis distances, to investigate relationships between soil bacterial communities and factors such as soil water content (SWC), soil temperature (ST), and inorganic nitrogen (NO3−-N, NH4+-N). Soil bacterial community composition in the study area was significantly influenced by environmental factors (soil water content, SWC; soil temperature, ST; NO3−-N; NH4+-N). The correlation coefficients and significance test values for these environmental factors on the ordination results are presented in Table 3.
The results revealed that during the UFP, SWC (R2 = 0.435, p = 0.01), ST (R2 = 0.333, p < 0.05), and NO3−-N (R2 = 0.545, p < 0.01) significantly shaped bacterial community composition across treatments (Figure 11). In the FP, SWC (R2 = 0.427, p < 0.01), NO3−-N (R2 = 0.357, p < 0.05), and NH4+-N (R2 = 0.277, p < 0.05) were significant drivers. During the UTP, SWC (R2 = 0.363, p < 0.05) was the main factor distinguishing community composition among treatments, while other factors showed non-significant effects (p > 0.05). In the TP, significant influences paralleled the FP patterns: SWC (R2 = 0.463, p < 0.01), NO3−-N (R2 = 0.392, p < 0.01), and NH4+-N (R2 = 0.449, p < 0.01).
Across the entire seasonal freeze–thaw process, all environmental factors exerted significant effects (p < 0.01), with ST (R2 = 0.653, p = 0.001) identified as the dominant driver of temporal variations in bacterial community composition.

4. Discussion

4.1. Effects of Seasonal Freeze–Thaw and Tillage on Bacterial Diversity

The results of this study demonstrate that freeze–thaw action significantly affects soil bacterial community diversity and structural composition during seasonal freeze–thaw processes, while tillage practices modulate the response patterns and resilience of these communities. Seasonal freeze–thaw processes dominated fluctuations in soil bacterial diversity, with α-diversity indices showing a pattern of first decreasing and then recovering. This shows that soil bacterial communities can self-reorganize during this process. This finding agrees with observations in an alpine forest ecosystem [30], where soil bacterial community diversity undergoes phases of disruption, adaptation, and reconstruction during seasonal freeze–thaw processes. This phenomenon may be related to the release of readily decomposable organic matter and increased microbial activity caused by rising temperatures [31,32]. Importantly, tillage practices modulated this process: bacterial communities under no-tillage with straw mulching (NTS) and straw burial incorporation (SBI) exhibited stronger resilience, while recovery was delayed under chopped–mixed straw incorporation (SCM) and short-term no-tillage (NT1 and NT2). This is consistent with findings by Wang et al. [33], indicating that whole-straw incorporation (mulching or deep burial) is more conducive to maintaining microbial resilience than mixing chopped straw into the soil. The primary reason for this difference is that straw mulching and deep burial provide physical protection and a stable carbon source for soil microorganisms, improving substrate availability and thereby enhancing resilience [34,35]. The restructuring capacity of soil bacterial communities under pure no-tillage increased with the duration of no-tillage application. This may be attributed to reduced soil disturbance under long-term no-tillage, which buffers the negative impacts of external environmental changes on the bacterial community [36]. Notably, this study also revealed temporal variations in the distribution patterns of soil bacterial communities. The influence of tillage practices was most pronounced during the FP and less significant in other periods. A similar result was reported in another maize field study, where five years of undisturbed soil practice led to more stable bacterial community richness and diversity across seasons, which also demonstrated that soil bacterial diversity and richness are driven by multiple factors, including land management practices and climatic conditions [37]. Following the FP, as the seasonal freeze–thaw process progressed, the distribution characteristics of soil bacteria trended towards convergence with the UFP, confirming that seasonal freeze–thaw processes drive bacterial communities towards a cyclical pattern of disturbance and recovery, culminating in a return to pre-freeze community states. The lower explanatory power of the PCoA axes is attributable to the high dimensionality of sequencing data and soil variability, a phenomenon also observed in similar studies [38]. Therefore, employing appropriate statistical analysis methods aids in understanding the results.

4.2. Shifts in Bacterial Community Composition and Taxon-Specific Responses

Seasonal freeze–thaw processes significantly change soil bacterial community composition, consistent with previous studies [13,39,40]. Actinomycetota maintained dominance throughout this process, probably because of low-temperature adaptation strategies involving cell membrane changes that support population size [41]. The relative abundance of Bacillota increased markedly during the FP, particularly under no-tillage treatments (NT1–3, NTS). This parallels findings [42] and reflects their stress tolerance via endospore formation. No-tillage practices further promoted Bacillota proliferation by providing stable carbon sources and microaerophilic microenvironments. Seasonal freeze–thaw reduced Nitrospirota abundance (consistent with soil temperature effects), though their metabolic activity recovered rapidly post-thaw [43]. LEfSe analysis [44] revealed treatment-specific bacterial responses, with the FP exhibiting the most pronounced community divergence (65 significantly different taxa; p < 0.05)—substantially exceeding other periods. This “freezing peak–thawing convergence” hierarchy (FP > UFP > UTP > TP) aligns with permafrost studies [45,46]. Extreme cold, ice formation, and associated physicochemical stresses (e.g., water phase changes, hypoxia) imposed strong environmental filtering during the FP, selecting for taxa with specialized stress resistance. Conversely, differentially abundant taxa decreased during thawing stages (UTP, TP), with communities converging structurally. This matches reports of stabilized microbial activity late in freeze–thaw cycles [43], likely due to reduced environmental pressure as temperatures rose and physical disturbances diminished. Tillage practices modulated taxon-specific responses; for example, Lachnospirales enrichment under NTS during the TP—linked to extracellular polymeric substance (EPS) production via cellulose decomposition—potentially contributed to straw-induced soil aggregation [47]. Cross-period analysis identified 52 period-specific indicator taxa, with 32 enriched during the FP (predominantly Bacillota), reinforcing earlier observations.

4.3. Adaptive Changes in Bacterial Co-Occurrence Networks

Seasonal freeze–thaw processes drive the reconstruction of soil bacterial co-occurrence networks by altering the intensity and stability of environmental pressures, with network topology exhibiting significant period-specific responses [13]. During the UFP, low-temperature physical disturbances disrupt microhabitat continuity, limit inter-species connection strength, and force communities to adopt simplified interaction modes to conserve energy. At this stage, the bacterial co-occurrence network relies primarily on a core module for critical stabilization functions. Module 1, dominated by Actinomycetota, maintained network stability. This corroborates findings [48,49] that soil bacterial communities (e.g., actinobacterial mycelial networks) reinforce local connections to sustain fundamental ecological niches [50], providing a primary stable structure—a mechanism crucial during the initial phase of seasonal freeze–thaw. During the FP, under stable low-temperature conditions, bacterial community cooperation increased, aligning with results from permafrost regions [51]. Increased network complexity and a decreased small-world index collectively revealed an adaptive shift in the microbiome, primarily driven by increased clustering and decreased path length. In this stable low-temperature environment, efficiency in global connectivity is sacrificed in favor of tight interactions for resource co-utilization [48,52]. The balanced distribution of modules signified the maturation of a multifunctional cooperative network. The drastic environmental shift during the UTP triggered network collapse. To buffer this perturbation, the communities utilized a dual strategy. The first line of defense was mediated by Pseudomonadota, the dominant phylum, which maintained core functions through centralized modules. This capacity stems from their psychrotrophic and cryotolerant nature, facilitated by physiological adaptations that include biofilm integrity and antifreeze protein synthesis [53,54]. Secondly, in agreement with Shi et al. [13], the communities also employed module segregation to mitigate the environmental change. By the TP, as the environment stabilized, the network underwent deep reorganization to reintegrate functions. High modularity and a structure dominated by the top four modules marked the completion of functional reorganization. Optimized inter-module connection pathways facilitated nutrient transfer, enabling efficient interactions between modules and supporting the rapid recovery of ecological functions. However, the current sampling resolution limited our ability to deeply investigate differences in soil bacterial co-occurrence networks under different tillage practices throughout the seasonal freeze–thaw process, which remains a key focus for future work.

4.4. Bacterial Community Assembly Under Seasonal Freeze–Thaw and Tillage

Neutral community model (NCM) and normalized stochasticity ratio (NST) analyses are valuable for delineating soil bacterial community assembly processes and effectively discerning the influence of deterministic versus stochastic drivers [55,56]. In this study, stochastic processes dominated soil bacterial community assembly throughout the seasonal freeze–thaw process. The NCM demonstrated high goodness-of-fit (R2 > 0.75) across all four periods, confirming the consistent predominance of stochastic dispersal. This fundamentally contrasts with a wetland restoration study in Northeast China, where deterministic processes prevailed [57]. This divergence arises from freeze–thaw’s unique physical mechanisms—altered soil pore structure [58] and water phase transitions [59], significantly weaken environmental filtering, making microbial dispersal the primary assembly driver. The dispersal limitation observed during the FP aligns with findings [60], where ice crystals impede microbial movement. Conversely, dispersal capacity recovered during the TP, supporting the theory of liquid water-mediated microbial migration [61,62]; increased liquid water content during thawing provides a dispersal medium. Further NST analysis revealed that tillage practices significantly regulated the stochastic–deterministic balance. Conventional tillage (CK) enhanced deterministic filtering during the UFP. In contrast, long-term no-tillage amplified stochasticity, likely by maintaining pore continuity to promote microbial dispersal [63]. Straw management further differentiated assembly patterns: straw chopped–mixed incorporation (SCM) intensified stochasticity during the FP, indicating that straw fragments enhance microhabitat heterogeneity to facilitate dispersal [33]. Conversely, no-tillage with straw mulching (NTS) strengthened deterministic processes during the TP, attributable to regulated hydrothermal gradients enhancing environmental selection [64].

4.5. Key Environmental Drivers of Bacterial Community Dynamics

Soil bacterial communities are intrinsically linked to environmental factors [65]. During seasonal freeze–thaw periods, drastic soil alterations occur, with tillage practices further modulating these environmental conditions [34]. The db-RDA analysis revealed that soil water content (SWC), soil temperature (ST), and available nitrogen collectively (NO3−-N, NH4+-N) governed bacterial community composition throughout the freeze–thaw process (p < 0.01). Crucially, ST emerged as the primary driver of temporal community shifts (R2 = 0.653, p = 0.001), directly regulating microbial activity. Declining temperatures during early freeze–thaw stages imposed cold stress, reducing bacterial diversity and triggering structural adaptations. During thawing phases, rising temperatures facilitated diversity recovery through self-reorganization—a mechanism paralleling observations in alpine forest ecosystems [30]. Tillage practices mediated these dynamics by regulating soil hydrothermal regimes [33,64]. For instance, straw mulching (NTS) enhanced SWC by reducing surface runoff and evaporation while simultaneously buffering atmospheric heat exchange [66]. SWC constituted the key driver of treatment-specific community variations across all freeze–thaw periods. Concurrently, differential nitrogen availability significantly shaped treatment-level community divergence [67]. However, contrasting with permafrost systems [43], rapid ammonium-N depletion occurred during initial thawing in seasonal frost due to reactivated nitrifying bacteria—consistent with our LEfSe results and explaining its limited influence during early thaw stages.
While this field-based study reveals significant short-term effects of tillage and freeze–thaw cycles on the soil bacterial community, our findings inevitably lead to questions about its long-term trajectory. Our experimental period captured initial responses but cannot determine how these microbial communities will evolve over years or decades of sustained agricultural practices. Future long-term monitoring under realistic field conditions is crucial to understand the legacy effects and potential ecological thresholds. Specifically, research should focus on the interplay between increasingly variable climate patterns and different soil management strategies. Such work would benefit from integrating multi-omics techniques (e.g., metagenomics, metatranscriptomics) with analyses of concomitant changes in soil physicochemical properties. Applying ecological theory to understand how soil ecosystems respond to future environmental change and the mechanisms underlying shifts in microbial stress resilience will be invaluable for developing sustainable agricultural practices.

5. Conclusions

The findings of this study confirm our hypotheses. Seasonal freeze–thaw processes are the primary factor influencing the dynamic changes in soil bacterial communities in this region, causing regular fluctuations in bacterial diversity that start with a decline and then rebound. Community structure undergoes a homeostatic recovery process of “disruption–adaptation–reconstruction,” revealing the widespread environmental adaptation and recovery strategies inherent to soil bacterial communities in freeze–thaw ecosystems. Tillage practices modulate community resilience by enriching specific phyla. Co-occurrence networks further clarify the stage specificity of bacterial interaction strategies throughout the seasonal freeze–thaw process. They serve as an essential foundation for communities to adapt to environmental changes, maintain functional stability, and ultimately restore ecological functions. Neutral community model and NST analyses confirmed stochastic processes as the core assembly driver (R2 > 0.75), which were regulated by tillage. No-till promoted dispersal (NST = 0.350 ± 0.087), while no-till with straw increased deterministic selection during thawing (NST = 0.608 ± 0.224). The db-RDA revealed soil temperature as the primary driver of temporal variations in bacterial community composition during the seasonal freeze–thaw process, while soil water content governed treatment-specific differences across all phases. These findings enhance our understanding of bacterial community evolution in seasonally frozen farmland, providing a theoretical basis for sustainable soil ecosystem management.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15202132/s1, Table S1: Methods for determining physical and chemical properties.

Author Contributions

Conceptualization, B.L. and B.W.; methodology, B.L. and A.R.; software, B.L.; formal analysis, B.L.; investigation, B.W., Z.S., A.R. and Y.S.; data curation, Z.S. and Y.H.; writing—original draft preparation, B.L.; writing—review and editing, B.W. and Z.S.; visualization, B.L.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Heilongjiang Provincial Natural Science Foundation Joint Guidance Project, grant number LH2023E115; the National Natural Science Foundation of China, grant numbers 52579038 and 52079050; and Integration and Demonstration of Soil Improvement, Water Retention, and Supplementary Irrigation Technologies for Dryland Farmland in Severe Drought-Prone Areas of Low-lying Plains, grant number 2024YFD150020401.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

We are grateful to the staff of the Water Conservancy Technology Research Station of the Heilongjiang Province Hydraulic Research Institute for their technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
UFPUnstable freezing period
FPFrozen period
UTPUnstable thawing period
TPThawed period
CKConventional tillage without straw
NT1No-Tillage 1 Year
NT2No-Tillage 2 Years
NT3No-Tillage 3 Years
NTSNo-tillage with straw mulching
SCMStraw chopped-and-mixed incorporation
SBIStraw burial incorporation
NCMNeutral community model
NSTNormalized stochasticity ratio
SWCSoil water content
STSoil temperature

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Figure 1. Field view of the study area: (a) after applying different tillage practices; (b) after the ground has completely frozen in winter.
Figure 1. Field view of the study area: (a) after applying different tillage practices; (b) after the ground has completely frozen in winter.
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Figure 2. Soil temperature and division of seasonal freeze–thaw periods. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period.
Figure 2. Soil temperature and division of seasonal freeze–thaw periods. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period.
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Figure 3. Soil bacterial community α-diversity indices: (a) Chao1; (b) Shannon. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Different lowercase letters indicate significant differences.
Figure 3. Soil bacterial community α-diversity indices: (a) Chao1; (b) Shannon. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Different lowercase letters indicate significant differences.
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Figure 4. Soil bacterial community β-diversity: (a) UFP; (b) FP; (c) UTP; (d) TP; (e) all freeze–thaw periods. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Statistical significance of group separation was assessed by ANOSIM. Ellipses represent 95% confidence intervals for each treatment group.
Figure 4. Soil bacterial community β-diversity: (a) UFP; (b) FP; (c) UTP; (d) TP; (e) all freeze–thaw periods. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Statistical significance of group separation was assessed by ANOSIM. Ellipses represent 95% confidence intervals for each treatment group.
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Figure 5. Relative abundance composition of soil bacterial communities at the phylum level: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Colors represent different bacterial phyla. The top ten most relatively abundant phyla are shown; others are grouped as ‘Others’.
Figure 5. Relative abundance composition of soil bacterial communities at the phylum level: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Colors represent different bacterial phyla. The top ten most relatively abundant phyla are shown; others are grouped as ‘Others’.
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Figure 6. LEfSe analysis of differentially abundant taxa across seasonal freeze–thaw periods. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. Colors represent period groups. Circles from inner to outer represent phylogenetic ranks from phylum to genus. p_: Phylum; c_: Class; o_: Order; f_: Family; g_: Genus.
Figure 6. LEfSe analysis of differentially abundant taxa across seasonal freeze–thaw periods. Note: UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. Colors represent period groups. Circles from inner to outer represent phylogenetic ranks from phylum to genus. p_: Phylum; c_: Class; o_: Order; f_: Family; g_: Genus.
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Figure 7. Linear discriminant analysis (LDA) scores: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Showing taxa with LDA scores above the set threshold. Bar length represents the effect size of the differential taxon (LDA score), p_: Phylum; c_: Class; o_: Order; f_: Family; g_: Genus.
Figure 7. Linear discriminant analysis (LDA) scores: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. Showing taxa with LDA scores above the set threshold. Bar length represents the effect size of the differential taxon (LDA score), p_: Phylum; c_: Class; o_: Order; f_: Family; g_: Genus.
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Figure 8. Soil bacterial community co-occurrence networks across seasonal freeze–thaw processes. Note: Network nodes represent bacterial taxa; connecting lines (edges) represent associations between nodes; colors represent module membership.
Figure 8. Soil bacterial community co-occurrence networks across seasonal freeze–thaw processes. Note: Network nodes represent bacterial taxa; connecting lines (edges) represent associations between nodes; colors represent module membership.
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Figure 9. Neutral community model (NCM) across seasonal freeze–thaw processes: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: Blue solid line: best-fit curve of the NCM; blue dashed lines: 95% confidence intervals predicted by the NCM; black dots: ASVs with occurrence frequencies within the 95% confidence interval; green dots: ASVs with occurrence frequencies above the 95% confidence interval; red dots: ASVs with occurrence frequencies below the 95% confidence interval.
Figure 9. Neutral community model (NCM) across seasonal freeze–thaw processes: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: Blue solid line: best-fit curve of the NCM; blue dashed lines: 95% confidence intervals predicted by the NCM; black dots: ASVs with occurrence frequencies within the 95% confidence interval; green dots: ASVs with occurrence frequencies above the 95% confidence interval; red dots: ASVs with occurrence frequencies below the 95% confidence interval.
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Figure 10. Normalized stochasticity ratio (NST) index across seasonal freeze–thaw periods: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. A value of 50% serves as the threshold between determinism and stochasticity.
Figure 10. Normalized stochasticity ratio (NST) index across seasonal freeze–thaw periods: (a) UFP; (b) FP; (c) UTP; (d) TP. Note: CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation. A value of 50% serves as the threshold between determinism and stochasticity.
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Figure 11. db-RDA analysis based on the Bray–Curtis distance: (a) UFP; (b) FP; (c) UTP; (d) TP; (e) all freeze–thaw periods. UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation; Species: the top five species in terms of relative abundance.
Figure 11. db-RDA analysis based on the Bray–Curtis distance: (a) UFP; (b) FP; (c) UTP; (d) TP; (e) all freeze–thaw periods. UFP: unstable freezing period; FP: frozen period; UTP: unstable thawing period; TP: thawed period. CK: conventional tillage; NT1–3: no-tillage for 1–3 years; NTS: no-tillage with straw mulching; SCM: straw chopped-and-mixed incorporation; SBI: straw burial incorporation; Species: the top five species in terms of relative abundance.
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Table 1. Basic soil properties.
Table 1. Basic soil properties.
Soil Depth (cm)Bulk Density (g·cm−3)pHAvailable
Nitrogen (mg·kg−1)		Available
Phosphorus (mg·kg−1)		Available
Potassium (mg·kg−1)		Organic
Matter Content (g·kg−1)
5–201.177.27154.440.1376.830.31
Note: Detailed measurement methods are provided in Table S1.
Table 2. Details of different tillage practice treatments.
Table 2. Details of different tillage practice treatments.
TreatmentTreatment ContentTreatment Details
Tillage MethodStraw Management
NT1One-year flat no-tillage, maize straw removed.Flat cultivation with no-tillage for one year, no tillage operations.Maize straw was removed from the field after harvest.
NT2Continuous two-year flat no-tillage, maize straw removed.Continuous flat cultivation with no-tillage for two years, no tillage operations.Maize straw was removed from the field after harvest.
NT3Continuous three-year flat no-tillage, maize straw removed.Continuous flat cultivation with no-tillage for three years, no tillage operations.Maize straw was removed from the field after harvest.
NTSFlat no-tillage with full straw mulching.Flat cultivation with no-tillage, no tillage operations.After harvest, maize straw was chopped (particle size < 10 cm) and evenly distributed on the soil surface.
SCMStraw chopped-and-mixed incorporation, ridge tillage.Conventional ridge tillage combined with topsoil plowing.After harvest, maize straw was chopped (particle size < 10 cm) and uniformly mixed into the 0–30 cm soil layer during plowing.
SBIStraw burial incorporation, ridge tillage.Conventional ridge tillage combined with topsoil plowing.After harvest, maize straw was chopped (particle size < 10 cm) and buried in the soil layer at a depth of 30 cm.
CKConventional ridge tillage.Conventional ridge tillage with plowing. All maize straw was removed from the field after harvest.
Table 3. db-RDA analysis results.
Table 3. db-RDA analysis results.
Period SWCSTNO3−-NNH4+-N
UFPR20.4350.3330.5450.287
p-value0.01 **0.033 *0.003 **0.051
FPR20.4270.1070.3570.277
p-value0.008 **0.3830.018 *0.047 *
UTPR20.3630.2270.2570.053
p-value0.02 *0.0870.0660.633
TPR20.4630.1510.3920.449
p-value0.005 **0.2370.008 **0.003 **
ALLR20.2270.6530.4220.118
p-value0.001 ***0.001 ***0.001 ***0.009 **
Note: * p < 0.05, ** p < 0.01, and *** p < 0.001.
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Liu, B.; Si, Z.; Huang, Y.; Sun, Y.; Wang, B.; Ren, A. Tillage Effects on Bacterial Community Structure and Ecology in Seasonally Frozen Black Soils. Agriculture 2025, 15, 2132. https://doi.org/10.3390/agriculture15202132

AMA Style

Liu B, Si Z, Huang Y, Sun Y, Wang B, Ren A. Tillage Effects on Bacterial Community Structure and Ecology in Seasonally Frozen Black Soils. Agriculture. 2025; 15(20):2132. https://doi.org/10.3390/agriculture15202132

Chicago/Turabian Style

Liu, Bin, Zhenjiang Si, Yan Huang, Yanling Sun, Bai Wang, and An Ren. 2025. "Tillage Effects on Bacterial Community Structure and Ecology in Seasonally Frozen Black Soils" Agriculture 15, no. 20: 2132. https://doi.org/10.3390/agriculture15202132

APA Style

Liu, B., Si, Z., Huang, Y., Sun, Y., Wang, B., & Ren, A. (2025). Tillage Effects on Bacterial Community Structure and Ecology in Seasonally Frozen Black Soils. Agriculture, 15(20), 2132. https://doi.org/10.3390/agriculture15202132

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