Assessment of Soil Health Through Metagenomic Analysis of Bacterial Diversity in Russian Black Soil

Abstract

Soil health is a critical determinant of agricultural productivity and environmental sustainability. Traditional assessment methods often fail to provide a comprehensive understanding of soil microbial communities and their functions. This study addresses this challenge by employing metagenomic techniques to assess the functionality of soil microbiomes in Russian black soil, renowned for its high fertility. We utilized shotgun metagenomic sequencing to analyze soil samples from Western Siberia subjected to different degrees of agro-soil disturbance. We identified functional genes involved in carbon (accA, argG, acsA, mphE, miaB), phosphorus (phoB, ppa, pstB, pnp, phnJ), and nitrogen (queC, amiF, pyrG, guaA, guaB, napA) metabolic pathways and associated with changes in microbial diversity, in general, and higher representation of certain bacterial species—Bradyrhizobium spp. The results demonstrated significant differences in microbial composition and functional potential between tillage treatments. No-Till technology and conventional tillage practices promoted beneficial microbial communities and enhanced soil health compared to long-term fallow soil. This work underscores the potential of metagenomic analysis in providing a comprehensive understanding of soil health, marking a significant advancement in the field.

1. Introduction

Soil health is a critical component of agricultural productivity, environmental sustainability, and overall ecosystem function. Healthy soils support plant growth by providing essential nutrients, water, and a stable structure for roots, while harboring beneficial microorganisms that enhance nutrient availability and protect plants from diseases. Soil health is linked to environmental sustainability through its role in carbon sequestration, water infiltration, and erosion control, which contribute to climate change mitigation and water quality protection [1,2]. Despite the importance of soil health, traditional assessment methods often fail to provide a comprehensive understanding of soil microbial communities and their functions [3].

Metagenomic analysis has emerged as a powerful tool for investigating soil health by characterizing microbial communities and their functional potential. This approach involves sequencing the collective genomes of microorganisms present in a soil sample, offering insights into the diversity, structure, and functional capabilities of the soil microbiome [4]. Traditional methods like culturing, PCR-based techniques, and Sanger sequencing are limited by biases and low throughput, often underrepresenting microbial diversity [5]. Metagenomics entails direct sequencing of total genomic DNA in a soil sample, bypassing the need to culture organisms and allowing the detection of microbial species that are not amenable to laboratory cultivation [6]. For example, many beneficial taxa (e.g., Azospirillum, Rhizobium, certain fungi) that are difficult to culture can still be identified in metagenomic data [7]. Additionally, metagenomics can assemble fragments of genomes from previously not characterized microorganisms, leading to the discovery of novel taxa or recovery of metagenome-assembled genomes (MAGs) [6]. Since shotgun sequencing does not rely on the amplification of a single gene target (which happens during 16S rRNA gene sequencing), it avoids primer biases that can skew the perceived community composition in amplicon studies. All DNA is sequenced according to its abundance in the sample, providing a more quantitative representation of dominant versus rare community members [8]. This is particularly important in soils, where certain groups might be over- or under-estimated by the 16S sequencing technique [9]. Metagenomics thus yields a truer picture of community structure, which is crucial when correlating microbial populations with soil health indicators or crop performance [7]. Additionally, unlike 16S rRNA gene sequencing that only profiles taxonomy, shotgun metagenomics reveals the functional genes and pathways present in the microbiome, linking community structure to ecological function in soil processes [6]. For instance, it allows for estimating the presence of functional genes involved in nutrient cycling—such as phosphorus, nitrogen, and carbon metabolism—[10] and even discovering previously unknown genes, such as those linked to antibiotic resistance or stress response mechanisms [11,12]. However, the high microbial diversity and variable evenness in soil pose challenges for metagenomic assembly, making it difficult to reconstruct microbial genomes from soil samples [13]. Consequently, metagenomes may remain fragmented, making it challenging to link specific genes (functions) to specific organisms [14].

Recent advances in metagenomic methods have allowed the characterization of microbial indicators of soil health as influenced by different types of tillage. These techniques can assess both compositional and functional changes in microbial communities, providing valuable insights into soil health [15]. Field-scale studies investigating microbial taxa from agricultural experiments are crucial for understanding the long-term effects of crop rotation and tillage on microbial indicator species [16] and to identify bioindicators of soil health by characterizing the changes caused by tillage [17]. Numerous studies have demonstrated that reduced tillage systems enhance microbial diversity and establish a distinct microbial community structure compared to conventionally plowed soils [18,19,20]. Metagenomic approaches provide a powerful tool for elucidating the beneficial effects of reduced tillage on a wide range of soil metabolic pathways, including carbon and nitrogen decomposition, carbon monoxide oxidation, nitrogen fixation, nitrate reduction, and phosphorus solubilization. Furthermore, metagenomics enables the identification of specific microbial taxa associated with these processes, such as Bradyrhizobium, Mesorhizobium, Nitrososphaera, Phenylobacterium, and Rhizobium [21].

Our research centers focus on evaluating soil health through metagenomic analysis of the microbiomes found in Russian black soil by examining soil samples from fields that have undergone different tillage methods: long-term fallow (for 16 years) and two types of grain–fallow crop rotation—conventional tillage and No-Till technology [22]. In the current study, soil samples were collected from the central forest–steppe of the Novosibirsk Priobye, located at the eastern edge of the Priob plateau, at the junction of the West Siberian Plain and the Kuznetsk Ala-Tau, Altai, and Sayan mountain systems (200–250 m a.s.l.). The region features a moderately warm, moderately humid climate, with a 38 °C annual temperature range, prolonged winters, stable snow cover (157–162 days), and soil freeze depths of 1.8–2 m. Leached black earths (Luvic Chernozem) are the most fertile soils of Western Siberia, crucial for grain crop cultivation. Since the 1980s, research has focused on soil-protective farming in this region [23], highlighting the role of reduced tillage in altering soil carbon dynamics [24]. Long-term permanent fallows are an informative tool for studying the problems of soil system stability under conditions of agrogenic degradation [25,26]. Previous research in this region has demonstrated that various tillage methods significantly impact soil quality, carbon and nitrogen stocks, and soil aggregates [27,28].

Renowned for its exceptional fertility [29], Russian black soil provides a distinctive setting for investigating soil microbial communities. Black soils have high organic matter content (often 10–15% in the surface), abundant nutrients, such as nitrogen and phosphorus, are neutral to slightly alkaline pH, and have high water-holding capacity [30]. Reduced tillage in black soil plots is associated with significantly higher soil microbial biomass and enzyme activity compared to conventional plowing [31] and promotes an increased abundance of bacterial groups involved in the nitrogen cycle [19], suggesting that decreased soil disturbance fosters microbial growth and metabolic activity. However, this relationship remains underexplored in the context of black soils in Russia. While individual studies report positive effects of reduced tillage over conventional plowing on microbial diversity and abundance, correlating with enhanced nutrient content and soil fertility [32,33,34], they lack a detailed analysis of changes in functional microbiome characteristics that could elucidate the underlying mechanisms driving soil quality improvement. By utilizing metagenomic sequencing with Illumina technology, we aimed to bridge the knowledge gap in characterizing the bacterial diversity of Russian black soil subjected to different tillage practices and to identify genes associated with phosphorus, nitrogen, and carbon cycling.

2. Materials and Methods

2.1. Study Area and Collection of Soil Samples

Soil samples were collected in August 2023 from medium-humus, medium-loamy black soil (Luvic Chernozem) in the forest–steppe region of the Ob area (coordinates: 54°53′13.5″ N, 82°59′36.7″ E). The size of the experimental plots occupied by wheat was 15 m × 18 m. Soil samples were collected at the 0–10 and 10–20 cm soil layers. The sample for analysis was an average sample of 5 individual ones, collected at each spatial replication of the experiment. On the long-term fallow, soil sampling was carried out according to the same scheme. Thus, 18 samples were obtained: 3 variants, 2 layers, 3 replicates. The soil samples were delivered to the laboratory, passed through a sieve with a cell diameter of 1 mm under sterile conditions, placed in sterile containers, and stored at a temperature of minus 80 degrees until analysis.

2.2. Analysis of Chemical Properties of Soil Samples

The total organic carbon (TOC) content was determined via the dichromate oxidation method [35]. The mortmass was separated by decanting the soil with water on a sieve with a cell diameter of 0.25 mm. The biomass was dried to an absolutely dry state. The carbon content was determined by thermal analysis on a Vario EL Cube elemental analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany) according to the manufacturer’s protocols. The amount of microbial biomass was determined using a substrate-induced respiration method [36]. N-NO₃ was determined by the ionometric method. Extractant 1 N K₂SO₄ solution at a soil/solution ratio of 1:2. The determination of nitrates was carried out using ion-selective electrodes [37]. Phosphatase activity in the soil samples was determined using sodium phenolphthalein phosphate as a substrate (pH 6.5), with incubation at 30 °C for 60 min [38].

2.3. DNA Extraction

DNA was extracted using the Quick-DNA Fungal/Bacterial Microprep Kit/Quick-DNA Fecal/Soil Microbe Microprep kit (Zymo Research, Irvine, CA, USA) following the manufacturer’s protocols. The extracted DNA was quantified using a Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and quality was assessed by electrophoresis in 1% agarose gel. Additionally, DNA quality was assessed according to the manufacturer’s instructions from Agilent Technologies (Santa Clara, CA, USA) using the 2100 Bioanalyzer (Santa Clara, CA, USA).

2.4. Shotgun Sequencing and Bioinformatic Data Processing

DNA samples were used to create libraries using Illumina DNA Prep, (M) Tagmentation (100 ng). Shotgun sequencing using Illumina Novaseq technology (San Diego, CA, USA) was performed using the manufacturer’s standard techniques. Q30 value: ≥93.00% (percentage of sequenced bases with a Phred score ≥ 30. This value is based on the entire sequencing run). Read length: 2 × 101 bp. Demultiplexing of sequencing reads was performed using Illumina bcl2fastq (2.20). Adapters were trimmed using Skewer software (version 0.2.2) [39]. The quality of FASTQ files was analyzed using FastQC software (version 0.11.5-cegat). After trimming, 99.56% of the reads remained. The average reading depth was 21,031,903 reads per sample after trimming. The data were uploaded to the NCBI under BioProject: PRJNA1226397.

Performing the bacterial analysis, we use an integrated approach. We analyze the abundance of species, genes, and signature pairs. Taxonomic analysis was conducted on reads using Kraken2 (v.2.1.13) [40] and relative abundance was calculated using Bracken (v.2.9) at the ‘species’ levels [17]. We used the Kraken2 database pluspf, which include bacteria, fungi, protozoa, viruses, and archaea. Alpha diversity was conducted using Shannon index calculating to establish the level of taxa diversity within the groups. Beta diversity was calculated using Bray–Curtis distance matrix, NMDS method to visualize distance matrix, and PERMANOVA analysis to identify the taxa differences between the groups. The vegan (Available online: https://CRAN.R-project.org/package=vegan, accessed on 1 February 2025), adonis [41], and edgeR (v.4.4.2) [42] packages were used. To visualize the results of Bray–Curtis, we used NMDS graphs.

To perform a functional analysis, it was necessary to collect the catalog of target amino acid sequences. To do this, we compiled a list of target carbon, nitrogen, and phosphorus exchange genes and taxa that we obtained at the stage of taxonomic analysis. Using these data, we conducted multiple searches in the UniProt (v.2024-04) [43] and KEGG (v.112.0) [44] databases. The catalog was compiled from the detected sequences. All sequences satisfy the following requirements: (i) the amino acid sequence belongs to the carbon, phosphorus, or nitrogen metabolic pathways; (ii) the amino acid sequence belongs to an organism that was obtained in the results of the taxonomic analysis (up to class level); and (iii) the sequence must have at least 2 annotations in the UniProt database. Full catalog information can be viewed in Table S1. Target genes from the compiled catalog were identified within the sequencing reads using blastx Diamond (v.2.1.11) [45]. To interpret the results, we conducted the following requirements: (i) the alignment must have at least 90% homology; and (ii) the alignment length must correspond to the length of the read. The readcounts were calculated for the detected coding sequences of the genes. The calculation was performed using our own code. Then, the readcounts were normalized using the Trimmed Mean of M-values (TMM) normalization method. The results of the taxonomic and functional analysis were compared into a signature matrix consisting of pairs (taxon; gene). Matrix data were processed with TMM normalization.

2.5. Statistics

Statistical analysis of the chemical parameters of the soil samples was performed applying one-way ANOVA with post hoc Tukey’s test. The data are presented as the mean ± standard error of the mean. Differences between the groups were considered to be significant at p < 0.05. Statistical analysis was performed using Prism 9.1 (GraphPad, San Diego, CA, USA).

The Wilcoxon–Mann–Whitney test (U-criterion) was used to identify statistically significant differences in the abundance of taxa, genes, and signature pairs between the soil tillage type groups. After that, we used False Positive Rate correction (FDR) by the Benjamini–Hochberg method based on U-criterion p-values. Further in the work, differences in the relative abundance of taxa, genes, or signature pairs for which the FDR p-values are less than 0.05 will be called ‘Statistically significant’.

To visualize the results, we selected taxa, genes, and signature pairs with statistically significant differences in abundance, calculated the median value for each soil tillage type group, and built graphs. The median values are chosen as the most convenient for presenting the result by group. The stats, matplotlib, numpy, and pandas libraries were used (available online: https://matplotlib.org/stable/index.html, (accessed on 1 February 2025), https://numpy.org/doc/, (accessed on 1 February 2025), https://api.semanticscholar.org/CorpusID:61539023, (accessed on 1 February 2025)).

3. Results

3.1. Assessment of Soil Chemical Parameters

The chemical properties of the soil under different tillage systems are summarized in

Table 1. Properties of leached medium-humus, medium-loamy chernozem (Luvic Chernozem) of Ob forest–steppe subjected to different types of tillage. Mean ± Std Err.

Soil Indicator	Long-Term Fallow	Conventional Tillage	No-Till Technology
TOC %	4.1 ± 0.07	4.2 ± 0.06	4.2 ± 0.04
C in mortmass, mg/kg	67 ± 12.5	817 ± 51.4	1133 ± 61.2
C in microbial biomass, μg/g	30 ± 5.5	100 ± 10,2	120 ± 12.5
N-NO3, mg/kg	78.6 ± 13.95	19.8 ± 7,78	4.4 ± 0.22
Phosphatase activity, μg, P2O5/g per hour	13.8 ± 0.43	14.6 ± 0,64	26.2 ± 0.84

. Total organic carbon (TOC) did not differ significantly among treatments (p = 0.38). No-till technology resulted in significantly higher carbon content in mortmass (1133 ± 61.2 mg/kg) compared to conventional tillage (817 ± 51.4 mg/kg). Both tillage practices led to markedly increased carbon content in mortmass compared to long-term fallow soil (67 ± 12.5 mg/kg). Microbial biomass carbon was significantly higher in No-Till (120 ± 12.5 μg/g) and conventional tillage (100 ± 10.2 μg/g) than in the fallow samples (30 ± 5.5 μg/g), though no statistically significant differences were observed between the two tillage systems. Nitrate content (N-NO₃) exhibited an inverse trend, with the highest concentration in long-term fallow (78.6 ± 13.95 mg/kg), followed by conventional tillage (19.8 ± 7.78 mg/kg), and the lowest in No-Till (4.4 ± 0.22 mg/kg). Phosphatase activity was markedly higher in No-Till soils (26.2 ± 0.84 μg P₂O₅/g/h) compared to conventional tillage (14.6 ± 0.64 μg P₂O₅/g/h) and fallow (13.8 ± 0.43 μg P₂O₅/g/h), with no statistically significant distinction detected between conventional tillage and fallow.

3.2. Taxonomy Abundance and Diversity Analysis

We identified 428 species with statistically significant differences in relative abundance between all the groups of soil tillage. The graph (Figure 1) shows the top-20 of these species with median values for each group. The relative abundance of species Streptomyces mobaraensis, Capillimicrobium parvum, Conexibacter woesei, Bradyrhizobium sp. 170, Bradyrhizobium sp. 200, Bradyrhizobium sp. CCBAU 051011, and Bradyrhizobium sp. S12-14-2 differs the most between the groups of tillage types. The differences in the abundance of these species are more than 0.5%. The abundance of species Streptomyces mobaraensis, Capillimicrobium parvum, and Conexibacter woesei is higher for soil samples with long-term fallow in comparison with the conventional tillage and No-Till technology groups. The species Bradyrhizobium sp. 170 is the most represented in the subgroup of No-Till technology and the least represented in the long-term fallow group, as well as the species Bradyrhizobium sp. 200, Bradyrhizobium sp. CCBAU 051011, Bradyrhizobium sp. S12-14-2, and Bradyrhizobium license.

The alpha diversity analysis revealed a high level of species diversity, with no statistically significant differences observed among the subgroups subjected to varying tillage practices. The Shannon diversity index, averaging approximately 7.7 across all groups, indicates a notably high level of species richness and evenness (Figure 2).

The beta diversity analysis, conducted using the Bray–Curtis dissimilarity matrix and PERMANOVA, revealed statistically significant differences among the subgroups. The NMDS graph (Figure 3) further corroborates these findings, demonstrating clear clustering of samples into three distinct subgroups corresponding to the tillage practices; the results of the PERMANOVA test indicate that this result is statistically significant (p-value = 0.01). The stress-value for NMDS analysis was 0.05. At the same time, the data exhibited considerable dispersion at the species level. The PERMANOVA results indicated that approximately 40% of the observed variability in the data set can be attributed to the classification of soil samples into the three tillage practice groups.

3.3. Functional Analysis

The genes of carbon, nitrogen, and phosphorus metabolism were searched using the catalog of homologues and reads. The genes with statistically significant differences in relative abundance between the soil subgroups are presented in Figure 4.

The relative abundance of the genes from Figure 4 is presented as the median relative abundance of the gene in the soil sample subgroups (Appendix A,

Table A1. Median relative abundances for genes with statistically significant differences.

Gene	Long-Term Fallow	Conventional Tillage (Median)	No-Till Technology (Median)
accA	1.53 × 106	15.15 × 106	12.48 × 106
acsA	2.55 × 108	8.09 × 108	8.08 × 108
amiF	0	5.38 × 103	9.43 × 103
argG	108.81 × 103	624.34 × 103	578.59 × 103
guaA	1.44 × 106	3.54 × 106	3.41 × 106
guaB	1.73 × 106	4.79 × 106	4.72 × 106
miaB	53.03 × 103	441.45 × 103	281.52 × 103
mphE	1.27 × 103	10.87 × 103	14.29 × 103
napA	106.36 × 103	3.67 × 106	3.42 × 106
phnJ	0.94 × 103	14.27 × 103	20.07 × 103
phoB	121.40 × 103	343.04 × 103	913.79 × 103
pnp	0	2.46 × 106	1.22 × 106
ppa	236.70 × 103	910.72 × 103	772.52 × 103
pstB	1.49 × 108	3.68 × 108	3.72 × 108
pyrG	2.22 × 106	5.18 × 106	4.96 × 106
queC	1.32 × 103	6.84 × 103	9.72 × 103

). The annotation (function and belonging to the metabolic pathway) for these genes is performed in

Table 2. Annotation for genes with statistically significant differences in relative abundance for their function and which metabolic pathway they belong to (carbon, nitrogen, phosphorus).

Gene Name	Enzyme Name	KEGG Enzyme Entry	Metabolic Pathway
accA	Acetyl-coenzyme A carboxylase carboxyl transferase subunit alpha	2.1.3.15	carbon
argG	Argininosuccinate synthase (Forming carbon-nitrogen bonds)	6.3.4.5
acsA	Acetyl-coenzyme A synthetase	6.2.1.1
mphE	4-hydroxy-2-oxovalerate aldolase	4.1.3.39
miaB	tRNA-2-methylthio-N(6)-dimethylallyladenosine synthase (catalyzes methylation)	2.8.4.3
phnJ	Alpha-D-ribose 1-methylphosphonate 5-phosphate C-P lyase	4.7.1.1	phosphorus
phoB	Phosphate regulon transcriptional regulatory protein	3.6.1.11
ppa	Inorganic pyrophosphatase	3.6.1.1
pstB	Phosphate import ATP-binding protein	7.3.2.1
pnp	Polyribonucleotide nucleotidyltransferase (catalyzes the phosphorolysis)	2.7.7.8
queC	7-cyano-7-deazaguanine synthase (Forming carbon-nitrogen bonds)	6.3.4.20	nitrogen
amiF	Formamidase (Acting on carbon-nitrogen bonds)	3.5.1.49
pyrG	CTP synthase (glutamine hydrolysing) (Forming carbon-nitrogen bonds)	6.3.4.2
guaA	GMP synthase [glutamine-hydrolyzing] (Forming carbon-nitrogen bonds)	6.3.5.2
guaB	Inosine-5′-monophosphate dehydrogenase (Acting on the CH-OH group of donors)	1.1.1.205
napA	Periplasmic nitrate reductase	1.9.6.1

.

The relative abundance of the genes differs significantly for soil subgroup ‘Long-term fallow’ and ‘Conventional tillage’, ‘Long-term fallow’ and ‘No-Till technology’. The difference between ‘Conventional tillage’ and ‘No-Till technology’ is not statistically significant. In all cases, the relative abundance of genes for ‘Long-term fallow’ is significantly lower than for the other subgroups. Genes acsA, accA, and pstB are the most represented (much more than 10⁶). Genes amiF and pnp are not represented in ‘Long-term fallow’ (the median value of the readcounts is zero). Genes mphE, phnJ, and queC are presented in ‘Long-term fallow’ at a significantly lower level compared to the other subgroups and genes (lower than 10⁴).

3.4. Signature Analysis

To provide the results, all pairs containing the species with the largest difference in representation between the subgroups were selected. These species are Streptomyces mobaraensis, Capillimicrobium parvum, Conexibacter woesei, Bradyrhizobium sp. 170, Bradyrhizobium sp. 200, Bradyrhizobium sp. CCBAU 051011, and Bradyrhizobium sp. S12-14-2. Then, a heatmap was built to visualize the differences in their median relative abundance (Figure 5). Here, we provide only the signature pairs with statistically significant differences in abundance.

The heatmap shows that the signature pair ‘Bradyrhizobium sp. CCBAU 051011|acsA’ has the highest abundance for ‘No-Till technology’ (over 1000 readcounts in median) and ‘Conventional tillage’ (about 1000 readcounts in median) in comparison with ‘Long-term fallow’. The signature pair ‘Bradyrhizobium sp. 200|pstB’ has a much high abundance (about 700 readcounts) in the tilled groups compared with the ‘Long-term fallow’ subgroup. These results support the results of taxonomic and functional analysis.

4. Discussion

Modern agriculture faces several challenges such as soil degradation, loss of fertility, and the need for sustainable agricultural practices. Integrated soil health assessment methods, particularly through metagenomic analysis, can help to address these challenges. Our study contributes to this growing body of knowledge by employing metagenomic tools to evaluate the microbial communities and functional potential of Russian black soil (chernozem), a soil type renowned for its high fertility and agricultural importance [29]. Despite its inherent fertility, chernozem is not immune to degradation, and its sustainable management is critical to preserving its productivity [46]. By employing metagenomic techniques, we were able to provide a comprehensive understanding of the microbial communities inhabiting this soil type and their functional potential. Using advanced metagenomic methods, we have gained insight into microbial diversity and genes that may be critical for maintaining soil health and productivity.

Our key findings highlight significant differences in microbial community composition under different tillage regimes. No-Till technology and conventional tillage practices promoted both bacterial diversity, according to the relative abundance of specific taxa in the groups (Figure 1) and alpha diversity (Figure 2) and beta diversity analysis (Figure 3), which may contribute to improved soil health through enhanced nutrient cycling and organic matter decomposition compared to long-term bare fallow soil. The relative representation of the major soil species, Streptomyces mobaraensis, Capillimicrobium parvum and Conexibacter woesei, inversely correlates with the increase in total microbial diversity (Figure 1). S. mobaraensis and C. woesei are known to form extended hyphal networks, and their abundance is associated with stable, structured soils where they thrive in protective microhabitats within intact soil aggregates [47,48]. C. parvum, a small-sized actinobacterium, is likely sensitive to rapid changes in soil moisture and organic matter distribution, which occur while applying regular tillage procedures [49]. Both tillage practices that break up soil aggregates can expose the described major bacterial species to harsher conditions—reducing their capacity to colonize and persist in the soil matrix, but at the same time, enabling the growth and development of other bacterial species. According to the alpha diversity results, we can see high levels of species richness and evenness [50], which suggests a substantial heterogeneity in species composition within each soil subgroup. These findings correspond to the increase in total carbon in microbial biomass (Table 1) and are consistent with previous studies demonstrating the positive impact of permanent tillage on soil microbial diversity and community stability [51]. At the same time, No-Till technology, which is considered a reduced tillage practice [52], was associated with a more heterogeneous soil environment than in the case of conventional tillage, providing diverse ecological niches that support a wider range of microbial lifestyles. This diversity is crucial for maintaining the resilience of soil ecosystems and their ability to provide essential services, such as nutrient availability and disease suppression, to plants and their environment [53]. The increased microbial diversity under reduced tillage conditions is consistent with previous findings [18,19,20] and can be attributed to the maintenance of soil structure and organic matter, which contribute to a more stable and supportive habitat for microbial communities. Additionally, these patterns align with observations in other fertile black soil systems. For example, in Northeast China’s Mollisol (black soil) region, no-tillage combined with residue mulching significantly increased the alpha diversity of soil bacteria and altered community composition, while increasing soil organic carbon and nutrient levels [54,55]. This parallel suggests that the benefits of reduced tillage on microbial diversity and function observed in our study are not unique to this Russian black soil but are likely applicable to other black soil-rich agricultural regions under comparable management practices. In contrast, conventional tillage disrupts soil aggregates, leading to the loss of organic matter, which results in less extensive microbial diversity [56]. In general, both tillage methods positively influenced soil microbial diversity, with reduced tillage No-Till technology demonstrating greater effectiveness.

The results of the study indicate that both treatment technologies led to a significant increase in the abundance of a certain microbial taxa—Bradyrhizobium spp.—which is in agreement with previously published data [57]. Bradyrhizobium spp. are well known symbiotic nitrogen-fixing bacteria that form nodules on the roots of leguminous plants [58] and are responsible for heavy metal resistance and bioremediation in a long-term heavy metal-contaminated ecosystem [59]. Bradyrhizobium spp. have been shown to stimulate plant growth and improve plant resistance to biotic and abiotic stresses, further highlighting their importance as indicators of soil health and plant vitality [60,61]. The extended presence of Bradyrhizobium spp. under reduced tillage conditions (No-Till technology) suggests that this practice not only enhances soil fertility but also contributes to the resilience of soil ecosystems in the face of environmental stressors.

The functional analysis (Figure 4) revealed that genes associated with carbon metabolism, such as accA, argG, acsA, mphE, and miaB, were more abundant in soils subjected to No-Till technology and conventional tillage practices. Among them, accA (acetyl-coenzyme A carboxylase) and acsA (acetyl-coenzyme A synthetase) are especially crucial for carbon fixation and the synthesis of acetyl-CoA, a key intermediate in the carbon cycle [62]. The increased abundance of these genes due to the application of tillage suggests enhanced carbon sequestration and organic matter decomposition, which are critical for maintaining soil fertility [56]. This assumption is supported by the fact that both methods of soil treatment led to significantly higher carbon content in microbial biomass and the mortmass, as well as increased CO₂ emission, compared to untreated soil (Table 1). Signature analysis (Figure 5) identified Bradyrhizobium sp. CCBAU 051011 as a crucial bacterial species associated with the increased abundance of acsA in tilled soils, highlighting its significant role in carbon fixation processes.

The optimization of carbon and nitrogen metabolism in soils under agricultural practices has been extensively studied in the literature. However, significantly less attention has been given to the dynamics of phosphorus metabolism [63]. In particular, the influence of soil cultivation methods on phosphorus metabolism remains underexplored. Nonetheless, a global meta-analysis of 5876 observations demonstrated that soil conservation practices significantly enhance soil phosphatase activity [64]. Our findings provide additional support for this conclusion through the lens of soil metagenomic analysis. Phosphorus metabolism was significantly affected by tillage practices, with genes such as phoB, ppa, pstB, pnp, and phnJ exhibiting higher abundance in soils subjected to treatment. These genes play essential roles in phosphorus uptake and regulation, processes vital for sustaining soil phosphorus availability and supporting plant nutrition [65]. Specifically, phoB, ppa, phnJ, and pstB encode key proteins involved in solubilizing soil phosphate, thereby enhancing its accessibility to plants [10]. For instance, phoB is crucial for the regulation of phosphate uptake and solubilization as well as P-starvation response, since it controls the expression of phosphatases and phosphate transporters [66]. In tilled soils, the higher abundance of phoB suggests an adaptive response to phosphorus scarcity, promoting the solubilization of inorganic phosphate and enhancing its availability to plants. The increase in phoB abundance was 3 times for plowing and 7 times for No-Till compared to fallow, which may be a sign of optimization of phosphorus metabolism conditions under soil conservation technology. The higher presence of ppa, encoding an inorganic pyrophosphatase, corresponds to elevated phosphatase activity (Table 1) in tilled soils, which is potentially associated with enhanced mineralization of organic phosphorus compounds, releasing orthophosphate for plant uptake [67]. The abundance of phnJ, a gene associated with the mineralization of organic phosphorus compounds [68], increased by two orders of magnitude compared to fallow conditions. The abundance of the pstB gene, encoding a phosphate transporter protein involved in phosphorus uptake and transport, was 2.5 times higher in tilled soils than fallow. Signature analysis (Figure 5) further highlighted that the elevated presence of pstB in tilled soils is linked to the increased abundance of Bradyrhizobium sp. 200, suggesting that this species plays a significant role in phosphorus solubilization.

In the context of nitrogen metabolism, genes such as queC, amiF, pyrG, guaA, guaB, and napA were more abundant in soils subjected to tillage practices. These genes are essential for nitrogen fixation and nitrate reduction, processes that are critical for soil fertility and plant growth. The increased abundance of these genes under tillage conditions is consistent with the higher levels of microbial biomass and the significant reduction in nitrate–nitrogen (N-NO₃) soil content (Table 1). N-NO₃ reduction is likely due to the enhanced activity of microbial communities carrying nitrogen metabolism genes, which promotes the immobilization of nitrogen in organic forms and reduces nitrate leaching [69]. Nitrate leaching is a major environmental concern, as it can lead to the contamination of groundwater and surface water bodies, contributing to eutrophication and other ecological imbalances [70]. By minimizing nitrate losses, tillage practices not only improve soil health but also contribute to broader environmental sustainability. The high presence of nitrogen metabolism genes in tilled soils is in agreement with the increased abundance of Bradyrhizobium spp., renowned for their ability to fix free nitrogen through symbiotic relationships with legumes and reported to carry the genes of this cluster [71]. However, none of the classical nif (nitrogen fixation) genes were detected in our metagenomic data. This apparent absence of nif gene sequences could indicate that the Bradyrhizobium populations present are predominantly free-living strains lacking the symbiotic nitrogen-fixation island, as has been observed in some non-diazotrophic Bradyrhizobium from other soils [61]. Environmental conditions might also play a role; for example, the absence of legume hosts and a relatively sufficient soil nitrogen status may reduce the need to retain nif genes, leaving them below the detection threshold of our sequencing.

Overall, our study reinforces the role of microbiome-driven processes in sustainable soil management. By showing that reduced tillage practices (like No-Till) enhance beneficial microbial communities and nutrient cycling in black soil, we contribute to the broader global effort to promote sustainable agriculture. These results echo calls to harness soil biodiversity for improving fertility and resilience [72], and they demonstrate how managing the soil microbiome can help balance agricultural productivity with environmental health on a long-term basis.

5. Conclusions

This study demonstrates that different tillage practices significantly influence the functional genes and microbial taxa associated with key biogeochemical cycles in Russian black soil. No-tillage technology and conventional tillage practices promote beneficial microbial communities and enhance soil health by improving nutrient cycling and organic matter decomposition. The increased abundance of genes involved in carbon, nitrogen, and phosphorus metabolism, as well as the microbial taxa responsible for these processes, underscores the importance of adopting sustainable tillage practices to maintain soil fertility and productivity. The use of metagenomic analysis provides a comprehensive understanding of soil microbial communities and their functions, demonstrating high correlation with traditional widely used physical and chemical methods evaluating soil quality/fertility and offering valuable insights for the development of sustainable agricultural practices.