Barley Rhizosphere Bacteriome Dynamics under Organic and Mineral Inputs: The importance of intercropping predecessor

595 Barley Rhizosphere Bacteriome Dynamics under Organic and Mineral Inputs: The importance of intercropping predecessor Stefan Shilev1, Mariana Petkova1, Vanya Popova1, Ivelina Neykova1, Ivan Rangelov2 1 Department of Microbiology and Environmental Biotechnologies, Agricultural University-Plovdiv, 4000 Plovdiv, Bulgaria 2 Index 11 JSC, Plovdiv 4000, Bulgaria Corresponding author: Stefan Shilev ([email protected]) Copyright: © Stefan Shilev et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract Integrating intercropping with green manure within crop rotation offers a sustainable pathway to enhance soil microbial diversity and overall agroecosystem health. This study evaluated the effects of diverse fertilisation practices on the soil microbiome in a barley (Hordeum vulgare L.) cropping system following the incorporation of an oatvetch (Avena sativa and Vicia sativa) and green manure incorporation versus single cropping of oats. The field experiment took place at the Agricultural University of Plovdiv, Bulgaria. Using high-throughput metagenomic sequencing, we assessed the structure and functions of the soil microbial community. The fertilisation treatments included mineral fertiliser, vermicompost, a combination of vermicompost and mineral fertiliser, and biochar, along with a non-fertilised control. The investigation compared two preceding cropping systems before barley cultivation: an oat-vetch intercrop used as green manure, incorporated into the soil at the ripening stage, and a conventional approach where barley was sown directly without prior green manuring. All treatments were conducted in triplicate. Sequencing allows for detailed taxonomic and functional profiling. The most abundant bacterial phyla identified were Actinobacteriota, Proteobacteria, and Bacteroidota, with Actinobacteriota showing increased abundance under fertilised conditions. Soils treated with compost and compost-mineral mixtures exhibited notably higher alpha diversity. Beta diversity analyses (PCA, PCoA, UPGMA) revealed clear differences in microbial community structure among the treatments. Certain genera, such as Sphingomonas, Noviherspirillum, and Agromyces, were particularly enriched in soils receiving vermicompost or green manure. Their presence suggests enhanced microbial functionality and nutrient cycling in those plots. Overall, the combination of green manure and organic inputs supported more diverse and functionally active microbial communities, contributing to the resilience and sustainability of barley-based cropping systems. This study focuses on the bacterial component of the soil microbiome; nutrient cycling and soil health are governed by the combined interactions among bacteria, fungi, and protozoa. Key words: Barley, high-throughput metagenomic sequencing, microbial diversity, plant-microbe interactions soil bacteriome, vermicompost Academic editor: Susheel Bhanu Busi Received: 30 July 2025 Accepted: 20 October 2025 Published: 18 November 2025 Citation: Shilev S, Petkova M, Popova V, Neykova I, Rangelov I (2025) Barley Rhizosphere Bacteriome Dynamics under Organic and Mineral Inputs: The importance of intercropping predecessor. Metabarcoding and Metagenomics 9: e167231. https:// doi.org/10.3897/mbmg.9.167231 Metabarcoding and Metagenomics 9: 595–620 (2025) DOI: 10.3897/mbmg.9.167231 596 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics Introduction Microorganisms in the soil are key drivers of agroecosystem functionality, contributing to essential processes such as nutrient mineralisation, maintenance of soil structure, and promotion of plant development. The concept of the microbiome has evolved from a purely taxonomic description to a functional, systems-level understanding of microbial interactions in specific habitats (Berg et al. 2020). These microbial populations are involved in fundamental processes such as nutrient cycling, organic matter decomposition, and the regulation of plant health, directly influencing soil fertility and crop yields (Fierer et al. 2007; Van der Heijden et al. 2008). The structure and function of these microbial assemblages are closely linked to land management decisions, including fertilisation methods, cropping systems, and tillage intensity (Larkin 2015). As concerns about environmental sustainability grow, there is increasing attention on how agricultural interventions can maintain or improve microbial biodiversity, which is critical for the long-term productivity and stability of agroecosystems (Escudero-Martinez et al. 2022). One strategy gaining traction is the integration of legumes into cropping systems through intercropping or green manuring. Legumes such as Vicia sativa L. can fix atmospheric nitrogen through symbiosis with rhizobia, enriching soil nutrient pools without the need for synthetic fertilisers (Peoples et al. 2009). The exudation of root metabolites from legumes can stimulate microbial activity and increase microbial richness in the rhizosphere. When incorporated into soil as green manure, legume biomass contributes organic inputs that enhance soil structure and biochemical function, creating a favourable environment for microbial growth and activity (Lazcano et al. 2013; Xiao et al. 2023). These effects often translate into improved performance of subsequent cereal crops, such as barley, through enhanced nutrient availability and disease suppression. Organic inputs, including compost, vermicompost, and biochar, are also widely used to stimulate microbial processes and restore soil health. Compost and vermicompost provide easily accessible carbon and microbial inoculants that support enzymatic activity and microbial biomass development (Arancon et al. 2006; Lazcano et al. 2013). Biochar, a carbon-rich material derived from the pyrolysis of biomass, is valued for its porous structure and high surface area, which offer physical niches for microbial colonisation and enhance nutrient and water retention in soil (Lehmann and Joseph 2015). These amendments can contribute to building resilient soil microbial networks and improving overall soil functionality, particularly under organic or reduced-input systems (Lehmann et al. 2011). Recent advances in sequencing technologies, including amplicon-based and metagenomic approaches, now enable researchers to dissect microbial community dynamics at high resolution. By targeting genes such as the 16S rRNA or applying shotgun metagenomics, scientists can simultaneously assess taxonomic diversity and the metabolic potential of soil microbial communities (Nissimov 2017; Prosser 2020). Despite these developments, field-based studies that integrate legume intercropping, organic amendments, and microbial community profiling remain limited, especially under temperate European conditions. This study investigates the impact of incorporating a legume-cereal mixture (Avena sativa × Vicia sativa) as green manure, followed by spring barley cultivation, on the composition and function of rhizosphere bacterial communities under varying fertilisation regimes. Microbial community structure and functional potential 597 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics were assessed using 16S rRNA gene sequencing and metagenomic analysis. The present study aims to reveal how fertiliser type and the legacy effects of cropping system influence soil bacteriome diversity in the rhizosphere of fodder winter barley. This study focuses on the bacterial component of the soil microbiome and recognises that nutrient cycling and soil health are shaped by the combined activities of bacteria, fungi, and protozoa within the broader soil food web. Materials and methods Field description and climatic conditions The field trial was carried out in 2023 at the Training and Experimental Station of the Agricultural University of Plovdiv, Bulgaria (42°08'15.4"N, 24°48'16.4"E). The soil at the site is classified as Mollic Fluvisol, according to the FAO system, formed on alluvial deposits with a humus-rich upper horizon and a transition into a carbonate layer. The region falls within the temperate continental climate zone of Southern Bulgaria, suitable for long-term agroecological experimentation (Shilev et al. 2024). The environmental conditions during the study could be characterized as average, taking into account the average values of multi-year data. The temperatures did not show significant variations from the optimal for the barley development. The average amount of precipitation at the end of 2022 was 75 mm, while at the beginning of 2023, it was 138 mm, with a lower amount in March. Experimental design and soil amendments The study was structured within a two-year crop rotation scheme, aimed at assessing the legacy effects of legume-based green manure and different fertilisation practices on soil microbial diversity. In spring of the first year (2022), two different cropping strategies were implemented using a randomized block layout with three replicates per treatment: (i) monoculture oats (Avena sativa L., cv. Max), and (ii) intercropped oats with vetch (Vicia sativa L., cv. Obrazets 666) at a 3:1 seeding ratio (vetch: oats), totaling 220 kg/ha. Each plot measured 5 × 7 m (35 m2). Sowing was done in March 2022. The monocrop oats were harvested at the end of June, while the intercropped biomass was ploughed into the soil as green manure at a depth of 30 cm. In the same year (2022), at beginning of November, all plots were cultivated with winter barley (Zemela cultivar) to evaluate residual and interactive effects of prior cropping and fertilisation schemes. Two main field setups were established: (1) plots previously enriched with green manure, and (2) conventional plots without green manure. Five fertilisation treatments were applied to both fields: no fertilisation (Control1, Control2); mineral fertiliser (MF1, MF2), NPK 15:15:15, 100 kg/ha, vermicompost (VC1, VC2), 12 t/ ha; combined mineral fertiliser and vermicompost (MF1.VC1, MF2.VC2) at half concentrations; and biochar (Bch1, Bch2), 10 t/ha. In this study, different organic amendments were applied and are defined as follows: Compost (Com) refers to mature plant-based compost applied alone; Vermicompost (VC) is compost processed through the activity of earthworms; and Biochar compost (BC or BC+Com) denotes a mixture of biochar blended with compost prior to soil application. For clarity, the term organic inputs is used throughout the manuscript as 598 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics a general descriptor for all organic treatments (Com, VC, and BC+Com), whereas individual abbreviations are employed when specific treatments are discussed. All treatments were arranged in a randomised complete block design with three replicates per treatment, resulting in a total of 30 barley plots. At the ripening stage of barley, rhizosphere soil samples were collected from each plot by randomly selected plants. This layout enabled the study of fertilisation and green manuring effects on rhizosphere microbial structure under field conditions (Shilev et al. 2024). Samples were kept on ice during transport, sieved to 2 mm, and stored at –80 °C until DNA extraction (Kulmatiski 2020; Natalio et al. 2024). DNA was extracted from 0.5 g of soil using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) following standard protocols. The analytic data showed sufficient concentrations of most macroelements as total content in the biochar (nitrogen, potassium, calcium, and magnesium) (Table 1). The phosphorus and iron content were higher in the vermicompost. The analytical procedure is described previously by Shilev et al. 2024. The soil analyses at the beginning of the study have shown a relatively low total organic carbon content (TOC) – 1.14% (Table 2). Soil reaction was slightly alkaline with low electrical conductivity (EC). The soil is poorly supplied with nitrogen and well stocked with phosphorus and potassium. Metagenomic sequencing DNA quality control Extracted DNA quality and quantity were assessed using a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA) and 1% agarose gel electrophoresis. All samples met the quality standards defined by Novogene Co. Ltd., including parameters for DNA integrity, purity, and concentration (Schloss et al. 2009). Table 1. Properties of vermicompost and biochar on a dry weight basis. Data show mean and standard error (n = 3). Unit Vermicompost Biochar TOC (%) 18.08 ± 1.48 65.00 pH 7.79 ± 0.01 – EC (µS cm-1)1685 ± 10.0 – Total N (%) 1.29 ± 0.17 1.31 P(%) 1.05 ± 0.08 0.64 K(%) 1.61 ± 0.21 7.95 Ca (%) 3.11 ± 0.32 4.28 Mg (%) 0.72 ± 0.07 3.06 Fe (%) 0.51 ± 0.05 0.13 Table 2. Physicochemical properties of the soil before application of amendments on a dry weight basis. Data show mean and standard error (n = 3). pH EC (µS cm-1) Accessible N (N-NH4+N-NO3) (mg 100 g-1) P2O5 (mg 100 g-1) K2O (mg 100 g-1)TOC (%) Soil 8.22 ± 0.1 121.0 ± 1.7 1.54 ± 0.03 3.58 ± 0.28 9.9 ± 0.1 1.14 ± 0.09 599 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics Library preparation and sequencing Metagenomic libraries were constructed through DNA fragmentation, end-repair, A-tailing, and adapter ligation, following Illumina’s standard protocol. Where necessary, libraries underwent PCR amplification. Following quantification and quality control, sequencing libraries were diluted to 1.5 nM and subjected to paired-end sequencing (2 × 150 bp) on the Illumina NovaSeq 6000 system (Quince et al. 2017). Bioinformatic analysis Data preprocessing and quality control The raw sequencing data underwent quality control with fastp v0.23.1 (Chen et al. 2018a), which involved removing adapter sequences, reads containing ambiguous nucleotides, and those with quality scores below Q20. Barcode and primer regions were trimmed before merging the high-quality paired-end reads using FLASH v1.2.11 (Magoč and Salzberg 2011). Detection and removal of chimeric sequences were performed using vsearch v2.16.0 (Rognes et al. 2016), referencing the SILVA v138 database (Quast et al. 2013) for improved accuracy. Feature table construction and taxonomic assignment Amplicon Sequence Variants (ASVs) were inferred using the DADA2 plugin in QIIME2 v2022.2 (Callahan et al. 2016). Taxonomic classification was conducted using a Naïve Bayes classifier trained on the 515F/806R region from the SILVA v138 reference set. ASV tables were rarefied to the minimum read depth for downstream comparisons. Diversity and community composition Alpha diversity indices (Chao1, Shannon, Simpson, Pielou’s evenness, Good’s coverage) and beta diversity metrics (Bray-Curtis, weighted and unweighted UniFrac) were calculated using QIIME2. Ordination analyses including Principal Coordinates Analysis (PCoA), Principal Component Analysis (PCA), and Nonmetric Multidimensional Scaling (NMDS) were conducted in R v4.2.0 (R Core Team 2022) using the vegan, ggplot2, and ade4 packages. Group-level differences were assessed via Analysis of Similarities (ANOSIM) and Permutational Multivariate Analysis of Variance (PERMANOVA) using the vegan package (Anderson 2001) Phylogenetic and statistical analyses Multiple sequence alignments of representative ASVs were performed with MAFFT (Katoh and Standley 2013), and phylogenetic trees were constructed using FastTree in QIIME2 (Price et al. 2010). Visualisation of microbial composition was carried out using stacked bar plots, heatmaps (heatmap), and ternary plots, implemented in R software v4.2.0 (R Core Team 2022; https://www.r-project.org/ accessed on 06.07.2025). Venn and flower diagrams representing shared and unique ASVs were generated using the VennDiagram package and custom SVG-based scripts. 600 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics Data availability The 16S rRNA sequencing data from examined soils were submitted to the NCBI gene bank under the BioProject accession number PRJNA1297989 with the submission ID: SUB15499394 released on 29 July 2025. The BioSample accessions of the analysed ten samples are SAMN50254222, SAMN50254223, SAMN50254224, SAMN50254225, SAMN50254226, SAMN50254227, SAMN50254228, SAMN50254229, SAMN50254230, and SAMN50254231. The data records are accessible using the following link: https://www.ncbi.nlm.nih. gov/bioproject/1297989, accessed on 29 July 2025. Results Sequencing data processing Row PE (Paired-End Reads) values range from 62,941 (MF2) to 78,820 (VC1), which is a reasonable range for 16S rRNA gene amplicon sequencing (Table 3). No sample shows an extreme outlier, meaning sequencing depth was fairly consistent. The combined (Merged Reads) step merges the paired-end reads into longer sequences. The merging rates are high across all samples (close to 100%), indicating good sequencing quality and proper overlapping read regions. Successful merging suggests accurate read alignment without major issues in adapter trimming or sequence quality. Qualified (Clean Tags in Table 3). The percentages remain high across all samples, showing data underwent minimal loss due to low-quality reads. This reflects that sequencing run had high quality, and data loss at this stage was minimal. Nochime (Effective Tags) indicates the presence of chimaeras. Control1 dropped from 63,766 to 54,656 (~14.3% reduction), and VC1 dropped from 77,093 to 70,624 (~8.4% reduction). Chimaera rates vary slightly between samples. MF1.VC1 and Bch2 show lower chimaera rates, indicating these samples had fewer artefacts. GC content ranges from 56.03% to 57.02%, which is biologically consistent and within the expected range for most microbial communities. All samples exhibit Q20 > 98% and Q30 > 95%, demonstrating excellent sequencing accuracy. The high Q20/ Q30 scores confirm minimal sequencing errors and high data reliability. Analysis of relative taxonomic abundance This stacked bar plot represents the relative abundance of bacterial taxa at the phylum level across different samples (Fig. 1). The bar plot of relative abundance at the phylum level illustrates consistent dominance of Proteobacteria, Actinobacteriota, and Acidobacteriota across all treatments, in line with their established roles in nutrient cycling and soil health. The proportional distribution of these and other phyla varied depending on the fertilisation regime and the application of green manure. Treatments VC, MF.VC, and to a lesser extent Bch, showed elevated levels of Actinobacteriota and Bacteroidota, phyla known for their saprophytic and degradative capabilities. This shift suggests increased availability of organic substrates following the incorporation of legume biomass, which can stimulate microbial taxa involved in organic matter decomposition and nutrient release. 601 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics In contrast, plots treated with mineral fertilizer or left as untreated controls exhibited relatively higher proportions of Acidobacteriota, often associated with oligotrophic conditions and more stable, slower nutrient cycling dynamics (Fig. 1). These treatments also showed reduced abundance of phyla such as Gemmatimonadota and Myxococcota, indicating a less diverse and less functionally dynamic microbial community. The biochar treatment presented a unique phylum distribution, with slightly elevated Verrucomicrobiota and Chloroflexi, consistent with previous findings that biochar alters microhabitat conditions such as aeration, pH, and moisture content, thereby selecting for specialized microbial taxa. The results underscore that green manure incorporation, particularly in combination with compost or mineral fertilizers, enhances microbial diversity and shifts the taxonomic structure toward copiotrophic, functionally active taxa. These microbial changes reflect improved soil fertility and biological functioning under intercropping-based, organic-amended systems. Table 3. Sequencing data analysis. Sample RawPE Combined Qualified Nochime Base(nt) Avglen(nt) GC, % Q20,% Q30,% Control1 65518 64580 63766 54656 22898990 418.97 56.65 98.71 95.43 MF1 66110 65551 64810 59174 24743668 418.15 56.10 98.87 95.80 VC1 78820 78024 77093 70624 29567160 418.66 56.03 98.86 95.81 MF1.VC1 68808 68308 67339 59460 24800258 417.09 56.27 98.74 95.35 Bch1 66344 65837 64947 56883 23728533 417.15 57.02 98.77 95.48 Control2 70347 69768 68827 64043 26761127 417.86 56.74 98.81 95.70 MF2 62941 62431 61527 55209 23012585 416.83 56.74 98.70 95.21 VC2 70455 69639 68724 62112 25927482 417.43 56.97 98.75 95.58 MF2.VC2 63834 63011 62111 54694 22854201 417.86 56.47 98.74 95.42 Bch2 64928 64102 63302 54227 22642699 417.55 56.51 98.75 95.54 Legend – Abbreviations of sample codes: Control1-unfertilized control after green manure, Control2 – Barley grown without fertilisation ; MF1-Barley grown after oat-vetch green manure and fertilized with mineral fertilizer, MF2 – fertilized with mineral fertilizer, VC1 - Barley grown after oat-vetch green manure and fertilized with vermicompost, VC2 –fertilized with vermicompost, MF1.VC1 – Barley grown after oat-vetch green manure, fertilized with compost + mineral fertilizer, MF2.VC2 – fertilized with compost + mineral fertilizer, Bch1 - Barley grown after oat-vetch green manure, fertilized with biochar, Bch2 – fertilized with biochar only. Figure 1. Relative taxonomy abundance of each sample in different taxonomic level. at phylum level. 602 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics Analysis of relative taxonomic abundance at the genus level The genus-level bar plot (Fig. 2) reveals a consistent dominance of Sphingomonas across all treatments, highlighting its ubiquity and potential ecological relevance in the barley rhizosphere. This genus, known for its capacity to degrade complex organic compounds and promote plant health, was most abundant in compostand biochar-amended soils, particularly in MF.VC and VC treatments. This suggests that green manure incorporation enriched copiotrophic genera linked to nutrient transformation and organic matter decomposition. Other notable genera include Lysobacter, Noviherspirillum, and Gemmatimonas, which together contributed to a moderate proportion of the community in Fig. 2. These genera are often associated with carbon and nitrogen cycling and showed slight increases in compost and biochar treatments, possibly reflecting the enhanced availability of root exudates and organic substrates. In contrast, control and mineral-only (MF) treatments exhibited lower richness and diversity at the genus level, with greater proportions classified under “Others,” indicating either lower taxonomic resolution or dominance of rare/uncultured taxa. Massilia and Arenimonas were more prominent in biochar-amended soils (Bch1, Bch2), suggesting their potential preference for the altered physicochemical properties (e.g., pH, porosity) associated with biochar. These results confirm that green manure, particularly when combined with organic amendments, enriches beneficial microbial taxa at the genus level, contributing to improved soil health and potential plant growth-promotion under intercropping systems. Heat map of genus levels The heatmap highlights the differential abundance of dominant bacterial genera across treatments, clustered both by taxonomic affiliation and treatment type (Fig. 3). Samples treated with green manure, especially MF.VC, and VC, exhibited clear shifts in microbial composition compared to mineral fertilisation (MF) and untreated controls. Genera such as Nocardioides, Agromyces, Rubrobacter, and Solirubrobacter (phylum: Actinobacteriota) were notably Figure 2. Relative taxonomy abundance of each sample in different taxonomic level at genus level. 603 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics enriched in green manure and biochar (Bch) treatments, reflecting increased microbial activity related to organic matter decomposition. These genera are often associated with cellulose degradation, plant-growth promotion, and resilience to environmental stress. Conversely, RB41, Lysobacter, and Sphingomonas were more abundant in control and mineral fertilizer treatments. Their prevalence in nutrient-limited environments may indicate a competitive advantage in lower-carbon soils. The relatively lower presence of copiotrophic genera in MF and Control samples suggests reduced microbial richness and functional diversity. Distinct patterns were also evident for Noviherspirillum (Proteobacteria), which showed Figure 3. Heatmap showing the relative abundance of the 35 dominant genera across all samples. The X-axis represents sample codes, and the Y-axis represents genera. Hierarchical clustering is shown on the left. The Z-score indicates deviation from the mean abundance, and the colour gradient reflects abundance levels: red/orange = higher, yellow = intermediate, blue = lower. 610 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics relative to Control1 (0.132; 0.451) and MF1 (0.114; 0.451), suggesting that this treatment alters microbial communities substantially. In contrast, the lowest dissimilarity was observed between Control1 and MF1 (0.098; 0.417), followed by MF1 and VC1 (0.143; 0.434), implying that these individual soil treatments showed limited effects on overall microbial composition, maintaining similarity with the control group. The combination MF1.VC1 resulted in a moderate shift from Control1 (0.182; 0.446), highlighting a possible interaction effect. While taxonomically closer to the control than Bch2, it still demonstrated moderate to high dissimilarity when compared with other treatments such as MF2 (0.146; 0.455) and MF1.VC1 (0.160; 0.424). This suggests that the specific Bacillus strain or formulation used in Bch1 induced a unique microbial response. Control1 and Control2 (0.132; 0.451) exhibited measurable dissimilarity due to micro-environmental variability or heterogeneity in microbial baseline conditions. Beta diversity matrix confirms that treatments containing biochar inoculants and MF2.VC2 drive the greatest divergence in microbial community structure, while MF1, VC1, and their individual applications show minimal deviation from the control. These findings are consistent with PCA and PCoA results, reinforcing the conclusion that combined biological amendments can reshape rhizosphere microbial communities, likely through synergistic nutrient effects, microbial interactions, and host plant responses. These results align with previous studies reporting that integrated organic and microbial inputs can significantly modify soil microbial assemblages and enhance functional diversity (Hartmann et al. 2015; Singh et al. 2020). Biochar amendments have been shown to enhance microbial diversity and alter community assembly by providing additional habitat and influencing soil chemistry (Lehmann et al. 2011). Vermicompost applications are consistently associated with improved soil structure and increased abundance of beneficial fungal taxa (Domínguez and Edwards 2011; Pathma and Sakthivel 2012). Likewise, compost inputs promote stable microbial networks and stimulate functional guilds involved in nutrient cycling (Bonanomi et al. 2016). Our results are in line with these studies, suggesting that the combined use of organic inputs and green manuring can foster resilient microbial communities that support both soil health and crop productivity. Discussion This study demonstrates that both fertilisation strategy and cropping system significantly shape the structure and functional potential of rhizosphere microbial communities in barley agroecosystems. Using 16S rRNA gene amplicon sequencing, we characterised the microbial taxonomic composition and metabolic capabilities across soils amended with mineral fertiliser, compost, compost-mineral blends, and biochar, under conventional monoculture and oat-vetch intercropping systems. Actinobacteriota and Proteobacteria dominated across all treatments, highlighting their central roles in soil ecosystem functioning (Hartmann et al. 2015; Fierer 2017) (Figs 1–4). The high abundance of Actinomycetota—particularly genera such as Nocardioides, Solirubrobacter, and Rubrobacter suggests a stable and resilient microbial core capable of organic matter decomposition and antibiotic production, aligning with observations in organically managed soils (Lehmann et al. 2011; Zhang et al. 2023) (Fig. 4). Biochar-treated soils displayed 611 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics the greatest community divergence, likely due to biochar’s capacity to modify soil structure, pH, and moisture retention, fostering unique microbial niches. The MF.VC and Bch treatments enriched a broader range of taxa, including both dominant phyla (e.g., Actinobacteriota, Bacteroidota) and less abundant genera with key functional roles in carbon and nitrogen cycling. Despite the presence of a shared core microbiome across treatments, microbial richness and evenness were significantly enhanced in organic and green manure-amended plots compared to mineral fertilised and control treatments (Fig. 5). This suggests that integrated nutrient management enhances ecological stability and functional redundancy, which are crucial for resilient soil systems. The phylogenetic and compositional differences observed further support the hypothesis that organic inputs introduce a greater variety of substrates and microhabitats, driving microbial diversification. The abundance of copiotrophic and functionally versatile genera, particularly in green manure plots, confirms the ecological benefit of these practices for sustaining soil biological health and crop productivity. The integration of leguminous green manure (oat-vetch) notably increased alpha diversity, particularly in intercropped and compost-treated plots, supporting previous evidence that legumes stimulate rhizosphere microbial richness through nitrogen fixation and exudate-mediated recruitment (Peoples et al. 2009; Shakya et al. 2013; Philippot et al. 2013; Brooker et al. 2015; Petkova et al. 2025) The elevated Shannon and Chao1 indices in VC1 and VC2 plots suggest that green manure improves niche availability and substrate heterogeneity, fostering more diverse and even microbial assemblages (Lazcano et al. 2013) (Table 4). The detection of a core microbiome across treatments—comprising over 560 shared species—suggests that barley maintains a stable microbial consortium irrespective of soil amendment. The presence of over 1,000 unique species in VC1 highlights the significant ecological impact of intercropping and green manure incorporation. This enrichment may reflect microbial adaptation to elevated nutrient availability and complex root architectures characteristic of mixed legume-cereal systems (Congreves and Van Eerd 2015; Finney et al. 2016). Importantly, while mineral fertilisers supported high microbial abundance, their sole application was associated with lower evenness and diversity compared with organic or mixed treatments, echoing concerns over long-term microbial homogenization under intensive input regimes (Chen et al. 2018b). Current results suggest that mineral inputs, when used in isolation, may favour copiotrophic taxa and reduce functional redundancy. Together, these findings underscore the synergistic benefits of intercropping and organic fertilisation for maintaining rich, diverse, and functionally robust microbial communities. By integrating oat-vetch green manure with compost-based amendments, farmers can foster a rhizosphere microbiome conducive to nutrient cycling, plant health, and agroecosystem sustainability. Beta diversity analysis in Fig. 8 revealed distinct community shifts driven by fertilisation type. Bch1 and Bch2 exhibited the most pronounced divergence from other treatments, consistent with biochar’s known capacity to alter soil physicochemical properties and provide microhabitats for specialised taxa (Lehmann et al. 2011; Liu et al. 2010). The clustering trees based on Unweighted and Weighted UniFrac distances reveal distinct patterns in bacterial community structure across treatments 612 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics (Fig. 9). Unweighted UniFrac (Fig. 9A) considers only the presence or absence of taxa and shows clear separation between treatment groups, especially between Control, MF, and MF.VC samples. This indicates that treatments influence the taxonomic composition, even among low-abundance taxa (Lozupone et al. 2005). In contrast, the Weighted UniFrac tree (Fig. 9B), which accounts for taxon abundances, highlights differences in dominant microbial lineages. MF1. VC1 and MF2.VC2 clusters closely, suggesting a shared enrichment of abundant phyla such as Proteobacteria, Actinobacteriota, and Acidobacteriota, as seen in the relative abundance bar plots (Zhang et al. 2023). The distinct clustering of Bch1 and Bch2 in both trees supports the idea that this treatment promotes a unique microbial assemblage, potentially driven by specific soil or plant-associated factors. These results emphasise that both community composition and abundance profiles are affected by management practices, and combining Unweighted and Weighted UniFrac analyses provides a more comprehensive view of microbial community dynamics (Lozupone and Knight 2011). Figure 9. Clustering tree based on Bray-Curtis distance in species abundance (A) unweighted and (B) weighted Unifrac distance. The dendrogram (left) coupled with a stacked bar plot of relative species abundance (right), offering insight into community composition and similarity among treatments. A B 613 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics The PCA plot in Fig. 10 provides a two-dimensional visualization of the variation in community structure among the treatment groups, based on principal component analysis. The first principal component (PC1) accounts for 14.53% of the total variance, while the second principal component (PC2) explains 12.15%, together capturing 26.68% of the observed variation. Distinct clustering patterns were observed among the treatments. Samples from Control1, MF1, and VC1 clustered closely in the upper left quadrant, indicating that these groups shared similar microbial or biochemical profiles and responded similarly to the tested conditions. In contrast, Control2 and MF2 were located in the lower right quadrant, clearly separated from Control1 and MF1, suggesting divergent profiles potentially associated with the effects of the treatments or environmental differences between replicates. MF2.VC2 and Bch2, grouped near the center of the plot, implying moderate or mixed responses that do not strongly drive variation along either PC1 or PC2. VC2 appeared as a clear outlier in the top right quadrant, reflecting a pronounced deviation in community structure, especially along PC2. Similarly, Bch1 was located distantly from other groups in the lower quadrant, suggesting a unique treatment effect or strain-specific response. MF1.VC1, positioned between the clusters of MF1 and VC1, displayed an intermediate profile, indicative of a partial interaction effect. Likewise, the combined treatment MF2.VC2 demonstrated a distinct but centrally located profile, suggesting a complex or balanced interaction between the individual components. PCA highlights the treatment-driven differentiation of the microbial or biochemical community structure. The close association of certain treatments (Control1, MF1, and VC1) and the divergence of others (MF2, VC2, Bch1) underscore the varied effects of experimental conditions. Bch2 also suggests potential intra-group variability or sensitivity to environmental or procedural factors. These findings emphasize the relevance of treatment-specific and interaction-dependent influences on the underlying biological system. Principal Coordinates Analysis (PCoA) in Fig. 11 was performed to assess beta-diversity and visualize differences in microbial community composition Figure 10. Principal Component Analysis (PCA) of Soil Microbial Communities under Different Fertilisation Treatments. 614 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics across the experimental treatments. This method allows for dimensionality reduction based on pairwise dissimilarity metrics and is commonly used to evaluate compositional shifts in microbial communities (Lozupone et al. 2005). In the left panel of Fig. 11, the first two principal coordinates explain 49.16% and 14.56% of the variance, respectively. A clear clustering of MF1, VC1, and Control1 is observed in the upper left quadrant, suggesting compositional similarity under these treatments. In contrast, MF2.VC2 and Bch2 are distinctly separated in the right half of the plot, reflecting a marked divergence in microbial community structure. Bch1 and MF1.VC1 show isolated positions, indicating unique microbial signatures that differ from both control and other treatment groups. The right panel of the PCoA plot, based on a different distance metric, reveals similar but more dispersed clustering. Here, PC1 and PC2 explain 21.07% and 12.73% of the total variation, respectively. Bch2 and MF2.VC2 cluster tightly in the upper right quadrant, indicating a strong treatment effect from the combined application of Bacillus-based biostimulants and vermicompost. VC2 appears as an outlier in the lower right quadrant, confirming a distinct microbial community composition. Meanwhile, MF1, Control1, and MF1.VC1 cluster in the upper left quadrant, reinforcing their compositional similarity. PCoA results confirm that specific treatments—particularly MF2, VC2, and Bch-based amendments—induce significant shifts in microbial community structure compared to the control. The clustering patterns indicate that combinations of microbial fertilizers and organic inputs can lead to synergistic or additive effects, reshaping the rhizosphere microbiome. These findings are consistent with previous studies showing that organic and microbial amendments drive distinct microbial assemblages and influence soil ecological function (Hartmann et al. 2015; Ai et al. 2015; Singh et al. 2020). The influence of various fertilisation strategies on soil microbial communities was assessed under two contrasting cropping systems: a conventional system with direct barley sowing (no prior cover crop), and an intercropping system utilising oat-vetch green manure before barley cultivation. In the absence of fertilisation (Control), microbial communities remained stable under Figure 11. Principal Coordinates Analysis (PCoA) of Soil Microbial Communities Based on UniFrac Distances. 615 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics conventional management, dominated by Actinomycetota. Тhe intercropped control plots supported slightly higher microbial richness and functional diversity, consistent with findings that legume-based green manures enhance soil microbiota even without nutrient amendments (Zhang et al. 2018; Zhou et al. 2021). Mineral fertilisation induced a selective increase in stress-tolerant taxa such as Nocardioides and Rubrobacteraceae, leading to reduced diversity in conventionally managed soils. In contrast, the green manure intercropping system preserved a more balanced microbial structure and facilitated the appearance of Acidobacteriota and Chloroflexota, phyla known for their roles in organic matter turnover and environmental resilience (Janssen 2006; Kielak et al. 2016). Compost application enriched decomposer bacteria under conventional barley, especially Nocardioides, but had a stronger effect under intercropping by promoting Solirubrobacteraceae and less abundant phyla. The inclusion of compost significantly enhanced microbial diversity and functionality, particularly when combined with green manure practices, aligning with previous reports that organic amendments stimulate microbial richness and promote beneficial taxa (Lazcano et al. 2013; Povilaitis et al. 2020). The combination of compost and mineral fertilisation led to strong dominance by Nocardioides and Frankiales under conventional management. Under intercropping, this combination promoted greater microbial evenness and enriched rare taxa such as Gaiella occulta. This suggests a synergistic effect between organic and inorganic inputs when used with green manure, enhancing microbial functional redundancy—an important indicator of soil ecosystem resilience (Banerjee 2021). Biochar application supported distinct microbial assemblages dominated by carbon-degrading bacteria under conventional barley cultivation. The intercropping-biochar combination resulted in the broadest microbial spectrum, including enrichment of niche-specific and rare taxa as Gaiella. These results corroborate the hypothesis that biochar in combination with legume-based green manures enhances microbial niche complexity and supports specialised functional groups (Quilliam et al. 2013; Lehmann et al. 2011). The oat-vetch green manure system improved microbial richness, evenness, and functional diversity across all fertilisation treatments. Intercropping not only mitigated the potential negative effects of mineral fertilisation but also enhanced the performance of organic amendments. These findings support the integration of green manures with compost or biochar as effective strategies to promote diverse and ecologically robust soil microbiomes in barley-based cropping systems. From an ecological standpoint, the application of legume-based green manure fostered microbial resilience and ecosystem complexity, supporting a broader range of taxa and potentially increasing functional redundancy. Functionally, these treatments are likely to enhance nutrient cycling, organic matter turnover, and plant-microbe interactions, which are critical for long-term soil fertility and crop productivity. These findings demonstrate that integrated nutrient management combining green manure with organic amendments is a viable strategy to promote diverse, ecologically stable, and functionally robust microbial communities in cereal cropping systems. Such practices are key to advancing soil health, sustainability, and agroecological resilience under modern agricultural pressures. While the present research concentrates on bacterial communities, we acknowledge that fungi and protozoa interact closely with bacteria in driving decomposition and nutrient mineralisation. Future studies 616 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics integrating bacterial, fungal, and protozoan analyses will provide a more comprehensive view of soil functional ecology under these agroecological practices. Conclusions Growing intercropped oat-vetch and incorporating them at the ripening stage as green manure, and applying vermicompost, fosters more diverse, functionally versatile, and ecologically resilient rhizosphere microbiomes compared to soils without green manure or mineral inputs.. The bacterial biome represents only a part of the complex soil microbiome, and that future metagenomic work should encompass fungal and protozoan contributions to fully elucidate soil ecosystem functioning. These findings advocate for the adoption of intercropping with legume green manure and tailored organic amendments to support sustainable cereal production. Ecological and functional benefits of the current study are the improved diversity under green manure and combined treatment, which likely translates into more robust nitrogen and carbon turnover, driven by a broader suite of degradative and biosynthetic pathways. Communities in vermicompost and combined fertilisation plots exhibited greater phylogenetic dispersion, suggesting improved capacity to withstand environmental perturbations. Biochar’s ability to stabilise soil pH and moisture appears to foster specialised consortia, potentially improving long-term soil structure and plant-microbe interactions. Additional information Conflict of interest The authors have declared that no competing interests exist. Ethical statement No ethical statement was reported. Use of AI No use of AI was reported. Funding This research was funded by the Bulgarian National “Science Fund”, grant number KP06-DO 02/5. The authors acknowledge the financial support through the partners of the Joint Call of the Cofund ERA-Nets SusCrop (grant No. 771134), FACCE ERA-GAS (grant No. 696356), ICT-AGRI-FOOD (grant No. 862665) and SusAn (grant No. 696231). Author contributions Conceptualization, S.S.; methodology, S.S. and M.P.; formal analysis, M.P., V.P., N.M. and I.N.; investigation, M.P., V.P., I.N. and S.S.; resources, S.S.; writing – original draft preparation, M.P. and S.S.; writing – review and editing, M.P., S.S., I.R.; supervision, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript. Author ORCIDs Stefan Shilev https://orcid.org/0000-0002-1172-881X Mariana Petkova https://orcid.org/0000-0001-5122-9575 617 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics Ivelina Neykova https://orcid.org/0009-0005-5163-3128 Data availability All of the data that support the findings of this study are available in the main text. References Ai C, Liang G, Sun J, He P, Tang S, Yang S, Zhou W, Wang X (2015) The alleviation of acid soil stress in rice by inorganic or organic ameliorants is associated with changes in soil enzyme activity and microbial community composition. Biology and Fertility of Soils 51(4): 465–477. https://doi.org/10.1007/s00374-015-0994-3 Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecology 26(1): 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x Arancon NQ, Edwards CA, Bierman P (2006) Influences of vermicomposts on field strawberries: Part 2. Effects on soil microbiological and chemical properties. Bioresource Technology 97(6): 831–840. https://doi.org/10.1016/j.biortech.2005.04.016 Banerjee A (2021) Biochar Production for Green Environment. Clean Coal Technologies: Beneficiation, Utilization, Transport Phenomena and Prospective. Springer International Publishing, Cham, 533–556. https://doi.org/10.1007/978-3-030-68502-7_21 Berg G, Rybakova D, Fischer D, Cernava T, Vergès MCC, Charles T, Chen X, Cocolin L, Eversole K, Corral GH, Kazou M, Kinkel L, Lange L, Lima N, Loy A, Macklin JA, Maguin E, Mauchline T, McClure R, Mitter B, Ryan MJ, Sarand I, Smidt H, Schelkle B, Roume H, Sessitsch A, Schlüter A, Schleper C (2020) Microbiome definition re-visited: Old concepts and new challenges. Microbiome 8: 103. https://doi.org/10.1186/ s40168-020-00875-0 Bonanomi G, De Filippis F, Cesarano G, La Storia A, Ercolini D, Scala F (2016) Organic farming induces changes in soil microbiota that affect agro-ecosystem functions. Soil Biology & Biochemistry 103: 327–336. https://doi.org/10.1016/j.soilbio.2016.09.005 Brooker RW, Bennett AE, Cong WF, Daniell TJ, George TS, Hallett PD, Hawes C, Iannetta PPM, Jones HG, Karley AJ, Li L, McKenzie BM, Pakeman RJ, Paterson E, Schöb C, Shen J, Squire G, Watson CA (2015) Improving intercropping: A synthesis of research in agronomy, plant physiology and ecology. The New Phytologist 206(1): 107–117. https://doi.org/10.1111/nph.13132 Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 13(7): 581–583. https://doi.org/10.1038/nmeth.3869 Chen S, Zhou Y, Chen Y, Gu J (2018a) fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34(17): i884–i890. https://doi.org/10.1093/bioinformatics/bty560 Chen Y, Liu J, Liu S (2018b) Effect of long-term mineral fertilizer application on soil enzyme activities and bacterial community composition. Plant, Soil and Environment 64(12): 571–577. https://doi.org/10.17221/658/2018-PSE Congreves KA, Van Eerd LL (2015) Nitrogen cycling and management in intensive horticultural systems. Nutrient Cycling in Agroecosystems 102: 299–318. https://doi. org/10.1007/s10705-015-9704-7 Domínguez J, Edwards CA (2011) Biology and ecology of earthworm species used for vermicomposting. In: Edwards CA, Arancon NQ, Sherman R (Eds) Vermiculture Tech- 618 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics nology: Earthworms, Organic Wastes, and Environmental Management. CRC Press, 27–40. https://doi.org/10.1201/b10453-4 Escudero-Martinez C, Coulter M, Alegria Terrazas R, Foito A, Kapadia R, Pietrangelo L, Bulgarelli D (2022) Identifying plant genes shaping microbiota composition in the barley rhizosphere. Nature Communications 13(1): 3443. https://doi.org/10.1038/ s41467-022-31022-y Fierer N (2017) Embracing the unknown: Disentangling the complexities of the soil microbiome. Nature Reviews. Microbiology 15(10): 579–590. https://doi.org/10.1038/ nrmicro.2017.87 Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88(6): 1354–1364. https://doi.org/10.1890/05-1839 Finney DM, White CM, Kaye JP (2016) Biomass production and carbon/nitrogen ratio influence ecosystem services from cover crop mixtures. Agronomy Journal 108(1): 39–52. https://doi.org/10.2134/agronj15.0182 Hartmann M, Frey B, Mayer J, Mäder P, Widmer F (2015) Distinct soil microbial diversity under long-term organic and conventional farming. The ISME Journal 9(5): 1177–1194. https://doi.org/10.1038/ismej.2014.210 Janssen PH (2006) Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Applied and Environmental Microbiology 72(3): 1719–1728. https://doi.org/10.1128/AEM.72.3.1719-1728.2006 Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Molecular Biology and Evolution 30(4): 772–780. https://doi.org/10.1093/molbev/mst010 Kielak AM, Barreto CC, Kowalchuk GA, van Veen JA, Kuramae EE (2016) The ecology of Acidobacteria: Moving beyond genes and genomes. Frontiers in Microbiology 7: 744. https://doi.org/10.3389/fmicb.2016.00744 Kulmatiski A (2020) Amplicon ITS and 16S rRNA sequencing of soil metagenome: quantifying plant-soil feedback effects in classic diversity-productivity experiments. Data Publication. Larkin RP (2015) Soil health paradigms and implications for disease management. Annual Review of Phytopathology 53: 199–221. https://doi.org/10.1146/annurev-phyto-080614-120357 Lazcano C, Gómez-Brandón M, Revilla P, Domínguez J (2013) Short-term effects of organic and inorganic fertilizers on soil microbial community structure and function: A field study with sweet corn. Biology and Fertility of Soils 49(6): 723–733. https://doi. org/10.1007/s00374-012-0761-7 Lehmann J, Joseph S (2015) Biochar for Environmental Management: Science, Technology and Implementation. 2nd edn. Routledge. https://doi.org/10.4324/9780203762264 Lehmann J, Rillig MC, Thies J, Masiello CA, Hockaday WC, Crowley D (2011) Biochar effects on soil biota – A review. Soil Biology & Biochemistry 43(9): 1812–1836. https://doi.org/10.1016/j.soilbio.2011.04.022 Li R, Khafipour E, Krause DO, Entz MH, de Kievit TR, Fernando WGD (2013) Pyrosequencing reveals the influence of organic and conventional farming systems on bacterial communities. PLoS ONE 8(12): e51897. https://doi.org/10.1371/journal. pone.0051897 Liu P, Liu WJ, Jiang H, Chen JJ, Li WW, Yu HQ (2012) Modification of bio-char derived from fast pyrolysis of biomass and its application in removal of tetracycline from aqueous solution. Bioresource Technology 121: 235–240. https://doi.org/10.1016/j. biortech.2012.06.085 619 Metabarcoding and Metagenomics 9: 595–620 (2025), DOI: 10.3897/mbmg.9.167231 Stefan Shilev et al.: Barley rhizosphere bacteriome dynamics Lozupone C, Knight R (2011) UniFrac: A new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71(12): 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 Lozupone C, Hamady M, Knight R (2005) UniFrac-an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics 6: 276. https://doi.org/10.1186/1471-2105-6-276 Magoč T, Salzberg SL (2011) FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics (Oxford, England) 27(21): 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 Natalio AI, Back MA, Richards A, Jeffery S (2024) Field-scale heterogeneity overrides management impacts following conversion to no-till within an arable system. Applied Soil Ecology 193: 105104. https://doi.org/10.1016/j.apsoil.2023.105104 Nissimov J (2017) A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551: 457–463. https://doi.org/10.1038/nature24621 Pathma J, Sakthivel N (2012) Microbial diversity of vermicompost bacteria that exhibit useful agricultural traits and waste management potential. SpringerPlus 1(1): 26. https://doi.org/10.1186/2193-1801-1-26 Peoples MB, Brockwell J, Herridge DF, Rochester IJ, Alves BJR, Urquiaga S, Boddey RM, Dakora FD, Bhattarai S, Maskey SL, Sampet C, Rerkasem B, Khan DF, Hauggaard-Nielsen H, Jensen ES (2009) The contributions of nitrogen-fixing crop legumes to the productivity of agricultural systems. Symbiosis 48(1–3): 1–17. https://doi.org/10.1007/ BF03179980 Petkova M, Shilev S, Popova V, Neykova I, Minev N (2025) Intercropping of Oats with Vetch Conducts to Improve Soil Bacteriome Diversity and Structure. Microorganisms 13(5): 977. https://doi.org/10.3390/microorganisms13050977 Philippot L, Raaijmakers JM, Lemanceau P, van der Putten WH (2013) Going back to the roots: The microbial ecology of the rhizosphere. Nature Reviews. Microbiology 11(11): 789–799. https://doi.org/10.1038/nrmicro3109 Povilaitis A, Matikienė J, Vismontienė R (2020) Effects of three types of amendments in woodchip-denitrifying bioreactors for tile drainage water treatment. Ecological Engineering 158: 106054. https://doi.org/10.1016/j.ecoleng.2020.106054 Price MN, Dehal PS, Arkin AP (2010) FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Molecular Biology and Evolution 27(7): 1641–1650. https://doi.org/10.1093/molbev/msp077 Prosser JI (2020) Putting science back into microbial ecology: a question of approach. Philosophical Transactions of the Royal Society B 375(1798): 20190240. https://doi. org/10.1098/rstb.2019.0240 Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research 41: D590–D596. https://doi.org/10.1093/ nar/gks1219 Quilliam RS, Glanville HC, Wade SC, Jones DL (2013) Life in the ‘charosphere’–Does biochar in agricultural soils provide a significant habitat for microorganisms? Soil Biology & Biochemistry 65: 287–293. https://doi.org/10.1016/j.soilbio.2013.06.004 Quince C, Walker AW, Simpson JT, Loman NJ, Segata N (2017) Shotgun metagenomics, from sampling to analysis. Nature Biotechnology 35(9): 833–844. https://doi. org/10.1038/nbt.3935 R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/