Soil management–dependent shifts in microbial diversity and nutrient-related functions ultimately shape the nutritional composition of maize and wheat

Soil management–dependent shifts in microbial diversity and nutrient-related functions ultimately shape the nutritional composition of maize and wheat | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Soil management–dependent shifts in microbial diversity and nutrient-related functions ultimately shape the nutritional composition of maize and wheat Njomza Gashi, Péter Fauszt, Péter Dávid, Zsombor Szőke, Piroska Bíróné Molnár, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8392734/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims : Understanding how tillage and fertilization influence soil microbes and crop quality is crucial for sustainable agriculture. This study examined how variations in tillage intensity and fertilization shape soil microbial communities and, through these shifts, affect the nutritional quality of maize and wheat. This approach revealed clear links between microbial community composition and crop nutrient outcomes. Methods : Field experiments were conducted within established long-term trial sites, using two tillage methods, ploughing and deep loosening combined with two fertilization regimes (moderate and high input) across fifteen nutrient treatments. Sixty-four soil and grain samples were analyzed. Soil microbial diversity and functional potential were assessed through metagenomic sequencing, while grain content was determined using standardized biochemical assays. Results : Within long-term trial contexts, it was demonstrated that tillage–fertilization combinations modulate soil microbiomes in ways that propagate to grain nutritional quality. Ploughing increased alpha diversity (p < 0.05) but favored generalist, stress-tolerant taxa associated with rapid nutrient turnover, aligning with higher carotenoid and lipophilic antioxidant contents. In contrast, deep loosening supported more crop-specific, functionally specialized, and apparently more stable microbiomes, reflected in greater accumulation of water-soluble antioxidants. Balanced fertilization was associated with denser, better-connected microbial networks, while excessive inputs disrupted ecological equilibrium and weakened the translation of soil functions to nutritional traits. Conclusions : Crop nutritional quality can be tuned via the soil microbiome: under moderated inputs, ploughing tends to raise carotenoids/lipophilic antioxidants, whereas deep loosening with balanced fertilization favors water-soluble antioxidants; excessive fertilization disrupts microbial balance and weakens these benefits. Soil microbiome long-term field trials Tillage–fertilization synergy Microbial fingerprints Maize- wheat grain nutritional quality Microbiome-aware management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Crops are the primary source of food for the global population. They are both nutritious and strategic, especially in times of crisis when demand for staple products such as flour rises sharply. A clear example was during the COVID-19 pandemic, when disruptions in crop availability, especially grains, led to widespread panic due to food shortages. This highlights the urgent need to develop strategies that not only increase crop production in an environmentally friendly manner but also enhance their nutritional quality. The foundation of our food system lies in the soil, the essential resource for growing crops. To ensure sufficient and high-quality production, maintaining soil health is critical. This is especially important considering that the formation of just one centimeter of soil can take over a thousand years (FAO 2015). Healthy soil is vital for supplying water and nutrients to plants, thereby supporting crop productivity. Beyond supporting crops, soil is also home to diverse microbial communities that play essential roles in ecosystem functioning. These microbes can be either beneficial or harmful to crops, yet all serve specific ecological functions meaning their absence can lead to significant imbalances. Soil is considered healthy when it supports the well-being of plants, animals, and humans in harmony. In fact, soil health is a fundamental component of the One Health approach, which recognizes the interconnectedness of environmental, human, and animal health. According to Lehmann et al. (2020), four key ecosystem services provided by soil include: plant production, water quality regulation, the promotion of human health, and climate change mitigation. Soil characteristics such as structure, texture, moisture, and porosity directly influence plant growth by affecting water retention, nutrient availability, and root development. A well-structured soil enhances air and water movement, facilitates nutrient uptake, and supports robust root systems, all of which are crucial for optimal plant health and yield (Abdul Khalil et al. 2015). To increase productivity, farmers globally apply various soil treatments, often without fully understanding their long-term impacts on soil health. Among these, tillage practices are commonly categorized into conventional and conservation tillage systems. Conservation tillage, which includes no-till, minimum-till, and reduced-till practices, aims to preserve soil structure, moisture, and nutrients. These methods have been shown to improve fertility, water retention, and drought resilience (Lv et al. 2023). Ashworth et al. (2017) reported that no-till systems foster distinct and responsive microbial communities over the long term, shaped by the plant-soil environment. Consequently, conservation tillage may promote more stable and diverse microbial communities. Zero tillage, in particular, avoids plowing before planting, helping to preserve microhabitats and microbial equilibrium. In contrast, conventional tillage involves deep plowing and soil inversion, which can degrade structure and fertility over time. Additionally, the removal of plant residues post-harvest often leaves the soil vulnerable to erosion (Angon et al. 2023). Fertilizer application is another crucial factor. While high fertilizer inputs are often intended to boost crop growth and nutrient content, excessive use can negatively affect soil health. Microbial fertilizers, for example, can reduce soil salinity and pH, aiding plants under salt-alkali stress. However, overuse may disrupt nutrient balances and harm both microbial communities and crop yields (Wu et al. 2024). Excessive phosphorus application, in particular, reduces its utilization efficiency and can degrade soil quality. It significantly lowers urease enzyme activity, vital for nitrogen cycling, and increases the abundance of potentially pathogenic fungi such as Fusarium, Gibberella , and Drechslera , thereby impairing soil microbial health (Liu et al. 2022). To prevent such outcomes, fertilizers must be applied at rates optimized for local conditions. These concerns extend beyond soil chemistry to include soil microbial life, which plays a central role in cycling key nutrients such as nitrogen, phosphorus, sulfur, and iron. These processes are fundamental to plant nutrition and broader ecological balance (Banerjee and van der Heijden 2023). Soil microbes decompose organic matter, fix nitrogen, and solubilize phosphorus, all of which are essential for nutrient cycling and improved plant growth (Chen et al. 2024b). Additionally, microbes around plant roots can trigger defense mechanisms, making plants more resistant to pests and diseases (Ali et al. 2024). Species such as Rhizobium and Azotobacter contribute to nitrogen fixation, while Pseudomonas and Bacillus assist in solubilizing phosphorus and potassium. Decomposers like Streptomyces and certain Ascomycota fungi also play a significant role in organic matter degradation, improving soil fertility and plant health (Chen et al. 2024b). However, soil can also harbor harmful bacteria that pose risks to human and animal health. These include members of the Enterobacteriaceae family such as Enterobacter, Escherichia, Klebsiella, Salmonella , and Shigella (Cruz et al. 2021). Poor water management and improper fertilizer use can lead to their proliferation, posing serious health hazards. While fertilizers have significantly improved food production worldwide, their inefficient and unbalanced use has also led to environmental pollution, nutrient imbalances, and suboptimal crop yields. Some regions face nutrient deficiencies due to limited access to fertilizers, while others suffer from over-fertilization. Imbalances, particularly in the nitrogen-to-phosphorus ratio, have disrupted ecosystems and biodiversity (Penuelas et al. 2023). Considering all these factors, the aim of this study is to comprehensively investigate how different combinations of tillage and fertilization practices affect the composition, diversity, and function of the soil microbial community. In parallel, the study explores how shifts in the soil microbiome translate into changes in the nutritional profile of crops. By examining both conventional and conservation tillage systems in combination with different fertilizer dosages, this research seeks to identify potential links between soil microbial dynamics and crop nutrient status. Ultimately, the goal is to provide insights into sustainable soil management practices that promote microbial health and functionality, enhance soil fertility, and improve the nutritional value of crops, thereby supporting both agricultural productivity and environmental resilience. Materials and Methods Experimental site and sampling process This field experiment was conducted at the Látókép Experimental Site of the University of Debrecen, Hungary (47°33′ N, 21°26′ E), situated on a chernozem soil (Tamás et al. 2023). The site is located in a region with a long-established tradition of maize and wheat cultivation and has served as a long-term experimental field for over 40 years, making it well suited for investigating the effects of different fertilization and tillage practices on soil processes and crop performance. The experiment aimed to explore how varying tillage methods and fertilization levels influence the soil microbiome and the nutritional quality of maize and wheat grains (Fig. 1). To characterize the baseline soil conditions at the experimental site, soil chemical properties were determined prior to the establishment of the treatments at two depths (0–25 and 25–50 cm) (Table 1). Soil parameters analyzed included pH (KCl), soil texture index (KA), organic matter content (humus), total nitrogen, mineral nitrogen (NO₃⁻ + NO₂⁻), AL-extractable phosphorus (P) and potassium (K), exchangeable magnesium and sodium, sulfate (SO₄²⁻), and selected micronutrients (Zn, Cu, Mn). These measurements were conducted to document the initial soil fertility status, chemical composition, and vertical nutrient stratification of the soil profile prior to the evaluation of treatment-specific effects related to tillage, fertilization, and crop type. Table 1 Soil chemical characteristics of the experimental site. Baseline soil properties were measured at two depths (0–25 and 25–50 cm) to document soil fertility and chemical stratification before the application of tillage and fertilization treatments. Soil parameters 0-25 cm soil depth 25-50 cm soil depth pH (KCl) 6.46 6.36 KA (soil texture index) 43.0 44.6 CaCO₃ (%) 0 0 Humus (%) 2.76 2.16 Total N (%) 0.150 0.120 NO₃⁻ + NO₂⁻ (mg kg -1 ) 6.20 1.74 AL-extractable P (mg kg -1 ) 133.4 48.0 AL-extractable K (mg kg -1 ) 239.8 173.6 Mg (mg kg -1 ) 332.4 405.4 Na (mg kg -1 ) 38.0 66.2 Zn (mg kg -1 ) 2.80 0.80 Cu (mg kg -1 ) 5.86 4.54 Mn (mg kg -1 ) 438 406 SO₄²⁻ (mg kg -1 ) 9.25 9.13 A total of 64 soil samples were collected, with 32 samples each from maize-planted and wheat-planted areas. Along with the soil samples, corresponding grain samples from the same plots were collected to evaluate the nutritional quality of the crops under different treatment conditions (Fig. 2). Soil sampling was conducted prior to harvest at crop-specific sampling times. For wheat, soil samples were collected in July, whereas for maize, soil sampling was performed in late September. In both cases, soil was sampled from a depth of 0-31 cm, a zone typically enriched with microbial activity and relevant for the study of microbial diversity. This sampling depth was selected based on prior knowledge that soil microbes, including bacteria, fungi, and archaea, predominantly thrive within this range due to adequate moisture, nutrient availability, and root activity (Aislabie and Deslippe 2013). Within each experimental plot, soil was collected from two parallel sampling lines located near the crop rows to ensure uniform exposure to the applied tillage and fertilization treatments while avoiding edge effects. At each sampling location, multiple soil cores were taken using a sterile soil auger and combined to obtain a composite sample representative of the plot. The collected subsamples were thoroughly homogenized, and visible plant residues and stones were removed prior to further processing. To minimize contamination, sterile tools and gloves were used throughout the sampling procedure. Immediately after collection, soil samples were placed in sterile containers, transported in insulated coolers, and stored at −80 °C until DNA extraction and microbial analyses. Two tillage methods were employed: deep loosening (45 cm), representing reduced tillage, and conventional ploughing (35 cm). These tillage practices were specifically selected because they are commonly used by farmers in Hungary and other parts of Europe. In addition to tillage methods, the experiment included two distinct nutrient regimes: a moderate nutrient setting (Setting M) and an extensive nutrient setting (Setting E), which were designed to simulate common farming practices under varying levels of input. Setting M, the moderate nutrient regime, reflects the typical fertilization practices used by farmers in the region, while Setting E represents a more intensive fertilization strategy to assess the effects of higher nutrient inputs on soil health, microbial diversity, and crop yields (Table 2). Nitrogen (N), phosphorus (P), and potassium (K) were applied as mineral fertilizers commonly used in long-term field experiments, with nutrient inputs expressed as elemental N, P, and K. The study incorporated 15 different fertilization combinations, ranging from a control (no fertilizer) to individual nutrient applications, dual nutrient combinations, and a full NPK treatment (Table 3). Soil samples were collected from two parallel lines near the planting area to ensure uniform exposure to treatments, avoiding side lines due to potential environmental variation. Sterile tools and gloves were used to prevent contamination. Samples were labeled, stored in insulated coolers, and transported under controlled conditions to the laboratory, where they were immediately frozen at -80 °C to preserve microbial integrity until pooling and DNA extraction. Table 2 Fertilization settings. Nutrients dosages in two fertilization settings M and E. Nutrient Setting M Setting E N (kg ha -1 ) 100 150 P (kg ha -1 ) 50 100 K (kg ha -1 ) 70 120 Table 3 Fertilizers combinations. Different combinations of fertilizers and their dosage in each setting: control, setting M and setting E. Setting Amount of fertilizer Control No fertilizer M(N) 100 (kg ha -1 ) N M(P) 50 (kg ha -1 ) P M(K) 70 (kg ha -1 ) K M(N+P) 100 (kg ha -1 ) N + 50 (kg ha -1 ) P M(N+K) 100 (kg ha -1 ) N + 70 (kg ha -1 ) K M(P+K) 50 (kg ha -1 ) P + 70 (kg ha -1 ) K M(N+P+K) 100 (kg ha -1 ) N + 50 (kg ha -1 ) P + 70 (kg ha -1 ) K E(N) 150 (kg ha -1 ) N E(P) 100 (kg ha -1 ) P E(K) 120 (kg ha -1 ) K E(N+P) 150 (kg ha -1 ) N + 100 (kg ha -1 ) P E(N+K) 150 (kg ha -1 ) N + 120 (kg ha -1 ) K E(P+K) 100 (kg ha -1 ) P + 120 (kg ha -1 ) K E(N+P+K) 150 (kg ha -1 ) N + 100 (kg ha -1 ) P + 120 (kg ha -1 ) Samples preparation and mechanical cell lysis Microbial DNA was extracted from soil by first transferring 0.25 grams of a well-homogenized soil sample into a PowerBead lysis tube (Qiagen, Hilden, Germany). To initiate the lysis process, 800 μL of CD1 lysis buffer (Qiagen, Hilden, Germany) was added to each tube. The mixture was vortexed briefly to ensure thorough mixing of the soil matrix within the lysis solution and to promote initial interaction between the microbial cells and the chemical reagents. Mechanical disruption of the microbial cells was then carried out using the MagNA Lyser instrument (Roche Applied Sciences, Penzberg, Germany) at 3000 rpm for 30 seconds. After a 2-minute incubation at 4 °C, the samples underwent a second round of bead-beating under the same conditions (3000 rpm for 30 seconds). Subsequently, the tubes were placed in a dry block thermostat (Bio TDB-100, Biosan, Riga, Latvia) and incubated at 60 °C for 10 minutes. After incubation, the lysates were centrifuged at 16,000 × g for 1 minute at room temperature to pellet insoluble debris, including soil particles, cell wall fragments, and denatured proteins. Between 500 and 600 μL of the clear supernatant was gently transferred to fresh, sterile 1.5 mL microcentrifuge tubes (Qiagen, Hilden, Germany), ensuring that the pellet remained undisturbed. This crude lysate was then used as the input for subsequent DNA purification procedures to remove humic substances, PCR inhibitors, and other residual contaminants commonly present in soil-derived samples. DNA Extraction Procedure DNA extraction was performed using the Qiagen DNeasy Power Soil Pro Kit (Cat. No. 47014, Qiagen, Hilden, Germany), following the manufacturer's protocol. Initially, 200 μL of CD2 solution was added to 500–600 μL of soil supernatant, which was then vortexed for 3 seconds and centrifuged at 15,000× g for 1 minute. The supernatant (700 μL) was transferred to a new microcentrifuge tube, and 600 μL of CD3 solution was added and vortexed for another 3 seconds. Next, 650 μL of the lysate was carefully applied to a QIAamp Mini Spin Column, and the column was centrifuged at 15,000× g for 1 minute. The flow-through was discarded, and the process was repeated with the remaining lysate. During the washing phase, 500 μL of EA solution was added to the column, followed by centrifugation at 15,000× g for 1 minute. The flow-through was discarded, and 500 μL of C5 solution was added, followed by another centrifugation at 15,000× g for 1 minute. Subsequently, 500 μL of 96–100% ethanol was added to the column, followed by centrifugation to remove any residual contaminants. The eluate was discarded, and the column was dried by centrifugation at 15,000× g for 3 minutes. To elute the purified DNA, 70 μL of C6 buffer was added to the column, incubated at room temperature for 3 minutes, and then centrifuged at 15,000× g for 1 minute. The column was discarded, and the purified DNA was collected in a fresh 1.5 mL tube. To ensure the quality and quantity of the extracted DNA, we measured the DNA concentration using a Qubit™ 4 Fluorometer (Thermo Fisher Scientific™, Waltham, MA, USA) and assessed its purity using a NanoDrop™ 2000 Spectrophotometer (Thermo Fisher Scientific™, Franklin, MA, USA). Library preparation, sequencing DNA library preparation and sequencing were carried out following the Illumina 16S Metagenomic Sequencing Library Preparation protocol (15044223 Rev. B) and according to the workflow described by Skopkó et al. (2023). Each reaction started from 12.5 ng of soil-derived DNA. The V3–V4 regions of the bacterial 16S rRNA gene were amplified using the universal primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 785R (5′-GACTACHVGGGTATCTAATCC-3′), both flanked by Illumina overhang adapters. Amplicons (~460 bp) were generated using 2× KAPA HiFi HotStart ReadyMix, indexed with the Nextera XT Index Kit, and purified with MagSI Pure Beads, yielding libraries of approximately 550–630 bp. Library integrity was confirmed using an Agilent D1000 ScreenTape system, quantified by qPCR, normalized, and pooled in equimolar concentrations. The 4 nM pooled library was denatured with 0.2 M NaOH, diluted to 8 pM, and sequenced on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 (600 cycles) following standard procedures. Nutritional analysis Grain samples were analyzed for key nutritional parameters, including protein content, antioxidant activity (both hydrophilic and lipophilic), and carotenoid concentration in maize. Protein levels were determined using the Pierce BCA Protein Assay Kit, a bicinchoninic acid-based colorimetric method. Water-soluble antioxidants were quantified through the Ferric Reducing Antioxidant Power assay (FRAP), while liposoluble antioxidants were assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. In maize, carotenoid content was analyzed using high-performance liquid chromatography (HPLC), allowing for precise quantification. Antioxidants content determination FRAP assay was conducted to determine the water-soluble antioxidants, by measuring the capacity of antioxidants to convert the Fe³⁺–TPTZ complex into its Fe²⁺ form in a low pH medium. The formation of the ferrous–TPTZ complex results in a blue coloration, and the increase in absorbance at 593 nm serves as an indicator of antioxidant activity. The reaction was performed in a 96-well microplate. In each well, 30 µL of distilled water and 10 µL of the diluted sample/ standard were added, followed by 200 µL of freshly prepared FRAP reagent. The reagent was composed of 10 volumes of 250 mM acetate buffer (pH 3.6), one volume of 20 mM ferric chloride, and one volume of 10 mM tripyridyl-s-triazin (TPTZ) dissolved in 40 mM HCl. The plate was then incubated at 37 °C, and absorbance was measured after 8 minutes at 593 nm. The antioxidant capacity obtained from the FRAP assay was calculated and expressed as milligrams of ascorbic acid equivalents per 100 g of sample (maize and wheat) (Benzie and Strain 1996; Nemes et al. 2018). The liposoluble antioxidant activity in the grain samples was determined using a modified DPPH microplate method, originally described by Blois (1958) and Brand-Williams et al. (1995). Approximately 25 mg of finely ground lyophilized grain sample was extracted with 1 mL of distilled water under vortex agitation for 1.5 min, followed by centrifugation at 10 000 rpm for 10 min. The supernatant was collected and used for the assay. A 0.1 mM DPPH solution was prepared daily by dissolving 2 mg of DPPH in 50 mL of methanol and stored in the dark until use. In each well of a 96-well microplate, 10 µL of the appropriately diluted aqueous extract or Trolox standard solution (31.25–1000 µM) was mixed with 50 µL of distilled water and 190 µL of the DPPH reagent. The plate was gently shaken and incubated for 30 min at 25 °C in the dark. The decrease in absorbance was measured at 517 nm using a microplate reader, with methanol and DPPH serving as blank. The DPPH radical scavenging activity was calculated from the Trolox calibration curve and expressed as milligrams of Trolox equivalents per 100 g of dry grain sample (mg Trolox/ 100g), representing the water-soluble antioxidant capacity of the sample (Nemes et al. 2018). Proteins content determination The total protein content of the grain samples was quantified using the bicinchoninic acid (BCA) method with the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, MAN0011430). The assay is based on the reduction of Cu²⁺ to Cu⁺ by peptide bonds under alkaline conditions, followed by colorimetric detection of the cuprous ion with bicinchoninic acid, forming a purple complex measurable at 562 nm. Briefly, 25 μL of the appropriately diluted grain extract or bovine serum albumin (BSA) standard (20–2000 μg/mL) was pipetted into each well of a 96-well microplate. Then, 200 μL of freshly prepared working reagent (reagents A and B mixed in a 50:1 ratio) was added. The plate was gently shaken and incubated at 37 °C for 30 min, after which the absorbance was measured at 562 nm using a microplate reader. Protein concentrations were determined from the BSA calibration curve and expressed as milligrams of protein per 100g of grain sample (mg/100g). Carotenoid content determination in maize samples Carotenoids were extracted from 0.20 g of homogenized feed maize using 1 mL of dichloromethane: acetone: methanol (2:1:1) and a Magna Lyser (6000 rpm, 30 s, twice). After centrifugation (10,000 rpm, 5 min), the supernatant was collected. The pellet was re-extracted with the same solvent and processed similarly. Combined extracts were evaporated at 40 °C, reconstituted in 300 µL of 80% acetone, and filtered (0.22 µm) prior to analysis. High-performance liquid chromatography (HPLC) was performed using a Waters Alliance e2695 system with a PDA detector (2998). Separation was achieved on a Waters XSelect C18 column (100 × 4.6 mm, 3.5 µm) at 30 °C, with a flow rate of 1.0 mL/min. The mobile phases were A - acetonitrile: water: triethylamine (90:10:0.1, v/v/v) and B - ethyl acetate. A gradient elution was applied over 35 minutes. Detection wavelengths were 450 nm and 470 nm. Chromatograms were evaluated using Empower 3 software, and carotenoids were identified based on retention times of known standards. Statistical analysis and data visualization All statistical analyses and visualizations were conducted in RStudio (v2025.09.0+387, Posit Software, 2025). Sequencing data were filtered, normalized, and transformed using the phyloseq package prior to downstream analyses. Alpha diversity was calculated with vegan, and treatment effects were evaluated using the Kruskal-Wallis and Wilcoxon rank-sum tests after verifying data non-normality with the Shapiro-Wilk test. Beta diversity was assessed using Bray-Curtis and UniFrac distance matrices and visualized through Principal Coordinate Analysis (PCoA) and Non-metric Multidimensional Scaling (MDS); group differences were tested with PERMANOVA. Differential abundance of microbial taxa and predicted functional genes was determined using DESeq2, with results visualized as volcano plots in ggplot2. Random Forest classification (randomForest package) was used to identify key genera contributing to community differentiation, with model accuracy evaluated via the out-of-bag (OOB) error rate and variable importance ranked by Mean Decrease Accuracy and Mean Decrease Gini. Correlations between microbial taxa and crop nutritional parameters were assessed using Spearman’s rank correlation and visualized as heatmaps and correlation matrices with pheatmap and corrplot. Microbial co-occurrence networks were constructed using NetCoMi from CLR-transformed abundance data based on Pearson correlations. Network topology was characterized by modularity, density, and degree centrality. Unless otherwise stated, statistical significance was determined at p < 0.05. All graphical outputs, including bar plots, heatmaps, pie charts, and network visualizations, were produced in ggplot2 and related R packages. Results Effects of tillage and fertilization practices on microbial alpha diversity Results indicated that conventional ploughing had a statistically significant positive effect on the alpha diversity of soil microbial communities in both maize and wheat (p<0.05). In both cropping systems, the measured diversity proved to be higher under treatment involving greater soil disturbance (ploughing) compared to the one under reduced tillage (deep loosening) (Fig. 3a and 3b). Fertilization treatments also had a clear impact on alpha diversity in both maize and wheat, with certain nutrient combinations consistently associated with lower or higher diversity (Fig. 3c and 3d). In maize, the lowest diversity values were generally observed in treatments with nitrogen or phosphorus alone, particularly under setting M (2M N and 3M P). In contrast, the highest diversity in maize was found in the treatment combining nitrogen and phosphorus (5M N+P), followed by the full NPK treatment under setting M (8M N+P+K). Similarly, in wheat, low diversity was also linked to nitrogen-only treatments (2M N) and the full NPK treatment in setting M (8M NPK). The highest diversity in wheat appeared under nitrogen with potassium (6M NK), phosphorus alone in setting E (3E P), and potassium alone (4E K). Management-driven shifts in soil microbial fingerprints and structural composition Variation in soil microbial community composition was closely associated with differences in crop type, tillage intensity, and fertilization level (Fig. 4). Distinct microbial fingerprints were observed between maize and wheat rhizosphere soils. Approximately 20.51% of taxa were unique to maize, 48.72% unique to wheat, and 30.77% shared between both crops (Fig. 4a). Maize-associated soils contained Gemmatirosa kalamazoonesis, Paenibacillus pectinilyticus, and Niastella yeongjuensis as characteristic members, whereas wheat soils were dominated by Archangium violaceum, Microvirga aerilata, and Pseudonocardia alaniniphila . The shared community consisted mainly of Oryzihumus leptocrescens, Bacillus asahii, and Agromyces ramosus . Among these, O. leptocrescens accounted for about 63.32% of its abundance in maize and 36.68% in wheat. Community composition also varied with tillage intensity. Under ploughing, 48.72% of species were unique, whereas 28.21% occurred only under deep loosening, and 23.08% were shared (Fig. 4b). Ploughed soils were characterized by Archangium violaceum, Microvirga aerilata, Pseudonocardia alaniniphila , and Azotobacter beijerinckii , while deep-loosened soils included Nocardioides islandensis and Gemmatirosa kalamazoonesis . The shared fraction, dominated by Oryzihumus leptocrescens, Bacillus asahii, Agromyces ramosus , and Nordella oligomobilis , comprised nearly half of all detected species, with B. asahii more prevalent in ploughed soils (73.82% of its total abundance). Distinct microbial assemblages were also evident among fertilization treatments. The control accounted for 12.82% unique species, setting M for 23.08%, and setting E for 30.77%, while the shared community made up 17.95% of the total detected species (Fig. 4c). Control soils contained Microvirga aerilata, Pseudonocardia alaniniphila, and Azotobacter beijerinckii as unique members; setting M favored Archangium violaceum and Ferruginibacter paludis ; and setting E included Roseimicrobium gellanilyticum, Paenibacillus pectinilyticus , and Niastella yeongjuensis . Across all treatments, a consistent core community, comprising Oryzihumus leptocrescens, Bacillus asahii, Agromyces ramosus, Nordella oligomobilis, Nocardioides islandensis, Noviherbaspirillum canariense, and Bacillus funiculus , remained present. Within this shared group, O. leptocrescens and B. asahii were mostly present. Following the compositional patterns observed in Figure 4, multivariate analyses were conducted to explore how management practices structured soil microbial communities. The PCoA plots (Fig. 5a-5c) show distinct clustering patterns according to crop type, tillage method, and fertilization settings. While fertilization showed no significant effect and produced substantial overlap among samples (p=0.405, R²=0.03), crop type (p=0.001, R²=0.09) and tillage (p=0.001, R²=0.07) resulted in clearer separations. To further explore the influence of soil disturbance, non-metric multidimensional scaling (MDS) analyses were performed separately for each tillage practice (Fig. 5d, 5e). Under ploughing, maize and wheat samples partially overlapped, reflecting a more mixed microbial composition and less crop specificity. In contrast, under deep loosening, maize and wheat formed more distinct clusters. To identify the microbial groups contributing most strongly to these clustering patterns, a Random Forest classification was applied, and the key genera were visualized in the barcode plots (Fig. 5f, 5g). Under ploughing, genera such as Bacillus, Gaiella, Bryobacter, and Gemmatimonas were among the major contributors to the observed variability. Conversely, deep loosening was characterized by Bacillus, Gaiella, Nocardioides , and Nitrospira . Functional profiling and predicted metabolic potential of soil microbiome To complement the taxonomic and community-level patterns, functional profiling was conducted to assess differences in functional gene abundance across crop type, tillage, and fertilization settings. This analysis was performed using computational predictions derived from the identified microbial taxa, rather than direct sequencing of functional genes, to estimate the potential metabolic functions associated with each management condition. The analysis revealed distinct variations in the metabolic potential of soil microbial communities under different agricultural practices (Fig. 6). Although most functional genes showed no significant change, specific subsets were differentially regulated depending on the factor considered. Across crop types, the observed functional divergence reflected the contrasting rhizosphere conditions of maize and wheat. Wheat soils exhibited a higher representation of functional genes related to ABC transporters and glycosyltransferases. Maize soils also contained genes within these categories but at comparatively lower abundance. Tillage exerted the strongest influence on functional differentiation. Deep loosening favored genes associated with transport and carbohydrate metabolism, including ABC transporters, glycosyltransferases, and two-component systems, consistent with a more stable and less disturbed soil environment that supports balanced nutrient cycling. In contrast, ploughing led to the enrichment of genes involved in peptidoglycan biosynthesis and degradation, antimicrobial resistance, peptidases, and bacterial toxin production. Under fertilization, functional shifts were less pronounced but still evident. High-input soils (fertilization E) showed enrichment of antimicrobial resistance genes and ABC transporters. Influence of tillage intensity on crop nutritional quality Different soil treatments influence not only the soil microbiome but also the nutritional profile of crops (Fig. 7). Regarding tillage, in maize, the distribution of nutritional categories was more balanced under ploughing, with similar proportions of high and moderate levels, while deep loosening produced a more uneven pattern dominated by low nutritional profiles (Fig. 7a). In wheat, a similar pattern was observed for the high nutritional profile, which was also promoted by ploughing; however, the medium nutritional profile was more prevalent under deep loosening, while low nutritional values were more common in ploughed soils (Fig. 7c). In this case, ploughing showed a more fragmented distribution, with high and low categories dominating and moderate values being scarce, whereas deep loosening resulted in a more balanced composition across the three nutritional levels. In addition, the distribution of nutritional categories under deep loosening appeared more fragmented, showing greater variability between high, moderate, and low levels, whereas ploughing resulted in a more balanced pattern with closer proportions among these categories. When examining specific nutrients, the effects of tillage varied between crops. Protein content was comparable across both tillage systems and crops, showing no significant differences. For liposoluble and water-soluble antioxidants, contrasting responses were recorded between maize and wheat. In maize, ploughing enhanced the concentration of liposoluble antioxidants, while deep loosening favored water-soluble antioxidants (Fig. 7b). Conversely, in wheat, the opposite trend was observed, with deep loosening significantly increasing liposoluble antioxidant levels (p<0.05) and ploughing supporting higher water-soluble antioxidant concentrations (Fig. 7d). In maize, carotenoid content was also evaluated, with ploughing significantly supporting their synthesis (p<0.001). Crop-specific nutritional responses to fertilization intensity Figure 8 illustrates the influence of different fertilization settings and tillage practices on the nutritional profile and composition of maize and wheat. Overall, the distribution of low, medium, and high nutritional profiles varied notably between crops. In maize, the highest nutritional profile was observed under the combined nutrient settings 7M P+K and 8M N+P+K, while medium nutritional levels were more frequent under the control, 5M N+P, and 8E N+P+K treatments. The lowest nutritional profile was predominantly associated with 6E N+K, 2M N, and 7E P+K (Fig. 9a). In wheat, the high nutritional profile was mostly found under 2M N, 5M N+P, and 5E N+P. Medium nutritional levels occurred under 8E N+P+K, 2E N, and 4E K, while low nutritional values were detected in the control, 7E P+K, and 8M N+P+K treatments (Fig. 9b). At the level of specific nutrients, consistent differences were observed between fertilization settings M and E for both crops. In maize, carotenoid and protein contents remained largely unchanged between the two settings, indicating that these parameters were not strongly influenced by fertilization intensity. However, both water-soluble and liposoluble antioxidants were slightly higher under setting E (Fig. 8c). In wheat, a similar pattern was detected for the antioxidant compounds, with both liposoluble and water-soluble antioxidants showing higher proportions under setting E, whereas protein content remained comparable between treatments, with a minor tendency to increase under setting M (Fig. 8c). Overall, higher fertilization levels enhanced antioxidant activity in both crops, while moderate fertilization maintained stable protein levels. In line with these findings, figure 8c illustrates the proportional contribution of major nutrient classes in maize and wheat. While the overall distribution remained relatively stable across treatments, several crop- and nutrient-specific trends were evident. In maize, the highest relative share of liposoluble antioxidants occurred under fertilization 5E, while water-soluble antioxidants peaked under 4E. Protein contribution reached its maximum under 4E, and carotenoids showed their highest proportion under 6E. In wheat, the highest proportion of liposoluble antioxidants appeared under 2E, whereas water-soluble antioxidants reached their maximum under 3E and protein levels were highest under 7M. Microbial correlations with crop nutritional parameters The correlation analysis revealed distinct linkages between microbial taxa and crop nutritional parameters in both maize and wheat, highlighting how specific microbial families and genera may influence or respond to the synthesis of key nutritional compounds (Fig. 9). In maize (Fig. 9a), positive correlations were observed between Clostridiaceae and liposoluble antioxidants (r=0.47), and between Microbacteriaceae and water-soluble antioxidants (r=0.45). Conversely, Bacillaceae exhibited a negative correlation with protein content (r=−0.44). At the genus level, Pirellula showed a positive relationship with protein content (r = 0.50), whereas Nocardioides correlated negatively with carotenoids (r=−0.46). In wheat (Fig. 9b), microbial associations displayed more consistent positive correlations with antioxidant compounds. Families such as Propionibacteriaceae (r=0.57) and Azospirillaceae (r=0.54) were positively correlated with liposoluble antioxidants, while Haliangiaceae showed a positive relationship with water-soluble antioxidants (r=0.48). In contrast, Planococcaceae (r=−0.74) and Bacillaceae (r=−0.67) were strongly negatively correlated with fat-soluble antioxidants. At the genus level, Microlunatus (r=0.58) and Skermanella (r=0.54) were positively correlated with liposoluble antioxidants, whereas Bacillus and Oryzihumus exhibited negative correlations, consistent with the family-level patterns. For water-soluble antioxidants, Haliangium again showed a positive association (r=0.48). Microbial network responses to management practices Co-occurrence network analysis revealed clear differences in microbial community structure across crop, tillage, and fertilization settings, with distinct topological patterns corresponding to variations in the nutritional profiles of maize and wheat (Fig. 10). Across all networks, modularity values ranged from 0.019 to 0.190, while density values varied between 0.276 and 0.460. The highest modularity values were observed in networks associated with lower nutritional profiles. In contrast, networks related to higher nutritional profiles displayed lower modularity and greater density, with tighter microbial associations and more integrated community organization. Within the crop-based networks, wheat showed slightly higher modularity (up to 0.091) compared with maize (0.065). The tillage-based networks revealed that deep loosening generated the highest modularity (0.162), while ploughing produced denser and less modular networks. Under fertilization settings, setting M exhibited intermediate modularity values (0.108–0.121) and higher density (up to 0.460), whereas setting E showed a broader modularity range (0.048–0.190). Discussion Tillage intensity and nutrient composition are key drivers of soil microbial diversity but act through different mechanisms. The findings of this study show that both tillage and fertilization substantially shape the diversity and structure of soil microbial communities in maize and wheat. The observed increase in microbial alpha diversity under ploughing suggests that intense soil disturbance creates dynamic microenvironments that stimulate microbial proliferation. Mechanical disturbance likely imposes stress conditions similar to those in disease ecology, where stress often triggers higher microbial activity. There is growing evidence that under combined environmental pressures, microbial diversity can increase through the coexistence of stress-tolerant taxa (Chen et al. 2024a). This supports the broader idea that stress in microbial communities during disturbance promotes adaptive responses rather than reflecting favorable growth conditions (Rocca et al. 2019). Enhanced aeration and organic-matter exposure following ploughing could further promote microbial growth, although such effects are transient. Galand et al. (2016) found that moderate disturbance increases microbial production and phylogenetic diversity in the short term, promoting substrate turnover and remineralization. Over time, however, excessive disturbance can degrade soil structure and reduce microbial stability, as reported by Sándor et al. (2020). Fertilization exerted a complex and crop-specific influence on microbial diversity. In maize, the highest diversity observed under the nitrogen-phosphorus (5M N+P) and full NPK (8M N+P+K) treatments suggests that balanced multi-nutrient inputs create complementary resource conditions that sustain broader microbial functionality. The simultaneous supply of nitrogen, phosphorus, and potassium likely enhances microbial cooperation and nutrient turnover through processes such as nitrogen fixation, phosphorus solubilization, and organic-matter decomposition. In contrast, single-nutrient inputs (2M N, 3M P) likely narrow the metabolic spectrum, favoring copiotrophic taxa and reducing overall heterogeneity. This pattern is consistent with Dai et al. (2018), who reported that long-term nitrogen fertilization alone decreases bacterial diversity due to acidification, whereas balanced NPK improves diversity by increasing soil organic carbon and pH. In wheat, however, diversity peaked under moderate nutrient availability (6M NK), implying that optimal competition occurs when resources are neither limiting nor excessive. The diversity decline under full NPK (8M NPK) suggests nutrient oversupply disrupts microbial equilibrium by accelerating plant uptake or favoring copiotroph dominance. Similar crop-dependent responses have been documented by Kong et al. (2023), who showed that long-term fertilization significantly affected soil bacterial communities, but these effects vary depending on the cropping system, highlighting the importance of crop type in microbial responses to nutrient input. These differences are mainly because of changes in root exudates, nutrient uptake, and soil microenvironments. Beyond diversity, distinct microbial fingerprints and community composition revealed clear crop-specific patterns shaped by tillage and fertilization. Nearly half of the detected taxa were unique to wheat, indicating that its carbon-rich rhizodeposits favor decomposer and biocontrol taxa such as Archangium violaceum and Pseudonocardia alaniniphila , which thrive in organic-matter-rich zones and degrade complex compounds, forming characteristic microbial fingerprints associated with wheat-dominated rhizospheres (Whatmough et al. 2024; Saraf and Sharma 2025). In contrast, maize enriched Paenibacillus pectinilyticus and Gemmatirosa kalamazoonesis , both linked to phosphorus solubilization and mineralization processes (DeBruyn et al. 2013; Li et al. 2017). Tillage further influenced microbial assembly through physical and oxygen-related stress. Ploughed soils favored Azotobacter beijerinckii and other fast-growing, oxygen-responsive taxa (Aasfar et al. 2021). Conversely, deep loosening supported Nocardioides islandensis and Gemmatirosa kalamazoonesis , indicating that reduced disturbance maintains stable habitats for microbes involved in long-term nitrogen and carbon cycling (Ma et al. 2023; Zhao et al. 2023). Nutrient balance also played a crucial role: extensive fertilization (setting E) promoted copiotrophic taxa such as Paenibacillus pectinilyticus , while moderate fertilization (setting M) maintained a functionally balanced microbiome. The control soils favored nitrogen-fixers like Microvirga aerilata , adapted to nutrient-poor conditions, consistent with evidence that long-term nutrient surpluses drive copiotrophic dominance and reduce functional equilibrium (Shu et al. 2023). Across all treatments, Bacillus asahii and Oryzihumus leptocrescens formed a stable core microbiome, highlighting their central role in decomposition, soil fertility, crop yield and nutrient recycling under variable management (Jiang et al. 2019). While the ecological role of O. leptocrescens has not yet been fully characterized, its consistent occurrence across treatments suggests potential functional importance within the soil microbial network. Their recurrent presence and central positioning indicate that these taxa may act as keystone species, representing promising targets for management strategies aimed at enhancing soil fertility and crop nutritional quality. Functional analyses further confirmed that tillage intensity was the dominant factor shaping both microbial structure and metabolic potential. Deep loosening enhanced rhizosphere-driven selection and maintained distinct crop-specific microbiomes, whereas ploughing increased microbial mixing and favored generalist taxa adapted to fluctuating conditions. These findings align with long-term studies showing that reduced disturbance increases microbial biomass and richness, while ploughing promotes saprotrophic fungi and generalist bacteria (Domnariu et al. 2025). Functional profiles mirrored these structural differences: deep loosening enhanced genes related to transport, carbohydrate metabolism, and nutrient cycling (e.g., ABC transporters, glycosyltransferases), indicating a stable soil environment that supports cooperative microbial functioning. Similar outcomes were reported by Dong et al. (2024) who found that deep tillage increased carbohydrate-metabolism genes, including glycoside hydrolases, improving organic-matter turnover and nutrient cycling. In contrast, ploughing increased stress- and defense-related genes, such as peptidases and antimicrobial resistance modules, indicating that disturbance triggers competitive microbial behavior (Wang et al. 2023). These microbial shifts were reflected in the nutritional profiles of maize and wheat. Ploughing generally promoted higher nutritional values, particularly in maize, where elevated carotenoid and liposoluble antioxidant levels likely resulted from improved aeration and nutrient mineralization stimulating secondary metabolism. Similar outcomes were reported by Xiao et al. (2025), who observed that plough tillage with straw incorporation improved soil nutrients, root growth, and nutrient content. Khan et al. (2024) also emphasized that maize productivity and nutrient status depend on enhanced nutrient turnover and aeration. In contrast, deep loosening favored water-soluble antioxidants, reflecting enhanced root–microbe interactions and stress-related defense responses under stable soil conditions. Wheat showed an opposite trend: deep loosening increased liposoluble antioxidants, while ploughing favored water-soluble ones, reflecting crop-specific differences in nutrient uptake and moisture distribution. The opposite trend in wheat may stem from its finer, shallower root system and higher sensitivity to soil aeration and moisture. Fertilization affected crop quality primarily through nutrient balance and intensity. In maize, moderate NPK input (setting M) produced the highest nutritional values, while over-fertilization or single-nutrient applications reduced quality. Wheat responded best to combined nitrogen and phosphorus, but high nutrient supply did not consistently enhance quality, likely due to metabolic saturation. Across both crops, higher fertilization increased antioxidant activity but not protein content, suggesting that nutrient excess stimulates secondary, stress-related metabolism rather than primary protein synthesis. This pattern highlights the potential of controlled fertilization regimes to modulate the synthesis of bioactive metabolites, which could be valuable for medical or functional food production where specific antioxidant or stress-related compounds are targeted. This agrees also with Ahmadi et al. (2024), who reported that conventional tillage combined with phosphorus fertilization improved wheat yield and quality, while no-till with phosphorus maintained high yields and enhanced bread quality. Correlations between microbial taxa and crop nutritional traits revealed that the links were both taxon- and crop-specific. In maize, decomposer and nutrient-cycling taxa such as Clostridiaceae and Microbacteriaceae correlated positively with antioxidants, while Bacillaceae correlated negatively with protein, indicating potential nutrient competition. In wheat, Azospirillaceae and Propionibacteriaceae showed positive associations with antioxidant accumulation, suggesting that nitrogen-fixing and metabolically versatile taxa enhance redox regulation (Jaiswal et al. 2022). This pattern supports evidence that beneficial microbes improve plant antioxidant genes activity and maintain redox homeostasis under stress (González et al. 2024). The stronger correlations between antioxidant compounds and microbial variability highlight the functional role of beneficial microbes, particularly nitrogen-fixing and metabolically active taxa such as Azospirillaceae, Microlunatus , and Propionibacteriaceae , in enhancing cereal nutritional quality through improved nutrient cycling and redox balance. The co-occurrence network analysis demonstrated that the structural organization of soil microbial communities influenced the nutritional outcomes of maize and wheat. Microbial networks exhibiting higher modularity values were composed of distinct, highly specialized ecological clusters with limited interconnections. Although such compartmentalized structures likely supported stable soil processes, they appeared less effective in facilitating nutrient transfer to crops, as these communities corresponded to grain samples with comparatively lower nutritional enrichment. Conversely, networks characterized by lower modularity and higher density formed more integrated and interactive microbial systems. The increased number of connections among taxa in these less modular networks likely enhanced nutrient cycling efficiency and rhizosphere cooperation, which coincided with greater accumulation of proteins, carotenoids, and both lipophilic and water-soluble antioxidants in maize and wheat. Intermediate configurations, with moderate modularity and density around, appeared to represent a transitional state in which the microbial community maintained both functional stability and efficient nutrient exchange. Overall, these findings indicate that higher microbial modularity promoted ecological compartmentalization but reduced nutrient translocation to plants, whereas lower modularity and higher network density supported more dynamic nutrient exchange, ultimately contributing to improved crop nutritional quality. This highlights that the balance between microbial specialization and connectivity is a key determinant of soil-plant nutrient interactions. These findings are consistent with recent evidence showing that the topological structure of microbial networks strongly regulates soil ecosystem functioning. Studies by Yang et al. (2024) and Byers et al. (2025) demonstrated that networks with lower modularity and higher connectivity promote greater nutrient cycling efficiency and multifunctionality, as enhanced inter-taxa interactions facilitate metabolic cooperation and resource exchange. Conversely, highly modular and compartmentalized networks, while stable, tend to reduce nutrient flow between microbial clusters and limit overall ecosystem productivity. This supports the idea that the balance between microbial specialization and connectivity governs the efficiency of soil–plant nutrient transfer and, ultimately, crop nutritional outcomes. Collectively, these results demonstrate that balanced nutrient input and minimal physical disturbance enhance microbial cooperation, functional diversity, and soil resilience, while intensive management simplifies network organization and reduces ecological specialization. The consistency between these findings and broader ecological studies reinforces the principle that environmental balance, through nutrient regulation and limited disturbance, is central to sustaining complex and resilient soil microbial ecosystems. Conclusions Soil management practices jointly shaped the ecological and functional organization of the soil microbiome and, consequently, the nutritional composition of maize and wheat. The interaction between tillage and fertilization regulated microbial diversity, specialization, and nutrient cycling efficiency. Although ploughing increased alpha diversity, it favored generalist and stress-tolerant taxa linked to rapid but unstable nutrient turnover. Conversely, deep loosening maintained crop-specific microbial networks with higher modularity and functional resilience, supporting more efficient nutrient transformation. Moderate, balanced fertilization reinforced these stable configurations by enhancing microbial connectivity and metabolic complementarity, whereas excessive or single-nutrient inputs promoted loss of ecological specialization. These microbial shifts were reflected in plant nutritional outcomes, with reduced disturbance favoring water-soluble antioxidants and ploughing stimulating carotenoid and lipophilic antioxidant synthesis. Overall, the results demonstrated that soil fertility and crop nutritional quality were emergent properties of microbiome organization, emphasizing that sustainable agriculture depends on optimizing, not intensifying, soil disturbance and nutrient supply. Future studies should integrate long-term field monitoring and functional metagenomic analyses to elucidate the mechanistic links between soil microbial networks, nutrient cycling, and crop nutritional outcomes under changing environmental and management conditions. Declarations Author’s contributions: Njomza Gashi, Melinda Paholcsek: Writing-original draft; Péter Fauszt: Data curation; Njomza Gashi: Visualization; Njomza Gashi, Péter Fauszt: Formal analysis; Péter Fauszt: Software; Péter Dávid, Péter Fauszt, Zsombor Szőke, Piroska Bíróné Molnár, Andrea Kun-Nemes, Erzsébet Szőllősi: Methodology; Njomza Gashi, László Zsombik, Péter Fauszt, Péter Dávid, Zsombor Szőke, Piroska Bíróné Molnár, Andrea Kun-Nemes, Erzsébet Szőllősi, László Stündl, Ferenc Gál, Judit Remenyik, Melinda Paholcsek: Writing-review & editing, Njomza Gashi, Péter Dávid, Zsombor Szőke, Melinda Paholcsek: Conceptualization, Melinda Paholcsek: Project administration, Judit Remenyik, Melinda Paholcsek: Funding acquisition, Melinda Paholcsek: Supervision. Acknowledgements: Supported by the University of Debrecen Scientific Research Bridging Fund (DETKA). Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests: The authors declare no conflict of interest. References Aasfar A, Bargaz A, Yaakoubi K, et al (2021) Nitrogen Fixing Azotobacter Species as Potential Soil Biological Enhancers for Crop Nutrition and Yield Stability. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.628379 Abdul Khalil HPS, Hossain MdS, Rosamah E, et al (2015) The role of soil properties and it’s interaction towards quality plant fiber: A review. Renewable and Sustainable Energy Reviews 43:1006–1015. https://doi.org/10.1016/j.rser.2014.11.099 Ahmadi H, Mirseyed Hosseini H, Moshiri F, et al (2024) Impact of varied tillage practices and phosphorus fertilization regimes on wheat yield and grain quality parameters in a five-year corn-wheat rotation system. Sci Rep 14:14717. https://doi.org/10.1038/s41598-024-65784-w Aislabie J, Deslippe JR (2013) Soil Microbes and Their Contribution to Soil Services. In: Ecosystem Services in New Zealand: Conditions and Trends. Manaaki Whenua Press, Lincoln, New Zealand, pp 143–161 Ali Q, Ali M, Jing H, et al (2024) Power of plant microbiome: A sustainable approach for agricultural resilience. Plant Stress 14:100681. https://doi.org/10.1016/j.stress.2024.100681 Angon PB, Anjum N, Akter MstM, et al (2023) An Overview of the Impact of Tillage and Cropping Systems on Soil Health in Agricultural Practices. Advances in Agriculture 2023:8861216. https://doi.org/10.1155/2023/8861216 Ashworth AJ, DeBruyn JM, Allen FL, et al (2017) Microbial community structure is affected by cropping sequences and poultry litter under long-term no-tillage. Soil Biology and Biochemistry 114:210–219. https://doi.org/10.1016/j.soilbio.2017.07.019 Banerjee S, van der Heijden MGA (2023) Soil microbiomes and one health. Nat Rev Microbiol 21:6–20. https://doi.org/10.1038/s41579-022-00779-w Benzie IFF, Strain JJ (1996) The Ferric Reducing Ability of Plasma (FRAP) as a Measure of “Antioxidant Power”: The FRAP Assay. Analytical Biochemistry 239:70–76. https://doi.org/10.1006/abio.1996.0292 Blois MS (1958) Antioxidant Determinations by the Use of a Stable Free Radical. Nature 181:1199–1200. https://doi.org/10.1038/1811199a0 Brand-Williams W, Cuvelier ME, Berset C (1995) Use of a free radical method to evaluate antioxidant activity. LWT - Food Science and Technology 28:25–30. https://doi.org/10.1016/S0023-6438(95)80008-5 Byers AK, Wakelin SA, Condron L, Black A (2025) Land Use Change Disrupts the Network Complexity and Stability of Soil Microbial Carbon Cycling Genes Across an Agricultural Mosaic Landscape. Microb Ecol 87:167. https://doi.org/10.1007/s00248-024-02487-9 Chen L, Li Z, Liu W, Lyu B (2024a) Impact of high temperature and drought stress on the microbial community in wolf spiders. Ecotoxicology and Environmental Safety 283:116801. https://doi.org/10.1016/j.ecoenv.2024.116801 Chen Q, Song Y, An Y, et al (2024b) Soil Microorganisms: Their Role in Enhancing Crop Nutrition and Health. Diversity 16:734. https://doi.org/10.3390/d16120734 Cruz D, Cisneros R, Benítez Á, et al (2021) Gram-Negative Bacteria from Organic and Conventional Agriculture in the Hydrographic Basin of Loja: Quality or Pathogen Reservoir? Agronomy 11:2362. https://doi.org/10.3390/agronomy11112362 Dai Z, Su W, Chen H, et al (2018) Long-term nitrogen fertilization decreases bacterial diversity and favors the growth of Actinobacteria and Proteobacteria in agro-ecosystems across the globe. Global Change Biology 24:3452–3461. https://doi.org/10.1111/gcb.14163 DeBruyn JM, Fawaz MN, Peacock AD, et al (2013) Gemmatirosa kalamazoonesis gen. nov., sp. nov., a member of the rarely-cultivated bacterial phylum Gemmatimonadetes. The Journal of General and Applied Microbiology 59:305–312. https://doi.org/10.2323/jgam.59.305 Domnariu H, Trippe KM, Botez F, et al (2025) Long-term impact of tillage on microbial communities of an Eastern European Chernozem. Sci Rep 15:642. https://doi.org/10.1038/s41598-024-84590-y Dong F, Wang L, Xu T, et al (2024) Multi-omics analysis of soil microbiota and metabolites in dryland wheat fields under different tillage methods. Sci Rep 14:24066. https://doi.org/10.1038/s41598-024-74620-0 FAO (2015) Healthy soils are the basis for healthy food production Galand PE, Lucas S, Fagervold SK, et al (2016) Disturbance Increases Microbial Community Diversity and Production in Marine Sediments. Front Microbiol 7:1950. https://doi.org/10.3389/fmicb.2016.01950 González M-C, Roitsch T, Pandey C (2024) Antioxidant Responses and Redox Regulation Within Plant-Beneficial Microbe Interaction. Antioxidants 13:1553. https://doi.org/10.3390/antiox13121553 Jaiswal S, Aneja B, Jagannadham J, et al (2022) Unveiling the Wheat Microbiome under Varied Agricultural Field Conditions. Microbiology Spectrum 10:e02633-22. https://doi.org/10.1128/spectrum.02633-22 Jiang H, Feng Y, Zhao F, Lin X (2019) Characteristics and Complete Genome Analysis of Bacillus asahii OM18, a Bacterium in Relation to Soil Fertility in Alkaline Soils Under Long-Term Organic Manure Amendment. Curr Microbiol 76:1512–1519. https://doi.org/10.1007/s00284-019-01766-w Khan H, Khan A, Khan S, et al (2024) Maize productivity and nutrient status in response to crop residue mineralization with beneficial microbes under various tillage practices. Soil and Tillage Research 239:106057. https://doi.org/10.1016/j.still.2024.106057 Kong W, Qiu L, Ishii S, et al (2023) Contrasting response of soil microbiomes to long-term fertilization in various highland cropping systems. ISME COMMUN 3:81. https://doi.org/10.1038/s43705-023-00286-w Lehmann J, Bossio DA, Kögel-Knabner I, Rillig MC (2020) The concept and future prospects of soil health. Nat Rev Earth Environ 1:544–553. https://doi.org/10.1038/s43017-020-0080-8 Li Y, Liu X, Hao T, Chen S (2017) Colonization and Maize Growth Promotion Induced by Phosphate Solubilizing Bacterial Isolates. International Journal of Molecular Sciences 18:1253. https://doi.org/10.3390/ijms18071253 Liu H, Li S, Qiang R, et al (2022) Response of Soil Microbial Community Structure to Phosphate Fertilizer Reduction and Combinations of Microbial Fertilizer. Front Environ Sci 10:. https://doi.org/10.3389/fenvs.2022.899727 Lv L, Gao Z, Liao K, et al (2023) Impact of conservation tillage on the distribution of soil nutrients with depth. Soil and Tillage Research 225:105527. https://doi.org/10.1016/j.still.2022.105527 Ma Y, Wang J, Liu Y, et al (2023) Nocardioides: “Specialists” for Hard-to-Degrade Pollutants in the Environment. Molecules 28:7433. https://doi.org/10.3390/molecules28217433 Nemes A, Szollosi E, Stündl L, et al (2018) Determination of Flavonoid and Proanthocyanidin Profile of Hungarian Sour Cherry. Molecules 23:3278. https://doi.org/10.3390/molecules23123278 Penuelas J, Coello F, Sardans J (2023) A better use of fertilizers is needed for global food security and environmental sustainability. Agriculture & Food Security 12:5. https://doi.org/10.1186/s40066-023-00409-5 Rocca JD, Simonin M, Blaszczak JR, et al (2019) The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities. Front Microbiol 9:. https://doi.org/10.3389/fmicb.2018.03272 Sándor Z, Magdolna T, Kincses I, et al (2020) Effect of various soil cultivation methods on some microbial soil properties. DRC Sustainable Future: Journal of Environment, Agriculture, and Energy 1:14–20. https://doi.org/10.37281/DRCSF/1.1.3 Saraf N, Sharma G (2025) Comparative genomic insight into the myxobacterial carbohydrate-degrading potential. Front Microbiol 16:. https://doi.org/10.3389/fmicb.2025.1550287 Shu X, Liu W, Huang H, et al (2023) Meta-Analysis of Organic Fertilization Effects on Soil Bacterial Diversity and Community Composition in Agroecosystems. Plants 12:3801. https://doi.org/10.3390/plants12223801 Skopkó B, Paholcsek M, Szilágyi-Rácz A, et al (2023) High-Throughput Sequencing Analysis of the Changes in the Salivary Microbiota of Hungarian Young and Adult Subpopulation by an Anthocyanin Chewing Gum and Toothbrush Change. Dentistry Journal 11:44. https://doi.org/10.3390/dj11020044 Tamás A, Kovács E, Horváth É, et al (2023) Assessment of NDVI Dynamics of Maize (Zea mays L.) and Its Relation to Grain Yield in a Polyfactorial Experiment Based on Remote Sensing. Agriculture 13:689. https://doi.org/10.3390/agriculture13030689 University of Debrecen Press (2017) A unique long-term field experiment in Europe | University of Debrecen. https://hirek.unideb.hu/en/unique-long-term-field-experiment-europe. Accessed 20 Oct 2025 Wang W, Shen P, Lu Z, et al (2023) Metagenomics reveals the abundance and accumulation trend of antibiotic resistance gene profile under long-term no tillage in a rainfed agroecosystem. Front Microbiol 14:. https://doi.org/10.3389/fmicb.2023.1238708 Whatmough B, Holmes NA, Wilkinson B, et al (2024) Microbe Profile: Pseudonocardia: antibiotics for every niche. Microbiology 170:001501. https://doi.org/10.1099/mic.0.001501 Wu Q, Chen Y, Dou X, et al (2024) Microbial fertilizers improve soil quality and crop yield in coastal saline soils by regulating soil bacterial and fungal community structure. Science of The Total Environment 949:175127. https://doi.org/10.1016/j.scitotenv.2024.175127 Xiao Y, Luo W, Yang K, et al (2025) Plow tillage with buried straw increases maize yield by regulating soil properties, root growth, photosynthetic capacity, and bacterial community assembly in semi-arid black soil farmlands. European Journal of Agronomy 164:127532. https://doi.org/10.1016/j.eja.2025.127532 Yang X, Song W, Yang X, et al (2024) Microbial network structure, not plant and microbial community diversity, regulates multifunctionality under increased precipitation in a cold steppe. Front Microbiol 14:. https://doi.org/10.3389/fmicb.2023.1349747 Zhao X, Qin X, Jing X, et al (2023) Key genes of electron transfer, the nitrogen cycle and tetracycline removal in bioelectrochemical systems. 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Gashi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2Nv0oDQRCHJwhns5J2q/gKIwHjQUheZeUg1RpbizOczXUm7RV5Cd9gl8VNs5J2AymsUl1xVbjqMJo7sdjElIL7FcP8+X0MgMfzJxFNQ0AUzW7XA56iyOw05RsCZ+THx4NKr6VeX8oYBrPZm1T9+PEe6J0U+cMaepQ5lTDRI0s1RJkdM8X1IkzomMm52UCYuRUU5tpiABFYgooHGoFyVBepArRHFFZBdLk0qG6qRql+UWQKAxS7ZCuNayU5pujR6mlK2ZXlKJ+nAgOSo5zrDUHz7las0qty2x92lqZblNsJts95t8jjdQcX7i9AxVe9TfajgqA+EGf8k/Y+O6zHycGgx+Px/F8+AMqsbsLP29NkAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-7384-5292","institution":"University of Debrecen: Debreceni Egyetem","correspondingAuthor":true,"prefix":"","firstName":"Njomza","middleName":"","lastName":"Gashi","suffix":""},{"id":565513931,"identity":"cf85a619-6957-451a-beb1-c31b26c7fd90","order_by":1,"name":"Péter 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Remenyik","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Judit","middleName":"","lastName":"Remenyik","suffix":""},{"id":565513941,"identity":"723075cd-c0ec-4f23-a477-1e7259210230","order_by":11,"name":"Melinda Paholcsek","email":"","orcid":"https://orcid.org/0000-0001-7171-245X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Melinda","middleName":"","lastName":"Paholcsek","suffix":""}],"badges":[],"createdAt":"2025-12-18 08:11:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8392734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8392734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99315797,"identity":"007fac34-e7d2-449a-a17a-ac3061ac3b4f","added_by":"auto","created_at":"2025-12-31 16:27:22","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11321,"visible":true,"origin":"","legend":"","description":"","filename":"plsoPLSOD2504946.xml","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/fc2a86a12234115340a4c68b.xml"},{"id":99129053,"identity":"4a27ca36-72cb-48de-87c9-d6210ae328a6","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1114,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD250494668036.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/d2b7ce89c97f5d60d51f380e.xml"},{"id":99129056,"identity":"a6ce88bb-18a8-4c5b-a4c4-7d280781acad","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":839,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD2504946Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/023eecf210dde3523666c19c.xml"},{"id":99129051,"identity":"5d7b017c-88b1-46f5-a99a-259409468568","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1428501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental field settings.\u003c/strong\u003e (a\u003csub\u003e1\u003c/sub\u003e) Map of Hungary indicating the location of the sampling site, with a picture from the site (a\u003csub\u003e2\u003c/sub\u003e) and a satellite image of the Látókép experimental field (a\u003csub\u003e3\u003c/sub\u003e) showing the layout used in this study. (b) Experimental design illustrating the combination of tillage practices (ploughing and deep loosening) and fertilization treatments: moderate (M) (figure b\u003csub\u003e2\u003c/sub\u003e) and extensive (E) (figure b\u003csub\u003e1\u003c/sub\u003e) fertilizer application levels, including their respective sub-settings. Figure marked with an asterisk (*) is adapted from University of Debrecen Press (University of Debrecen Press 2017).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/892d1467806666ba9c8d70ef.png"},{"id":99315550,"identity":"777c4368-e784-475c-9726-8a69643aac12","added_by":"auto","created_at":"2025-12-31 16:27:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiome and nutrient assessment.\u003c/strong\u003e The figure presents the microbiome and nutrient analyses performed in this study on soil and crop samples collected from maize and wheat. Asterisk (*) denotes samples analyzed using 16S rRNA gene-based taxonomic sequencing.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/08c01ff6cb82dafe8401d6f2.png"},{"id":99129047,"identity":"59e4bb94-0f53-40ef-ba21-d81e1ddf3abb","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity under different tillage and fertilization systems.\u003c/strong\u003ePanels (a) and (b) show Shannon diversity under different tillage settings for maize and wheat, respectively. Panels (c) and (d) present heatmaps of Shannon diversity under different fertilization treatments in maize and wheat. Colors represent changes relative to the control, where red indicates the lowest values, yellow the highest, and orange corresponds to the control level. M stands for moderate fertilization E stands for extensive fertilization. Asterisks indicate significant differences (p \u0026lt; 0.05*).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/d73959b708d35e5c80afe735.png"},{"id":99314697,"identity":"70bb36a0-d66e-467a-8389-e88093f46323","added_by":"auto","created_at":"2025-12-31 16:22:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":414371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial fingerprints under different crop, tillage, and fertilization systems. \u003c/strong\u003eVenn diagrams and relative frequency distributions illustrate the microbial fingerprints associated with key management factors: (a) crop type (maize vs. wheat), (b) tillage practice (ploughing vs. deep loosening), and (c) fertilization settings (control, setting M: moderate fertilization, and setting E: extensive fertilization). Top panels show the proportion of unique and shared microbial taxa among treatments, while bottom panels display the relative frequencies of these taxa, highlighting distinct crop-, tillage-, and fertilization-specific microbial fingerprints. For both unique and shared taxa, only those with a relative frequency ≥ 0.001 were retained for visualization and analysis. The complete list of unique and shared species, along with their corresponding percentage values, is provided in Online Resource 1.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/83afd5ff780d063e59bb01e1.png"},{"id":99315639,"identity":"dd6f85b5-d549-4ea8-a6ca-b5da8a1b8af4","added_by":"auto","created_at":"2025-12-31 16:27:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":339058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstrained ordination and classification of soil microbial communities under different management practices. \u003c/strong\u003e(a–c) Constrained Analysis of Principal Coordinates (CAP) showing community differentiation by crop type, tillage, and fertilization based on Bray-Curtis dissimilarities. (d–e) Non-metric Multidimensional Scaling (MDS) plots illustrate crop-specific clustering within each tillage system, with clearer separation under deep loosening. (f–g) Random Forest classification identifying key genera contributing to community differentiation under ploughing and deep loosening. M stands for moderate fertilization E stands for extensive fertilization. The complete list of genera and the corresponding results from Random Forest test are provided in Online Resource 2.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/a3b1c1aa4eb6a0d763c5366d.png"},{"id":99129060,"identity":"b8084770-4b1e-42f2-b292-e5bbbe388445","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":258734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots illustrating the differential abundance of functional genes under different agricultural settings: (a) crop type, (b) tillage, and (c) fertilization.\u003c/strong\u003e Each point represents a single enzyme. The x-axis shows the logarithm of fold change (log₂(FC)), while the y-axis shows the negative logarithm of the p-value (−log₁₀(p)), representing statistical significance. Functional genes were considered significantly differentially abundant when log₂(FC) \u0026gt;2 or \u0026lt;−2 and p\u0026lt;0.05. Gray points represent functional genes that did not meet these thresholds, while colored points denote significantly upregulated and downregulated genes, respectively. On the right side of each volcano plot, vertical bar charts display the top five significantly upregulated genes for each respective setting, based on their log₂(FC) values, alongside tables summarizing the main functional pathways associated with each gene. M stands for moderate fertilization E stands for extensive fertilization\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/a02a012c20eea70aad0c2060.png"},{"id":99315037,"identity":"7fa244f0-ef37-410d-b279-2295f5a06314","added_by":"auto","created_at":"2025-12-31 16:26:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":250502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNutritional composition of maize and wheat under different tillage practices.\u003c/strong\u003eProportions of low, medium, and high nutritional profiles in maize (a) and wheat (c) under ploughing and deep loosening tillage. Concentrations of carotenoids, liposoluble antioxidants, water-soluble antioxidants, and protein in maize (b) and wheat (d) under ploughing and deep loosening tillage. Error bars represent standard errors; significance: * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/a7f3d41b1225d1ed145a56c0.png"},{"id":99314612,"identity":"e781b6ec-61c7-4812-ad39-6f3d814a8b67","added_by":"auto","created_at":"2025-12-31 16:22:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":321777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of different fertilization settings on crop nutritional content.\u003c/strong\u003eNutritional levels (high, medium, low) are shown for maize (a) and wheat (b) in bar plots, while the proportional contribution of individual nutrients is illustrated for maize and wheat in pie charts (c). M stands for moderate fertilization E stands for extensive fertilization.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/be891af344069e8adba9c229.png"},{"id":99129062,"identity":"fb056b31-a5b0-4068-907e-fc2c666ec0f8","added_by":"auto","created_at":"2025-12-29 03:59:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":415551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between microbial taxa and nutrient composition in maize and wheat. \u003c/strong\u003eHeatmaps showing Spearman correlations between microbial taxa (family and genus level) and crops nutrients in (a) maize and (b) wheat. Positive (yellow) and negative (blue) correlations highlight how specific microbial groups are associated with protein, carotenoids, fat-soluble antioxidants, and water-soluble antioxidants.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/7452b9716653c4bb957f26dc.png"},{"id":99129058,"identity":"bff3b33a-9798-4748-a2f7-1aa88095d726","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1494977,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analysis of soil microbial communities associated with different nutritional profiles (low, medium, and high) of maize and wheat. Each panel represents the co-occurrence networks constructed for the corresponding crop (maize or wheat), tillage system (ploughing or deep loosening), and fertilization regime (M: moderate, E: extensive). Nodes represent microbial taxa, and edges indicate significant positive correlations among them (p\u0026lt;0.05). Node colors correspond to distinct modules (clusters) of co-occurring taxa identified through hierarchical clustering, while node size reflects the degree of connectivity, indicating the relative influence of each taxon within the network. Highly connected nodes denote potential hub taxa contributing to community stability and information flow. Mo denotes modularity, quantifying the strength of network subdivision into distinct microbial modules, and De denotes network density, representing the proportion of realized connections among all possible links.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/f42ed4b271c13c03af1cce9f.png"},{"id":99323444,"identity":"7175584a-da9d-45eb-8987-83d4cb71b3bc","added_by":"auto","created_at":"2025-12-31 16:45:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6769882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/11fd8b89-2718-45e6-a3f8-b0c7ab9d3134.pdf"},{"id":99315816,"identity":"6573aa35-4d57-423c-bd15-5fda391d76ed","added_by":"auto","created_at":"2025-12-31 16:27:22","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1372960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/4673259f7d60ab145b09d415.png"},{"id":99129046,"identity":"6a516859-8421-452e-9c0f-505bb0cf89de","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18030,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/512837937eb9fa0eff5efd1d.xlsx"},{"id":99129050,"identity":"3bc6d1ae-ce56-4bb2-9198-0fcf2d656a01","added_by":"auto","created_at":"2025-12-29 03:59:23","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":52565,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8392734/v1/8e573a795ac3319998d0a73d.xlsx"}],"financialInterests":"","formattedTitle":"Soil management–dependent shifts in microbial diversity and nutrient-related functions ultimately shape the nutritional composition of maize and wheat","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCrops are the primary source of food for the global population. They are both nutritious and strategic, especially in times of crisis when demand for staple products such as flour rises sharply. A clear example was during the COVID-19 pandemic, when disruptions in crop availability, especially grains, led to widespread panic due to food shortages. This highlights the urgent need to develop strategies that not only increase crop production in an environmentally friendly manner but also enhance their nutritional quality. The foundation of our food system lies in the soil, the essential resource for growing crops. To ensure sufficient and high-quality production, maintaining soil health is critical. This is especially important considering that the formation of just one centimeter of soil can take over a thousand years (FAO 2015). Healthy soil is vital for supplying water and nutrients to plants, thereby supporting crop productivity.\u003c/p\u003e\n\u003cp\u003eBeyond supporting crops, soil is also home to diverse microbial communities that play essential roles in ecosystem functioning. These microbes can be either beneficial or harmful to crops, yet all serve specific ecological functions meaning their absence can lead to significant imbalances. Soil is considered healthy when it supports the well-being of plants, animals, and humans in harmony. In fact, soil health is a fundamental component of the One Health approach, which recognizes the interconnectedness of environmental, human, and animal health. According to Lehmann et al. (2020), four key ecosystem services provided by soil include: plant production, water quality regulation, the promotion of human health, and climate change mitigation. Soil characteristics such as structure, texture, moisture, and porosity directly influence plant growth by affecting water retention, nutrient availability, and root development. A well-structured soil enhances air and water movement, facilitates nutrient uptake, and supports robust root systems, all of which are crucial for optimal plant health and yield (Abdul Khalil et al. 2015).\u003c/p\u003e\n\u003cp\u003eTo increase productivity, farmers globally apply various soil treatments, often without fully understanding their long-term impacts on soil health. Among these, tillage practices are commonly categorized into conventional and conservation tillage systems. Conservation tillage, which includes no-till, minimum-till, and reduced-till practices, aims to preserve soil structure, moisture, and nutrients. These methods have been shown to improve fertility, water retention, and drought resilience (Lv et al. 2023). Ashworth et al. (2017) reported that no-till systems foster distinct and responsive microbial communities over the long term, shaped by the plant-soil environment. Consequently, conservation tillage may promote more stable and diverse microbial communities. Zero tillage, in particular, avoids plowing before planting, helping to preserve microhabitats and microbial equilibrium. In contrast, conventional tillage involves deep plowing and soil inversion, which can degrade structure and fertility over time. Additionally, the removal of plant residues post-harvest often leaves the soil vulnerable to erosion (Angon et al. 2023).\u003c/p\u003e\n\u003cp\u003eFertilizer application is another crucial factor. While high fertilizer inputs are often intended to boost crop growth and nutrient content, excessive use can negatively affect soil health. Microbial fertilizers, for example, can reduce soil salinity and pH, aiding plants under salt-alkali stress. However, overuse may disrupt nutrient balances and harm both microbial communities and crop yields (Wu et al. 2024).\u003c/p\u003e\n\u003cp\u003eExcessive phosphorus application, in particular, reduces its utilization efficiency and can degrade soil quality. It significantly lowers urease enzyme activity, vital for nitrogen cycling, and increases the abundance of potentially pathogenic fungi such as \u003cem\u003eFusarium, Gibberella\u003c/em\u003e, and \u003cem\u003eDrechslera\u003c/em\u003e, thereby impairing soil microbial health (Liu et al. 2022). To prevent such outcomes, fertilizers must be applied at rates optimized for local conditions.\u003c/p\u003e\n\u003cp\u003eThese concerns extend beyond soil chemistry to include soil microbial life, which plays a central role in cycling key nutrients such as nitrogen, phosphorus, sulfur, and iron. These processes are fundamental to plant nutrition and broader ecological balance (Banerjee and van der Heijden 2023). Soil microbes decompose organic matter, fix nitrogen, and solubilize phosphorus, all of which are essential for nutrient cycling and improved plant growth (Chen et al. 2024b). Additionally, microbes around plant roots can trigger defense mechanisms, making plants more resistant to pests and diseases (Ali et al. 2024). Species such as \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003eAzotobacter\u003c/em\u003e contribute to nitrogen fixation, while Pseudomonas and Bacillus assist in solubilizing phosphorus and potassium. Decomposers like Streptomyces and certain \u003cem\u003eAscomycota\u003c/em\u003e fungi also play a significant role in organic matter degradation, improving soil fertility and plant health (Chen et al. 2024b). However, soil can also harbor harmful bacteria that pose risks to human and animal health. These include members of the \u003cem\u003eEnterobacteriaceae\u003c/em\u003e family such as \u003cem\u003eEnterobacter, Escherichia, Klebsiella, Salmonella\u003c/em\u003e, and \u003cem\u003eShigella\u003c/em\u003e (Cruz et al. 2021).\u0026nbsp;Poor water management and improper fertilizer use can lead to their proliferation, posing serious health hazards. While fertilizers have significantly improved food production worldwide, their inefficient and unbalanced use has also led to environmental pollution, nutrient imbalances, and suboptimal crop yields. Some regions face nutrient deficiencies due to limited access to fertilizers, while others suffer from over-fertilization. Imbalances, particularly in the nitrogen-to-phosphorus ratio, have disrupted ecosystems and biodiversity (Penuelas et al. 2023).\u003c/p\u003e\n\u003cp\u003eConsidering all these factors, the aim of this study is to comprehensively investigate how different combinations of tillage and fertilization practices affect the composition, diversity, and function of the soil microbial community. In parallel, the study explores how shifts in the soil microbiome translate into changes in the nutritional profile of crops. By examining both conventional and conservation tillage systems in combination with different fertilizer dosages, this research seeks to identify potential links between soil microbial dynamics and crop nutrient status. Ultimately, the goal is to provide insights into sustainable soil management practices that promote microbial health and functionality, enhance soil fertility, and improve the nutritional value of crops, thereby supporting both agricultural productivity and environmental resilience.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eExperimental site and sampling process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis field experiment was conducted at the L\u0026aacute;t\u0026oacute;k\u0026eacute;p Experimental Site of the University of Debrecen, Hungary (47\u0026deg;33\u0026prime; N, 21\u0026deg;26\u0026prime; E), situated on a chernozem soil (Tam\u0026aacute;s et al. 2023). The site is located in a region with a long-established tradition of maize and wheat cultivation and has served as a long-term experimental field for over 40 years, making it well suited for investigating the effects of different fertilization and tillage practices on soil processes and crop performance. The experiment aimed to explore how varying tillage methods and fertilization levels influence the soil microbiome and the nutritional quality of maize and wheat grains (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo characterize the baseline soil conditions at the experimental site, soil chemical properties were determined prior to the establishment of the treatments at two depths (0\u0026ndash;25 and 25\u0026ndash;50 cm) (Table 1). Soil parameters analyzed included pH (KCl), soil texture index (KA), organic matter content (humus), total nitrogen, mineral nitrogen (NO₃⁻ + NO₂⁻), AL-extractable phosphorus (P) and potassium (K), exchangeable magnesium and sodium, sulfate (SO₄\u0026sup2;⁻), and selected micronutrients (Zn, Cu, Mn). These measurements were conducted to document the initial soil fertility status, chemical composition, and vertical nutrient stratification of the soil profile prior to the evaluation of treatment-specific effects related to tillage, fertilization, and crop type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e \u003cstrong\u003eSoil chemical characteristics of the experimental site.\u003c/strong\u003e Baseline soil properties were measured at two depths (0\u0026ndash;25 and 25\u0026ndash;50 cm) to document soil fertility and chemical stratification before the application of tillage and fertilization treatments.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"73%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-25 cm soil depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25-50 cm soil depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003epH (KCl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e6.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eKA (soil texture index)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eCaCO₃ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eHumus (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eTotal N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eNO₃⁻ + NO₂⁻ (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eAL-extractable P (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e133.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eAL-extractable K (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e239.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e173.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eMg (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e332.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e405.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eNa (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e66.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eZn (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eCu (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eMn (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eSO₄\u0026sup2;⁻ (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e9.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA total of 64 soil samples were collected, with 32 samples each from maize-planted and wheat-planted areas. Along with the soil samples, corresponding grain samples from the same plots were collected to evaluate the nutritional quality of the crops under different treatment conditions (Fig. 2).\u003c/p\u003e\n\u003cp\u003eSoil sampling was conducted prior to harvest at crop-specific sampling times. For wheat, soil samples were collected in July, whereas for maize, soil sampling was performed in late September. In both cases, soil was sampled from a depth of 0-31 cm, a zone typically enriched with microbial activity and relevant for the study of microbial diversity. This sampling depth was selected based on prior knowledge that soil microbes, including bacteria, fungi, and archaea, predominantly thrive within this range due to adequate moisture, nutrient availability, and root activity\u0026nbsp;(Aislabie and Deslippe 2013). Within each experimental plot, soil was collected from two parallel sampling lines located near the crop rows to ensure uniform exposure to the applied tillage and fertilization treatments while avoiding edge effects. At each sampling location, multiple soil cores were taken using a sterile soil auger and combined to obtain a composite sample representative of the plot. The collected subsamples were thoroughly homogenized, and visible plant residues and stones were removed prior to further processing. To minimize contamination, sterile tools and gloves were used throughout the sampling procedure. Immediately after collection, soil samples were placed in sterile containers, transported in insulated coolers, and stored at \u0026minus;80 \u0026deg;C until DNA extraction and microbial analyses.\u003c/p\u003e\n\u003cp\u003eTwo tillage methods were employed: deep loosening (45 cm), representing reduced tillage, and conventional ploughing (35 cm). These tillage practices were specifically selected because they are commonly used by farmers in Hungary and other parts of Europe. In addition to tillage methods, the experiment included two distinct nutrient regimes: a moderate nutrient setting (Setting M) and an extensive nutrient setting (Setting E), which were designed to simulate common farming practices under varying levels of input. Setting M, the moderate nutrient regime, reflects the typical fertilization practices used by farmers in the region, while Setting E represents a more intensive fertilization strategy to assess the effects of higher nutrient inputs on soil health, microbial diversity, and crop yields (Table 2). Nitrogen (N), phosphorus (P), and potassium (K) were applied as mineral fertilizers commonly used in long-term field experiments, with nutrient inputs expressed as elemental N, P, and K. The study incorporated 15 different fertilization combinations, ranging from a control (no fertilizer) to individual nutrient applications, dual nutrient combinations, and a full NPK treatment (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil samples were collected from two parallel lines near the planting area to ensure uniform exposure to treatments, avoiding side lines due to potential environmental variation. Sterile tools and gloves were used to prevent contamination. Samples were labeled, stored in insulated coolers, and transported under controlled conditions to the laboratory, where they were immediately frozen at -80 \u0026deg;C to preserve microbial integrity until pooling and DNA extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e \u003cstrong\u003eFertilization settings.\u003c/strong\u003e Nutrients dosages in two fertilization settings M and E.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"316\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting M\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eN (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eP\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eK (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e120\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e \u003cstrong\u003eFertilizers combinations.\u003c/strong\u003e Different combinations of fertilizers and their dosage in each setting: control, setting M and setting E.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"438\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmount of fertilizer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 321px;\"\u003e\n \u003cp\u003eNo fertilizer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e50 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e70 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(N+P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N + 50 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(N+K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N + 70 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(P+K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e50 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P + 70 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eM(N+P+K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N + 50 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P + 70 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e150 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e120 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(N+P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e150 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N + 100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(N+K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e150 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N + 120 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(P+K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P + 120 (kg ha\u003csup\u003e-1\u003c/sup\u003e) K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003eE(N+P+K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 321px;\"\u003e\n \u003cp\u003e150 (kg ha\u003csup\u003e-1\u003c/sup\u003e) N + 100 (kg ha\u003csup\u003e-1\u003c/sup\u003e) P + 120 (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSamples preparation and mechanical cell lysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMicrobial DNA was extracted from soil by first transferring 0.25 grams of a well-homogenized soil sample into a PowerBead lysis tube (Qiagen, Hilden, Germany). To initiate the lysis process, 800 \u0026mu;L of CD1 lysis buffer (Qiagen, Hilden, Germany) was added to each tube. The mixture was vortexed briefly to ensure thorough mixing of the soil matrix within the lysis solution and to promote initial interaction between the microbial cells and the chemical reagents. Mechanical disruption of the microbial cells was then carried out using the MagNA Lyser instrument (Roche Applied Sciences, Penzberg, Germany) at 3000 rpm for 30 seconds. After a 2-minute incubation at 4 \u0026deg;C, the samples underwent a second round of bead-beating under the same conditions (3000 rpm for 30 seconds). Subsequently, the tubes were placed in a dry block thermostat (Bio TDB-100, Biosan, Riga, Latvia) and incubated at 60 \u0026deg;C for 10 minutes. After incubation, the lysates were centrifuged at 16,000 \u0026times; g for 1 minute at room temperature to pellet insoluble debris, including soil particles, cell wall fragments, and denatured proteins. Between 500 and 600 \u0026mu;L of the clear supernatant was gently transferred to fresh, sterile 1.5 mL microcentrifuge tubes (Qiagen, Hilden, Germany), ensuring that the pellet remained undisturbed. This crude lysate was then used as the input for subsequent DNA purification procedures to remove humic substances, PCR inhibitors, and other residual contaminants commonly present in soil-derived samples.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDNA Extraction Procedure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDNA extraction was performed using the Qiagen DNeasy Power Soil Pro Kit (Cat. No. 47014, Qiagen, Hilden, Germany), following the manufacturer\u0026apos;s protocol. Initially, 200 \u0026mu;L of CD2 solution was added to 500\u0026ndash;600 \u0026mu;L of soil supernatant, which was then vortexed for 3 seconds and centrifuged at 15,000\u0026times; g for 1 minute. The supernatant (700 \u0026mu;L) was transferred to a new microcentrifuge tube, and 600 \u0026mu;L of CD3 solution was added and vortexed for another 3 seconds. Next, 650 \u0026mu;L of the lysate was carefully applied to a QIAamp Mini Spin Column, and the column was centrifuged at 15,000\u0026times; g for 1 minute. The flow-through was discarded, and the process was repeated with the remaining lysate. During the washing phase, 500 \u0026mu;L of EA solution was added to the column, followed by centrifugation at 15,000\u0026times; g for 1 minute. The flow-through was discarded, and 500 \u0026mu;L of C5 solution was added, followed by another centrifugation at 15,000\u0026times; g for 1 minute. Subsequently, 500 \u0026mu;L of 96\u0026ndash;100% ethanol was added to the column, followed by centrifugation to remove any residual contaminants. The eluate was discarded, and the column was dried by centrifugation at 15,000\u0026times; g for 3 minutes. To elute the purified DNA, 70 \u0026mu;L of C6 buffer was added to the column, incubated at room temperature for 3 minutes, and then centrifuged at 15,000\u0026times; g for 1 minute. The column was discarded, and the purified DNA was collected in a fresh 1.5 mL tube.\u003c/p\u003e\n\u003cp\u003eTo ensure the quality and quantity of the extracted DNA, we measured the DNA concentration using a Qubit\u0026trade; 4 Fluorometer (Thermo Fisher Scientific\u0026trade;, Waltham, MA, USA) and assessed its purity using a NanoDrop\u0026trade; 2000 Spectrophotometer (Thermo Fisher Scientific\u0026trade;, Franklin, MA, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLibrary preparation, sequencing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDNA library preparation and sequencing were carried out following the Illumina 16S Metagenomic Sequencing Library Preparation protocol (15044223 Rev. B) and according to the workflow described by Skopk\u0026oacute; et al. (2023). Each reaction started from 12.5 ng of soil-derived DNA. The V3\u0026ndash;V4 regions of the bacterial 16S rRNA gene were amplified using the universal primers 341F (5\u0026prime;-CCTACGGGNGGCWGCAG-3\u0026prime;) and 785R (5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;), both flanked by Illumina overhang adapters. Amplicons (~460 bp) were generated using 2\u0026times; KAPA HiFi HotStart ReadyMix, indexed with the Nextera XT Index Kit, and purified with MagSI Pure Beads, yielding libraries of approximately 550\u0026ndash;630 bp. Library integrity was confirmed using an Agilent D1000 ScreenTape system, quantified by qPCR, normalized, and pooled in equimolar concentrations. The 4 nM pooled library was denatured with 0.2 M NaOH, diluted to 8 pM, and sequenced on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 (600 cycles) following standard procedures.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNutritional analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGrain samples were analyzed for key nutritional parameters, including protein content, antioxidant activity (both hydrophilic and lipophilic), and carotenoid concentration in maize. Protein levels were determined using the Pierce BCA Protein Assay Kit, a bicinchoninic acid-based colorimetric\u0026nbsp;method. Water-soluble antioxidants were quantified through the Ferric Reducing Antioxidant Power assay (FRAP), while liposoluble antioxidants were assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. In maize, carotenoid content was analyzed using high-performance liquid chromatography (HPLC), allowing for precise quantification.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAntioxidants content determination\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFRAP assay was conducted to determine the water-soluble antioxidants, by measuring the capacity of antioxidants to convert the Fe\u0026sup3;⁺\u0026ndash;TPTZ complex into its Fe\u0026sup2;⁺ form in a low pH medium. The formation of the ferrous\u0026ndash;TPTZ complex results in a blue coloration, and the increase in absorbance at 593 nm serves as an indicator of antioxidant activity. The reaction was performed in a 96-well microplate. In each well, 30 \u0026micro;L of distilled water and 10 \u0026micro;L of the diluted sample/ standard were added, followed by 200 \u0026micro;L of freshly prepared FRAP reagent. The reagent was composed of 10 volumes of 250 mM acetate buffer (pH 3.6), one volume of 20 mM ferric chloride, and one volume of 10 mM tripyridyl-s-triazin (TPTZ) dissolved in 40 mM HCl. The plate was then incubated at 37 \u0026deg;C, and absorbance was measured after 8 minutes at 593 nm. The antioxidant capacity obtained from the FRAP assay was calculated and expressed as milligrams of ascorbic acid equivalents per 100 g of sample (maize and wheat)\u0026nbsp;(Benzie and Strain 1996; Nemes et al. 2018).\u003c/p\u003e\n\u003cp\u003eThe liposoluble antioxidant activity in the grain samples was determined using a modified DPPH microplate method, originally described by Blois\u0026nbsp;(1958)\u0026nbsp;and Brand-Williams et al.\u0026nbsp;(1995). Approximately 25 mg of finely ground lyophilized grain sample was extracted with 1 mL of distilled water under vortex agitation for 1.5 min, followed by centrifugation at 10 000 rpm for 10 min. The supernatant was collected and used for the assay. A 0.1 mM DPPH solution was prepared daily by dissolving 2 mg of DPPH in 50 mL of methanol and stored in the dark until use. In each well of a 96-well microplate, 10 \u0026micro;L of the appropriately diluted aqueous extract or Trolox standard solution (31.25\u0026ndash;1000 \u0026micro;M) was mixed with 50 \u0026micro;L of distilled water and 190 \u0026micro;L of the DPPH reagent. The plate was gently shaken and incubated for 30 min at 25 \u0026deg;C in the dark. The decrease in absorbance was measured at 517 nm using a microplate reader, with methanol and DPPH serving as blank. The DPPH radical scavenging activity was calculated from the Trolox calibration curve and expressed as milligrams of Trolox equivalents per 100 g of dry grain sample (mg Trolox/ 100g), representing the water-soluble antioxidant capacity of the sample (Nemes et al. 2018).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProteins content determination\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe total protein content of the grain samples was quantified using the bicinchoninic acid (BCA) method with the Pierce\u0026trade; BCA Protein Assay Kit (Thermo Fisher Scientific, MAN0011430). The assay is based on the reduction of Cu\u0026sup2;⁺ to Cu⁺ by peptide bonds under alkaline conditions, followed by colorimetric detection of the cuprous ion with bicinchoninic acid, forming a purple complex measurable at 562 nm. Briefly, 25 \u0026mu;L of the appropriately diluted grain extract or bovine serum albumin (BSA) standard (20\u0026ndash;2000 \u0026mu;g/mL) was pipetted into each well of a 96-well microplate. Then, 200 \u0026mu;L of freshly prepared working reagent (reagents A and B mixed in a 50:1 ratio) was added. The plate was gently shaken and incubated at 37 \u0026deg;C for 30 min, after which the absorbance was measured at 562 nm using a microplate reader. Protein concentrations were determined from the BSA calibration curve and expressed as milligrams of protein per 100g of grain sample (mg/100g).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCarotenoid content determination in maize samples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCarotenoids were extracted from 0.20 g of homogenized feed maize using 1 mL of dichloromethane: acetone: methanol (2:1:1) and a Magna Lyser (6000 rpm, 30 s, twice). After centrifugation (10,000 rpm, 5 min), the supernatant was collected. The pellet was re-extracted with the same solvent and processed similarly. Combined extracts were evaporated at 40 \u0026deg;C, reconstituted in 300 \u0026micro;L of 80% acetone, and filtered (0.22 \u0026micro;m) prior to analysis. High-performance liquid chromatography (HPLC) was performed using a Waters Alliance e2695 system with a PDA detector (2998). Separation was achieved on a Waters XSelect C18 column (100 \u0026times; 4.6 mm, 3.5 \u0026micro;m) at 30 \u0026deg;C, with a flow rate of 1.0 mL/min. The mobile phases were A - acetonitrile: water: triethylamine (90:10:0.1, v/v/v) and B - ethyl acetate. A gradient elution was applied over 35 minutes. Detection wavelengths were 450 nm and 470 nm. Chromatograms were evaluated using Empower 3 software, and carotenoids were identified based on retention times of known standards.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis and data visualization\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses and visualizations were conducted in RStudio (v2025.09.0+387, Posit Software, 2025). Sequencing data were filtered, normalized, and transformed using the phyloseq package prior to downstream analyses. Alpha diversity was calculated with vegan, and treatment effects were evaluated using the Kruskal-Wallis and Wilcoxon rank-sum tests after verifying data non-normality with the Shapiro-Wilk test. Beta diversity was assessed using Bray-Curtis and UniFrac distance matrices and visualized through Principal Coordinate Analysis (PCoA) and Non-metric Multidimensional Scaling (MDS); group differences were tested with PERMANOVA. Differential abundance of microbial taxa and predicted functional genes was determined using DESeq2, with results visualized as volcano plots in ggplot2. Random Forest classification (randomForest package) was used to identify key genera contributing to community differentiation, with model accuracy evaluated via the out-of-bag (OOB) error rate and variable importance ranked by Mean Decrease Accuracy and Mean Decrease Gini. Correlations between microbial taxa and crop nutritional parameters were assessed using Spearman\u0026rsquo;s rank correlation and visualized as heatmaps and correlation matrices with pheatmap and corrplot. Microbial co-occurrence networks were constructed using NetCoMi from CLR-transformed abundance data based on Pearson correlations. Network topology was characterized by modularity, density, and degree centrality. Unless otherwise stated, statistical significance was determined at p \u0026lt; 0.05. All graphical outputs, including bar plots, heatmaps, pie charts, and network visualizations, were produced in ggplot2 and related R packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eEffects of tillage and fertilization practices on microbial alpha diversity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResults indicated that conventional ploughing had a statistically significant positive effect on the alpha diversity of soil microbial communities in both maize and wheat (p\u0026lt;0.05). In both cropping systems, the measured diversity proved to be higher under treatment involving greater soil disturbance (ploughing) compared to the one under reduced tillage (deep loosening) (Fig. 3a and 3b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFertilization treatments also had a clear impact on alpha diversity in both maize and wheat, with certain nutrient combinations consistently associated with lower or higher diversity (Fig. 3c and 3d). In maize, the lowest diversity values were generally observed in treatments with nitrogen or phosphorus alone, particularly under setting M (2M N and 3M P). In contrast, the highest diversity in maize was found in the treatment combining nitrogen and phosphorus (5M N+P), followed by the full NPK treatment under setting M (8M N+P+K). Similarly, in wheat, low diversity was also linked to nitrogen-only treatments (2M N) and the full NPK treatment in setting M (8M NPK). The highest diversity in wheat appeared under nitrogen with potassium (6M NK), phosphorus alone in setting E (3E P), and potassium alone (4E K).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eManagement-driven shifts in soil microbial fingerprints and structural composition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eVariation in soil microbial community composition was closely associated with differences in crop type, tillage intensity, and fertilization level (Fig. 4). Distinct microbial fingerprints were observed between maize and wheat rhizosphere soils. Approximately 20.51% of taxa were unique to maize, 48.72% unique to wheat, and 30.77% shared between both crops (Fig. 4a). Maize-associated soils contained \u003cem\u003eGemmatirosa kalamazoonesis, Paenibacillus pectinilyticus,\u003c/em\u003e and \u003cem\u003eNiastella yeongjuensis\u003c/em\u003e as characteristic members, whereas wheat soils were dominated by \u003cem\u003eArchangium violaceum, Microvirga aerilata,\u003c/em\u003e and \u003cem\u003ePseudonocardia alaniniphila\u003c/em\u003e. The shared community consisted mainly of \u003cem\u003eOryzihumus leptocrescens, Bacillus asahii,\u003c/em\u003e and \u003cem\u003eAgromyces ramosus\u003c/em\u003e. Among these, \u003cem\u003eO. leptocrescens\u003c/em\u003e accounted for about 63.32% of its abundance in maize and 36.68% in wheat.\u003c/p\u003e\n\u003cp\u003eCommunity composition also varied with tillage intensity. Under ploughing, 48.72% of species were unique, whereas 28.21% occurred only under deep loosening, and 23.08% were shared (Fig. 4b). Ploughed soils were characterized by \u003cem\u003eArchangium violaceum, Microvirga aerilata, Pseudonocardia alaniniphila\u003c/em\u003e, and \u003cem\u003eAzotobacter beijerinckii\u003c/em\u003e, while deep-loosened soils included \u003cem\u003eNocardioides islandensis\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Gemmatirosa kalamazoonesis\u003c/em\u003e. The shared fraction, dominated by \u003cem\u003eOryzihumus leptocrescens, Bacillus asahii, Agromyces ramosus\u003c/em\u003e, and \u003cem\u003eNordella oligomobilis\u003c/em\u003e, comprised nearly half of all detected species, with \u003cem\u003eB. asahii\u003c/em\u003e more prevalent in ploughed soils (73.82% of its total abundance). Distinct microbial assemblages were also evident among fertilization treatments. The control accounted for 12.82% unique species, setting M for 23.08%, and setting E for 30.77%, while the shared community made up 17.95% of the total detected species (Fig. 4c). Control soils contained \u003cem\u003eMicrovirga aerilata, Pseudonocardia alaniniphila, and Azotobacter beijerinckii\u003c/em\u003e as unique members; setting M favored \u003cem\u003eArchangium violaceum\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFerruginibacter paludis\u003c/em\u003e; and setting E included \u003cem\u003eRoseimicrobium gellanilyticum, Paenibacillus pectinilyticus\u003c/em\u003e, and \u003cem\u003eNiastella yeongjuensis\u003c/em\u003e. Across all treatments, a consistent core community, comprising \u003cem\u003eOryzihumus leptocrescens, Bacillus asahii, Agromyces ramosus, Nordella oligomobilis, Nocardioides islandensis, Noviherbaspirillum canariense,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Bacillus funiculus\u003c/em\u003e, remained present. Within this shared group, \u003cem\u003eO. leptocrescens\u003c/em\u003e and \u003cem\u003eB. asahii\u003c/em\u003e were mostly present.\u003c/p\u003e\n\u003cp\u003eFollowing the compositional patterns observed in Figure 4, multivariate analyses were conducted to explore how management practices structured soil microbial communities. The PCoA plots (Fig. 5a-5c) show distinct clustering patterns according to crop type, tillage method, and fertilization settings. While fertilization showed no significant effect and produced substantial overlap among samples (p=0.405, R\u0026sup2;=0.03), crop type (p=0.001, R\u0026sup2;=0.09) and tillage (p=0.001, R\u0026sup2;=0.07) resulted in clearer separations. To further explore the influence of soil disturbance, non-metric multidimensional scaling (MDS) analyses were performed separately for each tillage practice (Fig. 5d, 5e). Under ploughing, maize and wheat samples partially overlapped, reflecting a more mixed microbial composition and less crop specificity. In contrast, under deep loosening, maize and wheat formed more distinct clusters.\u003c/p\u003e\n\u003cp\u003eTo identify the microbial groups contributing most strongly to these clustering patterns, a Random Forest classification was applied, and the key genera were visualized in the barcode plots (Fig. 5f, 5g). Under ploughing, genera such as \u003cem\u003eBacillus, Gaiella, Bryobacter,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Gemmatimonas\u0026nbsp;\u003c/em\u003ewere among the major contributors to the observed variability. Conversely, deep loosening was characterized by \u003cem\u003eBacillus, Gaiella, Nocardioides\u003c/em\u003e, and \u003cem\u003eNitrospira\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional profiling and predicted metabolic potential of soil microbiome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo complement the taxonomic and community-level patterns, functional profiling was conducted to assess differences in functional gene abundance across crop type, tillage, and fertilization settings. This analysis was performed using computational predictions derived from the identified microbial taxa, rather than direct sequencing of functional genes, to estimate the potential metabolic functions associated with each management condition. The analysis revealed distinct variations in the metabolic potential of soil microbial communities under different agricultural practices (Fig. 6). Although most functional genes showed no significant change, specific subsets were differentially regulated depending on the factor considered. Across crop types, the observed functional divergence reflected the contrasting rhizosphere conditions of maize and wheat. Wheat soils exhibited a higher representation of functional genes related to ABC transporters and glycosyltransferases. Maize soils also contained genes within these categories but at comparatively lower abundance. Tillage exerted the strongest influence on functional differentiation. Deep loosening favored genes associated with transport and carbohydrate metabolism, including ABC transporters, glycosyltransferases, and two-component systems, consistent with a more stable and less disturbed soil environment that supports balanced nutrient cycling. In contrast, ploughing led to the enrichment of genes involved in peptidoglycan biosynthesis and degradation, antimicrobial resistance, peptidases, and bacterial toxin production. Under fertilization, functional shifts were less pronounced but still evident. High-input soils (fertilization E) showed enrichment of antimicrobial resistance genes and ABC transporters.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInfluence of tillage intensity on crop nutritional quality\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDifferent soil treatments influence not only the soil microbiome but also the nutritional profile of crops (Fig. 7). Regarding tillage, in maize, the distribution of nutritional categories was more balanced under ploughing, with similar proportions of high and moderate levels, while deep loosening produced a more uneven pattern dominated by low nutritional profiles (Fig. 7a). In wheat, a similar pattern was observed for the high nutritional profile, which was also promoted by ploughing; however, the medium nutritional profile was more prevalent under deep loosening, while low nutritional values were more common in ploughed soils (Fig. 7c). In this case, ploughing showed a more fragmented distribution, with high and low categories dominating and moderate values being scarce, whereas deep loosening resulted in a more balanced composition across the three nutritional levels. In addition, the distribution of nutritional categories under deep loosening appeared more fragmented, showing greater variability between high, moderate, and low levels, whereas ploughing resulted in a more balanced pattern with closer proportions among these categories.\u003c/p\u003e\n\u003cp\u003eWhen examining specific nutrients, the effects of tillage varied between crops. Protein content was comparable across both tillage systems and crops, showing no significant differences. For liposoluble and water-soluble antioxidants, contrasting responses were recorded between maize and wheat. In maize, ploughing enhanced the concentration of liposoluble antioxidants, while deep loosening favored water-soluble antioxidants (Fig. 7b). Conversely, in wheat, the opposite trend was observed, with deep loosening significantly increasing liposoluble antioxidant levels (p\u0026lt;0.05) and ploughing supporting higher water-soluble antioxidant concentrations (Fig. 7d). In maize, carotenoid content was also evaluated, with ploughing significantly supporting their synthesis (p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCrop-specific nutritional responses to fertilization intensity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 8 illustrates the influence of different fertilization settings and tillage practices on the nutritional profile and composition of maize and wheat. Overall, the distribution of low, medium, and high nutritional profiles varied notably between crops. In maize, the highest nutritional profile was observed under the combined nutrient settings 7M P+K and 8M N+P+K, while medium nutritional levels were more frequent under the control, 5M N+P, and 8E N+P+K treatments. The lowest nutritional profile was predominantly associated with 6E N+K, 2M N, and 7E P+K (Fig. 9a). In wheat, the high nutritional profile was mostly found under 2M N, 5M N+P, and 5E N+P. Medium nutritional levels occurred under 8E N+P+K, 2E N, and 4E K, while low nutritional values were detected in the control, 7E P+K, and 8M N+P+K treatments (Fig. 9b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the level of specific nutrients, consistent differences were observed between fertilization settings M and E for both crops. In maize, carotenoid and protein contents remained largely unchanged between the two settings, indicating that these parameters were not strongly influenced by fertilization intensity. However, both water-soluble and liposoluble antioxidants were slightly higher under setting E (Fig. 8c). In wheat, a similar pattern was detected for the antioxidant compounds, with both liposoluble and water-soluble antioxidants showing higher proportions under setting E, whereas protein content remained comparable between treatments, with a minor tendency to increase under setting M (Fig. 8c). Overall, higher fertilization levels enhanced antioxidant activity in both crops, while moderate fertilization maintained stable protein levels. In line with these findings, figure 8c illustrates the proportional contribution of major nutrient classes in maize and wheat. While the overall distribution remained relatively stable across treatments, several crop- and nutrient-specific trends were evident. In maize, the highest relative share of liposoluble antioxidants occurred under fertilization 5E, while water-soluble antioxidants peaked under 4E. Protein contribution reached its maximum under 4E, and carotenoids showed their highest proportion under 6E. In wheat, the highest proportion of liposoluble antioxidants appeared under 2E, whereas water-soluble antioxidants reached their maximum under 3E and protein levels were highest under 7M.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMicrobial correlations with crop nutritional parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis revealed distinct linkages between microbial taxa and crop nutritional parameters in both maize and wheat, highlighting how specific microbial families and genera may influence or respond to the synthesis of key nutritional compounds (Fig. 9). In maize (Fig. 9a), positive correlations were observed between \u003cem\u003eClostridiaceae\u003c/em\u003e and liposoluble antioxidants (r=0.47), and between \u003cem\u003eMicrobacteriaceae\u0026nbsp;\u003c/em\u003eand water-soluble antioxidants (r=0.45). Conversely, \u003cem\u003eBacillaceae\u003c/em\u003e exhibited a negative correlation with protein content (r=\u0026minus;0.44). At the genus level, \u003cem\u003ePirellula\u003c/em\u003e showed a positive relationship with protein content (r = 0.50), whereas \u003cem\u003eNocardioides\u0026nbsp;\u003c/em\u003ecorrelated negatively with carotenoids (r=\u0026minus;0.46). In wheat (Fig. 9b), microbial associations displayed more consistent positive correlations with antioxidant compounds. Families such as \u003cem\u003ePropionibacteriaceae\u0026nbsp;\u003c/em\u003e(r=0.57) and \u003cem\u003eAzospirillaceae\u003c/em\u003e (r=0.54) were positively correlated with liposoluble antioxidants, while \u003cem\u003eHaliangiaceae\u003c/em\u003e showed a positive relationship with water-soluble antioxidants (r=0.48). In contrast, \u003cem\u003ePlanococcaceae\u003c/em\u003e (r=\u0026minus;0.74) and \u003cem\u003eBacillaceae\u003c/em\u003e (r=\u0026minus;0.67) were strongly negatively correlated with fat-soluble antioxidants. At the genus level, \u003cem\u003eMicrolunatus\u0026nbsp;\u003c/em\u003e(r=0.58) and \u003cem\u003eSkermanella\u003c/em\u003e (r=0.54) were positively correlated with liposoluble antioxidants, whereas \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eOryzihumus\u0026nbsp;\u003c/em\u003eexhibited negative correlations, consistent with the family-level patterns. For water-soluble antioxidants, \u003cem\u003eHaliangium\u003c/em\u003e again showed a positive association (r=0.48).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMicrobial network responses to management practices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCo-occurrence network analysis revealed clear differences in microbial community structure across crop, tillage, and fertilization settings, with distinct topological patterns corresponding to variations in the nutritional profiles of maize and wheat (Fig. 10). Across all networks, modularity values ranged from 0.019 to 0.190, while density values varied between 0.276 and 0.460. The highest modularity values were observed in networks associated with lower nutritional profiles. In contrast, networks related to higher nutritional profiles displayed lower modularity and greater density, with tighter microbial associations and more integrated community organization. Within the crop-based networks, wheat showed slightly higher modularity (up to 0.091) compared with maize (0.065). The tillage-based networks revealed that deep loosening generated the highest modularity (0.162), while ploughing produced denser and less modular networks. Under fertilization settings, setting M exhibited intermediate modularity values (0.108\u0026ndash;0.121) and higher density (up to 0.460), whereas setting E showed a broader modularity range (0.048\u0026ndash;0.190).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTillage intensity and nutrient composition are key drivers of soil microbial diversity but act through different mechanisms. The findings of this study show that both tillage and fertilization substantially shape the diversity and structure of soil microbial communities in maize and wheat. The observed increase in microbial alpha diversity under ploughing suggests that intense soil disturbance creates dynamic microenvironments that stimulate microbial proliferation. Mechanical disturbance likely imposes stress conditions similar to those in disease ecology, where stress often triggers higher microbial activity. There is growing evidence that under combined environmental pressures, microbial diversity can increase through the coexistence of stress-tolerant taxa (Chen et al. 2024a). This supports the broader idea that stress in microbial communities during disturbance promotes adaptive responses rather than reflecting favorable growth conditions (Rocca et al. 2019). Enhanced aeration and organic-matter exposure following ploughing could further promote microbial growth, although such effects are transient. Galand et al. (2016) found that moderate disturbance increases microbial production and phylogenetic diversity in the short term, promoting substrate turnover and remineralization. Over time, however, excessive disturbance can degrade soil structure and reduce microbial stability, as reported by S\u0026aacute;ndor et al. (2020).\u003c/p\u003e\n\u003cp\u003eFertilization exerted a complex and crop-specific influence on microbial diversity. In maize, the highest diversity observed under the nitrogen-phosphorus (5M N+P) and full NPK (8M N+P+K) treatments suggests that balanced multi-nutrient inputs create complementary resource conditions that sustain broader microbial functionality. The simultaneous supply of nitrogen, phosphorus, and potassium likely enhances microbial cooperation and nutrient turnover through processes such as nitrogen fixation, phosphorus solubilization, and organic-matter decomposition. In contrast, single-nutrient inputs (2M N, 3M P) likely narrow the metabolic spectrum, favoring copiotrophic taxa and reducing overall heterogeneity. This pattern is consistent with Dai et al. (2018), who reported that long-term nitrogen fertilization alone decreases bacterial diversity due to acidification, whereas balanced NPK improves diversity by increasing soil organic carbon and pH. In wheat, however, diversity peaked under moderate nutrient availability (6M NK), implying that optimal competition occurs when resources are neither limiting nor excessive. The diversity decline under full NPK (8M NPK) suggests nutrient oversupply disrupts microbial equilibrium by accelerating plant uptake or favoring copiotroph dominance. Similar crop-dependent responses have been documented by Kong et al. (2023), who showed that long-term fertilization significantly affected soil bacterial communities, but these effects vary depending on the cropping system, highlighting the importance of crop type in microbial responses to nutrient input. These differences are mainly because of changes in root exudates, nutrient uptake, and soil microenvironments.\u003c/p\u003e\n\u003cp\u003eBeyond diversity, distinct microbial fingerprints and community composition revealed clear crop-specific patterns shaped by tillage and fertilization. Nearly half of the detected taxa were unique to wheat, indicating that its carbon-rich rhizodeposits favor decomposer and biocontrol taxa such as \u003cem\u003eArchangium violaceum\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePseudonocardia alaniniphila\u003c/em\u003e, which thrive in organic-matter-rich zones and degrade complex compounds, forming characteristic microbial fingerprints associated with wheat-dominated rhizospheres (Whatmough et al. 2024; Saraf and Sharma 2025). In contrast, maize enriched \u003cem\u003ePaenibacillus pectinilyticus\u003c/em\u003e and \u003cem\u003eGemmatirosa kalamazoonesis\u003c/em\u003e, both linked to phosphorus solubilization and mineralization processes (DeBruyn et al. 2013; Li et al. 2017).\u003c/p\u003e\n\u003cp\u003eTillage further influenced microbial assembly through physical and oxygen-related stress. Ploughed soils favored \u003cem\u003eAzotobacter beijerinckii\u003c/em\u003e and other fast-growing, oxygen-responsive taxa (Aasfar et al. 2021). Conversely, deep loosening supported \u003cem\u003eNocardioides islandensis\u003c/em\u003e and \u003cem\u003eGemmatirosa kalamazoonesis\u003c/em\u003e, indicating that reduced disturbance maintains stable habitats for microbes involved in long-term nitrogen and carbon cycling (Ma et al. 2023; Zhao et al. 2023). Nutrient balance also played a crucial role: extensive fertilization (setting E) promoted copiotrophic taxa such as \u003cem\u003ePaenibacillus pectinilyticus\u003c/em\u003e, while moderate fertilization (setting M) maintained a functionally balanced microbiome. The control soils favored nitrogen-fixers like \u003cem\u003eMicrovirga aerilata\u003c/em\u003e, adapted to nutrient-poor conditions, consistent with evidence that long-term nutrient surpluses drive copiotrophic dominance and reduce functional equilibrium (Shu et al. 2023). Across all treatments, \u003cem\u003eBacillus asahii\u003c/em\u003e and \u003cem\u003eOryzihumus leptocrescens\u003c/em\u003e formed a stable core microbiome, highlighting their central role in decomposition, soil fertility, crop yield and nutrient recycling under variable management (Jiang et al. 2019). While the ecological role of O. leptocrescens has not yet been fully characterized, its consistent occurrence across treatments suggests potential functional importance within the soil microbial network. Their recurrent presence and central positioning indicate that these taxa may act as keystone species, representing promising targets for management strategies aimed at enhancing soil fertility and crop nutritional quality.\u003c/p\u003e\n\u003cp\u003eFunctional analyses further confirmed that tillage intensity was the dominant factor shaping both microbial structure and metabolic potential. Deep loosening enhanced rhizosphere-driven selection and maintained distinct crop-specific microbiomes, whereas ploughing increased microbial mixing and favored generalist taxa adapted to fluctuating conditions. These findings align with long-term studies showing that reduced disturbance increases microbial biomass and richness, while ploughing promotes saprotrophic fungi and generalist bacteria (Domnariu et al. 2025). Functional profiles mirrored these structural differences: deep loosening enhanced genes related to transport, carbohydrate metabolism, and nutrient cycling (e.g., ABC transporters, glycosyltransferases), indicating a stable soil environment that supports cooperative microbial functioning. Similar outcomes were reported by Dong et al. (2024) who found that deep tillage increased carbohydrate-metabolism genes, including glycoside hydrolases, improving organic-matter turnover and nutrient cycling. In contrast, ploughing increased stress- and defense-related genes, such as peptidases and antimicrobial resistance modules, indicating that disturbance triggers competitive microbial behavior (Wang et al. 2023).\u003c/p\u003e\n\u003cp\u003eThese microbial shifts were reflected in the nutritional profiles of maize and wheat. Ploughing generally promoted higher nutritional values, particularly in maize, where elevated carotenoid and liposoluble antioxidant levels likely resulted from improved aeration and nutrient mineralization stimulating secondary metabolism. Similar outcomes were reported by Xiao et al. (2025), who observed that plough tillage with straw incorporation improved soil nutrients, root growth, and nutrient content. Khan et al. (2024) also emphasized that maize productivity and nutrient status depend on enhanced nutrient turnover and aeration. In contrast, deep loosening favored water-soluble antioxidants, reflecting enhanced root\u0026ndash;microbe interactions and stress-related defense responses under stable soil conditions. Wheat showed an opposite trend: deep loosening increased liposoluble antioxidants, while ploughing favored water-soluble ones, reflecting crop-specific differences in nutrient uptake and moisture distribution. The opposite trend in wheat may stem from its finer, shallower root system and higher sensitivity to soil aeration and moisture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFertilization affected crop quality primarily through nutrient balance and intensity. In maize, moderate NPK input (setting M) produced the highest nutritional values, while over-fertilization or single-nutrient applications reduced quality. Wheat responded best to combined nitrogen and phosphorus, but high nutrient supply did not consistently enhance quality, likely due to metabolic saturation. Across both crops, higher fertilization increased antioxidant activity but not protein content, suggesting that nutrient excess stimulates secondary, stress-related metabolism rather than primary protein synthesis. This pattern highlights the potential of controlled fertilization regimes to modulate the synthesis of bioactive metabolites, which could be valuable for medical or functional food production where specific antioxidant or stress-related compounds are targeted. This agrees also with Ahmadi et al. (2024), who reported that conventional tillage combined with phosphorus fertilization improved wheat yield and quality, while no-till with phosphorus maintained high yields and enhanced bread quality.\u003c/p\u003e\n\u003cp\u003eCorrelations between microbial taxa and crop nutritional traits revealed that the links were both taxon- and crop-specific. In maize, decomposer and nutrient-cycling taxa such as \u003cem\u003eClostridiaceae\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Microbacteriaceae\u003c/em\u003e correlated positively with antioxidants, while \u003cem\u003eBacillaceae\u0026nbsp;\u003c/em\u003ecorrelated negatively with protein, indicating potential nutrient competition. In wheat, \u003cem\u003eAzospirillaceae\u003c/em\u003e and \u003cem\u003ePropionibacteriaceae\u003c/em\u003e showed positive associations with antioxidant accumulation, suggesting that nitrogen-fixing and metabolically versatile taxa enhance redox regulation (Jaiswal et al. 2022). This pattern supports evidence that beneficial microbes improve plant antioxidant genes activity and maintain redox homeostasis under stress (Gonz\u0026aacute;lez et al. 2024). The stronger correlations between antioxidant compounds and microbial variability highlight the functional role of beneficial microbes, particularly nitrogen-fixing and metabolically active taxa such as \u003cem\u003eAzospirillaceae, Microlunatus\u003c/em\u003e, and \u003cem\u003ePropionibacteriaceae\u003c/em\u003e, in enhancing cereal nutritional quality through improved nutrient cycling and redox balance.\u003c/p\u003e\n\u003cp\u003eThe co-occurrence network analysis demonstrated that the structural organization of soil microbial communities influenced the nutritional outcomes of maize and wheat. Microbial networks exhibiting higher modularity values were composed of distinct, highly specialized ecological clusters with limited interconnections. Although such compartmentalized structures likely supported stable soil processes, they appeared less effective in facilitating nutrient transfer to crops, as these communities corresponded to grain samples with comparatively lower nutritional enrichment. Conversely, networks characterized by lower modularity and higher density formed more integrated and interactive microbial systems. The increased number of connections among taxa in these less modular networks likely enhanced nutrient cycling efficiency and rhizosphere cooperation, which coincided with greater accumulation of proteins, carotenoids, and both lipophilic and water-soluble antioxidants in maize and wheat. Intermediate configurations, with moderate modularity and density around, appeared to represent a transitional state in which the microbial community maintained both functional stability and efficient nutrient exchange. Overall, these findings indicate that higher microbial modularity promoted ecological compartmentalization but reduced nutrient translocation to plants, whereas lower modularity and higher network density supported more dynamic nutrient exchange, ultimately contributing to improved crop nutritional quality. This highlights that the balance between microbial specialization and connectivity is a key determinant of soil-plant nutrient interactions. These findings are consistent with recent evidence showing that the topological structure of microbial networks strongly regulates soil ecosystem functioning. Studies by Yang et al. (2024) and Byers et al. (2025) demonstrated that networks with lower modularity and higher connectivity promote greater nutrient cycling efficiency and multifunctionality, as enhanced inter-taxa interactions facilitate metabolic cooperation and resource exchange. Conversely, highly modular and compartmentalized networks, while stable, tend to reduce nutrient flow between microbial clusters and limit overall ecosystem productivity. This supports the idea that the balance between microbial specialization and connectivity governs the efficiency of soil\u0026ndash;plant nutrient transfer and, ultimately, crop nutritional outcomes.\u003c/p\u003e\n\u003cp\u003eCollectively, these results demonstrate that balanced nutrient input and minimal physical disturbance enhance microbial cooperation, functional diversity, and soil resilience, while intensive management simplifies network organization and reduces ecological specialization. The consistency between these findings and broader ecological studies reinforces the principle that environmental balance, through nutrient regulation and limited disturbance, is central to sustaining complex and resilient soil microbial ecosystems.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSoil management practices jointly shaped the ecological and functional organization of the soil microbiome and, consequently, the nutritional composition of maize and wheat. The interaction between tillage and fertilization regulated microbial diversity, specialization, and nutrient cycling efficiency. Although ploughing increased alpha diversity, it favored generalist and stress-tolerant taxa linked to rapid but unstable nutrient turnover. Conversely, deep loosening maintained crop-specific microbial networks with higher modularity and functional resilience, supporting more efficient nutrient transformation. Moderate, balanced fertilization reinforced these stable configurations by enhancing microbial connectivity and metabolic complementarity, whereas excessive or single-nutrient inputs promoted loss of ecological specialization. These microbial shifts were reflected in plant nutritional outcomes, with reduced disturbance favoring water-soluble antioxidants and ploughing stimulating carotenoid and lipophilic antioxidant synthesis. Overall, the results demonstrated that soil fertility and crop nutritional quality were emergent properties of microbiome organization, emphasizing that sustainable agriculture depends on optimizing, not intensifying, soil disturbance and nutrient supply. Future studies should integrate long-term field monitoring and functional metagenomic analyses to elucidate the mechanistic links between soil microbial networks, nutrient cycling, and crop nutritional outcomes under changing environmental and management conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions:\u0026nbsp;\u003c/strong\u003eNjomza Gashi, Melinda Paholcsek:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWriting-original draft; P\u0026eacute;ter Fauszt: Data curation; Njomza Gashi: Visualization; Njomza Gashi, P\u0026eacute;ter Fauszt: Formal analysis; P\u0026eacute;ter Fauszt: Software; P\u0026eacute;ter D\u0026aacute;vid, P\u0026eacute;ter Fauszt, Zsombor Szőke, Piroska B\u0026iacute;r\u0026oacute;n\u0026eacute; Moln\u0026aacute;r, Andrea Kun-Nemes, Erzs\u0026eacute;bet Szőllősi: Methodology; Njomza Gashi,\u0026nbsp;L\u0026aacute;szl\u0026oacute; Zsombik, P\u0026eacute;ter Fauszt, P\u0026eacute;ter D\u0026aacute;vid, Zsombor Szőke, Piroska B\u0026iacute;r\u0026oacute;n\u0026eacute; Moln\u0026aacute;r, Andrea Kun-Nemes, Erzs\u0026eacute;bet Szőllősi, L\u0026aacute;szl\u0026oacute; St\u0026uuml;ndl, Ferenc G\u0026aacute;l, Judit Remenyik, Melinda Paholcsek: Writing-review \u0026amp; editing, Njomza Gashi, P\u0026eacute;ter D\u0026aacute;vid, Zsombor Szőke, Melinda Paholcsek: Conceptualization, Melinda Paholcsek: Project administration, Judit Remenyik, Melinda Paholcsek: Funding acquisition, Melinda Paholcsek: Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eSupported by the University of Debrecen Scientific Research Bridging Fund (DETKA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAasfar A, Bargaz A, Yaakoubi K, et al (2021) Nitrogen Fixing Azotobacter Species as Potential Soil Biological Enhancers for Crop Nutrition and Yield Stability. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.628379\u003c/li\u003e\n\u003cli\u003eAbdul Khalil HPS, Hossain MdS, Rosamah E, et al (2015) The role of soil properties and it\u0026rsquo;s interaction towards quality plant fiber: A review. Renewable and Sustainable Energy Reviews 43:1006\u0026ndash;1015. https://doi.org/10.1016/j.rser.2014.11.099\u003c/li\u003e\n\u003cli\u003eAhmadi H, Mirseyed Hosseini H, Moshiri F, et al (2024) Impact of varied tillage practices and phosphorus fertilization regimes on wheat yield and grain quality parameters in a five-year corn-wheat rotation system. Sci Rep 14:14717. https://doi.org/10.1038/s41598-024-65784-w\u003c/li\u003e\n\u003cli\u003eAislabie J, Deslippe JR (2013) Soil Microbes and Their Contribution to Soil Services. In: Ecosystem Services in New Zealand: Conditions and Trends. Manaaki Whenua Press, Lincoln, New Zealand, pp 143\u0026ndash;161\u003c/li\u003e\n\u003cli\u003eAli Q, Ali M, Jing H, et al (2024) Power of plant microbiome: A sustainable approach for agricultural resilience. Plant Stress 14:100681. https://doi.org/10.1016/j.stress.2024.100681\u003c/li\u003e\n\u003cli\u003eAngon PB, Anjum N, Akter MstM, et al (2023) An Overview of the Impact of Tillage and Cropping Systems on Soil Health in Agricultural Practices. Advances in Agriculture 2023:8861216. https://doi.org/10.1155/2023/8861216\u003c/li\u003e\n\u003cli\u003eAshworth AJ, DeBruyn JM, Allen FL, et al (2017) Microbial community structure is affected by cropping sequences and poultry litter under long-term no-tillage. Soil Biology and Biochemistry 114:210\u0026ndash;219. https://doi.org/10.1016/j.soilbio.2017.07.019\u003c/li\u003e\n\u003cli\u003eBanerjee S, van der Heijden MGA (2023) Soil microbiomes and one health. Nat Rev Microbiol 21:6\u0026ndash;20. https://doi.org/10.1038/s41579-022-00779-w\u003c/li\u003e\n\u003cli\u003eBenzie IFF, Strain JJ (1996) The Ferric Reducing Ability of Plasma (FRAP) as a Measure of \u0026ldquo;Antioxidant Power\u0026rdquo;: The FRAP Assay. Analytical Biochemistry 239:70\u0026ndash;76. https://doi.org/10.1006/abio.1996.0292\u003c/li\u003e\n\u003cli\u003eBlois MS (1958) Antioxidant Determinations by the Use of a Stable Free Radical. Nature 181:1199\u0026ndash;1200. https://doi.org/10.1038/1811199a0\u003c/li\u003e\n\u003cli\u003eBrand-Williams W, Cuvelier ME, Berset C (1995) Use of a free radical method to evaluate antioxidant activity. LWT - Food Science and Technology 28:25\u0026ndash;30. https://doi.org/10.1016/S0023-6438(95)80008-5\u003c/li\u003e\n\u003cli\u003eByers AK, Wakelin SA, Condron L, Black A (2025) Land Use Change Disrupts the Network Complexity and Stability of Soil Microbial Carbon Cycling Genes Across an Agricultural Mosaic Landscape. Microb Ecol 87:167. https://doi.org/10.1007/s00248-024-02487-9\u003c/li\u003e\n\u003cli\u003eChen L, Li Z, Liu W, Lyu B (2024a) Impact of high temperature and drought stress on the microbial community in wolf spiders. Ecotoxicology and Environmental Safety 283:116801. https://doi.org/10.1016/j.ecoenv.2024.116801\u003c/li\u003e\n\u003cli\u003eChen Q, Song Y, An Y, et al (2024b) Soil Microorganisms: Their Role in Enhancing Crop Nutrition and Health. Diversity 16:734. https://doi.org/10.3390/d16120734\u003c/li\u003e\n\u003cli\u003eCruz D, Cisneros R, Ben\u0026iacute;tez \u0026Aacute;, et al (2021) Gram-Negative Bacteria from Organic and Conventional Agriculture in the Hydrographic Basin of Loja: Quality or Pathogen Reservoir? Agronomy 11:2362. https://doi.org/10.3390/agronomy11112362\u003c/li\u003e\n\u003cli\u003eDai Z, Su W, Chen H, et al (2018) Long-term nitrogen fertilization decreases bacterial diversity and favors the growth of Actinobacteria and Proteobacteria in agro-ecosystems across the globe. Global Change Biology 24:3452\u0026ndash;3461. https://doi.org/10.1111/gcb.14163\u003c/li\u003e\n\u003cli\u003eDeBruyn JM, Fawaz MN, Peacock AD, et al (2013) Gemmatirosa kalamazoonesis gen. nov., sp. nov., a member of the rarely-cultivated bacterial phylum Gemmatimonadetes. The Journal of General and Applied Microbiology 59:305\u0026ndash;312. https://doi.org/10.2323/jgam.59.305\u003c/li\u003e\n\u003cli\u003eDomnariu H, Trippe KM, Botez F, et al (2025) Long-term impact of tillage on microbial communities of an Eastern European Chernozem. Sci Rep 15:642. https://doi.org/10.1038/s41598-024-84590-y\u003c/li\u003e\n\u003cli\u003eDong F, Wang L, Xu T, et al (2024) Multi-omics analysis of soil microbiota and metabolites in dryland wheat fields under different tillage methods. Sci Rep 14:24066. https://doi.org/10.1038/s41598-024-74620-0\u003c/li\u003e\n\u003cli\u003eFAO (2015) Healthy soils are the basis for healthy food production\u003c/li\u003e\n\u003cli\u003eGaland PE, Lucas S, Fagervold SK, et al (2016) Disturbance Increases Microbial Community Diversity and Production in Marine Sediments. Front Microbiol 7:1950. https://doi.org/10.3389/fmicb.2016.01950\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez M-C, Roitsch T, Pandey C (2024) Antioxidant Responses and Redox Regulation Within Plant-Beneficial Microbe Interaction. Antioxidants 13:1553. https://doi.org/10.3390/antiox13121553\u003c/li\u003e\n\u003cli\u003eJaiswal S, Aneja B, Jagannadham J, et al (2022) Unveiling the Wheat Microbiome under Varied Agricultural Field Conditions. Microbiology Spectrum 10:e02633-22. https://doi.org/10.1128/spectrum.02633-22\u003c/li\u003e\n\u003cli\u003eJiang H, Feng Y, Zhao F, Lin X (2019) Characteristics and Complete Genome Analysis of Bacillus asahii OM18, a Bacterium in Relation to Soil Fertility in Alkaline Soils Under Long-Term Organic Manure Amendment. Curr Microbiol 76:1512\u0026ndash;1519. https://doi.org/10.1007/s00284-019-01766-w\u003c/li\u003e\n\u003cli\u003eKhan H, Khan A, Khan S, et al (2024) Maize productivity and nutrient status in response to crop residue mineralization with beneficial microbes under various tillage practices. Soil and Tillage Research 239:106057. https://doi.org/10.1016/j.still.2024.106057\u003c/li\u003e\n\u003cli\u003eKong W, Qiu L, Ishii S, et al (2023) Contrasting response of soil microbiomes to long-term fertilization in various highland cropping systems. ISME COMMUN 3:81. https://doi.org/10.1038/s43705-023-00286-w\u003c/li\u003e\n\u003cli\u003eLehmann J, Bossio DA, K\u0026ouml;gel-Knabner I, Rillig MC (2020) The concept and future prospects of soil health. Nat Rev Earth Environ 1:544\u0026ndash;553. https://doi.org/10.1038/s43017-020-0080-8\u003c/li\u003e\n\u003cli\u003eLi Y, Liu X, Hao T, Chen S (2017) Colonization and Maize Growth Promotion Induced by Phosphate Solubilizing Bacterial Isolates. International Journal of Molecular Sciences 18:1253. https://doi.org/10.3390/ijms18071253\u003c/li\u003e\n\u003cli\u003eLiu H, Li S, Qiang R, et al (2022) Response of Soil Microbial Community Structure to Phosphate Fertilizer Reduction and Combinations of Microbial Fertilizer. Front Environ Sci 10:. https://doi.org/10.3389/fenvs.2022.899727\u003c/li\u003e\n\u003cli\u003eLv L, Gao Z, Liao K, et al (2023) Impact of conservation tillage on the distribution of soil nutrients with depth. Soil and Tillage Research 225:105527. https://doi.org/10.1016/j.still.2022.105527\u003c/li\u003e\n\u003cli\u003eMa Y, Wang J, Liu Y, et al (2023) Nocardioides: \u0026ldquo;Specialists\u0026rdquo; for Hard-to-Degrade Pollutants in the Environment. Molecules 28:7433. https://doi.org/10.3390/molecules28217433\u003c/li\u003e\n\u003cli\u003eNemes A, Szollosi E, St\u0026uuml;ndl L, et al (2018) Determination of Flavonoid and Proanthocyanidin Profile of Hungarian Sour Cherry. Molecules 23:3278. https://doi.org/10.3390/molecules23123278\u003c/li\u003e\n\u003cli\u003ePenuelas J, Coello F, Sardans J (2023) A better use of fertilizers is needed for global food security and environmental sustainability. Agriculture \u0026amp; Food Security 12:5. https://doi.org/10.1186/s40066-023-00409-5\u003c/li\u003e\n\u003cli\u003eRocca JD, Simonin M, Blaszczak JR, et al (2019) The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities. Front Microbiol 9:. https://doi.org/10.3389/fmicb.2018.03272\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;ndor Z, Magdolna T, Kincses I, et al (2020) Effect of various soil cultivation methods on some microbial soil properties. DRC Sustainable Future: Journal of Environment, Agriculture, and Energy 1:14\u0026ndash;20. https://doi.org/10.37281/DRCSF/1.1.3\u003c/li\u003e\n\u003cli\u003eSaraf N, Sharma G (2025) Comparative genomic insight into the myxobacterial carbohydrate-degrading potential. Front Microbiol 16:. https://doi.org/10.3389/fmicb.2025.1550287\u003c/li\u003e\n\u003cli\u003eShu X, Liu W, Huang H, et al (2023) Meta-Analysis of Organic Fertilization Effects on Soil Bacterial Diversity and Community Composition in Agroecosystems. Plants 12:3801. https://doi.org/10.3390/plants12223801\u003c/li\u003e\n\u003cli\u003eSkopk\u0026oacute; B, Paholcsek M, Szil\u0026aacute;gyi-R\u0026aacute;cz A, et al (2023) High-Throughput Sequencing Analysis of the Changes in the Salivary Microbiota of Hungarian Young and Adult Subpopulation by an Anthocyanin Chewing Gum and Toothbrush Change. Dentistry Journal 11:44. https://doi.org/10.3390/dj11020044\u003c/li\u003e\n\u003cli\u003eTam\u0026aacute;s A, Kov\u0026aacute;cs E, Horv\u0026aacute;th \u0026Eacute;, et al (2023) Assessment of NDVI Dynamics of Maize (Zea mays L.) and Its Relation to Grain Yield in a Polyfactorial Experiment Based on Remote Sensing. Agriculture 13:689. https://doi.org/10.3390/agriculture13030689\u003c/li\u003e\n\u003cli\u003eUniversity of Debrecen Press (2017) A unique long-term field experiment in Europe | University of Debrecen. https://hirek.unideb.hu/en/unique-long-term-field-experiment-europe. Accessed 20 Oct 2025\u003c/li\u003e\n\u003cli\u003eWang W, Shen P, Lu Z, et al (2023) Metagenomics reveals the abundance and accumulation trend of antibiotic resistance gene profile under long-term no tillage in a rainfed agroecosystem. Front Microbiol 14:. https://doi.org/10.3389/fmicb.2023.1238708\u003c/li\u003e\n\u003cli\u003eWhatmough B, Holmes NA, Wilkinson B, et al (2024) Microbe Profile: Pseudonocardia: antibiotics for every niche. Microbiology 170:001501. https://doi.org/10.1099/mic.0.001501\u003c/li\u003e\n\u003cli\u003eWu Q, Chen Y, Dou X, et al (2024) Microbial fertilizers improve soil quality and crop yield in coastal saline soils by regulating soil bacterial and fungal community structure. Science of The Total Environment 949:175127. https://doi.org/10.1016/j.scitotenv.2024.175127\u003c/li\u003e\n\u003cli\u003eXiao Y, Luo W, Yang K, et al (2025) Plow tillage with buried straw increases maize yield by regulating soil properties, root growth, photosynthetic capacity, and bacterial community assembly in semi-arid black soil farmlands. European Journal of Agronomy 164:127532. https://doi.org/10.1016/j.eja.2025.127532\u003c/li\u003e\n\u003cli\u003eYang X, Song W, Yang X, et al (2024) Microbial network structure, not plant and microbial community diversity, regulates multifunctionality under increased precipitation in a cold steppe. Front Microbiol 14:. https://doi.org/10.3389/fmicb.2023.1349747\u003c/li\u003e\n\u003cli\u003eZhao X, Qin X, Jing X, et al (2023) Key genes of electron transfer, the nitrogen cycle and tetracycline removal in bioelectrochemical systems. Biotechnology for Biofuels and Bioproducts 16:174. https://doi.org/10.1186/s13068-023-02430-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Soil microbiome, long-term field trials, Tillage–fertilization synergy, Microbial fingerprints, Maize- wheat grain nutritional quality, Microbiome-aware management","lastPublishedDoi":"10.21203/rs.3.rs-8392734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8392734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground and Aims\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Understanding how tillage and fertilization influence soil microbes and crop quality is crucial for sustainable agriculture. This study examined how variations in tillage intensity and fertilization shape soil microbial communities and, through these shifts, affect the nutritional quality of maize and wheat. This approach revealed clear links between microbial community composition and crop nutrient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Field experiments were conducted within established long-term trial sites, using two tillage methods, ploughing and deep loosening combined with two fertilization regimes (moderate and high input) across fifteen nutrient treatments. Sixty-four soil and grain samples were analyzed. Soil microbial diversity and functional potential were assessed through metagenomic sequencing, while grain content was determined using standardized biochemical assays.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e: Within long-term trial contexts, it was demonstrated that tillage–fertilization combinations modulate soil microbiomes in ways that propagate to grain nutritional quality. Ploughing increased alpha diversity (p \u0026lt; 0.05) but favored generalist, stress-tolerant taxa associated with rapid nutrient turnover, aligning with higher carotenoid and lipophilic antioxidant contents. In contrast, deep loosening supported more crop-specific, functionally specialized, and apparently more stable microbiomes, reflected in greater accumulation of water-soluble antioxidants. Balanced fertilization was associated with denser, better-connected microbial networks, while excessive inputs disrupted ecological equilibrium and weakened the translation of soil functions to nutritional traits.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Crop nutritional quality can be tuned via the soil microbiome: under moderated inputs, ploughing tends to raise carotenoids/lipophilic antioxidants, whereas deep loosening with balanced fertilization favors water-soluble antioxidants; excessive fertilization disrupts microbial balance and weakens these benefits.\u003c/p\u003e","manuscriptTitle":"Soil management–dependent shifts in microbial diversity and nutrient-related functions ultimately shape the nutritional composition of maize and wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 03:59:18","doi":"10.21203/rs.3.rs-8392734/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-01-29T08:30:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-25T17:48:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-25T14:44:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-12-24T09:42:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-24T09:33:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-12-18T03:11:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"64be7b88-ef05-4f10-9355-945067c48232","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-26T13:14:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 03:59:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8392734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8392734","identity":"rs-8392734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}