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Article

Comparative Analysis of Effects of Nutrient Management Practices on Soil Microbiome and Rhizosphere Chemistry in Brinjal (Solanum melongena L.)

by
Sathasivam Bommi
1,
Ettiyagounder Parameswari
2,*,
Periyasamy Dhevagi
1,
Ramanujam Krishnan
2,
Ponnusamy Janaki
2,
Mariappan Suganthy
2,
Sundapalayam Palanisamy Sangeetha
3,
Gunasekaran Yazhini
1 and
Tamilselvan Ilakiya
4,*
1
Department of Environmental Science, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
2
Nammazhvar Organic Farming Research Centre, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
3
Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
4
Department of Horticulture, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu 603201, Tamil Nadu, India
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(17), 7832; https://doi.org/10.3390/su17177832
Submission received: 29 July 2025 / Revised: 25 August 2025 / Accepted: 27 August 2025 / Published: 30 August 2025
(This article belongs to the Special Issue Sustainable Agriculture and Food Security)
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Abstract

Brinjal (Solanum melongena) is one of the most tropical vegetable crops cultivated worldwide. Rhizosphere microbial dynamics play a crucial role in plant nutrition, providing valuable insights into soil fertility and sustainable agricultural practices. This study aims to identify sustainable nutrient management practices for brinjal, focusing on the rhizosphere microbiome by examining various nutrient management approaches, including integrated nutrient management (INM), inorganic fertilization, and organic fertilization. Root architectural analysis, LC-MS-based metabolite profiling, and shotgun metagenomics were employed to assess the various nutrient management-induced changes in metabolites and the microbial community. The result suggested that superior root features, including volume (16.3 cm3), surface area (399.48 cm2), and total root length (794.89 cm), were achieved under INM. Additionally, it encompassed the highest number and diversity of root metabolites, including both primary and secondary compounds. This can be the reason for INM maintaining a balance between the representation of bacteria (87.4%) and fungi (12.4%), with Actinomycota and Ascomycota being the dominant groups. Further diversity analyses revealed that INM soils supported the highest microbial richness and OTU abundance, while inorganic fertilization favored greater evenness of taxa but lower richness. Organic soils harbored unique, less abundant taxa, reflected in higher Fisher’s alpha values. The beta diversity analysis indicated distinct microbial community structures across different treatments. Therefore, INM is a sustainable solution for brinjal cultivation, since it improves crop performance, soil health, and microbial ecosystem services.

1. Introduction

Plant roots are surrounded by a dynamic zone known as the rhizosphere, which is essential to terrestrial ecosystems [1]. Complex interactions between soil constituents, microorganisms, roots, and soil solutions comprise some of its characteristics [2]. Diverse microbial communities are crucial for the health of plants and the fertility of the soil in the rhizosphere, a dynamic interface between soil and plants [3]. The microbial structure and function of the rhizosphere are influenced by the selection of plants and agricultural management practices [4]. These communities, which contain bacteria, fungi, and other microorganisms, are crucial for plant development, pathogen suppression, and the nutrient cycle [5]. Factors such as soil type, plant species, and management practices influence the composition of rhizosphere microbiomes [6]. The microbial community composition, co-occurrence patterns, and nitrogen-cycling processes in the rhizosphere are differentially influenced by organic and conventional management systems [4]. Different fertilization strategies have a significant impact on rhizosphere microbial communities, nutrient availability, organic matter decomposition, and overall soil health. Organic fertilizers enhance microbial interactions in the rhizosphere, while fertilization regimes modify critical microbial taxa and interactions. Particularly, they encourage the growth of fungal-dominated ecosystems, boost the population of fungi, and enhance soil organic carbon levels [7,8,9].
However, integrated nutrient management (INM) has been shown to improve economic returns, yield, and growth in brinjal cultivation. The application of vermicompost, combined with reduced chemical fertilizers, was efficacious in improving soil fertility and crop yield [10]. Although significant progress has been made, we still do not fully understand how different types of fertilizers, such as INM, organic, and inorganic nutrient management, influence soil microbes, plant root metabolites, and root growth. Understanding these intricate relationships is crucial in developing sustainable farming methods that leverage the rhizosphere microbiome’s ability to enhance crop yield and quality, while also addressing the environmental issues associated with large-scale food production [6].
Brinjal (Solanum melongena) is a vegetable crop cultivated globally and rich in nutrients [11]. The cultivation of brinjal is significantly influenced by root interactions, where root exudates play a key role in shaping microbial communities that affect plant health and growth [12]. Most research on brinjal cultivation to date has focused on either microbial community or root exudates [13]. Very few studies have examined their combined interaction under real farming conditions, but they remain underutilized in metagenomics [14]. In brinjal cultivation, however, knowledge of how different fertilization regimes influence microbial communities is still limited, underscoring the need for research to safeguard microbial diversity and design nutrient management strategies that promote beneficial microbes and enhance crop health [15]. Hence, to identify the most suitable nutrient management practices for sustainable brinjal cultivation, the following objectives were framed: (1) to explore the impact of different nutrient management practices on root architecture and their metabolites; (2) to explain the effects of nutrient management practices in structuring bacterial communities in brinjal.

2. Materials and Methods

2.1. Experimental Design

A field experiment was conducted in the Long-Term Fertilizer Experimental field (10 years) of the eastern block farm of Tamil Nadu Agriculture University (TNAU), Coimbatore (11.009203° N, 76.940493° E) during the Kharif season of 2024. The brinjal (Solanum melongena L. cv. Hybrid Lalita) was grown under three different nutrient management practices: INM, inorganic nutrient management, and organic nutrient management. The experiment details are given in Table 1, and sampling was conducted in November 2024 at the flowering stage of brinjal to investigate the root exudates and metagenomic profiling of the rhizosphere soil in brinjal (Solanum melongena). The supply of nutrients through organic sources, particularly vermicompost, was calculated on a nitrogen equivalent basis.

2.2. Characterization of Soil

Before initiation of the field experiment, soil samples were collected and analyzed for their physicochemical properties. Using a soil auger, soil samples were taken from a composite soil sample (0–15 cm depth), air-dried, and then sieved through a 2 mm mesh screen. The samples were stored at 4 °C for further analysis. Soil characteristics, such as pH, electrical conductivity (EC), organic carbon (OC), and nutrient levels, were measured using standard procedures [16]. A pH meter was used to measure the pH of the soil, and a conductivity meter was used to measure the EC. OC content was measured using the wet oxidation technique. The Kjeldahl method was used to estimate the amount of nitrogen, and colorimetric analysis was used to quantify the amount of phosphorus [17]. A flame photometer was used to analyze potassium [16].

2.3. Root Architecture

The WinRHIZO Pro imaging system (Regent Instruments, Inc., Quebec City, QC, Canada) was used to assess the root architectural attributes to examine plant–soil interactions and root morphological changes under various nutrient management regimes. The roots were sectioned into manageable lengths, thoroughly cleaned to remove any remaining dirt particles, and carefully removed to minimize breakage. To reduce root overlap, these portions were evenly distributed in a clear tray with water, and the WinRHIZO system was used to scan them. The root architectural attributes, including average root diameter (mm), total root length (m), root volume (cm3), and root surface area (cm2), were precisely measured using image analysis techniques. Analysis of each treatment was carried out independently, and mean results were noted for comparison [18]. Tukey’s test was calculated at p  <  0.05 to find out the significance among the different nutrient management practices in brinjal, including integrated nutrient management (INM) and inorganic and organic nutrient management.

2.4. Root Exudate Collection and Analysis

Root exudate collection and analysis protocols were used as follows [19,20]. To prevent any damage to the root structure, the brinjal plants from each treatment were carefully removed from the field during the flowering stage. After that, the root system was immersed in a bucket of tap water and washed to remove the soil particles until the water remained clear. This cleaning process was performed to minimize root damage and maintain root integrity for subsequent exudate analysis. After the cleaning, individual plants were then allocated to their respective treatment groups (INM, organic, and inorganic). To maintain uniformity and reduce diurnal fluctuation in root exudates, all samples were collected simultaneously, four hours after the experiment began, on the same day. The exudation setup consists of a 1 L glass beaker filled with 400 mL of ultrapure water. Breakers were wrapped to keep the root dark and maintain stems/leaves above the waterline. The recovery phase at 4 h was maintained, where plants were able to recover, followed by root cleansing. This phase allows the plant to repair itself and avoid contamination from damaged root cells. Exudates were collected at 4 h following the recovery phase. After collection, samples were stored in −80 °C in HDPE bottles to maintain their integrity. The replications of each treatment were pooled together as a single sample. For analysis, freeze-dried pooled samples were reconstituted by pipetting 2.5 mL of ultrapure water into HDPE bottles and then shaken vigorously for 1 min. The solution was transferred to a 5 mL reaction tube. Samples were rinsed with 2.5 mL of LC-MS grade methanol, vortexed, and sonicated in an ultrasonic bath (35 °C, 15 min). They were then centrifuged at 10,000× g for 3 min. The supernatant was collected, and the pellet was washed with 1 mL of 50% v/v methanol. The mixture was then vortexed, ultrasonicated for 15 min, and centrifuged. Further, the second supernatant was unified with the first. The supernatant was then transferred into LC-MS vials. The metabolite profiling was assessed using a Shimadzu LC-MS-8040 (Shimadzu UFLC-LC-20 AD, Kyoto, Japan) with an electrospray ionization detector. Liquid chromatographic separation was performed using a reversed-phase C18 analytical column (with TMS end-capping) measuring 4.6 mm × 250 mm and a particle size of 5 µm (SHIMADZU). The column temperature was maintained at 35 °C, and the total run time was 20 min for an injection sample volume of 10 µL, with an m/Z range of 100–1000. Compounds were eluted using a mobile phase consisting of 0.1% formic acid in water (A) and methanol (B) in gradient mode. The gradient started with 5% B for 2 min, followed by a linear increase to 90% B over 10 min, a decrease to 5% B over 15 min, and finally maintaining 5% B for 20 min. The flow rate was set at 0.2 mL/min using a binary pump. The chromatographic system was connected to a triple-quadrupole mass spectrometer (SHIMADZU). MS analysis was carried out in ESI-positive ionization mode with the following parameters: drying gas flow of 17 L/min, nebulizing gas flow of 3 L/min, and a total flow rate of 0.7 µL/min. Compounds were identified by comparing their mass spectra with those in the NIST 17 database (The NIST Mass Spectrometry Data Center, U.S. Department of Commerce, Gaithersburg, MD, USA). Chemical classification was visualized using a heatmap in OriginPro, Version 2021. OriginLab Corporation, Northampton, MA, USA [21].

2.5. DNA Isolation from the Soil Sample

Genomic DNA was extracted from soil samples using the Fast DNATM Spin kit for soil [22] (MP Biomedicals, Irvine, CA, USA), following the manufacturer’s protocol with slight modifications. Briefly, 500 mg of soil was suspended in 978 µL of sodium phosphate buffer and 122 µL of MT Buffer. The samples were centrifuged at 14,000× g for 5–10 min. The supernatant was carefully transferred to a 2 mL microcentrifuge tube, where 250 µL of protein precipitation solution reagent was added to induce protein precipitation. The mixture was thoroughly shaken 5 to 10 times and then centrifuged at 14,000× g for 5 min. The supernatant was transferred to a 15 mL tube, followed by the addition of 1 mL of binding matrix solution. The mixture was gently inverted for 2 min, and then, for binding, it was left undisturbed for 3 min. Then, 500 µL of the supernatant was discarded before proceeding to the DNA binding step.
For DNA binding, 600 µL of the mixture was loaded onto a Spin filter tube and centrifuged at 14,000× g for 1 min. This step was repeated until the remaining mixture exceeded 600µL. The filter was then washed with 500 µL of SEWS-M solution, followed by centrifugation at 14,000× g for 1 min. The SpinTM Filter was subsequently air-dried for 5 min at room temperature before undergoing final centrifugation at 14,000× g for 2 min. Elution of the DNA was performed using 50–100 µL of DES (DNase/Pyrogen-Free Water) elution solution, and the purified DNA was collected in a sterile microcentrifuge tube. The extracted DNA was quantified and stored at −20 °C for subsequent metagenomic analysis.

2.6. Library Preparation

DNA Quantification and Purification before Library Preparation: The input DNA was quantified using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) to ensure accurate concentration assessment. To minimize potential impurities, including residual RNA, nucleotides, and single-stranded DNA, and maintain integrity and consistency, the samples were eluted in 1× TE buffer. Fragmentation, end repair, and dA-tailing were carried out using the FS pro-DNALib prep kit V2. DNA was combined with FS Pro buffer I and FS Pro-Enzymes II (ABclonal Technology, Woburn, MA, USA), and the reaction was brought to a final volume of 50 µL with 1× TE buffer. The mixture was vortexed, centrifuged, and incubated in a thermal cycler preheated at 32 °C with a heated lid set to 82 °C. The fragmentation time was optimized based on the desired insert size, followed by incubation for 30 min at 72 °C for end repair and dA-tailing. The reaction was held at 4 °C for less than one hour. Following fragmentation, adapter ligation was performed. AFTMag NGS DNA clean beads were equilibrated at room temperature before use. Adapters were diluted appropriately using low-EDTA TE buffer, as per manufacturer instructions. The ligation reaction was set up on ice, comprising FS pro ligation buffer II, ligase enzymes, and the working adapter in a total volume of 80 µL. The reaction was incubated at 22 °C for 15 min. For post-ligation, a 0.8× bead purification step was performed, followed by ethanol washes and elution in 22 µL of nuclease-free water. Library amplification and PCR amplification of the adapter-ligated DNA were carried out using a 2× PCR Mix with either unique dual index (UDI) primers or full-length adapters. The reaction volume was 50 µL, and the thermal cycling conditions included an initial denaturation at 98 °C for 1 min, followed by 2–15 cycles of denaturation (98 °C, 10 s), annealing (60 °C, 30 s), and extension (72 °C, 30 s), with a final extension at 72 °C for 1 min. A final bead-based purification step (1×) was performed on the amplified libraries to remove primer dimers and reaction impurities. For libraries requiring size selection, a dual-step magnetic bead purification strategy was employed. For the initial selection step, a 0.65× bead ratio was used to retain larger fragments, and a subsequent 0.15× bead ratio was applied to remove smaller unwanted fragments. The purified DNA was eluted in 22 µL of nuclease-free water. Initial Library Purification and Storage Libraries were eluted in 30 µL of nuclease-free water and stored at −20 °C. Purified libraries remained stable for up to two weeks at 4 °C or −20 °C. Quality control was performed using an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) or an equivalent fragment analyzer to ensure accurate assessment of library size distribution and concentration [22]. This library preparation protocol ensured the production of high-quality sequencing libraries with consistent fragment sizes, minimal contamination, and optimal yield for downstream metagenomics analysis.

2.7. Metagenomic Data Processing and Bioinformatics Workflow

Shotgun metagenomic sequencing data underwent a thorough bioinformatics approach to investigate the structure of the rhizosphere microbial population linked to various soil treatments in brinjal (Figure S1). To ensure the evaluation of criteria, including per-base sequence quality, GC content, and the existence of adaptor contamination, the raw sequencing reads acquired from each treatment group were first examined for quality. Trimmomatic was then used to eliminate low-quality bases and remaining adapter sequences, utilizing a strict Phred quality score cutoff of Q30. To remove any potential biases, reads that were shorter than a certain length after trimming were eliminated. The sequences were subsequently aligned to a reference host genome using Kraken 2, a high-throughput metagenomic classification tool. Table 2 and Table 3 provide an overview of the raw and cleaned metagenomic sequencing data, respectively. After the inorganic and organic soil samples, the INM soil sample produced the highest data volume. The overall quality and integrity of the sequencing procedure were demonstrated by the high-quality Phred scores and uniform GC content of roughly 62–63% in all samples. Kraken, which utilizes marker genes to assign reads to bacterial, fungal, archaeal, and viral taxa, was employed to undertake the taxonomic classification of microbial communities after quality filtering. Krona and R-based visualization tools were used to visualize the organization of the microbial community at various taxonomic levels (phylum, genus, and species).

3. Results

3.1. Physicochemical Properties of Experimental Soil

The physicochemical characteristics of the soil varied depending on the fertilizer management regime (Table 2). The pH of the soil ranged from slightly alkaline to moderately alkaline, with the peak value recorded under inorganic fertilization (8.27) and the lowest value under organic management (8.14). The highest electrical conductivity (EC) was observed in the organic treatment (0.33 dS/m). In contrast, the lowest EC (0.27 dS/m) was found in the inorganic treatment. Organic carbon levels were significantly higher in plots managed organically (1.75%), followed by those with integrated nutrient management (INM) (0.95%), with the inorganic treatment showing the lowest content (0.33%). The available nitrogen content was most abundant under inorganic fertilization (344.96 kg/ha). In contrast, the INM (324.96 kg/ha) and organic (281 kg/ha) treatments presented relatively lower nitrogen levels. Potassium levels were highest in INM (223.8 kg/ha). In comparison, the lowest potassium content was found in organic management (188.64 kg/ha). Calcium concentrations were highest in the inorganic plots (1284.31 ppm), followed by the INM (830 ppm), with the organic treatment displaying the lowest level (634 ppm). Sulfur content exhibited a similar pattern, with the highest levels found in inorganic treatments (16.962 ppm), the lowest in organic treatments (13.79 ppm), and intermediate levels in INM (15.684 ppm).

3.2. Effect of Different Nutrient Management on Root Architecture Traits in Brinjal

The impact of the different nutrient management applications on root architecture (Figure 1) traits in brinjal plants is presented in Table 3. The total root length was highest in INM soil (794.89 cm), followed by inorganic soil (757.54 cm), and lowest in organic soil (738.49 cm). The root surface area showed a similar trend, with INM soil exhibiting the largest surface area (399.48 cm2), followed by inorganic soil (375.13 cm2) and organic soil (338.49 cm2). The average root diameter was highest in inorganic soil (1.85 mm), followed by INM soil (1.65 mm), and the lowest value was recorded in organic soil (1.45 mm). Root volume was also greater in INM soil (16.3 cm3), while inorganic soil had a value of 14.8 cm3, and organic soil had the lowest root volume (13 cm3). These differences underscore the substantial impact of soil treatment on root morphology and growth parameters, with INM soil exhibiting superior root development in terms of both length and surface area. In contrast, organic soil exhibited the least root growth.

3.3. Metabolite Profiling and Root Exudation Patterns in Brinjal: Insights from LC-MS Analysis

The LC-MS profiling of brinjal root exudates demonstrated significant differences in the composition and abundance of metabolites under the integrated nutrient management (INM), inorganic, and organic treatments. A total of 50+ compounds were identified, including amino acids, phenolic acids, fatty acids, alkaloids, and other secondary metabolites. A distinct difference in metabolite profiles was evident in the heatmap, which showed the presence of compounds in the three treatment blocks (INM, inorganic, and organic) (Figure 2). INM treatments exhibited a significant quantity of metabolites, encompassing substances such as citric acid trimethyl ester, acetic acid, aminoacetonitrile, indole-4-carboxaldehyde, esculin derivatives, and vanillic acid. Organic management was distinguished by the presence of many secondary metabolites, including mandelic acid, boric acid, and vanillic acid, as well as distinctive compounds such as esculin and homovanillyl alcohol. Inorganic management, in contrast, had a relatively limited profile, characterized by basic molecules such as acetic acid, propenyl derivatives, and dimethyl selenide, while lacking several complex metabolites found in INM and organic systems. Hence, unique chemical signatures of each treatment demonstrated how the nutrient source affected the chemical environment of the rhizosphere.

3.4. Microbial Diversity and Community Dynamics

High-throughput sequencing yielded enormous amounts of data upon sequencing brinjal rhizosphere soils under different nutrient management regimens (Table S1). For INM soil, 77,172,106 raw reads were obtained; for organic soil, 43,283,152; and for inorganic soil, 56,043,744. Clean sizing data were obtained after quality filtering using FastQC (Table S2). There were 77,083,458 clean reads retained in INM, including 55,964,132 inorganic reads and 21,118,326 organic reads. The clean data matched the raw outputs, indicating low read loss during quality filtering. Rarefaction curves were also generated (Figure S2). A rarefaction curve did not reach a plateau, suggesting additional sequencing reads could still capture more microbial diversity. INM had a total of 124,266,443 OTUs, while the inorganic management and the organic nutrient management had 8,298,338 and 6,819,440 OTUs, respectively. Bacteria were the dominant organisms in the domain-level taxonomic distribution, as determined by shotgun metagenomic sequencing. The proportion of total classified sequences classified as bacteria was 98.23% and 98.28% under INM and organic nutrient management strategies, respectively. A prominent bacterial dominance was also observed in the inorganic sample, with the bacterium being 97.75% dominant. These findings highlight the crucial ecological role of bacterial species in soil microbial ecosystems, regardless of the management strategies employed. Eukaryotic sequences were relatively low in abundance across all samples. The relative abundance of Eukaryotes was highest in the inorganic soil sample (1.03%–85178 OTUs), followed closely by the INM (0.65%–78927 OTUs) and organic nutrient management (0.53%–36199 OTUs). Similarly, low abundances of archaea were found: 1.13% in INM (140962 OTUs), 1.22% in inorganic fertilization (101492 OTUs), and 1.19% in organic fertilization (81260 OTUs) (Supplementary File S2).

3.4.1. Relative Abundance of Microbial Phyla

The microbial community composition at the phylum level exhibited substantial differences across the three soil treatments (INM, inorganic, and organic) (Figure 3a). In comparison to inorganic management, the phyla tended to be dominated by a greater proportion of INM treatments. At the phylum level, Bacillota constituted a significant portion of INM (44%); however, under inorganic (23%) and organic (33%) management, it was drastically reduced. In a similar vein, the abundances of Myxococcota (35%), Pseudomonadota (34%), and Bacteroidota (33%) were greater in INM compared to organic and inorganic treatments, which exhibited more uniform yet lower distributions. On the other hand, the prevalence of inorganic treatments has been reported to be lower across the majority of phyla, with Bacillota (23%) and Bacteroidota (32%) exhibiting the lowest percentages observed. Compared to other strategies, inorganic management showed a comparable representation of Actinomycetota (34%) and Pseudomonadota (34%). In organic management, the phyla were more evenly distributed, with Actinomycetota constituting 34%, Bacteroidota 35%, and Myxococcota 32%. The data indicate that INM increased the abundance of beneficial phyla such as Bacillota, Pseudomonadota, and Myxococcota. In contrast, inorganic management led to lower relative abundances, while organic treatments maintained proportions that were intermediate or balanced. Eukaryotic phyla distributions were varied in the three nutrient management strategies (Figure 3b). Ciliophora (65%), Evosea (48%), Fornicata (47%), and Microsporidia (41%) were among the phyla that were most prevalent in INM treatments. Likewise, other phyla were also prevalent under INM, such as Apicomplexa (38%), Parabasalia (39%), and Cercozoa (33%). Most phyla recorded moderate amounts of inorganic treatments, with the most significant contribution in Ascomycota (37%), followed by Microsporidia (31%), Cercozoa (30%), and Euglenozoa (28%). Relative abundances were lower for Evosea and Apicomplexa (22% each). Certain phyla were also more prevalent in organic fertilization, including Euglenozoa (43%), Basidiomycota (42%), Bacillariophyta (42%), Apicomplexa (40%), and Parabasalia (40%).
A notable diversity was observed in the representatives of archaeal phyla between the three different nutrient management techniques (Figure 3c). Some specific phyla such as Nanoarchaeota (37%), Thermoproteota (33%), Euryarchaeota (33%), Nitrososphaerota (35%), and Candidatus Thermoplasmatota (36%) were found to be greater in INM. Inorganic treatments demonstrated greater prevalences of certain phyla compared to the other approaches. Inorganic treatment yielded 63% Nanoarchaeota and 59% Candidatus Korarchaeota. Organic treatments enabled comparatively equal distributions among specific populations. The highest abundances were observed for Candidatus Nanohaloarchaeota (48%), followed by Candidatus Thermoplasmatota (41%), Candidatus Lokiarchaeota (38%), and Candidatus Micrarchaeota (37%).

3.4.2. Relative Abundance of Microbial Communities at the Genus Level

There was significant variation in the relative abundance of two dominant bacterial phyla, namely, Streptomyces and Pseudomonas, under the different nutrient management strategies (Figure 4). Following inorganic fertilizer application, Streptomyces had the highest relative abundance of 2.96%, whereas Pseudomonas showed a much lower abundance (1.09%). In the organic nutrient management system, Streptomyces (2.81%) was generally still more dominant than Pseudomonas (1.34%). Still, the variation in the diversity of the two genera was less notable than in the inorganic treatment. Although Streptomyces was always more abundant than Pseudomonas across all treatments, the degree of difference varied with treatment.
The community structure of eukaryota varied with different nutrient management strategies. In the case of INM, the highest species were Aspergillus (17.85%), Fusarium (9.33%), and Leishmania (8.84%), and the remaining species were Plasmodium, Purpureocillium, and Vanrija in less than the proportion (<2%). Fusarium (12.89%), Aspergillus (12.76% and 12.22%), and Leishmania (9.17%) were the three most abundant species, followed by Pyricularia (4.99%) and Trichoderma (3.99%) in terms of abundance during inorganic fertilization. Genera like Drechmeria and Thermothelomyces represented <2%, respectively. In organic management, the dominant groupings were Leishmania (14.00%), Pyricularia (7.24%), Aspergillus (6.50%), Fusarium (6.12%), and Toxoplasma (5.57%). Nutrient management strategies influenced the archaeal community structure, and Nitrososphaera predominated in INM (7.23%), inorganic fertilizer management (7.23%), and organic management (6.24%). The second most dominant genus was Halococcus, which showed similar proportions in both the INM (5.99%) and inorganic (5.99%) treatments, but was lower in the organic treatments (5.98%). The abundance of Natrialba was stable across systems, with a maximum of 1.53% under organic inputs, suggesting a predilection for organic materials. Comparatively, Candidatus nitrosocosmicus was present but in low abundance proportions (INM, 1.08%; organic, 1.12%) under INM and organic fertilization, whereas it was absent in the inorganic system, indicating a sensitivity to the nutrient source. INM maintained its advantage over key archaeal taxa, while inorganic inputs drove a relatively small number of taxa. In contrast, organic management increased specific sub-lineages, demonstrating the influence on the development of archaeal niches. These data highlight that nutrition management strategies govern archaeal diversity and abundance.

3.4.3. Alpha and Beta Diversity

Analysis of alpha diversity indicators revealed significant changes in microbial richness and diversity across the three nutrient management regimes (Figure 5). INM soils exhibited the highest species richness, as indicated by the highest observed OTUs, Chao1, and ACE indices. This suggests that the combination of organic and inorganic inputs in INM fosters a diverse range of microbial species. In contrast, the inorganic treatment had the highest Shannon, Simpson, and inverse Simpson indices, indicating a more evenly distributed microbial community with less dominance by specific taxa. This suggests that, while fewer taxa were present than in INM, the population was better balanced, probably due to the uniform supply of nutrients from chemical fertilizers. Organic soils had the greatest Fisher’s alpha value, indicating significant diversity in terms of uncommon taxa, which was most likely caused by the complexity of organic matter and the presence of micro-niches.
The beta diversity study, which employed principal coordinate analysis (PCoA) based on the Jaccard distance, revealed differential clustering patterns of microbial communities under various soil nutrient management conditions (Figure 6). The first two primary coordinates explained 100% of the difference in community composition, with Axis 1 and 2 accounting for 66.6% and 33.4%, respectively. Microbial communities from INM-treated soils grouped at the positive end of Axis 1, distinguishing themselves from those from inorganic and organic treatments. Inorganic soil samples were placed at the negative end of Axis 1, while organic soils occupied the upper middle quadrant, indicating a distinct microbial community rich in organic inputs. The non-overlapping distribution of clusters strongly supports a considerable difference in microbial composition between the three treatments.

4. Discussion

Long-term fertilizer management has a significant impact on the physicochemical properties of soils, which influence crop yield and the dynamics of microbial community structure [23]. The present investigation revealed the effect of long-term fertilizer management, including INM, inorganic, and organic fertilizer regimes, on soil properties [24]. Soil pH was moderately alkaline, with the maximum pH recorded under inorganic fertiliser (8.27) and the minimum recorded under organic inputs (8.14). The observed trend indicates that continuous chemical fertilizer usage leads to an alkalinizing impact, causing the accumulation of basic cations such as Ca2+ and Mg2+ [25] and application of organic inputs resulted in lower pH levels through mineralization of organic matter [26]. Organic carbon levels showed a pronounced disparity, with organic management yielding the highest concentration (1.75%), followed by integrated nutrient management (0.95%), whereas inorganic plots displayed negligible organic matter (0.33%) [27]. The results align with extensive studies in rice, wheat, and maize systems, indicating that a sole reliance on inorganic fertilizers results in the depletion of soil organic matter. At the same time, integrated nutrient management (INM) maintains moderate levels, and organic practices enhance soil carbon sequestration [28]. Similarly, the type of fertilizer and its nutrient-releasing pattern determine the variations in nutrient availability in soil [28].
The alteration of soil physicochemical properties by fertilizer regimes directly affected root architectural characteristics in brinjal. INM treatment consistently improved root length, surface area, and volume, whereas inorganic management resulted in thicker roots (larger diameter), and organic soils fostered the least widespread root systems [10]. The enhanced root morphology in INM soils can be attributed to the synergistic effect of continuous nutrient availability and improved soil structure, consistent with prior research on cereals and vegetables [29]. Furthermore, root exudate profiling confirmed these distinctions between the treatments, as INM-treated plants emitted various metabolites, encompassing organic acids (citric acid esters, acetic acid), amino acid derivatives, and phenolic substances. These chemicals are well known for their functions in signaling and microbial recruitment [30]. The presence of distinctive secondary metabolites such as mandelic acid and vanillic acid under organic fertilization implicitly suggested resource limitations [31], whereas inorganic fertilization, relying solely on chemical fertilizers, exhibits constrained chemical diversity in the rhizosphere, with a limited range of metabolites, particularly consisting of simpler compounds [32]. Thus, the present study confirmed that root metabolic activity and structure were well correlated with soil nutrient conditions, influencing the chemical environment accessible to rhizosphere microbes [33].
The alterations in soil conditions and rhizosphere inputs were evident in the dynamics of the microbial population [34]. High-throughput sequencing revealed that INM soils exhibited the greatest microbial richness and OTU abundance, whereas inorganic fertilization promoted increased evenness of taxa but reduced microbial richness. Organic soils had distinctive, less prevalent species, as shown by elevated Fisher’s alpha values. These patterns align with the ecological notion that varied nutrient inputs foster numerous microbial niches by offering a range of substrates, whereas inorganic fertilizers encourage uniformity and diminish niche differentiation [35]. The significant bacterial predominance (>97% across treatments) underscores the crucial role of bacteria in rhizosphere dynamics, particularly in nutrient cycling and the breakdown of organic matter. Notably, INM soils enhanced the abundance of the Bacillota, Pseudomonadota, and Myxococcota phyla, which have been recognized for their contributions to plant growth enhancement, disease suppression, and organic matter decomposition [36]. In contrast, beneficial phyla were less prevalent in inorganic soils, while beneficial phyla were evenly distributed but moderately prevalent in organic treatments [37]. This suggests that INM maintains preferable microbial communities, yielding a functional benefit for crop resilience. The variation at the genus level further emphasized the influence of nutritional protocols. Streptomyces, a significant genus in plant growth promotion and biocontrol, had larger and more abundant populations than Pseudomonas across all treatments, with the most notable differential reported under inorganic inputs [38]. Concurrently, organic soils maintained elevated levels of eukaryotic microflora, particularly fungi such as Basidiomycota and Ascomycota, indicative of organic substrate degradation. INM enhanced protozoan groups (Ciliophora, Evosea), which may strengthen nutrient turnover through microbial grazing [39]. Archaeal communities exhibited management-specific preferences: INM enriched Nitrososphaerota and other ammonia-oxidizing taxa essential for the nitrogen cycle, whereas inorganic treatments favored the dominance of Nanoarchaeota. Organic soils promoted the proliferation of Nanohaloarchaeota and Lokiarchaeota, indicating the selective impact of organic additions on archaeal ecology [4]. These findings suggest that nutrient management not only alters the relative abundances of primary domains but also refines the taxonomic composition of the rhizosphere microbiome.
The fluctuation in microbial abundance among INM, organic, and inorganic treatments is intricately linked to the presence or absence of particular soil metabolites. The heatmap indicates that INM treatments exhibit an increased concentration of metabolites, including citric acid, trimethyl ester, 2(3H)-furanone, mandelic acid, and methylthioacetic acid, corresponding with heightened abundances of taxa such as Nitrososphaera (7.23%), Pseudomonas (1.81%), and Streptomyces (2.61%). These chemicals offer accessible carbon sources and redox-balancing agents that promote microbial proliferation and functional diversity, particularly among nitrifiers and beneficial bacteria [40]. Conversely, organic treatments specifically comprise nitrogen-rich and signaling metabolites, including L-glutamine, L-threonine, and vanillic acid, which support lower root biomass, suggesting that resource limitation constrains exudation intensity. When added, these chemicals are well-documented as promoting advantageous microbial populations, such as Streptomyces (2.81%) and Pseudomonas (1.34%), while also appearing to support both beneficial and pathogenic fungi, including Leishmania (14.00%), Fusarium (6.12%), and Aspergillus (6.50%). L-glutamine and L-threonine act as amino acid sources that facilitate microbial protein synthesis, whereas vanillic acid, a phenolic molecule, influences microbial community dynamics by selectively promoting beneficial taxa [41]. Conversely, inorganic soils exhibited a relatively narrow metabolite spectrum, dominated by simpler molecules, which highlights the restricted chemical diversity of the rhizosphere and a transition toward dominance by opportunistic or stress-tolerant fungi, including Aspergillus (up to 12.76%) and Fusarium (12.89%). A microbial imbalance, presumably linked to decreased soil health and reduced nutrient cycling ability, is suggested by the abundance of these genera in inorganic systems [42]. Furthermore, the results indicate that organic treatments increase microbial diversity through metabolite contributions that are beneficial to both bacterial and fungal communities. In contrast, INM treatments favor bacterial and archaeal predominance, and inorganic systems encourage a more limited, potentially less advantageous, microbial spectrum.
This study demonstrates that nitrogen management influences soil characteristics, root structure, rhizosphere chemistry, and microbial communities in interrelated manners. INM promotes a functionally diversified rhizosphere characterized by enhanced microbial richness with beneficial taxa, facilitated by balanced nutrient availability and intricate exudation patterns. All of these findings indicate the ecological and agronomic benefits of integrated fertilization for brinjal production, including improved crop yields, healthier soil, and a more beneficial microbial environment.

5. Conclusions

The present investigation clearly showed the significant impact of different nutrient management techniques on soil characteristics, plant root architecture, rhizosphere metabolite profiles, and microbial community structure under brinjal cultivation. The INM helps create an environment conducive to the development of a long and dense root system, which fosters diverse root metabolites that alter the structure of the microbial community. Organic fertilization enhanced the soil’s organic carbon content, and the presence of distinct metabolites fostered fungal and archaeal diversity, although it restricted root development. In contrast, inorganic fertilization resulted in lower organic matter, altered metabolite spectra, and increased fungal dominance, raising concerns about long-term soil health. All the results have indicated that INM promotes soil fertility through regulating the microbial community. These results could highlight the ecological and agronomic benefits of integrating fertilization methods fully compared to organic or inorganic fertilization systems in sustainable brinjal cultivation.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/su17177832/s1. The preparatory methods for organic inputs. References [43,44] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, E.P. and R.K.; methodology, S.B. and E.P.; software, T.I. and S.P.S.; validation, S.B. and E.P.; formal analysis, S.B. and T.I.; investigation, S.B. and E.P.; resources, E.P. and S.P.S. data curation, S.B. and T.I.; writing—original draft preparation, S.B. and T.I.; writing—review and editing, E.P., P.J., M.S. and S.P.S.; visualization, P.D., P.J. and M.S.; supervision, E.P. and P.D.; project administration, E.P. and P.D.; funding acquisition, E.P. Writing, G.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Acknowledgments

The authors thank Nammazhvar Organic Farming Research Centre, Tamil Nadu Agricultural University, for their support.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. Root architecture of brinjal grown under different treatments.
Figure 1. Root architecture of brinjal grown under different treatments.
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Figure 2. LC-MS of brinjal root exudates under different soil treatments.
Figure 2. LC-MS of brinjal root exudates under different soil treatments.
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Figure 3. The relative abundance of (a) bacterial, (b) fungal, and (c) archaeal phyla (>1% contribution) in brinjal rhizosphere soil under INM and inorganic and organic nutrient management.
Figure 3. The relative abundance of (a) bacterial, (b) fungal, and (c) archaeal phyla (>1% contribution) in brinjal rhizosphere soil under INM and inorganic and organic nutrient management.
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Figure 4. The relative abundance of (a) bacterial, (b) fungal, and (c) archaeal genera (>1% contribution) in brinjal rhizosphere soil under INM and inorganic and organic nutrient management.
Figure 4. The relative abundance of (a) bacterial, (b) fungal, and (c) archaeal genera (>1% contribution) in brinjal rhizosphere soil under INM and inorganic and organic nutrient management.
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Figure 5. Alpha diversity indices of microbial communities in brinjal rhizosphere soils under different nutrient management regimes (INM, inorganic, and organic). Diversity measures, including observed species, Chao1, ACE, Shannon, Simpson, inverse Simpson, and Fisher, were calculated.
Figure 5. Alpha diversity indices of microbial communities in brinjal rhizosphere soils under different nutrient management regimes (INM, inorganic, and organic). Diversity measures, including observed species, Chao1, ACE, Shannon, Simpson, inverse Simpson, and Fisher, were calculated.
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Figure 6. Beta diversity indices of microbial communities in brinjal rhizosphere soils under different nutrient management regimes (INM, inorganic, and organic).
Figure 6. Beta diversity indices of microbial communities in brinjal rhizosphere soils under different nutrient management regimes (INM, inorganic, and organic).
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Table 1. Treatment details and experimental design for the brinjal.
Table 1. Treatment details and experimental design for the brinjal.
CropBrinjal
VarietyHybrid Lalita
Treatment details
INM soil25% nutrients through organic sources (kg/ha) + 25% nutrients through inorganic sources (kg/ha) + seed/seedling treatment with Beejamrit + application of Ghanajeevamrit @ 1000 kg/ha, Jeevamrit @ 1500 L/ha/time twice a month with irrigation water
Inorganic way of nutrient management100% inorganic fertilizers (200:150:100 N:P2O5:K2O kg/ha)
Organic way of nutrient managementSupply of 50% nutrients through organic sources + seed treatment with Beejamrit * + one-time application of Ghanajeevamrit * @ 1000 kg/ha, Jeevamrit * @ 1500 L/ha/time twice a month with irrigation water
Replications7
DesignRandomized block design
Spacing60 cm × 45 cm
Date of Planting14 August 2024
Crop duration180 days
* The preparation methods of Beejamrit, Ghanajeevamrit, and Jeevamrit are given in Supplementary File S1.
Table 2. Physicochemical properties of experimental soil under different nutrient management practices in brinjal cultivation.
Table 2. Physicochemical properties of experimental soil under different nutrient management practices in brinjal cultivation.
ParameterINMInorganicOrganic
pH8.198.278.14
EC (ds/m)0.290.270.33
Organic carbon (%)0.950.331.75
Nitrogen (Kg/ha)324.96344.96281
Phosphorus (Kg/ha)42.3539.4832.74
Potassium (Kg/ha)223.8209.68188.64
Calcium (ppm)8301284.31634
Sulfur (ppm)15.68416.96213.79
Table 3. Effect of different fertilisation, including integrated nutrient management and organic and inorganic nutrient management, on root morphological traits in brinjal.
Table 3. Effect of different fertilisation, including integrated nutrient management and organic and inorganic nutrient management, on root morphological traits in brinjal.
TreatmentTotal Root Length (cm)Root Surface Area (cm2)Average Root Diameter (mm)Root Volume (cm3)
INM soil794.89 ± 2.39 a399.48 ± 2.55 a1.65 ± 0.02 b16.3 ± 0.07 a
Inorganic soil757.54 ± 6.48 a375.13 ± 3.26 b1.85 ± 0.02 a14.8 ± 0.08 b
Organic soil738.49 ± 3.22 a338.49 ± 1.57 a1.45 ± 0.01 c13.0 ± 0.05 c
CD at 5%224.6419.870.1030.40
Values in each column are the mean of seven replications. Tukey’s test was used at a significance level of p < 0.05 to determine the significance of the treatments. ± followed by numbers is the standard error. Means sharing the same letters in column are not significantly different as determined by the Tukey test (p < 0.05).
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Bommi, S.; Parameswari, E.; Dhevagi, P.; Krishnan, R.; Janaki, P.; Suganthy, M.; Sangeetha, S.P.; Yazhini, G.; Ilakiya, T. Comparative Analysis of Effects of Nutrient Management Practices on Soil Microbiome and Rhizosphere Chemistry in Brinjal (Solanum melongena L.). Sustainability 2025, 17, 7832. https://doi.org/10.3390/su17177832

AMA Style

Bommi S, Parameswari E, Dhevagi P, Krishnan R, Janaki P, Suganthy M, Sangeetha SP, Yazhini G, Ilakiya T. Comparative Analysis of Effects of Nutrient Management Practices on Soil Microbiome and Rhizosphere Chemistry in Brinjal (Solanum melongena L.). Sustainability. 2025; 17(17):7832. https://doi.org/10.3390/su17177832

Chicago/Turabian Style

Bommi, Sathasivam, Ettiyagounder Parameswari, Periyasamy Dhevagi, Ramanujam Krishnan, Ponnusamy Janaki, Mariappan Suganthy, Sundapalayam Palanisamy Sangeetha, Gunasekaran Yazhini, and Tamilselvan Ilakiya. 2025. "Comparative Analysis of Effects of Nutrient Management Practices on Soil Microbiome and Rhizosphere Chemistry in Brinjal (Solanum melongena L.)" Sustainability 17, no. 17: 7832. https://doi.org/10.3390/su17177832

APA Style

Bommi, S., Parameswari, E., Dhevagi, P., Krishnan, R., Janaki, P., Suganthy, M., Sangeetha, S. P., Yazhini, G., & Ilakiya, T. (2025). Comparative Analysis of Effects of Nutrient Management Practices on Soil Microbiome and Rhizosphere Chemistry in Brinjal (Solanum melongena L.). Sustainability, 17(17), 7832. https://doi.org/10.3390/su17177832

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