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Article

Depth-Dependent Responses of Microbial Community Structure and Function to Reductive Soil Disinfestation

by
Xinyu Wang
1,2,
Hanlin Chen
1,
Juntao Zeng
1,
Jintao Chen
1,
Yanru Ma
1,
Qin Shao
1 and
Liangliang Liu
1,*
1
School of Life Sciences and Environmental Resources, Yichun University, Yichun 336000, China
2
School of Agricultural Sciences, Jiangxi Agricultural University, Nanchang 330100, China
*
Author to whom correspondence should be addressed.
Horticulturae 2026, 12(1), 35; https://doi.org/10.3390/horticulturae12010035
Submission received: 25 November 2025 / Revised: 16 December 2025 / Accepted: 25 December 2025 / Published: 27 December 2025
(This article belongs to the Special Issue Mitigating Soil-Borne Diseases in Horticultural Crops: Current Challenges and Management Strategies)
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Abstract

Reductive soil disinfestation (RSD) is an effective approach for controlling horticultural plant diseases by improving soil properties. However, its effects on microbial communities and their functional characteristics across soil depths remain poorly researched. In this study, we evaluated the impacts of RSD using solid (rice bran, RB) and liquid (molasses, MO) organic amendments in a Fusarium-infested field. Changes in biotic and abiotic properties were examined at two soil depths (0–15 cm and 15–30 cm) and the potential of different amendments to restore microecological functions in deeper soil was assessed. Both RSD treatments alleviated soil acidification and salinization compared with the control. The absolute abundances of Fusarium oxysporum and Fusarium solani were significantly reduced under both treatments, with MO-RSD showing stronger pathogen suppression in the 15–30 cm layer. MO-RSD exerted a greater influence on microbial community structure across soil depths, resulting in bacterial-fungal co-occurrence networks with higher complexity. Metabolic activity and carbon source utilization increased significantly following both RSD treatments, with the greatest enhancement observed in the 0–15 cm layer under MO-RSD. Furthermore, MO-RSD enriched a higher diversity and abundance of beneficial microorganisms such as Bacillus, Paenibacillus, and Tumebacillus in the 0–15 cm layer, and Azotobacter, Penicillium, and Neurospora in the 15–30 cm layer. These microbes were closely associated with enhanced metabolic activity and pathogen suppression. Overall, MO-RSD established a more integrated and functionally diverse microbiota across the 0–30 soil profile, likely due to the greater permeability and mobility of liquid organic amendments in shaping deeper soil microbial communities.

1. Introduction

High-intensity cultivation based on continuous cropping has become widespread due to its economic advantages. However, long-term adoption of this practice has resulted in severe degradation of arable land, including soil acidification, secondary salinization, and accumulation of soil-borne pathogens [1]. Collectively, these problems exacerbate soil-borne diseases, reduce soil productivity, and pose serious threats to global food security and ecological sustainability [2].
Several strategies, including soil disinfection, crop rotation, and the use of resistant cultivars, have been applied to mitigate soil degradation. Although these approaches can be effective, they often fail to provide long-term solutions due to the complexity of soil ecosystems [2,3,4]. For example, crop rotation requires substantial land availability and extended fallow periods, making it impractical in many production systems. Chemical soil fumigation can reduce pathogen abundance, but it also disrupts other indigenous microorganisms and does not address the underlying soil conditions that favor pathogen re-establishment [4]. As a result, pathogen populations may rapidly rebound after host crop planting resumes [5]. Hence, an effective soil management strategy should simultaneously suppress pathogens and improve soil conditions to prevent their recurrence.
As well known, soil microbial communities and their metabolic functions are essential for maintaining soil productivity and plant health [6,7]. Beneficial microorganisms such as plant growth-promoting rhizobacteria can suppress soil-borne pathogens through the secretion of antimicrobial compounds (e.g., antibiotics and siderophores) and competitive exclusion, while also enhancing plant stress tolerance by inducing systemic resistance [8,9]. Moreover, higher metabolic activity and functional diversity within soil microbial communities generally promote ecosystem stability, thereby increasing the resistance of plant rhizospheres to infection by soil-borne pathogens [10,11,12]. Notably, microorganisms inhabit not only surface soil but also deeper horizons, which are also vital for soil health and can be influenced by practices such as tillage, irrigation, and natural migration [13,14]. For example, previous studies have detected substantial populations of pathogens such as Fusarium oxysporum at depths of up to 30 cm in monoculture systems, indicating a hidden reservoir of disease risk [15,16]. Therefore, management strategies that promote beneficial microbial communities and enhance their metabolic and functional capacities across soil depths are crucial for sustainable soil management.
Reductive soil disinfestation (RSD) is implemented by incorporating large amounts of organic amendments to soil, followed by irrigating to saturation and sealing with plastic film to create a strongly anaerobic and reductive environment [17,18,19]. RSD can effectively suppress soil-borne pathogens and restructure soil microbial communities, thereby enhancing metabolic activity and functional diversity. However, its effectiveness strongly depends on the type and composition of the organic amendments applied. For example, Huang et al. [10] reported that sugarcane bagasse improved microbial community structure and metabolic function more effectively than ethanol, while studies by Zhao et al. [11] and Yan et al. [12] showed that combining organic materials further increased the abundance and activity of beneficial microorganisms. Notably, both solid and liquid forms of organic amendments are commonly used in RSD [18,19,20], but their contrasting physical properties (e.g., solubility, mobility, and distribution within the soil profile) are likely to differentially influence microbial communities across soil layers. However, the effects of solid versus liquid amendments on soil microbial communities and functions across different depths under RSD have not been systematically compared. This knowledge gap limits a comprehensive understanding of RSD mechanisms and constrains its broader application in agricultural systems.
Herein, we hypothesized that although both amendment types would improve soil microecological conditions across different depths, liquid amendments, owing to their greater mobility and permeability, would more effectively influence microbial communities in deeper soil layers, thereby promoting a more stable and functionally integrated microecosystem. To test this hypothesis, RSD was applied in a Fusarium-infested bitter gourd field using molasses (liquid) and rice bran (solid) as organic amendments. Changes in abiotic and biotic properties were evaluated at soil depths of 0–15 cm and 15–30 cm. Specifically, this study aimed to (1) characterize the vertical responses of soil microbiological properties to RSD and (2) compare the capacities of liquid and solid amendments to restore microecological function in deeper soil layers. The results of this study will provide mechanistic insights into RSD and offer guidance for improving soil health management at depth.

2. Materials and Methods

2.1. Field Site and Organic Amendments

The experiment was conducted in Nanmiao Town (28°11′ N, 114°42′ E), Yichun City, Jiangxi Province, China. The site experiences a subtropical monsoon climate, characterized by a mean annual temperature of 17.3 °C and annual precipitation of 1497 mm. Continuous greenhouse cultivation and limited rotation between bitter gourd (Momordica charantia L.) and amaranth (Amaranthus mangostanus L.) over the past five years resulted in a fusarium wilt incidence exceeding 30% in bitter gourd, leading to substantial yield losses. According to the FAO soil classification system, the soil is identified as a typical Ferralic Cambisols. The soil had the following characteristics: pH 4.11, electrical conductivity (EC) 406.3 μS·cm−1, total organic carbon (TOC) 26.02 g·kg−1, and total nitrogen (TN) 3.52 g·kg−1.
Rice bran (RB) was obtained from a local rice processing mill, with a particle size of < 0.5 cm. Its TOC, TN, and carbon/nitrogen ratio (C/N) were 406.0 g·kg−1, 4.6 g·kg−1, and 88.3, respectively. Molasses (MO) was provided by Yunnan Liran Agricultural Technology Co., Ltd., Kunming, China, with TOC, TN, and C/N values of 347.8 g·kg−1, 16.8 g·kg−1, and 20.7, respectively.

2.2. Experimental Design and Soil Sampling

The field experiment was conducted in a greenhouse from 10 July 2021 to 28 July 2021 (18 days) and included three treatments: (1) untreated soil maintained at 15–20% moisture as the control (CK); (2) soil amended with rice bran at 15 t∙ha−1, surface-applied and rototilled to a depth of 15–20 cm, followed by irrigating to saturation and sealing with 0.08 mm plastic film to establish anaerobic conditions (RB-RSD); and (3) soil treated after rototilling with a 20-fold diluted molasses solution (equivalent to 7.5 t·ha−1), followed by the same irrigation and plastic film covering as in RB-RSD (MO-RSD). Each treatment was replicated three times in plots measuring 60 m2 (15 m length × 4 m width). During the incubation period, average daily temperatures inside the greenhouse were approximately 40 °C during the day and 28 °C at night. Application rates for rice bran and molasses were based on preliminary trials and common field practices [12,21,22], allowing comparison of the two amendment types at their typical field application levels. After the incubation period, the plastic film was removed and the soil was allowed to dry. Soil samples were collected from two depths (0–15 cm and 15–30 cm) using an auger following an S-shaped sampling pattern within each plot (3 treatments × 3 replicates × 2 soil layers = 18 samples). Soil samples were sieved through a 2 mm-mesh and then split into two subsamples. One subsample was stored at 4 °C for analyses of physicochemical properties and carbon-source metabolic activity, while the other was stored at −20 °C for an assessment of microbial community composition.

2.3. Measurement of Soil Physicochemical Properties

Soil pH and EC were analyzed using a S220K pH meter and a S230K conductivity meter (Mettler-Toledo International Inc., Shanghai, China) at water-to-soil ratios of 2.5:1 and 5:1 (v/m), respectively. Soil TOC and TN were measured using the H2SO4–K2Cr2O7 wet digestion method [23] and semi-micro-Kjeldahl method [24], respectively. Soil nitrate nitrogen (NO3−-N) and ammonium nitrogen (NH4+-N) were extracted with 2 mol·L−1 KCl at a liquid-to-soil ratio of 5:1 (v/m) and then quantified according to a continuous flow analyzer (Skalar San++, Breda, The Netherlands). Available phosphorus (AP) and available potassium (AK) were extracted using 0.5 mol·L−1 NaHCO3 solution and 1 mol·L−1 NH4CH3CO2 solution, respectively, and were then correspondingly detected by molybdenum-antimony anti-spectrophotometric and flame photometry [25,26].

2.4. Determination of Soil Microbial Activity and Metabolic Activities

Total microbial activity of the soil was assessed through the fluorescein diacetate (FDA) hydrolysis method as described previously [27]. Soil metabolic activity was evaluated using Biolog EcoPlatesTM (Biolog, Inc., Hayward, CA, USA). These plates contained 31 carbon substrates and categorized into six groups: carbohydrates, carboxylic acids, amino acids, phenolic acids, amines, and polymers. The assay procedure was as follows: (1) a 10−1 soil suspension was prepared by homogenizing 10 g of fresh soil in 90 mL of sterile 0.85% NaCl solution and subsequently diluted to 10−3; (2) a 125 µL aliquot of the diluted suspension was inoculated into each well of the EcoPlate and incubated at 25 °C for 6 days; (3) optical density at 590 nm (OD590) was measured every 12 h using a microplate reader (BioTek Instruments Inc., Winooski, VT, USA); (4) based on the OD590 data, the value of Average Well Color Development (AWCD) was calculated, and metabolic diversity indices, including richness, evenness, and the Shannon index, were determined according to Garland [28] and Fisk et al. [29].

2.5. DNA Extraction and Quantitative PCR Analysis

Total genomic DNA was isolated from 0.5 g soil samples representing each replicate using the FastDNA® Spin Kit (MP Biomedicals, Santa Ana, CA, USA) according to supplied protocols. DNA purity and concentration were assessed with a DS-11 spectrophotometer (Denovix Inc., Wilmington, DE, USA) by measuring the A260/A280 and A230/A260 ratios. The absolute abundances of total bacteria, total fungi, and the pathogens F. oxysporum and F. solani were determined by a real-time PCR system (QuantStudio3, Applied Biosystems, Waltham, MA, USA). Each 20 μL qPCR reaction mixture contained 2 μL of DNA template, 10 μL of 2× SYBR Green Premix Taq, 1 μL each of forward and reverse primers (Table 1), and 6 μL of sterile water. Amplification conditions for each target gene are provided in Table 1, and standard curves were generated as described in previous studies [21,22].

2.6. MiSeq Sequencing and Bioinformation Analysis

To target the bacterial 16S rDNA V4-V5 and fungal internal transcribed spacer (ITS) regions, the primer pairs 515F/907R and ITS1/ITS2 (Table 1) were employed for PCR amplification, respectively. Each 30 μL PCR reaction mixture contained 10 μL of DNA template (1 ng·µL−1), 15 µL of 2× Phusion Master Mix, 1 μL each of forward and reverse primers, and 3 μL of sterile water. The resulting amplicons were purified using a Universal DNA Purification Kit (TianGen, Biochemical Technology Co., Ltd., Beijing, China), and equimolar amounts of high-quality products were pooled and sequenced on an Illumina NovaSeq platform by Beijing Novogene Bioinformatics Technology Co., Ltd. (Beijing, China).
Sequencing data were processed using Quantitative Insights Into Microbial Ecology 2 (QIIME2) environment (version 2019.1) [38] following the standard pipeline https://docs.qiime2.org/2019.1/ (accessed on 12 August 2025). Raw FASTQ files were imported using the qiime tools import plugin, followed by quality filtering, read merging, and chimeras removal with the dada2 plugin to generate amplicon sequence variants (ASVs). Taxonomic assignment of bacterial and fungal ASVs was performed using the feature-classifier and feature-table plugins against the SILVA [39] and UNITE [40] databases, respectively, with a 99% similarity threshold. All samples were rarefied to the minimum sequencing depth within each microbial group prior to calculation of alpha diversity indices using the core-diversity plugin.

2.7. Statistical Analysis

The effects of treatments and soil depth on microbial community composition and metabolic functional profiles were evaluated using principal coordinate analysis (PCoA), permutational multivariate analysis of variance (PERMANOVA), and variation partitioning analysis (VPA) with the phyloseq (version 1.52.0) [41] and vegan (version 2.7-1) [42] packages in R. Co-occurrence networks of bacterial-fungal core taxa (defined as taxa present in at least four replicates within each treatment and soil layer) were constructed based on correlations (|r| > 0.90, p < 0.01) using the psych (version 2.5.3) [43] package, and visualized with Gephi (version 0.9.2) [44]. Procrustes analysis and Mantel tests (performed with vegan) [42] were used to assess relationships among dissimilarities in microbial community structure, metabolic functional profiles, and pathogen abundance. Redundancy analysis (RDA) was conducted using phyloseq [41] to evaluate associations among dominant taxa, carbon source utilization, and pathogen abundance. Differences in soil physicochemical properties, absolute microbial abundances (log10-transformed), microbial diversity indices, and relative abundances of dominant taxa were analyzed using one-way ANOVA followed by the LSD’s test in SPSS 27.0.

3. Results

3.1. Soil Physicochemical Properties

In the 0–15 cm soil layer (Table 2), both RB-RSD and MO-RSD significantly increased soil pH and NH4+-N compared with CK (p < 0.05). In contrast, EC and NO3−-N were markedly reduced, with EC decreasing by 73.71% and 76.05% and NO3−-N by 99.46% and 99.69%, respectively (p < 0.05). Soil TOC was significantly higher in RB-RSD than in CK (p < 0.05), whereas no significant difference was observed under MO-RSD (p > 0.05). Neither RSD treatment significantly affected AK, whereas TN and AP were significantly reduced (p < 0.05) under both treatments compared to CK.
In the 15–30 cm soil layer, pH, EC, NH4+-N, NO3−-N, AK, and AP exhibited trends consistent with those observed in the 0–15 cm soil layer. However, TOC and TN did not differ significantly among treatments (p > 0.05). Across all treatments, pH, EC, TOC, TN, AK, and AP were significantly lower in the 15–30 cm soil layer than in the 0–15 cm soil layer (p < 0.05).

3.2. Soil Microbial Activity and Absolute Microbial Abundances

In the 0–15 cm soil layer (Figure 1A), total microbial activity was significantly higher under RB-RSD than under CK (p < 0.05), whereas no significant difference was observed under MO-RSD. Absolute bacterial abundances increased significantly in both RB-RSD (4.41 × 1010 copies g−1) and MO-RSD (9.49 × 1010 copies g−1) relative to CK (2.49 × 1010 copies g−1) (p < 0.05). In contrast, absolute fungal abundance was significantly reduced under RB-RSD (Figure 1B,C). The absolute abundances of F. oxysporum (2.19 × 104 copies g−1 and 8.43 × 104 copies g−1) and F. solani (6.71 × 104 copies g−1 and 2.09 × 104 copies g−1), as well as the total pathogen load in RB-RSD and MO-RSD, were markedly lower than in CK (F. oxysporum: 5.13 × 106 copies g−1; F. solani: 3.26 × 106 copies g−1). Corresponding disinfestation efficiencies ranged from 93.57% to 99.57% (p < 0.05). However, F. solani abundance and total pathogen load were higher in MO-RSD than in RB-RSD (Figure 1D–F).
In the 15–30 cm soil layer, total microbial activity under both RSD treatments followed trends similar to those observed in the 0–15 cm layer (Figure 1A). Absolute abundances of bacteria (2.46 × 1010 copies g−1 and 2.33× 1010 copies g−1), fungi (2.50 × 107 copies g−1 and 3.70× 106 copies g−1), F. oxysporum (2.05 × 104 copies g−1 and 4.03 × 103 copies g−1), F. solani (9.31 × 104 copies g−1 and 1.66 × 103 copies g−1), and the total pathogen load in RB-RSD and MO-RSD, were all significantly lower than in CK (bacteria: 3.38 × 1010 copies g−1; fungi: 8.80 × 107 copies g−1; F. oxysporum: 7.32 × 105 copies g−1; F. solani: 7.24 × 105 copies g−1) (p < 0.05). Corresponding reduction rates ranged from 27.33% to 31.19% for bacteria, 71.83% to 95.79% for fungi, 97.19% to 99.45% for F. oxysporum, 87.14 to 99.77% for F. solani, and 92.19% to 99.61% for total pathogen load (Figure 1B–E). Notably, in contrast to the 0–15 cm layer, F. solani abundance and total pathogen load were significantly lower under MO-RSD than under RB-RSD in the 15–30 cm layer (p < 0.05). Additionally, bacterial abundance across all treatments, as well as fungal and pathogen abundances under MO-RSD, were significantly lower in the 15–30 cm layer than in the 0–15 cm layer (p < 0.05).

3.3. Soil Microbial Community Diversity and Structure

In both the 0–15 cm and 15–30 cm soil layers, microbial community richness did not differ significantly among treatments. Similarly, the Shannon index under RB-RSD did not differ from that under CK, whereas MO-RSD resulted in a significant reduction in Shannon diversity (p < 0.05) (Figure 2A). PCoA and PERMANOVA revealed clear separation of both bacterial (p < 0.01) and fungal (p < 0.05) community structures among treatments and soil layers. The first two PCoA axes explained 54.8% and 23.0% of variation in bacterial communities and 50.3% and 19.0% variation in fungal communities, respectively (Figure 2B,C). VPA results further indicated that both treatment type and soil depth significantly influenced microbial community structure (Figure 2D,E). Treatment type explained a greater proportion of variation in bacterial (36.0%) and fungal (31.3%) communities than soil depth (23.3% and 1.89%, respectively). Additionally, soil depth exerted a stronger influence on bacterial community structure than on fungal community structure.

3.4. Microbial Community Composition

3.4.1. Bacterial-Fungal Co-Occurrence Networks

Compared to CK, both RB-RSD and MO-RSD substantially altered the interactive co-occurrence networks of bacterial-fungal communities, with distinct core taxa observed among treatments and soil depths (Figure 3A–C). The numbers of network nodes and edges were increased under the RB-RSD and MO-RSD treatments, respectively, relative to CK (Figure 3D), indicating enhanced microbial connectivity. In addition, both RSD treatments increased network modularity and the average clustering coefficient compared with CK (Figure 3D), suggesting that RSD enhanced the structural complexity and organization of bacterial-fungal interactions.

3.4.2. Composition of Dominant Phyla

Compared with CK, both RB-RSD and MO-RSD significantly altered soil microbial community composition (p < 0.05), and dominant microbial groups exhibited similar response patterns across the two soil layers. At the bacterial phylum level (Figure 4A), the relative abundances of Firmicutes, Acidobacteriota, Chloroflexi, Actinobacteriota, Bacteroidota, Verrucomicrobiota, Myxococcota, and Planctomycetota differed among treatments and soil layers (p < 0.05). Gemmatimonadota showed significant treatments effects in the 0–15 cm layer, whereas Proteobacteria, Desulfobacterota, Methylomirabilota, Nitrospirota, MBNT15, and Patescibacteria differed significantly among treatments in the 15–30 cm layer (p < 0.05). Notably, the relative abundance of Firmicutes increased under both RB-RSD and MO-RSD across soil layers compared with CK, whereas the relative abundances of Proteobacteria, Chloroflexi, and Actinobacteriota decreased (p < 0.05). For fungi (Figure 4B), the relative abundances of Ascomycota and Rozellomycota differed significantly among treatments and soil layers (p < 0.05). Both RB-RSD and MO-RSD considerably increased the relative abundance of Ascomycota while reducing that of Rozellomycota (p < 0.05).

3.4.3. Composition of Dominant Genera

At the bacterial genus level (Figure 4C), the relative abundances of Bacillus, Paenibacillus, Tumebacillus, Candidatus_Koribacter, Effusibacillus, Rummeliibacillus, Chujaibacter, Oxobacter, Cohnella, Thermoanaerobacterium, Hydrotalea, Acidothermus, Bryobacter, and Pullulanibacillus differed among treatments and soil layers (p < 0.05). The dominant genera Bryobacter, Chujaibacter, and Acidothermus were mainly enriched under CK (p < 0.05). Candidatus_Koribacter and Hydrotalea were predominantly enriched under RB-RSD, whereas most of the remaining dominant genera were mainly enriched under MO-RSD (p < 0.05). The relative abundance of Candidatus_Solibacter increased significantly in the 0–15 cm soil layer under RB-RSD, while Streptomyces and Alicyclobacillus increased significantly in the 15–30 cm soil layer (p < 0.05). The relative abundances of Azotobacter and Acetobacter increased significantly in the 15–30 cm soil layer under MO-RSD (p < 0.05). Although Alicyclobacillus, Azotobacter and Acetobacter also showed higher relative abundances in the 0–15 cm soil layer under MO-RSD, these differences were not statistically significant among treatments due to high variability.
For fungi (Figure 4D), the relative abundances of Trichoderma, Trichocladium, Fusarium, and Penicillium differed among treatments and soil layers (p < 0.05). Trichoderma, Trichocladium, and Fusarium were mainly enriched in CK (p < 0.05), while Penicillium was enriched under both RB-RSD and MO-RSD (p < 0.05). Additionally, Penicillifer and Chaetomium were significantly enriched in the 0–15 cm soil layer under RB-RSD and CK (p < 0.05), respectively. Mortierella was significantly enriched in the 15–30 cm layer under CK (p < 0.05), whereas Neurospora and Pseudeurotium were significantly enriched in the 15–30 cm soil layer under MO-RSD (p < 0.05). Similarly to the bacterial genera Alicyclobacillus, Azotobacter, and Acetobacter, no significant differences in Neurospora abundance were detected among treatments in the 0–15 cm layer due to high variability.

3.5. Microbial Metabolic Activity

Compared to CK, both RB-RSD and MO-RSD significantly increased the microbial metabolic activity (AWCD) across soil layers. However, AWCD values in the 15–30 cm layer were significantly consistently lower than those in the 0–15 cm layer across all treatments. In the 0–15 cm layer, AWCD was significantly higher under MO-RSD than under RB-RSD, whereas no significant difference was found between treatments in the 15–30 cm layer (Figure 5A). PCoA indicated that both RSD treatment and soil depth significantly influenced the composition of microbial metabolic functions (Figure 5B). The utilization intensity and diversity of carbohydrates, carboxylic acids, amino acids, phenolic acids, amines, and polymers increased significantly under both RB-RSD and MO-RSD. Moreover, carbon source utilization and metabolic diversity were considerably higher in the 0–15 cm layer than in the 15–30 cm layer across all treatments (Figure 5C,D). In the 0–15 cm layer, MO-RSD resulted in significantly greater utilization of five carbon source categories (excluding phenolic acids) and higher richness, Shannon index, and evenness of carbon source utilization compared with RB-RSD. These differences were not significant in the 15–30 cm layer.

3.6. Relationships Among Microbial Community Structure, Metabolic Function, and Pathogen Abundance

Procrustes analysis revealed that both bacterial (M2 = 0.451, p < 0.001) and fungal (M2 = 0.512, p < 0.001) community structures were significantly correlated with microbial metabolic functions across treatments and soil layers (Figure 6A,B). In addition, dissimilarities in bacterial and fungal community structures were significantly negatively correlated with differences in total pathogen abundance (Figure 6C,D). Furthermore, the relative abundances of dominant bacterial genera (Bacillus, Paenibacillus, Tumebacillus, Effusibacillus, Rummeliibacillus, Oxobacter, Cohnella, Thermoanaerobacterium, Streptomyces, and Alicyclobacillus) and fungal genera (Penicillium, Neurospora, and Talaromyces) under RB-RSD or MO-RSD were significantly positively correlated with carbon source utilization intensity, but significantly negatively correlated with total pathogen abundance (Figure 6C,D).

4. Discussion

4.1. RSD Alleviates Soil Acidification and Secondary Salinization Across the 0–30 cm Soil Profile

Soil acidification and secondary salinization are common consequences of continuous cropping systems and can indirectly disrupt soil microbial community structure and function, thereby inhibiting plant root growth and nutrient uptake [45,46]. Previous studies have shown that these conditions facilitate the proliferation of soil-borne pathogens and increase disease incidence [46,47]. In the present study, both RB-RSD and MO-RSD significantly increased soil pH and decreased EC in the 0–15 cm and 15–30 cm layers, demonstrating that RSD can effectively alleviate soil acidification and secondary salinization across the 0–30 cm soil profile. These changes improvements are likely associated with organic acid production during anaerobic decomposition, which neutralizes acidifying ions (e.g., H+ and Al3+), as well as the generation of OH− under reducing conditions [17,18]. In addition, RSD reduced NO3−-N content by more than 93% while increasing NH4+-N content, indicating enhanced denitrification and nitrogen immobilization. This shift likely limited nitrate accumulation and contributed to reduce soil salinity [48]. Notably, although increases in pH, NH4+-N, and TOC and decreases in EC were also observed in the 15–30 cm soil layer, these changes were less pronounced than those in the 0–15 cm soil layer. This suggests that the intensity of reducing conditions and nitrogen immobilization induced by RSD in deeper soil layers may be constrained by the downward movement and distribution of organic amendments.

4.2. Differential Pathogen Suppression by RSD Amendments Across the 0–30 cm Soil Profile

The accumulation of soil-borne pathogens is a common outcome of continuous cropping and poses a serious threat to crop production. Therefore, reducing pathogen abundance is essential for maintaining soil health and sustainable cultivation. In this study, both RB-RSD and MO-RSD significantly reduced populations of F. oxysporum and F. solani in the 0–15 cm and 15–30 cm soil layers, consistent with previous reports [17,18,19,20,21,22]. The strongly reducing conditions established during RSD facilitate anaerobic decomposition of organic materials, resulting in the production of volatile fatty acids (e.g., acetic, propionic, butyric, and isovaleric acids), hydrogen sulfide, and ammonia, all of which can directly inhibit pathogen growth [49]. Additionally, concurrent changes in soil physicochemical properties, such as increased pH and reduced EC, may further weaken pathogen survival [17,50].
Despite their overall effectiveness, the two RSD treatments exhibited distinct pathogen suppression patterns across soil depths. In the 0–15 cm layer, RB-RSD showed slightly stronger inhibition of F. solani and total pathogen population than MO-RSD. In contrast, MO-RSD resulted in lower pathogen abundances than RB-RSD in the 15–30 cm layer. This difference is likely related to the higher mobility of molasses as a liquid organic amendment, which allows it to diffuse more effectively into deeper (15–30 cm) soil layer, whereas rice bran, as a solid material, tends to remain concentrated near the soil surface. Moreover, the relatively high pathogen abundance observed in the 15–30 cm layer under CK indicates the presence of a pathogen refuge in deeper soil. Deep soil layers typically have lower oxygen availability and reduced microbial activity, making them less responsive to conventional management practices [13,14]. In this study, MO-RSD appeared to maintain a stronger disinfestation efficacy against pathogens in the 15–30 cm soil layer compared to RB-RSD, highlighting the potential advantage of liquid carbon sources in improving deep soil microenvironments. These findings suggest that integrating agronomic practices such as deep plowing or subsoiling to promote the downward distribution of organic amendments may promote more uniform pathogen suppression throughout the soil profile.

4.3. Liquid RSD Amendments More Strongly Shape Microbial Community Structure and Function Across the 0–30 cm Soil Profile

Soil microorganisms are key biological indicators of soil health, and beyond reducing pathogen loads, restructuring microbial community composition is essential for maintaining ecosystem function and supporting crop growth [5,16]. In this study, RSD treatments using different organic amendments significantly altered soil microbial community structure across soil depths. Notably, MO-RSD was more effective than RB-RSD in modulating microbial community structure throughout the 0–30 cm soil layer profile. Co-occurrence network analysis further indicated that both RB-RSD and MO-RSD enhanced cross-domain interactions between bacterial and fungal communities. Compared with CK and RB-RSD, MO-RSD generated a more complex network structure, as reflected by a higher number of edges and a greater average clustering coefficient [21,22]. These results suggest that molasses, as a liquid carbon source, may interact more readily with microbial communities across 0–30 cm soil layer due to greater vertical mobility. However, increased network connectivity may also reflect the dominance of a limited number of taxa or intensified co-occurrence patterns under amended conditions and does not necessarily indicate enhanced functional stability or ecological resilience. Further studies linking network topology to specific microbial functions are therefore needed to clarify these relationships.
Microbial metabolic activity directly reflects soil ecosystem functioning [7]. In this study, Biolog EcoPlates were used to assess the metabolic activity of fast-growing, culturable microorganisms in response to readily available carbon sources. In the 0–15 cm soil layer, MO-RSD significantly increased microbial AWCD values and the diversity of carbon source utilization. Specifically, the utilization intensities of carbohydrates, carboxylic acids, amino acids, amines, and polymers were higher under MO-RSD than under RB-RSD. These results suggest that molasses, as a readily decomposable and highly soluble liquid carbon source, can diffuse rapidly through soil pores and provide immediately available carbon and energy, thereby stimulating more active and diverse microbial metabolic responses. This finding is consistent with Yan et al. [12], who reported that molasses-based carbon sources enhance the metabolic potential of microbial communities in continuous cropping soils, particularly for functional groups involved in carboxylic acid and amino acid metabolism. Although microbial metabolic activity in the 15–30 cm layer was lower than in the 0–15 cm layer, likely due to limited resource and reduced microbial abundance, MO-RSD appeared to help maintain carbon utilization patterns in this deeper soil (15–30 cm). This observation suggests that liquid carbon sources such as molasses could supplement carbon-limited microbial communities at depth and partially activate their metabolic potential.
This study revealed that different RSD treatments exhibited distinct selectivity in enriching dominant microorganisms, with MO-RSD outperforming RB-RSD in both the diversity and abundance of enriched taxa. Specifically, RB-RSD significantly enriched Candidatus_Koribacter, Hydrotalea, and Penicillifer in the 0–15 cm layer, as well as Alicyclobacillus and Streptomyces in the 15–30 cm layer. In contrast, MO-RSD significantly enriched Bacillus, Paenibacillus, Tumebacillus, Effusibacillus, Rummeliibacillus, Oxobacter, Cohnella, Thermoanaerobacterium, and Pullulanibacillus in the 0–15 cm layer, and Azotobacter, Acetobacter, Neurospora, Penicillium, and Pseudeurotium in the 15–30 cm layer. Notably, most of these dominant taxa possess versatile metabolic capacities, enabling efficient utilization of diverse carbon sources and contributing to biocontrol and plant growth promotion. This is consistent with the observed positive correlations between these microorganisms and carbon source utilization intensity, as well as their negative correlations with pathogen abundance. For instance, Bacillus and Paenibacillus, which were enriched under MO-RSD, synthesize antimicrobial compounds such as lipopeptides and polyketides that directly inhibit soil-borne pathogens [21,51]. They also secrete plant growth regulators, including indole-3-acetic acid (IAA), which can enhance root development and disease resistance [52]. Azotobacter and Acetobacter contribute to biological nitrogen fixation, thereby improving nitrogen availability and the rhizosphere environment [53,54]. Tumebacillus, Pullulanibacillus, Effusibacillus, Rummeliibacillus, Oxobacter, and Thermoanaerobacterium are frequently reported as dominant taxa in RSD systems [10,11,12,21,22] and can antagonize pathogenic fungi through nutrient competition, production of intermediate metabolites (e.g., organic acids and alcohols), and involvement in sulfur and iron cycling, thereby indirectly suppressing pathogen colonization. Among fungi, Penicillium plays a role in decomposing complex organic substrates and producing antimicrobial metabolites such as penicillin [55]. Neurospora and Pseudeurotium, as common saprotrophs, degrade recalcitrant organic matter (e.g., lignin, cellulose, and polysaccharides like pullulan), releasing soluble carbon that supports bacterial growth [56,57]. Together, these processes may promote a synergistic fungal-bacterial carbon metabolism system that suppresses pathogen colonization through mechanisms such as competitive exclusion.
It is noteworthy that although the carbon input under MO-RSD was lower than under RB-RSD, the objective of this study was to compare the effects of amendment physical form (solid vs. liquid) on soil microbiota along the vertical soil profile, rather than to evaluate responses under equivalent carbon or nitrogen inputs. In contrast, previous studies have shown that organic amendments with lower C/N ratios and higher decomposability used in RSD can achieve greater disinfestation efficacy against soil-borne pathogens and promote the proliferation of beneficial microorganisms [18,19,20]. These findings further reinforces our inference that molasses-based RSD has advantages over rice bran, owing to molasses has a higher decomposability and lower C/N relative to rice bran. Nevertheless, this study examined only two types of organic amendments at two soil depth intervals (0–15 cm and 15–30 cm), which limits broader generalization regarding the effects of other organic materials on deeper soil layers (>30 cm). Future studies should therefore investigate RSD using a wider range of organic amendments and their combinations, as well as deeper soil profiles, to more fully elucidate the vertical regulatory effects of RSD and to provide a stronger theoretical basis for the management of soil-borne diseases.

5. Conclusions

This study demonstrates that RSD incorporating solid amendments (rice bran) achieved higher disinfestation efficacy against soil-borne pathogens in the 0–15 cm soil layer, whereas RSD incorporating liquid amendments (molasses) exerts a stronger capacity to regulate the soil microecological environment across the entire soil profile, owing to its greater mobility and permeability. Accordingly, MO-RSD represents a promising approach for restoring soil health in continuous cropping systems, and its effectiveness may be further enhanced by agronomic practices that promote the downward distribution of organic materials into subsoil layers. These findings also have practical implications for growers and soil managers: (1) when continuous cropping constraints are primarily confined to the topsoil, solid amendments may be sufficient; (2) when problems extend into deeper soil layers, liquid amendments are more appropriate; and (3) future management strategies could explore combinations of solid and liquid amendments to integrate the advantages of both forms and achieve more comprehensive soil health restoration.

Author Contributions

Conceptualization, L.L. and X.W.; methodology, X.W.; software, H.C.; validation, J.Z., J.C. and Y.M.; formal analysis, Q.S.; investigation, H.C.; resources, J.Z.; data curation, Y.M. and Q.S.; writing—original draft preparation, X.W. and L.L.; writing—review and editing, L.L.; visualization, Y.M.; supervision, Q.S. and L.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32460802, 32160748), Natural Science Foundation of Jiangxi Province (20252BAC240591), and the Key Research Basic Project of Yichun City, Jiangxi Province (2024ZDYFJH02).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Additionally, the raw data of MiSeq sequencing in this study are available in the NCBI repository under the accession number PRJNA890763, which can be found below: https://www.ncbi.nlm.nih.gov/ (accessed on 15 October 2022).

Acknowledgments

The authors acknowledge with gratitude the technical support provided by Yuanyuan Yan, Xing Zhou, and Xinqi Huang of Nanjing Normal University. We also extend our sincere thanks to the editors and reviewers for their insightful suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Soil microbial activity (A) and absolute abundances (log10-transformed) of bacteria (B), fungi (C), and pathogens (D–F). Error bars indicate SDs. Different lowercase and uppercase letters represent significant differences at p < 0.05 according to the LSD test. Asterisks indicate significant differences between soil layers at * p < 0.05, ** p < 0.01, and *** p < 0.001 based on independent samples t-tests. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
Figure 1. Soil microbial activity (A) and absolute abundances (log10-transformed) of bacteria (B), fungi (C), and pathogens (D–F). Error bars indicate SDs. Different lowercase and uppercase letters represent significant differences at p < 0.05 according to the LSD test. Asterisks indicate significant differences between soil layers at * p < 0.05, ** p < 0.01, and *** p < 0.001 based on independent samples t-tests. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
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Figure 2. Soil microbial community alpha diversity (A), structure (B,C), and contributing factors (D). Error bars in panel A represent SDs. Different lowercase and uppercase letters represent significant differences between treatments at p < 0.05 according to the LSD test. Asterisks indicate significant differences between soil layers at * p < 0.05 and ** p < 0.01 based on independent samples t-tests. Principal coordinate analyses (PCoA) of bacterial (B) and fungal (C) communities were conducted using Bray–Curtis distances calculated from ASV distributions across all treatments. Ellipses with the different colors in correspond to the 95% confidence intervals. Differences in microbial community tructure among treatments and soil layers were assessed using PERMANOVA with 999 permutations. The relative contributions of treatment and soil depth to microbial community dissimilarities were quantified using variation partitioning analysis (VPA) (D,E). CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
Figure 2. Soil microbial community alpha diversity (A), structure (B,C), and contributing factors (D). Error bars in panel A represent SDs. Different lowercase and uppercase letters represent significant differences between treatments at p < 0.05 according to the LSD test. Asterisks indicate significant differences between soil layers at * p < 0.05 and ** p < 0.01 based on independent samples t-tests. Principal coordinate analyses (PCoA) of bacterial (B) and fungal (C) communities were conducted using Bray–Curtis distances calculated from ASV distributions across all treatments. Ellipses with the different colors in correspond to the 95% confidence intervals. Differences in microbial community tructure among treatments and soil layers were assessed using PERMANOVA with 999 permutations. The relative contributions of treatment and soil depth to microbial community dissimilarities were quantified using variation partitioning analysis (VPA) (D,E). CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
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Figure 3. Co-occurrence networks of microbial communities under different treatments ((A), CK; (B), RB-RSD; (C), MO-RSD) and their topological characteristics (D). Each network includes core taxa (present in at least four replicates within each treatment) across different soil layers (six samples in total). CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
Figure 3. Co-occurrence networks of microbial communities under different treatments ((A), CK; (B), RB-RSD; (C), MO-RSD) and their topological characteristics (D). Each network includes core taxa (present in at least four replicates within each treatment) across different soil layers (six samples in total). CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
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Figure 4. Relative abundances of dominant microbial phyla ((A), bacteria; (B), fungi) and genera ((C), bacteria; (D), fungi) with proportions > 1%. Significant differences in relative abundance among RSD treatments at the 0–15 cm and 15–30 cm soil layers are indicated by * and #, respectively (p < 0.05). The values in (C,D) represent the average relative abundance of the corresponding genus, and the color gradient from blue to red in each row indicates the variation in relative abundance from the lowest to the highest for that genus. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
Figure 4. Relative abundances of dominant microbial phyla ((A), bacteria; (B), fungi) and genera ((C), bacteria; (D), fungi) with proportions > 1%. Significant differences in relative abundance among RSD treatments at the 0–15 cm and 15–30 cm soil layers are indicated by * and #, respectively (p < 0.05). The values in (C,D) represent the average relative abundance of the corresponding genus, and the color gradient from blue to red in each row indicates the variation in relative abundance from the lowest to the highest for that genus. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
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Figure 5. Effects of RSD treatments on microbial metabolic activity across soil layers. (A) Average well color development (AWCD) of 31 carbon substrates during incubation. (B–D) principal coordinate analysis (PCoA) of metabolic profiles (ellipses with the different colors in (B) correspond to the 95% confidence intervals), utilization patterns of categorized carbon sources, and diversity indices of microbial metabolic activity after six days of incubation. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
Figure 5. Effects of RSD treatments on microbial metabolic activity across soil layers. (A) Average well color development (AWCD) of 31 carbon substrates during incubation. (B–D) principal coordinate analysis (PCoA) of metabolic profiles (ellipses with the different colors in (B) correspond to the 95% confidence intervals), utilization patterns of categorized carbon sources, and diversity indices of microbial metabolic activity after six days of incubation. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
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Figure 6. Relationships among microbial community structure, metabolic function, and total pathogen abundance (F. oxysporum + F. solani). (A,B), Procrustes analyses showing correlations between bacterial and fungal community structures and microbial metabolic activity, respectively. Solid symbols represent the distribution of microbial community structures for each treatment, while hollow symbols indicate the distribution of microbial metabolic activity. The blue arrows connect the microbial community with its corresponding metabolic activity under the same treatment, and the length of each arrow is positively correlated with the strength of their association. (C,D), Mantel tests relating dissimilarities in bacterial and fungal community structures and total pathogens abundance, respectively. The gray shading denotes the 95% confidence intervals. (E,F), Redundancy analysis (RDA) illustrating associations of dominant bacterial and fungal genera with carbon source utilization intensity and total pathogen abundance. The size of each red dot indicates the relative abundance of its corresponding dominant genus.
Figure 6. Relationships among microbial community structure, metabolic function, and total pathogen abundance (F. oxysporum + F. solani). (A,B), Procrustes analyses showing correlations between bacterial and fungal community structures and microbial metabolic activity, respectively. Solid symbols represent the distribution of microbial community structures for each treatment, while hollow symbols indicate the distribution of microbial metabolic activity. The blue arrows connect the microbial community with its corresponding metabolic activity under the same treatment, and the length of each arrow is positively correlated with the strength of their association. (C,D), Mantel tests relating dissimilarities in bacterial and fungal community structures and total pathogens abundance, respectively. The gray shading denotes the 95% confidence intervals. (E,F), Redundancy analysis (RDA) illustrating associations of dominant bacterial and fungal genera with carbon source utilization intensity and total pathogen abundance. The size of each red dot indicates the relative abundance of its corresponding dominant genus.
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Table 1. Primers and amplification conditions used for qPCR and MiSeq sequencing.
Table 1. Primers and amplification conditions used for qPCR and MiSeq sequencing.
TargetPrimerSequence (5′-3′)Amplification ProceduresReferences
Bacterial 16S
rRNA		338FACTCCTACGGGAGGCAGCAGPre-denaturation at 95 °C for 1 min, followed by 39 cycles of denaturation at 95 °C for 5 s and annealing at 60 °C for 30 s[30]
518RATTACCGCGGCTGCTGG[31]
Fungal ITSITS1FCTTGGTCATTTAGAGGAAGTAA[32]
ITS2GCTGCGTTCTTCATCGATGC[33]
Fusarium solaniITS1FCTTGGTCATTTAGAGGAAGTAA[32]
AFP346GGTATGTTCACAGGGTTGATG[34]
Fusarium oxysporumITS1FCTTGGTCATTTAGAGGAAGTAAPre-denaturation at 95 °C for 1 min, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing at 58 °C for 15 s[32]
AFP308CGAATTAACGCGAGTCCCAAC[35]
16S rRNA sequencing515FGTGCCAGCMGCCGCGGPre-denaturation at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s and annealing at 50 °C for 30 s[36]
907RCCGTCAATTCMTTTRAGTTT[37]
ITS sequencingITS1FCTTGGTCATTTAGAGGAAGTAA[32]
ITS2RGCTGCGTTCTTCATCGATGC[33]
Table 2. Effects of RSD treatments on physicochemical properties at different soil depths.
Table 2. Effects of RSD treatments on physicochemical properties at different soil depths.
Property0–15 cm Soil Layer15–30 cm Soil Layer
CKRB-RSDMO-RSDCKRB-RSDMO-RSD
pH4.11 ± 0.11 b5.23 ± 0.03 a5.28 ± 0.08 a4.32 ± 0.06 C4.84 ± 0.02 A4.63 ± 0.08 B
EC (μS·cm−1)406.3 ± 33.06 a97.34 ± 10.52 b106.8 ± 5.72 b78.51 ± 7.92 A43.54 ± 1.27 B36.44 ± 0.41 B
NO3−-N (mg·kg−1)135.0 ± 2.25 a0.72 ± 0.38 b0.42 ± 0.11 b20.00 ± 2.19 A1.31 ± 0.84 B0.60 ± 0.10 B
NH4+-N (mg·kg−1)55.55 ± 16.41 b97.50 ± 2.45 a88.15 ± 5.27 a16.18 ± 1.87 C33.98 ± 4.99 A24.95 ± 2.17 B
TOC (g·kg−1)26.02 ± 0.46 b29.65 ± 0.70 a27.67 ± 1.57 ab11.56 ± 1.52 A12.13 ± 0.85 A11.67 ± 0.83 A
TN (g·kg−1)3.52 ± 0.11 a3.35 ± 0.06 b3.30 ± 0.02 b1.38 ± 0.15 A1.54 ± 0.16 A1.33 ± 0.10 A
AP (mg·kg−1)179.5 ± 6.77 a151.3 ± 10.09 b142.2 ± 9.75 b53.82 ± 0.68 A36.18 ± 4.39 B16.18 ± 5.51 C
AK (mg·kg−1)329.3 ± 37.54 a418.0 ± 93.72 a366.6 ± 16.65 a154.0 ± 109.48 A156.6 ± 38.69 A104.6 ± 12.70 A
Values are means ± standard deviations (SDs) (n = 3). Different lowercase letters (0–15 cm) and uppercase letters (15–30 cm) within the same row indicate significant differences among treatments at p < 0.05 according to the LSD test. CK, untreated control; RB-RSD and MO-RSD, RSD treatments with rice bran and molasses, respectively.
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Wang, X.; Chen, H.; Zeng, J.; Chen, J.; Ma, Y.; Shao, Q.; Liu, L. Depth-Dependent Responses of Microbial Community Structure and Function to Reductive Soil Disinfestation. Horticulturae 2026, 12, 35. https://doi.org/10.3390/horticulturae12010035

AMA Style

Wang X, Chen H, Zeng J, Chen J, Ma Y, Shao Q, Liu L. Depth-Dependent Responses of Microbial Community Structure and Function to Reductive Soil Disinfestation. Horticulturae. 2026; 12(1):35. https://doi.org/10.3390/horticulturae12010035

Chicago/Turabian Style

Wang, Xinyu, Hanlin Chen, Juntao Zeng, Jintao Chen, Yanru Ma, Qin Shao, and Liangliang Liu. 2026. "Depth-Dependent Responses of Microbial Community Structure and Function to Reductive Soil Disinfestation" Horticulturae 12, no. 1: 35. https://doi.org/10.3390/horticulturae12010035

APA Style

Wang, X., Chen, H., Zeng, J., Chen, J., Ma, Y., Shao, Q., & Liu, L. (2026). Depth-Dependent Responses of Microbial Community Structure and Function to Reductive Soil Disinfestation. Horticulturae, 12(1), 35. https://doi.org/10.3390/horticulturae12010035

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