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Article

Role of Reductive Soil Disinfestation and Chemical Soil Fumigation on the Fusarium Wilt of Dioscorea batatas Decne Suppression

by
Qin Shao
1,2,
Xiaopeng Li
1,
Tian Zhao
1,
Yiyang Wu
1,
Liqin Xiang
1,
Shengfu Pan
1,
Zihan Guo
1 and
Liangliang Liu
1,2,*
1
College of Life Science and Environmental Resources, Yichun University, Yichun 336000, China
2
Engineering Technology Research Center of Jiangxi Universities and Colleges for Selenium Agriculture, Yichun University, Yichun 336000, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11991; https://doi.org/10.3390/su151511991
Submission received: 7 July 2023 / Revised: 31 July 2023 / Accepted: 2 August 2023 / Published: 4 August 2023
(This article belongs to the Special Issue Soil Degradation, Soil Remediation and Sustainable Development)
Browse Figures
Versions Notes

Abstract

:
Reductive soil disinfestation (RSD) and chemical soil fumigation (CSF) comprise the most popular pre-planting soil management strategies. Their efficiency in suppressing several plant diseases in agricultural production systems has been compared. However, the disease-control effect of these methods on Fusarium wilt disease in Dioscorea batatas Decne (D. batatas) remains unclear. Importantly, dissimilarities in the impact of their bio-predictors on plant health have not been well characterized. Herein, four treatments, including no treatment (CK), RSD with gran chaff (GC-RSD) and molasses (MO-RSD), and CSF with dazomet (DA-CSF), were performed in a pot experiment using D. batatas-diseased soil. Compared with the CK treatment, the Fusarium oxysporum population significantly decreased by 88.89–97.78% following the DA-CSF, GC-RSD, and MO-RSD treatments. The bacterial community and functional composition of the soil were considerably altered by these treatments. However, the incidence of Fusarium wilt disease in D. batatas was significantly decreased in the two RSD-treated soils, rather than in DA-CSF-treated soils. Bacterial α-diversity and population as well as some key nitrogen-related functional gene expressions as bio-predictors were significantly lower in DA-CSF-treated soil than in RSD-treated soil. In particular, the core (e.g., Azotobacter, Phenylobacterium, Clostridium, Bradyrhizobium, Microvirga, and Caulobacter) and unique (e.g., Pseudomonas, Brevundimonas, Flavobacterium, Ochrobactrum, and Sphingobacterium) functional microbiomes in RSD-treated soil exerted a positive impact on soil functional composition of the soil and plant growth. Taken together, our results indicate that RSD outperformed CSF in promoting plant health by regulating the bacterial community and functional composition.

1. Introduction

Dioscorea batatas Decne (Purple yam) is a popular economic crop with high edible value in China. It is primarily distributed in the subtropical humid climate zones, such as Jiangxi, Hunan, and Guangxi Provinces [1]. Specifically, Jiangxi Province has the largest D. batatas cultivation area (8000 hm2) and an output of >1.2 billion RMB. Furthermore, D. batatas is considered an important geo-authentic crude drug, containing bioactive compounds, such as dioscin, anthocyanidin, and allantoin. It exerts positive pharmacological effects, including anticough, antiasthma, antitumor, hypolipidemic, and antiatherosclerotic activities [1,2,3].
However, owing to the increased consumption demand and limitation of suitable soil resources, continuous monoculture alongside overfertilization has become the major planting pattern for D. batatas in these areas. This planting pattern can degrade soil via soil acidification, salinization, and accumulation of soil-borne pathogens [4,5]. Recently, several studies have revealed that the Fusarium wilt disease caused by Fusarium oxysporum f. sp. dioscoreae seriously threatens the growth of D. batatas [6,7]; however, there is limited information regarding the suppression of Fusarium wilt in D. batatas. Thus, there is a need to develop effective strategies to control this plant disease.
Pre-planting soil disinfestation is an important strategy to control soil-borne diseases and ensure high crop yields. Among various physical, chemical, and biological strategies, chemical soil fumigation (CSF) using pesticides has been considered the most effective way to control plant diseases for decades [8,9]. For example, CSF with methyl bromide has been applied worldwide for over 40 years because it exhibits a broad-spectrum inhibitory effect on soil-borne pathogens and can only stay in the soil for a few days [8]. However, it is an ozone-depleting substance that has been prohibited for use according to the Montreal Protocol since 2004 [9]. The development of alternative chemical fumigants and even environmentally convenient soil disinfestation strategies has been a challenging task for plant protectionists. Dazomet (tetrahydro-3,5-dimethyl-2H-1,3,5-thiadiazine-2-thione), a chemical fumigant with low toxicity, is reportedly effective against a wide range of soil-borne pathogens, including fungi, some bacteria, pests, and nematodes, as it releases the compound methyl-isothiocyanate [10,11]. Recently, dazomet has been successfully applied for suppressing soil-borne diseases in numerous crops and represents a promising alternative to methyl bromide in China and worldwide [10,11,12,13].
In addition, reductive soil disinfestation (RSD), also known as anaerobic or biological soil disinfestation, is an environmentally friendly soil management strategy that involved incorporating organic materials (e.g., grain chaff, wheat bran, alfalfa meal, and molasses) into the soil, flooding to saturation, and covering with a plastic film at an average temperature of >25 °C for 2–4 weeks [14,15,16]. The highly anaerobic and reductive environment and the antimicrobial compounds (e.g., acetic acid, butyric acid, ammonia, Fe2+, and Mn2+) generated during RSD treatment can significantly eliminate various soil-borne pathogens, such as Fusarium oxysporum, Fusarium solani, Rhizoctonia solani, and Verticillium dahliae [17,18,19,20]. Moreover, RSD can improve soil physicochemical and microbial properties. For example, RSD can ameliorate soil acidification and salinization by reducing hydrogen and soluble base ions. The resulting acetic and butyric acid products may increase the soil’s nutritional quality by solubilizing non-available elements (such as phosphorous) [21,22,23,24]. In particular, the composition of the microbial community and its functions may also be optimized during RSD through stimulation with organic materials, which plays an important role in plant disease suppression during cultivation [21,25,26].
As mentioned previously, both CSF and RSD are promising strategies for soil disinfestation; however, which strategy can obtain a better control effect on the Fusarium wilt of D. batatas remains unknown. Importantly, to the best of our knowledge, the differences in the impact of their bio-properties in predicting plant health have not been well characterized, although the effects of these strategies on plant disease suppression have been compared in several studies [5,11]. Notably, according to these previous application cases, the cost efficiency of RSD practice (15,000–20,000 RMB per ha) is less than that of CSF with dazomet (20,000–24,000 RMB per ha). Therefore, to determine the role of RSD and CSF in suppressing D. batatas Fusarium wilt and clarify the key bio-indicators, studies employing CSF with dazomet (DA-CSF) and RSD incorporated with grain chaff (GC-RSD) and molasses (MO-RSD) in D. batatas-diseased soil (CK) were conducted. The effects of soil microbial population, bacterial community diversity, and functional composition on plant growth were determined.

2. Materials and Methods

2.1. Sampling Area and Materials Description

The sampling field was located in Wanzai County (114°43′ E, 28°12′ N), Jiangxi Province, China, where nearly 200 hm2 of D. batatas are grown. This area has a typical subtropical humid climate, with an annual average temperature of 16.93–18.29 °C and rainfall of 1742.5 mm. D. batatas has been planted in this field for >10 years, experiencing severe Fusarium wilt disease recently. During sampling, tillage layer soil (0–20 cm) was collected, and the soil had an initial electrical conductivity of 135.70 μS cm−1, pH of 5.91, and available potassium of 643.33 mg kg−1.
Grain chaff (particle size < 1 mm, total organic carbon 348 g kg−1, total nitrogen 5 g kg−1, and C:N 70) and molasses (total organic carbon 347.8 g kg−1, total nitrogen 16.8 g kg−1, and C:N 20.7) were collected from rice mills in the sampling area and sugar mills in Yunnan Province, China, respectively. Dazomet was purchased from Zhejiang Haizheng Chemical Co., Ltd., Taizhou, China. Before the experiment, the molasses was diluted 10-fold with water.

2.2. Experimental Design

Four treatments were evaluated in this study: (1) CK—9 kg soil with a moisture content of 18–30% was placed into a pot (top diameter × bottom diameter × height: 22 × 18 × 25 cm); (2) GC-RSD and MO-RSD—two lots of 9 kg soil were supplemented with 90 g (1% w/w) of grain chaff and molasses, respectively, and were placed into a pot, irrigated and sealed with plastic film; and (3) DA-CSF—9 kg soil fumigated with 2 g of dazomet (0.02% w/w) was placed into a pot, irrigated and sealed with plastic film. Each treatment included three replicates, and each replicate included three pots. The pots were incubated for 30 days at an average greenhouse temperature of 25–40 °C. Following incubation, the treated soils were naturally drained, sieved, and thoroughly mixed. The soil samples were collected in triplicate for each treatment by combining the soil from three pots (25 g for each pot), which were then stored at 4 °C for the subsequent determination of physicochemical properties and at −20 °C for microbial population and community assessment.
Soil from each replicate received 0.1 g kg–1 (w/w) of compound NPK fertilizer (20:20:20). Two D. batatas seedlings were subsequently sown into each pot (six seedlings for each replicate) followed by rain-shelter cultivation for 6 months (from 10 May to 10 November 2022). During this period, the same concentration of compound NPK fertilizer (0.1 g kg−1 of soil) was applied to each pot once a month. Each pot was irrigated with 450 mL water once a day during the summer season (from 10 May to 10 August) and then irrigated with 600 mL water once a week. After planting, the disease incidence, shoot length, shoot fresh weight, and underground fresh weight of D. batatas were recorded. The disease incidence for each treatment was determined using the following formula: (number of dead plants/6 plants in each replicate) × 100%.

2.3. Measurement of the Soil Physicochemical Properties

Soil pH was measured at a soil/deionized water ratio of 1:2.5 (w/v) using an S220 m (Mettler, Greifense, Switzerland). Soil NH4+–N was extracted using a 2 mol L–1 KCl solution at a soil/solution ratio of 1:5 and detected using a continuous flow analyzer (San ++; Skalar, Breda, The Netherlands). Soil-available potassium was extracted using a 1 mol L−1 ammonium acetate solution, followed by measurement using flame photometry.

2.4. Measurement of the Soil Microbial Population

Soil (0.5 g) from each replicate was used to extract microbial DNA according to the FastDNA Spin Kit (MP Biomedicals, Santa Ana, CA, USA). Extracted high-quality DNA was used to determine the microbial population based on a QuanStudio 3 Real-Time PCR system (Applied Biosystems, Waltham, MA, USA). The primers used for the amplification of bacteria, fungi, and F. oxysporum and nitrogen-related genes such as nitrate reduction (narG), nitrite reduction (nirK and nrfA), nitrous oxide reduction (nosZ), and nitrogen fixation (nifH), are listed in Table 1. The amplification mix used for each gene included 10 μM forward and reverse primers, 2 μL DNA template, 10 μL SYBR Green premix Taq (2×), and 6 μL sterile distilled water. The amplification protocols, specificity, and standard curves for each gene were previously described by Yan et al. [27].

2.5. Measurement of the Soil Bacterial Community and Function

Individual barcoded primers 515F/907R (Table 1) for the bacterial 16S rRNA gene at the V4–V5 region were used for Illumina MiSeq sequencing. The amplification mixes and protocols were obtained from Liu et al. [21]. Following purification and quantification, PCR products with equimolar polymerization were sequenced at Genesky Biotechnologies Inc. (Shanghai, China).
The raw sequencing data were processed using QIIME software (version 1.9.1) according to the method of Caporaso et al. [39]. Briefly, paired-end FASTQ sequences were merged using the default argument in multiple_join_paired_ends.py. The sequences underwent quality control and were clustered into operational taxonomic units (OTUs) according to the default arguments in multiple_split_libraries_fastq.py and pick_open_reference_otus.py, respectively. Chimeric, chloroplast, and mitochondrial DNA in all soil samples were identified and removed using the default arguments in parallel_identify_chimeric_seqs.py and filter_otus_from_otu_table.py. The taxonomic identities of the bacterial OTUs were annotated using the SLIVA 138/16s_bateria reference database at 97% sequence similarity [40]. Bacterial sequence counts across all the soil samples were rarefied to 73,291, and the alpha diversities were assessed using the default arguments in alpha_diversity.py. The functional composition of bacteria was predicted using FAPROTAX as described previously by Louca et al. [41].

2.6. Statistical Analysis

Significant differences (p < 0.05) in soil physicochemical properties, microbial quantifications (after log10-transformed), bacterial α-diversity, and plant properties were analyzed using analysis of variance (ANOVA) and least significant difference (LSD) test via IBM SPSS22.0. The significant differences in the soil bacterial community structure (β-diversity), functional composition, and core and unique microbiomes were analyzed using principal coordinate analysis (PCoA) and permutational multivariate ANOVA (PERMANOVA) based on the R “phyloseq” and “vegan” packages, respectively. The core and unique microbiomes for the different treatments were identified according to previous studies [42,43]. Relationships between the key indicators of the core microbiome, N-related genes, and plant properties were determined using Procrustes and heatmap correlation analyses via the Tutools platform (https://www.cloudtutu.com/, accessed on 15 March 2023).

3. Results

3.1. Soil Physicochemical Parameters

Compared with CK soil, the GC- and MO-RSD treatments significantly (p < 0.05) increased soil pH by 0.20 and 0.46, respectively, whereas the pH was not obviously (p > 0.05) different between CK and DA-CSF-treated soils (Table 2). The concentration of NH4+–N in the GC-RSD-, MO-RSD-, and DA-CSF-treated soils increased considerably (p < 0.05) by 2.37-, 7.47-, and 2.61-fold compared with that in CK soil, respectively (Table 2). Further, the highest (p < 0.05) soil AK content was found in GC-RSD-treated soil.

3.2. Soil Bacteria, Fungi, and Fusarium oxysporum Quantification

The bacterial quantifications in GC-RSD- (4.11 × 1010 copies g−1) and MO-RSD-treated (3.61 × 1010 copies g−1) soils and the fungal quantification in MO-RSD (3.52 × 108 copies g−1) treated soil were considerably (p < 0.05) increased compared with that in CK soil (bacteria: 2.64 × 1010 copies g−1; fungi: 1.15 × 108 copies g−1); whereas the bacterial and fungal quantification in DA-CSF-treated soil showed an opposite trend (p < 0.05) (Table 3). The quantifications of F. oxysporum in DA-CSF- (2.53 × 104 copies g−1), GC-RSD- (1.27 × 105 copies g−1), and MO-RSD-treated (9.34 × 104 copies g−1) soils decreased remarkably (p < 0.05) by 97.78%, 88.89%, and 91.84%, respectively, compared with that in CK soil (1.15 × 106 copies g−1) (Table 3).

3.3. Bacterial Community Diversity and Composition

3.3.1. Bacterial Community Diversity

The richness and Shannon index of bacterial α-diversity in the MO-RSD- and DA-CSF-treated soils considerably decreased (p < 0.05), whereas the Shannon index in the GC-RSD-treated soil considerably increased compared with that in the CK soil (Figure 1A,B). Notably, the lowest bacterial α-diversity was detected in DA-CSF-treated soil. In addition, bacterial β-diversity among the different treatments was obviously different (p < 0.01), whereas a similarity in bacterial β-diversity was found between the GC-RSD- and MO-RSD-treated soils compared with that in the DA-CSF-treated soil (Figure 1C).

3.3.2. Bacterial Community Composition

RSD and CSF practices significantly (p < 0.05) attenuated the soil bacterial community composition, with the regulations varying between each treatment (Figure 1D,E). At the phylum level, the relative abundances of the dominant phyla, i.e., Proteobacteria, Firmicutes, Actinobacteriota, Acidobacteriota, etc., were considerably different (p < 0.05) among the various treatments. The relative abundance of Firmicutes and Actinobacteriota in DA-CSF-, GC-RSD-, and MO-RSD-treated soils and that of Proteobacteria in MO-RSD-treated soil were considerably (p < 0.05) higher than those detected in CK soil (Figure 1D).
At the genus level, the relative abundances of all the dominant genera, except Dyella, were markedly different (p < 0.05) between the disinfestation- and CK-treated soils (Figure 1E). For example, the relative abundances of the dominant genera Azotobacter, Stenotrophomonas, and Sphingobacterium in MO-RSD-treated soil; Clostridium, Bradyrhizobium, Oryzihumus, and Pseudomona in the GC-RSD-treated soil; as well as Ramlibacter, Ruminiclostridium, and Sinomonas in the DA-CSF-treated soil were remarkably increased compared with that in the CK soil.

3.4. Soil Core and Unique Soil Microbiome Compositions

Significant differences in bacterial community composition were also observed between the core and unique microbiomes (Figure 2A–E). The size of the core OTUs in these treatments was 890, accounting for 38.83–50.83% of the retained OTUs. The number of unique OTUs in the CK-, DA-CSF-, GC-RSD-, and MO-RSD-treated soils was 663, 310, 337, and 269, respectively, accounting for 27.63%, 17.70%, 14.70%, and 13.52% of the corresponding retained OTUs (Figure 2A and Table 4).
Most core OTUs (66.11–77.76%) detected among the different treatments were classified into 21 genera (Figure 2D and Table 4). Specifically, the relative abundances of the core genera Azotobacter, Bordetella, and Nocardioides in MO-RSD-treated soil; Bradyrhizobium, Nocardia, and Piscinibacter in GC-RSD-treated soil; Caulobacter, Clostridium, Microvirga, Oryzihumus, and Phenylobacterium in both RSD-treated soils; and Citrifermentans, Noviherbaspirillum, Ramlibacter, Ruminiclostridium, and Sinomonas in DA-CSF soil were considerably (p < 0.05) increased compared with those observed in CK soil (Figure 2D, Table S1).
Most unique bacterial OTUs (4.16–8.99%) detected across all soils could be classified into 26 genera, with the CK, DA-CSF-, GC-RSD-, and MO-RSD-treated soils harboring 6, 6, 6, and 9 genera, respectively (Figure 2E, Table 4). In particular, the Alicyclobacillus, HN-HF16, Meiothermus, Nostoc_PCC-7524, Oxobacter, and Pelotomaculum genera were observed only in the DA-CSF-treated soil; Clostridium, Geothrix, Methylomagnum, Methylospira, Paludibacterium, and Pseudomonas genera were present only in the GC-RSD-treated soil; and Brevundimonas, Camelimonas, Chitinophaga, Flavobacterium, Leucobacter, Ochrobactrum, Sphingobacterium, Stenotrophomonas, and Taibaiella genera were only detected in the MO-RSD-treated soil (Figure 2E, Table S2).

3.5. Bacterial Functional and Nitrogen-Related Gene Compositions

Compared with the CK soil, the bacterial functional composition was significantly (p < 0.01) altered in the disinfestation-treated soils (Figure 3A). In particular, the relative abundances of denitrification, nitrate_ammonification, nitrous_oxide_denitrification, nitrite_respiration, and nitrite_denitrification in the MO-RSD-treated soil as well as those of nitrogen_fixation, nitrogen_respiration, nitrate_respiration, and nitrate_reduction in the DA-CSF-, GC-RSD-, and MO-RSD-treated soils were significantly (p < 0.05) higher compared with those in the CK soil (Figure 3B). Real-time PCR revealed that the expression of the nirK gene in both RSD-treated soils and the expression of the narG gene in GC-RSD soil was considerably (p < 0.05) higher than that in CK soil, whereas that was considerably (p < 0.05) lower in DA-CSF-treated soil. The expression of nrfA, nosZ, and nifH genes in the DA-CSF- and both RSD-treated soils were considerably (p < 0.05) higher compared with those in the CK soil. Notably, the expression of nifH in GC-RSD soil was obviously (p < 0.05) higher than that in DA-CSF soil (Figure 3C).

3.6. Relationships among Plant Properties, Core Microbiome, and N-Related Genes

After planting, the disease incidence in GC-RSD- (33.33%) and MO-RSD-treated (44.44%) soils decreased considerably by 50.0% and 33.33%, respectively, compared with that in CK soil (66.67%), whereas DA-CSF treatment (55.56%) showed no significant effect on disease incidence (Figure 4A). The shoot length of D. batatas was not significantly different among these soils. The shoot fresh weight of D. batatas in GC-RSD-treated soil and underground fresh weight in MO-RSD-treated soil were considerably higher than those in DA-CSF and CK-treated soils, respectively (Figure 4B–D).
Procrustes analysis showed that the ordination of the core microbial community was closely associated with that of plant properties (M2 = 0.58, p = 0.003) and N-related genes (M2 = 0.12, p = 0.001) (Figure 4E,F). Furthermore, the disease incidence was significantly and negatively correlated with the relative abundances of core genera, such as Oryzihumus, Azotobacter, Phenylobacterium, Clostridium, Bradyrhizobium, Microvirga, and Caulobacter (Figure 4G). The populations of nitrogen-related genes, such as narG, nirK, nosZ, and nifH, were considerably and positively correlated with the shoot fresh weight (Figure 4G).

4. Discussion

The results of this study indicate that both RSD treatments and DA-CSF treatment significantly decreased the population of F. oxysporum. However, after planting, both RSD treatments considerably reduced the disease incidence compared with DA-CSF treatment, indicating that RSD outperformed DA-CSF in promoting the health of D. batatas. Notably, although DA-CSF decreased the disease incidence of D. batatas, there was no significant difference compared with CK, which is inconsistent with previous findings that DA-CSF can significantly reduce soil-borne diseases in other plants (i.e., cucumber, lisianthus, and bell pepper) [11,12,13]. These results may be related to dissimilarities in the soil environment regulated by these different strategies and the strong selective effect of root exudates released by D. batatas on F. oxysporum proliferation.
Previous studies have revealed that numerous chemical fumigants used in CSF exhibit broad-spectrum activity against soil microorganisms and kill both pathogenic and beneficial microorganisms, thereby creating a microbial vacuum in the soil [44,45,46]. Under such environments, the root exudates that are released during crop cultivation can help stimulate a rapid rebound of soil-borne pathogens and accelerate the re-emergence of soil-borne diseases [44]. Conversely, RSD is a soil disinfestation strategy mediated by the composition of the microbial community [26]. Although soil-borne pathogens in RSD-treated soils can also significantly rebound during plant cultivation [5,47], the inheritance effects of RSD-regulated microbial properties can still protect plant health. Specifically, microbial populations and their diversity are important predictors of soil health, and a stable soil ecosystem usually exhibits a high microbial diversity, which can limit the rebound of pathogens to some extent [48,49]. Herein, the bacterial population of both RSD treatments and the fungal population following GC-RSD treatment significantly increased, whereas DA-CSF treatment considerably decreased the populations of bacteria and fungi and has the lowest bacterial richness and Shannon diversity index. Furthermore, although the MO-RSD and GC-RSD treatments showed different patterns of bacterial α diversities compared with the CK treatment, previous findings have shown that the microbial α diversities in the RSD-treated soils could be restored to the level of CK soils during plant cultivation; however, this is not possible in the CSF-treated soils [5]. Importantly, some studies have revealed that RSD-regulated soils still possess the ability to control soil-borne diseases even under conditions of equal pathogen populations in diseased soils obtained via pathogen reinoculation following disinfestation [20,21,22]. Taken together, these results suggest that the DA-CSF strategy may create a more fragile microhabitat than the RSD strategy in D. batatas-diseased soil. Meanwhile, if the root exudates of D. batatas exhibit a stronger driving effect on the rebound of soil-borne pathogens compared with other crops, potentially hampering the effective control of Fusarium wilt diseases of D. batatas in DA-CSF-treated soil, which should be verified in the future study.
In addition, it is accepted that the nitrogen-related functional genes of soil are direct participants in nitrogen cycling, which plays an important role in the uptake of effective nitrogen by crops [50,51]. In this study, we found that the abundances of some nitrogen-related functions and their related functional genes (i.e., nifH, nirK, and narG) in the MO-RSD- or GC-RSD-treated soil were significantly higher than those detected in the CK and DA-CSF-treated soils, revealing that RSD treatment can effectively promote the growth of D. batatas by improving the function of soil nutrient circulation. Additionally, the soil microbial community determines the occurrence of soil-borne diseases, deciphering the core and unique microbiomes of soils is essential for understanding the consistent and responsive microbial components across different treatments for predicting plant health [42,43]. Our findings indicate that the core and unique microbiomes were significantly different between the RSD and CSF treatments. Most RSD-enriched cores and unique microbes are closely linked with plant disease suppression, indicating they are the key potential plant health predictors. For RSD-enriched core species, the members of Azotobacter, Phenylobacterium, Bradyrhizobium, Microvirga, and Caulobacter are common plant-growth-promoting bacteria (PGP), which possess the ability to participate in nutrient cycling and can contribute to the building of defense systems against various soil-borne diseases by releasing various PGP factors (i.e., indole-3-acetic acid, siderophores, hydrogen cyanide, cytokinins, and auxins) [51,52,53,54,55]. For RSD-enriched unique species, the members of Clostridium, Pseudomonas, and Sphingobacterium are known to produce various antagonistic compounds [21,43], including acetic and butyric acids, 2,4-diacetylphlor-oglucinol and phenazines, and indophenol oxidase and hydrogen sulfide, respectively [56,57,58,59]. The genus Geothrix is considered an important reducer of iron, and its product (Fe2+) is one of the primary contributors to the effective killing of soil-borne pathogens by RSD [18,60]. Brevundimonas, Flavobacterium, Leucobacter, Ochrobactrum, and Stenotrophomonas often act as biocontrol agents in agricultural practices and can suppress soil-borne pathogens via direct parasitism, lytic enzymes, nutrient competition, biosurfactants, and volatile organic compounds [61,62,63,64,65]. Notably, our study primarily focused on the impact of different soil disinfestation strategies on the health of D. batatas by examining the regulation of bacterial communities, while the fungal community composition also plays an important role in plant disease suppression. For instance, the beneficial fungal genera, such as Aspergillus, Zopfiella, Penicillium, Acremonium, Chaetomium, etc., often increased in the RSD-treated soils, which can suppress the activity of soil-borne pathogens and ensure plant health via competing for ecological resources or producing antagonistic compounds [21,26,47].
The aforementioned results indicate that RSD is a viable alternative to CSF for managing soil-borne diseases; however, it is important to acknowledge that this study has a few limitations. First, we only investigated the physical parameters regarding plant growth in this study, while how these disinfestation strategies affect the key biochemical parameters related to plant stress, such as chlorophyll content, protein content, bioactive compounds, and proline level, remains unclear. Second, the control efficacy of RSD treatments on the Fusarium wilt of D. batatas in the pot experiment was only 33.3–50.0%, which does not yet meet the needs of field practice. Therefore, integrated strategies, such as the combination of RSD with the application of bioorganic fertilizer or indigenous biocontrol agents, should be proposed in the future to develop sustainable disease suppression of D. batatas.

5. Conclusions

This study revealed that bacterial α-diversity, microbial populations, and nitrogen-related gene expression—the global bio-indicators—were higher in RSD-treated soils than in CSF-treated soils. Moreover, specific bio-indicators, such as the beneficial members of the core and unique microbiomes, regulated by the RSD-treated soils exerted positive effects on disease suppression and functional composition. These results suggest that the RSD treatment outperformed the CSF treatment in predicting the health of D. batatas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su151511991/s1, Table S1: Relative abundance of the core taxa in each soil; Table S2: Relative abundance of the unique taxa in each soil.

Author Contributions

Conceptualization, L.L. and Q.S.; methodology, Q.S. and X.L.; software, T.Z.; validation, Q.S., Y.W. and L.X.; formal analysis, X.L.; investigation, S.P.; resources, Z.G.; data curation, Q.S.; writing—original draft preparation, Q.S. and L.L.; writing—review and editing, L.L.; visualization, L.L.; supervision, X.L.; project administration, Q.S. and L.L.; funding acquisition, Q.S. and 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 (32160748), the Primary Research and Development Plan of Jiangxi Province (Grant No. 20202BBFL63002), and the Science and Technology Research Project of the Education Department of Jiangxi Province (No. GJJ2201733, GJJ201618).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, accessed on 21 March 2023, PRJNA946924.

Acknowledgments

The authors thank Xinqi Huang of Nanjing Normal University for providing technical support. We would like to thank the editors and reviewers for their valuable comments and suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Impact of DA-CSF and RSD treatments on soil bacterial community diversity and composition. (A,B) Bacterial α-diversity. Error bars represent SDs and the different lowercase letters among different treatments indicate significant differences at p < 0.05, according to the LSD test. (C) Principal coordinate analysis of the bacterial β-diversity that was calculated using the Bray–Curtis distance of the bacterial OTUs. (D,E) Bacterial community composition at the phylum and genus levels, respectively. “**” indicates that the given taxa between the different treatments exhibited significant differences at p < 0.01, according to the LSD test. Definitions for the treatment abbreviations are listed in Table 2.
Figure 1. Impact of DA-CSF and RSD treatments on soil bacterial community diversity and composition. (A,B) Bacterial α-diversity. Error bars represent SDs and the different lowercase letters among different treatments indicate significant differences at p < 0.05, according to the LSD test. (C) Principal coordinate analysis of the bacterial β-diversity that was calculated using the Bray–Curtis distance of the bacterial OTUs. (D,E) Bacterial community composition at the phylum and genus levels, respectively. “**” indicates that the given taxa between the different treatments exhibited significant differences at p < 0.01, according to the LSD test. Definitions for the treatment abbreviations are listed in Table 2.
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Figure 2. Impact of DA-CSF and RSD treatments on the soil bacterial core and unique microbiome. (A) The number of core, unique, and overlapped OTUs in the different soil samples. (B,C) Principal coordinate analysis of the bacterial core and unique microbiomes, respectively. (D,E) Relative abundances of the dominant core and unique microbes at the genus level, respectively. “**” in the (D) plot indicates that the given core genus between different treatments showed significant differences at p < 0.01, according to the LSD test. Definitions for the treatment abbreviations are listed in Table 2.
Figure 2. Impact of DA-CSF and RSD treatments on the soil bacterial core and unique microbiome. (A) The number of core, unique, and overlapped OTUs in the different soil samples. (B,C) Principal coordinate analysis of the bacterial core and unique microbiomes, respectively. (D,E) Relative abundances of the dominant core and unique microbes at the genus level, respectively. “**” in the (D) plot indicates that the given core genus between different treatments showed significant differences at p < 0.01, according to the LSD test. Definitions for the treatment abbreviations are listed in Table 2.
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Figure 3. Impact of DA-CSF and RSD treatments on soil functions. (A) Principal coordinate analysis of the overall bacterial functions that were predicted using FAPROTAX. (B,C) The relative abundances of the nitrogen-related functions and the expression of the dominant nitrogen-related genes between different treatments, respectively. Error bars represent SDs. “*” or “***” in plot (B) and different lowercase letters in plot (C) indicate that the given nitrogen-related function and gene between the different treatments exhibited significant differences at p < 0.05 or p < 0.001, according to the LSD test, respectively. The nirK and nrfA, narG, nosZ, and nifH genes represent nitrite reduction, nitrate reduction, nitrous oxide reduction, and nitrogen fixation genes, respectively. Definitions of the treatment abbreviations are listed in Table 2.
Figure 3. Impact of DA-CSF and RSD treatments on soil functions. (A) Principal coordinate analysis of the overall bacterial functions that were predicted using FAPROTAX. (B,C) The relative abundances of the nitrogen-related functions and the expression of the dominant nitrogen-related genes between different treatments, respectively. Error bars represent SDs. “*” or “***” in plot (B) and different lowercase letters in plot (C) indicate that the given nitrogen-related function and gene between the different treatments exhibited significant differences at p < 0.05 or p < 0.001, according to the LSD test, respectively. The nirK and nrfA, narG, nosZ, and nifH genes represent nitrite reduction, nitrate reduction, nitrous oxide reduction, and nitrogen fixation genes, respectively. Definitions of the treatment abbreviations are listed in Table 2.
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Figure 4. Plant biomass properties and potential bio-indicators. (A–D) Disease incidence (DI), shoot length (SL), shoot fresh weight (SFW), and underground fresh weight (UGFW). Error bars represent SDs and the different lowercase letters among different treatments indicate significant differences at p < 0.05, according to the LSD test. (E,F) Procrustes analyses of PCoA ordination plots between the core microbiome and plant biomass properties and nitrogen-related genes, respectively. (G) Relationships between the abundances of the core microbes and nitrogen-related genes and plant biomass properties. Only the core microbes that were significantly and negatively correlated with disease incidence and the nitrogen-related genes that were significantly and positively correlated with shoot fresh weight are listed. “*”, “**”, and “***” represent p < 0.05, p < 0.01, and p < 0.001, respectively. Definitions of the treatment abbreviations are listed in Table 2.
Figure 4. Plant biomass properties and potential bio-indicators. (A–D) Disease incidence (DI), shoot length (SL), shoot fresh weight (SFW), and underground fresh weight (UGFW). Error bars represent SDs and the different lowercase letters among different treatments indicate significant differences at p < 0.05, according to the LSD test. (E,F) Procrustes analyses of PCoA ordination plots between the core microbiome and plant biomass properties and nitrogen-related genes, respectively. (G) Relationships between the abundances of the core microbes and nitrogen-related genes and plant biomass properties. Only the core microbes that were significantly and negatively correlated with disease incidence and the nitrogen-related genes that were significantly and positively correlated with shoot fresh weight are listed. “*”, “**”, and “***” represent p < 0.05, p < 0.01, and p < 0.001, respectively. Definitions of the treatment abbreviations are listed in Table 2.
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Table 1. Quantitative PCR and sequencing primers used in this study.
Table 1. Quantitative PCR and sequencing primers used in this study.
GenePrimersSequence (5′-3′)Reference
bacteriaEub338-FCCTACGGGAGGCAGCAG[28]
Eub518-R ATTACCGCGGCTGCTGG[29]
fungiITS1-FCTTGGTCATTTAGAGGAAGTAA[30]
ITS2-RGCTGCGTTCTTCATCGATGC[31]
F. oxysporumITS1-FCTTGGTCATTTAGAGGAAGTAA[30]
AFP308-RCGAATTAACGCGAGTCCCAAC[32]
narG1960m2-FTAYGTSGGGCAGGARAAACTG[33]
2050m2-RCGTAGAAGAAGCTGGTGCTGTT
nirKnirK-FGGMATGGTKCCSTGGCA[34]
nirK-RGCCTCGATCAGRTTRTGG
nosZNosZ-FAACGCCTAYACSACSCTGTTC[35]
NosZ-RTCCATGTGCAGNGCRTGGCAGAA
nifHPol-FTGCGAYCCSAARGCBGACTC[36]
Pol-RATSGCCATCATYTCRCCGGA
16S rRNA515-FGTGCCAGCMGCCGCGG[37]
907-RCCGTCAATTCMTTTRAGTTT[38]
F and R indicate forward and reverse primers.
Table 2. Soil physicochemical properties associated with different disinfestation strategies.
Table 2. Soil physicochemical properties associated with different disinfestation strategies.
TreatmentpHNH4+–N (mg kg−1)Available Potassium (mg kg−1)
CK5.91 ± 0.06 c18.56 ± 1.06 d643.3 ± 19.0 b
DA-CSF5.83 ± 0.02 c66.92 ± 0.58 b518.0 ± 115.9 b
GC-RSD6.11 ± 0.02 b62.61 ± 0.68 c878.0 ± 143.7 a
MO-RSD6.37 ± 0.06 a157.17 ± 0.82 a508.0 ± 129.9 b
Values (means ± standard deviation) in the same column followed by different lowercase letters represent significant differences at p < 0.05, according to the LSD test. CK—untreated soil with a moisture content of 18–30%; DA-CSF—soil fumigated with dazomet, flooded and sealed with plastic film; GC-RSD and MO-RSD—two lots of soil with added gran chaff and molasses, respectively, flooded and sealed with plastic film.
Table 3. Soil bacterial, fungal, and F. oxysporum populations associated with different disinfestation strategies.
Table 3. Soil bacterial, fungal, and F. oxysporum populations associated with different disinfestation strategies.
TreatmentBacteria
(lg 16S Copies g−1 Soil)		Fungi
(lg ITS Copies g−1 Soil)		F. oxysporum
(lg ITS Copies g−1 Soil)
CK10.42 ± 0.05 b8.06 ± 0.06 b6.06 ± 131.2 a
DA-CSF10.35 ± 0.02 c7.47 ± 0.04 c4.38 ± 70.6 c
GC-RSD10.62 ± 0.03 a8.15 ± 0.15 b5.01 ± 137.5 b
MO-RSD10.56 ± 0.02 a8.54 ± 0.11 a4.88 ± 69.9 bc
Values (means ± SD) in the same column followed by different lowercase letters indicate significant differences at p < 0.05, according to the LSD test. Definitions of the treatment abbreviations are listed in Table 2.
Table 4. Impact of different disinfestation strategies on the percentage of soil bacterial core and unique OTUs and their related sequences.
Table 4. Impact of different disinfestation strategies on the percentage of soil bacterial core and unique OTUs and their related sequences.
TreatmentCore (%)Unique (%)
OTUsSequencesOTUsSequences
CK38.8577.7627.634.53
DA-CSF50.8366.1117.705.98
GC-RSD38.8370.5814.704.16
MO-RSD44.7567.8713.528.99
Core—OTUs that consistently appeared in triple biological replicates for each treatment. Unique—OTUs that only presented in triple biological replicates of one treatment. Definitions for the treatment abbreviations are listed in Table 2.
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Shao, Q.; Li, X.; Zhao, T.; Wu, Y.; Xiang, L.; Pan, S.; Guo, Z.; Liu, L. Role of Reductive Soil Disinfestation and Chemical Soil Fumigation on the Fusarium Wilt of Dioscorea batatas Decne Suppression. Sustainability 2023, 15, 11991. https://doi.org/10.3390/su151511991

AMA Style

Shao Q, Li X, Zhao T, Wu Y, Xiang L, Pan S, Guo Z, Liu L. Role of Reductive Soil Disinfestation and Chemical Soil Fumigation on the Fusarium Wilt of Dioscorea batatas Decne Suppression. Sustainability. 2023; 15(15):11991. https://doi.org/10.3390/su151511991

Chicago/Turabian Style

Shao, Qin, Xiaopeng Li, Tian Zhao, Yiyang Wu, Liqin Xiang, Shengfu Pan, Zihan Guo, and Liangliang Liu. 2023. "Role of Reductive Soil Disinfestation and Chemical Soil Fumigation on the Fusarium Wilt of Dioscorea batatas Decne Suppression" Sustainability 15, no. 15: 11991. https://doi.org/10.3390/su151511991

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