1 Introduction

With rapid population growth, ensuring global economic crop welfare has become a serious concern. At present, due to the limited arable land available, the same crops are often planted in the same soil, giving rise to continuous cropping practices around the world (Tao et al. 2020; Zhang et al. 2023). A continuous-cropping system reduces crop yield and quality and increases disease incidence even with effective field management practices. Many crops, including tobacco, soybeans, potatoes, and Panax notoginseng, are adversely affected in continuous cropping, and pathogens which were previously detected from alterations in the abundance of Ascomycota, Basidiomycota, and Aspergillus arising during continuous-cropping soil give rise to plant rot in cotton (Bai et al. 2015; Tan et al. 2017, 2021). Besides, under soybean continuous cropping, the three dominant fungal pathogens, Thanatephorus, Fusarium, and Alternaria, were determined in rhizosphere soil (Bai et al. 2015), whereas the potential pathogens were Fusarium, Leotiomycetes, Mycocentrospora, and Cylindrocarpon in the continuous P. notoginseng soil (Tan et al. 2017). Meanwhile, in tobacco, the abundance of Thielaviopsis basicola, Ceratobasidium, and Fusarium sp. is responsible for the soil-borne diseases associated with root rot (Ding et al. 2021; Gai et al. 2021; Tan et al. 2022). Therefore, identifying pathogen composition and factors that affect these pathogens in continuous-cropping fields is vital for conditioning the soil for further improvement.

Under continuous-cropping obstacles, soil environmental factors interact with pathogens in the soil (Tan et al. 2021). Several obstacles from continuous-cropping soils have been reported, including soil nutrient deficiencies, allelopathic autotoxicity accumulation, enzyme activity reduction, microbial community disorder, and pathogen accumulation (Chen et al. 2022b; Liu et al. 2020a; Qian et al. 2023; Tan et al. 2017). Under these circumstances, the correlation between pH and the soil microbiome community has steadily weakened, which may result from the extensive utilization of fertilizers in continuous cropping (Borchers and Pieler 2010). In addition, elements changed by over-fertilization or a shortage of nutrients have a significant influence on soil pH. For instance, agrochemical fertilizers (nitrogen, phosphate, and potash fertilizer) can cause soil acidification (Borchers and Pieler 2010; Jiang et al. 2021). And the soil pH from organic acid accumulation is considerably relevant to the crop incidence rate, and the abundance of Ralstonia solanacearum increased at pH 5.5 (Shen et al. 2018). Similarly, the allelochemical accumulating from root metabolites (phenolic acids and alcohols) influenced the pathogens, such as Fusarium oxysporum, Talaromyces helices, and Kosakonia sacchari (Liu et al. 2020b; Wu et al. 2020). Moreover, the soil enzyme activities, including urease and catalase, were found to inhibit the disease infected by pathogens in the fungal community under continuous cropping (Wang et al. 2022a). However, polyphenol oxidase (PPO), which decomposes aromatic compounds of allelochemicals, affects the fungal community (Li et al. 2020). Therefore, it is critical to clarify the impacts of soil properties on the regulation and control of fungal pathogens by suppressing continuous-cropping obstacles.

Bio-organic fertilizers, antagonistic strains, and other technologies were used to alleviate continuous-cropping obstacles (Dzurendova et al. 2022; Huang et al. 2021). However, boundedness is observed in different soil environments. Swine manure has been applied as an organic manure to acidified croplands in southern China to suppress soil-borne disease (Chen et al. 2022a). Besides, an integrated microbiome agent decomposition of soybean cake powders and substrates from Pleurotus eryngii mushrooms with complex operations has been used as organic fertilizer to suppress tomato wilt (Zheng et al. 2020). In addition, the antagonism of a single strain applied to soil has limitations; for example, AMF biofertilizer was found to decrease the abundance of F. solani, whereas organic fertilizer inoculated with B. amyloliquefaciens W19 strain increased biofilm formation against the pathogen (Ji et al. 2022; Liu et al. 2020a; Tao et al. 2020).

The studies on biochar as a soil amendment alleviating continuous-cropping obstacles is extensive in agriculture system (Tarin et al. 2021; Wu et al. 2020; Yan et al. 2022). Biochar application significantly affected soil properties, such as nutrient bioavailability, enzyme activity, and pathogen enrichment, among which two main pathways for reducing crop diseases have been identified: the direct effect on the decrease of soil-borne pathogens and the indirect effect on the amelioration of soil properties (Lehmann et al. 2011; Meng et al. 2019; Smebye et al. 2016). In addition, soil total carbon and nutrients which could provide energy and nutrition for soil microbes affected the composition of fungi, particularly biochar application (Gorovtsov et al. 2020; Huang et al. 2018; Yan et al. 2022). Utilizing biochar has been demonstrated to reduce the abundance of pathogens such as Kosakonia sacchari, Talaromyces helices, F. oxysporum, and F. solani (Jaiswal et al. 2014; Wang et al. 2019; Wu et al. 2020). For F. oxysporum, biochar inhibits mycelial growth and abundance, which aims to reduce the virulence of crops (Ding et al. 2021; Tan et al. 2022). Nevertheless, the mechanism by which biochar application suppresses plant diseases and influences fungal composition by altering soil properties remains unclear.

To better understand the influence of biochar on the soil fungal community and pathogen abundance, we studied the fungal community, particularly pathogens, in continuous-cropping soil and the disease of tobacco root rot during the tobacco disease outbreak period. Treatments consisted of continuous cropping for 3 years (CT), 1st-year soil treated with biochar and then continuous cropping for 3 years (LB), and soil treated with biochar in the 3rd year (IB), in addition to a control treatment (first cultivated tobacco soil, CK). This study had several definite aims: (i) to determine how biochar application suppresses tobacco diseases; (ii) to explore the changes in pathogens and suppressing tobacco root rot by biochar application at different timing; and (iii) to clarify whether tobacco root rot is influenced by enzyme activity after biochar application.

2 Methods

2.1 Designing experimental and collecting samples

We established an experiment at the research station for tobacco (33° 48′ 0″ N, 113° 0′ 40″ E), located in Pingdingshan, Henan Province, China. The tobacco test material (ZhongYan 100) was provided by the Pingdingshan Tobacco Company of Henan Province. A conventional rate of 30 kg hm−2  of nitrogen was applied, and proportional dosages of phosphorus and potassium fertilizers were applied (N:P2O5:K2O = 1:1.5:3).

Four treatments were designed with randomized block: CK, the first cultivated tobacco soil; CT, soil from continuous cropping for 3 years; LB, soil treated with biochar in the 1st year followed by continuous cropping for 3 years; and IB, soil treated with biochar in the 3rd year. Peanut husks were used in this study from the Biochar Engineering Technology Research Center of Henan Province. Ultrapure water was used to wash the peanut husks to remove dust. And using muffle furnace, biochar from the peanut husks was prepared at 450 °C for 30 min. Biochar (20 tons hm−2) was applied to the continuous-cropping soil in both LB and IB treatments. All treatments were performed in triplicate.

We collected rhizosphere soil samples during the root rot outbreak period (100 days after transplantation) on August 1, 2019. The roots were dug carefully and non-rhizosphere soils were removed by shaking first, and then rhizosphere soil samples were collected by brushing the roots (≤ 0.2 cm distancing of the roots) using a sterilized brush. Each soil sample was divided equally which was mixed by five rhizosphere soils from each block. The soil factors were analyzed with different methods: air-dried to analyze the physicochemical properties, kept at 4 °C for determining enzyme activities, and frozen at −80 °C for extracting soil DNA.

2.2 Properties of soil and biochar

Using pH meter (HI2210; HANNA, Italy) and EC meter (731-ISM; Mettler Toledo, Switzerland), soil pH and conductivity (EC) (wsoil:vwater, 1:2.5) were examined (Hong et al. 2018). After extraction with NaHCO3, the soil available phosphorus (AP) was detected by UV spectrophotometer with Molybdenum Blue Method (Fan et al. 2020). Using a flame photometer, the amount of available potassium (AK) was extracted with neutral NH4OAC (Tang et al. 2020). Organic matter (OM) was detected using a paraffin oil bath at 175 °C for approximately 5 min using K2Cr2O7 and titrated with Fe2SO4 (Mitchell et al. 2015). To detect the content of soil available nitrogen (AN), we used the alkaline hydrolysis diffusion method (Fan et al. 2020). A continuous flow analyzer was used (AA3 Auto Analyzer III; Technicon, USA) to measure the amount of soil inorganic nitrogen (nitrate nitrogen: NO3−-N and ammonium nitrogen: NH4+-N) by 2 M KCl (wsoil:vKCl, 1:5) (Zhang et al. 2017). Soil total nitrogen (TN) and total carbon (TC) were determined by combustion method using a Automatic Elemental Analyzer (VARIO MACRO CUBE; Elementar, Germany). Multi N/C3100 (Analytic Jena, Germany) was used to measure the solubilization of soil total carbon (TDC), organic carbon (DOC), inorganic carbon (DIC), and total nitrogen (TDN) dissolved in ultrapure water (1:6 (w/v) for 30 min) (Zheng et al. 2019).

An ELISA kit (Sigma-Aldrich, USA) and a microplate reader (Varioskan LUX, Thermo Fisher Scientific, USA), were used to determine soil enzyme activities (protease, polyphenol oxidase (PPO), catalase (CAT), urease, and invertase).

 The characteristics of biochar are listed in Additional file 2: Table S1. The oxhydryl and carbon–carbon double bond were determined on the biochar using Fourier-transform Infrared Spectroscopy (FTIR, Tensor 27; Bruker, USA; Additional file 1: Fig. S1a). A focused beam of accelerated electrons was used to scan and create a complex structure by Scanning Electron Microscopy (SEM, Regulus 8100; Hitachi, Japan; Additional file 1: Fig. S1b), which showed a heterogeneous lamellar and pore structure with varying pore sizes.

2.3 Disease incidence

The incidence rate was defined as the percentage of tobacco plants that were diseased among the tobacco plants investigated. The severity of the tobacco disease was investigated using fixed plants. The score of disease severity ranged from 0 to 4: 0 = tobacco plants were asymptomatic; 1 = less than 50% of the afflicted leaves were wilted, or less than 50% of the diseased spot’s stem circumference, or fewer lesions; 2 = the leaves (1/2–2/3) around the stem circumference were diseased; 3 = the leaves (≥ 2/3) around the stem were diseased or completely diseased by the disease spot; 4 = the tobacco plant was dead or all leaves were wilted. The formula for calculating the disease index was derived from the following formula proposed in a study by Zhang et al. (2017):

$${\text{Incidence\, rate}}\left( \% \right) = \frac{{N_{D} }}{T} \times {100}$$
(1)
$${\text{Disease\, index}}\left( \% \right) = \frac{{\sum\limits_{{i = {0}}}^{4} {i \times N_{i} } }}{{T \times {4}}} \times {100,}$$
(2)

where ND = the number of diseased tobacco plants, T = the tobacco plants being surveyed, i = the score of disease tobacco being marked, and Ni = the number of disease tobacco corresponding to the score.

2.4 DNA extraction and amplicon sequencing

For soil samples, we used cetyltrimethylammonium bromide method (CTAB) to extract the total genomic DNA (Bai et al. 2019). And, the 1% agarose gel was used to monitor DNA concentration and purity. Using sterile water, the high concentration and pureness of DNA was diluted to 1 ng μL−1. Moreover, the primers were amplified in the ITS1 region: 5-1737F (5′-CTTCGGTCATTTAGAGGAAGTAA-3′) and 2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′). The Phusion® High-Fidelity PCR Master Mix (New England Biolabs; 15 μL), forward and reverse primers (0.2 μM), and template DNA (10 ng) were prepared for PCR reactions. Then, the main procedures of thermal cycling: denaturation (98 °C, 1 min, 30 cycles), anneal (50 °C, 30 s), elongate (72 °C, 30 s), and final extension (72 °C, 5 min).

Electrophoresis was used to detect the DNA with 2% agarose gel, which mixed the 1× loading buffer (contained SYB green) with equal amounts of PCR products. Then a Qiagen Gel Extraction Kit was used (Qiagen, Germany) to purify the qualified mixture of PCR products. The library which was generated on TruSeqDNA PCR-Free Sample Preparation Kit (Illumina, USA), was sequenced on an Illumina NovaSeq platform (Novogene Co., Ltd, Beijing, China) and 250 bp paired-end reads.

2.5 Bioinformatics analyses

The raw sequences were annotated following a few steps: raw tags were obtained from paired-end reads by FLASH (V1.2.7, http://ccb.jhu.edu/software/FLASH/); then using QIIME (http://qiime.org/scripts/split_libraries_fastq.html) for quality-controlled, high-quality clean tags were obtained under specific filtering conditions; and chimeras were removed from the Silva database (https://www.arb-silva.de/) using UCHIME (Yao et al. 2017). Finally, sequences with ≥ 97% similarity were assigned to the same OTUs which were annotated with the Silva Database using Uparse software (Huang X. et al. 2021; Tao et al. 2020).

The number of clustering effective tags with ≥ 97% similarity was used to determine the absolute abundance of the taxonomic group, and the percentage of the total number OTUs was used to determine the relative abundance. Basing on the output normalized data of OTUs, the alpha diversity (Shannon, Simpson, Chao1, and ACE index) and beta diversity were analyzed to identify the different composition and structures of fungal community. Principal Component Analysis (PCA) and Non-metric Multidimensional Scaling (NMDS) were performed on the differences of fungal community structure among the four treatments, and we  used R software (V4.2.2, R Core Team 2022, https://www.r-project.org) to calculate the beta diversity (Yao et al. 2017). The different biomarkers of fungi were separated by Linear Discriminant analysis Effect Size (LEfSe) among the four treatments (Li et al. 2020). Based on the FUNGuild (http://www.stbates.org/guilds/app.php), the OTUs were uploaded to predict fungal functional guild (Xie et al. 2021).

2.6 Statistical analysis

One-way analysis of variance (ANOVA) was used to analyze the difference in soil properties after biochar application by SPSS (V 23.0, IBM Corporation). Besides, Student’s t-test (t-test) and Tukey-HSD were conducted to examine the various soil fungal species by R software (Zheng et al. 2019). Meanwhile, PCA and NMDS were used to evaluate the influences of biochar on fungal community composition based on the Bray–Curtis distance in R using the “vegan” package (Yan et al. 2022). To identify the connection between the main soil properties and fungal community, redundancy analysis (RDA) was carried out by R software with the “vegan” package (Tarin et al. 2021). Galaxy website (http://huttenhower.sph.harvard.edu/galaxy) calculated the LDA that the taxa meeting with significance threshold was greater than 4 by the topological properties (Li et al. 2020). The direct connection between disease and soil factors (contained soil properties and fungal communities) was examined using line fitting and visualized by R software. Besides, the main soil properties and fungal species were identified by the co-occurrence network as those significantly more related, based on high and strong significance correlation (|r| > 0.6 and p < 0.05) using the “vegan” and “reshape2” packages in R (Wu et al. 2021). Partial Least Squares Path Modeling (PLS-PM) using linear statistics was established to predict the path relation between soil factors and disease of tobacco in R (“plspm” package) (Huang et al. 2021).

3 Results

3.1 Disease of tobacco affected by soil factors

The incidence rate and disease index of tobacco root rot decreased with the biochar application (LB and IB) (Fig. 1a). Compared with CT (32.33%), the incidence rate in the LB treatment (7.33%) significantly decreased to 77.32% (p < 0.05). However, no significant difference between the LB and CK treatments was observed (p > 0.05). In the LB treatment, the disease index decreased to 2.83% and was significantly lower than that in the CT and IB treatments (CT: 18.75%, IB: 9.92%, p < 0.05). Furthermore, biochar application alleviated tobacco disease of root rot, especially the LB treatment.

Fig. 1
figure 1

a Disease incidence of tobacco root rot among the four treatments (CK, CT, LB and IB). Letters: incidence rate (above error bar), disease index (below error bar). b Significant correlation between tobacco disease and soil properties among the four treatments. c Correlation between tobacco disease and soil fungal communities with average, and the OTUs screening from strong coefficient connection (|r| > 0.6) are shown in the heatmap (n = 5)

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The biochar application significantly affected soil properties (Additional file 1: Fig. S2). We found that the amounts of EC, AN, DOC, urease, and invertase showed no significant differences among the four treatments (p > 0.05). However, compared to the CK treatment, the continuous-cropping treatments (CT, LB, and IB treatment) exhibited a significant increase in the CAT activity (p < 0.01) and AP content (p < 0.001). The amounts of OM and PPO were decreased in the CT treatment. In addition, the amounts of TN, TC, and protease in IB treatment were decreased compared with the other treatments, whereas the amount of DIC was increased significantly in the IB treatment (p < 0.01). Biochar application improved the soil PPO activity, especially the LB treatment (p < 0.05). Furthermore, PPO activity in the LB treatment increased by 59.37%, whereas that in the IB treatment increased by 33.20%, comparing to the CT treatment.

To forecast the relationship between soil properties and root rot in tobacco, we used spearman correlation analysis to visualize in linear correlation (Additional file 2: Table S2). Figure 1b shows that, in the CK treatment, the incidence rate negatively and significantly correlated with soil protease activity (R2 = 0.999, p < 0.05), but positively and strongly connected with soil invertase activity (R2 = 0.999, p < 0.001). In addition, the disease index of tobacco root rot positively and significantly correlated with soil AN and DIC (AN: R2 = 0.990; DIC: R2 = 0.995; both p < 0.05). For the CT treatment, the soil invertase activity negatively and significantly correlated with the disease (incidence rate: R2 = 0.999, p = 0.008; disease index: R2 = 0.997, p = 0.025). However, no significant correlation between soil properties and tobacco disease was observed after biochar application. Therefore, biochar may compensate for the influences of soil properties on disease in continuous-cropping soil.

We generated a fungal community profile for each sample by targeting the ITS1 region, followed by sequencing (NovaSeq; Illumina). The 1,746,693 raw reads (the four treatments with five repeats, 64,609–99,981 reads per sample) were assigned to the OTUs with ≥ 97% similarity then 1545 OTUs to 733 OTUs were annotated by the sequences (raw data, effective tags, and OTUs in the Additional file 2: Table S4). Soil fungal communities play an important role in the development and spread of tobacco diseases. Therefore, we analyzed the relationship between fungal communities and tobacco diseases using Spearman’s correlation analysis (Fig. 1c). Our results showed that OTU53 (Dactylellina) significantly correlated with the disease index (r = − 0.947, p = 0.014) in the CK treatment. In the CT treatment, OTU49 (undentified_fungi) significantly correlated with the disease (incidence rate and disease index, both r = − 0.918, p = 0.028, especially), and OTU50 (Alternaria) exhibited a significant negative relation with the disease (both r = − 0.975, p = 0.005). OTU22 (Penicillium) showed a significant correlation with the disease index (r = − 0.975, p = 0.005) in the IB treatment. But there was no significant correlation between soil fungal species (OTUs) and tobacco disease in the LB treatment. As shown in Additional file 2: Table S3, the annotated OTUs for the correlation with tobacco root rot (|r| > 0.6) were listed.

3.2 Fungal community composition and diversity structure

After quality filtering and clustering of OTUs, the flat trend of the rarefaction curve indicated that the species were entirely annotated with the sequencing data (Additional file 1: Fig. S3a). Besides, the average number of OTUs in the CK treatment was higher than that in the other treatments (Additional file 2: Table S4). Across the fungal community, the continuous-cropping soil (CT treatment) decreased fungal diversity (Fig. 2a). Compared with CT treatment, the CK and IB treatments significantly increased (p < 0.05) Shannon and Simpson indices, especially, increasing Shannon index by 32.98% (CK) and 30.26% (IB), suggesting that biochar application following continuous-cropping has the potential to recover the fungal community diversity approach to the control treatment. However, Chao1 and ACE indices in the control treatment were higher than those in continuous-cropping soils (containing CT, LB, and IB treatments), indicating that the continuous-cropping soil decreased the soil fungal species, but all treatments showed no significant differences (p > 0.05; Additional file 1: Fig. S3b).

Fig. 2
figure 2

a Changes in alpha diversity of fungi across different treatments. b The cumulative histogram of fungal composition (the relative abundance of OTUs at the phylum level). c Heatmap and clustering analysis of the fungi at the class level. d Circos plot showing the species change with corresponding the different treatments for the relative abundance > 0.005% of OTUs (no blast hit was annotated the others, showing the annotation of OTUs)

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According to Fig. 2b, the result showed that Ascomycota, Mucoromycota, Basidiomycota, and Mortierellomycota were the majority fungal phyla, and a proportion of the top four fungal abundance of approximately 50.79–59.38% was observed. Moreover, Ascomycota and Mucoromycota were the dominant fungal phyla observed among the four treatments (CK, 22.65%, IB, 33.78%, CT, 30.03%, and LB, 32.80%). The other eight phyla accounted for the total relative abundance as follows:1.45% (CK), 0.51% (CT), 0.81% (LB), and 0.44% (IB).

The fungal community was categorized into 46 classes belonging to 12 phyla, and was significantly different among the four treatments (Fig. 2c). Among them, 12 classes belong to Basidiomycota, 11 classes belong to Ascomycota, and 6 classes belong to Chytridiomycota. The following classes were the most abundant (top five) in all treatments: Mucoromycetes (20.21%), which showed the greatest abundance, followed by the next more abundant, namely Sordariomycetes (14.84%), Tremellomycetes (7.05%), Mortierellomycetes (5.76%), and Eurotiomycetes (2.45%). Furthermore, the abundance of Sordariomycetes was increased while the abundance of Mucoromycetes and Mortierellomycetes were decreased in the IB treatment. In contrast, Agaricostilbomycetes, Archaeorhizomycetes, and Microsporidea were significantly enriched in the LB treatment. At the genus level, it was observed that Actinomucor was the most frequently annotated genera for all OTUs, particularly in the CT and LB treatments (Fig. 2d). However, compared with the CK treatment, continuous-cropping soils (CT, LB, and IB treatments) decreased the abundance of Mortierella from 94.03% to 47.08% and Rhizopus (98.91–92.28%). In addition, the biochar treatments (LB and IB) decreased the abundance of Solicoccozyma, but the IB treatment increased the abundance of Fusarium, Humicola, and Penicillium.

PCA analysis showed a separation in fungal community among the four treatments at the phylum level, the two components extracted by the PCA explained 45.7% of the variance (Fig. 3a). From the first principal coordinates, the IB treatment was separated from the other treatments, indicating that the transient effect of biochar was the largest source of variation. Meanwhile, the fungal community in the LB treatment was approached from the CT treatment along PCA1, whereas it was separated from the CK treatment, suggesting that biochar aged for 3 years in soil had less effect. According to the NMDS analysis, fungal community (the relative abundance of OTUs > 0.005% at the phylum level) was clustered and the ordination was well-fitted (stress value = 0.056, R2 = 0.99; Fig. 3b). These two components (NMDS1 and NMDS2) were close to those in the LB treatment, demonstrating that the clusters were characterized by a similar fungal community structure.

Fig. 3
figure 3

a PCA plot depicts fungal communities (the relative abundance of OTUs > 0.005% at the phylum level) among the four treatments. b Non-metric multidimensional scaling (NMDS) ordinations of fungal community composition among the four treatments (relative abundance > 0.005% at the phylum level). c Cladogram shows the significant differences in fungal taxa among the four treatments. Taxon marked with colored dots shows significant differences among the four treatments. These represent the phylum, class, order, family, genus, and species levels from the center outward. The yellow nodes indicate no significant difference among the four treatments

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To further distinguish the differences in fungal species among the four treatments, we used the LEfSe to determine the fungal community at the LDA > 4 (Fig. 3c). Most fungi were significantly enriched in the IB treatment, whereas only four clades showed an abundance advantage in the CT treatment. Specifically, Pleosporales (order), Boletales (order), Boletaceae (family), and Strobilomyces (genus) were significantly enriched in the CT treatment, comparing to the other treatments. Moreover, 38 genera were significantly (p < 0.05) different as examined by Metastat analysis among the four treatments (Fig. 4). It was observed that 19 genera were significantly enriched (p < 0.05) in the CT treatment, comparing to the CK treatment. Compared with biochar application, the CT treatment significantly increased the relative abundance of Monosporascus (p < 0.05). Meanwhile, biochar application significantly decreased (p < 0.05) the relative abundance of Monosporascus and Ceratobasidium, fungi related to the tobacco disease. Besides, the LB treatment significantly increased (p < 0.05) the relative abundance of fungal genera, such as Dactylellina, Thielavia, Cryptococcus, Dactylella, Conlarium, Monographella, Humicola, Lecanicillium, Ochroconis, unidentified_Mortierellomycota, Cunninghamella, and Fusicolla, compared with the CT treatment (Additional file 1: Fig. S4).

Fig. 4
figure 4

The relative abundance of differences pathogens at the genus level among the four treatments (t-test, n = 5; *p < 0.05, **p < 0.01, ***p < 0.001)

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3.3 Association between soil properties and soil fungal community

The soil properties (Additional file 1: Fig. S2) which showed significant differences among the four treatments were used to examine the association with fungal community (the relative abundance ≥ 0.005% at the class level). We used RDA to conduct a further analysis of the correlation between the soil properties and fungal community (Fig. 5). RDA explained 45.27% and 16.26% of the relationships between soil properties and fungi at the class level, respectively, which removed collinearity. In addition, soil factors, such as TN, IN, PPO, and EC (R2TN = 0.68, R2IN = 0.64, R2PPO = 0.37, all p < 0.001; R2EC = 0.35, p < 0.05; Additional file 2: Table S5) significantly affected the fungal community. Furthermore, variance partitioning analysis of the soil properties and fungal class indicated that the physicochemical properties explained 26.58% and 17.25% of the enzyme activity, respectively. Nevertheless, the soil properties had no common interpretation owing to non-collinearity (Additional file 1: Fig. S5).

Fig. 5
figure 5

The RDA result shows a significant correlation between the soil properties and fungi (as analyzed by the relative abundance at the class level > 0.005%) among the four treatments

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To identify the connection between the fungal genera (OTUs relative abundance > 0.005%) and the soil properties, the co-occurrence network was used based on Spearman’s correlation analysis (|r| > 0.6, p < 0.05). The results of co-occurrence network are shown in Fig. 6 and Additional file 2: Table S6. We found that the density of co-occurrence network was significantly increased in the biochar treatments (LB and IB treatment), comparing to the non-biochar treatments (CK and CT treatments). The average number of neighbors in the CK treatment and LB treatment was significantly increased than that in the CT treatment. These findings indicated that association and connectivity of soil properties related with fungal genera were increased after biochar application. Furthermore, in the CK treatment, soil PPO exhibited a significant positive association (r > 0.89, p < 0.05) with fungal genera that included Ramicandelaber, Humicola, Cephalotrichum, Acremonium, Dichotomopilus, and Stachybotrys. In the CT treatment, soil CAT was a main soil property and significantly correlated with Fusarium, Rhizopus, Mortierella, Sagenomella, Neonectria, and Penicillium (all r = 0.95, p < 0.05), while the soil PPO positively correlated with Loma (r = 0.92, p < 0.05). In the LB treatment, soil protease was significantly negatively correlated with the Solicoccozyma, Microsphaeropsis, Paramyrothecium, Thermoascus, and Craterellus (both r = − 0.97, p < 0.05), while positively correlated with Sarocladium (r = 0.95, p < 0.05). Soil AK, EC, DOC, TDC, and PPO were mainly connected to the co-occurrence in the IB treatment and significantly correlated with Actinomucor, Trichoderma, Psilocybe, and Neocosmospora (0.95 < |r| < 0.97, p < 0.05). Owing to the special characteristics of biochar, nutrients in the soil will be retained for microbe survival (Qian et al. 2023). In our study, we found that soil properties such as PPO, CAT, AK, EC, and DOC were improved after application of biochar in the continuous-cropping soil. The results demonstrated that application of biochar in soil could provide adequate nutrition for fungal genera, enriching the diversity of fungal community, which was consistent with the Shannon and Simpson indices. Notably, the fungal genera related with soil properties were not the specifical pathogens of tobacco root rot.

Fig. 6
figure 6

Co-occurrence network of fungal and soil property interactions based on top species (relative abundance > 0.005%) in the different treatments. The remaining lines denote a significant Spearman correlation (|r| > 0.6, p < 0.05). Red indicates a positive correlation, while blue represents a negative correlation

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3.4  Influence of soil properties on the fungal functional guilds

Functional prediction of the fungal community was performed using Fungal Function Guild software (FUNGuild) during secondary succession. The trophic mode, which comprised symbiotroph, pathotroph, and saprotroph, differed among the four treatments (Fig. 7a). The relative abundance of symbiotroph and saprotroph in the CK significantly (ANOVA, p < 0.01) increased compared with the IB treatment. Besides, the IB treatment significantly increased the pathotroph fungi abundance compared to the other treatments, whereas IB treatment decreased the symbiotroph fungi abundance. Further analyzing the eight guild categories of the three trophic modes, we found that in the LB treatment, Animal.Pathogen fungi abundance was significantly correlated with the disease (R2incidence rate = 0.786, R2disease index = 0.821, both p < 0.05), and Endophyte fungi abundance had a significant correlation with the disease (R2incidence rate = 0.781, R2disease index = 0.816, both p < 0.05; Fig. 7b, Additional file 2: Table S7). However, no significant correlations (p > 0.05) among the soil properties and Animal.Pathogen and Endophyte were observed (Additional file 2: Table S8). Besides, the Plant.Pathogen had a low correlation with soil pH, IN, TDN, AP, AK, CE, TC, DIC, TDC, invertase, and protease (the range of R2: 0.38–0.54, p < 0.05). Similarly, soil AP and PPO significantly affected Arbuscular.Mycorrhizal (the range of R2 = 0.17–0.23, p < 0.05), and the AP, AN, and CAT significantly affected Dung.Saprotroph (R2AP = 0.16, R2AN = 0.23, both p < 0.05; R2CAT = 0.43, p < 0.01). The dominant trophic mode of fungi was changed with the relative abundance of fungi, thereby affecting the incidence of the tobacco root rot. Our findings indicated that soil properties exhibited a weak significant correlation with functional fungi, especially Animal.Pathogen and Endophyte fungi. In conjunction with the diversity of fungi, we considered that soil properties have a direct influence on the abundance of fungal community, as indirect factors for the occurrence of tobacco disease.

Fig. 7
figure 7

a Mean relative abundance of main three trophic modes using FUNGuild software. The absolute abundance of OTUs was selected by 0.005%. One-way ANOVA significant difference was used for two pairwise compositions (* indicates p ≤ 0.05, ** indicates p ≤ 0.01). b Relationship between fungi guild categories and disease of tobacco

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3.5 Soil environmental factors influenced tobacco disease

The PLS-PM model was used to identify the complex relationship among the soil properties, fungal community and the disease of tobacco root rot (Fig. 8). The results indicated that the goodness of fit (GOF = 0.607) was good. Soil enzyme activities, rather than soil physicochemical properties, had a significant direct effect (total coefficient = − 1.34) on the disease, whereas enzyme activities had a significant effect on pathogenic fungi (total coefficient = − 0.606). Soil physicochemical properties showed an indirect effect on disease, with a path coefficient of 0.767. Enzyme activities indirectly affected pathogenic fungi, with a path coefficient of − 0.703 and a total coefficient of − 0.606. Specifically, PPO could explain the influence on disease, with a cross-loading of − 0.614. Moreover, Monosporascus (cross-loading = 0.929) and Ceratobasidium (cross-loading = 0.720) significantly affected the tobacco disease. The correlation coefficient between the block of soil enzyme activities and the tobacco disease was − 0.708, whereas that between pathogenic fungi and the disease was 0.554 (Additional file 2: Table S9).

Fig. 8
figure 8

A PLS-PM analysis of the soil properties and tobacco root rot shows the direct and indirect effects. Disease blocks were chosen based on significant differences between the treatments [physicochemical properties (IN: inorganic nitrogen, TN: total nitrogen, TDN: solubilization of soil total nitrogen, AP: available phosphorus, AK: available potassium, pH, OM: organic matter, TC: total carbon, DIC: solubilization of inorganic carbon, TDC: solubilization of soil total carbon), enzyme activity (protease, PPO: polyphenol oxidase, CAT: catalase), α-diversity (Shannon and Simpson indices), pathogenic fungi (Monosporascus and Ceratobasidium), and disease (incidence rate and disease index)]

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4 Discussion

4.1 Biochar application in advance effectively suppressed tobacco disease in continuous cropping soil

As a potentially eco-friendly agricultural material, biochar was found to significantly alleviate tobacco disease caused by soil-borne pathogens in continuous-cropping soil (Singh et al. 2022; Zhao et al. 2021). In our study, we also found that biochar as a soil amendment could be widely applicated in continuous-cropping soil to suppress the soil-borne disease (Fig. 1a). The incidence of tobacco root rot disease after biochar application (LB and IB treatments) was significantly decreased, compared with CT treatment, especially in the biochar application forward continuous-cropping soil (LB treatment; Fig. 1a). The result is in line with Sigmumd reports. Their study considered that during the continuous-cropping processing, biochar in the soil significantly suppressed the disease of plants and increased system stability to balance the environmental condition (Sigmund et al. 2017). In addition, our data showed no significant correlation between fungal species (OTUs) and the incidence of root rot in the LB treatment, while the OTUs significantly correlated with incidence of root rot in the other treatments based on the Spearman’s correlation analysis (|r| > 0.6, p < 0.05; Fig. 1c). The result indicated that application of biochar in advance established a stable rhizosphere soil-pathogen system in continuous-cropping soil and effectively reduced the incidence of disease compared to biochar application following continuous-cropping soil (Yang et al. 2022; Yao et al. 2017). Based on genus, further analysis found that the relative abundance of pathogens, such as Ceratobasidium and Monosporascus was decreased in both biochar treatments, and especially, the LB treatment significantly reduced the relative abundance of Ceratobasidium compared with the IB treatment (Fig. 4). Application of biochar on continuous-cropping soil in our experiment directly decreased the abundance of soil-borne pathogens in both biochar treatments (Wang et al. 2019; Yao et al. 2017). Although biochar was aged in soil during continuous 3 years tobacco cultivation, the fungal pathogens and incidence rate of rot root are more effectively inhibited by application of biochar forward tobacco continuous-cropping soil than by biochar application following continuous cropping. Our finding provided the important data for suppressing continuous cropping obstacle through accurate timing of biochar application.

4.2 Application of biochar early significantly decreased the relative abundance of the soil fungal pathogens

The critical soil fungal species play an important role for the diseases which are occasioned by the soil-borne pathogens (Collado et al. 2002; Yao et al. 2017). Our results showed that fungal species have no significant decrease as measured by the Chao1 and ACE indices in all treatments. However, the fungal diversity determined by the Simpson and Shannon indices in the CT treatment was observed to be lower than that in the other treatments. It was indicated that the incidence rate of tobacco root rot was attributed to the relative abundance of specific fungal pathogens, rather than the species of fungal genera, aligning with the previous research (Li et al. 2020; Xie et al. 2021). Metastat analysis (p < 0.05) identified 38 different fungal genera across the four treatments to screen the specific pathogens which correlated with the tobacco disease (Fig. 4 and Additional file 1: Fig. S4). Our study proved the specific fungal species, such as Monosporascus and Ceratobasidium belonging to the top fungal phyla (Ascomycota and Basidiomycota), were the prime pathogens responsible for tobacco root rot (Collado et al. 2002; Dzurendova et al. 2022; Tan et al. 2017; Zhang et al. 2023), and the relative abundance of fungal pathogens was significantly decreased by biochar treatments. As a result of the CT treatment, the Ceratobasidium, a pathogen infecting roots, was significantly and positively correlated with the tobacco root rot, which is consistent with previous study (Tan et al. 2022). Meanwhile, Ricks confirmed that the Monosporascus, which is a typical endophyte of potential root rot pathogens, is enriched in continuous-cropping fields (Ricks et al. 2021). Although we also found the other fungal pathogens in the continuous-cropping tobacco soil, such as Penicillium and Fusarium infecting wilt of tobacco at a low abundance, the wilt disease was not manifested in the study (Wang et al. 2022b). Our results indicated that the relative abondance of the special fungal pathogens can result in root rot in continuous cropping soil. The application of biochar in the continuous-cropping soil decreased the relative abundance of soil fungal pathogens, rather than the fungal species, and decreased the incidence rate of disease. Notably, it was observed that the application of biochar forward continuous cropping resulted in a lower abundance of pathogens that suppressed the incidence of tobacco disease, comparing to the biochar application following continuous cropping, indicating the significant prophylaxis of biochar.

4.3 Biochar ameliorated soil quality for alleviating tobacco disease

The soil quality including chemical properties and enzyme activities is an important factor that affects the incidence rate of disease in the continuous-cropping soil, disturbing the abundance of fungal pathogens (Bai et al. 2015; Jiang et al. 2021). The soil chemical properties provided adequate nutrients for fungi, and the soil enzyme fulfilled a vital role in growth metabolism of fungi to enrich fungal community (Jiang et al. 2021; Yao et al. 2017). However, in our study, we found that the interaction of fungi with soil properties was the weakest in continuous-cropping soil (CT) compared with the other treatments based on the co-occurrence network (Fig. 6). Meanwhile, by linear correlation analysis, the direct correlation between soil properties and incidence of tobacco root rot was attenuated by biochar application (LB and IB) comparing to the continuous-cropping soil (Fig. 1b). The result originated from the fact that biochar improved soil nutrition and altered the soil properties (Wang et al. 2019). On the other hand, it was showed that the soil enzyme activities and fungal pathogens are strongly correlated with tobacco disease, with higher enzyme activities and a lower abundance of pathogens being instrumental in suppressing disease (Yan et al. 2022; Zhao et al. 2021). Besides, the soil PPO and CAT activities were affected by biochar application, such as increasing N-cycling (14%) and reducing C-cycling (6.3%) based on meta-analysis, which has potential effects on soil properties (Palansooriya et al. 2022; Zhang et al. 2019). We found that soil enzymes, such as PPO in the CK treatment and CAT in the CT treatment significantly and negatively correlated with the soil fungal pathogens. We suppose that biochar led to a reduction in the relative abundance of fungal pathogens by improving enzyme activities, suppressing the incidence rate of tobacco root rot. Additionally, our findings provided further evidence of a significant correlation between PPO (cross-loading of −0.93) and the tobacco disease, as determined by PLS-PM analysis (Fig. 8). Similarly, Wang et al. (2022a, b) demonstrated that PPO, which could effectively suppress the occurrence of disease, may be beneficial for the release of nutrients and degradation of root exudation (e.g., phenolic acids) (Wang et al. 2022a). Notably, for biochar application in continuous-cropping soil, PPO activity in the LB treatment was increased by 59.37%, whereas that in the IB treatment increased by 33.20% compared to the CT treatment, indicating that biochar application in advance could suppress the incidence of disease. PPO, a defense enzyme, has the potential to confer resistance to pathogen invasion, and disrupt pectin which is exclusively located in the interface formed around the Ceratobasidium (Paduano et al. 2011; Salari et al. 2012; Wu et al. 2018). In our results, we also proved the relative abundance of Ceratobasidium was significantly decreased, consequently suppressing the incidence of tobacco root rot, especially in the LB treatment. Overall, biochar directly reduced the relative abundance of soil pathogens (Monosporascus and Ceratobasidium), or indirect soil enzyme activity (PPO) effects on the fungal community may contribute to the suppression of tobacco root rot in continuous cropping soil. Furthermore, biochar application forward continuous cropping increased the PPO activity and decreased the relative abundance of Ceratobasidium compared to the biochar application following continuous cropping. Our results indicated that the inhibition of tobacco root rot was strengthened by application of biochar in advance, which is consistent with our above results that emphasize the importance of biochar application forward continuous cropping in alleviating continuous cropping obstacle.

5 Conclusion

In the study, it was observed that the soil quality was altered by biochar application, which attenuated the relationship with the incidence rate of tobacco disease. In addition, biochar application in continuous-cropping soil improved the fungal community composition, enriched the diversity (Shannon and Simpson index) and decreased the relative abundance of fungal pathogens (Monosporascus and Ceratobasidium). Furthermore, the co-occurrence network analysis displayed a negative interaction between the relative abundance of pathogens and soil PPO activity, and the PLS-PM model represented a significant and negative correlation between PPO activity and the tobacco disease. This study demonstrated that indirect at the higher soil enzyme activities and direct at the lower relative abundance of soil pathogens jointly resulted in suppressing the incidence rate of tobacco root rot, especially in biochar application forward continuous cropping. The research provided insight into the relationship between soil enzyme activity and pathogens and tobacco disease in the continuous-cropping soil, and biochar application in advance may be a good practice to alleviate the continuous-cropping obstacle.