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Article

Effects of the Continuous Cropping and Soilborne Diseases of Panax Ginseng C. A. Meyer on Rhizosphere Soil Physicochemical Properties, Enzyme Activities, and Microbial Communities

by
Fuhui Chen
1,
Yongjun Xie
1,
Qingwen Jia
2,
Shuyan Li
1,
Shiyong Li
1,
Naikun Shen
1,
Mingguo Jiang
1 and
Yibing Wang
1,*
1
Guangxi Key Laboratory for Polysaccharide Materials and Modifications, School of Marine Sciences and Biotechnology, Guangxi Minzu University, Nanning 530008, China
2
Baishan Institute of Science and Technology, Baishan 134300, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(1), 210; https://doi.org/10.3390/agronomy13010210
Submission received: 18 December 2022 / Revised: 4 January 2023 / Accepted: 9 January 2023 / Published: 10 January 2023
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Abstract

:
Continuous cropping and soilborne diseases affect soil properties and soil microbial diversity and structure, which are the main factors posing obstacles to the continuous cropping of ginseng. This study explored the response of the physicochemical properties, enzyme activity, and microbial community of ginseng rhizosphere soil to continuous cropping and soilborne disease (root rot of ginseng). We used woodland soil without ginseng planting as a control to study these changes. The results showed that continuous cropping and soilborne disease significantly affected soil physicochemical properties, enzyme activities, and microbial communities. The levels of total nitrogen, hydrolyzable nitrogen, organic matter, and soil pH decreased significantly, while available phosphorus, available potassium, total phosphorus, and total potassium showed significant accumulation after continuous cropping. The activities of urease, catalase, sucrase, acid phosphatase, alkaline phosphatase, and polyphenol oxidase decreased significantly after continuous cropping. Using MiSeq high-throughput sequencing, we found that the alpha diversity and the number of bacterial and fungal communities significantly changed after continuous cropping and soilborne disease. A redundancy analysis suggests that soil physicochemical properties and enzyme activities also affect soil microbial communities. In summary, this study revealed the effects of continuous cropping and soilborne disease on soil and provides a theoretical basis for alleviating soilborne disease in ginseng.

1. Introduction

Panax ginseng C. A. Meyer, a perennial herbaceous plant in the Araliaceae family, is a precious Chinese herbal medicine that is mainly produced in Northeast China [1]. Ginseng contains many active ingredients, such as saponins, polysaccharides, volatile oils, amino acids, and polyacetylenes [2]. It is commonly used to treat nervous system [3], cardiovascular [4], and cerebrovascular diseases and as an antitumor agent [5]. Because wild ginseng resources are scarce, the cultivation of ginseng can be traced back to 1660 years ago in the late Western Jin Dynasty. The ginseng cultivation industry expanded due to the increase in market demand. Currently, most commonly cultivated ginseng is available on the market, among which ginseng cultivated in forests is more popular because of its quality and chemical composition, which are similar to wild ginseng.
The planting period of ginseng is very long, as it is usually harvested 5–6 years after planting. However, the long-term single-cultivation mode easily causes pathogen growth in the soil and soilborne diseases [6], which are caused by fungal or bacterial pathogens and nematodes in the soil, and these pathogens can invade the roots or stems of plants under specific conditions. Fungi are the main pathogens, and Fusarium is the main cause of soilborne diseases in ginseng [7]. Previous studies showed that soil nutrient imbalance [8], soil acidification, decreased soil enzyme activities [9], pathogenic microorganism enrichment, and microbial community changes are the main reasons for soilborne diseases of ginseng [10]. The abundant microorganisms contained in soil are an important part of the soil ecosystem [11]. Soil enzymes, which are mainly derived from soil and plant root exudates, have the function of rapid catalysis and are very active in soil ecosystems. At the same time, soil enzymes are closely related to the contents of nutrient elements in soil, so they are often used as indicators to evaluate soil fertility [12]. Microorganisms maintain ecological balance, ensure the normal growth of plants, and promote nutrient circulation and energy flow [13]. The diversity of soil microbes and the soil microbial community structure reflect soil fertility and soil health [14]. Thus, soil physicochemical properties, soil enzyme activities, and microbial communities have a major influence on the growth, yield, and quality of ginseng.
In the present study, we observed healthy ginseng seedlings and ginseng seedlings showing signs of the soilborne disease root rot. The ginseng root rot caused by Fusarium oxysporum is a serious disease that impacts ginseng production. Two opposite phenomena occurred after biennial ginseng seedlings were transplanted and continuously cropped for four years. We hypothesized that continuous cropping and soilborne diseases would further affect the composition, structure, and diversity of soil bacterial and fungal communities by affecting soil physicochemical properties and soil enzyme activities and finally lead to a decline in ginseng products and quality. To test this hypothesis, we measured the soil physicochemical properties, enzyme activities, and soil microbial communities, using woodland soil without ginseng planting as a control. The differences between continuous cropping and control as well as between soilborne disease and healthy ginseng soil samples were analyzed. This study provides a basis for the study of soil factors with healthy ginseng and soilborne diseases of ginseng, which have great significance for the cultivation and management of ginseng in forests.

2. Materials and Methods

2.1. Soil Sample Collection

The ginseng rhizosphere soil samples analyzed in this study were collected by the Baishan City Institute of Science and Technology from native and vegetated plant communities, as shown in Figure 1. The experimental base was located in Heixiagou village, Sanpeng Lake, Mayihe town, Linjiang city, Jilin Province (41°51′02.80″ N latitude, 127°16′21.81″ E longitude; 833 m above sea level) (Figure 2), and the initial soil samples are described in Table 1 and Table 2. A five-point sampling method was adopted. The 10 cm surface soil layer of the selected area was removed, and the rhizosphere soil of ginseng was collected and evenly mixed. Pictures of soil samples are shown in Figure 3. The samples were naturally air-dried and sieved through a 1 mm mesh sieve for the determination of soil physicochemical properties and soil enzyme activities, and other samples were stored at −80 °C for subsequent DNA extraction. The following treatments were analyzed: CK: woodland soil without ginseng planting; CH: biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy rhizosphere woodland soil; and CD: biennial ginseng seedlings that were transplanted and then continuously cropped for four years in severely diseased (root rot) rhizosphere woodland soil. All treatments were the same as CH. Each experimental treatment (CK, CH, and CD) was analyzed using three biological replicates.

2.2. Soil Physicochemical Analysis

The soil physicochemical properties were measured according to the method described by Bao et al. [15]. The total nitrogen (TN) content in soil was determined using the semimicro Kjeldahl method following reference standard NY/T53-1987 [16]. The total phosphorus (TP) content in soil was determined using the sodium hydroxide melting molybdenum antimony anti-colorimetric method following reference standard NY/T88-1988 [17]. The total potassium (TK) content in soil was determined via hydrofluoric acid digestion and flame photometry following reference standard NY/T87-1988 [18]. The available phosphorus (AP) content in soil was determined using the hydrochloric acid–sulfuric acid double acid extraction colorimetric method following reference standard NY/T1121.7-2014 [19]. The available potassium (AK) content in soil was determined using the neutral ammonium acetate extraction and flame photometric method following reference standard LY/T1234-2015 [20]. The organic matter (OM) content in soil was determined using the high-temperature external thermal potassium dichromate oxidation capacity method following reference standard NY/T1121.6-2006 [21]. The hydrolyzable nitrogen (HN) content in soil was determined using the alkaline hydrolysis diffusion method following reference standard LY/T1228-2015 [22]. Soil samples were sent to Guangxi Yipu Testing Technology Co., Ltd. for the determination of the above soil nutrient indices. According to soil reference standard LY/T1239-1999 [23], soil pH was measured using deionized water extraction with a soil–water ratio of 1:2.5 and a pH meter (Leici, Shanghai, China). The samples from each experimental treatment were analyzed using three biological replicates.

2.3. Soil Enzyme Activities

The soil sucrase activity (S-SC) was determined using the 3,5-dinitrosalicylic acid colorimetry method [24]; the soil urease activity (S-UE) was determined using the indophenol blue colorimetry method [25,26]; the soil catalase activity (S-CAT) was determined using the potassium permanganate titration method [27]; the soil cellulase activity (S-CL) was determined using the potassium permanganate titration method [28]; the soil acid phosphatase activity (S-ACP) and soil alkaline phosphatase activity (S-AKP) were determined using the organic group content method [29]; and the soil polyphenol oxidase activity (S-PPO) was determined using the purple gallic acid colorimetry method [30]. The samples from each experimental treatment were analyzed using three biological replicates.

2.4. DNA Extraction and MiSeq Sequencing

The extraction of total microbial DNA was completed by Shanghai Majorbio Technology Company Limited (Shanghai, China) using the E.Z.N.A® soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The DNA concentration and purity were determined using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA), and the DNA completeness was determined using 1% agarose gel electrophoresis (Biowest agarose, Spain).
The V3–V4 hypervariable region of the bacterial 16S rRNA genes was amplified via PCR using the following primer pairs [31]: 338F: 5′-CCTACGGGNGGCWGCAG-3′ and 806R: 5′-GGACTACHVGGGTATCTAAT-3′. The fungal ITS region was amplified with the following primer pairs [32]: ITS1F: 5′-GATGAAGAACGYAGYRAA-3′ and ITS2R: 5′-GGACTACHVGGGTATCTAAT-3′. The PCR amplification was performed in a 20 µL reaction solution consisting of 4 µL of 5× FastPfu Buffer, 2 µL of dNTPs (2.5 mM), 1.6 µL of forward and reverse primers (5 µM), 0.4 µL of FastPfu DNA Polymerase, 0.2 µL of BSA, and 10 ng of template DNA. The PCR amplification conditions were as follows: initial denaturation at 95 °C for 3 min; followed by 30 cycles of 95 °C for 30 s, 72 °C for 30 min, and 72 °C for 45 s; and a final extension at 72 °C for 10 min. The PCR products were detected via electrophoresis using a 2% agarose gel and recovered using an AxyPrep DNA Gel kit (Axygen Biosciences, Union City, CA, USA). The PCR product was purified using an AxyPrep DNA Gel Extraction Kit. The PCR products were detected and quantified using a Quantus™ Fluorometer. The amplified products were sequenced using a NEXTflex Rapid DNA-Seq Kit. Illumina MiSeq high-throughput sequencing was performed by Majorbio Technology Company Limited (Shanghai, China).

2.5. Sequencing Data Analysis

Quality control was carried out using fastp [33] software version 0.20.0 (https://github.com/OpenGene/fastp, accessed on 5 October 2022). Splicing was carried out using FLASH [34] software version 1.2.7 (http://www.cbcb.umd.edu/software/flash accessed on 5 October 2022). UPARSE [35] software version 7.1 (http://drive5.com/uparse/ accessed on 5 October 2022) was used to perform a cluster analysis of the sequences by operational taxonomic units (OTUs) according to a similarity of 97%. The chloroplast and mitochondrial sequences annotated in all samples were removed. Then, the number of all bacterial 16S rRNA gene sequences was flattened to 34,849, and the number of all fungal ITS regions was flattened to 47,479. After flattening, the Good’s coverage of every sample still reached 98.65%. The OTU species taxonomy was annotated using the RDP classifier [36] version 2.11 (http://rdp.cme.msu.edu/ accessed on 5 October 2022) and compared with the Silva gene database (v138) and UNITE gene database (8.0), and the confidence threshold was 70%. The community composition of each sample was counted at different classification levels. The raw data were deposited in the NCBI database (https://www.ncbi.nlm.nih.gov, accessed on 13 December 2022) with the deposition numbers PRJNA906659 and PRJNA907963.
The bacterial and fungal alpha diversity estimations of the Chao1 richness index, ACE index, Shannon index, Simpson index, and coverage index were calculated using Mothur [37] software (http://www.mothur.org/wiki/Calculators accessed on 5 October 2022). A one-way analysis of variance (ANOVA) was used for the quantitative analysis of alpha diversity between groups. Based on the Bray–Curtis algorithm of three samples, a principal coordinate analysis (PCoA) was carried out. The PCoA was used to test the similarity of the microbial community structure among samples, and combined with a non-parametric ANOVA it was used to analyze whether the differences in the microbial community structure among sample groups were significant. An unweighted pair group method with arithmetic mean (UPGMA) clustering analysis based on the Bray–Curtis algorithm was used to reveal the relationship of microbiome composition. A heatmap analysis was used to compare the top 30 classified genera in each sample using the ggplot2 package in R (R i385 4.0.4). A redundancy analysis (RDA) was used to examine the correlations between the top 10 phyla of the bacterial and fungal communities in different samples and different environmental factors. In this experiment, 15 environmental factors were determined, including the soil nutrient indices TN, TP, TK, AP, AK, HN, OM, and pH and the soil enzyme activity indices S-SC, S-UE, S-CAT, S-CL, S-ACP, S-AKP, and S-PPO. Environmental factors were selected based on the variance inflation factor (VIF); environmental factors with p > 0.05 or VIF > 20 were removed from the subsequent analysis. The VIF values of TN, HN, S-ACP, S-AKP, and S-PPO were lower than 20, and thus these indices were selected. A detrended correspondence analysis (DCA) showed that the largest axis lengths were 1.14 at the phylum level of bacterial communities and 1.05 at the phylum level of fungal communities. Consequently, RDA was selected to analyze the possible influences of environmental factors and different soil samples on the phyla of bacterial and fungal communities. We used a biomarker algorithm combining linear discriminant analysis and LefSe to analyze the differences in taxa among the three samples, and high LDA scores reflect significantly higher abundances of certain taxa.

2.6. Statistical Analysis

An ANOVA was used to analyze the soil physicochemical properties and soil enzyme activity using SPSS Statistics 21 software. The soil physiochemical properties, soil enzyme activity, bacterial and fungal taxa, and microbial alpha diversity indices were compared using Student’s t test. A p value of <0.05 was considered statistically significant.

3. Results

3.1. Soil Physicochemical Properties

Our results showed significant differences between the three soil samples in soil pH, TN, TP, TK, AP, AK, HN, and OM (p < 0.05) (Table 3). The soil pH, TN, HN, and OM in CH and CD were significantly lower than those in CK. Compared with CK, the TN, HN, and OM of CH decreased by 61.39%, 7.51%, and 57.57%, respectively, and the TN, HN, and OM of CD decreased by 24.52%, 13.91%, and 28.60%, respectively. The TP, TK, AP, and AK in CH and CD were significantly higher than those in CK. Compared with CK, the TP, TK, AP, and AK of CH increased by 0.3420, 0.4829, 4.9445, and 1.5878 times, respectively, and the TP, TK, AP, and AK of CD increased by 0.4304, 0.4158, 3.8541, and 0.3653 times, respectively. These results indicate that the continuous cropping of ginseng and soilborne disease resulted in significant decreases in soil pH, TN, HN, and OM and significant accumulations of TP, TK, AP, and AK, indicating that continuous cropping resulted in a soil nutrient imbalance.

3.2. Soil Enzyme Activity

The activities of S-UE, S-CAT, S-SC, S-ACP, S-AKP, and S-PPO in CH and CD were all lower than those in CK (Table 4). After continuous cropping, the most evident change was the activity of S-SC, which decreased significantly by 86.02% and 81.96% in CH and CD, respectively, compared with that of CK (p < 0.05). However, the S-CL activity of CH increased by 26.25%, while that of CD decreased by 1.97% compared with that of CK. These results show that the continuous cropping of ginseng resulted in a decrease in the soil enzyme activities of S-UE, S-CAT, S-SC, S-ACP, S-AKP, and S-PPO but not S-CL.

3.3. Correlations between Soil Physicochemical Properties and Soil Enzyme Activities

The analysis of the correlations between the soil physicochemical properties and soil enzyme activities is shown in Table 5. The activity of S-SC was positively correlated with TN, HN, OM, and pH and negatively correlated with TP, TK, AP, and AK. The activity of S-UE was positively correlated with TN, OM, and pH and negatively correlated with TK, AP, AK, and TP. The activity of S-CL was positively correlated with AK and negatively correlated with TN and OM. The activity of S-ACP was positively correlated with TN, OM, and pH and negatively correlated with AK, TK, and AP. The activity of S-AKP was positively correlated with TN, OM, and pH and negatively correlated with AP, AK, and TK. The activity of S-CAT was positively correlated with TN, OM, and pH and negatively correlated with AK, TK, and AP. The activity of S-PPO was positively correlated with HN and pH and negatively correlated with TP, TK, AP, and OM. These results showed that there were correlations between the soil enzyme activity and soil physicochemical properties.

3.4. Soil Bacterial and Fungal Abundance

In this study, 455,749 and 503,096 sequences were obtained from bacteria and fungi, respectively. There were, on average, 34,808 and 47,479 effective sequences per bacterial and fungal sample, respectively. The average fragment lengths of bacteria and fungi were 414 bp and 242 bp, respectively. In total, 3346 bacterial OTUs and 2089 fungal OTUs were obtained (Figure 4A,B). Additionally, 1939, 1432, and 2577 bacterial OTUs were obtained for the CK, CH, and CD treatments, respectively, with 750 OTUs common to all three samples. In contrast, 1125, 554, and 1222 fungal OTUs were obtained for the CK, CH, and CD treatments, respectively, with 189 OTUs common to all three samples.
The OTUs of the three soil samples were annotated and included 1 kingdom, 35 phyla, 94 classes, 243 orders, 377 families, 361 genera, and 338 species of bacteria and 1 kingdom, 14 phyla, 50 classes, 131 orders, 276 families, 431 genera, and 671 species of fungi (removing unclassified, unidentified, and no rank results) (Table 6). We defined the bacterial community with a relative abundance greater than 10% as the dominant flora and the bacterial phyla with relative abundances greater than 1% and less than 10% as the subdominant flora. The dominant flora and the subdominant flora are collectively referred to as the dominant flora.
At the bacterial-phylum level, the dominant phyla in the three samples are shown with a histogram in Figure 5A. The relative abundance of bacterial communities at the phylum level are listed in Table S3. The dominant phyla of CK were Proteobacteria (33.93%), Acidobacteriota (25.36%), and Actinobacteriota (17.57%). The subdominant phyla were Chloroflexi (6.60%), Firmicutes (1.66%), Verrucomicrobiota (4.40%), Gemmatimonadota (1.03%), Bacteroidota (1.27%), Myxococcota (1.62%), Planctomycetota (2.44%), and Methylomirabilota (1.02%). The dominant phyla of CH were Proteobacteria (22.98%), Chloroflexi (22.93%), Firmicutes (21.11%), and Actinobacteriota (20.86%), and the subdominant phyla were Acidobacteriota (3.98%), Gemmatimonadota (1.30%), Bacteroidota (1.48%), Patescibacteria (2.52%), and WPS-2 (1.31%). The dominant phyla of CD were Actinobacteriota (24.38%), Proteobacteria (22.27%), Chloroflexi (16.46%), and Acidobacteriota (13.66%), and the subdominant phyla were Firmicutes (9.27%), Verrucomicrobiota (1.77%), Gemmatimonadota (3.19%), Bacteroidota (1.62%), Myxococcota (1.85%), and Methylomirabilota (1.42%).
The relative abundances of Proteobacteria and Acidobacteriota in CH and CD were significantly lower (p < 0.05) than those of CK, while the relative abundances of Actinobacteriota, Chloroflexi, and Firmicutes were significantly higher (p < 0.05) than those of CK. At the bacterial-phylum level, the relative abundances of Chloroflexi and Firmicutes changed considerably.
At the fungal-phylum level, the dominant phyla in the three samples are shown with a histogram in Figure 5B. The relative abundance of fungal communities at the phylum level are listed in Table S4. The dominant phyla of CK were Basidiomycota (53.95%) and Ascomycota (34.15%), and the subdominant phyla were Mortierellomycota (8.36%), unclassified_k__Fungi (1.80%), and Rozellomycota (1.34%). The dominant phyla of CH were Ascomycota (80.04%) and Basidiomycota (13.06%), and the subdominant phylum was Mortierellomycota (5.47%). The dominant phyla of CD were Ascomycota (60.78%), Mortierellomycota (21.78%), and Basidiomycota (13.46%), and the subdominant phylum was unclassified_k__Fungi (2.06%).
At the fungal-phylum level, the relative abundance of Ascomycota in CH and CD was significantly higher (p < 0.05) than that in CK, while that of Basidiomycota was significantly lower (p < 0.05) than that in CK. The relative abundance of Mortierellomycota in CD was the highest among the three soil samples.
Figure 6A,B shows the effects of continuous cropping and soilborne disease on soil bacterial and fungal communities at the genus level. The relative abundance of bacterial and fungal communities at the genus level are listed in Tables S5 and S6, respectively. Blue, white, and gold represent the relative abundances of soil microbial communities from high to low.
Regarding soil bacterial genera, Acidothermus, Bradyrhizobium, Candidatus Udaeobacter, Bryobacter, Mycobacterium, Candidatus Solibacter, and Roseiarcus had higher relative abundances in CK than in CH and CD. Bacillus, Rhodanobacter, HSB_OF53-F07, Chujaibacter, and Paenibacillus displayed higher relative abundances in CH than in CK and CD. The relative abundances of Gaiella and Sphingomonas were higher in CD than in CK and CH. Regarding soil fungal genera, Russula, Tomentella, Oidiodendron, Microglossum, Sebacina, Lactarius, Cladophialophora, Laccaria, Clavulinopsis, and Hygrocybe had higher relative abundances in CK than in CH and CD. Trichocladium, Neocosmospora, Chaetomium, Phialophora, and Aspergillus displayed higher relative abundances in CH than in CK and CD. Mortierella, Penicillium, Schizothecium, Paracylindrocarpon, Cladorrhinum, Gibellulopsis, Neonectria, Pleotrichocladium, Paraphaeosphaeria, Truncatella, and Helvellosebacina displayed higher relative abundances in CD than in CK and CH.

3.5. Soil Microbial Diversity

3.5.1. Alpha Diversity Indices of the Bacterial and Fungal Communities

Microbial diversity is an important index for measuring the biological composition of a community. The alpha diversity reflects the abundance and diversity of microorganisms in soil. In this study, we compared the diversity indices of bacteria and fungi (Figure 7). The diversity and richness indices of bacterial communities are listed in Table S1, and fungal in Table S2. The results show that the ACE, Chao1, and Shannon indices of bacteria and fungi in CH were significantly lower than those in CK, while the Simpson index was significantly higher than that in CK. The ACE, Chao1, and Shannon indices of bacteria and fungi in CD were significantly higher than those in CH, while the Simpson index was significantly lower than that in CH. The ACE, Chao1, Shannon, and Simpson indices of bacteria showed significant differences among the three samples. The ACE, Chao1, Shannon, and Simpson indices of fungi showed no significant differences between CK and CD but showed significant differences between CH and CD. The coverage index values of the three samples of bacteria were 98.95%, 99.25%, and 98.65%, respectively, which indicated that the sequencing depth reflected the sequencing results, representing the real situation of the microorganisms in the samples, and that the experimental results are highly reliable. The coverage index values of the three samples of fungi were 99.66%, 99.84, and 99.74%, respectively, which indicated that the sequencing depth reflected the sequencing results, representing the real situation of the microorganisms in the samples, and that the experimental results are highly reliable.

3.5.2. Beta Diversity Indices of the Bacterial and Fungal Communities

A principal coordinate analysis (PCoA) can be used to explain the relationships between several samples. Our results showed that the three replicates of each sample were clustered together, which indicates that the bacterial community structures and fungal community structures were well replicated. Comparing the three soil samples, the contribution rate of the first principal component of the bacterial community to the sample difference was 60.24%, and that of the second principal component was 28.88% (Figure 8A). Based on the abscissa direction, the bacterial community composition of CH was distinguished from that of CK and CD. Based on the ordinate direction, the bacterial community composition of CD was distinguished from that of CK and CH. The contribution rate of the first principal component of the fungal community to the sample difference was 61.03%, and that of the second principal component was 29.79% (Figure 8B). The compositions and structures of the fungal communities in the three samples were quite different.
Based on the OTU annotation results, the bacterial communities and fungal communities of the three samples were analyzed by nonmetric multidimensional scaling (NMDS) using the Bray–Curtis distance algorithm. The differences in bacterial and fungal community composition among CK, CH, and CD were studied using NMDS (Figure 9A,B). The results showed that there were significant differences in the bacterial and fungal microbial communities among the three samples (bacteria: R = 1.000, p = 0.001; fungi: R = 1.000, p = 0.001), and the stress values were all 0, indicating that continuous cropping and soilborne disease can significantly affect the microbial community composition in the soil.
Using the Bray–Curtis distance algorithm, an OTU-level UPGMA cluster analysis was carried out on the distance matrix of the bacterial and fungal communities. The samples were clustered using the average value, and OTUs with abundances less than 5% were merged with others. The results showed that the bacterial and fungal communities in CK, CH, and CD were significantly different. The diversity of the bacterial community in CH was the highest, and the diversity of the fungal community in CD was the highest (Figure 10A,B). CK and CD had a high level of similarity in the bacterial community, and CH and CD had a high level of similarity in the fungal community.

3.6. Effects of Environmental Factors on Bacterial and Fungal Communities

The RDA results at the phylum level of bacteria showed that the eigenvalue of the first axis was 82.16%, and that of the second axis was 8.96% (Figure 11A). The RDA results at the phylum level of fungi showed that the first-axis eigenvalue was 86.89%, and the second-axis eigenvalue was 9.24% (Figure 11B). The RDA plot at the phylum level of bacteria indicated that TN was the most important factor for the bacterial community, while S-ACP was the most important factor for the fungal community. TN was positively correlated with Acidobacteriota and Proteobacteria but negatively correlated with Firmicutes. S-ACP was positively correlated with Mortierellomycota and negatively correlated with Ascomycota.
The correlation heatmap shows the relationship between the top 30 genera of bacteria, soil physicochemical properties, and enzyme activities (Figure 12A). The environmental factors HN and S-PPO showed a significant positive correlation with Bryobacter and Acidothermus. The environmental factors S-ACP, TN, and S-AKP were significantly positively correlated with Candidatus Solibacter, Mycobacterium, Candidatus Udaeobacter, and Bradyrhizobium. The environmental factor HN was significantly negatively correlated with Arthrobacter. The environmental factor S-PPO was significantly negatively correlated with Arthrobacter and Sphingomonas. The environmental factors S-ACP, TN, and S-AKP showed significant negative correlations with Bacillus, Chujaibacter, and Paenibacillus. The correlation heatmap showed the relationship between the top 30 genera of fungi, soil physicochemical properties, and enzyme activities (Figure 12B). The environmental factors HN and S-PPO showed significant positive correlations with Oidiodendron, Cladophialophora, and Microglossum, and the environmental factor S-PPO showed a significant positive correlation with Trichoderma. The environmental factors S-ACP, TN, and S-AKP were significantly positively correlated with Russula, Tomentella, Lactarius, and Sebacina; the environmental factor S-ACP was significantly positively correlated with Hypomyces; and the environmental factor TN was significantly positively correlated with Microglossum. The environmental factors HN and S-PPO showed significant negative correlations with Penicillium, Schizothecium, Paracylindrocarpon, Cladorrhinum, and Gibellulopsis. The environmental factors TN and S-AKP showed significant negative correlations with Chaetomium, Neocosmospora, Saitozyma, Trichocladium, and Fusarium. The environmental factor S-AKP showed significant negative correlations with Chaetomium, Trichocladium, and Neocosmospora.
A linear discriminant analysis (LDA) of effect size (LEfSe) determined the taxa most likely to explain the differences between CH, CD, and CK. Significant differences were found between the bacterial and fungal communities from the three soil samples. The results confirmed the significant enrichment of Firmicutes, Chloroflexi, Sordariomycetes, and Ascomycota in CH and an enrichment of Vicinamibacteria, KD4-96, Anaerolineae, Acidimicrobiia, and Mortierellomycetes in CD (Figure 13).

4. Discussion

Soil physicochemical properties and soil enzyme activities reflect the level of soil fertility and are thus important indicators for evaluating soil fertility [38]. Rational fertilization can not only reduce the use of fertilizer, improve the utilization rate of fertilizer, and prevent soil compaction but can also improve the yield and quality [39]. In this study, we found that continuous cropping led to significant decreases in TN, HN, and OM as well as soil acidification, which is in accordance with the findings of other studies [40]. Hao et al. [41] reported that denitrification occurred during the hydrolysis and loss of soil nitrate nitrogen, which resulted in soil acidification, providing a reasonable explanation for the simultaneous decrease in soil HN and pH in our study. Ginseng with soilborne disease showed a significant decrease in consumed TN and OM compared with healthy ginseng. However, due to improper topdressing, phosphorus and potassium accumulated extensively in rhizosphere soil. The above results further indicate the importance of rational fertilization in ginseng cultivation.
Soil enzyme activities underwent different changes after the continuous cropping of ginseng. Compared with CK, the activities of S-UE, S-CAT, S-SU, S-ACP, S-AKP, and S-PPO in CH and CD decreased. However, compared with CK, the enzyme activity of S-CL in CH increased significantly. The most considerable change in soil enzyme activity was observed for S-SC. The changes in S-ACP and S-AKP were consistent with the results of earlier research [42]. Urease activity is closely related to soil quality, which can reflect the nitrogen content in soil and indirectly adjust the pH of soil [43]. Based on this, it was speculated that soil enzyme activity is closely related to the continuous cropping obstacle of ginseng.
In our study, we confirmed that there were correlations between soil physicochemical properties and soil enzyme activities. The activities of S-UE, S-SC, S-CL, S-CAT, S-ACP, and S-AKP were significantly correlated with TN, and S-SC was significantly correlated with TP and AP, which was consistent with Zhang et al. [44]. The enzyme activities of S-UE, S-SC, S-CAT, S-ACP, and S-AKP were significantly correlated with OM. The activities of S-SC, S-UE, S-ACP, and S-AKP were extremely positively correlated with the soil pH, and the activity of S-CAT was positively correlated with the soil pH, which is consistent with the results of Zhao et al. [45]. Soil physicochemical properties and soil enzyme activities restrict and influence each other, which means that any change will damage the soil ecological balance and affect soil fertility. Rational fertilization is the key to improving soil enzyme activity and alleviating the obstacle of ginseng continuous cropping.
As an important part of soil ecology, the dynamic balance of the microbial community is very important for the growth of ginseng. In this study, we analyzed the microbial diversity and the microbial community structure of three soil samples, and the differences in rhizosphere microorganisms between soils with healthy ginseng and ginseng showing signs of soilborne diseases were studied. Moreover, the effects of continuous cropping on the species and the relative abundance of soil microorganisms were clarified, which provides an important basis for regulating the distribution of the soil microbial community in the process of ginseng planting and effectively avoiding soilborne disease.
This study found that the alpha diversity of bacteria and fungi was CD > CK > CH. Chen [46] showed that the continuous cropping obstacles of many crops were related to changes in soil microbial community structure and composition. In this study, it was found that the structure of the soil microbial community in the three soil samples was significantly changed by continuous cropping and soilborne disease.
At the phylum level, we found a total of eight dominant phyla (relative abundance >10%), and the relative abundances in the three soil samples were significantly different (p < 0.05). The relative abundance of Firmicutes in CH was significantly higher than those in CK and CD. Firmicutes contains broad-spectrum biocontrol bacteria such as Bacillus [47]. It was speculated that soilborne disease did not occur after continuous ginseng cropping due to the high abundance of biocontrol bacteria in CH. The relative abundance of Actinomycetes followed the order CD (24.38%) > CH (20.86%) > CK (17.57%). Actinomycetes contains a large number of biocontrol bacteria that prevent soilborne diseases of ginseng, which can produce secondary metabolites to inhibit the growth of pathogenic bacteria and induce ginseng to produce defense enzymes to enhance its resistance to pathogenic bacteria [48]. It is speculated that the occurrence of soilborne diseases leads to an increase in the relative abundance of actinomycetes in the soil to antagonize the pathogenic bacteria of ginseng. The relative abundances of Proteobacteria and Acidobacteriota in CK were significantly higher than those in CH and CD. The relative abundance of Chloroflexi in CH and CD was significantly higher than that in CK. Proteobacteria, Chloroflexi, and Acidobacteriota have the functions of material transformation and the decomposition of plant residues, promoting soil nutrient circulation and maintaining the ecological balance of soil, which are important for material circulation in soil. It is speculated that the planting of ginseng leads to a decrease in the relative abundance of the abovementioned strains, which leads to an imbalance in the soil microecology [49]. The total relative abundance of Ascomycota and Basidiomycetes in the three soil samples was 93.1% in CH > 88.10% in CK > 74.25% in CD. Ascomycetes and Basidiomycetes are the two main fungal groups in soil and have the function of maintaining soil microecology stability [50]. Therefore, we infer that the decrease in Ascomycota and Basidiomycota relative abundance was a reason for the occurrence of soilborne diseases of ginseng. The relative abundance of Mortierellomycota in CD was higher than those in CK and CH. It is suggested that Mortierellomycota may contain a pathogen that can cause soilborne disease of ginseng.
A total of six dominant bacterial genera (relative abundance >10%) were found in this study. The relative abundances of Bacillus, Trichocladium, and Neocosmospora in CH were significantly higher than those in CK and CD. The relative abundance of Mortierella in CD was significantly higher than those in CK and CH. The relative abundance of Russula was significantly higher in CK than in CH and CD. The relative abundances of Fusarium in CH and CD were significantly higher than that in CK. Bacillus has been shown to have broad-spectrum biological control in most studies [51] and was the main bacteria for the biological control of soilborne diseases in ginseng [52]. For instance, Bacillus velezensis has a broad-spectrum antibacterial effect and has an evident control effect on Alternaria panax Whetz [53]. Kim et al. [54] found that Bacillus amyloliquefaciens AK-0 had a good inhibitory effect on the pathogens of ginseng rust rot, ginseng root rot, and ginseng gray mold in vitro. Paenibacillus also showed good bacteriostatic potential against the abovementioned soilborne diseases [55]. Therefore, it can be inferred that because of the high abundance of Bacillus in CH, ginseng cultivated in this soil sample could grow healthily, even though it was continuously cropped for four years. Most of the strains of Trichocladium are endophytic fungi, suggesting that Trichocladium is closely related to plant growth and development [56]. This study also confirmed that Trichocladium was the dominant fungus in CH. Ratledge and Wynn [57] confirmed that Mortierella, represented by Mortierella alpina, could produce a wide range of polyunsaturated fatty acids, such as arachidonic acid, γ linolenic acid, and eicosapentaenoic acid. These fatty acids are rich in carbon sources, which may change the habitat of soil microorganisms. Therefore, inoculation with Mortierella may affect the soil microbial community by changing nutrient absorption and the soil microhabitat, thus indirectly affecting soil nutrient transformation and availability. Therefore, it is inferred that the increase in the relative abundance of Mortierella strains in the continuous cropping of ginseng with soilborne disease in the rhizosphere soil may be a response mechanism of the soil microbial community to soilborne diseases.
Through the linear discriminant analysis (LDA) of effect size (LEfSe) study, significant differences were found between the bacterial and fungal communities from the three soil samples. The Firmicutes, Chloroflexi, and Ascomycota in CH were all significantly enriched, and there was also an enrichment of Mortierellomycetes in CD. All these results were consistent with the analysis of relative abundance.
Our PCoA, NMDS, and UPGMA results suggested that the soil bacterial and fungal community structures were changed due to the continuous cropping of ginseng. The PCoA results suggested that the three replicates of each sample were clustered together and that the compositions and structures of the bacterial and fungal communities in the three samples were quite different. The NMDS results also suggested that the three replicates of each sample were clustered together and that the compositions and structures of the bacterial and fungal communities in CK were quite different from those in CH and CD. However, the UPGMA results suggested that the bacterial community composition in CH was quite different from those in CK and CD, while the fungal community composition in CK was quite different from those in CH and CD. According to the UPGMA results, the relative abundances of OTU2413 (Bacillus aryabhattai) in CH and OTU2347 (Bradyrhizobium elkanii) in CK were significantly higher than those in CD. Related research has confirmed that Bacillus aryabhattai [58] and Bradyrhizobium elkanii [59] have a positive influence on crop growth. Thus, soil bacterial and fungal communities were affected by continuous cropping and soilborne diseases.
RDA was carried out to describe the relationships between soil microbial communities and soil environmental factors at the phylum level. The correlation heatmap shows the relationships between soil microbial communities and soil environmental factors at the genus level. Our study showed that there were significant difference among CK, CH, and CD in the bacterial and fungal communities. Five environmental factors, TN, HN, S-ACP, S-AKP, and S-PPO, were selected, and our results showed that these five factors were associated with the soil microflora composition. HN and S-ACP were the most important soil environmental factors affecting the soil bacterial and fungal community compositions, respectively. At the fungal-phylum level, Ascomycota was negatively correlated with S-ACP, S-AKP, and TN. The results of Zhu et al. [60] showed that the relative abundance of Ascomycota increases when the soil nutrient status decreases. However, Fusarium is the pathogen of ginseng root rot in Ascomycota [61]. Therefore, a reasonable adjustment of the content of TN may be beneficial to the inhibition of Fusarium pathogenic bacteria, thus alleviating the continuous cropping obstacle of ginseng. At the bacterial genus level, Sphingomonas, Bacillus, and Paenibacillus were almost negatively correlated with environmental factors. These bacterial genera have been confirmed to have biocontrol functions [62,63,64]. However, the Bradyrhizobium [65] genera, which also have biocontrol functions, were positively correlated with environmental factors. We will carry out relevant studies on this topic in subsequent experiments. Overall, our results indicate that changes in soil physicochemical properties and enzyme activities may be the main factors modulating microflora communities, and the changes in microflora composition may be the main cause of soilborne diseases of ginseng.

5. Conclusions

Our study demonstrated that after continuous cropping, the levels of TN, HN, OM, and soil pH in the rhizosphere soil of ginseng were significantly lower than those in CK, and AP, AK, TP, and TK all showed significant accumulation. S-CL increased after the continuous cropping of ginseng but decreased significantly after soilborne disease of ginseng. S-CL is derived from soil microorganisms and can promote the decomposition of cellulose into glucose, thus providing available carbon-source nutrients for soil microorganisms. The bacterial and fungal community compositions, structures, and diversity in CD were more diverse than those in CH. The order of influence of the five soil environmental factors selected by variance expansion on the community structures of bacteria and fungi was HN > S-ACP > TN > S-AKP > S-PPO. In addition, RDA showed that at the phylum level, HN was positively correlated with Proteobacteria in the bacterial community and Basidiomycota in the fungal community but negatively correlated with Actinobacteriota in the bacterial community and Mortierellomycota in the fungal community. Our research shows that the continuous cropping and soilborne diseases of ginseng resulted in significant changes to soil physicochemical properties and enzyme activities as well as the bacterial and fungal community composition, structure, and diversity, ultimately leading to a decline in ginseng yield and quality. The above results provide a theoretical basis for the sustainable development of the ginseng cultivation industry and the amendment and maintenance of the soil ecological environment.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy13010210/s1, Table S1: Diversity and richness indices of bacterial communities; Table S2: Diversity and richness indices of fungal communities; Table S3: The relative abundances of bacterial communities at the phylum level; Table S4: The relative abundances of fungal communities at the phylum level; Table S5: The relative abundances of bacterial communities at the genus level; Table S6: The relative abundances of fungal communities at the genus level.

Author Contributions

Q.J. performed the sample collection. F.C. participated in the bioinformatics and statistical analyses, wrote the original draft, and performed the review and editing. F.C., Y.X., S.L. (Shuyan Li) and S.L. (Shiyong Li) were responsible for of the software and methodology of the manuscript. Y.W., N.S. and M.J. oversaw project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Major Project of Guangxi (AA18242026) and the Key Research and Development Program of Guangxi (AB21196019).

Data Availability Statement

The raw readings were submitted to the Sequence Read Archive (SRA) of the NCBI database (accession numbers: PRJNA906659 and PRJNA907963).

Acknowledgments

We thank Qingwen Jia for collecting samples.

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Agronomy 13 00210 g001 550
Figure 1. Pictures of the native and vegetated plant communities of soil samples. (a) Picture of the CH and CD collection place. (b) Picture of the CK collection place. (c) Picture of native and vegetated plant communities. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 1. Pictures of the native and vegetated plant communities of soil samples. (a) Picture of the CH and CD collection place. (b) Picture of the CK collection place. (c) Picture of native and vegetated plant communities. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 2. Map of the study plots.
Figure 2. Map of the study plots.
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Figure 3. Pictures of soil samples. (a) Picture of soil sample CH. (b) Picture of soil sample CD. (c) Picture of soil sample CK. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 3. Pictures of soil samples. (a) Picture of soil sample CH. (b) Picture of soil sample CD. (c) Picture of soil sample CK. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 4. Venn diagrams of bacterial and fungal OTUs in the three soil samples. (A) Venn diagram of bacterial OTUs. (B) Venn diagram of fungal OTUs. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 4. Venn diagrams of bacterial and fungal OTUs in the three soil samples. (A) Venn diagram of bacterial OTUs. (B) Venn diagram of fungal OTUs. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 5. Bacterial and fungal compositions and community structures in the three soil samples. (A) Bar plot of the bacterial community at the phylum level. (B) Bar plot of the fungal community at the phylum level. The relative abundance in each sample was calculated on the basis of the percentage of the total effective sequences, which were classified using the RDP classifier and compared with the Silva database and the UNITE gene database. Phyla with relative abundances less than 1% were classified as ‘other’. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 5. Bacterial and fungal compositions and community structures in the three soil samples. (A) Bar plot of the bacterial community at the phylum level. (B) Bar plot of the fungal community at the phylum level. The relative abundance in each sample was calculated on the basis of the percentage of the total effective sequences, which were classified using the RDP classifier and compared with the Silva database and the UNITE gene database. Phyla with relative abundances less than 1% were classified as ‘other’. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 6. Heatmaps of soil bacterial and fungal communities at the genus level in the different soil samples. (A) Heatmap of the bacterial community at the genus level. (B) Heatmap of the fungal community at the genus level. The relative abundance in each sample was calculated on the basis of the percentage of the total effective sequences, which were classified using the RDP classifier and compared with the Silva database and the UNITE gene database. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 6. Heatmaps of soil bacterial and fungal communities at the genus level in the different soil samples. (A) Heatmap of the bacterial community at the genus level. (B) Heatmap of the fungal community at the genus level. The relative abundance in each sample was calculated on the basis of the percentage of the total effective sequences, which were classified using the RDP classifier and compared with the Silva database and the UNITE gene database. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 7. Boxplots of the alpha diversity of bacteria and fungi in the rhizosphere soil of the three samples. (a) Shannon index of bacteria and fungi in the rhizosphere of the three samples. (b) Simpson index of bacteria and fungi in the rhizosphere of the three samples. (c) ACE index of bacteria and fungi in the rhizosphere of the three samples. (d) Chao 1 index of bacteria and fungi in the rhizosphere of the three samples. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting; * represents a significant correlation (p < 0.05), whereas ** represents an extremely significant correlation (p < 0.01).
Figure 7. Boxplots of the alpha diversity of bacteria and fungi in the rhizosphere soil of the three samples. (a) Shannon index of bacteria and fungi in the rhizosphere of the three samples. (b) Simpson index of bacteria and fungi in the rhizosphere of the three samples. (c) ACE index of bacteria and fungi in the rhizosphere of the three samples. (d) Chao 1 index of bacteria and fungi in the rhizosphere of the three samples. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting; * represents a significant correlation (p < 0.05), whereas ** represents an extremely significant correlation (p < 0.01).
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Figure 8. (A) PCoA analysis of bacterial community structure. (B) PCoA analysis of fungal community structure. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 8. (A) PCoA analysis of bacterial community structure. (B) PCoA analysis of fungal community structure. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 9. (A) NMDS analysis of bacterial community structure. (B) NMDS analysis of fungal community structure. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 9. (A) NMDS analysis of bacterial community structure. (B) NMDS analysis of fungal community structure. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 10. (A) UPGMA analysis of the bacterial community structure. (B) UPGMA analysis of the fungal community structure. OTUs representing less than 5% of the total composition were classified as ‘other’. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 10. (A) UPGMA analysis of the bacterial community structure. (B) UPGMA analysis of the fungal community structure. OTUs representing less than 5% of the total composition were classified as ‘other’. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Figure 11. Correlations between bacterial and fungal phyla, soil physicochemical properties, and enzyme activities. (A) Results of the redundancy analysis (RDA) of the top 10 bacterial phyla, soil physicochemical properties, and soil enzyme activities. (B) Results of the RDA of the top 10 fungal phyla, soil physicochemical properties, and soil enzyme activities. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting. TN—total nitrogen, HN—hydrolyzable nitrogen, S-ACP—soil acid phosphatase activity, S-AKP—soil alkaline phosphatase activity, and S-PPO—soil polyphenol oxidase activity.
Figure 11. Correlations between bacterial and fungal phyla, soil physicochemical properties, and enzyme activities. (A) Results of the redundancy analysis (RDA) of the top 10 bacterial phyla, soil physicochemical properties, and soil enzyme activities. (B) Results of the RDA of the top 10 fungal phyla, soil physicochemical properties, and soil enzyme activities. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting. TN—total nitrogen, HN—hydrolyzable nitrogen, S-ACP—soil acid phosphatase activity, S-AKP—soil alkaline phosphatase activity, and S-PPO—soil polyphenol oxidase activity.
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Figure 12. (A) Heatmap of bacterial communities at the genus level with soil physicochemical properties and enzyme activities. (B) Heatmap of fungal communities at the genus level with soil physicochemical properties and enzyme activities. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting. TN—total nitrogen, HN— hydrolyzable nitrogen, S-ACP—soil acid phosphatase activity, S-AKP—soil alkaline phosphatase activity, and S-PPO—soil polyphenol oxidase activity.
Figure 12. (A) Heatmap of bacterial communities at the genus level with soil physicochemical properties and enzyme activities. (B) Heatmap of fungal communities at the genus level with soil physicochemical properties and enzyme activities. CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting. TN—total nitrogen, HN— hydrolyzable nitrogen, S-ACP—soil acid phosphatase activity, S-AKP—soil alkaline phosphatase activity, and S-PPO—soil polyphenol oxidase activity.
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Figure 13. LDA scores of the differentially abundant taxa. (a) Taxa enriched in bacterial microbiota from CK, CH, or CD are indicated with LDA scores (taxa with relative abundances >0.001 and LDA scores >4 are shown). (b) Taxa enriched in fungal microbiota from CK, CH, or CD are indicated with LDA scores (taxa with relative abundances >0.001 and LDA scores >4 are shown). CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Figure 13. LDA scores of the differentially abundant taxa. (a) Taxa enriched in bacterial microbiota from CK, CH, or CD are indicated with LDA scores (taxa with relative abundances >0.001 and LDA scores >4 are shown). (b) Taxa enriched in fungal microbiota from CK, CH, or CD are indicated with LDA scores (taxa with relative abundances >0.001 and LDA scores >4 are shown). CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
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Table
Table 1. Pedological characterization and classification of three initial soil samples.
Table 1. Pedological characterization and classification of three initial soil samples.
SamplesSoil ColorSoil StructureSoil TextureSoil LayerTopographic PositionSoil Taxonomy
CKdark browngranular soilclayhumus layerplainphaeozem
CHdark browngranular soilclayhumus layerplainphaeozem
CDdark browngranular soilclayhumus layerplainphaeozem
Abbreviations: CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Table
Table 2. Mineralogy and chemical compositions of three initial soil samples.
Table 2. Mineralogy and chemical compositions of three initial soil samples.
SamplesAvailable Iron
(mg·kg−1)		Available Manganese
(mg·kg−1)		Cu
(mg·kg−1)		Zn (mg·kg−1)Available Boron (mg·kg−1)Mo (mg·kg−1)
CK233.67 ± 3.51 a21.73 ± 0.85 b15.50 ± 0.46 b55.17 ± 1.46 c0.97 ± 0.05 b0.55 ± 0.04 c
CH167.00 ± 3.61 b35.67 ± 1.40 a14.80 ± 0.20 b63.33 ± 0.67 b1.38 ± 0.02 a0.69 ± 0.02 b
CD85.70 ± 1.14 c23.60 ± 0.36 b18.53 ± 0.57 a80.30 ± 1.87 a0.23 ± 0.02 c0.83 ± 0.04 a
Abbreviations: Different letters in columns indicate significant differences (p < 0.05, n = 3). CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting.
Table
Table 3. Soil physicochemical properties of three soil samples.
Table 3. Soil physicochemical properties of three soil samples.
SamplesTN (mg·kg−1)TP (mg·kg−1)TK (mg·kg−1)AP (mg·kg−1)AK (mg·kg−1)HN (mg·kg−1)OM (mg·kg−1)pH
CK6100.00 ± 0.00 a700.00 ± 0.00 b18,600.00 ± 0.00 c6.60 ± 0.11 c210.67 ± 9.65 c398.47 ± 184.17 a145,970.00 ± 7.37 a7.47 ± 0.07 a
CH2300.00 ± 0.00 c900.00 ± 0.00 a27,600.00 ± 0.00 a39.25 ± 0.76 a545.17 ± 7.20 a368.53 ± 5.56 b61,930.00 ± 2.88 c4.52 ± 0.00 c
CD4600.00 ± 0.00 b1000.00 ± 0.00 a26,300.00 ± 0.00 b32.05 ± 0.41 b287.63 ± 31.70 b343.03 ± 13.90 c104,230.00 ± 0.26 b5.48 ± 0.11 b
Abbreviations: TN—total nitrogen, TP—total phosphorus, TK—total potassium, AP—available phosphorus, AK—available potassium, HN—hydrolyzable nitrogen, and OM—organic matter; CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting. Different letters in columns indicate significant differences (p < 0.05, n = 3).
Table
Table 4. Enzyme activities of three soil samples.
Table 4. Enzyme activities of three soil samples.
SamplesS-UE
(U·g−1)		S-CAT
(U·g−1)		S-CL
(U·g−1)		S-SC
(U·g−1)		S-ACP
(U·g−1)		S-AKP
(U·g−1)		S-PPO
(U·g−1)
CK1166.50 ± 186.58 a2.88 ± 0.00 a6.59 ± 0.01 b110.63 ± 3.50 a35.34 ± 1.01 a30.76 ± 1.23 a36.53 ± 0.67 a
CH557.22 ± 1.40 c0.69 ± 0.02 b8.32 ± 0.23 a15.47 ± 0.01 c29.31 ± 0.64 b23.07 ± 0.15 c28.34 ± 0.30 b
CD806.13 ± 9.82 b2.08 ± 0.00 a6.46 ± 0.09 b19.96 ± 0.03 b34.36 ± 0.39 a28.40 ± 0.84 b25.85 ± 0.41 c
Abbreviations: S-UE—soil urease activity, S-CAT—soil catalase activity, S-CL—soil cellulase activity, S-SC—soil sucrase activity, S-ACP—soil acid phosphatase activity, S-AKP—soil alkaline phosphatase activity, and S-PPO—soil polyphenol oxidase activity; CH—biennial ginseng seedlings that were transplanted and then continuously cropped for four years in healthy ginseng rhizosphere woodland soil, CD—biennial ginseng seedlings that were transplanted and then continuously cropped for four years with severe soilborne disease in ginseng rhizosphere woodland soil, CK—soil sample from woodland without ginseng planting. An S-SC invertase activity unit is defined as a soil invertase activity unit at 37 °C that produces 1 mg of reducing sugar per g of soil sample every day. An S-UE invertase activity unit is defined as a soil invertase activity unit that produces 1 μg of NH3−N per g of soil sample every day. An S-CAT invertase activity unit is defined as a soil invertase activity unit that catalyzes the degradation of 1 mmol of H2O2 per g of air-dried soil sample per day. An S-CL invertase activity unit is defined as a soil invertase activity unit that produces 1 mg of glucose per g of soil sample every day. An S-ACP invertase activity unit is defined as a soil invertase activity unit at 37 °C that releases 1 nmol of phenol per g of soil sample every day. An S-AKP invertase activity unit is defined as a soil invertase activity unit at 37 °C that releases 1 nmol of phenol per g of soil sample every day. An S-PPO invertase activity unit is defined as a soil invertase activity unit that produces 1 mg of purple gallic acid per g of soil sample every day. Different letters in columns indicate significant differences (p < 0.05, n = 3).
Table
Table 5. Correlation analysis between soil physicochemical properties and enzyme activities in three soil samples.
Table 5. Correlation analysis between soil physicochemical properties and enzyme activities in three soil samples.
Correlation CoefficientTNTPTKAPAKHNOMpH
S-SC0.827 **−0.936 **−0.994 **−0.985 **−0.708 *0.836 **0.883 **0.946 **
S-UE0.975 **−0.787 *−0.956 **−0.978 **−0.918 **0.6050.993 **0.980 **
S-CL−0.853 **0.2130.5300.5980.911 **0.017−0.796 *−0.662
S-ACP0.939 **−0.393−0.699 *−0.748 *−0.965 **0.1510.904 **0.807 **
S-AKP0.973 **−0.574−0.796 *−0.847 **−0.973 **0.3600.954 **0.871 **
S-CAT0.946 **−0.382−0.670 *−0.730 *−0.986 **0.1350.903 **0.787 *
S-PPO0.645−0.960 **−0.932 **−0.900 **−0.4950.908 **0.727 *0.852 **
Abbreviations: TN—total nitrogen, TP—total phosphorus, TK—total potassium, AP—available phosphorus, AK—available potassium, HN—hydrolyzable nitrogen, and OM—organic matter; S-UE—soil urease activity, S-CAT—soil catalase activity, S-CL—soil cellulase activity, S-SC—soil sucrase activity, S-ACP—soil acid phosphatase activity, S-AKP—soil alkaline phosphatase activity, and S-PPO—soil polyphenol oxidase activity; * represents a significant correlation (p < 0.05), whereas ** represents an extremely significant correlation (p < 0.01).
Table
Table 6. Quantities of bacterial and fungal communities at different levels.
Table 6. Quantities of bacterial and fungal communities at different levels.
Microbial CommunityKingdomPhylaClassesOrdersFamiliesGeneraSpecies
bacteria13594243377361338
fungi11450131267431671
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Chen, F.; Xie, Y.; Jia, Q.; Li, S.; Li, S.; Shen, N.; Jiang, M.; Wang, Y. Effects of the Continuous Cropping and Soilborne Diseases of Panax Ginseng C. A. Meyer on Rhizosphere Soil Physicochemical Properties, Enzyme Activities, and Microbial Communities. Agronomy 2023, 13, 210. https://doi.org/10.3390/agronomy13010210

AMA Style

Chen F, Xie Y, Jia Q, Li S, Li S, Shen N, Jiang M, Wang Y. Effects of the Continuous Cropping and Soilborne Diseases of Panax Ginseng C. A. Meyer on Rhizosphere Soil Physicochemical Properties, Enzyme Activities, and Microbial Communities. Agronomy. 2023; 13(1):210. https://doi.org/10.3390/agronomy13010210

Chicago/Turabian Style

Chen, Fuhui, Yongjun Xie, Qingwen Jia, Shuyan Li, Shiyong Li, Naikun Shen, Mingguo Jiang, and Yibing Wang. 2023. "Effects of the Continuous Cropping and Soilborne Diseases of Panax Ginseng C. A. Meyer on Rhizosphere Soil Physicochemical Properties, Enzyme Activities, and Microbial Communities" Agronomy 13, no. 1: 210. https://doi.org/10.3390/agronomy13010210

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