1. Introduction
In contrast to crop rotation, continuous cropping (CC) refers to the repeated continuous cropping of monoculture plant types in the same agricultural field over many years [
1]. Continuous cropping of the same crop results in reduced yield, decreased quality, and poor growth. These phenomena are defined as continuous-cropping obstacles (CCOs). Certain crops are resistant to continuous cropping, and these crops show better growth with continuous cropping; such as Achyranthes (
Achyranthes aspera Linn.), in which the yield can be significantly increased through continuous cropping, resulting in the so-called “replanting benefit” [
2]. However, CCOs are common in most crops. CCO leads to a decline in crop yield and quality, an increase in pests and diseases, deterioration of soil properties, changes in soil microbial communities, and slow crop growth, making them an urgent problem to be solved in agricultural production [
3].
Studies have identified the main factors involved in CCOs, such as imbalances in the soil microbial community, plant autotoxicity, and variations in soil properties [
4,
5,
6,
7]. For example, a decrease in soil pH due to continuous cropping can alter soil microbial diversity, reducing the abundance of microorganisms critical for the C, N, and P cycles, thus resulting in CCOs [
8]. Compared with other plants, medicinal plants are more prone to CCOs during cultivation, especially medicinal plants that have tuberous roots and tubers [
9,
10]. Approximately 70% of tuber/root medicinal plants (
Salvia,
Rehmannia glutinosa,
Panax ginseng,
Pinellia,
Panax notoginseng, etc.) exhibit CCOs during planting [
11]. For example, with increasing continuous-cropping years (CCYs), the biomass and yield of
Pseudostellaria heterophylla have shown a decreasing trend [
12]. However, the primary CCO factors vary among different medicinal plants. Liu et al. (2021) reported that a decrease in soil pH with increasing CCYs was the main factor causing CCOs in American ginseng [
13]. For
Boehmeria nivea (L.) Gaudich., an increase in the diversity of soil bacterial microorganisms and a decrease in Actinomycetes diversity with increasing CCYs are key factors driving CCOs [
14]. In
Atractylodes macrocephala Koidz., the accumulation of autotoxic substances such as 2,4-di-tert-butyl phenol is the main factor affecting CCOs [
15].
Codonopsis Radix is a plant of the genus
Codonopsis in the
Campanulaceae family, also known as
Dangshen in China, and is an important medicinal plant resource in China, Japan, and Korea. This genus includes over 60 species, with approximately 39 species present in China. In China, the roots of three particular
Codonopsis Radix plants,
Codonopsis pilosula (Franch.) Nannf.,
Codonopsis pilosula Nannf. var. modesta (Nannf.) L. T. Shen, and
Codonopsis tangshen Oliv. are traditionally used to increase immunity in the human body, increase learning and memory capacity, and lower blood pressure [
16]. Owing to increasing CCYs, there has been a notable decline in the root vitality, yield, and quality of
Codonopsis Radix plants, along with an increase in the incidence of root rot.
To date, two primary factors have been identified as contributing to CCOs in
Codonopsis Radix. One perspective suggests that continuous cropping of
Codonopsis Radix could change the soil microbial diversity and community structure in the rhizosphere microenvironment. As the number of CCYs of
Codonopsis Radix increases, the soil properties deteriorate, and the soil becomes more acidified, leading to a decline in production [
17]. These changes in soil properties also result in changes in the microbial community structure. Zhang et al. (2021) reported that with increasing CCYs of
C. tangshen, the abundance of beneficial soil microorganisms decreased, whereas that of harmful soil microorganisms increased, resulting in a shift in the rhizosphere microenvironment from the “bacterial type” to the “fungal type” [
18]. Furthermore, the fungal/bacterial ratio in the soil increased, which led to a greater abundance of fungi in the soil, destabilizing the rhizosphere microenvironment [
18,
19]. Another viewpoint is that the accumulation of autotoxic allelochemicals causes CCOs in
Codonopsis Radix. Studies have indicated that in continuous-cropping systems, autotoxic allelochemicals, which are major factors driving declines in crop yield and quality, are present in the soil [
19,
20]. Additionally, other substances such as terpenes, phenols, alkaloids, and cyanogenic glycosides have been linked to the autotoxicity of medicinal plants [
20]. Xie et al. (2017) identified terpenoid esters as major contributors to CCOs in
C. pilosula (Franch.) Nannf., with codonopilate-A identified as the main autotoxic substance [
21]. These studies indicate that changes in the soil microbial community structure and the accumulation of autotoxic substances in the
Codonopsis Radix rhizosphere might be the primary causes of CCOs. However, researchers have not yet found an efficient solution to address CCOs in
Codonopsis Radix.
The application of microbiomics and metabolomics to analyze the continuous-cropping effects of crops allows for a precise understanding of the complex biological processes occurring in soils and aids in identifying biomarkers of metabolites related to specific diseases [
22]. Therefore, we explored the changes in the soil bacterial community structure and the differences in soil metabolites in response to the continuous cropping of
Codonopsis Radix. The objectives of this study were (1) to determine the relationships between changes in soil microbial communities and metabolites and the continuous cropping of
Codonopsis Radix and (2) to identify potential biomarkers for CCOs in this medicinal plant.
2. Materials and Methods
2.1. Field Experiment and Soil Sampling
The research was carried out in Weiyuan County, Dingxi city, Gansu Province, China (one of the main production areas of
Codonopsis Radix in China, 103°51′08″ N, 35°06′18″ E), which has a temperate continental climate. In the study area, the average altitude is 2321 m, the average annual temperature is 6.8 °C, and the average annual precipitation is 500 mm, which primarily occurs in July and August. The soil type is calcaric cambisols, according to the FAO classification [
23].
Codonopsis pilosula (Franch.) Nannf. (C.
pilosula) is the main species planted in the study area and served as the test material.
C. pilosula was planted on 11 April 2023 (with a plant spacing of 10 cm and row spacing of 15 cm) and harvested on 22 October 2023. The period of vegetative growth was from August to October, and soil samples were collected in mid-September (the farmland used in this experiment had been fallow for 4 years, and no crops had been planted). In September 2023, sample plots with different numbers of years of continuous cropping (1Y, 2Y, 3Y and 4Y) were selected for sampling. To minimize errors in the experiment, similar field management practices were employed in all the plots. Field sampling was performed via a randomized design with six replicates per plot with different years of continuous cropping, and six quadrats (5 × 5 m each) were established within each plot. Soil samples were collected via the five-point method (i.e., soil samples were collected at five random points in the nearby area within the same plot). A sampling shovel was used to completely excavate the whole root of
C. pilosula, after which the rhizosphere soil was collected. Rhizosphere soil is defined as the soil that remains attached to roots after the roots are gently shaken [
24]. The rhizosphere soils were collected from the same quadrat, combined, and mixed in equal amounts to form one replicate sample.
Each sample was placed in a sterile bag and stored on dry ice. The samples were quickly transported to the laboratory. A portion of the samples was used for soil microbiological and metabonomic analyses (stored at −80 °C), while the remainder was air-dried in a dark location and passed through a 0.25 mm mesh for soil physicochemical analyses. Each treatment had six replicate samples.
2.2. Analysis of Soil Properties
The soil pH was measured via a pH meter at a water/soil ratio of 5:1 (
v/
m) (ST-3100, Ohaus Co., Ltd., Parsippany, NJ, USA). The soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), and available potassium (AK) contents were determined via standard procedures [
25,
26,
27].
2.3. Soil Microbiome Analysis
A Power-Soil DNA Isolation Kit (ID: 47016) was used to extract total genomic DNA from the soils (Mo-Bio Laboratories Inc., Carlsbad, CA, USA). The DNA concentration and purity were evaluated on a 1% agarose gel and subsequently diluted to 1 ng/µL with sterile water according to the concentration. A spectrophotometer (NanoDrop 2000C, Thermo Scientific, Inc., Waltham, MA, USA) was used to evaluate the DNA concentration. A fungal rRNA internal transcribed spacer (ITS) sequence was amplified with the primers ITS1F and ITS4R (5′-CTTGGTCATTTAGAGGAAGTAA-3′ and 5′-TCCTCCGCTTATTGATATGC-3′). The bacterial 16S rRNA gene V3-V4 region primers used were 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (50-GGACTACHVGGGTWTCTAAT-30). All PCRs were carried out with 15 µL of Phusion® High-Fidelity PCR Master Mix (New England Biolabs Inc., Ipswich, MA, USA), 2 µM of forward and reverse primers, and approximately 10 ng of template DNA. Thermal cycling consisted of initial denaturation at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 30 s. Finally, the samples were heated at 72 °C for 5 min. An equal amount of 1X TAE buffer was mixed with the PCR products, which were tested on a 2% agarose gel. The PCR products were mixed at equal density ratios. The PCR products were purified via the Qiagen Gel Extraction Kit (1.1.3) (Qiagen, Valencia, CA, USA). Sequencing libraries were generated via the TruSeq DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA), index codes were added, and quality checks were conducted on a Qubit 2.0 fluorometer (Thermo Scientific Inc., Waltham, MA, USA). The library was sequenced on an Illumina platform, and 250 bp paired-end reads were generated. All the raw sequences were deposited in the NCBI Sequence Read Archive (SRA) database (PRJNA1147451).
The raw data were imported into a format operable by the QIIME2 system via the QIIME tools importer. The sequence of each sample was quality-trimmed, and raw reads with tail-end quality scores less than 20 were removed [
28]. Sequence analysis was performed via Uparse software (Uparse v7.0.1001). The sequences were categorized into different groups on the basis of their similarity. The sequences were divided into operational taxonomic units (OTUs) at a similarity threshold of ≥97% for further analysis [
29]. A detailed description of the high-throughput sequencing of the microbial community is provided in the
Supplementary Data (Text S1).
2.4. Soil Metabonomic Analysis
The soils were freeze-dried and then ground into powder via a grinder set to 30 Hz for 30 s. A 0.5 g soil sample was combined with 1 mL of methanol (CH3OH)/isopropanol (C3H8O)/water (H2O) at a 3:3:2 (v/v/v) ratio. The samples were homogenized with a ball mill at 45 Hz for three minutes, followed by ultrasonication for 20 min in ice water. The supernatant was carefully collected, and 0.020 mL of an internal standard (suberic acid-d4, 10 μg/mL) was added. The mixture was then evaporated under a nitrogen stream and freeze-dried via a lyophilizer. The residue was stored at −80 °C for derivatization.
For the derivatization process, the sample was mixed with 0.1 mL of methoxyamine hydrochloride in pyridine (0.015 g/mL) and incubated at 37 °C for two hours. After homogenization, 0.1 mL of bis-(trimethylsilyl)-trifluoroacetamide (with 1% chlorotrimethylsilane) was added, and the mixture was incubated at 37 °C for 30 min. A 0.2 mL aliquot of the derivatization solution was accurately pipetted, diluted to 1 mL with n-hexane, and filtered through an organic phase syringe, and the filtrate was stored at −20 °C for analysis within 24 h. Metabolites were analyzed via an Agilent 8890 gas chromatograph (GC) coupled with a 5977B mass spectrometer (MS), which was equipped with a DB-5MS column (30 × 0.25 mm, 0.25 μm coating thickness) (J&W Scientific, Inc., Folsom, CA, USA). A 1 μL sample was injected into the column at a split ratio of 5:1. The injector and detector (FID) temperatures were set to 250 °C and 290 °C, respectively. The temperature program involved increasing the temperature from 40 to 300 °C at a rate of 15 °C/min and maintaining it at 300 °C for five minutes. Helium (99%) was used as the carrier gas at a flow rate of 1.2 mL/min. All the samples were analyzed in scan mode (the mass range was from 40 to 450 amu at 0.5 s). The ion source and transfer line temperatures were 230 °C and 280 °C, respectively. The mass spectrometry data were acquired in full-scan mode with the
m/
z range of 50–500. The detailed determination of metabolites is shown in the
Supplementary Data (Text S2).
2.5. Statistical Analyses
The core diversity plugin in QIIME2 was used to calculate diversity metrics. Alpha diversity indices, including observed OTUs, and the Chao1, Shannon, and Simpson indices, were used to estimate microbial diversity within individual samples. To reveal changes in the soil microbiota and soil metabolites among different CCYs, supervised models, including principal coordinate analysis (PCoA), orthogonal partial least squares discriminant analysis (OPLS-DA), and principal component analysis (PCA) were employed, and these analyses were executed via the “vegan” and “plsda” packages in R v4.4.1. Linear discriminant analysis effect size (LEfSe) analysis was executed via the “microeco” package in R v4.4.1.
Metabolites that were differentially abundant between different groups (VIP > 1, p < 0.05) were identified through a combination of univariate and multivariate analyses on the basis of the variable importance in the project (VIP) values generated by OPLS-DA. Pearson’s correlation analysis was conducted to explore the relationships between primary soil microbes and metabolites, which were visualized via the heatmap package in R v4.4.1. Piecewise structural equation modeling (piecewise SEM) was applied to evaluate the connections between continuous cropping and various factors, such as soil properties, microbial diversity, and soil metabolites. These analyses were conducted with the “piecewiseSEM”, “nlme”, and “lme4” packages in R v4.4.1. The overall model fit and goodness of fit were assessed via the chi-square test via Fisher’s C test value, where a p-value greater than 0.05 indicated that the model was reasonable. Statistical analyses were performed via SPSS 23.0 (SPSS Inc., Chicago, IL, USA), with the significance set to p < 0.05.
4. Discussion
Multiple factors contribute to CCOs, including variations in soil properties, soil microbial community structures, and the accumulation of autotoxic substances. This study revealed that the continuous cropping of
C. pilosula significantly affects soil properties, with notable changes in soil pH, total phosphorus (TP), and available potassium (AK) across different CCYs. Specifically, the soil pH decreased with increasing CCY and was lowest at 4 years. Research has demonstrated that continuous cropping results in soil acidification, nutrient imbalances, and deterioration of the soil structure. Soil acidification is a critical factor influencing various soil processes and microbial activities. Low pH can adversely affect the microbial community structure by reducing the abundance of beneficial microbes and increasing the abundance of acid-tolerant, potentially harmful microorganisms such as Acidobacteria. Furthermore, decreased pH promotes the proliferation of acidophilic microorganisms while inhibiting those that thrive under neutral to alkaline conditions, thus altering the microbial community structure. Additionally, soil acidification can lead to the solubilization of toxic metals, reduced availability of essential nutrients, and overall soil degradation [
30]; these changes disrupt nutrient cycling and reduce soil fertility, contributing to CCOs [
13].
The significant increase in TP and AK observed with increasing CCYs can be attributed to the accumulation of fertilizers and the decomposition of organic matter. Phosphorus and potassium are essential nutrients for plant growth, and an increase in the availability of these nutrients might initially support plant health. The accumulation of the nutrients can lead to nutrient imbalances and exacerbate CCOs [
13]. Generally, the SOC and TN contents decrease with increasing CCY [
31]. However, in this study, the contents of SOC and TN did not exhibit clear trends with increasing CCY, suggesting that other factors, such as microbial activity and crop uptake, might influence these parameters. In contrast, compared with continuous cropping for 1 year, continuous cropping for 2 years resulted in greater increases in SOC and TN. SOC influences soil structure and microbial activity, both of which are crucial for maintaining soil fertility under continuous-cropping systems. Additionally, high SOC levels can improve soil aggregation and porosity, promoting better root growth and providing more microbial habitat; however, nitrogen can sometimes become immobilized at high SOC levels, making it temporarily unavailable to plants [
32]. Continuous cropping also affects N-cycling processes such as mineralization, nitrification, and denitrification. Generally, continuous cropping can lead to decreases in soil TN, and nitrogen deficiencies can lead to reduced crop yields and increased crop susceptibility to pests and diseases [
32]. In this study, the increase in the soil TN content might have been beneficial for crop growth, but it may also have had some negative effects on the soil health in the long term, e.g., nutrient imbalances, variations in the soil microbial community structure and soil acidification [
33]. Our results are consistent with those of previous studies, indicating that continuous cropping significantly alters soil properties, potentially leading to CCOs [
3]. In this study, the observed trends in soil acidification and nutrient accumulation indicate the need for careful management of soil pH and nutrient levels in continuous-cropping systems to maintain soil health and productivity.
Continuous cropping of
C. pilosula significantly alters soil microbial diversity and community structure, impacting both bacterial and fungal communities. The abundance of beneficial microbial populations, such as those involved in nutrient cycling and disease suppression, decreases, whereas that of harmful microbes increases, and this shift from bacteria-dominated to fungus-dominated communities destabilizes the soil ecosystem [
7,
18]. In the present study, continuous cropping significantly affected soil microbial community structure, impacting both the bacterial and fungal communities. The alpha diversity metrics indicated a peak in bacterial diversity at two years of continuous cropping, followed by a significant decline by the fourth year. This decline in microbial diversity can reduce the resilience and functional capacity of soil ecosystems, increasing the susceptibility of crops to diseases and decreasing the efficiency of nutrient cycling in the soil [
7].
A reduction in soil microbial diversity is a key characteristic of continuously cropped systems, and diverse microbial communities are important for maintaining soil health, supporting plant growth, and preventing pathogen outbreaks [
3]. The results demonstrated distinct separations in microbial community structures between different CCYs. The relative abundances of major bacterial phyla such as Proteobacteria, Firmicutes, and Actinobacteria decreased with increasing CCY, whereas those of Acidobacteria increased. This shift suggests that continuous cropping favors the growth of acidophilic bacteria due to the decrease in soil pH. Proteobacteria and actinomycetes are beneficial bacteria for soil health and play important roles in soil N-cycling and disease suppression [
34]. At the genus level, the present study revealed a decrease in beneficial genera, such as Pseudomonas and Bacillus, which are known for their roles in promoting plant growth and suppressing pathogens. Conversely, the increased abundance of potential pathogenic genera such as Fusarium indicates a shift toward a more disease-prone microbial community. This shift is consistent with the findings of Zhang et al. (2021), who reported similar trends in
C. tangshen under a continuous-cropping system [
18]. In terms of fungal communities, the study revealed a significant decrease in Basidiomycota and an increase in Rozellomycota with increasing CCY. The decline in Basidiomycota, which includes many beneficial fungi, and the increase in Rozellomycota, which may include opportunistic pathogens, further highlight the negative impact of continuous cropping on soil health. This shift toward a fungus-dominated community can destabilize the rhizosphere environment, contributing to CCOs [
35].
Metabolomic analysis revealed significant changes in the soil metabolite profile with continuous cropping, indicating alterations in soil nutrient cycling and microbial metabolism. A total of 101 known metabolites were identified, with distinct variations were observed across different CCYs. The relative abundances of lipid compounds, organic acids, and phenols decreased, whereas those of carbohydrate compounds increased with increasing CCY. These shifts suggest changes in soil microbial activity and nutrient cycling processes [
22]. Lipid compounds and organic acids are crucial for microbial membrane integrity and metabolic functions, and a reduction in their abundance can indicate a decline in microbial health and activity. The accumulation of carbohydrate compounds, such as glucose and mannitol, might be linked to changes in microbial activity and carbon cycling. An increase in carbohydrate content could result from reduced microbial utilization of carbohydrates due to low microbial diversity and activity, as indicated by the decline in the abundance of beneficial bacteria and fungi [
24]. Specifically, a decrease in metabolites such as octadecanoic acid and n-hexadecanoic acid, which are essential for soil health and plant growth, indicates a decline in soil quality. The increase in autotoxicants, such as 2,6-di-tert-butylphenol, indicates that autotoxicity plays an important role in CCOs during
C. pilosula cultivation in the study area. The observed metabolomic changes are consistent with previous studies showing that continuous cropping can lead to the accumulation of autotoxic substances and changes in soil metabolite profiles [
36]. Notably, even for the same plant, the main factors causing continuous-cropping problems can vary greatly due to differences in climatic conditions, soil characteristics, environmental factors, etc., in the growing areas. For example, in this study, the autotoxic substance that potentially caused CCOs in
C. pilosula in the study area was 2,6-di-tert-butylphenol, whereas other studies have reported that 2,4-di-tert-butylphenol is an autotoxic substance that causes CCOs [
21].
This study revealed that continuous cropping of
C. pilosula significantly affected the soil microbial community structure by altering the soil properties and metabolite composition. A previous study revealed that soil properties significantly impact microbial diversity and metabolite composition [
37]. Soil properties (total nitrogen (TN), total organic carbon (SOC), total phosphorus (TP), etc.) are important environmental factors that drive microbial community structure [
38]. For example, soil pH, a critical soil property, directly influences microbial community composition. In this study, the soil pH decreased with increasing CCY, which was conducive to the growth of acidophilic microorganisms and led to a decrease in microbial diversity [
13]. This shift impacts soil functions such as nutrient cycling and disease suppression. The availability of nutrients, as indicated by TN, TP, and AK, also influences microbial activity and diversity. High contents of P and K can alter microbial community structures by promoting or inhibiting the growth of specific microbial groups. For example, excessive phosphorus could lead to eutrophication and alter the balance of microbial communities, potentially promoting pathogenic microbes [
27]. Soil metabolites are derived mainly from microbial metabolites and root exudates [
39], and soil properties also affect metabolite profiles. A low pH can lead to changes in microbial metabolism, and research has indicated that at low pH, the production of essential metabolites such as organic acids and lipids, which are crucial for microbial health and activity, is inhibited [
22].
Changes in the soil microbial community structure can also influence soil metabolite profiles. For example, beneficial microbes such as
Pseudomonas and
Bacillus produce metabolites that promote plant growth and suppress pathogens, and a decrease in their abundance in soil results in a reduction in the production of beneficial metabolites and increased accumulation of autotoxic substances, further exacerbating CCOs [
18]. Soil metabolites are both products of microbial activity and important factors affecting soil health and microbial diversity. In this study, some beneficial metabolites, such as organic acids and lipids, contributed to nutrient availability and microbial activity; however, the observed decrease in their content indicates a decline in microbial activity and soil health with increasing CCY [
24]. Furthermore, some autotoxic substances, such as 2,4-di-tert-butyl-phenol, that accumulate in the soil under continuous cropping inhibit microbial activity and root growth, and the accumulation of these autotoxic substances creates a feedback loop in which reduced microbial activity leads to a decrease in the production of beneficial metabolites and further accumulation of allelopathic substances, exacerbating soil health deterioration and continuous-cropping obstacles [
3,
13,
21].