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Article

Summer Rice–Winter Potato Rotation Suppresses Various Soil-Borne Plant Fungal Pathogens

by
Yuanping Zhou
1,2,3,†,
Wenjiao Luo
1,2,3,†,
Maoxing Li
1,2,3,
Qiong Wang
2,3,4,
Yongxin Liu
5,* and
Huachun Guo
1,2,3,*
1
College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China
2
Yunnan Engineering Research Center of Tuber and Root Crop Bio-Breeding and Healthy Seed Propagation, Yunnan Agricultural University, Kunming 650201, China
3
Tuber and Root Crop Institute, Yunnan Agricultural University, Kunming 650201, China
4
Faculty of Mechanical & Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China
5
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(8), 2143; https://doi.org/10.3390/agronomy13082143
Submission received: 30 June 2023 / Revised: 12 August 2023 / Accepted: 14 August 2023 / Published: 16 August 2023
(This article belongs to the Section Farming Sustainability)
Browse Figures
Versions Notes

Abstract

:
Growing potatoes (Solanum tuberosum) using the idle rice fields in Southern China and the Indo-Gangetic Plains of India in the winter season through the rice–potato rotation (RC) system could support future food security. However, the modulation capacity of the RC system on soilborne fungal pathogens is still unclear. In the current study, a pot experiment was designed and conducted to monitor the dynamics of soil fungal community composition between the potato monoculture (CC) system and the RC system, where the two systems were set with the same soil conditions: autoclaving with fertilization; autoclaving without fertilization; autoclave-free with fertilization; and autoclave-free without fertilization. Then, the uncultivated soil (CK) and root-zone soil samples of conditions under the two systems were collected, and then soil physiochemical properties and enzymatic activities were determined. Next, the high-variable region (V5–V7) of fungal 18S rRNA genes of the samples were amplified and sequenced through the PCR technique and the Illumina Miseq platform, respectively. Finally, the fungal species diversity and composition, as well as the relative abundance of fungal pathogens annotated against the Fungiuld database in soil samples, were also investigated. The results showed that the RC could significantly (p < 0.05) increase soil fungal species diversity and decrease the relative abundance of soil fungal pathogens, where the RC could suppress 23 soil fungal pathogens through cultivating the rice during the summer season and 93.75% of the remaining pathogens through winter-season cultivation. Seven-eighths of the conditions under RC have lower pathogenic MGIDI indices (6.38 to 7.82) than those of the CC (7.62 to 9.63). Notably, both rice cultivation and winter planting reduced the abundance of the pathogenic strain ASV24 under the Colletotrichum genus. The bipartite fungal network between the pathogens and the non-pathogens showed that the pathogenic members could be restricted through co-occurring with the non-pathogenic species and planting crops in the winter season. Finally, the redundancy analysis (RDA) indicated that soil pH, electronic conductivity, available phosphorus content, and various enzyme activities (cellulase, urease, sucrase, acid phosphatase, catalase, polyphenol oxidase) could be the indicators of soil fungal pathogens. This experiment demonstrated that the rice–potato rotation system outperformed the potato monoculture on suppressing soilborne fungal pathogenic community.

1. Introduction

It is estimated that a 100% increase in food production will be needed to meet the food requirement of nine billion people on the planet in 2050 due to the growing global population [1,2]. Restricting various soilborne fungal diseases through reasonably combining crops in agricultural system is one of the important measures to contribute to food security. Recently, a new rice–potato rotation system (RC) was gradually adopted by farmers and promoted by the governments of China and India, where the crops (rice and potato) were grown in the rice fields in Southern China [3] and in the Indo-Gangetic Plains of India [4] by alternatively cultivating rice in the summer season and planting potato in the winter season, respectively. Previous research has investigated the possibility of utilizing the unused rice fields left in the winter season to cultivate potato crops to ensure future food security due to potato growth favoring the cool climate during the winter season in these areas. However, although its potential benefits in food promotion have been investigated [3,4], the potential suppressiveness of the RC system on various soilborne fungal pathogens as a community has not been explored and evaluated yet.
Potato (Solanum tuberosum L.) and rice (Oryza sativa L.) are the staple food sources for the world population [5], but they could be infected by many soilborne fungal pathogens, such as the most frequently studied pathogenic members from fungal genera of the Colletotrichum [6,7], Fusarium [8], and Rhizoctonia [9,10,11], which could seriously affect crop yield and quality; previous research has estimated that plant soilborne diseases could result in about 10–20% crop yield loss on average [12]. Therefore, healthy cultivated soil is always the option for adopting a potential agricultural system on the existing field [13]. These researchers consistently reported that the cultivated soil under reasonable cropping systems maintains a higher microbial diversity and a lower pathogenic abundance, a higher complexity in microbial network, and a more suitable soil physiochemical property. For example, various researchers verified that a more diversified community [14] and a more complex microbial network always associated with a more stable and well-functioned microbial population [15,16], which confer resistance to biotic and abiotic disruptions, such as warming [17] and pathogens invasion [18] in agricultural ecosystems. However, due to the lack of a comprehensive database for fungal species identification, the investigation of the existence and development of fungal pathogens from a community scale in a farming system was complicated and previously inaccessible. With the development of high-throughput sequencing technology and the construction of various fungal databases, such as the curated fungal sequence databases NCBI, SILVA, UNITE [19], and RDP, and notably, with the accumulation of fungal sequence information, the fungal community species composition can be grouped into different functional groups based on their biotrophic mode; for example, through the FUNGuild database [20] and the FungalTraits [21], the investigation and surveillance of fungal pathogens in soil samples of farming systems have been gradually realized [22,23]. Hence, potential suppressive ability of various agricultural measures (i.e., the rice–potato rotation) on the soilborne fungal pathogenic community could be explored and evaluated by referencing these fungal databases.
The present study designed a pot experiment that contains rice–potato rotation and potato monoculture systems to comprehensively characterize the suppression capacity of the rice–potato rotation system on various soilborne fungal pathogens. Firstly, to profile the modulation effect of fertilizer and indigenous microbial community on soil fungal community in these systems, soil autoclaving (two levels) and fertilization (two levels) were adopted as factors to set four soil conditions (autoclaving with fertilization, autoclaving without fertilization, autoclave-free with fertilization, and autoclave-free without fertilization) with three replicates each, which combined with the alternative cultivation (different seasons, two levels); these experiments validated effects of four factors in total (cropping systems, soil fertilization, soil autoclaving, and cultivation seasons) on soil fungal community. Subsequently, after soil samples were collected when harvesting crops, soil physiochemical properties and enzymatic activities in samples were determined; soil fungal community diversity and composition were profiled; soil pathogenic species in the fungal community were annotated against the FUNGuild database, and the bipartite co-occurrence network between the pathogenic species and the non-pathogenic species in the fungal community was constructed. Finally, redundancy analysis was conducted between the fungal pathogenic composition and the soil environmental variables (soil physiochemical properties and enzymatic activities) to reveal the indicators of fungal pathogenic species for field fungal disease management in practice. In this study, we characterized the effects of cropping system (rice–potato rotation & potato monoculture), fertilization (fertilizer & fertilizer-free), autoclaving (autoclaving & autoclave-free), and cultivation season (summer & winter) on soil fungal community, and explored the potential determinants of soil fungal pathogenic community, which could provide some guidance for field fungal disease management for the rice–potato rotation practice in the future.

2. Materials and Methods

2.1. Experiment Description

2.1.1. Soil Pots Preparation

This experiment was carried out in a greenhouse on the trial field of Yunnan Agricultural University (YAU), Kunming, China, by simulating the previously stated rice–potato rotation system (25.13 N, 102.75 E). Three uncultivated soil mixture (collected before crop cultivation) were collected as control samples for the entire experiment (CK, three replicates). The soil layer in the greenhouse field from 0 to 20 cm depth was collected and thoroughly blended. The soil mixture was then divided into 80 pots (water-tight, 30 cm in diameter and 45 cm in height), with 25 kg of soil each. Half of the 80 soil pots were then packaged separately in clear plastic bags and autoclaved for 45 min at 126 °C using a vertical pressure steam sterilizer purchased from Boxun Industrial Co., Ltd. (Shanghai, China). Then, the autoclaved soil was put back into each of the 40 pots. Finally, the 80 soil pots were placed in the greenhouse according to the experimental design (cropping system × autoclaving × fertilization) shown in Figure 1. The amount of fertilizer applied per pot under the fertilized conditions is 500 g organic fertilizer (integrated fertility > 5%), 1.85 g pure nitrogen, 1.56 g pure P2O5, and 1.40 g pure K2O, which was following the amount of fertilizer practiced in fields by Chinese farmers for tuber and rice crops between 2011 and 2015. The conditions in this study were labeled as CCAF, CCAnF, CCunAF, and CCunAnF under the potato monoculture system and RCAF, RCAnF, RCunAF, and RCunAnF under the rice–potato rotation system, with ten pots per condition. Where CC denoted potato monoculture, RC denoted rice–potato rotation; A denoted autoclaving; unA denoted autoclave-free; F denoted fertilization, and nF denoted non-fertilization.

2.1.2. Experimental Arrangements

The experimental materials were Dianza-36, a rice cultivar adapted to the experimental region, and Qingshu-9, a staple potato cultivar in China. To begin, on 18 April 2016, healthy, nearly equal-sized seed tubers for the potato monoculture system were sown in pots (one tuber per pot). Meanwhile, the rice germination was carried out in the lab at 30 °C with a suitable moisture environment for the rice–potato rotation system; after emergence, the rice buds were transplanted to floating plates that had been prepared in advance for seedlings growth (one bud per chamber) until its 3rd leaf showed up; the seedlings were then transplanted to and cultivated in pots under the conditions of the rice–potato system on 10 June 2016. Second, the potatoes under the potato monoculture system were harvested on 7 August 2016; and on 13 October 2016, the rice under the rice–potato rotation system was also harvested. Third, on 20 November 2016, 80 pots of post-harvested soil were subjected to potato cultivation during the winter season, and the potatoes were harvested on 3 April 2017, completing a full round of the CC and RC systems, respectively. The fertilized conditions for winter cultivation received the same amount of fertilizer as those of the summer cropping season, but for the autoclaved conditions, the soil would not be subjected to autoclaving again before potato cultivation in the winter season. Notably, during the growing periods of the two crops, the grass in the pots was promptly removed, and the potting soil was watered following the needs of the rice and potato.

2.1.3. Soil Sampling and Preservation

When harvesting the crops, the root-zone soil samples were immediately collected from the tightly bound soil around the roots. In a nutshell, this experiment was carried out to determine how the root-modulated soil’s fungal community and the soilborne pathogenic species responded to the 4 experimental factors. Fifty-one soil samples of the 17 conditions were separately collected into the 50 mL centrifuge tubes (clean, DNase-free), each condition with 3 replicates (soil mixture from every three plants under the corresponding conditions). For future differentiation, the conditions for samples were further labeled as CK, CCAFs, CCAnFs, CCunAFs, CCunAnFs, RCAFs, RCAnFs, RCunAFs, RCunAnFs, CCAFw, CCAnFw, CCunAFw, CCunAnFw, RCAFw, RCAnFw, RCunAFw, and RCunAFw, where the lower case of s and w denoted the samples from the summer and the winter seasons, respectively. Meanwhile, another 2 duplicates of the 51 soil samples were collected to determine their enzymatic activities and physiochemical properties. Finally, the soil samples were placed in an ice box and transported to the lab, where the soil samples for DNA extraction were stored in a refrigerator at −80 °C, while those for enzymatic activities and physiochemical properties determination were air-dried and stored in the lab for the subsequent testing.

2.2. Determination of Soil Physiochemical Properties and Enzymatic Activities

In this experiment, the content of soil organic matter (SOM), total nitrogen (TN), total Olsen-phosphorus (TP), total potassium (TK), alkaline hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), and the soil electrical conductivity (EC) and soil pH were determined following the protocols used in [24]. The soil enzymatic activities of polyphenol oxidase (S-PPO), catalase (S-CAT), sucrase (S-SC), cellulase (S-CL), acid phosphatase (S-ACP), and urease (S-UE) were determined using the testing kit purchased from Suzhou-Keming Biotech Co., Ltd. (Suzhou, China) developed following the principles described in [25].

2.3. Fungal Community Analysis through Miseq Sequencing of Fungal 18S rRNA Genes

2.3.1. DNA Extraction and PCR Amplification

The total genomic DNA of the samples was extracted separately from 0.5 g soil using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions. The DNA extraction quality was evaluated on a 1% agarose gel; then, the DNA concentration and purity were determined using a NanoDrop-2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, NC, USA). Following that, the V5–V7 hypervariable region of the fungal 18S rRNA gene of each sample’s template DNA was amplified using the primer sets 817F (5′-TTAGCATGGAATAATRRAATAGGA-3′) and 1196R (5′-TCTGGACCTGGTGAGTTTCC-3′) on an ABI GeneAmp®9700 PCR thermocycler (ABI, Los Angeles, CA, USA). The PCR procedures are as follows: (1) the PCR mixture contains 4 μL TransStart FastPfu buffer, 2 μL 2.5 mM dNTPs, 0.8 μL forward primer (5 μM), 0.8 μL reverse primer (5 μM), 0.4 μL TransStart FastPfu DNA Polymerase, 10 ng template DNA, and the remaining ddH2O up to 20 μL; (2) the PCR amplification procedure is as follows: initial denaturation at 95 °C for 3 min, followed by 27 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 45 s, and then followed by a single extension at 72 °C for 10 min, and finally hold at 4 °C; (3) for each sample, the PCR reaction was performed in triplicate; (4) finally, the PCR product was extracted from a 2% agarose gel, purified using the Axy-Prep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and quantified using the QuantusTM Fluorometer (Promega, Madison, WI, USA).

2.3.2. Illumina Miseq Sequencing

Using an Illumina Miseq PE300 platform (Illumina, San Diego, CA, USA), the purified amplicons were pooled in an equimolar fashion and paired-end sequenced using the established techniques by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The raw amplicon gene sequencing reads were demultiplexed, quality-filtered with Fastp v-0.20.0, and merged with FLASH v-1.2.7 according to the following standards: (i) The 300 bp reads were truncated at any site with an average quality score of 20 over a 50 bp sliding window, and reads shorter than 50 bp were discarded; reads with ambiguous characters were also discarded. (ii) Only overlapping sequences longer than 10 bp were assembled in the order in which they overlapped. The overlap region’s maximum mismatch ratio is 0.2. Reads that were unable to be assembled were discarded. (iii) Samples were discriminated based on the barcode, and then the sequence direction was adjusted, with exact barcode matching as the overarching principle, and a maximum of two nucleotide mismatches allowed for primer matching.
The clean data of samples obtained from the company were deposited in the Genome Sequence Archive (GSA) database under Accession Number: CRA006404.

2.3.3. Processing of Sequencing Data

The exact sequence variants (ASVs) with 99% similarity were grouped using the unoise3 algorithm in USEARCH v-10.0, and the chimeric sequences were identified and eliminated using the reference-based chimera identification approach in VSEARCH v-2.15.0 after obtaining clean reads. Following that, the taxonomy of each ASV representative sequence was assigned using the SINTAX algorithm in USEARCH against the UNITE database (accessed on 10 May 2021) with a confidence threshold of 0.5. Finally, the read counts in each sample were normalized by resampling to the same number as in the sample with the minimum reads (24,910 reads per sample) following the pipeline we used by R v-4.0.2 for further alpha- and beta-diversity analysis [26].

2.3.4. Fungal Guilds Annotation and Analysis

The resulting fungal ASVs taxonomic table generated following the pipeline we used were annotated through the FUNGuildR package by searching the FUNGuild database (accessed on 5 September 2022) (FUNGuild: Home). Following that, the ASVs being annotated into the plant pathogenic category were regarded as fungal soilborne pathogenic species in the fungal community of each soil sample for subsequent further analysis.

Comprehensive Evaluation of Fungal Pathogenic Suppressiveness of the Conditions

The annotated pathogenic ASVs were adopted for the comprehensive evaluation of disease suppressiveness of each condition by calculating the MGIDI index [27] concerning the plant pathogens, where the known destructive pathogens infecting rice and potato were assigned a higher weight of 10 over the remaining plant pathogens that have not been reported as pathogens of rice or potato for their devastating effect. The MGIDI indices of conditions were calculated following the pipeline provided in [27].

Differential Analysis of the Plant Pathogens under Factors

To characterize the effect of the four factors (cropping system, cultivation season, autoclaving, and fertilization) on plant pathogens, the significance of the pathogenic members between the 2 levels under each factor of the four factors was separately characterized by using the STAMP v-2.1.3 software [28].

2.3.5. Bipartite Fungal Co-Occurrence Network Analysis between Plant Pathogens and the Remaining Non-Pathogenic Members

To characterize the effect of different factors on the interactive relationships between soilborne plant fungal pathogens and the remaining non-pathogenic members, the fungal ASV-level bipartite co-occurrence network between the annotated plant pathogens (ASVs) and the remaining non-pathogenic ASVs under levels of the aforementioned factors were constructed, respectively. First, the network under different levels of factors was constructed through the Metware Cloud platform (https://cloud.metware.cn/#/home) (accessed on 20 October 2022), where the interactive pairs with Pearson association coefficient ≥ 0.6 among members of the soilborne plant pathogenic fungi and the remaining non-pathogenic fungi were selected for network construction under different levels of different factors. Second, the networks were revisualized in the Cytoscape v-3.8 software and ggClusterNet package [29] by using a network file that was suitable for network construction generated from the Metware Cloud platform. Finally, the topological features of all the networks were calculated using the “Analyze Network” module under the “Tools” menu of the Cytoscape software.

2.3.6. Redundancy Analysis (RDA) of the Plant Fungal Pathogens and the Soil Physiochemical Properties and Enzymatic Activities

Redundancy analysis was used to profile the correlating relationship of soil physiochemical properties and enzymatic activities with the soilborne plant fungal pathogenic fungi, the data on soil environmental indicators (physiochemical properties and enzymatic activities) were coupled with the data on soilborne plant fungal pathogenic fungi for subsequent redundancy analysis (RDA) through the vegan package v-2.5.6, where the data of soil environmental indicators were regarded as environmental variables, and the species compositional data of soil fungal pathogens were regarded as the responsive variables.

2.4. Statistical Analysis

The amplicon R package was used to perform the differential analysis and visualization of fungal diversity (ACE and Richness indices), where the one-way ANOVA with the post-hoc test (Tukey-HSD) was used to conduct the differential analysis on fungal community diversity among the conditions. The differential analysis of plant pathogens between different levels under the four factors was separately conducted in the STAMP v-2.1.3 software, where the test method is Welch’s t-test. The redundancy analysis of the pathogenic community and the physiochemical properties and enzymatic activities were conducted through the vegan v-2.5.6 package, where the significance of each RDA axis is calculated through permutation test (n = 999) to test the significant effects of soil indicators on soilborne plant pathogenic fungal composition. In this experiment, the significance level is α = 0.05 for all analysis.

3. Results

3.1. Taxon Compositional and Differential Analysis of Fungal Community under Different Taxonomic Levels among the Conditions of CK, CC, and RC

This study was composed of 51 soil samples from 17 conditions, and the number of sequences in each sample was finally rarefied to 24,910 reads. A total of 580 fungal ASVs were detected in this experiment, which was annotated to 7 phyla, 21 classes, 32 orders, 41 families, and 36 genera against the fungal Unite database (accessed on 10 May 2021).
The LDA analysis of fungal community composition under different classification taxonomies revealed that potato–potato monoculture (CC) could significantly enrich the fungal species from the phylum of Monoblepharomycota, from the classes of Monoblepharidomycetes and Microbotryomycetes; from the orders of Glomerellales, Hypocreales, Harpochytriales, and Pleosporales; from the families of Plectosphaerellaceae, Glomerellaceae, Pyronemataceae, Rhizophydiaceae, and Harpochytriaceae; from the genera of Colletotrichum, Plectosphaerella, Rhizophydium, Harpochytrium, and the fungal strain ASV24 (Colletotrichum_destructivum_SH2219021.08FU). While uncultivated soils (CK) preferred to significantly enrich the fungal species from the phylum of Ascomycota, from the classes of Dothideomycetes, Spizellomycetes, and Orbiliomycetes; from the orders of Spizellomycetales and Orbiliales; from the families of Trichocomaceae, Spizellomycetaceae, and Orbiliaceae; from the genera of Thermomyces, Hyalorbilia, and Rhizophagus; and the fungal strains of ASV70 (Thermomyces_lanuginosus_SH1531481.08FU) and ASV351 (Hyalorbilia_erythrostigma_SH2708266.08FU). However, the rice–potato rotation could merely significantly enrich the fungi in 5 categories, which belong to the Chytridiomycetes class, Chytridiales order, Clavicipitaceae family, and Conoideocrella genus, respectively (Figure 2).

3.2. Fungal α- and β-Diversity Analysis

After the summer growing season, fungal diversity of the autoclaved soils was significantly reduced in all conditions (ACE and Richness indices) compared with the control (CK) (Figure A1A,B; p < 0.05, ANOVA, Tukey’s HSD). However, after the following winter growing season, the rice–potato rotation (RC) recovered the fungal diversity (ACE and richness indices) to the levels as those in the control (CK) (Figure 3A,B, and the diversity indices (ACE and Richness) under non-fertilized conditions in the RC were significantly higher than those of the CC. However, the potato monoculture could not recover the fungal richness index to the levels in the control and had significantly lower fungal diversity indices than the control (Figure 3A,B). In contrast with the autoclaved conditions, the rice–potato rotation and the potato monoculture did not result in significant differences in fungal diversity when compared with the control after the summer growing season under the un-autoclaved conditions (Figure A1A,B), and there were no significant differences in fungal diversity indices between conditions under the rice–potato rotation and the potato monoculture systems. However, the rice–potato rotation produced slightly lower soil fungal diversity indices after the summer season cultivation but resulted in slightly higher fungal diversity indices after the subsequent winter season cultivation (ACE and Richness indices) than the conditions under potato monoculture (Figure A1A,B and Figure 3A,B) and the control.
The principal coordinate analysis (PcoA) showed that the cropping system and the autoclaving significantly reshaped the soil fungal community (Figure A1C and Figure 3C), which was further corroborated with the constrained version of the PcoA analysis (p < 0.05, Figure A1D and Figure 3D).
The genus-level fungal community composition analysis showed that rice–potato rotation resulted in a different fungal composition in comparison with those of the potato monoculture system, in which there were three remarkable dominant genera, including the Humicola, Plectosphaerella, and the Colletotrichum, while the dominant genera with higher abundance under the rice–potato rotation system were the Humicola or Serendipita and Plectosphaerella after the summer season cultivation (Figure A1E,F). However, after the subsequent winter potato cultivation, the fungal community species composition of the rice–potato rotation system was still different from those of the potato monoculture system (Figure 3E,F), where the fungal community in the un-autoclaved soil under RC was shifted to a community similar to those of the CK (Figure 3F). Fungal composition in autoclaved soil was shifted to a different state when soil followed rice–potato rotation (Figure A1F and Figure 3F).

3.3. The Suppressiveness of the Summer Rice–Winter Potato Rotation System on Soilborne Plant Fungal Pathogens

In the present study, to characterize the dominant suppressive effect of the rice–potato rotation system on various soilborne fungal plant pathogens, the fungal community in soil samples was annotated against the FUNGuild database, which could give comprehensive information on the roles of each fungal species. After that, the members annotated as plant pathogens were selected to be pathogenic indicators of the soil samples for further evaluation of pathogenic suppressiveness among conditions according to their original relative abundance.
As shown in Figure 4A, a total of 39 of the 580 ASVs were annotated to plant pathogens, the heatmap of the ranked raw data on plant pathogens validated that rice cultivation under the rice–potato rotation system decreased the abundance of 23 pathogens in comparison with the potato monoculture system following the summer season cultivation. Meanwhile, the remaining 15 pathogens (except for the ASV2) of the 39 plant pathogens could be sharply restricted following the winter season cultivation. However, it is notable that potato cultivation seems to always result in an accumulation of the above-mentioned 23 plant pathogens, whether in the rice–potato rotation system or the potato monoculture system during the winter season, which further verifies the necessity of rice cultivation for soilborne fungal pathogens’ suppression during the summer season, which could remarkably modulate and restrict the development of soilborne fungal pathogens through rice–potato rotation.

3.3.1. Comprehensive Evaluation of Suppressiveness of the Conditions

To characterize the differential suppressiveness of each condition in this study, a comprehensive evaluation was performed based on a calculated index (MGIDI) concerning the 39 plant fungal pathogens. The results showed that MGIDI indices of conditions (RCAFw, RCAnFw, and RCAnFs) under the rice–potato rotation system were significantly lower than the other conditions. Notably, only the RCunAnFw condition under the rice–potato rotation system had a slightly higher but not significantly different MGIDI index than the condition of its counterparts, the CCunAnFw, under the potato monoculture system (Figure 4B).

3.3.2. Suppressive Effect of Factors on Soilborne Plant Fungal Pathogens

There were eight ASVs of the 39 plant pathogens with significantly higher abundance in the un-autoclaved soil samples than those of the autoclaved soil samples; meanwhile, there were two ASVs of the 39 plant pathogens enriched in the autoclaved soil samples (Figure A2A). Surprisingly, potato monoculture accumulated significantly higher abundant pathogens from the genus of Colletotrichum, Pestalotiopsis, Neophaeosphaeria, and one unassigned ASVs (Figure 4C). In comparison with the effect of the other factors, fertilization seems merely to result in a lesser suppressive effect on plant fungal pathogens, where only three ASVs with significant differences between the fertilized soil samples and the non-fertilized soil samples were observed (Figure A2C). When characterizing the suppressive effect of cultivation season on plant fungal pathogens, there were 10 ASVs with significantly higher abundance in soil samples after the winter cultivation than those soil samples after the summer cultivation (Figure A2B). Interestingly, ASV70 under the Thermomyces genus was diminished following the winter cultivation (Figure A2B), despite that it could be significantly promoted in the rice–potato rotation system during the summer season (Figure 4C). Meanwhile, both rice–potato rotation and winter cultivation could significantly reduce the abundance of the ASV24 under the Colletotrichum genus, which are destructive pathogens for potato cultivation.

3.3.3. Bipartite Network between the Annotated Plant Pathogens and the Remaining Non-Pathogenic Fungal Species

The bipartite co-occurrence network between the 39 plant pathogens and the remaining 541 fungal species was constructed separately following their conditions based on the correlation threshold of 0.6. The results showed that the un-autoclaved soil samples constituted a network with more participated nodes (N), more edges (E), higher average number of neighbors (AVNN), shorter network diameter (Nd), shorter network radius (Nr), and lower characteristic path length (CPL) than those of its counterparts the autoclaved soil samples, and at the same time, the un-autoclaved soil tends to enrich more pathogenic fungal species with significantly higher abundance in its network than the autoclaved soil; meanwhile, the plant pathogen ASV24 (Colletotrichum) appeared in soil samples under the two conditions of autoclaving, in which it has been tightly connected with the remaining non-pathogenic fungal species (Figure A3A,B). The soil samples from the rice–potato rotation system constitute a network with fewer nodes (N), fewer edges (E), and smaller network diameter (Nd) than those of its counterparts, the soil samples under the potato monoculture system; notably, the bipartite fungal network of soil samples under the potato monoculture system contained four fungal pathogenic species with significantly higher abundance than in its counterparts, the soil samples under the rice–potato rotation system, whereas the bipartite fungal network of soil samples under the rice–potato rotation system only contained two pathogenic species with significantly higher abundance in comparison with those of the soil samples under potato monoculture systems (Figure 5A,B) and where the pathogenic Colletotrichum_ASV24 was tightly connected with the other non-pathogenic species under the RC system. The soil samples following the fertilization could generate a network with much more different topological features from those of the soil samples without prior fertilization (Figure A3C,D); the former resulted in a network with significantly fewer participated nodes (N), remarkably fewer edges (E), lower average number of neighbors (AVNN), but longer network diameter (Nd), higher characteristic path length (CPL), and more connected component (Cc). There was no significant difference in the relative abundance of the Colletotrichum_ASV24, and it only contained one or two pathogens with significantly higher abundance to each other, respectively. However, it is notablethat the soil samples following the winter season cultivation resulted in a network with fewer nodes (N), fewer edges (E), and smaller characteristic path length (CPL) but with higher network density (Nds), higher network heterogeneity (Nh), and higher network centralization (Nc); and where there were more plant pathogenic fungal species with significantly higher abundance appeared in the bipartite fungal network in soil samples following the winter season cultivation, notably, the ASV24 (Colletotrichum) were observed to be significantly less abundant in the bipartite fungal network in the soil samples following the winter season cultivation (Figure 5C,D).

3.3.4. The Potential Relationships between the Environmental Indicators (Enzymatic Activity and Physiochemical Properties) and the Soilborne Fungal Pathogens

The data of the soil environmental variables were determined and presented in Table A1 and Table A2; the subsequent redundancy analysis (RDA) revealed that soil physiochemical properties and soil enzymatic activities significantly influenced soilborne plant fungal pathogenic community (Figure A4, RDA1, 14.74%, p < 0.001; RDA2, 7.97%, p < 0.001, n = 999). Specifically, ASV24 (Colletotrichum) and ASV95 (Unassigned genus) presented a negative correlation with soil pH value, soil organic matter content (SOM), total nitrogen (TN), total phosphorus (TP), and electronic conductivity (EC) but a positive correlation with soil enzymatic activities of catalase (S-CAT), polyphenol oxidase (S-PPO), and acid phosphatase (S-ACP). ASV71 (Unassigned genus) and ASV231 (Unassigned genus) presented a positive correlation with soil enzymatic activities of S-CAT and S-PPO but a negative correlation with soil pH value, TN, SOM, EC, and TP. ASV227 (Pestalotiopsis), ASV661 (Pestalotiopsis), and ASV658 (Plectosphaerella) presented a positive correlation with soil available nitrogen (AN), TN, EC, TP, available potassium (AK), and soil cellulase activity (S-CL). ASV2 (Plectosphaerella) showed a positive correlation with soil pH value but a negative correlation with soil available phosphorus (AP), total potassium (TK), available potassium (AK), and the enzymatic activities of sucrase (S-SC), urease (S-UR), S-ACP, and cellulase (S-CL). ASV264 (Pestalotiopsis) and ASV19 (Plectosphaerella) had a positive correlation with soil TP, EC, SOM, TN, AN, TK, AP, and soil enzymatic activities of S-ACP, S-SC, S-UE, and S-CL.

4. Discussion

The present study was originally conducted to explore whether and how the rice–potato rotation system could generate a significantly different fungal community from those of the potato monoculture system and to validate whether the fertilization, autoclaving, and cultivation season (which are closely related factors affecting microbial community composition in agricultural practices) could modulate the overall fungal community, especially whether the rice–potato rotation system and these related factors could effectively restrict various soilborne fungal pathogens to provide research evidence for its adoption or further optimization. At the same time, the present study managed to explore the restriction mechanisms that exist under different factors.

4.1. Rice–Potato Rotation (RC) Could Significantly Change Soil Fungal Community Composition Compared with the Uncultivated (CK) and the Potato Monoculture (CC) Soil Samples

Soil microbial community dynamics are one of the most important causes of crop monoculture obstacles; the variation of the microbes in the community might adjust the abundance of the pathogenic species. Potato monoculture could promote taxa abundance under the fungal Ascomycota phylum [30], while our results revealed that these taxa in the uncultivated soil (CK) were more abundant than those of the CC; a recent study further validated that fungal species under the Ascomycota phylum could be plant pathogens [31]; at the same time, the CC could also significantly enrich the pathogens [32] under the Colletotrichum (Colletotrichum_destructivum_SH2219021.08FU). In addition, both the CK and CC could enrich much more fungal taxa than the RC, which might demonstrate that RC has reshaped the fungal community to a more balanced community than the CK and the CC to contribute to its soilborne pathogens’ restriction (Figure 2). The previous research reported that fungal species under the Plectosphaerellaceae family or Plectosphaerellla genus and Hypocreales order are plant pathogens [33,34,35]; these taxa were significantly enriched in the CC soils (Figure 2), which further verified that CC might have accumulated more abundant soilborne fungal pathogens.

4.2. The Rice–Potato Rotation System Could Promote the Fungal Species Diversity Compared with the Potato Monoculture System

In this study, the rice–potato rotation system can significantly increase soil fungal diversity, which demonstrates that the rice–potato rotation system constitutes capacities in regulating soil fungal community from the ecosystem scale. Since previous studies have validated that healthy soil was associated with an increasing bacterial diversity [36,37], the higher fungal diversity might demonstrate a higher suppressiveness in RC. Furthermore, the results also indicated that the RC system had a very different fungal composition in comparison with those of the CC system. However, it is notable that following the summer season cultivation, the significant diversity differences between the RC system and the CC system only appeared in their species composition (Figure A1E,F), while after the potato cultivation in the winter season, the fungal community under the RC system presented a relatively higher diversity than that of the CC system (Figure 3A,B). Meanwhile, the fungal composition in soil samples of the RC system was still different from that of the CC system and those of the CK shown in Figure 3F. The above results indicated that the rice–potato system could effectively modulate soil fungal community and promote the soilborne fungal diversity from a community scale in comparison with those of the CC system, which further validated that RC reshaped a more balanced fungal community [38,39,40] when combined with its much lesser enriched fungal taxa, thus, to effectively suppress the soilborne pathogens. However, when it comes to bacterial diversity, a diversified bacterial community does not denote soil health; for example, several studies concluded that higher bacterial diversity was associated with soil sickness [16,41].

4.3. The Rice–Potato Rotation System Could Suppress the Soil Fungal Pathogens in Comparision with the Potato Monoculture System

The fungal species differential analysis on conditions concerning CK, CC, and RC revealed that strains under the Colletotrichum genus were significantly enriched in the CC system (Figure 2) due to Colletotrichum could be the causal pathogens of the potato black dot [6], which revealed that RC could effectively restrict these taxa in soils. The raw data presented in Figure 4A indicated that rice–potato rotation constitutes a set of perfect suppressing weapons either by the rice cultivation or the climatic limitation during the winter season to restrict nearly all of the known pathogens in soil samples to some extent, which is inconsistent with the previous conclusion that rotation could restrict soilborne disease [14]. Despite the suppressive capacity might be not as strong as some of the marketed specific fungicides, the rice–potato rotation could constitutively restrict soilborne fungal pathogens in an eco-friendly and overall-level manner, which is inspiring for potato promotion in the large number of idle rice fields left during the winter season in the South of China and in the Indo-Gangetic Plains of India. Subsequently, to characterize the suppression capacity of the 17 conditions, the MGIDI index on soil fungal pathogens of the 17 conditions was calculated (Figure 4B), which screened out 3 conditions under the RC system with significant suppressing capacity over the remaining 14 conditions. In the remaining 14 conditions, only 1 condition under the CC systems had a slightly higher fungal pathogenic MGIDI index than its corresponding condition under the RC system. Although most of the fungal pathogens detected in this study are not reported to be major pathogens of potatoes or rice, they appeared in these systems and can be modulated by cultivating these crops, which further validated that the rice–potato system is an effective system for the suppression of soilborne fungal pathogens.
The previous study validated that the rice–potato rotation could significantly restrict potato common scab caused by pathogenic bacteria [42]; the present study characterized the fungal pathogenic suppression capacity of the four factors involved (Figure 4C and Figure A2), and we noticed that there were more fungal pathogens enriched significantly in the un-autoclaved soil samples when compared with those of the autoclaved soil samples, which revealed that the fungal pathogens could be accumulated in soil environment if there was no effective farming practices. Notably, the rice–potato rotation could remarkably restrict ASV24 (Colletrotrichum) but significantly enrich ASV70 (Thermomyces), but fortunately, the ASV70 could be remarkably restricted by the subsequent winter cultivation, which validated the strong capacity of cultivation season on fungal community modulation [22]. Meanwhile, despite the fact that the winter cultivation decreasing the abundance of the ASV24 (Colletotrichum) and the ASV70 (Thermomyces), the soil samples following the winter potato cultivation enriched more abundant fungal pathogens than those of the soil samples following the summer cultivation, which seems to be contradicted with the traditional concept that climate in winter season could restrict soil pathogens. However, as illustrated in Figure 4A, the rice cultivation resulted in an overall-level suppression of soilborne fungal pathogens, and thus, many fungal pathogens demonstrated a significantly lower abundance in the soil samples under the RC system after the summer cultivation; meanwhile, these results also confirmed that cultivating potato could accumulate fungal pathogens in soil samples, especially those soilborne fungal pathogens affecting potato; thus, the alternative cultivation through planting rice in the rice–potato rotation system are critical measures in restricting the soilborne potato fungal pathogens.
The bipartite fungal co-occurrence network revealed that autoclaving (Figure A3A,B) could suppress the abundance of the fungal pathogens and decrease the number of the pathogens which have significantly higher abundance, while the un-autoclaved soil samples accumulated more fungal pathogens which have a significantly higher abundance; notably, the ASV24 under the Colletotrichum genus was co-occurred in both of the two networks of the autoclaved and the un-autoclaved soil samples; thus, their abundance in the two communities are of no significant difference. When characterizing the bipartite fungal networks of the CC and the RC systems, we noticed that the CC system generated more fungal pathogens with significantly higher abundance, and in which the ASV24 under the Colletotrichum genus significantly enriched; meanwhile, the pathogens with significantly higher abundance in the two systems were totally different, and the ASV24 were tightly connected with the other non-pathogens in network of the RC system (Figure 4C and Figure A2B). When characterizing the bipartite fungal network in soil samples concerning fertilization (Figure A3C,D), we noticed that fertilization resulted in a remarkable decrease in nodes’ number and edges’ number in its network, which revealed the complementary ecological functions of the fungal community [43] to abiotic stress (fertilization limitation). Finally, when characterizing the bipartite network in soil samples concerning different cultivation seasons, we observed more fungal pathogens with significantly higher abundance appeared in the fungal network following the winter cultivation, but the winter season restricted abundance of the ASV24 in comparison with its significantly higher abundance in the fungal network following the summer cultivation. However, it seems that winter cultivation could significantly impact the bipartite network in the whole community, which evidenced the significant effects of cultivation season on fungal community [22]. Thus, this study indicated that the bipartite fungal co-occurrence network between the pathogenic fungi and the non-pathogenic fungi was a good indicator to profile the ecological fitness of the fungal community.
The redundancy analysis revealed that some of the soil environmental variables might have the capacity to decrease the abundance of fungal pathogens (Figure A4). Specifically, soil pH value, TP, TN, and SOM might limit the abundance of the pathogenic ASV24 (Colletotrichum), ASV71, and ASV231; the pathogenic ASV2 might be restricted by adjusting the content of AP, AK, and TK by promoting the enzymatic activities of the S-SC, S-CL, S-UE, and S-ACP. However, it is notable that the ASV19, ASV264, ASV661, ASV637, ASV227, and ASV658 are plant fungal pathogens without obvious reliable associated soil environmental variables; whilethe suppression of these ASVs could recur to the restriction mediated by the other microbial antagonist [44].
Potato monoculture is regularly reported to induce severe soilborne infections, and crop rotation is a well-known soilborne disease management strategy. The study was designed to validate whether potatoes cultivated in idle rice fields during the winter season (summer rice-winter potato rotation, RC) could systematically suppress soilborne fungal pathogens in cultivated land from a community scale. Thus, it is noteworthy that the impacts of rice monoculture on soil fungal pathogens were not explored in this study. However, previous research has shown that rice monoculture can accumulate Pyricularia oryzae, a fungal pathogen in the Sordariomycetes class [45], indicating that rice monoculture does not perform well when compared to its counterpart condition (rice-pasture rotation), even though the rice-pasture rotation and rice monoculture generate a comparable fungal community when comparing fungal diversity and composition. Another study found that when compared to ancient rice terraces cultivated fields, dry land could transform the trophic modes of soil fungal communities into biotrophic fungi; additionally, dry land could generate fungal networks with significantly more nodes and edges than rice fields; and most importantly, soil dissolved organic carbon, soil total nitrogen, and soil total phosphorus are important factors in shaping soil fungal communities [46]. The present work demonstrated a similar network with higher nodes and edges in the CC than those of the RC for the bipartite network, which is consistent with the conclusion in the previous research [46] because the CC does not experience the drying-rewetting process in the RC. Similar to the result of the previous research [45], the present research could not detect the major fungal pathogens of potatoes or rice, which is because the present work was performed under healthy soil conditions, where the major fungal pathogens are absent before crop cultivation, at the same time, the current study also indicated that the major fungal pathogens regularly reported didn’t accumulate in the soil environment under the natural cultivation conditions of rice and potatoes. However, in the future experiment, research works could focus on validating the suppression effect of the RC on the major fungal pathogens (e.g., Rhizoctonia, Fusarium, Alternaria, Magnaporthe, and Verticillium) of potatoes and rice.
In summary, this experiment primarily validated the suppression effect of the rice–potato rotation system on various soilborne fungal pathogenic species in comparison with the potato monoculture system by resorting to rice cultivation in the summer season and potato cultivation in the winter season, which could prevent the accumulation of fungal pathogens in rice–potato system as in the potato monoculture system.

Author Contributions

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

Funding

The work was supported by the Science and Technology Projects in Yunnan Province (202102AE090018) and the National Natural Science Foundation of China (32260543).

Data Availability Statement

The clean data have been deposited into the Genome Sequence Archive (GSA) database (Genome Sequence Archive—CNCB–NGDC), Accession Number: CRA006528.

Acknowledgments

We thank Yongxin Liu from the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, for providing advice on data analysis of the present work.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

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Figure A1. The soil fungal diversity following the summer cultivation. (A). Richness indices of fungal community in different conditions following summer cultivation. ANOVA, Tukey’s HSD post hoc test, α = 0.05. (B). ACE indices of fungal community in different conditions following summer cultivation. ANOVA, Tukey’s HSD post hoc test, α = 0.05. (C). Principal coordinate analysis (PCoA) of fungal community composition on Bray–Curtis distance. (D). Constrained PCoA of fungal community composition. Permutation test, n = 499, α = 0.01. (E,F). Genus-level fungal community composition displayed based on single samples or groups, respectively. Note: In (C–F), due to the relatively less effect of fertilization on the fungal community composition than the agricultural system and autoclaving, their samples (CCAs contain the samples of the conditions of CCAFs and CCAnFs; CCunAs contain the samples of the conditions of CCunAFs and CCunAnFs; RCAs contains the samples of the conditions of RCAFs and RCAnFs; RCunAs contains the samples of the conditions of RCunAFs and RCunAnFs) are grouped (C,D) or averaged (E,F) to illuminate community composition pattern. Different letters within a cultivar indicate significant differences between groups (p < 0.05).
Figure A1. The soil fungal diversity following the summer cultivation. (A). Richness indices of fungal community in different conditions following summer cultivation. ANOVA, Tukey’s HSD post hoc test, α = 0.05. (B). ACE indices of fungal community in different conditions following summer cultivation. ANOVA, Tukey’s HSD post hoc test, α = 0.05. (C). Principal coordinate analysis (PCoA) of fungal community composition on Bray–Curtis distance. (D). Constrained PCoA of fungal community composition. Permutation test, n = 499, α = 0.01. (E,F). Genus-level fungal community composition displayed based on single samples or groups, respectively. Note: In (C–F), due to the relatively less effect of fertilization on the fungal community composition than the agricultural system and autoclaving, their samples (CCAs contain the samples of the conditions of CCAFs and CCAnFs; CCunAs contain the samples of the conditions of CCunAFs and CCunAnFs; RCAs contains the samples of the conditions of RCAFs and RCAnFs; RCunAs contains the samples of the conditions of RCunAFs and RCunAnFs) are grouped (C,D) or averaged (E,F) to illuminate community composition pattern. Different letters within a cultivar indicate significant differences between groups (p < 0.05).
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Figure A2. Differential analysis of pathogens among samples of the conditions concerning factors on autoclaving (A), fertilization (B) and cultivation seasons (C). Welch’s t−test; α = 0.05.
Figure A2. Differential analysis of pathogens among samples of the conditions concerning factors on autoclaving (A), fertilization (B) and cultivation seasons (C). Welch’s t−test; α = 0.05.
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Figure A3. The pathogenic and non-pathogenic fungal bipartite co-occurrence networks concerning cropping systems and cultivation seasons. (A). Network on the autoclaved and cultivated soil samples. (B). Network on the un-autoclaved and cultivated soil samples. (C). Network on the fertilized and cultivated soil samples. (D). Network on the non-fertilized and cultivated soil samples. Note: the nodes (potential species) with yellow points and red text mean a species with significantly higher abundance in its condition than in its counterpart conditions. The nodes with yellow points and yellow text denote the Colletotrichum_ASV24 participated in network construction. The red and yellow points represent the pathogens; the green points represent the rest non-pathogenic nodes (potential species), and the green lines represent potential interaction relationships. The characters listed above each figure mean topological features of networks (N denotes the participated nodes numbers; AVNN denotes the average number of neighbors; Nd denotes the network diameter; Nr denotes the network radius; Cpl denotes the characteristic path length; Nds denotes the network density; Nh denotes the network heterogeneity; Nc denotes the network centralization, and Cc denote the numbers of the connected component in networks).
Figure A3. The pathogenic and non-pathogenic fungal bipartite co-occurrence networks concerning cropping systems and cultivation seasons. (A). Network on the autoclaved and cultivated soil samples. (B). Network on the un-autoclaved and cultivated soil samples. (C). Network on the fertilized and cultivated soil samples. (D). Network on the non-fertilized and cultivated soil samples. Note: the nodes (potential species) with yellow points and red text mean a species with significantly higher abundance in its condition than in its counterpart conditions. The nodes with yellow points and yellow text denote the Colletotrichum_ASV24 participated in network construction. The red and yellow points represent the pathogens; the green points represent the rest non-pathogenic nodes (potential species), and the green lines represent potential interaction relationships. The characters listed above each figure mean topological features of networks (N denotes the participated nodes numbers; AVNN denotes the average number of neighbors; Nd denotes the network diameter; Nr denotes the network radius; Cpl denotes the characteristic path length; Nds denotes the network density; Nh denotes the network heterogeneity; Nc denotes the network centralization, and Cc denote the numbers of the connected component in networks).
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Figure A4. The redundancy analysis of fungal community composition and soil environmental variables. Note: The legends at the top left of Figure represent samples from different conditions, where the fertilized and the fertilizer-free conditions are grouped (CK denotes the uncultivated soil samples; CCAs denotes the samples from the conditions of the CCAFs and CCAnFs; CCAw denotes the samples from the conditions of the CCAFw and CCAnFw; CCunAs denotes the samples from the conditions of the CCunAFs and CCunAnFs; CCunAw denotes the samples from the conditions of the CCunAFw and CCunAnFw; RCAs denotes the samples from the conditions of the RCAFs and RCAnFs; RCAw denotes the samples from the conditions of the RCAFw and RCAnFw; RCunAs denotes the samples from the conditions of the RCunAFs and RCunAnFs; RCunAw denotes the samples from the conditions of the RCunAFw and RCunAnFw, respectively) to profile fungal pathogen composition pattern of samples. The red arrows indicate abundance of pathogens (Genus taxonomy + ASV labels), and the blue arrows indicate soil environment variables (pH denote soil potential of hydrogen; EC denotes soil electronic conductivity; SOM denotes soil organic matter content; TN denotes soil total nitrogen content; AN denote soil hydrolysable nitrogen content; TP denotes soil total Olsen-phosphorus content, AP denotes soil available phosphorus content; TK denotes soil total potassium content; AK denotes soil available potassium content).
Figure A4. The redundancy analysis of fungal community composition and soil environmental variables. Note: The legends at the top left of Figure represent samples from different conditions, where the fertilized and the fertilizer-free conditions are grouped (CK denotes the uncultivated soil samples; CCAs denotes the samples from the conditions of the CCAFs and CCAnFs; CCAw denotes the samples from the conditions of the CCAFw and CCAnFw; CCunAs denotes the samples from the conditions of the CCunAFs and CCunAnFs; CCunAw denotes the samples from the conditions of the CCunAFw and CCunAnFw; RCAs denotes the samples from the conditions of the RCAFs and RCAnFs; RCAw denotes the samples from the conditions of the RCAFw and RCAnFw; RCunAs denotes the samples from the conditions of the RCunAFs and RCunAnFs; RCunAw denotes the samples from the conditions of the RCunAFw and RCunAnFw, respectively) to profile fungal pathogen composition pattern of samples. The red arrows indicate abundance of pathogens (Genus taxonomy + ASV labels), and the blue arrows indicate soil environment variables (pH denote soil potential of hydrogen; EC denotes soil electronic conductivity; SOM denotes soil organic matter content; TN denotes soil total nitrogen content; AN denote soil hydrolysable nitrogen content; TP denotes soil total Olsen-phosphorus content, AP denotes soil available phosphorus content; TK denotes soil total potassium content; AK denotes soil available potassium content).
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Appendix B

Table
Table A1. Soil physiochemical properties in each condition.
Table A1. Soil physiochemical properties in each condition.
ConditionsSOMpHECTNANTPAPTKAK
CK4.61 ± 0.326.64 ± 0.17408.00 ± 10.000.20 ± 0.0142.12 ± 1.330.70 ± 0.0282.79 ± 7.251.87 ± 0.07489.89 ± 33.89
CCAFs7.19 ± 0.166.61 ± 0.18497.00 ± 15.000.26 ± 0.0173.97 ± 0.730.84 ± 0.00137.83 ± 10.772.44 ± 0.12416.34 ± 16.82
CCAFw8.45 ± 0.436.82 ± 0.08640.00 ± 14.000.27 ± 0.0174.08 ± 1.230.83 ± 0.03138.40 ± 6.422.36 ± 0.12494.26 ± 66.77
CCAnFs5.99 ± 0.546.81 ± 0.04236.00 ± 8.000.23 ± 0.0273.03 ± 1.410.77 ± 0.0697.34 ± 2.551.94 ± 0.09365.01 ± 51.07
CCAnFw8.13 ± 0.306.85 ± 0.08609.00 ± 22.000.27 ± 0.0073.73 ± 2.050.81 ± 0.02127.11 ± 11.442.39 ± 0.12439.17 ± 30.12
CCunAFs7.19 ± 0.376.81 ± 0.05516.00 ± 12.000.26 ± 0.0276.07 ± 1.760.77 ± 0.04121.71 ± 3.932.62 ± 0.19524.09 ± 31.93
CCunAFw8.01 ± 0.576.80 ± 0.11575.00 ± 7.000.27 ± 0.0280.27 ± 3.180.81 ± 0.03139.83 ± 8.952.37 ± 0.21545.20 ± 48.03
CCunAnFs4.57 ± 0.426.85 ± 0.18209.00 ± 10.000.19 ± 0.0065.22 ± 2.020.67 ± 0.0181.45 ± 3.131.77 ± 0.06350.87 ± 36.55
CCunAnFw5.02 ± 0.326.95 ± 0.03288.00 ± 14.000.21 ± 0.0163.70 ± 1.850.73 ± 0.0382.40 ± 2.301.60 ± 0.08394.05 ± 26.45
RCAFs7.50 ± 0.616.81 ± 0.04545.00 ± 10.000.26 ± 0.0377.82 ± 1.410.82 ± 0.0689.46 ± 5.492.40 ± 0.08468.67 ± 62.38
RCAFw8.02 ± 0.426.92 ± 0.12743.00 ± 25.000.28 ± 0.0273.73 ± 3.590.84 ± 0.0495.61 ± 8.282.42 ± 0.25529.84 ± 68.73
RCAnFs5.64 ± 0.257.03 ± 0.03269.00 ± 17.580.21 ± 0.0071.05 ± 2.450.73 ± 0.0350.36 ± 0.591.51 ± 0.06183.91 ± 12.49
RCAnFw7.17 ± 0.656.97 ± 0.04828.00 ± 17.000.25 ± 0.0370.23 ± 2.460.80 ± 0.0762.46 ± 5.961.84 ± 0.19373.33 ± 51.26
RCunAFs7.46 ± 0.696.85 ± 0.06598.67 ± 11.370.26 ± 0.0274.20 ± 4.260.84 ± 0.0294.03 ± 1.032.83 ± 0.23620.99 ± 21.98
RCunAFw9.12 ± 0.306.84 ± 0.081122.33 ± 55.900.30 ± 0.00101.73 ± 1.330.92 ± 0.02156.07 ± 4.303.33 ± 0.07685.39 ± 88.05
RCunAnFs5.08 ± 0.337.01 ± 0.11256.00 ± 9.000.21 ± 0.0069.65 ± 4.040.78 ± 0.0266.43 ± 1.781.75 ± 0.09305.58 ± 26.43
RCunAnFw5.78 ± 0.527.02 ± 0.03304.00 ± 18.000.22 ± 0.0172.57 ± 4.230.78 ± 0.0573.97 ± 5.901.77 ± 0.06328.13 ± 38.52
Note: SOM, soil organic matter content (%). EC, soil electronic conductivity (us/cm). TN, soil total nitrogen content (g/kg). AN, soil available nitrogen content (mg/kg). TP, soil total phosphorus content (g/kg). AP, soil available phosphorus content (mg/kg). TK, soil total potassium content (g/kg). AK, soil available potassium content (mg/kg).
Table
Table A2. Soil enzymatic activities.
Table A2. Soil enzymatic activities.
ConditionsS-PP0S-CATS-SCS-CLS-UES-ACP
CK25.98 ± 0.6413.89 ± 0.3436.09 ± 5.2833.95 ± 0.811217.51 ± 44.1612,300.43 ± 669.61
CCAFs32.84 ± 3.4636.31 ± 0.1037.81 ± 7.3539.20 ± 7.45712.34 ± 58.5616,116.82 ± 935.68
CCAFw16.16 ± 2.1522.07 ± 0.6833.53 ± 9.4940.54 ± 0.80941.03 ± 29.4711,163.51 ± 1542.02
CCAnFs40.56 ± 0.7632.65 ± 3.5633.02 ± 2.0940.48 ± 3.11575.41 ± 10.5012,414.70 ± 1187.62
CCAnFw19.96 ± 1.9023.06 ± 6.4432.87 ± 0.7341.42 ± 1.00955.02 ± 12.339746.65 ± 2062.82
CCunAFs35.46 ± 2.4737.67 ± 2.0245.24 ± 0.2536.59 ± 3.07988.82 ± 80.1220,144.60 ± 565.69
CCunAFw21.57 ± 1.9627.51 ± 1.8640.52 ± 1.5641.27 ± 1.311007.46 ± 38.2016,676.72 ± 541.19
CCunAnFs23.71 ± 1.5931.94 ± 2.5721.50 ± 4.2723.84 ± 6.741002.51 ± 71.0111,517.73 ± 2792.63
CCunAnFw19.91 ± 1.0433.55 ± 2.3835.27 ± 2.6437.27 ± 0.451204.11 ± 60.1216,928.09 ± 389.46
RCAFs25.24 ± 5.3730.52 ± 2.4112.45 ± 3.7830.69 ± 4.83259.32 ± 84.996850.08 ± 1191.45
RCAFw17.60 ± 1.8725.34 ± 2.5222.65 ± 6.5842.35 ± 0.78510.74 ± 111.2811,592.00 ± 4642.37
RCAnFs20.70 ± 1.9826.98 ± 1.037.80 ± 1.5020.44 ± 1.89233.97 ± 34.065564.62 ± 1372.05
RCAnFw19.30 ± 1.7319.20 ± 4.8025.68 ± 10.0540.46 ± 1.36483.06 ± 64.4610,083.73 ± 252.68
RCunAFs46.15 ± 9.1133.20 ± 2.0143.32 ± 0.8836.31 ± 0.96943.08 ± 13.9916,008.27 ± 1671.69
RCunAFw28.34 ± 0.8723.80 ± 1.8632.22 ± 2.7841.67 ± 0.24973.96 ± 28.0810,917.85 ± 312.77
RCunAnFs28.16 ± 2.9526.73 ± 4.6219.14 ± 2.8923.87 ± 6.27904.03 ± 16.6113,865.84 ± 238.11
RCunAnFw20.00 ± 2.2323.76 ± 3.8926.39 ± 4.1340.05 ± 1.17994.93 ± 337.5012,906.03 ± 2850.36
Note: S-PPO, soil polyphenol oxidase activity (mg/d·g). S-CAT, soil catalase activity (μmol/d·g). S-SC, soil sucrase activity (mg/d·g). S-CL, soil cellulase activity (mg/d·g). S-UE, soil urease activity (μg/d·g). S-ACP, soil acid phosphatase activity (nmol/d·g).

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Figure 1. The diagram of this experiment.
Figure 1. The diagram of this experiment.
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Figure 2. Biomarkers selected through the lefse analysis of fungal community composition. Note: CK (soil samples before crops cultivation, 3 replicates); potato–potato monoculture (soil samples under the monoculture conditions; 24 samples of the 8 conditions of CCAFs, CCAFw, CCAnFs, CCAnFw, CCunAFs, CCunAFw, CCunAnFs, and CCunAnFw under the CC system); rice–potato rotation (soil samples under the rotation system, 24 samples of the 8 conditions of RCAFs, RCAFw, RCAnFs, RCAnFw, RCunAFs, RCunAFw, RCunAnFs, and RCunAnFw under the RC system). Significant level is α = 0.05.
Figure 2. Biomarkers selected through the lefse analysis of fungal community composition. Note: CK (soil samples before crops cultivation, 3 replicates); potato–potato monoculture (soil samples under the monoculture conditions; 24 samples of the 8 conditions of CCAFs, CCAFw, CCAnFs, CCAnFw, CCunAFs, CCunAFw, CCunAnFs, and CCunAnFw under the CC system); rice–potato rotation (soil samples under the rotation system, 24 samples of the 8 conditions of RCAFs, RCAFw, RCAnFs, RCAnFw, RCunAFs, RCunAFw, RCunAnFs, and RCunAnFw under the RC system). Significant level is α = 0.05.
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Figure 3. The soil fungal diversity following the winter cultivation. (A). Richness indices of fungal community in different conditions following winter cultivation. ANOVA, Tukey-HSD post-hoc test, α = 0.05. (B). ACE indices of fungal community in different conditions following winter cultivation. ANOVA, Tukey-HSD post-hoc test, α = 0.05. (C). Principal coordinate analysis (PCoA) of fungal community composition on Bray–Curtis distance. (D). Constrained-PcoA of fungal community composition. Permutation test, n = 499, α = 0.01. (E,F). Genus-level fungal community composition displayed based on single samples or groups, respectively. Note: In (C–F), due to the relatively lower effect of fertilization on the fungal community composition than the agricultural system and autoclaving, their samples (CCAw contains the samples of the conditions of CCAFw and CCAnFw, CcunAw contains the samples of the conditions of CcunAFw and CcunAnFw, RCAw contains the samples of the conditions of RCAFw and RCAnFw; RcunAw contains the samples of the conditions of RcunAFw and RcunAnFw) are grouped (C,D) or averaged (E,F) to illuminate community composition pattern. Different letters within a cultivar indicate significant differences between groups (p < 0.05).
Figure 3. The soil fungal diversity following the winter cultivation. (A). Richness indices of fungal community in different conditions following winter cultivation. ANOVA, Tukey-HSD post-hoc test, α = 0.05. (B). ACE indices of fungal community in different conditions following winter cultivation. ANOVA, Tukey-HSD post-hoc test, α = 0.05. (C). Principal coordinate analysis (PCoA) of fungal community composition on Bray–Curtis distance. (D). Constrained-PcoA of fungal community composition. Permutation test, n = 499, α = 0.01. (E,F). Genus-level fungal community composition displayed based on single samples or groups, respectively. Note: In (C–F), due to the relatively lower effect of fertilization on the fungal community composition than the agricultural system and autoclaving, their samples (CCAw contains the samples of the conditions of CCAFw and CCAnFw, CcunAw contains the samples of the conditions of CcunAFw and CcunAnFw, RCAw contains the samples of the conditions of RCAFw and RCAnFw; RcunAw contains the samples of the conditions of RcunAFw and RcunAnFw) are grouped (C,D) or averaged (E,F) to illuminate community composition pattern. Different letters within a cultivar indicate significant differences between groups (p < 0.05).
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Figure 4. The effect of the rice–potato rotation on the soilborne fungal pathogenic community. (A). The raw abundance data of 39 ASVs (features) in the 17 conditions. (B). Evaluation of fungal pathogenic restricting ability for the 17 conditions by calculating one index on pathogens abundance. Differential analysis among conditions was conducted using ANOVA and LSD test; α = 0.05. (C). Differential analysis of pathogens between samples of the rice–potato rotation system (RC) and samples of the potato monoculture system (CC). Welch’s t-test; α = 0.05. Note: The abundance of each cell in (A) is the mean of every 3 replicates under the 17 conditions, respectively.
Figure 4. The effect of the rice–potato rotation on the soilborne fungal pathogenic community. (A). The raw abundance data of 39 ASVs (features) in the 17 conditions. (B). Evaluation of fungal pathogenic restricting ability for the 17 conditions by calculating one index on pathogens abundance. Differential analysis among conditions was conducted using ANOVA and LSD test; α = 0.05. (C). Differential analysis of pathogens between samples of the rice–potato rotation system (RC) and samples of the potato monoculture system (CC). Welch’s t-test; α = 0.05. Note: The abundance of each cell in (A) is the mean of every 3 replicates under the 17 conditions, respectively.
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Figure 5. The pathogenic and non-pathogenic fungal bipartite co-occurrence networks concerning cropping systems and cultivation seasons. (A). Network on soil samples in the potato–potato monoculture system. (B). Network on soil samples in the rice–potato rotation system. (C). Network on soil samples cultivated during the summer season. (D). Network on soil samples cultivated during the winter season. Note: the nodes (potential species) with yellow points and red text mean a species with significantly higher abundance in its condition than which in its counterpart condition. The nodes with yellow points and yellow text denote the Colletotrichum_ASV24 participated in network construction. The red and yellow points mean pathogens; the green points mean the rest non-pathogenic nodes (potential species); the green lines mean potential interaction relationships. The characters listed above each figure mean topological features of networks (N denotes the participated nodes numbers; AVNN denotes the average number of neighbors; Nd denotes the network diameter; Nr denotes the network radius; Cpl denotes the characteristic path length; Nds denotes the network density; Nh denotes the network heterogeneity; Nc denotes the network centralization, and Cc denotes the numbers of the connected component in networks).
Figure 5. The pathogenic and non-pathogenic fungal bipartite co-occurrence networks concerning cropping systems and cultivation seasons. (A). Network on soil samples in the potato–potato monoculture system. (B). Network on soil samples in the rice–potato rotation system. (C). Network on soil samples cultivated during the summer season. (D). Network on soil samples cultivated during the winter season. Note: the nodes (potential species) with yellow points and red text mean a species with significantly higher abundance in its condition than which in its counterpart condition. The nodes with yellow points and yellow text denote the Colletotrichum_ASV24 participated in network construction. The red and yellow points mean pathogens; the green points mean the rest non-pathogenic nodes (potential species); the green lines mean potential interaction relationships. The characters listed above each figure mean topological features of networks (N denotes the participated nodes numbers; AVNN denotes the average number of neighbors; Nd denotes the network diameter; Nr denotes the network radius; Cpl denotes the characteristic path length; Nds denotes the network density; Nh denotes the network heterogeneity; Nc denotes the network centralization, and Cc denotes the numbers of the connected component in networks).
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Zhou, Y.; Luo, W.; Li, M.; Wang, Q.; Liu, Y.; Guo, H. Summer Rice–Winter Potato Rotation Suppresses Various Soil-Borne Plant Fungal Pathogens. Agronomy 2023, 13, 2143. https://doi.org/10.3390/agronomy13082143

AMA Style

Zhou Y, Luo W, Li M, Wang Q, Liu Y, Guo H. Summer Rice–Winter Potato Rotation Suppresses Various Soil-Borne Plant Fungal Pathogens. Agronomy. 2023; 13(8):2143. https://doi.org/10.3390/agronomy13082143

Chicago/Turabian Style

Zhou, Yuanping, Wenjiao Luo, Maoxing Li, Qiong Wang, Yongxin Liu, and Huachun Guo. 2023. "Summer Rice–Winter Potato Rotation Suppresses Various Soil-Borne Plant Fungal Pathogens" Agronomy 13, no. 8: 2143. https://doi.org/10.3390/agronomy13082143

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