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Article

Conservation Agricultural Practices Increase Soil Fungal Diversity and Network Complexity in a 13-Year-Duration Conservation Agriculture System in the Loess Plateau of China

by
Yajie Wang
1,
Rongchun Zheng
1,
Wanqing Dong
1,
Ping Gao
1,2 and
Tingyu Duan
1,*
1
State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, Engineering Research Center of Grassland Industry, Ministry of Education, Gansu Tech Innovation Center of Western China Grassland Industry, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
2
Eco-Environment and Plant Protection Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(18), 8411; https://doi.org/10.3390/app14188411
Submission received: 14 April 2024 / Revised: 22 August 2024 / Accepted: 29 August 2024 / Published: 19 September 2024
(This article belongs to the Section Agricultural Science and Technology)
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Abstract

:
Agricultural practices can affect the diversity and community structure of soil fungi. This study investigates the impact of long-term agricultural practices on soil fungal diversity in the Loess Plateau of northwestern China. Different tillage practices have been implemented for 13 years, and their impact on soil fungi is assessed using high-throughput Illumina Sequencing. This study found a total of 2071 fungal Amplicon Sequence Variants (ASVs), and these were assigned to 25 different phyla, 372 families, and 496 genera. The fungal communities were dominated by Ascomycota (52.1%), followed by Zygomycota (14.3%) and Basidiomycota (9.0%). In general, the soil exhibited higher fungal community abundance, richness, and diversity in winter than in summer. Notably, no-tillage or stubble retention resulted in greater diversity than conventional tillage, with no-tillage combined with stubble retention resulting in the highest fungal richness, diversity, and network complexity in both summer and winter. These findings indicate that no-tillage with stubble retention is beneficial for biological soil components, which favors the establishment of abundant and diverse soil fungal communities in the Loess Plateau of China. The present study expands the knowledge of fungal communities in agro-ecosystems and the long-term ecosystem benefits of tillage practices.

1. Introduction

As important members of the soil microbial flora, fungi take part in matter cycling and energy flows in ecosystems [1,2]. Soil fungi participate in the decomposition and humification of inorganic and organic matter, thus improving the fertility and structure of soils and maintaining nutrient cycling. Organic matter is degraded and converted by soil fungi, eventually forming simple compounds (e.g., CO2, H2O, SO42−, and PO43−) that are returned to the environment, thus completing matter cycles. The activity of soil fungi in soil can be modified by tillage and crop production practices [3]. Soil fungi are sensitive to changes in soil organic matter and they respond to changes in soil factors and environmental conditions [4]. The fungal community composition in soil can reflect the health and quality of soil [5]. In agricultural ecosystems, factors including management practices, crop type, soil structure, and chemical characteristics can affect the composition and number of soil fungi [6,7]. For example, Trichoderma was abundant in low-pH and high-organic-content soil [8]. The dominant genus of arbuscular mycorrhizal fungi (AMF), Glomus, was increased significantly in soil after deep tillage [9]. The soil fungal diversity was higher in “organic” soil than in “conventional” soil, with the two types of soils containing more soil fungi in the cold temperatures of January than in the warm temperatures of April and October [10]. In soil, the distribution and activity of soil fungi usually lead to an increase in soil fertility and nutrient transformation [11,12].
Conservation tillage is a common agricultural practice that can affect fungal communities in soils [13]. No-tillage (NT) areas usually contain higher soil fungal diversity [14]. However, a study found higher fungal diversity under conventional tillage than reduced tillage. And the authors suggested that the specific local set of environmental conditions (a loess-derived soil and an oceanic temperate climate) may explain the results [15]. Florine et al. (2017) found that tillage, crop, and growing stage were significant determinants of microbial community structure [16]. This is linked to the different ecological niches created by different agricultural practices [17].
Approximately 3.4 billion tons of crop stubble are produced across the world in a year, and the application of previous crop residues is an important conservation agricultural practice [5]. The residue of crop stubble contains a large content of mineral elements (e.g., N, P, and K). This residue can improve the soil temperature and water content of soil, as well as soil structure, the content of soil organic carbon [18,19], and the soil microbial activity [20,21], therefore improving crop yield [22]. Crop stubble residue affords abundant C and N sources and improves the growth and reproduction of soil fungi, resulting in changes in the soil fungal community [23].
The Loess Plateau of China is one of the most heavily eroded areas worldwide. A huge loss of soil nutrients and 16.4 × 108 t of soil from this area to the Yellow River was caused by soil erosion, leading to poor soil fertility in the special topography of this area of valleys and steep slopes [24]. Conservation agriculture management has been undertaken in the Loess Plateau since 2001 to improve crop yield and soil fertility, and to reduce soil erosion. Yang (2013) found that the amounts of fungi and actinomycetes were higher under NT and residue retention in a rotation system of winter wheat (Triticum aestivum), maize (Zea mays), and soybean (Glycine max) than under CT [25]. Although many studies have reported the effects of agricultural management strategies on soil organisms [26], little is known about the effect of conservation agricultural practices on soil fungal diversity in long-term rain-fed agriculture systems. Due to the close relationship between soil fungal diversity and targeted agriculture management, we hope to clarify the responses of soil fungi to agricultural practices in this crop-producing region of the Loess Plateau.
High-throughput sequencing, especially the sequencing of internal transcribed space (ITS), has been widely used to quantify and characterize soil fungal diversity and communities [27,28,29]. Soil fungal diversity and richness are important for the functional diversity of fungi related to environmental and agricultural management practices. In this study, we used high-throughput Illumina Sequencing to assess the fungal diversity in different conservation tillage practices in June and December from an experimental station in the Loess Plateau. The aim was to clarify the effects of conservation management practices on soil fungal quantity and diversity in summer and winter to provide knowledge relevant to selecting optimal agricultural practices from the viewpoint of soil fungal diversity. We hypothesized that agricultural practice alters the diversity of soil fungal communities, and no-tillage and soil residue cover result in high soil fungal diversity when compared to conventional tillage.

2. Materials and Methods

2.1. Description of Trial Site

A long-term field trial was established in the year of 2001, in the middle of the Loess Plateau, China (35°39′ N, 107°51′ E). Here, the altitude is approximately 1297 m, the temperature range is −22.4–39.6 °C, the annual rainfall is around 480–660 mm, and the growing season is 255 days per year. The soil is Calcisols layer soil [30]; soil organic C was below 1.70 g kg−1, and Olsen phosphorus (P) was below 25 mg kg−1 [22].
The trial was set up with a rotation system of maize, winter wheat, and soybean in 2001. A conventional tillage (CT) process, whereby the top soil (20 cm) is tilled using a Chisel plow in September or October once a year, was carried out in the same way with local farmers. The CT was taken as the control treatment. Three conservational practices were employed in the study, including conventional tillage plus previous crop residue (CTR), no-tillage (NT), and no-tillage plus previous crop residue (NTR) [22]. For each treatment, there were four replicates randomly arranged with a total of 16 plots. The size of the plot was 4 m × 14 m (56 m2) with 1 m space between plots.
Local farmers sow maize, winter wheat, and soybean varieties according to traditional practices. The growth stage for maize extends from late April to late September, followed by the sowing of winter wheat, which is harvested in late June or early July of the subsequent year. Soybean is then sown immediately after and harvested in October, followed with a 6-month period of bare fallow. Maize is sown again to initiate a new growth cycle after a fallow period.
No irrigation is applied, and 300 kg/ha of diammonium phosphate (DAP) is applied to both the winter wheat and maize treatment before sowing. Additionally, 150 kg/ha of CO(NH2)2 is added to winter wheat at the jointing stage, while 300 kg/ha is applied to maize during its late growth stage. For soybean, only 63 kg/ha of phosphorus (P2O5) is applied before sowing.

2.2. Soil Sampling and DNA Extraction

In June 2014 (summer), 12 plots (3 plots for each of the four treatments, denoted as S-NT, S-CT, S-NTR and S-CTR) out of the total 16 plots for the same rotation cycle were chosen for soil sampling in the maize field. Layers of 0–20 cm of soil were collected using soil cores (total length—107 cm; auger length—16.3 cm; auger outer diameter—6.3 cm; auger inside diameter—5.8 cm) from 16 points from each plot, and then mixed and homogenized by passing through a <2 mm sieve and stored at −80 °C prior to analysis by following Lin et al. (2012) [31]. For each soil sample, 0.5 g moist soil was used to extract genomic DNA with the SDS-CTAB method on the day after sampling [32]. The purity and concentration of the DNA in the 12 soil samples were monitored on 1% agarose gels. After transferring the appropriate volumes of samples to centrifuge tubes, we used sterile water to dilute samples to 1 ng µL−1 and stored the samples at −20 °C until further use. The soils were resampled in December 2014 (winter) from the same sites under winter wheat with the methods mentioned above, and here, soil samples for each treatment are denoted as W-NT, W-CT, W-NTR and W-CTR.

2.3. PCR Amplification

Polymerase chain reaction (PCR) amplification of the ITS1 region was performed with primer pair ITS1F (5′-ACTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′) [33]. PCR products were determined by electrophoresis on 2% agarose gel. The mixture of PCR products was then purified with GeneJET Gel Extraction Kit (Thermo Scientific, Waltham, MA, USA). Sequencing libraries were constructed using NEB Next® UltraTM DNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA). The constructed library quality was sequenced on an Illumina MiSeq sequencer at the Beijing Novogenne bio-information technology Ltd., Beijing, China.

2.4. Sequencing Data Processing and ASVs Analysis

2.4.1. Sequencing Data Processing

For data analysis, reads were processed using QIIME 2 (Quantitative Insights Into Microbial Ecology) and its plugins [34]. Briefly, to obtain amplicon sequence variants (ASVs), DADA2 was used to remove low-quality reads and noises, including chimeras [35]. The ASVs were then assigned to taxonomic groupings based on comparisons with the Unite database version 10.0 (Only fungi remain) [36] database using the q2-feature-classifier plugin [37].

2.4.2. ASVs Analysis

Alpha diversity indices (Observed features, Chao1 and Shannon index) and beta diversity metrics (Bray–Curtis) were calculated using the q2-diversity plugin [34]. Observed features was used to estimate the number of unique ASVs found in each sample, the Chao1 index was used to estimate the species richness, and the Shannon index was used to indicate the community diversity. Principal coordinates analyses (PCoA) on the ASVs level were performed using Bray–Curtis dissimilarities through the community ecology package, R package Vegan [38].
Network inference was performed based on Spearman correlation at the ASVs level (abundance > 0.1%), with coefficients of >0.8 or <0.8, and false discovery rate-corrected p-values < 0.05 were used to construct networks [39]. To analyze the topology of the co-occurrence networks, we used a set of measures, such as the numbers of edges, nodes and clusters, clustering coefficients, modularity, connectedness, and average degree, using R package igraph 1.2.6 [40]. Co-occurrence network visualization was carried out in Gephi 9.0.

2.5. Statistical Analysis

The differences in the alpha diversity index of the soil between the treatments were tested using a one-way analysis of variance (ANOVA) in SPSS 22.0 (SPSS, Chicago, IL, USA) [41]. Tukey’s HSD was used for all pairwise comparisons. To identify differences in microbial communities under the different treatments, we performed permutational multivariate one-way analysis of variance (PERMANOVA) based on the Bray–Curtis dissimilarities [42] using R package vegan. The species annotations and abundance information at the genus level for all samples were analyzed to identify the top 35 genera in terms of abundance. These were then selected to create a heatmap using R package pheatmap 1.0.12 [43], which displays the abundance information for each sample. In all tests, a p value < 0.05 was considered statistically significant.

3. Results

3.1. Sequencing Data

Sequencing information is summarized in Supplementary Table S1. Illumina-based analysis of the universal ITS1 region of genes produced 1,105,390 total reads across all samples. After filtering and removing low-quality reads, noise, and chimeras, a total of 861,712 taxon tags were obtained. These sequences were assigned to 2071 fungal ASVs.
The Venn diagram results show that the summer group had 1057 ASVs, with 128 ASVs (12%) found to be in common. The winter group had 1579 ASVs, with 249 ASVs (16%) found to be in common (Figure 1).
The rarefaction curves of ASVs were used to characterize soil fungal species. All amplified rarefaction curves reached a plateau when the sequences exceeded 10,000, indicating that the sequence-derived diversity and richness in this study were sufficient to characterize fungal species in each sample (Supplementary Figure S1).

3.2. Fungal Community Analysis

The taxonomic assignment of fungal ASVs revealed that the fungal community was dominated by Ascomycota (70.61%), followed by the Basidiomycota (6.71%) and Chytridiomycota (0.20%) (Figure 2A). Almost all soil fungi could be found in all soil samples; however, there was a noticeable variation in the relative compositions of fungal communities among the individual soils from the 24 samples (Figure 2B).

3.3. Alpha Diversity

The Chao1 index and Shannon index responded positively to growth season, and there was a significant difference between seasons. The Chaol index revealed that species richness in the winter samples was higher than in the summer ones. The value of the Chao1 index for the no-tillage plus previous crop residue treatment is significantly higher than for the other three agricultural management practices, in both the winter and summer soil samples (Figure 3A). The Shannon index indicates that community diversity in the winter samples is significantly higher than in the summer ones; however, the differences among the four agricultural management practices were not significant in the summer samples. Treatment NTR produced the lowest Shannon index in both the winter and summer samples (Figure 3B). Residue return increased species richness (p = 0.055) and no-tillage increased fungal community diversity (p = 0.064), but neither was significant (Table 1).

3.4. Beta Diversity

Analyses of fungal diversity in all soils using Bray–Curtis distances showed the separation of different treatment soils. PCoA indicated that the community structures differed significantly between different agricultural practices, where PCo1 and PCo2 explained 40.55% and 10.68% of the variance, respectively (PERMANOVA; p = 0.001) (Figure 4). Moreover, ANOSIM analyses showed significant differences in fungal community structure between growing seasons (p = 0.001). Tillage and residue return had no significant effect on fungal community structure (Table 2).

3.5. Heatmap of Fungal Groups

The shifts in the fungal community compositions were further corroborated by the clear clustering of the dominant fungal genera (ranking of abundance at the top 35), corresponding to different treatments in the heat map, as shown in Figure 5. The figure clearly shows abundance information of fungal genera in the eight treatments. The results reveal that the fungal compositions in soils were significantly different between summer and winter, of which 12 genera belong to the summer samples, while 23 genera belong to winter samples, indicating that the genera of soil fungi were richer in winter than in summer. The fungal genera in the summer samples were mainly Coprinellus, Pyrenochaeta, Talaromyces, Pseudogymnoascus, Trichocladium, Stachybotrys and Entoloma. The fungal genera in winter samples were mainly Plectosphaerella, Neocosmospora, Octospora, Schizothecium, Hydropisphaera, Cladosporium, Articulospora and Myrmecridium (Figure 5).

3.6. Co-Occurrence Network Analysis

To investigate the changes in interconnections among soil fungal community members and their relationships with different agricultural practices, this study performed a microbial co-occurrence network analysis of soil fungal communities and calculated the network’s topological characteristics. The analysis revealed that winter soil fungi exhibited higher network complexity than summer soil fungi. Additionally, the complexity under no-tillage and stubble retention conditions was higher than under conventional tillage and stubble retention in both summer and winter (Figure 6). The topological analysis results further highlight the differences among microbial networks. Compared to conventional tillage and stubble retention, no-tillage and stubble retention had more nodes and edges, a higher average degree, and a higher average weighted degree, with the NT treatment showing the highest numbers in both summer and winter. The clustering coefficient and modularity also varied (Table 3).

4. Discussion

The sequencing results and the alpha and beta diversity indexes indicate that soil fungal community structures, abundance and diversity varied among the four different tillage treatments and in different seasons. The alpha diversity index responded positively to season. The fungal community was significantly different under different seasons, tillages and crop residue treatments. Residue return with no-tillage increased soil fungal richness, community diversity and network complexity, and seasonal differences were stronger than under agronomic treatments.
Agriculture practices have different influences on soil fungi; for instance, conservation tillage had positive effects on arbuscular mycorrhizal fungi, while saprotrophic fungi and bacteria were not affected by soil tillage [19]. As we expected, NT showed higher richness and diversity of soil fungi as compared with CT. The effects of tillage on soil fungi were diverse, and lots of studies recorded positive contributions of NT to the soil fungus community, such as showing increased diversity and abundance [14,44] as well as biomass [3]. The fact that the soil biological component was improved in NT changed the soil properties and reduced disturbance. NT soils exhibited significantly higher dehydrogenase, beta-glucosidase, phosphatase, urease and BAA protease activities than conventional deep -tillage soils [45]. Tillage, even if shallow and performed infrequently, had a negative effect on organic C and N pools [46]. Moreover, NT reduced the disturbances imposed on soil fauna, signifying that it provided a more stable environment for soil fauna [47]. The abundance and diversity of soil arthropods were significantly higher in no-tillage than in conventional tillage plots [48]. Zaitlin et al. (2004) found the negative effect of tillage on culturable soil-specific actinomycete communities [49], while Bayman and Turgut (2018) and Carneiro et al. (2019) found that tillage practices had no effect on soil fungus and soil AMF communities [16,50].
Soil tillage usually decreases the amount of organic C stored in soils, and the return of crop residue reduces the negative impact of soil tillage, thus being beneficial to soil microbial systems [19]. The NTR system had higher fungal richness than the NT, indicating that stubble retention increased the number of fungal species. This confirms our hypothesis that soil residue cover results in higher soil fungal diversity relative to CT. Miura et al. (2013) also found that NT amended with bagasse mulch had the highest soil microbial biomass and diversity (especially litter fungi, by 2 to 2.5 times) in sugarcane plantations [3]. Straw contains a large content of C, N, P and other nutrients, as well as organic substances, such as cellulose, hemicellulose and lignin, protein and sugar, and can be utilized by soil fungi. The NTR approach has been previously shown to significantly increase soil organic matter, total N and P, and the activity of phosphatase and sucrase, as well as reducing water evaporation and improving the water content of soil [51]. In addition, the dominant fungi we identified, Ascomycota and Zygomycota, are associated with litter decomposition, which accelerate residue decomposition, resulting in higher soil nutrients for microorganisms and crops. Silvia et al. (2014) also found that microbial biomass was highest in the NT with bagasse mulch compared to the NT without bagasse mulch, as well as the CT with or without bagasse mulch [52]. Thus, the diversity of soil fungi could well reflect soil nutrient conditions.
Soil fungi diversity shows seasonal dynamics, e.g., the number of fungi increased significantly during the flag leaf phenological period compared to in other periods [16]. The abundance and richness of fungal genera in soil samples were higher in winter than in summer. The possible driving forces regulating the temporal dynamics of fungal communities in different seasons may include the types of host plants or host plant carbon changing supply [53]. In our study, maize (grown in the summer) has been shown to be a typical C4 plant, whereas winter wheat (grown in the winter) is a typical C3 plant. A possible explanation of the seasonal differences in fungal community structure is that Coprinellus, Pyrenochaeta, Talaromyces, Pseudogymnoascus, Trichocladium, Stachybotrys and Entoloma can easily survive in a C4 plant rhizosphere. In contrast, Plectosphaerella, Neocosmospora, Octospora, Schizothecium, Hydropisphaera, Cladosporium, Articulospora and Myrmecridium may be better suited to surviving in a C3 plant rhizosphere. Besides the host plant, climate and environmental constitutions can also affect fungal communities [16,54]. In our study, soil fungal community abundance and the diversity index were generally higher in winter than in summer, indicating that the lower soil temperature in the winter is more suitable for soil fungi, and thus the soil fungal community structure showed a recovery trend.
In our study, no-tillage and stubble retention conditions exhibited higher network complexity than conventional tillage and stubble retention. This indicates that agricultural practices significantly influence the complexity and stability of soil microbial networks. A study on crop rotation has shown that network complexity plays a greater role in enhancing soil multifunctionality than microbiome diversity and community composition, highlighting the crucial role of network complexity in maintaining soil functions [55]. Recent research has found that ecosystem functions are maintained by soil microbiome interactions in various environments, and the loss of complex associations between soil microorganisms can impair ecosystem functions and services [56]. Sustainable practices that enhance soil biodiversity and structure tend to support more complex and beneficial microbial interactions, contributing to overall soil health and agricultural productivity [57,58].
The above conclusions indicate that no-tillage with stubble retention is beneficial to biological soil components, as it favored the establishment of more abundant and diverse soil fungal communities compared to conventional tillage in the Loess Plateau of China.

5. Conclusions

The present study reveals that the compositions of fungal communities were significantly different under different growing seasons, tillage methods, and crop residues. Here, 13 years of the continuous application of tillage treatments, including those of conservation agriculture, resulted in changes in the abundance, diversity, and network complexity of the fungal community in the soil. The soil showed higher fungal community abundance, richness, and diversity indexes in winter than in summer, and stubble retention increased the number of fungal species in both summer and winter under NT and CT practices. The study provides baseline information on soil fungal diversity in the Loess Plateau under different agricultural practices across two growing seasons.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app14188411/s1, Figure S1: Rarefaction curves of ASVs for different treatments. S = summer, W = winter; CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue. Table S1: Sequencing information obtained by Illumina sequencing platform in this study. S = summer, W = winter; CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.

Author Contributions

Writing—original draft preparation, Y.W., P.G. and W.D.; writing—review and editing, R.Z.; visualization, R.Z.; supervision, T.D.; funding acquisition, T.D. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the National Nature Fund (31100368) and China Agriculture Research System–Forage Grass Research System (CARS-34).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 14 08411 g001
Figure 1. Venn diagrams of the ASVs in different samples of conservation agriculture practices. The numbers inside the diagram indicate the numbers of ASVs. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
Figure 1. Venn diagrams of the ASVs in different samples of conservation agriculture practices. The numbers inside the diagram indicate the numbers of ASVs. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
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Figure 2. The relative distribution of species at the phylum level for all soil samples (A) and the relative abundance of species at the genus level (B) for seven treatments. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
Figure 2. The relative distribution of species at the phylum level for all soil samples (A) and the relative abundance of species at the genus level (B) for seven treatments. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
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Figure 3. Chao1 (A) and Shannon (B) index of different treatments, including no-tillage (NT), conventional tillage (CT), no-tillage plus previous crop residue (NTR) and conventional tillage plus previous crop residue (CTR) in summer and winter. Different lowercase letters indicate significant differences between different treatments (p < 0.05).
Figure 3. Chao1 (A) and Shannon (B) index of different treatments, including no-tillage (NT), conventional tillage (CT), no-tillage plus previous crop residue (NTR) and conventional tillage plus previous crop residue (CTR) in summer and winter. Different lowercase letters indicate significant differences between different treatments (p < 0.05).
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Figure 4. Principal co-ordinates analysis (PCoA) of fungal diversity in all soil samples. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
Figure 4. Principal co-ordinates analysis (PCoA) of fungal diversity in all soil samples. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
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Figure 5. Heatmap analysis of the highly represented fungal taxa at the genus level. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
Figure 5. Heatmap analysis of the highly represented fungal taxa at the genus level. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.
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Figure 6. Co-occurrence networks of soil fungi between different agricultural practices. S = summer, W = winter, NT = no-tillage, CT = conventional tillage, NR = stubble retention, R = crop residue. Different colors in the figure represent the phylum to which different bacteria belong.
Figure 6. Co-occurrence networks of soil fungi between different agricultural practices. S = summer, W = winter, NT = no-tillage, CT = conventional tillage, NR = stubble retention, R = crop residue. Different colors in the figure represent the phylum to which different bacteria belong.
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Table 1. ANOVA result for effects of season, agricultural practices and their interactions on the listed variables.
Table 1. ANOVA result for effects of season, agricultural practices and their interactions on the listed variables.
Parameter Season (S)Tillage (T)Residue (R)S × TS × RT × RS × T × R
Chao1F36.9544.2703.2490.6800.0091.9110.010
p<0.00010.0550.0900.4220.9270.1680.922
ShannonF81.0781.8013.9661.6841.7880.1961.024
p<0.00010.1980.0640.2130.2000.6640.327
Table 2. Effects of tillage, residue and growing season on fungal community composition.
Table 2. Effects of tillage, residue and growing season on fungal community composition.
TreatmentdfFR2p
Tillage11.30240.055890.218
Residue11.97990.082560.067
Season114.2370.392890.001 (***)
Note: Effects of tillage, residue and growing season as assessed by multivariate permutational analysis of variance (PERMANOVA). Values represent the pseudo-F ratio (F), the permutation-based level of significance [p (perm)] and the proportion of variance explained by each factor (R2). Significant results are labeled in asterisk (*** p ≤ 0.001).
Table 3. Topological properties of soil fungi networks.
Table 3. Topological properties of soil fungi networks.
Topological PropertiesSummerWinterSummerWinter
NTCTNRRNTCTNRR
Number of nodes122268133140142133244226228213
Number of edges1176826874985085412532198821871515
Average degree1.9185.0910.3317.1147.1558.13520.75417.59319.18414.225
Average weighted degree0.00800380.2090.1530.1450.1680.4040.3510.3780.290
Clustering coefficient0.4360.4320.5590.4830.5060.5550.5780.5490.5270.548
Modularity0.9110.8570.4790.6690.7000.6400.5200.5660.5370.584
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Wang, Y.; Zheng, R.; Dong, W.; Gao, P.; Duan, T. Conservation Agricultural Practices Increase Soil Fungal Diversity and Network Complexity in a 13-Year-Duration Conservation Agriculture System in the Loess Plateau of China. Appl. Sci. 2024, 14, 8411. https://doi.org/10.3390/app14188411

AMA Style

Wang Y, Zheng R, Dong W, Gao P, Duan T. Conservation Agricultural Practices Increase Soil Fungal Diversity and Network Complexity in a 13-Year-Duration Conservation Agriculture System in the Loess Plateau of China. Applied Sciences. 2024; 14(18):8411. https://doi.org/10.3390/app14188411

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Wang, Yajie, Rongchun Zheng, Wanqing Dong, Ping Gao, and Tingyu Duan. 2024. "Conservation Agricultural Practices Increase Soil Fungal Diversity and Network Complexity in a 13-Year-Duration Conservation Agriculture System in the Loess Plateau of China" Applied Sciences 14, no. 18: 8411. https://doi.org/10.3390/app14188411

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