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Article

The Application of Carbon-Based Fertilizer Changed the Microbial Composition and Co-Occurrence Network Topological Properties of Vineyard Soil

by
Ping Sun
1,
Jiaqi Wu
1,
Xianrui Lin
1,
Chenfei Chen
1,
Jianxi Zhu
1,
Yi Wang
1,
Jian Zhou
2,
Huaxin Wang
1,
Jiansheng Shen
1,* and
Huijuan Jia
3,*
1
Jinhua Academy of Agricultural Sciences (Zhejiang Institute of Agricultural Machinery), Jinhua 321000, China
2
Jiangxi Agricultural Technology Extension Center, Nanchang 330046, China
3
College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
*
Authors to whom correspondence should be addressed.
Horticulturae 2024, 10(8), 871; https://doi.org/10.3390/horticulturae10080871
Submission received: 8 July 2024 / Revised: 1 August 2024 / Accepted: 16 August 2024 / Published: 18 August 2024
(This article belongs to the Section Viticulture)
Browse Figures
Versions Notes

Abstract

:
Charcoal-based fertilizer could be used extensively and is environmentally friendly. An experiment was designed to investigate the effects of different charcoal-based fertilizer application methods on soil microbiology and grape quality in a vineyard to guide the cultivation of ‘Shine-Muscat’. A control treatment without fertilization and six other treatments were set up. Four treatments applied carbon-based fertilizer as a base fertilizer with or without potassium fulvic acid, a complex microbial agent, or Bacillus subtilis, and two treatments were only applied with two applications of carbon-based fertilizer or compound fertilizer during the expansion period. The results showed that the bacterial phyla were mainly Proteobacteria and Bacteroidetes. Ascomycota, Basidiomycota, and Mortierellomycota dominated the fungal community. At the genus level, the composition of fungi, compared to bacteria, varied significantly, while the dominant flora differed among fertilization practices. Application of charcoal-based fertilizer enriched beneficial microorganisms, while chemical fertilizers enriched pathogenic microorganisms. The addition of microbial fungicides and biostimulants for a period reduced the size of the microbial network, lowered positive correlations, and enhanced resistance to adverse conditions and diseases and there was no significant correlation between agronomic traits and microbial network topology. A combination of soil microbial and grape agronomic traits suggests that a charcoal-based fertilizer base, with microbial fungicides applied, is the optimal fertilization regimen for grape.

1. Introduction

Soil, which is an important ecosystem supporting plant growth and development, is closely related to agricultural production as well as the safety of agricultural products. In addition to soil physical and chemical indicators, soil microorganisms are one of the most important indicators used to evaluate soil health. It has been noted that alterations in the structure and function of the microbial community of the soil can produce abnormalities in the soil, such as a depletion of soil nutrients, erosion of soil physicochemical properties, and an accumulation of a plants’ own toxins [1]. Soil microorganisms play an important role in the process of elemental cycling, pollutant degradation, soil-borne disease prevention and control, and so on; the community characteristics of soil microorganisms can sensitively reflect changes in soil nutrients, pH, and other external conditions. Fang et al. [2] found that soil carbon, nitrogen, and phosphorus transformations were associated with some specific microbial communities. Zhang et al. [3] showed that Al3+ content increased significantly at pH less than 5.1, and that acidic soils were rich in Al-tolerant microorganisms and had a more complex network structure than neutral and alkaline soils. The study of the community characteristics of the soil microbial can help land managers respond to the health of the soil and provide a basis for decision making during agricultural production.
The structure and diversity of soil microbial communities are strongly influenced by cultivation practices, climatic characteristics, and cropping patterns. Fertilizer application is one of the cultivation measures that has a major impact on soil microorganisms [4]. Current agricultural production is very dependent on chemical fertilizers; however, the long-term use of these fertilizers will change the species composition of soil microorganisms, strongly changing the structure and functional diversity of soil species [5]. Environmentally friendly fertilizers such as charcoal-based and biofungal fertilizers are gradually entering the market and being applied in actual production. Charcoal-based fertilizers, as ecologically sound and environmentally friendly fertilizers, can increase the content of charcoal-based and organic matter in the soil, improve soil structure, and achieve a water–salt balance [6,7]. In addition, the use of waste biomass converted into biochar, when buried in the ground, reduces soil greenhouse gas emissions [8]. Microbial fungicide is one of the hotspots in the research related to chemical fertilizer substitutes worldwide, which uses plant growth-promoting rhizobacteria to achieve the purpose of improving soil enzyme activity and fertilizer use while improving crop yield and quality [9,10,11]. Humic acids are the core of soil humus, which promotes soil desalination and inhibits soil salt return by changing soil salt movement [12]. However, charcoal-based and other new fertilizers have a higher price when compared to chemical fertilizers; if farmers do not master the scientific application method, this will reduce their profit. Therefore, it is necessary to study the type, application rate, and timing of fertilizer application in order to improve the soil and the quality of fruit while reducing costs.
Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and continuous environments [13]. Co-occurrence networks are structural models that characterize the interrelationships between species at the community level, and are now widely used in ecology, agriculture, and medicine [13,14]. The topological characteristic parameters of co-occurrence networks (e.g., modularity, centrality, network nodes and edges, etc.) can reflect microbial community stability and complexity, and reveal the functions and characteristics of the community [15]. The study of microbial covariance networks will be a step further to understanding the effect of fertilization on soil microbial ecosystems in orchards, which is important for screening appropriate fertilization methods.
Grapes have a unique flavor, can be used for fresh food and winemaking, have rich nutritional value, and are one of the four major fruits produced commercially worldwide. Jinhua, a prefectural level city, was chosen as the test site because it is the largest grape producing area in southern China; however, the long-term use of chemical fertilizers has caused a decline in soil fertility, soil element scarcity, and other problems, which affects the quality of grapes. Based on the results of previous studies, we hypothesized that the use of charcoal-based fertilizers with microbial fungicides could improve the soil microbiological environment, cultivate beneficial flora to inhibit the growth of pathogenic bacteria, and improve the quality of grapes. In this experiment, charcoal-based fertilizer, potassium fulvic acid, and a compound microbial fungal agent were used as research objects. Different application rates and application times were applied to explore the effects of different fertilization treatments on the characteristics of soil microbial communities and agronomic traits in vineyards through field experiments and high-throughput technology. The goal was to provide data support for the rational use of new types of fertilizers.

2. Materials and Methods

2.1. Overview of the Experimental Site

The experimental site was located in the grape production area of Jiangtang Town, Wucheng District, Jinhua, Zhejiang Province, China (29.05° N, 119.46° E). This region experiences a mid-subtropical monsoon climate, with four distinct seasons, obvious dry and wet seasons, average annual rainfall of about 1424 mm, and an average annual air temperature of about 17.5 °C. The region has sandy loam soil which was used as the soil in this study for three consecutive years. In addition, a composite microbial fungal agent was used for three consecutive years.

2.2. Fertilizer Used in the Experiment

The test variety, ‘Shine-Muscat’, was planted for more than 6 years. Viticulture was carried out at 2 m × 3 m spacing, using V-frames. A biomass-type charcoal-based compound fertilizer (N-P2O5-K2O:17-13-10) produced by Tuonong Biomass New Material Co., Ltd. (Xinxiang, China) was used in the experiments. The particle size was 2–5 mm. The biochar was made from straw by slow pyrolytic carbonization at medium temperature, and the content of biochar (in terms of C) is ≥6%. Potassium fulvic acid (fulvic acid content ≥ 40%, total nitrogen content ≥ 3.0%, total phosphorus content ≥ 0.4%, total potassium content ≥ 12%, and organic matter content ≥ 66%, as well as plant growth of a variety of enzymes, sugars, proteins, and vitamin C, vitamin E, and a large number of vitamin B and other nutrients) was purchased from Fengguan Biological Co., Ltd. (Weifang, China), and the composite microbial bacterial agent, Bacillus subtilis, was purchased from Haicheng Biotechnology Co., Ltd. (Yangzhou, China); the content of effective live bacteria was ≥2 × 1010 CFU/g. The composite microbial bacterial agent contains azotobacter, B. amyloliquefaciens, and B. megaterium as well as Actinomycete spp. and their protectants. Compound fertilizer (N-P2O5-K2O:17-13-10) was purchased from Jinhua Biomass Industry Science and Technology Research Institute (Jinhua, China).

2.3. Experimental Design

Seven fertilizer application methods were set up, as shown in Table 1. The basal fertilizer was applied on 24 March 2021, and the fruit-expanding fertilizer was applied on 14 May 2021. The interval between the two fertilizer applications of Cbf2 and Cf was 20 days.

2.4. Soil Collection

Soil samples were collected during the ripening period of grapes. Two points were sampled next to each of nine vines for a total of 18 soil sampling points for each of the treatments, covering all directions of the vines. Prior to sampling, the undecomposed apomictic layer on the soil surface was removed; then, 5 cm of soil on the surface layer was removed using sterilized shovels. Next, 10–15 g of soil for each sample was placed in a sterile plastic bag and sealed; the samples from two points for each vine were evenly mixed to create a combined sample. The collected samples were labeled, placed in an ice box, and returned to the laboratory where plant and animal residues, gravel and other impurities were removed. The large pieces of soil in the samples were crushed, sieved through a 2 mm sieve, and then divided into 2 mL Eppendorf® tubes (Eppendorf China, Shanghai, China), immediately frozen in liquid nitrogen, and stored in a refrigerator at −80 °C. Three parallel samples were set up in each treatment for extraction of microbial DNA from the soil.

2.5. DNA Extraction, PCR Amplification and Sequencing

Total soil DNA was extracted from 0.25 g of each soil sample according to the procedure described in a PowerSoil® DNA Isolation kit (Mobio Laboratories, Carlsbad, CA, USA). The full-length primers for the bacterial 16S rRNA V1 to V9 region were 27F (5′-AGRGTTTGATYNTGGCTCAG-3′) and 1492R (5′-TASGGHTACCTTGTTASGACTT-3′). The fungal ITS rRNA full-length primers were ITS (5′CTTGGTCATTTAGAGGAAGTAA-3′) and ITS4 (5′-TCCTCCGCTTATTGATATATGC-3′). The above primers were used to synthesize the specific primers with Barcode for PCR amplification. The reaction system was as follows: 10 μL of KOD FX Neo Buffer (2×), 4 μL of dNTP (2 mmol/L), 0.4 μL of KOD FX Neo (TOYOBO), 5 ng of DNA template, 1 μL of forward primer (10 μmol/L), and 1 μL of reverse primer (10 μmol/L); then, 10 μL of the reaction system was prepared with dd H2O. The procedure was as follows: pre-denaturation at 95 °C for 5 min, denaturation at 95 °C for 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 1 min, 30 cycles, and extension at 72 °C for 5 min. The PCR-amplified products were purified, quantified, and homogenized to form a sequencing library using the SMRTBell template library; the constructed library was first subjected to library QC, and the QC-qualified library was sequenced on the PacBio Sequel platform. All the above-mentioned procedures were completed by Biomarker Technologies (Beijing, China).

2.6. Data Analysis

Circular consensus sequencing (CCS) reads were obtained with SMRTLink v8.0 software after correcting the raw sub-reads. The CCS reads were barcode-identified and length-filtered; the chimeras were removed thereafter using UCHIME v.8.1 (Edgar et al., 2011), and finally, a set of optimized-CCS reads were obtained. The optimized-CCS reads were clustered into operational taxonomic units (OTUs) at 97% similarity using USEARCH v.10.0 software [16], and the representative OTU sequences were annotated using the SILVA bacterial 16S rRNA database (Release132) [17] and the UNITE fungal ITS database (Release 8.1) [18] by a QIIME-based wrapper of RDP-classifier v.2.2 software [19] with a confidence cutoff of 0.8. The detected communities were identified and annotated at different taxonomic levels. Further analysis was performed to calculate the alpha diversity and richness of OTUs, and the community composition of each sample was determined at different classification levels. Finally, based on the above analysis, two alpha diversity indices were applied to analyze the microbial diversity, specifically, the Shannon and Ace indices. Principal co-ordinates analysis (PCoA) was applied to reduce the dimension of the original variables based on the binary Jaccard distances using R v.3.5.3 software [20]. Linear discriminant analysis (LDA) coupled with effect size measurement (LEfSe) analysis was applied to search for statistically different biomarkers between different groups using LEfSe software v1.0. [21]. Pearson correlation coefficients (relative abundance > 0.001, frequency of occurrence > 1/3) between different colonies were calculated using the igraph package of R software v.4.3.2. A correlation coefficients matrix and p-value matrix were obtained to determine the threshold for the existence of interactions between species (r > 0.8, p < 0.001), and to compute the topological attributes of the co-occurrence network. Network mapping was performed using Gephi 0.9.2 software [22].

2.7. Measurement of Agronomic Traits in Grapes

Individual leaves of the outer canopy were randomly selected, and their chlorophyll content was determined using the leaf Meter Model SPAD-502 (Minolta Camera Co., Ltd., Osaka, Japan). Its principle is based on the chlorophyll spectral absorption law, with the use of two different light-emitting tube irradiation leaves, through the measurement of the intensity of the light through the leaf to calculate the chlorophyll content within the leaf. Ten samples were collected per vine, with 27 sample vines per treatment for a total of 270 samples. Three clusters of grapes were randomly selected from each vine after full ripening, the total soluble solids (TSS) were measured with a refractometer, which is based on the principle of calculating the TSS content by measuring the refractive index of grape juice. Individual fruit weights, cluster weights, and yields were weighed on an electronic balance.

3. Results

3.1. Effects of Different Fertilization Methods on the Diversity of Soil Microbial Communities in Vineyards

The alpha diversity indices (Figure 1A,B,D,E) showed that different fertilization practices affected the microbial diversity of vineyard soils. The bacterial ACE (Abundance-based Coverage Estimator) indices of the Cbf, CbfB, and CbfP treatments were significantly lower than that of the CK treatment. The ACE index of CbfB was the lowest, and it was significantly different from that of the Cf, Cbf2, CbfM, CbfP, and CK treatments. The ACE index of CbfM was the highest, and it was significantly different from that of Cbf, Cf, CbfB, and CbfP. The Shannon index showed that the CbfM treatment had the highest bacterial diversity and was significantly different from Cbf, Cbf2, CbfB, and CK. The Shannon index of CbfB was significantly lower than that of CF. The ACE index of fungi in each treatment group showed that the fungal abundance of Cf was significantly higher than that of Cbf2. Based on the Shannon index, different fertilization methods had little effect on fungal diversity. No significant difference was observed between the treatment groups except that the fungal diversity of Cbf2 was significantly lower than that of Cf and CbfM.
The β-diversity of microorganisms in each treatment group was analyzed at the OTU level (Figure 1C,F). The results of β-diversity analysis for bacteria showed that Cf and CbfM were closer in spatial distance, indicating that the structure of the bacterial community was similar in these two treatments than in other treatments. The Cbf2 and CK treatments were aggregated in the fourth quadrant, and the bacterial community structures of these were similar. The distance in β-diversity analysis between CbfB, Cbf, and CbfP was more distant, indicating the structures of their bacterial community structure were more different than those of other treatments. The PcoA results for fungi showed that a greater difference in the fungal community structure existed within the CbfP group when compared with the other treatment groups. The β-diversities of the CbfB and Cbf2 treatments were closer together, indicating these had similar community structures. The β-diversities of CbfM and Cbf were also similar, indicating these had similar community structures.

3.2. Differences in Soil Bacterial Community Fractions in Vineyards with Different Fertilization Practices

Sequencing data showed that a total of 273,420 CCS sequences were obtained from the seven treatments after identification by Barcode analysis, and a total of 207,793 OTUs were obtained after clustering. Species annotation revealed that, at the phylum level, the bacteria in the vineyard soil were mainly in the phyla Proteobacteria and Bacteroidetes. In addition, Patescibacteria, Acidobacteria, and Nitrospirota had a small relative abundance in each treatment group (Figure 2A). The relative abundance of Firmicutes in the Cbf2 treatment group was significantly higher than the other treatment groups. Gemmatimonadetes was not significantly different among any of the treatment groups.
The results of genus level analysis showed that only Chujaibacter, Nitrospira, Sphingomonas, and Pseudolabrys could be accurately categorized, indicating that there were additional unknown microorganisms in the soil (Figure 2B). Chujaibacter was significantly less frequent in the CbfM and Cf treatments than in the other treatment groups. Nitrospiraea and Sphingomonas were significantly less or more frequent in CbfP and CbfM than in the other treatment groups, respectively.

3.3. Differences in Soil Fungal Community Fractions in Vineyards with Different Fertilization Practices

Sequencing data showed that a total of 268,970 CCS sequences were obtained from the seven treatments after Barcode identification; a total of 464 OTUs were obtained after clustering. Species annotation revealed that, at the phylum level, the fungi in the vineyard soil were mainly from the phyla Ascomycota, Basidiomycota, and Mortierellomycota (Figure 3A). Ascomycota had the highest relative abundance in CbfP at 83.38%. The relative abundance of Ascomycota in the Cbf and CbfB treatments also reached more than 50%, while the relative abundance of Ascomycota in Cbf2, CbfM, and CK did not differ significantly, which were all around 40%. The relative abundance of Ascomycota in Cbf2 was significantly higher than that in other treatment groups. The lowest relative abundance of Ascomycota was 5.44% in the Cbf treatment. The relative abundance of Aspergillus varied considerably among the treatment groups, with the highest relative abundance of 33.25% in Cbf, followed by (33.25%) in Cbf, and the lowest (3.87%) in CbfP. Rozellomycota had the highest relative abundance in CbfB (14.55%), followed by 2.02% and 2.03% in Cbf2 and CbfP, respectively. The relative abundance of Chytridiomycota in CbfM was 13.15% and was less than 10% in all other treatment groups. The relative abundance of the other phyla was less than 0.1% in all treatment groups.
The results of genus level analysis (Figure 3B) showed that the relative abundance of Mortierella varied greatly among treatment groups, with the highest relative abundance of 31.74% in CK, the second highest in Cf (27.72%), and the lowest in CbfP (3.88%). The relative abundance of Trichoderma in Cbf2 and CbfB was 16.36% and 23.50%, respectively, and 5.03% in Cf, and less than 1% in all other treatment groups. The relative abundance of Leucocoprinus in Cbf2 (32.49%) was significantly higher than in the other treatment groups. Enterocarpus had a high relative abundance of 17.48% in CbfP and on the low extreme 5.75%, 4.26%, and 2.17% in CbfM, Cbf, and CK, respectively. The relative abundance of Coprinellus was 13.46% and 12.78% in CK and CbfM, respectively. The relative abundance of Fusarium in the treatments in descending order was Cf (9.97%), CbfM (5.23%), Cbf (5.21%), CK (3.95%), CbfB (3.02%), Cbf2 (1.91%), and CbfP (1.07%). The relative abundance of Chaetomium in CbfP was significantly higher than the other treatment groups, and it was the genus with the highest relative abundance in CbfP. The relative abundance of Acrophialophora in Cbf, CbfM, and CbfP was around 8%. The relative abundance of Schizothecium and Trechispora in CbfB was significantly higher than the other treatment groups.
At the species level, the species with the highest relative abundance were inconsistent across treatment groups (Figure 3C). Mortierella rishikesha had a higher relative abundance in Cf and CK. Trichoderma crassum had a relative abundance greater than 10% in both Cbf2 and CbfB. Leucocoprinus straminellus had a relative abundance of 32.27% in Cbf2. Chaetomium grande had the highest relative abundance of 27.99% in CbfP.

3.4. Analysis of Soil Microbial Variability in Vineyards with Different Fertilization Methods

A total of 38 bacterial biomarkers and 54 fungal biomarkers were detected using LEfSe analysis with the threshold set at 3 (Figure 4). The results for bacteria showed no biomarkers for the CbfM treatment. There was only one biomarker for Cbf, while Xanthomonadales, Cytophagales, and Microscillaceae were biomarkers for CbfP. A total of 10 biomarkers were observed for Cf involving one species, two genera, two families, two orders, and three phyla. Biomarkers for CbfB included Acidobacteriia, Acidobacteriia1, Chitinophagaceae, Acidobacteriaceae Subgroup 1, and one uncultured genus and one uncultured species. Biomarkers for Cbf2 contained Acidobacteria, Bacilli, Rhizobiales, Bacillales, Rhodanobacteraceae, Bacillaceae, Chujaibacter, and C. soli. Biomarkers for CK contained Gemmatimonadetes, Gemmatimonadales, KF JG30 C25, Gemmatimonadaceae, and uncultured groups including one family, two genera, and two species.
The results for fungi showed that there were six biomarkers for Cf, containing GS10, Trichosphaeriales, Trichosphaeriaceae, Fusarium, Nigrospora, and M. rishikesha. The CbfP treatment had the greatest number of biomarkers, comprising two orders, three phyla, three families, four genera, and three species. Ustilaginomycetes, Saccharomycetes, Ustilaginales, Saccharomycetales, Ustilaginaceae, and N. rubi were the biomarkers for CbfM. The CbfB treatment had 10 biomarkers, including Rozellomycota as one phylum, two orders such as Hypocreales, three families such as Hypocreaceae, three genera such as Trichoderma, and one species of T. crassum. Basidiomycota, Agaricales, Agaricaceae, Leucocoprinus, and L. straminellus were the biomarkers of Cbf2. F. equiseti, Botryotrichum, and B. domesticum were the biomarkers of Cbf. There were nine biomarkers of CK, containing one phylum, one class, one order, two families, two genera, and two species.

3.5. Co-Occurrence Network of Vineyard Soil Microorganisms

The same threshold (r > 0.8, p < 0.01) was used to construct a co-occurrence network of vineyard microorganisms under different fertilization conditions to explore the interactions between the bacterial and fungal communities (Figure 5). The results showed that the microbial co-occurrence network was mainly dominated by bacterial nodes, and the co-occurrence network under different fertilization conditions did not change significantly. The network topological properties (Table 2)showed that the degree of modularity of the co-occurrence network was high, always between 0.534 and 0.897, indicating that the bacterial and fungal co-occurrence networks were clustered into modules that were resistant to changes in the external environment. The lowest number of nodes and edges was observed in CbfB, and the highest number of nodes and edges was observed in Cbf. Positive effects were greater than negative effects in the co-occurrence networks of all treatment groups except CbfM. The Cbf treatment had the highest average degree of co-occurrence network, indicating that the microorganisms were most closely related to each other. The CbfP had the highest network density, indicating that the microbial networks had the densest interactions with each other.

3.6. Grape Agronomic Traits and Their Relationship with Microbial Network Topological Attribute Parameters

Different fertilization methods can affect soil microorganisms, which in turn affects grape quality. The chlorophyll content, cluster weight, the total soluble solids (TSS), and the yield of grapes treated with different fertilization methods were determined (Table 3). The lowest chlorophyll content was found in CK, and the highest chlorophyll content was found in Cbf2. A significant difference was observed between Cbf2 and the other treatment groups. The chlorophyll contents of Cbf and CbfB were significantly higher than those of CbfP, Cf, and CK. The cluster weight was significantly higher in Cbf and Cbf2 than that of the other treatment groups. The lowest TSS content of 16.83% was recorded in CK, which was significantly lower than the treatments CbfP, CbfM, Cf, Cbf, and CbfB. The TSS content of CbfM (17.10%) was significantly higher than in the other treatment groups.
Grape agronomic traits and microbial network topology attributes were analyzed by Spearman analysis and no significant correlation was found between grape agronomic traits and microbial network topology attribute parameters (Figure 6). The only correlation coefficients greater than 0.6 were network density between the chlorophyll content and single cluster weight.

4. Discussion

4.1. Effects of Different Fertilization Methods on Soil Microbial Community Diversity

As one of the important components of soil ecosystems, soil microorganisms play an irreplaceable role in the decomposition of organic matter as well as nutrient transformation in soil [23]. Different fertilization methods can drive changes in soil physicochemical properties and affect the conditions required for the survival of soil microorganisms, thus altering the composition and diversity of microbial communities in soil [24]. At the experimental site, the soil microbial abundance and diversity were already at a high level with the use of compound microbial fungicides all year round. Charcoal-based fertilizers, when applied to the soil, can increase the availability of soil organic matter and quick-acting nutrient content through adsorption and retention, providing nutrients for microbial growth and reproduction. Numerous studies have shown that the application of charcoal-based fertilizers can increase the abundance and diversity of soil bacteria and fungi [25,26,27]. However, Lu et al. [28] found that a threshold value exists for the effects of biochar on the bacterial community, and exceeding that biochar threshold value will result in a decrease instead of an increase in the bacterial content of the soil. The results of the present study showed that application of 400 g/plant of charcoal-based fertilizer as a basal fertilizer significantly reduced the abundance of soil bacteria but had no significant effect on the abundance and diversity of fungi. No significant difference was observed in bacterial diversity and abundance when compared to two applications of charcoal-based fertilizer and of compound fertilizer during the expansion period. Nevertheless, the abundance and diversity of fungi were significantly higher after the application of compound fertilizer than after charcoal-based fertilizer was applied. The reason for this result might be that the soil microbial richness and diversity were already at a high level in the experimental site based on the past use of a year-round compound microbial fungal agent, and the single application of charcoal-based fertilizer in the experiment was too large, resulting in the content of biochar being too high, which decreased the soil microbial richness and the diversity of fungi. As a humic acid, potassium fulvic acid can effectively promote soil nitrogen cycling [29]. The community structure of both soil bacteria and fungi changed significantly after the application of potassium fulvic acid. Zhang et al. [30] also found that the application of potassium fulvic acid in citrus significantly altered the microbial community structure. Microbial fungicides are commonly used as a agronomic measure to increase the population size and abundance of beneficial microorganisms in the soil, and to inhibit the propagation of pathogenic bacteria; different application methods can affect the effectiveness of the fungicides [31,32]. The application of charcoal-based fertilizer in the first stage and the application of a composite microbial fungicide and B. subtilis in the later stage of fertilizer application had no significant effect on the abundance and diversity of fungi, which was consistent with the results of Huang et al. [33]. As a commonly used fungicide in agricultural production, B. subtilis can improve the physicochemical properties of the soil, increase the soil bacterial abundance and diversity, decrease the abundance of soil harmful bacteria, and increase the crop yield [34]. However, in the present study, the application of B. subtilis at the later stage increased the proportion of negative interactions between soil microorganisms and caused a decrease in soil bacterial abundance and diversity compared to the application of charcoal-based fertilizer at the earlier stage only. This phenomenon suggests that the addition of a single bacterial group by land managers may cause a decrease in bacterial abundance and diversity in soils that previously had high bacterial abundance and diversity.

4.2. Effects of Different Fertilizer Application Methods on Soil Microbial Composition

In the present study, it was found that the dominant bacterial community of different fertilization treatments at the phylum level were similar, all of which were Proteobacteria and Bacteroidetes. The study showed that Bacteroidetes was more abundant in the soil and had a higher use rate of carbon in the soil [35], which can indirectly indicate a higher content of available organic carbon (SOC) in the soil. Most Acidobacteria taxa are oligotrophic, and an increase in nutrients in the soil will inhibit the growth of this type of bacteria, so the relative abundance of Acidobacteria was lower than that of CK in all treatment groups except Cbf2 after fertilizer application. Acidobacteria are acidophilic bacteria, and Egidi et al. [36] found that the relative abundance of the Acidobacteria subsequently showed a decreasing and then increasing trend with an increased amount of biochar application, which corroborated the results of the present experiment. Chitinophagaceae are a group of disease-suppressing bacterial taxa, which were shown to inhibit the growth of pathogenic fungi because they contain enzymes related to the degradation of fungal cell walls [37]. Rhodanobacteraceae are beneficial bacterial taxa that inhibit the growth of pathogenic bacteria such as Xanthomonadaceae and Rhizobiaceae while enhancing plant resistance to conditions that are adverse to growth such as drought [36]. Studies have shown that after amending polycyclic aromatic hydrocarbon (PAH)-contaminated soils with biochar, it was found that the abundance of Saccharimonadales was positively correlated with polycyclic aromatic hydrocarbon degradation products such as benzoin, and other aromatic hydrocarbon degradation products were positively correlated and may be resistant to PAH stress [38]. The microorganisms with higher relative abundance in each treatment group were beneficial microorganisms, indicating good soil conditions. LEfSe analysis showed that application of charcoal-based fertilizers enriched beneficial bacteria such as Rhodanobacteraceae and Bacillaceae, thereby reducing grapevine morbidity.
Compared to bacteria, the composition of fungi varied significantly under different fertilization conditions, with significant differences in the dominant flora of different treatment groups at the genus level. Ascomycota, Basidiomycota, and Mortierellomycota dominated the fungal communities in all treatments, which is similar to the composition of fungal communities in dry farmland [39]. This result can be explained by the fact that grapes are watered less frequently during the late ripening period to increase brix and storage tolerance, resulting in low soil moisture content. Yang et al. [40] found that the soil microbial community is mainly regulated by soil pH and organic matter, and Thickettsia can serve as a signature microorganism for over-fertilization. The results of the present study showed that the soil microbial community is mainly regulated by soil pH and organic matter. The relative abundance of Firmicutes in Cbf2 was higher than that in other treatment groups, and Agaricales was the signature fungus, which could also indicate that the charcoal-based fertilizer application in this experiment was too large and the soil was high in organic matter. Some species of Nigrospora infect plant roots and leaves, causing leaf spot and leaf blight [41,42,43]. Fusarium is an important plant pathogen, and studies have shown that F. solani can cause grapevine leaf veins to turn grayish brown and yellow so that plant height is significantly reduced [44]. In the present study, F. solan and Nigrospora had the highest relative abundance in the Cf treatment group, suggesting that the application of compound fertilizers increases the relative abundance of potential pathogens.

4.3. Effect of Different Fertilization Methods on Agronomic Traits of Grapes

Reasonable fertilization methods can improve crop quality and yield. In areas with good cultivation conditions, the yield of ‘Shine-Muscat’ can reach 6 kg/m2, but if the yield is too high, it will affect the grape’s flavor and sweetness, so the producers of ‘Shine-Muscat’ will generally control the yield at 3–3.75 kg/m2. Studies have shown that the use of biochar can improve soil fertility, reduce negative agro-environmental impacts, and increase yields of crops such as maize, peanuts, and rice [27,45,46,47]. The results of the present study indicate that the application of charcoal-based fertilizers alone or charcoal-based fertilizers with either microbial fungicides or potassium fulvic acid can increase grape yield and TSS. The more direct role of B. subtilis as a biopreventive bacterium lies in its ability to defend against the invasion of pathogens and to reduce plant morbidity, so the effect on grape quality enhancement was not obvious in soils with more beneficial flora. Potassium fulvic acid promotes plant uptake of nitrogen and phosphorus and is effective in increasing tomato and citrus yields under adverse conditions [48]. Based on the basal application of a charcoal-based fertilizer to grapes, the late application of potassium fulvic acid did not significantly enhance the TSS of grapes, probably because the effect of potassium fulvic acid was not significant under conditions of high soil fertility and the absence of climatic extremes. Interestingly, the application of a charcoal-based fertilizer as a base fertilizer and late application of microbial composite enriched with Saccharomycetes yeasts as bioprophilic bacteria reduced the relative abundance of harmful soil bacteria, improved soil fertility, and had a positive effect on enhancing grape aroma and wine fermentation [49].
As the ecological network of soil microbes is related to the stable operation of the ecosystem, a large number of complex interactions among microorganisms are necessary to accomplish important ecological functions such as carbon and nitrogen cycling. Fertilization is one of the measures land managers can use to change the properties of soil microbial networks [50]. The present study found that Cbf had the highest nodes, greatest number of edges, highest average degree of co-occurrence network, and highest proportion of positive correlation between various microbial groups, which indicated that the microbial network was large. This finding may be caused by the nutrients brought by charcoal-based fertilizer that alleviate competition among microbial groups. Previous studies have shown that the negative correlation between various microbial groups can promote network stability [51]. The addition of microbial fungi and biostimulants during the expansion stage of grape reduced the size of the microbial network, decreased the proportion of positive correlation between various microbial groups, and enhanced the ability of grape to withstand adverse environmental conditions and diseases. Zhang et al. [52] found that the complexity of soil bacterial and fungal communities had the most stable contribution to maize yield. Iman et al. [51] were able to estimate potato yield using information related to the structure of the fungal community. In contrast, the present study found no significant correlation between any of the microbial network topological attribute parameters and yield, which may be caused by the need to control yield in order to produce high-quality ‘Shine-Muscat’.

5. Conclusions

In recent years, large amounts of waste biomass using inappropriate recycling techniques have significantly increased greenhouse gas emissions. Converting waste biomass into biochar and processing it into charcoal-based fertilizers can improve soil quality while achieving soil carbon sequestration. The application of charcoal-based fertilizer can change the structure and abundance of soil microorganisms and different application methods have different effects. The combination of soil microorganisms and agronomic traits of grapes as well as the cost of fertilizers suggests that the use of charcoal-based fertilizers as a base fertilizer and microbial fungicides at the later stage of fruit expansion provides the best fertilization options.

Author Contributions

Methodology, P.S. and J.S.; data curation, C.C., X.L. and J.Z. (Jianxi Zhu); writing—original draft preparation, H.J., J.W. and P.S.; writing—review and editing, J.W., Y.W. and P.S.; visualization, J.Z. (Jian Zhou) and H.W.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Zhejiang Province Fruit Industry Technology Project.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://ngdc.cncb.ac.cn/gsa./ (accessed on 14 June 2024), CRA017064.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Analysis of soil microbial diversity in different treatment groups: (A) bacterial ACE index; (B) bacterial Shannon index; (C) bacterial principal co-ordinates analysis (PCoA); (D) fungal ACE index; (E) fungal Shannon index; (F) fungal PCoA. Note: PC1 and PC2, principal co-ordinates 1 and 2, respectively; CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period; * and ** indicate p < 0.05 and p < 0.01, respectively.
Figure 1. Analysis of soil microbial diversity in different treatment groups: (A) bacterial ACE index; (B) bacterial Shannon index; (C) bacterial principal co-ordinates analysis (PCoA); (D) fungal ACE index; (E) fungal Shannon index; (F) fungal PCoA. Note: PC1 and PC2, principal co-ordinates 1 and 2, respectively; CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period; * and ** indicate p < 0.05 and p < 0.01, respectively.
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Figure 2. Relative abundance of bacteria in different treatment groups at the (A) phylum and (B) genus levels. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
Figure 2. Relative abundance of bacteria in different treatment groups at the (A) phylum and (B) genus levels. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
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Figure 3. Relative abundance of fungi in different treatment groups at the (A) phylum, (B) genus, and (C) species levels. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
Figure 3. Relative abundance of fungi in different treatment groups at the (A) phylum, (B) genus, and (C) species levels. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
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Figure 4. Results of linear discriminant effect size analysis for different treatments for: (A) bacteria and (B) fungi using Log10 Linear Discriminant Analysis (LDA) (>3). CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
Figure 4. Results of linear discriminant effect size analysis for different treatments for: (A) bacteria and (B) fungi using Log10 Linear Discriminant Analysis (LDA) (>3). CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
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Figure 5. Bacterial and fungal co-occurrence network diagram for the seven treatment groups. Node size and the number of node-connected edges are positively correlated. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
Figure 5. Bacterial and fungal co-occurrence network diagram for the seven treatment groups. Node size and the number of node-connected edges are positively correlated. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
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Figure 6. Microbial network topological complementary attributes correlated with grape agronomic traits. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
Figure 6. Microbial network topological complementary attributes correlated with grape agronomic traits. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
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Table 1. Fertilizer application in different treatment groups.
Table 1. Fertilizer application in different treatment groups.
TreatmentBase FertilizerFruit-Expanding Fertilizer
CK//
Cbf400 g/plant charcoal-based fertilizer/
CbfP400 g/plant charcoal-based fertilizer40 g/plant potassium fulvic acid
CbfM400 g/plant charcoal-based fertilizer10 g/plant composite microbial fungus
CbfB400 g/plant charcoal-based fertilizer10 g/plant Bacillus subtilis
Cbf2/two applications of 200 g/plant charcoal-based fertilizer
Cf/two applications of 100 g/plant composite fertilizer
Table 2. Topological complementary properties of bacterial and fungal networks in different treatment groups.
Table 2. Topological complementary properties of bacterial and fungal networks in different treatment groups.
TreatmentNodesEdgesPositive Edges (%)Negative Edges (%)Average DegreeNetwork DensityModularity
Cbf140303458.47%41.53%43.3430.3120.616
CbfP122236651.94%48.06%38.7870.3210.602
CbfM124224449.29%50.71%36.1940.2940.646
CbfB115209750.79%49.21%36.4700.3200.615
Cbf2117213951.75%48.25%36.5640.3150.616
Cf132244650.65%49.35%37.0610.2830.664
CK126234250.43%49.57%37.1750.2970.610
CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP: 400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
Table 3. Agronomic traits of grapes in each of seven treatment groups.
Table 3. Agronomic traits of grapes in each of seven treatment groups.
TreatmentChlorophyll Content (SPAD)Cluster Weight (g)Total Soluble Solids (%)Yield (kg/m2)
Cbf39.61 ± 2.60 bc1057.77 ± 99.72 b18.12 ± 1.05 d3.57 ± 0.02 c
CbfP38.07 ± 1.21 a890.73 ± 54.49 a17.5 ± 0.7 bc3.55 ± 0.06 bc
CbfM38.28 ± 2.03 ab857.17 ± 76.6 a19.1 ± 1.55 e3.5 ± 0.06 bc
CbfB39.45 ± 1.80 bc899.61 ± 107.29 a17.88 ± 1.13 cd3.54 ± 0.06 bc
Cbf240.16 ± 1.35 d1101.66 ± 36.57 b17.21 ± 1.17 ab3.58 ± 0.08 c
Cf37.58 ± 2.05 a795.9 ± 100.02 a17.99 ± 1.42 d3.47 ± 0.06 b
CK37.14 ± 1.98 a884 ± 116.19 a16.83 ± 1.79 a3.33 ± 0.03 a
Lowercase letters indicate significance at the 0.05 level. CK: no fertilizer; Cbf: 400 g/plant charcoal-based fertilizer as the base fertilizer; CbfP:400 g/plant charcoal-based fertilizer as the base fertilizer + 40 g/plant potassium fulvic acid in the expansion period; CbfM: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant composite microbial fungus in the expansion period; CbfB: 400 g/plant charcoal-based fertilizer as the base fertilizer + 10 g/plant Bacillus subtilis in the expansion period; Cbf2: two applications of 200 g/plant charcoal-based fertilizer in the expansion period; Cf: two applications of 100 g/plant composite fertilizer in the expansion period.
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Sun, P.; Wu, J.; Lin, X.; Chen, C.; Zhu, J.; Wang, Y.; Zhou, J.; Wang, H.; Shen, J.; Jia, H. The Application of Carbon-Based Fertilizer Changed the Microbial Composition and Co-Occurrence Network Topological Properties of Vineyard Soil. Horticulturae 2024, 10, 871. https://doi.org/10.3390/horticulturae10080871

AMA Style

Sun P, Wu J, Lin X, Chen C, Zhu J, Wang Y, Zhou J, Wang H, Shen J, Jia H. The Application of Carbon-Based Fertilizer Changed the Microbial Composition and Co-Occurrence Network Topological Properties of Vineyard Soil. Horticulturae. 2024; 10(8):871. https://doi.org/10.3390/horticulturae10080871

Chicago/Turabian Style

Sun, Ping, Jiaqi Wu, Xianrui Lin, Chenfei Chen, Jianxi Zhu, Yi Wang, Jian Zhou, Huaxin Wang, Jiansheng Shen, and Huijuan Jia. 2024. "The Application of Carbon-Based Fertilizer Changed the Microbial Composition and Co-Occurrence Network Topological Properties of Vineyard Soil" Horticulturae 10, no. 8: 871. https://doi.org/10.3390/horticulturae10080871

APA Style

Sun, P., Wu, J., Lin, X., Chen, C., Zhu, J., Wang, Y., Zhou, J., Wang, H., Shen, J., & Jia, H. (2024). The Application of Carbon-Based Fertilizer Changed the Microbial Composition and Co-Occurrence Network Topological Properties of Vineyard Soil. Horticulturae, 10(8), 871. https://doi.org/10.3390/horticulturae10080871

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