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Article

Evaluating the Effect on Cultivation of Replacing Soil with Typical Soilless Growing Media: A Microbial Perspective

by
Jie Li
,
Wanlai Zhou
,
Rui Yang
,
Hong Wang
,
Dongdong Zhang
,
Yujia Li
,
Zhiyong Qi
and
Wei Lin
*
Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(1), 6; https://doi.org/10.3390/agronomy13010006
Submission received: 12 October 2022 / Revised: 16 December 2022 / Accepted: 16 December 2022 / Published: 20 December 2022
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Abstract

:
Bacteria and fungi are good indicators for soil health as well as soilless growing media (SGM) health. However, there is very limited information about the fungal and bacterial communities for SGM. In the present study, coir substrate and peat-based substrate were used as typical SGM under drip irrigation and tidal irrigation to understand the situation of fungal and bacterial communities by high-throughput sequencing technology. In this study, both environmental factors and microbial communities were significantly affected by SGM type and irrigation pattern, in which SGM type played a major role and irrigation pattern played a minor role. The bacterial phyla Actinobacteriota and Proteobacteria and the fungal phyla Ascomycota were more closely related to environmental factors including EC, pH, NO3−, NH4+ and ω as well as urease and phosphatase. The bacterial and fungal communities in the two SGM had some similarities with those in soil. In addition, the functions of the soil, including key soil organisms, carbon mineralization, wood decomposition, nitrification, denitrification, carbon fixation, nitrogen fixation and methanotrophy, could be basically performed by the two SGM. In general, the SGM should possess common soil capabilities according to bacterial and fungal analyses, but there are numerous fungi of unknown function that need be addressed in the future. Meanwhile, these results improve our understanding of the correlation between the environmental factors and the microbiome, and provide basic guidance for management and research on SGM in the future.

1. Introduction

Urbanization has contributed to a rapid development of greenhouse vegetable production in China, resulting in serious problems in soil due to the overuse of agricultural inputs [1]. In order to overcome the soil problems, soilless culture systems (SCS) used as substitutes are garnering more and more attention [2]. Currently, the common SCSs include soilless growing media (SGM) and hydroponic systems. However, the SGM are more applied because of the suitable characteristics for the growth of most plants, such as adequate porosity, low soluble salt content and high content of available water [3]. The SGM with many advantages are developing rapidly in urban and peri-urban areas to take the role of soil. Correspondingly, while research on SGM is increasing, most of it focuses on their physical and chemical properties rather than their biological properties [4]. Since the microbiomes in SGM have not been studied as extensively as soil, there is limited information about the microbiomes in SGM. Therefore, it is necessary to explore microbiomes in SGM that may play an important role in plant growth.
Many studies have shown that the microbiome in soil is closely related to the health and biomass of plants [5,6,7]. Soil stores not only the organic matter but also the live heterotrophic soil organisms, including protists, bacteria, archaea, fungi and animals. The diverse microbiome living in soil is critical for organic matter decomposition and nutrient cycling. They, especially rhizosphere microbes, can manage the soil fertility by regulating the absorption and release of the main elements of soil [5]. This is mainly related to the fact that soil microbiomes can transform organic matter into inorganic matter and provide effective nutrients for plants [8]. Some rhizosphere microbes can also secrete vitamins and plant growth-stimulating hormones to promote plant growth and improve the tolerance of adverse environmental conditions [9,10]. Among them, bacteria and fungi dominate soil habitats in biomass, biodiversity and essential-soil processes [11]. Community analysis based on bacteria and fungi reveals that soil microbiomes vary in structure and function in different ecosystems [12]. Many studies have proposed that soil type might be the determinant factor that shapes the composition of bacterial and fungal communities, as each soil has its own physicochemical characteristics and thus maintains a specific community under equilibrium conditions [13]. In addition, the structure and function of bacteria and fungi (for a specific soil) are also highly dependent on abiotic environmental factors in the soil. Previous studies have investigated the effects of soil physicochemical characteristics on microbial communities, such as water input, pH, organic matter content, salinity and soil particle size [14,15].
However, the bacterial and fungal communities in SGM should be largely affected by the material of SGM [13]. In theory, any material that meets plant growth can be used as an SGM, such as peat, coir, mineral wool and sand. This leads to the essential differences in physical, chemical and biological properties among different SGM, and between SGM and soil. Therefore, it is difficult to study SGM because of their complexity and diversity. Currently, coir and peat substrates are the most widely used and representative organic SGM in SCSs in China as well as in the world, accounting for over 80% of the organic SGM [2,16]. With the development of soilless culture technology, these substrates will be used more and more in the future, and so they should be studied first. There may be differences in pH, electrical conductivity (EC), organic matter (OM), water content and nutrient adsorption between the two SGM due to different material composition—which may be the main abiotic environmental factors affecting bacteria and fungi. Remarkably, the rhizosphere microbiome may play a key role in SGM, wherein roots tend to fill due to limited space. Consequently, a study of microbial communities of SGM may contain the rhizosphere microbiome. In addition, it is also very vital that irrigation as an agronomic management practice impacts bacterial and fungal preference. In SCSs, drip irrigation and tidal irrigation are common irrigation patterns. Therefore, the objectives of this study are (1) to evaluate the bacterial and fungal communities of the coir and peat substrates under drip irrigation and tidal irrigation; (2) to clarify the effect of the physicochemical properties of the two SGM under the two irrigation patterns on bacteria and fungi; and (3) to reveal the structure and function of bacteria and fungi for the SGM in replacing soil. The results will help to understand the structure and function of the microbiome in the typical SGM under common irrigation patterns, and to establish guidance towards optimal management practices for plant cultivation in SCSs.

2. Materials and Methods

2.1. Experimental Design

Greenhouse trials were conducted at the Chengdu Academy of Agricultural and Forestry Sciences (139° E, 30.7° N), situated in Chengdu, Sichuan Province, China, in 2020. Two typical SGM, including a coir substrate imported from a foreign source (Galuku Pty Ltd., Sydney, Australia) and a peat-based substrate including 60% peat, 20% perlite, 18% vermiculite and 2% plant fiber (China patent No. CN202011106379.6), were evaluated with an equal volume on the planting of tomatoes (Solanum lycopersicum cv. SV7846TH) under drip irrigation and tidal irrigation systems. The peat-based substrate with drip irrigation and tidal irrigation were named DL and TL treatments, and the coir substrate with drip irrigation and tidal irrigation were named DY and TY treatments, respectively. There were 4 replicates (plots) for each treatment. The water and nutrients were supplied with the irrigation systems to meet plant growth needs (see Supplementary Table S1). Initial characteristics of both coir substrate and peat-based substrate were listed in Supplementary Table S2.

2.2. Cultivation and Sampling

Seeds were sown into expanded polystyrene trays with an inverted pyramid design and filled with the two SGM on 12 March 2020. The uniform seedlings were transplanted from a growth chamber (125 cm × 50 cm × 128 cm), after 16 h in the light and 8 h in the dark, to a U-shaped slot in a greenhouse (60 m × 40 m) on 9 April 2020. After being cultivated for 50 days, the plants were hand-pulled from the SGM, and the roots were shaken vigorously to obtain SGM samples. The SGM samples were carefully collected with a sterile tube. After removing residual plant residues (e.g., roots) and passing the samples through a 2 mm sieve, one portion of the samples was stored in a −80 °C freezer for future microbial analysis, and the other portion was extracted directly or air-dried for chemical analysis.

2.3. Determination of Soilless Growing Media Characteristics

A part of the fresh SGM samples was used to measure mass water content (ω). The remainder was shaken with 2M KCl solution (1:10 w/v) for 1 h. The content of NH4+ and NO3− in KCl extracts was measured using a continuous flow analyzer (QuikChem8000, LACHAT, Milwaukee, USA). The air-dried SGM samples were extracted with de-ionized water (1:20, w/v) by shaking for 1 h. The pH and electrical conductivity (EC) in the water extracts were measured using a pH meter (FE20; Mettler Toledo, Schwerzenbach, Switzerland) and a conductivity meter (SG3, Mettler Toledo, Schwerzenbach, Switzerland), respectively. Urease and neutral phosphatase activities that are closely related to substrate fertility were also determined using soil enzyme testing kits (Solarbio, Beijing, China) according to the manufacturer’s instructions. In addition, we obtained the tomato yield by weighing.

2.4. Extraction of DNA and Illumina MiSeq DNA Sequencing

The total microbial genomic DNA of the different SGM samples was extracted. The DNA extraction was performed using the Fast DNA SPIN Kit for soil (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s protocols. The extracted all-parallel samples for each treatment were mixed together before sequencing [17,18]. The DNA concentration and quality were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, NC, USA). The V4–V5 hypervariable regions of the bacterial 16S rRNA gene were amplified with universal primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 909R (5′-CCCCGYCAATTCMTTTRAGT-3′) as described previously [19]. The internal transcribed spacer (ITS) regions of ribosomal RNA derived from fungi were amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-TGCGTTCTTCATCGATGC-3′) [20]. PCR product purification and Illumina sequencing were performed as reported previously [21].

2.5. Data Processing and Analysis

Raw fastq files were quality-filtered by Trimmomatic and merged by FLASH; low-quality sequences and chimeras were removed [22,23]. Then, the sequences were clustered into operational taxonomic units (OTUs, 97% similarity) using Uparse [24]. The taxonomy of each 16S rRNA gene sequence was analyzed by the RDP Classifier algorithm (http://rdp.cme.msu.edu/, accessed on 20 July 2022) against the Silva 16S rRNA database [25] and the Unite database [26] for bacteria and fungi, respectively. Moreover, chloroplasts and mitochondria were removed for downstream analysis.
The sequence was normalized to obtain the minimum read counts of all samples. The alpha-diversity index was calculated using MOTHUR software [27]. The circus was created using R (version 4.0.2) to show the bacteria and fungi at the phylum level. The hierarchically clustered heatmap, based on Bray-Curtis similarity index, was created using R to show the bacteria and fungi at the genus level (top 30 genera). Principal co-ordinates analysis (PCoA) was also analyzed using the R package vegan. The correlation network analysis based on Spearman’s correlation was conducted using Cytoscape (version 3.9.1) to evaluate the correlation between the environmental factors and primary genera (mean relative abundance > 1%). The bacterial and fungal functional profiles were predicted by PICRUSt 2 depending on the KEGG pathway database and by FunGuild according to an open fungal taxa annotation, respectively. One-way and two-way analyses of variance (ANOVA) were used to assess differences between treatment groups in terms of the physicochemical characteristics of the SGM using R. Differences were considered significant at p < 0.05, with a separation of mean values by a Tukey test.

3. Results

3.1. Characteristics of the Different Treatments

The pH values for all treatments were circumneutral, ranging from 6.46 to 7.19 (Figure 1A). In addition, the EC, OM, NO3−, NH4+, ω, urease activity and phosphatase activity varied in the different treatments, ranging from 272 to 1207 μS/cm, 220.4 to 415.9 g/kg, 878.5 to 2566.6 mg/kg, 22.9 to 95.0 mg/kg, 2.2 to 5.8, 2192 to 6434 U/g, and 2426 to 36799 U/g, respectively (Figure 1B–H). The values for EC, OM, NO3−, NH4+, ω and phosphatase activity in the DY and TY treatments were significantly larger than that in the DL and TL treatments, while the values for pH and urease activity were opposite (Table 1, p < 0.01). There were interactive effects on pH, EC, NO3− and NH4+ between SGM type and irrigation pattern (Table 1, p < 0.05).

3.2. Bacterial and Fungal Diversity and Structure of the Different Treatments

A total of 195,431 bacterial 16S rRNA (DL: 50,756; TL: 48,096; DY: 50,392; TY: 46,187) and 282,035 fungal ITS (DL: 66,420; TL: 73,427; DY: 70,279; TY: 71,909) high-quality sequences were obtained from the four treatments, which respectively were given into 1830 and 397 OTUs (Table 2). The coverage was close to 1, showing that the results were reliable (Table 2). The OTU numbers and Chao1 richness in the TL and DL treatments were obvious larger than that in the TY and DY treatments for both bacteria and fungi, while Simpson diversity showed this trend only for bacteria and Shannon diversity showed the same result only for fungi (Table 2). In addition, each treatment had some common and unique bacterial and fungal OTUs based on the Venn diagram (Figure S1).
As shown in Figure 2A,B, the relative abundance was analyzed for bacteria and fungi at the phylum level. For bacteria, Proteobacteria, as the largest phylum in the DL (37.09%) and TL (36.05%) treatments, were lower than that in the DY (46.69%) and TY (54.19%) treatments. However, Actinobacteria and Planctomycetes in the DL (32.16%, 6.07%) and TL (21.12%, 7.22%) treatments were higher compared to in the DY (5.52%, 5.55%) and TY (6.97%, 5.24%) treatments. In addition, Acidobacteria in the DY treatment, Firmicutes in the TY treatment and Cyanobacteria in the TL treatment had higher relative abundance than other treatments. For fungi, Ascomycota was the dominant phylum in all treatments, especially in the DL treatment, followed by Rozellomycota in the TL and TY treatments, which was obviously higher than that in the DL and DY treatments. Remarkably, there were many unclassified fungal phyla in the four treatments, with the DY treatment having the most.
In addition, the hierarchically clustered heatmap showed the relationship of bacteria and fungi among the four treatments (Figure 2C,D). For bacteria, there were close relationships between DL and TL treatments and between DY and TY treatments. For fungi, a close relationship was found between TL and TY treatments and between DL and DY treatments. It can be also observed that the Devosia, Bacillus, Actinoallomurus, Pseudolabrys, Pantalinema, Streptomyces, Saccharopolyspora, Bauldia, Pir4_lineage and Pseudomonas were the representative bacterial genera, and Penicillium, Ramophialophora, Pseudogymnoascus, Aspergillus, Zopfiella and Plectosphaerella were the representative fungal genera.

3.3. Effects of Environmental Parameters on Microbial Community under Different Treatments

To reveal the correlation between environmental parameters and primary genera, correlation network analysis was performed based on Spearman’s correlation coefficients (p < 0.01). As shown in Figure 3A,B, the urease, phosphatase, OM and ω were significantly correlated with norank_f__Microscillaceae, norank_f__BIrii41, norank_f__Pirellulaceae, Ilumatobacter, norank_f__Gemmatimonadaceae, Lacunisphaera, Hydrogenophaga and IS-44 (p < 0.05), but there was a reverse correlation between urease and phosphatase, and OM and ω. The EC, NO3− and NH4+ were significantly correlated with Bacillus, Actinoallomurus, Pseudolabrys, unclassified_f__Micromonosporaceae, Saccharopolyspora, unclassified_f__Sphingomonadaceae and unclassified_f__Xanthobacteraceae (p < 0.01), while pH was not significantly correlated with these bacterial genera at p < 0.01 level. It was also found that actinobacteriota containing four influential genera and proteobacteria containing five influential genera were the core bacterial phyla in SGM. For fungi, the urease, phosphatase, OM and ω were significantly correlated with unclassified_k__Fungi, Pseudogymnoascus, Candida, unclassified_o__Onygenales, Blastobotrys, Trichoderma and Byssochlamys (p < 0.05), but there was an opposite correlation between the urease and phosphatase, OM and ω. In addition, EC, NO3− and NH4+ were significantly correlated with Penicillium, Ramophialophora, Aspergillus, unclassified_p__Ascomycota, Phialemonium and unclassified_p__Chytridiomycota (p < 0.05); pH was significantly correlated with Saitozyma, unclassified_f__Pezizaceae, unclassified_f__Pseudeurotiaceae, Solicoccozyma and Meliniomyces (p < 0.05). The core fungal phyla were ascomycota containing 14 influential genera.

3.4. Bacterial and Fungal Function Prediction

As shown in Figure 4A, the bacterial functions predicted metabolism (76.34~78.56%), genetic information processing (5.86~6.29%), environment information processing (5.70~6.32%), cellular processes (4.37~5.10%), human diseases (3.60~4.23%) and organismal systems (1.87~1.95%). In a deeper level, the global and overview maps (39.11~40.19%), carbohydrate metabolism (8.68~9.12%) and amino acid metabolism (7.89~8.24%) were main bacterial functions. Furthermore, there was no significant difference in these bacterial functions among the four treatments. The fungal functions were assigned into saprotrophy, symbiotrophy and pathotrophy. As shown in Figure 4B, the fungi could perform one, two or even three of the three functions. Apart from unknown functional fungi, fungi performing single saprotrophy had the highest abundance, with 4.3~53.14%, followed by fungi performing all three functions, with 2.8~18.9%. It also can be seen that there were obvious differences in these fungal functions among the four treatments.
In the four treatments, some common functions performed by soil organisms were also predicted based on the same bacteria or fungi with soil (Figure 4C) [28]. The bacterial functions involved key soil organisms, carbon mineralization, wood decomposition, nitrification, denitrification, carbon fixation, nitrogen fixation and methanotrophy. The fungal functions included key soil organisms, carbon mineralization, wood decomposition and mycorrhizal association.

4. Discussion

Most of the current research mainly focuses on soil rather than soilless growing media (SGM). Accordingly, irrigation patterns based on SGM in soilless culture systems (SCS) are less studied. Therefore, the peat-based substrate and coir substrate under drip irrigation and tidal irrigation were sampled and analyzed in this study. The EC, OM, NO3−, NH4+, ω and phosphatase activity in the coir substrate was higher than the peat-based substrate, whereas the pH and urease activity were the opposite (Figure 1). These distinct differences should be related to the components of the two SGM that come from different materials. The difference for EC, NO3− and NH4+ mainly is due to the fact that coir substrate contains or absorbs more inorganic salt compared to peat-based substrate [29]. A similar phenomenon is also found between different soil ecosystems with complex environmental conditions [30]. However, the SGM, as a kind of SCS, very much differs from soil ecosystems and are rarely studied. Therefore, we cannot obtain decisive information based on the different physicochemical properties of the two SGM. Furthermore, the pH, EC, OM, NO3−, NH4+, urease and neutral phosphatase were also significantly affected by irrigation patterns (Table 1, p < 0.05), indicating that the importance of irrigation pattern in SCS is the same as in many soil ecosystems [31]. In summary, the SGM type and irrigation pattern are important for the tomato-growing environment in SCSs, although tomato yield was not sensitive to different SGM types or irrigation patterns (Figure S2).
The microbiomes of SGM in CSC remain unclear, but they are critical for the health and biomass of crops [32]. In this study, hence, bacteria and fungi as the main microbiome were analyzed to ascertain their situation in two typical SGM under two common irrigation patterns. From the OTU numbers and α diversity perspective, the bacteria and fungi of the SGM samples in the four treatments were distinctly lower than those in common soil ecosystems [33,34,35], and were larger than those in raw SGM (below detection limit). Therefore, most bacteria and fungi in the SGM samples might be adsorbed from exogenous sources, such as irrigation water and the atmosphere. However, different SGM types and irrigation patterns may also change the adsorption effect on the microbiome. As observed, the four treatments exhibited unique OTUs (Figure S1) and differed in diversity for bacteria and fungi (Table 2). At the phylum level, these differences caused by SGM were also embodied in the bacterial community, while the differences in the fungal community were more susceptible to irrigation patterns (Figure 2A,B). The analysis of the hierarchically clustered heatmap at the genus level showed to be more intuitive for this point (Figure 2C,D). In any case, the PCoA results can also prove these affinity relationships (Figure S3). Similarly, many studies on soil ecosystems also confirmed that bacteria are sensitive to soil type [5,36], and fungi are sensitive to irrigation pattern [11,37]. Moreover, Proteobacteria, Actinobacteria, and Acidobacteria were essential members for the bacterial phyla (Figure 2A), which is consistent with previous studies on soil [7,38]. The fungal phyla, including Ascomycota, Rozellomycota and Basidiomycota (Figure 2B), have been reported to be the majority groups in the many soil ecosystems [39,40]. What is noteworthy is that unclassified k Fungi accounted for an average of 42.49% of the fungal community in all treatments, which was quite different from previous studies on soil fungal communities [35,40]. This result suggested that the two SGM had numerous unknown fungi which may be associated with the material composition and fabrication process of coir substrate and peat-based substrate. Therefore, these unknown fungi need further investigation in the future. In addition, the bacterial genera and fungal genera from the two SGM can also be observed in many soil ecosystems [18,41], indicating that there may be some similarities in the microbial communities between the SGM and soil.
Previous studies demonstrated that microbial communities vary with environmental conditions such as pH [42,43], organic matter content [33,44], water content [45,46], various nutrients content [14], soil particle size [47] and other physicochemical properties of soil [12]. Compared to soil, there are more diverse physical and chemical characteristics among SGM, and thus different SGM may differ in the diversity and structure of their microbiome. In this study, the EC, pH, OM, NO3−, NH4+, ω, urease and phosphatase were analyzed as environmental parameters. According to the correlation network analysis between environmental parameters and primary genera, there was a similar effect among EC, NO3− and NH4+; among OM, ω and phosphatase; and between pH and urease. In addition, pH had a more significant effect on the fungal community, based on this network analysis. Although pH and EC are regarded as important factors on affecting the microbial community in the soil [48,49], they do not show such differences in this study. This might be associated with the widening of differences caused by salt accumulation in the SGM in this study—the same phenomenon is found in soil with salinity gradients [30]. Furthermore, Actinobacteria, Proteobacteria and Ascomycota were core phyla of this study, and they are also more important phyla in saline soil compared to non-saline soil [35,50]. However, high EC values were observed only in the coir substrate (Figure 1B). Therefore, we speculate that the salinization problems are more likely to occur in coir substrate. It also was clearly observed that there were many bacterial or fungal phyla significantly associated with urease and phosphatase, as well as EC, pH, OM, NO3−, NH4+ and ω, suggesting that the nutrient function of SGM influenced the multiple bacteria and fungi. Therefore, a comprehensive understanding of the microbiome helps to explore more function of SGM.
In this study, the functions performed by bacteria and fungi were predicted by PICRUSt 2 and FunGuild, respectively. For bacteria, metabolism was the primary function, while its abundance in the SGM was higher than that in common soil [51] and compost [18]. On a deeper level, overview maps, carbohydrate metabolism and amino acid metabolism were more abundant. Carbohydrate metabolism can degrade hemicellulose and cellulose [52], and amino acid metabolism can provide carbon and energy sources for microbial growth and activity [53]. Therefore, it can be inferred that the transformation of organic matter played vital roles in this study. For fungi, saprotrophy was the main function apart from an unknown function, especially in peat-based substrate, indicating a large number of dead organisms in the peat-based substrate and coir substrate. Similarly, key soil organisms, carbon mineralization and wood decomposition were observed based on the same bacteria and fungi from the previous study [28], which supports our judgment above. Meanwhile, other soil functions, such as nitrification, denitrification, carbon fixation, nitrogen fixation and methanotrophy, were also observed in the two SGM [28]. Furthermore, Pseudomonas and Bacillus were the most representative bacterial genera in the two SGM. They play a vital role in biocontrol and plant-growth-promoting activity in the soil [54]. In summary, either the peat-based substrate or the coir substrate can basically perform soil functions in terms of their microbiome.

5. Conclusions

Identifying the microbiome information is essential for improving mutually favorable relationships between soilless growing media (SGM) and plants, and to guide management practices in soilless culture systems (SCS). In this study, both environmental factors and microbial communities were significantly affected by SGM type and irrigation pattern, in which SGM type played a major role and irrigation pattern played a minor role. In addition, although there were some differences in bacterial and fungal community structures among different treatments, the microbial functions and tomato yield had no significant differences between the two SGM. We also found that the common soil functions can basically be performed by the two SGM. These results indicated that peat-based substrate and coir substrate are very good SGM and can be used to replace the soil. Furthermore, it is likely that the bacteria and fungi are primarily derived from exogenous adsorption rather than from the SGM itself. Therefore, it is speculated that other SGM can also perform similar functions. In addition, numerous unknown fungi and other environmental factors in the two SGM, such as porosity and nutrient content, need be investigated in later research. In general, the SGM should possess common soil capabilities, but there are some problems that need be addressed in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13010006/s1, Figure S1: Venn diagram of bacteria (a) and fungi (b) at OUT level for different treatment; Figure S2: The tomato yield of different treatments; Figure S3: Principal Co-ordinates Analysis (PCoA) of bacteria (a) and fungi (b) at OTU level for different treatments; Table S1: Composition of nutrient solution; Table S2: Initial characteristics of soilless growing media.

Author Contributions

J.L.: Conceptualization, methodology, investigation, data curation, formal analysis, writing, review and editing. W.L.: conceptualization, methodology, investigation, writing, review and editing, funding acquisition, project administration. W.Z.: data curation, writing, review and editing. R.Y.: data curation, writing, review and editing. H.W.: investigation, writing, review and editing. D.Z.: formal analysis, writing, review and editing. Y.L.: writing, review and editing. Z.Q.: writing, review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by The National Natural Science Foundation of China (42107040), The Agricultural Science and Technology Innovation Program (ASTIP), and the Local Financial of National Agricultural Science & Technology Center, Chengdu (NASC).

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its Supplementary Information files. The sequencing raw reads were deposited in the NCBI Sequence Read Archive (SRA) database with the following accession numbers: PRJNA695038 (https://www.ncbi.nlm.nih.gov).

Acknowledgments

The authors would like to acknowledge Zheng Wang and Tao Huang for technical assistance in the field experiments.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 13 00006 g001 550
Figure 1. Characteristics parameters of different treatments. (A) pH; (B) EC; (C) NH4+ content; (D) NO3− content; (E) OM content; (F) mass water content/ω; (G) urease activity; (H) neutral phosphatase activity; DL, drip irrigation + peat-based substrate; DY, drip irrigation + coir substrate; TL, tidal irrigation + peat-based substrate; TY, tidal irrigation + coir substrate. The same below. Different lowercase letters indicate statistically significant differences at 5% level. Results are average values of the treatments ± SD (n = 4).
Figure 1. Characteristics parameters of different treatments. (A) pH; (B) EC; (C) NH4+ content; (D) NO3− content; (E) OM content; (F) mass water content/ω; (G) urease activity; (H) neutral phosphatase activity; DL, drip irrigation + peat-based substrate; DY, drip irrigation + coir substrate; TL, tidal irrigation + peat-based substrate; TY, tidal irrigation + coir substrate. The same below. Different lowercase letters indicate statistically significant differences at 5% level. Results are average values of the treatments ± SD (n = 4).
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Figure 2. Microbial community structure of the soilless growing media. (A) Circus analysis for bacteria at phylum level; (B) circus analysis for fungi at phylum level; (C) hierarchically clustered heatmap for bacteria at genus level; (D) hierarchically clustered heatmap for fungi at genus level. Data show as mean value. The same below.
Figure 2. Microbial community structure of the soilless growing media. (A) Circus analysis for bacteria at phylum level; (B) circus analysis for fungi at phylum level; (C) hierarchically clustered heatmap for bacteria at genus level; (D) hierarchically clustered heatmap for fungi at genus level. Data show as mean value. The same below.
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Figure 3. The correlation between environmental parameters and genera for different treatments. (A) Network analysis for bacteria; (B) network analysis for fungi. The grey solid line indicates a positive correlation, whereas the grey dotted line indicates a negative correlation (A,B). The bigger the circle, the higher relative abundance (A,B).
Figure 3. The correlation between environmental parameters and genera for different treatments. (A) Network analysis for bacteria; (B) network analysis for fungi. The grey solid line indicates a positive correlation, whereas the grey dotted line indicates a negative correlation (A,B). The bigger the circle, the higher relative abundance (A,B).
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Figure 4. Function prediction of microbial communities for soilless growing media. (A) Bacterial functional profiles; (B) fungal functional profiles; (C) common functional profiles.
Figure 4. Function prediction of microbial communities for soilless growing media. (A) Bacterial functional profiles; (B) fungal functional profiles; (C) common functional profiles.
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Table
Table 1. Mean values of different treatments in pH, EC, OM, NO3−, NH4+, ω, urease and neutral phosphatase.
Table 1. Mean values of different treatments in pH, EC, OM, NO3−, NH4+, ω, urease and neutral phosphatase.
TreatmentspHECOMNO3−NH4+ωUreasePhosphatase
μs/cmg/kgmg/kgmg/kg%U/gU/g
DL6.87 ± 0.05286 ± 43271.9 ± 3.4985.2 ± 106.729.8 ± 6.92.4 ± 0.24.90 ± 0.3511.98 ± 2.98
DY6.68 ± 0.10525 ± 43412.0 ± 5.32050.1 ± 216.169.9 ± 5.35.2 ± 0.62.96 ± 0.5630.10 ± 7.28
TL7.11 ± 0.12375 ± 67249.7 ± 13.21242.9 ± 116.232.9 ± 8.02.4 ± 0.15.55 ± 0.625.12 ± 2.60
TY6.68 ± 0.081096 ± 161401.2 ± 12.52343.3 ± 223.387.3 ± 7.84.8 ± 0.43.72 ± 0.2322.00 ± 6.12
Irrigation0.025 *<0.01 **0.032 *<0.01 **<0.01 **0.4890.011 *0.013 *
SGM<0.01 **<0.01 **<0.01 **<0.01 **<0.01 **<0.01 **<0.01 **<0.01 **
Irrigation×SGM0.022 *<0.01 **0.4140.030*0.011 *0.5140.8240.814
EC, electric conductivity; OM, organic matter; ω, mass water content; DL, drip irrigation + peat-based substrate; DY, drip irrigation + coir substrate; TL, tidal irrigation + peat-based substrate; TY, tidal irrigation + coir substrate. The same below. * and ** are significant difference at 5% and 1% level, respectively. Values are shown as average ± standard deviation (n = 4).
Table
Table 2. MiSeq sequencing coverage and alpha diversity of bacterial 16S rRNA gene and fungal ITS.
Table 2. MiSeq sequencing coverage and alpha diversity of bacterial 16S rRNA gene and fungal ITS.
DomainTreatmentsOTUsChao1ShannonSimpsonCoverage
BacteriaDL12921504.35.2930.02099.258
DY12441432.05.6280.00999.317
TL13531487.05.4470.01899.373
TY11921425.35.4540.01399.283
FungiDL271279.43.2620.10099.965
DY185203.92.8510.09299.955
TL283315.83.1500.09999.929
TY225240.52.9590.10099.953
Data show as mean value (n = 3).
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Li, J.; Zhou, W.; Yang, R.; Wang, H.; Zhang, D.; Li, Y.; Qi, Z.; Lin, W. Evaluating the Effect on Cultivation of Replacing Soil with Typical Soilless Growing Media: A Microbial Perspective. Agronomy 2023, 13, 6. https://doi.org/10.3390/agronomy13010006

AMA Style

Li J, Zhou W, Yang R, Wang H, Zhang D, Li Y, Qi Z, Lin W. Evaluating the Effect on Cultivation of Replacing Soil with Typical Soilless Growing Media: A Microbial Perspective. Agronomy. 2023; 13(1):6. https://doi.org/10.3390/agronomy13010006

Chicago/Turabian Style

Li, Jie, Wanlai Zhou, Rui Yang, Hong Wang, Dongdong Zhang, Yujia Li, Zhiyong Qi, and Wei Lin. 2023. "Evaluating the Effect on Cultivation of Replacing Soil with Typical Soilless Growing Media: A Microbial Perspective" Agronomy 13, no. 1: 6. https://doi.org/10.3390/agronomy13010006

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