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Article

Effects of Corn Stover Biochar on Soil Bacterial and Fungal Biomarkers in Greenhouse Tomatoes Under Mulched Drip Irrigation

by
Jianglong An
1,2,
Lijian Zheng
1,2,
Ruifeng Sun
1,2,
Xufeng Li
3,
Li Ma
4 and
Juanjuan Ma
1,2,*
1
College of Water Resource Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Shanxi Key Laboratory of Collaborative Utilization of River Basin Water Resources, Taiyuan 030024, China
3
Shanxi Institute of Organic Dryland Farming, Shanxi Agricultural University, Taiyuan 030031, China
4
College of Resources and Environmental Sciences, Inner Mongolia Agricultural University, Hohhot 010011, China
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(9), 1143; https://doi.org/10.3390/horticulturae11091143
Submission received: 31 July 2025 / Revised: 15 September 2025 / Accepted: 18 September 2025 / Published: 19 September 2025
(This article belongs to the Section Plant Nutrition)
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Abstract

Although the role of biochar in enhancing soil quality has been extensively studied, its specific effects on the changes of soil bacteria and fungi in greenhouse tomato under mulched drip irrigation are not yet fully understood. In order to understand the above-mentioned changes, a two-year experiment on greenhouse tomatoes with mulched drip irrigation was conducted. The objective of this experiment was to explore the relationship between different irrigation levels (W1: 50–70% of the field capacity W2: 60–80% of the field capacity, and W3: 70–90% of the field capacity) and different biochar application rates (B0: 0 t/ha, B1: 15 t/ha, B2: 30 t/ha, B3: 45 t/ha, and B4: 60 t/ha) on soil bacteria and fungi. The results demonstrated that the soil bacterial Chao index was influenced by biochar application and water-biochar interactions, while the soil fungal α-diversity index and bacterial and fungal β-diversity were predominantly impacted by the irrigation level. The random forest modelling indicated that soil bacterial biomarkers were predominantly rare genera, while fungal biomarkers contained both dominant and rare genera. In comparison with the B0 treatment, biochar application resulted in an enhancement of the abundance of bacterial biomarkers associated with nutrient cycling, including Galbibacter (400.90–2216.22%) at the W3 levels. The B4 treatment at both W3 and W2 levels reduced the relative abundance of the pathogenic fungus Aspergillus sp., but the rest of the biochar treatments enhanced it by 4.69–108.16% and 55.86–213.30%, respectively. The Mantel test demonstrated that soil water content was the most significant influencing factor for all soil bacterial and fungal biomarkers. Biochar application significantly altered major bacterial biomarker functions in mulched drip irrigation, while fungal biomarker functions were mainly affected by irrigation levels and water-biochar interactions. At the W3 level, biochar application significantly reduced the relative abundance of Saprotroph–Symbiotroph by 83.44–97.92%. These results serve as a reminder of the critical importance of soil health sustainability in integrated crop management decisions and provide valuable insights for improving soil quality under mulched drip irrigation.

1. Introduction

Tomato (Solanum lycopersicum L.) is the primary greenhouse-cultivated crop in China, and mulched drip irrigation is one of its commonly used irrigation methods [1]. This method can effectively reduce soil water loss and improve soil quality and crop yield by enhancing soil structure, as well as soil microbial diversity and function. However, long-term implementation may also result in soil compaction and reduced microbial diversity [2], which necessitates further soil improvement. Biochar is a prevalent soil amendment, and its incorporation has been demonstrated to induce alterations in the composition, diversity, and functionality of soil microbial communities by modulating the soil structure [3,4]. Furthermore, biochar has been shown to enhance the efficiency of nutrient utilisation and possesses the potential to ameliorate infertile or acidic soils [5]. Consequently, the concomitant utilisation of biochar in conjunction with mulched drip irrigation has the potential to further enhance soil quality by modifying soil microbial community changes.
Soil microbial diversity and community structure have been identified as significant indicators of soil health [6,7], and are also critical for maintaining soil fertility and sustainable production [8]. Soil microbial ecosystems are dominated by fungi and bacteria, and the increase in soil microbial activity in mulched drip irrigation is mainly due to the improvement of the soil water, air, and thermal environments. However, residual film residues may also lead to a decrease in soil microbial activity [9]. Meanwhile, disparate irrigation conditions yield divergent effects on soil microorganisms. It has been demonstrated that soil bacterial community diversity increases as the relative soil water content increases within a certain range [10], while fungi are more resistant to water [11]. However, excessively high soil moisture conditions can also lead to a decrease in soil microbial diversity [12]. Reduced soil moisture can alter soil microbial community composition and diversity [13]; but it may only change bacterial community structure without affecting diversity [14]. Biochar application exerts variable effects on soil microorganisms, ranging from positive to negative or even none. It is a commonly held belief that the application of biochar exerts a direct influence on the physical and chemical properties of the soil, which in turn affects the associated properties of soil microorganisms [15,16]. Biochar is characterised by a high porosity, a substantial surface area, and a rich content of surface functional groups, which collectively foster the survival, colonization, and proliferation of microorganisms [17]. However, the presence of organic compounds, such as furan and phenol, in biochar may also act as an inhibitor to microorganisms, thereby resulting in a proportional decrease in microbial community with increasing biochar application [18]. Nevertheless, it has also been found that long-term application of biochar alone maintains the stability of the microbial community and exerts minimal effect on microbial activity [19]. The divergent conclusions may be attributed to factors such as the type of crop species, biochar application rate, water supply, and other alterations of the soil environment [20,21,22].
However, while the focus of academics is currently on changes in overall soil microbial diversity, community structure, and function, there is a wide variety of bacteria and fungi in the soil, with different variations in diversity and composition of abundant, intermediate, and rare taxonomic populations, and their responses to environmental disturbances [23]. It has been demonstrated that not all soil microbial communities are sensitive to changes in soil moisture, with Chloroflexi, Firmicute and Proteobacteria being more drought-tolerant than Gram-negative bacteria [24,25]. Concurrently, the response of diverse soil fungal and bacterial communities to biochar is not uniform. Biochar has been shown to promote fungal growth, while the abundance of actinomycetes does not respond significantly to biochar application [26]. Nevertheless, it has also been found that biochar promotes the relative abundance of Actinospica, Streptomyces and Massilia, while Aspergillus responds differently to different types of biochar [27]. Microbial biomarkers are used to denote microorganisms that are sensitive to different soil environmental responses, which is commonly used to distinguish microbial communities in different soil environments [28]. These biomarkers may also drive the composition and function of the whole community through metabolism or the regulation of some intermediate and effector groups [29]. Furthermore, some studies have suggested that biomarkers may be responsible for explaining and predicting changes in soil quality conditions [30,31,32]. Extant research on the effects of biochar and irrigation on soil microorganisms is predominantly oriented towards the examination of biochar application rates or irrigation conditions as individual factors. Nevertheless, research that explores the synergistic effects of mulched drip irrigation combined with biochar application on soil microbial biomarkers is less prevalent. It has been demonstrated that biochar application has altered the composition and diversity of soil microorganisms under mulched drip irrigation [33], but the alterations in structural and functional composition of microbial biomarkers induced by biochar and irrigation remain to be elucidated, and it is not feasible to predict the soil microbial environmental preferences solely from the phylum level [34]. It is therefore imperative that further exploration be conducted into which key microbial biomarkers (soil fungi and bacteria) are affected by water-biochar interactions on greenhouse tomatoes.
Accurately identifying the most sensitive microbial biomarkers from a wide variety of microorganisms that are responsive to environmental changes is fundamental to determining whether different field test conditions act individually or synergistically. Recent studies have demonstrated the potential of machine learning models in identifying key biomarkers under various soil environmental conditions [35,36,37]. The Random Forest model has been shown to exhibit high accuracy and low average error rate [38]. Therefore, the objectives of this study were (1) to investigate the effects of different irrigation levels and biochar application under mulched drip irrigation on the diversity of soil bacteria and fungi, and (2) to identify biomarkers of the most important genera of biochar impacts on soil bacteria and fungi under mulched drip irrigation by using the random forest model, and to further explore in depth the composition, function, and correlation of these genera with environmental factors.

2. Materials and Methods

2.1. Experimental Design

The experimental site was situated in the Liujiabao Tomato Industrial Park, Taiyuan City, Shanxi Province, China (112°29′11.51″ E, 37°38′44.30″ N). The area under consideration is characterised by a temperate continental semi-arid climate, with the mean annual temperature of 9.5 °C and the mean annual sunshine duration of 2675.8 h over multiple years. The experimental greenhouse occupies an area of 600 m2, with each test plot measuring 28.8 m2 (length: 8 m; width: 3.6 m). The plots were distributed equitably, with three ridges and grooves. The ridges were 0.8 m in width, while the grooves were 0.4 m. Each ridge was planted with two rows of tomatoes, with a row spacing of 50 cm and a plant spacing within the row of 50 cm. The ground surface was fully covered with a black mulch that measured 0.008 mm in thickness.
The experiment was conducted in a completely randomized block design with 15 treatments, three replicates per treatment, three irrigation levels and five biochar application levels. The irrigation levels were defined as follows: W1: 50–70% θf, W2: 60–80% θf, and W3: 70–90% θf, with θf denoting soil field capacity. The biochar levels were categorised as follows: B0: 0 t/ha, B1: 15 t/ha, B2: 30 t/ha, B3: 45 t/ha, and B4: 60 t/ha. The schematic layout of the experiment is illustrated in Figure 1, and for the various treatment, irrigation, and biochar settings, refer to Table S1. The irrigation method was mulched drip irrigation, and impermeable membranes were buried between the test plots to prevent lateral exchange of water. The irrigation treatments were the same in 2021 and 2022. Water meters were installed in each plot to measure the amount of water used for each irrigation, and the irrigation water was uniformly sourced from local groundwater. The biochar under consideration was procured from Liao Ning Golden Future Agriculture Technology Co., Ltd. (Anshan, China). It was subjected to high-temperature pyrolysis (400–500 °C) to yield corn stover biochar, with a particle diameter of less than 2 mm. On 30 April 2021, biochar was evenly spread on the soil surface and tilled into the 0–30 cm soil layer with a rotary tiller, along with 20,000 kg/ha of organic fertilizer (cow manure). No applicational biochar was applied in 2022, while the same rate of organic fertiliser was applied every year.
The tomato “Shouyan PT326” were planted on 17 May 2021 and 25 May 2022, and they were harvested on 22 September 2021 and 30 September 2022. Other field managements were consistent across all experimental plots and years, including practices such as weeding and the application of a smoke generator containing isoprocarb to control greenhouse whiteflies. The physical and chemical properties of soil and biochar were tested prior to the experiment and are presented in Table S2. The microclimatic data for the 2021 and 2022 test periods are illustrated in Figure S1.

2.2. High-Throughput Sequencing of Soil Microorganisms

After two full years, on 15 May 2023, microbial sampling of the bulk soil was carried out, with each treatment sampled using the 5-point method on the ridges. The 5-point sampling method was used for each treatment, taking 0–30 cm of ploughed soil diagonally. Following thorough mixing, 10 g of soil samples were collected in sterilised self-sealing bags and stored at –80 °C for subsequent molecular analysis.
Total microbial genomic DNA was extracted from soil samples using the E.Z.N.A.® soil DNA Kit (Omega Biotek, Norcross, GA, USA) according to the manufacturer’s instructions. The quality and concentration of DNA were determined by 1.0% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). Using the above-extracted DNA as a template, the bacterial 16S rRNA gene was amplified with primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) and the fungal ITS rRNA gene was amplified with primer pairs ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) by T100 Thermal Cycler PCR thermocycler (BIO-RAD, USA) by T100 Thermal Cycler PCR thermocycler (BIO-RAD, Hercules, CA, USA). The PCR amplification procedures, product recovery and library construction were the same as in previous studies [39]. Sequencing was performed using the Illumina PE300 platform (Illumina, San Diego, CA, USA). The raw sequencing reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: SRP601579).

2.3. Determination of Chemical and Physical Soil Properties

The soil water content was measured using a time domain reflectometer (TRIME-PICO-IPH, IMKO, Ettlingen, Germany) at 7 d intervals, with applicational measurements taken before and after irrigation. The soil temperature was monitored online using a temperature logger with a high-precision negative temperature coefficient (NTC) temperature sensor (i500-E8T, Hang Zhou Dlogtech Co., Ltd., Hangzhou, China) and the measurement points are detailed in Figure 1. In the years 2021 and 2022, samples were collected from the 0–30 cm soil layer on the ridges at 10 cm intervals during the seedling, flowering, fruit expansion, and ripening and harvesting stages of tomato, respectively, for the purpose of determining soil pH and redox potential; The measurement of these two indicators were determined using an FJA-6 redox potential depolarisation automatic determination system (FJA-6, Nanjing Chuan-Di Instrument & Equipment Co., Ltd., Nanjing, China). The soil-water ratio was 1:2.5.

2.4. Statistical Analysis

Using IBM SPSS Statistics version 27.0, the data were first assessed for normality and homogeneity of variance. Upon confirmation that these assumptions were satisfied, one-way and two-way analyses of variance (ANOVA) followed by Duncan’s post hoc tests were performed to identify significant differences in soil microbial α-diversity index, relative abundance of soil bacterial and fungal biomarkers, and their functional profiles. In cases where the assumption of homogeneity of variance was violated, Welch’s ANOVA was applied as a robust alternative. Soil microbial α-diversity index analysis was performed using mothur (version v.1.30.2). Non-metric multidimensional scaling (NMDS) analysis, correlation analysis and redundancy analysis were performed using vegan package (version 2.6–10) in R (version 4.5.0). Random forest analysis was performed to identify the significantly abundant taxa of bacteria and fungi biomarkers among the different groups using the R software (version 4.5.0) randomForest package (version 4.7.1.2). The decision tree was set to 1000 and the tenfold cross-validation method was used to validate the model results (Figure S2). The top 30 bacterial genera and the top 80 fungal genera with the lowest error rates were selected (Tables S3 and S4). Function prediction of soil bacteria and fungi biomarkers was performed using PICRUSt2 (v2.2.0–b) and FUNGuild (v1.0) software. Graphs were generated using OriginPro 2021 software and Rstudio (version 2025.05.0) ggplot2 package (version 3.5.2).

3. Results

3.1. Analysis of Soil Microbial Community Diversity in Different Treatments

A significant variation in the soil bacterial Chao index was identified between treatments at the biochar level or water-biochar interactions, but not at the irrigation level. At the W1 and W2 levels, soil bacterial Chao index exhibited a comparable trend of decline and subsequent increase with the augmentation of biochar application, and the W1B2 treatment was the only statistically different to W1B0. Although W2B4 was the highest average, it was not statistically different from W2B0. The Chao index and Shannon index of soil fungi exhibited a highly significant divergence across treatments, exclusively at the irrigation level alone. The soil fungal Chao index of the treatments at the W2 level was observed to be at its maximum in comparison to the same biochar application rate (Table 1). NMDS analysis based on the Bray–Curtis distance demonstrated highly significant differences in the community structure of both soil bacteria (p = 0.002) and fungi (p = 0.001) across treatments, with samples from the same irrigation level exhibiting closer distribution (Figure 2a,b). Consequently, a further grouping of the diverse treatments was conducted into distinct irrigation levels versus biochar levels. The NMDS outcomes of these analyses revealed highly significant disparities (p = 0.001) in soil bacteria and fungi across treatments only at varying irrigation levels (Figure 2c–f).

3.2. Soil Bacterial and Fungal Biomarkers in Different Treatments

In order to provide further clarification on the effects of biochar on soil bacterial and fungal communities in mulched drip irrigation, a random forest model was employed to screen soil bacterial and fungal biomarkers at the genus level (Figure 3). The total value of the relative abundance of bacterial biomarkers in different treatments was only 2.15–6.82%, which was mainly composed of rare bacterial genera (relative abundance < 0.01%). Among these, the total relative abundance of bacterial biomarkers with relative abundance > 0.001% ranged from 1.69% to 6.02% and was statistically significant for all factors, while those with relative abundance < 0.001% ranged from 0.46% to 0.87% (Figure 3a). At the W3 irrigation level, the total relative abundance of bacterial biomarkers exhibited an increasing trend, followed by a subsequent decrease, peaking at values significantly higher than those at other irrigation levels under equivalent biochar application. The total relative abundance of soil fungal biomarkers across treatments ranged from 71.55% to 90.13%. This comprised fungal biomarkers with relative abundance > 0.01% (63.79–83.00%), those between 0.001% and 0.01% (4.28–7.48%), and those < 0.001% (0.69–1.09%) (Figure 3b). The differences observed in the total relative abundance of fungal biomarkers and in the total relative abundance of fungal biomarkers with relative abundance >0.01% were found to be statistically significant under the biochar level and water-biochar interactions.
In the experiment, the bacterial and fungal biomarkers present in the soil samples were analysed using a two-way ANOVA. The results demonstrated that bacterial biomarkers exhibited a significant response to variations in irrigation level, with 12 genera being exclusively influenced by this factor alone (Table S5). No genera responded solely to biochar level or water-biochar interactions, and 7 genera responded to all factors (Table S6). Tables S5 and S6 demonstrated that 25 genera of fungal biomarkers were exclusively influenced by the irrigation level, 8 genera were impacted by all factors, 1 genus by biochar level alone, 3 genera solely by water-biochar interactions, and 2 genera were impacted by both biochar level and water-biochar interactions. The findings demonstrated that bacterial biomarkers exhibited heightened sensitivity to irrigation in comparison to fungal biomarkers. Furthermore, biochar application was observed to exert an influence on the particular soil bacterial and fungal biomarkers.

3.3. Changes in the Relative Abundance of Soil Bacterial and Fungal Biomarkers

Biochar application differentially affected soil bacterial and fungal biomarkers across irrigation levels. At the W3 level compared to B0 treatment, the relative abundance of soil bacterial biomarkers Galbibacter, Glycomyces, Myceligenerans, Anaerolinea, and Hydrogenophaga initially increased then decreased with increasing biochar, and Galbibacter showed the most significant enhancement (400.90–2216.22%). Conversely, Paracoccus and Dokdonella abundance decreased. At the W2 level, biochar application resulted in a decrease in the relative abundance of Dokdonella by 56.48–70.65%, whereas the relative abundance of Paracoccus increased by 68.97–525.86%. With the exception of Dokdonella, the relative abundance of bacterial biomarkers was lowest in the high-biochar application (B4) treatment at the W1 level (Figure 4a). Phylogenetic analysis revealed that these bacterial biomarkers clustered within specific clades, predominantly belonging to Actinobacteriota, Bacteroidota, and Proteobacteria (Figure S3a).
Compared to the B0 treatment, the relative abundance of Aspergillus was increased by 4.69–108.16% at the W3 level and by 55.86–213.30% at the W2 level under B1–B3 treatments, while B4 treatment reduced it by 66.66% (W3) and 12.77% (W2). Biochar application increased the relative abundance of Talaromyces by 26.67–850.00% (W3) and 60.52–392.11% (W2). At the W3 level, the relative abundance of Oedocephalum decreased by 91.04–100.00% with biochar application. At the W2 level, the relative abundance of Lophotrichus initially increased and then decreased with the increase of biochar application, whereas Zopfiella and Zygopleurage gradually decreased by 64.97–98.89% and 50.36–98.17%, respectively. At the W1 level, biochar application significantly reduced the relative abundance of these fungal biomarkers, except for the B2 treatment of Aspergillus (Figure 4b).
It was observed that the B1 treatment led to a significant reduction in the relative abundance of the pathogenic fungus Didymella, with a range of 40.00–96.82% compared to the B0 treatment. Conversely, the other biochar application treatments at the W3 level exhibited an increase, ranging from 40.00% to 4420.00%, most markedly in W3B3 treatment (Figure 4c). Biochar application increased the relative abundance of Pseudallescheria and Wardomyces at the W3 level, while the opposite was observed at the W2 level. The relative abundance of Leucothecium increased by 168.75–1125.00% with biochar application at the W2 level. At the W1 level, biochar application significantly decreased the relative abundance of Pseudallescheria, Wardomyces and Leucothecium (Figure 4d). Compared to the B0 treatment, the relative abundance of the Metarhizium increased most in the B4 treatment at the W3 and W2 levels, by 17.39% and 140.36% respectively. At the W1 level, its value increased by 245.32% in the B3 treatment (Figure 4e). Phylogenetic analysis revealed that fungal biomarkers clustered within specific clades, predominantly belonging to Ascomycota (Figure S3b).

3.4. Correlation Analysis Between Soil Bacterial and Fungal Biomarkers with Environmental Factors

Soil water content (SWC), soil temperature (T), soil pH, and redox potential (EH) are key factors influencing soil microbial communities. A Mantel test was conducted to compare overall soil bacterial and fungal biomarkers with the annual average values of four soil physicochemical parameters from 2021 and 2022. Both bacterial and fungal biomarkers showed a statistically significant positive correlation with SWC over the two years (p < 0.001), notably stronger in 2022 than in 2021. However, the data demonstrated low correlations and lacked statistical significance for pH, T, and EH (Table 2). The findings indicated that SWC was the most significant influencing factor for the entire soil bacterial and fungal biomarkers. The redundancy analysis (RDA) further confirmed the above-mentioned finding, and under the W1 irrigation level, these environmental factors explained a relatively larger proportion of the variance (Figure S4).
Soil bacterial biomarkers (Galbibacter, Glycomyces, Myceligenerans, and Hydrogenophaga) exhibited a significant positive correlation with SWC and a significant negative correlation with EH (Figure 5a); Paracoccus demonstrated a significant positive correlation exclusively with SWC. Soil fungal biomarkers Aspergillus and Leucothecium correlated negatively with both pH and EH. Zopfiella, Talaromyces, Zygopleurage, and Lophotrichus showed negative correlations with SWC but positive with EH; whereas Oedocephalum and Pseudallescheria demonstrated the reverse pattern (Figure 5b–d). It was found that only Dokdonella, Leucothecium, and Aphanoascus were significantly correlated with T. The findings suggested that the presence of bacterial and fungal biomarkers, influenced by irrigation and biochar application, exhibits heightened sensitivity to variations in SWC and EH.

3.5. Predictive Analysis of the Functions of Soil Bacterial and Fungal Biomarkers

Functional prediction analysis using PICRUSt2 revealed that the primary functions of bacterial biomarkers in different treatment were as follows: Function unknown (17.22–17.97%), Amino acid transport and metabolism (10.69–10.96%), Translation, Ribosomal structure and biogenesis (7.35–7.86%), Energy production and conversion (7.19–7.44%), Cell wall/membrane/envelope biogenesis (6.21–6.69%), Transcription (5.62–6.30%), Carbohydrate transport and metabolism (5.70–5.85%), Inorganic ion transport and metabolism (5.63–5.99%), Replication, recombination, and repair (4.72–4.85%), and Coenzyme transport and metabolism (4.52–4.64%) (Figure S3a). Soil bacterial biomarker functional relative abundance exhibited relatively small changes under different treatments. The primary functions of bacterial biomarkers, namely amino acid transport and metabolism, transcription, and carbohydrate transport and metabolism, were statistically significant at varying levels of irrigation or biochar application, while they were non-significant under water-biochar interactions. (Table S7). A comparison of the W3 level with the B0 treatment reveals that biochar application resulted in an increase in the relative abundance of carbohydrate transport and metabolism by 0.42–4.07% and a decrease in the relative abundance of energy production and conversion by 0.83–5.73%. The application of biochar at the W2 level resulted in an increase in the relative abundance of amino acid transport and metabolism and energy production and conversion by 1.22–3.95% and 0.10–3.90%, respectively. Conversely, at the W1 level, biochar application led to a reduction in the relative abundance of carbohydrate transport and metabolism by 0.45–7.35% (Figure 6a,b,d). At the W3 and W1 levels, the B3 and B4 treatments exhibited an augmentation in the relative abundance of Translation, ribosomal structure and biogenesis, concomitant with a diminution in the relative abundance of Inorganic ion transport and metabolism and Transcription (Figure 6c,f). However, no significant differences were observed between the treatments with and without biochar application at the same irrigation level.
FUNGuild functional prediction analysis revealed that the predominant trophic guilds of soil fungal biomarkers in different treatments was Saprotroph (57.19–73.10%), followed by Unknown (8.54–19.79%), Pathotroph–Saprotroph–Symbiotroph (10.63–31.26%), Pathotroph (0.48–12.55%), Pathogen–Saprotroph–Symbiotroph (0.48–3.72%), Pathotroph–Saprotroph (0.01–0.19%), Saprotroph–Symbiotroph (0.00–3.60%), and Pathotroph–Symbiotroph (0.01–0.30%) (Figure S3b). The combined effects of irrigation, biochar, and water-biochar interactions were significantly different between the Pathotroph–Saprotroph–Symbiotroph. Saprotroph–Symbiotroph was influenced by irrigation and water-biochar interactions, while Pathotroph–Symbiotroph was primarily affected by irrigation (Table S7). In comparison with B0 treatment, the relative abundance of Pathotroph–Saprotroph–Symbiotroph was reduced by 10.56–44.89% at the W2 level with biochar application. However, the differences observed among the treatments did not reach statistical significance (Figure 6g). At the W3 irrigation level, the relative abundance of Saprotroph–Symbiotroph was significantly diminished by 83.44–97.92% with biochar application (Figure 6h).
Interestingly, it was found that the functional composition of soil bacterial and fungal biomarkers was similar to that of the entire soil bacterial and fungal functional composition (Figure S5). To further investigate this, we conducted correlation analyses between the function of soil bacteria and fungi biomarkers and the function of overall soil bacteria and fungi. The Mantel test revealed a significant positive correlation between the function of bacterial biomarkers and total soil bacteria (r = 0.441, p < 0.001), and the same be-tween the function of soil fungi and total soil fungi (r = 0.841, p < 0.001) (Table S8). Procrustes analysis indicated significant structural similarity between the functional composition of soil bacterial biomarkers and total soil bacteria (M2 = 0.594, p = 0.001), as well as between soil fungal biomarker and total soil fungi (M2 = 0.259, p = 0.001) (Figure 7).

4. Discussion

4.1. The Effects of Irrigation and Biochar Application on Microbial Diversity

It is well established that soil microorganisms exhibit sensitivity to water [40], and the introduction of biochar may have an influence on this sensitivity. The findings of the present study demonstrated that the Chao index of soil bacteria was influenced by biochar and the interaction between biochar and irrigation. In contrast, the Chao index and Shannon index of soil fungi, as well as the β-diversity of soil bacteria and fungi, were predominantly impacted by irrigation levels (Table 1 and Figure 2). In comparison with fungi, bacteria exhibit higher levels of diversity and greater functional redundancy [41]. Consequently, under varying moisture conditions, although bacterial β-diversity undergoes changes, soil bacterial α-diversity remains stable; Ning et al. also found that reduced irrigation altered the soil bacterial community structure (β-diversity) but did not alter α-diversity [42]. It was evident that the application of biochar at relatively low rates has the capacity to diminish bacterial diversity within soil. This phenomenon may be attributed to the biochar’s propensity to adsorb easily decomposable organic carbon present within the soil [43]. Consequently, this results in a deficiency of carbon sources available to soil bacteria, leading to an intensification of competition for microbial resources, subsequently reducing soil bacterial activity. Conversely, biochar application at elevated rates has been observed to exert a synergistic effect, thereby augmenting the soil’s capacity to retain moisture [44]. Appropriate increases in soil moisture have been demonstrated to enhance soil organic matter mineralisation [45], while the slow-release capacity of biochar for easily decomposable organic carbon is strengthened [46], thereby increasing soil bacterial diversity. It is noteworthy that our results demonstrated a discrepancy between the bacterial Chao index and the Shannon index (Table 1). This discrepancy may be attributed to the divergent calculation principles employed by the Chao index and the Shannon index. The former is more sensitive to changes in low-abundance species, while the latter is more sensitive to changes in dominant species [47]. The divergent outcomes observed in the assessment of bacterial and fungal biomarkers, as determined by random forest analysis, serve to indirectly corroborate this result.

4.2. The Effects of Irrigation and Biochar Application on Microbial Biomarkers

Keystone taxa have been shown to regulate soil microbial composition and function through metabolism or interaction [29]. The majority of previous studies have concentrated on alterations in dominant soil species, thereby diminishing the significance of rare species [23]. However, this may potentially lead to the loss of crucial information pertaining to taxonomic changes. Biochar has the capacity to promote the growth of specific groups of microorganisms, whilst concomitantly inhibiting the growth of others. The results of the random forest analysis indicated that soil bacterial biomarkers in different treatments were predominantly rare genera, while fungal biomarkers comprised both dominant and rare genera (Figure 3). Soil microorganisms exhibit divergent responses to environmental disturbances, contingent upon their respective abundances, as rare species may not be essential for maintaining function; however, they may possess “insurance effects” that are activated under specific environmental conditions [48]. The influence of bacterial biomarkers is subject to the synergistic effects of irrigation and biochar application. At the W3 irrigation level, biochar application resulted in a significant increase in the relative abundance of bacterial biomarkers, while fungal biomarkers exhibited a response to biochar and water-biochar interactions. This phenomenon may be attributed to the increased soil organic carbon content resulting from biochar application [49], and bacteria exhibit a preference for the utilisation of easily decomposable organic carbon, and the enhanced soil moisture levels serve to mitigate the constraints on carbon diffusion. In contrast, fungi demonstrate a heightened resistance to water stress and exhibit a preference for the utilisation of recalcitrant organic compounds [46].
At the W3 level, biochar increased the abundance of bacterial biomarkers related to the carbon-nitrogen cycle to a certain extent. Niesleny et al. also observed that low-abundance species exerted a more significant impact on soil carbon cycling than high-abundance species [50]. The present study revealed that the biochar application into the soil resulted in an augmentation in the relative abundance of Galbibacter, Glycomyces, Myceligenerans, and Anaerolinea (Figure 4a). Galbibacter has been identified as a beneficial soil bacterium that has the capacity to inhibit the growth of pathogenic fungi [51]. It belongs to the Proteobacteria (Figure S3a), which is primarily responsible for soil nutrient cycling. Zhang et al. discovered that, under identical conditions, biochar application can enhance the relative abundance of Galbibacter in compost [52]. Our experimental treatments were uniformly treated with organic fertilizer, and biochar addition may have enhanced the utilization of nutrients in the organic fertilizer by Galbibacter. Anaerolinea has been identified as a degrader of carbohydrates and polycyclic aromatic hydrocarbons [53], while Hydrogenophaga sp. has been found to contain genes that encode hydrogenase, oxidase, and RuBisCO, which are involved in the soil carbon cycle [54]. Interestingly, biochar application has been demonstrated to enhance the availability of carbon sources, thereby stimulating the growth of these microorganisms. Biochar application yielded consistent alterations in the relative abundance of Dokdonella at the W3 and W2 levels. In contrast, Paracoccus exhibited divergent patterns. This discrepancy may be attributable to their distinct characteristics. Paracoccus is an aerobic or facultative anaerobic denitrifying bacterium, whereas Dokdonella is a strictly aerobic denitrifying bacterium. Biochar application resulted in a reduction of soil bulk density and an enhancement of water holding capacity, while under the conditions of mild water stress, the soil exhibited satisfactory aeration levels. Furthermore, correlation analysis also indicated that Paracoccus was more sensitive to water changes (Figure 5a). This may also be due to interactions between different types of denitrifying bacteria [55]. The application of biochar at varying irrigation levels also exerted differential effects on soil pathogenic fungi, including Aspergillusi, Oedocephalum, Didymella, and Pseudallescheria, as well as potential beneficial soil fungi such as Talaromyces, Lophotrichus, Zopfiella, Leucothecium, Wardomyces, and Metarhizium (Figure 4b–e). Talaromyces, Lophotrichus, and Zopfiella have been shown to possess the capacity to inhibit the growth of pathogenic fungi [56,57,58], while Leucothecium and Metarhizium are classified as entomopathogenic fungi [59,60]. In our previous research, it was established that the incorporation of 30–60 t/ha and 45–60 t/ha of biochar at the W1 and W2 levels, respectively, had the capacity to counterbalance the deleterious effects of diminished irrigation on tomato source and sink characteristics, and the W2B3 treatment was found to yield the maximum yield [1]. However, it was also determined that the W2 treatment, which exhibited comparatively superior water, air, and heat conditions under mulched drip irrigation, may have facilitated the proliferation of potential pathogenic fungi in the soil [39]. Furthermore, elevated levels of organic carbon may stimulate the growth of pathogenic fungi [61]. Aspergillus is a prevalent pathogenic species in soil [62]. The findings of this study indicate that while the application of biochar reduced the relative abundance of Oedocephalum, it also promoted the growth of Aspergillus. Furthermore, the W3B3 treatment resulted in an enrichment of Didymella and Pseudallescheria. This further underscores that while biochar enhances tomato growth and yield under mulched drip irrigation, its impacts on soil bacterial/fungal communities must be considered for long-term soil health sustainability.

4.3. The Effects of Environmental Factors on Microbial Biomarkers

We selected key environmental drivers altered by irrigation and biochar application—specifically soil moisture and nutrient status—for correlation and redundancy analyses with soil bacterial and fungal biomarkers. The findings indicated that SWC is the primary factor influencing all soil bacterial and fungal biomarkers (Table 2 and Figure S3), which is consistent with previous studies that have identified soil moisture as the strongest predictor of soil bacteria and fungi [63]. Soil moisture significantly influences the metabolic activity of most microorganisms. Despite the influence of different biochar types and application rates on soil properties, soil moisture remains the predominant factor governing microbial community composition, with biochar acting primarily by buffering water stress and improving soil water retention [64,65]. A decrease in water content results in reduced microbial respiration and nutrient mineralization [40]. The RDA results further demonstrated that both soil bacteria and fungi exhibit a more sensitive response to low irrigation levels. The Mantel test outcomes also revealed that soil T, pH, and EH exhibited low correlations with all soil bacterial and fungal biomarkers and were not statistically significant. This finding is in contrast with the conclusions of previous studies, which suggested that soil pH and EH exert a greater influence on soil microorganisms than on other factors [66,67]. However, a recent study found that rare microbial communities may be more influenced by random processes and exhibit weaker responses to soil environmental factors [68]. Furthermore, Spearman correlation heatmap analysis revealed that soil EH was also a major factor significantly influencing specific soil bacterial and fungal biomarkers under irrigation and biochar application (Figure 5). Our study revealed a significant negative correlation between soil EH and the presence of pathogenic fungi, including Aspergillus, Oedocephalum, and Pseudallescheria. In contrast, potentially beneficial soil fungi such as Talaromyces exhibited a significant positive correlation with soil EH. Research has demonstrated that elevated microbial activity and elevated levels of available carbon compounds can also diminish soil EH of well-aerated soils [67]. Furthermore, the antagonistic interaction between pathogenic and beneficial fungi may also lead to different responses to soil EH.

4.4. The Effects of Irrigation and Biochar Application on the Function of Microbial Biomarkers

Our study revealed that irrigation and biochar application altered functional relative abundances of specific soil bacterial and fungal biomarkers (Table S7). At the W3 level, biochar application resulted in an increase in the relative abundance of the soil bacterial biomarker Carbohydrate transport and metabolism (Figure 6b), which is consistent with the increase in the relative abundance of nutrient cycling-related bacteria such as Galbibacter due to biochar. Concurrently, comparatively elevated biochar application rates exerted an influence on soil bacterial biomarkers associated with Inorganic ion transport and metabolism, Translation, ribosomal structure and biogenesis, and Transcription (Figure 6c–e). This phenomenon may be attributed to alterations in the availability of carbon sources or the adsorption of inorganic and organic compounds induced by biochar application [69]. Increased biochar application has been demonstrated to increase the soil carbon to nitrogen ratio, enhance the adsorption of inorganic ions, and reduce the availability of nitrogen sources in the soil, thereby inhibiting bacterial transport of inorganic ions [70,71]. Biochar application altered the relative abundances of fungal biomarkers with Pathotroph–Saprotroph–Symbiotroph and Saprotroph–Symbiotroph trophic modes (Figure 6g,h). These fungi, predominantly affiliated with the Ascomycota (Figure S3b), demonstrate nutrient availability-dependent responses. In conditions of full irrigation, biochar application resulted in a decrease in the relative abundance of fungi belonging to the Saprotroph–Symbiotroph trophic group, a finding that is consistent with those reported by Lei et al. [36]. The synergistic effect of biochar and irrigation may have further adjusted the availability and stability of soil nutrients, thereby reducing the relative abundance of this group of fungi. The synergistic effect of biochar and irrigation may have further adjusted the availability and stability of soil nutrients, thereby reducing the relative abundance of this group of fungi. The Mantel test and Procrustes analysis further confirmed that soil bacterial and fungal biomarker functions are similar to total soil bacterial and fungal functions, with fungal biomarker functions showing higher similarity (Table S8 and Figure 7). This similarity may be attributed to ecological niche overlap and functional redundancy of soil microorganisms.

5. Conclusions

Biochar application had a significant impact on the Chao index of soil bacteria in greenhouse tomatoes under mulched drip irrigation facilities, while the diversity of soil bacteria and fungi was predominantly influenced by the levels of irrigation. Bacterial biomarkers were predominantly composed of rare genera (relative abundance < 0.01%), while fungal biomarkers comprised both dominant and rare genera. The application of biochar at the W3 level resulted in an increase in the relative abundance of carbon and nitrogen cycle-related bacterial biomarkers, such as Galbibacter. However, at the W3 and W2 levels, the application of 15–45 t/ha biochar also stimulated the growth of the pathogenic fungi Aspergillus and Pseudallescheria, in comparison to no biochar application. In practice, it is imperative to maintain the balance of enhancing tomato growth and yield using biochar with a sustainable soil health status. It was determined that soil moisture was the most crucial factor affecting all soil bacterial and fungal biomarkers. Biochar application had a significant impact on the primary functions of soil bacterial biomarkers. In contrast, the functions of fungal biomarkers were predominantly influenced by water-biochar interactions. The response of soil bacteria and fungi to biochar is a complex process, and the long-term application of biochar may result in changes to microbial functional genes. In future research, there is a need to combine the functional genes of soil bacterial and fungal biomarkers in order to explore the intrinsic mechanism of the effect of long-term application of biochar on soil bacterial and fungal biomarkers in mulched drip irrigation.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/horticulturae11091143/s1, Figure S1: The daily meteorological data during the experiment period; Figure S2: Validation plot of the results of random forest analysis based on ten-fold cross-validation; Figure S3: Phylogenetic trees of bacterial and fungal biomarkers; Figure S4: Redundancy analysis (RDA) of microbial biomarkers and environmental factors; Figure S5: Prediction of soil bacterial and fungal functions in different treatments; Table S1: Factors and levels of experiment; Table S2: The physical and chemical properties of soil and biochar; Table S3: Random Forest analysis of soil bacteria at the genus level in different treatments; Table S4: Random Forest analysis of soil fungi at the genus level in different treatments; Table S5: Two-way ANOVA results of soil bacteria and fungi biomarkers which solely influenced by the irrigation factor; Table S6: Two-way ANOVA results of soil bacterial and fungal biomarkers in different treatments; Table S7: Two-way ANOVA results of soil bacterial and fungal biomarkers functions in different treatments; Table S8: Mantel test of the function of biomarkers and soil bacteria and fungi in different treatments.

Author Contributions

Formal analysis, J.A., R.S. and X.L.; investigation, J.A., R.S. and X.L.; resources, L.Z. and J.M.; data curation, J.A., R.S. and X.L.; writing—original draft preparation, J.A.; writing—review and editing, J.A., L.Z., R.S., X.L., J.M. and L.M.; visualization, J.A.; supervision, L.Z., R.S., X.L., J.M. and L.M.; project administration, L.Z. and J.M.; funding acquisition, L.Z. and J.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the National Natural Science Foundation of China (52079085, 52109061) and the Natural Science Research Project of Shanxi Province (202403021211047).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that no conflict of interest.

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Figure 1. Schematic layout of the experiment.
Figure 1. Schematic layout of the experiment.
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Figure 2. NMDS analysis of β-diversity of soil bacteria and fungal genera in soil receiving varying levels of biochar and irrigation (see text for legend explanation): (a) soil bacteria in different treatments and (b) soil fungi in different treatments and (c) soil bacteria in different irrigation groups and (d) soil bacteria in different biochar groups and (e) soil fungi in different irrigation groups and (f) soil fungi in different biochar groups. Stress quantifies the discrepancy between the original and reduced-dimensional distances in NMDS. A stress value < 0.2 typically indicates that the ordination plot provides a meaningful interpretation of the underlying data structure. R represents the ANOSIM statistic R value.
Figure 2. NMDS analysis of β-diversity of soil bacteria and fungal genera in soil receiving varying levels of biochar and irrigation (see text for legend explanation): (a) soil bacteria in different treatments and (b) soil fungi in different treatments and (c) soil bacteria in different irrigation groups and (d) soil bacteria in different biochar groups and (e) soil fungi in different irrigation groups and (f) soil fungi in different biochar groups. Stress quantifies the discrepancy between the original and reduced-dimensional distances in NMDS. A stress value < 0.2 typically indicates that the ordination plot provides a meaningful interpretation of the underlying data structure. R represents the ANOSIM statistic R value.
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Figure 3. Soil bacterial and fungal biomarkers in soil receiving varying levels of biochar and irrigation (see text for legend explanation): (a) total relative abundance of the top 30 significance biomarkers of bacterial genera, total relative abundance of bacterial biomarkers with relative abundance greater than or less than 0.001%, and (b) the total relative abundance of the top 80 significance biomarkers of fungal genera, total relative abundance of fungal biomarkers with relative abundance less than 0.001%, between 0.001–0.01%, and greater than 0.01%. *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments.
Figure 3. Soil bacterial and fungal biomarkers in soil receiving varying levels of biochar and irrigation (see text for legend explanation): (a) total relative abundance of the top 30 significance biomarkers of bacterial genera, total relative abundance of bacterial biomarkers with relative abundance greater than or less than 0.001%, and (b) the total relative abundance of the top 80 significance biomarkers of fungal genera, total relative abundance of fungal biomarkers with relative abundance less than 0.001%, between 0.001–0.01%, and greater than 0.01%. *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments.
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Figure 4. The relative abundance of soil fungal and bacterial biomarkers in different treatments affected by various factors: (a) soil bacterial biomarkers affected by irrigation, biochar and water-biochar interactions; (b) soil fungal biomarkers affected by irrigation, biochar and water-char synergism; (c) soil fungal biomarkers affected by the biochar alone; (d) soil fungal biomarkers affected by water-biochar interactions alone; (e) soil fungal biomarkers affected by both the biochar level and water-biochar interactions. *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments.
Figure 4. The relative abundance of soil fungal and bacterial biomarkers in different treatments affected by various factors: (a) soil bacterial biomarkers affected by irrigation, biochar and water-biochar interactions; (b) soil fungal biomarkers affected by irrigation, biochar and water-char synergism; (c) soil fungal biomarkers affected by the biochar alone; (d) soil fungal biomarkers affected by water-biochar interactions alone; (e) soil fungal biomarkers affected by both the biochar level and water-biochar interactions. *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments.
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Figure 5. Heatmaps of Spearman correlations between soil fungal/bacterial biomarkers and environmental factors (mean values across two years): (a) soil bacterial biomarkers influenced by irrigation, biochar, and water-biochar interactions factors; (b) soil fungal biomarkers influenced by irrigation, biochar, and water-biochar interactions factors; (c) soil fungal biomarkers influenced by biochar or water-biochar interactions factors; (d) soil fungal biomarkers influenced by biochar and water-biochar interactions factors. *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments.
Figure 5. Heatmaps of Spearman correlations between soil fungal/bacterial biomarkers and environmental factors (mean values across two years): (a) soil bacterial biomarkers influenced by irrigation, biochar, and water-biochar interactions factors; (b) soil fungal biomarkers influenced by irrigation, biochar, and water-biochar interactions factors; (c) soil fungal biomarkers influenced by biochar or water-biochar interactions factors; (d) soil fungal biomarkers influenced by biochar and water-biochar interactions factors. *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments.
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Figure 6. Analysis of major differential functions in bacterial and fungal biomarkers in soil receiving varying levels of biochar and irrigation (see text for legend explanation): (a–f) The functions of soil bacteria in soil receiving varying levels of biochar and irrigation (see text for legend explanation). (g,h) The trophic modes of soil fungi in different treatments. Different lowercase letters indicate significant differences between treatments (p < 0.05).
Figure 6. Analysis of major differential functions in bacterial and fungal biomarkers in soil receiving varying levels of biochar and irrigation (see text for legend explanation): (a–f) The functions of soil bacteria in soil receiving varying levels of biochar and irrigation (see text for legend explanation). (g,h) The trophic modes of soil fungi in different treatments. Different lowercase letters indicate significant differences between treatments (p < 0.05).
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Figure 7. Procrustes analysis of the functions of soil microorganisms and biomarkers: (a) The functions of soil bacteria and soil bacterial biomarkers. (b) The trophic modes of soil fungi and soil fungal biomarkers. Yellow arrow length indicates the residual magnitude between paired samples.
Figure 7. Procrustes analysis of the functions of soil microorganisms and biomarkers: (a) The functions of soil bacteria and soil bacterial biomarkers. (b) The trophic modes of soil fungi and soil fungal biomarkers. Yellow arrow length indicates the residual magnitude between paired samples.
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Table 1. α-diversity index of soil microbial communities in different treatments.
Table 1. α-diversity index of soil microbial communities in different treatments.
TreatmentBacteriaFungi
ChaoShannonChaoShannon
W3B43960.73 ± 205.43 b7.52 ± 0.03 a358.87 ± 27.53 ab3.60 ± 0.24 a
W3B34079.94 ± 82.43 ab7.19 ± 0.15 a347.83 ± 15.38 abc3.77 ± 0.15 a
W3B24211.84 ± 191.72 ab7.34 ± 0.12 a354.02 ± 30.56 abc3.66 ± 0.38 a
W3B13848.81 ± 134.22 b7.40 ± 0.03 a355.01 ± 12.03 abc3.72 ± 0.08 a
W3B04234.10 ± 112.08 ab7.44 ± 0.06 a311.61 ± 13.51 bcd3.31 ± 0.19 ab
W2B44522.13 ± 12.70 a7.28 ± 0.11 a381.17 ± 2.15 a3.36 ± 0.02 ab
W2B34054.39 ± 68.68 ab7.44 ± 0.00 a377.14 ± 8.85 a3.61 ± 0.12 a
W2B23723.42 ± 156.38 b7.34 ± 0.06 a377.14 ± 2.00 a3.54 ± 0.12 ab
W2B13845.74 ± 46.32 b7.48 ± 0.01 a382.85 ± 22.39 a3.43 ± 0.13 ab
W2B04089.45 ± 69.99 ab7.47 ± 0.02 a377.22 ± 10.64 a3.35 ± 0.16 ab
W1B43871.19 ± 66.45 b7.34 ± 0.05 a334.82 ± 21.56 abcd3.10 ± 0.25 ab
W1B33905.42 ± 139.20 b7.48 ± 0.01 a288.47 ± 27.86 d2.87 ± 0.27 b
W1B23234.67 ± 487.96 c6.82 ± 0.57 a330.43 ± 19.04 abcd3.17 ± 0.25 ab
W1B13983.03 ± 67.83 ab7.39 ± 0.03 a298.44 ± 13.11 cd3.12 ± 0.20 ab
W1B04149.22 ± 1.39 ab7.45 ± 0.03 a374.15 ± 7.26 a3.39 ± 0.15 ab
Wnsns*****
B*nsnsns
W × B*nsnsns
Results are presented as mean ± standard error. W and B represent different irrigation levels and biochar application rates, respectively: W1: 50–70% θf, W2: 60–80% θf, and W3: 70–90% θf (θf = soil field capacity); B0: 0 t/ha, B1: 15 t/ha, B2: 30 t/ha, B3: 45 t/ha, and B4: 60 t/ha. Different lowercase letters indicate significant differences between treatments (p < 0.05). *** represents highly statistically significant (p < 0.001), ** represents highly statistically significant (p < 0.01), * represents statistically significant (p < 0.05), and ns represents not significant among different treatments, the same as below.
Table 2. Mantel test between soil bacterial/fungal biomarkers and environmental factors.
Table 2. Mantel test between soil bacterial/fungal biomarkers and environmental factors.
20212022
Bacteria biomarkersSWCTpHEHSWCTpHEH
Mantel testr0.418 −0.213 −0.018 0.060 0.437 0.120 −0.102 0.212
p0.004 0.942 0.507 0.322 0.003 0.193 0.768 0.097
Fungi biomarkersSWCTpHEHSWCTpHEH
Mantel testr0.440 −0.207 −0.052 −0.001 0.452 0.107 −0.114 0.123
p0.004 0.911 0.604 0.402 0.003 0.233 0.775 0.210
Mantel tests were based on Bray–Curtis distance with 9999 permutations; r and p represent the correlation coefficient and p-value, respectively, from the Mantel test assessing the relationship between the microbial biomarker distance matrix and the environmental variable distance matrix. SWC: volumetric soil water content, T: soil temperature, pH: soil acidity and alkalinity, EH: soil redox potential, the same as below.
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An, J.; Zheng, L.; Sun, R.; Li, X.; Ma, L.; Ma, J. Effects of Corn Stover Biochar on Soil Bacterial and Fungal Biomarkers in Greenhouse Tomatoes Under Mulched Drip Irrigation. Horticulturae 2025, 11, 1143. https://doi.org/10.3390/horticulturae11091143

AMA Style

An J, Zheng L, Sun R, Li X, Ma L, Ma J. Effects of Corn Stover Biochar on Soil Bacterial and Fungal Biomarkers in Greenhouse Tomatoes Under Mulched Drip Irrigation. Horticulturae. 2025; 11(9):1143. https://doi.org/10.3390/horticulturae11091143

Chicago/Turabian Style

An, Jianglong, Lijian Zheng, Ruifeng Sun, Xufeng Li, Li Ma, and Juanjuan Ma. 2025. "Effects of Corn Stover Biochar on Soil Bacterial and Fungal Biomarkers in Greenhouse Tomatoes Under Mulched Drip Irrigation" Horticulturae 11, no. 9: 1143. https://doi.org/10.3390/horticulturae11091143

APA Style

An, J., Zheng, L., Sun, R., Li, X., Ma, L., & Ma, J. (2025). Effects of Corn Stover Biochar on Soil Bacterial and Fungal Biomarkers in Greenhouse Tomatoes Under Mulched Drip Irrigation. Horticulturae, 11(9), 1143. https://doi.org/10.3390/horticulturae11091143

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