Two Distinct Soil Disinfestations Differently Modify the Bacterial Communities in a Tomato Field

Differently Modify the Bacterial Communities a Tomato Field. Abstract: Reductive soil disinfestation (RSD) and soil solarization (SS) were evaluated based on environmental factors, microbiome, and suppression of Fusarium oxysporum in a tomato ﬁeld soil. Soil environmental factors (moisture content, electric conductivity, pH, and redox potential (RP)) were measured during soil disinfestations. All factors were more strongly inﬂuenced by RSD than SS. 16S rRNA amplicon sequencing of RSD- and SS-treated soils was performed. The bacterial communities were taxonomically and functionally distinct depending on treatment methods and periods and signiﬁcantly correlated with pH and RP. Fifty-four pathways predicted by PICRUSt2 (third level in MetaCyc hierarchy) were signiﬁcantly different between RSD and SS. Quantitative polymerase chain reaction demonstrated that both treatments equally suppressed F. oxysporum . The growth and yield of tomato cultivated after treatments were similar between RSD and SS. RSD and SS shaped different soil bacterial communities, although the effects on pathogen suppression and tomato plant growth were comparable between treatments. The existence of pathogen-suppressive microbes, other than Clostridia previously reported to have an effect, was suggested. Comparison between RSD and SS provides new aspects of unknown disinfestation patterns and the usefulness of SS as an alternative to RSD. remain unknown. This study compared the RSD and SS soil disinfestation methods based on soil environmental factors and tomato plants cultivated


Introduction
Increasing food production is one of the most important challenges to meet the global population growth of the 21st century. Chemical fertilizers and synthetic pesticides have been used for stable crop growth, pest control, and yield increase worldwide [1]. In conventional agriculture, pest control depends on chemical pesticides; for example, soil fumigants, such as chloropicrin, metam-sodium, and dazomet, are used for broadspectrum pest control [2][3][4]. However, conventional agricultural systems simultaneously degrade the global environment (e.g., land, water, biodiversity, and climate) and consume a tremendous amount of natural resources [5]. Organic farming, the management system limiting chemical fertilizers and synthetic pesticides, is a promising solution for these negative impacts. A meta-analysis to compare organic and conventional farming showed that organic farming has positive environmental impacts per area unit, but not necessarily per product unit [6]. Nevertheless, organic farming has been shown to improve soil fertility and reduce global warming potential [7]. Although organic farming is considered more environmentally friendly than conventional farming, it produces lower crop yields than

Soil Disinfestations and Sampling
Field experiments were performed in a plastic multispan greenhouse (4 m × 24 m × 6 m) located in Kyoto Prefecture, Japan (35.0 • N, 135.4 • E), where the type of soil was clay accumulation red yellow. Three plots were designed for RSD (Plots 1-3) and SS (Plots 4-6) treatments (a total of six plots; size, 1.2 m × 20 m per plot). Both treatments were started on 6 August 2019. Wheat bran (2% N and 40% C, estimated available nitrogen and carbon supply; Tamagoya-Shoten, Ibaraki, Japan) was applied at 1.2 kg m −2 to RSD, and BLOF compost (0.9% N and 20.7% C, estimated available nitrogen and carbon supply; Japan Bio Farm Ltd., Nagano, Japan) was applied at 2.5 kg m −2 to SS. The initial (estimated) volumetric moisture content (MC) was adjusted to about 40% for RSD and about 30% for SS by irrigation, and the soil surface was covered with plastic films. The temperature in the greenhouse was controlled not to exceed 50 • C in the daytime by the environmental controller and the other temperature was entrusted. Changes in air temperatures were shown in Supplemental Figure S1 and the mean temperature during this period was 29.8 • C. The films were removed on 27 August 2019, and the temperature was changed to ambient temperature (mean of 30 • C). Soils were sampled on 6 August 2019 before the start, 13, 16, 20, 24, 27, and 30 August and 3, 6, 10, and 13 September 2019, during the treatment period, and 13 November 2019 and 22 January 2020, during the tomato cultivation period. Soils were collected from three different points with a core size of 17 mm in diameter at a depth of 0-15 cm from the surface in each plot for one sample. These soils were used for the measurement of environmental factors and DNA extraction.

Environmental Factor Measurement
The soils sampled during the treatment period were dried at 60 • C for 48 h. MC, electric conductivity (EC), and pH were measured. MC was calculated by subtracting the weight of the dry soil from the weight of the moist soil divided by the weight of the dry soil. A 1:5 (w/v) ratio of dried soil and water was used for measuring EC and pH by LAQUAtwin EC-11 conductivity meter and LAQUAtwin pH-11 pH meter, respectively (HORIBA Ltd., Kyoto, Japan). Besides, redox potential (RP) was continuously monitored by an FV-702 RP sensor (Fujiwara Seisakusho Ltd., Tokyo, Japan).

DNA Extraction and 16S rRNA Amplicon Sequencing
DNA was extracted from the soils sampled on 6, 13, 20, and 27 August, 3 and 10 September, and 13 November 2019 and 22 January 2020, using the DNeasy PowerSoil Kit (QIAGEN K.K., Tokyo, Japan). The DNA concentration was measured by a Qubit dsDNA HS Assay Kit and Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Polymerase chain reaction (PCR) amplification of the V4 region of bacterial 16S rRNA was performed using KOD FX Neo (TOYOBO, Osaka, Japan) with the following primer set: 515F (5 -ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAGCMGCCGCGGTAA-3 ) and 806R (5 -GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGACTACHVGGGTW TCTAAT-3 ) in technical triplicate. PCR was conducted according to the following program: 94 • C for 2 min and 20 cycles of 98 • C for 10 s, 50 • C for 30 s, and 68 • C for 30 s. Purification of the PCR products was carried out using AMPure XP (Beckman Coulter, Danvers, MA, USA), and the purified DNA was used as a template for PCR amplification for MiSeq (Illumina, San Diego, CA, USA) adapter attachment. PCR amplification was performed using KOD FX Neo with primers provided by FASMAC Co., Ltd. (Kanagawa, Japan) in technical duplicate. PCR was conducted according to the following program: 94 • C for 2 min and 10 cycles of 98 • C for 10 s, 59 • C for 30 s, and 68 • C for 30 s. The PCR products were purified as described above. Equal amounts of DNA were mixed and applied for Agronomy 2021, 11, 1375 4 of 15 2 × 250 bp paired-end sequencing using MiSeq in FASMAC. The acquired sequence data has been registered in the DNA Data Bank of Japan Sequence Read Archive (DRA012344).

Microbiome Data Analysis
16S rRNA amplicon sequence data were analyzed using the QIIME2 environment version 2019.7 [38]. The raw sequences were processed using the q2-dada2 plugin in QIIME2 to trim the bases other than the 21st to 200th of all paired reads and construct error-corrected amplicon sequence variants (ASVs) from the trimmed reads [39]. The taxonomic assignment of ASVs was performed using the naïve Bayes classifier pretrained on the Silva 16S rRNA database release 132 [40,41]. The obtained ASV sequences were aligned using MAFFT [42]. Phylogenetic trees were constructed using FastTree2 [43]. The core-metrics-phylogenetic pipeline in the q2-diversity plugin within QIIME2 was used to calculate the αand β-diversity indices of the sampled soils with a subsampled ASV dataset with 19,000 sequences per sample. Bacterial metagenome functional profiles were predicted from the obtained ASV sequences and ASV profiles using the q2-picrust2 plugin [44] with the MetaCyc database of metabolic pathways and enzymes [45]. The predicted pathways by PICRUSt2 were grouped into parent classes based on the MetaCyc pathway hierarchy version 23.5 (released on December 18, 2019; Supplemental Table S1) and used for subsequent analysis.

Quantification of F. oxysporum in Soils
DNA from the soils sampled on 6, 13, 20, and 27 August and 3 and 10 September 2019, were used for real-time PCR to quantify F. oxysporum in soils as templates. PCR was performed using KOD FX Neo with the following primer set: CLOX1 (5 -CAGCAAAGCATCAGACCACTATAACTC-3 ) and CLOX2 (5 -CTTGTCAGTAACTGGAC GTTGGTACT-3 ) [46]. The PCR program was conducted as follows: 98 • C for 2 min and 50 cycles of 98 • C for 10 s, 60 • C for 10 s, and 68 • C for 30 s, followed by ramping up from 65 • C to 95 • C at 0.1 • C s −1 . The absolute abundance of F. oxysporum was estimated using a calibration curve constructed using a dilution series of plasmids with cloned fragments of the specific gene.

Tomato Cultivation and Evaluation of the Growth and Yield
Cherry tomato (Solanum lycopersicum cv. Benisuzume; Institute for Horticultural Plant Breeding, Chiba, Japan) was cultivated after soil disinfestations. The seeds were sown in pots, and the seedlings were grown at the greenhouse for about 50 days. They were planted into the field on 23 September 2019, after disinfestations and grown with two leaders in 1.7 plants m −2 . Plant growth was measured on 16 and 30 October and 13 and 27 November 2019. The length of the main stem was defined as its distance from the shoot apex to the soil surface position. The perimeter of the main stem was measured at 15 to 20 cm from the shoot apex. The leaf area was defined as the average of the products of width and length of the first upper and lower expanded leaves at 50 cm from the shoot apex of the main stem. The length of the internode was defined as the average of internode lengths from the first upper inflorescence with flowers of the main stem to the first upper and lower inflorescences. All items were measured in five plants per plot. Ripe fruits from more than 100 plants for each treatment were harvested and weighed to determine the fruit yield per unit area.

Statistical Analysis
The Welch's t-test was carried out using the t.test function of R package "stats." Tukey's test was performed using TukeyHSD function of R package "multcomp." Hierarchical clustering was performed using the complete linkage method with the hclust function in the R package "stats." Permutational multivariate analysis of variance (PERMANOVA) was performed with the adonis function in the R package "vegan" [47]. Correlation analysis between soil bacterial communities and soil properties was performed by the Mantel test using the weighted UniFrac (WUF) distance matrix and the Euclidian distance matrix of environmental factors in Plots 2 and 5. The R software package "vegan" was used with 10,000 permutations (* p < 0.05; ** p < 0.01). ANOVA-like differential expression (ALDEx2) analysis [48,49] was performed to detect differentially abundant taxa at the class level and pathways predicted by PICRUSt2 between RSD and SS with the R software package "ALDEx2" [false discovery rate (FDR) < 0.01, Welch's t-test corrected by the Benjamini-Hochberg method]. Low abundant taxa (mean relative abundance of < 0.1%) were filtered out for ALDEx2 analysis.

Fluctuation of Environmental Factors during Soil Disinfestations
To assess the soil disinfestation methods without chemical fertilizers and pesticides, the effects of RSD and SS in a cherry tomato greenhouse were compared. Both treatments were initiated by adding organic substrates and water and covering them with plastic films. Four parameters (i.e., MC, EC, pH, and RP) were monitored for 6 weeks. MC reached about 40% and 30% after the addition of water in RSD and SS, respectively, which were then decreased after 4 weeks in both conditions. MC in RSD remained continuously higher than that in SS during the test period ( Figure 1A). In both conditions, EC decreased to 0.2 mS cm −1 for 1 week after initiation and returned to the initial levels (0.5-0.6 mS cm −1 ) after 5 weeks. EC in RSD was generally lower than that in SS ( Figure 1B). pH increased from 6.4 to 7.4 in 3 weeks and then decreased to 6.6 in RSD; in SS, it increased from 6.4 to 7.2 in 1 week, maintained for 18 days, and then decreased to 6.9 ( Figure 1C). Soil anaerobicity is described as follows: aerobic (RP> +300 mV) and anaerobic [moderately reductive (+300 to 0 mV), reductive (0 to −200 mV), and highly reductive (< −200 mV)] [50]. RP decreased to −200 mV after 10 days in RSD, maintained for 3 weeks, and then rapidly returned to the levels at the start (about +600 mV); in SS, it decreased to +250 mV for 1 week, maintained for 2 weeks, and then returned to the levels at the start 3 weeks later ( Figure 1D). Based on this observation, RSD and SS formed highly and moderately reduced conditions, respectively. The measurement of soil parameters revealed that RSD and SS caused different influences on the soil environmental factors.

Changes in Bacterial Communities by Soil Disinfestations
To evaluate the influence of RSD and SS on bacterial communities, a 16S rRNA amplicon sequence of soils during treatments was conducted. Both weighted and unweighted UniFrac (WUF and UUF)-based principal coordinate analysis (PCoA) showed a variation of the bacterial communities depending on the treatment methods and periods (PER-MANOVA, p = 0.001; Figure 2).
The differentially abundant test was performed using ALDEx2 analysis, and nine bacterial taxa at the class level were significantly different between RSD and SS (FDR < 0.01; Supplemental Table S2). Three classes, such as Bacilli and Clostridia of Firmicutes and Deltaproteobacteria of Proteobacteria, were more abundant in RSD than in SS, and six classes, such as Acidimicrobiia of Actinobacteria, Chloroflexia and Gitt-GS-136 of Chloroflexi, Gemmatimonadetes and S0134 terrestrial group of Gemmatimonadetes, and Verrucomicrobiae of Verrucomicrobia, were more abundant in SS than in RSD (Supplemental Figure S2 and Table S2). The Mantel test was used to assess the relationships between soil bacterial communities and environmental factors. It showed that soil RP and pH were significantly correlated with the bacterial communities (p < 0.05; Figure 3). These results suggested that the difference of changes in bacterial communities depends on the soil disinfestation methods and that fluctuation of environmental factors affects the communities. Agronomy 2021, 11, x 6 of 16

Changes in Bacterial Communities by Soil Disinfestations
To evaluate the influence of RSD and SS on bacterial communities, a 16S rRNA amplicon sequence of soils during treatments was conducted. Both weighted and unweighted UniFrac (WUF and UUF)-based principal coordinate analysis (PCoA) showed a variation of the bacterial communities depending on the treatment methods and periods (PERMANOVA, p = 0.001; Figure 2). The differentially abundant test was performed using ALDEx2 analysis, a bacterial taxa at the class level were significantly different between RSD and SS 0.01; Supplemental Table S2). Three classes, such as Bacilli and Clostridia of Fi results suggested that the difference of changes in bacte soil disinfestation methods and that fluctuation of enviro munities.

Changes in Bacterial Pathway Composition by Soil Disinfestations
The predicted pathway composition of the bacterial metagenomes in the disinfested soils was inferred with a 16S rRNA gene sequence using the PICRUSt2 pipeline developed from PICRUSt1 to predict the functional potential of a bacterial community [44,51]. By PICRUSt2, 445 metabolic pathways were predicted for the data, and for understanding with simplicity, these pathways were grouped into parent classes based on the MetaCyc pathway hierarchy (Supplemental Table S1). Top-level (eight categories) and third level (176 categories) in the class hierarchy were used for subsequent analysis. Principal component analysis (PCA) of the functional profiles at third and eighth levels (most divided) showed a difference in the pathway compositions depending on the methods and periods of disinfestations by the first and second axes, respectively (Figure 4; Supplemental Figure S3). Based on functional categories at level 1 of the MetaCyc pathway, the function "Biosynthesis" accounted for about 70%, and "Degradation/Utilization/Assimilation" and "Generation of Precursor Metabolite and Energy" accounted for about 15% ( Figure 5A). ALDEx2 analysis showed that 54 categories at level 3 of the MetaCyc pathway were significantly different between RSD and SS (FDR < 0.01; Supplemental Table S3). Focusing on periods when environmental factors were especially different between RSD and SS (Figure 1), three categories belonging to "Degradation/Utilization/Assimilation" peaked at 2 weeks later in RSD, whereas the other eight categories peaked at 1 to 3 weeks later in SS ( Figure 5B). Intriguingly, the category "Siderophore Biosynthesis" belonging to "Biosynthesis" was enriched in SS, particularly in soils 2 weeks later ( Figure 5B). These results implied that the potential of siderophore production is higher in SS than in RSD.

Changes in Bacterial Pathway Composition by Soil Disinfestations
The predicted pathway composition of the bacterial metagenomes in soils was inferred with a 16S rRNA gene sequence using the PICRUSt2 pipe from PICRUSt1 to predict the functional potential of a bacterial commu PICRUSt2, 445 metabolic pathways were predicted for the data, and for with simplicity, these pathways were grouped into parent classes based o pathway hierarchy (Supplemental Table S1). Top-level (eight categories) (176 categories) in the class hierarchy were used for subsequent analysis. ponent analysis (PCA) of the functional profiles at third and eighth levels showed a difference in the pathway compositions depending on the metho of disinfestations by the first and second axes, respectively (Figure 4; Suppl S3). Based on functional categories at level 1 of the MetaCyc pathway, the synthesis" accounted for about 70%, and "Degradation/Utilization/Ass "Generation of Precursor Metabolite and Energy" accounted for about 15 ALDEx2 analysis showed that 54 categories at level 3 of the MetaCyc pat nificantly different between RSD and SS (FDR < 0.01; Supplemental Tabl on periods when environmental factors were especially different betwe (Figure 1), three categories belonging to "Degradation/Utilization/Assim at 2 weeks later in RSD, whereas the other eight categories peaked at 1 to SS ( Figure 5B). Intriguingly, the category "Siderophore Biosynthesis" bel synthesis" was enriched in SS, particularly in soils 2 weeks later ( Figure 5B implied that the potential of siderophore production is higher in SS than i

Difference of F. oxysporum Density between Two Soil Disinfestations
To compare the effects of disinfestation methods on plant pathogens, a tomato pathogen F. oxysporum was quantified with PCR during RSD and SS treatments. F. oxysporum decreased over time in both RSD and SS. It was reduced to 6.8% of the initial amount in RSD and was below the limit of quantification in SS 5 weeks later ( Figure 6). There is no significant difference between RSD and SS on the abundance of F. oxysporum, although these practices differentially affect the soil environmental factors and soil bacterial communities.   To compare the effects of disinfestation methods on plant pathogens, a tomato pathogen F. oxysporum was quantified with PCR during RSD and SS treatments. F. oxysporum decreased over time in both RSD and SS. It was reduced to 6.8% of the initial amount in RSD and was below the limit of quantification in SS 5 weeks later ( Figure 6). There is no significant difference between RSD and SS on the abundance of F. oxysporum, although these practices differentially affect the soil environmental factors and soil bacterial communities.

Effects of Soil Disinfestation on Soil Bacterial Communities and Tomato Growth during Cultivation
It is unclear how long the effects of disinfestations last. To analyze whether the difference of bacterial communities between RSD and SS was maintained during the cherry tomato cultivation period, bacterial communities in soils sampled during the tomato cultivation period after these treatments were analyzed. WUF-and UUF-PCoA showed a clear distinction of the bacterial communities between RSD and SS (PERMANOVA, p = 0.019 for WUF and p = 0.003 for UUF; Figure 7). In contrast, the bacterial communities of both soils were not altered between 14 and 24 weeks later (PERMANOVA, p = 0.058 for WUF and p = 0.237 for UUF; Figure 7). These data indicated that the effects of RSD and SS continue even after 6 months.

Effects of Soil Disinfestation on Soil Bacterial Communities and Tomato Growth during Cultivation
It is unclear how long the effects of disinfestations last. To analyze whether the difference of bacterial communities between RSD and SS was maintained during the cherry tomato cultivation period, bacterial communities in soils sampled during the tomato cultivation period after these treatments were analyzed. WUF-and UUF-PCoA showed a clear distinction of the bacterial communities between RSD and SS (PERMANOVA, p = 0.019 for WUF and p = 0.003 for UUF; Figure 7). In contrast, the bacterial communities of both soils were not altered between 14 and 24 weeks later (PERMANOVA, p = 0.058 for WUF and p = 0.237 for UUF; Figure 7). These data indicated that the effects of RSD and SS continue even after 6 months. Differences in bacterial communities between RSD and SS during the tomato cultivation period were confirmed. The effects of RSD and SS on the growth of cherry tomatoes during cultivation were also investigated. The length and perimeter of the main stem and length of internode on October 30 and the length of the main stem on November 27 were significantly different between RSD and SS (Table 1). Fruit yield showed a slight increase in SS (5.64 kg m −2 ) than in RSD (5.23 kg m −2 ) but not statistically tested due to singlicate.  Differences in bacterial communities between RSD and SS during the tomato cultivation period were confirmed. The effects of RSD and SS on the growth of cherry tomatoes during cultivation were also investigated. The length and perimeter of the main stem and length of internode on October 30 and the length of the main stem on November 27 were significantly different between RSD and SS (Table 1). Fruit yield showed a slight increase in SS (5.64 kg m −2 ) than in RSD (5.23 kg m −2 ) but not statistically tested due to singlicate.

Discussion
To improve the profitability of organic farming, it is required to effectively control pathogens without using pesticides to grow plants healthily. Fusarium wilt is a major soil-borne tomato disease caused by FOL [52] and is difficult to prevent without pesticides. Both RSD and SS strongly suppressed the abundance of F. oxysporum. The suppressive effect by RSD was previously reported for several pathogens, including Agrobacterium tumefaciens, F. oxysporum, Ralstonia solanacearum, Rhizoctonia solani, Pythium spp., and Verticillium dahliae [53][54][55][56][57][58]. Pathogen suppression mediated by RSD is supposed to involve various chemical and biological mechanisms in a complicated manner [18,19]. The decrease in RP coincided with pathogen suppression [15], but the reductive condition alone was not lethal for F. oxysporum [18]. The results of the study suggested that even a moderately reduced condition is enough to suppress F. oxysporum and that the sequential chemical and biological modulations are essential. Another possible mechanism for pathogen control is the accumulation of toxic organic acids mediated by the anaerobic decomposition of organic substrates added at the start of disinfestation, accompanied by the pH decrease during RSD [59]. However, this study, including some previous works, indicated pH increase and pathogen suppression during RSD [60][61][62]. Nevertheless, the fluctuation of pH during RSD had different results depending on added organic substrates and initial pH at the start and the geographic locations, although the treatments showed pathogen suppression [63,64]. Thus, it remains unclear why the pH fluctuations during RSD differ among several studies and which pathogens are suppressed by organic acids. However, under the conditions set in this study, the observed suppression of F. oxysporum would be caused by factors other than organic acid generation with pH decrease.
The quantity, diversity, and community of soil-borne microbes are important indicators of soil quality and play key roles in plant growth and defense [24,65]. RSD treatment formed different bacterial and fungal communities from untreated soil by culture-dependent and culture-independent investigation [66]. At the phylum level of bacteria, members of Firmicutes (from both classes Clostridia and Bacilli) increased in the RSD-treated soil [25][26][27]67]. Because RSD makes soil reducing/anaerobic, it is reasonable to increase anaerobic microbes, such as Clostridia. In accordance with these findings, the relative abundance of class Clostridia tended to be inversely proportional to soil RP (Supplemental Figure S2B). Regarding the involvement of microbes in pathogen suppression during RSD treatment, specific strains of Clostridia and Bacilli from treated soils could have a disease control effect [30,32]. The families Ruminococcaceae, Lachnospiraceae, and Clostridiaceae belonging to Clostridia increased during RSD and produced toxic compounds against plant pathogens [68][69][70]. Time-series analysis of soil microbiome and metabolome during RSD demonstrated that composition of Firmicutes abundance continuously changes throughout the treatment period and that population dynamics of Clostridium spp. were correlated with temporal changes in abundances of organic acids, p-cresol, and methyl sulfides with antimicrobial activities [71]. These previous studies argue that Clostridia is responsible for suppressing pathogens. In this study, the suppression effects of F. oxysporum between RSD and SS treatments were similar, although Clostridia was not increased in SS as much as RSD (Supplemental Figure S2B), suggesting that microbes other than Clostridia may also be involved in the suppression. Indeed, the relative abundances of some groups belonging to the bacterial order Sphingobacteriales and fungal order Sordariales also increased by RSD, which were negatively correlated with disease incidence [60]. In addition to the pathogenic control effect, the abundances of the nitrogenase and ammonia monooxygenase genes related to nitrification increased in RSD-treated soil [68,72], suggesting the improvement of nutritional availability by RSD. Recently, SS was reported to induce organic nitrogen to increase plant growth [34]. The comparison between RSD and SS revealed that SS might be involved in siderophore production. This result gave a possibility of plant growth promotion by SS, although there was no significant difference in the effects of RSD and SS on cherry tomato plant growth. Furthermore, the legacy effect on the bacterial community was confirmed 3 months after treatment [73], in accordance with the observation of distinct bacterial communities between RSD and SS even during the tomato cultivation period. Thus, this study suggests that the effects of soil disinfestation on soil microbiota last during the cultivation period in organic farming.

Supplementary Materials:
The following are available online at https://www.mdpi.com/article/ 10.3390/agronomy11071375/s1, Figure S1: Air temperature observed outside the green house, Figure S2: Relative abundances of bacterial taxa in the soil samples under reductive soil disinfestation (RSD) and soil solarization (SS), Figure S3: Principal component analysis (PCA) of the pathway composition (eighth level in MetaCyc hierarchy) predicted by PICRUSt2 in the soil bacterial metagenomes under ( ) reductive soil disinfestation (RSD) and ( ) soil solarization (SS), Table S1: MetaCyc_pathway_hierarchy_(version 23.5), Table S2. Relative abundances of bacterial classes (mean relative abundance > 0.1%) in the soil samples under reductive soil disinfestation (RSD) and soil solarization (SS), Table S3. Relative abundances of the predicted pathways in the soil samples under reductive soil disinfestation (RSD) and soil solarization (SS). Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.
Data Availability Statement: All the data is available in the paper.