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Article

Conversion to Greenhouse Cultivation from Continuous Corn Production Decreases Soil Bacterial Diversity and Alters Community Structure

by
Yaqiong Fan
1,2,
Yamin Jia
1,
Xinyang Zhang
1,
Guoqiang Geng
1,
Ronghao Liu
1,
Lixia Shen
1,
Jingjuan Hu
1 and
Xinmei Hao
2,3,*
1
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733009, China
3
Center for Agricultural Water Research in China, College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(9), 2144; https://doi.org/10.3390/agronomy14092144
Submission received: 4 July 2024 / Revised: 10 September 2024 / Accepted: 18 September 2024 / Published: 20 September 2024
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
Review Reports Versions Notes

Abstract

:
Changes in crop types and long-term monoculture substantially impact soil microbial communities. Exploring these changes and their influencing factors is of great significance for addressing the challenges posed by continuous cropping. Soil surface layer samples from greenhouse tomatoes fields cultivated for 5 (Y5), 9 (Y9), 13 years (Y13), and a surrounding corn field (CK) as a control were analyzed. The Y13 sample showed a significant increase in the relative abundance of Pseudomonadota (43.1%) and a decrease in Actinobacteria (50.3%) compared to the CK sample. Soil bacterial alpha diversity generally declined from the CK to Y13 (0.1–22.2%) sample, with a small peak in Y9 for Chao1 and Observed_species. Significant differences in Chao1 and Observed_ species were observed between the CK and Y13 samples. Beta diversity analysis revealed a pronounced variation in soil bacterial community structure across planting years, with the divergence from the CK sample intensifying over time. In comparison to the Y5 vs. CK samples, Y9 and Y13 exhibited marked differences from the CK across the same and broader metabolic pathways, suggesting a potential convergence of microbial activities over time. The Y9 and Y13 samples showed significantly higher biosynthesis abundance (7.50% and 6.36%, respectively) than the CK. In terms of soil physicochemical indices, the carbon–nitrogen ratio was the primary factor influencing soil bacterial composition. In conclusion, we found that crop alteration and continued planting changed the soil’s bacterial composition and increasing planting years suppressed the soil’s bacterial diversity, leading to a stable bacterial ecology after nine years. Implementing appropriate measures during this critical period is vital for optimal soil utilization.

1. Introduction

Microorganisms play a pivotal role in nutrient cycles and energy flow within soil, serving as indicators of soil structure, fertility, stability, and sustainable use. They play an indispensable role in regulating the intricate feedback mechanisms between plants and soil, thereby fostering a harmonious and productive environment. Both the alteration in crop cultivation practices and the implementation of continuous planting strategies serve as pivotal measures in adapting to shifts in human dietary preferences. Delving into their respective impacts on microbial communities holds immense significance, as this facilitates a deeper understanding of the theory underpinning soil microbial community evolution.
The structure and activity of microbial communities vary significantly across cultivation years due to changes in soil property [1]. Dominant bacterial abundance and diversity correlate with soil carbon, nitrogen, and phosphorus levels [2,3,4]. Zhang et al. [5] discovered that the relative abundance of the Acidobacteria phylum significantly increased with an increase in soil nitrogen input, whereas the Actinobacteria phylum showed contrasting results, indicating a decrease in its relative abundance under similar conditions. Furthermore, factors such as higher soil organic content and varying carbon-to-nitrogen ratios can significantly affect soil microbial diversity [6] and the relative abundance of Proteobacteria, Bacteroidetes, and Acidobacteria [7,8]. Specifically, the relative abundance of the Acidobacteria phylum was observed to be diminished in soils enriched with high concentrations of organic carbon. In contrast, b-Proteobacteria and Bacteroidetes, both exhibiting copiotrophic characteristics, demonstrated the highest relative abundances in soils with high availability of carbon [7].
With long-term cultivation, Xiong et al. [9] revealed that the continuous cropping of black pepper led to a decrease in soil bacterial abundance and alterations in soil microbial community composition and structure, which were related to black pepper growth. Tong et al. [10] reported that an increase in cultivation years of P. ginseng in farmland and deforestation fields significantly altered the diversity of soil microbial communities. Similarly, soil bacterial and fungal richness decreased with continuous coffee cropping [11]. However, several studies have illuminated a nonlinear pattern in soil bacterial composition and diversity, featuring a pivotal point we designate as the “limit year effect”. Li et al. [12] uncovered a notable decrease in the Shannon and Simpson indices within the first 9 years, followed by an increase in year 13 in greenhouse vegetable fields. Analogous trends were observed in a rice–cherry tomato rotation system, where the alpha diversity of soil microbial communities peaked at year 5, subsequently declining gradually until year 10, with certain beneficial microorganisms experiencing a decline after years 5 or 7 [13]. These discoveries underscore the intricate response of soil microbiome indices to continuous agricultural practices, initially escalating and then declining, or exhibiting a reversal in trend.
Research on temporal variations in soil microbial community diversity across planting cycles uncovers varied patterns. These patterns frequently reflect the unique soil conditions and distinct cultivation practices of different regions. However, studies examining soil bacterial community shifts in response to crop alteration and continued greenhouse cultivation in the arid northwest of China are still limited, resulting in uncertainties about whether soil bacterial composition and diversity undergo a gradual transformation over successive planting years or experience an abrupt change (known as the “limit year effect”).
Moreover, a typical phenomenon is the transition of fields originally cultivated for food crops, such as corn, to vegetable cultivation to meet the escalating demand for vegetables. This transition, meticulously planned, has led to a cultivation area exceeding 4 million hectares in 2021 [12]. A previous study has highlighted the substantial alteration in soil microbiome diversity and composition when American ginseng is continuously planted compared to traditional crops [14]. Similarly, Oh et al. [15] and Mahnert et al. [16] have indicated that soil microbial community composition was affected by vegetation type and plant community structure, respectively.
Given these insights, it is logical to infer that converting conventional fields to greenhouse tomato cultivation would initially result in a notable difference in soil microorganisms, primarily driven by the change in plant species. Notably, tomato is a globally popular and representative vegetable cultivated under greenhouse conditions in northern China [17,18]. In practical production, continuous planting of greenhouse tomatoes is prevalent due to cost considerations and the desire to maximize soil utilization. However, long-term monocropping of greenhouse tomatoes has been reported to severely deteriorate soil quality, leading to a sharp decline (2–38%) in tomato yields [19,20,21]. Additionally, this practice often results in the accumulation of autotoxic substances, degradation of soil physiochemical properties, disruption of native soil microbial communities, and the buildup of soil-borne pathogens [22,23]. Studies have also warned that monocropped or intensively managed greenhouse production systems can result in the risk of soil acidification and salinization, with prolonged cultivation causing substantial changes in soil community structure and abundance [13,24].
Therefore, selecting research subjects that have transitioned from field corn cultivation to continuous greenhouse tomato cultivation is not only representative but also reasonable for analyzing the research gap mentioned above, aiming to better understand the impact of such transitions and continue planting on soil microbial communities.
In this study, soil from greenhouse tomatoes used in monocropping for 5 years (Y5), 9 years (Y9), and 13 years (Y13) were selected as study objects and the surrounding open soil planted with corn (CK) was selected as the comparison item. The objectives of the study were (1) to investigate the shifts in soil bacterial communities after converting corn fields to greenhouse tomato cultivation; (2) to explore soil bacterial diversity and composition under different planting years, as well as the associated predicted functions of different bacterial groups; and (3) to determine whether there is a “limit year effect” in greenhouse tomato continuous cropping. We hypothesized that converting corn fields to greenhouse tomatoes would significantly alter soil microbial structure. Moreover, based on our prior findings regarding soil physical and chemical properties [25], we speculated that 9 years of continuous cropping represents a tipping point for changes in soil microbial characteristics.

2. Materials and Methods

2.1. Study Site Description

The study was conducted at Qingyuan Town, Liangzhou District, Wuwei City, Gansu Province. The study area spans a latitude range of 37°50′16″ N to 37°50′31″ N and a longitude range of 102°52′28″ E to 102°52′49″ E, with altitudes varying between 1570 m and 1580 m. The region experiences a temperate continental climate, characterized by an annual average rainfall of 164.4 mm, although evaporation rates are high, averaging 2000 mm annually from water surfaces. Furthermore, the region boasts abundant light and heat resources, with an average annual sunshine duration exceeding 3000 h, a frost-free period lasting over 150 days, and an annual average temperature of 8.8 °C.

2.2. Survey Respondents

The study selected centralized greenhouse tomato planting and corn planting as sampling sites. Notably, corn cultivation has a rich history here, spanning over two decades. Conversely, the greenhouse tomato sites were recently established, having been restructured from portions of these established corn fields in recent years. Consequently, tomato greenhouses that had undergone continuous monocropping for 5, 9, and 13 years were chosen in the vicinity (designated as Y5, Y9, and Y13, respectively), and the adjacent corn fields naturally served as the control (CK), providing a valuable benchmark for comparison. To ensure reproducibility, three greenhouses/corn fields were included for each study year.
This type of solar greenhouse features an earth brick structure, with a planting area of approximately 500 m2 (the external length × width × height is about 80 m × 70 m × 3.5 m). The soil in the corn field and tomato greenhouse is primarily composed of sandy loam. The corn field is an open corn field, usually planted in mid-April and harvested in early October. Tomatoes (Seminis 4224) are cultivated twice annually, with growth cycles extending from early April to late August and then from early October until the end of February the subsequent year. Sampling occurred in late August 2021, when a few greenhouses were preparing to replant with ginseng fruits in less than 2 weeks. Consequently, any potential differences stemming from diverse crops were disregarded in this study. The greenhouse lacked heating and was equipped with a ventilation system that automatically opened a 0.5-m-wide vent at its apex whenever temperatures surpassed 30 °C. Typically, this ventilation system remained operational from late April onwards, with additional ventilation achieved by rolling up the lower 70 cm of the greenhouse walls from late May until the harvest in August. During winter, straw mats were placed atop the greenhouse to ensure interior temperatures remained above 5 °C, and the ventilation was manually opened to approximately 20 cm for roughly 5 h, starting from midday.
Furthermore, before each transplanting, the shallow soil (0–20 cm) is manually tilled, and after a decade of harvesting, the deeper soil (>40 cm) undergoes mechanical turning in greenhouse cultivation. Before planting corn every year, the soil is mechanically rotary-plowed to a maximum depth of no more than 40 cm. The irrigation method adopted in the solar greenhouse involves trench irrigation, with approximately 17 irrigations over the growth period, totaling 350 mm. The corn field in the control group was irrigated on the surface, with an irrigation interval of around 30 to 50 days, and a total irrigation volume of approximately 480 mm. Prior to transplantation, micronutrient fertilizer preceded vegetable transplantation, while a nitrogen, phosphorus, and potassium compound fertilizer was used during growth. For corn cultivation, only nitrogen, phosphorus, and potassium fertilizer were applied. All greenhouses adhered to a uniform and standardized management protocol. The specific geographical location, management measures, etc., of the 12 plots are listed in Supplementary Materials Table S1.

2.3. Soil Sample Collection and Determination of Soil’s Physicochemical and Microbial Properties

Mixed samples were collected from the surface layer (0–20 cm) of each corn field and greenhouse, utilizing the plum blossom distribution method. Therefore, there were a total of 12 soil samples in this study. Part of the soil was naturally air-dried to determine its physical and chemical properties, while the other part was securely stored at −80 °C to facilitate soil DNA extraction. Soil bulk density (ρ) was determined using the cutting ring method, while soil pH was determined by a glass electrode pH meter with a soil-to-water ratio of 1:5. Soil electrical conductivity (EC) was determined by a conductivity meter in a 1:5 soil water (w/v) suspension. Soil NH4+-N and NO3−-N content were determined using a flow analyzer, following extraction with a KCl solution (50 mL KCl for 10 g soil). Soil available phosphorus (AP) was determined by following extraction with sodium bicarbonate solution. Soil available potassium (AK) content was determined by a flame photometry, following extraction with a CH3COONH4 solution. Soil total nitrogen (TN) content was analyzed using the Kjeldahl method. Soil total organic carbon (OC) content was determined using the Total Organic Carbon Analyzer (Analytik Jena AG, Jena, Germany).
DNA was extracted from 0.5 g of soil samples using the Fast DNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) and subsequently purified with the PowerClean DNA Clean-up Kit (Mobio, Carlsbad, CA, USA), adhering strictly to the manufacturer’s protocols. The quality and concentration of the extracted DNA were assessed through gel electrophoresis (0.8% agarose) and a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The purified DNA was then stored at −20 °C.
The V3–V4 regions of bacterial 16S rRNA genes were amplified using primers 338F (ACTCCTACGGGAGGCAGCA) and 806R (GGACTACHVGGGTWTCTAAT). PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified utilizing the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After individual quantification, amplicons were pooled in equal molar concentrations, and pair-end 2 × 250 bp sequencing was carried out on the Illumina NovaSeq platform with the NovaSeq 6000 SP Reagent Kit (500 cycles) at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).
Sequence data analyses were primarily conducted using QIIME2 (version 2019.4) and R packages (version 3.2.0). The obtained sequences underwent amplicon sequence variant (ASV) clustering, denoising, and dereplication to generate a comprehensive ASV species abundance profile (Table S2). The soil bacterial taxonomy in the study was assigned using the Silva 138.1 database.

2.4. Statistical Analysis

The soil bacterial diversity indices (ASVs, the relative abundance of bacterial phyla and classes, alpha diversity indexes, and relative abundance of metabolic pathways) in different years were compared by one-way ANOVA. Significant effects were further determined using Fisher’s least significant difference (LSD) test. Statistical analyses were carried out using SPSS (version 20.0). The richness (Chao1 and Observed species), diversity (Shannon and Simpson), and evenness (Pielou_e) index were used to compare soil microbial alpha diversity. The differences in soil microbial community structures were analyzed by Non-metric multi-dimensional scaling analysis (NMDS). Redundancy analysis (RDA) was used to explore the relationship between soil microbial communities based on the predominant phyla and gene and soil physicochemical properties. The MetaCyc database (https://metacyc.org/, accessed on 17 September 2024) was used to predict and compare soil function.

3. Results

3.1. Changes in Soil Physicochemical Properties

The results showed that soil ρ decreased with increasing planting years while soil TN exhibited an opposite trend. Notably, soil EC, pH, AP, AK, and NH4+-N values generally reached a small peak or dipped to a trough in the Y9 sample with continued planting. Meanwhile, the Y5 and Y13 samples had significantly higher soil TN, AP, AK, OC, and NH4+-N than the CK; the opposite results were found in soil C:N and NO3−-N levels ( as shown in Table 1). No significant differences were observed in soil ρ, EC, and pH between CK and other planting years.

3.2. Soil Bacterial Species Composition

Regarding the four situations, the denoised sequence count of high quality falls within a range of 40,793 to 51,922 (see Table S2). There was no significant variation in the abundance of bacterial ASVs across the different situations at the phyla, family, and genus taxonomic levels. However, a notable significant difference was observed specifically when Y9 and Y13 was compared to Y5 at the class taxonomic level as well as Y9 and Y13 when compared to the CK at the order taxonomic level. Furthermore, when compared to the CK, Y5, Y9, and Y13 all demonstrated a significant increase of 60.5%, 67.4%, and 51.2%, respectively, in ASV counts at the species taxonomic level (Table 2).
The bacteria belonging to Actinobacteria emerged as the most abundant bacterial phylum across all four situations, accounting for an average of 24.4% of all taxa, followed by Pseudomonadota (23.7%), Chloroflexi (15.8%), and Firmicutes (9.6%). A significantly higher abundance of Pseudomonadota and Firmicute were found in Y13 than in the CK while the opposite results were found in an abundance of Actinobacteria, Acidobacteria, and Gemmatimonadetes (p < 0.05) (see Figure 1a). At the class taxonomic level, a significantly higher relative abundance of Actinobacteria was found in the CK than in Y9 and Y13 (p < 0.05), while Y9 and Y13 resulted in a significantly higher relative abundance of Anaerolineae and Gammaproteobacteria, respectively, than the CK and Y5 (p < 0.05) (see Figure 1b).

3.3. Soil Bacterial Diversity

The indices of Shannon exhibited a decline as the planting years progressed. However, Chao1 and Observed species indices trended downward with a minor spike in year 9. The opposite pattern was found in Pielou_e and Simpson with a higher value in Y13 than the CK. A significantly higher value (p < 0.05) was found when the CK was compared to Y13 for Chao1 and Observed_species, with the gap reaching 28.8% and 24.5%, respectively (see Table 3).
The soil bacterial beta diversity among situations was evaluated through NMDS analysis. The profiles of bacterial beta diversity for the CK were separated from those at any greenhouse planting situation (Figure 2a). Further analysis of the differences between groups revealed that the distance from the CK increased with the length of planting years, although this trend did not reach statistical significance (see Figure 2b).

3.4. Soil Bacterial Species Difference

Venn diagrams were constructed to visually represent the unique and shared ASVs among the various situations. The overlaps between Y5 and CK, Y9 and CK, as well as Y13 and CK, revealed 611, 453, and 281 shared soil bacterial ASVs, respectively. These shared ASVs constituted 16.3%, 11.8%, and 7.5% of the ASVs observed in the CK. Consequently, alterations in crop species and continuous planting led to variations in soil bacterial composition, with the differences gradually increasing. The plant transformation strongly altered the species composition while the endemic soil bacteria proportion remained stable during the continuous tomato cultivation. The unique bacterial ASVs for CK, Y5, Y9, and Y13 were 2960, 2108, 2098, and 1935, respectively, accounting for 79.2%, 55.1%, 55.8%, and 57.0% of the total ASVs in each situation (see Figure 3).
In the cluster heat map of the top 20 phyla based on their relative abundance in the soil bacterial community, each situation was clustered into individual categories, except for Y5 and Y13, which formed a single clade (Figure 4). The dominant soil bacterial phyla in CK and different planting years situations were distinctly different and the dominant soil genera could be clustered into two groups. Specifically, the relative abundance of Actinobacteriota, Entotheonellaeota, Acidobacteriota, and Gemmatimonadota was notably higher in the CK than in others, gradually decreasing with an increase in planting years. Conversely, the bacterial phyla Planctomycetota, Desulfobacterota, Latescibacterota, NB1-j, Pseudomonadota, Bacteroidota, and Patescibacteria dominated the Y13.
The redundancy analysis (RDA) disclosed that the carbon–nitrogen ratio (C:N) and soil NH4+-N content had a profound impact on the dominant soil bacterial phyla and genera, as depicted in Figure 5. The two canonical axes explained 55.21% and 19.06% of the variation in bacterial phyla, respectively, with axis 1 effectively segregating the CK from the others (see Figure 5a). Soil pH, C:N, ρ, and NO3−-N content emerged as positive indices influencing the relative abundance of Actinobacteria, Entotheonellaeota, Acidobacteriota, and Gemmatimonadota. The correlation between soil physicochemical properties and the bacterial community structure among soil bacterial genera accounted for 71.13% of the total variation in bacterial community composition, with the distribution of Y13 being more concentrated compared to others (see Figure 5b). Additionally, soil NH4+-N content, organic matter (OM), total nitrogen content (TN), available phosphorus content (AP), and available potassium (AK) exhibited strong positive relationships among themselves, jointly influencing the relative abundance of Pseudomonadota phylum and the genera Actinomadura and Bacillus primarily.

3.5. Metabolic Functional Traits of Soil Bacterial Communities

The PICRUSt analysis revealed distinct metabolic pathway patterns among the four situations, as evidenced by the second-level metabolism of the MetaCyc database. The core components of soil bacterial metabolism encompassed biosynthesis, degradation/utilization/assimilation, and the generation of precursor metabolites and energy (see Figure 6). The results showed that the metabolic pathway of biosynthesis and macromolecule modification were generally increased with the increase of planting years; Y13 had a significantly higher relative abundance than the CK. Amino Acid Biosynthesis constitutes the paramount aspect of the biosynthesis process, contributing approximately a quarter of its overall functionality. Notably, Y9 and Y13 exhibited significantly elevated levels compared to Y5. The specific second-level metabolism difference is shown in the Supplementary Material (see Table S3).
The analysis of metabolic pathway differences showed that 14 identical pathways had significant differences (p < 0.05) between each tomato cultivation years and the CK. However, overall, Y9 and Y13 showed significant differences from the CK in more metabolic pathways (see Figure 7). Meanwhile, the pathway of PWY-4361 (S-methyl-5-thio-&alpha;-D-ribose 1-phosphate degradation), PWY-7527 (L-methionine salvage cycle III), PWY-6588 (pyruvate fermentation to acetone) and PYRIDOXSYN-PWY (pyridoxal 5′-phosphate biosynthesis I) showed an extremely significant difference (p < 0.001) when the CK was compared with Y9 and Y13. However, no significant difference was found between Y5 and the CK.

4. Discussion

4.1. The Effect of Crop Alteration and Continued Planting on Soil Bacterial Communities

The composition of soil bacterial communities underwent significant alterations when corn land was converted to greenhouse vegetable cultivation. The significant differences in soil bacterial ASVs quantity, composition, diversity, and metabolic function all proved that the change of crops and planting environment was the primary factor affecting soil microorganisms. An et al. [26] have demonstrated that the establishment of H. ammodendron on shifting sand dunes not only altered the structure of their bacterial communities, but also enhanced their diversity and richness compared to non-vegetated shifting sand dunes. Similarly, the transition of virgin deserts into oasis farmland triggered profound changes in the microbial community and boosted soil enzyme activities by modifying soil moisture and nutritional conditions [27]. Additionally, the application of organic fertilizers in greenhouse tomato cultivation led to distinct differences in soil microbial growth patterns compared to corn fields fertilized with inorganic sources [28]. Therefore, the changes in soil bacterial composition and diversity were habitat-specific.
During greenhouse vegetable cultivation, the soil bacterial abundance and composition exhibited a distinct pattern as the number of vegetable planting years increased. For example, bacterial phyla Actinobacteria and Proteobacteria were dominant phyla; these play an important role in the soil carbon cycle and can degrade recalcitrant carbon [29]. Interestingly, the combined relative abundance of Actinobacteriota and Pseudomonadota was observed to be lower in Y9 and Y13 compared to Y5, displaying an initial decline followed by a subsequent increase. This variation was related to the changes in soil salinity, despite the observed increase in soil OC with the extension of cultivation years. In this study, the micronutrient fertilizer used for greenhouse cultivation contains a certain amount of calcium, magnesium, potassium, and other ions, which lead to an increase in soil conductivity. Moreover, the temperature in solar greenhouses is generally higher than that in field environments, and the upward movement of soil moisture is more intense. As a result, the salt content in the soil is more likely to move upward with the evaporation of water, leading to a significant increase in soil salinity and the risk of soil salinization [13,24]. The increase in salinity in Y9 and Y13 limits the efficient use of soil carbon, leading to a lower relative abundance of these two dominant phyla. The same result was found in a study of surface soils based on a natural sodicity/salinity gradient, which can strongly structure soil microbial communities [30]. An et al. [26] also found lowest relative abundance of α-and γ-Proeteobacteria was related to an increase in soil salinity.
The declines after successive planting in these soil bacteria phyla were consistent with previous findings in the Saudi desert, Horqin sandy land, sand–stabilization plantations, rice–cherry tomato rotations, and greenhouse vegetable fields [9,12,31,32]. Moreover, the relative abundances of Chloroflexi and Acidobacteria also showed the same trend, which was probably related to a change in soil nutrients in shallow soil layers [33]. Additionally, Duc et al. and Rashid et al. [34,35] have highlighted Proteobacteria and Firmicutes as key prokaryotic microorganisms responsible for fixing atmospheric N2 into NH3 via their normal metabolic processes. Thus, the lower relative abundance of Firmicutes in Y9 than in Y5 and Y13 in our study suggested a limited nitrogen-fixing capacity in the soil, and the deep plowing of soil in the 10th year could bring it back to a certain extent.
The decrease in soil bacterial richness and diversity observed over planting years suggests that the continuous cultivation of greenhouse vegetables creates a restrictive environment for bacterial community growth in the soil. Prolonging the monoculture period is likely to further reduce soil microbial diversity. This trend in microbial community changes aligns with previous studies conducted by An et al. and Deng et al. [13,26]. Specifically, Li et al. [12] reported a significant decline in microbial diversity after 9 years of greenhouse vegetable cultivation. Similarly, Zhao et al. [11] found that soil bacterial Chao 1, ACE, and Shannon indices decreased 7.6%, 7.7%, and 4.3%, respectively, from the 4th to the 18th year in a coffee experiment. These findings suggest that continuous crop barriers may pose limitations to the sustainable and healthy development of the soil microbial communities in greenhouse vegetable fields. Additionally, our study revealed a decrease in soil pH, which could potentially inhibit microbial growth. Numerous studies have emphasized the crucial role of soil pH in shaping the structure of soil bacterial communities, regardless of whether organic or inorganic fertilizers are used [12,36,37,38]. Furthermore, the increase in pathogenic bacteria and the accumulation of self-toxicity of crops are also the reasons that cause microbial mortality during long-term monoculture growth [39].

4.2. Limit Year Effect of Soil Microorganisms in Long-Term Monoculture Continuous Cropping

The comparison of soil bacterial composition, beta diversity, and cluster condition, especially soil microbial function, revealed that after about 9 years of continuous cropping of greenhouse tomato, soil microbial properties reached a stable state. The majority of predicted sequences are linked to functions such as biosynthesis, the generation of precursor metabolites and energy, as well as degradation, utilization, and assimilation. The Y9 and Y13 samples significantly enhanced the soil’s metabolic ability for biosynthesis. Previous research also demonstrates that soil microbial characteristics attain their peak or nadir levels following a specific duration of continuous cultivation. Liu et al. [40] found that 15 years of biological soil crust development during vegetation restoration led to the highest bacterial richness and abundance. Similarly, in a study comparing greenhouse vegetable fields cultivated for 0, 3, 9, and 13 years, the number of bacteria first increased and then decreased with the increase in planting, peaking at year 9 [12]. Consistent with these findings, a study on coffee plantations also revealed a similar trend [11]. The study by Deng et al. [13] on soil microbial beta diversity in a rice–cherry rotation system revealed significant differences in microbial community structure between 7a, 10a, and 1a when compared to 3a rotations. We believe that such a peak phenomenon is related to changes in the soil’s physical and chemical properties, as most of the soil physicochemical indexes were peaked or dived. Similarly, in the Shandong Province of China, studies have also shown that the content of soil’s organic carbon, nitrogen, phosphorus, and potassium follows a pattern of steady growth and then decline, with the peak point appearing at around 10 years, and the maximum growth multiple reaching 4.5 times (available phosphorus) [41]. Specifically, the peak in the metabolic pathway for Amino Acid Biosynthesis was observed in Y9, which was intimately tied to the level of soil OC. A previous study proved a significant linear correlation between organic matter and protease activities [36]. Thus, a higher organic matter level and micronutrient fertilizer utilization in greenhouse cultivation created a favorable environment for protease enhancement, thereby potentially providing more available substrates for Amino Acid Biosynthesis.
Above all, these observed “limit year effects” serve as a reminder of potential obstacles in continuous cropping systems. In our research, planting years emerge as a crucial factor in shaping soil bacterial diversity and functionality, with these patterns primarily influenced by soil physical and chemical properties. The soil’s comprehensive health index was largely decreased in years 9a (0.59, 0.58) and 13a (0.58, 0.57) compared to year 5a (0.96, 0.97) with a soil depth of 0~80 cm and 0~60 cm, respectively [25]. On the other hand, the local greenhouse planting management practices typically undergo deepening after a decade, leading to improvements in certain soil properties, such as pH and EC. This accounts for the relatively minor differences in microbial structure and function between the Y9 and Y13 greenhouse soils. Previous studies have demonstrated that deep ploughing prompts rhizosphere soil microorganisms to adapt to changes in soil nutrients and enzymatic activities, enabling the community to acclimate to the new soil environment [42].

5. Conclusions

When the open field soil planted with corn for many years was converted to greenhouse tomato soil, its bacterial composition underwent significant changes. As the greenhouse tomato planting years progressed, significant alterations were observed in the soil bacterial structure and diversity. Notably, the number of ASVs increased while the alpha diversity of bacteria had significantly declined. The primary bacterial phyla, Pseudomonadota and Actinobacteria, exhibited a reversed trend as the planting years increased. Among the bacterial classes, Actinobacteria was dominant, but its abundance decreased significantly due to continuous planting. Key soil characteristics, such as the carbon–nitrogen ratio and pH, had a significant impact on bacterial phyla and genera in the soil. Remarkably, although the bacterial community structure changed, it stabilized after 9 years, with soil functions and metabolic pathways showing similarities between Y9 and Y13. Soil biosynthesis stood out as the most crucial soil function. For greenhouse tomato fields continuously cultivated for over 9 years, new soil bacterial adaptations gradually emerged, and deep tillage measures contributed to maintaining this stability to a certain extent. Identifying this critical year holds immense significance in overcoming obstacles and enhancing our understanding of soil bacterial development in continuous cropping systems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14092144/s1, Table S1. Geographical location and management of the sampling site for different treatments. Table S2. The obtained sequences for different treatments. Table S3. The second-level metabolic functional traits and categories according to MetaCyc pathways database. Table S4. The Description of Metabolic Pathways.

Author Contributions

Conceptualization, Y.F. and X.H.; methodology, X.H.; software, X.Z.; validation, Y.F., Y.J. and R.L.; formal analysis, G.G.; investigation, G.G. and X.Z.; resources, Y.F.; data curation, Y.J.; writing—original draft preparation, Y.F.; writing—review and editing, Y.J., L.S. and J.H.; visualization, R.L.; supervision, X.H.; project administration, Y.F.; funding acquisition, Y.F. and R.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture (grant number NORSOAE-ZZ(KF)-2021009) and the Natural Science Foundation of the Shanxi Province (grant numbers 20210302124249 and 202203021211139).

Data Availability Statement

Data is contained within the article.

Acknowledgments

We are grateful to the Shanghai Peisenol Biotechnology Co., Ltd. for their detection of soil bacterial diversity.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) Relative abundance of bacterial phyla for different situations; (b) Relative abundance of bacterial classes for different situations. Different lowercase letters indicate significant differences (p < 0.05) between the situations. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
Figure 1. (a) Relative abundance of bacterial phyla for different situations; (b) Relative abundance of bacterial classes for different situations. Different lowercase letters indicate significant differences (p < 0.05) between the situations. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
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Figure 2. (a) Non-metric multi-dimensional scaling analysis (NMDS) of the beta diversity of soil bacteria based on Bray–Curtis distances among different situations; (b) Analysis of differences between groups based on Bray–Curtis distances and the results were compared by the Permanova-test method. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
Figure 2. (a) Non-metric multi-dimensional scaling analysis (NMDS) of the beta diversity of soil bacteria based on Bray–Curtis distances among different situations; (b) Analysis of differences between groups based on Bray–Curtis distances and the results were compared by the Permanova-test method. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
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Figure 3. Unique and overlapped ASVs of soil bacteria in different situations. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
Figure 3. Unique and overlapped ASVs of soil bacteria in different situations. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
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Figure 4. Cluster heat map analysis of the top 20 phyla by relative abundance in the soil bacterial community among four situations. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
Figure 4. Cluster heat map analysis of the top 20 phyla by relative abundance in the soil bacterial community among four situations. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
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Figure 5. (a) Redundancy analysis (RDA) of soil bacterial phylum of different situations; (b) Redundancy analysis (RDA) of soil bacterial genus of different situations. C:N: soil carbon–nitrogen ratio, NH4: soil NH4+-N content, OM: soil organic matter, TN: soil total nitrogen, AP: soil available phosphorus, AK: soil available potassium, OC: soil organic carbon, NO3: soil NO3−-N content, ρ: soil bulk density, EC: soil electric conductivity. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
Figure 5. (a) Redundancy analysis (RDA) of soil bacterial phylum of different situations; (b) Redundancy analysis (RDA) of soil bacterial genus of different situations. C:N: soil carbon–nitrogen ratio, NH4: soil NH4+-N content, OM: soil organic matter, TN: soil total nitrogen, AP: soil available phosphorus, AK: soil available potassium, OC: soil organic carbon, NO3: soil NO3−-N content, ρ: soil bulk density, EC: soil electric conductivity. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration.
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Figure 6. The first-level metabolic functional traits and categories according to MetaCyc pathways database. The columns represent the relative abundance of all samples on that metabolic pathway, expressed in parts of per million. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration. Different lowercase letters indicate significant differences (p < 0.05) between the situations.
Figure 6. The first-level metabolic functional traits and categories according to MetaCyc pathways database. The columns represent the relative abundance of all samples on that metabolic pathway, expressed in parts of per million. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration. Different lowercase letters indicate significant differences (p < 0.05) between the situations.
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Figure 7. Analysis of metabolic pathways with significant differences between greenhouse cultivation situations and CK. The X-axis represented log2 (Fold change), positive values indicated higher results in greenhouse cultivation situation than CK, and the negative values indicated the opposite. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration. The Description of each metabolic pathways is provided in Supplementary Materials (see Table S4).
Figure 7. Analysis of metabolic pathways with significant differences between greenhouse cultivation situations and CK. The X-axis represented log2 (Fold change), positive values indicated higher results in greenhouse cultivation situation than CK, and the negative values indicated the opposite. Where CK is the field with corn planting; Y5 is the greenhouse with a 5-year planting duration; Y9 is the greenhouse with a 9-year planting duration; Y13 is the greenhouse with a 13-year planting duration. The Description of each metabolic pathways is provided in Supplementary Materials (see Table S4).
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Table 1. Soil physicochemical properties for different planting situations.
Table 1. Soil physicochemical properties for different planting situations.
SituationCK 1Y5Y9Y13
ρ 21.58 ± 0.11 a 31.45 ± 0.20 a1.41 ± 0.09 a1.29 ± 0.16 a
EC (μS/cm)185.83 ± 31.47 a236.40 ± 31.36 a449.93 ± 31.71 a367.57 ± 7.16 a
pH7.99 ± 0.19 a8.01 ± 0.07 a7.73 ± 0.14 a7.84 ± 1.16 a
TN (mg/kg)762.33 ± 145.31 b1632.00 ± 145.82 a1843.67 ± 145.94 a1901.00 ± 9.16 a
AP (mg/kg)26.33 ± 9.66 b310.00 ± 9.69 a318.33 ± 9.83 a263.67 ± 8.16 a
AK (mg/kg)100.67 ± 47.68 b375.33 ± 47.96 a266.67 ± 47.77 ab353.67 ± 7.16 a
OC (g/kg)9.05 ± 1.32 b17.01 ± 1.73 a18.68 ± 1.02 a19.55 ± 0.16 a
C:N (mg/kg)11.96 ± 0.49 a10.46 ± 0.31 b10.31 ± 0.62 b10.34 ± 6.16 b
NH4 (mg/kg)0.05 ± 0.01 b0.68 ± 0.07 a0.73 ± 0.01 a0.65 ± 0.16 a
NO3 (mg/kg)101.39 ± 27.58 a5.77 ± 27.85 c49.83 ± 27.02 b54.92 ± 0.16 b
1 CK: the field with corn planting; Y5: the greenhouse with a 5-year planting duration; Y9: the greenhouse with a 9-year planting duration; Y13: the greenhouse with a 13-year planting duration. 2 ρ: soil bulk density, EC: soil electric conductivity, TN: soil total nitrogen, AP: soil available phosphorus, AK: soil available potassium, OC: soil organic carbon, C:N: soil carbon-nitrogen ratio, NH4: soil NH4+-N content, NO3: soil NO3−-N content. 3 Values are means ± standard deviation (n = 3). Different lowercase letters indicate significant differences among situations based on one-way ANOVA followed by an LSD test (p < 0.05).
Table 2. The ASV number in taxa for different situations.
Table 2. The ASV number in taxa for different situations.
SituationsCK 1Y5Y9Y13
Phylum27 ± 1 a 228 ± 1 a28 ± 1 a29 ± 1 a
Class69 ± 3 ab67 ± 5 b76 ± 4 a75 ± 3 a
Order141 ± 7 b148 ± 7 ab159 ± 3 a163 ± 9 a
Family193 ± 18 a207 ± 15 a217 ± 4 a220 ± 7 a
Genus265 ± 35 a286 ± 32 a292 ± 12 a286 ± 4 a
Species43 ± 13 b69 ± 3 a72 ± 3 a65 ± 5 a
1 CK: the field with corn planting; Y5: the greenhouse with a 5-year planting duration; Y9: the greenhouse with a 9-year planting duration; Y13: the greenhouse with a 13-year planting duration. 2 Values are means ± standard deviation (n = 3). Different lowercase letters indicate significant differences among situations based on one-way ANOVA followed by an LSD test (p < 0.05).
Table 3. Soil bacterial alpha diversity indexes for different situations.
Table 3. Soil bacterial alpha diversity indexes for different situations.
SituationCK 1Y5Y9Y13
Chao11757.7 ± 225.6 a 21540.8 ± 128.8 ab1579.0 ± 122.6 ab1368.3 ± 31.9 b
Observed_species1661.4 ± 185.5 a1489.7 ± 103.5 ab1515.3 ± 116.6 ab1334.7 ± 9.3 b
Pielou_e0.9063 ± 0.0262 a0.9125 ± 0.0062 a0.9021 ± 0.0188 a0.9054 ± 0.0160 a
Shannon9.69 ± 0.42 a9.62 ± 0.10 a9.53 ± 0.28 a9.40 ± 0.16 a
Simpson0.9963 ± 0.0027 a0.9976 ± 0.0003 a0.9968 ± 0.0012 a0.9969 ± 0.0012 a
1 CK: the field with corn planting; Y5: the greenhouse with a 5-year planting duration; Y9: the greenhouse with a 9-year planting duration; Y13: the greenhouse with a 13-year planting duration. 2 Values are means ± standard deviation (n = 3). Different lowercase letters indicate significant differences among situations based on one-way ANOVA followed by an LSD test (p < 0.05).
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Fan, Y.; Jia, Y.; Zhang, X.; Geng, G.; Liu, R.; Shen, L.; Hu, J.; Hao, X. Conversion to Greenhouse Cultivation from Continuous Corn Production Decreases Soil Bacterial Diversity and Alters Community Structure. Agronomy 2024, 14, 2144. https://doi.org/10.3390/agronomy14092144

AMA Style

Fan Y, Jia Y, Zhang X, Geng G, Liu R, Shen L, Hu J, Hao X. Conversion to Greenhouse Cultivation from Continuous Corn Production Decreases Soil Bacterial Diversity and Alters Community Structure. Agronomy. 2024; 14(9):2144. https://doi.org/10.3390/agronomy14092144

Chicago/Turabian Style

Fan, Yaqiong, Yamin Jia, Xinyang Zhang, Guoqiang Geng, Ronghao Liu, Lixia Shen, Jingjuan Hu, and Xinmei Hao. 2024. "Conversion to Greenhouse Cultivation from Continuous Corn Production Decreases Soil Bacterial Diversity and Alters Community Structure" Agronomy 14, no. 9: 2144. https://doi.org/10.3390/agronomy14092144

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