Green manure (Raphanus stativus L.) alters soil microbial structure and promotes acetochlor degradation

2.1 Reagents

ACE standard (purity 99.40 %) was purchased from Beijing Manhag Biotechnology Co., Ltd (Beijing, China). Emulsifiable ACE concentrates (81.5 %) were obtained from Nantong Nanshen Plant protection Technology Development Co., Ltd. (Jiangsu, China). Acetonitrile (ACN), methanol (MeOH), and sodium chloride (NaCl) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Primary secondary amines (PSA) were obtained from Shanghai McLean Biochemical Technology Co., Ltd. (Shanghai, China). Syringe filters (0.22 μm, nylon) were from Tianjin Jinteng Experimental Equipment Co., Ltd. (Tianjin, China).

2.2 Materials

The soil used was a local yellow soil in Guizhou. The physical and chemical properties of the soil include soil pH, soil organic matter, total nitrogen, total phosphorus, total potassium, alkali-hydrolyzed nitrogen, soil-available phosphorus, and soil-available potassium, which are 7.08, 17.39 g·kg⁻¹, 1.26 g·kg⁻¹, 0.4 g·kg⁻¹, 15.52 g·kg⁻¹, 146.79 mg·kg⁻¹, 24.41 mg·kg⁻¹ and 222.62 mg·kg⁻¹ respectively.

The seeds of green manure were obtained from Guizhou Academy of Agricultural Sciences, and were sown in October 2022 in each plot of the experimental field (26°29′56″N, 106°39′34″E). Management of green manure cultivation was carried out, and the details of the nutrient composition of green manure are shown in Table S1.

2.3 Experimental design

The pot incubation test was conducted by selecting pots (25 × 45 cm) with 8 kg soils (Erenoğlu et al., 2023). During the experiment, the pesticide was applied according to the recommended dosage (110–155 mL·ha⁻¹), and evenly sprayed into the soil by spraying method. Except for the control (CK) treatment, the green manures were cut into small sections of 10–15 cm, wiped off the surface water and then pressed into the soil. The uniform overturning capacity of the three green manures was 22500 kg·ha⁻¹ (Wang et al., 2022a). In this experiment, four kinds of treatment containing different green manure were selected (Table S1): CK (without adding green manure), GMV (with Vicia villosa Roth as green manure), GML (with Lolium perenne L. as green manure) and GMR (with Raphanus stativus L.as green manure). The experiment was designed with three parallel treatments, totaling 12 treatments, conducted in a shaded area. In the pre-test, the amount of water applied was 200 mL to ensure that the water could moisten the soil and the pesticides would not be lost with the water, and the amount of water added to each treatment was consistent to avoid human error.

Different soil depths (0–20 cm; mixed) were obtained according to the time period of 0, 1, 3, 7, 14, 30, and 60 days. The mixed soil samples were separated into two parts after removing impurities such as stones and twigs and passing through a sieve (per size, 2 mm). Some of the soil samples were air-dried in a cool, well-ventilated room to test for pesticide residues and to assess soil properties, while another set of samples was stored at −80 °C for future microbiological analysis (with sterilized tools used for sampling).

2.4 Test method

As for physicochemical properties of the soil, the soil samples were first dried at 35℃ for 4 h and then pulverized using a pulverizer (Feng et al., 2023). Detailed steps for the determination of soil physical and chemical properties, including pH, soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzed nitrogen (AHN), soil-available phosphorus (AP), soil-available potassium (AK), and soil organic matter (SOM) refer to Wang et al. (2022a).

The QuEChERS (Anastassiades et al., 2003) technique was used to extract ACE from soil using ACN as the extracting agent and PSA as the purifying agent. The degradation of ACE was monitored by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Detailed information on sample preparation and determination of ACE can be found in Supplemental material Text S1, while specifics on the elution process are outlined in Table S2 and optimized MS/MS parameters can be found in Table S3. The method was validated, with matrix effect, limit of detection (LOD) and limit of quantitation (LOQ) among the specific validation parameters presented in Table S4. The recoveries of ACE in soil samples, together with the corresponding relative standard deviations, are documented in Table S5.

2.5 Microbial community detection

DNA was extracted from 0.5 g soil using the PowerSoil® DNA Isolation Kit from Mo Bio Laboratories. High-throughput sequencing was used to study microbial communities. Mi Seq amplicons derived from the V3-V4 region of the bacterial 16S rRNA gene were sequenced using specific barcode-labeled primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTA-CHVGGGTATCAAT-3′). Similarly, Mi Seq amplicons from the fungal ITS2 region were sequenced with primers specifically designed for the barcode markers ITS1-F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2-R (5′-GCTGCGTTCTTCATCGATGC-3′). Sequencing and subsequent bioinformatic analysis were performed at Shanghai Majorbio Bio-pharm Technology Co. Ltd (Yan et al., 2021).

2.6 Data analysis

The degradation of ACE was determined using the first-order kinetic equation C_t = C₀ e^-kt as described by Yu et al. (2019). In this equation, C_t represents the concentration of ACE on day t (mg·kg⁻¹), C₀ is the initial concentration of ACE (mg·kg⁻¹), k (d^-1) is the rate constant of ACE degradation, and t_1/2 is the half-life of ACE. Data processing was performed in Microsoft Excel 2016, and analysis of Duncan's multiple range test data was performed using IBM SPSS Statistics version 24.0. Bioinformatic statistical analysis of operational taxonomic units (OTUs) at the 97 % similarity level was performed using Uparse (version 7.0). Taxonomic analysis of fungal and bacterial communities was performed using the fungal database Unite (release 8.0 at https://unite.ut.ee/index.php) and the bacterial 16S rRNA database Silva (release 138 at https://www.arb-silva.de). Statistical analyses and graphs were performed using the R language (version 3.3.1) and Circos-0.67–7 (https://circos.ca/). The significance of differences between two groups was tested using Welch's t-test, with p-values corrected for multiple testing using Benjamini-Hochberg and a two-tailed test with a confidence interval of 0.95. Tools for taxonomic analysis of fungal communities were used through the software FUNGuild (http://www.funguild.org/).

3.1 Effect of different green manures on the degradation of ACE

The degradation of ACE in the CK with different green manure treatments were shown in Table 1 and Fig. S1. The half-life of ACE was 23.10 days in the blank treatment, and the half-life of several other treatments was significantly lower than that of CK. Among them, GMR (Raphanus stativus L.) showed the best effect as green manure, and the degradation time of the soil was shortened to 5.82 days, which improved the degradation efficiency by 74.81 %. This indicated that the ACE degradation was positively affected by the different green manure treatments, and the presence of fertilizer sources may improve the existing environmental system, leading to differences in tpesticide degradation (Zhang et al., 2022). Studies have demonstrated that green manure treatment can increase the adsorption of pesticides in soil, promote the degradation of pesticides and reduce soil residues (Ferreira et al., 2021). The ACE degradation rate of green manure treatment was higher than that of the CK treatment, indicating that green manure was conducive to the ACE degradation in soil (Cai et al., 2007).

3.2 Soil microbial diversity under different green manures

A thorough analysis of different types of green manures was conducted to evaluate their influence on pesticide by examining the microbial community structure and diversity. Venn diagram illustrates the distribution of soil OTUs across various treatment conditions (Fig. 1). A total of 583 bacterial OTUs were found to be common to all four soil types (Fig. 1A), suggesting that these OTUs were not significantly affected by ACE. A total of 419 fungal OTUs were identified in all (Fig. 1B). Among these, CK, GMV, GML, and GMR contained 160, 187, 241, and 230 OTUs, respectively, indicating that these endemic fungi effected ACE degradation.

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Fig. 1

Venn diagram showing the number of unique and shared OTUs of microbial communities in different green manure soils exposed to ACE. (A) bacteria;(B) fungi. Principal coordinates analysis (PCoA) based on the relative abundance of microbial OTUs showing different microbial community structure: (C) bacteria; (D) fungi. CK: without green manure; GMV: Vicia villosa Roth; GML: Lolium perenne L.; GMR: Raphanus stativus L.

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Chao, Shannon, and Simpson indices represent the abundance and diversity of microbial species (Wang et al., 2022b). In our study, Chao and OTUs varied consistently, with CK having the lowest value (Table 2). The Shannon index indicated that soil microbial community diversity was higher and more evenly distributed in the green manure treatment group. The diversity of fungal communities under the four treatments was ranked in the following order: GML > GMR > GMV > CK, and the richness was ranked in the order GMV > GML > GMR > CK (Table 3). Furthermore, we found that application of GMV, GML, and GMR increased the abundance and diversity of bacterial and fungal microbial communities in the soil compared with CK. Xu et al. (2023) showed that green manure ryegrass can alter the microbial taxa involved in soil carbon, nitrogen, and sulfur cycling, thereby increasing the α-diversity of soil bacteria.

Principal coordinate analysis (PCoA) was performed evaluate the modifications in the composition of microbial communities ACE after exposure. Fig. 1D illustrates that approximately 36.13 % of the variability in the microbial community was accounted for by 20.73 % in the first dimension and 15.4 % in the second dimension. The fungal community composition significantly differed among the four treatment groups, with GMR showing a distinct composition compared to GMV and GML which exhibited a bacterial community composition more similar to that of CK soil as per PCoA. Moreover, bacterial PCoA indicated that 24.08 % and 16.15 % of the variation could explained by PCoA1 and PCoA2, respectively (Fig. 1C). The sample sites were tightly clustered, so there were no significant differences in bacterial community composition between treatments.

3.3 Structure of soil microbial communities under different green manures

3.3.1

3.3.1 Variations in bacterial community composition

The top five dominant bacterial communities at the phylum level were Proteobacteria (20.69 %-28.53 %), Actinobacteriota (22.24 %-25.42 %), Chloroflexi (17.20 %-19.62 %), Acidobacteriota (9.11 %-15.41 %), and Bacteroidata (3.50 %-3.95 %), accounting for more than 72.74 %-92.93 % of the bacterial sequences (Fig. 2A). The prevalence of Proteobacteria, Actinobacteria, Chlorobacteria, and Acidobacteria in this study was consistent with previous findings (Gao et al., 2023; Zhang et al., 2023). The proportion of both Actinobacteriota and Acidobacteriota were higher in the green manure treatment than in the CK. Acidobacteria isis widespread in highly acidic fibrous tissue soils rich in soil organic matter and effective phosphorus and can survive in extreme environments and nutrient-poor soils (Geng et al., 2020). Similarly, Actinobacteriota is widely present in soil and can be cleaned up by bioremediation, making them a good choice for contaminated soil (Alvarez et al., 2017).

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Fig. 2

Distribution of microbial community for each sample at phylum and genus level. The data were visualized by Circos. The width of the bars from each phylum and genus indicates the relative abundance of that phylum and genus in the sample. (A), (B) phylum level; (C), (D) genus level. CK: without green manure; GMV: Vicia villosa Roth; GML: Lolium perenne L.; GMR: Raphanus stativus L.

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Microbial communities were further tested for differentiation at the genus level. Fifteen genera were detected in bacterial communities affected by GMV, GML, and GMR (Fig. 3A). The five genera with the highest relative abundance were norank_f__Vicinamibacteraceae, norank_f__Roseiflexaceae, norank_f__norank_o__Vicinamibacterales, Arthrobacter, and norank_f__norank_o__norank_c__KD4-96, which accounted for 13.22 %-24.49 % of the bacterial community (Fig. 2C). Among the four treatments, the green manure treatment had higher norank__f__Vicinamibacteraceae, norank__f__norank__o___Vicinamibacterales, and norank__f__norank__o__norank__c__KD4-96 than the control. Ellin6067 was less abundant in all treatments, whereas Arthrobacter was more abundant in the GMR treatment (Fig. 3A). Aerobic bacteria (Arthrobacter) can establish populations in the rhizosphere that facilitate the rapid degradation of pesticides in soil when they are present, and are a promising bioremediation agent for contaminated soils (Bazhanov et al., 2017). The varying effects of different fertilization conditions on Arthrobacte could be attributed to the distinct soil properties present in the four treatments (Li et al., 2022). Studies have shown that bacteria of the genus Arthrobacter can degradate environmental organic pollutants such as benzene derivatives, polycyclic aromatic hydrocarbons (PAHs), N-heterocyclic compounds, and other environmental organic pollutants. And they have an abundance of selective factors and resilience such as oxidative stress, which is important for the degradation of pollutants in complex and variable environments (Hasan et al., 2011; Vandera et al., 2015). Therefore, considering the pesticide remediation properties of Arthrobacter spp. (Actinobacteriota) in soils, it was speculated that they might be involved in the degradation of ACE.

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Fig. 3

Plots showing the genera with differential abundance in soils with GMV, GML and GMR comparing the CK. (A) bacteria;(B) fungal. P-values were calculated based on Welch’s t-test with Benjamini-Hochberg correction (p < 0.05). CK: without green manure; GMV: Vicia villosa Roth; GML: Lolium perenne L.; GMR: Raphanus stativus L.

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3.4 Analysis of microbial communities in relation to soil properties

Earlier findings have indicated that soil physicochemical properties significantly influence both compositional and functional changes in soil microbial communities (Li et al., 2021a). The thermograms showed that soil pH, soil total phosphorus (TP) and soil total potassium (TK) had a significant effect on bacterial microbial communities, and soil pH and soil total phosphorus (TP) had a significant effect on the microbial communities of fungi (Fig. 4). Therefore, the present study focused on the effect of common influencing factors pH and TP on soil microbial communities. This finding is in agreement with that of other studies (Li et al., 2021b). Among the most well-known environmental factors influencing how the organization of soil microbial communities varies is pH (Shen et al., 2013). It has recently been shown that soil phosphorus addition mainly influences microorganisms by affecting soil carbon cycling and chemical properties (e.g., pH) (Xiao et al., 2022). Correlation analysis showed that pH was mainly positively correlated with Myxococcota (Fig. 4A, p < 0.05), which was similar to previous research results (Chen et al., 2021). In addition, several other studies have shown positive associations between TP and certain bacterial phyla, including Protcobacteria and Myxococcota (Dao et al., 2023). Protcobacteria is the more abundant phylum in the bacterial microbial community (Fig. 2A), and previous studies have shown that Actinobacteriota and Proteobacteria, among others, are the dominant bacteria in soils where amide herbicides have been applied, and that the duration of herbicide exposure is a major determinant of bacterial community variability (Wang et al., 2022b). In addition, bacteria belonging to α-, β-, and γ-Proteobacteria can utilize hydrocarbons as carbon sources and degrade pesticide contaminants into simpler forms (Klase et al., 2019). This suggests that Proteobacteria may contribute to the degradation of ACE. Among the fungal communities, only Basidiomycota and Kickxellomycota were positively associated with pH and TP, whereas the other phyla were not significantly correlated with pH and TP (Fig. 4B) (Shen et al., 2013). Overall, there was a correlation between soil physico-chemical properties and changes in soil bacterial and fungal community structure under different green manure treatments. However, soil is a complex environment that influenced by many factors, making it difficult to determine correlations between soil, pesticides, and microbial communities (Han et al., 2022).

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Fig. 4

The correlation heatmap analysis between environmental factors and species of bacterial by Bray-Curtis distances. (A) bacteria; (B) fungi. TP: total phosphorus.

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3.5 Prediction of soil microbial communities for degradation of ACE

Microbial biodegradation is an important process in the removal of organic pollutants from soil and that fungi are prominent in the degradation of metolachlor in soil (Sun et al., 2020). FUNGuild was used to categorize the fungi based on their functions to study the impact of microorganisms on soil ACE degradation in the four different treatments. A total of nine trophic modes were identified based on Fig. 5A, of which the three main trophic modes were pathotrophic, saprotrophic, and symbiotrophic. Pathotrophic fungi had the highest relative abundance in GMR, while the relative abundance of saprotrophic fungi and symbiotic fungi were higher in CK treatment than green manure treatment group (Table S6). The application of green manure altered the relative abundance of saprotroph, pathotroph, and symbiotroph. The presence of highly pesticide-sensitive fungal genera in the saprophytic fungal phylum is associated with the residues deethylatrazine, pyrimethamine, and S-isopropylmethachlor (Walder et al., 2022). The decrease in saprophytic fungi under green manure treatments in this study may be linked to the degradation of ACE in the soil by these fungi (Table S6). The three main nutrient modes were further analyzed by guilds, and 11 major dominant taxa were found, of which the top five guilds in abundance were undefined saprotrophs, plant pathogens, dung saprotrophs, animal pathogens, and arbuscular mycorrhizalss (Fig. 5B). (According to the above diversity analysis, there was no significant difference in bacteria, therefore, it was not be repeated). According to Fig. 5C, the test of fungi related to the undefined saprotrophic fungi and arbuscular mycorrhizal fungi (AMF) showed that the dominant fungal groups were Ascomycota and Glomeromycota. According to Fig. 2B and 3B, application of green manure can change the abundance of fungal microbial communities. Among them, the abundance of Ascomycota increased under green manure treatment, the difference of Cystofilobasidium was more significant, and the relative abundance of GMR was much higher than that of CK. Egidi et al. (2019) showed that Ascomycota has a strong ability to withstand environmental pressure and utilize more resources, which is more conducive to its survival in soil. Sláviková and Vadkertiová (2003) found that Cystofilobasidium was resistant to pesticides, and there were some differences in pesticide inhibition. As soil saprotrophic fungus, Ascomycota can degrade lignified plant residues in the soil and promote rapid growth and reproduction of the fungal community (Boer et al., 2005). Whiteway et al. (2015) in their study found that the phylum Ascomycetes and Ascomycetes can degrade harmful aromatic pollutants in soil. In the CK treatment, the mycorrhizal fungus Glomeromycota was most abundant (Fig. 5D). The presence of arbuscular mycorrhizal fungi (AMF) enhanced the plant root response to atrazine by upregulating relevant enzymes, alleviating toxic effects, and promoting soil atrazine degradation (Huang et al., 2009). Based on the results of bubble abundance maps and previous studies, we concluded that Ascomycota and Glomeromycota may be involved in ACE degradation. However, because of the numerous unknown factors in the soil environment, further analysis is required to fully understand the degradation pathways (Chen et al., 2023).

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Fig. 5

Variations in fungal function (A), composition of fungal functional groups (guilds) inferred by FUNGuild (B), and comparison of fungal distribution in fungal function groups and different samples (C-D). CK: without green manure; GMV: Vicia villosa Roth; GML: Lolium perenne L.; GMR: Rhaphanus stativus L.

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