Next Article in Journal
Effects of Sowing Date and Nitrogen (N) Application Rate on Grain Yield, Nitrogen Use Efficiency and 2-Acetyl-1-Pyrroline Formation in Fragrant Rice
Previous Article in Journal
Plant Growth Promoting Filamentous Fungi and Their Application in the Fertilization of Pastures for Animal Consumption
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 12
Issue 12
10.3390/agronomy12123034
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of the Sulfamethazine on Nitrogen Conversion in Alternate Wet and Dry Paddy Fields

by
Lina Chen
1,2,3,4,
Haiming Wang
1,2,3,
Jiang Li
1,*,
Wenbo Wu
1,2,3 and
Guomian Guo
1,4
1
College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China
2
Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 211100, China
3
College of Environment, Hohai University, Nanjing 210098, China
4
Key Laboratory of Lower Yellow River Channel and Estuary Regulation, Ministry of Water Resources, Zhengzhou 450003, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3034; https://doi.org/10.3390/agronomy12123034
Submission received: 25 October 2022 / Revised: 11 November 2022 / Accepted: 26 November 2022 / Published: 30 November 2022
Browse Figures
Versions Notes

Abstract

:
Aquaculture wastewaters have been used in rice irrigation directly or indirectly. Antibiotics in aquaculture wastewater entering the rice fields with irrigation will affect the soil physicochemical properties, microbial properties, and nitrogen conversion. A pot experiment irrigated with simulated aquaculture wastewater was performed to study the effects of sulfamethazine in aquaculture wastewater on nitrogen concentration and conversion-related microorganisms in rice fields with different irrigation modes. Sulfamethazine (500 ng/L, 1500 ng/L, and 3000 ng/L) decreased the NH4+ concentration at the late tillering stage and NO3− concentration at the late tillering and jointing–booting stages (p < 0.05) but increased the NH4+ concentration at the late tillering stage (p < 0.05). Sulfamethazine (3000 ng/L) promoted the lowest nitrogen conversion gene (amoA, nirS, and nirK) abundances and the most special community structure of nitrogen conversion microorganism under mild alternate wetting and drying (AWD). Furthermore, Nitrosospira_sp._KAN8, belonging to ammonia-oxidizing bacteria (AOB), was sensitive to sulfamethazine. Flora with the same nitrogen conversion genes exhibited different variations under the same treatment. The results show that antibiotic and mild AWD caused more serious adverse effects to soil nitrogen conversion and nitrogen conversion microorganisms, which will increase the environmental risks of sulfamethazine. It can provide a basis for the scientific and rational use of aquaculture water to irrigate rice fields.

1. Introduction

The global overuse of ground and surface water resources has caused serious environmental problems [1]. Due to greenhouse gas emissions, droughts of the future are likely to be more frequent, severe, and longer-lasting than they have been in recent decades [2]. Water scarcity is increasingly becoming a key constraint on global development. To obtain more available water resources, the development and utilization of unconventional water resources such as wastewater has attracted more and more attention from various countries [3,4]. Agriculture consumes the most water resources, agricultural water consumption accounts for nearly 60% of the total water consumption in China, and irrigation consumes 90% of the total agricultural water consumption. In the meantime, unconventional water resources such as aquaculture wastewater contain the nitrogen and phosphorus required for crops. Therefore, the usage of unconventional water resources for irrigation has important prospects and significance in coping with water shortages [5].
Aquaculture wastewater, one of the common unconventional water resources, is rich in nutrients such as nitrogen and phosphorus owing to excreta and unused feed [6]. Its application to irrigation is an effective means to achieve “solving aquaculture pollution emissions and alleviating irrigation water shortages” [7,8]. However, some new pollutants, such as antifungal compounds, hormones, and antibiotic, are produced in aquaculture production [9,10,11]. These pollutants will be a potential contributor to farmland pollution. Residual antibiotics are one of the most typical pollutants. They have been widely detected in natural water bodies, which is mainly sulfamethazine [12,13,14,15]. The concentration of antibiotics in water is often less than 3200 ng/L [12,16].
Antibiotics are used in animal husbandry as antimicrobial growth promoters to control infectious diseases and accelerate animal growth. Antibiotics used in animals are not fully absorbed. They are excreted and enter the farmland with fertilization or irrigation [17,18]. Antibiotics are widespread in the water and soil [19,20], and it has been found that the concentration of sulfonamide antibiotics in agricultural soils is 0–27.93 µg/kg [21], and the average concentration is 6.92 µg/kg [22]. A study along the Mekong Delta in Vietnam found that the highest sulfamethazine concentration was 86.1 µg/kg [23] in rice field soils. Previous studies have shown that antibiotics in soils can not only lead to changes in the community structure of soil microbes [24,25] but also have an impact on the nitrogen conversion process [26,27,28,29].
Aquaculture ponds and rice fields are staggered in the plain lake areas of South China, and aquaculture wastewater has been used directly or indirectly for irrigation [30]. At the same time, rice has different irrigation methods, such as flood irrigation and alternate wetting and drying (AWD) [31]. AWD reduces water consumption and changes the long-term anaerobic environment of flood-irrigated rice fields. It leads to changes in the soil physicochemical characteristics of rice fields [32] and will cause differences in soil antibiotic residues. These changes will lead to differences in soil microbes and nitrogen conversion, whereas very few studies have examined this.
The objective of this study was to reveal the effects of antibiotics on soil nitrogen and nitrogen conversion microorganisms in paddy fields under different intensities of AWD to study the adverse effects of antibiotics on rice fields in aquaculture wastewater and ways to reduce their adverse effects. We hypothesized that AWD would promote antibiotic degradation and thus mitigate the effects on soil nitrogen conversion in paddy fields. This study can provide a basis for the scientific and rational use of aquaculture water to irrigate rice fields.

2. Materials and Methods

2.1. Experimental Design

The experiment was performed in the water-saving park of Hohai University, Jiangning District, Nanjing. The soil was taken from a paddy field in Nanjing, and it is loam. It was mixed well before experiment and passed through a coarse sieve (5 mm). The rice cultivar is Nanjing 46 (Oryza sativa L.), and the whole growth period is 167 days. The rice was transplanted on July 10, 2019 and harvested on 8 October 2019. A pot experiment was used. The soil pots used in the experiment were 60 cm high and 30 cm in diameter, 22 kg soil was filled inside each pot in layers, and three plants were planted in each pot. Two factors, antibiotic concentrations (A) and irrigation methods (W), were set: (1) The antibiotic concentration is the concentration of sulfamethazine in irrigation water, and 4 gradients were set, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). (2) Three irrigation methods were set, including flood irrigation (W1, except for the midseason drainage and preharvest drain, the shallow water layer was maintained during the experiment period), mild AWD (W2, each irrigation brought field water depth to 20 mm, and the next irrigation flooding is 3 days later than the disappearance of the field water), and severe AWD (W3, each irrigation brought field water depth to 20 mm, and the next irrigation flooding is 5 days later than the disappearance of the field water). There were 12 treatments, with three replicates every treatment (Table 1). The irrigation water was artificial simulated aquaculture wastewater, and the formula was: C6H12O6: 33.3 mg/l, NH4Cl: 68.76 mg/L, NaHCO3: 71.4 mg/L, NaHPO4: 18.86 mg/L, and mix trace elements 1 ml/L. Mixed trace elements: MnSO4·H2O: 33.8 mg/L, H3BO3: 49.4 mg/L, ZnSO4·7H2O: 43.1 mg/L, FeSO4·7H2O: 97.3 mg/L, and CuSO4·5H2O: 25.0 mg/L [33]. During the experiment, the precipitation was isolated.
The base-fertilizer was 750 kg/hm2 of compound fertilizer according to the nitrogen application level in the local area, the nutrients of the compound fertilizer were N:P2O5:K2O = 15:15:15, and the topdressing was 300 kg/hm2 of urea. Topdressing was applied about 60 days after transplanting (at the jointing–booting stage). Agronomic measures such as weeding were carried out in accordance with local customs.

2.2. Soil Sampling

Soil samples were collected regularly combined with irrigation and fertilization at various growth stages of rice, the sampling depth was 5–10 cm, which was taken every 5–7 days, and the measurement was added after fertilization. Then, soil samples were kept at −20 °C in a freezer until analysis for soil nitrogen and functional genes.

2.3. Measurement of Soil Nitrogen

Soil NH4+ and NO3− were extracted with 50 mL of 0.5 M K2SO4 from 5.0 g fresh soils and determined using a continuous flow spectrophotometer (Auto Analyzer 3-AA3, Seal Analytical, Norderstedt, Germany).

2.4. DNA Extraction, PCR, and Sequencing of the Nitrogen Conversion Functional Genes

According to the supplier’s instructions, the DNA of each sample was extracted from soil samples (A0W2, A3W1, A3W2, and A3W3) at the late tillering and yellow ripening stages, respectively, using the E.Z.N.A.® stool DNA Kit (Omega Bio-tek, Norcross, GA, USA). The quality of DNA was determined using 1% agarose gel electrophoresis and spectrophotometry. All extracted DNA samples were stored at −20 °C for the subsequent analysis.
The amoA, nirS, and nirK are important genes in nitrification and denitrification. AOA amoA and AOB amoA are involved in the first, often limiting, step of nitrification (oxidation of ammonium into nitrite) [34]; nirK and nirS encode nitrite reductases [35]. Referring to the methods of Liao et al. [36], we used specific primers for AOA amoA [37], AOB amoA [38], nirK, and nirS [39] to quantify the samples individually using the fluorescence PCR technique. The sequences of these primers are show in Table 2. During PCR amplification, R2 > 0.98, and the amplification efficiency > 92%.
The Illumina Hi-seq (Allwegene, Beijing, China) platform was used to target ammonia oxidizing archaea (AOA) amoA, AOB amoA, nirS, and nirK genes perform high-throughput sequencing. The raw sequencing data were filtered and pruned using the PReprocessing and INformation of SEQuence data (PRINSEQ) and Moetur tools. The UCLUST tool in Quantitative Insights Into Microbial Ecology (QIIME 2) was used to conduct a cluster analysis on sequences with a sequence similarity threshold of 97% and calculate the Alpha Diversity Index (including Shannon and Chaol) for each set of samples [40].

2.5. Statistical Analysis

The data were analyzed and plotted using StataMP 16.0 (StataCrop, College Station, TX, USA) and Origin 2021 (OriginLab Corporation, Northampton, MA, USA). The Kruskal–Wallis method was used for ANOVA, and Dunn’s test was used for paired comparisons. The Pearson correlation analysis was performed.

3. Results

3.1. NH4+ and NO3− Concentration at Each Stage

NH4+ and NO3− concentrations are shown in Figure 1. The NH4+ concentration decreased with changes of the rice growth stages (p < 0.05), but the NO3− concentration of different growth stages varied less. The NH4+ concentration decreased with the increase of AWD intensity at the late tiller stage and heading and flowering stage (p < 0.05) but did not change significantly under different irrigation methods at other stages and even increased with the increase of AWD intensity (p < 0.05) at the milk ripe stage (Figure S1). The NO3− concentration showed significant differences under different irrigation methods (p < 0.05) at all stages, except the jointing–booting stage (Figure S2). The NO3− concentration decreased with the increase of AWD intensity in the late tillering period (r = −0.4313, p < 0.05), but the AWD significantly promoted the NO3− concentration in the other three periods. Significant differences under different sulfamethazine treatments were shown at the late tillering and the jointing stages (p < 0.05). Sulfamethazine significantly reduced the soil NH4+ concentration at the late tillering stage but significantly increased the soil NH4+ concentration at the jointing–booting stage. At the late tillering and jointing–booting stages, sulfamethazine significantly reduced the NO3− concentration.

3.2. Absolute Abundance and Diversity of Nitrogen Conversion Genes

Based on the NH4+ and NO3− concentrations, the late tillering stage was selected as the representative period with a significant response to sulfamethazine, and the yellow ripening stage was selected as the representative period without a significant response to sulfamethazine for further study. During these two stages, A3 treatments with significant differences in the NH4+ and NO3− concentrations were selected, and the microbial indicators were extracted and determined. As shown in Figure 2, there were large differences between the absolute abundances of the nitrogen conversion genes. The abundance of the AOA amoA and AOB amoA were smaller. The minimum abundance of the nirK gene was above 4 × 107 copies/g soil, much higher than other genes. Apart from AOA amoA, all gene abundances showed smaller difference at the yellow ripening stage than that at the late tillering stage, while the genes in A3W2 showed lower absolute abundances compared to the other A3 treatments at the late tillering or yellow ripening stages. At the yellow ripening stage, the AOA amoA appeared with lower abundance in A3W2, while the abundances of AOB amoA, nirS, and nirK were similar to that of other A3 treatments. In summary, A3W2 had the lowest AOA amoA and AOB amoA abundances at the late tillering and yellow ripening stages and the lowest nirS and nirK abundances at the late tillering stage. The lowest nitrogen conversion gene abundance of A3W2 attests to the special NH4+ and NO3− concentrations exhibited at A3W2 over multiple stages.

3.3. Alpha Diversity of Nitrogen Conversion Genes Communities

The Chao1 and Shannon diversity indices were used to study the differences in the microbial community structure under different treatments. The gene (AOA amoA, AOB amoA, nirS, and nirK) diversity indices are shown in Table 3.
The trends of the chao1 and Shannon indices were the same basically. The Shannon index of the AOB amoA had the greatest difference under different conditions, while the Shannon indices of other genes were smaller. The lowest Shannon index also appeared in the AOB amoA, while the nirK gene generally had a higher Shannon index. At the same time, the AOB amoA gene in the A0W2 had the highest Shannon index compared with others at the yellow ripening stage.

3.4. Community Structure of Nitrogen Conversion Genes

3.4.1. Community Structure of AOA amoA and AOB amoA

A total of 30 OTUs were detected, of which the species detected were only able to annotate to the level of the classes in all treatments, and three phyla were identified except for the bacteria that failed to be annotated. Figure 3a,b shows a histogram of the phyla level and the classes level. The structure of the AOA amoA community changed little in all the treatments. The highest relative abundance of Crenarchaeota appeared in the A3W3_L, and the highest relative abundance of Unspecified_Archaea appeared in A3W2_Y. With the change of the growth stages, the composition of AOA amoA in the phyla and classes level underwent some changes. Crenarchaeota with relative abundances above 75% at the late tillering stage decreased below 75% at the yellow ripening stage (A3W2_Y slightly above 75%), while the relative abundances of Unspecified_Archaea and Thaumarchaeota increased with changes of the rice growth. In A3 treatment, Crenarchaeota had a higher relative abundance at the yellow ripening stage, while other phyla were inhibited. AWD also promoted Crenarchaeota while inhibiting other phyla, although this change may be affected by the growth period. Unspecified_Archaea has the highest relative abundance in A0W2_Y.
A total of 57 OTUs were detected in AOB amoA, and a total of three phyla were annotated. The relative abundance of the phyla and genera levels are shown in Figure 3c,d. The relative abundance of Proteobacteria was the highest in A0W2_L. The relative abundance of Proteobacteria in A3W2_Y was the lowest (70.2%), but the relative abundance of Proteobacteria in A3W1_Y and A3W3_Y were above 99.5%. At the late tillering stage, the relative abundance of Proteobacteria in A3 was reduced compared with A0W2_L, while the relative abundance of the other phyla increased. At the yellow ripening stage, except for A3W2_Y, antibiotics improved the relative abundance of Proteobacteria compared with A0W2_Y (more than 99.2%). The abundance distribution at the genera level is similar to that at the phyla level. Nitrosospira was the most abundant at the genera level, and the presence of sulfamethazine increased the abundance of Nitrosospira at the late tillering stage, but the abundance of Nitrosospira at the yellow ripening stage decreased. These changes varied due to the difference in the AWD intensity. A3W2_Y had the highest environmental_samples_environmental_samples abundance and the lowest Nitrosospira abundance of all sulfamethazine treatments. A0W2_Y had the most uniform species composition at the species level and was related to the diversity of nitrogen conversion genes in the preceding article. Nitrosomonas_sp._Nm86 had the highest abundance at the species level.

3.4.2. Community Structure of nirS and nirK

A total of 77 OTUs of the nirS were detected in all treatments. Five phyla were detected, and Figure 4a,b shows the community structure at the phyla and genera levels. Proteobacteria was the most abundant of all treatments, with only Proteobacteria included in A3W2_L and A3W3_L. Other phyla emerged in A3W2_Y and A3W3_Y, including Planctomycetes, Actinobacteria, and Acidobacteria. With the change of the growth stages, Acidobacteria was replaced by Chloroflexi in A0W2, Planctomycetes and Acidobacteria were replaced by Actinobacteria in A0W3. At the genera level, the relative abundance of the microorganisms under different treatments varies greatly. The abundance distribution of Noviherbaspirillum had a large range different from other gene-based strains, and with the highest relative abundance always occupying higher abundances, Noviherbaspirillum had a relative abundance of only 3.8% in A3W3_L. At the same time, the relative abundance of Unspecified_Burkholderiales became the largest relative abundance in the treatment (40.8%), and the relative abundance of others also increased. Similarly, the relative abundance of Noviherbaspirillum in A3W1_L was only 29.8%, while the relative abundance of Sandaracinus reached 60.7% and became the most relatively abundant phylum in this treatment. A3W2_L had the highest Noviherbaspirillum abundance in the late tillering stage, while the other sulfamethazine treatments were low. In addition, with the change of the growth stage, the composition of the genera-level community structure also underwent succession, and Noviherbaspirillum became the genus with the largest relative abundance in A3W1 at the yellow ripening stage (92.2%). The relative abundance of Noviherbaspirillum in A3W3 rose to 37.4%, but the largest relative abundance of bacteria was Pseudodogulbenkiania (39.0%). With the changes of the growth stages, the relative abundance of Noviherbaspirillum decreased to a certain extent in A0W2, but the relative abundance of other bacteria increased.
A total of 450 OTUs were detected in the nirK, all of which belong to three phyla. Figure 4c,d shows the community structure at the phyla and genera levels. Of all treatments at the late tillering stage, A0W2_L had the highest relative abundance of Proteobacteria. The relative abundance of Proteobacteria gradually decreased with the increase of the AWD intensity in the A3 treatments. Of all the treatments at the yellow ripening stage, A0W2_Y had the lowest relative abundance of Proteobacteria, while the A3 treatments had a higher Proteobacteria abundance than A0W2_Y. At the phyla level, A3W2_Y had the lowest Proteobacteria abundance of all A3 treatments; A3W2_L and A3W2_Y had the lowest Nitrosospira abundances at the genera level. The different community structures of A3W2_Y may be related to the special values of the NO3− concentration. The genus with the highest level of abundance was generally Nitrosospira, and sulfamethazine decreased the abundance of Nitrosospira at the late tillering stage, while the relative abundance of other species increased. The relative abundance of Nitrosospira was higher under the A3 treatment at the yellow ripening stage, but the relative abundance of Nitrosospira in A3W2_Y was lower.

3.5. Correlation Analysis

As shown in Figure 5, most bacteria did not show a significant correlation with sulfamethazine. Nitrosospira_sp._KAN8, belonging to AOB amoA, can still be found with a significant negative correlation with sulfamethazine (p < 0.001). This suggested that both sulfamethazine-sensitive and -insensitive bacteria are present in the nitrogen conversion microorganism and that irrigation water containing sulfamethazine may have a huge impact on these antibiotic-sensitive bacteria. Except for nirK, other genes were not significantly associated with the irrigation methods, and Bradyrhizobium_sp._s23321, Ochrobactrum, and Bradyrhizobium_sp._STM_3843 showed a negative correlation to irrigation (p < 0.05). The microorganisms of the four nitrogen conversion genes all responded to changes in the growth period of rice. Thaummarchaeota and Crenachaeota belonging to AOA amoA showed a diametrically opposed correlation with the growth periods (p < 0.01), indicating that some microbes may have adapted to the antibiotic-containing environment and underwent ecological niche changes. Other nitrogen conversion gene microorganisms correlated with the growth stage were Janibacter_indicus, Pseudogulbenkiania_sp._NH8B, and Bradyrhizobium_diazoefficiens positively (p < 0.05), and Nitrosospira_sp._En13, Rhodopseudomonas_palustris, and Rhodopseudomonas_sp._2_8 were correlated with the growth stages negatively (p < 0.05). The strains of various nitrogen conversion-related genes showed a certain correlation with the NH4+ and NO3− concentrations. Among them, Crenachaeota, Archaea, Rhodopseudomonas_palustris, and Rhodopseudomonas_sp._2_8 were correlated with the NH4+ concentration negatively (p < 0.05). Nitrosospira_sp._9ss1 and Afipia_sp._1NLS2 were correlated with the NO3− concentration positively (p < 0.05). Although some nitrogen conversion microorganisms are involved in the same nitrogen conversion process and have the same gene, their responses to the same environmental variables varied. This may be due to their different sensitivities to different factors or the competitive relationships that exist between them. Under the antibiotics, changes in the nitrogen conversion processes and rates in paddy fields may be related to more detailed changes in different nitrogen conversion bacteria.

4. Discussion

4.1. Soil Nitrogen

The NH4+ concentration decreased significantly with the growth stages (Figure 1a). This is roughly the same as the results of Li. [41]. However, the concentration of soil NH4+ is also related to the input of exogenous fertilizers, the absorption of nitrogen by rice, the volatile loss of nitrogen ammonia, etc. In this study, there was no significant increase in NH4+, and NH4+ did not change much in the late tillering and flowering stages of panicle extraction (Figure 1a), which is because the input of fertilizer compensated for the loss of NH4+ in the rice growth process. The change of the NO3− concentration at different growth stages only showed significant differences at the late jointing and the yellow ripening stages (p < 0.05). The regression analysis showed that the regression coefficient is only −0.10 (p = 0.257), indicating that NO3− did not change significantly in this study. NO3− with a negative charge is difficult to be adsorbed by soil colloidal particles but easy to disperse into the environment through leaching [42]. The NO3− concentration did not change significantly in this study, and this difference may be because NO3− cannot be removed from the pot experiment system. The NO3− concentration increased significantly with the increase of the AWD intensity over certain periods, while the NH4+ concentration decreased significantly over certain periods (Figure S2). Cao et al. [43] and Zhang et al. [44] found similar laws to this study in culture experiments. However, the trend of the NO3− concentration changing with the AWD intensity gradually changed with the growth period (Figure S2), while the degree of decline of the NH4+ concentration with the increase of the intensity of AWD was gradually less obvious with the change of the growth period (Figure S1). The rice demand for nitrogen is mainly concentrated during the reproductive growth period [45]. At the same time, although rice also absorbs NO3−, NH4+ is still the main source of nitrogen utilization for rice [46]. Therefore, the difference in the responses of NH4+ and NO3− to AWD may be explained because of the differences in the rice nutrient requirements with growth periods and differences in the rice demand for different types of nitrogen.
Antibiotics can affect the soil nitrogen conversion, but the effects are also related to the type and concentration of antibiotics. Antibiotics affect soil nitrogen conversion by influencing different microorganisms. Revellin et al. [47] found that low concentrations of antibiotics change the diversity and characteristics of nitrogen-fixing bacteria, and many studies have found multiple antibiotics can inhibit nitrification and denitrification [48]. However, some antibiotics, including sulfamethazine, facilitate the denitrification process [27]. Trace antibiotic concentrations were used in this study, but the significant difference of NO3− and NH4+ still can be observed (p < 0.05). Studies have found that low concentrations of antibiotics can promote denitrification, but it only has a significant effect under soil moisture conditions of 40% of the water-filled pore space (WFPS) [49]. The moisture conditions of severe and mild AWD in the study were much greater than 40% WFPS, which may explain the effect of antibiotics in this study is not significant in some cases.

4.2. Nitrogen Conversion-Related Microorganisms

Nitrogen conversion-related microorganisms are important components of the soil microbial community, which includes a variety of microbes involving a variety of nitrogen conversion functional genes. Changes in soil nitrogen conversion-related microorganisms can significantly affect the nitrogen conversion in soil. Many studies have found that, although AOA and AOB participate in the same process of nitrification, their presence in the environment is different. Archaea typically make up 0–10% of the total native microflora in the soil, the most abundant of which is AOA [50]. AOA has been found to occupy higher abundances than AOB in many studies [51,52,53,54]. AOA and AOB respond differently to factors such as pH, salinity, NH4+, and NO3− [55]. AOA is better adapted to low NH4+ concentrations, and it has higher abundance at higher NO3− concentrations, while AOB tends to have a higher abundance at higher NH4+ concentrations [56,57,58]. In this study, all treatments had higher AOB amoA abundances at the late tillering stage, except for A3W2, and A3W1 and A3W3 had higher AOA amoA abundances at the yellow ripening stage (Figure 2). Except for A3W2, all treatments had a higher AOA amoA/AOB amoA ratio at the yellow ripening stage than that at the late tillering stage, which may be due to the high soil NH4+ concentration at the late tillering stage. As NH4+ was consumed in soils, AOA had a greater advantage in soils. The above discussions show that the main force of nitrification may change as the NH4+ and NO3− concentrations change in the soil.
The nirS and nirK genes are important denitrification genes. Abundances of nirS are higher in some studies [59]; however, nirK have higher abundances in other studies [60,61,62]. Yoshida et al. [39] found that the abundance of nirK was approximately 10 times more than nirS throughout the field sampling season. NirK is more dominant in this study, and this is due to the fact that nirS and nirK have different niches. In this study, the Shannon index of AOA amoA was between 2.687998 and 3.306445, and the Shannon index of AOB amoA was 0.240285–3.391534 (Table 3). NirK had the highest Shannon index, because the nirK gene had the highest number of species compared to other genes. The community structure of AOA amoA changed little and is more stable than AOB amoA (Figure 3). At the same time, it was found that AOB amoA had a higher correlation coefficient with NH4+ and NO3− than AOA amoA, although they were not significant. However, this may indicate that the intensity change of nitrification in rice fields is mainly contributed to by AOB amoA.
Under the impact of antibiotics or moisture conditions, the soil microbial diversity and community structure and the nitrogen cycle processes are changed [63,64,65]. Further studies show that antibiotics can affect the nitrification and denitrification of soil microorganisms [48,66,67]. This study found that the presence of antibiotics changed the community structure of nitrogen conversion microorganisms, but it also varied at different growth stages of rice (Figure 3 and Figure 4). It is worth noting that, except for AOA amoA, A3W2 showed significant differences in the community structures of the other three genes. Although there is a lack of direct evidence that the community structure of AOA amoA is more stable than AOB amoA under the impact of antibiotics, AOA has been found to be insensitive to antibiotics [68,69]. The results of this study show that sulfamethazine has a stronger effect on the nitrogen conversion microbial community and nitrogen conversion at a mild AWD. This result may be related to the distribution of antibiotics under such irrigation. Surface tension causes the narrowest diameter pores to control moisture distribution during wetting and drying due to the ink effect. During wetting, the narrow inlet of the large hole slows the infiltration of water, and during the drying process, they obstruct drainage, resulting in water retention, which affects the distribution of nutrients and sulfamethazine in the soil [70]. Therefore, due to the ink effect, the most uniform distribution of nutrients and sulfamethazine may occur under A3W2. Such results suggest that mild AWD may contribute to the effects of antibiotics on soil nitrogen conversion communities and nitrogen conversion processes.
Only Nitrosospira_sp._KAN8, belonging to AOB amoA, was significantly and negatively correlated with sulfamethazine concentrations (Figure 5a), which may explain why no significant effect of antibiotics on nitrogen conversion has been found in some studies [71,72]. Since the NH4+ concentration also changed with the change of the rice growth stages, this study cannot fully distinguish the effects of sulfamethazine on the microbial community structure under long-term action. All the microorganisms involved in this study were nitrifying and denitrifying microorganism. However, they responded to NH4+ and NO3− differently, and some studies found that AOA amoA and AOB amoA, nirS, and nirK respond to environmental factors differently [69,73]. This study further demonstrates that even microbiota with the same nitrogen conversion gene respond significantly differently to environmental factors such as NH4+, NO3−, and antibiotics. This study may provide a basis for a more in-depth explanation of soil microbial community structures and nitrogen conversion changes.

5. Conclusions

This study found that the presence of antibiotics changes the NH4+ and NO3− concentrations at the late tillering and jointing–booting stages (p < 0.05). Experimental data showed that mild AWD promoted the adverse effect of sulfamethazine (3000 ng/L) on soil nitrogen conversion microorganisms. Microbiota containing the same nitrogen conversion gene exhibited different responses to environmental factors. This study can provide a basis for the use of aquaculture water containing antibiotics to irrigate rice fields. Further research is needed, especially the response of nitrification and denitrification rates to antibiotics and how long antibiotics take to form the effect on nitrogen conversion.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12123034/s1: Figure S1: Responses of NH4+ concentration to irrigation methods at different growth stages. Figure S2: Responses of NO3− concentration to irrigation methods at different growth stages.

Author Contributions

Conceptualization, L.C.; methodology, L.C. and H.W.; formal analysis, L.C., H.W. and W.W.; investigation, W.W. and G.G.; resources, L.C.; data curation, H.W.; writing—original draft preparation, L.C. and H.W.; writing—review and editing, J.L.; visualization, H.W.; project administration, L.C.; and funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, (52209052) and the Fundamental Research Funds for the Central Universities (B200204033 and B210202117).

Data Availability Statement

The datasets were deposited in the Sequence Read Archive (SRA) repository under accession# PRJNA891876.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jasechko, S.; Perrone, D. Global groundwater wells at risk of running dry. Science 2021, 372, 418–421. [Google Scholar] [CrossRef]
  2. Ault, T.R. On the essentials of drought in a changing climate. Science 2020, 368, 256–260. [Google Scholar] [CrossRef] [PubMed]
  3. Elimelech, M.; Phillip, W.A. The Future of Seawater Desalination: Energy, Technology, and the Environment. Science 2011, 333, 712–717. [Google Scholar] [CrossRef] [PubMed]
  4. Yan, J.; Zhu, J.Q.; Ma, Y.; Jiang, S.Y.; Wu, Q.X. Effects of Pond Aquaculture Wastewater lrrigation and Reducing Fertilization on Grain Yield, Nitrogen and Phosphorus Mobility in Soil. Water Sav. Irrig. 2017, 2017, 65–69. (In Chinese) [Google Scholar]
  5. de Araújo, A.F.B.; Cavalcante, E.S.; Lacerda, C.F.; de Albuquerque, F.A.; Sales, J.R.D.S.; Lopes, F.B.; Ferreira, J.F.D.S.; Costa, R.N.T.; Lima, S.C.R.V.; Bezerra, M.A.; et al. Fiber Quality, Yield, and Profitability of Cotton in Response to Supplemental Irrigation with Treated Wastewater and NPK Fertilization. Agronomy 2022, 12, 2527. [Google Scholar] [CrossRef]
  6. Nie, X.; Mubashar, M.; Zhang, S.; Qin, Y.; Zhang, X. Current progress, challenges and perspectives in microalgae-based nutrient removal for aquaculture waste: A comprehensive review. J. Clean. Prod. 2020, 277, 124209. [Google Scholar] [CrossRef]
  7. Leogrande, R.; Pedrero, F.; Nicolas, E.; Vitti, C.; Lacolla, G.; Stellacci, A.M. Reclaimed Water Use in Agriculture: Effects on Soil Chemical and Biological Properties in a Long-Term Irrigated Citrus Farm. Agronomy 2022, 12, 1317. [Google Scholar] [CrossRef]
  8. Chen, L.; Feng, Q.; Li, C.; Wei, Y.; Zhao, Y.; Feng, Y.; Zheng, H.; Li, F.; Li, H. Impacts of aquaculture wastewater irrigation on soil microbial functional diversity and community structure in arid regions. Sci. Rep. 2017, 7, 10. [Google Scholar] [CrossRef] [Green Version]
  9. Bao, W.; Zhu, S.; Jin, G.; Ye, Z. Generation, characterization, perniciousness, removal and reutilization of solids in aquaculture water: A review from the whole process perspective. Rev. Aquac. 2018, 11, 1342–1366. [Google Scholar] [CrossRef]
  10. Ahmad, A.; Kurniawan, S.B.; Abdullah, S.R.S.; Othman, A.R.; Abu Hasan, H. Contaminants of emerging concern (CECs) in aquaculture effluent: Insight into breeding and rearing activities, alarming impacts, regulations, performance of wastewater treatment unit and future approaches. Chemosphere 2022, 290, 15. [Google Scholar] [CrossRef]
  11. He, Z.; Cheng, X.; Kyzas, G.Z.; Fu, J. Pharmaceuticals pollution of aquaculture and its management in China. J. Mol. Liq. 2016, 223, 781–789. [Google Scholar] [CrossRef]
  12. Zhang, Q.Y.; Zhou, J.Z.; Zhao, W.W.; Wu, W. Analysis of Sulfonamides Residues and lsolation of Sulfonamides Resistant Strains in the Aquaculture Environment. Ecol. Environ. 2015, 24, 1354–1360. (In Chinese) [Google Scholar] [CrossRef]
  13. Carvalho, I.T.; Santos, L. Antibiotics in the aquatic environments: A review of the European scenario. Environ. Int. 2016, 94, 736–757. [Google Scholar] [CrossRef]
  14. Liu, X.; Steele, J.C.; Meng, X.-Z. Usage, residue, and human health risk of antibiotics in Chinese aquaculture: A review. Environ. Pollut. 2017, 223, 161–169. [Google Scholar] [CrossRef] [PubMed]
  15. Zhuo, L.; Wang, M.H.; Shi, Y.G.; Wang, Y.Y.; Zhuang, X.; Xu, R.F.; Ren, M.Z. Occurrence, Distribution, and Ecological Risk of Antibiotics in Surface Water of Typical Drinking Water Sources and Aquaculture in South China. Asian J. Ecotoxicol. 2019, 14, 164–175. (In Chinese) [Google Scholar] [CrossRef]
  16. Su, H.; Xu, W.; Hu, X.; Xu, Y.; Wen, G.; Cao, Y. Spatiotemporal variations and source tracking of antibiotics in an ecological aquaculture farm in Southern China. Sci. Total Environ. 2020, 763, 143022. [Google Scholar] [CrossRef]
  17. Christou, A.; Agüera, A.; Bayona, J.M.; Cytryn, E.; Fotopoulos, V.; Lambropoulou, D.; Manaia, C.M.; Michael, C.; Revitt, M.; Schröder, P.; et al. The potential implications of reclaimed wastewater reuse for irrigation on the agricultural environment: The knowns and unknowns of the fate of antibiotics and antibiotic resistant bacteria and resistance genes—A review. Water Res. 2017, 123, 448–467. [Google Scholar] [CrossRef] [Green Version]
  18. Camacho-Arévalo, R.; García-Delgado, C.; Mayans, B.; Antón-Herrero, R.; Cuevas, J.; Segura, M.; Eymar, E. Sulfonamides in Tomato from Commercial Greenhouses Irrigated with Reclaimed Wastewater: Uptake, Translocation and Food Safety. Agronomy 2021, 11, 1016. [Google Scholar] [CrossRef]
  19. Zhang, J.-J.; Chen, J.; Wang, C.; Wang, P.-F.; Gao, H.; Hu, Y. Distribution characteristics and controlling factors of typical antibiotics and antibiotic resistance genes in river networks in western area of Wangyu River, China. Water Sci. Eng. 2022, 15, 318–327. [Google Scholar] [CrossRef]
  20. Lyu, J.; Yang, L.; Zhang, L.; Ye, B.; Wang, L. Antibiotics in soil and water in China–a systematic review and source analysis. Environ. Pollut. 2020, 266, 115147. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Cheng, D.M.; Xie, J.; Zhang, Y.T.; Wan, Y.; Zhang, Y.Q.; Shi, X.J. Impacts of farmland application of antibiotic–contaminated manures on the occurrence of antibiotic residues and antibiotic resistance genes in soil: A meta–analysis study. Chemosphere 2022, 300, 13. [Google Scholar] [CrossRef] [PubMed]
  22. Wu, J.; Wang, J.; Li, Z.; Guo, S.; Li, K.; Xu, P.; Ok, Y.S.; Jones, D.L.; Zou, J. Antibiotics and antibiotic resistance genes in agricultural soils: A systematic analysis. Crit. Rev. Environ. Sci. Technol. 2022, 1–18. [Google Scholar] [CrossRef]
  23. Braun, G.; Braun, M.; Kruse, J.; Amelung, W.; Renaud, F.G.; Khoi, C.M.; Duong, M.V.; Sebesvari, Z. Pesticides and antibiotics in permanent rice, alternating rice–shrimp and permanent shrimp systems of the coastal Mekong Delta, Vietnam. Environ. Int. 2019, 127, 442–451. [Google Scholar] [CrossRef] [PubMed]
  24. Grenni, P.; Ancona, V.; Caracciolo, A.B. Ecological effects of antibiotics on natural ecosystems: A review. Microchem. J. 2018, 136, 25–39. [Google Scholar] [CrossRef]
  25. Kumar, M.; Jaiswal, S.; Sodhi, K.K.; Shree, P.; Singh, D.K.; Agrawal, P.K.; Shukla, P. Antibiotics bioremediation: Perspectives on its ecotoxicity and resistance. Environ. Int. 2019, 124, 448–461. [Google Scholar] [CrossRef]
  26. Cui, H.; Wang, S.-P.; Fu, J.; Zhou, Z.-Q.; Zhang, N.; Guo, L. Influence of ciprofloxacin on microbial community structure and function in soils. Biol. Fertil. Soils 2014, 50, 939–947. [Google Scholar] [CrossRef]
  27. D’Alessio, M.; Durso, L.M.; Miller, D.N.; Woodbury, B.; Ray, C.; Snow, D.D. Environmental fate and microbial effects of monensin, lincomycin, and sulfamethazine residues in soil. Environ. Pollut. 2019, 246, 60–68. [Google Scholar] [CrossRef] [Green Version]
  28. DeVries, S.L.; Loving, M.; Li, X.; Zhang, P. The effect of ultralow-dose antibiotics exposure on soil nitrate and N2O flux. Sci. Rep. 2015, 5, 16818. [Google Scholar] [CrossRef] [Green Version]
  29. Guan, A.; Qi, W.; Peng, Q.; Zhou, J.; Bai, Y.; Qu, J. Environmental heterogeneity determines the response patterns of microbially mediated N-reduction processes to sulfamethoxazole in river sediments. J. Hazard. Mater. 2021, 421, 126730. [Google Scholar] [CrossRef]
  30. Sun, X.L.; Hu, Z.Y.; Liu, L.; Li, S.Y.; Liu, F.L. Pollutant Reduction Systems for Controlling Agricultural Non–Point–SourcePollution in Town District of River Network Area Based on Reuse of Wastewater and Nutrient: A Case Study in Xinjian Town, Taihu Lake. J. Ecol. Rural Environ. 2019, 35, 582–592. (In Chinese) [Google Scholar] [CrossRef]
  31. Ananna, F.H.; Amin, M.M.; Islam, D.; Ahmed, T.; Ashrafuzzaman, M.; Aziz, M.G. Groundwater contamination risks with manure-borne microorganisms under different land-application options. Water Sci. Eng. 2021, 14, 314–322. [Google Scholar] [CrossRef]
  32. Haque, A.; Uddin, K.; Sulaiman, M.; Amin, A.; Hossain, M.; Aziz, A.; Mosharrof, M. Impact of Organic Amendment with Alternate Wetting and Drying Irrigation on Rice Yield, Water Use Efficiency and Physicochemical Properties of Soil. Agronomy 2021, 11, 1529. [Google Scholar] [CrossRef]
  33. Zhou, H.-Y.; Han, M.-L.; Qiu, T.-L.; Gao, M.; Sun, X.-B.; Wang, X.-M. Ammonia Removal Rate and Microbial Community Structures in Different Biofilters for Treating Aquaculture Wastewater. Huan Jing Ke Xue 2017, 38, 2444–2452. [Google Scholar]
  34. Srikanthasamy, T.; Barot, S.; Koffi, F.K.; Tambosco, K.; Marcangeli, Y.; Carmignac, D.; N’Dri, A.B.; Gervaix, J.; Leloup, J.; Le Roux, X.; et al. Effects of vegetation cover and season on soil nitrifiers in an African savanna: Evidence of archaeal nitrifier inhibition by grasses. Geoderma 2022, 416, 115775. [Google Scholar] [CrossRef]
  35. Zhang, X.; Qiu, Y.; Gilliam, F.S.; Gillespie, C.J.; Tu, C.; Reberg-Horton, S.C.; Hu, S. Arbuscular Mycorrhizae Shift Community Composition of N-Cycling Microbes and Suppress Soil N2O Emission. Environ. Sci. Technol. 2022, 56, 13461–13472. [Google Scholar] [CrossRef] [PubMed]
  36. Liao, J.; Hu, A.; Zhao, Z.; Liu, X.; Jiang, C.; Zhang, Z. Biochar with large specific surface area recruits N2O-reducing microbes and mitigate N2O emission. Soil Biol. Biochem. 2021, 156, 12. [Google Scholar] [CrossRef]
  37. Gong, Y.; Sun, F.; Wang, F.M.; Lambers, H.; Li, Y.W.; Zhong, Q.P.; Chen, J.; Li, Z.; Wang, M. The mechanisms and potentially positive effects of seven years of delayed and wetter wet seasons on nitrous oxide fluxes in a tropical monsoon forest. Geoderma 2022, 412, 11. [Google Scholar] [CrossRef]
  38. Lv, B.; Zhang, D.; Chen, Q.; Cui, Y. Effects of earthworms on nitrogen transformation and the correspond genes (amoA and nirS) in vermicomposting of sewage sludge and rice straw. Bioresour. Technol. 2019, 287, 121428. [Google Scholar] [CrossRef]
  39. Yoshida, M.; Ishii, S.; Otsuka, S.; Senoo, K. Temporal shifts in diversity and quantity of nirS and nirK in a rice paddy field soil. Soil Biol. Biochem. 2009, 41, 2044–2051. [Google Scholar] [CrossRef]
  40. Chen, L.; Zhao, Z.; Li, J.; Wang, H.; Guo, G.; Wu, W. Effects of muddy water irrigation with different sediment particle sizes and sediment concentrations on soil microbial communities in the Yellow River Basin of China. Agric. Water Manag. 2022, 270, 107750. [Google Scholar] [CrossRef]
  41. Li, S.H. Effects of Different Tillage Patterns on greenhouse Gas Emission and Nitrogen Uptake of Rice. Ph.D. Thesis, Huazhong Agricultural University, Wuhan, China, 2021. (In Chinese). [Google Scholar]
  42. Pandey, A.; Suter, H.; He, J.-Z.; Hu, H.W.; Chen, D. Dissimilatory nitrate reduction to ammonium dominates nitrate reduction in long–term low nitrogen fertilized rice paddies. Soil Biol. Biochem. 2019, 131, 149–156. [Google Scholar] [CrossRef]
  43. Cao, X.C.; Liu, X.X.; Ma, C.; Tian, C.; Zhu, L.F.; Wu, L.L.; Zhang, J.H.; Jin, Q.Y.; Zhu, C.Q.; Kong, Y.L.; et al. Alternative dry–wet irrigation improves the rhizospheric oxygen environment and nitrogen transformation, and increases nitrogen absorption by rice plants. Plant Nutr. Fert. Sci. 2022, 28, 1–14. (In Chinese) [Google Scholar] [CrossRef]
  44. Zhang, Z.Z.; Wang, D.M.; Li, M.X. Effect of Drying–rewetting Intensity on Soil Nutrient Availability. J. Soil Water Conserv. 2021, 35, 265–270. (In Chinese) [Google Scholar] [CrossRef]
  45. Zhang, S.; Zhang, Y.; Li, K.; Yan, M.; Zhang, J.; Yu, M.; Tang, S.; Wang, L.; Qu, H.; Luo, L.; et al. Nitrogen Mediates Flowering Time and Nitrogen Use Efficiency via Floral Regulators in Rice. Curr. Biol. 2021, 31, 671–683. [Google Scholar] [CrossRef] [PubMed]
  46. Yang, H.-C.; Kan, C.-C.; Hung, T.-H.; Hsieh, P.-H.; Wang, S.-Y.; Hsieh, W.-Y.; Hsieh, M.-H. Identification of early ammonium nitrate-responsive genes in rice roots. Sci. Rep. 2017, 7, 16. [Google Scholar] [CrossRef] [Green Version]
  47. Revellin, C.; Hartmann, A.; Solanas, S.; Topp, E. Long-Term Exposure of Agricultural Soil to Veterinary Antibiotics Changes the Population Structure of Symbiotic Nitrogen-Fixing Rhizobacteria Occupying Nodules of Soybeans (Glycine max). Appl. Environ. Microbiol. 2018, 84, 9. [Google Scholar] [CrossRef] [Green Version]
  48. DeVries, S.L.; Zhang, P. Antibiotics and the Terrestrial Nitrogen Cycle: A Review. Curr. Pollut. Rep. 2016, 2, 51–67. [Google Scholar] [CrossRef] [Green Version]
  49. DeVries, S.L.; Block, K.A.; Loving, M.; Logozzo, L.; Zhang, P. The effects of trace narasin on the biogeochemical N-cycle in a cultivated sandy loam. Sci. Total Environ. 2020, 716, 137031. [Google Scholar] [CrossRef]
  50. Sterngren, A.E.; Hallin, S.; Bengtson, P. Archaeal Ammonia Oxidizers Dominate in Numbers, but Bacteria Drive Gross Nitrification in N–amended Grassland Soil. Front. Microbiol. 2015, 6, 8. [Google Scholar] [CrossRef] [Green Version]
  51. Xiao, R.; Qiu, Y.; Tao, J.; Zhang, X.; Chen, H.; Reberg-Horton, S.C.; Shi, W.; Shew, H.D.; Zhang, Y.; Hu, S. Biological controls over the abundances of terrestrial ammonia oxidizers. Glob. Ecol. Biogeogr. 2019, 29, 384–399. [Google Scholar] [CrossRef]
  52. Wang, C.; Tang, S.; He, X.; Ji, G. The abundance and community structure of active ammonia-oxidizing archaea and ammonia-oxidizing bacteria shape their activities and contributions in coastal wetlands. Water Res. 2020, 171, 115464. [Google Scholar] [CrossRef] [PubMed]
  53. Xiao, R.; Ran, W.; Hu, S.; Guo, H. The response of ammonia oxidizing archaea and bacteria in relation to heterotrophs under different carbon and nitrogen amendments in two agricultural soils. Appl. Soil Ecol. 2020, 158, 103812. [Google Scholar] [CrossRef]
  54. Qin, Y.; Liu, W.; He, F.; Li, X. Nitrogen and Phosphorus Fertilization Reshapes the Abundance and Structure of Ammonia Oxidizers on a Leymus chinensis Steppe in Northern China. Agronomy 2022, 12, 2515. [Google Scholar] [CrossRef]
  55. Huang, D.; Li, X.; Luo, X. Response of Nitrifier and Denitrifier Abundance to Salinity Gradients in Agricultural Soils at the Yellow River Estuary. Agronomy 2022, 12, 1642. [Google Scholar] [CrossRef]
  56. Ying, J.; Li, X.; Wang, N.; Lan, Z.; He, J.; Bai, Y. Contrasting effects of nitrogen forms and soil pH on ammonia oxidizing microorganisms and their responses to long-term nitrogen fertilization in a typical steppe ecosystem. Soil Biol. Biochem. 2017, 107, 10–18. [Google Scholar] [CrossRef]
  57. Zou, W.; Lang, M.; Zhang, L.; Liu, B.; Chen, X. Ammonia-oxidizing bacteria rather than ammonia-oxidizing archaea dominate nitrification in a nitrogen-fertilized calcareous soil. Sci. Total Environ. 2021, 811, 151402. [Google Scholar] [CrossRef]
  58. Dong, X.; Zhang, J.; Qiu, H.; Zhang, H.; Luo, C.; Deng, D.; Shen, Q.; Jia, Z. Chronic Nitrogen Fertilization Modulates Competitive Interactions Among Microbial Ammonia Oxidizers in a Loess Soil. Pedosphere 2019, 29, 24–33. [Google Scholar] [CrossRef]
  59. Bowen, H.; Maul, J.E.; Cavigelli, M.A.; Yarwood, S. Denitrifier abundance and community composition linked to denitrification activity in an agricultural and wetland soil. Appl. Soil Ecol. 2020, 151, 103521. [Google Scholar] [CrossRef]
  60. Liang, Y.; Wu, C.; Wei, X.; Liu, Y.; Chen, X.; Qin, H.; Wu, J.; Su, Y.; Ge, T.; Hu, Y. Characterization of nirS- and nirK-containing communities and potential denitrification activity in paddy soil from eastern China. Agric. Ecosyst. Environ. 2021, 319, 107561. [Google Scholar] [CrossRef]
  61. Xiao, X.; Xie, G.X.; Yang, Z.H.; He, N.; Yang, D.; Liu, M.D. Variation in abundance, diversity, and composition of nirK and nirS containing denitrifying bacterial communities in a red paddy soil as affected by combined organic–chemical fertilization. Appl. Soil Ecol. 2021, 166, 10. [Google Scholar] [CrossRef]
  62. Wang, X.; Li, Y.; Ciampitti, I.A.; He, P.; Xu, X.; Qiu, S.; Zhao, S. Response of soil denitrification potential and community composition of denitrifying bacterial to different rates of straw return in north-central China. Appl. Soil Ecol. 2022, 170, 10. [Google Scholar] [CrossRef]
  63. Heděnec, P.; Rui, J.; Lin, Q.; Yao, M.; Li, J.; Li, H.; Frouz, J.; Li, X. Functional and phylogenetic response of soil prokaryotic community under an artificial moisture gradient. Appl. Soil Ecol. 2018, 124, 372–378. [Google Scholar] [CrossRef]
  64. Wepking, C.; Badgley, B.; Barrett, J.E.; Knowlton, K.F.; Lucas, J.M.; Minick, K.J.; Ray, P.P.; Shawver, S.E.; Strickland, M.S. Prolonged exposure to manure from livestock-administered antibiotics decreases ecosystem carbon-use efficiency and alters nitrogen cycling. Ecol. Lett. 2019, 22, 2067–2076. [Google Scholar] [CrossRef]
  65. Zhang, L.; Zhang, C.; Lian, K.; Liu, C. Effects of chronic exposure of antibiotics on microbial community structure and functions in hyporheic zone sediments. J. Hazard. Mater. 2021, 416, 126141. [Google Scholar] [CrossRef] [PubMed]
  66. Yin, G.Y.; Hou, L.J.; Liu, M.; Zheng, Y.L.; Li, X.F.; Lin, X.B.; Gao, J.; Jiang, X.F.; Wang, R.; Yu, C.D. Effects of multiple antibiotics exposure on denitrification process in the Yangtze Estuary sediments. Chemosphere 2017, 171, 118–125. [Google Scholar] [CrossRef]
  67. Li, W.; Shi, C.; Yu, Y.; Ruan, Y.; Kong, D.; Lv, X.; Xu, P.; Awasthi, M.K.; Dong, M. Interrelationships between tetracyclines and nitrogen cycling processes mediated by microorganisms: A review. Bioresour. Technol. 2020, 319, 124036. [Google Scholar] [CrossRef] [PubMed]
  68. Tu, R.; Jin, W.; Han, S.-F.; Zhou, X.; Wang, T.; Gao, S.-H.; Wang, Q.; Chen, C.; Xie, G.-J.; Wang, Q. Rapid enrichment and ammonia oxidation performance of ammonia-oxidizing archaea from an urban polluted river of China. Environ. Pollut. 2019, 255, 113258. [Google Scholar] [CrossRef]
  69. Tang, S.; Wang, C.; Ji, G. Response of ammonia-oxidizing archaea and bacteria to streptomycin sulfate and penicillin in coastal wetlands along the Bohai Rim. Land Degrad. Dev. 2021, 32, 1917–1926. [Google Scholar] [CrossRef]
  70. Banerjee, S.; Helgason, B.; Wang, L.; Winsley, T.; Ferrari, B.C.; Siciliano, S.D. Legacy effects of soil moisture on microbial community structure and N2O emissions. Soil Biol. Biochem. 2016, 95, 40–50. [Google Scholar] [CrossRef]
  71. Liu, H.; Yang, Y.; Sun, H.; Zhao, L.; Liu, Y. Effect of tetracycline on microbial community structure associated with enhanced biological N&P removal in sequencing batch reactor. Bioresour. Technol. 2018, 256, 414–420. [Google Scholar] [CrossRef]
  72. Rahman, M.M.; Shan, J.; Yang, P.P.; Shang, X.X.; Xia, Y.Q.; Yan, X.Y. Effects of long–term pig manure application on antibiotics, abundance of antibiotic resistance genes (ARGs), anammox and denitrification rates in paddy soils. Environ. Pollut. 2018, 240, 368–377. [Google Scholar] [CrossRef] [PubMed]
  73. Deng, F.; Zhang, D.; Yang, L.; Li, L.; Lu, Y.; Wang, J.; Fan, Y.; Zhu, Y.; Li, X.; Zhang, Y. Effects of antibiotics and heavy metals on denitrification in shallow eutrophic lakes. Chemosphere 2021, 291, 132948. [Google Scholar] [CrossRef] [PubMed]
Agronomy 12 03034 g001 550
Figure 1. Soil (a) NH4+ and (b) NO3− concentrations during different growth stages. * indicates a significant difference from the A0 treatment in the same stage. A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD).
Figure 1. Soil (a) NH4+ and (b) NO3− concentrations during different growth stages. * indicates a significant difference from the A0 treatment in the same stage. A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD).
Agronomy 12 03034 g001
Agronomy 12 03034 g002 550
Figure 2. Absolute abundance of AOA amoA (a), AOB amoA (b), nirS (c), and nirK (d). A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD).
Figure 2. Absolute abundance of AOA amoA (a), AOB amoA (b), nirS (c), and nirK (d). A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD).
Agronomy 12 03034 g002
Agronomy 12 03034 g003 550
Figure 3. Relative abundance of AOA amoA (a,b) and AOB amoA (c,d) at the phyla, classes, and genera levels. A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD). _L and _Y indicate the late tillering and yellow ripening periods, respectively.
Figure 3. Relative abundance of AOA amoA (a,b) and AOB amoA (c,d) at the phyla, classes, and genera levels. A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD). _L and _Y indicate the late tillering and yellow ripening periods, respectively.
Agronomy 12 03034 g003
Agronomy 12 03034 g004 550
Figure 4. Relative abundance of nirS (a,b) and nirK(c,d) at the phyla and genera levels. A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD). _L and _Y indicate the late tillering and yellow ripening periods, respectively.
Figure 4. Relative abundance of nirS (a,b) and nirK(c,d) at the phyla and genera levels. A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD). _L and _Y indicate the late tillering and yellow ripening periods, respectively.
Agronomy 12 03034 g004
Agronomy 12 03034 g005a 550Agronomy 12 03034 g005b 550
Figure 5. Heat map of the correlation between nitrogen conversion flora and environmental factors. *, **, and *** indicate that the significance levels of the correlation coefficients are p < 0.05, p < 0.01, and p < 0.001, respectively. Antibiotics, the concentration gradient of the antibiotic sulfamethazine. Irrigation, irrigation methods. NH4+-N, the concentration of NH4+. NO3−-N, the concentration of NO3−. Stage, growth stages of rice.
Figure 5. Heat map of the correlation between nitrogen conversion flora and environmental factors. *, **, and *** indicate that the significance levels of the correlation coefficients are p < 0.05, p < 0.01, and p < 0.001, respectively. Antibiotics, the concentration gradient of the antibiotic sulfamethazine. Irrigation, irrigation methods. NH4+-N, the concentration of NH4+. NO3−-N, the concentration of NO3−. Stage, growth stages of rice.
Agronomy 12 03034 g005aAgronomy 12 03034 g005b
Table
Table 1. Processing numbers.
Table 1. Processing numbers.
TreatmentsAntibiotic Concentrations (A)Irrigation Methods (W)
A0W1A0W1
A0W2A0W2
A0W3A0W3
A1W1A1W1
A1W2A1W2
A1W3A1W3
A2W1A2W1
A2W2A2W2
A2W3A2W3
A3W1A3W1
A3W2A3W2
A3W3A3W3
Table
Table 2. Primer information used in the experimental procedure.
Table 2. Primer information used in the experimental procedure.
GenesName of PrimerSequence (5′ to 3′)
AOA amoAArch-amoA26FGACTACATMTTCTAYACWGAYTGGGC
Arch-amoA417RGGKGTCATRTATGGWGGYAAYGTTGG
AOB amoAamoB-FGGGGTTTCTACTGGTGGT
amoB-rCCCCTCKGSAAAGCCTTCTTC
nirKnirKF1aCuATCATGGTSCTGCCGCG
nirKR3CuGCCTCGATCAGRTTGTGGTT
nirScd3aFGTSAACGTSAAGGARACSGG
R3cdGASTTCGGRTGSGTCTTGA
Table
Table 3. Nitrogen conversion gene’s observed Chao1 and Shannon indices.
Table 3. Nitrogen conversion gene’s observed Chao1 and Shannon indices.
Growth StagesTreatmentsAOA amoAAOB amoAnirSnirK
Chao1ShannonChao1ShannonChao1ShannonChao1Shannon
late tilleringA0W2243.113744240.768873342.060788299.42864.729482
A3W1233.065076191.155675222.764163284.63644.776766
A3W2232.904411150.85763717.52.110334292.55264.368037
A3W3242.68799821.333333.39153419.53.050141255.455.180928
yellow ripeningA0W2243.129178220.24028520.23.7079953273.452603
A3W123.53.306445130.392306202.956571278.08824.347746
A3W2213.279984130.47802442.461542.113301207.22.755407
A3W3243.206926111.74004183.4589212425.106296
Note: A, the concentration gradient of the antibiotic sulfamethazine, including A0 (0 ng·L−1), A1 (500 ng·L−1), A2 (1500 ng·L−1), and A3 (3000 ng·L−1). W, irrigation methods, including W1 (flood irrigation), W2 (mild AWD), and W3 (severe AWD).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chen, L.; Wang, H.; Li, J.; Wu, W.; Guo, G. Effect of the Sulfamethazine on Nitrogen Conversion in Alternate Wet and Dry Paddy Fields. Agronomy 2022, 12, 3034. https://doi.org/10.3390/agronomy12123034

AMA Style

Chen L, Wang H, Li J, Wu W, Guo G. Effect of the Sulfamethazine on Nitrogen Conversion in Alternate Wet and Dry Paddy Fields. Agronomy. 2022; 12(12):3034. https://doi.org/10.3390/agronomy12123034

Chicago/Turabian Style

Chen, Lina, Haiming Wang, Jiang Li, Wenbo Wu, and Guomian Guo. 2022. "Effect of the Sulfamethazine on Nitrogen Conversion in Alternate Wet and Dry Paddy Fields" Agronomy 12, no. 12: 3034. https://doi.org/10.3390/agronomy12123034

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop