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Article

Effects of Biological Nitrification Inhibitor on Nitrous Oxide and nosZ, nirK, nirS Denitrifying Bacteria in Paddy Soils

by
Xingchen Huang
1,†,
Yuning Zou
1,†,
Cece Qiao
1,
Qiumeng Liu
1,
Jingwen Liu
1,
Rui Kang
1,
Lantian Ren
1,* and
Wenge Wu
2,*
1
Anhui Engineering Research Center for Smart Crop Planting and Processing Technology, Anhui Science and Technology University, Fengyang 233100, China
2
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230041, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2023, 15(6), 5348; https://doi.org/10.3390/su15065348
Submission received: 14 January 2023 / Revised: 28 February 2023 / Accepted: 15 March 2023 / Published: 17 March 2023
(This article belongs to the Special Issue Green and Sustainable Agricultural Practices in Times of Climate Change)
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Abstract

:
This study aimed to investigate the effects of a biological nitrification inhibitor on nitrous oxide emission and rice yield quality in paddy soils and its effects on denitrifying the bacteria of nosZ, nirK, and nirS types. Two treatments were performed: (1) using a local conventional fertilizer as the control CK; (2) using the partial application of a conventional fertilizer + biological nitrification inhibitor as SW. N2O emission was measured using gas chromatography; qPCR amplification was performed using primers for the targeted functional genes, nosZ, nirS, and nirK, and denitrifying functional gene abundance and denitrifying microbial community structure were analyzed using fluorescence quantification and high–throughput sequencing, respectively. The results reveal that the biological nitrification inhibitor resulted in a 41.83% reduction in N2O, relative to the normal fertilizer treatment. Meanwhile, rice yield increased by 15.45% and related quality indexes were also improved. This can promote the reproduction of bacteria with the nosZ gene while inhibiting the growth of bacteria with nirS and nirK genes. The core bacteria, Nitrosospira, Rhodanobacter, Bradyrhizobium, Tardiphaga, Rhodopseudomonas, and Paracoccus, positively correlated with N2O emissions, while core bacteria Azospirillum, Burkholderia, and Mesorhizobium negatively correlated with N2O emissions. Therefore, the application of a biological nitrification inhibitor could be an effective measure to promote rice yield and quality, reduce N2O emissions, and affect key denitrifying bacteria.

1. Introduction

Global warming has elicited widespread attention in recent years. According to projections made by the Intergovernmental Panel on Climate Change (IPCC) in 2018, the global average temperature is expected to rise by about 1.5 °C between 2030 and 2052. Nitrous oxide (N2O) is another greenhouse gas, along with carbon dioxide (CO2) and methane (CH4), that has a significant environmental impact, with a warming potential 265 times higher than that of CO2 [1]. Excessive fertilizer application has led to the production of atmospheric N2O mainly from agricultural soils, which contributes to approximately 69% of total global N2O, respectively [2,3,4,5]. Rice, as the primary food crop in China, has an average annual planting area of 28.6 million hm2. During rice growth, farmers often adopt water management patterns, such as flooding and intermittent irrigation to increase yields. Anaerobic conditions are conducive to the growth of denitrifying microorganisms, and, with the presence of large amounts of NO3−−N provided by nitrification, denitrification can release large amounts of N2O, thus making paddy fields an important source of N2O emissions. According to relevant studies, N2O emissions from rice fields in China account for 11.4% of the total emissions from farmland [6]. Thus, as a large agricultural country, it is urgent to reduce N2O emissions and develop low–carbon agriculture in China. In recent years, an increasing number of scholars have been exploring methods of reducing N2O emissions from various aspects, such as innovating fertilizer application methods and applying nitrification inhibitors.
As an effective way to reduce N2O emissions, a nitrification inhibitor mainly comprises chemical nitrification and biological nitrification inhibitors. Chemical nitrification inhibitors include DCD, nitrapyrin, and DMPP. DCD is a white crystalline powder that is nonflammable. It has a melting point of 207–209 °C, has a relative density of 1.400 (25/4 °C), and is soluble in water and liquid ammonia. Nitrapyrin is a white crystal with a molecular weight of 230.9, a melting point of 62–63 °C, and a vapor pressure of 0.373 N/m2 at 23 °C. It has a low solubility in water (40 mg/L) but can dissolve 400 g of nitrapyrin (20 °C) per kg of anhydrous ammonia. It can easily dissolve in organic solvents such as propylene and toluene. DMPP is a new nitrification inhibitor produced by BASF in Germany. It is composed of NH4NO3 and (NH4)2SO4 in a 2:1 ratio, containing 18.5% NH4+−N and 7.5% NO3−N and 14% S and 0.29% DMPP. The application of chemical synthetic nitrification inhibition can effectively inhibit the conversion of ammonium nitrogen to nitrate nitrogen in the soil, significantly increasing crop yields and reducing nitrate levels, resulting in a 38.9% reduction in N2O emission factors [7,8]. However, its agricultural production cost is high, while the application of chemicals may pose a significant risk to the environment and food security. In contrast, the use of a biological nitrification inhibitor with components is an emerging and effective method. In the natural ecosystem, among the nitrification inhibitors secreted by plants, phenolic compounds, isothiochlorate vinegar, and alkaloids have attracted much attention [9,10,11,12]. At present, researchers use a “biological nitrification inhibitor” to describe the natural organic compounds secreted by plant roots that have specific inhibitory effects on nitrification and their inhibitory capacity [13,14,15].
A biological nitrification inhibitor is environmentally and crop friendly and has the potential to reduce greenhouse gas levels and increase nitrogen efficiency and crop productivity. In a 3-year field experiment conducted by Subbarao et al. [16] at the Colombian Tropical International Agricultural Center, biological nitrification inhibitors in experimental plots suppressed 90% of N2O emissions compared to soybean or control experimental plots. Zhang et al. [17] found an 18.1% reduction in N2O emissions and a 10.3 t·hm−2·a−1 increase in yield in vegetable plots after a crop rotation with sorghum, with biological nitrification inhibitors being applied in a 2-year field trial of vegetables. It is evident that biological nitrification inhibitors play a critical role in increasing yields and reducing N2O emissions.
Biological nitrification inhibitors have yielded effective results in paddy fields; however, their mechanism of action in paddy soils is an underexplored domain. As an essential factor in crop yield, the nitrogen cycle includes a denitrification process, nitrification process, denitrification process by nitrifying microorganisms, and nitrate isomerization reduction to ammonium, all of which can produce N2O. One of the most prominent forms of N2O release occurs in agricultural soils in the denitrification process. It was found that functional genes related to the nitrogen cycle (nosZ, nirS, nirK) are closely related to N2O release during denitrification [18]. Among them, nirS and nirK genes encode nitrite reductases that easily compete with N2O reductase for electrons, and thus inhibit N2O reductase, which in turn causes N2O accumulation. In contrast, the nosZ gene encodes an oxidoreductase that can reduce N2O to N2, which in turn reduces N2O emissions. Meanwhile, Aamer et al. [19] proved that the (nirS + nirK)/nosZ ratio associated with N2O emissions was positively correlated with N2O emissions.
Microorganisms are the main drivers of soil elemental cycling transformations, controlling and participating in key processes related to the nitrogen cycle [20,21]. Therefore, researching microorganisms carrying nosZ, nirS, and nirK, which are functional genes of the nitrogen cycle, is pivotal for decreasing N2O emissions. In this study, we investigated the effects of biological nitrification inhibitors on rice yield, quality, and N2O emissions in rice fields, using rice as the research object, and studied the core microorganisms associated with N2O emission inhibition by labeling functional genes related to the nitrogen cycle, namely, nosZ, nirS, and nirK. This study provides a new pathway to achieve a reduction in greenhouse gas emissions in the future.

2. Materials and Methods

2.1. Experimental Materials

The experiment was conducted from May 2021 to November 2021 at the Straw Industry Research Institute of Anhui Institute of Science and Technology (117°33′39″ E, 32°52′49″ W), where the average annual temperature was 15 °C, the average annual precipitation reached 1200 mm, and the frost-free period was 230 d. Rice was planted after a wheat harvest in June in yellow-brown soil with a tillage layer organic matter content of 20.8 g·kg−1, an alkaline nitrogen content of 110.9 mg·kg−1, fast-acting potassium content of 115.2 mg·kg−1, and effective phosphorus content of 25.8 mg·kg−1. The rice variety used in the experiment was Tsuen 9 U063. The biological nitrification inhibitor used in this experiment was methyl 3-(4-hydroxyphenyl) propionate (MHPP), which is one of the first Biological Nitrification Inhibitors (BNIs) found in the root secretion of Sorghum bicolor, a highly BNI-active variety of sorghum [22].

2.2. Experimental Treatments and Methods

The two experimental treatments in the rice field were CK and SW: 750 kg/hm2 compound fertilizer + 375 kg/hm2 urea for CK treatment and 600 kg/hm2 compound fertilizer + 300 kg/hm2 urea + 6 times (4 L/time) MHPP for SW treatment. Three replications were set for each treatment, and the plot size was 7 m × 2 m. The plots were all proportioned according to the amount of releasable N 148.5 kg·hm2 of chemical fertilizer and compost, and 29.7% of total N released from compost was calculated. Randomized groups were arranged. The nitrogen, phosphorus, and potassium contents of compost were 1.03%, 0.87%, and 1.35%, respectively, and 47.8% for organic matter with pH 6.67. The seedlings were raised in seedling trays on May 16, and 75 g of rice seeds was used in each plot. The land was prepared on June 18, and all of the compost and 60% urea were applied on June 25. The remaining 40% of urea was applied on 15 August 2021, and the rice was harvested on 30 October 2021.

2.3. Measurement and Methods

2.3.1. Gas Sample Collection and Determination

The gas emitted from the soil was collected once a week using a static box dark box sampling method according to the seasonal temperature. This gas was collected using a sampling syringe every 5 min during the sampling process and for a total of 3 times per treatment. While collecting gas, the temperature inside the static box dark box and the soil surface temperature were also recorded. After all the gas was collected, the syringe was brought to the laboratory, and the emission fluxes of CO2, NH4, and N2O were determined using a gas chromatograph Agilent 798A (Agilent, USA). Then, the unobserved daily emission fluxes were calculated using the interpolation method [23], after which the daily measured values were added to the calculated values to obtain the N2O emissions. The greenhouse gas emission fluxes were calculated as follows.
F is the greenhouse gas emission flux (mg/(m2‧h)); dc/dt is the slope of the regression curve of the gas volume fraction with time at the time of sampling; M is the molar mass of the gas (g/mol); V0 is the molar volume of the gas at the standard gas (22.41 L/mol); P and P0 are the air pressure at the sampling point (Pa) and the air pressure at the standard state (101,325 Pa); T and T0 are the absolute temperature at the sampling point (K) and the absolute temperature at the standard state (273.15 K); H is the height of the sampling box (m). The greenhouse gas intensity (GHGI, IGHG) is calculated using the following formula [24]: IGHG = PGW × (FN2O)/Y.

2.3.2. Biological Nitrification Inhibitor (BNI) Hydroponics and Application Method

The sorghum seeds were soaked in tap water for 8–10 h. The seeds were spread on a wet towel and placed in a long porcelain tray at 28 °C for germination for 8 h until they were white. Then, the seeds were evenly spread on vermiculite, after which a layer of vermiculite was spread on the seed surface to cover it. The seeds were watered daily with distilled water to keep the vermiculite moist until germination, and the long leaves reached about 2–3 cm in length. Sorghum was then cultivated with vermiculite for a total of 21 days for extraction.
Every 6 sorghum seedlings were divided into groups, wrapped with a sponge to expose the roots, and placed in a foam in a reservoir of distilled water (0.06607 g ammonium sulfate–L). The procedure was repeated three times with one group for one liter of water and maintained at approximately 28 °C for 24 h before application. The cultured biological nitrification inhibition (BNI) was poured into a spray bottle and sprayed evenly in the test plots.

2.3.3. DNA Extraction and Quantitative PCR

In total, 36 samples were collected, whereby there were three replicate samples per treatment, with two treatments and six time points. Soil samples were also collected during each gas collection and stored at −70 °C. Cold–dried soil samples were weighed 0.25 g and DNA was extracted from the soil samples using a Mobio kit (PowerSoil DNA Isolation Kit, USA). The extracted DNA purity index (A260/A280 ratio 1.8 to 2.0) was measured using a NanoDrop 2000 spectrophotometer. The purity index of the extracted DNA was measured using the corresponding primers nosZ2F/nosZ2R (CGCRACGGCAAS AAGGTSMSSGT/CAKR−TGCAKSGCRTGGCAGAA), cd3aF/R3cd (GTSAACGTSAAGGARACSGG/GASTTCGGRTGS−GTCTTGA), and FlaCu/R3Cu (ATCATGGTSCTG−CCGCG/TTGGTGTTRGACTAGCTCCG) for qPCR amplification of the nosZ, nirS, and nirK functional genes. The SYBR Premix ExTaq TM kit (TaKaRa, Dalian, China) was used according to the SYBR qPCR Mix (10 μL), Primer–F (0.5 μL), Primer–R (0.5 μL), 50 × Rox (0.4 μL), SBA (0.4 μL), sterile water (6.2 μL), and DNA samples (2 μL) of the system. Additionally, fluorescent quantitative PCR amplification reactions were performed using the ABI 7500 Sequence Detection System (Applied Biosystems, California, USA) according to corresponding cycling systems in nosZ, nirS, and nirK [25]. Sample reactions were set up in three parallels. Analytical calculations were performed using the 7500 SDS System with software, and the abundance values of denitrification for functional genes nosZ, nirS, and nirK were obtained from the standard curve. The amplification efficiency of the standard samples ranged from 94.2 to 98.4%, and the R2 of the standard curve was greater than 0.98.

2.4. High–Throughput Sequencing and Processing

The nosZ, nirS, and nirK functional genes were amplified from DNA extracts utilizing polymerase chain reaction (PCR), following the amplification system in Henry et al. [24]. The PCR amplification products were purified, employing the Promega Agarose Gel DNA (Promega, Madison, WI, USA) purification kit; then, the concentrations were determined using NanoDrop 2000. Finally, high–throughput sequencing was performed on the Illumina MiSeq 2500 platform (Guangdong Megagen Technology Co., Ltd., Guangzhou, China). The sequenced norZ, nirS, and nirK sequences were processed via quality control; the high–throughput sequences were processed using QIIME, Uchime, Mothur, and Bioedit software, and the processed sequences were uploaded to the National Center of Biotechnology Information (NCBI) database for protein sequence comparison and annotation.

2.5. Data Processing and Statistical Analysis

All the statistical tests were considered statistically significant at p < 0.05. Bacterial abundance and diversity were assessed by adopting Chao 1 and Shannon indices, respectively. Principal component analysis of β–diversity patterns and processes for different compost treatments employed the ggplot2 package in R. PERMANOVA, and ANOSIM tests were performed with the vegan package in R to determine the (un)similarity of bacterial communities in the soil. Bray–Curtis variance matrices were calculated at the operational taxonomic unit (OTU) level for paddy vertical soil samples, employing R language software as the principal axis. Spearman correlation analysis between the relative abundance of core bacterial genera and N2O emissions was conducted with the vegan package in R.

3. Results

3.1. Dynamics of Soil N2O Emission Fluxes, Emission Reduction, and Rice Yield Increase in Paddy Fields under Different Treatments

The N2O emission intensities of CK and SW were 0.1028 and 0.598 mg·kg−1, respectively, and the reduction effect of the biological nitrification inhibitor treatment was 41.83%. This indicates that the application of biological inhibitors can effectively reduce N2O emissions from rice fields. As the effect of the rice yield increased, the yield of rice with a biological nitrification inhibitor application was 10,194 kg·hm−2 compared with 8830 kg·hm−2 in the control group CK, and the yield of rice in the SW treatment increased by 15.45% compared with the CK treatment (Table 1).

Effect of Different Treatments on the Quality of Rice

As shown in Table 2, chalkiness and chalky grain rate change depending on whether biological nitrification inhibitors are applied, and the appearance of the rice changes accordingly. Compared with CK in the control group, the chalkiness and chalky grain rate in the SW treatment significantly decreased by 26.54% and 27.98%, respectively, whereas the brown rice chalky grain rate only decreased by 4.82%.
The improvement of rice quality was related to the application of the biological nitrification inhibitor, and the protein content and whole grain percentage increased by 2.85% and 5.99% in the SW treatment, while the straight chain starch decreased by 2.76%. Overall, the quality of rice was improved rather than decreased after the application of biological nitrification inhibitors.

3.2. Effects of Different Treatments on Bacterial Abundance and Diversity in Paddy Soils

An estimation of the bacterial community using Chao1 and the Shannon index can determine the richness and diversity of species. The Chao1 index of all treatments ranged from 352,81 to 1174,29, and the nirS, nirK, and nosZ in CK showed an increasing trend from July to September, while the SW treatment with a nitrification inhibitor application behaved differently (Figure 1). In nirK, although the SW treatment presented an increasing trend, bacterial abundance was consistently lower than that of the CK treatment. The nirS–type denitrifying bacteria indicated an increasing trend followed by a decreasing trend in SW treatment. Although the nosZ–type denitrifying bacteria in the SW treatment followed the same trend as nirK, monthly bacterial abundance was significantly higher than that in the CK treatment. The Shannon index values changed, ranging from 2.81 to 7.63. Moreover, in the CK treatment, nirK and nirS, increased and then decreased between July and September, while the SW treatment illustrated a slow declining trend. Different from nirK and nirS, the trends of the two treatments sharply increased in nosZ, and the increasing trend of SW was significantly higher than that of CK.

3.3. Community Composition and Microbial Interactions of NosZ, NirS, and NirK Denitrifying Bacteria

PCoA is commonly utilized to distinguish similarities in bacterial community composition between different samples in soil treatments and times. As shown in Figure 2, the total shows that the degrees of nirK−, nirS−, and nosZ−type denitrifying bacteria on both axes were 48.88%, 39.33%, and 47.45%, respectively, with relatively low explained values. This indicates that the genetic affinity between the samples is not high, whereas the nosZ−, nirS−, and nirK−type denitrifying bacterial communities still have significant differences. In Figure 2, the denitrifying bacteria of the nirK type achieved a low dispersion in CK and SW treatments and indicated a similar community structure in both treatments. The dispersion of nirS denitrifier was higher than that of the nirK denitrifier under both treatments. Conversely, the degree of denitrifying bacteria isolation and community differences were greater in CK and SW treatments for nosZ. Therefore, the application of biological nitrification inhibitors affected the structure of nirK−, nirS−, and nosZ−type denitrifying bacterial flora in paddy soil and had a crucial effect on nosZ−type denitrifying bacteria.

3.4. Changes in Average Relative Abundances of Bacteria in Soil under Different Treatments

Among the CK treatments at the genus level, the relative abundances of part of the top 20 taxonomic bacterial genera demonstrated significant changes on July 4. Simultaneously, there was a significant increase in the abundance of the genera Ferrovibrio, Nitrosospira, Rhodanobacter, Bradyrhizobium, Rhodopseudomonas, and Paracoccus, which accounted for 13.56%, 9.24%, 16.13%, 9.57%, 10.55%, and 10.52%, respectively. Nitrosospira, Bradyrhizobium, Rhodopseudomonas, and Tardiphaga continued to increase on 25 July compared with the increases on 4 July of 25.93%, 25.37%, 36.84%, and 14.29%, respectively. The relative abundances of each bacterial genus gradually decreased and became balanced. Similarly, the abundances of Ferrovirio, Nitrosospira, Rhodanobacter, Bradyrhizobium, Rhodopseudomonas, and Paracoccus exhibited a downward trend on July 4, with the percentage of abundance being 2.37%, 1.56%, 2.84%, 1.58%, and 0.52%, respectively, under SW treatment. Meanwhile, the genera Burkholderia, Mesorhizobium, and Azospirillum exhibited a large growth trend, and their relative abundances to proportion were 17.62%, 15.74%, and 19.85%, respectively. The relative abundance of each bacterial genus gradually decreased after 25 July (Figure 3).

3.5. Correlation Analysis of Core Bacteria and N2O Emissions

Spearman’s correlation analysis was employed to identify possible bacterial groups associated with composting efficiency (Figure 4). Only bacterial genera significantly associated with compost maturity were analyzed. The bacterial genera included Nitrosospira (r = −0.56; p = 0.027), Rhodanobacter (r = −0.48; p = 0.031), Bradyrhizobium (r = −0.79; p < 0.001), Paracoccus (r = −0.52; p = 0.028), Rhodopseudomonas (r = −0.54; p = 0.024), Tardiphaga (r = −0.68; p < 0.001), Azospirillum (r = 0.57; p = 0.009), Burkholderia (r = 0.49; p = 0.031), and Mesorhizobium (r = 0.63; p < 0.001).
The bacterial genera Nitrosospira, Rhodanobacter, Bradyrhizobium, Paracoccus, Rhodopseudomonas, and Tardiphaga demonstrated significantly positive correlations with N2O emissions in the Spearman analysis. In contrast, the Azospirillum, Burkholderia, and Mesorhizobium genera had negative correlations.

4. Discussion

4.1. Effect of Biological Nitrification Inhibitors on N2O Emission Flux and Rice Yield Quality in Paddy Soils

Nitrification inhibitors can be utilized to slow the oxidation of NH4+−N to NO3−−N in soil, the volatilization of NH3, and the loss of gaseous N from N2 and N2O [26]. The results of this study indicate that the application of biological nitrification inhibitors not only significantly suppressed N2O emissions from paddy soils, but also increased rice yields and improved rice quality.
These experimental results are also supported by a related study, in which Zhang et al. [27] found a measure for reducing carbon and increasing yield in rice. Yao et al. reveal that, due to the addition of urease, nitrification inhibitors can reduce greenhouse gas emissions in rice fields, and that this is a feasible and effective method of inhibiting nitrification using MHPP. Root zone fertilization + MHPP significantly reduced the peak N2O flux, thus reducing NO3− production; moreover, its total N2O emissions were 79% lower than the control while maximizing rice yield with reduced N2O emissions. Thus, the biological nitrification inhibitor was able to reduce emissions and increase yields and improve quality.

4.2. Changes in Community Composition of nosZ−, nirS−, and nirK−type Denitrifying Bacteria

Through α diversity, we investigated the richness and diversity of denitrifying bacteria with nirS, nirK, and nosZ functional genes. The treatments with biological nitrification inhibitors illustrated a consistent increase in both bacterial abundance and the diversity of bacteria carrying the nosZ functional gene from July to September. Although bacteria with nirK and nirS functional genes also indicated an increasing trend in bacterial abundance, they demonstrated the opposite trend in diversity. Correspondingly, in the β−diversity analysis, the largest differences in the nosZ−type bacterial community were observed between the control and the biological nitrification inhibitor treatment. Given the significant difference in greenhouse gas nitrous oxide between the two treatments, it is speculated that denitrifying bacteria with functional genes of nosZ type are closely associated with N2O emissions.
Conversely, the only biological pathway for soil N2O digestion is the reduction of N2O to N2, catalyzed by N2O reductase and encoded by the nosZ gene [28]. Therefore, the nosZ gene has been intensely studied as a molecular marker to reveal the regulatory mechanism of N2O reduction by denitrifying microorganisms [29]. It has been observed that the direct inoculation of nosZ−containing strains into soil can substantially increase the potential of soil N2O reduction [30]. These nosZ−containing microorganisms contribute to a reduction in soil N2O emissions. Therefore, this indicates that the abundance and diversity of nosZ−type denitrifying bacterial communities increased under biological nitrification inhibitor treatment, and that these bacteria significantly differ from nirS− and nirK−type bacterial communities. This further proves that biological nitrification inhibitors can indirectly inhibit N2O emissions by affecting the corresponding functional genes.

4.3. Effect of Biological Nitrification Inhibitors on Core Bacteria at the Level of Bacterial Genera in Paddy Soil

As observed by the abundance of each functional gene, the abundance of bacteria in the CK fraction significantly increased on July 4 with a sudden increase in nitrous oxide. The levels of Nitrosospira, Rhodanobacter, Bradyrhizobium, Tardiphaga, Rhodopseudomonas, and Paracoccus were higher than the corresponding genera under the SW treatment. Interestingly, these bacteria were positively correlated with N2O emission fluxes, and it seems that an increase in these core bacteria contributes to an increase in N2O emissions. We also observed that these bacteria belonged to the nirS and nirK functional genotype bacteria. The balance between N2O−producing and N2O−reducing microorganisms (nir/nos ratio) can partially determine the net N2O emissions from soils [31], and that the nir/nos ratio was positively correlated with N2O emissions in lakes and grasslands [32]. All these studies are consistent with our findings.
In previous studies, some scholars identified these core bacteria. Liu et al. [33] found a significant positive correlation between Nitrosospira in nirK−type denitrifying bacteria and N2O production potential in paddy soils. Nitrosospira were found to contribute to N2O release among nirK−type denitrifying bacteria in paddy soils. Obando et al. [34] found that Bradyrhizobium among nirK−type denitrifying bacteria was correlated with N2O production potential, and that Bradyrhizobium could release N2O. Rhodanobacter is a genus of bacteria capable of denitrification at a low pH [35], and the Rhodanobacter of nirS−type denitrifying bacteria in increasing the vertical depth of soil in rice fields had a significant positive correlation with N2O production potential. In a series of indoor soil slurry pot experiments, Zhang et al. [36] investigated the effect of trivalent iron (Fe(III)) reduction on denitrifying bacteria abundance, community structure, and N2O production. Paracoccus and Rhodopseudomonas were considered the core genus of N2O−producing bacteria.
Some of the bacterial genera, Azospirillum, Burkholderia, and Mesorhizobium, exhibited a significant growth trend on July 25 under biological nitrification inhibitor treatment relative to the control CK. Simultaneously, these bacteria negatively correlated with the N2O emission flux, which belonged to the nosZ functional gene type. Denitrifying bacteria with the nosZ gene (i.e., N2O−reducing bacteria) reduces N2O to N2 [37,38]. NosZ−reducing bacteria have the nosZ gene encoding N2OR and produce N2OR to reduce N2O to N2. This explains the phenomenon that all bacterial genera negatively associated with N2O emission fluxes are nosZ−functional genes. Gao et al. [39,40] further screened N2O−reducing bacteria isolated from rice soils in different regions of Japan for the plant inter−rooted probiotic nitrogen−fixing spirochete strain Azospirillum with high probiotic properties. Ishii et al. [41] isolated N2O−reducing bacteria containing nirS and nosZ genes from Japanese rice soils. Burkholderia sp. presented with very low denitrification activity for NO3− and NO2−reduction but strong N2O reduction activity. Shi et al. [42] applied biochar + organic fertilizer to investigate its effect on nitrous oxide (N2O) flux and composition of nitrifying and denitrifying bacteria in saline soils. It was found that the genus Mesorhizobium may be the main contributor to the reduction in N2O emissions.
Consequently, it was revealed that the proportion of nirS− and nirK−type bacteria was reduced, and that the abundance of bacteria carrying nosZ−type genes was increased in the presence of biological nitrification inhibitors. These nosZ−type core bacteria can enhance the reduction in N2O, and thus decrease N2O emissions in soil.

5. Conclusions

(1)
The application of biological nitrification inhibitors not only promoted the yield and quality of rice but also played a critical role in reducing N2O emissions;
(2)
Biological nitrification inhibitors affect nosZ, nirS, and nirK bacterial communities related to the nitrogen cycle in paddy soil. Simultaneously, they can stimulate the reproduction of nosZ−type bacteria and inhibit the growth of nirS− and nirK−type bacteria at the same time;
(3)
Core bacteria, Nitrosospira, Rhodanobacter, Bradyrhizobium, Tardiphaga, Rhodopseudomonas, and Paracoccus, positively correlated with N2O emission in paddy soil. Core bacteria, Azospirillum, Burkholderia, and Mesorhizobium, negatively correlated with nitrous oxide emissions.

Author Contributions

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

Funding

This research was funded by the Natural Science Foundation of Anhui province, China, grant number 2008085QD181; and the Major Provincial Science and Technology Projects, grant numbers 201903A06020001, 201903A06020023, 202003a06020003; and Anhui Xiaogang National Agricultural Science and Technology Park Science and Technology Plan Project, China, grant number yq202201; and Project “Research and Development of Rice–wheat (oil) Mechanization and Intelligent Production Management Technology and Equipment” grant numbers 2022YFD2301402.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Bacterial richness (Chao 1) and diversity (Shannon). The asterisk indicates that there is a statistically significant difference between treatments at the 0.05 probability level determined using a multivariate analysis of variance (MANOVA) test (* p < 0.05, ** p < 0.01). Notes: (A): nirK−type denitrifying bacteria, (B): nirS−type denitrifying bacteria, (C): nirZ−type denitrifying bacteria.
Figure 1. Bacterial richness (Chao 1) and diversity (Shannon). The asterisk indicates that there is a statistically significant difference between treatments at the 0.05 probability level determined using a multivariate analysis of variance (MANOVA) test (* p < 0.05, ** p < 0.01). Notes: (A): nirK−type denitrifying bacteria, (B): nirS−type denitrifying bacteria, (C): nirZ−type denitrifying bacteria.
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Figure 2. PCoA of denitrifying bacteria of nosZ, nirS, and nirK types in paddy soil samples.
Figure 2. PCoA of denitrifying bacteria of nosZ, nirS, and nirK types in paddy soil samples.
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Figure 3. Average relative abundance of genus level bacterial groups in all soil samples (ranking first to nineteenth in relative abundance). “−UN” means that other bacteria cannot be identified.
Figure 3. Average relative abundance of genus level bacterial groups in all soil samples (ranking first to nineteenth in relative abundance). “−UN” means that other bacteria cannot be identified.
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Figure 4. Spearman correlation coefficient (r) between relative abundance of bacteria at genus level and N2O emissions. * p < 0.05, ** p < 0.01, *** p < 0.1.
Figure 4. Spearman correlation coefficient (r) between relative abundance of bacteria at genus level and N2O emissions. * p < 0.05, ** p < 0.01, *** p < 0.1.
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Table
Table 1. Dynamic changes in N2O emission fluxes from rice fields and increased effects on emission reduction and rice yield during different treatments.
Table 1. Dynamic changes in N2O emission fluxes from rice fields and increased effects on emission reduction and rice yield during different treatments.
TreatmentDateN2O Flux//(μg·m−2·h−1)Yield/(kg
·hm−2)		Increase Rate (%)Yield–Scaled N2O Emission/(mg·kg−1)Reduction Percent (%)
06–2007–0407–2508–2209–0609–20
CK41.75 ± 27.19151.43 ± 53.6353.76 ± 21.4347.38 ± 11.3532.48 ± 10.3215.77 ± 4.738830 ± 125b-0.1028 ± 0.07a-
SW26.38 ± 15.6384.92 ± 23.2837.28 ± 12.9234.72 ± 9.4628.52 ± 12.6212.49 ± 7.3010,194 ± 276a15.450.0598 ± 0.06b41.83
Table
Table 2. Effects of different treatments on rice quality.
Table 2. Effects of different treatments on rice quality.
TreatmentProtein Content/%Head Milled Rice Rate/%Brown Rice Rate/%Chalkiness Degree/%Chalkiness Rate/%Amylose Content/%
CK7.38 ± 0.45 a72.58 ± 0.14 b80.58 ± 0.45 a22.53 ± 0.26 a27.48 ± 0.24 a16.29 ± 0.57 a
SW7.59 ± 0.27 a76.93 ± 0.38 a76.70 ± 0.82 b16.55 ± 0.32 b19.79 ± 0.29 b15.84 ± 0.39 a
Notes: Different letters indicate significant differences between treatments at p < 0.05.
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Huang, X.; Zou, Y.; Qiao, C.; Liu, Q.; Liu, J.; Kang, R.; Ren, L.; Wu, W. Effects of Biological Nitrification Inhibitor on Nitrous Oxide and nosZ, nirK, nirS Denitrifying Bacteria in Paddy Soils. Sustainability 2023, 15, 5348. https://doi.org/10.3390/su15065348

AMA Style

Huang X, Zou Y, Qiao C, Liu Q, Liu J, Kang R, Ren L, Wu W. Effects of Biological Nitrification Inhibitor on Nitrous Oxide and nosZ, nirK, nirS Denitrifying Bacteria in Paddy Soils. Sustainability. 2023; 15(6):5348. https://doi.org/10.3390/su15065348

Chicago/Turabian Style

Huang, Xingchen, Yuning Zou, Cece Qiao, Qiumeng Liu, Jingwen Liu, Rui Kang, Lantian Ren, and Wenge Wu. 2023. "Effects of Biological Nitrification Inhibitor on Nitrous Oxide and nosZ, nirK, nirS Denitrifying Bacteria in Paddy Soils" Sustainability 15, no. 6: 5348. https://doi.org/10.3390/su15065348

APA Style

Huang, X., Zou, Y., Qiao, C., Liu, Q., Liu, J., Kang, R., Ren, L., & Wu, W. (2023). Effects of Biological Nitrification Inhibitor on Nitrous Oxide and nosZ, nirK, nirS Denitrifying Bacteria in Paddy Soils. Sustainability, 15(6), 5348. https://doi.org/10.3390/su15065348

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