Changes of Soil Nitrogen Fractions and nirS -Type Denitriﬁer Microbial Community in Response to N Fertilizer in the Semi-Arid Area of Northeast China

: The denitriﬁcation process is one of the important processes in the soil nitrogen (N) cycle, and is closely related to the loss of soil N fertilizer. Five treatments were included in this study: N0 (control, no N application); N90 (N application rate 90 kg ha − 1 ); N150 (N application rate 150 kg ha − 1 ); N210 (N application rate 210 kg ha − 1 ); and N270 (N application rate 270 kg ha − 1 ), to study the effect of different N application rates on the soil nirS -type denitrifying bacterial community structure, the inﬂuence of key enzyme activities during the denitriﬁcation process, and the main environmental factors affecting the variation of the denitrifying bacterial community in maize ﬁeld soil under the mulched fertigation system in the semi-arid region of Northeast China. The results showed that increasing N fertilizer application signiﬁcantly increased the contents of soil inorganic N and acid-hydrolyzable organic N, but signiﬁcantly decreased pH. N fertilizer signiﬁcantly increased nitrate reductase (NAR) activity and nitrite reductase (NIR) activity. Excessive application of N fertilizer signiﬁcantly increased the nirS gene copy numbers, and, at the same time, signiﬁcantly decreased the diversity of nirS -type soil denitrifying bacteria. Proteobacteria was the dominant denitrifying phylum in all treatments, and N application promoted the growth of Bradyrhizobium belonging to this phylum. The application of N fertilizer signiﬁcantly changed the community structure of nirS denitrifying bacteria, and the NO 3 − -N content was the most important factor for this observation. Soil organic matter (SOM) and non-hydrolyzable N (NHN) indirectly affected the denitrifying bacterial community structure through NAR activity and NIR activity, while soil total N (TN) and nitrate N (NO 3 − -N) indirectly affected yield through denitrifying bacterial community structure. Although increasing N fertilizer was beneﬁcial in increasing soil nutrients, the community structure of nirS -type denitrifying bacteria changed signiﬁcantly. This is attributed to the increase in soil NO 3 − -N accumulation caused by a large amount of N application. The results of this research provide an important scientiﬁc basis for further research on the response mechanism of farmland soil denitrifying microorganisms to different N fertilizer managements under the mulched fertigation system in the semi-arid region of Northeast China.


Introduction
In agricultural ecosystems, nitrogen (N) is the limiting nutrient for primary productivity, and the application of N fertilizers in agricultural practice can increase soil net primary production capacity and output, and also increase crop yield and quality [1,2]. In recent years, the amount of N fertilizer applied has exceeded the most economical amount and has also exceeded the amount required for the highest yield. This has resulted in a decline been used to analyze the community structure and abundance of denitrifying bacteria in soil [24]. Dandie et al. [25] reported that quantitative analysis of nirS denitrification functional genes and high-throughput sequencing analysis can comprehensively measure nirS gene abundance, diversity of nirS-type denitrifying bacteria, and community structure.
Denitrification in dryland soil is mainly affected by factors such as soil moisture content, aeration status, soil temperature, crop root types, tillage practices, and N application rate; however, the effect of N application rate is the most significant factor [26,27]. Existing research results have shown that the intensity of denitrification in the soil increases with the increase in N fertilizer application [28], and long-term N application will not only increase the intensity of denitrification in soil but also significantly change the denitrification bacterial community structure [29]. Most of the studies on denitrification have focused on ecosystems such as rice soils, wetlands, and winter-wheat farmlands [30][31][32]. There are few reports on the denitrification of maize field soil in semi-arid regions under the mulched fertigation system. Based on this, a real-time quantitative PCR detecting system (qPCR) and Illumina MiSeq sequencing technology were applied in this study to analyze soil acid-hydrolyzable organic N components and denitrification enzyme activities under different N fertilizer application conditions. We hypothesized that soil nirS-type denitrifying bacterial community structure would be distinct under different N fertilizer rates and that N fertilizer could reduce nirS-type denitrifying bacteria community diversity. This study will help us to understand how different N fertilizer application rates affect the abundance, community composition, and diversity of nirS-type denitrifying bacteria in the soil. Moreover, the findings of this study can clarify the relative contribution of soil N grouping to the changes of soil nirS-type denitrifying bacterial community.

Experimental Design
The experimental site of this study was located in Minle Village (45 • 26 N, 125 • 88 E), Jilin Province, Northeast China. The area has a mid-temperate continental monsoon climate and the annual rainfall is concentrated in May-September. The annual average sunshine hours are about 2867 h, the frost-free period is 135-140 d, and the annual average temperature is 5.6 • C. The long-term field experiment was set up in 2017 under a filmmulching drip irrigation system and different fertilizer application rates. Five treatments were included in this study: N0 (control, no N application); N90 (N application rate 90 kg ha −1 ); N150 (N application rate 150 kg ha −1 ); N210 (N application rate 210 kg ha −1 ); and N270 (N application rate 270 kg ha −1 ). All treatments received 90 kg P 2 O 5 ha −1 and 90 kg K 2 O ha −1 as base fertilizer whereby 30% of N and 50% of phosphorus and potassium of the base fertilizer were applied, and the remaining percentages were applied by drip irrigation. Maize (Xiangyu 998 variety) was sown in May and harvested in early October every year and the planting density was 70,000 plants/hm 2 . After setting up the experiment, the field management procedures such as fertilization method, planting density, and fertilization amount were consistent with those of local farmers.
In September 2021, the "S" point sampling method was used to collect soil samples from the 0-20 cm depth of the soil. The soil samples from 5 points from each replicate were mixed to form a single composite soil sample. Each treatment was composed of 3 replicated composite samples. A total of 15 soil samples was collected. The collected samples were put in an ice box and taken to the laboratory, where dead branches, roots, stones, and other debris were removed from the soil samples. The samples were passed through a 2 mm sieve for the determination of soil properties. Part of the fresh soil was used to determine enzyme activity, and part was stored in the refrigerator at −80 • C for the extraction of soil DNA. The remaining part was air-dried at room temperature for soil N component analysis.

Determination of Soil Chemical Properties and Soil Acidolysis N Components
The soil organic matter (SOM) content was measured by the potassium dichromate oxidation-external heating method, the soil pH was measured by a pH meter (soil-to-water ratio 1:2.5), the soil total N (TN) content was measured by the Kjeldahl method, and the soil alkaline N content (ALN) was determined by the alkaline-solution diffusion method. The contents of ammonium N (NH 4 + -N) and nitrate N (NO 3 − -N) were determined by using a continuous flow analyzer (SKALAR San++, Skalar, Holland) [33].
The content of each acid-hydrolyzable N component in the soil was determined by the Bremner method where the content of acid-hydrolyzable N (AHN) was determined by Kjeldahl distillation, and the content of acid-hydrolyzable ammoniacal N (AN) was determined by the MgO-Kjeldahl distillation method. AN and acid-hydrolyzable amino sugar N (ASN) content was determined by the phosphate-borax buffer distillation method and the acid-hydrolyzable amino acid N (amino acid N, AAN) content was oxidized with ninhydrin and determined by phosphate-borax buffer distillation assay. The content of non-hydrolyzable N (NHN) was the soil TN minus the AHN, and the acid hydrolyzed unknown N (UN) was the content of AHN minus AN minus ASN minus AAN content [34].

Determination of Soil Enzyme Activity and Maize Yield
Soil nitrate reductase (NAR) was determined using KNO 3 as a substrate where soil samples were placed in a closed test tube, submerged in water at 25 • C for 24 h, and 2,4-dinitrophenol was used to inhibit the activity of nitrite reductase. After the cultivation, the released NO 2 − was leached with KCl solution, and the color was measured with a spectrophotometer at 520 nm. Soil nitrite reductase (NIR) was determined by using sodium nitrite as substrate, the soil samples were anaerobically cultured at 30 • C for 24 h, and the reduction of NO 2 − -N per unit time was measured by sulfonamide colorimetry, at 520 nm with a spectrophotometer. The yield was measured at the maturity stage where 20 m 2 was designated for each plot to harvest all maize. The yield was measured after natural air drying at the standard water content (14%).

Extraction of Total Soil DNA and Fluorescent Quantitative PCR of nirS Gene of Denitrifying Bacteria
The total DNA of soil microorganisms was extracted using the FastDNA ® SPIN Kit for Soil (MP Biomedicals, Irvine, CA, USA). In this method, 0.5000 g of fresh soil for each sample is accurately weighed, and the total DNA of the soil is extracted and dissolved in DES buffer according to the operating instructions of the kit. After the DNA extraction was completed, it was detected by 1% agarose gel electrophoresis, and the concentration was determined by NanoDrop 2000 (Thermo Scientific, Wilmington, DE, USA), and stored at −20 • C for future use.
The nirS gene of denitrifying bacteria was detected by fluorescent quantitative PCR, and the primers were cd3aF (GTS AAC GTS AAG GAR ACS GG) and R3cd (GAS TTC GGR TGS GTC TTGA A) [35]. The quantitative PCR reaction system was as follows: 2 × Master Mix: 10 µL, 10 uM PCR-specific primer F: 0.5 µL, 10 uM PCR-specific primer R: 0.5 µL, add water to a total volume of 18 µL. The quantitative PCR reaction program was: 95 • C for 30 s, 40 cycles: 95 • C for 5 s, 60 • C for 40 s, and 50 • C for 60 s.

Illumina MiSeq sequencing of the nirS Gene of Denitrifying Bacteria
All samples were subjected to high-throughput sequencing using the above-mentioned cd3aF and R3cd denitrifying bacteria primers, and 6 bp specific tag sequences (barcode) were added at the front and rear primer ends to distinguish different samples. PCR amplification reaction system: DNA sample: 30 ng, forward primer (5 µM): 1 µL, reverse primer (5 uM): 1 µL, BSA (2 ng/µL): 3 µL, 2xTaq Plus Master Mix: 12.5 µL, and ddH 2 O: 7.5 µL. The PCR reaction program was: 94 • C for 5 min, 35 cycles; 94 • C for 30 s; 57 • C for 30 s; 72 • C for 60 s; and 72 • C for 7 min. Each sample was repeated in triplicate, and the PCR amplification products were mixed, identified, and separated by 2% agarose gel electrophoresis (80 V, 20 min), using a gel recovery kit (Agarose Gel DNA Purification Kit, TaKaRa). The PCR products were gel-purified, and then detected and quantified with the QuantiFluor™-ST Fluorescence Quantitative System (Promega, Madison, WI, USA), mixed in equal amounts according to the sequencing volume requirements of the samples, and then sequenced with the Beijing Aoweisen Illumina MiSeq sequencer.

High-Throughput Data Analysis Method
Illumina MiSeq sequencing results were analyzed using QIIME software (Quantitative Insights Into Microbial Ecology, Version 1.9.0) [36]. Firstly, the label sequence (barcode) and primer sequence were removed. This was followed by removing the sequences that were smaller than 200 bp and having more than 6 uncertain bases with an average quality score lower than 25. The chimeric sequences were removed by the Uchime algorithm [37]. Finally, high-quality sequences were obtained for subsequent analysis using the UPARSE method [38] to perform OTU clustering according to the 97% similarity. The sequence with the largest number in each OTU was set as the representative sequence (representative set of sequences) to make an OTU table. The obtained representative OTU sequences were screened on the FunGene (http://fungene.cme.msu.edu/) (accessed on 13 November 2022) platform. All sequences that could not be translated into nirS amino acids were eliminated, and all the remaining sequences that could be translated were carried out in GenBank Sequence alignment (minimum similarity 0.70). All representative sequences were aligned to the reference sequence using the PyNAST tool (Python Nearest Alignment Space Termination) [39]. Phylogenetic trees were constructed using Fasttree 2.1.11 software [40]. All raw sequences generated in this study have been deposited in NCBI under the accession SRP408526.

Statistical Analysis
The significant differences in soil chemical properties, acid-hydrolyzable organic N grouping, soil enzyme activity, maize yield, nirS gene copy number, the relative abundance of denitrifying bacteria at each taxonomic level, and α-diversity index among different treatments were determined using one-way ANOVA. The correlation between nirS gene copy number and soil chemical properties, soil acid-hydrolyzable organic N components, and soil enzyme activity were determined using Pearson correlation analysis. The above analyzes were all carried out in SPSS software version 20. Based on the Bray-Curtis distance matrix, principal coordinates analysis (PCoA) was performed using the vegan package of R (Version 3.4.2). Canonical analysis (RDA) was used in Canoco (Version 5.0) to clarify the relationship between nirS gene community structure and soil chemical properties, and acid-hydrolyzable organic N components. A structural equation model (SEM) constructed by Amos (Version 23) was used to illustrate the direct and indirect effects of soil acidhydrolyzable organic N components and soil nitrifying microbial biological traits on maize yield. Data were analyzed using the chi-square test (p > 0.05), the free Chi-square ratio (CMIN/DF < 3), and fitness index (CFI > 0.9).

Soil Chemical Properties and Soil Acid-Hydrolyzable Organic N Components
The application of N fertilizer had a significant effect on soil chemical properties (Table 1). Compared with N0, N application significantly increased soil SOM, TN, NH 4 + -N, NO 3 − -N, and ALN content, but significantly decreased pH (p < 0.05). The results for soil acid-hydrolyzable organic N according to chemical composition are presented in Table 2. Compared with N0, N application significantly increased the content of AHN and NHN. Moreover, N270 significantly increased the content of AN; N150, N210, and N270 significantly increased the content of AAN; and N210 and N270 significantly increased  Table S1). The low N treatment had higher ASN content compared to AAN and the high N treatment had higher AAN content compared to ASN.

Soil Enzyme Activity and Maize Yield
Compared with N0, N application increased the NAR and NIR activities ( Figure 1). The NAR activities of N150, N210, and N270 were significantly increased by 23.4%, 23.3%, and 34.4%, respectively, and the NIR activities of N150, N210, and N270 were significantly increased by 29.0%, 50.2%, and 43.8% respectively (p < 0.05). NAR and NIR activities were significantly positively correlated with SOM, NH 4 + -N, NO 3 − -N, ALN, TN, AN, AAN, UN, AHN, and NHN, but significantly negatively correlated with pH (Supplementary Figure S2). Compared with N0, N fertilization significantly increased maize yield. Moreover, maize yield tended to increase with the increase in N fertilization rate (Supplementary Figure S1).

Abundance of Denitrifying Bacteria
The total copy number of nirS denitrifying bacteria under the different N fertilizer application rates was 4.91 × 10 5 -6.25 × 10 5 (Figure 2). Compared with N0, the copy number of denitrifying bacteria in low N treatments (N90 and N150) was slightly different while that of the high N treatments (N210 and N270) significantly increased by 13.6% and 27.3%, respectively (p < 0.05). The nirS gene copy number was significantly positively correlated with NAR, NIR, NH 4 + -N, NO 3 − -N, ALN, TN, AAN, UN, AHN, and NHN, but significantly negatively correlated with pH (Supplementary Figure S2). yield. Moreover, maize yield tended to increase with the increase in N fertilization rate (Supplementary Figure S1).

Abundance of Denitrifying Bacteria
The total copy number of nirS denitrifying bacteria under the different N fertilizer application rates was 4.91 × 10 5 -6.25 × 10 5 (Figure 2). Compared with N0, the copy number of denitrifying bacteria in low N treatments (N90 and N150) was slightly different while that of the high N treatments (N210 and N270) significantly increased by 13.6% and 27.3%, respectively (p < 0.05). The nirS gene copy number was significantly positively correlated with NAR, NIR, NH4 + -N, NO3 − -N, ALN, TN, AAN, UN, AHN, and NHN, but significantly negatively correlated with pH (Supplementary Figure S2).

Denitrifying Bacterial Community Composition
A total of 1,055,088 high-quality sequences were obtained by performing Illumina MiSeq sequencing, sequence quality screening, and translation into corresponding amino acids on the 15 samples. The sequences that could not be translated into nitrite reductase

Abundance of Denitrifying Bacteria
The total copy number of nirS denitrifying bacteria under the different N fertilizer application rates was 4.91 × 10 5 -6.25 × 10 5 (Figure 2). Compared with N0, the copy number of denitrifying bacteria in low N treatments (N90 and N150) was slightly different while that of the high N treatments (N210 and N270) significantly increased by 13.6% and 27.3%, respectively (p < 0.05). The nirS gene copy number was significantly positively correlated with NAR, NIR, NH4 + -N, NO3 − -N, ALN, TN, AAN, UN, AHN, and NHN, but significantly negatively correlated with pH (Supplementary Figure S2).

Denitrifying Bacterial Community Composition
A total of 1,055,088 high-quality sequences were obtained by performing Illumina MiSeq sequencing, sequence quality screening, and translation into corresponding amino acids on the 15 samples. The sequences that could not be translated into nitrite reductase

Denitrifying Bacterial Community Composition
A total of 1,055,088 high-quality sequences were obtained by performing Illumina MiSeq sequencing, sequence quality screening, and translation into corresponding amino acids on the 15 samples. The sequences that could not be translated into nitrite reductase amino acids were eliminated. The dominant phylum in all soil samples was Proteobacteria, with a relative abundance of 55.28-61.77% (Table 3). Three dominant bacterial classes were also detected, namely Gammaproteobacteria, Betaproteobacteria, and Alphaproteobacteria, with relative abundances of 12.43-24.75%, 13.79-22.00%, and 14.23-20.45%, respectively (Table 3). Compared with N0, N210 and N270 significantly increased the relative abundance of Gammaproteobacteria and Alphaproteobacteria, but significantly decreased the relative abundance of Betaproteobacteria. In addition, nine denitrifying bacterial orders and nine denitrifying bacterial families were detected. Compared with N0, N210 and N270 significantly increased the relative abundance of Rhodospirillales, Neisseriales, and Oceanospirillales, and the relative abundances of Rhodobacteraceae, Rhodospirillaceae, Bradyrhizobiaceae, and Halomonadaceae. The dominant families of N0 were Rhodobacteraceae, Rhodospirillaceae, and Pseudomonadaceae. After N application, the relative abundance of Bradyrhizobiaceae increased significantly to become the dominant family, and also caused a significant change in the relative abundance of denitrifying bacteria at the genus level. At the genus level, N fertilization significantly increased the relative abundances of Azospirillum, Cupriavidus, Bradyrhizobium, Pseudogulbenkiania, and Halomonas  Hydrogenophaga 2.60 ± 0.03a 2.46 ± 0.07b 1.69 ± 0.03c 1.65 ± 0.04c 0.74 ± 0.04d Azoarcus 0.24 ± 0.05b 0.19 ± 0.01b 0.16 ± 0.02b 0.13 ± 0.02b 1.81 ± 0.15a Halomonas 0.13 ± 0.02d 0.39 ± 0.04c 0.59 ± 0.10b 1.06 ± 0.06a 1.15 ± 0.21a Paracoccus 0.86 ± 0.03b 0.93 ± 0.20b 0.80 ± 0.01b 1.11 ± 0.22ab 1.36 ± 0.26a Values are means ± SD, different letters indicate significant differences among samples (p < 0.05, ANOVA). N0, N90, N150, N210, and N270 represent N fertilizer being used at a level of 0, 90, 150, 210, and 270 kg ha −1 , respectively. . The x-axis is the difference in the average abundance between the two groups, the y-axis is the multiple difference in the average abundance between the two groups. Gray p < 0.05, white p > 0.05, and black p is NA.

Denitrifying Bacterial Community Diversity
After analyzing the difference in the diversity index of denitrifying bacterial communities in all samples, the results showed that compared with N0, N application significantly reduced the Shannon index and Chao1 index, and the Shannon index showed a . The x-axis is the difference in the average abundance between the two groups, the y-axis is the multiple difference in the average abundance between the two groups. Gray p < 0.05, white p > 0.05, and black p is NA.

Denitrifying Bacterial Community Diversity
After analyzing the difference in the diversity index of denitrifying bacterial communities in all samples, the results showed that compared with N0, N application significantly reduced the Shannon index and Chao1 index, and the Shannon index showed a decrease with the increase in N application rate (Figure 4). The denitrifying bacterial community structure of all soil samples was analyzed by PCoA ( Figure 5A). Different N fertilizer application rates had an impact on the denitrifying bacterial community structure where N0, N90, and N150 clustered together and were separated from N210 and N270 on the PCA1 axis ( Figure 5A). High N application significantly changed the denitrifying bacterial community structure, while low N application had little effect on the denitrifying bacterial community structure.
decrease with the increase in N application rate (Figure 4). The denitrifying bacterial community structure of all soil samples was analyzed by PCoA ( Figure 5A). Different N fertilizer application rates had an impact on the denitrifying bacterial community structure where N0, N90, and N150 clustered together and were separated from N210 and N270 on the PCA1 axis ( Figure 5A). High N application significantly changed the denitrifying bacterial community structure, while low N application had little effect on the denitrifying bacterial community structure.

Correlation Analysis of Denitrifying Bacterial Community and Soil Nutrients
RDA showed that NO3 − -N was the main driving factor for the changes in denitrifying bacterial community structure, and the contribution rate was as high as 57.5% (p < 0.05) ( Figure 5B and Supplementary Table S3). The SEM model fully fitted the experimental data (χ2/df = 0.05, p = 1.00, CFI = 1.00), and the equation model could explain 60% of the changes in the community structure of soil denitrifying bacteria and 97% of the changes in yield (Figure 6). TN and NO3 − -N had a direct impact on the denitrifying bac- decrease with the increase in N application rate (Figure 4). The denitrifying bacterial community structure of all soil samples was analyzed by PCoA ( Figure 5A). Different N fertilizer application rates had an impact on the denitrifying bacterial community structure where N0, N90, and N150 clustered together and were separated from N210 and N270 on the PCA1 axis ( Figure 5A). High N application significantly changed the denitrifying bacterial community structure, while low N application had little effect on the denitrifying bacterial community structure.

Correlation Analysis of Denitrifying Bacterial Community and Soil Nutrients
RDA showed that NO3 − -N was the main driving factor for the changes in denitrifying bacterial community structure, and the contribution rate was as high as 57.5% (p < 0.05) ( Figure 5B and Supplementary Table S3). The SEM model fully fitted the experimental data (χ2/df = 0.05, p = 1.00, CFI = 1.00), and the equation model could explain 60% of the changes in the community structure of soil denitrifying bacteria and 97% of the changes in yield (Figure 6). TN and NO3 − -N had a direct impact on the denitrifying bac-

Correlation Analysis of Denitrifying Bacterial Community and Soil Nutrients
RDA showed that NO 3 − -N was the main driving factor for the changes in denitrifying bacterial community structure, and the contribution rate was as high as 57.5% (p < 0.05) ( Figure 5B and Supplementary Table S3). The SEM model fully fitted the experimental data (χ 2 /df = 0.05, p = 1.00, CFI = 1.00), and the equation model could explain 60% of the changes in the community structure of soil denitrifying bacteria and 97% of the changes in yield ( Figure 6). TN and NO 3 − -N had a direct impact on the denitrifying bacterial community structure, and NO 3 − -N had the greatest impact on the denitrifying bacterial community structure (λ = 1.93). SOM and NHN indirectly affected the denitrifying bacterial community structure through NAR and NIR activities. The denitrifying bacterial community structure had a direct impact on the yield. NHN, NH 4 + -N, TN, and SOM had a direct impact on the yield, while TN and NO 3 − -N had an indirect impact on the yield through the denitrifying bacterial community structure. terial community structure, and NO3 − -N had the greatest impact on the denitrifying bacterial community structure (λ = 1.93). SOM and NHN indirectly affected the denitrifying bacterial community structure through NAR and NIR activities. The denitrifying bacterial community structure had a direct impact on the yield. NHN, NH4 + -N, TN, and SOM had a direct impact on the yield, while TN and NO3 − -N had an indirect impact on the yield through the denitrifying bacterial community structure. Figure 6. The structural equation model (SEM) between the soil nutrient content, denitrifying enzyme activity, nirS-type denitrifier community, and maize yield. The arrow width is proportional to the intensity of the path coefficient. Solid arrows represent positive path coefficients, dotted arrows represent negative path coefficients, and gray arrows represent path coefficients of 0. *, **, and *** represent p < 0.05, p < 0.01, and p < 0.001, respectively. AHN, acid-hydrolyzable organic nitrogen; NO3 − -N, nitrate nitrogen; AN, ammonia nitrogen; TN, total nitrogen; SOM, soil organic matter.

Changes in Soil Chemical Properties and Acid-hydrolyzable Organic N Components under Different N Fertilizer Application Rates
In this study, the long-term application of chemical N fertilizers increased SOM, TN, NH4 + -N, and NO3 − -N, while significantly reducing soil pH and causing soil acidification. Fertilization is an important measure that leads to changes in soil chemical properties. Previous studies have shown that long-term application of chemical fertilizers, especially excessive N fertilizers, will lead to adverse results such as soil acidification, reduced available nutrient content, and poor structure [41]. The content of NO3 − -N was more sensitive to N fertilizer as it increased with the increase in N application rate. NO3 − -N was identified as an important factor for soil acidification since it had a significant negative correlation with pH.
The content of each component of soil organic N is closely related to the farmland environment, the type and amount of fertilizer applied, and other factors. Excessive N application in this study led to significant changes in soil N components, which in turn affected soil N supply capacity. High N treatments significantly increased the content of soil acid-hydrolyzable organic N components, and the content of AAN and UN were most sensitive to N fertilizer. Jiao et al. [42] indicated that the continuous application of N fertilizer increased the content of acid-hydrolyzable total N in the soil. Moreover, the contents of acid-hydrolyzable total N in each soil layer increased with the increase in N Figure 6. The structural equation model (SEM) between the soil nutrient content, denitrifying enzyme activity, nirS-type denitrifier community, and maize yield. The arrow width is proportional to the intensity of the path coefficient. Solid arrows represent positive path coefficients, dotted arrows represent negative path coefficients, and gray arrows represent path coefficients of 0. *, **, and *** represent p < 0.05, p < 0.01, and p < 0.001, respectively. AHN, acid-hydrolyzable organic nitrogen; NO 3 − -N, nitrate nitrogen; AN, ammonia nitrogen; TN, total nitrogen; SOM, soil organic matter.

Changes in Soil Chemical Properties and Acid-hydrolyzable Organic N Components under Different N Fertilizer Application Rates
In this study, the long-term application of chemical N fertilizers increased SOM, TN, NH 4 + -N, and NO 3 − -N, while significantly reducing soil pH and causing soil acidification. Fertilization is an important measure that leads to changes in soil chemical properties. Previous studies have shown that long-term application of chemical fertilizers, especially excessive N fertilizers, will lead to adverse results such as soil acidification, reduced available nutrient content, and poor structure [41]. The content of NO 3 − -N was more sensitive to N fertilizer as it increased with the increase in N application rate. NO 3 − -N was identified as an important factor for soil acidification since it had a significant negative correlation with pH.
The content of each component of soil organic N is closely related to the farmland environment, the type and amount of fertilizer applied, and other factors. Excessive N application in this study led to significant changes in soil N components, which in turn affected soil N supply capacity. High N treatments significantly increased the content of soil acid-hydrolyzable organic N components, and the content of AAN and UN were most sensitive to N fertilizer. Jiao et al. [42] indicated that the continuous application of N fertilizer increased the content of acid-hydrolyzable total N in the soil. Moreover, the contents of acid-hydrolyzable total N in each soil layer increased with the increase in N application rate and later had a decreasing trend. AN and AAN are the sources and sinks of soil organic N that are easy to mineralize. Long-term application of N fertilizers increases soil N mineralization potential thereby leading to the accumulation of AN and AAN. Moreover, due to the low mineralization rate of UN, it is a component that is difficult to mineralize in the soil acid-hydrolyzable organic N pool, and it is usually easy to accumulate in the soil.

Effects of Different N Fertilizer Application Rates on Denitrifying Bacterial Community Structure
The significant increase in the nirS gene copy number under the high N treatment may have been due to the accumulation of NO 3 − -N caused by excessive N application, which provided sufficient reaction substrates for denitrifying microorganisms and promoted their growth. Also, decreases in pH due to continuous N fertilization were the key factor that led to changes in the number of denitrifying bacteria as nirS gene is sensitive to pH changes. Liu et al. [43] also showed that the application of N fertilizer in rice had a significant effect on yield. This is because N fertilizer can significantly increase the abundance of denitrifying bacteria in paddy soil. In contrast, Yang et al. [44] found that increased N application significantly reduced the copy numbers of the nirS gene in dryland wheat soil, indicating that the differential response of the nirS gene to fertilization was due to differences in soil types or planted crops.
Proteobacteria was the dominant denitrifying bacterial phylum in northeastern farmland soils, which was consistent with most studies [22,45]. However, Rhodocyclices and Burkholderiales are the main denitrifying bacteria in wastewater, sediment, paddy field soil, etc. The main genera of denitrifying bacteria in the semi-arid area were Rhodanobacter, Azospirillum, Rubrivivax, and Bradyrhizobium. N fertilization significantly increased the relative abundance of Bradyrhizobium. Most Bradyrhizobium belongs to heterotrophic microorganisms and thrives better in acidic environments [46]. Therefore, the findings of this study were attributed to the increase in soil nutrients such as carbon and N and the decrease in soil pH in the N fertilizer treatments, which promoted bacterial growth.
Although chemical N fertilizer application significantly increased the abundance of soil nirS-type denitrifying bacteria, it decreased the diversity and richness of denitrifying bacteria, and N270 was the lowest. This indicates that excessive use of N fertilizer will lead to a decrease in the richness and diversity of denitrifying bacterial populations in semi-arid areas, resulting in changes in the community structure of soil-denitrifying bacteria. This is contrary to the findings of Rui et al. [47] on denitrifying bacteria in lime soil. The reason for this difference may be related to the soil pH. The pH of the lime soil is alkaline, but the soil pH of the four N fertilization treatments in this study was lower than 7, and a large number of research reports have pointed out that the bacterial community in acidic soil is diverse. The results showed that the bacterial community has a significant positive correlation with soil pH.

Driving Factors Affecting Changes in Denitrifying Bacterial Communities
Soil acidification and the increase in NO 3 − -N content caused by high-N treatment made the denitrifying bacterial community significantly different from the no-N fertilizer treatment. Soil NO 3 − -N was the most important factor affecting the structure of the soil nirS-type denitrifying bacterial community. At the same time, NO 3 − -N contributed to the increase in the denitrifying bacterial community in N270, which may have been due to the excessive N fertilizer application, resulting in a change in the microbial community structure and diversity. Li et al. [48] also found that NO 3 − -N content significantly affected the gene abundance of nirS. Yin et al. [49] and Wang et al. [46] also showed that denitrifying bacteria were closely related to TN, NO 3 − -N, and NH 4 + -N under long-term fertilization conditions. There are two main reasons for the change in the diversity of soil denitrifying microorganisms with different fertilization. The first reason is that fertilization directly leads to differences in the carbon source and input, N source, and other nutrient content, culminating in the addition of special bacteria [46]. Secondly, fertilization changes the soil environment and changes the living conditions of denitrifying microorganisms. TN and NO 3 − -N had an indirect effect on yield through denitrifying bacterial community structure. N fertilization affects the growth and development of crops by affecting the proportion and activity of soil microbial populations and the ability of soil to supply available N.

Conclusions
After studying the nirS-type denitrifying bacterial community in the semi-arid region of Northeast China under different N fertilizer application rates, it was found that N application significantly altered the soil properties and acid-hydrolyzable organic N components and also led to soil acidification. N fertilization significantly increased the activities of NAR and NIR. Although high N significantly increased the copy numbers of the nirS gene, it significantly reduced the diversity of the nirS-type denitrifying bacterial community, and at the same time significantly changed the structure of the nirS-type denitrifying bacterial community. SOM and NHN can indirectly affect the denitrifying bacterial community structure through the activities of NAR and NIR. The accumulation of soil NO 3 − -N caused by application of N fertilizer was the main factor affecting the variation of denitrification bacterial communities in the region. This study showed that the application of N fertilizer had a significant impact on the community of soil nirS-type denitrifying bacteria in the semi-arid region of Northeast China. This study provided a theoretical basis for the process of nirS-type denitrification bacterial community changes mediated by different fertilizer management in farmland in semi-arid areas of China.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/agronomy13092212/s1, Figure S1: Maize yield with different N fertilizer application rates; Figure S2: Correlation analysis of nirS-type denitrifier abundance, soil enzyme activities, and nutrients; Table S1: Ratio of soil acid-hydrolyzable organic nitrogen fraction content to total nitrogen content at different N fertilizer application rates (%); Table S2: Illumina MiSeq sequenced denitrifying bacterial data based on the nirS gene; Table S3: The explanation rate and contribution rate in RDA of soil nirS-type denitrifier community structure and soil nutrients under different N fertilizer application rates.