The test site was located in Minle Village, Ningjiang District, Songyuan City, Jilin Province (45° 26′ N,125° 88′ E). The rainfall period is normally from May to September. The test soil is the meadow soil and the field trial started in 2017, with three treatments set up. Treatment 1: traditional flooding irrigation (FP, all fertilizers were applied as base fertilizer at one time); Treatment 2: shallow buried drip irrigation (DI, 30% N fertilizer and 50% phosphate and potassium fertilizer were applied as the base fertilizer, and the remaining fertilizer was applied with water for 4 times in the later stage); Treatment 3: mulched film drip irrigation (MF, white transparent plastic film was covered on the corn and drip irrigation belt, 30% N fertilizer and 50% phosphorus and potassium fertilizer were applied as the base fertilizer, and the remaining fertilizer was applied with water for 4 times in the later stage). The N application rates for treatments DI and MF were pre-seeding (30%), jointing stage (30%), flowering stage (20%), silking stage (10%) and filling stage (10%), and the application rates for phosphate (P) and potassium (K) fertilizer were pre-seeding (50%), jointing stage (20%), flowering stage (15%), silking stage (10%) and filling stage (5%). The experimental maize variety was Xiangyu 998, and the planting density was 70 thousand hm^–2. The same rates of N, P, and K were applied in all treatments, and the amount of irrigation was the same. The fertilizer dosage of N-P₂O₅-K₂O was 210-90-90 kg⋅hm^–2, the base fertilizer was urea (N 46%), diammonium phosphate (N-P₂O₅-K₂O: 18-46-0), potassium chloride (K₂O 60%). Topdressing was done using urea (N 46%), water-soluble fertilizer mono-ammonium phosphate (N-P₂O₅-K₂O: 12-61-0), and water-soluble potassium chloride (K₂O 60%). The size of each experimental plot was 10 m long, 3.6 m wide, and 36 m² in area, and three repeated random plots were arranged. The experiment adopted a large-ridge double-row cultivation mode, with a narrow row spacing of 40 cm and a wide row spacing of 80 cm. The drip irrigation belt was laid in the middle of the narrow row during seeding, and the inner diameter of the drip irrigation belt was 16 mm with a drop head spacing of 30 cm. The source of water for the experiment was underground well water, and each district was connected to an independent fertilizer tank. During the fertilization process, the fertilizer tank was filled with the amount of fertilizer required by each district and thoroughly stirred for the fertilizer to be fully dissolved. Before fertilizing, the fertilizer tank valve was opened and the completely dissolved fertilizer was dropped. After fertilization, the water was allowed to continue dropping for 30 min.

In this study, soil samples were collected from the 0∼20 cm layer during the harvest period in 2021, and the soil samples from 5 randomly selected points were collected for each plot and mixed evenly to form a composite soil sample. After collection, the samples were returned to the laboratory in an ice box, and impurities such as roots and stones were removed, followed by passing the samples through a 2 mm sieve. Fresh soil samples were used for the determination of soil enzyme activity. Part of the soil samples were stored in a −80°C refrigerator at low temperature for the extraction of soil DNA, and the remaining soil samples were air-dried at normal temperature for the determination of soil physical and chemical properties.

Soil organic matter (SOM) was determined the by potassium dichromate oxidation and ferrous sulfate titration method; soil moisture was determined by the drying method; pH was measured by pH meter (water-to-soil ratio was 2.5:1); soil total nitrogen (TN) was determined by Kjeldahl method; soil alkali-hydrolyzed nitrogen (AN) was determined by diffusion method; soil ammonium nitrogen (NH₄⁺-N) and soil nitrate nitrogen (NO₃^–-N) were determined by the continuous flow analyzer (Lu, 1999).

Fresh soil samples (2 g) were extracted by ultrasonic wave for 10 min in 5 mL cell lysis solution (50 mmolL^–1 Tris HCl (pH7.5), 10% (m/v) sucrose solution, 300 mmolL^–1 sodium chloride and 90 mmolL^–2 EDTA disodium salt). The extraction volume was adjusted to 25 mL with a buffer solution (pH 7.8) consisting of 50 mmolL^–1 TrishCl and 5 mmolL^–1 magnesium chloride. The extracts were centrifuged at 12000 r/min for 20 min, and enzyme activity was measured with supernatant. AMO activity was determined using the AMOELISA kit (SaintBio, Shanghai) according to the manufacturer’s instructions. The activity of HAO was measured by Abramyan et al. (1989).

The total DNA was extracted from 0.5 g fresh Soil according to the FastDNA^® SPIN Kit for Soil (MP Biomedicals, USA) kit. The DNA concentration was determined by NanoDrop 2000 UV-VIS spectrophotometer (Thermo Scientific, USA), and the DNA quality was determined by 1% agarose gel electrophoresis. The primer sequences for the soil nitrification microbial amoA gene were A26F (5′-GACTACATMTTCTAYACWGAYTGGGC-3′) and A416R (5′-GGKGTCATRTATGG WGGYAAYGTTGG-3′) (Francis et al., 2005). The AOA amoA and AOB amoA genes were amplified with primer sequences amoA-1F (5′-GGGGTTTCTACTGGTGGT-3′) and amoA-1R (5′-CCCCTCKGSAAAGCCTTCTTC-3′), respectively (Chang et al., 2017). The reaction system was as follows: 10 μM upstream primer and downstream primer 1 μL, 1 μL SYBR^®PremixExTaq™II (TliRNaseHPlus), ROXplusL, 25 μL 2 × TaqMasterMix, and 22 μL water, making a total of 50 μL. The PCR amplification conditions of the amoA gene were: predenaturation at 94°C for 5 min, 94°C 30 s, 55°C 30 s, 72°C 30 s, 30 cycles, and then extended at 72°C for 10 min after the last cycle was completed. Each sample was repeated 3 times, and the standard curve was established by soil ammonia-oxidizing microorganisms with known copy numbers.

The amoA gene was amplified using the total DNA extracted from soil microorganisms as a template. An 8 bp barcode sequence was added to the upstream 5′ end and downstream 3′ end of the primer in AOA amoA and AOB amoA to distinguish different samples. PCR reaction system (total system 25 μL): 12.5 μL 2× TaqPlusMasterMix, 3 μL BSA (2 ng μL^–1), 1 μL ForwardPrimer (5 μM), 1 μL ReversePrimer (5 μM), 2 μL DNA (total added DNA was 30 ng), finally, 5.5 μL ddH₂O was added to 25 μL. Reaction parameters: predenaturation at 95°C for 5 min, denatured at 95°C for 45 s, annealed at 55°C for 50 s, extended at 72°C for 45 s, 28 cycles, extended at 72°C for 10 min. The PCR products were detected by 1% agarose gel electrophoresis and purified by EncourAGee TampureXP nucleic acid purification kit. The PCR products were used to construct the microbial diversity sequencing library and were sequenced at Beijing Ovison Gene Technology Co., LTD.

The disembarkation data is segmented by QIME (V1.8.0) software according to the barcode sequence, and Pear (V0.9.6) software was used to filter and concatenate the data. Scores that were lower than 20, containing ambiguous base and primer mismatches were removed. The minimum overlap during splicing is set to 10 bp and the mismatch rate was 0.1. The Denovo method of Vsearch (V2.7.1) software was used to remove short sequences and chimeric sequences. The Vsearch UPARSE algorithm (V2.7.1) software was used to process the classification unit OTU (Operational Taxonomic Units) for high-quality sequence clustering, and the similarity threshold was 97%. The Blast algorithm was used to compare GenBank non-redundant nucleotide databases (nt) and a threshold of 1e−5 was set to obtain a species taxonomic information corresponding to each OTU. All raw sequences generated in this study have been deposited in NCBI under the accession SRP479978.

Using SPSS 23.0, F test in one-way ANOVA, multiple comparison of means (P < 0.05) was conducted with a Fisher’s protected least significant difference (LSD) in statistical analysis in soil physical and chemical properties, soil enzyme activities, and microbial diversities of ammonia oxidation among the different treatments. Pearson correlation analysis among soil physicochemical properties, soil enzyme activities, and abundance was performed using SPSS (V23.0). Canoco (V5.0) software was used for redundancy analysis (RDA) to elucidate the relationship between soil enzyme activities and physicochemical properties. Heatmap was done using R (V3.4.2) ‘pheatmap’ and ‘ggplot2’ packages (R Core Team, 2016). Principal component analysis (PCoA) and Anosim were performed using the Bray-Curtis algorithm in R software (V3.4.2). The ternary graph was created using “ggplot2” in R (V3.4.2). The structural equation model (SEM) was constructed by Amos (V23), chi-square test (P > 0.05), degree of freedom chi-square ratio (CMIN) / DF < 3, fitness index (CFI > 0.9). The visualization of cooccurrence network analysis was done by using the “Phych” packet of R (V3.4.2) software to generate the species correlation matrix. In addition, Gephi (V0.9.2) was used to draw the microbial network map and calculate the topological parameters. The function of nodes in a co-occurrence network was determined by inter-module connectivity (Pi) and intra-module connectivity (Zi) representation. According to inter-module connectivity (Pi) and intra-module connectivity (Zi), nodes were classified into peripheral nodes (Zi < 2.5 and Pi < 0.62), connectors (Zi < 2.5 and Pi > 0.62), module hubs (Zi > 2.5 and Pi < 0.62) and network hubs (Zi > 2.5 and Pi > 0.62).

Compared with FP, DI, and MF significantly increased the content of AN, NO₃^–-N, and soil moisture, and significantly decreased the content of NH₄⁺-N. Compared with DI, MF significantly increased Moisture content, while pH and TN had no significant difference between different treatments (Table 1). The irrigation methods significantly changed soil enzyme activity. Compared with FP, DI and MF significantly increased the activities of AMO and HAO, where DI increased AMO and HAO activities by 1.64 and 1.28 times while MF increased by 1.26 and 1.18 times, respectively. Compared with DI, MF significantly reduced the activity of AMO and HAO by 1.3 and 1.03 times, respectively (Figure 1). The correlation between enzyme activities and soil physicochemical properties showed that the activity of HAO was positively correlated with AN (r = 0.862, P < 0.01), SOM (r = 0.723, P < 0.05) and NH₄⁺-N (r = 0.783, P < 0.05). AMO was significantly positively correlated with AN (r = 0.810, P < 0.01), and SOM (r = 0.854, P < 0.01) (Supplementary Figure 1).

Among the three irrigation methods, the abundance of AOA ranged from 1.53 to 2.60 × 10⁶ g^–1 soil, and the abundance of AOB ranged from 1.26 to 4.26 × 10⁴g^–1 soil, and the abundance of AOA was significantly higher than that of AOB (Figure 2). Compared with FP, DI significantly increased the abundance of AOA, 1.63 times than that of FP (P < 0.05) (Figure 2A). Compared with DI, MF significantly decreased the abundance of AOA, and DI was 1.60 times higher than that of MF (P < 0.05). Compared with FP, DI, and MF significantly increased the abundance of AOB by 3.4 times and 2.3 times, respectively (P < 0.05). Compared with DI, MF significantly decreased the abundance of AOB, and DI was about 1.48 times higher than that of MF (P < 0.05) (Figure 2B). The abundance ratio of AOA amoA and AOB amoA genes ranged from 58.64 to 121.16, which was not similar to the trend of AOA amoA and AOB amoA gene abundance. Compared with FP, DI and MF treatments significantly reduced the abundance ratio of AOA amoA and AOB amoA genes (Figure 2C). The correlation analysis of AOA and AOB abundance and soil physicochemical properties under three irrigation treatments showed that AOA abundance was significantly positively correlated with AN (r = 0.668, P < 0.05), SOM (r = 0.682, P < 0.05), and AOB abundance was significantly positively correlated with SOM (r = 0.710, P < 0.05), NH₄⁺-N (r = 0.795, P < 0.05), AN (r = 0.911, P < 0.001) (Supplementary Figure 1). Correlation analysis of AOA and AOB abundance and enzyme activities under the three irrigation methods showed that AOA abundance was significantly positively correlated with AMO (r = 0.821, P < 0.01) and HAO (r = 0.737, P < 0.05), AOB abundance was significantly positively correlated with AMO (r = 0.931, P < 0.001) and HAO (r = 0.963, P < 0.001) (Supplementary Figure 1).

Sequencing from Illumina MiSeq, in order to analyze the microbial communities at the same sequencing depth, the lowest sequencing number of 73,184 sequences for AOA amoA gene and 30,160 sequences for AOB amoA gene were for randomly selected per sample. 799,214 high-quality sequences were obtained from all samples of the AOA amoA gene, each sample had 75,159–115,274 high-quality sequences, and the sequence length was optimized from 200 to 540 bp (Supplementary Table 1). Moreover, 385,327 optimized sequences were obtained from all samples of the AOB amoA gene, with 33,440 to 56,157 high-quality sequences per sample, and optimized sequence lengths of 200 to 540 bp (Supplementary Table 2). The OTU distribution among the samples revealed the OTU sharing of the microbiome in different irrigation treatments. The ternary phase diagram analysis of AOA and AOB based on three irrigation methods. In the ternary phase diagram of AOA, species were evenly distributed in the three treatments. The center of the triplet diagram is the influence of soil microbiome selection on the three irrigation treatments, and the core microbiome spans FP, DI, and MF treatments. FP recorded the highest relative abundance of Thaumarchaeota (80%), MF recorded the highest relative abundance of Proteobacteria (60%), and DI recorded the highest relative abundance of Crenarchaeota (80%) (Figure 3A). In the ternary phase diagram of AOB, the distribution of species in the three treatments was not very uniform. The relative abundance of Proteobacteria, Gemmatimonadetes, and Actinobacteria was higher in FP, DI, and MF treatments respectively (Figure 3B). Venn analysis revealed that FP, DI, and MF had 128 AOA OTUs and 988 AOB OTUs (Supplementary Figure 2). A total of 20 different OTUs were selected under AOA. Among them, OTU42 was the dominant bacterium with the relative abundance of 6.11–11.33%. Moreover, MF recorded a significant increase in the relative abundance of OTU42 (Figure 4A). BLAST analysis indicated that OTU42 (KM595440.1) might be an uncultured Nitrososphaerota (Supplementary Table 3). A total of 20 different OTUs were selected by AOB, among which OTU100 (MH589282.1) and OTU95 (KP212533.1) were the dominant bacteria, and their relative abundances were 10.64–14.75% and 7.81–11.87%, respectively. BLAST analysis showed that OTU100 (MH589282.1) could be uncultivated Nitrososphaerota and OTU95 (KP212533.1) may be uncultured Nitrosospira sp. (Supplementary Table 4). The heatmap showed that in AOA, compared with FP, DI significantly increased the relative abundance of OTU174, OTU99, OTU142, OTU9, OTU69 and OTU245 while MF significantly increased the relative abundance of OTU208, OTU68, OTU28, OTU7, OTU42, OTU42, and OTU149 (Figure 4A). In AOB, compared with FP, DI significantly increased the relative abundance of OTU139, OTU491, OTU499, OTU99, OTU58, OTU747, OTU707, OTU103, OTU144, OTU435, OTU887, OTU116 and OTU830, while MF significantly increased the relative abundance of OTU134, OTU949, OTU48, OTU808, OTU250, OTU125, OTU18, OTU119, and OTU738 (Figure 4B).

Compared with FP and DI, MF significantly reduced the Shannon index and Chao1 index of AOA (Figures 5A, B). In AOB, the Shannon index and Chao1 index showed no significant difference in the different irrigation treatments (Figures 5C, D). The PCoA of soil AOM showed that for the PCA results of AOA, the contribution values of the first principal component and the second principal component were 77.30 and 12.40%, respectively, totaling 89.70%, reflecting the main reason for the difference among the three irrigation treatments. The relative distance between FP and MF treatment was relatively close, which was different from that of DI treatment (Figure 6A). The PCA results of AOB showed that the contribution values of the first principal component and the second principal component were 37.89 and 17.80%, respectively, totaling 55.69%, which was also the main reason for the difference among the three different irrigation treatments. The samples of FP and DI treatments were relatively clustered, and there was a difference between them and MF treatment (Figure 6B). In order to further determine whether the difference in soil AOA and AOB between groups and within groups was significant, Anosim analysis was performed on soil microbial colony structure, and the results showed that the difference between groups of the three irrigation treatments was significantly greater than the difference within groups (P < 0.05) (Figures 6C, D). In conclusion, irrigation methods significantly affected the community structure of AOA and AOB.

RDA showed that in AOA, AN was the main driver of AOA community structural changes (Supplementary Table 5), and SOM, TN, AN, NH₄⁺-N were positively correlated with DI. Moreover, pH, NO₃^–-N, and soil moisture were positively correlated with MF (Figure 7A). In AOB, soil moisture was the main driving factor of AOB community structural changes (Supplementary Table 6). The pH, NO₃^–-N and soil moisture were positively correlated with MF, while AN, SOM, TN, and NH₄⁺-N were positively correlated with DI (Figure 7B). For AOA, SEM perfectly fitted the experimental data (χ²/df = 0.450, P = 0.77, CFI = 1.00), and the equation model explained 85% of the variation in AMO, 84% of the variation in HAO, and 80% of the variation in AOA community (Figure 7C). TN, AN, NO₃^–-N, NH₄⁺-N and Moisture had direct effects on AMO, HAO and AOA communities, among which AN had the greatest effect on AMO (λ = 0.86, P < 0.001), HAO (λ = 1.56, P < 0.01) and AOA (λ = 1.046, P < 0.001) community. AMO and HAO had direct effects on AOA community, and AMO (λ = 0.96, P < 0.001) had the greatest effect on AOA community. For AOB, SEM fully fitted the experimental data (χ²/df = 1.119, P = 3.26, CFI = 0.998), and the equation model could explain 83% of the variation in the AMO, 81% of the variation in HAO, and 86% of the variation in AOB community (Figure 7D). TN, AN, NO₃^–-N, NH₄⁺-N and Moisture had direct effects on AMO, HAO and AOB community, among which AN had the greatest effect on AMO (λ = 0.86, P < 0.001) and HAO (λ = 1.56, P < 0.01), Moisture (λ = 1.103, P < 0.05) had the greatest effect on AOB community. AMO and HAO had direct effects on AOB community, and HAO (λ = 1.57, P < 0.01) had the greatest effect on AOB community.

The AOA and AOB symbiotic networks of the three irrigation treatments were used to test whether irrigation methods showed unique topological properties that affect microbial communities. Through the analysis of the average degree, clustering coefficient, average path length, complexity, density, and modularity characteristics of AOM networks, it was revealed that each network has some typical personalized modularity characteristics (Table 2). The AOA symbiotic network consisted of 153 (FP), 157 (DI), 144 (MF) nodes and 961 (FP), 1137 (DI), and 849 (MF) edges. These results showed that DI increased the number of nodes in the network compared to FP and MF. Also, DI increased the edge of the interaction of AOA species, the complexity of soil AOA networks, and enhanced the interactions between AOA species (Figures 8A–C). The AOB symbiotic network consisted of 241 (FP), 249 (DI), 276 (MF) nodes and 3034 (FP), 4442 (DI), 5514 (MF) edges. Compared with FP and DI, MF increases the number of nodes in the network and also increases the interaction edges of AOB species. MF increased the complexity of the soil AOB network and enhanced the interaction between AOB species (Figures 8D–F). Compared with AOA networks, AOB networks had the highest average degree, the shortest average path length, and the highest average clustering coefficient, indicating that AOB networks had the highest interspecific interaction degree and the highest connection tightness with closer OTUs connections.

Most of the nodes in the AOA and AOB symbiotic networks under the three irrigation modes were peripheral nodes, and none of the nodes in the AOA and AOB networks fell within the network hub. In the AOA symbiotic network: FP had 1 node in the module hub and 12 nodes in the connector; DI had 1 node in the module hub and 5 nodes in the connector; MF had 1 node in the module hub and 3 nodes in the connector (Figures 9A–C). In the AOB symbiotic network: FP had 6 nodes in the module hub and 3 nodes in the connector; DI had 1 node in the module hub; MF had 5 nodes in the module hub and 1 node in the connector (Figures 9D–F).

Compared with FP, DI and MF significantly increased soil water content. Qu et al. (2019) also indicated that mulching film significantly increased soil water content in arid and semi-arid areas. This may be due to the slow and uniform immersion of soil water into the soil during drip irrigation, which keeps the soil water-holding capacity relatively suitable and stable while not destroying the soil structure, thereby reducing the evaporation of soil water (Liu and Xu, 2023). To a certain extent, mulching film mitigates the impact of air convection, increases temperature, and preserves soil moisture, thus inhibiting the evaporation of surface water. This improves soil hydrothermal conditions and promotes the early growth of crops while saving water and fertilizer, thereby improving water use efficiency (Yu et al., 2018). There was no significant difference in SOM under different irrigation modes, which was consistent with the study by Núez et al. (2022). In this study, there was no significant difference in TN content among the three irrigation methods, which may be due to the dynamic balance of N in the soil (Lu et al., 2021). Compared with FP, DI, and MF significantly increased the content of AN in soil, which may be because the content of AN in the soil is not stable enough and is easily affected by soil hydrothermal and biological activities. Mulching increased soil temperature, intensified microbial activity, promoted nutrient release, and increased the content of AN (Wang et al., 2021a; Araujo et al., 2023). Compared with FP, DI and MF significantly increased NO₃^–-N content, while decreasing soil NH₄⁺-N content, which may be due to faster evaporation of soil water after drip irrigation, better soil aeration, and soil surface N accumulation under film mulching treatment (Chen et al., 2020). Moreover, the film coating can slow down the downward migration of NO₃^–-N, leading to an increase in NO₃^–-N content under mulched drip irrigation (Wang et al., 2022). With the increase in soil aeration and soil temperature, the activity of soil nitrifying bacteria was enhanced, and the nitrification reaction was intensified, resulting in decreases in NH₄⁺-N content (Li et al., 2022).

This study showed that the abundance of AOA amoA gene was significantly higher than that of AOB amoA gene, which was consistent with earlier studies that indicated that AOA amoA gene abundance dominates in some weakly acidic soils (Sun et al., 2019; Yao et al., 2022). Compared with FP, DI significantly increased the abundance of AOA amoA and AOB amoA genes, and MF significantly increased the abundance of AOB amoA gene. Drip irrigation promotes the nitrification of soil such that under drip irrigation conditions, water droplets slowly enter the soil and distribute around crop roots under the action of capillary force and gravity. In this region, the nitrification effect is higher than the denitrification effect (Sarula et al., 2023), thus increasing the abundance of soil AOA amoA and AOB amoA genes. In this study, the correlation between soil physicochemical properties and the abundance of AOA amoA and AOB amoA genes was studied by Pearson correlation analysis. The results showed that the soil AOA amoA abundance was positively correlated with AN and SOM, and the soil AOB amoA abundance was positively correlated with AN, SOM, and NH₄⁺-N. Studies have shown that AOA is more adaptable to environmental conditions with low available ammonium N concentration, and plays a leading role in the nitrification process than AOB (Lin et al., 2021). On the contrary, AOB is more dominant in the nitrification process of neutral soils with high alkaline and N abundance (Wang et al., 2021b). The results indicated that the change of NH₄⁺-N content had a more obvious effect on AOB abundance due to the different irrigation methods.

At the phylum level, soil AOB community composition was significantly affected by irrigation methods, while soil AOA community changes were less affected. This may be due to the fact that the ecological niche of soil AOA is smaller than that of AOB, that is, it occupies a narrower range of resources and space in the soil, while soil AOB has a wider ecological niche and can survive under different conditions such as temperature, pH and N elements (Guo et al., 2023). Compared with FP, DI, and MF significantly changed the community structure and abundance of AOM in soil. The dominant microorganisms are relatively abundant in soil and play an important role in the regulation of ecological functions. The results of this study indicated that Thaumarchaeota, Crenarchaeota, and Proteobacteria are the dominant phyla in AOA. Thaumarchaeota is a typical ammonia-oxidizing archaea, and some studies have suggested that Thaumarchaeota plays an important role in the ammonia-oxidizing process (Yang et al., 2018; Wang N. et al., 2019). The significant increase in the relative abundance of Thaumarchaeota in FP may be due to the application of N fertilizer. Araujo et al. (2023) showed that Thaumarchaeota is the dominant archaea under N application systems. The research of Luo et al. (2023) also showed that Thaumarchaeota is significantly positively correlated with soil NH₄⁺-N content and other related indicators, suggesting that Thaumarchaeota can accelerate catalytic ammonia oxidation to obtain energy for autotrophic growth (Wu et al., 2020). Studies have shown that most OTUs in AOA belong to Crenarchaeota (Wang and Huang, 2021), and an increase in the relative abundance of Crenarchaeota is conducive to promoting the rate of soil ammonia oxidation. An appropriate combination of fertilization and irrigation frequency can regulate soil AOA community composition, and the physicochemical properties also have different effects on Crenarchaeota. The highly relative abundance of Crenarchaeota in DI treatment may be due to the high SOM content and better soil fertility. Proteobacteria have a wide ecological range and strong suitability and can form a relatively stable ecological niche in different environments (Jiang et al., 2021). The highly relative abundance of Proteobacteria in MF may be due to the difference in soil water content. The findings of this study are consistent with Chen et al. (2020) who reported that Proteobacteria propagated in large numbers under high soil-water conditions.

In AOB, Firmicutes, Gemmatimonadetes and Actinobacteria are dominant bacteria. The Bacillus in Firmicutes produces spores that can resist drying, dehydration, and extreme environments. In the case of drought, spores can prolong the hydration of cells and provide nutrients to cells, so that cells can resist dehydration and other extreme environments (Li et al., 2020). The highly relative abundance of Firmicutes in FP treatment may be due to the fluctuations in moisture and the proliferation of these bacteria in low water content environments (Veach and Zeglin, 2020). Gemmatimonadetes was the dominant phylum of DI. Gemmatimonadetes can adapt to low-humidity environments, but cannot resist moisture fluctuations caused by dry and wet cycles. Moreover, studies have shown that Gemmatimonadetes is negatively correlated with soil organic matter (Zhang et al., 2019b). The highly relative abundance of Actinobacteria in MF may be due to the influence of SOM content. Javed et al. (2021) confirmed that the abundance of Actinobacteria bacteria can promote the formation of humus and decomposition of organic matter in soil, and also have a certain effect on the soil N cycle. As Actinobacteria tends to grow in soil with low SOM content, film mulching reduces the organic carbon content of rhizosphere soil, which is conducive to promoting the growth and propagation of Actinobacteria (Chen et al., 2017). According to Blast analysis, Nitrososphaerota is an important strain of AOA, and Nitrososphaera belongs to Nitrososphaerota. Previous studies have shown that the main AOA group in the black soil of Northeast China is Nitrososphaera (Ding et al., 2020), and it may be the main driving factor for nitrification of acidic soil in China (Li W. et al., 2021). Nitrosospira sp. was the dominant genus of AOB, which is consistent with previous studies on agricultural soil (Zhang et al., 2019c; Hu et al., 2021). Nitrosospira sp. produces nitrite by oxidizing soil nitrifying bacteria, which plays a crucial role in the nitrification process, making nitrogen easily absorbed by crops.

In this study, the RDA of environmental factors and AOA community structure showed that AN was the main driving factor affecting change in the AOA community. The results of this study are consistent with the results of Sarula et al. (2023), who similarly reported that AOA community structure mainly depends on AN and pH. On the contrary, soil pH did not have much influence on the AOA community probably because there was little change in soil pH in this study. Sun et al. (2019) showed that pH plays an important role in shaping AOA community structure as similarly reported by other studies (Gubry-Rangin et al., 2011; Hu et al., 2013). Moreover, the RDA of environmental factors and AOB community structure showed that water content was the main driving factor affecting the change of the AOB community, which may be caused by the response of AOB to oxygen conditions. Similarly, Yang et al. (2018) showed that there was a significant correlation between the AOB community and soil water which indicates that soil water content significantly affects the community structure of nitrifying microorganisms in soil (Cong et al., 2023).

In general, the higher the microbial community diversity index, the more complex the community structure and the better the stability (Jiang et al., 2020). The AOB symbiotic network was more complex than the AOA symbiotic network in the three irrigation methods, indicating that the three irrigation methods had better stability and stress resistance effects on the soil AOB community. This may be due to the significant increase in some key species in AMO in our study, thereby improving the network stability and connectivity of species in soil (Xiang et al., 2020). At the same time, the high stability of microbial communities is an important guarantee for the realization of ecological functions. Therefore, AOB plays an important role in enhancing the function of farmland soil under different irrigation methods. The study also identified key species of microbial communities by analyzing the topological structure of symbiotic networks, which change the composition of microbial communities (Zhou et al., 2021). In a microbial symbiotic network, nodes represent microbiota species in the biome, and the topological characteristics of different nodes can be used to determine the key species. Generally, node attributes are divided into four types; peripheral nodes (Zi < 2.5 and Pi < 0.62), connectors (Zi < 2.5 and Pi > 0.62), module hubs (Zi > 2.5 and Pi < 0.62), and network hubs (Zi > 2.5 and Pi > 0.62), where all nodes of the network hub are key species in the network. Nodes that fall within connectors and module hubs play an important role between and within modules (Ya et al., 2021). DI recorded the highest number of network edges with more complex structures and the interaction between AOA communities was closer. The high connectivity within the AOA community in DI results in higher complexity and stability, thereby maintaining the versatility and sustainability of the ecosystem. In addition, the interaction between AOB communities was closer under MF due to the high number of network edges which made the structure more complex. This indicated that MF had better connectivity and higher energy transfer efficiency between AOB communities.