A method for synergistic promotion of soil nitrogen transformation and regulation of the microbiome by bacteria and fertilizer

Abstract

This invention belongs to the field of crop cultivation technology, specifically relating to a method for synergistic promotion of soil nitrogen transformation and regulation of the microbiome through microbial-fertilizer incorporation. The invention involves applying basal fertilizer before corn planting, topdressing with potassium fertilizer and reduced nitrogen fertilizer during the corn seedling stage, and applying reduced nitrogen fertilizer again during the corn's large tasseling stage. Furthermore, after a period of time following corn planting, synergistic nitrogen-fixing bacteria and arbuscular mycorrhizal fungi are regularly applied. The synergistic nitrogen-fixing bacteria and arbuscular mycorrhizal fungi synergistically improve soil nutrient conditions and increase soil nitrogenase activity, promoting the soil nitrogen transformation process. Combined with the application of reduced nitrogen fertilizer, this enhances the nitrogen fertilizer absorption and utilization rate of corn, promoting crop growth. Simultaneously, the combined inoculation with nitrogen fertilizer increases beneficial bacteria, inhibits pathogens, improves the soil microbial community structure, and maintains soil health. This promotes the stable functioning of the soil microbiome (bacteria, fungi, archaea, and protists), fosters the sustainable and healthy development of the rhizosphere micro-ecosystem, and promotes healthy crop growth.

Description

Technical Field

[0001] This invention belongs to the field of crop cultivation technology, specifically relating to a method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of microorganisms and fertilizers. Background Technology

[0002] The quality and high yield of corn depend on a sufficient supply of nitrogen, typically requiring large amounts of nitrogen fertilizer in production. However, in practice, farmers often over-fertilize and apply nitrogen fertilizer in excess of their capacity in pursuit of higher yields and economic benefits. Excessive nitrogen fertilizer application not only leads to low nitrogen fertilizer utilization efficiency and high production costs, but also results in the loss of most nitrogen fertilizer after it is applied to the soil through surface runoff, ammonia volatilization, nitrogen leaching, nitrification, and denitrification, causing various environmental and ecological problems such as eutrophication of water bodies, excessive nitrate levels in groundwater, and N2O emissions. Chemical nitrogen fertilizers, such as urea, have very low utilization rates, ranging from 30% to 35%. Therefore, reducing the input of chemical nitrogen fertilizers, improving nitrogen utilization efficiency, mitigating the environmental risks associated with nitrogen surpluses, and developing environmentally friendly and resource-efficient corn production models are critical issues that urgently need to be addressed in current corn production.

[0003] Biological nitrogen fixation (BNF) converts atmospheric nitrogen into ammonia through nitrogenase, increasing soil nitrogen content and making it a major contributor to nitrogen in the biosphere. BNF can serve as a sustainable nitrogen source for crops, effectively improving nitrogen fertilizer use efficiency and reducing nitrogen fertilizer application. It can also enhance crop stress resistance and reduce nitrogen loss from farmland ecosystems. Furthermore, the mycorrhizal symbiosis formed by arbuscular mycorrhizal fungi (AMF) and plant roots can regulate soil microbial community structure and control soil nitrogen cycling. The nitrogen cycle is one of the core elements in soil ecosystem cycling. The application of nitrogen fertilizer and rhizosphere-promoting bacteria both cause fluctuations in nitrogen levels throughout the soil nitrogen cycle, thus affecting soil microbial diversity, community structure, and functional changes. The nitrogen utilization and cycling processes of crops are primarily driven by the micro-ecosystem composed of the root-rhizosphere-soil microbiome.

[0004] Based on the relationship between nitrogen-fixing microorganisms and host crops, biological nitrogen fixation can be divided into three types: autotrophic nitrogen fixation, symbiotic nitrogen fixation, and associative nitrogen fixation. Inoculation with nitrogen-fixing bacteria and other inoculants is one of the effective ways to improve nitrogen fertilizer utilization and reduce nitrogen fertilizer application. However, compared with symbiotic nitrogen fixation, associative nitrogen-fixing bacteria are more susceptible to environmental factors such as indigenous microorganisms, nitrogen application levels, and planting patterns. For example, adding exogenous nitrogen-fixing bacteria can disrupt the original microecological environment of the rhizosphere soil, affecting soil nutrient content and community structure. Indigenous microbial communities also dynamically respond to the introduction of exogenous microbial communities. This change can either promote or inhibit the effects of exogenous nitrogen-fixing bacteria on plants and soil. Therefore, the growth-promoting effect and nitrogen-fixing effect of associative nitrogen-fixing bacteria on host plants are uncertain. To fully realize the nitrogen-fixing potential of associative nitrogen-fixing bacteria on gramineous plants, it is necessary to conduct in-depth research on their interaction with the natural soil microbiome.

[0005] Therefore, promoting the linkage mechanism between changes in microbial community structure, interspecific interactions, and nitrogen use efficiency by combining nitrogen-fixing bacteria with other microbial agents and controlling nitrogen application levels is a key link in developing environmentally friendly and resource-efficient crop production models. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for synergistic promotion of soil nitrogen transformation and regulation of the microbiome by microorganisms and fertilizers. This invention explores the characteristics of nitrogen changes in rhizosphere soil of gramineous crops under exogenous inoculation and different nitrogen application treatments from the perspectives of nitrogen cycle and microbiome, revealing the molecular driving mechanism of nitrogen transformation, and investigating the dynamic responses of microbiome community abundance, diversity, structural composition, and interspecific interactions. This aims to help improve rhizosphere soil microecological conditions and provide a theoretical basis for the mechanism of growth promotion in gramineous crops and for promoting green and sustainable agricultural development.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a method for synergistic promotion of soil nitrogen transformation and regulation of the microbiome by microorganisms and fertilizers, comprising the following steps: (1) Apply base fertilizer before planting gramineous crops; (2) Regularly inoculate nitrogen-fixing bacteria and arbuscular mycorrhizal fungi after planting gramineous crops; (3) Apply seedling fertilizer during the seedling stage of gramineous crops, wherein the seedling fertilizer includes potassium fertilizer and reduced amount of nitrogen fertilizer; (4) Apply top dressing fertilizer during the tasseling stage of grass crops, wherein the top dressing fertilizer is a reduced amount of nitrogen fertilizer.

[0008] Furthermore, in step (1), the base fertilizer includes potassium fertilizer, phosphorus fertilizer and nitrogen fertilizer.

[0009] Furthermore, in step (1), the amount of potassium fertilizer applied is 150-230 kg·K₂O·hm⁻².-2 Phosphate fertilizer 200-330 kg·P₂O₅·hm -2 Nitrogen fertilizer application: urea at a rate of 80-120 kg·N·hm. -2 .

[0010] Furthermore, in step (1), the amount of potassium fertilizer applied is 180 kg·K₂O·hm⁻². -2 268 kg·P₂O₅·hm² of phosphate fertilizer -2 Nitrogen fertilizer application: urea at a rate of 100 kg N·hm. -2 .

[0011] Further, in step (2), the regular inoculation of syn-nitrogenous bacteria and arbuscular mycorrhizal fungi refers to inoculating syn-nitrogenous bacteria and arbuscular mycorrhizal fungi every 7-14 days after planting the grass crops 10-30 days after planting.

[0012] Furthermore, in step (2), the periodic inoculation with syn-nitrogenous bacteria and arbuscular mycorrhizal fungi refers to inoculating the syn-nitrogenous bacteria and arbuscular mycorrhizal fungi every 10 days after the 12th day of planting the grass crops.

[0013] Further, in step (2), the combined nitrogen-fixing bacteria are *Azospirillum brasilense* and *Herbaspirillum frisingense*, and the arbuscular mycorrhizal fungus is arbuscular mycorrhizal fungus (AMF).

[0014] Furthermore, in step (3), the amount of potassium fertilizer applied is 150-230 kg·K₂O·hm⁻². -2 Reduce nitrogen fertilizer application and apply urea at a rate of 60-90 kg·N·hm. -2 .

[0015] Furthermore, in step (3), the amount of potassium fertilizer applied is 180 kg·K₂O·hm⁻². -2 Reduce nitrogen fertilizer application and apply urea at a rate of 75 kg N·hm. -2 .

[0016] Further, in step (4), the amount of urea applied in the reduced nitrogen fertilizer application is 120-160 kg·N·hm. -2 .

[0017] Furthermore, in step (4), the reduced nitrogen fertilizer application of urea is carried out at a rate of 140 kg·N·hm. -2 .

[0018] Furthermore, the total amount of urea applied in the base fertilizer, seedling fertilizer, and bud fertilizer is approximately 60% of the total amount of nitrogen fertilizer applied conventionally.

[0019] Furthermore, the gramineous crop is at least one of maize, wheat, and rice.

[0020] Compared with the prior art, the beneficial effects of the present invention are: (1) The compound inoculation and nitrogen fertilizer application of this invention mainly increases TN, AK and NH in the soil. 4+ The MBN content in the AHA treatment was significantly higher than that in the AH treatment. Combined inoculation with reduced nitrogen fertilizer increased the aboveground dry weight and underground fresh weight of maize, with the AHA treatment showing better results than the AH treatment. Combined inoculation improved the aboveground nitrogen content and nitrogen fertilizer absorption and utilization rate of maize, significantly increasing both under nitrogen-free and reduced-application conditions. The AHA treatment showed a more pronounced improvement compared to the AH treatment, demonstrating a synergistic effect between nitrogen-fixing bacteria and AMF in promoting crop growth.

[0021] (2) The present invention can increase the activity of nitrogenase in soil under different nitrogen application levels through compound inoculation. The AHA treatment is more effective than the AH treatment in increasing the activity of nitrogenase in soil, and the effect is more significant when combined with reduced nitrogen fertilizer. The interaction between different inoculation modes and nitrogen application can significantly affect the nitrogen transformation rate of maize rhizosphere soil. Compound inoculation significantly increased the net ammonification rate in soil under no nitrogen application and reduced nitrogen application levels. Compound inoculation can affect the expression level of core functional genes of nitrogen transformation in soil. In addition to reducing the expression level of amoA gene under conventional nitrogen application level, compound inoculation under different nitrogen application levels increased the expression levels of nifH (nitrogen fixation process), chiA (organic nitrogen mineralization process), amoA (nitrification process), and nirK, nirS, and nosZ (denitrification process) genes in soil, thus promoting the nitrogen transformation process in soil.

[0022] (3) The combined inoculation with nitrogen fertilizer of this invention can change the structural composition of soil microbial communities, increasing the relative abundance of bacteria such as Patescibacteria and Bacteroidota, fungi such as Ascomycota, archaea such as Crenarchaeota, and protists such as SAR_d__Eukaryota, while decreasing the relative abundance of fungi such as Basidiomycota and Glomeromycota, and protists such as Nematozoa. At the same time, the combined inoculation with nitrogen fertilizer can promote the growth of beneficial bacteria in soil microorganisms such as bacteria such as Rhodanobacter and Chujaibacter, fungi such as Saitozyma and Penicillium, archaea such as unclassified_f__Nitrososphaeraceae and Candidatus nitrososphaera, and protists such as unclassified_f__Colpodea and norank_c__Trebouxiophyceae, while inhibiting the growth of harmful bacteria in soil such as fungi such as Fusarium and Melanconiella, thus promoting the healthy development of the soil ecosystem.

[0023] (4) The combined inoculation of bacteria in this invention affects the interaction relationships of the microbial network. Under different treatments, the microbial community mainly exhibits a cooperative relationship, and the competition between bacteria and protists is intensified under combined inoculation with nitrogen fertilizer. On the other hand, combined inoculation affects the topological properties of the soil microbial network. Under the nitrogen-free treatment, combined inoculation reduces the number of nodes and edges in the microbial network, and some microbial groups and their interrelationships decrease. Under the combined inoculation with nitrogen fertilizer treatment, different microbial communities show different dynamic responses. Combined inoculation with reduced nitrogen fertilizer promotes interspecific communication between bacterial and archaea communities, making the microbial network more complex, while the fungal community shows the opposite trend, with reduced interspecific communication and network complexity. Protists are most sensitive to environmental changes, with reduced network complexity under the two-bacterial treatment and increased network complexity under the three-bacterial treatment. Combined inoculation with conventional nitrogen fertilizer reduces the number of nodes and edges in the bacterial, fungal, and archaea networks, thus reducing the complexity of the microbial network. Interspecific material information exchange in protozoan communities is more frequent, and microbial networks are more complex. This may be related to the fact that protozoa can selectively feed on bacteria and fungi. Increased availability of resources and food promotes the growth and reproduction of protozoan communities.

[0024] In summary, combined inoculation with microorganisms can improve soil nutrient conditions and increase soil nitrogenase activity, promoting soil nitrogen transformation. Furthermore, when combined with reduced nitrogen fertilizer application, it enhances nitrogen fertilizer absorption and utilization in maize, thus promoting crop growth. Simultaneously, combined inoculation with nitrogen fertilizer increases beneficial bacteria, inhibits pathogens, improves soil microbial community structure, and maintains soil health. While the soil microbiome (bacteria, fungi, archaea, and protozoa) responds differently to combined inoculation and nitrogen fertilizer addition, they collectively maintain the stability of soil microbial community function and promote the sustainable and healthy development of the rhizosphere micro-ecosystem. Attached Figure Description

[0025] Figure 1 The graph shows the comparison of nitrogen content in the aboveground parts of maize under different treatments. Different lowercase letters above the bars indicate significant differences using the Duncan test (p<0.05).

[0026] Figure 2 The graph shows the comparison of nitrogen fertilizer absorption and utilization rates in maize under different treatments. Different lowercase letters above the bars indicate significant differences using the Duncan test (p<0.05).

[0027] Figure 3 This is a comparison of nitrogenase activity in maize rhizosphere soil under different treatments. Different lowercase letters above the columns indicate significant differences using the Duncan test (p<0.05).

[0028] Figure 4 This is a comparison of the expression levels (copies / g soil) of core functional genes for nitrogen transformation in soil under different treatments. Different lowercase letters above the bars indicate significant differences using the Duncan test (p<0.05).

[0029] Figure 5 Dilution curves of bacterial communities under different treatments.

[0030] Figure 6 The composition of soil bacterial communities at different treatment levels.

[0031] Figure 7 PCoA analysis of soil bacterial community structure under different treatments.

[0032] Figure 8 RDA analysis of soil bacterial communities and environmental factors under different treatments.

[0033] Figure 9 This is a network diagram showing the co-occurrence of bacterial communities under different treatments.

[0034] Figure 10 The dilution curves of fungi under different treatments are shown.

[0035] Figure 11The composition of soil fungal communities at different treatment levels.

[0036] Figure 12 PCoA analysis of soil fungal community structure under different treatments.

[0037] Figure 13 RDA analysis of soil fungal communities and environmental factors under different treatments.

[0038] Figure 14 This is a network diagram showing the co-occurrence of fungal communities under different treatments.

[0039] Figure 15 Dilution curves of archaea microorganisms under different treatments.

[0040] Figure 16 The composition of soil archaea communities at different treatment levels.

[0041] Figure 17 PCoA analysis of soil archaeal community structure under different treatments.

[0042] Figure 18 RDA analysis of soil archaeal communities and environmental factors under different treatments.

[0043] Figure 19 This is a network diagram showing the co-occurrence of archaeal communities under different treatments.

[0044] Figure 20 Dilution curves of native biological communities under different treatments.

[0045] Figure 21 The composition of native soil communities at different treatment levels.

[0046] Figure 22 PCoA analysis of soil native biological community structure under different treatments.

[0047] Figure 23 RDA analysis of soil native communities and environmental factors under different treatments.

[0048] Figure 24 This is a network diagram showing the co-occurrence of native biological communities under different treatments. Detailed Implementation

[0049] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the experimental materials used in the following embodiments are all available through conventional commercial channels.

[0051] Example 1: Experiment on the synergistic effect of bacteria and fertilizer in promoting nitrogen transformation in maize soil and regulating the microbiome Soil used in the experiment: Soil was taken from a cornfield at the teaching experimental farm of South China Agricultural University. The soil was mixed thoroughly, weighed, and placed in pots. Each pot contained 4 kg of soil, and one corn plant was planted in each pot. The basic physicochemical properties of the soil are as follows: pH 5.54, SOC content 14.63 g / kg, total nitrogen (TN) 0.64 g / kg, total phosphorus (TP) 1.01 g / kg, total potassium (TK) 27.36 g / kg, available phosphorus (TP) 71.50 mg / kg, available potassium (AK) 62.46 mg / kg, and ammonium nitrogen (NH4+)... + The concentration of nitrogen (NO3-N) was 2.22 g / kg, and the concentration of nitrate nitrogen (NO3) was... - The concentration of -N was 53.18 mg / kg.

[0052] The tested maize was Huazhen sweet corn, and the seeds were purchased from Hezhiyuan Seed Industry Co., Ltd.

[0053] The tested nitrogen-fixing bacteria and arbuscular mycorrhizal fungi (AMF) were: *A. brasilense* and *H. frisingense*, purchased from BNCC (Beina Chuanglian Biotechnology Co., Ltd.), with serial numbers BNCC361938 and BNCC363210 respectively. Both were Gram-negative and aerobic. Morphology: Colonies were 1-2 mm in diameter, round, with neat edges, opaque, grayish-white on the front, convex in the center, smooth, bright, moist, and easily picked up. The tested AMF was an endophytic fungal bio-inoculum purchased from Beijing Jinbilai Biotechnology Co., Ltd., with the active species being *Rhizophmgus irregularis*.

[0054] Test culture media: NA (Nutrient agar) medium is a solid medium used for plate culture, with a formulation of peptone: 10 g, beef extract: 3 g, sodium chloride: 5 g, and agar: 15 g. NB (Nutrient broth) medium is a liquid medium used for shake flask culture, with a formulation of peptone: 10 g, beef extract: 3 g, and sodium chloride: 5 g. Add the above components to 1000 ml of distilled water, stir well, and adjust the pH to 7.0 ± 0.2. Dispense into test tubes or culture flasks, autoclave at 121 ℃ for 15 min, and use after cooling.

[0055] Experimental Methods: The pot experiment was conducted from May 15th to July 18th, 2025, in the greenhouse of the Science and Technology Building at South China Agricultural University. This experiment included three nitrogen application levels: N0 = 0 kg·hm². -2 N1 = 315 kg·hm -2 N2 = 525.8 kg·hm -2 Three inoculation modes were used: CK (no inoculation), AH = A. brasilense + H. frisingense, and AHA = A. brasilense + H. frisingense + AMF. This experiment included 9 treatments, with 5 replicates per treatment, for a total of 45 samples. Specific treatments are shown in Table 1. Before the experiment, a sufficient number of maize seeds were selected, first soaked in 70% ethanol for 3 min, then soaked in 5% sodium hypochlorite for 3 min, and finally rinsed 3 times with sterile distilled water for surface disinfection. The seeds were then placed at 28℃ until germination. Seeds with sprouting roots and uniform growth were selected for subsequent planting. Fertilizer was applied 3 times during the entire experiment. Base fertilizer was applied before planting, and all treatments received a uniform 190 kg·K₂O·hm⁻² potassium fertilizer. -2 Phosphate fertilizer 268 kg·P2O5·hm -2 Nitrogen treatment: 100 kg N·hm -2 Apply topdressing fertilizer during the corn seedling stage (around 25 days, V6-7), and uniformly apply 180 kg·K2O·hm² of potassium fertilizer. -2 Conventional nitrogen application treatment uses urea at a rate of 125 kg·N·hm. -2 Reduced nitrogen application treatment: 75 kg N·hm² of urea. -2 Top-dress with fertilizer during the corn's large trumpet stage (around 60 days, V11-22). Apply urea at 300 kg N·hm² for both conventional and reduced nitrogen application treatments. -2 140 kg·N·hm -2In the inoculation treatment, the streak plating method was used to evenly spread *A. brasilense* and *H. frisingense* strains onto NA medium, and then incubated overnight at 28°C to activate the bacterial community. After single colonies grew on the medium, the bacterial culture was picked up using a disposable inoculation loop in a clean bench. The inoculation loop was then rinsed in NB medium. After completion, the medium was sealed, and the shaker was set to 30°C and 200 rpm for overnight incubation (12 h), while observing whether turbidity appeared in the medium. 200 μl of each NB medium containing different bacteria was taken and measured at 600 nm using a microplate reader to obtain an OD value of 0.6–0.7, which was used to prepare the target bacterial suspension. 200 ml of the mixed bacterial suspension (*A. brasilense* and *H. frisingense* bacterial suspension in a 1:1 ratio) was poured into the rhizosphere soil of maize treated with AH. For AHA-treated corn, the first step was to use the hole-digging method. Two holes were carefully dug in the soil near the corn roots, exposing some of the fine roots. 2 g of inoculant was applied to each hole, and the holes were then covered. 200 ml of mixed inoculant solution was then poured into the soil around the corn roots (as above). Both inoculation treatments were inoculated every 10 days starting from the 12th day after corn planting.

[0056] Table 1 Test Example 1: Determination of Soil Chemical Properties and Nutrient Content The physicochemical properties of maize rhizosphere soil were determined according to the method of Bao Shidan (2000), including: pH (glass electrode method (soil:water = 1:2.5)), SOC (potassium dichromate-external heating method), TN (semi-micro Kjeldahl nitrogen determination method), TP (molybdenum-antimony colorimetric method), TK (flame photometry), AP (colorimetric method), AK (atomic absorption spectrometry), and NH4. + -N (spectrophotometry), NO3 - -N (spectrophotometric method). Soil microbial biomass nitrogen (MBN) was determined using a chloroform fumigation-K2SO4 extraction-flow injection nitrogen analyzer method.

[0057] Under the same nitrogen application level, the soil physicochemical properties of the three inoculation methods showed significant differences. At the N0 level, the C / N and AP ratios were significantly increased in the CK treatment, while TN and NH4+ were significantly increased. + -N and MBN were significantly reduced; TN and NH4+ were significantly reduced after AH treatment. + -N, NO3 - -N and MBN were significantly higher than AHA, while C / N was significantly lower than AHA, but there were no significant differences in SOC, TK, AP, and AK between the two. At N1 level, CK-treated NO3... - -N and C / N ratios increased significantly, TN and NH4++ -N was significantly reduced; NH4 treated with AH was significantly reduced. + -N was significantly higher than AHA, while MBN was significantly lower than AHA. Inoculation mode had no significant effect on pH, SOC, TK, AP, and AK. At N2 levels, CK treatment significantly decreased TN, AK, and MBN, and significantly increased C / N; AH treatment significantly increased TK and NH4+. + -N and MBN were significantly higher than AHA, but NO3- was much higher. - -N was significantly lower than AHA; there were no significant differences in pH, SOC, and AP among the three inoculation modes. There were no significant differences in pH among the three inoculation modes at each nitrogen application level.

[0058] Soil physicochemical properties varied under different nitrogen application levels and inoculation patterns. In the control (CK) treatment, the C / N ratio and AP of N0 were significantly higher than those of N1 and N2, while the NO3- content of N1 was lower. - -N and MBN were significantly higher than N2 and NH4. + -N was significantly lower than N2, while pH, SOC, TN, and AK showed no significant differences among different nitrogen application levels. In the AH treatment, pH and MBN were significantly higher in N0 than in N1 and N2, while NO3 in N1 was significantly higher. - -N and C / N were significantly higher than N2, while TN and MBN were significantly lower than N2. SOC, TK, AP, and AK showed no significant differences among different nitrogen application levels. In the AHA treatment, NO3- from N0... - -N is significantly lower than N1 and N2. TK, C / N, and MBN of N1 are significantly higher than those of N2. SOC, AP, AK, and NH4 + -N showed no significant difference among different nitrogen application levels. TP showed no significant difference among treatments.

[0059] Test Example 2: Determination of Soil Nitrogenase Activity The activity of nitrogenase in soil samples was determined using a soil nitrogenase enzyme-linked immunosorbent assay (ELISA) kit (Shanghai Enzyme-Link Biotechnology Co., Ltd.). The specific operating steps are as follows: (1) Adding standard samples: Set up standard sample wells and sample wells, and add 50 μl of standard sample of different concentrations to each standard sample well.

[0060] (2) Sample addition: Set up blank wells (blank control wells do not contain sample or enzyme-labeled reagent, and the remaining steps are the same) and sample wells. First, add 40 μl of sample diluent to the sample wells on the enzyme-labeled plate, and then add 10 μl of the sample to be tested (the final sample dilution is 5 times). When adding the sample, add it to the bottom of the well, trying not to touch the well wall, and gently shake to mix.

[0061] (3) Add enzyme: except for the blank well, add 100 μl of enzyme labeling reagent to each well.

[0062] (4) Incubation: After sealing with a sealing film, incubate at 37 ℃ for 60 min.

[0063] (5) Solution preparation: Dilute the 20-fold concentrated washing solution with distilled water 20 times and set aside.

[0064] (6) Washing: Carefully peel off the sealing film, discard the liquid, shake dry, fill each well with washing liquid, let stand for 30 seconds and then discard, repeat this 5 times, and pat dry.

[0065] (7) Color development: Add 50 μl of color developer A to each well, then add 50 μl of color developer B, gently shake to mix, and develop color at 37 ℃ in the dark for 15 min.

[0066] (8) Termination: Add 50 μl of stop solution to each well to terminate the reaction (at this time, the blue color will immediately turn yellow).

[0067] (9) Measurement: Zero the instrument with the blank well and measure the absorbance (OD value) of each well in sequence at a wavelength of 450 nm. The measurement should be performed within 15 minutes after adding the stop solution. Finally, plot the standard curve on graph paper with the concentration of the standard as the x-axis and the OD value as the y-axis. Find the corresponding concentration of the sample from the standard curve based on the OD value of the sample, and then multiply it by the dilution factor to obtain the actual concentration of the sample.

[0068] Nitrogenase activity is a key indicator reflecting soil nitrogen cycling and nitrogen supply capacity. Figure 3 At all nitrogen application levels, nitrogenase activity was consistently AHA > AH > CK, with significant differences, indicating that combined inoculation significantly increased nitrogenase activity in maize rhizosphere soil, with AHA showing the strongest effect. Within the same inoculation pattern, CK nitrogenase activity was significantly higher at N0 and N2 levels than at N1, with N2-CK being significantly higher than N1-CK, while there was no significant difference between N0-CK and N2-CK. AH and AHA nitrogenase activities were significantly higher at N1 level than at N0 level, with no significant difference between the two at N0 and N2 levels. N1-AH was significantly higher than N2-AH, while there was no significant difference between N1-AHA and N2-AHA. Overall, combined inoculation, especially the AHA treatment, can significantly enhance soil nitrogenase activity, thereby improving nitrogen supply capacity.

[0069] Test Example 3: Determination of Nitrogen-Related Indicators of Maize Agronomic Traits (1) Agronomic traits Plant height: During the tasseling stage of maize, plant height and stem diameter are measured. Plant height is the distance from the base of the plant to the highest point of the plant (the top of the tassel).

[0070] Stem diameter: During the corn tasseling period, the diameter of the stem (widest part) at the second internode above the base of the plant is measured with vernier calipers.

[0071] Biomass: During the corn harvest period, the plants were separated into aboveground and underground parts, and the fresh weight was measured. Then, the plants were blanched for 30 minutes (105 ℃) and dried (80 ℃) to constant weight, and the dry weight was measured.

[0072] Agronomic traits of maize are key characteristics affecting its growth and development. Under the same nitrogen application level, there were significant differences in agronomic traits among the three inoculation modalities. At the N0 level, there was no significant difference in plant height; stem diameter and aboveground fresh weight showed a significant order of AHA > AH > CK; the aboveground dry weight, underground fresh weight, and dry weight of CK were significantly lower than those of AHA, but not significantly different from those of AH. At the N1 level, there was no significant difference in stem diameter and underground dry weight; the aboveground dry weight and underground fresh weight of CK were significantly lower than those of AH and AHA; there were no significant differences in any indicators between the AH and AHA treatments. At the N2 level, there were no significant differences in plant height, aboveground fresh weight, and underground fresh weight and dry weight; the stem diameter of CK was significantly greater than that of AH, and the aboveground dry weight was significantly greater than that of AHA.

[0073] Under different nitrogen application levels, maize agronomic traits varied among inoculation patterns. In the CK treatment, plant height showed no significant difference; stem diameter, aboveground fresh weight, and dry weight all showed a significant order of N2 > N1 > N0; the underground dry weight of N0 was significantly lower than that of N1 and N2, and its underground fresh weight was significantly lower than that of N2. In the AH treatment, plant height showed no significant difference; stem diameter and underground fresh weight of N0 were significantly lower than those of N1; the aboveground fresh weight and dry weight, as well as the underground dry weight of N0, were significantly lower than those of N1 and N2, with no significant difference between N1 and N2. In the AHA treatment, plant height, stem diameter, aboveground fresh weight, and underground fresh weight and dry weight showed no significant differences among different nitrogen application levels; the aboveground dry weight of N2 was significantly lower than that of N1, but not significantly different from that of N0.

[0074] Two-way ANOVA showed that nitrogen application level had no significant effect on plant height, but nitrogen application level and inoculation pattern had significant effects on other agronomic traits. The interaction between the two significantly affected stem diameter, aboveground fresh weight and dry weight, and underground dry weight.

[0075] (2) Nitrogen content in the aboveground parts of the plant: The aboveground parts of the dried corn plant were crushed and the total nitrogen content in the aboveground parts of the plant was determined by the Kjeldahl method.

[0076] Under nitrogen application levels of N0 and N1, the aboveground nitrogen content of maize under different inoculation patterns showed a significant order of AHA > AH > CK. Under nitrogen application level of N2, there was no significant difference in aboveground nitrogen content among different inoculation patterns. However, under the same inoculation pattern, the aboveground nitrogen content of maize under the CK treatment showed a significant order of N2 > N1 > N0. The aboveground nitrogen content of maize under the N0-AH treatment was significantly lower than that under the N1-AH and N2-AH treatments, with no significant difference between the N1-AH and N2-AH treatments. The aboveground nitrogen content of maize under the AHA treatment showed no significant difference among different nitrogen application levels. Figure 1 ).

[0077] (3) Nitrogen fertilizer absorption and utilization rate: The nitrogen fertilizer absorption and utilization rate (100%) was calculated according to the method of Huang Qiaoyi et al. (2017).

[0078] Nitrogen uptake by aboveground parts (kg·hm) -2 = Aboveground biomass × Aboveground nitrogen content Nitrogen fertilizer absorption and utilization rate (%) = (Nitrogen uptake by aboveground parts in the nitrogen-applied area - Nitrogen uptake by aboveground parts in the control area) × 100 / nitrogen application rate Using the nitrogen fertilizer uptake and utilization rate under the N0 nitrogen application treatment as a benchmark, at the N1 nitrogen application level, the nitrogen fertilizer uptake and utilization rates of different inoculation treatments showed the order AHA > AH > CK, with significant differences. At the N2 nitrogen application level, there was no significant difference in nitrogen fertilizer utilization rates among different inoculation methods. Under the same inoculation method, there was no significant difference in nitrogen fertilizer utilization rates between N1-CK and N2-CK; however, the nitrogen fertilizer uptake and utilization rates of the AH and AHA treatments at the N1 nitrogen application level were significantly higher than those at the N2 nitrogen application level. Figure 2 ).

[0079] (4) Net ammoniation rate and net nitrification rate: Net ammoniation rate and net nitrification rate were calculated according to the method of Xu Junshan et al. (2021).

[0080] Soil net ammonification rate = (Ammonium nitrogen concentration after incubation – Ammonium nitrogen concentration before incubation) / Incubation time Soil net nitrification rate = (nitrate nitrogen concentration after incubation – nitrate nitrogen concentration before incubation) / incubation time Soil ammonification and nitrification are the core processes of soil nitrogen cycling, and different nitrogen application levels and inoculation patterns significantly affect the net ammonification rate (NAR). Under N0 and N1 nitrogen application levels, the NAR of different inoculation patterns was AH > AHA > CK, with significant differences. Under N2 nitrogen application level, the NAR of the AH treatment was significantly higher than that of the CK and AHA treatments, while there was no significant difference between the N2-CK and N2-AHA treatments. Under the same inoculation pattern, the N2-CK treatment had a significantly higher NAR than N1-CK, but no significant difference from N0-CK; the N0-AH treatment had a significantly higher NAR than N2-AH, while there was no significant difference between N1-AH and N2-AH; the AHA treatment showed no significant difference in NAR under different nitrogen application levels. Therefore, different nitrogen application levels and inoculation patterns significantly affect the net nitrification rate (NAR) of soil. At the N0 nitrogen application level, the net nitrification rate (NNR) of different inoculation modalities was AH > CK > AHA, with significant differences. At the N1 nitrogen application level, there was no significant difference in the NNR of AH and AHA, and both were significantly lower than the CK treatment. At the N2 nitrogen application level, the NNR of different inoculation modalities was AHA > CK > AH, with significant differences. Under the same inoculation modalities, the NNR of CK and AHA treatments was significantly higher than the no-nitrogen treatment at the nitrogen application level, and the NNR of N1-CK was significantly higher than that of N2-CK, while there was no significant difference between N1-AHA and N2-AHA. The NNR of N2-AH treatment was significantly lower than that of N0-AH and N1-AH, and there was no significant difference in the NNR of N0-AH and N1-AH treatments. Two-way ANOVA showed that, except for nitrogen application level which had no significant effect on net ammonification rate, different nitrogen application levels and inoculation patterns had significant effects on net ammonification rate and net nitrification rate. Furthermore, the interaction between different inoculation patterns and nitrogen application amount significantly affected the nitrogen transformation rate in maize rhizosphere soil.

[0081] Test Example 4: DNA Extraction and Absolute Quantitative qPCR To quantitatively regulate key genes involved in nitrification (amoA), denitrification (nirK, nirS, nosZ), nitrogen fixation (nifH), and organic nitrogen mineralization (chiA), 0.5 g of maize rhizosphere soil was extracted from each treatment. Total DNA was extracted from soil samples using the Power Soil DNA Isolation Kit (MoBio, USA), and the concentration and purity of the extracted soil DNA were determined using a Nanodrop 2000 (Thermo, USA). SYBR® Premix Ex Taq was used. TMThe dedicated Real-Time PCR kit (TaKaRaBiotechnology, Otsu, Shiga, Japan) was used for absolute quantitative PCR analysis on an Applied Biosystems 7500 / 7500 Fast Real-Time PCR System (Thermo Fisher Scientific, Inc., UK). Each sample was analyzed in triplicate. The standard curve was prepared using a 10-fold dilution of the corresponding functional gene's cloning recombinant plasmid, with eight dilution gradients, each with three replicates. Sterile deionized water was used as a negative control. Specific primers and qPCR amplification procedures for the functional genes are shown in Table 2. The amplification system included 2×SYBR® PremixExTaq. TM II. 10 μl of primer, 0.4 μl each of forward and reverse primers (10 μmol / L), and 2 μl of template DNA were added, and the volume was brought to 20 μl with sterile deionized water. The specificity of the amplified product was confirmed by melting curve analysis. Finally, the gene copy number was calculated using a standard curve and normalized to the copy number per gram of dry soil (dried at 60 °C for 24 h) (copies / g dry soil).

[0082] Table 2 Note: After the reaction is complete, add a melting curve program (95℃, 15s; 60℃, 60s; 95℃, 30s; 60℃, 15s).

[0083] The effects of different nitrogen application levels and inoculation patterns on functional genes involved in nitrogen cycling processes in maize rhizosphere soil, such as... Figure 4As shown: nitrogen fixation (nifH), organic nitrogen mineralization (chiA), nitrification (amoA), and denitrification (nirK, nirS, nosZ). Under the N0 nitrogen application level, inoculation treatment increased the expression levels of nifH and amoA genes, while the expression levels of chiA, nirK, nirS, and nosZ genes were significantly higher under the N0-AHA treatment than under the N0-CK and N0-AH treatments, but there were no significant differences between the N0-CK and N0-AH treatments. Except for the N2-CK treatment, where the amoA gene expression level was significantly higher than that under the N2-AH and N2-AHA treatments, and there was no significant difference in amoA gene expression levels between the N2-AH and N2-AHA treatments, there were no significant differences in the expression levels of core functional genes for soil nitrogen transformation among different inoculation patterns under the N1 and N2 nitrogen application levels. Under different inoculation patterns, nitrogen application decreased the expression level of the nifH gene and increased the expression level of the amoA gene in the CK treatment. The expression levels of chiA, nirK, nirS, and nosZ genes did not differ significantly under different nitrogen application levels, while the amoA gene expression level in the N2-CK treatment was significantly higher than that in the N0-CK and N1-CK treatments. The expression levels of core functional genes for soil nitrogen transformation under AH treatment did not differ significantly under different nitrogen application levels. Under AHA treatment, the expression levels of nifH, amoA, and nirS genes did not differ significantly under different nitrogen application levels, while the expression levels of chiA, nirK, and nosZ genes under the N0 nitrogen application level were significantly higher than those under the N1 and N2 nitrogen application levels, and there was no significant difference between the N1-AHA and N2-AHA treatments.

[0084] Test Example 5: High-throughput sequencing Primers for sequencing soil DNA samples were selected from the V4-V5 region of bacterial 16S rRNA, the ITS1-ITS2 region of fungal ITS, the V3-V5 region of archaea 16S rRNA, and the V4 region of protist 18S rRNA (Table 3). Soil DNA was extracted using the Power Soil DNA Isolation Kit (MoBio, USA) according to the prescribed steps. Amplification was performed using the Takara Premix Taq Version 2.0 kit (Takara Biotechnology Co., Dalian, China). Each sample was amplified three times. The PCR amplification system and procedure were as follows: the PCR reaction volume was 25 μl, including 10 ng template DNA, 1 μl upstream primer (10 μM), 1 μl downstream primer (10 μM), 12.5 μl 2×Taq PCR Master Mix, and sterile ddH2O to a final volume of 25 μl. The amplification program consisted of 30 cycles of pre-denaturation at 95°C for 5 min (95°C denaturation for 30 s, 55°C annealing for 30 s, 72°C extension for 30 s), followed by a stabilization extension at 72°C for 10 min. The amplified products were examined by 1.2% agarose gel electrophoresis. The gels were extracted using the EZNAGel Extraction Kit (Omega, USA). The PCR products were then analyzed using QuantiFluor. TM After comparing concentrations using the -ST blue fluorescence quantitative system (Promega, USA), the required volume for each sample was calculated based on the principle of equal mass, and the amplified products were mixed. The library construction procedure was performed according to NEBNext® Ultra. TM The DNA Library Prep Kit for Illumina® (New England Biolabs, USA) was used for standardized procedures, and the constructed amplicon library was sequenced using the Illumina PE250 platform (Meiji Biotechnology, Shanghai). The raw data has been uploaded to the National Center for Bioinformatics (NCBI), accession number PRJNA1190904.

[0085] Table 3 Raw sequencing data preprocessing: Sample data were distinguished according to the index sequence to obtain fastq format paired-end reads files (fq1 and fq2). Flash software was used to assemble single sequences according to the overlap relationship of PE reads. Then, Trimmomatic software was used for quality control optimization: (1) Filtering bases with a tail quality value less than 20, and truncating the tail bases if the average quality score within a 50bp sliding window is less than 20, and removing reads below 50bp and those containing N bases. (2) Assembling according to the overlap relationship of PE reads, with a minimum length of 10 bp. (3) The maximum mismatch ratio in the overlap region is 0.2, and sequences that do not meet the requirements are discarded. (4) Samples were distinguished according to primers and barcodes, and the sequence orientation was arranged. The barcode mismatch number is 0, and the maximum primer mismatch number is 2.

[0086] OTU and Species Community Alpha Diversity Analysis: Using UPARSE (version 7.1) software, sequences with a similarity threshold of 97% or higher were grouped into one OTU (Operational Taxonomic Unit). Non-repetitive sequences were extracted from the optimized sequences, individual sequences were deleted, and the remaining sequences were clustered after removing chimeras based on 97% similarity to obtain representative OTU sequences. All optimized sequences (15) were aligned with the representative OTU sequences, and sequences with a similarity higher than 97% were selected to generate an OTU table. The RDP classifier Bayesian algorithm was used to perform taxonomic analysis on the representative OTU sequences, and community composition was statistically analyzed. The Alpha diversity index was analyzed using MOTHUR (v.1.30.1). The dilution curve data were constructed using the QIIME software package, and curves were created using R language tools. A certain number of sequences were randomly selected from the sample, and the Alpha diversity index of these sequences for the corresponding samples was calculated. A curve was plotted with the amount of extracted data on the x-axis and the Alpha diversity index value on the y-axis. Whether the curve reached a flat point indicated whether the amount of sequencing data was sufficient.

[0087] Comparative Analysis of Samples: Beta diversity analysis compares the species diversity of microbial communities under different treatments to explore the similarities or differences in community composition among different groups of samples. Principal coordinates analysis (PCoA) is a multivariate statistical analysis method used for dimensionality reduction and visualization, suitable for displaying the similarities or differences between samples. The original data is standardized, a distance matrix is ​​calculated based on Bray-Curtis distance, and then centered to obtain a centered matrix. Eigenvalue decomposition is performed on the centered matrix to extract eigenvalues ​​and eigenvectors. Finally, the top two principal coordinates are selected for visualization based on the magnitude of the eigenvalues. PCoA statistical analysis and plotting are performed using R language to analyze the community composition of different samples and reflect the differences and distances between samples. Simultaneously, the ANOSIM nonparametric similarity test (Analysis of similarities) is used to examine the significant differences between different groups.

[0088] Correlation analysis of environmental factors: Based on environmental factor data and OTU abundance tables, redundancy analysis (RDA) was performed using the vegan package in R to reflect the relationship between the microbiome and environmental factors and to create graphs. Measured values ​​of environmental variables included pH, TN, TP, TK, AK, AP, and NH4. + -N, NO3 - Environmental factors such as -N, MBN, and Nitrogenase activity were included. The OTU abundance table and environmental factor data were standardized, and the significance of the model was tested using the permutation test. The contribution rate of environmental factors to the model was extracted, and then the RDA bi-order plot was drawn using the `plot` function and plotted using the `ggplot2` package.

[0089] Molecular ecological network structure data analysis: OTU data obtained from high-throughput sequencing were standardized. In the R language platform, the pairwise similarity between different 16 OTUs was calculated using the Pearson correlation coefficient, constructing a Pearson correlation matrix. Based on Random Matrix Theory (RMT), by setting appropriate thresholds, the correlation between OTUs can be determined. After running the algorithm, node attribute files and edge attribute files of the network are obtained. These files are then imported into Gephi software for network visualization, revealing the network structure between microbial communities under different nitrogen application levels and inoculation patterns. In Gephi software, the "Layout" option in the overview page is set to "Fruchterman Reingold," and the algorithm is run. Simultaneously, the constructed network is analyzed in the "Statistics" option, yielding network topology attributes including the number of nodes, edges, average degree, network density, connecting components, and modularity. Network nodes represent species within a microbial community, and the connections between nodes indicate interactions between communities. Average degree is the average degree of all nodes in the network. Network density is the ratio of the actual number of edges to the theoretical maximum possible number of edges; the closer the density is to 1, the closer the network is to complete connectivity. Connecting components are subsets of nodes that are reachable from each other in the network; the number of connecting components reflects the network structure, and a larger number indicates a more dispersed network. Modularity is an indicator of the structural strength of a network community, reflecting the modular characteristics of molecular ecological networks.

[0090] (1) α-diversity analysis of maize rhizosphere bacterial community: A total of 3,013,044 bacterial sequences, including 33,854 ASVs, were obtained under different nitrogen application levels and inoculation patterns. Dilution curves ( Figure 5 The data level tended to plateau with increasing sequencing volume, indicating that the data volume had reached saturation and met the analysis requirements. α-diversity was used to assess community richness and diversity. The results showed that the Chao and Ace indices did not differ significantly among different treatments; at the same nitrogen application level, the Shannon and Simpson indices of the three inoculation treatments did not differ significantly; at different nitrogen application levels, the Shannon index of N0-AH was significantly higher than that of N2-AH, and the Simpson index of N1-CK was significantly higher than that of N0-CK.

[0091] (2) Analysis of the bacterial community composition of maize rhizosphere soil showed that: under different nitrogen application levels and inoculation patterns ( Figure 6Proteobacteria, Firmicutes, Actinobacteria, and Chloroflexi were dominant phyla in all treatments (72.05%–77.30%). Acidobacteriola (8.44%) and WPS-2 (4.20%) had relatively high abundance in N0-CK; Patescibacteria also had high abundance in N1-AH, N1-AHA, N2-CK, N2-AH, and N2-AHA treatments (8.86%–10.13%). Further analysis of the top ten dominant genera revealed that different treatments significantly affected the relative abundance of genera. Bacillus was significantly elevated in N1-CK; norank_o__Gaiellales showed no significant difference at the same nitrogen level, but was significantly lower in N2-AH than in N0-AH; norank_f__JG30-KF-AS9 was significantly lower in N0-AHA than in N0-CK; Sphingomonas was significantly higher in N2-CK than in other treatments; Rhodanobacter showed no difference at N0 level, but the inoculation treatment under nitrogen was significantly higher than CK; FCPS473 was significantly higher in N0-CK than in N0-AHA; norank_f__LWQ8 showed no difference at the same nitrogen level, but N2 was significantly higher than N0 in CK and AH treatments, while AHA treatment showed no significant difference; Chujaibacter showed no difference at N0, but AH was significantly higher than CK under nitrogen; Tumebacillus significantly decreased in CK treatment with increasing nitrogen, and was higher in N0 and N1 than inoculation treatment; norank_o__C01199 was significantly higher in N0-AHA than in other treatments.

[0092] (3) PCoA analysis of β-diversity of maize rhizosphere soil bacterial community ( Figure 7 The results showed that PC1 and PC2 explained 29.80% and 21.39% of the community differences, respectively. At N0 and N1 nitrogen application levels, CK was clearly separated from AH and AHA, and AH and AHA aggregated; at the N2 level, all three aggregated. This indicates that under N0 and N1 conditions, there were significant differences in community structure between the uninoculated and inoculated treatments, but no significant difference between AH and AHA; under N2 conditions, there were no significant differences among the treatments.

[0093] Under the same inoculation pattern, the control group (CK) was clearly separated between different nitrogen application levels. In the AH and AHA treatments, N0 was clearly separated from N1 and N2, while N1 and N2 clustered together. This indicates that the uninoculated treatment was significantly affected by the nitrogen application level. In the inoculated treatment, N0 differed significantly from N1 and N2, while there was no significant difference between N1 and N2.

[0094] (4) RDA analysis of maize rhizosphere soil bacterial community and environmental factors ( Figure 8The results showed that the first two axes explained 24.22% and 9.36% of the community variation, respectively. The bacterial community was affected differently by environmental factors under different treatments, with the C / N ratio (R...) being a key indicator. 2 =0.4401, P= 0.001), TN (R 2 =0.3684, P= 0.001), AP (R 2 =0.3782, P= 0.001), NH4 + -N(R) 2 =0.1761, P= 0.025) and nitrogenase activity (R 2 = 0.3068, P = 0.001) The effect was significant.

[0095] At the N0 nitrogen application level, the control (CK) was mainly affected by the aspirin (AP), while the inoculation treatments (AH, AHA) were mainly affected by the mycelium (MBN) and total phosphorus (TP). At the N1 level, the CK was mainly affected by the C / N ratio and pH, while the inoculation treatments were mainly affected by the acetic acid (AK) and NH4+. + -N influence; at N2 levels, CK is mainly affected by TP, while inoculation treatment is mainly affected by NH4. + The effects of -N, nitrogenase activity, TN, and SOC.

[0096] (5) Molecular ecological network analysis of maize rhizosphere soil bacteria communities showed that co-occurrence networks ( Figure 9 The nodes in the network are mainly composed of bacteria from the phyla Chloroflexi, Proteobacteria, Actinobacteriota, Firmicutes, Acidobacteriota, Patescibacteria, Gemmatimonadota, and Bacteroidota. Network topology attributes show that bacterial community interaction patterns differ under different treatments: the ratio of positive to negative correlations is relatively balanced in N2-AH and N2-AHA, while synergistic effects are dominant in the other treatments.

[0097] Under nitrogen application levels N0 and N2, the number of nodes and edges in the control group (CK) was significantly higher than that in the inoculation treatment. Furthermore, the number of nodes and edges in the N0-CK group was higher than that in the nitrogen application treatment, while those in the N1-CK group were lower than those in the inoculation treatment, especially lower than those in the AHA group. The average degree of the bacterial molecular network was highest in the N0-AH group (9.628), indicating that this network had the highest number of node connections. Nitrogen application reduced the average degree of the AH treatment. Compared to N1, the average degree of the three inoculation treatments was significantly increased under nitrogen application at N2, suggesting that conventional nitrogen application is more beneficial than reduced nitrogen application in increasing the average number of node connections.

[0098] The network density was lowest in N0-CK (0.013) and the most connecting components (100), indicating a more dispersed network with more independent subnetworks. N0-AH had a higher network density (0.028) and fewer connecting components (57), resulting in a more compact structure and better connectivity. Nitrogen application significantly increased the network density of both CK and AHA treatments and reduced the number of connecting components in CK, while the connection components in AHA were less affected by nitrogen application. Conversely, nitrogen application significantly reduced the network density and increased the number of connecting components in AH, promoting inter-community interactions.

[0099] Network modularity was lowest at N0-AH (0.909), indicating that the modular structure was not obvious. Nitrogen application improved network modularity under the AH treatment, which is beneficial for interspecific material and information exchange. At both N1 and N2 nitrogen application levels, the network modularity of AH was significantly higher than that of AHA, indicating that under nitrogen application conditions, the AH inoculation mode is more effective in promoting bacterial community modularity than AHA.

[0100] (6) α-diversity analysis of maize rhizosphere fungal communities showed that, based on Illumina MiSeq (PE300) sequencing, a total of 3,502,732 original fungal sequences were obtained under different nitrogen application levels and inoculation patterns, and classified into 3,929 ASVs. Dilution curves ( Figure 10 The results showed that while the Sobs index continued to rise with increasing sequencing depth, it tended to level off, indicating that the sequencing depth had reached saturation and met the analytical requirements. The fungal α diversity index was used to assess community richness and diversity.

[0101] At the N1 nitrogen application level, the Chao and Ace indices of N1-CK were significantly higher than those of N1-AHA; at the N0 level, the Chao and Ace indices of all three inoculation treatments were significantly higher than those of the N2 level. The Shannon index of N1-CK was significantly higher than that of N1-AHA, but not significantly different from that of N1-AH; in the N2-CK treatment, the Shannon index was significantly higher than that of the inoculation treatment, and there was no significant difference between N2-AH and N2-AHA. The Simpson index did not differ significantly among all nitrogen application levels and inoculation patterns.

[0102] (7) Analysis of the fungal community composition in maize rhizosphere soil showed that, at the phylum level, the relative abundance of Ascomycota and Basidiomycota was relatively high in all treatments (N0-CK, N0-AH, N0-AHA, N1-CK, N1-AH, N1-AHA, N2-CK, N2-AH, N2-AHA) (approximately 83.84%–92.50%). The relative abundance of Glomeromycota in N0-CK (5.66%), N1-CK (2.77%), and N2-CK (3.10%) was significantly higher than that in the inoculation treatment (all less than 0.5%), while the relative abundance of Mortierellomycota increased significantly in the inoculation treatment.

[0103] Analysis of the top ten dominant genera at the genus level showed that *Saitozyma* did not differ significantly among different inoculation modalities at the same nitrogen application level, but its relative abundance under N2 nitrogen application was significantly lower than that under N0 nitrogen application in all three inoculation treatments. *Chaetothyriales* was significantly higher in N1-AHA than in N1-CK, but showed no significant difference among the three inoculation modalities at N0 and N2 levels; its abundance under N1 nitrogen application was significantly higher than under N0 nitrogen application, while the CK treatment showed no significant difference with nitrogen application level. At the N2 level, *Penicillium* was significantly lower in N2-CK than in N2-AH and N2-AHA, and its relative abundance under all three inoculation modalities at the N2 level was significantly higher than that at N0 and N1 levels.

[0104] Fusarium showed significantly higher relative abundance in N1-CK than in N2-CK and N1-AHA, while no significant differences were found among the three inoculation modalities at N0 and N2 levels. Trichoderma showed significantly higher abundance in N1-AHA than in N1-CK, and significantly higher abundance in N2-AH than in N0-AH, N2-CK, and N2-AHA. Unclassified_k__Fungi and Talaromyces showed no significant differences among nitrogen application levels and inoculation modalities. Entomortierella showed significantly higher abundance in N0-AHA than in N0-CK, with no significant differences among the other treatments; Melanconiella had the highest relative abundance in N2-CK, while Condenascus showed no significant differences among different treatments.

[0105] (8) PCoA analysis of β-diversity of maize rhizosphere soil fungal community ( Figure 12The results showed that PC1 and PC2 explained 34.51% and 14.05% of the differences in community structure, respectively. At the N0 nitrogen application level, CK and AHA were clearly separated, while AH aggregated with CK and AHA; at the N1 and N2 levels, CK, AH, and AHA all aggregated, indicating that the fungal community structure difference between CK and AHA was significant at the N0 level, while the differences among the three treatments were not significant at the N1 and N2 levels.

[0106] Under the same inoculation pattern, CK and AH were clearly separated at different nitrogen application levels; AHA was clearly separated between N0 and N1, and N2, but aggregated between N1 and N2. The results indicate that the fungal community structure of CK and AH differed significantly under different nitrogen application levels, while AHA showed significant differences between N0 and N1, and N2, but no significant difference between N1 and N2.

[0107] (9) RDA analysis of maize rhizosphere soil fungal community and environmental factors ( Figure 13 The results showed that the first two axes explained 29.34% and 6.74% of the variation in the fungal community, respectively, with TN (R²=0.1406, P=0.05), AP (R²=0.2231, P=0.007), and C / N (R²=0.2004, P=0.009) being the main driving factors. At the N0 nitrogen application level, the control (CK) was mainly affected by C / N and AP, while the inoculation treatments (AH, AHA) were significantly affected by C / N, pH, TP, and AP. At the N1 level, the CK was mainly affected by TP and pH, while the inoculation treatments were mainly affected by NO3. - -N influence; at N2 levels, CK and AHA are less affected by environmental factors, while AH is mainly affected by TN.

[0108] (10) Molecular ecological network analysis of maize rhizosphere soil fungal communities showed that: co-occurrence network ( Figure 14 The nodes in the fungal network are mainly composed of phyla such as Ascomycota, unclassified_k__Fungi, Fungi_phy_Incertae_sedis, Basidiomycota, Glomeromycota, Mortierellomycota, and Chytridiomycota. Under each treatment, the fungal network showed a predominance of positive correlation and synergistic effects, particularly prominent in N2-CK (87.50%) and N2-AHA (81.52%).

[0109] The number of nodes, edges, and mean degree of CK were significantly higher than those of the inoculation treatment under nitrogen application levels N0, N1, and N2, indicating that inoculation simplified the fungal co-occurrence network and reduced the degree of connection between nodes. Under the same inoculation mode, the number of nodes and edges decreased with increasing nitrogen application, while the mean degree increased in N1-CK (2.752), N1-AH (2.392), and N2-AHA (2.272), indicating that there were more connections between individual nodes and other nodes in these treatments.

[0110] Compared to N0, the three inoculation modalities showed increased network density and fewer connecting components at nitrogen application levels N1 and N2, indicating that nitrogen application made the fungal network more compact and more connected. N2-AH had the lowest network modularity (0.879), suggesting less interspecific material and information exchange. The modularity of CK and AH decreased with increasing nitrogen application, while AHA showed significantly higher modularity at N0 and N1 than at N2, especially N1-AHA (0.954). This indicates that low nitrogen is beneficial for enhancing the modularity of the fungal network in AHA treatment, while conventional nitrogen application weakens its modularity.

[0111] (11) α-diversity analysis of maize rhizosphere soil archaea communities showed that, based on Illumina MiSeq (PE300) sequencing, a total of 2,769,443 original archaea sequences were obtained under different nitrogen application levels and inoculation patterns, and classified into 7,906 ASVs. Dilution curves ( Figure 15 The results showed that although the Sobs index continued to rise with the increase in sequencing depth, it tended to level off, indicating that the sequencing depth had reached saturation and met the analysis requirements. The archaeal α diversity index was used to assess community richness and diversity.

[0112] The Chao and Ace indices were significantly higher in the N2-CK treatment than in the other treatments, while the differences among the other treatments were not significant. At N0 and N1 nitrogen application levels, there were no significant differences in the Shannon index among the three inoculation modalities; at the N2 level, the Shannon index of N2-CK was significantly higher than that of the inoculation treatment, and there was no significant difference between N2-AH and N2-AHA. Simultaneously, the Shannon index of N2-CK was significantly higher than that of N0-CK and N1-CK. The Simpson index was significantly higher in the N0-AHA treatment than in the N0-CK treatment, while there were no significant differences among the inoculation modalities at the N1 and N2 levels; among nitrogen application levels, the Simpson index of N0-AHA was significantly higher than that of N1-AHA, but not significantly different from that of N2-AHA.

[0113] (12) Analysis of the archaeological community composition in maize rhizosphere soil showed that, at the phylum level, Crenarchaeota was the dominant phylum in all treatments (N0-CK, N0-AH, N0-AHA, N1-CK, N1-AH, N1-AHA, N2-CK, N2-AH, N2-AHA), with a relative abundance of 60.99%–90.24%; unclassified_k__norank_d__Archaea also accounted for a large proportion in all treatments (6.82%–34.72%). Compared with other treatments, the relative abundance of Nanoarchaeota was significantly increased in N0-CK (2.93%), and Halobacterota and Euryarchaeota were significantly increased in N1-CK (7.77%, 3.85%).

[0114] Analysis of the top ten dominant genera showed that *Nitrososphaeraceae* was significantly more abundant in N0-AHA than in N0-CK, with no significant difference among the three inoculation modes at N1 and N2 levels. Among nitrogen application levels, N0-AHA was significantly more abundant than the nitrogen-applied treatment, while there was no significant difference between N1-AHA and N2-AHA. *Nitrososphaeraceae* was significantly more abundant in N0-CK than in N0-AHA and N1-CK, and significantly more abundant in N2-AHA than in N0-AHA. *Archaea* was significantly more abundant in N2-CK than in N2-AHA, N0-CK, and N1-CK, with no significant differences among the other treatments. *Candidatus nitrocosmicus*, *Nitrosotaleaceae*, *Methanocella*, and *Methanobacterium* showed no significant differences among the treatments. norank_o__Group_1.1c was significantly higher than N0-AHA in N0-CK, with no significant differences among the other treatments; Candidatus nitrososphaera was significantly lower than N0-AHA and N1-CK in N0-CK; norank_o__Woesearchaeales was significantly higher than other treatments in N0-CK, with no significant differences among the other treatments.

[0115] (13) PCoA analysis of β-diversity of maize rhizosphere soil archaea community ( Figure 17The results showed that PC1 and PC2 explained 39.32% and 26.15% of the differences in archaeal community structure, respectively. At the N0 nitrogen application level, CK and AHA were clearly separated, while AH aggregated with CK and AHA; at the N1 and N2 nitrogen application levels, CK, AH, and AHA all aggregated. This indicates a significant difference in archaeal community structure between CK and AHA at the N0 level, while no significant differences were found among the three treatments at the N1 and N2 levels.

[0116] Under the same inoculation pattern, CK showed clear separation between nitrogen application levels N0 and N1, but aggregated with N0 and N1 at the N2 level; AH and AHA aggregated at all nitrogen application levels. The results indicate that the archaeal community structure differed significantly between N0 and N1 levels in the non-inoculated treatment, but not significantly between N2 and N0 or N1; the overall archaeal community structure did not differ significantly under different nitrogen application levels in the inoculated treatment.

[0117] (14) Effects of environmental factors on archaea communities in maize rhizosphere soil: Based on RDA analysis, the effects of environmental factors on archaea communities in maize rhizosphere soil under different nitrogen application levels and inoculation patterns were explained. The results showed that the first two axes of environmental factors explained 24.31% and 7.88% of the community variation in archaea communities, respectively. Figure 18 Furthermore, the distribution of archaeal communities was not significantly affected by soil environmental factors. Compared to other environmental factors, at the N0 nitrogen application level, the CK treatment was less affected by soil NH3. 3- -N content has a significant impact; inoculation treatments (AH and AHA) are affected by NH content. 4+ -N and SOC content have a significant impact; at the N1 nitrogen application level, the CK treatment is significantly affected by NH3 in the soil. 3- -N and C / N ratios have a significant impact, while inoculation treatment is significantly affected by SOC content; at N2 nitrogen application levels, the CK treatment is significantly affected by the C / N ratio in the soil, the AH treatment is significantly affected by TK content, and the AHA treatment is significantly affected by NH4+. 4+ -N content has a significant impact.

[0118] (15) Molecular ecological network analysis of maize rhizosphere soil archaea communities showed that: co-occurrence network ( Figure 19 The nodes in the network are mainly composed of phyla such as *Archaea*, *Crenarchaeota*, *Nanoarchaeota*, *Aenigmarchaeota*, *Euryarchaeota*, and *Halobacterota*. Topological attribute results show that the archaeal co-occurrence network under each treatment is predominantly positively correlated, with synergistic effects being dominant, especially in the N2-AH treatment where the positive correlation ratio is the highest (91.30%).

[0119] At nitrogen application levels N0 and N2, the number of nodes and edges in CK was higher than that in the inoculation treatment, but lower at level N1. Meanwhile, the number of nodes and edges in CK and AH was significantly higher at level N2 than in N0 and N1, while AHA's number of nodes and edges decreased at levels N1 and N2 compared to level N0. At level N0, the average degree of CK was lower than that of the inoculation treatment, but higher at level N2. The average degree of N2-CK was the highest (2.613), indicating that each node in its network had the most connections to other nodes.

[0120] At the N0 and N2 levels, the network density of CK decreased while the number of connecting components increased, indicating that the N0-CK and N2-CK networks were relatively dispersed with many independent subnetworks, while inoculation increased network density and enhanced connectivity. Under different nitrogen application levels, the network density of CK increased in N1, AH in N0, and AHA in N2, while the number of connecting components decreased, indicating that the networks were more compact and had better connectivity in these treatments. Furthermore, the nitrogen application level had a significant impact on the archaeal network structure under the three inoculation patterns. The N2-AHA treatment exhibited the lowest archaeal co-occurrence network modularity (0.790), indicating that its modular structure was not obvious and the interspecies interactions were relatively evenly distributed. The modularity of CK and AH increased at the N0 and N2 levels, respectively, while the modularity of AHA did not change significantly at the N0 and N1 levels, and was significantly higher than that of N2-AHA, indicating that different nitrogen application levels had significant differences in the modularity of archaeal networks under the three inoculation patterns.

[0121] (16) α-diversity analysis of maize rhizosphere protozoan communities showed that, based on Illumina MiSeq (PE300) sequencing, a total of 2,561,086 original protozoan sequences were obtained under different nitrogen application levels and inoculation patterns, and were classified into 9,817 ASVs. Dilution curves ( Figure 20 The results showed that while the Sobs index continued to rise with increasing sequencing depth, it tended to level off, indicating that the sequencing depth had reached saturation and met the analytical requirements. The protist alpha diversity index was used to assess community richness and diversity.

[0122] (17) At the N0 nitrogen application level, the Chao and Ace indices of N0-CK were significantly higher than those of N0-AHA; at the N2 nitrogen application level, N2-CK was significantly lower than that of the inoculation treatment, and there was no significant difference between N2-AH and N2-AHA; at the same time, the Chao and Ace indices of N2-CK were significantly lower than those of N0-CK and N1-CK. The Shannon index was significantly lower in N0-AHA than in N0-CK and N0-AH, and significantly lower in N2-CK than inoculation treatment, and there was no significant difference between N2-AH and N2-AHA; among different nitrogen application levels, the Shannon index of N2-CK was significantly lower than that of N0-CK and N1-CK, while that of N0-AHA was significantly lower than that of N1-AHA and N2-AHA. The Simpson index was significantly higher in N0-AHA than in N0-CK and N0-AH, and significantly higher in N2-CK than in N2-AH and N2-AHA. Among nitrogen application levels, the Simpson index of N2-CK was significantly higher than that of N0-CK and N1-CK, and that of N0-AHA was significantly higher than that of N1-AHA and N2-AHA.

[0123] (18) Analysis of the protist community composition in maize rhizosphere soil showed that there were differences in the relative abundance of protists among treatments at the phylum level. Nematozoa, SAR_d__Eukaryota, and norank_k__Chloroplastida were the common dominant groups in all treatments (the total relative abundance was greater than 60%). Compared with other treatments, norank_k__Amoebozoa had higher relative abundance in N1-CK (9.77%), N1-AH (12.62%), N2-AH (13.16%), and N2-AHA (8.51%), while Phragmoplastophyta had a significantly higher relative abundance in N2-CK (11.39%) than in other treatments.

[0124] Analysis of the top ten dominant genera showed that *Tylenchida* was significantly higher than both *N0-AHA*, *N0-CK*, and *N0-AH* in N0-AHA, and significantly higher than all nitrogen-applied treatments in N1-CK, *N1-AH* in N1-CK, and significantly higher than the inoculation treatment in N2-CK. *Enoplea*, *Mucoromycota*, *Heteromita*, and *Vermamoeba* showed no significant differences among treatments. *Chlorophyceae* was significantly higher than *N0-AHA*, *N1-AH*, and *N2-AH* in N0-AH, with no significant differences among the remaining treatments. *Eukaryota* was significantly higher than *N0-CK* in N0-AH, significantly higher than *N1-AHA* in N1-AH, but significantly lower than the inoculation treatment in N2-CK. The unclassified_f__Colpodea treatment was significantly lower than the inoculation treatment in N1-CK, significantly higher than N0-AH and N2-CK in N2-AH, and significantly higher than N0-AHA in N1-AHA. The norank_c__Trebouxiophyceae treatment was significantly higher than N1-CK in N1-AHA, with no significant differences among the three inoculation modalities at N0 and N2 levels. The norank_k__Chloroplastida treatment was significantly higher than other treatments in N0-AH, with no significant differences among the remaining treatments.

[0125] (19) PCoA analysis of β-diversity of maize rhizosphere soil native communities ( Figure 22 The results showed that PC1 and PC2 explained 28.60% and 12.28% of the differences in community structure, respectively. At nitrogen application levels of N0, N1, and N2, CK was clearly separated from AH and AHA, while AH and AHA clustered together, indicating significant differences in protozoan community structure between the uninoculated and inoculated treatments at each nitrogen application level, while no significant difference was found between AH and AHA.

[0126] Under the same inoculation pattern, CK clustered together across different nitrogen application levels, indicating that its community structure did not change significantly with nitrogen application. AH and AHA were clearly separated between N0 and N2 nitrogen application levels, but clustered with N0 and N2 at the N1 level. This suggests that the protozoan community structure did not differ significantly under different nitrogen application levels in the non-inoculated treatment, while the difference between N0 and N2 was significant in the inoculated treatment, and the difference between N1 and N0 and N2 was not significant.

[0127] (20) RDA analysis of maize rhizosphere soil native biological community and environmental factors ( Figure 23The results showed that the first two axes explained 33.78% and 5.52% of the community variation, respectively. The protozoan communities under different treatments were affected differently by environmental factors, including C / N (R²=0.5323, P=0.001), TN (R²=0.4095, P=0.001), AP (R²=0.2643, P=0.001), AK (R²=0.2226, P=0.005), and NH₄⁺. + -N (R2=0.2554, P=0.002) and nitrogenase activity (R2=0.1671, P=0.031) were significant driving factors.

[0128] At the N0 nitrogen application level, CK is mainly affected by AP, AH by MBN and TN, and AHA by TP and AP. At the N1 level, CK is less affected by environmental factors, while inoculation treatment is mainly affected by SOC and TK. At the N2 level, CK is also less affected by environmental factors, while inoculation treatment is mainly affected by TN and NH4++-N.

[0129] (21) Molecular ecological network analysis of maize rhizosphere soil protozoan community showed that: co-occurrence network ( Figure 24 The nodes in the network are mainly composed of phyla such as SAR_d__Eukaryota, unclassified_d__Eukaryota, norank_k__Chloroplastida, norank_k__Amoebozoa, Gracilipodida, Nematozoa, Mucoromycota, and Phragmoplastophyta. Network topology attributes show that the native biological communities under each treatment are predominantly positively correlated, with synergistic effects playing a dominant role.

[0130] Compared to other treatments, N0-CK showed significantly higher node and edge counts; under nitrogen application conditions N1 and N2, the AHA treatment showed a significant increase in node and edge counts. At the N1 level, the average degree of CK was higher than that of the inoculation treatments (AH, AHA); the average degree of AHA decreased significantly at the N0 level but increased significantly at the N2 level, and the average degree of N2-CK was lower than that of the inoculation treatment, indicating that inoculation under nitrogen application in N1 reduced the average number of connections of nodes in the protozoan network, while inoculation under nitrogen application in N2 increased this indicator.

[0131] At the N0 level, the network density of CK was low and the number of connecting components was high; at the N2 level, the network density increased and the number of connecting components decreased; at the N1 level, the difference was not significant compared to N1-AH. The network density of CK increased and the number of connecting components decreased with increasing nitrogen application, while AHA showed the opposite trend. This indicates that inoculation (AH, AHA) and nitrogen application (N1, N2) can improve the connectivity of protozoan networks under CK treatment, while the network of AHA treatment is relatively more dispersed under nitrogen application conditions.

[0132] At the N0 level, the network modularity of CK was significantly higher than that of the inoculated treatment, while at the N2 level it was significantly lower than that of the inoculated treatment. At the N1 level, the network modularity of AHA was higher than that of CK and AH. Compared with N1 and N2, CK showed higher modularity at the N0 level, while AHA showed lower modularity at the N0 level, indicating that the effect of inoculation on network modularity differed significantly under different nitrogen application levels: under the N2 nitrogen application condition, inoculation was beneficial to promoting the exchange of matter and information among protist communities, while CK showed better protist network modularity and more intimate community interactions at the N0 level.

[0133] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A method for promoting soil nitrogen transformation and regulating microbial community by bacteria-fertilizer synergism, characterized in that, Includes the following steps: (1) Apply base fertilizer before planting gramineous crops; (2) Regularly inoculate nitrogen-fixing bacteria and arbuscular mycorrhizal fungi after planting gramineous crops; (3) Apply seedling fertilizer during the seedling stage of gramineous crops, wherein the seedling fertilizer includes potassium fertilizer and reduced amount of nitrogen fertilizer; (4) Apply top dressing fertilizer during the tasseling stage of grass crops, wherein the top dressing fertilizer is a reduced amount of nitrogen fertilizer.

2. The method for promoting soil nitrogen transformation and regulating microbial community by bacteria-fertilizer synergy according to claim 1, characterized in that, In step (1), the base fertilizer includes potassium fertilizer, phosphorus fertilizer and nitrogen fertilizer.

3. The method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of microorganisms and fertilizers according to claim 2, characterized in that, Step (1), the amount of potassium fertilizer applied is 150-230 kg·K2O·hm -2 Phosphate fertilizer 200-330 kg·P2O5·hm -2 Nitrogen fertilizer application: urea at a rate of 80-120 kg·N·hm. -2 .

4. The method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of microorganisms and fertilizers according to claim 1, characterized in that, In step (2), the regular inoculation with syn-nitrogenous bacteria and arbuscular mycorrhizal fungi refers to inoculating syn-nitrogenous bacteria and arbuscular mycorrhizal fungi every 7-14 days after planting 10-30 days after planting gramineous crops.

5. The method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of microorganisms and fertilizers according to claim 4, characterized in that, In step (2), the combined nitrogen-fixing bacteria are *Azospirillum brasilense* and *Herbaspirillum frisingense*, and the arbuscular mycorrhizal fungus is arbuscular mycorrhizal fungus (AMF).

6. The method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of microorganisms and fertilizers according to claim 3, characterized in that, In step (3), the application amount of the potassium fertilizer is 150-230 kg·K2O·hm -2 ; and the application amount of urea as the reduced nitrogen fertilizer is 60-90 kg·N·hm -2 .

7. The method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of bacteria and fertilizer according to claim 6, characterized in that, In step (4), the reduced amount of nitrogen fertilizer is applied as urea at an application rate of 120-160 kg-N-hm -2 .

8. The method for promoting soil nitrogen transformation and regulating the microbiome through synergistic effects of microorganisms and fertilizers according to claim 1, characterized in that, The gramineous crops mentioned are at least one of maize, wheat, and rice.

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