Introduction

Agricultural non-point source pollution is an important part of eutrophic pollution sources of various lakes in China (Ongley et al. 2010). Improper application of nitrogen fertilizer and extensive irrigation in traditional rice farming have resulted in a large quantity of nitrogen (N) loss and reduced N use efficiency of crops, thus aggravating agricultural pollution (Srinivasarao et al. 2014). China is one of the mainly rice-producing countries, accounting for nearly 20% of the rice planting area worldwide and stably supplying about 30% of the world’s rice production (Zhe et al. 2021). Therefore, scholars have carried out much research on water-fertilizer optimum management, including water-saving irrigation (WSI), controlled drainage technology, fertilizer management practices, microorganisms improvement, and soil amendments (Chen et al. 2021; Deng et al. 2019; Liu et al. 2019a, 2019b). Controlled irrigation (CI) is one of the WSI technologies. Nowadays, the research found that rice CI can mitigate the greenhouse effects, reduce nitrogen and phosphorus losses, improve crop productivity, but the alternating process of wet and dry water of rice CI can also speed up the soil organic matter decomposition (Hale et al. 2021; Yang et al. 2018). The utilization and efficiency enhancement technology of straw charcoal-based fertilizer was listed as one of the top ten leading promotion technologies in 2020 by the MARA (Ministry of Agriculture and Rural Affairs). Biochar application easily forms large aggregates in soil, which have a good adsorption effect on nitrogen ions and can reduce nitrogen volatilization (Hale et al. 2021), so biochar application plays a positive role in soil nitrogen fixation and reducing nitrogen loss. Controlled irrigation can reduce the amount of infiltration and thus decrease the loss of nitrogen ions with infiltration (Yang et al. 2013). Therefore, combining biochar with CI is an effective method for achieving sustainable utilization of water and nitrogen resources in paddy fields. At present, most studies focused on the effects of different biochar contents on soil nitrogen concentration in farmland (Cui et al. 2019; Guo et al. 2021). The mechanism of action on nitrogen transformation-related microorganisms of biochar addition in CI paddy fields is still unclear.

Soil microorganisms are quite sensitive to response to soil ecological changes and environmental stresses and play vital roles in soil structural dynamics, nutrient cycling, and some other ecological processes (Cruz-Paredes et al. 2017; Yan et al. 2022). The changes in soil microbial properties can widely monitor soil quality trends and maintain soil fertility (Guo et al. 2019). Nitrogen-fixing microorganisms undertake the role of regulating nitrogen nutrition and are one of the important functional microbial flora in the rice field ecosystem (Wang et al. 2019). All nitrogen-fixing microorganisms contain the nifH gene ferritin in encoding molybdenum-dependent nitrogenase, which is the best marker gene to study community structure (Kang et al. 2013). Ammonia oxidation is the rate-limiting process of nitrification, mainly catalyzed by ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB), which oxidize ammonia to nitrite (Fu et al. 2020). The functions of nitrogen-fixing microorganisms and ammonia-oxidizing microorganisms widely studied in agro-ecosystems are mainly affected by soil environmental factors, including temperature, water content, pH, soil texture, and fertilization (Gao et al. 2020; Lai et al. 2019; Yang et al. 2020). Although the abundance and uniformity of microorganisms can be improved when water is sufficient (Cai et al. 2020), AOA is more adaptable to water-deficient environments than AOB (Yang et al. 2017). Without a doubt, it has a profound effect on the microbial community structure of nitrogen transformation, soil nutrient forms, and migration patterns with different irrigation methods.

Biochar is a farmland carbon management measure with great emission reduction potential. Biochar can adsorb soil inorganic N and affects the microbial degradation process in the soil, reducing the loss of ammonia and nitrate, while potentially controlling the slow release of N to plant roots and improving N use efficiency (Haider et al. 2017; Wang et al. 2016; Yu et al. 2017). Biochar input leads to an increase in soil pH, which affects the abundance and activity of AOA and AOB (Zhang et al. 2019). For example, Song et al. (2014) found that cotton stalk biochar increased the diversity of AOB and reduced the diversity of AOA. Liu et al. (2019a, 2019b) study demonstrated that low-volume biochar (20 t/hm2) and compound fertilizer amendment increased the nifH gene abundance and altered the community structure of soil nifH. Same studies on the effects of applicated biochar on soil microbial activity have drawn inconsistent conclusions, mainly because the biochar type, amount, and use time were different (Maroušek et al. 2020; Stávková and Maroušek, 2021). Currently, most studies only focused on the impact of a single factor of irrigation pattern or biochar application on soil microorganisms (Jiang et al. 2021; Wang et al. 2021). Therefore, under the synergistic effect of biochar and CI, there are few reports on the microbial mechanism of soil nitrogen transformation from the gene level.

The aim of this paper clarified the dynamic changes of soil available N (NH4+–N and NO3−–N) in paddy soil under the joint control of BA and CI. In-depth exploration of the response mechanism and regulation process of nitrogen-fixing and ammonia-oxidizing microorganisms in paddy soil analyzes the environmental factors affecting the microbial OUT abundance of functional genes during growth. The results provide a new perspective on the mechanism of nitrogen migration and transformation, realizing a scientific basis for the sustainable utilization of soil and water resources in paddy fields.

Materials and methods

Description of the study area

The experimental site was located at the Kunshan Experimental Station (34°15′21″N, 121°05′22″E) of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering of Hohai University, in the east Taihu Lake region of China (Fig. 1). This area belongs to the subtropical monsoon climate zone in southeastern China. The annual average temperature, evaporation, precipitation, and sunshine hours are 15.5 °C, 1375.9 mm, 1097.1 mm, and 2104.9 h, respectively, and the frost-free period is 232 days/year. The locals were accustomed to the rice-wheat rotation. The soil type of the experimental field was dark-yellow hydromorphic paddy soil. The soil bulk density of the surface layer 0~30 cm was 1.32 g/cm3. The biochar prepared by straw pyrolysis used in this experiment was obtained from Zhejiang Biochar Engineering Technology Research Center. The biochar was spread in the pots manually and incorporated into the soil (about 20 cm) using a shovel 1 day before the transplantation of rice in 2016. The basic properties of soil and biochar were shown in Table 1.

Fig. 1
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Study area

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Table 1 Basic parameters of soil and biochar properties
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Experimental design

The tested rice variety was Su Xiang Jing, the plant spacing and rowing were 25cm, and the emergence quantity per hole was 3~4. Rice was transplanted on June 25 and harvested on October 24 in 2019. The experiment adopted a randomized block design in 12 lysimeters (2.5×2 m). Four treatments with different combinations of irrigation and biochar were designed. There were two irrigation modes, namely controlled irrigation (CI) and flooding irrigation (FI). Under CI, the three biochar treatments were CA (0 t/hm2 biochar), CB (20 t/hm2 biochar), and CC (40 t/hm2 biochar); under FI conditions were FC (40 t/hm2 biochar) during the whole growth period of rice (re-greening stage to harvest stage).

In the CI treatment, the water depth of the paddy field was only kept at 5~25 mm in the re-greening stage; the paddy field avoided the water layer after irrigation in other stages. The irrigation quota and irrigation time of other stages were determined by taking the soil moisture of the root-soil layer as the control index. The upper limit of soil moisture control was the saturated water content, and the lower limit was 60~80% of the saturated soil moisture content (Liu et al. 2019a, 2019b). Except for the late tillering and yellow maturity stages, the water depth of the paddy field after transplanting was maintained at 30~50 mm in the FI treatment. The fertilization time and fertilization amount were carried out according to local farmers’ habits (Table 2).

Table 2 Type, date, and amount of fertilizer application
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Soil sample collection and measurement

The three-point mixing method was used to collect soil samples at 0–10cm during the re-greening stage (7/12), tillering stage (7/23), and harvest stage (10/25), which were marked by 1, 2, 3, respectively. A total of 36 soil samples were sequenced, including 4 treatments (CA, CB, CC, FC), 3 growth stages, and 3 replicates. The collected soil samples were taken back to the laboratory after removing plant roots and gravel. The average T during the soil sampling period was 20.4 °C. A part of the fresh soil was leached with 1mol/L KCL solution, which determines the NH4+–N, NO3−–N content; the other soil was air-dried and passed through a 20mm mesh sieve and a 100mm mesh sieve to determine the soil urease activity (Urease). In addition, it took the soil of about 2g from each sampling point, mixed, sealed, and stored in a refrigerator at −20 °C for soil microbial genome extraction and analysis. The ultraviolet spectrophotometry measured the content of NH4+–N and NO3−–N. The phenol-sodium hypochlorite colorimetric method determined the content of soil urease, simultaneously monitoring conventional physical and chemical indicators such as Eh, soil moisture (θ) of soil samples.

Soil DNA extraction and PCR amplification

The overall process of soil microbial genome extraction and analysis includes 6 parts: DNA extraction, synthetic primer barcode, PCR amplification and purification, PCR product quantification, PCR free sequencing libraries, and computer sequencing, provided by the Genepioneer Biotechnologies (Nanjing). High-throughput sequencing based on the Illumina MiSeq PE250 platform was used to study nifH, AOA, and AOB gene diversity composition spectrum sequencing. Functional gene-specific primers are shown in Table 3.

Table 3 Primers used for PCR amplification
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Raw FASTQ files were screened for high-quality sequences (Phred Quality Score > 20) by removing low-quality reads, unrecognized reverse primers, and any ambiguous base calls. Effective bacterial sequences were clustered into OTUs (Operational Taxonomic Units) at a 97% sequence similarity level using UPARSE software; then, the dominant sequence was chosen as the representative sequence for each OUT (Guo et al. 2019). Calculate the alpha diversity index based on the abundance of non-rare OUT in each sample, and obtain the species classification information corresponding to each OTU by comparing the OTU sequence to the proGenomes (http://progenomes.embl.de/) database.

Analytical methods

Microbiome data and drawing-related charts were processed using Microsoft 2019. R 3.1.0 software was used to calculate the high-throughput sequencing diversity index. Statistical analysis was performed using SPSS 22.0, which determines the effects of irrigation treatment and biochar on alpha-diversity indices and microbiome OTU in different periods. Heatmap analysis uses color gradients to represent the size of values in the data matrix and can be clustered according to species or sample abundance similarity. Linear regression analyses, including Pearson correlation coefficients, were used to examine the correlations between microbial diversity and soil properties under different biochar and irrigation treatments.

Results

Effect of biochar application and irrigation on soil NH4 +–N and NO3 −–N

The soil NH4+–N content of different BA treatments under different irrigation conditions showed a single peak change trend and reached the peak at the rice tillering stage (Fig. 2). The average concentrations of soil NH4+–N in CA2, CB2, and CC2 were 21.844mg/kg, 29.452mg/kg, and 47.148mg/kg, respectively. During the whole growth period of rice (from the re-greening stage to the harvest stage), under CI conditions, the average concentration of soil NH4+–N of CA, CB, CC were 8.465mg/kg, 10.755mg/kg, 16.565mg/kg, respectively. So this shows that the application of biochar had different soil NH4+–N retention effects in CI rice fields. The application of high amounts of biochar had the best adsorption effect on soil NH4+–N and can reduce the risk of NH4+–N leaching. Under the biochar management conditions, the average content of soil NH4+–N of CC2 was 20.278 mg/kg higher than that of FC2; the average content of soil NH4+–N of CC was 6.261 mg/kg higher than that of FC during the whole growth period. It is mainly related to controlling water in the rice field. On the one hand, the CI are beneficial in improving the utilization efficiency of nitrogen fertilizer and can inhibit the ammonia volatilization loss after urea hydrolysis, so the soil colloid can absorb more NH4+–N. On the other hand, FI may promote water infiltration, which causes soluble ions such as NH4+–N to migrate to deep soil layers.

Fig. 2
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Soil NH4+–N and NO3−–N concentrations after rice transplanting

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Under CI conditions, the soil NO3−–N content of different BA rates reached the peak of rice (Fig. 2), CA2, CB2, CC2 were 18.792mg/kg, 23.013mg/kg, 25.403mg/kg, respectively. Compared with CA1, CB1, CC1, the contents of NO3−–N all decreased by 71.62%, 55.77%, 59.41%, respectively. During the whole growth period of paddy, the average soil NO3−–N content of each treatment group (CA, CB, CC, FC) was 14.179mg/kg, 15.182mg/kg, 15.641mg/kg, and 12.399mg/kg, respectively, so under CI conditions, the application of biochar soil NO3−–N content increased by 7.07% ~10.31%. The average level of soil NO3−–N concentration under FC was lower than under CC, with an average reduction of 3.242 mg/kg, indicating that CC can effectively reduce the loss of soil NO3−–N. The biochar improved the holding capacity of soil NH4+–N and NO3−–N under CI, and NH4+–N was more affected by biochar during the whole growth period. In terms of irrigation management, compared with traditional FI, the contents of NH4+–N and NO3−–N were higher in the surface soil of the paddy field under CI, so losing nitrogen ions with the infiltration water was few. The joint regulation of biochar and CI enhanced soil retention of soil NH4+–N and NO3−–N, thus reducing nutrient leaching.

Gene alpha-diversity analyses under biochar application and irrigation

Alpha-diversity of the nifH gene

After a series of preprocessing such as quality control filtering, splicing and chimera removal, the final effective sequence number was 80023~103020. The OUT number was 3305~4193 by using effective sequence for clustering (Table 4). The Good’s coverage for the observed nifH gene OTUs was 0.99, indicating that the species sample was nearly all covered. There were differences in the α-diversity of bacterial communities under different BA and irrigation. During the whole growth period of rice, with the increase in BA, the Chao1 index showed an increasing trend, and the content of CA, CB, CC was 3884, 4100, 4309, respectively. The Chao1 index of the CB and CC groups increased significantly by 5.57% and 10.95% compared with the CA group. The Shannon index was the highest at the tillering stage, followed by the re-greening stage and the lowest harvest stage. In the tillering stage of rice, the CB2, CA3 of chan1 and Shannon were higher than the CA2, increasing by 18.58~26.55% and 6.96~7.41%, respectively. The FC average index of Chao1 was 4398, which increased by 2.06% compared with CC. At different times, the Shannon of FI was the highest. The Shannon indexes of FC1, FC2, FC3 were 9.61, 9.71, 9.32, respectively, which increased by 0.11, 0.18, and 0.04 compared with CC1, CC2, CC3. Except for the control group, the Shannon index of each treatment group showed a trend of increasing and then decreasing. In summary, the high-carbon treatment had the highest abundance of nifH, but CI did not improve the abundance and diversity of nifH in the rice field.

Table 4 The α-diversity analysis results of nifH gene
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Alpha-diversity of the AOA-amoA gene and AOB-amoA gene

The AOA-amoA and AOB-amoA gene sequencing results and the a-diversity index are shown in Table 5. The Good’s coverage for the observed OTUs was 0.995~1; the sequencing results can reflect the true condition of the microorganisms in the sample. NH3/NH4+ was the common substrate for AOA and AOB to participate in the ammonia oxidation reaction. Therefore, the AOA and AOB abundance and relative contribution rate were easily affected by the concentration of soil fertility in the paddy. The AOA-amoA gene OUT number increased at the re-greening stage and tillering stage, and then decreased by 73.88~133.87% at the harvest stage. The AOB-amoA gene number of OTUs of each sample was the highest during the re-greening stage and then decreased. When the soil fertility content was higher in the tillering stage, AOA dominated the ammonia oxidation reaction because of its high affinity for ammonia in the paddy soil ecosystem; in the harvest stage, when the soil fertility content was low, AOB accounted for domination (Fig. 3).

Table 5 The α-diversity analysis results of AOA-amoA gene and AOB-amoA gene
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Fig. 3
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The number of OTUs of the AOA-amoA gene and AOB-amoA gene

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The chao1 index was higher from the AOA-amoA gene in Table 5 during the re-greening stage and tillering stage, because of the large and frequent application of base fertilizer and tillering fertilizer. During the harvest stage, the soil N content dropped to a low level; chao1 index was lower. So, fertilization was valid and conducive to increasing the abundance of AOA in paddy fields. From the perspective of community diversity, there was no significant change in the Shannon index of each treatment at the re-greening stage and tillering stage. During the re-greening stage, the medium amount of biochar (20 t/hm2) had inhibition on the abundance and diversity of AOA, and the high amount of biochar (40 t/hm2) showed the promoting effect. Under the same BA conditions, the chao1 and Shannon indexes of the CC1 were higher than the FC1, increasing by 7.57%, 4.51%, respectively. During the whole growth period, the chao1 index of CC increased by 0.36% compared with the FC. It shows that there was no significant influence in the composition of the AOA community by irrigation methods.

From the AOB-amoA gene in Table 5, the chao1 and Shannon indexes of each treatment decreased to varying degrees at the re-greening stage to tillering stage, and there was a significant rise at the harvest stage. The abundance and diversity of the AOB at the tillering stage were the smallest. Compared with the CA2, the CB2 had a promotion effect on chao1 and Shannon indexes, increasing by 1.69% and 1.16%; the CC2 had an inhibitory effect on chao1, decreasing by 10.42%. In irrigation modes, the chao1 index of CC1 was higher than that of FC1; the chao1 index of FC2 and FC3 was significantly higher than that of CC2 and CC3. The CC2 Shannon index was the highest in the tillering stage; FC1 and FC3 were the highest in other stages. The Shannon index of FC was higher than CC, an increase of 6.09%, showing that AOB was more susceptible to irrigation patterns than AOA. The chao1 index of CC was higher than CA, increasing by 4.91%, indicating that the application of high amounts of biochar increased the abundance of AOB.

Bacterial community structure under biochar application and irrigation

Comparison of dominant microbial

The relative abundance of dominant microbial species in each of the columns at the genus level showed the top 20 species abundance classification results, which contain the other microbial (Fig. 4a). The species composition of nitrogen-fixing bacteria encoded by the nifH gene in paddy soils under different water and carbon management modes was similar. The dominant microbial population in the columns were Bradyrhizobium, Unassigned, Geobacter, Anaeromyxobacter, and Geoalkalibacter, respectively. These five species accounted for nearly 80% of all the soil systems. In the CI mode, the cumulative abundance of the top 5 dominant microbial in CA1 was the lowest (76.83%); CB1, CC1 were 82.74%, and 85.67%, respectively, indicating that microbial promoted the growth of dominant microbial in the re-greening stage. The abundance of Bradyrhizobium and Geobacter increased in CB2 and CC2 compared with CB1, and the abundance of Anaeromyxobacter decreased, but the cumulative total amount of dominant microbial had little difference. In the harvest stage, the N content of paddy soil was at a relatively low level. The CA3, CB3, CC3, and FC3 nitrogen-fixing microbial communities had the highest abundance of Bradyrhizobium, 41.03%, 33.60%, 43.75%, and 43.32%, respectively. The cumulative abundance of the dominant microorganism in the harvest stage was higher than that in the other two stages. In each growth period, the abundance of dominant microorganism in CI was significantly higher than that of flooded irrigation, indicating that the inadequate irrigation mode increased soil permeability, which was beneficial to increase the microbial quantity and promote nitrogen fixation. Especially after fertilization, the cumulative abundance of the dominant bacteria in FC1 and FC2 was lower than that of CC1 and CC2, which were reduced by 5.68% and 5.33%.

Fig. 4
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Composition of the microbial community at genus level in different treatments

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Figure 4b shows the top 9 species abundance classification results for the relative abundance of AOA-amoA dominant microbial species in each column at the genus level. The species composition of the AOA-amoA gene was similar under different treatment modes in paddy soil. The dominant microbial population were Candidatus Nitrosocosmicuss and Nitrososphaera. These two species accounted for 85.65~92.34% of all the soil samples. From the time scale, there was little difference in the microbial species distribution of the samples at the re-greening and tillering stages. Except for the CC2, Nitrososphaera had the highest relative abundance, accounting for 47.39%, and the remaining treatments were Candidatus Nitrosocosmicus with the highest relative abundance, accounting for 43.17~50.83%. In the harvest stage, the distribution of AOA-amoA microbial changed significantly, Nitrososphaera had the highest relative abundance, and the cumulative abundance of dominant species of CA3, CB3, and CC3 increased slightly, to 90.42%, 92.34%, and 90.40%, respectively. Compared with CA1, CA2, and CA3, new microbial Candidatus Nitrosotenuis, Nitrosarchaeum, and Nitrosopumilus appeared in CC1, CC2, and CC3 groups, respectively. It showed that the application of biochar was beneficial to the increase of AOA-amoA microbial diversity. Under the same BA conditions, the cumulative abundance of dominant microbial in CC was higher than that of FC, which increased by 3.03%.

The relative abundance of the top 8 species of AOB-amoA dominant microbial species at the genus level is shown in Fig. 4c. The Unassigned were the dominant microbial population, and relative abundance was 56.58~87.32% in the re-greening stage and harvest stage, and it was 99.8% at the tillering stage. In the re-greening stage and harvest stage, Nitrosomonas was widely present but not detected in samples at the tillering stage. Mycolicibacterium only existed in each treatment sample at the tillering stage. In the CI mode, BA had little effect on the distribution and abundance of dominant species of AOB-amoA microbial, but it will stimulate the increase of species diversity. CC1 and CB1 samples detect 5 and 4 bacterial microbial, respectively, but CA1 only contains 3. The rare microbial of Nitrosococcus and Methylocystis appeared in the application biochar treatments, indicating that the addition of biochar increased the diversity of AOB-amoA microorganisms in rice soil. Analyzing the influence of water-carbon methods on the AOB-amoA community composition, the relative abundance of the optimal microbial community in flooded irrigation was higher than that of CI in different periods, increasing by 0.07~30.57%. Compared with AOA-amoA microbial composition, rare microbial of the AOB-amoA only appears in samples at certain specific periods. The number of microbes detected by AOB-amoA in a single sample was lower than that in AOA-amoA, indicating that the species diversity of AOA was more abundant.

Cluster analysis of microbial abundance

To visualize general differences in the composition of microbial communities among different treatments, the heatmap was used to cluster analyze the nifH, AOA-amoA, AOB-amoA genes at the family level. The resulting heatmap showed similarities in the abundance of various species or samples. The nifH communities showed more clusters than AOA-amoA and AOB-amoA communities, indicating greater complexity and diversity of nifH communities (Fig. 5). Each treatment affected nifH gene species composition in different periods (Fig. 5a). The nifH gene communities of each treatment were divided into 5 clusters: the first type was FC3, CA3, CC3; the second type was CA1; the third type was CC1 and CA2; the fourth type was FC2, CB1, CB2; the fifth type was CB3, FC1, CC2. Figure 5b shows that each sample of the AOA-amoA gene communities was divided into 4 clusters: the first type was FC3; the second type was FC2, FC1, CB1, CB2, CC2, CC1, CA1, CA2 in the re-greening and tillering stages; the third type was CB3; the fourth type was CA3 and CC3. The species composition of different clusters was similar. During the harvest stage, the abundance of FC3 was significantly higher than that of CC3, which was consistent with the conclusions in Table 5. Figure 5c showed that each sample of the AOB-amoA gene communities was divided into 2 clusters. The first category was all treatment groups at the tillering stage, in which the AOB-amoA gene was greatly affected by external fertilization. The second type was all treatment groups in the re-greening stage and harvest stage, indicating that the species composition of these two periods was similar.

Fig. 5
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Heatmap based on cluster analysis at the family level

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Environmental factors related to soil nitrogen transformation genes

Effect of biochar application and irrigation on urease

The urease of the paddy field was higher during the rice tillering stage and then declined, remaining at the initial level (Fig. 6). Affected by tillering fertilizer, the urease of each treatment was the largest in the tillering stage, and it was positively correlated with the amount of biochar applied. The CA2, CB2, and CC2 treatments were 0.779, 0.903, 1.004 mg/g, respectively. The Urease under FI treatment was higher than that under CI, and FC2 increased by 0.159 mg/g compared to CC2. During the whole growth period of rice, the average urease of each treatment (CA, CB, CC, FC) was 0.442, 0.488, 0.548, 0.614 mg/g, respectively. The biochar treatments (CB, CC) increased soil urease by 10.31~24.00% compared with the CA, indicating that biochar was beneficial for urease. FC increased urease than CC 11.99%, indicating that the urease activity under FI was higher. In short, Fig. 2 shows that the content of increasing soil NH4+–N and NO3−–N of CC was higher than that of CB, which was 2.29 mg/kg and 0.459 mg/kg, respectively, during the whole growth period of paddy. The urease of CC was higher than that of CB in Fig. 7. In addition, the bacterial community structure under biochar application was similar. In the future, we propose to treat the soil with CC (40 t/hm2 biochar).

Fig. 6
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The urease under different water-carbon methods

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Fig. 7
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Pearson correlation analysis of soil nitrogen conversion factors

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Correlation analysis of soil nitrogen transformation genes

The effects of biochar and CI on soil nitrogen transformation function were further investigated. The relationship among nifH, AOA-amoA, AOB-amoA, urease, NH4+–N and NO3−–N content, Eh, T, θ was analyzed through the Pearson correlation and further visualized in Fig. 7. Each treatment group had different correlations with their parameters. The abundance of AOA-amoA gene OTU was positively correlated with T, NH4+–N, and NO3−–N, and the correlation coefficients were 0.78~0.87, 0.26~062, and 0.62~0.86. In Fig. 7b, urease was significantly positively correlated with NH4+–N and NO3−–N, with correlation coefficients of 0.94** (P<0.01) and 0.92* (P<0.05). Comparing Fig. 7a, b, the correlation between the OTU abundance of nifH gene and soil NH4+–N and NO3−–N increased in the 40 t/hm2 biochar treatment group (CC), and the correlation coefficients were 0.73 and 0.71. Under FC conditions, the correlations of other parameters except for AOA-amoA and AOB-amoA genes were reduced, indicating that NH4+–N and NO3−–N were greatly affected by water-saving irrigation. In Fig. 7b, the AOA-amoA and AOB-amoA gene OTU abundances had higher correlations with θ than other treatment groups; AOA-amoA was greater than AOB-amoA because the AOA was more able to adapt to the water-deficient environment than AOB. The combined use of CI and biochar affected soil permeability, soil organic matter content, T, pH, Eh, etc., which affected urease, urea hydrolysis, and ammonia volatilization processes.

Discussion

Effects of BA and CI on nitrogen-transforming functional genes and inorganic nitrogen

The application of biochar affects the microbial degradation process, the abundance, activity, and diversity of a series of soil nitrogen cycle-related microorganisms such as nitrogen-fixing bacteria, AOA and AOB. Anderson et al. (2011) added different biochar in pots, and the results showed biochar increased the relative abundance of many known nitrogen-fixing bacteria, including Forlan’s population and Rhizobium. This experiment showed that the 40 t/hm2 biochar treatment had the highest abundance of nifH under CI; the abundance of dominant microbial (Bradyrhizobium, Unassigned, Geobacter, Anaeromyxobacter, and Geoalkalibacter) increased by 79.68–86.19%. This was consistent with the results of biochar compound fertilizer amendment that increased the nifH gene abundance and altered the community structure of soil nitrogen-fixing bacteria (Liu et al. 2019a, 2019b). Firstly, because biochar as a kind of organic C can potentially manipulate the rates of N cycle in soil systems through adsorption of ammonia and increase of NH4+ storage, thereby reducing NO3−–N leaching and improving microbial nitrogen fixation (Lan et al. 2022). Second, because the CI of repeated alternating dry and wet irrigation significantly increases the amount of dissolved oxygen and redox potential inside and outside the rhizosphere of rice (Cheng et al. 2022) enhances nitrification and promotes the transformation of nitrogen forms, thereby increasing the content of NH4+ and NO3− in paddy soil. Under CI, the soil immobilization of NH4+–N, NO3−–N can increase by 60.77% and 26.14% compared with FI in this paper.

The transformation process of soil nitrogen, especially the intensity of nitrification, determines whether the dominant form of soil inorganic nitrogen is dominated by NH4+–N or NO3−–N (Zhang et al. 2019a). In this paper, we found that the use of biochar increased the diversity of the AOA community, and Nitrosopumilus appeared in CB3 and CC3 (Fig. 4b). Because NH4+–N was between 1.190 ~2.410 at the harvest stage, the low NH4+–N environments favored the growth of AOA (Liu et al. 2017). In Fig. 2, we found that the transformation rate of NH4+–N to NO3−–N in the CC3 treatment group was the highest, which was 80.84%. This is consistent with Wu et al. (2018) enhancement of the nitrification rate by biochar amendment. Daims et al. (2015) point to completely nitrifying Nitrospira as key components of nitrogen-cycling microbial communities. The use of biochar increased the abundance of Nitrospira and promoted the diversity of the AOB community (Fig. 4c), because biochar incorporation into the soil may increase the bulk of the soil pH, elevate NH4+ and dissolved organic C, and thereby enhance nitrification activity via AOB activity (Mierzwa-Hersztek et al. 2018). An increased pH due to biochar input into soils usually had affected the abundance and activity of AOA and AOB genes (Zhang et al. 2019). In this study area, soil pH was 7.4, which was weakly alkaline. The application of a high amount of biochar willed increase the abundance of AOB, but the change of AOA was not obvious. As an alkaline material, biochar increases soil pH through its composition of alkaline substances and adsorption of exchangeable acid cations (Wu et al. 2020). Therefore, ammonia-oxidizing microorganisms also have different response mechanisms to the changes and stress in environmental conditions.

Different irrigation modes, such as internal drainage, alternate wetting and drying irrigation, soil water potential, would affect the ecological distribution of microorganisms in rice rhizosphere soil (Zhang et al. 2016). Ma et al. (2019) found that the number of OTUs of the AOA-amoA gene was larger than that of the AOB-amoA gene under different irrigation water salinity treatments. This is consistent with AOB being more susceptible to irrigation patterns than AOA under the same BA in our research because AOB dominates over AOA in drylands, while AOA dominates in waterlogged soils (Li et al. 2021). The abundances, diversity, and community structure of AOA and AOB varied largely among all the samples, which is attributable to the application of CI that affects the niche differentiation of AOA and AOB by changing the water content and oxygen concentration of the soil (Ma et al. 2019).

Influencing factors of soil nitrogen transformation genes

The Pearson correlation analysis showed that the properties of soil showed various significant correlations with bacterial community composition. This paper found that the correlation coefficient between AOA gene OTU abundance and T was 0.78~0.87. This is mainly because the average T during the sampling period was 20.4 °C, and the suitable growth T of ammonia-oxidizing microorganisms is 20–37 °C (Onodera et al. 2010), so the nitrification rate was positively correlated with T. Du et al. (2019) found that the application of biochar affected the correlation between AOA and pH, SOM, AN, NH4+–N, and NO3−–N. This paper found that AOA is positively correlated with NH4+–N and NO3−–N because biochar can slow down the reduction of NH4+–N by promoting organic nitrogen mineralization or reducing microbial assimilation of inorganic nitrogen. Biochar also can promote soil nitrification rate and increase the abundance of nitrifying crops in the soil (Prommer et al. 2014; Ball et al. 2010). Therefore, the abundance and diversity of AOA were more sensitive to soil environmental factors, and the AOA-amoA genes OTU abundance was highly correlated with T and θ, while AOB was low in Fig. 7. Numerous studies have demonstrated the cellular, genomic, and physiological differences between AOA and AOB (Prosser and Nicol 2012), and their divergent nitrification pathways and responses to environmental and climatic factors (Hu et al. 2015), which might lead to differential patterns between AOA and AOB in paddy soils.

The correlation coefficients between nifH gene OTU abundance and soil NH4+–N and NO3−–N in Fig. 7b were the largest, which were 0.73 and 0.71. This is because CI promoted the growth of nitrogen-fixing bacteria and reduced the leaching of nutrients (Zhao et al. 2022; Meng et al. 2014), under biochar application, thus decreasing the loss of soil NH4+–N and NO3−–N. Under CC, urease was significantly positively correlated with NH4+–N (P<0.001) and NO3−–N (P<0.05), which were 0.94** and 0.92*. The same research found that the urease activity of the treatment with biochar was increased by 0.64 to 42.17%, and the increase in urease activity was the most by the treatment with 4500 kg/hm2 of biochar (Chen et al. 2020). The effect of biochar on urease is mainly through the adsorption of reaction substrates or enzyme molecules to promote the progress of enzymatic reactions, thereby affecting the increase of soil enzyme activity. At the same time, the improvement of nitrogen nutrition promoted the reproduction of soil microorganisms, so that they secreted more urease into the soil (Petra et al. 2003). In general, BA and CI affected the correlation between soil properties and soil nitrogen transformation functional genes and caused community changes.

Conclusions

Biochar is a farmland carbon management measure with great emission reduction potential, which can reduce the impact of CI on ecology footprint. The BA improves the soil NH4+–N and NO3−–N contents of CI paddy fields, increasing by 27.05 ~ 95.68% and 7.07 ~ 10.31%, respectively. That is because BA can improve the richness and diversity of nifH at tillering stage and promote biological nitrogen fixation. High-throughput sequencing results illustrated that the application of high-volume biochar increased the Chao1 indexes of nifH and AOB-amoA by 10.95% and 4.91% under CI, but AOA-amoA did not change significantly. Because biochar can increase soil pH, AOA-amoA was accountable for ammonia oxidation in a low pH environment. The rare microbial of Nitrosopumilus, Nitrosococcus, and Methylocystis separately appeared in the BA treatments under CI, showing the diversity of AOA-amoA and AOB-amoA microorganisms increase in paddy. The combination of CI and BA increased the correlation between nifH, AOA-amoA and NH4+–N, NO3−–N. In summary, this paper creatively analyzes the nitrogen transformation mechanism in the combined BA and CI from the perspective of microbial genes, which can provide data and theoretical support for the sustainable use of farmland water and soil resources in the future.