Understanding the role of soil microbes and enzymes in regulating nitrogen dynamics: Promoting sustainable crop rotation systems

Understanding the role of soil microbes and enzymes in regulating nitrogen dynamics: Promoting sustainable crop rotation systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding the role of soil microbes and enzymes in regulating nitrogen dynamics: Promoting sustainable crop rotation systems Liqiang Zhang, Wenxiu Ji, Xinbo Jiang, Yunlong Wang, Xiaoya An, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4008531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Soil nitrogen is recognized as a vital nutrient influencing soybean growth and yield. Hence, a comprehensive understanding of the intricate connections between shifts in nitrogen patterns and the behaviors of soil microbial communities and crucial enzymes in the nitrogen cycle is highly desirable. Methods This study involved a rotation positioning experiment spanning 9 to 11 years. Measurement of soil microenvironment changes during the mature period for three consecutive years, focusing on the corn-soybean rotation with varying fertilizer application rates. Six distinct treatment groups were established for investigation. Based on these groups, the study delved into the alterations in nitrogen patterns within the soybean rotation, examining both soil enzyme activity and microbial community dynamics. Results Long-term crop rotation and nitrogen application led to an increase ranging from 2.16% to 108.34% in the nine components of soil nitrogen. The variations in total nitrogen, heavy fraction organic nitrogen, and light fraction organic nitrogen were primarily influenced by the enrichment of the Actinobacteriota phylum. The environmental factors affecting the changes in inorganic nitrogen, alkaline hydrolyzable nitrogen, exchangeable ammonium and acid hydrolyzable nitrogen were linked to the Ascomycota phylum. The Proteobacteria phylum and urease were key factors in the variations of organic nitrogen and nitrate-nitrogencomponents, respectively. Conclusions Changes in inorganic nitrogen and total organic nitrogen resulting from crop rotation enhanced the richness of soil microbial communities, reducing their diversity. This alteration influenced the bacterial and fungal communities composition, ultimately augmenting their functional capacities. soybean maize rotation nitrogen cycling nitrogen form soil microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Leguminous crops inherently possess nitrogen-fixing capabilities, which provide nitrogen nutritional supplies for subsequent cereal crops. The soil environment following the harvest of leguminous crops' root systems stands as an essential prerequisite for bolstering nitrogen fixation in leguminous flora (Aparicio et al., 2007; Freitas et al. 2022 ; Peoples et al. 2021 ). The biological nitrogen fixation capacity of China's soybean-growing areas can reach up to a maximum of 150 kg/ha (Guan et al., 2014 ). In China's intensive agricultural landscapes, the widespread use of high-nitrogen fertilizers has exceeded 15% of the total arable land area (Sun et al., 2006 ). This excessive application has led to poor nitrogen utilization efficiency, soil compaction, and a decrease in soil organic matter content. Simultaneously, the unretrieved portion enters the soil, atmosphere, and water bodies, polluting the ecological environment (Raza et al., 2014; Moritz et al., 2023 ). Hence, establishing a green and sustainable crop rotation model is of paramount significance, especially in intensively cultivated areas. Vance et al. (2001) highlighted that ~ 100 kg/ha of nitrogen in subsequent-season crops emanates from preceding-season leguminous cover crops. A three-year field experiment demonstrated that crop rotation with leguminous crops augmented maize yield and nitrogen utilization efficiency in the subsequent season by 35.5% and 33.0%, respectively (Gan et al., 2015 ). Similarly, maize yields in France exhibited an average annual decline of 0.035 t/ha over a 15-year span, when leguminous crop cultivation was reduced (Brisson et al., 2010 ). Hence, a comprehensive exploration of the impact of leguminous crops within crop rotation systems on nitrogen supply mechanisms for succeeding crops holds substantial theoretical and practical implications. This endeavour is pivotal for reducing nitrogen inputs, enhancing nitrogen utilization efficiency, and producing crops with excellent quality and yield. Soil nitrogen levels, forms, and transformations have a direct influence on the nutritional capacity of crops (Jacynthe, 2022 ). Inorganic nitrogen (TIN) is primarily derived from residual soil sources as well as the mineralization of organic nitrogen from soil or applied organic materials (Jakob et al., 2021 ). Soil acid hydrolyzable nitrogen (AHN), exchangeable ammonium (AN), nitrate nitrogen (NN), and alkaline hydrolyzable nitrogen (AAN) constitute the main forms of nitrogen, comprising ~ 2.61–14.85% of total nitrogen. These forms serve as the principal sources of nitrogen nutrition for soil plants, with direct absorption and assimilation (Torres et al., 2020 ; Fajri et al., 2020). Organic nitrogen (TON) accounts for more than 90% of total nitrogen (TN) in soil, representing the predominant nitrogen form. Its content and distribution are closely linked to soil organic matter (Soman et al., 2016 ). Labile organic nitrogen (LON), a highly prevalent category of nitrogen compounds, typically constitutes 20–60% of total soil nitrogen (Lisa et al., 2022). This category mainly resides within soil organic matter, including proteins and peptides (Cecilia et al., 2021 ). Soil hydrolysates also include light organic nitrogen, accounting for about 5–10% of total soil nitrogen (Leinweber et al., 2013 ). It generally exists in the form of polysaccharide structures and can interact with small molecules like antimicrobial substances in conjunction with sticky peptides and proteins. In soil, it potentially serves a dual role, acting as a source of energy for plant growth and assisting the development of a sound soil structure (Feng et al., 2018 ; Braos et al., 2017 ). Crop rotation enhances nutrient cycling, boosts soil organic carbon, and augments soil fertility. A rice-bean rotation system effectively mitigates continuous cropping obstacles, resulting in increased yields and better crop quality. Concurrently, a notable surge in both the abundance and diversity of subterranean microorganisms is observed, imparting a transformative enhancement to the intricate tapestry of soil microstructures (Matt et al., 2020). The enzymatic activities of soil exhibit significant correlations with indicators such as soil nitrogen content and microbial communities (Sun et al., 2022 ). Within the realm of soil, enzymes such as urease (SU), protease (SP), and nitrate reductase (SNR) exhibit enhanced activity (Park et al., 2016). A plethora of investigations have demonstrated crop rotation's ability to foster microbial diversity in soil, while concomitantly bolstering enzymatic activity in the arable strata (Jia et al., 2016 ). According to Yang et al. ( 2016 ), conventional cultivation does not generate discernable discrepancies in soil enzymatic activity across varying soil depths, whereas rotated cultivation unveils a distinct stratification of enzyme activity in congruence with soil depth (Mori et al., 2023 ; Yuhei et al., 2023). Under divergent crop rotation regimes, the activity of distinct enzymes is variably influenced. Within the maize-soybean rotation paradigm, enzymes like SU, SP, and SNR demonstrate noteworthy distinctions in activity when compared to monoculture soybean and continuous maize scenarios (Muhammad et al., 2020). The investigation of the complex interaction between crop rotation and the intricate tapestry of soil's nitrogen-fixing bacterial communities has been predominantly guided by rudimentary methodologies, such as age-old breeding practices. Although traditional, these approaches fall short of capturing nuanced abundance, precise evaluation of nitrogen fixation, and the subtle shifts within bacterial populations in the intricate milieu of complex soil ecosystems (Wu et al., 2020 ). The higher the abundance of microorganisms such as bacteria, fungi, and actinomycetes in the soil, the greater the stability of their communities, thereby enhancing soil fertility and promoting plant growth (Sun et al., 2020 ; Liu et al., 2023 ). Continuous cropping has been shown to limit the amount of rhizobia and inhibit root nodule formation, thereby fostering the development of certain diseases (Gao et al., 2023). Prolonged crop rotation increases the population of Bacillus , Streptomyces , and Acidobacteria in the soil, whereas Bacillus , Rhodococcus , and yeast encourage crop growth and yield, ultimately improving the structure and functionality of the soil's microbial community (Surendra et al., 2019). Similarly, Perez et al. ( 2014 ) found that long-term continuous cropping of soybeans increased soil fungal and plant diseases, with fungal populations significantly lower in soybeans under crop rotation with rice. Several studies have reported continuous cropping significantly reduces the quality of soybeans, the quantity and vitality of coexisting rhizobia, as well as nitrogen fixation., A 5-year soybean planting cycle induces substantial diversity shifts within nitrogen-fixing bacterial populations in the soil as compared to maize-soybean rotation. Different planting strategies reduce the types and quantities of nitrifying bacteria to varying degrees, accompanied by distinct alterations in the bacterial community structure of the soil (Narayana et al., 2022 ). Building upon the aforementioned literature, it was postulated that the alterations in nitrogen forms caused by nitrogen application in long-term soybean rotations might be closely linked to key species within the soil's microbial community and enzymatic activity. To validate this hypothesis, soil samples were collected from plots subjected to continuous 9–11 year rotations of soybean and maize along with fertilization. The quantitative analyses of key enzymatic activities related to nitrogen cycling were conducted. High-throughput sequencing techniques were deployed to characterize the composition of bacterial and fungal communities. The objectives of this study are to (1) explore the influence of long-term maize-soybean rotations coupled with nitrogen application on soil nitrogen forms, enzymatic activities, and microbial community structure; (2) elucidate the effects of soil enzymes and microbial communities on changes in various forms of nitrogen; and (3) microscopically decipher the theoretical underpinnings behind shifts in metabolic functionality. 2. Materials and Methods 2.1. Site profile and experimental design The experiment was conducted from April 2020 to October 2022 at the Yàn Míng Lake Seed Company base in Shaheyan, Guandi Town, Dunhua City, Yanbian Korean Autonomous Prefecture, Jilin Province (128.3592°E, 43.4400°N). The climate is temperate semi-humid, and the soil type is Albic Luvisol. The study was carried out on three equal-sized plots. Two of these plots were dedicated to a crop rotation system involving soybean and maize, while the remaining one was used for continuous soybean cultivation. The experiment featured four treatment groups: MS1 - Fertilized soybean-maize rotation, MS0 - Non-fertilized soybean-maize rotation, SS1 - Fertilized continuous soybean cultivation, and SS0 - Non-fertilized continuous soybean cultivation. Set three repetitions for each processing set. Each experimental plot covered an area of 315 m 2 . The crops within each sub-plot were planted in 12 rows, with each row spanning 65 cm and a row spacing of 60 cm, The planting distribution is shown in Fig. 1 . The soybean planting density ranged from 20.0 to 22.0 thousand plants per hectare, whereas the planting density of maize ranged from 5.5 to 6.0 thousand plants per hectare. Two nitrogen (N) application levels were employed: 0 kg N ha − 1 and 60 kg N ha − 1 . Additionally, phosphorus (P) and potassium (K) fertilizers were applied at rates of 75 kg P 2 O 5 ha − 1 and K 2 O ha − 1 respectively. The experiment was designed to investigate the impact of these treatments on crop yields and soil quality in the region. 2.2. Soil Sampling and Analysis The soil sampling experiment was conducted between October 1, 2020, and October 1, 2022. Collect soil samples from the 0-20cm cultivation layer during the soybean maturity period for three consecutive years. Three sampling points were established within each plot, using the soil augering method. Root samples were extracted in situ from the 0–20 cm soil depth on the ridges. Fresh soil specimens were collected and homogenized thoroughly before being placed in ice chests. Subsequently, extraneous elements such as stones and vegetative residues were eliminated. A portion of the soil samples was air-dried and reserved for chemical analysis, while another fraction, designated for the quantification of microbial biomass and enzymatic activity was stored in a refrigerator at -80°C. 2.2.1. Analytical methods Soil total nitrogen (TN) was determined using the Kjeldahl method for digestion (Rong et al., 2023 ), followed by filtration through a 0.45 µm PES membrane. A continuous flow analyzer (AA3, AutoAnalyzer 3, Technicon, Windows/NT) was used for analysis. Soil inorganic nitrogen (TIN), ammonium nitrogen (AN), and nitrate nitrogen (NN) were extracted with 2 mol/L CaCl 2 , shaken at 180 rpm for 60 min, allowed to settle for 30 min, and then subjected toAA3 analysis (Han et al., 2022 ; Bao, 1999 ). Soil alkali hydrolyzable nitrogen (AAN) and acid hydrolyzable nitrogen (AHN) were determined using the alkaline diffusion method (Hou et al., 2021 ; Zhang et al., 2019 ). Total organic nitrogen (TON), light fraction organic nitrogen (LON), and heavy fraction organic nitrogen (RON) were quantified using the semi-micro Kjeldahl method (Jiao et al., 2018 ). Soil urease (SU) activity was assayed using the urease colorimetric method with urea as the substrate. The soil sample was incubated at a constant temperature for 24 hours in a C6H8O7 buffer solution at 37 ℃ and pH 6.7, and measured at 578nm using a spectrophotometer. (Kyung et al., 2021 ). Soil protease (SP) activity was determined using the casein colorimetric method. The soil sample was incubated at a constant temperature for 24 hours in a phosphate buffer solution at 37℃ and pH 5.5, and measured at 650nm using a spectrophotometer. (Tanjila et al., 2022). Soil nitrate reductase (SNR) activity was assessed through anaerobic cultivation followed by the phenol-sulfuric acid colorimetric method. The soil sample was incubated at a constant temperature for 24 hours in a glucose buffer solution at 30 ℃ and pH 7.0, and measured at 400-500nm using a spectrophotometer (An et al., 2012 ). The soil pH and electrical conductivity (EC) values were extracted from the soil-water mixture at a ratio of 5:1. The mixture was agitated at 180 rpm for 5 min and then left undisturbed for 30 min. The pH was subsequently measured using a pH meter (pH-100A, 100-2000rpm, LICHEN, Shanghai, CN), while the EC was determined using a conductivity meter (DDSJ-11A-307, YUEPING, Shanghai, CN). For the analysis of total potassium (TK) and total phosphorus (TP) components in the soil, a digestion method involving concentrated perchloric acid and sulfuric acid was employed. The digested solution was filtered through a 0.45 µm PES membrane and the content was quantified using flame photometry (FP6400, INESA, Shanghai, CN) and the AA3 analyzer, respectively. The readily available phosphorus (AP) component in the soil was extracted using a sodium bicarbonate solution. The extraction process involved shaking at 180 rpm per minute for 2 h, followed by a 30 min settling period. After filtration through a 0.45 µm PES membrane, the concentration of AP was measured using the AA3 analyzer. Similarly, the available potassium (AK) component in the soil was extracted using an ammonium acetate solution. The extraction process involved shaking at 180 rpm for 2 h, followed by a 30 min settling period. After filtration through a 0.45 µm PES membrane, the concentration of AK was determined using flame photometry (fp6400, INESA, Shanghai, CN). The soil's organic matter content was determined using the potassium dichromate volumetric method combined with the dilution-heat approach, as outlined by Bao ( 1999 ). 2.3. PCR Amplification and High-Throughput Sequencing The MN NucleoSpin 96 Soil DNA Extraction Kit was used to extract DNA from soil samples. The resulting DNA concentration was measured using the NanoDrop 2000, and the quality of the extracted DNA was assessed through 1% agarose gel electrophoresis. For the amplification of bacterial 16S rRNA gene fragments corresponding to the V3-V4 region, the primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') were employed. In the case of fungal 18S rRNA gene amplification targeting the ITS1 region, the primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2 (5'-GCTGCGTTTCTTTCATCGATGC-3') were utilized. Following the amplification, the PCR products were purified, quantified, and standardized to generate sequencing libraries. The 16S rRNA gene libraries were prepared as paired-end (PE) 2 × 300, while the 18S rRNA gene libraries were prepared as PE 2 × 250. The constructed libraries were subjected to rigorous quality control. Subsequently, the qualified libraries were subjected to sequencing on the Illumina NovaSeq 6000 platform, as outlined in Hou et al. (2022). The registration number for this biological project is MJ20221021156-MJ-M-20221024089. 2.4. Statistical Analysis The SPSS 22.0 software was used to conduct statistical analysis. A two-way analysis of variance (ANOVA) was used to evaluate the impact of nitrogen fertilizer levels and cultivation methods on nitrogen components during various growth stages and soil parameters. To assess the primary effects of fertilization, growth stages, and their interactions on soil nitrogen forms and enzymatic activity, a bidirectional analysis of variance was employed. Pearson correlation tests were carried out to evaluate the relationships between microbial modules, enzymes, relative abundances of nitrogen forms, and the physicochemical properties of the soil along with enzyme activity. Beta diversity analysis based on the Bray-Curtis dissimilarity coefficient and PCA analysis was used to compare the similarity of species community diversity among different samples. A co-occurrence network model at the genus level was established for continuous and rotational soil microbial communities, comparing the interactions between soil microbial communities under different cultivation modes. The PICRUSt and FUNGuild functional prediction methods were constructed to forecast the functions of soil bacteria and fungi under various treatments and identify the abundance of nitrogen metabolism-related enzymes and gene expressions in soil samples under different treatments. 3. Results 3.1. Fundamental physicochemical characteristics of soil From 2020 to 2022, the fundamental physicochemical properties of twelve distinct soil samples were assessed, as documented in Table 1 . Except for TP, crop rotation regimen outcomes differed significantly (P < 0.05) from those of the SS1 and SS0 treatments. When compared to continuous monoculture, soil pH and SOM demonstrated augmentation in the crop rotation systems, exhibiting increments of 0.48–14.31% and 20.33–77.36%, respectively. Conversely, EC, TK, AP, and AK constituents experienced a reduction. It is noteworthy that despite the amplified soil pH and SOM under crop rotation practices, the introduction of fertilizers interrupted this growth trend. Furthermore, increased fertilizer application exacerbated the inhibitory impact. Within the continuous monoculture approach, soil EC, AP, and AK showed a propensity for post-fertilization augmentation. Overall, nitrogen-based fertilizers application led to a decline in soil pH, SOM, TP, and TK, regardless of the duration of the crop rotation cycle, while concurrently fostering a rise in EC, AP, and AK. Table 1 Changes in soil physicochemical parameters under different treatments from 2020 to 2022 (n = 3). Statistically significant differences (P < 0.05) between the two treatments are denoted by distinct letters (a, b, c). SOM: organic matter; TP: total phosphorus; TK: total potassium; AP: available phosphorus; AK: available potassium. pH EC SOM TP TK AP AK (g kg − 1 ) (g kg − 1 ) (g kg − 1 ) (mg kg − 1 ) (mg kg − 1 ) 2020 MS1 6.76 ± 0.31 b 40.29 ± 3.00 a 37.25 ± 0.33 b 0.82 ± 0.11 c 6.28 ± 0.02 c 37.54 ± 0.85 b 55.06 ± 1.79 c MS0 6.94 ± 0.02 a 13.98 ± 0.35 c 46.23 ± 3.39 a 1.05 ± 0.14 a 6.98 ± 0.01 b 15.36 ± 0.38 c 44.25 ± 1.67 d SS1 6.29 ± 0.06 d 42.98 ± 4.92 a 28.16 ± 1.14 c 1.00 ± 0.05 b 7.20 ± 0.02 a 83.45 ± 0.93 a 97.01 ± 1.59 a SS0 6.44 ± 0.03 c 12.28 ± 1.22 c 26.64 ± 0.81 c 0.98 ± 0.01 b 7.18 ± 0.07 a 19.61 ± 1.43 c 76.29 ± 0.23 b 2021 SM1 6.78 ± 0.08 b 39.73 ± 1.60 a 33.44 ± 5.12 c 0.93 ± 0.11 c 6.44 ± 0.02 c 48.73 ± 1.85 b 56.37 ± 3.07 c SM0 7.03 ± 0.03 a 17.57 ± 1.38 c 40.30 ± 6.73 a 1.07 ± 0.05 a 6.71 ± 0.08 b 17.37 ± 0.59 d 35.53 ± 3.40 d SS1 6.28 ± 0.06 d 42.77 ± 4.92 a 27.95 ± 1.14 d 0.99 ± 0.05 b 6.99 ± 0.02 a 73.56 ± 0.42 a 103.80 ± 2.31 a SS0 6.52 ± 0.03 c 14.32 ± 0.92 c 29.19 ± 0.69 d 0.96 ± 0.01 b 7.06 ± 0.07 a 17.15 ± 0.73 d 82.83 ± 3.01 b 2022 MS1 6.18 ± 0.01 c 32.93 ± 3.36 b 35.19 ± 6.06 b 1.03 ± 0.01 b 6.53 ± 0.06 bc 40.55 ± 1.82 b 61.67 ± 0.47 b MS0 6.86 ± 0.03 a 12.53 ± 0.80 c 47.25 ± 3.98 a 1.17 ± 0.06 a 6.41 ± 0.17 c 19.67 ± 2.71 c 34.23 ± 0.67 c SS1 6.15 ± 0.03 c 51.17 ± 2.17 a 36.44 ± 1.49 c 1.02 ± 0.02 b 6.93 ± 0.03 ab 72.62 ± 0.88 a 93.66 ± 5.40 a SS0 6.34 ± 0.13 b 17.28 ± 4.16 c 39.45 ± 1.21 c 1.05 ± 0.01 ab 7.09 ± 0.03 a 12.63 ± 0.93 d 91.93 ± 0.38 a 3.2. Transformation of various nitrogen forms in soil An analysis of the findings presented in Fig. 2 -A reveals that crop rotation increases soil TN content as compared to continuous cropping. The increase in TIN content was even more pronounced. In the comparison between MS1 and SS0 treatments, MS1 exhibited a substantial rise of 273.43% in TIN content. In the crop rotation treatments, as nitrogen application rates, increased, both soil TN and TON content gradually rose, while TIN content progressively declined. Notably, in the treatments without nitrogen fertilization, MS0 and SS0 exhibited higher TN and TON content than the nitrogen-fertilized treatments. Among these, MS0 displayed the highest TN content, surpassing SS0 by 32.11%, though its TIN content was 57.02% lower than that of MS1. Examining the distribution of TON and its components among the five treatments ( Fig. 2 . B) , it becomes evident that crop rotation enhances the content of LON. Specifically, the MS0 treatment presented the highest LON content, outperforming MS1 and SS0 by 31.98% and 108.34% respectively. Among the crop rotation treatments, MS1 accounted for 36.79% of the LON proportion, while MS0 and SS0 constituted 28.60% and 17.30% of the total respectively. The distribution of AAN and its three components across the five treatments ( Fig. 2 . C) demonstrates that MS1 and SS1 treatments have significantly higher AAN content than the others. This implied a close relationship between soil AAN content and nitrogen application rates. Across all treatments, AHN, NN, and AN constituted 68.53%-87.04%, 8.35%-16.47%, and 2.16%-15.03% of AAN, respectively. Notably, crop rotation resulted in an 11.45% lower proportion of AHN compared to continuous cropping. Contrarily, NN and AN proportions were higher by 3.87% and 7.58%, respectively. Reduced nitrogen application rates across all treatments corresponded to an increase in AHN proportion and a decrease in NN and AN proportions. From the aforementioned analysis, it is evident that compared to SS0, crop rotation significantly enhanced the content of LIN, LON, AAN, NN, and AN in the soil. Furthermore, with an increase in the number of years of crop rotation, these components displayed an ascending trend. During the soybean maturation stage, nitrogen fertilizer application notably augmented AAN content. It is worth noting that both nitrogen application rates and years of cultivation exhibited interactive effects on the transformation of soil nitrogen forms. 3.3. Changes in key enzyme activities during the nitrogen conversion process Table 2 shows that the activities of three enzymes pertinent to the nitrogen cycle exhibited pronounced disparities between distinct cultivation methodologies with or without nitrogen fertilization treatments. In comparison to the N0 treatment, both SU and SP activities were elevated by 3.95–29.64% and 38.82–76.83%, respectively, under the rotational cropping regimen. Meanwhile, the SNR of the rotational cropping regimen exhibited a reduction of 17.41–51.86% in relation to the SS0 treatment. Concurrent with the augmentation of nitrogen levels, analogous patterns of enzymatic activity alterations were observed for both rotational and continuous cropping approaches. In the former, the activities of SU and SNR rose, with increments of 19.79% and 41.72% respectively in MS1 compared to MS0, and increments of 4.07% and 23.56% respectively in SS1 compared to SS0. Conversely, the activity of SP declined, with reductions of 21.50% in MS1 compared to MS0 and 7.84% in SS1 compared to SS0. Comparing the years 2020 and 2022, rotational cropping showcased an incremental and steady rise in SR and SP activities, alongside a reduction in SNR activity. In contrast, the continuous cropping approach demonstrated an opposing trend in the alteration of SU, SP, and SNR activities. Notably, the interplay between nitrogen application rates and years of cultivation exerts a mutual influence on key soil enzymatic activities across all treatments. Table 2 Activities of key enzymes involved in nitrogen transformation processes during 2020–2022.: SU: urease: SP: protease; and SNR: nitrate reductase. SU SP SNR mg g − 1 d − 1 mg g − 1 d − 1 µm g − 1 d − 1 2020 MS1 4.32 ± 0.08 a 54.14 ± 3.18 b 9.18 ± 0.18 c MS0 3.57 ± 0.05 bc 64.23 ± 3.78 a 6.23 ± 0.08 d SS1 3.65 ± 0.04 bc 29.65 ± 0.24 c 13.71 ± 0.47 a SS0 3.49 ± 0.02 c 32.84 ± 0.96 c 11.17 ± 0.67 b 2021 MS1 4.53 ± 0.08 a 55.68 ± 1.08 b 6.56 ± 0.06 bc MS0 3.59 ± 0.06 b 70.94 ± 0.87 a 3.21 ± 0.03 d SS1 3.61 ± 0.02 b 33.68 ± 0.09 c 10.46 ± 0.32 a SS0 3.46 ± 0.04 b 37.89 ± 1.01 c 8.06 ± 0.07 b 2022 MS1 4.59 ± 0.08 a 43.24 ± 1.44 b 5.81 ± 0.02 b MS0 3.62 ± 0.05 b 59.80 ± 0.96 a 3.12 ± 0.05 c SS1 3.55 ± 0.05 b 38.29 ± 0.07 b 9.96 ± 0.11 a SS0 3.42 ± 0.08 b 39.53 ± 0.74 b 6.86 ± 0.05 b 3.4. Changes in soil microbial communities 3.4.1. Composition of the bacterial and fungal communities The present study identified a total of 37 phyla, 107 classes, 220 orders, 411 families, and 786 genera of bacteria over the period of three years. A minimum of 30,566 sequences were obtained per sample, after conducting rarefaction. Likewise, the fungal analysis revealed 27 phyla, 59 classes, 93 orders, 119 families, and 144 genera. Following rarefaction, each sample yielded at least 35,257 sequences. The Proteobacteria, Actinobacteriota, Acidobacteriota, and Chloroflexi were found as dominant phyla within the bacterial domain(Fig. 3 ). These accounted for average relative abundances of 25.48%, 23.31%, 11.61%, and 11.54%, respectively, collectively comprising 71.94% of the total. Ascomycota and Basidiomycota emerged as the prominent phyla in fungi, accounting for 91.53% of the total with average relative abundances of 71.52% and 20.01%, respectively. The relative abundance of Proteobacteria in both SS1 and SS0 exhibited a declining trend, decreasing by an average of 27.11% by the year 2022. Meanwhile, relative abundances of Acidobacteriota, Acidobacteriota, and Chloroflexi rose gradually, with average increments of 4.9%, 40.14%, and 16.59% by the year 2022. For MS1 and MS0, shifts in dominant bacterial phyla mirrored the effects of rotation and continuous cropping. Proteobacteria decreased by 50.23%, whereas Actinobacteriota, Acidobacteriota, and Chloroflexi showed growth of 3.55%, 47.35%, and 5.67% respectively. Furthermore, the rotation and continuous cropping treatments increased the relative abundance of Ascomycota, with average increments of 5.39% and 34.81%, respectively, by the year 2022. Conversely, Basidiomycota's relative abundance declined by an average of 9.9% and 61.86% by the year 2022 in the context of rotation and continuous cropping, respectively. 3.4.2. PCA analysis of bacteria and fungi The Beta diversity study was carried out over 999 iterations using the Bray-Curtis dissimilarity coefficient and the Beta diversity index, with PCA selected as the analytical approach. This enabled a comparison of the degree of similarity in species community diversity across different samples (Fig. 4 ). For the period spanning 2020 to 2022, at the bacterial phylum level (97% similarity), PCA revealed an average explanatory rate of 42.89% for the first principal component (PC1) and 32.28% for the second principal component (PC2). In the crop rotation treatments, namely SM0, SM1, SS1, and SS0, clear differentiation was observed for both PC1 and PC2.AAN, TON, RON, and NN displayed significant contributions to PC1, with rates of 82.16%, 52.95%, 51.32%, and 42.46%, respectively. AN and AK demonstrated notable contributions to the PC2, with rates of 77.64% and 61.92% respectively. SU and SP were aligned with the direction of MS1 and SM1 treatments, highlighting the substantial influence of their magnitudes on the soil bacterial community in these two treatments. Turning attention to the fungal community analysis under varying treatments, for the years 2020 through 2022, the Beta diversity analysis demonstrated an average explanatory rate of 40.42% for the PC1 and 34.65% for the PC2. As seen in bacterial communities, the varying treatments viz. SM0, SM1, SS1, and SS0 exhibited substantial differences in fungal populations. Notably, ANN and NN made notable contributions to the PC1, with rates of 65.26% and 51.92% respectively, while AK demonstrated a significant contribution of 71.62% to the PC2. Soil attributes SU, SP, and SNR did not significantly impact the variations in fungal communities across the crop rotation treatments. 3.4.3. Co-occurrence networks and ecological assemblages of bacteria and fungi Under different cultivation modes, distinct agroecosystem models were established, showcasing continuous cropping (where SS1 and SS0 were fitted via 97% similarity) and crop rotation (where MS1 and MS0 were fitted via 97% similarity). These models delineated the co-occurrence networks of soil microbial communities at the genus level (Fig. 5 ). Table 3 presents a compilation of topological parameters for the network models from 2020 to 2022. This compilation was used to contrast the interrelations among soil microbial communities under varying cultivation practices. Crop rotation resulted in a reduction in soil microbial nodes as well as an increment in edge numbers. This implied decreased microbial diversity in crop-associated soil following crop rotation. However, interrelationships among different microbial phyla are complex. Table 3 Topological property index of soil microbial co-occurrence networks. Continuous soybean cropping (CC) versus soybean-corn rotation (CR). Treatment Node Bacteria (%) Fungus (%) Edge Positive (%) Negative (%) Average degree Average weighting Cluster coefficient modularity 2020 CC 414 70.62 29.38 3333 53.17 46.83 640.88 160.22 0.53 4.15 CR 409 72.37 27.63 3562 55.44 44.56 652.59 163.15 0.56 4.29 2021 CC 426 69.01 30.99 3679 55.56 44.44 758.39 189.6 0.61 4.77 CR 424 78.87 21.13 4121 56.12 43.88 764.22 191.06 0.64 4.92 2022 CC 440 57.59 42.41 3661 53.56 46.44 814.37 253.59 0.78 5.84 CR 437 59.79 40.21 4317 57.04 42.96 817.42 254.36 0.81 5.99 A comparison of microbial proportions between continuous cropping and crop rotation for the same year revealed that bacterial prevalence exceeded fungal prevalence in all treatments. Notably, crop rotation increased the proportion of bacteria, with a magnitude ranging between 1.75% and 9.86%. The positive correlations outweighed negative correlations in all treatments, with an average proportion of 55.15% and 44.85%, respectively. The crop rotation induced an augmentation in the proportion of positive correlations, with an increase ranging from 0.56–3.48%. The crop rotation treatments outperformed continuous cropping treatments in terms of average degree, average weighted degree, average clustering coefficient, and modularity. This pattern indicated that the interconnectivity among network nodes was stronger within the crop rotation treatments, featuring a more intricate and abundant web of connections. Nevertheless, both crop rotation and continuous cropping treatments showed an upward increase in node numbers, fungal proportions, edge numbers, negative correlation proportions, average degree, average weighted degree, and average clustering coefficient as planting years progressed. 3.5. Prediction analysis of soil microbial communities 3.5.1. Prediction analysis of soil bacterial functions The gene functional annotation data derived from the macro-genomic sequencing was sorted in line with the predictive capacity of gene functions by PICRUSt. The enzymes and gene expression abundances associated with nitrogen metabolism were selectively extracted for subsequent multi-sample abundance analysis (Table 4 ). Notably, continuous cropping consistently exhibited greater expression of the nitrification enzymes i.e., HAD, MMO, and AMM compared to crop rotation, with average increases of 41.98%, 8.55%, respectively. Additionally, the abundances of these enzymes in MS1 over MS0 showed respective increments of 42.14% and 7.68%. Contrarily, in the denitrification process, apart from the gene expression of nitrate reductase, the abundances of other enzyme genes were consistently higher in crop rotation than in continuous cropping, with rise of 14.13%, 1.82%, 4.15%, and 3.03% respectively. Table 4 Bacterial nitrogen metabolism enzymes and their corresponding gene expression abundances in different samples (n = 3). Metabolic Pathway Enzyme number Enzymes MS1 MS0 SS1 SS0 2020 Nitrification 1.7.2.6 HAD 69 85 99.5 63 1.14.18.3 MMO 59 58 73 70 1.14.99.39 AMM 59 58 73 70 Denitrification 1.7.2.5 NO reductase 2195.4 2564.6 1241.3 898.65 1.7.2.1 Nitrite reductase 3157.4 3542.1 2421.0 2099.6 1.7.99.1 HRR 769.32 664.72 649.03 649.43 1.7.2.4 N 2 O reductase 1502.6 1662.6 1270.5 1195.1 1.7.7.2 Nitrate reductase 695.66 790.99 706.65 597.82 Ammoniation 3.5.5.1 Nitrile hydrolase 1845.5 1847.6 2242.5 2212.5 1.4.1.4 GDH 3703.4 3292.5 3701.3 3346.5 2.7.2.2 Carbamate kinase 1325.7 1261.1 2353.5 1761.5 3.5.1.49 Carboxylase 2036.3 2043.3 3708.9 3123.0 4.2.1.104 Cyanogenase 1729.5 1805.4 2813.8 2884.1 Nitrogen fixation 1.18.6.1 Nitrogenase 1438.3 1660.1 2846.8 3032.0 2021 Nitrification 1.7.2.6 HAD 135 83 80 143 1.14.18.3 MMO 178 194 162 250 1.14.99.39 AMM 178 194 162 250 Denitrification 1.7.2.5 NO reductase 6000.6 2100.3 2249.6 2176 1.7.2.1 Nitrite reductase 8228.4 3741.2 4049.3 4378.1 1.7.99.1 HRR 951.86 815.82 769.25 747.36 1.7.2.4 N 2 O reductase 4714.1 2900.7 2828.3 3166.5 1.7.7.2 Nitrate reductase 697.32 653.67 684.34 566.49 Ammoniation 3.5.5.1 Nitrile hydrolase 3043.9 2936.3 3009.8 3148.3 1.4.1.4 GDH 4617.6 3922.8 3702.2 4054.2 2.7.2.2 Carbamate kinase 2502.2 2984.3 2986.7 2778.6 3.5.1.49 Carboxylase 2964.5 3527.4 2669.5 3070.9 4.2.1.104 Cyanogenase 1975 2761.2 2168.7 2542.6 Nitrogen fixation 1.18.6.1 Nitrogenase 3198.9 4073.0 2743.2 3965.9 2022 Nitrification 1.7.2.6 HAD 76.5 33.5 110.5 52.5 1.14.18.3 MMO 153 102 138 101 1.14.99.39 AMM 153 102 138 101 Denitrification 1.7.2.5 NO reductase 1655.4 782.63 1332.4 991.01 1.7.2.1 Nitrite reductase 2565.8 1527.0 2660.9 2175.9 1.7.99.1 HRR 1731.9 1205 1014.9 1057.8 1.7.2.4 N 2 O reductase 1412.8 1005 1353.2 1442.1 1.7.7.2 Nitrate reductase 874.16 527 789.67 744.15 Ammoniation 3.5.5.1 Nitrile hydrolase 3128.6 2869.1 3333.1 2823.0 1.4.1.4 GDH 5317.5 4077.1 4872.4 4683.3 2.7.2.2 Carbamate kinase 2527.8 2786.9 2424.1 2990.0 3.5.1.49 Carboxylase 2788.5 2562.4 3820.2 3096.5 4.2.1.104 Cyanogenase 2405.1 2293.1 3059.8 2611.5 Nitrogen fixation 1.18.6.1 Nitrogenase 2124.0 1151.3 1160.1 790.32 In continuous cropping, nitrate reductase expression was 21.36% lower. Furthermore, when MS1 was compared with MS0, five enzyme gene abundances experienced elevations of 53.54%, 30.41%, 13.14%, 11.46%, and 15.03%, respectively. As for the ammonia assimilation process, the abundances of the five enzymes paralleled those of nitrification, with continuous cropping outnumbering crop rotation. There were average gains of 1.6%, 25.81%, 6.87%, 17.04%, and 8.63%. In the nitrogen fixation process, enzyme gene abundances also exhibited a preference for crop rotation, with a 10.29% rise in expression levels and a further 11.46% increment when comparing MS1 with MS0 treatment. Within the crop rotation treatment, nitrification, ammonia assimilation, and nitrogen fixation displayed augmentations of 77.56%, 56.78%, and 15.17% respectively, while denitrification diminished by 6.74% as the cultivation years progressed. 3.5.2. Prediction and analysis of soil fungal function In contrast to the functional gene prediction for bacteria, the prediction for fungi relies primarily on the abundance of genes associated with nitrogen metabolism, particularly the process of ammonification ( Table 5 ) . The enzymes involved in nitrile hydrolysis, glutamate dehydrogenase, and formamidase within the ammonification process exhibited significantly higher abundances under continuous cropping compared to crop rotation, with an average rise of 7.72%, 1.19%, and 14.56% respectively. When the MS1 treatment was compared to the MS0 treatment, there were reductions of 7.92%, 4.41%, and 18.65% respectively for these enzymes. As the cultivating years progressed, both continuous cropping and crop rotation treatments displayed a decline in the abundance of enzymes related to the ammonification process, with an average reduction of 46.93% and 32.81% respectively. Table 5 Fungal nitrogen metabolic enzymes and their corresponding gene expression abundances in samples. Metabolic Pathway Enzyme number Enzymes MS1 MS0 SS1 SS0 2020 Ammoniation 3.5.5.1 Nitrile hydrolase 29977.6 31011.67 34166.46 36103.33 1.4.1.4 GDH 16116.55 18049.16 17261.76 18668.62 3.5.1.49 Carboxylase 12178.34 12422.32 16404.09 18720.96 2021 3.5.5.1 Nitrile hydrolase 25075.92 23285.69 23425.88 24334.87 1.4.1.4 GDH 13964.87 12769.77 12829.34 12887.55 3.5.1.49 Carboxylase 11092.28 11427.93 11995.78 11501.22 2021 3.5.5.1 Nitrile hydrolase 23134.33 24119.1 25433.44 26707.24 1.4.1.4 GDH 13517.06 13270.52 12009.52 13570.78 3.5.1.49 Carboxylase 9927.89 10697.26 10506.35 11597.28 3.6. Microbial communities and enzymatic activity's influence on nitrogen transformation A random forest model that preserved the intended meaning was constructed using data on key species and enzyme activity. This model elucidated the regulatory mechanisms of TN and its eight components. It accounted for 90–97% of the variations in TN, TON, TIN, RON, LON, AAN, AN, and AHN ( Fig. 6 ) . Factors were selected based on IncMSE (Incremental mean squared error) > 10% and p < 0.05 for comparison. Among these factors, the four dominant phyla including Actinobacteriota, as well as SU and SP, were identified as significant variables impacting changes in TN, RON, and LON ( Fig. 6 . A, D, E) . For TIN, AAN, AN, and AHN, environmental factors such as Ascomycota and SU played pivotal roles ( Fig. 6 . C, F, G, I) . Proteobacteria and SU were respectively identified as critical determinants affecting variations in TON and NN components ( Fig. 6 . B, H) . SNR had a negative correlation with NN, while Basidiomycota showed negative associations with TN, TIN, and LON, yielding significant effects. 3.7. Analysis of functional factors of soil microbial community based on structural equation model The analysis results of the structural equation model (SEM) are presented in Fig. 7 . It indicated that both crop rotation and fertilization could significantly enhance soil TIN and TON, ranging from 32.11–57.02% (P < 0.05). The rhizospheric influence of nitrogen components was identified as the primary driving factor for soil microbial abundance. Crop rotation exerted a positive effect on the bacterial community composition and a negative effect on the fungal community composition. Interestingly, fertilizer addition showed the opposite pattern, with a negative effect on the former and a positive effect on the latter. Soil inorganic nitrogen content demonstrated a positive effect on bacterial community richness but a negative effect on fungal community richness, significantly impacting both fungal and bacterial community diversities. Soil organic nitrogen had a favourable influence on bacterial and fungal community richness, with a greater impact on bacteria than fungi. However, it had a deleterious impact on the diversities of both fungal and bacterial communities, with bacteria being affected more than fungi. This study underscores that different forms of nitrogen exert significantly varying impacts on the compositions of bacterial and fungal communities (P < 0.05). As the proportion of soil TON components increased, the diversity of soil bacteria and fungi decreased, leading to alterations in their community compositions. Within this study, both bacterial and fungal community compositions exhibited positive effects on their respective community functionalities. The mechanisms through which crop rotation and fertilization influence microbial communities encompass an increase in inorganic and organic nitrogen content, thereby enhancing the abundance of soil microbial communities while reducing their diversity. This alteration in the community composition consequently enhances the functionalities of these microbial communities. Therefore, in subsequent research that employs models to predict soil nitrogen cycling, accounting for the connection between nitrogen forms and soil microbial community composition becomes imperative. 4. Discussion 4.1. Long-term crop rotation and nitrogen application-induced shifts in soil nitrogen forms Within the context of soybean-corn rotation, corn often gains a competitive edge in soil nitrogen acquisition, as its nitrogen consumption exceeds that of soybeans. This, in turn, stimulates soybean's nitrogen fixation capacity. Wang et al. ( 2022 ), in their study concerning the impact of rotation on soil physicochemical properties in black calcareous soil regions, corroborated a notable decrease of 15.5% in soil total nitrogen content following crop rotation. In the current work, the rotational practice significantly lowered the soil's total nitrogen content during the maturation phase. This phenomenon primarily arose from the mutualistic interaction between corn and soybeans. During periods of low nitrogen availability, leguminous plants' nitrogen fixation capacity adapts to enhance fixation rather than diminish it. Consequently, soybeans transfer nitrogen to corn, strengthening the nutrient translocation that augments corn's nitrogen nourishment and stimulates its growth (Batista et al., 2020 ). Within this study, the soil's post-rotation levels of AHN and NN were noticeably greater than those of AN. This divergence can be attributed to the transformation of AN nitrogen into AHN and NN during the nitrification process. A greater disparity between AHN and NN leads to higher availability of nitrate nitrogen for translocation (Ding et al., 2022 ). Hohman et al. (2020) reported enhanced NN content in soybean-corn rotation systems. Unlike continuous cultivation, the extended rotation of corn and soybean substantially elevates soil TON levels, with LON content exerting the greatest influence. As compared to the MS0 and SS0 treatments, long-term fertilization had a considerable influence on TON content. Within the components of TON, rotation, and fertilization primarily elevated soil LON, whose concentration increased with the duration of the rotation. The order of RON content across treatments was: rotation > continuous cultivation, fertilized > non-fertilized, signifying that the combination of fertilization and crop rotation can enhance RON. This outcome primarily stems from the differing cultivation practices. In conventionally tilled soils, nitrogen predominantly transforms into amide nitrogen, while amino sugar nitrogen and amino acid nitrogen prevail in rotated soils (Alison et al., 2020). 4.2. The prolonged practice of crop rotation and nitrogen applications alters the structure of soil microbial communities The dynamics of soil nitrogen reflect shifts in the overall microbial population within the soil. In the context of long-term rotation between rice and soybean, the bacterial composition at the phylum and genus levels remains quite similar. The relative abundance of these has been consistently higher in rotational soil compared to continuous cropping, indicating that cultivation practices have a major influence on the distribution of bacterial phyla and genera. This could be attributed to the influence of alternating crops on bacterial composition (Fan et al., 2022). Microbial community analysis via PCA and functional prediction revealed that after rotation, Proteobacteria and Acidobacteriota significantly contributed to soil nitrogen content. This contribution was intertwined with soil urease and protease activities, consequently promoting nitrification and ammonification processes within the soil. However, there was a negative correlation with the key enzyme for denitrification, nitrate reductase, thereby reducing nitrogen loss. This could be explained by the aerobic nature of both Proteobacteria and Acidobacteriota. Rotational cultivation of leguminous and graminaceous crops enhances soil aeration, and these two bacterial phyla can fix atmospheric nitrogen under low oxygen pressure (Liu, 2020 ; Zhang et al., 2019 ). Construction of a co-occurrence network model revealed that rotation increased the edges among soil bacteria, leading to a more complex interplay among phyla. As planting years grow, soil microbial communities display increased species richness and reduced inter-species competition. This shift is due to improved survival conditions for dominant bacterial phyla post-rotation, resulting in strengthened mutualistic relationships and reduced competitive interactions (Rong et al., 2019). Rotation effectively enhances the relative abundance of Ascomycota while decreasing that of Basidiomycota. Basidiomycota, being a large and complex fungal group, includes several plant pathogens like Chaetomium and Fusarium . These pathogens, which are major drivers of soybean root rot, often increase in abundance during continuous soybean cropping, potentially elevating the incidence of crop diseases (Zhou et al., 2018 ). In this study, Ascomycota effectively increased soil nitrogen content while simultaneously boosting the activities of soil urease and protease enzymes. This effect can be attributed to yeast symbionts in the soil, which proliferate around plant roots. Their gelatinous secretions enhance soil structure by increasing looseness, aeration, water retention, and nutrient preservation. This, in turn, decomposes nitrogen, phosphorus, potassium, and other immobilized elements in the soil, transforming them into nutrients that plants can directly absorb and utilize. As a result, the utilization efficiency of fertilizers is enhanced (Naumova et al., 2017 ; Danka et al., 2022 ). 4.3. Impact of long-term crop rotation on nitrogen form transformation: Insights from microbial community dynamics and enzyme activities In this study, the pivotal species and enzymatic activities associated with variations in nitrogen forms were identified using PCA analysis and a random forest predictive model. Within the TN group, the proportions of RON and AAN components were notably predominant in TON and TIN, respectively. This phenomenon is most likely due to the modulation of key microbial communities and enzymatic activities regulating the formation of RON and AAN. AAN contains elements like AN and NN that are plausible nitrogen sources for plant uptake and constitute one of the most dynamic nitrogen reservoirs for crop growth (Brown et al., 2022 ). The fluctuation of AAN in the soil could be linked to variations in AHN, given its significant presence within AAN and its role as a swiftly releasable fraction. Proteobacteria and Acidobacteriota as well as Ascomycota have previously been identified as members of the Dissimilatory Nitrate Reduction to Ammonium (DNRA) community (Sakuntala et al., 2016 ). Crop rotation and nitrogen fertilization might induce ammonium reduction, curbing nitrogen loss via ammonia volatilization and thereby promoting AN and NN accumulation. The production of chitinase by Ascomycota has been reported (Hu et al., 2022 ) and thus, chitinase decomposition of organic nitrogen products could add to the pool of AN and NN. Furthermore, recent reports indicate Ascomycota's participation in crop pathogen suppression, assistance in crop growth, and the accumulation of AN and NN components through enhanced root exudation (Challacombe et al., 2019). The significant influence of SU, SP, and SNR on the changes in AN components was documented in the present work. SP is engaged in the breakdown of chitin and lignin, key constituents of bacterial and fungal cell walls (Zhang et al., 2020 ). Following microbial cell wall shedding, decomposition generates low-molecular-weight organic compounds like free amino acids and amino sugars, contributing to the TIN pool. On the other hand, UR's end product is NH 4 + , a precursor of AN. However, when nitrogen saturation occurs, SNR may lead to a reduction in AN and NN content (Alizadeh et al., 2017). In the SM1 treatment, AN and NN content significantly surpassed those of MS0 and SS0 treatments (Fig. 1 ), suggesting that Proteobacteria, Acidobacteriota, and Ascomycota excel in modulating AAN content compared to SNR. Certain fungal species have been reported to possess genes associated with amino acids (Frey et al., 2000 ), participating in nitrogen cycling. Their ability to produce an amino acid oxidase has also been observed (Isobe et al., 2014 ). The lower ammonia-producing capability following crop rotation is explained by decreased fungal richness relative to continuous cropping. Notably, in this study, Fungi demonstrate greater resilience to lower pH levels than bacteria in microbial communities (Zhang et al., 2020 ). Thus, the fungal-to-bacterial richness ratio rose with nitrogen fertilization due to the anticipated pH decrease caused by nitrogen input. This possibly elucidates the critical relevance of fungi in AAN morphological changes, as AAN is intricately tied to microbial metabolism (Durani et al., 2016 ). 5. Conclusion This study's findings reveal that under soybean-corn rotation conditions, both LON and ANN, as well as their constituent forms, namely AHN, AN, and NN, increased significantly. Each nitrogen form was subjected to a random forest model for the prediction of dominant soil bacterial and fungal phyla or three crucial nitrogen-cycling enzymes (SU, SP, SNR). This analysis provided pertinent insights into microbial nitrogen metabolic functions encompassing nitrification, denitrification, ammonification, and nitrogen fixation. It is critical to account for soil microenvironmental and functional factors in the investigation of soil nitrogen cycling across distinct rotation systems, given their direct impact on the alteration of soil nitrogen forms. Declarations Credit authorship contribution statement H.Z. and W.Z. designed the experiment. Y.W. and L.Z. wrote the first manuscript and performed all of the statistical analyses. Y.W., L.Z., F.M., Z.L., X.A., and X.J. collected the data in the field and lab. All authors have read and agreed to the published version of the manuscript. Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgments This research was supported by the China Agriculture Research System of MOF and MARA (No. CARS-04-PS14), the Young and Middle-aged Scientific and Technological Innovation and Entrepreneurship Outstanding Talent (team) Project (20210509012RQ), and Science and Technology Development Plan Project of Jilin Province (No.YDZJ202201ZYTS578). References Alison, E.K., Kirsten, S.H., 2017. Diversified cropping systems support greater microbial cycling and retention of carbon and nitrogen. Agriculture, Ecosystems and Environment 240, 66-67. https://doi.org/10.1016/j.agee.2017.01.040. Alizadeh, H., Kandula, R.D., Hampton, G.J., Stewart, A., Leung, W.D., Edwards, Y., Smith, C., Urease producing microorganisms under dairy pasture management in soils across New Zealand. Geoderma Regional 11, 78-85. https://doi.org/10.1016/j.geodrs.2017.10.003. An, N.N., Fan, W.G., Tan, Q.P., Li, D.M., Gao, D.S., 2012. Effects of different cultivating media on the growth of malus hupehensis seedlings,microc organism and nitrate reductase in soil. Chinese Journal of Applied and Environmental Biology 18 (06),1022-1026. https://doi.org/10.3724/SP.J.1145.2012.01022. Aparicio, V., Costa, J.L., 2007. Soil quality indicators under continuous cropping systems in the Argentinean Pampas. Soil and Tillage Research 96 (1-2), 155-165. https://doi.org/10.1016/j.still.2007.05.006. Azeem, M., Sun, D.Q., Crowley, D., Hayat, R., Hussain, Q., Ali, A., Tahir, M.I., Jeyasundar, P.G.S.A., Rinklebe, J., Zhang Z.Q., 2020. Crop types have stronger effects on soil microbial communities and functionalities than biochar or fertilizer during two cycles of legume-cereal rotations of dry land. Science of the Total Environment 715, 136958. https://doi.org/10.1016/j.scitotenv.2020.136958. Bao, S.D., 1999. Analysis on Soil and Agricultural Chemistry (in Chinese). China Agricultural Press, Beijing. Batista, M.S., Silva, A.V., Silva, R.D.M., Oliveira, L., Silva, C.M., Mielezrski, F., 2020. Productive potential and economic viability of soybeans in response to potassium application. Journal of Agricultural Studies 8 (3), 221. https://doi.org/10.5296/jas.v8i3.16558. Braos, B.B., Ferreira E.M., Cruz, D.P.C.M., Braos, L.B., Barbosa, J.C., 2017. Mild and Moderate Extraction Methods to Assess Potentially Available Soil Organic Nitrogen. Revista Brasileira de Ciência do Solo 40 (0). https://doi.org/10.1590/18069657rbcs20151059. Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F.X., Huard, F., 2010. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Research 119 (1), 201-212. https://doi.org/10.1016/j.fcr2010.07.012. Brown, F.R., Sala, E.O., Sinsabaugh, L.R., Collins, S.L., 2022. Temporal effects of monsoon rainfall pulses on plant available nitrogen in a chihuahuan desert grassland. Journal of Geophysical Research. Biogeosciences 127 (6). https://doi.org/10.1029/2022JG006938. Cecilia, C., Nicolás, W., Hernán, S.R., Pedro, B., Mirian, B., Vicente, G., Pablo, B., 2021. Improving soil organic nitrogen and sulfur pools by cover cropping and crop fertilization in soybean-based cropping systems. Soil & Tillage Research 213. https://doi.org/10.1016/J.STILL.2021.105138. Danka, R., Vera, K., Jelena, D., Jelena, J.P., Igor, K., Blazo, L.,Vera, R., 2022. Soil yeasts promoting plant growth: benefits for the development of common wheat and white mustard. Zemdirbyste-Agriculture 109, 27-34. https://doi.org/10.13080/Z-A.2022.109.004. Ding, G.M., Chen, H.R., Gu, H.F., Zhang, Y.Q., Li, R.M., Zhang, S.F., 2022. Inorganic nitrogen uptake characteristics of three typical bloom-forming algae in the east china sea. Water 14 (16), 2580. https://doi.org/10.3390/W14162580. Durani, A., Brar, B.S., Dheri, G.S., 2016. Soil nitrogen fractions in relation to rice-wheat productivity: effects of long-term application of mineral fertilizers and organic manures.J. Crop Improv. 30 (4), 399–420. https://doi.org/10.1080/15427528.2016.1172145. Gan, Y.T., Chantal, H., John, T.O.D., Herb, C., Robert, P.Z., Con, A.C., Niu, Y.N., Lee, P., 2015. Diversifying crop rotations with pulses s enhances system productivity. Scientific Reports 49 (4), 6-10. https://doi.org/10.1038/srep14625. Fajri, A., Makoto, K., 2020. Effect of tillage treatments of hairy vetch residues on soil inorganic-N distributions and corn growth in a subtropical region. Applied and Environmental Soil Science, 7049161. https://doi.org/10.1155/2020/7049161. Fan, K.K., Delgado, B.M., Guo, X.S., Wang, D.Z., Zhu, Y.G., Chu, H.Y., 2020. Microbial resistance promotes plant production in a four-decade nutrient fertilization experiment. Soil Biology and Biochemistry 141, 107679. https://doi.org/10.1016/j.soilbio.2019.107679. Feng, J., Turner, L.B., Wei, K., Tian, J.H., Chen, Z.H., Lü, X.T., Wang, C., Chen, L.J., 2018. Divergent composition and turnover of soil organic nitrogen along a climate gradient in arid and semiarid grasslands. Geoderma 327, 36-44. https://doi.org/10.1016/j.geoderma.2018.04.020. Freitas, V.F., Cerezini, P., Hungria, M., Nogueira, M.A., 2022. Strategies to deal with drought-stress in biological nitrogen fixation in soybean. Applied Soil Ecology 172. https://doi.org/10.1016/J.APSOIL.2021.104352. Frey, S.D., Elliott, E.T., Paustian, K., Peterson, G.A., 2000. Fungal translocation as a mechanism for soil nitrogen inputs to surface residue decomposition in a no-tillage agroecosystem. Soil Biology and Biochemistry 32(5), 689-698. https://doi.org/10.1016/S0038-0717(99)00205-9. Gao, H.J., Li, Q., Peng, C., Zhang, X.Z., Jiao, Y.F., Gao, J.C., Zhu, P., 2022. Effects of different crop rotation and straw returning methods on bacterial community structure in black soil. Journal of Jilin Agricultural University 44 (03), 336-344. https://doi.org/10.13327/j.jjlau.2021.1135. Guan, D.W., Li, L., Yue, X.L., Ma, M.C., Zhang, W., Li, J., 2014. Study on potential of biological nitrogen fixation of soybean in China. Journal of Plant Nutrition and Fertilizer 20 (06), 1497-1504. https://doi.org/10.11674/zwyf.2014.0620. Han, X., Yuan, C.Y., Li, J.H., Hong, Z.W., Liu, X., Du, T., Li, H., You, C.M., Tan, B., Zhu, P., Xv, Z.F., 2022. Effects of Tree Species and Soil Layers on Soil Extractable Nitrogen Content. Ecology and Environmental Sciences. 31 (11), 2143-2151. https://doi.org/10.16258/j.cnki.1674-5906.2022.11.005. Hohman, S.P., Smyth, A.R., Bean, E.Z., Reisinger, A.J., 2021. Internal nitrogen dynamics in stormwater pond sediments are influenced by pond age and inorganic nitrogen availability. Biogeochemistry 156 (2), 255-278. https://doi.org/10.1007/S10533-021-00843-2. Hou, J.W., Xing, C.F., Yang, L.L., 2021. Optimization reform of soil available nitrogen determination method. Journal of Southwest China Normal University (Natural Science Edition) 46 (07), 45-49. https://doi.org/10.13718/j.cnki.xsxb.2021.07.006. Hou, K.X., Cheng, C., Shi B.H., Liu, W., Du, Z.K., Li, B., Wang, J., Wang, J.H., Zhu, L.S., New insights into the effects of chlorpyrifos on soil microbes: Carbon and nitrogen cycle related microbes in wheat/maize rotation agricultural field. Environmental Pollution 318, 120908. https://doi.org/10.1016/J.ENVPOL.2022.120908. Hu, M.H., Liu, L.L., Zhang, X., Lin, Y., Shen, X.C., Long, S.H., Kang, J.C., Wijayawardene, N.N., Li, Q.R., Long, Q.D., 2022. New species and records of Neomassaria, Oxydothis and Roussoella (Pezizomycotina, Ascomycota) associated with palm and bamboo from China. MycoKeys 93, 165-191. https://doi.org/10.3897/MYCOKEYS.93.89888. Isobe, K., Taira, R., Hoshi, Y., Matsuda, S., Yamada, M., Hibi, M., Kishino, S., Ogawa, J., 2014. Characterization of a novel l -amino acid oxidase with protein oxidizing activity from Penicillium steckii AIU 027. Journal of Bioscience and Bioengineering 117 (6), 690-695. https://doi.org/1016/j.jbiosc.2013.11.008. Jacynthe, D.R., 2022. Soil nitrogen supply: Linking plant available N to ecosystem functions and productivity. Nitrogen 3 (3), 455-457. https://doi.org/10.3390/NITROGEN3030030. Jakob, H., Wolfgang, W., Ye, T., Kwatcho, K.S., Werner, B., Andreas, S., Erich, I., 2021. No effect of long-term soil warming on diffusive soil inorganic and organic nitrogen fluxes in a temperate forest soil. Soil Biology and Biochemistry, 108261. https://doi.org/10.1016/J.SOILBIO.2021.108261. Jean, F.C., Cedar, N.H., Bramer, L.M., Ann, M.C.L., Mary, L., Samuel, P., Carrie, N., Verne, G.G.L., Andrea, P.A., Cheryl, R.K., 2019. Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis. BMC genomics 20 (1), 976. https://doi.org/10.1186/s12864-019-6358-x. Jesmin T., Margenot, A.J., Mulvaney, R.L., 2022. A comprehensive method for casein-based assay of soil protease activity. Communications in Soil Science and Plant Analysis 53 (4), 507-520. https://doi.org/10.1080/00103624.2021.2017954. Jia, J., Zhu, J.F., Du, X.Z., Wang, M.M., Xv, Z.C., 2016. Effects of different croppina patterns on Soil EnzvmeFlue-cured tobacco growth and aroma substances content of tobacco leaf. Journal of Agricultural Science and Technology 18 (03), 141-149. https://doi.org/10.13304/j.nykjdb.2015.499. Jiao, H., Li, T.L., Gao, J.W., Li, Y., He, B., Li, S., 2018. Effects of Fertilization on Light and Heavy Fractions Organic Nitrogen in Reclaimed Soil. Journal of Soil and Water Conservation 32 (05), 208-213+221. https://doi.org/10.13870/j.cnki.stbcxb.2018.05.034. Kyung, J.L., Jun, H.P., Ju, S.C., Ju, K.S., Kee, P.J., 2021. Effect of pyroligneous acid on soil urease, amidase, and nitrogen use efficiency by Chinese cabbage (Brassica campestris var. Pekinensis). Environmental Pollution 291, 118132. https://doi.org/10.1016/J.ENVPOL.2021.118132. Leinweber, P., Kruse, J., Baum, C., Arcand, M., Knight, J.D., Farrell, R., Eckhardt, K.U., Kiersch, K., Jandl, G., 2013. Advances in understanding organic nitrogen chemistry in soils using state-of-the-art analytical techniques. Advances in Agronomy 119, 83-151. https://doi.org/10.1016/B978-0-12-407247-3.00002-0. Liebman, M., Nichols, V.A., 2020. Cropping system redesign for improved weed management: A modeling approach illustrated with giant ragweed (Ambrosia trifida). Agronomy 10 (2), 262. https://doi.org/10.3390/agronomy10020262. Liu, X., Pang, R.Y., Zhao, J., Si, T., Wang, Y.F., Zou, X.X., Jiao, Y.L. 2023. Effects of different rotation modes on the concentrations of soil activate carbon fractions and characteristics of carbon mineralization. Journal of Agro-Environment Science, 1-13 https://doi.org/10.11654/jaes.2022-1165. Liu, Z.X., 2020. Molecular analysis of the changes of soil microbial communities in continuous cropping of soybean in the black soil of Northeast China (in Chinese). Northeast Institute of Geograpy and Agroecology, Changchun. https://doi.org/10.27536/d.cnki.gccdy.2020.000043. Mori, T., Wang, S., Peng, C., Wang, C., Mo, J.M., Zheng, M.H., Zhang, W., 2023. Importance of considering enzyme degradation for interpreting the response of soil enzyme activity to nutrient addition: Insights from a field and laboratory study. Forests 14 (6). https://doi.org/10.3390/F14061206. Moritz, L., Marc, C., Samuel, M., Wanjiku, M.M.M., Daniel, M., Magdalena, N., Marijn V.B., Wycliffe,W., Bernard, V., Johan, S., 2023. Combining manure with mineral N fertilizer maintains maize yields: Evidence from four long-term experiments in Kenya. Field Crops Research 291. https://doi.org/10.1016/J.FCR.2022.108788. Nakayama, Y., Wade, J., Li C.Y., Daughtridge, R.C., Margenot, A.J., 2023. Quantifying the relative importance of controls and assay conditions for reliable measurement of soil enzyme activities with para-nitrophenol substrates. Geoderma 429. https://doi.org/10.1016/J.GEODERMA.2022.116234. Narayana, N.K., Kingery, W.L., Jacobs, A.A., Allison, J.K., Ganapathi, S.S., 2022. Effects of tillage and winter cover management in a maize soybean rotation on soil bacterial and fungal community composition. Land 11 (12), 2259. https://doi.org/10.3390/LAND11122259. Naumova, E.S., Lee, C.F., Kondratieva, V.I., Sadykova, A.Z., Naumov, G.I., 2017. Molecular genetic polymorphism of soil yeasts of the genus Williopsis from Taiwan Island. Russian Journal of Genetics 53 (5), 561-567. https://doi.org/10.1134/s1022795417040111. Noll, L., Zhang S.S., Zheng, Q., Hu, Y.T., Hofhansl, F., Wanek, W., 2022. Climate and geology overwrite land use effects on soil organic nitrogen cycling on a continental scale. Biogeosciences 19 (23), 5419-5433. https://doi.org/10.5194/BG-19-5419-2022. Peoples, M.B., Giller, K.E., Jensen, E.S., Herridge, D.F., 2021. Quantifying country-to-global scale nitrogen fixation for grain legumes: I. Reliance on nitrogen fixation of soybean, groundnut and pulses. Plant and Soil 469 (1-2), 1-14. https://doi.org/10.1007/S11104-021-05167-6. Perez, B.C., Arzeno, J.L., Huidobro, J., Grumberg, B., Hilton, S., Bending, G.D., Meriles, J. M., Vargas, G.S., 2014. The effect of crop sequences on soil microbial, chemical and physical indicators and its relationship with soybean sudden death syndrome (complex of Fusarium species). Spanish Journal of Agricultural Research 12 (1), 252-264. https://doi.org/10.5424/sjar/2014121-4654. Raza, M.A., Khalid, M.H.B., Zhang, X., Feng, L.Y., Khan, I., Hassan M.J., Ahmed, M., Ansar, M., Chen, Y.K., Fan, Y.F., Yang, F., Yang, W.Y., 2019. Effect of planting patterns on yield, nutrient accumulation and distribution in maize and soybean under relay crop rotation systems. Scientific reports 9 (1), 4947-4961. https://doi.org/10.1038/s41598-019-41364-1. Ree, P.K., Sol, P.C., Gyun, K.B., 2016. An enzyme complex increases in vitro dry matter digestibility of corn and wheat in pigs. SpringerPlus 5 (1), 598. https://doi.org/10.1186/s40064-016-2194-5. Rong, G.H., Zhou, J.Y., Wu, H.Y., Wei, X.R., 2023. Comparative study on the flow analyzer and the kjeldahl nitrogen analyzer for measuring soil total nitrogen. Research of Soil and Water Conservation 30 (01), 204-208. https://doi.org/10.13869/j.cnki.rswc.2023.01.030. Rong, Z.Y., Ling, J., Ling, H.X., Zhang, L., Zhang, Y.Y., Wang, L., 2022. Analysis on soil microbial community structure and diversity in fodder-crop rotation system. Journal of Gansu Agricultural University 57 (06), 155-163. https://doi.org/10.13432/j.cnki.jgsau.2022.06.019. Sakuntala, S., Akinori, A., Ryuya, I., Makoto, S., Jun, O., 2016. Analysis of microbial community and nitrogen transition with enriched nitrifying soil microbes for organic hydroponics. Bioscience, biotechnology, and biochemistry, 1-8. https://doi.org/10.1264/jsme2.2000.103. Singh, S., Savoy, H.J., Yin, X.H., Schneider, L., Jagadamma, S., 2019. Phosphorus and potassium fertilizer rate verification for a corn–wheat–soybean rotation system in Tennessee. Agronomy Journal 111 (4), 2060-2068. https://doi.org/10.2134/agronj2018.12.0749. Soman, C., Li, D.F., Wander, M.M., Kent, A.D., 2016. Long-term fertilizer and crop-rotation treatments differentially affect soil bacterial community structure. Plant and Soil 413, 145-159. https://doi.org/10.1007/s11104-016-3083-y. Sun, Q., Wu, H.L., Chen, F., Kang, J.H., 2020. Characteristics of soil nutrients and fungal community composition in crop rhizosphere under different rotation patterns. Environmental Science 41 (10), 4682-4689. https://doi.org/10.13227/j.hjkx.202001031. Sun, Y.P., Chen, X.B., Yang, J.S., Luo Y.M., Yao, R.J., Wang, X.P., Xie, W.P., Zhang, X., 2022. Biochar effects coastal saline soil and improves crop yields in a maize-barley rotation system in the tidal flat reclamation zone, China. Water 14 (20), 3204. https://doi.org/10.3390/W14203204. Sun, Z.M., Wu, Z.J., Chen, L.J., Liu, Y.G., 2006. Research advances in nitrogen fertilization and its environmental effects. Chinese Journal of Soil Science 04, 782-786. https://doi.org/10.19336/j.cnki.trtb.2006.04.037. Torres, M.C., Pinheiro, S.D.D.A., Hungria, D.C.L., Husny, J.C.E., Sacramento, P.P., Andrade, I.P., 2020. Inorganic nitrogen fractions in soil under different uses and management systems in the Brazilian eastern Amazon. Journal of Agricultural Studies 8 (4), 32. https://doi.org/10.5296/jas.v8i4.17080. Vance, C.P., 2001. Symbiotic nitrogen fixation and phosphorus acquisition.Plant nutrition in a world of declining renewable resources. Plant Physiology 127 (2), 390-397. https://doi.org/10.1104/pp.127.2.390. Wang, N., Wang, L., Song, C.H., Yang, H.N., Xiao, J., Jiao, Y.G., 2022. Effect of crop rotation on crop yield and soil physical and chemical properties in the black soil area. Bulletin of Agricultural Science and Technology (09), 71-74. Wu, P.B., Li, L.J., Zhang, Y.L., Li, X.T., Yang, F., 2020. Effects of rotation and fertilization on soil organic carbon and its fractions and soil nutrients. Chinese Journal of Soil Science 51 (02), 416-422. https://doi.org/10.19336/j.cnki.trtb.2020.02.20. Yang, W.Y., Du, Q., Yang, H., Fu, Z.D., Pang, T., Yang, H., Yong, T.W., 2016. Effects of different varieties and root barriers on soybean nodule nitrogen fixation and nitrogen uptake in maize/soybean intercropping system. Journal of Sichuan Agricultural University 34 (01), 1-5. https://doi.org/10.16036/j.issn.1000-2650.2016.01.001. Zhang, F.W., Qiao, Z.H., Yao, C.T., Sun S.A., Liu, W.T. Wang, J.X., 2020. Effects of the novel HPPD-inhibitor herbicide QYM201 on enzyme activity and microorganisms, and its degradation in soil. Ecotoxicology, 1-11. https://doi.org/10.1007/s10646-020-02302-4. Zhang, P., Sun, J., Li, L., Wang, X., Qu, J., 2019. Effect of Soybean and Maize Rotation On Soil Microbial Community Structure. Agronomy 9 (2), 42. https://doi.org/10.3390/agronomy9020042. Zhang, X.M., 2019. Determination of hydrolyzed nitrogen in soil by chemical nitrogen tube distillation. Xinjiang non-ferrous metals 42 (03), 69-70. https://doi.org/10.16206/j.cnki.65-1136/tg.2019.03.030. Zhang, Y., Dai, S.F., Huang, X.Q., Zhao, Y., Zhao, J., Cheng, Y., Cai, Z.C., Zhang, J.B., 2020. pH-induced changes in fungal abundance and composition affects soil heterotrophic nitrification after 30 days of artificial pH manipulation. Geoderma 366 (C), 114255. https://doi.org/10.1016/j.geoderma.2020.114255. Zhou, Q.X., Li, N.N., Chang, K.F., Hwang, S.F., Strelkov, S.E., Conner, R.L., McLaren, D.L., Fu, H.T., Harding, M.W., Turnbull, G.D., 2018. Genetic diversity and aggressiveness of Fusarium species isolated from soybean in Alberta, Canada. Crop Protection 105, 49-58. https://doi.org/10.1016/j.cropro.2017.11.005. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4008531","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276300643,"identity":"366f267c-7530-47be-8559-611a43127689","order_by":0,"name":"Liqiang Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Jilin University College of Plant Science","correspondingAuthor":true,"prefix":"","firstName":"Liqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":276300644,"identity":"edc0b58a-c093-4d93-8798-ee9848b060b8","order_by":1,"name":"Wenxiu Ji","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenxiu","middleName":"","lastName":"Ji","suffix":""},{"id":276300645,"identity":"d9a0f92e-3c0d-4ad4-886e-1a9094e62bcd","order_by":2,"name":"Xinbo Jiang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinbo","middleName":"","lastName":"Jiang","suffix":""},{"id":276300646,"identity":"b19ba302-54cf-4dc5-b3fe-1e6b104180d0","order_by":3,"name":"Yunlong Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Wang","suffix":""},{"id":276300647,"identity":"2339a182-66cc-4025-9509-587596153da8","order_by":4,"name":"Xiaoya An","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaoya","middleName":"","lastName":"An","suffix":""},{"id":276300648,"identity":"e99c15f5-296f-4a1e-ad9a-b98c39bca0ea","order_by":5,"name":"Demin Rao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Demin","middleName":"","lastName":"Rao","suffix":""},{"id":276300649,"identity":"41a7d314-2243-4e5d-8f6b-58c09a1fde49","order_by":6,"name":"Fangang Meng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fangang","middleName":"","lastName":"Meng","suffix":""},{"id":276300650,"identity":"aaad7523-bd4c-46d8-bdbd-2c679ba4c7b9","order_by":7,"name":"Jinhu Cui","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jinhu","middleName":"","lastName":"Cui","suffix":""},{"id":276300651,"identity":"0b3dabbc-01ae-40f9-a2a9-3922dbfeb530","order_by":8,"name":"Wei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":276300652,"identity":"a06797ad-33ce-44fc-9f00-a205c3003f5c","order_by":9,"name":"Hongyan Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-03-03 12:16:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4008531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4008531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52079388,"identity":"b95b9346-0aca-4e12-976d-b40c2eb866dc","added_by":"auto","created_at":"2024-03-06 10:54:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408435,"visible":true,"origin":"","legend":"\u003cp\u003ePlanting distribution map\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/883bfc32e5c3fe9f319400a2.jpg"},{"id":52079389,"identity":"734cf076-fa27-411a-9d57-23879ef27237","added_by":"auto","created_at":"2024-03-06 10:54:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":440126,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in different nitrogen forms (n=3). For each treatment, data from three columns corresponds to the years 2020, 2021, and 2022 respectively. The standard deviations of the three replicates are represented by error bars, while asterisk denote significant differences between treatments (*P \u0026lt; 0.05；**P \u0026lt; 0.01；***P \u0026lt; 0.001；****P \u0026lt; 0.0001). TN: total nitrogen; TON: total organic nitrogen; TIN: total inorganic nitrogen; RON: heavy fraction organic nitrogen; LON: light fraction organic nitrogen; AAN: alkali hydrolyzable nitrogen; AHN: acid hydrolyzable nitrogen; AN: ammonium nitrogen; and NN nitrate nitrogen.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/41ac4b9ae0be672fd958da71.jpg"},{"id":52079390,"identity":"283696d5-0532-4938-ac55-61ba27cf4079","added_by":"auto","created_at":"2024-03-06 10:54:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":350632,"visible":true,"origin":"","legend":"\u003cp\u003eA comparative assessment of the relative abundance of microbial phyla in soil, in light of divergent agronomic systems (n=3) during the years 2020 to 2022.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/4df7b79712a572cecd953b12.jpg"},{"id":52079387,"identity":"280efaf4-efad-4a46-9778-2f1ba3a66bef","added_by":"auto","created_at":"2024-03-06 10:54:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":572559,"visible":true,"origin":"","legend":"\u003cp\u003eThe PCA plot illustrating the Bray-Curtis distances based on OTUs (operational taxonomic units) of bacterial and fungal communities in soil across four experimental groups during the 2020- 2022 period. The plot's uppercase letters and red arrows represent environmental factors.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/d5881439dd5f7311b85ef6c2.jpg"},{"id":52079394,"identity":"28fad57a-e432-439f-989b-b56ddc7ce3b9","added_by":"auto","created_at":"2024-03-06 10:54:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2194229,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence networks of soil microbial communities, sculpted by the exquisite dance of correlation. Within this intricate tapestry, each circle symbolizes a distinct biological entity, an embodiment of species diversity. The dimensions of these circles represent the tapestry's complex weaving of species' relative abundances. Interwoven between these circlets, filaments of connection manifest as slender bridges, indicating intricate relationships between pairs of organisms. The girth of these filaments signifies their interdependence.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/07730312c4521216cc824d0c.jpg"},{"id":52079393,"identity":"df51ae66-c56f-452e-ac70-950d13b12f51","added_by":"auto","created_at":"2024-03-06 10:54:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":589875,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of key species and enzymatic activity on variations in TN (A), TON (B), TIN (C), RON (D), LON (E), AAN (F), AN (G), NN (H), and AHN (I) as inferred via the random forest model. Statistically significant influential factors are denoted by asterisks (*P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/591d60da9388673ee08c940f.jpg"},{"id":52079392,"identity":"b8b7bd15-42f3-48f9-9a53-89a11ba6e522","added_by":"auto","created_at":"2024-03-06 10:54:51","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":371770,"visible":true,"origin":"","legend":"\u003cp\u003eSEM-based functional analysis of the intricate interconnections among cultivation practices, nitrogen levels, and soil microbial communities. Positive associations are indicated by red arrows, while negative relationships are depicted by blue arrows. Statistical significance is denoted by asterisks (*P \u0026lt; 0.05; **P \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/2eb59053b120c77124e4c5d4.jpg"},{"id":53506162,"identity":"edf6847a-e41e-423c-8210-bc60247478ef","added_by":"auto","created_at":"2024-03-26 20:15:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1629243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4008531/v1/8d5b892b-4c6e-4f9b-8749-e1c02b3ed77c.pdf"}],"financialInterests":"","formattedTitle":"Understanding the role of soil microbes and enzymes in regulating nitrogen dynamics: Promoting sustainable crop rotation systems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLeguminous crops inherently possess nitrogen-fixing capabilities, which provide nitrogen nutritional supplies for subsequent cereal crops. The soil environment following the harvest of leguminous crops' root systems stands as an essential prerequisite for bolstering nitrogen fixation in leguminous flora (Aparicio et al., 2007; Freitas et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peoples et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The biological nitrogen fixation capacity of China's soybean-growing areas can reach up to a maximum of 150 kg/ha (Guan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In China's intensive agricultural landscapes, the widespread use of high-nitrogen fertilizers has exceeded 15% of the total arable land area (Sun et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This excessive application has led to poor nitrogen utilization efficiency, soil compaction, and a decrease in soil organic matter content. Simultaneously, the unretrieved portion enters the soil, atmosphere, and water bodies, polluting the ecological environment (Raza et al., 2014; Moritz et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence, establishing a green and sustainable crop rotation model is of paramount significance, especially in intensively cultivated areas.\u003c/p\u003e \u003cp\u003eVance et al. (2001) highlighted that ~\u0026thinsp;100 kg/ha of nitrogen in subsequent-season crops emanates from preceding-season leguminous cover crops. A three-year field experiment demonstrated that crop rotation with leguminous crops augmented maize yield and nitrogen utilization efficiency in the subsequent season by 35.5% and 33.0%, respectively (Gan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, maize yields in France exhibited an average annual decline of 0.035 t/ha over a 15-year span, when leguminous crop cultivation was reduced (Brisson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Hence, a comprehensive exploration of the impact of leguminous crops within crop rotation systems on nitrogen supply mechanisms for succeeding crops holds substantial theoretical and practical implications. This endeavour is pivotal for reducing nitrogen inputs, enhancing nitrogen utilization efficiency, and producing crops with excellent quality and yield.\u003c/p\u003e \u003cp\u003eSoil nitrogen levels, forms, and transformations have a direct influence on the nutritional capacity of crops (Jacynthe, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Inorganic nitrogen (TIN) is primarily derived from residual soil sources as well as the mineralization of organic nitrogen from soil or applied organic materials (Jakob et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Soil acid hydrolyzable nitrogen (AHN), exchangeable ammonium (AN), nitrate nitrogen (NN), and alkaline hydrolyzable nitrogen (AAN) constitute the main forms of nitrogen, comprising\u0026thinsp;~\u0026thinsp;2.61\u0026ndash;14.85% of total nitrogen. These forms serve as the principal sources of nitrogen nutrition for soil plants, with direct absorption and assimilation (Torres et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fajri et al., 2020). Organic nitrogen (TON) accounts for more than 90% of total nitrogen (TN) in soil, representing the predominant nitrogen form. Its content and distribution are closely linked to soil organic matter (Soman et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Labile organic nitrogen (LON), a highly prevalent category of nitrogen compounds, typically constitutes 20\u0026ndash;60% of total soil nitrogen (Lisa et al., 2022). This category mainly resides within soil organic matter, including proteins and peptides (Cecilia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil hydrolysates also include light organic nitrogen, accounting for about 5\u0026ndash;10% of total soil nitrogen (Leinweber et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It generally exists in the form of polysaccharide structures and can interact with small molecules like antimicrobial substances in conjunction with sticky peptides and proteins. In soil, it potentially serves a dual role, acting as a source of energy for plant growth and assisting the development of a sound soil structure (Feng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Braos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Crop rotation enhances nutrient cycling, boosts soil organic carbon, and augments soil fertility. A rice-bean rotation system effectively mitigates continuous cropping obstacles, resulting in increased yields and better crop quality. Concurrently, a notable surge in both the abundance and diversity of subterranean microorganisms is observed, imparting a transformative enhancement to the intricate tapestry of soil microstructures (Matt et al., 2020).\u003c/p\u003e \u003cp\u003eThe enzymatic activities of soil exhibit significant correlations with indicators such as soil nitrogen content and microbial communities (Sun et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within the realm of soil, enzymes such as urease (SU), protease (SP), and nitrate reductase (SNR) exhibit enhanced activity (Park et al., 2016). A plethora of investigations have demonstrated crop rotation's ability to foster microbial diversity in soil, while concomitantly bolstering enzymatic activity in the arable strata (Jia et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to Yang et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), conventional cultivation does not generate discernable discrepancies in soil enzymatic activity across varying soil depths, whereas rotated cultivation unveils a distinct stratification of enzyme activity in congruence with soil depth (Mori et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yuhei et al., 2023). Under divergent crop rotation regimes, the activity of distinct enzymes is variably influenced. Within the maize-soybean rotation paradigm, enzymes like SU, SP, and SNR demonstrate noteworthy distinctions in activity when compared to monoculture soybean and continuous maize scenarios (Muhammad et al., 2020).\u003c/p\u003e \u003cp\u003eThe investigation of the complex interaction between crop rotation and the intricate tapestry of soil's nitrogen-fixing bacterial communities has been predominantly guided by rudimentary methodologies, such as age-old breeding practices. Although traditional, these approaches fall short of capturing nuanced abundance, precise evaluation of nitrogen fixation, and the subtle shifts within bacterial populations in the intricate milieu of complex soil ecosystems (Wu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The higher the abundance of microorganisms such as bacteria, fungi, and actinomycetes in the soil, the greater the stability of their communities, thereby enhancing soil fertility and promoting plant growth (Sun et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Continuous cropping has been shown to limit the amount of rhizobia and inhibit root nodule formation, thereby fostering the development of certain diseases (Gao et al., 2023).\u003c/p\u003e \u003cp\u003eProlonged crop rotation increases the population of \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, and \u003cem\u003eAcidobacteria\u003c/em\u003e in the soil, whereas \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, and yeast encourage crop growth and yield, ultimately improving the structure and functionality of the soil's microbial community (Surendra et al., 2019). Similarly, Perez et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that long-term continuous cropping of soybeans increased soil fungal and plant diseases, with fungal populations significantly lower in soybeans under crop rotation with rice. Several studies have reported continuous cropping significantly reduces the quality of soybeans, the quantity and vitality of coexisting rhizobia, as well as nitrogen fixation., A 5-year soybean planting cycle induces substantial diversity shifts within nitrogen-fixing bacterial populations in the soil as compared to maize-soybean rotation. Different planting strategies reduce the types and quantities of nitrifying bacteria to varying degrees, accompanied by distinct alterations in the bacterial community structure of the soil (Narayana et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding upon the aforementioned literature, it was postulated that the alterations in nitrogen forms caused by nitrogen application in long-term soybean rotations might be closely linked to key species within the soil's microbial community and enzymatic activity. To validate this hypothesis, soil samples were collected from plots subjected to continuous 9\u0026ndash;11 year rotations of soybean and maize along with fertilization. The quantitative analyses of key enzymatic activities related to nitrogen cycling were conducted. High-throughput sequencing techniques were deployed to characterize the composition of bacterial and fungal communities. The objectives of this study are to (1) explore the influence of long-term maize-soybean rotations coupled with nitrogen application on soil nitrogen forms, enzymatic activities, and microbial community structure; (2) elucidate the effects of soil enzymes and microbial communities on changes in various forms of nitrogen; and (3) microscopically decipher the theoretical underpinnings behind shifts in metabolic functionality.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Site profile and experimental design\u003c/h2\u003e \u003cp\u003eThe experiment was conducted from April 2020 to October 2022 at the Y\u0026agrave;n M\u0026iacute;ng Lake Seed Company base in Shaheyan, Guandi Town, Dunhua City, Yanbian Korean Autonomous Prefecture, Jilin Province (128.3592\u0026deg;E, 43.4400\u0026deg;N). The climate is temperate semi-humid, and the soil type is Albic Luvisol. The study was carried out on three equal-sized plots. Two of these plots were dedicated to a crop rotation system involving soybean and maize, while the remaining one was used for continuous soybean cultivation. The experiment featured four treatment groups: MS1 - Fertilized soybean-maize rotation, MS0 - Non-fertilized soybean-maize rotation, SS1 - Fertilized continuous soybean cultivation, and SS0 - Non-fertilized continuous soybean cultivation. Set three repetitions for each processing set. Each experimental plot covered an area of 315 m\u003csup\u003e2\u003c/sup\u003e. The crops within each sub-plot were planted in 12 rows, with each row spanning 65 cm and a row spacing of 60 cm, The planting distribution is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The soybean planting density ranged from 20.0 to 22.0 thousand plants per hectare, whereas the planting density of maize ranged from 5.5 to 6.0 thousand plants per hectare. Two nitrogen (N) application levels were employed: 0 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 60 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Additionally, phosphorus (P) and potassium (K) fertilizers were applied at rates of 75 kg P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and K\u003csub\u003e2\u003c/sub\u003eO ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively. The experiment was designed to investigate the impact of these treatments on crop yields and soil quality in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Soil Sampling and Analysis\u003c/h2\u003e \u003cp\u003eThe soil sampling experiment was conducted between October 1, 2020, and October 1, 2022. Collect soil samples from the 0-20cm cultivation layer during the soybean maturity period for three consecutive years. Three sampling points were established within each plot, using the soil augering method. Root samples were extracted in situ from the 0\u0026ndash;20 cm soil depth on the ridges. Fresh soil specimens were collected and homogenized thoroughly before being placed in ice chests. Subsequently, extraneous elements such as stones and vegetative residues were eliminated. A portion of the soil samples was air-dried and reserved for chemical analysis, while another fraction, designated for the quantification of microbial biomass and enzymatic activity was stored in a refrigerator at -80\u0026deg;C.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Analytical methods\u003c/h2\u003e \u003cp\u003eSoil total nitrogen (TN) was determined using the Kjeldahl method for digestion (Rong et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), followed by filtration through a 0.45 \u0026micro;m PES membrane. A continuous flow analyzer (AA3, AutoAnalyzer 3, Technicon, Windows/NT) was used for analysis. Soil inorganic nitrogen (TIN), ammonium nitrogen (AN), and nitrate nitrogen (NN) were extracted with 2 mol/L CaCl\u003csub\u003e2\u003c/sub\u003e, shaken at 180 rpm for 60 min, allowed to settle for 30 min, and then subjected toAA3 analysis (Han et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bao, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Soil alkali hydrolyzable nitrogen (AAN) and acid hydrolyzable nitrogen (AHN) were determined using the alkaline diffusion method (Hou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal organic nitrogen (TON), light fraction organic nitrogen (LON), and heavy fraction organic nitrogen (RON) were quantified using the semi-micro Kjeldahl method (Jiao et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Soil urease (SU) activity was assayed using the urease colorimetric method with urea as the substrate. The soil sample was incubated at a constant temperature for 24 hours in a C6H8O7 buffer solution at 37 ℃ and pH 6.7, and measured at 578nm using a spectrophotometer. (Kyung et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Soil protease (SP) activity was determined using the casein colorimetric method. The soil sample was incubated at a constant temperature for 24 hours in a phosphate buffer solution at 37℃ and pH 5.5, and measured at 650nm using a spectrophotometer. (Tanjila et al., 2022). Soil nitrate reductase (SNR) activity was assessed through anaerobic cultivation followed by the phenol-sulfuric acid colorimetric method. The soil sample was incubated at a constant temperature for 24 hours in a glucose buffer solution at 30 ℃ and pH 7.0, and measured at 400-500nm using a spectrophotometer (An et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe soil pH and electrical conductivity (EC) values were extracted from the soil-water mixture at a ratio of 5:1. The mixture was agitated at 180 rpm for 5 min and then left undisturbed for 30 min. The pH was subsequently measured using a pH meter (pH-100A, 100-2000rpm, LICHEN, Shanghai, CN), while the EC was determined using a conductivity meter (DDSJ-11A-307, YUEPING, Shanghai, CN). For the analysis of total potassium (TK) and total phosphorus (TP) components in the soil, a digestion method involving concentrated perchloric acid and sulfuric acid was employed. The digested solution was filtered through a 0.45 \u0026micro;m PES membrane and the content was quantified using flame photometry (FP6400, INESA, Shanghai, CN) and the AA3 analyzer, respectively. The readily available phosphorus (AP) component in the soil was extracted using a sodium bicarbonate solution. The extraction process involved shaking at 180 rpm per minute for 2 h, followed by a 30 min settling period. After filtration through a 0.45 \u0026micro;m PES membrane, the concentration of AP was measured using the AA3 analyzer. Similarly, the available potassium (AK) component in the soil was extracted using an ammonium acetate solution. The extraction process involved shaking at 180 rpm for 2 h, followed by a 30 min settling period. After filtration through a 0.45 \u0026micro;m PES membrane, the concentration of AK was determined using flame photometry (fp6400, INESA, Shanghai, CN). The soil's organic matter content was determined using the potassium dichromate volumetric method combined with the dilution-heat approach, as outlined by Bao (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3. PCR Amplification and High-Throughput Sequencing\u003c/h2\u003e \u003cp\u003eThe MN NucleoSpin 96 Soil DNA Extraction Kit was used to extract DNA from soil samples. The resulting DNA concentration was measured using the NanoDrop 2000, and the quality of the extracted DNA was assessed through 1% agarose gel electrophoresis. For the amplification of bacterial 16S rRNA gene fragments corresponding to the V3-V4 region, the primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') were employed. In the case of fungal 18S rRNA gene amplification targeting the ITS1 region, the primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2 (5'-GCTGCGTTTCTTTCATCGATGC-3') were utilized. Following the amplification, the PCR products were purified, quantified, and standardized to generate sequencing libraries. The 16S rRNA gene libraries were prepared as paired-end (PE) 2 \u0026times; 300, while the 18S rRNA gene libraries were prepared as PE 2 \u0026times; 250. The constructed libraries were subjected to rigorous quality control. Subsequently, the qualified libraries were subjected to sequencing on the Illumina NovaSeq 6000 platform, as outlined in Hou et al. (2022). The registration number for this biological project is MJ20221021156-MJ-M-20221024089.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe SPSS 22.0 software was used to conduct statistical analysis. A two-way analysis of variance (ANOVA) was used to evaluate the impact of nitrogen fertilizer levels and cultivation methods on nitrogen components during various growth stages and soil parameters. To assess the primary effects of fertilization, growth stages, and their interactions on soil nitrogen forms and enzymatic activity, a bidirectional analysis of variance was employed. Pearson correlation tests were carried out to evaluate the relationships between microbial modules, enzymes, relative abundances of nitrogen forms, and the physicochemical properties of the soil along with enzyme activity.\u003c/p\u003e \u003cp\u003eBeta diversity analysis based on the Bray-Curtis dissimilarity coefficient and PCA analysis was used to compare the similarity of species community diversity among different samples. A co-occurrence network model at the genus level was established for continuous and rotational soil microbial communities, comparing the interactions between soil microbial communities under different cultivation modes. The PICRUSt and FUNGuild functional prediction methods were constructed to forecast the functions of soil bacteria and fungi under various treatments and identify the abundance of nitrogen metabolism-related enzymes and gene expressions in soil samples under different treatments.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Fundamental physicochemical characteristics of soil\u003c/h2\u003e \u003cp\u003eFrom 2020 to 2022, the fundamental physicochemical properties of twelve distinct soil samples were assessed, as documented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Except for TP, crop rotation regimen outcomes differed significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from those of the SS1 and SS0 treatments. When compared to continuous monoculture, soil pH and SOM demonstrated augmentation in the crop rotation systems, exhibiting increments of 0.48\u0026ndash;14.31% and 20.33\u0026ndash;77.36%, respectively. Conversely, EC, TK, AP, and AK constituents experienced a reduction. It is noteworthy that despite the amplified soil pH and SOM under crop rotation practices, the introduction of fertilizers interrupted this growth trend. Furthermore, increased fertilizer application exacerbated the inhibitory impact. Within the continuous monoculture approach, soil EC, AP, and AK showed a propensity for post-fertilization augmentation. Overall, nitrogen-based fertilizers application led to a decline in soil pH, SOM, TP, and TK, regardless of the duration of the crop rotation cycle, while concurrently fostering a rise in EC, AP, and AK.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in soil physicochemical parameters under different treatments from 2020 to 2022 (n\u0026thinsp;=\u0026thinsp;3). Statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the two treatments are denoted by distinct letters (a, b, c). SOM: organic matter; TP: total phosphorus; TK: total potassium; AP: available phosphorus; AK: available potassium.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e103.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Transformation of various nitrogen forms in soil\u003c/h2\u003e \u003cp\u003eAn analysis of the findings presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A reveals that crop rotation increases soil TN content as compared to continuous cropping. The increase in TIN content was even more pronounced. In the comparison between MS1 and SS0 treatments, MS1 exhibited a substantial rise of 273.43% in TIN content. In the crop rotation treatments, as nitrogen application rates, increased, both soil TN and TON content gradually rose, while TIN content progressively declined. Notably, in the treatments without nitrogen fertilization, MS0 and SS0 exhibited higher TN and TON content than the nitrogen-fertilized treatments. Among these, MS0 displayed the highest TN content, surpassing SS0 by 32.11%, though its TIN content was 57.02% lower than that of MS1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExamining the distribution of TON and its components among the five treatments \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eB)\u003c/b\u003e, it becomes evident that crop rotation enhances the content of LON. Specifically, the MS0 treatment presented the highest LON content, outperforming MS1 and SS0 by 31.98% and 108.34% respectively. Among the crop rotation treatments, MS1 accounted for 36.79% of the LON proportion, while MS0 and SS0 constituted 28.60% and 17.30% of the total respectively. The distribution of AAN and its three components across the five treatments \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eC)\u003c/b\u003e demonstrates that MS1 and SS1 treatments have significantly higher AAN content than the others. This implied a close relationship between soil AAN content and nitrogen application rates. Across all treatments, AHN, NN, and AN constituted 68.53%-87.04%, 8.35%-16.47%, and 2.16%-15.03% of AAN, respectively. Notably, crop rotation resulted in an 11.45% lower proportion of AHN compared to continuous cropping. Contrarily, NN and AN proportions were higher by 3.87% and 7.58%, respectively.\u003c/p\u003e \u003cp\u003eReduced nitrogen application rates across all treatments corresponded to an increase in AHN proportion and a decrease in NN and AN proportions. From the aforementioned analysis, it is evident that compared to SS0, crop rotation significantly enhanced the content of LIN, LON, AAN, NN, and AN in the soil. Furthermore, with an increase in the number of years of crop rotation, these components displayed an ascending trend. During the soybean maturation stage, nitrogen fertilizer application notably augmented AAN content. It is worth noting that both nitrogen application rates and years of cultivation exhibited interactive effects on the transformation of soil nitrogen forms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Changes in key enzyme activities during the nitrogen conversion process\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the activities of three enzymes pertinent to the nitrogen cycle exhibited pronounced disparities between distinct cultivation methodologies with or without nitrogen fertilization treatments. In comparison to the N0 treatment, both SU and SP activities were elevated by 3.95\u0026ndash;29.64% and 38.82\u0026ndash;76.83%, respectively, under the rotational cropping regimen. Meanwhile, the SNR of the rotational cropping regimen exhibited a reduction of 17.41\u0026ndash;51.86% in relation to the SS0 treatment. Concurrent with the augmentation of nitrogen levels, analogous patterns of enzymatic activity alterations were observed for both rotational and continuous cropping approaches. In the former, the activities of SU and SNR rose, with increments of 19.79% and 41.72% respectively in MS1 compared to MS0, and increments of 4.07% and 23.56% respectively in SS1 compared to SS0. Conversely, the activity of SP declined, with reductions of 21.50% in MS1 compared to MS0 and 7.84% in SS1 compared to SS0. Comparing the years 2020 and 2022, rotational cropping showcased an incremental and steady rise in SR and SP activities, alongside a reduction in SNR activity. In contrast, the continuous cropping approach demonstrated an opposing trend in the alteration of SU, SP, and SNR activities. Notably, the interplay between nitrogen application rates and years of cultivation exerts a mutual influence on key soil enzymatic activities across all treatments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActivities of key enzymes involved in nitrogen transformation processes during 2020\u0026ndash;2022.: SU: urease: SP: protease; and SNR: nitrate reductase.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026micro;m g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Changes in soil microbial communities\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Composition of the bacterial and fungal communities\u003c/h2\u003e \u003cp\u003eThe present study identified a total of 37 phyla, 107 classes, 220 orders, 411 families, and 786 genera of bacteria over the period of three years. A minimum of 30,566 sequences were obtained per sample, after conducting rarefaction. Likewise, the fungal analysis revealed 27 phyla, 59 classes, 93 orders, 119 families, and 144 genera. Following rarefaction, each sample yielded at least 35,257 sequences. The Proteobacteria, Actinobacteriota, Acidobacteriota, and Chloroflexi were found as dominant phyla within the bacterial domain(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These accounted for average relative abundances of 25.48%, 23.31%, 11.61%, and 11.54%, respectively, collectively comprising 71.94% of the total. Ascomycota and Basidiomycota emerged as the prominent phyla in fungi, accounting for 91.53% of the total with average relative abundances of 71.52% and 20.01%, respectively. The relative abundance of Proteobacteria in both SS1 and SS0 exhibited a declining trend, decreasing by an average of 27.11% by the year 2022. Meanwhile, relative abundances of Acidobacteriota, Acidobacteriota, and Chloroflexi rose gradually, with average increments of 4.9%, 40.14%, and 16.59% by the year 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor MS1 and MS0, shifts in dominant bacterial phyla mirrored the effects of rotation and continuous cropping. Proteobacteria decreased by 50.23%, whereas Actinobacteriota, Acidobacteriota, and Chloroflexi showed growth of 3.55%, 47.35%, and 5.67% respectively. Furthermore, the rotation and continuous cropping treatments increased the relative abundance of Ascomycota, with average increments of 5.39% and 34.81%, respectively, by the year 2022. Conversely, Basidiomycota's relative abundance declined by an average of 9.9% and 61.86% by the year 2022 in the context of rotation and continuous cropping, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. PCA analysis of bacteria and fungi\u003c/h2\u003e \u003cp\u003eThe Beta diversity study was carried out over 999 iterations using the Bray-Curtis dissimilarity coefficient and the Beta diversity index, with PCA selected as the analytical approach. This enabled a comparison of the degree of similarity in species community diversity across different samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For the period spanning 2020 to 2022, at the bacterial phylum level (97% similarity), PCA revealed an average explanatory rate of 42.89% for the first principal component (PC1) and 32.28% for the second principal component (PC2). In the crop rotation treatments, namely SM0, SM1, SS1, and SS0, clear differentiation was observed for both PC1 and PC2.AAN, TON, RON, and NN displayed significant contributions to PC1, with rates of 82.16%, 52.95%, 51.32%, and 42.46%, respectively. AN and AK demonstrated notable contributions to the PC2, with rates of 77.64% and 61.92% respectively. SU and SP were aligned with the direction of MS1 and SM1 treatments, highlighting the substantial influence of their magnitudes on the soil bacterial community in these two treatments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTurning attention to the fungal community analysis under varying treatments, for the years 2020 through 2022, the Beta diversity analysis demonstrated an average explanatory rate of 40.42% for the PC1 and 34.65% for the PC2. As seen in bacterial communities, the varying treatments viz. SM0, SM1, SS1, and SS0 exhibited substantial differences in fungal populations. Notably, ANN and NN made notable contributions to the PC1, with rates of 65.26% and 51.92% respectively, while AK demonstrated a significant contribution of 71.62% to the PC2. Soil attributes SU, SP, and SNR did not significantly impact the variations in fungal communities across the crop rotation treatments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3. Co-occurrence networks and ecological assemblages of bacteria and fungi\u003c/h2\u003e \u003cp\u003eUnder different cultivation modes, distinct agroecosystem models were established, showcasing continuous cropping (where SS1 and SS0 were fitted via 97% similarity) and crop rotation (where MS1 and MS0 were fitted via 97% similarity). These models delineated the co-occurrence networks of soil microbial communities at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a compilation of topological parameters for the network models from 2020 to 2022. This compilation was used to contrast the interrelations among soil microbial communities under varying cultivation practices. Crop rotation resulted in a reduction in soil microbial nodes as well as an increment in edge numbers. This implied decreased microbial diversity in crop-associated soil following crop rotation. However, interrelationships among different microbial phyla are complex.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTopological property index of soil microbial co-occurrence networks. Continuous soybean cropping (CC) versus soybean-corn rotation (CR).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFungus\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEdge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003cp\u003edegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003cp\u003eweighting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003cp\u003ecoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003emodularity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e640.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e160.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e652.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e163.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e758.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e189.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e764.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e191.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e814.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e253.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e817.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e254.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA comparison of microbial proportions between continuous cropping and crop rotation for the same year revealed that bacterial prevalence exceeded fungal prevalence in all treatments. Notably, crop rotation increased the proportion of bacteria, with a magnitude ranging between 1.75% and 9.86%. The positive correlations outweighed negative correlations in all treatments, with an average proportion of 55.15% and 44.85%, respectively. The crop rotation induced an augmentation in the proportion of positive correlations, with an increase ranging from 0.56\u0026ndash;3.48%. The crop rotation treatments outperformed continuous cropping treatments in terms of average degree, average weighted degree, average clustering coefficient, and modularity. This pattern indicated that the interconnectivity among network nodes was stronger within the crop rotation treatments, featuring a more intricate and abundant web of connections. Nevertheless, both crop rotation and continuous cropping treatments showed an upward increase in node numbers, fungal proportions, edge numbers, negative correlation proportions, average degree, average weighted degree, and average clustering coefficient as planting years progressed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Prediction analysis of soil microbial communities\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1. Prediction analysis of soil bacterial functions\u003c/h2\u003e \u003cp\u003eThe gene functional annotation data derived from the macro-genomic sequencing was sorted in line with the predictive capacity of gene functions by PICRUSt. The enzymes and gene expression abundances associated with nitrogen metabolism were selectively extracted for subsequent multi-sample abundance analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, continuous cropping consistently exhibited greater expression of the nitrification enzymes i.e., HAD, MMO, and AMM compared to crop rotation, with average increases of 41.98%, 8.55%, respectively. Additionally, the abundances of these enzymes in MS1 over MS0 showed respective increments of 42.14% and 7.68%. Contrarily, in the denitrification process, apart from the gene expression of nitrate reductase, the abundances of other enzyme genes were consistently higher in crop rotation than in continuous cropping, with rise of 14.13%, 1.82%, 4.15%, and 3.03% respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBacterial nitrogen metabolism enzymes and their corresponding gene expression abundances in different samples (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetabolic Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnzyme number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnzymes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14.18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14.99.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenitrification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2195.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2564.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1241.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e898.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrite reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3157.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3542.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2421.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2099.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e769.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e664.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e649.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e649.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1502.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1662.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1270.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1195.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrate reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e695.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e790.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e706.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e597.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmoniation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrile hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1845.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1847.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2242.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2212.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4.1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3703.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3292.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3701.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3346.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7.2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbamate kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1325.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1261.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2353.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1761.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2036.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2043.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3708.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3123.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2.1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyanogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1729.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1805.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2813.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2884.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen fixation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18.6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1438.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1660.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2846.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3032.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14.18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14.99.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenitrification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6000.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2100.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2249.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrite reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8228.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3741.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4049.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4378.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e951.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e815.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e769.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e747.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4714.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2900.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2828.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3166.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrate reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e697.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e653.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e684.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e566.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmoniation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrile hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3043.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2936.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3009.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3148.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4.1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4617.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3922.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3702.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4054.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7.2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbamate kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2502.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2984.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2986.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2778.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2964.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3527.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2669.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3070.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2.1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyanogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2761.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2168.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2542.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen fixation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18.6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3198.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4073.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2743.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3965.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14.18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14.99.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenitrification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1655.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e782.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1332.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e991.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrite reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2565.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1527.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2660.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2175.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1731.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1014.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1057.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1412.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1353.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1442.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7.7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrate reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e874.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e789.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e744.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmoniation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrile hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3128.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2869.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3333.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2823.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4.1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5317.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4077.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4872.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4683.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7.2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbamate kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2527.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2786.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2424.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2990.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2788.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2562.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3820.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3096.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2.1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyanogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2405.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2293.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3059.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2611.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen fixation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18.6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2124.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1151.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1160.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e790.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn continuous cropping, nitrate reductase expression was 21.36% lower. Furthermore, when MS1 was compared with MS0, five enzyme gene abundances experienced elevations of 53.54%, 30.41%, 13.14%, 11.46%, and 15.03%, respectively. As for the ammonia assimilation process, the abundances of the five enzymes paralleled those of nitrification, with continuous cropping outnumbering crop rotation. There were average gains of 1.6%, 25.81%, 6.87%, 17.04%, and 8.63%. In the nitrogen fixation process, enzyme gene abundances also exhibited a preference for crop rotation, with a 10.29% rise in expression levels and a further 11.46% increment when comparing MS1 with MS0 treatment. Within the crop rotation treatment, nitrification, ammonia assimilation, and nitrogen fixation displayed augmentations of 77.56%, 56.78%, and 15.17% respectively, while denitrification diminished by 6.74% as the cultivation years progressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2. Prediction and analysis of soil fungal function\u003c/h2\u003e \u003cp\u003eIn contrast to the functional gene prediction for bacteria, the prediction for fungi relies primarily on the abundance of genes associated with nitrogen metabolism, particularly the process of ammonification \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The enzymes involved in nitrile hydrolysis, glutamate dehydrogenase, and formamidase within the ammonification process exhibited significantly higher abundances under continuous cropping compared to crop rotation, with an average rise of 7.72%, 1.19%, and 14.56% respectively. When the MS1 treatment was compared to the MS0 treatment, there were reductions of 7.92%, 4.41%, and 18.65% respectively for these enzymes. As the cultivating years progressed, both continuous cropping and crop rotation treatments displayed a decline in the abundance of enzymes related to the ammonification process, with an average reduction of 46.93% and 32.81% respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFungal nitrogen metabolic enzymes and their corresponding gene expression abundances in samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetabolic Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnzyme number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnzymes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMS0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSS0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAmmoniation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrile hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29977.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31011.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34166.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36103.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4.1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16116.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18049.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17261.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18668.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12178.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12422.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16404.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18720.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrile hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25075.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23285.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23425.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24334.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4.1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13964.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12769.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12829.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12887.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11092.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11427.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11995.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11501.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrile hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23134.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24119.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25433.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26707.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4.1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13517.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13270.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12009.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13570.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5.1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarboxylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9927.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10697.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10506.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11597.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Microbial communities and enzymatic activity's influence on nitrogen transformation\u003c/h2\u003e \u003cp\u003eA random forest model that preserved the intended meaning was constructed using data on key species and enzyme activity. This model elucidated the regulatory mechanisms of TN and its eight components. It accounted for 90\u0026ndash;97% of the variations in TN, TON, TIN, RON, LON, AAN, AN, and AHN \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Factors were selected based on IncMSE (Incremental mean squared error)\u0026thinsp;\u0026gt;\u0026thinsp;10% and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for comparison. Among these factors, the four dominant phyla including Actinobacteriota, as well as SU and SP, were identified as significant variables impacting changes in TN, RON, and LON \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cb\u003eA, D, E)\u003c/b\u003e. For TIN, AAN, AN, and AHN, environmental factors such as Ascomycota and SU played pivotal roles \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cb\u003eC, F, G, I)\u003c/b\u003e. Proteobacteria and SU were respectively identified as critical determinants affecting variations in TON and NN components \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cb\u003eB, H)\u003c/b\u003e. SNR had a negative correlation with NN, while Basidiomycota showed negative associations with TN, TIN, and LON, yielding significant effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e3.7. Analysis of functional factors of soil microbial community based on\u003c/em\u003e structural equation model\u003c/h2\u003e \u003cp\u003eThe analysis results of the structural equation model (SEM) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. It indicated that both crop rotation and fertilization could significantly enhance soil TIN and TON, ranging from 32.11\u0026ndash;57.02% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The rhizospheric influence of nitrogen components was identified as the primary driving factor for soil microbial abundance. Crop rotation exerted a positive effect on the bacterial community composition and a negative effect on the fungal community composition. Interestingly, fertilizer addition showed the opposite pattern, with a negative effect on the former and a positive effect on the latter. Soil inorganic nitrogen content demonstrated a positive effect on bacterial community richness but a negative effect on fungal community richness, significantly impacting both fungal and bacterial community diversities. Soil organic nitrogen had a favourable influence on bacterial and fungal community richness, with a greater impact on bacteria than fungi. However, it had a deleterious impact on the diversities of both fungal and bacterial communities, with bacteria being affected more than fungi.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study underscores that different forms of nitrogen exert significantly varying impacts on the compositions of bacterial and fungal communities (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As the proportion of soil TON components increased, the diversity of soil bacteria and fungi decreased, leading to alterations in their community compositions. Within this study, both bacterial and fungal community compositions exhibited positive effects on their respective community functionalities. The mechanisms through which crop rotation and fertilization influence microbial communities encompass an increase in inorganic and organic nitrogen content, thereby enhancing the abundance of soil microbial communities while reducing their diversity. This alteration in the community composition consequently enhances the functionalities of these microbial communities. Therefore, in subsequent research that employs models to predict soil nitrogen cycling, accounting for the connection between nitrogen forms and soil microbial community composition becomes imperative.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Long-term crop rotation and nitrogen application-induced shifts in soil nitrogen forms\u003c/h2\u003e \u003cp\u003eWithin the context of soybean-corn rotation, corn often gains a competitive edge in soil nitrogen acquisition, as its nitrogen consumption exceeds that of soybeans. This, in turn, stimulates soybean's nitrogen fixation capacity. Wang et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in their study concerning the impact of rotation on soil physicochemical properties in black calcareous soil regions, corroborated a notable decrease of 15.5% in soil total nitrogen content following crop rotation. In the current work, the rotational practice significantly lowered the soil's total nitrogen content during the maturation phase. This phenomenon primarily arose from the mutualistic interaction between corn and soybeans. During periods of low nitrogen availability, leguminous plants' nitrogen fixation capacity adapts to enhance fixation rather than diminish it. Consequently, soybeans transfer nitrogen to corn, strengthening the nutrient translocation that augments corn's nitrogen nourishment and stimulates its growth (Batista et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this study, the soil's post-rotation levels of AHN and NN were noticeably greater than those of AN. This divergence can be attributed to the transformation of AN nitrogen into AHN and NN during the nitrification process. A greater disparity between AHN and NN leads to higher availability of nitrate nitrogen for translocation (Ding et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hohman et al. (2020) reported enhanced NN content in soybean-corn rotation systems. Unlike continuous cultivation, the extended rotation of corn and soybean substantially elevates soil TON levels, with LON content exerting the greatest influence. As compared to the MS0 and SS0 treatments, long-term fertilization had a considerable influence on TON content. Within the components of TON, rotation, and fertilization primarily elevated soil LON, whose concentration increased with the duration of the rotation. The order of RON content across treatments was: rotation\u0026thinsp;\u0026gt;\u0026thinsp;continuous cultivation, fertilized\u0026thinsp;\u0026gt;\u0026thinsp;non-fertilized, signifying that the combination of fertilization and crop rotation can enhance RON. This outcome primarily stems from the differing cultivation practices. In conventionally tilled soils, nitrogen predominantly transforms into amide nitrogen, while amino sugar nitrogen and amino acid nitrogen prevail in rotated soils (Alison et al., 2020).\u003c/p\u003e \u003cp\u003e \u003cem\u003e4.2. The prolonged practice of crop rotation and nitrogen applications alters the structure of soil microbial communities\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe dynamics of soil nitrogen reflect shifts in the overall microbial population within the soil. In the context of long-term rotation between rice and soybean, the bacterial composition at the phylum and genus levels remains quite similar. The relative abundance of these has been consistently higher in rotational soil compared to continuous cropping, indicating that cultivation practices have a major influence on the distribution of bacterial phyla and genera. This could be attributed to the influence of alternating crops on bacterial composition (Fan et al., 2022). Microbial community analysis via PCA and functional prediction revealed that after rotation, Proteobacteria and Acidobacteriota significantly contributed to soil nitrogen content. This contribution was intertwined with soil urease and protease activities, consequently promoting nitrification and ammonification processes within the soil. However, there was a negative correlation with the key enzyme for denitrification, nitrate reductase, thereby reducing nitrogen loss. This could be explained by the aerobic nature of both Proteobacteria and Acidobacteriota.\u003c/p\u003e \u003cp\u003eRotational cultivation of leguminous and graminaceous crops enhances soil aeration, and these two bacterial phyla can fix atmospheric nitrogen under low oxygen pressure (Liu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Construction of a co-occurrence network model revealed that rotation increased the edges among soil bacteria, leading to a more complex interplay among phyla. As planting years grow, soil microbial communities display increased species richness and reduced inter-species competition. This shift is due to improved survival conditions for dominant bacterial phyla post-rotation, resulting in strengthened mutualistic relationships and reduced competitive interactions (Rong et al., 2019). Rotation effectively enhances the relative abundance of Ascomycota while decreasing that of Basidiomycota. Basidiomycota, being a large and complex fungal group, includes several plant pathogens like \u003cem\u003eChaetomium\u003c/em\u003e and \u003cem\u003eFusarium\u003c/em\u003e. These pathogens, which are major drivers of soybean root rot, often increase in abundance during continuous soybean cropping, potentially elevating the incidence of crop diseases (Zhou et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, Ascomycota effectively increased soil nitrogen content while simultaneously boosting the activities of soil urease and protease enzymes. This effect can be attributed to yeast symbionts in the soil, which proliferate around plant roots. Their gelatinous secretions enhance soil structure by increasing looseness, aeration, water retention, and nutrient preservation. This, in turn, decomposes nitrogen, phosphorus, potassium, and other immobilized elements in the soil, transforming them into nutrients that plants can directly absorb and utilize. As a result, the utilization efficiency of fertilizers is enhanced (Naumova et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Danka et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003e4.3. Impact of long-term crop rotation on nitrogen form transformation: Insights from microbial community dynamics and enzyme activities\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn this study, the pivotal species and enzymatic activities associated with variations in nitrogen forms were identified using PCA analysis and a random forest predictive model. Within the TN group, the proportions of RON and AAN components were notably predominant in TON and TIN, respectively. This phenomenon is most likely due to the modulation of key microbial communities and enzymatic activities regulating the formation of RON and AAN. AAN contains elements like AN and NN that are plausible nitrogen sources for plant uptake and constitute one of the most dynamic nitrogen reservoirs for crop growth (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The fluctuation of AAN in the soil could be linked to variations in AHN, given its significant presence within AAN and its role as a swiftly releasable fraction. Proteobacteria and Acidobacteriota as well as Ascomycota have previously been identified as members of the Dissimilatory Nitrate Reduction to Ammonium (DNRA) community (Sakuntala et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrop rotation and nitrogen fertilization might induce ammonium reduction, curbing nitrogen loss via ammonia volatilization and thereby promoting AN and NN accumulation. The production of chitinase by Ascomycota has been reported (Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and thus, chitinase decomposition of organic nitrogen products could add to the pool of AN and NN. Furthermore, recent reports indicate Ascomycota's participation in crop pathogen suppression, assistance in crop growth, and the accumulation of AN and NN components through enhanced root exudation (Challacombe et al., 2019). The significant influence of SU, SP, and SNR on the changes in AN components was documented in the present work. SP is engaged in the breakdown of chitin and lignin, key constituents of bacterial and fungal cell walls (Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Following microbial cell wall shedding, decomposition generates low-molecular-weight organic compounds like free amino acids and amino sugars, contributing to the TIN pool. On the other hand, UR's end product is NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, a precursor of AN. However, when nitrogen saturation occurs, SNR may lead to a reduction in AN and NN content (Alizadeh et al., 2017).\u003c/p\u003e \u003cp\u003eIn the SM1 treatment, AN and NN content significantly surpassed those of MS0 and SS0 treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that Proteobacteria, Acidobacteriota, and Ascomycota excel in modulating AAN content compared to SNR. Certain fungal species have been reported to possess genes associated with amino acids (Frey et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), participating in nitrogen cycling. Their ability to produce an amino acid oxidase has also been observed (Isobe et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The lower ammonia-producing capability following crop rotation is explained by decreased fungal richness relative to continuous cropping. Notably, in this study, Fungi demonstrate greater resilience to lower pH levels than bacteria in microbial communities (Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the fungal-to-bacterial richness ratio rose with nitrogen fertilization due to the anticipated pH decrease caused by nitrogen input. This possibly elucidates the critical relevance of fungi in AAN morphological changes, as AAN is intricately tied to microbial metabolism (Durani et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study\u0026apos;s findings reveal that under soybean-corn rotation conditions, both LON and ANN, as well as their constituent forms, namely AHN, AN, and NN, increased significantly. Each nitrogen form was subjected to a random forest model for the prediction of dominant soil bacterial and fungal phyla or three crucial nitrogen-cycling enzymes (SU, SP, SNR). This analysis provided pertinent insights into microbial nitrogen metabolic functions encompassing nitrification, denitrification, ammonification, and nitrogen fixation. It is critical to account for soil microenvironmental and functional factors in the investigation of soil nitrogen cycling across distinct rotation systems, given their direct impact on the alteration of soil nitrogen forms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Z. and W.Z. designed the experiment. Y.W. and L.Z. wrote the first manuscript and performed all of the statistical analyses. Y.W., L.Z., F.M., Z.L., X.A., and X.J. collected the data in the field and lab. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the China Agriculture Research System of MOF and MARA (No. CARS-04-PS14), the Young and Middle-aged Scientific and Technological Innovation and Entrepreneurship Outstanding Talent (team) Project (20210509012RQ), and Science and Technology Development Plan Project of Jilin Province (No.YDZJ202201ZYTS578).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlison, E.K., Kirsten, S.H., 2017. Diversified cropping systems support greater microbial cycling and retention of carbon and nitrogen. Agriculture, Ecosystems and Environment 240, 66-67. https://doi.org/10.1016/j.agee.2017.01.040. \u003c/li\u003e\n\u003cli\u003eAlizadeh, H., Kandula, R.D., Hampton, G.J., Stewart, A., Leung, W.D., Edwards, Y., Smith, C., Urease producing microorganisms under dairy pasture management in soils across New Zealand. Geoderma Regional 11, 78-85. https://doi.org/10.1016/j.geodrs.2017.10.003. \u003c/li\u003e\n\u003cli\u003eAn, N.N., Fan, W.G., Tan, Q.P., Li, D.M., Gao, D.S., 2012. Effects of different cultivating media on the growth of malus hupehensis seedlings,microc organism and nitrate reductase in soil. Chinese Journal of Applied and Environmental Biology 18 (06),1022-1026. https://doi.org/10.3724/SP.J.1145.2012.01022. \u003c/li\u003e\n\u003cli\u003eAparicio, V., Costa, J.L., 2007. Soil quality indicators under continuous cropping systems in the Argentinean Pampas. Soil and Tillage Research 96 (1-2), 155-165. https://doi.org/10.1016/j.still.2007.05.006. \u003c/li\u003e\n\u003cli\u003eAzeem, M., Sun, D.Q., Crowley, D., Hayat, R., Hussain, Q., Ali, A., Tahir, M.I., Jeyasundar, P.G.S.A., Rinklebe, J., Zhang Z.Q., 2020. Crop types have stronger effects on soil microbial communities and functionalities than biochar or fertilizer during two cycles of legume-cereal rotations of dry land. Science of the Total Environment 715, 136958. https://doi.org/10.1016/j.scitotenv.2020.136958. \u003c/li\u003e\n\u003cli\u003eBao, S.D., 1999. Analysis on Soil and Agricultural Chemistry (in Chinese). China Agricultural Press, Beijing. \u003c/li\u003e\n\u003cli\u003eBatista, M.S., Silva, A.V., Silva, R.D.M., Oliveira, L., Silva, C.M., Mielezrski, F., 2020. Productive potential and economic viability of soybeans in response to potassium application. Journal of Agricultural Studies 8 (3), 221. https://doi.org/10.5296/jas.v8i3.16558. \u003c/li\u003e\n\u003cli\u003eBraos, B.B., Ferreira E.M., Cruz, D.P.C.M., Braos, L.B., Barbosa, J.C., 2017. Mild and Moderate Extraction Methods to Assess Potentially Available Soil Organic Nitrogen. Revista Brasileira de Ci\u0026ecirc;ncia do Solo 40 (0). https://doi.org/10.1590/18069657rbcs20151059. \u003c/li\u003e\n\u003cli\u003eBrisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F.X., Huard, F., 2010. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Research 119 (1), 201-212. https://doi.org/10.1016/j.fcr2010.07.012.\u003c/li\u003e\n\u003cli\u003eBrown, F.R., Sala, E.O., Sinsabaugh, L.R., Collins, S.L., 2022. Temporal effects of monsoon rainfall pulses on plant available nitrogen in a chihuahuan desert grassland. Journal of Geophysical Research. Biogeosciences 127 (6). https://doi.org/10.1029/2022JG006938. \u003c/li\u003e\n\u003cli\u003eCecilia, C., Nicol\u0026aacute;s, W., Hern\u0026aacute;n, S.R., Pedro, B., Mirian, B., Vicente, G., Pablo, B., 2021. Improving soil organic nitrogen and sulfur pools by cover cropping and crop fertilization in soybean-based cropping systems. Soil \u0026amp;amp; Tillage Research 213. https://doi.org/10.1016/J.STILL.2021.105138. \u003c/li\u003e\n\u003cli\u003eDanka, R., Vera, K., Jelena, D., Jelena, J.P., Igor, K., Blazo, L.,Vera, R., 2022. Soil yeasts promoting plant growth: benefits for the development of common wheat and white mustard. Zemdirbyste-Agriculture 109, 27-34. https://doi.org/10.13080/Z-A.2022.109.004. \u003c/li\u003e\n\u003cli\u003eDing, G.M., Chen, H.R., Gu, H.F., Zhang, Y.Q., Li, R.M., Zhang, S.F., 2022. Inorganic nitrogen uptake characteristics of three typical bloom-forming algae in the east china sea. Water 14 (16), 2580. https://doi.org/10.3390/W14162580. \u003c/li\u003e\n\u003cli\u003eDurani, A., Brar, B.S., Dheri, G.S., 2016. Soil nitrogen fractions in relation to rice-wheat productivity: effects of long-term application of mineral fertilizers and organic manures.J. Crop Improv. 30 (4), 399\u0026ndash;420. https://doi.org/10.1080/15427528.2016.1172145. \u003c/li\u003e\n\u003cli\u003eGan, Y.T., Chantal, H., John, T.O.D., Herb, C., Robert, P.Z., Con, A.C., Niu, Y.N., Lee, P., 2015. Diversifying crop rotations with pulses s enhances system productivity. Scientific Reports 49 (4), 6-10. https://doi.org/10.1038/srep14625. \u003c/li\u003e\n\u003cli\u003eFajri, A., Makoto, K., 2020. Effect of tillage treatments of hairy vetch residues on soil inorganic-N distributions and corn growth in a subtropical region. Applied and Environmental Soil Science, 7049161. https://doi.org/10.1155/2020/7049161. \u003c/li\u003e\n\u003cli\u003eFan, K.K., Delgado, B.M., Guo, X.S., Wang, D.Z., Zhu, Y.G., Chu, H.Y., 2020. Microbial resistance promotes plant production in a four-decade nutrient fertilization experiment. Soil Biology and Biochemistry 141, 107679. https://doi.org/10.1016/j.soilbio.2019.107679. \u003c/li\u003e\n\u003cli\u003eFeng, J., Turner, L.B., Wei, K., Tian, J.H., Chen, Z.H., L\u0026uuml;, X.T., Wang, C., Chen, L.J., 2018. Divergent composition and turnover of soil organic nitrogen along a climate gradient in arid and semiarid grasslands. Geoderma 327, 36-44. https://doi.org/10.1016/j.geoderma.2018.04.020. \u003c/li\u003e\n\u003cli\u003eFreitas, V.F., Cerezini, P., Hungria, M., Nogueira, M.A., 2022. Strategies to deal with drought-stress in biological nitrogen fixation in soybean. Applied Soil Ecology 172. https://doi.org/10.1016/J.APSOIL.2021.104352. \u003c/li\u003e\n\u003cli\u003eFrey, S.D., Elliott, E.T., Paustian, K., Peterson, G.A., 2000. Fungal translocation as a mechanism for soil nitrogen inputs to surface residue decomposition in a no-tillage agroecosystem. Soil Biology and Biochemistry 32(5), 689-698. https://doi.org/10.1016/S0038-0717(99)00205-9. \u003c/li\u003e\n\u003cli\u003eGao, H.J., Li, Q., Peng, C., Zhang, X.Z., Jiao, Y.F., Gao, J.C., Zhu, P., 2022. Effects of different crop rotation and straw returning methods on bacterial community structure in black soil. Journal of Jilin Agricultural University 44 (03), 336-344. https://doi.org/10.13327/j.jjlau.2021.1135. \u003c/li\u003e\n\u003cli\u003eGuan, D.W., Li, L., Yue, X.L., Ma, M.C., Zhang, W., Li, J., 2014. Study on potential of biological nitrogen fixation of soybean in China. Journal of Plant Nutrition and Fertilizer 20 (06), 1497-1504. https://doi.org/10.11674/zwyf.2014.0620. \u003c/li\u003e\n\u003cli\u003eHan, X., Yuan, C.Y., Li, J.H., Hong, Z.W., Liu, X., Du, T., Li, H., You, C.M., Tan, B., Zhu, P., Xv, Z.F., 2022. Effects of Tree Species and Soil Layers on Soil Extractable Nitrogen Content. Ecology and Environmental Sciences. 31 (11), 2143-2151. https://doi.org/10.16258/j.cnki.1674-5906.2022.11.005. \u003c/li\u003e\n\u003cli\u003eHohman, S.P., Smyth, A.R., Bean, E.Z., Reisinger, A.J., 2021. Internal nitrogen dynamics in stormwater pond sediments are influenced by pond age and inorganic nitrogen availability. Biogeochemistry 156 (2), 255-278. https://doi.org/10.1007/S10533-021-00843-2. \u003c/li\u003e\n\u003cli\u003eHou, J.W., Xing, C.F., Yang, L.L., 2021. Optimization reform of soil available nitrogen determination method. Journal of Southwest China Normal University (Natural Science Edition) 46 (07), 45-49. https://doi.org/10.13718/j.cnki.xsxb.2021.07.006.\u003c/li\u003e\n\u003cli\u003eHou, K.X., Cheng, C., Shi B.H., Liu, W., Du, Z.K., Li, B., Wang, J., Wang, J.H., Zhu, L.S., New insights into the effects of chlorpyrifos on soil microbes: Carbon and nitrogen cycle related microbes in wheat/maize rotation agricultural field. Environmental Pollution 318, 120908. https://doi.org/10.1016/J.ENVPOL.2022.120908. \u003c/li\u003e\n\u003cli\u003eHu, M.H., Liu, L.L., Zhang, X., Lin, Y., Shen, X.C., Long, S.H., Kang, J.C., Wijayawardene, N.N., Li, Q.R., Long, Q.D., 2022. New species and records of Neomassaria, Oxydothis and Roussoella (Pezizomycotina, Ascomycota) associated with palm and bamboo from China. MycoKeys 93, 165-191. https://doi.org/10.3897/MYCOKEYS.93.89888. \u003c/li\u003e\n\u003cli\u003eIsobe, K., Taira, R., Hoshi, Y., Matsuda, S., Yamada, M., Hibi, M., Kishino, S., Ogawa, J., 2014. Characterization of a novel l -amino acid oxidase with protein oxidizing activity from Penicillium steckii AIU 027. Journal of Bioscience and Bioengineering 117 (6), 690-695. https://doi.org/1016/j.jbiosc.2013.11.008. \u003c/li\u003e\n\u003cli\u003eJacynthe, D.R., 2022. Soil nitrogen supply: Linking plant available N to ecosystem functions and productivity. Nitrogen 3 (3), 455-457. https://doi.org/10.3390/NITROGEN3030030. \u003c/li\u003e\n\u003cli\u003eJakob, H., Wolfgang, W., Ye, T., Kwatcho, K.S., Werner, B., Andreas, S., Erich, I., 2021. No effect of long-term soil warming on diffusive soil inorganic and organic nitrogen fluxes in a temperate forest soil. Soil Biology and Biochemistry, 108261. https://doi.org/10.1016/J.SOILBIO.2021.108261. \u003c/li\u003e\n\u003cli\u003eJean, F.C., Cedar, N.H., Bramer, L.M., Ann, M.C.L., Mary, L., Samuel, P., Carrie, N., Verne, G.G.L., Andrea, P.A., Cheryl, R.K., 2019. Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis. BMC genomics 20 (1), 976. https://doi.org/10.1186/s12864-019-6358-x. \u003c/li\u003e\n\u003cli\u003eJesmin T., Margenot, A.J., Mulvaney, R.L., 2022. A comprehensive method for casein-based assay of soil protease activity. Communications in Soil Science and Plant Analysis 53 (4), 507-520. https://doi.org/10.1080/00103624.2021.2017954. \u003c/li\u003e\n\u003cli\u003eJia, J., Zhu, J.F., Du, X.Z., Wang, M.M., Xv, Z.C., 2016. Effects of different croppina patterns on Soil EnzvmeFlue-cured tobacco growth and aroma substances content of tobacco leaf. Journal of Agricultural Science and Technology 18 (03), 141-149. https://doi.org/10.13304/j.nykjdb.2015.499. \u003c/li\u003e\n\u003cli\u003eJiao, H., Li, T.L., Gao, J.W., Li, Y., He, B., Li, S., 2018. Effects of Fertilization on Light and Heavy Fractions Organic Nitrogen in Reclaimed Soil. Journal of Soil and Water Conservation 32 (05), 208-213+221. https://doi.org/10.13870/j.cnki.stbcxb.2018.05.034. \u003c/li\u003e\n\u003cli\u003eKyung, J.L., Jun, H.P., Ju, S.C., Ju, K.S., Kee, P.J., 2021. Effect of pyroligneous acid on soil urease, amidase, and nitrogen use efficiency by Chinese cabbage (Brassica campestris var. Pekinensis). Environmental Pollution 291, 118132. https://doi.org/10.1016/J.ENVPOL.2021.118132. \u003c/li\u003e\n\u003cli\u003eLeinweber, P., Kruse, J., Baum, C., Arcand, M., Knight, J.D., Farrell, R., Eckhardt, K.U., Kiersch, K., Jandl, G., 2013. Advances in understanding organic nitrogen chemistry in soils using state-of-the-art analytical techniques. Advances in Agronomy 119, 83-151. https://doi.org/10.1016/B978-0-12-407247-3.00002-0.\u003c/li\u003e\n\u003cli\u003eLiebman, M., Nichols, V.A., 2020. Cropping system redesign for improved weed management: A modeling approach illustrated with giant ragweed (Ambrosia trifida). Agronomy 10 (2), 262. https://doi.org/10.3390/agronomy10020262. \u003c/li\u003e\n\u003cli\u003eLiu, X., Pang, R.Y., Zhao, J., Si, T., Wang, Y.F., Zou, X.X., Jiao, Y.L. 2023. Effects of different rotation modes on the concentrations of soil activate carbon fractions and characteristics of carbon mineralization. Journal of Agro-Environment Science, 1-13 https://doi.org/10.11654/jaes.2022-1165. \u003c/li\u003e\n\u003cli\u003eLiu, Z.X., 2020. Molecular analysis of the changes of soil microbial communities in continuous cropping of soybean in the black soil of Northeast China (in Chinese). Northeast Institute of Geograpy and Agroecology, Changchun. https://doi.org/10.27536/d.cnki.gccdy.2020.000043. \u003c/li\u003e\n\u003cli\u003eMori, T., Wang, S., Peng, C., Wang, C., Mo, J.M., Zheng, M.H., Zhang, W., 2023. Importance of considering enzyme degradation for interpreting the response of soil enzyme activity to nutrient addition: Insights from a field and laboratory study. Forests 14 (6). https://doi.org/10.3390/F14061206. \u003c/li\u003e\n\u003cli\u003eMoritz, L., Marc, C., Samuel, M., Wanjiku, M.M.M., Daniel, M., Magdalena, N., Marijn V.B., Wycliffe,W., Bernard, V., Johan, S., 2023. Combining manure with mineral N fertilizer maintains maize yields: Evidence from four long-term experiments in Kenya. Field Crops Research 291. https://doi.org/10.1016/J.FCR.2022.108788. \u003c/li\u003e\n\u003cli\u003eNakayama, Y., Wade, J., Li C.Y., Daughtridge, R.C., Margenot, A.J., 2023. Quantifying the relative importance of controls and assay conditions for reliable measurement of soil enzyme activities with para-nitrophenol substrates. Geoderma 429. https://doi.org/10.1016/J.GEODERMA.2022.116234. \u003c/li\u003e\n\u003cli\u003eNarayana, N.K., Kingery, W.L., Jacobs, A.A., Allison, J.K., Ganapathi, S.S., 2022. Effects of tillage and winter cover management in a maize soybean rotation on soil bacterial and fungal community composition. Land 11 (12), 2259. https://doi.org/10.3390/LAND11122259. \u003c/li\u003e\n\u003cli\u003eNaumova, E.S., Lee, C.F., Kondratieva, V.I., Sadykova, A.Z., Naumov, G.I., 2017. Molecular genetic polymorphism of soil yeasts of the genus Williopsis from Taiwan Island. Russian Journal of Genetics 53 (5), 561-567. https://doi.org/10.1134/s1022795417040111. \u003c/li\u003e\n\u003cli\u003eNoll, L., Zhang S.S., Zheng, Q., Hu, Y.T., Hofhansl, F., Wanek, W., 2022. Climate and geology overwrite land use effects on soil organic nitrogen cycling on a continental scale. Biogeosciences 19 (23), 5419-5433. https://doi.org/10.5194/BG-19-5419-2022. \u003c/li\u003e\n\u003cli\u003ePeoples, M.B., Giller, K.E., Jensen, E.S., Herridge, D.F., 2021. Quantifying country-to-global scale nitrogen fixation for grain legumes: I. Reliance on nitrogen fixation of soybean, groundnut and pulses. Plant and Soil 469 (1-2), 1-14. https://doi.org/10.1007/S11104-021-05167-6. \u003c/li\u003e\n\u003cli\u003ePerez, B.C., Arzeno, J.L., Huidobro, J., Grumberg, B., Hilton, S., Bending, G.D., Meriles, J. M., Vargas, G.S., 2014. The effect of crop sequences on soil microbial, chemical and physical indicators and its relationship with soybean sudden death syndrome (complex of Fusarium species). Spanish Journal of Agricultural Research 12 (1), 252-264. https://doi.org/10.5424/sjar/2014121-4654. \u003c/li\u003e\n\u003cli\u003eRaza, M.A., Khalid, M.H.B., Zhang, X., Feng, L.Y., Khan, I., Hassan M.J., Ahmed, M., Ansar, M., Chen, Y.K., Fan, Y.F., Yang, F., Yang, W.Y., 2019. Effect of planting patterns on yield, nutrient accumulation and distribution in maize and soybean under relay crop rotation systems. Scientific reports 9 (1), 4947-4961. https://doi.org/10.1038/s41598-019-41364-1. \u003c/li\u003e\n\u003cli\u003eRee, P.K., Sol, P.C., Gyun, K.B., 2016. An enzyme complex increases in vitro dry matter digestibility of corn and wheat in pigs. SpringerPlus 5 (1), 598. https://doi.org/10.1186/s40064-016-2194-5. \u003c/li\u003e\n\u003cli\u003eRong, G.H., Zhou, J.Y., Wu, H.Y., Wei, X.R., 2023. Comparative study on the flow analyzer and the kjeldahl nitrogen analyzer for measuring soil total nitrogen. Research of Soil and Water Conservation 30 (01), 204-208. https://doi.org/10.13869/j.cnki.rswc.2023.01.030. \u003c/li\u003e\n\u003cli\u003eRong, Z.Y., Ling, J., Ling, H.X., Zhang, L., Zhang, Y.Y., Wang, L., 2022. Analysis on soil microbial community structure and diversity in fodder-crop rotation system. Journal of Gansu Agricultural University 57 (06), 155-163. https://doi.org/10.13432/j.cnki.jgsau.2022.06.019. \u003c/li\u003e\n\u003cli\u003eSakuntala, S., Akinori, A., Ryuya, I., Makoto, S., Jun, O., 2016. Analysis of microbial community and nitrogen transition with enriched nitrifying soil microbes for organic hydroponics. Bioscience, biotechnology, and biochemistry, 1-8. https://doi.org/10.1264/jsme2.2000.103. \u003c/li\u003e\n\u003cli\u003eSingh, S., Savoy, H.J., Yin, X.H., Schneider, L., Jagadamma, S., 2019. Phosphorus and potassium fertilizer rate verification for a corn\u0026ndash;wheat\u0026ndash;soybean rotation system in Tennessee. Agronomy Journal 111 (4), 2060-2068. https://doi.org/10.2134/agronj2018.12.0749. \u003c/li\u003e\n\u003cli\u003eSoman, C., Li, D.F., Wander, M.M., Kent, A.D., 2016. Long-term fertilizer and crop-rotation treatments differentially affect soil bacterial community structure. Plant and Soil 413, 145-159. https://doi.org/10.1007/s11104-016-3083-y. \u003c/li\u003e\n\u003cli\u003eSun, Q., Wu, H.L., Chen, F., Kang, J.H., 2020. Characteristics of soil nutrients and fungal community composition in crop rhizosphere under different rotation patterns. Environmental Science 41 (10), 4682-4689. https://doi.org/10.13227/j.hjkx.202001031. \u003c/li\u003e\n\u003cli\u003eSun, Y.P., Chen, X.B., Yang, J.S., Luo Y.M., Yao, R.J., Wang, X.P., Xie, W.P., Zhang, X., 2022. Biochar effects coastal saline soil and improves crop yields in a maize-barley rotation system in the tidal flat reclamation zone, China. Water 14 (20), 3204. https://doi.org/10.3390/W14203204.\u003c/li\u003e\n\u003cli\u003eSun, Z.M., Wu, Z.J., Chen, L.J., Liu, Y.G., 2006. Research advances in nitrogen fertilization and its environmental effects. Chinese Journal of Soil Science 04, 782-786. https://doi.org/10.19336/j.cnki.trtb.2006.04.037. \u003c/li\u003e\n\u003cli\u003eTorres, M.C., Pinheiro, S.D.D.A., Hungria, D.C.L., Husny, J.C.E., Sacramento, P.P., Andrade, I.P., 2020. Inorganic nitrogen fractions in soil under different uses and management systems in the Brazilian eastern Amazon. Journal of Agricultural Studies 8 (4), 32. https://doi.org/10.5296/jas.v8i4.17080. \u003c/li\u003e\n\u003cli\u003eVance, C.P., 2001. Symbiotic nitrogen fixation and phosphorus acquisition.Plant nutrition in a world of declining renewable resources. Plant Physiology 127 (2), 390-397. https://doi.org/10.1104/pp.127.2.390. \u003c/li\u003e\n\u003cli\u003eWang, N., Wang, L., Song, C.H., Yang, H.N., Xiao, J., Jiao, Y.G., 2022. Effect of crop rotation on crop yield and soil physical and chemical properties in the black soil area. Bulletin of Agricultural Science and Technology (09), 71-74. \u003c/li\u003e\n\u003cli\u003eWu, P.B., Li, L.J., Zhang, Y.L., Li, X.T., Yang, F., 2020. Effects of rotation and fertilization on soil organic carbon and its fractions and soil nutrients. Chinese Journal of Soil Science 51 (02), 416-422. https://doi.org/10.19336/j.cnki.trtb.2020.02.20. \u003c/li\u003e\n\u003cli\u003eYang, W.Y., Du, Q., Yang, H., Fu, Z.D., Pang, T., Yang, H., Yong, T.W., 2016. Effects of different varieties and root barriers on soybean nodule nitrogen fixation and nitrogen uptake in maize/soybean intercropping system. Journal of Sichuan Agricultural University 34 (01), 1-5. https://doi.org/10.16036/j.issn.1000-2650.2016.01.001. \u003c/li\u003e\n\u003cli\u003eZhang, F.W., Qiao, Z.H., Yao, C.T., Sun S.A., Liu, W.T. Wang, J.X., 2020. Effects of the novel HPPD-inhibitor herbicide QYM201 on enzyme activity and microorganisms, and its degradation in soil. Ecotoxicology, 1-11. https://doi.org/10.1007/s10646-020-02302-4. \u003c/li\u003e\n\u003cli\u003eZhang, P., Sun, J., Li, L., Wang, X., Qu, J., 2019. Effect of Soybean and Maize Rotation On Soil Microbial Community Structure. Agronomy 9 (2), 42. https://doi.org/10.3390/agronomy9020042. \u003c/li\u003e\n\u003cli\u003eZhang, X.M., 2019. Determination of hydrolyzed nitrogen in soil by chemical nitrogen tube distillation. Xinjiang non-ferrous metals 42 (03), 69-70. https://doi.org/10.16206/j.cnki.65-1136/tg.2019.03.030.\u003c/li\u003e\n\u003cli\u003eZhang, Y., Dai, S.F., Huang, X.Q., Zhao, Y., Zhao, J., Cheng, Y., Cai, Z.C., Zhang, J.B., 2020. pH-induced changes in fungal abundance and composition affects soil heterotrophic nitrification after 30 days of artificial pH manipulation. Geoderma 366 (C), 114255. https://doi.org/10.1016/j.geoderma.2020.114255. \u003c/li\u003e\n\u003cli\u003eZhou, Q.X., Li, N.N., Chang, K.F., Hwang, S.F., Strelkov, S.E., Conner, R.L., McLaren, D.L., Fu, H.T., Harding, M.W., Turnbull, G.D., 2018. Genetic diversity and aggressiveness of Fusarium species isolated from soybean in Alberta, Canada. Crop Protection 105, 49-58. https://doi.org/10.1016/j.cropro.2017.11.005. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"soybean maize rotation, nitrogen cycling, nitrogen form, soil microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4008531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4008531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eAims\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSoil nitrogen is recognized as a vital nutrient influencing soybean growth and yield. Hence, a comprehensive understanding of the intricate connections between shifts in nitrogen patterns and the behaviors of soil microbial communities and crucial enzymes in the nitrogen cycle is highly desirable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved a rotation positioning experiment spanning 9 to 11 years. Measurement of soil microenvironment changes during the mature period for three consecutive years, focusing on the corn-soybean rotation with varying fertilizer application rates. Six distinct treatment groups were established for investigation. Based on these groups, the study delved into the alterations in nitrogen patterns within the soybean rotation, examining both soil enzyme activity and microbial community dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLong-term crop rotation and nitrogen application led to an increase ranging from 2.16% to 108.34% in the nine components of soil nitrogen. The variations in total nitrogen, heavy fraction organic nitrogen, and light fraction organic nitrogen were primarily influenced by the enrichment of the Actinobacteriota phylum. The environmental factors affecting the changes in inorganic nitrogen, alkaline hydrolyzable nitrogen, exchangeable ammonium and acid hydrolyzable nitrogen were linked to the Ascomycota phylum. The Proteobacteria phylum and urease were key factors in the variations of organic nitrogen and nitrate-nitrogencomponents, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChanges in inorganic nitrogen and total organic nitrogen resulting from crop rotation enhanced the richness of soil microbial communities, reducing their diversity. This alteration influenced the bacterial and fungal communities composition, ultimately augmenting their functional capacities.\u003c/p\u003e","manuscriptTitle":"Understanding the role of soil microbes and enzymes in regulating nitrogen dynamics: Promoting sustainable crop rotation systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 10:54:35","doi":"10.21203/rs.3.rs-4008531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a7f87bf-7306-44d1-a029-eb56f5998b77","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-26T20:07:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 10:54:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4008531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4008531","identity":"rs-4008531","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}