Microbial diversity in rhizosphere soil of soybean grass under different cultivation methods in an alpine region

Microbial diversity in rhizosphere soil of soybean grass under different cultivation methods in an alpine region | 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 Microbial diversity in rhizosphere soil of soybean grass under different cultivation methods in an alpine region Ying Zhang, Wenhui Liu, Xilai Li, Zhiying Zhang, Beibei Su, Ri-na Dao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-31329/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 Background A large number of studies have shown that soybean grass with mixed seeding cultivation can significantly improve the yield and quality of forage grass compared with clean culture cultivation.This study explores the differences in the characteristics of the composition and diversity of the microbial community in the rhizosphere of soybean grasses between clean culture and mixed seeding methods in an alpine region. We used high-throughput sequencing technology to determine the microbial diversity and analytical methods to determine the physicochemical characteristics of plant rhizosphere soil of Avena sativa L. and Vicia sativa L. Results There were no significant differences in pH, total nitrogen, total phosphorus, and total potassium in the rhizosphere soil samples of soybean grasses under the clean culture and mixed seeding methods, while there were significant differences in the available nitrogen, available phosphorus, available potassium, and organic matter content ( P < 0.05). The bacterial diversity of the rhizosphere soil of Avena sativa L. was the highest under the clean culture method, and the fungal diversity of the rhizosphere soil of Vicia sativa L. was the highest under the clean culture method. Furthermore, the microbial diversity of the rhizosphere was significantly different under the different cultivation methods ( P < 0.05). The differences between the microbial species in the rhizosphere of the treated soil were at three class level. The abundance of Alphaproteobacteria and Actinobacteria in the rhizosphere of Avena sativa L. and Vicia sativa L. under the mixed seeding method was conspicuously higher than that of Avena sativa L. and Vicia sativa L. under the clean culture method, while the abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia were significantly lower than that obtained under the clean culture method. Regarding fungal predominance, Mortierellomycetes was the most abundant (32.66%) under the mixed seeding method, while the abundance of Sordariomycetes and Leotiomycetes were significantly lower than that under clean culture. The distribution of bacterial and fungal community species in the rhizosphere differed significantly between the treatments. The Kyoto Encyclopedia of Genes and Genomes metabolism analysis showed that the metabolic pathways of functional genes in the soil microbial communities were similar. Conclusions Mixed sowing changed the diversity of plant rhizosphere microbial community structure and promoted plant yield. General Microbiology Applied & Industrial Microbiology Avena sativa L. Vicia sativa L. high-throughput sequencing clean culture mixed seeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Forage is a necessity for the survival of animal husbandry and the fundamental driving force for ensuring the sustainable development of animal husbandry. The development of the forage industry aims to promote the development of forage cultivation and pursue high-yield cultivation. A change in cultivation methods is a new technique for the high-yield cultivation of forage. A large number of studies have shown that soybean grass with mixed seeding cultivation can significantly improve the yield and quality of forage grass compared with clean culture cultivation, and the development of mixed soybean grassland is a feasible method to increase forage yield and quality [ 1 – 8 ]. Avena sativa L. and Vicia sativa L. are fine forage grasses widely cultivated in alpine areas with strong resistance and of excellent quality [ 9 – 12 ]. They are widely cultivated in China and abroad to provide high-quality feed for domestic animals in mixed cultivation [ 13 , 14 ]. Grassland soil microorganisms play an important role in the grassland ecosystem [ 15 , 16 ]. There are a large number of microorganisms in the rhizosphere soil of plants. Due to the combined effect of microbial activities and plant metabolism, differences in soil characteristics, microbial community composition, nutrient content, and other aspects exist between the rhizosphere soil and non-rhizosphere soil of plants [ 17 , 18 ]. The diversity of soil microorganisms is an important indicator that can directly reflect the stability and diversity of the plant ecosystem, and it can also visually demonstrate the differences in the function, communities, and species of soil microorganisms in different regions[ 19 ]. As an important indicator to describe the characteristics of the soil microbial community, the functional diversity of the soil microbial community can reflect the basic information of the soil [ 20 ]. The structure and diversity of the microbial community in rhizosphere soil are usually influenced by cultivation conditions, the planting environment, plant species, and other factors [ 21 – 23 ]. Compared with previously employed methods, such as traditional DNA sequencing, high-throughput sequencing technology can greatly reduce the research cost, save time, and yield a large amount of information with a higher level of accuracy and sensitivity [ 24 – 26 ]. At present, there have been many studies on different mixed-planting ratios and grass yield in seeding methods of soybean grass [ 27 – 29 ], but there are few studies of the rhizosphere microbial diversity of forage under different cultivation methods. In this paper, we used the rhizosphere soils of Avena sativa L. and Vicia sativa L. under different cultivation methods in an alpine region as research objects to analyze the changes of microbial diversity using high-throughput sequencing so as to reveal the differences in microbial diversity of the rhizosphere soil of plants under different cultivation methods and to analyze structural and composition changes in the microbial community, thereby providing a basis for the study of microbial ecology of the rhizosphere soil of cultivated forages. Results Soilphysicochemical properties soil The physicochemical properties of the soil samples are shown in Table 1 . There was no significant difference in pH or in the total nitrogen, total phosphorus, or total potassium content of the soil samples with the three different treatments. Overall, the pH value was 7.50–7.73, the total nitrogen content was 3.16–4.13 g•kg − 1 , the total phosphorus content was low at 1.63–1.91 g•kg − 1 , and the total potassium content was 22.01–22.92 g•kg − 1 . The content of available nitrogen, available phosphorus, available potassium, and organic matter in the soil samples were significantly different among the three treatments (P < 0.05). The available nitrogen content was 210.11–275.17 mg•kg − 1 , available phosphorus was 8.41–18.71 mg•kg − 1 , and available potassium content was 67.11–12.18 mg•kg − 1 . The available nitrogen, available potassium, and organic matter contents in the CYCH treatment soil were significantly higher than those in the other treatments ( P < 0.05), and the contents of available phosphorus in the CYD treatment soil were significantly higher than those in the other treatments ( P < 0.05). Table 1 Soil nutrient content Soil indicators CWD CYD CYCH pH 7.73 ± 0.45 a 7.50 ± 0.36 a 7.65 ± 0.38 a Total nitrogen (g/kg) 3.16 ± 0.22 a 3.97 ± 0.27 a 4.13 ± 0.33 a Total phosphorus (g/kg) 1.63 ± 0.06 a 1.91 ± 0.08 a 1.85 ± 0.05 a Total potassium (g/kg) 22.92 ± 1.24 a 22.01 ± 1.18 a 22.01 ± 1.24 a Available nitrogen (mg/kg) 210.11 ± 13.2 c 248.08 ± 17.14 b 275.17 ± 22.07 a Available phosphorus (mg/kg) 11.24 ± 2.36 b 18.71 ± 3.57 a 8.41 ± 2.68 c Available potassium (mg/kg) 94.07 ± 8.41 b 67.11 ± 7.65 c 120.18 ± 10.18 a The organic matter (g/kg) 51.67 ± 4.07 c 69.35 ± 5.58 b 75.83 ± 6.47 a Note: Different lowercase letters on the same line indicate significant differences ( P < 0.05) Otu-level Analysis Of Soil Bacteria And Fungi The results of the OTU statistical analysis are shown in Table 2 . The 16S rDNA V3–V4 region of soil samples from all three treatments (CWD, CYD, and CYCH) was sequenced to obtain 48,988–50,302 effective tags. The tags lengths were highly concentrated, averaging 421 bp, 420 bp, and 417 bp for the CWD, CYD, and CYCH treatments, respectively. The sequence length of the 16S rDNA V3–V4 region in each treatment was roughly consistent. Following sequencing analysis, 32,031–42,291 effective tags were obtained from the ITS regions of the DNA obtained from the soil samples of the three treatments Generally, the sequence length was 230–280 bp, and the average length was 243, 230, and 280 bp, for the CWD, CYD, and CYCH treatments, respectively. Rarefaction curves (Fig. 1 a and b) were prepared based on the results. It can be seen from Fig. 1 that the OTU level of the sample increased rapidly with the increase of sequencing fragments, and remained stable after reaching the peak. Table 2 Tag information and OTU statistical analysis of rhizosphere soil samples Target Sample Tags Effective tags Average tags (bp) Percentage of effective (%) No. OTUs 16S rDNA CWD 67249 ± 243 48988 ± 241 421 ± 15 61.12 ± 1.24 1162 ± 1.76 CYD 67594 ± 326 50302 ± 276 420 ± 17 63.06 ± 1.33 1209 ± 1.58 CYCH 67396 ± 358 49918 ± 318 417 ± 12 62.75 ± 1.27 1127 ± 1.29 ITS CWD 74762 ± 572 36891 ± 424 243 ± 18 46.31 ± 1.24 261 ± 2.65 CYD 74496 ± 427 42291 ± 267 230 ± 14 53.17 ± 1.36 260 ± 1.87 CYCH 74611 ± 410 32031 ± 211 280 ± 13 40.06 ± 1.18 211 ± 1.87 Analysis Of Soil Microbial Species Composition The richness of microbial species composition was > 0.1% higher in the three soil treatment samples (Fig. 2 ). We detected many phyla and classes of bacteria and fungi, and most of them contained very few species. For the convenience of observation, only the top ten abundant fungi and bacteria are shown in Fig. 2 and the remaining have been grouped as Others. The Unclassified and Unknown groups represent species that were not taxonomically annotated. The ordinate ( y -axis) shows the relative abundance. Figure 2 a and b, respectively, show the top 10 classes of bacteria and fungi, respectively, according to their relative abundance level. For all three treatments, the soil bacteria were mainly distributed in the following 10 classes: Alphaproteobacteria, Betaproteobacteria, Blastocatellia, Acidobacteria subgroup 6, Gemmatimonadetes, Nitrospira, Acidimicrobiia, Actinobacteria, Gammaproteobacteria, and Holophagae(Fig. 2 a). Alphaproteobacteriawas the most abundant, with a relative abundance of 17.83%, 24.24%, and 18.67%, for the CWD, CYCH, and CYD treatments, respectively. For the CYCH treatment, the abundance of Alphaproteobacteria and Actinobacteria were significantly higher than for the CWD and CYD treatment. For the CYCH treatment, the abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia was significantly lower than that of the CWD and CYD treatment. With the exception of Actinobacteria, the relative abundance of the main bacterial species in the CWD and CYD soils was relatively close and it is quite different from that in CYCH soil. As can be seen in Fig. 2 b, for all three treatments, the soil fungi were mainly distributed in the following 9 groups: Mortierellomycetes, Sordariomycetes,Leotiomycetes, Dothideomycetes, Tremellomycetes, Agaricomycetes, Archaeorhizomycetes, Eurotiomycetes, Spizellomycetes, or Unclassified, of which Mortierellomycetes was the most abundant the CYCH treatment soil (32.66%). Compared with the CWD and CYD treatment soil, the abundance of Sordariomycetes and Leotiomycetes in the CYCH treatment soil was significantly decreased, while the distribution of fungal communities was significantly different between the soil treatments. Analysis Of Soil Microbial Diversity Alpha diversity analysis As can be seen from the alpha diversity index results (Table 3 ), all three soil treatment samples have a high bacterial and fungal community diversity. The order of abundance-based coverage estimator (ACE) and Chao1 indices of bacterial alpha diversity in the three soil treatment samples were CYD > CYCH > CWD, indicating the highest abundance of bacterial species in the CYD treatment soil. The Simpson index showed CYCH > CWD > CYD. The Shannon index showed CYD > CYCH > CWD, indicating the richest diversity of bacterial microorganisms in the CYD treatment soil. The ACE and Chao1 indices of fungal alpha diversity in the three soil treatment samples were CWD > CYD > CYCH, showing that the fungal species abundance was highest in HNY soil. The Simpson index showed that CYCH > CYD > CWD. The Shannon index was ranked as CWD > CYD > CYCH. Thus fungal microbial diversity of the CWD treatment soil was the most abundant among the three treatments. Table 3 Diversity index of soil microbial community in rhizosphere soil Diversity indexes CWD CYD CYCH Bacterial ACE 1244.7398 ± 5.42 1276.6826 ± 5.22 1247.6747 ± 4.89 Chao1 1259.2143 ± 3.41 1286.5981 ± 3.24 1285.6355 ± 2.42 Simpson 0.0068 ± 0.32 0.0063 ± 0.14 0.0086 ± 0.16 Shannon 5.9163 ± 1.08 6.044 ± 1.23 5.9639 ± 1.18 Fungal ACE 274.0142 ± 13.36 269.1001 ± 10.68 218.8791 ± 12.21 Chao1 280.0909 ± 6.48 270.0 ± 3.79 217.6667 ± 5.66 Simpson 0.029 ± 0.05 0.0298 ± 0.04 0.0526 ± 0.02 Shannon 4.2945 ± 1.15 4.2487 ± 1.05 3.5755 ± 1.13 Note: Different lowercase letters on the same line indicate significant differences ( P < 0.05) Analysis Of Beta Diversity Heatmaps represent samples through interspecies distance relationships to obtain the sequence relationship between samples, which are drawn by means of matrix language tool software. By analyzing the heatmap, the difference between the two samples can be intuitively reflected by the range of color transformation. Figure 3 shows the heatmap of the three soil treatment samples. The bacterial flora (Fig. 3 a) and fungal flora (Fig. 3 b) in the CWD and CYD treatment soil had a high level of similarity. The beta microbial diversity analysis was conducted to obtain a distance matrix. Then, the UPGMA function of R software was used for hierarchical clustering comparison of samples, and the similarity relationship of the species composition in each sample was described. The closer the samples were, the shorter the branch length was, indicating that the species composition of the two samples was more similar. Fig. 4 shows that the OTU level of bacteria and fungi in the CWD treatment soil samples had a relatively high consistency, while the similarity of bacteria and fungi in the CYCH treatment soil sample was relatively low. Functional gene prediction PICRUSt software (http://picrust.github.io/picrust /) was used to compare the species composition information obtained from the 16S sequencing tags to infer the functional gene composition of the samples and then analyze the functional differences between the different soil treatments. According to the of Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolism histogram (Fig. 5 ), the functional genes of the soil microbial community for each treatment were basically similar in their metabolic pathways. Discussion Previous studies by many scholars have confirmed that the abundance and flora composition of soil microbial populations are closely related to pH, organic matter content, water content, the content of various nutrients, soil particle size, and other physical and chemical properties of soil [ 37 – 42 ]. In this study, there were no significant differences in pH, total nitrogen, total phosphorus, and total potassium in the three plant rhizosphere soil treatment samples of Avena sativa L. and Vicia sativa L. under clean culture and mixed seeding cultivation, but there were significant differences in available nitrogen, available phosphorus, available potassium, and organic matter content ( P < 0.05). The content of available nitrogen, available potassium, and organic matter of soil of Avena sativa L. and Vicia sativa L. in the mixed seeding treatment were significantly higher than those in the other treatments ( P < 0.05), and the content of available phosphorus of Avena sativa L. in the clean culture treatment was significantly higher than in the other treatments ( P < 0.05). The relationship between plant rhizosphere microorganisms and soil nutrient content requires further study. Rhizosphere microorganisms play an important role in connecting the soil and plant microecosystems. Plant root secretions can provide energy for the life activities of microorganisms, and microorganisms can affect plants by decomposing organic matter and transforming nutrient substances or indirectly affect plant growth and the dynamics of non-rhizosphere microorganisms via material cycling and energy flow [ 43 , 44 ]. Yu et al. [ 45 ] studied the diversity of rhizosphere and rhizome microorganisms cultivated by Impatiensbalsamina in Hainan, China, and the seasonal variation of its community at different altitudes. The results showed that the number of rhizospheric fungi and bacterial OTUs, Shannon–Weiner index, and richness index varied greatly with altitude and with the wet and dry seasons. Lijun et al. [ 46 ] studied the microbial diversity in rhizosphere soil of Davidiainvolucrata in different regions. They found that the microbial communities in the rhizosphere soil of Davidiainvolucrata in three regions all had high biodiversity, but there were differences in species composition and distribution. Yating et al. [ 47 ] studied the rhizosphere microbial community diversity of maize in Yunnan, China, and they concluded that altitude and variety type were important factors influencing the rhizosphere microbial community diversity of maize. In our study, the microbial diversity in rhizosphere soil of soybean grass was studied under different cultivation methods. The results showed that the bacterial microorganism diversity in rhizosphere soil was the highest when Avena sativa L. was under clean culture cultivation, while the bacterial microorganism diversity in rhizosphere soil of Vicia sativa L. under clean culture cultivation was the lowest. The fungal microorganism diversity of rhizosphere soil of Vicia sativa L. under clean culture cultivation was the highest, and the fungal microorganism diversity of rhizosphere soil under mixed seeding of Avena sativa L. and Vicia sativa L. was the lowest. The diversity of rhizosphere microorganisms was significantly different under different cultivation methods, which was consistent with the results of other researchers. In similar environments, the structure of microbial communities is determined by the plant types, and the structure composition of soil microbial communities varies between different vegetation types [ 48 ]. The change in soil microbial diversity depends on soil water content, pH, aeration, nitrogen content, and organic carbon affected by vegetation. Based on the characteristic analysis of rhizosphere exudates and plant litters, the main factors affecting soil microbial diversity may be vegetation type and quantity [ 49 ]. In this study, the species structure of the plant rhizosphere microbial community under the three treatments was at class level, and the abundance of Alphaproteobacteria and Actinobacteria was significantly higher in the mixed sowing treatment of Avena sativa L. and Vicia sativa L. than that in clean culture treatment of both Vicia sativa L. and Avena sativa L. The abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia was significantly lower than that of soybean grass under clean culture. The Mortierellomycetes of fungi were most abundant under mixed seeding treatment (32.66%), while the abundance of Sordariomycetes and Leotiomycetes was significantly lower than that of soybean grass under clean culture, while the species distribution of bacteria and fungi communities in soil was significantly different under the different various treatments. The KEGG metabolic analysis showed that the functional genes of soil microbial communities were similar in their metabolic pathways. The structure and diversity of the microbial community in the rhizosphere of plants changes greatly under different cultivation modes, and the influence of plants, soil, and the environment required further study. Conclusions In this study, there were no significant differences in pH, total nitrogen, total phosphorus, and total potassium in the three plant rhizosphere soil treatment samples of Avena sativa L. and Vicia sativa L. under clean culture and mixed seeding cultivation, but there were significant differences in available nitrogen, available phosphorus, available potassium, and organic matter content ( P < 0.05). The contents of available nitrogen, available potassium, and organic matter of soil of Avena sativa L. and Vicia sativa L. under mixed seeding treatment were significantly higher than those under the other treatments ( P < 0.05), and the available phosphorus content in the soil of Avena sativa L. under clean culture treatment was significantly higher than those of other treatments ( P < 0.05). We investigated the microbial diversity in rhizosphere soil of soybean grass under different cultivation methods. The results showed that the bacterial microbial diversity in rhizosphere soil of Avena sativa L. was the highest under clean culture, while the bacterial microbial diversity in rhizosphere soil of Vicia sativa L. was the lowest under clean culture. The fungi microbial diversity in rhizosphere soil of Vicia sativa L. was the highest under clean culture and the lowest under the mixed cultivation of Avena sativa L. and Vicia sativa L. There were significant differences in rhizosphere microbial diversity under different cultivation methods. In this study, the species structure of the rhizosphere microbial community was at the class level under the three treatments. The abundance of Alphaproteobacteria and Actinobacteria under the mixed cultivation of Avena sativa L. and Vicia sativa L. was significantly higher than that of Avena sativa L. and Vicia sativa L. under clean culture cultivation, and the abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia was significantly lower than that of soybean grass under clean culture. The Mortierellomycetes class of fungi was the most abundant under mixed seeding treatment (32.66%), while the abundance of Sordariomycetesand Leotiomycetes was significantly lower than that of soybean grass under clean culture. The species distribution of the bacteria and fungi community in soil microorganisms was significantly different between the soil treatments. KEGG metabolic analysis showed that the functional genes of soil microbial communities were similar in their metabolic pathways. Methods Overview of the experimental area The experiment was conducted at Sanjiangyuan Field Ecological Observation Station of Qinghai University (33°24'30" N, 97°18'00" E), which is located in Zhenqin Town, Chengduo County, Yushu Prefecture, Qinghai province, China. The experimental area is situated at an altitude is 4,270 m and has a typical plateau continental climate, with an annual average temperature of − 5.6 °C to 3.8 °C and an annual average precipitation of 562.2 mm, mainly distributed from June to September. Its alpine meadow soil has a rich humus content but poor soil fertility due to poor decomposition. The soil pH is 7.32, organic matter content is 58 g •kg − 1 , and available potassium, phosphorus, and nitrogen is 76.5, 7.0, and 14.0 mg •kg − 1 , respectively, of which ammonia nitrogen is 5.1 mg •kg − 1 and nitrate nitrogen is 8.9 mg •kg − 1 . The area is irrigated during droughts. Experimental Materials And Design This experiment was designed as a single-factor randomized block study. The experimental materials were Avena sativa L. and Vicia sativa L., the seeds were provided by the Key Laboratory of Superior Forage Germplasm in the Qinghai-Tibetan Plateau. We set up three treatments: Vicia sativa L. with clean culture cultivation (CWD), Avena sativa L. with clean culture cultivation (CYD), and a mixture of Avena sativa L. and Vicia sativa L. in equal proportions (CYCH). There were three plots of 4 × 5 m 2 each. The plots were drilled, and the rows were spaced at intervals of 30 cm. Diammonium phosphate (30 kg/mu) was applied prior to sowing. The amount of seed used in the plot was calculated according to the plot area. The amount of Avena sativa L. under clean culture cultivation was 15 kg/mu, that of Vicia sativa L. under clean culture cultivation was 5 kg/mu, and the mixed sowing amount is was 7.5 kg/mu of Avena sativa L. plus 2.5 kg/mu of Vicia sativa L. Sample Collection The experiment initiated in May 2019 and the experimenter visited the plot in August 2019. Using a five-point sampling method, root samples ≤ 20 cm long were collected in triplicate from Avena sativa L. and Vicia sativa L.. The large soil particles were shaken off using the shake-down method [ 30 ], and then a brush was used to brush off excess soil surrounding the roots, leaving a layer of 0–5 mm of soil, which was considered the rhizosphere soil. The two species were evenly mixed, placed into sterile Ziplock bags, stored at a low temperature, and brought back to the laboratory as quickly as possible. The collected soil samples for each treatment were divided into two parts, one to determine the nutrient content and the other to determine the microbial diversity index of the soil. Determination Of Soil Physicochemical Properties The indices of the physicochemical properties of the soil samples were determined by conventional analysis methods [ 31 ]. Soil pH was determined using a pH potentiometer method. Organic matter content was determined by high-temperature potassium dichromate oxidation capacity, available nitrogen content by a potassium chloride extraction-spectrophotometric method, available phosphorus content by sodium bicarbonate extraction and molybdenum-antimony colorimetry, and available potassium content by an ammonium acetate extraction-flame photometer method. Total nitrogen content was determined by Nessler colorimetry, total phosphorus content by molybdenum-antimony colorimetry, and total potassium content by a flame photometer method. Total Dna Extraction From Soil Microorganisms The total genomic DNA was extracted from the soil microorganisms using an OMEGA soil DNA extraction kit. Each sample was mixed before processing in triplicate to reduce the chances of missing DNA from organisms that were sparsely distributed. Following extraction, polymerase chain reaction (PCR) amplification of the V3–V4 region of the bacterial 16S rRNA gene and the fungal internal transcribed spacer (ITS)1 region was performed using universal primers (bacterial V3–V4 region: 341F, 5ʹ-CCTACGGGNGGCWGCAG-3ʹ and 805R, 5ʹ-GACTACHVGGGTATCTAATCC-3ʹ and fungal ITS1 region: ITS1F, 5ʹ-CTTGGTCATTTAGAGGAAGTAA-3ʹ and ITS2R, GCTGCGTTCTTCATCGATGC-3ʹ). The PCR amplification conditions were performed with reference to the method used by Zhao et al. [ 32 ]. The amplified products were purified and quantified, and a sequencing library was constructed, which was sequenced by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China) on an Illumina HiSeq PE250 high-throughput sequencing platform. Bioinformatics Analysis After cutting the barcode and primer sequences from raw data, the reads of each sample were spliced to obtain the raw tags. The raw tags were inspected and the chimeras were removed to obtain effective tags. We used Qiime software ( http://qiime.org/ ) to perform operational taxonomic unit (OTU) clustering when the sequence similarity was > 97%. We used mothur software ( https://www.mothur.org/ ) [ 33 ], the Silva ribosomal RNA sequence database ( https://www.arb-silva.de/ ), and the UNITE fungal DNA database ( https://unite.ut.ee/ ) [ 34 ]to annotate species (threshold value, 0.8–1.0) to determine the classification information of the sequence and obtain taxa at all levels of phylum, class, order, family, and genus. The PyNAST tool ( https://omictools.com/pynast-tool ) [ 35 ] was used for multi-sequence comparison with data information in the Greengenes database ( https://greengenes.secondgenome.com/ ) [ 36 ]. Finally, the tags were standardized, and Qiime l.7.0 software was used to calculate the alpha diversity index and perform the beta diversity analysis. We plotted the dilution curve using the vegan package of R 2.15.3 software ( https://www.r-project.org/ ). The R language tool was used to draw the heatmap, and the unweighted pair-group method with arithmetic mean (UPGMA) was used to draw the phylogenetic tree by hierarchical clustering. Data analysis All data were processed and analyzed using SPSS 21.0 software (IBM Corp. in Armonk, NY). One-way analysis of variance and Duncan’s new multiple range test were used to analyze the significant differences.A probability value ( P ) of < 0.05 was considered statistically signifificant. Abbreviations ACE: abundance-based coverage estimator; CWD:Vicia sativa L. with clean culture cultivation; CYCH:a mixture of Avena sativa L. and Vicia sativa L. in equal proportions; CYD:Avena sativa L. with clean culture cultivation; ITS:internal transcribed spacer; KEGG:Kyoto Encyclopedia of Genes and Genomes; OTU:operational taxonomic unit; PCR:polymerase chain reaction; UPGMA:unweighted pair-group method with arithmetic mean Declarations Anailability of data and materials All data generated of analysed of this study are described in this paper. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no conflict of interest. Funding This study was supported by the National Natural Science Foundation of China [grant numbers 41561006, 31260025] and the Science and Technology Program of Qinghai Province [grant number 2019-ZJ-7008]andKey Laboratory of Superior Forage Germplasm in the Qinghai-Tibetan Plateau [grant number 2020-ZJ-Y03]. Authors’ Contributions YZ and ZZ designed the experiments. BS, RD and YW conducted the experiments. YZ and BS performed the experiments and wrote the main manuscript text. RD and YW prepared the Figs. YZ, ZZ, BS, RD and YW analyzed the data and reviewed the manuscript. All authors have read the manuscript and approved its final version. Acknowledgements The authors thank Prof. Tou YAO for his analysis of the metabolome date. References Qu YC, He ZG, Hao WY, Lan TH, Li YF. The situation and development countermeasure of oat production in China. Horticulture & Seed, 2006, 26(3):233-235. Yang HP, Sun ZM. Avena sativa in China . Beijing: Agriculture Press, 1989:1-5. Cao ZH. 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Beijing: China Agriculture Press, 2000. Zhao F, ZhaoMZ, Wang Y, Pang FH. Biodiversity of bacterial and fungi in rhizosphere of strawberry with different continuous cropping years. Microbiology China, 2017, 44(6):1377-1386. Magoč T, Salzberg SL. Flash: fast length adjustment of short reads to improve genome assemblies. Bioinformatics, 2011, 27(21):2957-2963. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 2007, 73(16):5261-5267. Quast C, Pruesse E, Yilmaz P,Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO. The Silva ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 2013, 41:590-596. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, Schweer T, Peplies J, Ludwig W, Glockner FO. The Silva and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Research, 2014, 42:643-648 Li XZ, Qu QH. Soil microbial biomass carbon and nitrogen in Mongolian grassland. Acta PedologicaSinica, 2002, 39(1):97-104. Liu J, Sui YY, Yu ZH, Yao Q, Shi Y, Chu HY, Jin J, Liu XB, Wang GH. Diversity and distribution patterns of acidobacterial communities in the black soil zone of northeast China. Soil Biology and Biochemistry, 2016, 95:212-222. Romanowicz KJ, Freedman ZB, Upchurch RA, Argiroff WA, Zak DR. Active microorganisms in forest soils differ from the total community yet are shaped by the same environmental factors: the influence of pH and soil moisture. FEMS Microbiology Ecology, 2016, 92(10):pii: fiw149. Li HY, Yao T, Zhang JG, Gao YM, Ma YC, Lu XW, Zhang HR, Yang XL. Relationship between soil bacterial community and environmental factors in the degraded grassland of eastern Qilian Mountains, China. Chinese Journal of Applied Ecology, 2018, 29(11):3793-3801. Li HY, Yao T, Ma YC, Zhang HR, Lu XW, Yang XL, Xia DH, Zhang JG, Gao YM. Soil bacterial community change across a degradation gradient in alpine meadow grassland in the central Qilian Mountains. Acta PrataculturaeSinica, 2019, 28(8):170-179. Li HY, Yao T, Gao YM, Zhang JG, Ma YC, Lu XW, Yang XL, Zhang HR, Xia DH. Relationship between soil fungal community and soil environmental factors in degraded alpine grassland. Acta MicrobiologicaSinica, 2019, 59(4):678-688. Grice EA, Kong HH, Deming SCCB, Davis J, Young AC, Bouffard GG,BlakesleyRW, Murray P.R, Green ED, Turner ML, Julia A, Segre JA. Topographical and temporal diversity of the human skin microbiome. Science, 2009, 324(5931):1190-1192. He JZ, Wang JT. Mechanisms of community organization and spatiotemporal patterns of soil microbial communities. Acta EcologicaSinica, 2015, 35(20):6575-6583. Liu Y. Seasonal variation of rhizosphere and root microbial diversity and community in Impatiens hainanensis (Balsaminaceae) on different altitude gradient. Hainan: Hainan University, 2018: pp 5. Cheng LJ, Liu JJ, Wang SM, Shi R. Study of isolation and diversity of endophytic fungi of Davidiainvolucrata var. vilmoriniana. Modern Horticulture, 2018, 1:5-6. Wang YT, Fu LN, Ji GH, Yang J, Wang X, Zhang JH, Wang YF, Liu Q. A study of the microbial community diversity of corn rhizosphere in Yunnan province based on high-throughput sequencing technique. Acta Agriculturae Universitatis Jiangxiensis, 2019(3):491-500. Bi JT, He DH. Research advances in effects of plant on soil microbial diversity. Chinese Agricultural Science Bulletin, 2009, 25(9):244-250. Kowalchuk GA, Buma DS, De Boer W, Klinkhamer PGL, Veen JA. Effects of above-ground plant species composition and diversity on the diversity of soil-borne microorganisms. Antonie Van Leeuwenhoek, 2002, 81(1-4):509-520. 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Number of sequencing (Fungus)","description":"","filename":"Figure1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-31329/v1/Figure1.JPG"},{"id":1238235,"identity":"aefb2b33-dd38-4252-b4a7-6749e2c04b96","added_by":"auto","created_at":"2020-06-03 15:31:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52841,"visible":true,"origin":"","legend":"Distribution of bacteria and fungi in rhizosphere soil samples. a. Community distribution of Bacteria; b. Community distribution of Fungus","description":"","filename":"Figure2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-31329/v1/Figure2.JPG"},{"id":1238236,"identity":"7c32365e-724e-4161-9edd-f98a7c83abd7","added_by":"auto","created_at":"2020-06-03 15:31:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36641,"visible":true,"origin":"","legend":"Heatmap diagram of weighted UniFrac for rhizosphere soil samples. a.Bacteria community; b. Fungus community","description":"","filename":"Figure3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-31329/v1/Figure3.JPG"},{"id":1238237,"identity":"7887d602-37b9-4226-8d46-223c2249c90e","added_by":"auto","created_at":"2020-06-03 15:31:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21205,"visible":true,"origin":"","legend":"Dendrogram of rhizosphere soil samples based on out. a.Bacteria community; b. Fungus community","description":"","filename":"Figure4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-31329/v1/Figure4.JPG"},{"id":1238238,"identity":"771c8303-ea67-4960-9242-7901a443b85e","added_by":"auto","created_at":"2020-06-03 15:31:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72807,"visible":true,"origin":"","legend":"Histogram of KEGG metabolism of soil samples","description":"","filename":"Figure5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-31329/v1/Figure5.JPG"},{"id":13536734,"identity":"50982a68-b9d4-4b6c-8312-8830ffff49cb","added_by":"auto","created_at":"2021-09-17 01:33:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":578686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-31329/v1/eb075948-d0c7-40ce-a2d0-4388d498b2a4.pdf"}],"financialInterests":"","formattedTitle":"Microbial diversity in rhizosphere soil of soybean grass under different cultivation methods in an alpine region","fulltext":[{"header":"Background","content":" \u003cp\u003eForage is a necessity for the survival of animal husbandry and the fundamental driving force for ensuring the sustainable development of animal husbandry. The development of the forage industry aims to promote the development of forage cultivation and pursue high-yield cultivation. A change in cultivation methods is a new technique for the high-yield cultivation of forage. A large number of studies have shown that soybean grass with mixed seeding cultivation can significantly improve the yield and quality of forage grass compared with clean culture cultivation, and the development of mixed soybean grassland is a feasible method to increase forage yield and quality [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. are fine forage grasses widely cultivated in alpine areas with strong resistance and of excellent quality [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They are widely cultivated in China and abroad to provide high-quality feed for domestic animals in mixed cultivation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrassland soil microorganisms play an important role in the grassland ecosystem [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. There are a large number of microorganisms in the rhizosphere soil of plants. Due to the combined effect of microbial activities and plant metabolism, differences in soil characteristics, microbial community composition, nutrient content, and other aspects exist between the rhizosphere soil and non-rhizosphere soil of plants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The diversity of soil microorganisms is an important indicator that can directly reflect the stability and diversity of the plant ecosystem, and it can also visually demonstrate the differences in the function, communities, and species of soil microorganisms in different regions[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As an important indicator to describe the characteristics of the soil microbial community, the functional diversity of the soil microbial community can reflect the basic information of the soil [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The structure and diversity of the microbial community in rhizosphere soil are usually influenced by cultivation conditions, the planting environment, plant species, and other factors [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Compared with previously employed methods, such as traditional DNA sequencing, high-throughput sequencing technology can greatly reduce the research cost, save time, and yield a large amount of information with a higher level of accuracy and sensitivity [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. At present, there have been many studies on different mixed-planting ratios and grass yield in seeding methods of soybean grass [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], but there are few studies of the rhizosphere microbial diversity of forage under different cultivation methods.\u003c/p\u003e \u003cp\u003eIn this paper, we used the rhizosphere soils of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under different cultivation methods in an alpine region as research objects to analyze the changes of microbial diversity using high-throughput sequencing so as to reveal the differences in microbial diversity of the rhizosphere soil of plants under different cultivation methods and to analyze structural and composition changes in the microbial community, thereby providing a basis for the study of microbial ecology of the rhizosphere soil of cultivated forages.\u003c/p\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSoilphysicochemical properties soil\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the soil samples are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was no significant difference in pH or in the total nitrogen, total phosphorus, or total potassium content of the soil samples with the three different treatments. Overall, the pH value was 7.50\u0026ndash;7.73, the total nitrogen content was 3.16\u0026ndash;4.13 g\u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the total phosphorus content was low at 1.63\u0026ndash;1.91 g\u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the total potassium content was 22.01\u0026ndash;22.92 g\u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The content of available nitrogen, available phosphorus, available potassium, and organic matter in the soil samples were significantly different among the three treatments \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The available nitrogen content was 210.11\u0026ndash;275.17 mg\u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, available phosphorus was 8.41\u0026ndash;18.71 mg\u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and available potassium content was 67.11\u0026ndash;12.18 mg\u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The available nitrogen, available potassium, and organic matter contents in the CYCH treatment soil were significantly higher than those in the other treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the contents of available phosphorus in the CYD treatment soil were significantly higher than those in the other treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eSoil nutrient content\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCYD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCYCH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal nitrogen (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal phosphorus (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal potassium (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable nitrogen (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210.11\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248.08\u0026thinsp;\u0026plusmn;\u0026thinsp;17.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275.17\u0026thinsp;\u0026plusmn;\u0026thinsp;22.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable phosphorus (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable potassium (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.07\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.65\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe organic matter (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.83\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Different lowercase letters on the same line indicate significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003ch2\u003eOtu-level Analysis Of Soil Bacteria And Fungi\u003c/h2\u003e \u003cp\u003eThe results of the OTU statistical analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The 16S rDNA V3\u0026ndash;V4 region of soil samples from all three treatments (CWD, CYD, and CYCH) was sequenced to obtain 48,988\u0026ndash;50,302 effective tags. The tags lengths were highly concentrated, averaging 421\u0026nbsp;bp, 420\u0026nbsp;bp, and 417\u0026nbsp;bp for the CWD, CYD, and CYCH treatments, respectively. The sequence length of the 16S rDNA V3\u0026ndash;V4 region in each treatment was roughly consistent. Following sequencing analysis, 32,031\u0026ndash;42,291 effective tags were obtained from the ITS regions of the DNA obtained from the soil samples of the three treatments Generally, the sequence length was 230\u0026ndash;280\u0026nbsp;bp, and the average length was 243, 230, and 280\u0026nbsp;bp, for the CWD, CYD, and CYCH treatments, respectively. Rarefaction curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and b) were prepared based on the results. It can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e that the OTU level of the sample increased rapidly with the increase of sequencing fragments, and remained stable after reaching the peak.\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\u003eTag information and OTU statistical analysis of rhizosphere soil samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTags\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffective tags\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage tags (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of\u003c/p\u003e \u003cp\u003eeffective (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo. OTUs\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\u003e16S rDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e67249\u0026thinsp;\u0026plusmn;\u0026thinsp;243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e48988\u0026thinsp;\u0026plusmn;\u0026thinsp;241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e421\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e61.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1162\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e67594\u0026thinsp;\u0026plusmn;\u0026thinsp;326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e50302\u0026thinsp;\u0026plusmn;\u0026thinsp;276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e420\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e63.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1209\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\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\u003eCYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e67396\u0026thinsp;\u0026plusmn;\u0026thinsp;358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e49918\u0026thinsp;\u0026plusmn;\u0026thinsp;318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e417\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e62.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1127\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e74762\u0026thinsp;\u0026plusmn;\u0026thinsp;572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36891\u0026thinsp;\u0026plusmn;\u0026thinsp;424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e243\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e46.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e261\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\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 \u003cp\u003eCYD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e74496\u0026thinsp;\u0026plusmn;\u0026thinsp;427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e42291\u0026thinsp;\u0026plusmn;\u0026thinsp;267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e230\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e53.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e260\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\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\u003eCYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e74611\u0026thinsp;\u0026plusmn;\u0026thinsp;410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32031\u0026thinsp;\u0026plusmn;\u0026thinsp;211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e280\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e40.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e211\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\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\u003e \u003c/p\u003e \u003ch2\u003eAnalysis Of Soil Microbial Species Composition\u003c/h2\u003e \u003cp\u003eThe richness of microbial species composition was \u0026gt;\u0026thinsp;0.1% higher in the three soil treatment samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We detected many phyla and classes of bacteria and fungi, and most of them contained very few species. For the convenience of observation, only the top ten abundant fungi and bacteria are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the remaining have been grouped as Others. The Unclassified and Unknown groups represent species that were not taxonomically annotated. The ordinate (\u003cem\u003ey\u003c/em\u003e-axis) shows the relative abundance. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b, respectively, show the top 10 classes of bacteria and fungi, respectively, according to their relative abundance level.\u003c/p\u003e \u003cp\u003eFor all three treatments, the soil bacteria were mainly distributed in the following 10 classes: Alphaproteobacteria, Betaproteobacteria, Blastocatellia, Acidobacteria subgroup 6, Gemmatimonadetes, Nitrospira, Acidimicrobiia, Actinobacteria, Gammaproteobacteria, and Holophagae(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Alphaproteobacteriawas the most abundant, with a relative abundance of 17.83%, 24.24%, and 18.67%, for the CWD, CYCH, and CYD treatments, respectively. For the CYCH treatment, the abundance of Alphaproteobacteria and Actinobacteria were significantly higher than for the CWD and CYD treatment. For the CYCH treatment, the abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia was significantly lower than that of the CWD and CYD treatment. With the exception of Actinobacteria, the relative abundance of the main bacterial species in the CWD and CYD soils was relatively close and it is quite different from that in CYCH soil.\u003c/p\u003e \u003cp\u003eAs can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, for all three treatments, the soil fungi were mainly distributed in the following 9 groups: Mortierellomycetes, Sordariomycetes,Leotiomycetes, Dothideomycetes, Tremellomycetes, Agaricomycetes, Archaeorhizomycetes, Eurotiomycetes, Spizellomycetes, or Unclassified, of which Mortierellomycetes was the most abundant the CYCH treatment soil (32.66%). Compared with the CWD and CYD treatment soil, the abundance of Sordariomycetes and Leotiomycetes in the CYCH treatment soil was significantly decreased, while the distribution of fungal communities was significantly different between the soil treatments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003ch2\u003eAnalysis Of Soil Microbial Diversity\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAlpha diversity analysis\u003c/h2\u003e \u003cp\u003eAs can be seen from the alpha diversity index results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), all three soil treatment samples have a high bacterial and fungal community diversity. The order of abundance-based coverage estimator (ACE) and Chao1 indices of bacterial alpha diversity in the three soil treatment samples were CYD\u0026thinsp;\u0026gt;\u0026thinsp;CYCH\u0026thinsp;\u0026gt;\u0026thinsp;CWD, indicating the highest abundance of bacterial species in the CYD treatment soil. The Simpson index showed CYCH\u0026thinsp;\u0026gt;\u0026thinsp;CWD\u0026thinsp;\u0026gt;\u0026thinsp;CYD. The Shannon index showed CYD\u0026thinsp;\u0026gt;\u0026thinsp;CYCH\u0026thinsp;\u0026gt;\u0026thinsp;CWD, indicating the richest diversity of bacterial microorganisms in the CYD treatment soil. The ACE and Chao1 indices of fungal alpha diversity in the three soil treatment samples were CWD\u0026thinsp;\u0026gt;\u0026thinsp;CYD\u0026thinsp;\u0026gt;\u0026thinsp;CYCH, showing that the fungal species abundance was highest in HNY soil. The Simpson index showed that CYCH\u0026thinsp;\u0026gt;\u0026thinsp;CYD\u0026thinsp;\u0026gt;\u0026thinsp;CWD. The Shannon index was ranked as CWD\u0026thinsp;\u0026gt;\u0026thinsp;CYD\u0026thinsp;\u0026gt;\u0026thinsp;CYCH. Thus fungal microbial diversity of the CWD treatment soil was the most abundant among the three treatments.\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\u003eDiversity index of soil microbial community in rhizosphere soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity indexes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCYD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCYCH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBacterial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1244.7398\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1276.6826\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1247.6747\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1259.2143\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1286.5981\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1285.6355\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.0068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.0063\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.0086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.9163\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.044\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.9639\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFungal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e274.0142\u0026thinsp;\u0026plusmn;\u0026thinsp;13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e269.1001\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e218.8791\u0026thinsp;\u0026plusmn;\u0026thinsp;12.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e280.0909\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e270.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e217.6667\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.0298\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.0526\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.2945\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.2487\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.5755\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Different lowercase letters on the same line indicate significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003ch2\u003eAnalysis Of Beta Diversity\u003c/h2\u003e \u003cp\u003eHeatmaps represent samples through interspecies distance relationships to obtain the sequence relationship between samples, which are drawn by means of matrix language tool software. By analyzing the heatmap, the difference between the two samples can be intuitively reflected by the range of color transformation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the heatmap of the three soil treatment samples. The bacterial flora (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and fungal flora (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) in the CWD and CYD treatment soil had a high level of similarity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The beta microbial diversity analysis was conducted to obtain a distance matrix. Then, the UPGMA function of R software was used for hierarchical clustering comparison of samples, and the similarity relationship of the species composition in each sample was described. The closer the samples were, the shorter the branch length was, indicating that the species composition of the two samples was more similar. Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the OTU level of bacteria and fungi in the CWD treatment soil samples had a relatively high consistency, while the similarity of bacteria and fungi in the CYCH treatment soil sample was relatively low.\u003c/p\u003e \u003ch2\u003eFunctional gene prediction\u003c/h2\u003e \u003cp\u003e PICRUSt software \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e(http://picrust.github.io/picrust\u003c/span\u003e\u003c/span\u003e/) was used to compare the species composition information obtained from the 16S sequencing tags to infer the functional gene composition of the samples and then analyze the functional differences between the different soil treatments. According to the of Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolism histogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the functional genes of the soil microbial community for each treatment were basically similar in their metabolic pathways.\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003ePrevious studies by many scholars have confirmed that the abundance and flora composition of soil microbial populations are closely related to pH, organic matter content, water content, the content of various nutrients, soil particle size, and other physical and chemical properties of soil [\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, there were no significant differences in pH, total nitrogen, total phosphorus, and total potassium in the three plant rhizosphere soil treatment samples of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under clean culture and mixed seeding cultivation, but there were significant differences in available nitrogen, available phosphorus, available potassium, and organic matter content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The content of available nitrogen, available potassium, and organic matter of soil of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. in the mixed seeding treatment were significantly higher than those in the other treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the content of available phosphorus of \u003cem\u003eAvena sativa\u003c/em\u003e L. in the clean culture treatment was significantly higher than in the other treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The relationship between plant rhizosphere microorganisms and soil nutrient content requires further study.\u003c/p\u003e \u003cp\u003eRhizosphere microorganisms play an important role in connecting the soil and plant microecosystems. Plant root secretions can provide energy for the life activities of microorganisms, and microorganisms can affect plants by decomposing organic matter and transforming nutrient substances or indirectly affect plant growth and the dynamics of non-rhizosphere microorganisms via material cycling and energy flow [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Yu et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] studied the diversity of rhizosphere and rhizome microorganisms cultivated by \u003cem\u003eImpatiensbalsamina\u003c/em\u003e in Hainan, China, and the seasonal variation of its community at different altitudes. The results showed that the number of rhizospheric fungi and bacterial OTUs, Shannon\u0026ndash;Weiner index, and richness index varied greatly with altitude and with the wet and dry seasons. Lijun et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] studied the microbial diversity in rhizosphere soil of \u003cem\u003eDavidiainvolucrata\u003c/em\u003e in different regions. They found that the microbial communities in the rhizosphere soil of \u003cem\u003eDavidiainvolucrata\u003c/em\u003e in three regions all had high biodiversity, but there were differences in species composition and distribution. Yating et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] studied the rhizosphere microbial community diversity of maize in Yunnan, China, and they concluded that altitude and variety type were important factors influencing the rhizosphere microbial community diversity of maize. In our study, the microbial diversity in rhizosphere soil of soybean grass was studied under different cultivation methods. The results showed that the bacterial microorganism diversity in rhizosphere soil was the highest when \u003cem\u003eAvena sativa\u003c/em\u003e L. was under clean culture cultivation, while the bacterial microorganism diversity in rhizosphere soil of \u003cem\u003eVicia sativa\u003c/em\u003e L. under clean culture cultivation was the lowest. The fungal microorganism diversity of rhizosphere soil of \u003cem\u003eVicia sativa\u003c/em\u003e L. under clean culture cultivation was the highest, and the fungal microorganism diversity of rhizosphere soil under mixed seeding of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. was the lowest. The diversity of rhizosphere microorganisms was significantly different under different cultivation methods, which was consistent with the results of other researchers. In similar environments, the structure of microbial communities is determined by the plant types, and the structure composition of soil microbial communities varies between different vegetation types [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The change in soil microbial diversity depends on soil water content, pH, aeration, nitrogen content, and organic carbon affected by vegetation. Based on the characteristic analysis of rhizosphere exudates and plant litters, the main factors affecting soil microbial diversity may be vegetation type and quantity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In this study, the species structure of the plant rhizosphere microbial community under the three treatments was at class level, and the abundance of Alphaproteobacteria and Actinobacteria was significantly higher in the mixed sowing treatment of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. than that in clean culture treatment of both \u003cem\u003eVicia sativa\u003c/em\u003e L. and \u003cem\u003eAvena sativa\u003c/em\u003e L. The abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia was significantly lower than that of soybean grass under clean culture. The Mortierellomycetes of fungi were most abundant under mixed seeding treatment (32.66%), while the abundance of Sordariomycetes and Leotiomycetes was significantly lower than that of soybean grass under clean culture, while the species distribution of bacteria and fungi communities in soil was significantly different under the different various treatments. The KEGG metabolic analysis showed that the functional genes of soil microbial communities were similar in their metabolic pathways. The structure and diversity of the microbial community in the rhizosphere of plants changes greatly under different cultivation modes, and the influence of plants, soil, and the environment required further study.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn this study, there were no significant differences in pH, total nitrogen, total phosphorus, and total potassium in the three plant rhizosphere soil treatment samples of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under clean culture and mixed seeding cultivation, but there were significant differences in available nitrogen, available phosphorus, available potassium, and organic matter content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The contents of available nitrogen, available potassium, and organic matter of soil of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under mixed seeding treatment were significantly higher than those under the other treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the available phosphorus content in the soil of \u003cem\u003eAvena sativa\u003c/em\u003e L. under clean culture treatment was significantly higher than those of other treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eWe investigated the microbial diversity in rhizosphere soil of soybean grass under different cultivation methods. The results showed that the bacterial microbial diversity in rhizosphere soil of \u003cem\u003eAvena sativa\u003c/em\u003e L. was the highest under clean culture, while the bacterial microbial diversity in rhizosphere soil of \u003cem\u003eVicia sativa\u003c/em\u003e L. was the lowest under clean culture. The fungi microbial diversity in rhizosphere soil of \u003cem\u003eVicia sativa\u003c/em\u003e L. was the highest under clean culture and the lowest under the mixed cultivation of \u003cem\u003eAvena sativa\u003c/em\u003eL. and \u003cem\u003eVicia sativa\u003c/em\u003e L. There were significant differences in rhizosphere microbial diversity under different cultivation methods.\u003c/p\u003e \u003cp\u003eIn this study, the species structure of the rhizosphere microbial community was at the class level under the three treatments. The abundance of Alphaproteobacteria and Actinobacteria under the mixed cultivation of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. was significantly higher than that of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under clean culture cultivation, and the abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia was significantly lower than that of soybean grass under clean culture. The Mortierellomycetes class of fungi was the most abundant under mixed seeding treatment (32.66%), while the abundance of Sordariomycetesand Leotiomycetes was significantly lower than that of soybean grass under clean culture. The species distribution of the bacteria and fungi community in soil microorganisms was significantly different between the soil treatments. KEGG metabolic analysis showed that the functional genes of soil microbial communities were similar in their metabolic pathways.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOverview of the experimental area\u003c/h2\u003e \u003cp\u003eThe experiment was conducted at Sanjiangyuan Field Ecological Observation Station of Qinghai University (33\u0026deg;24'30\" N, 97\u0026deg;18'00\" E), which is located in Zhenqin Town, Chengduo County, Yushu Prefecture, Qinghai province, China. The experimental area is situated at an altitude is 4,270\u0026nbsp;m and has a typical plateau continental climate, with an annual average temperature of \u0026minus;\u0026thinsp;5.6\u0026nbsp;\u0026deg;C to 3.8\u0026nbsp;\u0026deg;C and an annual average precipitation of 562.2\u0026nbsp;mm, mainly distributed from June to September. Its alpine meadow soil has a rich humus content but poor soil fertility due to poor decomposition. The soil pH is 7.32, organic matter content is 58\u0026nbsp;g \u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and available potassium, phosphorus, and nitrogen is 76.5, 7.0, and 14.0\u0026nbsp;mg \u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, of which ammonia nitrogen is 5.1\u0026nbsp;mg \u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and nitrate nitrogen is 8.9\u0026nbsp;mg \u0026bull;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The area is irrigated during droughts.\u003c/p\u003e \u003c/div\u003e \u003ch2\u003eExperimental Materials And Design\u003c/h2\u003e \u003cp\u003eThis experiment was designed as a single-factor randomized block study. The experimental materials were \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L., the seeds were provided by the Key Laboratory of Superior Forage Germplasm in the Qinghai-Tibetan Plateau. We set up three treatments: \u003cem\u003eVicia sativa\u003c/em\u003e L. with clean culture cultivation (CWD), \u003cem\u003eAvena sativa\u003c/em\u003e L. with clean culture cultivation (CYD), and a mixture of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. in equal proportions (CYCH). There were three plots of 4\u0026thinsp;\u0026times;\u0026thinsp;5\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e each. The plots were drilled, and the rows were spaced at intervals of 30\u0026nbsp;cm. Diammonium phosphate (30\u0026nbsp;kg/mu) was applied prior to sowing. The amount of seed used in the plot was calculated according to the plot area. The amount of \u003cem\u003eAvena sativa\u003c/em\u003e L. under clean culture cultivation was 15\u0026nbsp;kg/mu, that of \u003cem\u003eVicia sativa\u003c/em\u003e L. under clean culture cultivation was 5\u0026nbsp;kg/mu, and the mixed sowing amount is was 7.5\u0026nbsp;kg/mu of \u003cem\u003eAvena sativa\u003c/em\u003e L. plus 2.5\u0026nbsp;kg/mu of \u003cem\u003eVicia sativa\u003c/em\u003e L.\u003c/p\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003eThe experiment initiated in May 2019 and the experimenter visited the plot in August 2019. Using a five-point sampling method, root samples\u0026thinsp;\u0026le;\u0026thinsp;20\u0026nbsp;cm long were collected in triplicate from \u003cem\u003eAvena sativa\u003c/em\u003e L. and\u003cem\u003eVicia sativa\u003c/em\u003e L.. The large soil particles were shaken off using the shake-down method [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and then a brush was used to brush off excess soil surrounding the roots, leaving a layer of 0\u0026ndash;5\u0026nbsp;mm of soil, which was considered the rhizosphere soil. The two species were evenly mixed, placed into sterile Ziplock bags, stored at a low temperature, and brought back to the laboratory as quickly as possible. The collected soil samples for each treatment were divided into two parts, one to determine the nutrient content and the other to determine the microbial diversity index of the soil.\u003c/p\u003e \u003ch2\u003eDetermination Of Soil Physicochemical Properties\u003c/h2\u003e \u003cp\u003eThe indices of the physicochemical properties of the soil samples were determined by conventional analysis methods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Soil pH was determined using a pH potentiometer method. Organic matter content was determined by high-temperature potassium dichromate oxidation capacity, available nitrogen content by a potassium chloride extraction-spectrophotometric method, available phosphorus content by sodium bicarbonate extraction and molybdenum-antimony colorimetry, and available potassium content by an ammonium acetate extraction-flame photometer method. Total nitrogen content was determined by Nessler colorimetry, total phosphorus content by molybdenum-antimony colorimetry, and total potassium content by a flame photometer method.\u003c/p\u003e \u003ch2\u003eTotal Dna Extraction From Soil Microorganisms\u003c/h2\u003e \u003cp\u003eThe total genomic DNA was extracted from the soil microorganisms using an OMEGA soil DNA extraction kit. Each sample was mixed before processing in triplicate to reduce the chances of missing DNA from organisms that were sparsely distributed. Following extraction, polymerase chain reaction (PCR) amplification of the V3\u0026ndash;V4 region of the bacterial 16S rRNA gene and the fungal internal transcribed spacer (ITS)1 region was performed using universal primers (bacterial V3\u0026ndash;V4 region: 341F, 5ʹ-CCTACGGGNGGCWGCAG-3ʹ and 805R, 5ʹ-GACTACHVGGGTATCTAATCC-3ʹ and fungal ITS1 region: ITS1F, 5ʹ-CTTGGTCATTTAGAGGAAGTAA-3ʹ and ITS2R, GCTGCGTTCTTCATCGATGC-3ʹ). The PCR amplification conditions were performed with reference to the method used by Zhao et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The amplified products were purified and quantified, and a sequencing library was constructed, which was sequenced by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China) on an Illumina HiSeq PE250 high-throughput sequencing platform.\u003c/p\u003e \u003ch2\u003eBioinformatics Analysis\u003c/h2\u003e \u003cp\u003eAfter cutting the barcode and primer sequences from raw data, the reads of each sample were spliced to obtain the raw tags. The raw tags were inspected and the chimeras were removed to obtain effective tags. We used Qiime software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://qiime.org/\u003c/span\u003e\u003c/span\u003e) to perform operational taxonomic unit (OTU) clustering when the sequence similarity was \u0026gt;\u0026thinsp;97%. We used mothur software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mothur.org/\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], the Silva ribosomal RNA sequence database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arb-silva.de/\u003c/span\u003e\u003c/span\u003e), and the UNITE fungal DNA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unite.ut.ee/\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]to annotate species (threshold value, 0.8\u0026ndash;1.0) to determine the classification information of the sequence and obtain taxa at all levels of phylum, class, order, family, and genus. The PyNAST tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omictools.com/pynast-tool\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was used for multi-sequence comparison with data information in the Greengenes database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://greengenes.secondgenome.com/\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Finally, the tags were standardized, and Qiime l.7.0 software was used to calculate the alpha diversity index and perform the beta diversity analysis. We plotted the dilution curve using the vegan package of R 2.15.3 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003c/span\u003e). The R language tool was used to draw the heatmap, and the unweighted pair-group method with arithmetic mean (UPGMA) was used to draw the phylogenetic tree by hierarchical clustering.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll data were processed and analyzed using SPSS 21.0 software (IBM Corp. in Armonk, NY). One-way analysis of variance and Duncan\u0026rsquo;s new multiple range test were used to analyze the significant differences.A probability value (\u003cem\u003eP\u003c/em\u003e) of \u0026lt;\u0026thinsp;0.05 was considered statistically signifificant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eACE: abundance-based coverage estimator; CWD:Vicia sativa L. with clean culture cultivation; CYCH:a mixture of Avena sativa L. and Vicia sativa L. in equal proportions; CYD:Avena sativa L. with clean culture cultivation; ITS:internal transcribed spacer; KEGG:Kyoto Encyclopedia of Genes and Genomes; OTU:operational taxonomic unit; PCR:polymerase chain reaction; UPGMA:unweighted pair-group method with arithmetic mean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAnailability of data and materials\u003c/h2\u003e\u003cp\u003eAll data generated of analysed of this study are described in this paper.\u003c/p\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China [grant numbers 41561006, 31260025] and the Science and Technology Program of Qinghai Province [grant number 2019-ZJ-7008]andKey Laboratory of Superior Forage Germplasm in the Qinghai-Tibetan Plateau [grant number 2020-ZJ-Y03].\u003c/p\u003e \u003ch2\u003eAuthors\u0026rsquo; Contributions\u003c/h2\u003e \u003cp\u003eYZ and ZZ designed the experiments. BS, RD and YW conducted the experiments. YZ and BS performed the experiments and wrote the main manuscript text. RD and YW prepared the Figs. YZ, ZZ, BS, RD and YW analyzed the data and reviewed the manuscript. All authors have read the manuscript and approved its final version.\u003c/p\u003e \u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank Prof. Tou YAO for his analysis of the metabolome date.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQu YC, He ZG, Hao WY, Lan TH, Li YF. The situation and development countermeasure of oat production in China. Horticulture \u0026amp; Seed, 2006, 26(3):233-235.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eYang HP, Sun ZM. \u003cem\u003eAvena sativa\u003c/em\u003e in China . Beijing: Agriculture Press, 1989:1-5.\u003c/li\u003e\n\u003cli\u003eCao ZH. 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Study of isolation and diversity of endophytic fungi of Davidiainvolucrata var. vilmoriniana. Modern Horticulture, 2018, 1:5-6.\u003c/li\u003e\n\u003cli\u003eWang YT, Fu LN, Ji GH, Yang J, Wang X, Zhang JH, Wang YF, Liu Q. A study of the microbial community diversity of corn rhizosphere in Yunnan province based on high-throughput sequencing technique. Acta Agriculturae Universitatis Jiangxiensis, 2019(3):491-500.\u003c/li\u003e\n\u003cli\u003eBi JT, He DH. Research advances in effects of plant on soil microbial diversity. Chinese Agricultural Science Bulletin, 2009, 25(9):244-250.\u003c/li\u003e\n\u003cli\u003eKowalchuk GA, Buma DS, De Boer W, Klinkhamer PGL, Veen JA. Effects of above-ground plant species composition and diversity on the diversity of soil-borne microorganisms. Antonie Van Leeuwenhoek, 2002, 81(1-4):509-520.\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":"Avena sativa L., Vicia sativa L., high-throughput sequencing, clean culture, mixed seeding","lastPublishedDoi":"10.21203/rs.3.rs-31329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-31329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA large number of studies have shown that soybean grass with mixed seeding cultivation can significantly improve the yield and quality of forage grass compared with clean culture cultivation.This study explores the differences in the characteristics of the composition and diversity of the microbial community in the rhizosphere of soybean grasses between clean culture and mixed seeding methods in an alpine region. We used high-throughput sequencing technology to determine the microbial diversity and analytical methods to determine the physicochemical characteristics of plant rhizosphere soil of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were no significant differences in pH, total nitrogen, total phosphorus, and total potassium in the rhizosphere soil samples of soybean grasses under the clean culture and mixed seeding methods, while there were significant differences in the available nitrogen, available phosphorus, available potassium, and organic matter content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The bacterial diversity of the rhizosphere soil of \u003cem\u003eAvena sativa\u003c/em\u003e L. was the highest under the clean culture method, and the fungal diversity of the rhizosphere soil of \u003cem\u003eVicia sativa\u003c/em\u003e L. was the highest under the clean culture method. Furthermore, the microbial diversity of the rhizosphere was significantly different under the different cultivation methods (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The differences between the microbial species in the rhizosphere of the treated soil were at three class level. The abundance of Alphaproteobacteria and Actinobacteria in the rhizosphere of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under the mixed seeding method was conspicuously higher than that of \u003cem\u003eAvena sativa\u003c/em\u003e L. and \u003cem\u003eVicia sativa\u003c/em\u003e L. under the clean culture method, while the abundance of Gemmatimonadetes, Nitrospira, and Acidimicrobiia were significantly lower than that obtained under the clean culture method. Regarding fungal predominance, Mortierellomycetes was the most abundant (32.66%) under the mixed seeding method, while the abundance of Sordariomycetes and Leotiomycetes were significantly lower than that under clean culture. The distribution of bacterial and fungal community species in the rhizosphere differed significantly between the treatments. The Kyoto Encyclopedia of Genes and Genomes metabolism analysis showed that the metabolic pathways of functional genes in the soil microbial communities were similar.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMixed sowing changed the diversity of plant rhizosphere microbial community structure and promoted plant yield.\u003c/p\u003e","manuscriptTitle":"Microbial diversity in rhizosphere soil of soybean grass under different cultivation methods in an alpine region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-03 15:31:14","doi":"10.21203/rs.3.rs-31329/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":"bb8ead94-8c87-4885-b692-e3917f1581a1","owner":[],"postedDate":"June 3rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":112270,"name":"General Microbiology"},{"id":112271,"name":"Applied \u0026 Industrial Microbiology"}],"tags":[],"updatedAt":"2020-07-20T21:29:49+00:00","versionOfRecord":[],"versionCreatedAt":"2020-06-03 15:31:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-31329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-31329","identity":"rs-31329","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}