Comparison of rhizosphere soil microbial diversity of different introduced alfalfa varieties

Alfalfa (Medicago sativa L.) is an important forage legume in farming and animal husbandry systems. In this study, MiSeq high-throughput sequencing was applied to assess the relationship between bacterial and fungal community structures and alfalfa growth characteristics and soil physical and chemical properties induced by different cultivars alfalfa (Victoria, Kangsai, Aohan) in the grey desert soil. The results showed that the diversity of bacterial and fungal in Victoria was higher, and the bacterial diversity was signicantly lower for alfalfa with Aohan than for the others, and the fungal diversity was lower for alfalfa with Kangsai than for the others. Heatmap showed that total nitrogen, fresh weight, pH and organic have signicantly affect fungal community structure, whereas pH and organic carbon also signicant effects on bacterial community structure. LefSe analysis showed that the growth adaptability of introduced alfalfa is mainly related to fungal and bacterial species, and the benecial microorganisms with signicant differences and relative high abundance are signicantly enriched in Victoria. Pathogens with high relative abundance are mainly concentrated in Aohan alfalfa soil. Based on our ndings, Victoria is the high-yield alfalfa suitable for planting in gray desert soil, while planting Kangsai and Aohan alfalfa needs probiotic for adjuvant.


Introduction
Alfalfa is a kind of legume forage with high protein content, which plays an important role in the development of livestock breeding . Different types of soil has signi cant differences on the growth and development of introduced alfalfa, which seriously affects the land utilization rate and alfalfa yield (He et al. 2017). In particular, after autumn, the growth difference of alfalfa is more obvious.
It can be caused by issues such as the decrease of outside temperature and light (Fiutak et al. 2019).
Therefore, in recent years, introduction has become a focus of improving alfalfa yield, although the relevant research remains lacking.
The soil microbial community is highly diverse (Berendsen et al. 2012) and plays a vital role in increasing nutrient availability to plant roots and improving soil structure (Xia et al. 2019). However, compared to non-rooted bulk soil, the rhizosphere, which is the important place where microorganisms interact with plants (Berg et al. 2014), and is considered as one of the most complex ecosystems (Bakker et al. 2013).
The increase microbial abundance and activities in rhizosphere environment are due to the release of organic carbon by plant root exudation (Tkacz et al. 2015). Soil microorganisms are chemotactically attracted to root exudates, which allow them to proliferate in this carbon rich environment (Raaijmakers et al., 2009). Similarly, rhizosphere microbial communities can also directly and / or indirectly affect the composition and biomass of plant communities in natural and agricultural ecosystems (Philippot et al., 2013). The study of plant-micro interaction reveals the profound in uence on plant growth, development, nutrition, disease and productivity. Plants and microorganisms are adapted to use their close ties to achieve mutual bene t. This is best exempli ed by the symbiosis between rhizobia and legumes (Ji et al. 2018). The rhizosphere microorganisms can release carbon dioxide and metabolize acids, which dissolve insoluble minerals like phosphorous and increase their absorption via plant roots (Bakker et al. 2013).
They also secrete plant growth-stimulating substances, such as indoleacetic acid (Abd et al. 2020).
Accordingly, the rhizosphere soil microbiota is crucial for the growth and development of its host. When the normal microbiota is affected by change of external environment, its self-balance will be broken, which may threaten the health of soil or even induce disease and insect pests of the host. Recent studies have shown that the types of microbes in the soil are abundant and can be affected by the external environment. Besides the physical and chemical characteristics of soil , ages and physiological conditions of plants also affect the structure of rhizosphere microbial community . However, the characteristics of rhizosphere soil microbiota in alfalfa with different cultivars remain unclear.
This study aim to utilize high-throughput 16S rRNA and fungal ITS genes sequencing to comprehensively analyze and compare the bacterial and fungal community structures in soil of alfalfa with different cultivars. A better understanding of the biotic and abiotic components of planting different cultivars alfalfa, with the goal of providing a scienti c basis for planting alfalfa, managing soil and reasonable application of bacterial fertilizer.

Materials And Methods
Sample collection and processing Samples were collected from the experimental herbage station of Shihezi University in Shihezi City, Xinjiang Uygur Autonomous Region, China (N44 ° 20 ', E88 ° 30', altitude 420 m). The site is characterized by a temperate continental arid climate, mean annual precipitation of 225 mm, and mean annual evaporation of 1250 mm. In addition, the region experiences short and hot summers, and long and cold winter, with an average annual temperature of 8.1°C. The site was divided into 3 variety zones in May 2018, and the Victoria (VT, Fall Dormancy (FDC)=6.0, Medicago sativa L, Canada), Kangsai (KS, FDC=4.0, Medicago sativa L, America) and Aohan (AH, Medicago sativa L, Inner Mongolia of China) were planted, respectively. Each experimental zone measured 8 m × 6 m, and the plant and row spacing were 0.2 m and 0.6 m respectively. Rhizosphere samples were collected in mid-September 2019 from 3 random quadrats (1 m × 1 m) per zone by the "shaking root method" (Chen et al. 2020;Bungonsiri et al. 2019). Then six rhizosphere soil samples were collected from each quadrat and immediately pooled into a single soil sample. Finally, three soil samples were obtained for each variety zone. The rhizosphere soil samples were passed through a 2 mm sieve to remove any organic contaminants and snap frozen in liquid nitrogen. One part was stored at 80°C for high-throughput sequencing and another for physicochemical analysis. In addition, alfalfa plants from each quadrat were collected to determine plant height (PHT), number of branches (NOB), number of internodes (NOI), internode length (ILH) and fresh weight (FW).
Analysis of soil physicochemical properties pH was measured using the PHS-3C pH meter (Model PHS-3C pH Meter, Shanghai INESA Scienti c Instrument Co.Ltd., Shanghai, China). The content of organic carbon (OC) and available nitrogen (AN) were respectively measured with the potassium cyclo pate-external heating method and alkali solution diffusion method. The content of available phosphorus (AP) in the sodium bicarbonate-extracted soil samples was determined by molybdenum-antimony colorimetry. Available potassium (AK) was extracted with 1mol L −1 NH 4 OAc, and then determined by ame absorption spectroscopy. The samples were digested in HCIO 4 and H 2 SO 4 /HCl respectively to measure total nitrogen (TN) and total phosphorus (TP). The content of total potassium (TK) was determined in the molten sodium hydroxide soil extract by ame photometry (Bungonsiri et al. 2019).

DNA Extraction, Ampli cation and Sequencing
Total DNA was extracted using the PowerSoil DNA isolated Kit (Mo Bio Laboratories, Solana Beach, CA, USA) according to the manufacturer's protocol. The quality and concentration of the extracted DNA were measured using a NanoDrop spectrophotometer (ND-1000, NanoDrop Technologies, Wilmington, DE, United States). The 16S rRNA and ITS genes were ampli ed using primers 338F-806R (Xu et al. 2016) and ITS1F-ITS2R respectively. The PCR products were mixed with the same volume 2× loading buffer and were subject to 1.8% agarose gel electrophoresis for detection. Then, the mixsure of PCR products was puri ed using the Axyprep DNA gel extraction kit (Axygen Biosciences, Union City, CA, USA). PCR priducts were quanti ed using the QuantiFluor TM -ST Fluorometer (Promega Biotech, Beijing, China), and the samples were adjusted as required for Sequencing. Sequencing were conducted by Shanghai Majorbio Bio-PharmTechnology (Shanghai, China) on the Illumina Miseq platform.

Bioinformatics analyses and functional annotation
The raw reads were quality ltered under speci c ltering conditions to obtain hight-quality clean tags on the basis of the QIIME (V1.7.0) quality control process. Sequences that were shorter than 200 bp, ambiguous bases and those with an average mass less than 25 were removed. Chimeric sequences were removed using USEARCH v7.1. The unique sequences with ≥97% similarity were clustered into operational taxonomic units (OTUs) using the UPARSE software (Edgar, 2013). The sequences were then classi ed and annotated using the SILVA database. They were then taxonomically classi ed to different levels (phylum, class, order, family, genus, and species) using the Ribosomal Database Program (RDP) classi er.

Statistical Analysis
One-way ANOVA of soil physical and chemical properties was performed using SPSS (version 19.0; SPSS, Chicago, IL, USA). Signi cance was calculated by Tukey's test (p < 0.05). Alpha diversity indices (i.e., ACE, Sob and Shannon) were calculated by QIIME from rare ed samples using for richness and diversity indices of the bacterial community. β-diversity was calculated using unweighted UniFrac and non-metric multidimensional scaling (NMDS), after which Intra-group and Inter-group beta distance boxplot diagrams were generated. The Canoco program for Windows 4.5 (Biometris, Wageningen, the Netherlands) was used for principal component analysis (PCA). Heatmap evaluates the correlation between microbial classi cation and environmental variables by pheatmap. The relationship between soil microbial community structure and each affecting factor was analyzed by Redundancy analysis (RDA).
RDA eliminates redundant variables depending on other measured variables, automatically selecting variables with large effects, and on the variance in ation factor values to gradually remove redundant parameters, and the signi cance levels are based on 999 Monte Carlo permutations. Linear discriminant analysis (LDA) effect size (LefSe) analysis was performed to reveal the signi cant ranking of abundant modules in different cultivars samples. A size-effect threshold of 2.0 on the logarithmic LDA score was used for discriminative functional biomarkers.

Results
The alfalfa cultivars differ in terms of growth characteristics and the physicochemical properties of rhizosphere soil The VT alfalfa variety showed a signi cantly higher plant height internode length and fresh weight, along with increased TP, TK and AK levels in the rhizosphere soil samples compared to the KS and AH varieties (P < 0.05). In contrast, the number of branches and soil AN were signi cantly higher for KS compared to that of VT (P<0.01), while AP and AK contents were signi cantly lower than that of VT and AH (P < 0.01). The average number of internodes per plant and soil pH were similar across the different cultivars. Finally, soil TN and OC of AH were signi cantly higher than that of VT and KS (P < 0.05) ( Table 1).
The diversity of the soil microbiome differs among the alfalfa cultivars A total of 515,407 and 577,685 quality-ltered and chimera-checked 16S/ITS rRNA gene sequences were obtained with an average length of 417 and 234 bp across all samples, respectively. The ends of the rarefaction curves tapered off with increasing numbers of sequences per sample, as is commonly observed with sequencing data (Supplement Fig. 1A, B).
Bacterial and fungal community relative abundances (Sob, Ace) and diversity (Shannon; a-diversity) index values were compared for different alfalfa soil ( Table 2). The Sob and Ace estimator indicated that bacterial community abundances in KS was signi cantly higher than those in VT and AH (P < 0.05); the fungal community abundance was higher in KS than in the others (P > 0.05). The Shannon indices showed that VT and KS bacterial communities was signi cantly higher than AH (P < 0.05), while the fungal community was lower in KS than others. β-diversity is used to analyze the temporal and spatial changes in species composition, re ecting whether there is difference in microbial communities between groups. The PCA clearly grouped the bacterial and fungal communities according to soil of different alfalfa varieties. The rst two axes (PC1 and PC2) explained 43.3 and 30.7%, respectively, of the total variance in the bacterial and fungal species in soil of different alfalfa varieties. (Fig. 1A, B). The NMDS plot showing the dissimilarity of community and also revealed a distinct structure between VT, KS and AH (Fig. 1C, D).

Relative Abundance and Core Microbiota
The bacterial phyla with high relative abundance were Actinobacteria, Proteobacteria, Chloro exi and Acidobacteria ( Fig. 2A). These bacteria accounted for 83.3, 81.9 and 82.9% of the detectable reads in the VT, KS and AH samples, respectively. The phyla with signi cant differences in relative abundance between VT and KS was Actinobacteria (P < 0.05). The phyla with signi cant differences in relative abundance between VT and AH was Proteobacteria (P < 0.05). The phyla with signi cant differences in relative abundance between KS and AH were Actinobacteria and Acidobacteria (P < 0.05) (Supplement Table 1). On genus level, Arthrobacter, JG30-KF-CM45 and Gemmatimonadaceae were the dominant genera (Fig. 2B). In the top 10 genera in relative abundance, the genera with signi cant differences in relative abundance between VT and KS were Arthrobacter and Rokubacteriales (P < 0.05). The genera with signi cant differences in relative abundance between VT and AH were Arthrobacter and Rokubacteriales (P < 0.05) (Supplement Table 2).
The fungal phyla with relatively higher abundance were Ascomycota and Basidiomycota (Fig. 2C). These bacteria accounted for 93.6, 89.6 and 94.7% of the detectable reads in the VT, KS and AH samples, respectively.The phyla with signi cant differences in relative abundance between VT and KS was Ascomycota (P < 0.05). The phyla with signi cant differences in relative abundance between VT and AH was Basidiomycota (P < 0.05). The phyla with signi cant differences in relative abundance between KS and AH was Ascomycota (P < 0.05) (Supplement Table 1). In the top 10 genera in relative abundance, the genera with signi cant differences in relative abundance between VT and KS were Cladosporium, Sordariomycetes, Stachybotryaceae, Alternaria and Penicilium (P < 0.05). The genera with signi cant differences in relative abundance between VT and AH were Fusarium, Cladosporium, Sordariomycetes, Talaromyces, Mortierella, Hypocreales, Nectriaceae, Alternaria and Penicilium (P < 0.05). The genera with signi cant differences in relative abundance between KS and AH were Fusarium, Sordariomycetes, Stachybotryaceae, Talaromyces, Mortierella, Hypocreales and Nectriaceae (P < 0.05) (Supplement Table  2).
Relationship between the microbial community structure and the soil and plant characteristics RDA revealed that the microbial community structure was formed by primary environmental characteristics (TN AP OC pH NOB NOI FW). After removal of the redundant variables, seven factors were chosen for RDA. As shown in Supplement Fig. 2, the contribution of soil environmental and plant growth factors to bacterial and fungal communities were 68.16% and 73.67%, respectively. As shown in Fig. 3, on genus level, Arthrobacter, Gaiella, Cladosporium and Lectera showed a signi cant relationship with organic carbon; Thanatephorus, Talaromyces, Stachybotryaceae and Fusarium showed a signi cant relationship with fresh weight; Sordariomycetes, Talaromyces, Lectera and Fusarium showed a signi cant relationship with soil total nitrogen; Geminicoccaceae, Sordariomycetes, Cladosporium and Mortierella showed a signi cant relationship with pH.
Microbial communities with statistically signi cant differences Apart from determining αand βdiversities, another primary goal of comparing microbial communities is to identify specialized communities in samples. LefSe analysis was performed to reveal the signi cant ranking of abundant modules. The cladogram (Fig. 4A, C) showed differences in 18 taxa among VT, KS and AH. The plot from LefSe analysis (Fig. 4B, D) displays LDA scores of microbial taxa with signi cant differences among VT, KS and AH. On genus level, the bacteria signi cant differences between the VT group and the other two groups were Phycisphaeraceae (genus), Novosphingobium (genus), Dongia (order to genus) and AKYG1722 (family to genus); Lecanicillium (genus), Cordycipitaceae (genus), Nectria (genus), Thielaviopsis (family to genus) and Volvariella (order Agaricales and family Pluteaceae to genus) were the differential fungi. The bacteria signi cant differences between the KS group and the other two groups were S085 (class to genus), Saccharimonadales (phylum to genus) and Latescibacteria (phylum to genus); Coniochaetaceae (order to genus) and Trichoderma (family Hypocreaceae to genus) were the differential fungi. In contrast, the AH rhizosphere soil did not show signi cant enrichment of bacteria; Ascochyta (family Didymellaceae to genus) and Didymellaceae (family to genus) were the only differential fungi (Fig. 4A, B).

Discussion
In this study, three sample plots of alfalfa with different cultivars were set up in the experimental herbage station of Shihezi University to analyze the relationships between soil bacterial and fungal communities and alfalfa cultivars and the interaction between plant characteristics and soil physicochemical properties resulting from cultivars and bacterial and fungal communities. The predominant ora in the bacterial and fungal communities of rhizosphere soil was generally consistent between the three cultivars regimens, but there were differences in relative abundance, and additionally, each regimen had its own unique microbial populations. Bacterial abundance and diversity were highest in Kangsai (P < 0.05). Fungal abundance were highest in KS, but the diversity is the lowest. And fungal diversity were highest in Victoria. Soil microbial biomass and/or diversity can increase, decrease, or remain the same depending on the type of soil, the management system, ages and physiological conditions of plants .
Although bacterial and fungal communities respond to the different alfalfa soil in different ways with regard to relative abundances and diversity, our study showed that the soil microbial community structure changed signi cantly along the cultivars of alfalfa, which was relative with difference in soil and plant growth characteristics. Although similar ndings have been reported, these previous studies often relied on culture plate counting (Wang and Sheng 2012) or denaturing gradient gel electrophoresis (Clegg 2006) to determine the effects of plant cultivars on microbes. As for microbe identi cation and classi cation, these methods may not be accurate and/or can analyze only a limited set of microbial species in samples from complex environments. Thus, it's impossible to comprehensive understanding of the effects of planting different cultivars alfalfa on grey desert soil microbial communities. Therefore, this study aimed to provide a more complete analysis of the microbial community in order to achieve a better understanding of the contributions made by the microbial communities.
We found that the different cultivars alfalfa soil physicochemical and plant growth characteristics contribute differently to different microbial groups in the community. And the environmental factors evaluated explained over 70% of the shift in the microbial communities, which suggests that they are the primary factors in uencing microbial community structure (Supplement Fig. 2A, B). The shifts in dominant fungi communities showed a signi cant relationship with fresh weight, soil total nitrogen, organic carbon and pH; while shifts in dominant bacterial communities showed a signi cant relationship with soil organic carbon and pH (Fig. 3). The bacterial community structure in our study area was not as signi cant correlation as the fungal community structure to plant biomass changes. This may be because fungi are more likely to degrade lignocellulose from different plants than bacteria, allowing them to rst obtain resources from many of the relevant available substances (Chapin et al. 2011). In addition, we found signi cant direct relationships of bacterial and fungal community structures with soil organic carbon and pH. The dominant rhizosphere microbial including Actinobacteria, Proteobacteria and Acidobacteria usually survive in acidic soils (Luo et al. 2019), and also correlated with low pH in our study. In addition, the contents of organic carbon, available nitrogen and total nitrogen in alfalfa soil with Victoria were signi cantly lower than Kangsai and Aohan (Table 1). Nitrogen is one of the most important nutrients for life (Ramara et al. 2016); therefore, plant and microbial activities may gradually reduce the content of nitrogen in soil. However, correlations between microbial communities and environmental factors must be carefully explained because it is often very di cult to rmly establish the relationship between microbial communities and soil nutrient cycling (Bardgett 2005). In addition, this study employed 16S rRNA and ITS sequencing to analyze microbial communities; the gene abundances indicate genetic potential and not necessarily microbial activity. In this study, the productivity of Victoria alfalfa is signi cantly higher than Kangsai and Aohan.
Compared with soil microbial communities of three introduced alfalfa cultivars, the histogram of relative phylum abundance showed that regardless of what variety, Actinobacteria, Proteobacteria, and Chloro exi comprised the main dominant bacteria; and Ascomycota and Basidiomycota comprised the main dominant fungi ( Fig. 2A, C). However, the relative abundance of Actinobacteria and Ascomycota in the soil of Victoria were higher than that of Kangsai and Aohan. Notably, Actinomycetes can utilize nitrogenase that catalyzes conversion of nitrogen to nitrate, which is easily absorbed by the plant roots. In addition, Actinomycetes play an active role in carbon cycling by decomposing organic matter, promotes the formation of soil aggregates and improves soil structure, all of which are conducive to plant growth and development (Chaurasia et al. 2018). The relative abundance of Proteobacteria and Chloro exi in the soil of Aohan soil were higher than that of Kangsai and Victoria. Notably, Proteobacteria show rich species and genetic diversity, and promotes nitrogen absorption in addition to controlling plant diseases, and improve the yield of plants (Shang et al. 2016). The phosphate solubilizing function of Bacillus can enhance soil phosphate fertilizer and promote plant growth (Ramani 2011). Chloro exus can synthesize amino acids, nitrogen compounds and other substances, and is an important ora in fertile soil (Speirs et al. 2019). In this study, the nitrogen content in Victoria soil is lower than that in Kangsai and Aohan, which may be related to the action of Actinomycetes, Proteobacteria and Chloro exi.
LefSe analysis showed that on genus level (Fig. 4), most of the signi cantly enriched microbial clades in Victoria showed a signi cant correlation with total nitrogen (Supplement Table 3). These microbial clades (Novosphingobium, Lecanicillium, Cladosporium, Cordycipitaceae and Nectria) all have a close relationship with the growth of plant. Natalia et al. (2020) found that Novosphingobium sp. P6W can degradation abscisic acid (ABA) and participates in carbon and nitrogen metabolism. The entomogenous fungi of Lecanicillium spp. are important pathogens of insect pests and can maintain the healthy growth of plants (Zhou et al., 2020). Ramy et al. (2020) found that metabolites of Cladosporiumin can effectively inhibit pathogenic microorganisms. Nectria haematococca can signi cantly improve plant growth parameters and promote plant growth under abiotic stress (Prema et al., 2018). Enrichment for Lecanicillium, Novosphingobium, Cladosporium, Cordycipitaceae and Nectria may be caused by a better growth performance in Victoria than in the two other. No signi cant correlation was detected between Actinobacteria and the main environmental factors measured (Supplement Table 3). This may be because correlations between higher taxonomic ranks and nutrient indicators may conceal relations with environmental parameters at the subfamily level (Cruz-Martínez et al., 2012).In addition, we found that the abundance of Arthrobacter in Victoria soil was signi cantly higher than that in Kangsai and Aohan (Fig. 2B). He et al. (2020) found that Arthrobacter are effective nitrogen-removing bacteria that can absorb dissolved nitrogen from the environment and synthesize nitrite. The soil nutrients in Victoria may have created the most appropriate growth environment for Arthrobacter, which might have accumulated the soil nitrogen in their cells, thereby signi cantly reducing the soil nitrogen content (Table 1). In Fig. 2D, the abundance of Cladosporiumin, Nectriaceae and Fusarium in Aohan soil was signi cantly higher than that in Kangsai and Victoria. Virtually all Fusarium species synthesize toxic mycotoxins that threaten the healthy growth of plants (Perincherry et al. 2019). Some species of Nectriaceae are harmful to the roots of plants and can threaten their healthy growth (Parkinson et al. 2019). And the abundance of Talaromyces, Hypocreales, Penicilium and Sordariomycetes in Victoria soil was signi cantly higher than that in Kangsai and Aohan (Supplementary Table 2). Sharda et al. (2019) found that Talaromyces contains high levels of indoleacetic acid (IAA) and phosphate dissolving activity, which is very important for promoting plant growth. Hypocreales are the fungus that controls nematodes and is reported as an insect pathogen (Toledo et al. 2019). As a whole, the abundance of bene cial bacteria in is higher than that of harmful bacteria. This may also be one of the factors for high yield of alfalfa in Aohan. Mason et al. (2020) found that Saccharimonadales were identi ed as candidate biological indicators of high phosphorus utilization, and their relative abundance decreased with the increase of P. This is consistent with the results of this study (Table 1). Trichoderma can effectively alter soil properties and elevate plant growth and crop productivity. In addition, mowing can greatly accelerate plant shoot regrowth (Zhang et al. 2020). In this study, the number of branches and internodes in Kangsai soil were signi cantly higher than that in Victoria and Aohan. Aohan was primarily enriched for Didymellaceae, which can degrade plant litters and promote carbon cycle (Habtewold et al. 2020). This is consistent with the results of this study (Table 1).
In summary, the distribution difference of microbial communities in three introduced alfalfa soils was studied by partial 16S/ITS gene analysis. MiSeq results showed that the diversity of bacterial community in Victoria and Kangsai are higher, but the dominant microorganisms with signi cant differences and relative high abundance are signi cantly enriched in Victoria. And most of the bene cial bacteria clades with high abundance were signi cantly enriched in Victoria alfalfa soil. And part of the harmful bacteria clades with high abundance were mainly enriched in Aohan alfalfa soil. Therefore, in order to improve the adaptability of introduced alfalfa varieties in the current study, appropriate use of probiotics should be considered for adjuvant. In addition, changes in microbial communities were primarily attributable to plant fresh weight, pH, and soil total nitrogen and organic carbon. Considering the microbial community structure and environmental factors, we found that Victoria is the high-yield alfalfa suitable for planting in gray desert soil, while planting Kangsai and Aohan alfalfa needs probiotic assistance. Owing to the limited sampling and restricted sampling area, these conclusions cannot be generalized to other gray desert soil. However, our foundings provide a theoretical basis for the introduction of alfalfa, and has guiding value for reasonable application of bacterial fertilizer and improving the adaptability of alfalfa.    Heatmap analysis of the microorganisms and environmental factors. Note: The X-axis and Y-axis were environmental factors and species, respectively, and the correlation R value and P value were obtained through calculation. The R values are shown in different colors in the gure. If the P value is less than 0.05, it is marked with *, * 0.01 < P ≤ 0.05, ** 0.001 < P ≤ 0.01, *** P ≤ 0.001.

Figure 4
The phylogenetic distribution of the soil microbes from alfalfa with different cultivars.