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Article

Cultivation of Two Barnyard Varieties Improves Physicochemical Properties of Saline-Alkali Land through Mediating Rhizospheric Microbiome and Metabolome

by
Xueqin Wang
1,2,
Xing Xu
1,
Anqiao Lu
1,
Xin Li
3 and
Fengju Zhang
4,*
1
Agricultural College, Ningxia University, Yinchuan 750021, China
2
Ningxia Institute of Science and Technology Development Strategy and Information, Yinchuan 750021, China
3
Shizuishan Agricultural and Rural Bureau, Shizuishan 750021, China
4
Northwest Land Degradation and Ecological Restoration of State Key Laboratory Cultivation Base, College of Ecological Environment, Ningxia University, Yinchuan 750021, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(6), 1322; https://doi.org/10.3390/agronomy12061322
Submission received: 23 March 2022 / Revised: 15 May 2022 / Accepted: 21 May 2022 / Published: 30 May 2022
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Abstract

:
The present study was conducted to compare the effect of two barnyard millet varieties viz. Echinochloa frumentacea (L.) (HNJZ) and Echinochloa crusgalli (L.) var. austro-japonensis (BZ), on fertility and physicochemical properties of alkaline soil of Ningxia, China. Soil rhizosphere of BZ and HNJZ with three replicates (5 plants from each replicate) were collected for bacterial communities metagenomic profiling and six rhizosphere soil samples from each treatment for untargeted-metabolomic analysis. Results revealed significant (p < 0.05) improvement in soil fertility for both millet varieties at 0–20 cm depth than 20–40 cm by decreasing the alkalinity and pH, while increasing the organic matter. Proteobacteria, Firmicutes, and Actinobacteria were the most abundant phyla, whereas Megamonas, uncharacterized_Acidobacteria, and Halomonas were the most abundant genera. No difference in bacterial alpha diversity parameters was observed between HNJZ and BZ rhizospheres. Relative abundance of Cellvibrio and Devosia was increased (p < 0.05) in HNJZ, while Arenimonas, Gillisia, Sphingomonas, uncharacterized_Gammaproteobacteria, and Lysobacter were increased significantly (p < 0.05) in BZ rhizospheres. Beta diversity analysis revealed more diverse bacterial communities structure in HNJZ rhizosphere with higher Firmicutes ratio. Non-targeted metabolomic analysis revealed biosynthesis of alkaloids, histamine H2/H3 receptor agonists/antagonists, and arginine/proline metabolism as top three enriched KEGG pathways. The present study indicated that both millet varieties contributed to the reclamation of saline-alkali soil through reducing pH, total salts, and alkalinity while increasing the organic matter.

1. Introduction

Soil salinization is a widely spread problem for plant cultivation and growth in the world and covers almost 7.3% of the total land surface area of the earth which is about 9.5 × 106 km2 [1]. Alkaline soil is about 37% of the total world’s cultivated area, whereas saline soils cover around 23%. In China, saline and alkaline soils are also a major concern for crop cultivation in northern, northeastern, and northwestern areas, where it covers around 346,000 km2 of cultivated area (92,000 km2 is alkali soil and the rest is saline soil) accounting for 36% of the total area [2]. The saline-alkali soil decreases the soil fertility resulting in decreased agricultural turnover [3,4]. The Ningxia province is located in the northwest of China and it is affected by the saline-alkali soil which covers about 75.6% of the total farmland area. An afforestation project was started in 2007 in Pingluo city in northern Ningxia which suggested that salt-tolerant species with proper irrigation systems have better growth and survival rate [5].
Salt-tolerant plants species can be cultivated for phytoremediation of saline-alkali soils and can improve the soil quality [6]. The cultivation of such plants can influence the salt distribution as well other physicochemical properties of soil [7]. Echinochloa frumentacea (L.) and Echinochloa crusgalli (L.) belong to the genus Echinochloa and tribe Paniceae. Commonly, some of the members of this genus are also known as barnyard grass or cockspur grass. Echinochloa frumentacea (Indian barnyard millet) is cultivated for human use and also as fodder for livestock majorly in India, China, Japan, and Korea [8]. This crop has good nutritive value, less growing time, and can withstand drought conditions very well [9,10]. In vitro and in vivo study revealed that Echinochloa frumentacea (L.) is salt tolerant and can be cultivated in saline soils [11]. Echinochloa crusgalli (L.) is considered a weed for 36 crops and is prevalent in the paddy fields [12,13]. This grass is a global weed that has been introduced regularly to tropical and subtropical regions to be used as fodder and forage for livestock [14]. Echinochloa crusgalli (L.) can be grown for habitat rehabilitation and alkaline soil reclamation as it is being tested for its salt tolerance ability [15,16].
The interactions between plants and microbial communities of rhizosphere soil can improve the soil quality. These microorganisms within rhizosphere soil play a key role in plant growth and development, and can also help plant to withstand against adverse conditions [17]. The morphology and biodiversity of soil microorganisms can have a positive or negative impact on soil quality and plant development [18]. More than 30,000 distinct prokaryotic species may be found in the rhizosphere, which can harbor up to 1011 microbial cells per gram root [19]. These microbes are affected by the plant root exudates and their diversity and composition also depends upon the variability of exudates [20]. Every plant species host a specific microbiome that is dependent on soil structure [21]. In this regard, metagenomic sequencing technology is being used recently to explore useful diverse rhizospheric microbiome of the plant [22]. Moreover, metabolomics profiling is carried out for identifying active metabolites synthesized by rhizosphere microbes [23]. The 16S rDNA metagenomic sequencing of the rhizosphere has been exploited to better understand bacterial species diversity in distinct environments [24,25,26]. Rhizosphere metabolomics is a new area that incorporates impartial investigation of the full metabolite complement (metabolome) to better comprehend the complicated physiological, pathological, symbiotic, and other interactions among the rhizosphere’s inhabitants [27].
We hypothesize that both Echinochloa frumentacea (L.) Roxb. and E. crusgalli var. austro-japonensis (L.) are relatively salt-tolerant varieties and have different rhizosphere bacterial communities and metabolites that can affect rhizospheric soil physicochemical properties. So, we undertook 16S rDNA metagenomics and metabolomics profiling of rhizosphere samples of E. crusgalli (L.) and E. frumentacea (L.) to assess the differences in bacterial communities and metabolites cultivated in the alkaline soil of Ningxia and screen out the better salt-tolerant variety with diverse bacterial communities and metabolites.

2. Materials and Methods

2.1. Experimental Site Location and Soil Properties

The experiment was carried out for two years (17 April to 20 October 2018 and 20 April to 15 October 2019) in the field area of Dongfeng Village, Gaozhuang Township, Pingluo County, Shizuishan City, Ningxia (38°47′ N, 106°18′ E). The average annual precipitation of the experimental site was 172.5 mm and the annual average temperature was 8.5 °C. The soil in the test area was cracked alkaline soil and the anatomical surface of the soil can be divided into the alkaline layer, transition layer, and parent material layer from top to bottom. The clay mineral composition of the alkaline layer soil was mainly hydromica, followed by chlorite, kaolinite, and smectite, mainly including the crust layer and prismatic structure layer [28]. The basic physical and chemical properties, and soil salinity parameters of experimental site before start of experiment are listed in Tables S1–S3.

2.2. Experimental Design and Samples Collection

The seeds of two plants Echinochloa frumentacea (Roxb.) Link and E. crusgalli var. austro-japonensis were provided by the School of Ecology and Environment, Ningxia University, China. Before sowing, the test site and the seeds were prepared. The two types of plant seeds were sown by drilling. They were managed in a unified manner throughout the growth period, and the inter-cultivation, weeding, and irrigation were carried out on time. The experiment was carried out under normal field conditions without any mineral supplementation and fertilization. The experiment was performed with a randomized complete block design (RCBD) with three replications of each treatment (Echinochloa frumentacea (L.): HNJZ1, HNJZ2 and HNJZ3 and; E. crusgalli var. austro-japonensis (L.): BZ1, BZ2 and BZ3. The experimental site was divided into three areas (blocks), each area included two plots. The plot was 7 m long and 5 m wide with a total area of 35 m2. The area spacing is 1.5 m; plot spacing is 1 m. A 5-m wide protection line was set up to isolate the test area. Before planting, three soil samples (5-cm diameter × 30 cm depth) were collected randomly from each plot. Both millet varieties (HNJZ and BZ) were cultivated for two successive years and their seeds were sown at 17 April 2018 and 20 April 2019. The rhizosphere soil samples of BZ and HNJZ plants were collected on 20 August 2019. A total of 5 plants were randomly selected from each plot, and the rhizosphere soils of the 5 plants were mixed (rhizosphere samples from replicate were mixed) For sample collection, soil was dug about 20 cm in depth around the roots of both plants after removing the litter and dirt before the sample collection. The soil attached to 1–2 mm of the roots of plants was collected and placed in a sterilized 50 mL labeled centrifuge tube. The rhizosphere soil samples were placed in a dry ice sampling box, and immediately brought back to the real face room. A part of the soil samples was taken out, air-dried, and sieved, and then the soil physicochemical properties were determined according to the “Soil Agrochemical Analysis Method”. The remaining samples were stored at −80 °C for later use (metagenomic analysis). The statistical analysis of physicochemical properties was performed by taking means of each parameter at start and end of experiment.

2.3. DNA Extraction, Library Preparation, and Metagenomic Sequencing

The genomic DNA from rhizosphere samples was extracted using the Cetyl trimethyl ammonium bromide (CTAB) method [29]. The quality and quantity of extracted DNA were analyzed by 1% agarose gel electrophoresis and NanoDrop 2000-UV spectrophotometer (Thermo Scientific, Waltham, MA, USA), respectively. Using diluted genomic DNA (1 ng/μL) as a template, specific primers for 16S DNA region 16S V4 (515F and 806R) for identification of bacterial diversity, Phusion® High-Fidelity PCR master mix with GC buffer (Biolabs, New England), and high-potency high-fidelity enzymes were used for PCR to ensure amplification efficiency and accuracy. Additionally, amplification of 16S (V4–V5, V8) archaeal-bacterial regions was performed. The PCR product was detected with 2% agarose gel electrophoresis and purity was analyzed using the purification kit (GeneJET Gel Extraction Kit designed by Thermo Fisher Scientific, USA) following the manufacturer’s recommendations. The genomic library was built using the Thermofisher’s Ion Plus Fragment Library Kit 48 rxns Library Builder Kit. After passing Qubit quantification and library testing Thermofisher’s Ion S5TMXL was used for hands-on sequencing.

2.4. Metagenomic Sequence Statistical Analysis

Sequenced data were run through quality control using the software Cutadapt V1.9.1 (http://cutadapt.readthedocs.io/en/stable/, 22 March 2022) to remove low-quality reads and then chimera sequences were detected (https://github.com/torognes/vsearch/, 22 March 2022) and removed with the UCHIME algorithm in USEARCH to obtain clean reads [30]. All qualified sequences were clustered into operational taxonomic units (OTUs) at 97% similarity [31], by using the Uparse software V7.0.1001 (http://www.drive5.com/uparse/, 22 March 2022) and sequences with the highest frequency were selected as the representative sequence of OTUs. Species annotation analysis was performed for each taxonomic level (kingdom, phylum, class, order, family, genus, species) with the Mothur method, and the SSUrRNA database of SILVA132 (http://www.arb-silva.de/, 22 March 2022) at the threshold was set at 0.8–1, and taxonomic information was obtained and analyzed separately in each classification level. Using MUSCLE [32], Version 3.8.31 (http://www.drive5.com/muscle/, 22 March 2022) software for rapid multiple sequence alignment, the phylogeny of all OTUs sequences was obtained. Further, the data for each sample were normalized by comparing with the sample having the least amount of data for standardization. Alpha diversity (Observed-species, ACE, Shannon, Simpson, Good’s-coverage, Chao1), and beta diversity analyses (Unifrac distance, UPGMA clustering, PCA, PCoA, and NMDS) were performed using the Qiime software (Version 1.9.1) and the results were further analyzed with R software (Version 2.15.3). LEfSe software with a threshold LDA score of 4 with default parameters was applied to identify biomarker taxa in both groups. For the Metastats analysis, a permutation test was applied in R software to the obtained p value. Further, the p-value was corrected by applying the Benjamini and Hochberg False Discovery Rate method, and a q-value was obtained [33]. To find the correlation of the environmental factors CCA and RDA functions in the vegan package of R software were applied.

2.5. Non-Targeted Metabolomics Analysis

For the metabolomic analysis, a total of 12 fresh rhizosphere samples, 6 from each group (E. crusgalli var. austro-japonensis (L.) (BZ): and E. frumentacea (L.) (HNJZ), were collected, weighed, and labeled as BZ1, BZ2, BZ3, BZ4, BZ5, BZ6 and HNJZ1, HNJZ2 HNJZ3, HNJZ4, HNJZ5 and HNJZ6, respectively. These samples were then stored in liquid nitrogen at −800°C, then freeze-dried and crushed to powdered form for the DNA extraction later.
Non-targeted metabolome LC-MS detection and analysis techniques were used to analyze metabolites of six HNJZ and six BZ samples. From each rhizosphere soil sample, 100 mg soil was taken and grounded with liquid nitrogen. Then the homogenate obtained was suspended with 0.1% formic acid and chilled methanol. Before the centrifugation of samples at 15,000 rpm for 5 min at 40 °C, incubation was performed for 5 min with the ice. LC-MS grade water was used for the dilution of supernatant. Second round centrifugation was performed at 15,000 rpm for 10 min at 40 °C after sample filtration using a 0.22 μm filter. Samples were filtered and then injected into the LC-MS/MS system. The Vanquish UHPLC system was utilized from the Thermo Fisher for the analysis and this system was coupled with the mass spectrometer (Orbitrap Q Exactive HF-X, Thermo Fisher, San Jose, CA, USA). The filtered samples were introduced at a flow rate (0.2 mL/min) into the Gold column. The eluents for the positive and negative polarity modes were (A1: 0.1% formic acid and B: methanol and; A2: 5 mM ammonium acetate (pH 9.0) and B2: methanol), respectively. The solvent gradient was set as follows: 2% B, 1.5 min; 2–100% B, 12.0 min; 100% B, 14.0 min; 100–2% B, 14.1 min; 2% B, 16 min. The mass spectrometer was run in positive/negative polarity mode with the following set protocols (aux gas flow rate: 10 arb; sheath gas flow rate: 35 arb; spray voltage: 3.2 kV and; capillary temperature: 320 °C). For the analysis of peak picking, peak alignment, and each metabolite quantitation, the raw data produced was further processed through Compound Discoverer 3.0 from Thermo Fisher. The peak intensities results were standardized to obtain total spectral intensity. Using the standardized data molecular formula was predicted based on fragment ions, additive ions, and molecular ion peaks. Correct qualitative and relative quantitative results were obtained by comparing the peaks data with the databases like ChemSpider and mzCloud. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used for the metabolite enrichment analysis and to determine the important pathways in the enriched terms [34].
Unsupervised principal component analysis (PCA) was applied in R software using the prcomp function. Hierarchical Cluster Analysis (HCA) was performed through R software, and a heatmap was constructed for the samples and metabolites. Using the cor function in R software, Pearson correlation coefficients were measured between samples. Significant metabolites among the groups were measured by using the results values from the first PCA of OPLS-DA analysis and then applying VIP ≥ 1 and absolute log2FC (fold change) ≥1. Before the OPLS-DA analysis, the data were mean-centered and log-transformed (log2). A permutation test was performed to validate the OPLS-DA model as reported previously [35].

3. Results

3.1. Soil Physicochemical Properties

The effect of HNJZ and BZ groups on soil physicochemical properties like pH, total salt, alkalinity, bulk density, organic matter, total nitrogen content, available phosphorus, and fast acting potassium were determined at two depths (0–20 cm and 20–40 cm) and results of change in physicochemical properties of the soil before and after experiment are shown in Table 1. Results revealed that cultivation of both varieties resulted in decreased (p < 0.05) soil pH at 0–20 cm depth as pH decreased from 9.17 to 8.42 for HNJZ and from 9.17 to 8.70 for the BZ group. However, at 20–40 cm depth, no effect (p < 0.05) on soil pH was observed (Table 1). Similarly, Soil alkalinity was decreased (p < 0.05) in HNJZ and BZ groups at both depths. A significant decrease (p < 0.05) in total salts was observed at 0–20 cm for both groups, whereas no significant decrease (p < 0.05) was noted at 20–40 cm soil depth for both groups. Bulk density showed a decrease (p < 0.05) while increasing organic matter (p < 0.05) in HNJZ and BZ at both soil depths (Table 1). Moreover, the total N and available P were increased (p < 0.05) at both depths, whereas fast-acting K was decreased (p < 0.05) at both depths.

3.2. Operational Taxonomic Units (OTUs) and Annotation Results of Bacterial Communities

The IonS5TMXL sequencing platform revealed an average of 83,677 reads per sample and an average of 79,028 clean reads (94.47%) were obtained after quality control. A total of 3587 OTUs were classified at each taxonomic level through the Silva database (release132). A total of 3400 (94.81%) and 1356 (37.80%) OTUs were annotated at the phylum and genus levels, respectively. The dominant bacterial phyla were mainly Proteobacteria, Firmicutes, and Actinobacteria, while dominant genera included Megamonas, uncharacterized_Acidobacteria, and Halomonas. Further, the Venn diagram revealed a total of 1464 and 1611 OTUs common within HNJZ and BZ samples, respectively (Supplementary Figure S1A,B). Additionally, the rarefaction curve revealed the equally diverse bacterial communities in each sample (Supplementary Figure S2A). Similarly, the Rank Abundance curve showed the least number of bacterial communities in HNJZ3 (Supplementary Figure S2B).

3.3. Alpha Diversity

The alpha diversity analysis (ACE, CHAO1, Observed species, Shannon, and Simpson indices) showed no significant differences (p > 0.05) in the bacterial communities between HNJZ and BZ (Table 2). Furthermore, The beeswarm graphs were drawn using the Wilcoxon rank test and Shannon index (diversity index) also didn’t reveal significant differences between HNJZ and BZ (Supplementary Figure S3A,B).

3.4. Relative Abundance of Bacterial Communities

The top ten abundant bacterial phyla observed between HNJZ and BZ groups are presented in Figure 1. Both groups were enriched with the same bacterial phyla but their relative abundance varied between the groups. Proteobacteria was the dominant phyla in both groups, while Thaurmarchaeota was the least abundant phyla in both groups. The second most abundant phyla were Firmicutes and Actinobacteria in HNJZ and BZ groups, respectively (Figure 1). Further, a t-test was applied to find the most significant bacterial groups at different taxonomic levels (Phylum, Class, Order, Family, Genus, and Species) between HNJZ and BZ groups. Gemmatimonadetes were found the most significantly different bacterial group (p < 0.05) between HNJZ and BZ with an average abundance of 2.0% and 5.1%, respectively (Supplementary Figure S4). Further, the metastat method was used to screen the bacterial communities which had significant differences at p’s which were corrected to get q-values. The relative abundance distribution box map of 35 genera was plotted between HNJZ and BZ (Figure 2). Relative abundance of Cellvibrio and Devosia was significantly higher (p < 0.05) in HNJZ while Arenimonas, Gillisia, Sphingomonas, uncharacterized_Gammaproteobacteria, and Lysobacter were increased significantly (p < 0.05) in BZ rhizospheres.
The LEfSe analysis was performed to analyze the significant relative abundant bacterial taxa (biomarker) in the HNJZ and BZ groups (Figure 3A,B). The 14 biomarkers taxa with LDA score > 4 belonging to the HNJZ group were found enriched with Bacilli, Rhodobacterales, uncharacterized _Actinobacteria, and Oceanosprillales. Whereas, the BZ group was found enriched with Xanthomonadaceae, Gemmatimonadetes, Nitrosomonadaceae, and Acidobacteria (Figure 3A). Further, Cladogram showed the significantly enriched bacterial taxa (phylum to genus/species level) in both HNJZ and BZ groups (Figure 3B).

3.5. Beta Diversity

Principal component analysis was performed to reveal true similarities and differences between groups and samples (HNJZ: HNJZ1, HNJZ2, and HMJZ3; and BZ; BZ1, BZ2, and BZ3) by reducing the dimensionality of large data (Figure 4A). The PC1 revealed the 36.21% variation, whereas PC2 explained the 18.47% of the total variation. The BZ samples (BZ1, BZ2, and BZ3) showed a more similar bacterial community structure than the HNJZ group samples (HNJZ1, HNJZ2, and HNJZ3). Further, the Non-Metric Multi-Dimensional Scaling (NMDS) analysis showed the BZ samples were clustered at one point showing the high similarity in bacterial community structure in this group, whereas, the HNJZ group showed more differences as samples from this group were separated apart (Figure 4B). The NMDS is a nonlinear model and can overcome the shortcomings of linear models (including PCA and PCoA) and better reflect the nonlinear structure of ecological data.

3.6. Environmental Factors Affecting Microbial Communities

A canonical correspondence analysis (CCA) plot was designed to check the correlation of environmental factors with the bacterial community structure of HNJZ and BZ groups (Figure 5). The first axis CCA1 revealed the 60.92% variation in the bacterial-environment relationship, whereas the second axis CCA2 explains 24.83% of the total variation. For the first axis, the more important environmental factors were pH, total salt (T.S), and soil bulk density (B.D) because these factors had a small angle with the first axis (11.7°, 80.1°, and 72.5°, respectively), and the pH had the most pronounced effect on BZ group samples as it had the longer length along the axis. The important environmental factors constrained on the second axis were T.S, B.D, and pH. The total salt (TS) had a major effect on HNJZ bacterial communities.
Further, the spearman correlation analysis was determined between the environmental variables and relative abundant bacterial genera at p < 0.05 (Supplementary Figure S5). A highly significant negative correlation was observed between pH and Bacteroids (p < 0.05), whereas a highly significant positive correlation was observed between bulk density (BD) and Pseudomonas (p < 0.05) (Supplementary Figure S5).

3.7. Differential Metabolite Analysis

Principal component analysis revealed both groups (HNJZ and BZ) were separated apart. The BZ group samples had a more similar metabolites structure between them as compared to the HNJZ group samples (Supplementary Figure S6).
Differential metabolite analysis was performed for both groups (HNJZ vs. BZ) and important metabolites were screened through a t-test. A total of 370 metabolites were identified, out of which 78 were found significantly enriched (p < 0.05). Of these 78 important metabolites, 10 were involved in up-regulation, while 68 were found involved in down-regulation. Further, the volcano map was drawn to visualize the overall distribution of differential metabolites between both groups (Figure 6). The abscissa indicates the change in the expression of multiple metabolites in different groups (log2FoldChange), and the vertical coordinate indicates the difference in significance level (−log10p).

3.8. Metabolite Functions and Classification Annotations

The KEGG analysis showed metabolism as the most dominant category with a total of 68 metabolites followed by organismal systems (4), environmental information processing (2), and genetic information processing (2) (Figure 7). In the metabolism category, global and overview maps had most metabolites (19) followed by amino acid metabolism (8), metabolism of cofactor and enzymes (8), and biosynthesis of other secondary metabolites (7) importantly (Figure 7).
Most functionally enriched pathways observed were Indole alkaloid biosynthesis, biosynthesis of alkaloids derived from shikimate pathway, Histamine H2/H3 receptor agonists/antagonists, Arginine and proline metabolism, metabolic pathways, and biosynthesis of secondary metabolites (Figure 8).

4. Discussion

4.1. Physicochemical Properties of Soil

Soil salinization is a dynamic problem worldwide and is expected to aggravate in the coming years owing to the recent climate change scenario. It not only reduces net cultivable area but also challenges crop productivity and quality, cultivation practices, biodiversity, water quality, and industry. It is suggested that soil salinization causes annual global economic losses of about 27.3 billion US$ [36]. The cultivation of salt-tolerant crops is one of the key measures required for the restoration and management of saline-alkali soils for land expansion and improvement of crop productivity to ensure global future food security.
In the present study, the cultivation of two millet varieties (HNJZ and BZ) withstand the alkaline soil well and our result are in agreement with studies conducted on E. frumentacea (L.) [11], and E. crusgalli (L.) [15,16] showing these plant species can tolerate salt well and can be cultivated in alkaline soils. Almost all physicochemical parameters showed significant improvement in soil quality at 0–20 cm depth than 20–40 cm depth. The soil pH, alkalinity, total salt, bulk density and fast acting K were significantly decreased at 0–20 cm soil depth, whereas organic matter, total N and available P were increased. Similarly at 20–40 cm soil depth, a decrease in alkalinity, bulk density, and fast acting K was observed, whereas an increase in the organic matter, total N and available P was observed. However, no effect on soil pH and total salts was observed at 20–40 cm depth. The previous studies conducted in saline-alkali soil by cultivating the salt-tolerant plant species have also shown improvement in soil physicochemical properties [37,38]. Organic matter is crucial for the improvement of soil quality as it provides nutrients to the soil and improves the water holding capacity of the plant. Organic matter also provides aeration for seed germination and root growth [39]. Moreover, increased organic matter or nutrient content subsequently decreases soil alkalinity owing to the well-established negative correlation between nutrient content and soil alkalinity [37]. Plant growth-promoting bacteria (PGPB) present in rhizosphere soil also play a crucial role in the salt tolerance and improvement of soil physicochemical properties. It is mainly mediated by slowing down the evaporation process and increasing the water availability to plants, ultimately enhancing the time for plants to adjust metabolic changes under drought stress [40,41].

4.2. Metagenomics Profiling and Bacterial Diversity

The rhizosphere is the principal site of direct contact between the host plant and soil microorganisms [19,42]. The microbiome diversity in the rhizosphere is influenced by host plant habitat, seasonal variations, and root exudates [43,44,45]. In response, these microbial communities may influence root functions, as well as plant health and growth. The rhizospheric microbiome is known as the “second genome” of the plant because of its potential implications on plant development and survival [19,46]. The comparative metagenomic and metabolomic studies have shown to reveal the microbial diversity and important metabolites of host plant’s rhizosphere soil that are important for their growth and development [26,47]. Alpha diversity is used to analyze microbial community diversity within samples [48]. In the present study, no difference in alpha diversity parameters was observed for all samples within HNJZ and BZ. Proteobacteria, Firmicutes, and Actinobacteria were the dominant phyla in both groups. The previous studies have also revealed higher enrichment of these bacteria in saline-alkali soils [49,50,51]. Proteobacteria have also been revealed as the most abundant phyla in different rhizosphere soils [26,52]. Proteobacteria play an important role in the recycling of nutrients like Fe, N, C and S [53,54]. An increase in levels of Proteobacteria is associated with increased nutrient levels in saline soil [55,56,57]. An increase in nutrient levels usually leads to decrease the soil alkalinity [37]. The second most abundant phyla in HNJZ and BZ groups were Firmicutes and Actinobacteria, respectively. Actinobacteria play a crucial role in soil decomposition and carbon cycling globally [58,59]. Carbon cycling is negatively associated with salt stress, so increase in carbon cycling will subsequently decrease the salt stress [60]. Actinobacteria have also shown significant enrichment in the rhizosphere and root samples of four millet species under drought conditions [61]. An increased abundance of Actinobacteria bacteria was observed under soil alkalinity [62], while Firmicutes were found as abundant phyla in saline-alkali soil in the arid region of northwest China [48]. Further, Firmicutes have also been observed abundantly in a higher association with the arid saline soil of Antarctica [63,64]. Similarly, Firmicutes and Proteobacteria were labeled as special indicators in the high saline-alkali soil of Songnen Plain in northeast China [50].
Metastat analysis of the top 35 genera revealed that the relative abundance of Cellvibrio and Devosia was significantly higher (p < 0.05) in the HNJZ group. These both genera are found to be involved in nitrogen fixation in rhizosphere soil [65,66], which can results in increase N content of the soil and can improve quality of the salt affected soils [67]. Similarly, Arenimonas, Gillisia, Sphingomonas, uncharacterized_Gammaproteobacteria, and Lysobacter were increased (p < 0.05) in BZ rhizospheres. Arenimonas contain a gene that is associated with alkaline phosphatase, a metal enzyme linked to Cd, which can cause this genus to solidify or mineralize metals by biosorption leading to lowering heavy metal stress on plants [68]. Lysobacter and Sphingomonas were observed as abundant genera in alkaline soil spiked with anthracene [69]. Sphingomonas play a crucial role in plant growth [70,71], and are also involved in the degradation of organic pollutants [72,73]. In the present study, LefSe analysis identified Bacilli, Rhodobacterales, unicharacterized_Actinobacteria, and Oceanosprillales as biomarker taxa for HNJZ. While, BZ group was found enriched with Xanthomonadaceae, Gemmatimonadetes, Nitrosomonadaceae and Acidobacteria. Bacillus is important for plant growth by acting against the phytopathogens and serves as a biocontrol agent [74]. Xanthomonadaceae is related to the Gammaproteobacteria and is considered pathogenic for plant growth [75]. Gemmatimonadetes were found adaptive to low moisture soils in arid climate zone [76]. The Nitrosomonadaceae family is involved in the N cycle and can improve nitrogen availability for plant growth by converting ammonia into nitrate [77]. Acidobacteria is abundant phyla in most of the soil types and is usually found in soils with low pH and nutrients [78]. Acidobacteria are involved in the C degradation cycle by utilizing different carbon sources like cellulose, hemicellulose, and chitin [79], and can help to improve soil fertility.

4.3. Environmental Correlation

Environmental factors affect bacterial growth, survival, and diffusion under different soil conditions [80,81]. In the current study, the effect of three environmental factors (pH, total salts, and bulk density) was observed through CCA analysis, which revealed pH and total salts had a greater effect on BZ and HNJZ groups, respectively. The pH plays an important role in shaping the bacterial community structure and it drives the growth of different bacteria with slight changes within a narrow range [50,82,83,84]. In our bacterial diversity results, BZ group samples had less diverse bacterial communities so that might be under effect of pH slight changes. Similarly, the salt concentration is important in soils and a higher concentration can lead to salt stress in plants [85]. The more diverse bacterial communities’ structure of HNJZ samples might be under influence of salt stress to cope with it. Microbial communities in the rhizosphere play important roles in the adaptation of plants to salt stress [86,87]. These microbial communities usually consist of plant growth-promoting rhizosphere (PGPR) bacteria [25]. Further, the spearman correlation analysis revealed a high positive correlation between Pseudomonas and bulk density, whereas a high negative correlation between Bacteroids and pH. In previous studies, contrary to our results a positive correlation was observed between Bacteroids and pH [88], and a negative correlation was observed between Pseudomonas fluorescens distribution and bulk density of soil [89,90]. These contrasting results might be due to differences in study sites, as previous studies suggest variable correlations can be observed due to different experimental sites [91,92].

4.4. Differential Metabolite Analysis

Rhizosphere metabolomics is a new area that incorporates impartial investigation of the full metabolite complement (metabolome) to better comprehend complicated physiological, pathological, symbiotic, and other interactions among the rhizosphere’s inhabitants. The rhizosphere is a complex and dynamic microenvironment, and metabolomic investigations of the rhizosphere are difficult [23,35]. The type of root exudates, secretions from rhizobacteria, fungus, and other soil organisms are the primary determinants of rhizosphere metabolite composition. The type of these root exudates, on the other hand, has an impact on microbial proliferation in the rhizosphere, either directly or indirectly. Some substances promote growth, while others have antimicrobial properties [27].
In the present study, PCA analysis revealed the HNJZ and BZ groups were separated but metabolites in the BZ group had a more similar composition than the HNJZ group. This might be explained as HNJZ group had more diverse bacterial communities. Out of 78 significantly enriched metabolites, 10 were involved in up-regulation, while 68 were involved in down-regulation. The KEGG analysis results revealed metabolism as the most dominant category for HNJZ and BZ groups. Most enriched pathways observed were Indole alkaloid biosynthesis, biosynthesis of alkaloids derived from shikimate pathway, Histamine H2/H3 receptor agonists/antagonists, arginine and proline metabolism, metabolic pathways, and biosynthesis of secondary metabolites. Plant-based indole alkaloids are pharmaceutically important compounds and possess strong biological activities against viruses, bacteria, fungus, and various environmental stresses [93,94,95]. The plant produces a very minute quantity of indole alkaloids. Arginine and proline metabolism pathways play a key role in stress management in plants [96,97]. Another differential metabolite enriched category was secondary metabolites, which were found involved majorly in changes in the color of flowers and seeds and resistance to external stresses [98,99,100]. These pathways are involved in potentially crucial physiological functions and their enrichment in rhizospheres indicates their potential involvement in salt stress management and improving soil physicochemical properties of saline-alkali soils.

5. Conclusions

Our study revealed that both varieties of millet (HNJZ and BZ) significantly improved the soil physicochemical properties, especially at 0–20 cm depth. Metagenomic profiling indicated that E. frumentacea (L.) had a more diverse bacterial community structure than the E. crusgalli (L.) as revealed by the beta diversity analysis. Moreover, the relative abundance of Firmicutes was higher in E. frumentacea (L.). Furthermore, Bacilli, Rhodobacterales, Actinobacteria, and Oceanosprillales were identified as biomarker taxa for the E. frumentacea (L.), whereas, Xanthomonadaceae, Gemmatimonadetes, Nitrosomonadaceae, and Acidobacteria were biomarker taxa for the E. crusgalli (L.). Metabolomic analysis revealed important pathways like indole alkaloid biosynthesis, arginine and proline metabolism and secondary metabolite pathways which could have played role in salt stress management. The findings of the present study indicated the potential of both varieties for reclamation of alkaline soils as both tolerated the salts and pH quite well while increasing the organic matter content of the soil.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12061322/s1, Figure S1: Venn diagram showing common and unique OTUs in both rhizosphere groups (A) E. crusgalli var. austro-japonensis: BZ1, B2, and BZ3 (B) E. frumentacea (Roxb.) Link: HNJZ1, HNJZ 2, and HNJZ3. Figure S2: (A) Rarefaction curve showing the observed number of OTUs (Operational Taxonomic Units) with respect to the number of sequencing bars drawn randomly for each sample of E. crusgalli var. austro-japonensis: BZ1, BZ2, and BZ3 and E. frumentacea (Roxb.) Link: HNJZ1, HNJZ2, and HNJZ3 (B) Rank Abundance curve showing the relative abundance and species rank for each sample of E. crusgalli var. austro-japonensis: BZ1, BZ2, and BZ3 and E. frumentacea (Roxb.) Link: HNJZ1, HNJZ2, and HNJZ3. Figure S3: Beeswarm plot of alpha diversity analysis (S1A) Wilcoxon test rank showing observed species differences between E. crusgalli var. austro-japonensis (BZ) and E. frumentacea (Roxb.) Link (HNJZ) and (S1B) Shannon index for E. crusgalli var. austro-japonensis (BZ) and E. frumentacea (Roxb.) Link (HNJZ). Figure S4: T-test showing bacterial groups with significant differences at various taxonomic levels (Phylum, Class, Order, Family, Genus, Species) between E. crusgalli var. austro-japonensis (BZ) and E. frumentacea (Roxb.) Link (HNJZ) at p < 0.05. Figure S5: Spearman correlation was performed between relative abundant bacterial genera and environmental variables. The longitudinal is the environmental factor information, the horizontal is the species information, the corresponding value of the intermediate heat map is the Spearman correlation coefficient r, between −1, 1, r < 0 is negatively correlated, r > 0 is a positive correlation, and the scale * indicates the significance test p < 0.05. Figure S6: Principal component analysis (PCA) of differential metabolites showing differences between groups and samples (E. frumentacea (Roxb.) Link: (HNJZ) and; E. crusgalli var. austro-japonensis (BZ). Table S1: Basic parameters linked to soil salinity before start of experiment. Table S2: Basic physical properties of experimental site before start of experiment. Table S3: Basic chemical properties of experimental site before start of experiment.

Author Contributions

Conceptualization, X.W. and F.Z.; methodology, X.W. and X.X.; software, X.L.; validation, A.L. and X.L.; formal analysis, X.W., X.X. and X.L.; investigation, A.L.; resources, F.Z.; writing—original draft preparation, X.X. and X.L.; writing—review and editing, F.Z. and X.X.; supervision, F.Z. and X.X.; project administration and funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Ningxia Natural Science Foundation (2020AAC03078), Key Research & Development Program of Ningxia Hui Autonomous Region (2019BBF02001), National Key Research & Development Program of China (2016YFC0501300) and Joint Open Research Fund Program of State key Laboratory of Hydroscience and Engineering and Tsinghua–Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance (sklhse-2022-Iow05).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Metternicht, G.I.; Zinck, J.A. Remote sensing of soil salinity: Potentials and constraints. Remote Sens. Environ. 2003, 85, 1–20. [Google Scholar] [CrossRef]
  2. Wang, S.J.; Chen, Q.; Li, Y.; Zhuo, Y.Q.; Xu, L.Z. Research on saline-alkali soil amelioration with FGD gypsum. Resour. Conserv. Recycl. 2017, 121, 82–92. [Google Scholar] [CrossRef]
  3. Reddy, M.P.; Shah, M.T.; Patolia, J.S. Salvadora persica, a potential species for industrial oil production in semiarid saline and alkali soils. Ind. Crops Prod. 2008, 28, 273–278. [Google Scholar] [CrossRef]
  4. Ondrasek, G.; Rengel, Z.; Veres, S. Soil salinisation and salt stress in crop production. In Abiotic Stress in Plants. Mechanisms and Adaptation; Shanker, A., Venkateswarlu, B., Eds.; IntechOpen: London, UK, 2011. [Google Scholar] [CrossRef] [Green Version]
  5. Cao, B.; Song, L.H. Discussion on Afforestaion Techniques in Salt-alkaline Soil in Northern Ningxia. Adv. Mat. Res. 2012, 518, 5276–5280. [Google Scholar] [CrossRef]
  6. Panta, S.; Flowers, T.; Lane, P.; Doyle, R.; Haros, G.; Shabala, S. Halophyte Agriculture: Success Stories. Environ. Exp. Bot. 2014, 107, 71–83. [Google Scholar] [CrossRef]
  7. Yue, Y.; Shao, T.; Long, X.; He, T.; Gao, X.; Zhou, Z.; Liu, Z.; Rengel, Z. Microbiome Structure and Function in Rhizosphere of Jerusalem Artichoke Grown in Saline Land. Sci. Total Environ. 2020, 724, 138259. [Google Scholar] [CrossRef] [PubMed]
  8. Upadhyaya, H.D.; Dwivedi, S.L.; Singh, S.K.; Singh, S.; Vetriventhan, M.; Sharma, S. Forming core collections in barnyard, kodo, and little millets using morphoagronomic descriptors. Crop. Sci. 2014, 54, 2673–2682. [Google Scholar] [CrossRef]
  9. Dwivedi, S.L.; Upadhyaya, H.D.; Senthilvel, S.; Hash, C.T.; Fukunaga, K.; Diao, X.; Santra, D.; Baltensperge, D.; Prasad, M. Millets: Genetic and genomic resources. Plant. Breed. Rev. 2012, 35, 247–375. Available online: http://oar.icrisat.org/id/eprint/5375 (accessed on 22 March 2022).
  10. Saleh, A.S.M.; Zhang, Q.; Chen, J.; Shen, Q. Millet grains: Nutritional quality, processing, and potential health benefits. Compr. Rev. Food Sci. Food Saf. 2013, 12, 281–295. [Google Scholar] [CrossRef]
  11. Subramanian, A.; Raj, R.N.; Jeyaprakash, P. In vitro and in vivo screening of barnyard millet (Echinochloa frumentacea (Robs.) link) germ plas for salinity tolerance. Plant Arch. 2020, 20, 7389–7397. [Google Scholar]
  12. Holm, L.G.; Plucknett, D.L.; Pancho, J.V.; Herberger, J.P. The World’s Worst Weeds. Distribution and Biology; University Press of Hawaii: Honolulu, HI, USA, 1977. [Google Scholar]
  13. Ottis, B.V.; Talbert, R.E. Barnyardgrass (Echinochloa crus-galli) control and rice density effects on rice yield components. Weed Technol. 2007, 21, 110–118. [Google Scholar] [CrossRef]
  14. Fao Nicodemo, M.L.F.; Souza, F.H.D.; Pezzopane, J.R.M.; Mendes, J.C.T.; Tholon, P.; Santos, P.M. Frequências de cortes em nove leguminosas forrageiras tropicais herbáceas cultivadas ao sol e sob plantação florestal. Arq. Bras. Med. Vet. Zootec. 2015, 67, 809–818. [Google Scholar] [CrossRef] [Green Version]
  15. Abogadallah, G.M.; Quick, W.P. Vegetative salt tolerance of barnyard grass mutants selected for salt tolerant germination. Acta Physiol. Plant. 2009, 31, 815–824. [Google Scholar] [CrossRef]
  16. Aslam, Z.; Salim, M.; Qureshi, R.H.; Sandhu, G.R. Salt Tolerance of Echinochloa crusgalli. Biol. Plant. 1987, 29, 66–69. [Google Scholar] [CrossRef]
  17. Philippot, L.; Raaijmakers, J.M.; Lemanceau, P.; Van Der Putten, W.H. Going Back to the Roots: The Microbial Ecology of the Rhizosphere. Nat. Rev. Microbiol. 2013, 11, 789–799. [Google Scholar] [CrossRef]
  18. Köberl, M.; Schmidt, R.; Ramadan, E.M.; Bauer, R.; Berg, G. The microbiome of medicinal plants: Diversity and importance for plant growth, quality and health. Front. Microbiol. 2013, 4, 400. [Google Scholar] [CrossRef] [Green Version]
  19. Berendsen, R.L.; Pieterse, C.M.J.; Bakker, P.A.H.M. The rhizosphere microbiome and plant health. Trends Plant. Sci. 2012, 17, 478–486. [Google Scholar] [CrossRef]
  20. Compant, S.; Clément, C.; Sessitsch, A. Plant growth-promoting bacteria in the rhizo-and endosphere of plants: Their role, colonization, mechanisms involved and prospects for utilization. Soil Biol. Biochem. 2010, 42, 669–678. [Google Scholar] [CrossRef] [Green Version]
  21. Smalla, K.; Wieland, G.; Buchner, A.; Zock, A.; Parzy, J.; Kaiser, S.; Roskot, N.; Heuer, H.; Berg, G. Bulk and rhizosphere soil bacterial communities studied by denaturing gradient gel electrophoresis: Plant-dependent enrichment and seasonal shifts revealed. Appl. Environ. Microbiol. 2001, 67, 4742–4751. [Google Scholar] [CrossRef] [Green Version]
  22. Zhou, X.; Wang, J.-T.; Wang, W.-H.; Tsui, C.K.M.; Cai, L. Changes in Bacterial and Fungal Microbiomes Associated with Tomatoes of Healthy and Infected by Fusarium oxysporum f. sp. lycopersici. Microb. Ecol. 2021, 81, 1004–1017. [Google Scholar] [CrossRef]
  23. Grim, C.M.; Luu, G.T.; Sanchez, L.M. Staring into the void: Demystifying microbial metabolomics. FEMS Microbiol. Lett. 2019, 366, fnz135. [Google Scholar] [CrossRef] [PubMed]
  24. Youssef, N.; Sheik, C.S.; Krumholz, L.R.; Najar, F.Z.; Roe, B.A.; Elshahed, M.S. Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys. Appl. Environ. Microbiol. 2009, 75, 5227–5236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Lozupone, C.A.; Turnbaugh, P.J.; Fierer, N.; Knight, R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 2011, 108, 4516–4522. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Shi, R.; Gu, H.; He, S.; Xiong, B.; Huang, Y.; Horowitz, A.R.; He, X. Comparative Metagenomic and Metabolomic Profiling of Rhizospheres of Panax notoginseng Grown under Forest and Field Conditions. Agronomy 2021, 11, 2488. [Google Scholar] [CrossRef]
  27. Reuben, S.; Bhinu, V.S.; Swarup, S. Rhizosphere metabolomics: Methods and applications. In Secondary Metabolites in Soil Ecology; Springer: Berlin/Heidelberg, Germany, 2008; pp. 37–68. [Google Scholar]
  28. Gong, Z.; Zhang, G.; Chen, Z. Development of Soil Classification in China. In Soil Classification; CRC Press: Boca Raton, FL, USA, 2002; pp. 101–126. ISBN 0429125380. [Google Scholar]
  29. Fu, Z.-Y.; Song, J.-C.; Jameson, P.E. A rapid and cost effective protocol for plant genomic DNA isolation using regenerated silica columns in combination with CTAB extraction. J. Integr. Agric. 2017, 16, 1682–1688. [Google Scholar] [CrossRef]
  30. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef] [Green Version]
  31. Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef] [Green Version]
  32. Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef] [Green Version]
  33. White, J.R.; Nagarajan, N.; Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 2009, 5, e1000352. [Google Scholar] [CrossRef]
  34. Marco-Ramell, A.; Palau-Rodriguez, M.; Alay, A.; Tulipani, S.; Urpi-Sarda, M.; Sanchez-Pla, A.; Andres-Lacueva, C. Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinform. 2018, 19, 1. [Google Scholar] [CrossRef]
  35. Swenson, T.L.; Jenkins, S.; Bowen, B.P.; Northen, T.R. Untargeted soil metabolomics methods for analysis of extractable organic matter. Soil Biol. Biochem. 2015, 80, 189–198. [Google Scholar] [CrossRef] [Green Version]
  36. Qadir, M.; Quillérou, E.; Nangia, V.; Murtaza, G.; Singh, M.; Thomas, R.J.; Drechsel, P.; Noble, A.D. Economics of salt-induced land degradation and restoration. Nat. Resour. Forum. 2014, 38, 282–295. [Google Scholar] [CrossRef]
  37. Zhao, J.; Xie, H.-J.; Zhang, J. Microbial diversity and physicochemical properties of rhizosphere microenvironment in saline-alkali soils of the Yellow River Delta. Huan Jing Ke Xue 2020, 41, 1449–1455. [Google Scholar] [CrossRef]
  38. An, X.; Wang, Z.; Teng, X.; Zhou, R.; Wang, X.; Xu, M.; Lian, B. Rhizosphere bacterial diversity and environmental function prediction of wild salt-tolerant plants in coastal silt soil. Ecol. Indic. 2022, 134, 108503. [Google Scholar] [CrossRef]
  39. Zia, M.S. Soil Fertility Evaluation and Management for Flooded Lowland-Rice Soils of Pakistan. Ph.D. Thesis, Kyoto University, Kyoto, Japan, 1993. [Google Scholar]
  40. Zheng, W.; Zeng, S.; Bais, H.; LaManna, J.M.; Hussey, D.S.; Jacobson, D.L.; Jin, Y. Plant growth-promoting rhizobacteria (PGPR) reduce evaporation and increase soil water retention. Water Resour. Res. 2018, 54, 3673–3687. [Google Scholar] [CrossRef]
  41. Ren, H.; Huang, B.; Fernández-García, V.; Miesel, J.; Yan, L.; Lv, C. Biochar and rhizobacteria amendments improve several soil properties and bacterial diversity. Microorganisms 2020, 8, 502. [Google Scholar] [CrossRef] [Green Version]
  42. Priyadharsini, P.; Rojamala, K.; Ravi, R.K.; Muthuraja, R.; Nagaraj, K.; Muthukumar, T. Mycorrhizosphere: The extended rhizosphere and its significance. In Plant-Microbe Interaction: An Approach to Sustainable Agriculture; Springer: Berlin/Heidelberg, Germany, 2016; pp. 97–124. [Google Scholar]
  43. Haichar, F.E.Z.; Marol, C.; Berge, O.; Rangel-Castro, J.I.; Prosser, J.I.; Balesdent, J.M.; Heulin, T.; Achouak, W. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2008, 2, 1221–1230. [Google Scholar] [CrossRef]
  44. Narula, N.; Kothe, E.; Behl, R.K. Role of root exudates in plant-microbe interactions. J. Appl. Bot. Food Qual. 2012, 82, 122–130. [Google Scholar]
  45. Saleem, M.; Law, A.D.; Sahib, M.R.; Pervaiz, Z.H.; Zhang, Q. Impact of root system architecture on rhizosphere and root microbiome. Rhizosphere 2018, 6, 47–51. [Google Scholar] [CrossRef]
  46. Pieterse, C.M.J.; Zamioudis, C.; Does, D.V.; Van Wees, S.C.M. Signalling Networks Involved in Induced Resistance. Induced Resistance for Plant Defence: A Sustainable Approach to Crop Protection, 2nd ed.; John Wiley and Sons: New York, NY, USA, 2014; pp. 58–80. [Google Scholar]
  47. Zhao, X.; Lin, C.; Yuan, W.; Ruan, S.; Qi, G.; Wang, R.; Tan, J. Intergrated metagenomics and metabolomics analysis discovers nematicidal microbes, enzymes and metabolites from the plant rhizosphere microbiota. Res. Sq. 2021. preprint. [Google Scholar] [CrossRef]
  48. Li, B.; Zhang, X.; Guo, F.; Wu, W.; Zhang, T. Characterization of tetracycline resistant bacterial community in saline activated sludge using batch stress incubation with high-throughput sequencing analysis. Water Res. 2013, 47, 4207–4216. [Google Scholar] [CrossRef]
  49. Ma, B.; Gong, J. A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils. World J. Microbiol. Biotechnol. 2013, 29, 2325–2334. [Google Scholar] [CrossRef]
  50. Wang, S.; Sun, L.; Ling, N.; Zhu, C.; Chi, F.; Li, W.; Hao, X.; Zhang, W.; Bian, J.; Chen, L. Exploring soil factors determining composition and structure of the bacterial communities in saline-alkali soils of Songnen Plain. Front. Microbiol. 2020, 10, 2902. [Google Scholar] [CrossRef] [Green Version]
  51. Nan, L.; Guo, Q.; Cao, S.; Zhan, Z. Diversity of bacterium communities in saline-alkali soil in arid regions of Northwest China. BMC Microbiol. 2022, 22, 11. [Google Scholar] [CrossRef]
  52. She, S.; Niu, J.; Zhang, C.; Xiao, Y.; Chen, W.; Dai, L.; Liu, X.; Yin, H. Significant relationship between soil bacterial community structure and incidence of bacterial wilt disease under continuous cropping system. Arch. Microbiol. 2017, 199, 267–275. [Google Scholar] [CrossRef]
  53. Hedrich, S.; Schlömann, M.; Johnson, D.B. The iron-oxidizing proteobacteria. Microbiology 2011, 157, 1551–1564. [Google Scholar] [CrossRef] [Green Version]
  54. Rosenzweig, N.; Tiedje, J.M.; Quensen Iii, J.F.; Meng, Q.; Hao, J.J. Microbial communities associated with potato common scab-suppressive soil determined by pyrosequencing analyses. Plant Dis. 2012, 96, 718–725. [Google Scholar] [CrossRef] [Green Version]
  55. Smit, E.; Leeflang, P.; Gommans, S.; van den Broek, J.; van Mil, S.; Wernars, K. Diversity and seasonal fluctuations of the dominant members of the bacterial soil community in a wheat field as determined by cultivation and molecular methods. Appl. Environ. Microbiol. 2001, 67, 2284–2291. [Google Scholar] [CrossRef] [Green Version]
  56. Liu, D.; Yang, Y.; An, S.; Wang, H.; Wang, Y. The biogeographical distribution of soil bacterial communities in the Loess Plateau as revealed by high-throughput sequencing. Front. Microbiol. 2018, 9, 2456. [Google Scholar] [CrossRef] [Green Version]
  57. Yang, J.; Zhan, C.; Li, Y.; Zhou, D.; Yu, Y.; Yu, J. Effect of salinity on soil respiration in relation to dissolved organic carbon and microbial characteristics of a wetland in the Liaohe River estuary, Northeast China. Sci. Total Environ. 2018, 642, 946–953. [Google Scholar] [CrossRef]
  58. Upchurch, R.; Chiu, C.-Y.; Everett, K.; Dyszynski, G.; Coleman, D.C.; Whitman, W.B. Differences in the composition and diversity of bacterial communities from agricultural and forest soils. Soil Biol. Biochem. 2008, 40, 1294–1305. [Google Scholar] [CrossRef]
  59. Fradin, E.F.; Thomma, B.P.H.J. Physiology and molecular aspects of Verticillium wilt diseases caused by V. dahliae and V. albo-atrum. Mol. Plant. Pathol. 2006, 7, 71–86. [Google Scholar] [CrossRef] [PubMed]
  60. Jaiswal, L.K.; Singh, P.; Singh, R.K.; Nayak, T.; Tripathi, Y.N.; Upadhyay, R.S.; Gupta, A. Effects of Salt Stress on Nutrient Cycle and Uptake of Crop Plants. In Physiology of Salt Stress in Plants: Perception, Signalling, Omics and Tolerance Mechanism; John Wiley & Sons Ltd. (Wiley library): Hoboken, NJ, USA, 2021; pp. 129–153. [Google Scholar] [CrossRef]
  61. Simmons, T.; Styer, A.B.; Pierroz, G.; Gonçalves, A.P.; Pasricha, R.; Hazra, A.B.; Bubner, P.; Coleman-Derr, D. Drought drives spatial variation in the millet root microbiome. Front. Plant. Sci. 2020, 11, 599. [Google Scholar] [CrossRef] [PubMed]
  62. Jeanbille, M.; Buée, M.; Bach, C.; Cébron, A.; Frey-Klett, P.; Turpault, M.P.; Uroz, S. Soil parameters drive the structure, diversity and metabolic potentials of the bacterial communities across temperate beech forest soil sequences. Microb. Ecol. 2016, 71, 482–493. [Google Scholar] [CrossRef]
  63. Van Horn, D.J.; Okie, J.G.; Buelow, H.N.; Gooseff, M.N.; Barrett, J.E.; Takacs-Vesbach, C.D. Soil microbial responses to increased moisture and organic resources along a salinity gradient in a polar desert. Appl. Enviro. Microbiol. 2014, 80, 3034–3043. [Google Scholar] [CrossRef] [Green Version]
  64. Geyer, K.M.; Altrichter, A.E.; Takacs-Vesbach, C.D.; Van Horn, D.J.; Gooseff, M.N.; Barrett, J.E. Bacterial community composition of divergent soil habitats in a polar desert. FEMS Microbiol. Ecol. 2014, 89, 490–494. [Google Scholar] [CrossRef]
  65. Suarez, C.; Ratering, S.; Kramer, I.; Schnell, S. Cellvibrio Diazotrophicus sp. nov., a nitrogen-fixing bacteria isolated from the rhizosphere of salt meadow plants and emended description of the genus cellvibrio. Int. J. Syst. Evol. Microbiol. 2014, 64, 481–486. [Google Scholar] [CrossRef]
  66. Rivas, R.; Velázquez, E.; Willems, A.; Vizcaíno, N.; Subba-Rao, N.S.; Mateos, P.F.; Gillis, M.; Dazzo, F.B.; Martínez-Molina, E. A new species of Devosia that forms a unique nitrogen-fixing root-nodule symbiosis with the aquatic legume Neptunia natans (Lf) Druce. Appl. Environ. Microbiol. 2002, 68, 5217–5222. [Google Scholar] [CrossRef] [Green Version]
  67. Mukherjee, R.; Sen, S. Agricultural Sustainability through Nitrogen Fixation: Approaches and Techniques. Harvest 2021, 6, 48–55. [Google Scholar] [CrossRef]
  68. Radziemska, M.; Gusiatin, Z.M.; Cydzik-Kwiatkowska, A.; Cerdà, A.; Pecina, V.; Bęś, A.; Datta, R.; Majewski, G.; Mazur, Z.; Dzięcioł, J. Insight into metal immobilization and microbial community structure in soil from a steel disposal dump phytostabilized with composted, pyrolyzed or gasified wastes. Chemosphere 2021, 272, 129576. [Google Scholar] [CrossRef]
  69. Castro-Silva, C.; Ruíz-Valdiviezo, V.M.; Valenzuela-Encinas, C.; Alcántara-Hernández, R.J.; Navarro-Noya, Y.E.; Vázquez-Núñez, E.; Luna-Guido, M.; Marsch, R.; Dendooven, L. The bacterial community structure in an alkaline saline soil spiked with anthracene. Electron. J. Biotechnol. 2013, 16, 10. [Google Scholar] [CrossRef]
  70. Khan, A.L.; Waqas, M.; Kang, S.-M.; Al-Harrasi, A.; Hussain, J.; Al-Rawahi, A.; Al-Khiziri, S.; Ullah, I.; Ali, L.; Jung, H.-Y. Bacterial endophyte Sphingomonas sp. LK11 produces gibberellins and IAA and promotes tomato plant growth. J. Microbiol. 2014, 52, 689–695. [Google Scholar] [CrossRef]
  71. Pan, F.; Meng, Q.; Wang, Q.; Luo, S.; Chen, B.; Khan, K.Y.; Yang, X.; Feng, Y. Endophytic bacterium Sphingomonas SaMR12 promotes cadmium accumulation by increasing glutathione biosynthesis in Sedum alfredii Hance. Chemosphere 2016, 154, 358–366. [Google Scholar] [CrossRef]
  72. Kunihiro, M.; Ozeki, Y.; Nogi, Y.; Hamamura, N.; Kanaly, R.A. Benz [a] anthracene biotransformation and production of ring fission products by Sphingobium sp. strain KK22. Appl. Environ. Microbiol. 2013, 79, 4410–4420. [Google Scholar] [CrossRef] [Green Version]
  73. Niharika, N.; Sangwan, N.; Ahmad, S.; Singh, P.; Khurana, J.P.; Lal, R. Draft genome sequence of Sphingobium chinhatense strain IP26T, isolated from a hexachlorocyclohexane dumpsite. Genome Announc. 2013, 1, e00680-13. [Google Scholar] [CrossRef] [Green Version]
  74. Andrić, S.; Meyer, T.; Ongena, M. Bacillus responses to plant-associated fungal and bacterial communities. Front. Microbiol. 2020, 11, 1350. [Google Scholar] [CrossRef]
  75. Büttner, D.; Bonas, U. Regulation and secretion of Xanthomonas virulence factors. FEMS Microbiol. Rev. 2010, 34, 107–133. [Google Scholar] [CrossRef] [Green Version]
  76. DeBruyn, J.M.; Nixon, L.T.; Fawaz, M.N.; Johnson, A.M.; Radosevich, M. Global biogeography and quantitative seasonal dynamics of Gemmatimonadetes in soil. Appl. Environ. Microbiol. 2011, 77, 6295–6300. [Google Scholar] [CrossRef] [Green Version]
  77. Daims, H.; Nielsen, J.L.; Nielsen, P.H.; Schleifer, K.-H.; Wagner, M. In situ characterization of Nitrospira-like nitrite-oxidizing bacteria active in wastewater treatment plants. Appl. Environ. Microbiol. 2001, 67, 5273–5284. [Google Scholar] [CrossRef] [Green Version]
  78. Barns, S.M.; Takala, S.L.; Kuske, C.R. Wide distribution and diversity of members of the bacterial kingdom Acidobacterium in the environment. Appl. Environ. Microbiol. 1999, 65, 1731–1737. [Google Scholar] [CrossRef] [Green Version]
  79. De Chaves, M.G.; Silva, G.G.Z.; Rossetto, R.; Edwards, R.A.; Tsai, S.M.; Navarrete, A.A. Acidobacteria subgroups and their metabolic potential for carbon degradation in sugarcane soil amended with vinasse and nitrogen fertilizers. Front. Microbiol. 2019, 10, 1680. [Google Scholar] [CrossRef] [Green Version]
  80. Hwang, G.B.; Jung, J.H.; Jeong, T.G.; Lee, B.U. Effect of hybrid UV-thermal energy stimuli on inactivation of S. epidermidis and B. subtilis bacterial bioaerosols. Sci. Total Environ. 2010, 408, 5903–5909. [Google Scholar] [CrossRef]
  81. Zhong, X.; Qi, J.; Li, H.; Dong, L.; Gao, D. Seasonal distribution of microbial activity in bioaerosols in the outdoor environment of the Qingdao coastal region. Atmos. Environ. 2016, 140, 506–513. [Google Scholar] [CrossRef]
  82. Jiang, Y.; Lei, Y.; Yang, Y.; Korpelainen, H.; Niinemets, Ü.; Li, C. Divergent assemblage patterns and driving forces for bacterial and fungal communities along a glacier forefield chronosequence. Soil Biol. Biochem. 2018, 118, 207–216. [Google Scholar] [CrossRef] [Green Version]
  83. Vasar, M.; Davison, J.; Sepp, S.-K.; Öpik, M.; Moora, M.; Koorem, K.; Meng, Y.; Oja, J.; Akhmetzhanova, A.A.; Al-Quraishy, S. Arbuscular mycorrhizal fungal communities in the soils of desert habitats. Microorganisms 2021, 9, 229. [Google Scholar] [CrossRef]
  84. Hu, Y.; Zhang, Z.; Huang, L.; Qi, Q.; Liu, L.; Zhao, Y.; Wang, Z.; Zhou, H.; Lv, X.; Mao, Z. Shifts in soil microbial community functional gene structure across a 61-year desert revegetation chronosequence. Geoderma 2019, 347, 126–134. [Google Scholar] [CrossRef]
  85. Golldack, D.; Li, C.; Mohan, H.; Probst, N. Tolerance to drought and salt stress in plants: Unraveling the signaling networks. Front. Plant. Sci. 2014, 5, 151. [Google Scholar] [CrossRef] [Green Version]
  86. De Zelicourt, A.; Al-Yousif, M.; Hirt, H. Rhizosphere microbes as essential partners for plant stress tolerance. Mol. Plant 2013, 6, 242–245. [Google Scholar] [CrossRef] [Green Version]
  87. Qin, Y.; Druzhinina, I.S.; Pan, X.; Yuan, Z. Microbially mediated plant salt tolerance and microbiome-based solutions for saline agriculture. Biotechnol. Adv. 2016, 34, 1245–1259. [Google Scholar] [CrossRef]
  88. Lammel, D.R.; Barth, G.; Ovaskainen, O.; Cruz, L.M.; Zanatta, J.A.; Ryo, M.; de Souza, E.M.; Pedrosa, F.O. Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures. Microbiome 2018, 6, 106. [Google Scholar] [CrossRef]
  89. Rattray, E.-A.S.; Tyrrell, J.A.; Prosser, J.I.; Glover, L.A.; Killham, K.S. Effect of soil bulk-density and temperature on wheat rhizosphere colonization by lux-marked Pseudomonas-fluorescens. Eur. J. Soil Biol. 1993, 29, 73–82. [Google Scholar]
  90. Juyal, A.; Otten, W.; Baveye, P.C.; Eickhorst, T. Influence of soil structure on the spread of Pseudomonas fluorescens in soil at microscale. Eur. J. Soil Sci. 2021, 72, 141–153. [Google Scholar] [CrossRef]
  91. Rousk, J.; Bååth, E.; Brookes, P.C.; Lauber, C.L.; Lozupone, C.; Caporaso, J.G.; Knight, R.; Fierer, N. Soil Bacterial and Fungal Communities across a PH Gradient in an Arable Soil. ISME J. 2010, 4, 1340–1351. [Google Scholar] [CrossRef] [PubMed]
  92. Zhalnina, K.; Dias, R.; de Quadros, P.D.; Davis-Richardson, A.; Camargo, F.A.O.; Clark, I.M.; McGrath, S.P.; Hirsch, P.R.; Triplett, E.W. Soil PH Determines Microbial Diversity and Composition in the Park Grass Experiment. Microb. Ecol. 2015, 69, 395–406. [Google Scholar] [CrossRef]
  93. Omar, F.; Tareq, A.M.; Alqahtani, A.M.; Dhama, K.; Sayeed, M.A.; Emran, T.B.; Simal-Gandara, J. Plant-based indole alkaloids: A comprehensive overview from a pharmacological perspective. Molecules 2021, 26, 2297. [Google Scholar] [CrossRef]
  94. Cordell, G.A. Fifty Years of Alkaloid Biosynthesis in Phytochemistry. Phytochemistry 2013, 91, 29–51. [Google Scholar] [CrossRef]
  95. Zhu, W.; Yang, B.; Komatsu, S.; Lu, X.; Li, X.; Tian, J. Binary Stress Induces an Increase in Indole Alkaloid Biosynthesis in Catharanthus Roseus. Front. Plant Sci. 2015, 6, 582. [Google Scholar] [CrossRef] [Green Version]
  96. Li, M.; Li, Y.; Zhang, W.; Li, S.; Gao, Y.; Ai, X.; Zhang, D.; Liu, B.; Li, Q. Metabolomics analysis reveals that elevated atmospheric CO2 alleviates drought stress in cucumber seedling leaves. Anal. Biochem. 2018, 559, 71–85. [Google Scholar] [CrossRef]
  97. Sun, L.; Cao, X.; Tan, C.; Deng, Y.; Cai, R.; Peng, X.; Bai, J. Analysis of the effect of cadmium stress on root exudates of Sedum plumbizincicola based on metabolomics. Ecotoxicol. Environ. Saf. 2020, 205, 111152. [Google Scholar] [CrossRef]
  98. Tanaka, Y.; Sasaki, N.; Ohmiya, A. Biosynthesis of plant pigments: Anthocyanins, betalains and carotenoids. Plant. J. 2008, 54, 733–749. [Google Scholar] [CrossRef]
  99. Valentinuzzi, F.; Mason, M.; Scampicchio, M.; Andreotti, C.; Cesco, S.; Mimmo, T. Enhancement of the bioactive compound content in strawberry fruits grown under iron and phosphorus deficiency. J. Sci. Food Agric. 2015, 95, 2088–2094. [Google Scholar] [CrossRef]
  100. Zhang, T.-J.; Chow, W.S.; Liu, X.-T.; Zhang, P.; Liu, N.; Peng, C.-L. A magic red coat on the surface of young leaves: Anthocyanins distributed in trichome layer protect Castanopsis fissa leaves from photoinhibition. Tree Physiol. 2016, 36, 1296–1306. [Google Scholar] [CrossRef] [Green Version]
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Figure 1. Relative abundance of top ten bacteria phyla between both rhizosphere groups E. crusgalli var. austro-japonensis (L.) (BZ) and E. frumentacea (L.) (HNJZ).
Figure 1. Relative abundance of top ten bacteria phyla between both rhizosphere groups E. crusgalli var. austro-japonensis (L.) (BZ) and E. frumentacea (L.) (HNJZ).
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Figure 2. Metastat analysis of top 35 bacterial genera between E. crusgalli var. austro–japonensis (L.) (BZ) and E. frumentacea (L.) (HNJZ) and their significance (p < 0.01, <0.05, and >0.05). The color codes are based on Z-score.
Figure 2. Metastat analysis of top 35 bacterial genera between E. crusgalli var. austro–japonensis (L.) (BZ) and E. frumentacea (L.) (HNJZ) and their significance (p < 0.01, <0.05, and >0.05). The color codes are based on Z-score.
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Figure 3. (A) Linear discriminant analysis effect size (LEfSe) for bacterial taxa (biomarker) in rhizosphere of E. crusgalli var. austro-japonensis (L.) (BZ) and E. frumentacea (L.) (HNJZ), (B) Cladogram showing significantly enriched bacterial taxa (from phylum to family level) for both rhizosphere groups HNJZ and BZ.
Figure 3. (A) Linear discriminant analysis effect size (LEfSe) for bacterial taxa (biomarker) in rhizosphere of E. crusgalli var. austro-japonensis (L.) (BZ) and E. frumentacea (L.) (HNJZ), (B) Cladogram showing significantly enriched bacterial taxa (from phylum to family level) for both rhizosphere groups HNJZ and BZ.
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Figure 4. (A) Principal component analysis (PCA) showing the difference between groups and the distribution of samples in each group (E. crusgalli var. austro–japonensis (L.): BZ1, BZ2, and BZ3 and E. frumentacea (L.): HNJZ1, HNJZ2, and HNJZ3 and (B) NDMS analysis of both groups and their samples (E. crusgalli var. austro–japonensis (L.): BZ1, BZ2, and BZ3 and E. frumentacea (L.): HNJZ1, HNJZ2, and HNJZ3), each point represents the different sample and the distance between points indicates the degree of difference. Both group samples are colored differently.
Figure 4. (A) Principal component analysis (PCA) showing the difference between groups and the distribution of samples in each group (E. crusgalli var. austro–japonensis (L.): BZ1, BZ2, and BZ3 and E. frumentacea (L.): HNJZ1, HNJZ2, and HNJZ3 and (B) NDMS analysis of both groups and their samples (E. crusgalli var. austro–japonensis (L.): BZ1, BZ2, and BZ3 and E. frumentacea (L.): HNJZ1, HNJZ2, and HNJZ3), each point represents the different sample and the distance between points indicates the degree of difference. Both group samples are colored differently.
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Figure 5. Canonical correspondence analysis (CCA) shows the relationships between environmental factors and rhizosphere groups bacterial communities in each sample (E. crusgalli var. austro–jponesis (L.): BZ1, BZ2, and BZ3 and E. frumentacea (L.): HNJZ1, HNJZ2, and HNJZ3).
Figure 5. Canonical correspondence analysis (CCA) shows the relationships between environmental factors and rhizosphere groups bacterial communities in each sample (E. crusgalli var. austro–jponesis (L.): BZ1, BZ2, and BZ3 and E. frumentacea (L.): HNJZ1, HNJZ2, and HNJZ3).
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Figure 6. Volcano map, the abscissa indicates the change in the expression of multiple metabolites in different groups (HNJZ vs. BZ) (log2 Fold Change), the vertical coordinate indicates the different significance level (−log10p), each point in the volcano chart represents a metabolite, the significantly up-regulated metabolite is represented by a red dot, the significantly down-regulated metabolite is represented by a green dot.
Figure 6. Volcano map, the abscissa indicates the change in the expression of multiple metabolites in different groups (HNJZ vs. BZ) (log2 Fold Change), the vertical coordinate indicates the different significance level (−log10p), each point in the volcano chart represents a metabolite, the significantly up-regulated metabolite is represented by a red dot, the significantly down-regulated metabolite is represented by a green dot.
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Figure 7. KEGG pathway analysis showing the number of metabolites and enriched functional class of E. crusgalli var. austro-japonensis (L.) (BZ) vs. E. frumentacea (L.) (HNJZ).
Figure 7. KEGG pathway analysis showing the number of metabolites and enriched functional class of E. crusgalli var. austro-japonensis (L.) (BZ) vs. E. frumentacea (L.) (HNJZ).
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Figure 8. KEGG enrichment bubble diagram showing enriched pathways for E. frumentacea (L.) (HNJZ) vs. E. crusgalli var. austro–japonensis (L.) (BZ).
Figure 8. KEGG enrichment bubble diagram showing enriched pathways for E. frumentacea (L.) (HNJZ) vs. E. crusgalli var. austro–japonensis (L.) (BZ).
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Table
Table 1. Effect of E. crusgalli L. (BZ) and E. frumentacea L. (HNJZ) on soil physicochemical properties (mean values) at two soil depths (0–20 and 20–40 cm).
Table 1. Effect of E. crusgalli L. (BZ) and E. frumentacea L. (HNJZ) on soil physicochemical properties (mean values) at two soil depths (0–20 and 20–40 cm).
ParameterSampleDepth (cm)At Start of ExperimentAt End of ExperimentSEMp
pHHNJZ0–209.178.420.04080.001
20–409.209.180.08240.872
BZ0–209.178.700.07960.014
20–409.209.180.08210.872
Total salt (g/kg)HNJZ0–204.694.370.03570.003
20–404.103.960.10860.413
BZ0–204.694.370.07060.033
20–404.103.960.10870.414
AlkalinityHNJZ0–2019.6315.500.03990.001
20–4021.5019.990.12630.001
BZ0–2019.6316.330.04160.001
20–4021.5020.270.12280.002
Bulk density (g/cm3)HNJZ0–201.581.530.00410.001
20–401.631.610.00620.039
BZ0–201.581.550.00410.007
20–401.631.610.00620.039
Organic matter (g/kg)HNJZ0–205.067.930.01990.001
20–402.172.550.00780.001
BZ0–205.066.510.01900.001
20–402.172.240.00970.004
Total nitrogen (g/kg)HNJZ0–200.420.500.00330.001
20–400.210.240.00330.001
BZ0–200.420.470.00240.001
20–400.210.220.00330.005
Available phosphorus (mg/kg)HNJZ0–2010.610.950.00670.001
20–402.292.300.00780.294
BZ0–2010.610.810.00670.001
20–402.292.280.01050.115
Fast acting potassium (mg/kg)HNJZ0–20176.30142.920.12300.001
20–40101.5788.180.11120.001
BZ0–20176.30147.170.11800.001
20–40101.5790.600.17150.001
Table
Table 2. Alpha diversity parameters analysis for E. crusgalli var. austro-japonensis (BZ) and E. frumentacea (Roxb.) Link (HNJZ) group.
Table 2. Alpha diversity parameters analysis for E. crusgalli var. austro-japonensis (BZ) and E. frumentacea (Roxb.) Link (HNJZ) group.
ParameterHNJZBZSEMp Value
ACE2459.22432.348.9560.7170
Chao12428.92427.460.7510.9874
Observed Species2267.02310.744.5220.5262
Shannon8.99009.55530.15570.0622
Simpson0.99370.99700.0090.0668
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Wang, X.; Xu, X.; Lu, A.; Li, X.; Zhang, F. Cultivation of Two Barnyard Varieties Improves Physicochemical Properties of Saline-Alkali Land through Mediating Rhizospheric Microbiome and Metabolome. Agronomy 2022, 12, 1322. https://doi.org/10.3390/agronomy12061322

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Wang X, Xu X, Lu A, Li X, Zhang F. Cultivation of Two Barnyard Varieties Improves Physicochemical Properties of Saline-Alkali Land through Mediating Rhizospheric Microbiome and Metabolome. Agronomy. 2022; 12(6):1322. https://doi.org/10.3390/agronomy12061322

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Wang, Xueqin, Xing Xu, Anqiao Lu, Xin Li, and Fengju Zhang. 2022. "Cultivation of Two Barnyard Varieties Improves Physicochemical Properties of Saline-Alkali Land through Mediating Rhizospheric Microbiome and Metabolome" Agronomy 12, no. 6: 1322. https://doi.org/10.3390/agronomy12061322

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