Soil bacterial diversity and community structure of cotton rhizosphere under mulched drip- irrigation in arid and semi-arid regions of Northwest China

Soil bacterial diversity and community structure of cotton rhizosphere under mulched drip- irrigation in arid and semi-arid regions of Northwest China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Soil bacterial diversity and community structure of cotton rhizosphere under mulched drip- irrigation in arid and semi-arid regions of Northwest China Man Zhang, Yang Hu, Yue Ma, Tianyu Hou, Juanhong Wang, Qingxuan Che, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5689151/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Xinjiang is situated in an arid and semi-arid region, where abundant heat and sunlight create highly favorable conditions for cotton cultivation. Xinjiang's cotton output accounts for nearly one-quarter of global production. Moreover, the implementation of advanced planting techniques, such as 'dwarfing, high-density, early-maturing' strategies combined with mulched drip irrigation, ensures stable and high yields in this region. Despite these advancements, limited research has focused on the microbial mechanisms in cotton fields employing these advanced planting methods. Results The bacterial and phoD communities in the cotton rhizosphere were predominantly composed of nine bacterial phyla (i.e., Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Chloroflexi, Bacteroidetes, Rokubacteria, Firmicutes, and Nitrospirae) and five phoD phyla (i.e., Proteobacteria, Actinobacteria, Planctomycetes, Acidobacteria, and Firmicutes), respectively. Alpha diversity analysis indicated that the medium yield cotton field (MYF) exhibited higher bacterial richness and diversity indices compared to low yield (LYF) and high yield (HYF) fields. The symbiotic network analysis of LYF revealed greater values of average degree, number of edges, and modularity, suggesting a more complex network structure in both bacterial and phoD communities. The Mantel test, RDA, and PLS-PM model identified soil pH, electrical conductivity (EC), organic phosphorus (OP), available phosphorus (AP), total nitrogen (TN), microbial biomass carbon (MBC), and clay content as the main driving factors influencing changes in the rhizosphere bacterial community diversity and network structure. Conclusion These findings provide a theoretical basis for future research aimed at improving soil quality and cotton yield. Soil microbial community Co-occurrence network Rhizosphere Cotton Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Cotton (Gossypium hirsutum L. ) is a vital global commodity, playing a critical role in the textile industry and significantly contributing to the economies of numerous [ 1 ]. It is one of the most extensively cultivated crops, with applications spanning textiles, oil production, animal feed and the medical industry [ 2 ]. As the world’s leading producer of cotton, China produced approximately 5.61 million tonnes in 2023, accounting for more than 25% of global production [ 3 ]. Cotton plays an essential role in regional economic growth and ecological balance [ 4 ]. Nonetheless, the sustainability of cotton production is influenced by several key factors, including climate, environmental conditions, management practices, pest control, and soil properties, with soil fertility being a primary determinant of yield potential [ 5 ]. Therefore, understanding the variations in soil fertility and microbiome characteristics in cotton fields is crucial for addressing yield constraints and maintaining sustainable. Soil microorganisms play a significant role in soil ecosystem biochemical processes, including soil structure formation, nutrient cycling, organic matter decomposition, and other soil biochemical activities. Consequently, soil microorganisms contribute to the regulation of soil fertility and health, crop productivity, and sustainability of cultivated plants [ 6 – 9 ]. The present study focuses on fundamental research into the core plant microbiome, soil microbial activities, abundance, community structure and functions, as well as microbial co-occurrence networks [ 10 – 14 ]. Core plant microbiomes refer to microbial taxa consistently present in a particular environment, playing essential roles in nutrient availability and plant growth. For instance, Rhizobium and Azospirillum enhance nitrogen availability, promoting root and shoot development in crops [ 14 ]. Pseudomonas and Bacillus enhance phosphorus availability to plants, thereby boosting cotton fiber production [ 15 ]. Actinobacteria facilitate to the breakdown of organic matter, promoting soil health and enhancing plant growth [ 16 ]. The stability and function of soil ecosystems largely depend on the activities, abundance and community structure of soil microorganism [ 17 ]. Previous studies have demonstrated that regulating microbial activities, diversities, and community compositions influences carbon cycling, nitrogen transformation processes and phosphorus availability [ 18 – 21 ]. The phoD gene is responsible for alkaline phosphatase expression in soil, and the composition and diversity phoD -harboring bacterial community composition with the potential effects on organic P transformation [ 22 – 24 ]. Microbial communities in soil interact in complex microbial networks, where species form relationships that can be cooperative (mutualism), competitive, or neutral [ 25 ]. Networks may reveal the complexity and stability of soil microbial community structure [ 26 ]. The microbial networks with higher modularity or complexity were beneficial for enhancing soil functions, such as nutrient cycling, organic matter decomposition, pathogen control and plant productivity [ 27 – 30 ]. Therefore, understanding the core plant microbiome, soil microbial community structure and functions, and microbial co-occurrence networks can provide important information on the cultivation of soil ecological niche and functions. In recent years, increasing attention has been directed the intricate relationship between soil microorganisms and environmental factors [ 31 ]. Previous studies have demonstrated that soil microorganisms modified their environment in various ways including altering soil properties and establishing mineral structures [ 32 , 33 ]. Environmental factors are also known to significantly influence soil microbial communities, their composition, and microbial co-occurrence networks [ 31 , 34 , 35 ]. Several studies have identified soil properties such as pH, temperature, moisture, soil organic carbon, saline-alkali stress, and soil texture as key determinants of microbial community structure and function [ 36 , 37 ]. Therefore, understanding the feedback mechanisms between microorganisms and the soil environment is of great agronomic importance for maintaining soil health and enhancing agricultural productivity. Xinjiang is located in a typical temperate inland region, where abundant heat and sunlight create particularly favorable conditions for cotton cultivation. Xinjiang serves as the largest commercial cotton production base in China, accounting for over 90% of the nation's total cotton output [ 1 ]. However, significant differences in cotton yield per unit area arise due to variations in planting patterns, regional climate, soil conditions, and management practices. Consequently, most studies have focused on improving cotton production through strategies such as optimizing plant density [ 38 , 39 ], fertilization practices [ 40 , 41 ], irrigation techniques [ 42 , 43 ], and enhancing soil fertility and quality [ 44 , 45 ], Nevertheless, research on the changes in soil microbial community composition, network structure, and environmental variables across different cotton yield levels remains limited. In this study, soil samples were collected from 26 cotton fields at Xinhu Farm in Xinjiang Province and analyzed using high-throughput Illumina MiSeq sequencing to investigate the diversity, structure, composition, and interaction networks of soil bacterial and phoD communities. The objectives of this study were to (i) determine the abundance, diversity, and structural composition of bacterial and phoD communities in response to varying cotton yield levels, (ii) identify the core taxa within cotton rhizosphere microbiome, and (iii) elucidate the most important factors influencing the bacterial and phoD community and network structure in the cotton of rhizosphere. This study aims to provide theoretical support and a reference basis for an in-depth understanding of the microbial mechanism underlying high crop yields. Material and methods Study description The study area was located at Xinhu Farm in Manas County (44°20′-44°55′N, 86°15′-86°35′E), Xinjiang Province, Northwest China, which is characterized by a typical semi-arid to arid climate. The study sites experience a mean annual rainfall of 194.3 mm, a mean annual surface evaporation of 1988.5 mm, and a mean annual temperature of 7.1 ℃. The altitude ranges from 400 to 500 m above sea level, with a frost-free period of 160 to 190 days, an annual sunshine duration of 3110 h, and total annual solar radiation is 5560 MJ m -2 . Xinhu farm has 22,666 hectares of arable land, with cotton as its primary crop, covering 18,666 hectares. Over the past five years, the average seed cotton yield has been 6215 kg ha -1 . The cotton (Gossypium hirsutum L.) was cultivated using a mulched drip irrigation system. The mulched drip irrigation pattern consists of "one mulch, three rows of drip tape, and six rows of cotton". Specifically, six rows of cotton, with spacing of 0.13 m + 0.63 m+ 0.13 m + 0.63 m+ 0.13 m, were seeded under one 2.05-m-wide strip of plastic film, with three rows of drip irrigation belts placed in the narrow rows (0.13m) (Wang et al., 2022). The nitrogen, phosphorus and potassium fertilizer application were 300-350 kg N ha -1 , 100-150 kg P 2 O 5 ha -1 and 75-90 kg K 2 O ha -1 , respectively. Basal nitrogen fertilizer was applied at rate of 60 kg ha -1 . And the remaining dosage was fertigated through the mulched drip irrigation system at a ratio of 3:4:3 during squaring, early flowering boll, and late flowering boll stages. Phosphorus and potassium fertilizers were applied prior to sowing using a broadcast method. Drip irrigation was performed 8-10 times throughout the growth period. Site selection and soil sampling Soil samples were collected from 26 study sites at Xinhu Farm, which are shown in Fig. 1. At each study site (covering approximately 10-20 hectares), three sampling lines were selected with an angle of approximately 120 degrees between adjacent lines. Along each sampling line, ten rhizosphere soil cores were collected at 5-meter intervals to a depth of 0-20 cm. The loosely adhered soil was gently shaken off the roots, and the more tightly bound rhizosphere soil was collected using a brush. The collected soil cores from each sampling line were combined to obtain approximately 200 grams of rhizosphere soil per site, resulting in a total of 78 samples collected in August 2023 (boll stage of cotton). Samples were thoroughly mixed, placed in zip-lock bags, transported to the laboratory, and stored at -80 ℃. The rhizosphere soil samples were divided into two portions using the quartering method: one portion was air-dried, sieved through a 2 mm mesh, and subjected to chemical and physical analyses, while the other portion was stored at -80 ℃ for subsequent high-throughput sequencing analysis. Measurements of soil properties and cotton yield Soil pH and electronic conductivity (EC) were measured in a 1:5 soil-to-water solution using a pH meter and a conductivity meter, respectively. Soil total carbon (TC) content was determined using a Vario Max element analyzer (Elementar, Hanau, Germany). Soil total nitrogen (TN) was determined via the Kjeldahl distillation-titration method. For total potassium (TK) and total phosphorus (TP) determination, HF-HClO 4 was used for digestion, and TP was measured using the molybdenum-blue method, while TK was detected using a flame photometer. Available phosphorus (AP) was determined using the molybdo-vanadophosphate method in air-dried soil extracted with 0.5 M NaHCO 3 (pH 8.5) at 25 ℃. Organic phosphorus (OP) was estimated using a high-temperature combustion method. Air-dried soil samples were combusted at high temperatures and subsequently digested with half-strength sulfuric acid (1/2 H 2 SO 4 ). The released phosphate was detected using the molybdenum blue colorimetric method, and the organic phosphorus content was calculated by subtracting the amount of inorganic phosphorus extracted by 1/2 H 2 SO 4 from the same quantity of uncombusted soil. OP was also determined using the molybdo-vanadophosphate method, with soil extracted using 0.25 M NaOH-0.05 M EDTA in a 1:10 (w/v) ratio for 16 hours on a horizontal shaker [46]. Soil texture was measured using the hydrometer method, categorizing the soil into clay (<0.002 mm), silt (0.002-0.05 mm), and sand (0.05-2 mm). Microbial biomass carbon (MBC) and microbial biomass phosphorus (MBP) were analyzed on moist soil using the chloroform fumigation-extraction method [47,48]. Alkaline phosphatase (ALP) activity was determined using the method described by Tabatabai and Bremner [49]. For each study site, seed cotton was harvested from a 6.67 m² area in the center of the plastic mulch on October 3, 2023 (cotton harvest stage). The seed cotton weight was recorded to determine yield. Based on seed cotton yield, the study samples were categorized into three experimental groups: low yield (6500 kg ha -1 ). DNA extraction, PCR ampliffcation, Illumina MiSeq sequencing and sequencing data processing Soil genomic DNA from 26 fresh soil samples were extracted using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s instructions [50]. The extracted DNA quality was checked by 1% agarose gel electrophoresis, and then stored at -20 °C prior to further analysis. Next generation sequencing was performed in Personalbio, Inc. (Shanghai, China). The primer pair 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') was used to amplify the V3–V4 region of bacterial 16S rRNA genes. The bacterial phoD (alkaline phosphatase) genes were amplified using the primer pairsALPSF730/ALPSR1101(CAGTGGGACGACCACGAGGT/GAGGCCGATCGGCATGTCG) [51]. Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. The PCR components contained 5 μl of buffer (5×), 0.25 μl of Fast pfu DNA Polymerase (5U/μl), 2 μl (2.5 mM) of dNTPs, 1 μl (10 uM) of each Forward and Reverse primer, 1 μl of DNA Template, and 14.75 μl of ddH 2 O. The PCR amplification conditions for 16S rRNA genes were as follows: pre-denaturation at 98 °C for 5 min, followed by 25 cycles consisting of denaturation at 98 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 45 s, with a final extension of 5 min at 72 °C. The PCR amplification conditions for phoD genes were as follows: pre-denaturation at 94 °C for 3 min, followed by 35 cycles consisting of denaturation at 94 °C for 60 s, annealing at 57 °C for 60 s, and extension at 72 °C for 120 s, with a final extension of 7 min at 72 °C. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and pair-end 2×250 bp sequencing was performed using the Illlumina NovaSeq platform with NovaSeq 6000 SP Reagent Kit (500 cycles) at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China). The obtained original sequences were spliced and filtered using Flash v1.2.11 and Trimmomatic v0.33 software to obtain high quality sequences. The UCLUST consensus taxonomy assigner (UCLUST v1.2.22q) was used to cluster the sequences with 97 % or higher similarity into operational taxonomic units (OTUs) [52]. Species annotation was performed using the GreenGene Database based on the Ribosomal Data Project (RDP) database. The software and algorithms used in the analyses are included in the QIIME platform.The bacterial phoD gene sequences obtained in this study were deposited in the National Centre for Biotechnology Information Sequence Reads Archive (Accession Number MD2023033110054FAG). Statistical analyses All data was pre-processed using Excel 2016 and SPSS 27. ArcGIS was used to map sample points across different seed cotton yield types, and a box plot was employed to illustrate the changes in different groups’ soil properties and bacterial community diversity index. To investigate the similarities and differences in community composition among samples of different groups, the species diversity of microbial communities was compared by the non-metric location scaling method (NMDS). Origin 2021 software was used to draw the relationship between microbial communities and environmental factors. The bacterial symbiotic network map, functional annotation, and phylum-level bacterial dominant species abundance map were drawn using R 4.1.1 software. The Mantel test was used to analyze the relationship among microbial communities and diversity and environmental factors. Finally, to explore the possible pathways of bacterial and phoD composition, network structure, and environmental variables, the partial least squares path model (PLS-PM) model was used for analysis. Results Soil physicochemical properties and seed cotton yield There were significant differences ( p < 0.05 ) in seed cotton yield, EC, TC, TN, TP, AP, OP, Texture, MBC, MBP and ALP among 26 sites in study area (Table S1). According to the yield level, 26 points were divided into low yield field (6, 8,11 and 21 plots), medium yield field (3, 4, 7, 9, 10, 13, 14, 16, 17, 18, 19, 22 and 23 plots) and high yield field (1, 2, 5, 12, 15, 20, 24, 25 and 26 plots). The TC, TP and MBC significantly differed among low, medium and high yield field ( p < 0.05 ), and the TC was highest in the high yield field (HYF), followed by the medium yield field (MYF), and finally the low yield field (LYF), and the TP was highest in the MYF, followed by in the HYF, and finally in the LYF; and the MBC was highest in the HYF, followed by the LYF, and finally in the MYF; the highest and lowest levels were 22.5 g/kg, 1.55g/kg, 525 mg/kg and 11.8 g/kg, 1.14 g/kg, 353 mg/kg, respectively. There were not significant differences in EC, pH, TN, AP, OP, texture, MBP and ALP among low, medium and high yield field. Diversity analysis of rhizosphere microbial communities The Chao1 index obtained in the α-diversity analysis (Fig. 3 A) showed that the soil bacterial richness levels of the three groups (LYF, MYF and HYF) were significantly different ( p < 0.05 ). The richness of the bacterial community was highest in the MYF, followed by the HYF, and the lowest richness was detected in the LYF. The Goods coverage index showed significant differences in the true nature of the bacterial community among different groups (p < 0.05), the MYF holds the lowest index (0.972), and the LYF holds the highest index (0.981). The Shannon Simpson, Observed and Species index of bacterial community in different groups presented a consistent trend as follows: MYF > HYF > LYF (Fig. 3 A), but there was no significant difference. There was no significant difference in the Chao1, Goods coverage, Shannon Simpson, Observed and Species index of phoD community in different groups (Fig. 3 B). According to nonmetric multidimensional scaling (NMDS) analysis, the composition of the rhizosphere soil bacterial and phoD communities was not differed significantly among LYF, MYF and HYF (Fig. 4 A, B). Analysis of the composition of the rhizosphere microbial communities In total, 31 phyla of bacteria and 19 phyla of phoD were identified in the 78 soil samples of the three groups (LYF, MYF, and HYF) (Fig. 5 A, C). The abundance of dominant phylum of bacteria was 98.1%, 98.3%, and 98.4% in LYF, MYF, and HYF. Among all samples, the nine most abundant phyla of bacteria were Proteobacteria (29.3%), Actinobacteria (19.5%), Acidobacteria (17.8%), Gemmatimonadetes (16.5%), Chloroffexi (6.5%), Bacteroidetes (2.4%), Rokubacteria (2.3%), Firmicutes (1.6%), and Nitrospirae (1.5%) (Fig. 5 A). ANOVA revealed that except for Actinobacteria, Gemmatimonadetes and Bacteroidetes, other bacterial phyla showed no significant differences in three groups ( p > 0.05) (Fig. 5 B). The abundance of Actinobacteria phyla was the highest in MYF. The abundance of Gemmatimonadetes and Bacteroidetes phyla was the highest in HYF (Fig. 5 B). The abundance of dominant phylum of phoD was 67.9%, 59.6%, and 60.4% in LYF, MYF, and HYF (Fig. 5 C). Among all samples, the nine most abundant phyla of phoD were Proteobacteria (48.9%), Actinobacteria (13.3%), Planctomycetes (0.2%), Acidobacteria (0.1%), Firmicutes (0.1%), Bacteroidetes (0.01%), Cyanobacteria, Euryarchaeota and Verrucomicrobia (Fig. 5 C). ANOVA revealed that except for Acidobacteria and Firmicutes, other phoD phyla showed no significant differences in three groups ( p > 0.05) (Fig. 5 D). The abundance of Acidobacteria phyla was the highest in LYF. The abundance of Firmicutes phyla was the highest in MYF (Fig. 5 D). Overall, 20 genus of bacteria and 50 genus of phoD were identified in the 78 soil samples of the three groups (LYF, MYF, and HYF) (Fig. 6 A, C). The abundance of dominant genus of bacteria was 38.0%, 38.4%, and 40.3% in LYF, MYF, and HYF (Fig. 6 A). Among all samples, the nine most abundant genus of bacteria were Subgroup_6 (10.4%), MND1 (2.6%), S0134_terrestrial_group (2.4%), Rokubacteriales (2.3%), RB41 (1.9%), Sphingomonas (1.7%), Gemmatimonas (1.6%), bacteriap25 (1.6%), and JG30-KF-CM45 (1.5%) (Fig. 6 A). ANOVA revealed that except for MND1 and Rokubacteriales , other bacterial genus showed no significant differences in three groups ( p > 0.05) (Fig. 6 B). The abundance of MND1 and Rokubacteriales genus was the highest in HYF. The abundance of dominant genus of phoD was 4.4%, 4.9%, and 4.0% in LYF, MYF, and HYF (Fig. 6 C). Among all samples, the nine most abundant genus of phoD were Streptomyces (1.5%), Sinorhizobium (1.1%), Amycolatopsis (0.4%), Bradyrhizobium (0.4%), Saccharopolyspora (0.3%), Stella (0.2%), Variibacter (0.1%), Frankia (0.1%) and Pseudomonas (0.1%) (Fig. 6 C). ANOVA revealed that except for Frankia and Pseudomonas , other phoD genus showed no significant differences in three groups ( p > 0.05) (Fig. 6 D). The abundance of Frankia genu s was the highest in MYF. The abundance of Pseudomonas genus was the highest in LYF (Fig. 6 D). Co-occurrence networks of the rhizosphere microbial communities To understand the differences in rhizosphere microbial network interactions among different bacterial and phoD groups, the symbiotic network between bacterial and phoD species under different groups were constructed using OTUs (Fig. 7 A, B). Nodes in the bacterial and phoD co-occurrence network included five bacterial phyla (Actinobacteria, Proteobacteria, Acidobacteria, Chloroffexi and Gemmatimonadetes) and four phoD phyla (Proteobacteria, Actinobacteria, Firmicutes and Pseudomonadota; Fig. 7 A, B), respectively. A unique microbial co-occurrence network formed in each treatment. The most complex network was in LYF, followed by the HYF, and the lowest in the MYF. Compared with MYF and HYF networks, LYF networks had more significant values of average degree, number of edges and modularity in bacterial communities and average degree and number of edges in phoD communities (Fig. 7 C). The average path length of bacterial communities in MYF was the largest, and LYF’s was the smallest (Fig. 7 C). The average path length of phoD communities in HYF was the largest, and MYF’s was the smallest (Fig. 7 C). Relationship between rhizosphere microbial communities and environmental factors According to the Mantel test results, bacterial community structure showed significant positive correlations with AP, MBC and TN and exhibited extremely significant positive correlation with pH, EC, OP and clay content (Fig. 8 ). The bacterial Simpson index showed a highly significant positive association with EC and a significant positive association with TP and clay content. The phoD community structure was significantly positively correlated with EC and TP. However, no notable correlation was observed between the phoD Simpson index and environmental factors. The RDA indicated that environmental factors exerted strong influences on rhizosphere microbial communities, and that RDA1 and RDA2 explained 37.04%, 57.75% and 16.64%, 13.64% of the bacterial and phoD differences at phylum-level, and that RDA1 and RDA2 explained 41.16%, 20.65% and 15.08%, 9.47% of the bacterial and phoD differences at genus-level, respectively (Fig. 9 ). EC (R 2 = 0.447, p = 0.002) and clay (R 2 = 0.308, p = 0.013) for the bacterial community at phylum-level and TP (R 2 = 0.270, p = 0.019) for the phoD community at phylum-level reached significant levels (Fig. 9 A, C). At the genus level, TN (R 2 = 0.398, p = 0.002), EC (R 2 = 0.410, p = 0.001) SALP (R 2 = 0.298, p = 0.019) and TC (R 2 = 0.260, p = 0.029) were significant factors for the bacterial community (Fig. 9 B). Dominant populations play a crucial role in stabilizing the structure and function of microorganisms, which were mainly affected by surrounding environmental factors. Therefore, the species with the highest coverage of identifiable microorganisms at the phylum level and the genus level were selected for correlation analysis with soil physicochemical properties (Fig. S1). The number of bacterial communities in the most common phyla was significantly correlated with the soil EC, AP, TC, TN and ALP (Fig. S1A). Proteobacteria showed extremely significant positive correlations with soil EC and significant negative correlations with OP. Actinobacteria was significant negatively correlated with AP and TC. Acidobacteria exhibited significant positive correlations with TN and ALP, and significant negative correlations with soil EC. Gemmatimonadetes was significant positively correlated with soil EC, AP, TC, TN and MBC. Chloroflexi was extremely significant negatively correlated with soil EC. Rokubacteria showed significant positive correlations with TC and OP and significant negative correlations with EC. Bacteroidetes was significant positively correlated with soil EC and significant negatively correlated with OP and ALP. Firmicutes exhibited significant negative correlations with soil EC, TN and ALP. Nitrospirae was significant positively correlated with AP and TP and significant negatively correlated with soil EC and pH. Planctomycetes was significant negatively correlated with MBP. AP, TC and TN were closely correlated with the abundance of the dominant phoD phyla (Fig. S1C). Proteobacteria was significant positively correlated with soil EC and significant negatively correlated with TC. Actinobacteria showed significant negative correlations with AP, TP, TN and ALP, and significant positive correlations with pH. Acidobacteria was significant negatively correlated with AP and TC. Firmicutes was extremely significant negative correlations with TC. Bacteroidetes was significant negatively correlated with TN. Correlation analysis results showed that the abundance of bacterial communities in the most common genera was significantly correlated with soil EC, TC, TN, MBC, and ALP (Fig. S1B). Subgroup_6 showed significant positive correlations with AP, TN and ALP. MND1 was significant positively correlated with TC, TN and ALP. Rokubacteriales exhibited significant positive correlations with TC and MBP, and significant negative correlations with soil EC. S0134_terrestrial_group was significant positively correlated with soil EC and pH and significant negatively correlated with TN, OP and ALP. RB41 was extremely significant negatively correlated with soil EC and significant positively correlated with AP. Sphingomonas showed significant positive correlations with soil EC and MBC and significant negative correlations with OP and ALP. Bacteriap25 was significant positively correlated with TN and ALP. 67 − 14 exhibited significant negative correlations with soil EC and MBC. Gimmatimonas was significant positively correlated with soil EC, TC and MBC. JG30-KF-CM45 was significant negatively correlated with soil EC and TC and positively correlated with MBP. AP, ALP and TN were closely correlated with the abundance of the dominant phoD genera (Fig. S1D). Sinorhizobium and Saccharopolyspora were significant positively correlated with AP, TN and ALP. Stella, Frankia and Luteitalen exhibited significant positive correlations with TN. PLS-PM model of rhizosphere microbial communities, network structure, and environmental factors To further elucidate the direct and indirect effects of environmental factors on bacterial and phoD communities across different groups, PLS-PM was employed based on the results described above (Bacterial: goodness of fit (GOF) = 0.59, phoD : GOF = 0.42) (Fig. 10 ).The conceptual model for the direct and indirect effects of environmental factors and bacterial communities explained 60.9% of the variation in bacterial diversity, 66.6% of the variation in the bacterial structure, and 14.0% of the variation in bacterial network (Fig. 10 A). Soil pH, EC, nutrients, ALP and physical properties affected the prediction of bacterial structure with standardized path coefficient (SPC), respectively (SPC = -0.366, 0.690, 0.561, -0.483,0.386). Soil nutrients affected the prediction of bacterial network (SPC = 0.374) and soil physical properties affected the prediction of bacterial diversity (SPC = 0.535) (Fig. 10 A). Similarly, the conceptual model for the direct and indirect effects of environmental factors on phoD communities explained 56.8% of the variation in phoD diversity and 17.4% of the variation in the phoD network (Fig. 10 B). Soil nutrients significantly influence the phoD network (SPC = 0.417), whereas soil physical properties and EC effected on phoD diversity (SPC = -0.432, 0.337) (Fig. 10 B). Discussion Cotton is an important cash crop widely cultivated under film-mulched drip irrigation in Xinjiang province, an arid and semi-arid region that contributes approximately 90% of China's cotton output annually [ 1 ]. However, substantial differences in cotton yield levels exist in this area, primarily influenced by soil fertility, field management techniques, cotton varieties, local climate, and other environmental factors [ 53 , 54 ]. Soil microbial diversity and composition are potential indicators of soil fertility quality, and play crucial roles in organic matter decomposition, soil nutrient cycling and transformation, soil biodiversity, soil ecosystem stability, plant nutrient absorption and utilization, and ultimately crop yield [ 37 , 55 ]. In this study, we investigated changes in soil properties, soil bacterial community composition, and network structure under different cotton yield types. Based on our findings, we identified core taxa within the cotton rhizosphere microbiome and elucidated the most important factors affecting the bacterial community and network structure. Our study provides insights into the biological mechanisms underlying high cotton yield. Soil physicochemical properties of cotton fields with different yield Soil physicochemical properties are not only fundamental components of soil fertility but are also key indicators of sustainable soil sustainable development and improve crop productivity [ 56 ]. In the present study, we found that the soil physicochemical properties in HYF were superior to those in MYF and LYE, especially, the TC, TP and MBC in high-yield field were significantly higher than those in low-yield field. This further supports the notion that soil physicochemical properties are critical to soil quality and serve as an important foundation for achieving high yields [ 53 ]. Moreover, the high-yield cotton fields contributed substantial straw residues to the soil, which promoted the formation of soil organic matter and carbon transformation, thereby increasing the content of TC, TP, and MBC—results consistent with several previous studies [ 57 – 60 ]. However, no significant differences were observed in EC, pH, total nitrogen (TN), available phosphorus (AP), organic phosphorus (OP), texture, microbial biomass phosphorus (MBP), and ALP activity among the different yield fields. This could be attributed to the fact that all soil samples were collected from the same farm, which shares similar climate, cultivation practices, water and fertilizer management, and soil conditions. Rhizosphere soil microbial community structure of cotton fields with different yield Soil microbial composition and diversity are potential indicators of soil quality and important components and the most active constituents in agroecosystems, as well as important factors in soil formation and development, soil organic carbon transformation, nutrient cycles, ecosystem balance, soil function maintenance [ 37 , 61 – 64 ]. They are associated with the level of crop yields. The results of soil bacterial communities based on high throughput sequencing showed that the Chao1 index and the Goods coverage index of dominant species varied significantly under different yield types. The Chao1 index of the bacterial community was highest in the MYF and lowest in LYF, consistent with previous studies. Chen et al. [ 65 ] showed that the higher Chao1 indices were observed from the NPK and NP treatments with moderate wheat biomass, and the similar lower values from the NK and Nil treatments with low wheat biomass. Feng et al. [ 66 ] found higher Chao1 index in moderate saline-alkali soil with middle cotton yield, lower values in heavily saline-alkali soil with lowest cotton yield. In this study, the MYF holds the lowest Goods coverage index and the LYF holds the highest Goods coverage index. Fu et al. [ 67 ] reported alfalfa yield was detected as the strongest factor, which simultaneously associated with the bacterial α-diversity (the maximum Chao1 index appears at the moderate yield level). These results suggested that there was a suitable soil environmental threshold for soil microbial diversity [ 68 , 69 ]. The Shannon Simpson, Observed and Species index of bacterial community and the Chao1, Goods coverage, Shannon Simpson, Observed and Species index of phoD community in different groups were not significantly differ, as well as the composition of the rhizosphere soil bacterial and phoD communities was not differed significantly among LYF, MYF and HYF based on NMDS analysis. One possible explanation is that similar soil conditions, cultivation patterns and management techniques affecting soil microbial composition and diversity in different groups [ 70 – 72 ]. The underlying mechanisms require further investigation. Soil function and niche depends on not only the diversity of soil microbes, but also on community compositions [ 73 , 74 ]. Microorganisms are important components and decomposers of soil, which is affected by different crop, soil environment, ecosystem, management model and other factors. Liang et al. [ 75 ] found the Planctomycetes, Acidobacteria, Proteobacteria, Gemmatimonadetes, Verrucomicrobia, Chloroflexi, Bacteroicetes, Nitrospirae and Saccharlbacteria were dominant bacterial phyla of wheat in Northwest China. Hou et al. [ 76 ] reported the dominant phyla of bacteria in the corn field in Northeast China were Proteobacteria, Thaumarchaeota, Actinobacteria, Acidobacteria and Verrucomicrobia, which altogether accounted for 82%-87%. Iqbal et al. [ 77 ] showed that the top five dominant phyla in rice fields in South China were Chloroflexi, Proteobacteria, Firmicutes, Acidobacteria and Planctomycetes, which reported more than 70% of the relative abundance of the bacterial communities. The dominant bacterial phyla tested using in the kiwifruit orchard soil were Proteobacteria, Acidobacteria, Bacteroidetes, and Actinobacteria [ 78 ]. In the present study, the nine most abundant phyla of bacteria in cotton rhizosphere soil were Proteobacteria (29.3%), Actinobacteria (19.5%), Acidobacteria (17.8%), Gemmatimonadetes (16.5%), Chloroffexi (6.5%), Bacteroidetes (2.4%), Rokubacteria (2.3%), Firmicutes (1.6%), and Nitrospirae (1.5%), which agreed with prior reports in cotton field soil [ 79 ]. These results indicated Proteobacteria, Actinobacteriota and Acidobacteria might be the core phyla of soil bacteria in agroecosystem. Additionally, we also found that the species composition of the soil bacterial community was similar under different cotton yield levels, but the relative abundance of the soil bacterial community was different under different cotton yield levels. In our study, the abundance of Actinobacteria phyla was the higher in MYF and LYF. Actinobacteria is a ubiquitous bacterial groups and persistent population in agroecosystem, which is predominant in dry alkaline soil [ 16 , 80 ]. Actinobacteria play major roles in the cycling of organic matter, decompose complex mixtures of polymer in plant litter results in production of many extracellular enzymes which are conductive to crop production. Actinobacteria identified as a group of copiotrophic taxa and thrive in the condition of high C availability [ 81 ], was revealed negatively correlated with cotton yield in our study. This result is consistent with the research results of Fu et al. [ 67 ] on alfalfa, but not consistent with the research results of Dang et al. [ 70 ] on Proso millet ( Panicum miliaceum L.). Therefore, we regarded that crop growth also had great potential in shaping Actinobacteria distribution, but the mechanism needs further study to understand. Gemmatimonadetes have a cosmopolitan distribution in terrestrial systems, which as persistent and important members of soil communities, especially in arid and saline soils [ 82 , 83 ]. Gemmatimonadetes can be capable of anaerobic photosynthesis, which play critical roles in regulating the cycling of carbon and facilitating the degradation of cellulose in soil, as well as important participant in biogeochemical transformations in soils under salinity and drought [ 72 ]. In this study, soil samples were collected from saline-alkali soil in arid and semi-arid area, and the abundance of Gemmatimonadetes phyla was the highest in HYF (TC and MBC were the highest). These results are useful for understanding the living environment and ecological function for Gemmatimonadetes species. [ 67 , 84 , 85 ]. Bacteroidetes are a ubiquitous bacterial group prevalent in various soil ecosystems, due to their remarkable versatility in ecological niches adaptation and genomic plasticity [ 86 ]. Bacteroidetes play a critical role in maintaining soil health and ecosystem function, such as degrading organic matter, promoting nutrient cycling, producing growth-stimulating phytohormones, and stabilizing microbial communities [ 66 , 87 , 88 , 89 ]. Bacteroidetes, generally regarded as r-strategists in eutrophic environments, were found to be abundant in HYF, supporting their association with high soil fertility [ 89 – 91 ]. At the genus level, the abundance of dominant genus of bacteria was 38.0-40.3% in cotton field soil. Subgroup_6 (10.4%), MND1 (2.6%), S0134_terrestrial_group (2.4%), Rokubacteriales (2.3%), RB41 (1.9%), Sphingomonas (1.7%), Gemmatimonas (1.6%), bacteriap25 (1.6%), and JG30-KF-CM45 (1.5%) were the dominant genus in our study, which are almost the same as those in previous studies. In cotton soil in arid areas, Gemmatimonas, Sphingomonas, Subgroup_6 and Rokubacteriales are the dominant genus [ 79 ]. In cotton rhizosphere soil in saline-alkaline soil, Sphingomonas , Gemmatimonas and bacteriap25 are the main genus [ 92 ]. On continuous cotton field, Gemmatimonas , MND1 and Rokubacteriales are the most abundant genus [ 93 ]. The dominant bacterial genera in three groups (LYE, MYE and HYE) were not significantly different except for MND1 and Rokubacteriales . The abundance of MND1 and Rokubacteriales genus was the highest in HYF. MND1 may play a role in the nitrogen cycle, including processes like ammonia oxidation, nitrification, or denitrification [ 94 ]. Rokubacteriales may be involved in the biogeochemical cycles of carbon and sulfur, helping to transform these elements and maintain ecological balance [ 95 ]. These results indicate that higher carbon and nitrogen conditions increase the relative abundance of MND1 and Rokubacteriales, enhancing their functional roles in soil [ 96 ]. The cotton field in Xinjiang province, Northwest of China, are predominantly distributed on alkaline soils with higher pH and calcium carbonate (CaCO₃), which often restrict the availability of phosphorus (P) to plants [ 97 ]. Alkaline phosphatase (ALP) is one of the primary enzymes responsible for the release of available inorganic P from organic P in soil [ 98 ]. The phoD gene is one of ALP-encoding genes, which occurs in a broad range of terrestrial and aquatic ecosystems [ 99 , 100 ]. Thus, understanding the microbial community structure associated with the phoD gene can be an effective strategy for enhancing the bioavailability of phosphorus and improving the production of cotton in alkaline soils. In the present study, the abundance of dominant phylum and genus of phoD was 67.9%, 59.6%, 60.4% and 4.4%, 4.9%, 4.0% in LYF, MYF, and HYF, respectively. Additionally, the most abundant phyla and genus (relative abundance > 1%) of phoD were Proteobacteria (48.9%), Actinobacteria (13.3%), Planctomycetes (0.2%), Acidobacteria (0.1%), Firmicutes (0.1%), and Streptomyces (1.5%), Sinorhizobium (1.1%), Amycolatopsis (0.4%), Bradyrhizobium (0.4%), Saccharopolyspora (0.3%), Stella (0.2%), Variibacter (0.1%), Frankia (0.1%) and Pseudomonas (0.1%) across all of the samples in our study, respectively. These are consistent with the findings in cotton soil under organic fertilization [ 101 ], but the results were different from the findings in maize-wheat-cotton rotation under long-term field experiment [ 97 ]. These results indicated the core phylum and genus of phoD in agroecosystem was not very stable, and was affected by factors such as cultivation systems [ 102 ], fertilization strategies [ 24 , 103 , 104 ] and land-use patterns [ 105 ]. Co-occurrence networks and main factors driving rhizosphere microbial community structure of cotton fields with different yield Microbial co-occurrence networks are crucial for elucidating potential microbial interactions and relationships, thereby enhancing our understanding of microbial ecology and function [ 106 – 109 ]. In microbial symbiosis networks, nodes, edges, path length, and modularity each have specific meanings that help describe the structure and dynamics of microbial communities [ 110 ]. The results of our study showed that low yield field increased the microbial network complexity (number of nodes, edges, and modules). Increased network complexity in low yield field may be caused by higher environmental stress [ 111 ], greater microbial diversity [ 112 ], higher functional redundancy [ 113 ], dynamic network adaptation [ 114 ]. The Mantel test, redundancy analysis (RDA) and PLS-PM revealed the possible pathways between bacterial communities and environmental variables [ 115 , 116 ]. Soil physicochemical properties significantly influence the composition, diversity, and functioning of microbial communities in the soil [ 69 ]. In the present study, we found that pH, EC, OP, AP, TN, MBC and clay were the main factors driving the changes in bacterial communities structure and diversity, but EC and TP were the main factors driving the changes in phoD community structure. Soil pH is a key factor that determines the availability of nutrients and can affect microbial activity. Previous studies have shown that different microbial groups thrive in specific pH ranges, regulating the structure and diversity of bacterial communities [ 117 , 118 ]. In saline-alkali regions, EC was the main factors regulating the activity, diversity and structure of the soil microbial communities [ 37 , 119 , 120 ]. The availability of macronutrients and micronutrients plays a vital role in shaping microbial communities. Nutrient-rich soils often support higher microbial diversity and activity, which can enhance soil health and fertility [ 53 , 121 ]. Soil texture, particularly clay content, is another essential factor in determining microbial community structure [ 121 , 122 ]. Due to their large surface area and chemical binding capacity, clay particles can reduce bacterial diversity [ 123 ]. In conclusion, our research demonstrated that soil physical properties and nutrient availability are the primary factors influencing bacterial community composition and network structure. Conclusion This study found that the soil bacterial communities in the cotton rhizosphere under mulched drip irrigation in semi-arid and arid climate regions were mainly composed of 9 phyla: Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Chloroffexi, Bacteroidetes, Rokubacteria, Firmicutes, and Nitrospirae. At the genus level, the dominant genera of the bacterial community were Subgroup_6 , MND1 , S0134_terrestrial_group , Rokubacteriales , RB41 , Sphingomonas , Gemmatimonas , bacteriap25 , and JG30-KF-CM45 .The high-throughput sequencing data demonstrated that soil bacterial community diversity, composition, and network structure significantly differed between cotton yield types. Medium-yield field exhibited the highest diversity and composition of soil bacterial communities, whereas low-yield fields had the most complex network structure. Furthermore, the soil microbial characteristics of the cotton rhizosphere under mulched drip irrigation were primarily associated with soil physicochemical properties. Soil pH, EC, OP, AP, TN, MBC, and clay content were identified as the key driving factors influencing the changes in rhizosphere bacterial community diversity and network structure. This study, which assessed the characteristic changes and main influencing factors of soil bacterial community and network structure under three cotton yield types in Xinjiang, provides valuable methods and data to support further research aimed at enhancing soil quality and cotton yield in the future. Declarations Data Availability Data will be made available on request. Acknowledgments This work was supported by the Project for Young Top-Notch Talents in Science and Technology of Xinjiang Uygur Autonomous Region (Grant No. 2022TSYCCX0085), the Sponsored by Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2024D01E06), the National Natural Science Foundation of China (Grant No. 32360793, 31960629), the Key Research and Development Project in Xinjiang Uygur Autonomous Region (Grant No. 2022B02033-1), and Special Topics of Major Science and Technology in Xinjiang Uygur Autonomous Region (Grant No. 2022A02007-2). We thank, PhD Jorus Sunstrider, for editing the English text of a draft of this manuscript. Authors’ contributions Man Zhang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. Yang Hu ： Writing – review & editing, Software, Data curation. Yue Ma: Investigation, Data curation. Tianyu Hou: Investigation. Juanhong Wang: Data curation. Qinxuan Che: Data curation. 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Manure application enhanced cotton yield by facilitating microbially mediated P bioavailability. Field Crop. Res. 2023;304:10915. Gou X, Cai Y, Wang C, Li B, Zhang R, Zhang Y, et al. Effects of different long-term cropping systems on phoD harboring bacterial community in red soils. J. Soil. Sediment. 2020;21:376-87. Liu W, Ling N, Luo G, Guo J, Zhu C, Xu Q, et al. Active phoD-harboring bacteria are enriched by long-term organic fertilization. Soil Biol. Biochem. 2021;152:108071. Guo L, Wang C, Feng T, Shen R. Short-term application of organic fertilization impacts phosphatase activity and phosphorus-mineralizing bacterial communities of bulk and rhizosphere soils of maize in acidic soil. Plan Soil. 2023;484:95-113. Siles JA, Starke R, Martinovic T, Fernandes MLP, Orgiazzi A, Bastida F. Distribution of phosphorus cycling genes across land uses and microbial taxonomic groups based on metagenome and genome mining. Soil Biol. Biochem. 2022;174:108826. Chaffron S, Rehrauer H, Pernthaler J, Mering CV. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 2010;20(7):947-959. Barber´an A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6: 343-51. Jiao S, Wang J, Wei G, Chen W, Lu Y. Dominant role of abundant rather than rare bacterial taxa in maintaining agro-soil microbiomes under environmental disturbances. Chemosphere. 2019;235:248-59. Chen J, Liu KX, Hu TM, Zhang YJ. Soil microbial network complexity predicts ecosystem function along elevation gradients on the Tibetan Plateau. Soil Biol. Biochem. 2022;172:108766. Li D, Xiao X, Shun B, Liang YT. Characteristics of bacterial community symbiotic network in black soil under hydrothermal increase. Chinese. J. Microbiol. 2021;61:1715-27. Zhou JZ, Xue K, Xie JP, Deng Y, Wu LY, Cheng XH, et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change, 2011;1(2):54-8. Maron PA, Mougel C, Ranjard L. Soil microbial diversity: Methodological strategy, spatial overview and functional interest. C.R. Biol. 2011;334(5-6):403-11. Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 2018;16(9):567-76. Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 2012;3:417. Ding B, Bai Y, Guo S, He Z, Wang B, Liu H, et al. Effect of irrigation water salinity on soil characteristics and microbial communities in cotton fields in southern Xinjiang, China. Agronomy. 2023;13:1679. Liu B, Dai Y, Cheng X, He X, Bei Q, Wang Y, et al. Straw mulch improves soil carbon and nitrogen cycle by mediating microbial community structure and function in the maize field. Front. Microbiol. 2023;14:1217966. Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U. S. A. 2006;103(3):626-31. Li J, Yang Y, Wen J, Mo F, Liu Y. Continuous manure application strengthens the associations between soil microbial function and crop production: Evidence from a 7-year multisite field experiment on the Guanzhong Plain. Agr. Ecosyst. Environ. 2022;338:108082. Chandrasekaran M, Boughattas S, Hu S, Oh SH, Sa T. A meta-analysis of arbuscular mycorrhizal effects on plants grown under salt stress. Mycorrhiza. 2014;24:611-25. Yang H, Hu J, Long X, Liu Z, Rengel Z. Salinity altered root distribution and increased diversity of bacterial communities in the rhizosphere soil of Jerusalem artichoke. Sci. Rep. 2016;6:20687. Lauber CL, Strickland MS, Bradford MA, Fierer N. The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biol. Biochem. 2008;40(9):2407-15. Johnson MJ, Lee KY, Scow KM. DNA fingerprinting reveals links among agricultural crops, soil properties, and the composition of soil microbial communities. Geoderma. 2003; 114:279-303. Ge N, Wei X, Wang X, Liu X, Shao M, Jia X, et al. Soil texture and phosphorous under two contrasting land use types in the Loess Plateau. Catena. 2019;172:148-57. Additional Declarations No competing interests reported. Supplementary Files TableS1FigureS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5689151","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409564518,"identity":"93887d18-c310-4b69-af2e-6b71a8f412fc","order_by":0,"name":"Man Zhang","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Zhang","suffix":""},{"id":409564519,"identity":"911bb83a-6c33-401b-aca0-4cd73e2535b4","order_by":1,"name":"Yang Hu","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Hu","suffix":""},{"id":409564520,"identity":"775f8bbd-ebd1-4a68-99f8-59cdafe476ed","order_by":2,"name":"Yue Ma","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Ma","suffix":""},{"id":409564521,"identity":"63ac5b1e-ef9a-4fea-b244-6d7d782a1eb5","order_by":3,"name":"Tianyu Hou","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tianyu","middleName":"","lastName":"Hou","suffix":""},{"id":409564522,"identity":"030a959f-0764-4af4-93d8-2d581d6aa619","order_by":4,"name":"Juanhong Wang","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Juanhong","middleName":"","lastName":"Wang","suffix":""},{"id":409564523,"identity":"3714dfcd-37a1-4f3e-9b83-c2913b8826a4","order_by":5,"name":"Qingxuan Che","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Qingxuan","middleName":"","lastName":"Che","suffix":""},{"id":409564524,"identity":"bfa00eb2-0c1d-46c0-b9a3-1e54ff0ddfaa","order_by":6,"name":"Bolang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCSjNz8zY+CDBwIaBjWgtku3Nhw0eVKSRoMXgzLE0yQdnDhN2F//s5mOPeWru2DXcyDGTSGw7b88n3fyA4UfFNtyW3DmWbsxz7Fly44wcY4vEttuJbTLHDBh7ztzGqcVAIsdMOoftcDKzRI7hDaCWBDaJBANmxjZ8WvK/Sef8O5zMJpFjAHTYOXs2ifQPBLTksEnnth224+E5liSRcOYAYxtQL14tEjfSzKT/9h1OkGAHBnJCRXIiUEvBQXx+4Z+R/ExyxrfD9vaHGRsf/jCws5efkb7xwY8K3FpgILEBmXeAoHogsCdG0SgYBaNgFIxQAABSI1oQo1jBfQAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Bolang","middleName":"","lastName":"Chen","suffix":""},{"id":409564525,"identity":"63615057-d48c-40e4-8a40-69a902170f29","order_by":7,"name":"Qinghui Wang","email":"","orcid":"","institution":"Xinjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qinghui","middleName":"","lastName":"Wang","suffix":""},{"id":409564526,"identity":"f685906d-4312-4ab9-8b66-5a3269165688","order_by":8,"name":"Gu Feng","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Gu","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-12-21 10:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5689151/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5689151/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75357512,"identity":"a4f67ddc-ae2b-41ef-8957-25938ace2cdb","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":422392,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of sampling sites (n = 26) in the study area.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/e2a7f34fe720b86a0a5ad23c.png"},{"id":75357513,"identity":"66b876c7-7b5f-40c6-b715-732a49593417","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140605,"visible":true,"origin":"","legend":"\u003cp\u003eSeed cotton yield and soil chemical and biological properties at different groups. SY: seed cotton yield; EC: electrical conductivity; TC: total carbon; TN: total nitrogen; TP: total phosphorus; AP: available phosphorus; OP: organic phosphorus; MBC: microbial carbon; MBP: microbial phosphorus; ALP: alkaline phosphatase.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/79a8ef512bd38869c9f5d816.png"},{"id":75357520,"identity":"2c3c8a40-72b7-4ff1-9331-243b941b2d52","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262451,"visible":true,"origin":"","legend":"\u003cp\u003eChao1, Goods coverage, Shannon index, Simpson index, and Observed species of bacterial (A) and \u003cem\u003ephoD\u003c/em\u003e (B) communities at different groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/c6cb59e6221389dec5e57284.png"},{"id":75357514,"identity":"4e4e1290-cad4-441d-9ce8-208093351b54","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81679,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial(A) and\u003cem\u003e phoD\u003c/em\u003e (B) community beta diversity at different groups.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/3f2899055f953cd983d2f7ab.png"},{"id":75357528,"identity":"4ea55221-ea22-46ef-b085-d22ead19f5e5","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":295489,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of bacterial and \u003cem\u003ephoD\u003c/em\u003ecommunities at phylum level in different groups. Phylum-level composition of (A) bacterial and (C) \u003cem\u003ephoD\u003c/em\u003e communities. Analysis of differences in relative abundances of (B) bacterial phyla in A and (D) \u003cem\u003ephoD\u003c/em\u003ephyla in C. Names of the nine most abundant phyla are shown, and other phyla were grouped as “Others.”\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/4acca3e51249302492b756c7.png"},{"id":75358132,"identity":"78a13f90-9128-4cc9-be9c-702516a67ebb","added_by":"auto","created_at":"2025-02-03 17:21:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":270340,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of bacterial and \u003cem\u003ephoD\u003c/em\u003ecommunities at genus-level in different groups. Genus-level composition of (A) bacterial and (C) \u003cem\u003ephoD\u003c/em\u003e communities. Analysis of differences in relative abundances of (B) bacterial genus in A and (D) \u003cem\u003ephoD\u003c/em\u003e genus in C. Names of the nine most abundant genus are shown, and other genus were grouped as “Others.”\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/955ad50f2e9dd896a16df031.png"},{"id":75357521,"identity":"1cd0f112-a8a5-4095-9638-7110313c13ae","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":473002,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence networks of bacterial (A) and phoD (B)communities in different groups. Each node in a network represents a phylum of bacteria or phoD. Indices related to topological structure of the co-occurrence interaction network of bacterial- phoD communities (C).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/158a7a200aae95c06130e85d.png"},{"id":75357542,"identity":"c5a62496-d65f-4e0f-b6ba-a0777d2a9319","added_by":"auto","created_at":"2025-02-03 17:13:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":200246,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between soil properties and microbial communities.\u003cstrong\u003e \u003c/strong\u003eEC: electrical conductivity; TC: total carbon; TN: total nitrogen; TP: total phosphorus; AP: available phosphorus; OP: organic phosphorus; MBC: microbial carbon; MBP: microbial phosphorus; SALP: soil alkaline phosphatase. ex. The correlation values are mapped by the color of the heat map: the darker the shade of blue, the stronger the negative correlation, and the closer the color to red, the stronger the positive correlation. * means \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** means \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** means \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. The thickness of the line between the three nodes and each environmental factor indicates the magnitude of the correlation: the thicker the line, the stronger the correlation, and the weaker, the opposite. The color of the line between the node and the environmental factor indicates the \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/4c0a721cf7b2741fb4248d97.png"},{"id":75357523,"identity":"18412e94-d7e6-492d-89e1-daed6e4b59e7","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":64475,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) of soil microbial communities and environmental indices in different groups.\u003cstrong\u003e \u003c/strong\u003ePhylum-level composition of (A) bacterial and (C) \u003cem\u003ephoD\u003c/em\u003e communities. Genus-level composition of (B) bacterial and (D) phoD communities. EC: electrical conductivity; TC: total carbon; TN: total nitrogen; TP: total phosphorus; AP: available phosphorus; OP: organic phosphorus; MBC: microbial carbon; MBP: microbial phosphorus; SALP: soil alkaline phosphatase.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/53f7435fad5e5fc866a848f1.png"},{"id":75357532,"identity":"1bad5ff8-3006-4ab8-b673-c39f92538bd5","added_by":"auto","created_at":"2025-02-03 17:13:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":309432,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between soil bacterial (A, GFI=0.59) and \u003cem\u003ephoD\u003c/em\u003e (B, GFI=0.42) communities under different groups with soil properties by Partial least squares-path modeling (PLS-PM). Phys: physical included sand, silt and clay; EC: electrical conductivity; Nutri: nutrients included total carbon, total nitrogen, total phosphorus, available phosphorus, organic phosphorus, microbial carbon, microbial phosphorus.; S-ALP: soil alkaline phosphatase; Bacteria stru: bacteria structure included bacteria tsOTUS taxa; Bacteria.der: bacteria diversity included α-diversity and β-diversity; phoD stru: phoD structure included phoD tsOTUS taxa; phoD.der: phoD diversity included α-diversity and β-diversity; GFI: goodness of fit index.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/e295439cd423fa396531a6ed.png"},{"id":75460380,"identity":"5d3b8a8c-eed3-4db5-b55e-e93f74db3f65","added_by":"auto","created_at":"2025-02-04 22:46:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3725206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/ea80aba6-3a47-49a4-acab-a4062ea75a19.pdf"},{"id":75357511,"identity":"d7d3991e-c634-4da5-8779-63b1f0c13d4d","added_by":"auto","created_at":"2025-02-03 17:13:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":208600,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5689151/v1/e93fd398c94cf8d72df30013.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soil bacterial diversity and community structure of cotton rhizosphere under mulched drip- irrigation in arid and semi-arid regions of Northwest China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCotton (Gossypium \u003cem\u003ehirsutum L.\u003c/em\u003e) is a vital global commodity, playing a critical role in the textile industry and significantly contributing to the economies of numerous [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is one of the most extensively cultivated crops, with applications spanning textiles, oil production, animal feed and the medical industry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the world\u0026rsquo;s leading producer of cotton, China produced approximately 5.61\u0026nbsp;million tonnes in 2023, accounting for more than 25% of global production [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cotton plays an essential role in regional economic growth and ecological balance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nonetheless, the sustainability of cotton production is influenced by several key factors, including climate, environmental conditions, management practices, pest control, and soil properties, with soil fertility being a primary determinant of yield potential [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, understanding the variations in soil fertility and microbiome characteristics in cotton fields is crucial for addressing yield constraints and maintaining sustainable.\u003c/p\u003e \u003cp\u003eSoil microorganisms play a significant role in soil ecosystem biochemical processes, including soil structure formation, nutrient cycling, organic matter decomposition, and other soil biochemical activities. Consequently, soil microorganisms contribute to the regulation of soil fertility and health, crop productivity, and sustainability of cultivated plants [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The present study focuses on fundamental research into the core plant microbiome, soil microbial activities, abundance, community structure and functions, as well as microbial co-occurrence networks [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Core plant microbiomes refer to microbial taxa consistently present in a particular environment, playing essential roles in nutrient availability and plant growth. For instance, \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003eAzospirillum\u003c/em\u003e enhance nitrogen availability, promoting root and shoot development in crops [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e enhance phosphorus availability to plants, thereby boosting cotton fiber production [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cem\u003eActinobacteria\u003c/em\u003e facilitate to the breakdown of organic matter, promoting soil health and enhancing plant growth [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The stability and function of soil ecosystems largely depend on the activities, abundance and community structure of soil microorganism [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Previous studies have demonstrated that regulating microbial activities, diversities, and community compositions influences carbon cycling, nitrogen transformation processes and phosphorus availability [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The \u003cem\u003ephoD\u003c/em\u003e gene is responsible for alkaline phosphatase expression in soil, and the composition and diversity \u003cem\u003ephoD\u003c/em\u003e-harboring bacterial community composition with the potential effects on organic P transformation [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Microbial communities in soil interact in complex microbial networks, where species form relationships that can be cooperative (mutualism), competitive, or neutral [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Networks may reveal the complexity and stability of soil microbial community structure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The microbial networks with higher modularity or complexity were beneficial for enhancing soil functions, such as nutrient cycling, organic matter decomposition, pathogen control and plant productivity [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, understanding the core plant microbiome, soil microbial community structure and functions, and microbial co-occurrence networks can provide important information on the cultivation of soil ecological niche and functions.\u003c/p\u003e \u003cp\u003eIn recent years, increasing attention has been directed the intricate relationship between soil microorganisms and environmental factors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous studies have demonstrated that soil microorganisms modified their environment in various ways including altering soil properties and establishing mineral structures [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Environmental factors are also known to significantly influence soil microbial communities, their composition, and microbial co-occurrence networks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Several studies have identified soil properties such as pH, temperature, moisture, soil organic carbon, saline-alkali stress, and soil texture as key determinants of microbial community structure and function [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, understanding the feedback mechanisms between microorganisms and the soil environment is of great agronomic importance for maintaining soil health and enhancing agricultural productivity.\u003c/p\u003e \u003cp\u003eXinjiang is located in a typical temperate inland region, where abundant heat and sunlight create particularly favorable conditions for cotton cultivation. Xinjiang serves as the largest commercial cotton production base in China, accounting for over 90% of the nation's total cotton output [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, significant differences in cotton yield per unit area arise due to variations in planting patterns, regional climate, soil conditions, and management practices. Consequently, most studies have focused on improving cotton production through strategies such as optimizing plant density [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], fertilization practices [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], irrigation techniques [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and enhancing soil fertility and quality [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], Nevertheless, research on the changes in soil microbial community composition, network structure, and environmental variables across different cotton yield levels remains limited.\u003c/p\u003e \u003cp\u003eIn this study, soil samples were collected from 26 cotton fields at Xinhu Farm in Xinjiang Province and analyzed using high-throughput Illumina MiSeq sequencing to investigate the diversity, structure, composition, and interaction networks of soil bacterial and \u003cem\u003ephoD\u003c/em\u003e communities. The objectives of this study were to (i) determine the abundance, diversity, and structural composition of bacterial and \u003cem\u003ephoD\u003c/em\u003e communities in response to varying cotton yield levels, (ii) identify the core taxa within cotton rhizosphere microbiome, and (iii) elucidate the most important factors influencing the bacterial and \u003cem\u003ephoD\u003c/em\u003e community and network structure in the cotton of rhizosphere. This study aims to provide theoretical support and a reference basis for an in-depth understanding of the microbial mechanism underlying high crop yields.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area was located at Xinhu Farm in Manas County (44°20′-44°55′N, 86°15′-86°35′E), Xinjiang Province, Northwest China, which is characterized by a typical semi-arid to arid climate. The study sites experience a mean annual rainfall of 194.3 mm, a mean annual surface evaporation of 1988.5 mm, and a mean annual temperature of 7.1 ℃. The altitude ranges from 400 to 500 m above sea level, with a frost-free period of 160 to 190 days, an annual sunshine duration of 3110 h, and total annual solar radiation is 5560 MJ m\u003csup\u003e-2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eXinhu farm has 22,666 hectares of arable land, with cotton as its primary crop, covering 18,666 hectares. Over the past five years, the average seed cotton yield has been 6215 kg ha\u003csup\u003e-1\u003c/sup\u003e. The cotton (Gossypium hirsutum L.) was cultivated using a mulched drip irrigation system. The mulched drip irrigation pattern consists of \"one mulch, three rows of drip tape, and six rows of cotton\". Specifically, six rows of cotton, with spacing of 0.13 m + 0.63 m+ 0.13 m + 0.63 m+ 0.13 m, were seeded under one 2.05-m-wide strip of plastic film, with three rows of drip irrigation belts placed in the narrow rows (0.13m) (Wang et al., 2022). The nitrogen, phosphorus and potassium fertilizer application were 300-350 kg N ha\u003csup\u003e-1\u003c/sup\u003e, 100-150 kg P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e ha\u003csup\u003e-1\u003c/sup\u003e and 75-90 kg K\u003csub\u003e2\u003c/sub\u003eO ha\u003csup\u003e-1\u003c/sup\u003e, respectively. Basal nitrogen fertilizer was applied at rate of 60 kg ha\u003csup\u003e-1\u003c/sup\u003e. And the remaining dosage was fertigated through the mulched drip irrigation system at a ratio of 3:4:3 during squaring, early flowering boll, and late flowering boll stages. Phosphorus and potassium fertilizers were applied prior to sowing using a broadcast method. Drip irrigation was performed 8-10 times throughout the growth period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSite selection and\u0026nbsp;soil sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil samples were collected from 26 study sites at Xinhu Farm, which are shown in Fig. 1. At each study site (covering approximately 10-20 hectares), three sampling lines were selected with an angle of approximately 120 degrees between adjacent lines. Along each sampling line, ten rhizosphere soil cores were collected at 5-meter intervals to a depth of 0-20 cm. The loosely adhered soil was gently shaken off the roots, and the more tightly bound rhizosphere soil was collected using a brush. The collected soil cores from each sampling line were combined to obtain approximately 200 grams of rhizosphere soil per site, resulting in a total of 78 samples collected in August 2023 (boll stage of cotton). Samples were thoroughly mixed, placed in zip-lock bags, transported to the laboratory, and stored at -80 ℃. The rhizosphere soil samples were divided into two portions using the quartering method: one portion was air-dried, sieved through a 2 mm mesh, and subjected to chemical and physical analyses, while the other portion was stored at -80 ℃ for subsequent high-throughput sequencing analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements of soil properties and cotton yield\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil pH and electronic conductivity (EC) were measured in a 1:5 soil-to-water solution using a pH meter and a conductivity meter, respectively. Soil total carbon (TC) content was determined using a Vario Max element analyzer (Elementar, Hanau, Germany). Soil total nitrogen (TN) was determined via the Kjeldahl distillation-titration method. For total potassium (TK) and total phosphorus (TP) determination, HF-HClO\u003csub\u003e4\u003c/sub\u003e was used for digestion, and TP was measured using the molybdenum-blue method, while TK was detected using a flame photometer. Available phosphorus (AP) was determined using the molybdo-vanadophosphate method in air-dried soil extracted with 0.5 M NaHCO\u003csub\u003e3\u003c/sub\u003e (pH 8.5) at 25 ℃. Organic phosphorus (OP) was estimated using a high-temperature combustion method. Air-dried soil samples were combusted at high temperatures and subsequently digested with half-strength sulfuric acid (1/2 H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e). The released phosphate was detected using the molybdenum blue colorimetric method, and the organic phosphorus content was calculated by subtracting the amount of inorganic phosphorus extracted by 1/2 H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e from the same quantity of uncombusted soil. OP was also determined using the molybdo-vanadophosphate method, with soil extracted using 0.25 M NaOH-0.05 M EDTA in a 1:10 (w/v) ratio for 16 hours on a horizontal shaker \u0026nbsp;[46]. Soil texture was measured using the hydrometer method, categorizing the soil into clay (\u0026lt;0.002 mm), silt (0.002-0.05 mm), and sand (0.05-2 mm). Microbial biomass carbon (MBC) and microbial biomass phosphorus (MBP) were analyzed on moist soil using the chloroform fumigation-extraction method [47,48]. Alkaline phosphatase (ALP) activity was determined using the method described by Tabatabai and Bremner [49].\u003c/p\u003e\n\u003cp\u003eFor each study site, seed cotton was harvested from a 6.67 m² area in the center of the plastic mulch on October 3, 2023 (cotton harvest stage). The seed cotton weight was recorded to determine yield. Based on seed cotton yield, the study samples were categorized into three experimental groups: low yield (\u0026lt;6000 kg ha\u003csup\u003e-1\u003c/sup\u003e), medium yield (6000-6500 kg ha\u003csup\u003e-1\u003c/sup\u003e), and high yield (\u0026gt;6500 kg ha\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction, PCR ampliffcation, Illumina MiSeq sequencing and sequencing data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil genomic DNA from 26 fresh soil samples were extracted using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s instructions [50]. The extracted DNA quality was checked by 1% agarose gel electrophoresis, and then stored at -20 °C prior to further analysis.\u003c/p\u003e\n\u003cp\u003eNext generation sequencing was performed in Personalbio, Inc. (Shanghai, China). The primer pair 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') was used to amplify the V3–V4 region of bacterial 16S rRNA genes. The bacterial phoD (alkaline phosphatase) genes were amplified using the primer pairsALPSF730/ALPSR1101(CAGTGGGACGACCACGAGGT/GAGGCCGATCGGCATGTCG) \u0026nbsp;[51]. Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. The PCR components contained 5 μl of buffer (5×), 0.25 μl of Fast pfu DNA Polymerase (5U/μl), 2 μl (2.5 mM) of dNTPs, 1 μl (10 uM) of each Forward and Reverse primer, 1 μl of DNA Template, and 14.75 μl of ddH\u003csub\u003e2\u003c/sub\u003eO. The PCR amplification conditions for 16S rRNA genes were as follows: pre-denaturation at 98 °C for 5 min, followed by 25 cycles consisting of denaturation at 98 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 45 s, with a final extension of 5 min at 72 °C. The PCR amplification conditions for \u003cem\u003ephoD\u003c/em\u003e genes were as follows: pre-denaturation at 94 °C for 3 min, followed by 35 cycles consisting of denaturation at 94 °C for 60 s, annealing at 57 °C for 60 s, and extension at 72 °C for 120 s, with a final extension of 7 min at 72 °C. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and pair-end 2×250 bp sequencing was performed using the Illlumina NovaSeq platform with NovaSeq 6000 SP Reagent Kit (500 cycles) at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China).\u003c/p\u003e\n\u003cp\u003eThe obtained original sequences were spliced and filtered using Flash v1.2.11 and Trimmomatic v0.33 software to obtain high quality sequences. The UCLUST consensus taxonomy assigner (UCLUST v1.2.22q) was used to cluster the sequences with 97 % or higher similarity into operational taxonomic units (OTUs) [52]. Species annotation was performed using the GreenGene Database based on the Ribosomal Data Project (RDP) database. The software and algorithms used in the analyses are included in the QIIME platform.The bacterial \u003cem\u003ephoD\u003c/em\u003e gene sequences obtained in this study were deposited in the National Centre for Biotechnology Information Sequence Reads Archive (Accession Number MD2023033110054FAG).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data was pre-processed using Excel 2016 and SPSS 27. ArcGIS was used to map sample points across different seed cotton yield types, and a box plot was employed to illustrate the changes in different groups’ soil properties and bacterial community diversity index. To investigate the similarities and differences in community composition among samples of different groups, the species diversity of microbial communities was compared by the non-metric location scaling method (NMDS). Origin 2021 software was used to draw the relationship between microbial communities and environmental factors. The bacterial symbiotic network map, functional annotation, and phylum-level bacterial dominant species abundance map were drawn using R 4.1.1 software. The Mantel test was used to analyze the relationship among microbial communities and diversity and environmental factors. Finally, to explore the possible pathways of bacterial and \u003cem\u003ephoD\u003c/em\u003e composition, network structure, and environmental variables, the partial least squares path model (PLS-PM) model was used for analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSoil physicochemical properties and seed cotton yield\u003c/h2\u003e \u003cp\u003eThere were significant differences (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) in seed cotton yield, EC, TC, TN, TP, AP, OP, Texture, MBC, MBP and ALP among 26 sites in study area (Table S1). According to the yield level, 26 points were divided into low yield field (6, 8,11 and 21 plots), medium yield field (3, 4, 7, 9, 10, 13, 14, 16, 17, 18, 19, 22 and 23 plots) and high yield field (1, 2, 5, 12, 15, 20, 24, 25 and 26 plots). The TC, TP and MBC significantly differed among low, medium and high yield field (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), and the TC was highest in the high yield field (HYF), followed by the medium yield field (MYF), and finally the low yield field (LYF), and the TP was highest in the MYF, followed by in the HYF, and finally in the LYF; and the MBC was highest in the HYF, followed by the LYF, and finally in the MYF; the highest and lowest levels were 22.5 g/kg, 1.55g/kg, 525 mg/kg and 11.8 g/kg, 1.14 g/kg, 353 mg/kg, respectively. There were not significant differences in EC, pH, TN, AP, OP, texture, MBP and ALP among low, medium and high yield field.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiversity analysis of rhizosphere microbial communities\u003c/h3\u003e\n\u003cp\u003eThe Chao1 index obtained in the α-diversity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) showed that the soil bacterial richness levels of the three groups (LYF, MYF and HYF) were significantly different (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). The richness of the bacterial community was highest in the MYF, followed by the HYF, and the lowest richness was detected in the LYF. The Goods coverage index showed significant differences in the true nature of the bacterial community among different groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the MYF holds the lowest index (0.972), and the LYF holds the highest index (0.981). The Shannon Simpson, Observed and Species index of bacterial community in different groups presented a consistent trend as follows: MYF\u0026thinsp;\u0026gt;\u0026thinsp;HYF\u0026thinsp;\u0026gt;\u0026thinsp;LYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), but there was no significant difference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was no significant difference in the Chao1, Goods coverage, Shannon Simpson, Observed and Species index of \u003cem\u003ephoD\u003c/em\u003e community in different groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). According to nonmetric multidimensional scaling (NMDS) analysis, the composition of the rhizosphere soil bacterial and \u003cem\u003ephoD\u003c/em\u003e communities was not differed significantly among LYF, MYF and HYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the composition of the rhizosphere microbial communities\u003c/h2\u003e \u003cp\u003eIn total, 31 phyla of bacteria and 19 phyla of \u003cem\u003ephoD\u003c/em\u003e were identified in the 78 soil samples of the three groups (LYF, MYF, and HYF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, C). The abundance of dominant phylum of bacteria was 98.1%, 98.3%, and 98.4% in LYF, MYF, and HYF. Among all samples, the nine most abundant phyla of bacteria were Proteobacteria (29.3%), Actinobacteria (19.5%), Acidobacteria (17.8%), Gemmatimonadetes (16.5%), Chloroffexi (6.5%), Bacteroidetes (2.4%), Rokubacteria (2.3%), Firmicutes (1.6%), and Nitrospirae (1.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). ANOVA revealed that except for Actinobacteria, Gemmatimonadetes and Bacteroidetes, other bacterial phyla showed no significant differences in three groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The abundance of Actinobacteria phyla was the highest in MYF. The abundance of Gemmatimonadetes and Bacteroidetes phyla was the highest in HYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The abundance of dominant phylum of \u003cem\u003ephoD\u003c/em\u003e was 67.9%, 59.6%, and 60.4% in LYF, MYF, and HYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Among all samples, the nine most abundant phyla of \u003cem\u003ephoD\u003c/em\u003e were Proteobacteria (48.9%), Actinobacteria (13.3%), Planctomycetes (0.2%), Acidobacteria (0.1%), Firmicutes (0.1%), Bacteroidetes (0.01%), Cyanobacteria, Euryarchaeota and Verrucomicrobia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). ANOVA revealed that except for Acidobacteria and Firmicutes, other \u003cem\u003ephoD\u003c/em\u003e phyla showed no significant differences in three groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The abundance of Acidobacteria phyla was the highest in LYF. The abundance of Firmicutes phyla was the highest in MYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, 20 genus of bacteria and 50 genus of \u003cem\u003ephoD\u003c/em\u003e were identified in the 78 soil samples of the three groups (LYF, MYF, and HYF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C). The abundance of dominant genus of bacteria was 38.0%, 38.4%, and 40.3% in LYF, MYF, and HYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among all samples, the nine most abundant genus of bacteria were \u003cem\u003eSubgroup_6\u003c/em\u003e (10.4%), \u003cem\u003eMND1\u003c/em\u003e (2.6%), \u003cem\u003eS0134_terrestrial_group\u003c/em\u003e (2.4%), \u003cem\u003eRokubacteriales\u003c/em\u003e (2.3%), \u003cem\u003eRB41\u003c/em\u003e (1.9%), \u003cem\u003eSphingomonas\u003c/em\u003e (1.7%), \u003cem\u003eGemmatimonas\u003c/em\u003e (1.6%), \u003cem\u003ebacteriap25\u003c/em\u003e (1.6%), and \u003cem\u003eJG30-KF-CM45\u003c/em\u003e (1.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). ANOVA revealed that except for \u003cem\u003eMND1\u003c/em\u003e and \u003cem\u003eRokubacteriales\u003c/em\u003e, other bacterial genus showed no significant differences in three groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The abundance of \u003cem\u003eMND1 and Rokubacteriales\u003c/em\u003e genus was the highest in HYF. The abundance of dominant genus of \u003cem\u003ephoD\u003c/em\u003e was 4.4%, 4.9%, and 4.0% in LYF, MYF, and HYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Among all samples, the nine most abundant genus of \u003cem\u003ephoD\u003c/em\u003e were \u003cem\u003eStreptomyces\u003c/em\u003e (1.5%), \u003cem\u003eSinorhizobium\u003c/em\u003e (1.1%), \u003cem\u003eAmycolatopsis\u003c/em\u003e (0.4%), \u003cem\u003eBradyrhizobium\u003c/em\u003e (0.4%), \u003cem\u003eSaccharopolyspora\u003c/em\u003e (0.3%), \u003cem\u003eStella\u003c/em\u003e (0.2%), \u003cem\u003eVariibacter\u003c/em\u003e (0.1%), \u003cem\u003eFrankia\u003c/em\u003e (0.1%) and \u003cem\u003ePseudomonas\u003c/em\u003e (0.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). ANOVA revealed that except for \u003cem\u003eFrankia\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, other \u003cem\u003ephoD\u003c/em\u003e genus showed no significant differences in three groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The abundance of \u003cem\u003eFrankia\u003c/em\u003e genu\u003cem\u003es\u003c/em\u003e was the highest in MYF. The abundance of \u003cem\u003ePseudomonas\u003c/em\u003e genus was the highest in LYF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCo-occurrence networks of the rhizosphere microbial communities\u003c/h2\u003e \u003cp\u003eTo understand the differences in rhizosphere microbial network interactions among different bacterial and \u003cem\u003ephoD\u003c/em\u003e groups, the symbiotic network between bacterial and \u003cem\u003ephoD\u003c/em\u003e species under different groups were constructed using OTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Nodes in the bacterial and \u003cem\u003ephoD\u003c/em\u003e co-occurrence network included five bacterial phyla (Actinobacteria, Proteobacteria, Acidobacteria, Chloroffexi and Gemmatimonadetes) and four \u003cem\u003ephoD\u003c/em\u003e phyla (Proteobacteria, Actinobacteria, Firmicutes and Pseudomonadota; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B), respectively. A unique microbial co-occurrence network formed in each treatment. The most complex network was in LYF, followed by the HYF, and the lowest in the MYF. Compared with MYF and HYF networks, LYF networks had more significant values of average degree, number of edges and modularity in bacterial communities and average degree and number of edges in \u003cem\u003ephoD\u003c/em\u003e communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The average path length of bacterial communities in MYF was the largest, and LYF\u0026rsquo;s was the smallest (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The average path length of \u003cem\u003ephoD\u003c/em\u003e communities in HYF was the largest, and MYF\u0026rsquo;s was the smallest (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between rhizosphere microbial communities and environmental factors\u003c/h2\u003e \u003cp\u003eAccording to the Mantel test results, bacterial community structure showed significant positive correlations with AP, MBC and TN and exhibited extremely significant positive correlation with pH, EC, OP and clay content (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The bacterial Simpson index showed a highly significant positive association with EC and a significant positive association with TP and clay content. The \u003cem\u003ephoD\u003c/em\u003e community structure was significantly positively correlated with EC and TP. However, no notable correlation was observed between the \u003cem\u003ephoD\u003c/em\u003e Simpson index and environmental factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe RDA indicated that environmental factors exerted strong influences on rhizosphere microbial communities, and that RDA1 and RDA2 explained 37.04%, 57.75% and 16.64%, 13.64% of the bacterial and \u003cem\u003ephoD\u003c/em\u003e differences at phylum-level, and that RDA1 and RDA2 explained 41.16%, 20.65% and 15.08%, 9.47% of the bacterial and \u003cem\u003ephoD\u003c/em\u003e differences at genus-level, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). EC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.447, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and clay (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.308, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) for the bacterial community at phylum-level and TP (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.270, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) for the \u003cem\u003ephoD\u003c/em\u003e community at phylum-level reached significant levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, C). At the genus level, TN (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.398, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), EC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.410, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) SALP (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.298, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and TC (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.260, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) were significant factors for the bacterial community (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDominant populations play a crucial role in stabilizing the structure and function of microorganisms, which were mainly affected by surrounding environmental factors. Therefore, the species with the highest coverage of identifiable microorganisms at the phylum level and the genus level were selected for correlation analysis with soil physicochemical properties (Fig. S1). The number of bacterial communities in the most common phyla was significantly correlated with the soil EC, AP, TC, TN and ALP (Fig. S1A). Proteobacteria showed extremely significant positive correlations with soil EC and significant negative correlations with OP. Actinobacteria was significant negatively correlated with AP and TC. Acidobacteria exhibited significant positive correlations with TN and ALP, and significant negative correlations with soil EC. Gemmatimonadetes was significant positively correlated with soil EC, AP, TC, TN and MBC. Chloroflexi was extremely significant negatively correlated with soil EC. Rokubacteria showed significant positive correlations with TC and OP and significant negative correlations with EC. Bacteroidetes was significant positively correlated with soil EC and significant negatively correlated with OP and ALP. Firmicutes exhibited significant negative correlations with soil EC, TN and ALP. Nitrospirae was significant positively correlated with AP and TP and significant negatively correlated with soil EC and pH. Planctomycetes was significant negatively correlated with MBP. AP, TC and TN were closely correlated with the abundance of the dominant \u003cem\u003ephoD\u003c/em\u003e phyla (Fig. S1C). Proteobacteria was significant positively correlated with soil EC and significant negatively correlated with TC. Actinobacteria showed significant negative correlations with AP, TP, TN and ALP, and significant positive correlations with pH. Acidobacteria was significant negatively correlated with AP and TC. Firmicutes was extremely significant negative correlations with TC. Bacteroidetes was significant negatively correlated with TN.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis results showed that the abundance of bacterial communities in the most common genera was significantly correlated with soil EC, TC, TN, MBC, and ALP (Fig. S1B). \u003cem\u003eSubgroup_6\u003c/em\u003e showed significant positive correlations with AP, TN and ALP. \u003cem\u003eMND1\u003c/em\u003e was significant positively correlated with TC, TN and ALP. \u003cem\u003eRokubacteriales\u003c/em\u003e exhibited significant positive correlations with TC and MBP, and significant negative correlations with soil EC. \u003cem\u003eS0134_terrestrial_group\u003c/em\u003e was significant positively correlated with soil EC and pH and significant negatively correlated with TN, OP and ALP. \u003cem\u003eRB41\u003c/em\u003e was extremely significant negatively correlated with soil EC and significant positively correlated with AP. \u003cem\u003eSphingomonas\u003c/em\u003e showed significant positive correlations with soil EC and MBC and significant negative correlations with OP and ALP. \u003cem\u003eBacteriap25\u003c/em\u003e was significant positively correlated with TN and ALP. \u003cem\u003e67\u0026thinsp;\u0026minus;\u0026thinsp;14\u003c/em\u003e exhibited significant negative correlations with soil EC and MBC. \u003cem\u003eGimmatimonas\u003c/em\u003e was significant positively correlated with soil EC, TC and MBC. \u003cem\u003eJG30-KF-CM45\u003c/em\u003e was significant negatively correlated with soil EC and TC and positively correlated with MBP. AP, ALP and TN were closely correlated with the abundance of the dominant \u003cem\u003ephoD\u003c/em\u003e genera (Fig. S1D). \u003cem\u003eSinorhizobium\u003c/em\u003e and \u003cem\u003eSaccharopolyspora\u003c/em\u003e were significant positively correlated with AP, TN and ALP. \u003cem\u003eStella, Frankia and Luteitalen\u003c/em\u003e exhibited significant positive correlations with TN.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePLS-PM model of rhizosphere microbial communities, network structure, and environmental factors\u003c/h2\u003e \u003cp\u003eTo further elucidate the direct and indirect effects of environmental factors on bacterial and \u003cem\u003ephoD\u003c/em\u003e communities across different groups, PLS-PM was employed based on the results described above (Bacterial: goodness of fit (GOF)\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003ephoD\u003c/em\u003e: GOF\u0026thinsp;=\u0026thinsp;0.42) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003e).The conceptual model for the direct and indirect effects of environmental factors and bacterial communities explained 60.9% of the variation in bacterial diversity, 66.6% of the variation in the bacterial structure, and 14.0% of the variation in bacterial network (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Soil pH, EC, nutrients, ALP and physical properties affected the prediction of bacterial structure with standardized path coefficient (SPC), respectively (SPC = -0.366, 0.690, 0.561, -0.483,0.386). Soil nutrients affected the prediction of bacterial network (SPC\u0026thinsp;=\u0026thinsp;0.374) and soil physical properties affected the prediction of bacterial diversity (SPC\u0026thinsp;=\u0026thinsp;0.535) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Similarly, the conceptual model for the direct and indirect effects of environmental factors on \u003cem\u003ephoD\u003c/em\u003e communities explained 56.8% of the variation in \u003cem\u003ephoD\u003c/em\u003e diversity and 17.4% of the variation in the \u003cem\u003ephoD\u003c/em\u003e network (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Soil nutrients significantly influence the \u003cem\u003ephoD\u003c/em\u003e network (SPC\u0026thinsp;=\u0026thinsp;0.417), whereas soil physical properties and EC effected on \u003cem\u003ephoD\u003c/em\u003e diversity (SPC = -0.432, 0.337) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCotton is an important cash crop widely cultivated under film-mulched drip irrigation in Xinjiang province, an arid and semi-arid region that contributes approximately 90% of China's cotton output annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, substantial differences in cotton yield levels exist in this area, primarily influenced by soil fertility, field management techniques, cotton varieties, local climate, and other environmental factors [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Soil microbial diversity and composition are potential indicators of soil fertility quality, and play crucial roles in organic matter decomposition, soil nutrient cycling and transformation, soil biodiversity, soil ecosystem stability, plant nutrient absorption and utilization, and ultimately crop yield [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this study, we investigated changes in soil properties, soil bacterial community composition, and network structure under different cotton yield types. Based on our findings, we identified core taxa within the cotton rhizosphere microbiome and elucidated the most important factors affecting the bacterial community and network structure. Our study provides insights into the biological mechanisms underlying high cotton yield.\u003cb\u003eSoil physicochemical properties of cotton fields with different yield\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSoil physicochemical properties are not only fundamental components of soil fertility but are also key indicators of sustainable soil sustainable development and improve crop productivity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In the present study, we found that the soil physicochemical properties in HYF were superior to those in MYF and LYE, especially, the TC, TP and MBC in high-yield field were significantly higher than those in low-yield field. This further supports the notion that soil physicochemical properties are critical to soil quality and serve as an important foundation for achieving high yields [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Moreover, the high-yield cotton fields contributed substantial straw residues to the soil, which promoted the formation of soil organic matter and carbon transformation, thereby increasing the content of TC, TP, and MBC\u0026mdash;results consistent with several previous studies [\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. However, no significant differences were observed in EC, pH, total nitrogen (TN), available phosphorus (AP), organic phosphorus (OP), texture, microbial biomass phosphorus (MBP), and ALP activity among the different yield fields. This could be attributed to the fact that all soil samples were collected from the same farm, which shares similar climate, cultivation practices, water and fertilizer management, and soil conditions.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere soil microbial community structure of cotton fields with different yield\u003c/h2\u003e \u003cp\u003eSoil microbial composition and diversity are potential indicators of soil quality and important components and the most active constituents in agroecosystems, as well as important factors in soil formation and development, soil organic carbon transformation, nutrient cycles, ecosystem balance, soil function maintenance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. They are associated with the level of crop yields. The results of soil bacterial communities based on high throughput sequencing showed that the Chao1 index and the Goods coverage index of dominant species varied significantly under different yield types. The Chao1 index of the bacterial community was highest in the MYF and lowest in LYF, consistent with previous studies. Chen et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] showed that the higher Chao1 indices were observed from the NPK and NP treatments with moderate wheat biomass, and the similar lower values from the NK and Nil treatments with low wheat biomass. Feng et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] found higher Chao1 index in moderate saline-alkali soil with middle cotton yield, lower values in heavily saline-alkali soil with lowest cotton yield. In this study, the MYF holds the lowest Goods coverage index and the LYF holds the highest Goods coverage index. Fu et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] reported alfalfa yield was detected as the strongest factor, which simultaneously associated with the bacterial α-diversity (the maximum Chao1 index appears at the moderate yield level). These results suggested that there was a suitable soil environmental threshold for soil microbial diversity [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The Shannon Simpson, Observed and Species index of bacterial community and the Chao1, Goods coverage, Shannon Simpson, Observed and Species index of \u003cem\u003ephoD\u003c/em\u003e community in different groups were not significantly differ, as well as the composition of the rhizosphere soil bacterial and \u003cem\u003ephoD\u003c/em\u003e communities was not differed significantly among LYF, MYF and HYF based on NMDS analysis. One possible explanation is that similar soil conditions, cultivation patterns and management techniques affecting soil microbial composition and diversity in different groups [\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. The underlying mechanisms require further investigation.\u003c/p\u003e \u003cp\u003eSoil function and niche depends on not only the diversity of soil microbes, but also on community compositions [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Microorganisms are important components and decomposers of soil, which is affected by different crop, soil environment, ecosystem, management model and other factors. Liang et al. [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] found the Planctomycetes, Acidobacteria, Proteobacteria, Gemmatimonadetes, Verrucomicrobia, Chloroflexi, Bacteroicetes, Nitrospirae and Saccharlbacteria were dominant bacterial phyla of wheat in Northwest China. Hou et al. [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] reported the dominant phyla of bacteria in the corn field in Northeast China were Proteobacteria, Thaumarchaeota, Actinobacteria, Acidobacteria and Verrucomicrobia, which altogether accounted for 82%-87%. Iqbal et al. [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] showed that the top five dominant phyla in rice fields in South China were Chloroflexi, Proteobacteria, Firmicutes, Acidobacteria and Planctomycetes, which reported more than 70% of the relative abundance of the bacterial communities. The dominant bacterial phyla tested using in the kiwifruit orchard soil were Proteobacteria, Acidobacteria, Bacteroidetes, and Actinobacteria [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. In the present study, the nine most abundant phyla of bacteria in cotton rhizosphere soil were Proteobacteria (29.3%), Actinobacteria (19.5%), Acidobacteria (17.8%), Gemmatimonadetes (16.5%), Chloroffexi (6.5%), Bacteroidetes (2.4%), Rokubacteria (2.3%), Firmicutes (1.6%), and Nitrospirae (1.5%), which agreed with prior reports in cotton field soil [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. These results indicated Proteobacteria, Actinobacteriota and Acidobacteria might be the core phyla of soil bacteria in agroecosystem. Additionally, we also found that the species composition of the soil bacterial community was similar under different cotton yield levels, but the relative abundance of the soil bacterial community was different under different cotton yield levels. In our study, the abundance of Actinobacteria phyla was the higher in MYF and LYF. Actinobacteria is a ubiquitous bacterial groups and persistent population in agroecosystem, which is predominant in dry alkaline soil [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Actinobacteria play major roles in the cycling of organic matter, decompose complex mixtures of polymer in plant litter results in production of many extracellular enzymes which are conductive to crop production. Actinobacteria identified as a group of copiotrophic taxa and thrive in the condition of high C availability [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], was revealed negatively correlated with cotton yield in our study. This result is consistent with the research results of Fu et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] on alfalfa, but not consistent with the research results of Dang et al. [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] on Proso millet (\u003cem\u003ePanicum miliaceum\u003c/em\u003e L.). Therefore, we regarded that crop growth also had great potential in shaping Actinobacteria distribution, but the mechanism needs further study to understand. Gemmatimonadetes have a cosmopolitan distribution in terrestrial systems, which as persistent and important members of soil communities, especially in arid and saline soils [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Gemmatimonadetes can be capable of anaerobic photosynthesis, which play critical roles in regulating the cycling of carbon and facilitating the degradation of cellulose in soil, as well as important participant in biogeochemical transformations in soils under salinity and drought [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In this study, soil samples were collected from saline-alkali soil in arid and semi-arid area, and the abundance of Gemmatimonadetes phyla was the highest in HYF (TC and MBC were the highest). These results are useful for understanding the living environment and ecological function for Gemmatimonadetes species. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Bacteroidetes are a ubiquitous bacterial group prevalent in various soil ecosystems, due to their remarkable versatility in ecological niches adaptation and genomic plasticity [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Bacteroidetes play a critical role in maintaining soil health and ecosystem function, such as degrading organic matter, promoting nutrient cycling, producing growth-stimulating phytohormones, and stabilizing microbial communities [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Bacteroidetes, generally regarded as r-strategists in eutrophic environments, were found to be abundant in HYF, supporting their association with high soil fertility [\u003cspan additionalcitationids=\"CR90\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the genus level, the abundance of dominant genus of bacteria was 38.0-40.3% in cotton field soil. \u003cem\u003eSubgroup_6\u003c/em\u003e (10.4%), \u003cem\u003eMND1\u003c/em\u003e (2.6%), \u003cem\u003eS0134_terrestrial_group\u003c/em\u003e (2.4%), \u003cem\u003eRokubacteriales\u003c/em\u003e (2.3%), \u003cem\u003eRB41\u003c/em\u003e (1.9%), \u003cem\u003eSphingomonas\u003c/em\u003e (1.7%), \u003cem\u003eGemmatimonas\u003c/em\u003e (1.6%), \u003cem\u003ebacteriap25\u003c/em\u003e (1.6%), and \u003cem\u003eJG30-KF-CM45\u003c/em\u003e (1.5%) were the dominant genus in our study, which are almost the same as those in previous studies. In cotton soil in arid areas, \u003cem\u003eGemmatimonas, Sphingomonas, Subgroup_6\u003c/em\u003e and \u003cem\u003eRokubacteriales\u003c/em\u003e are the dominant genus [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. In cotton rhizosphere soil in saline-alkaline soil, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eGemmatimonas\u003c/em\u003e and \u003cem\u003ebacteriap25\u003c/em\u003e are the main genus [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. On continuous cotton field, \u003cem\u003eGemmatimonas\u003c/em\u003e, \u003cem\u003eMND1\u003c/em\u003e and \u003cem\u003eRokubacteriales\u003c/em\u003e are the most abundant genus [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. The dominant bacterial genera in three groups (LYE, MYE and HYE) were not significantly different except for \u003cem\u003eMND1\u003c/em\u003e and \u003cem\u003eRokubacteriales\u003c/em\u003e. The abundance of \u003cem\u003eMND1\u003c/em\u003e and \u003cem\u003eRokubacteriales\u003c/em\u003e genus was the highest in HYF. \u003cem\u003eMND1\u003c/em\u003e may play a role in the nitrogen cycle, including processes like ammonia oxidation, nitrification, or denitrification [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. \u003cem\u003eRokubacteriales\u003c/em\u003e may be involved in the biogeochemical cycles of carbon and sulfur, helping to transform these elements and maintain ecological balance [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. These results indicate that higher carbon and nitrogen conditions increase the relative abundance of MND1 and Rokubacteriales, enhancing their functional roles in soil [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cotton field in Xinjiang province, Northwest of China, are predominantly distributed on alkaline soils with higher pH and calcium carbonate (CaCO₃), which often restrict the availability of phosphorus (P) to plants [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Alkaline phosphatase (ALP) is one of the primary enzymes responsible for the release of available inorganic P from organic P in soil [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. The \u003cem\u003ephoD\u003c/em\u003e gene is one of ALP-encoding genes, which occurs in a broad range of terrestrial and aquatic ecosystems [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Thus, understanding the microbial community structure associated with the \u003cem\u003ephoD\u003c/em\u003e gene can be an effective strategy for enhancing the bioavailability of phosphorus and improving the production of cotton in alkaline soils. In the present study, the abundance of dominant phylum and genus of \u003cem\u003ephoD\u003c/em\u003e was 67.9%, 59.6%, 60.4% and 4.4%, 4.9%, 4.0% in LYF, MYF, and HYF, respectively. Additionally, the most abundant phyla and genus (relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;1%) of \u003cem\u003ephoD\u003c/em\u003e were Proteobacteria (48.9%), Actinobacteria (13.3%), Planctomycetes (0.2%), Acidobacteria (0.1%), Firmicutes (0.1%), and \u003cem\u003eStreptomyces\u003c/em\u003e (1.5%), \u003cem\u003eSinorhizobium\u003c/em\u003e (1.1%), \u003cem\u003eAmycolatopsis\u003c/em\u003e (0.4%), \u003cem\u003eBradyrhizobium\u003c/em\u003e (0.4%), \u003cem\u003eSaccharopolyspora\u003c/em\u003e (0.3%), \u003cem\u003eStella\u003c/em\u003e (0.2%), \u003cem\u003eVariibacter\u003c/em\u003e (0.1%), \u003cem\u003eFrankia\u003c/em\u003e (0.1%) and \u003cem\u003ePseudomonas\u003c/em\u003e (0.1%) across all of the samples in our study, respectively. These are consistent with the findings in cotton soil under organic fertilization [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], but the results were different from the findings in maize-wheat-cotton rotation under long-term field experiment [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. These results indicated the core phylum and genus of \u003cem\u003ephoD\u003c/em\u003e in agroecosystem was not very stable, and was affected by factors such as cultivation systems [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], fertilization strategies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e] and land-use patterns [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eCo-occurrence networks and main factors driving rhizosphere microbial community structure of cotton fields with different yield\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMicrobial co-occurrence networks are crucial for elucidating potential microbial interactions and relationships, thereby enhancing our understanding of microbial ecology and function [\u003cspan additionalcitationids=\"CR107 CR108\" citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. In microbial symbiosis networks, nodes, edges, path length, and modularity each have specific meanings that help describe the structure and dynamics of microbial communities [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. The results of our study showed that low yield field increased the microbial network complexity (number of nodes, edges, and modules). Increased network complexity in low yield field may be caused by higher environmental stress [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], greater microbial diversity [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e], higher functional redundancy [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], dynamic network adaptation [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Mantel test, redundancy analysis (RDA) and PLS-PM revealed the possible pathways between bacterial communities and environmental variables [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. Soil physicochemical properties significantly influence the composition, diversity, and functioning of microbial communities in the soil [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. In the present study, we found that pH, EC, OP, AP, TN, MBC and clay were the main factors driving the changes in bacterial communities structure and diversity, but EC and TP were the main factors driving the changes in \u003cem\u003ephoD\u003c/em\u003e community structure. Soil pH is a key factor that determines the availability of nutrients and can affect microbial activity. Previous studies have shown that different microbial groups thrive in specific pH ranges, regulating the structure and diversity of bacterial communities [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. In saline-alkali regions, EC was the main factors regulating the activity, diversity and structure of the soil microbial communities [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. The availability of macronutrients and micronutrients plays a vital role in shaping microbial communities. Nutrient-rich soils often support higher microbial diversity and activity, which can enhance soil health and fertility [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Soil texture, particularly clay content, is another essential factor in determining microbial community structure [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. Due to their large surface area and chemical binding capacity, clay particles can reduce bacterial diversity [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. In conclusion, our research demonstrated that soil physical properties and nutrient availability are the primary factors influencing bacterial community composition and network structure.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that the soil bacterial communities in the cotton rhizosphere under mulched drip irrigation in semi-arid and arid climate regions were mainly composed of 9 phyla: Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Chloroffexi, Bacteroidetes, Rokubacteria, Firmicutes, and Nitrospirae. At the genus level, the dominant genera of the bacterial community were \u003cem\u003eSubgroup_6\u003c/em\u003e, \u003cem\u003eMND1\u003c/em\u003e, \u003cem\u003eS0134_terrestrial_group\u003c/em\u003e, \u003cem\u003eRokubacteriales\u003c/em\u003e, \u003cem\u003eRB41\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eGemmatimonas\u003c/em\u003e, \u003cem\u003ebacteriap25\u003c/em\u003e, and \u003cem\u003eJG30-KF-CM45\u003c/em\u003e.The high-throughput sequencing data demonstrated that soil bacterial community diversity,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecomposition, and network structure significantly differed between cotton yield types. Medium-yield field exhibited the highest diversity and composition of soil bacterial communities, whereas low-yield fields had the most complex network structure. Furthermore, the soil microbial characteristics of the cotton rhizosphere under mulched drip irrigation were primarily associated with soil physicochemical properties. Soil pH, EC, OP, AP, TN, MBC, and clay content were identified as the key driving factors influencing the changes in rhizosphere bacterial community diversity and network structure. This study, which assessed the characteristic changes and main influencing factors of soil bacterial community and network structure under three cotton yield types in Xinjiang, provides valuable methods and data to support further research aimed at enhancing soil quality and cotton yield in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Project for Young Top-Notch Talents in Science and\u003c/p\u003e\n\u003cp\u003eTechnology of Xinjiang Uygur Autonomous Region (Grant No. 2022TSYCCX0085), the Sponsored by Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2024D01E06), the National Natural Science Foundation of China (Grant No. 32360793, 31960629), the Key Research and Development Project in Xinjiang Uygur Autonomous Region (Grant No. 2022B02033-1), and Special Topics of Major Science and Technology in Xinjiang Uygur Autonomous Region (Grant No. 2022A02007-2).\u0026nbsp;We thank, PhD Jorus Sunstrider, for editing the English text of a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMan Zhang:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Methodology, Investigation, Conceptualization.\u003cstrong\u003e\u0026nbsp;Yang Hu\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Software, Data curation.\u003cstrong\u003e\u0026nbsp;Yue Ma:\u0026nbsp;\u003c/strong\u003eInvestigation, Data curation. \u003cstrong\u003eTianyu Hou:\u0026nbsp;\u003c/strong\u003eInvestigation. \u003cstrong\u003eJuanhong Wang:\u003c/strong\u003e Data curation.\u003cstrong\u003e\u0026nbsp;Qinxuan Che:\u003c/strong\u003e Data curation.\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Bolang Chen:\u0026nbsp;\u003c/strong\u003eProject administration, Formal analysis, Writing \u0026ndash; review \u0026amp; editing, Funding acquisition.\u003cstrong\u003e\u0026nbsp;Qinghui Wang:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Investigation, Formal analysis. \u003cstrong\u003eGu Feng:\u003c/strong\u003e Funding acquisition, Formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed the final version of the manuscript and agree to its submission to this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGuo RS, Zhang N, Wang L, Lin T, Zheng ZP, Cui JP, et al. 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Catena. 2019;172:148-57. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil microbial community, Co-occurrence network, Rhizosphere, Cotton","lastPublishedDoi":"10.21203/rs.3.rs-5689151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5689151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinjiang is situated in an arid and semi-arid region, where abundant heat and sunlight create highly favorable conditions for cotton cultivation. Xinjiang's cotton output accounts for nearly one-quarter of global production. Moreover, the implementation of advanced planting techniques, such as 'dwarfing, high-density, early-maturing' strategies combined with mulched drip irrigation, ensures stable and high yields in this region. Despite these advancements, limited research has focused on the microbial mechanisms in cotton fields employing these advanced planting methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bacterial and \u003cem\u003ephoD\u003c/em\u003e communities in the cotton rhizosphere were predominantly composed of nine bacterial phyla (i.e., Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Chloroflexi, Bacteroidetes, Rokubacteria, Firmicutes, and Nitrospirae) and five \u003cem\u003ephoD\u003c/em\u003e phyla (i.e., Proteobacteria, Actinobacteria, Planctomycetes, Acidobacteria, and Firmicutes), respectively. Alpha diversity analysis indicated that the medium yield cotton field (MYF) exhibited higher bacterial richness and diversity indices compared to low yield (LYF) and high yield (HYF) fields. The symbiotic network analysis of LYF revealed greater values of average degree, number of edges, and modularity, suggesting a more complex network structure in both bacterial and \u003cem\u003ephoD\u003c/em\u003e communities. The Mantel test, RDA, and PLS-PM model identified soil pH, electrical conductivity (EC), organic phosphorus (OP), available phosphorus (AP), total nitrogen (TN), microbial biomass carbon (MBC), and clay content as the main driving factors influencing changes in the rhizosphere bacterial community diversity and network structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings provide a theoretical basis for future research aimed at improving soil quality and cotton yield.\u003c/p\u003e","manuscriptTitle":"Soil bacterial diversity and community structure of cotton rhizosphere under mulched drip- irrigation in arid and semi-arid regions of Northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 17:13:41","doi":"10.21203/rs.3.rs-5689151/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7624e67a-05ca-476b-9c27-a87522ad936e","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-04T22:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 17:13:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5689151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5689151","identity":"rs-5689151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}