Next Article in Journal
Faba Bean–Oat Mixtures Modify Rhizosphere Microbiota and Nutrient–Biomass Regulation on the Qinghai–Tibetan Plateau
Previous Article in Journal
Recirculating Aquaculture Biosolids Are Comparable to Synthetic Fertilizers for Grain Protein and Yield in Durum Wheat
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 9
10.3390/agronomy15092238
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Bio-Organic Fertilizers Enhance Yield in Continuous Cotton Cropping Systems Through Rhizosphere Microbiota Modulation and Soil Nutrient Improvement

by
Mengmeng Yu
,
Hao He
,
Liyang Cheng
,
Shuai Li
,
Tingting Wan
,
Jie Qin
and
Junhua Li
*
Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, College of Agriculture, Shihezi University, Shihezi 832003, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(9), 2238; https://doi.org/10.3390/agronomy15092238
Submission received: 20 August 2025 / Revised: 16 September 2025 / Accepted: 19 September 2025 / Published: 22 September 2025
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

The application of bio-organic fertilizers (BOFs) represents a promising strategy for mitigating soil degradation in continuous monoculture systems, yet their long-term mechanistic impacts in aged cotton fields remain poorly elucidated. This study aims to uncover how BOFs enhance soil health, reshape microbial communities, and sustain cotton productivity under decades-long continuous cropping in Xinjiang, China. A two-year field experiment compared conventional chemical fertilization (CK, N−P−K: 300–180–150 kg·ha−1) with combined chemical and BOF treatment (BOF, N−P−K: 300−180−150 kg·ha−1, BOFs: 4159 kg·ha−1 in 2023 and 4545 kg·ha−1 in 2024). The BOFs used in this study contained ≥40.0% organic matter and ≥0.20 × 108 CFU·g−1 of Bacillus amyloliquefaciens. The results demonstrated that BOF application significantly increased seed cotton yield by 19.82–28.17% and total plant biomass by 56.66–61.97%, with the latter reflecting improved root development and nutrient acquisition—key factors contributing to yield gains. Soil analysis indicated substantial elevations in organic matter (12.05–17.72%) and available nutrients without altering pH. Metagenomic sequencing revealed that the BOF treatment enriched beneficial taxa (e.g., Lysobacter increased by 50.53%), suppressed Fusarium (decreased by 36.08%), enhanced microbial network complexity, and reinforced disease-suppressive functions. These findings provide mechanistic insights into the role of BOFs in restoring rhizosphere ecology and promoting soil resilience. This study supports the practical integration of BOFs as a sustainable measure for rejuvenating degraded cotton monoculture systems and optimizing fertilizer management in arid agroecosystems.

1. Introduction

Cotton (Gossypium spp.), cultivated across more than 100 countries, including China, the United States, and Australia, fulfills approximately 31% of global fiber demands [1,2]. As a strategic agricultural commodity in China, it serves as the cornerstone of the textile industry. Xinjiang Autonomous Region, accounting for over 95% of the national cotton cultivation area [3], has emerged as a pivotal production hub due to its suitable agroecological conditions [4]. However, intensive monoculture practices have precipitated widespread continuous cropping systems in this region. This agricultural paradigm has triggered multifactorial challenges, including progressive soil nutrient depletion [5,6] and aggravated soil-borne pathogen prevalence (Fusarium spp. and Verticillium dahliae) [7]. Prolonged cotton monoculture reduces microbial diversity [8] and depletes beneficial taxa (e.g., Bacteroidota) [9,10]. Such synergistic deteriorations ultimately compromise crop productivity and fiber quality metrics [11].
Continuous cropping obstacles primarily stem from rhizosphere microbiome dysbiosis [6]. Currently, the mainstream methods to overcome this obstacle include crop rotation, soil sterilization, and chemical control [12,13], but these methods have obvious limitations. Crop rotation requires a relatively long period to affect the viability and distribution of pathogens [14]. Soil disinfection and chemical substances have a significant impact on the soil environment, may cause secondary damage to the soil ecology, and are difficult to repair [15]. It has recently been demonstrated that introducing beneficial microorganisms into soils used for continuous cropping is a quick and efficient way to lessen crop succession disorders [16]. Crop residues, food waste, animal manure, and brewing residues undergo harmless fermentation and maturation to become organic carriers. These carriers are then mixed with specific functional microorganisms and processed via temperature-controlled fermentation to produce bio-organic fertilizers (BOFs) [5,17]. This type of fertilizer not only has balanced and comprehensive nutrients, long-lasting fertility, and high levels of functional microorganisms, but also features a simple and easy application method. It has been widely used in fields such as plant growth promotion and soil-borne disease control [13]. Compared with other methods, applying BOFs more safely, effectively and conveniently alleviates soil degradation and crop yield reduction from continuous cropping. However, compared with chemical fertilizers, BOFs have a relatively slow nutrient release rate [18]. Applying BOFs alone is often insufficient to meet the nutrient demands of crops throughout their entire life cycle [19]. Therefore, it is necessary to apply BOFs in combination with chemical fertilizers. Chemical fertilizers have high nutrient content and clear composition, with fast nutrient release and rapid efficacy [20,21], which can meet the high nutrient demand of cotton during its key growth stages.
Previous research shows BOF application reduces crop Malondialdehyde (MDA) levels [22] and enhances defensive enzyme activity (e.g., superoxide dismutase (SOD), catalase (CAT), peroxidase (POD)) [13,23]. Malondialdehyde (MDA) is a widely used marker for oxidative lipid damage induced by environmental stress [24], and SOD, CAT, and peroxidase POD are core enzymes in the antioxidant defense system of crops [25,26,27,28,29]. This indicates that BOFs are beneficial for enhancing the antioxidant capacity of crops. Moreover, concurrent application of BOFs and chemical fertilizer harmonizes the supply of organic and inorganic nutrients. This enhances crops’ net photosynthetic rate and chlorophyll content [30], thereby promoting dry matter accumulation [31]. The combined application of BOFs and chemical fertilizers boosts crop yield and fertilizer use efficiency by enhancing crop nutrient uptake and yield components [32,33,34]. BOFs have been shown to be effective in boosting soil nutrient availability and enzyme activities [35,36], decreasing soil bulk density [37,38], and improving soil aggregation [39,40]. Moreover, BOFs use boosts soil water retention [41], aids soil moisture and nutrient storage [42,43], and creates a favorable environment for crop root growth. These enhancements in soil characteristics contribute to improved soil quality and subsequently enhance crop productivity.
Agroecosystems rely on soil microorganisms as key regulatory elements. These microorganisms support crop nutrient supply and growth via multiple biological mechanisms (e.g., nutrient transformation, growth-regulating substance secretion) [44]. Furthermore, these microorganisms have been shown to be crucial in maintaining soil material circulation, facilitating bioremediation, and contributing to ecosystem balance [38,45]. BOFs, a blend of organic fertilizer and probiotic microorganisms, have been demonstrated to stimulate the growth of beneficial soil bacteria. The inherent functional bacteria, along with the activated indigenous beneficial bacteria, can effectively prevent and control plant diseases by producing antibacterial compounds and competing for ecological niches [46]. It has been reported that BOFs can increase the abundance of saprophytic fungi, thereby enhancing the metabolism of mineral elements [38]. Prior research has demonstrated that BOFs use enhances beneficial bacteria (e.g., Sphingomonas, Lysobacter, Nocardia, Bacillus [5,38,47]) that contribute to biological control and plant growth. Meanwhile, it diminishes soil populations of pathogenic bacteria such as Fusarium and Verticillium dahlia [23,48]. The main functional bacterium carried by the BOFs used in this study is Bacillus amyloliquefaciens. Previous studies have confirmed that this bacterium can inhibit the growth of a variety of pathogenic bacteria [49,50]. Moreover, multiple studies [17,35,51,52,53,54] show BOFs enhance soil microbial diversity, abundance, and interaction complexity. These effects play a crucial role in alleviating the adverse impacts stemming from excessive fertilizer application on soil microorganisms [55].
Current BOF research predominantly focuses on pepper, banana, and tomato systems [37,45,56], with limited mechanistic insights into microbiome succession and yield enhancement in decade-long cotton monocultures. Metagenomic sequencing overcomes cultivation limitations through direct soil DNA extraction, enabling comprehensive taxonomic profiling and functional annotation [57,58]. Therefore, this study selected cotton fields with over 20 years of continuous cropping as the research object. A 2-year field experiment explored the effects of combined BOFs and chemical fertilizer application on yield, soil nutrients, and rhizosphere microorganisms in continuous cropping cotton fields. The aim was to clarify the mechanisms underlying the benefits of this combined application. The hypotheses are as follows: (1) BOFs may increase cotton yield by improving soil nutrients; (2) BOFs may increase the diversity of rhizosphere soil microbial communities; (3) Compared with the single application of chemical fertilizer, the combined application of BOFs and chemical fertilizer may reduce the content of pathogenic bacteria and improve the soil environment.

2. Materials and Methods

2.1. Experimental Site Description

The study was conducted between April 2023 and October 2024 in a cotton field with 23 years of continuous cropping history at the 144th Regiment of Xinjiang Production and Construction Corps, China (44°32′24″ N, 85°43′41″ E, elevation 335 m). This location is situated at the northern foot of the Tianshan Mountains and the western part of the southern edge of the Junggar Basin. The region has a temperate continental arid climate The meteorological data during the experiment are shown in Figure 1. During the two-year cotton growing period, the monthly average temperature was 21.88 °C and the monthly average rainfall was 24.31 mm. The initial properties of the tilled soil (0–20 cm) were as follows: soil organic matter (SOM) 8.27 g·kg−1, alkali-hydrolyzed nitrogen (AN) 51.47 mg·kg−1, available phosphorus (AP) 22.15 mg·kg−1, available potassium (AK) 358.90 mg·kg−1, and pH 8.35.

2.2. Experimental Design

CK treatment: conventional chemical fertilizer with N−P−K (300−180−150 kg·ha−1). BOF treatment: conventional chemical fertilizer combined with BOFs; the N−P−K application rate was consistent with CK (300−180−150 kg·ha−1). Each treatment had three replicates, resulting in a total experimental area of 1.2 ha (600 m × 20 m). Individual plots (0.2 ha, 200 m × 10 m) were arranged in strip blocks (Figure 2). Cotton varieties used were “Xinluzao 66” (2023) and “Xinshi K24” (2024) (varieties independently selected by local farmers, both of which are major high-yield cultivars in the region). Chemical fertilizers included urea (N ≥ 46.4%), monoammonium phosphate (N ≥ 12%, P2O5 ≥ 61%), and potassium sulfate (K2O ≥ 51%). The BOFs (organic matter ≥ 40.0%, Bacillus amyloliquefaciens ≥ 0.20 × 108·g−1, produced by Jiangsu Hexi Biotechnology Co., Ltd., Jiangsu, China) were applied as base fertilizer on April 9th each year using a large-scale fertilizer spreader. Application rates 4159 kg·ha−1 (2023) and 4545 kg·ha−1, respectively.
The cultivation system featured a 1-membrane, 3-tube, 6-row configuration (Figure 2; with membrane width 2.05 m, row spacing 66 cm + 10 cm, and sowing width 4.56 m). Seeds were sown mechanically on 15 April each year at a density of 225,000 plants·ha−1, with drip irrigation under plastic mulch: irrigation volumes were 750 m3·ha−1 for seedling emergence and 30–40 days after emergence, followed by 375 m3·ha−1 every 7–10 days, totaling 4500 m3·ha−1. Chemical fertilizers were applied through the drip irrigation system. Phosphorus fertilizer: applied from the second irrigation, with three total applications (each accounting for 1/3 of the total phosphorus rate). Nitrogen and potassium fertilizers: applied from the second irrigation, with seven total applications: the first and last applications each accounted for 1/20 of their respective total rates, the second application for 1/10, and the 3rd to 6th applications each for 1/5. Manual topping was conducted annually from 28 June to 5 July. Defoliant (375 g·ha−1, a mixture of thidiazuron and diuron) was sprayed on 12 September 2023, and 8 September 2024.

2.3. Sample Collection and Measurement

2.3.1. Plant Sample

Plant samples were collected at the mid-boll opening stage annually (10 September 2023, and 4 September 2024), when approximately 50% of cotton bolls had dehisced. Sampling commenced 40 m from the plot boundary, where one plant was randomly sampled from the middle row. Subsequently, one plant was sampled at every 40 m interval along the row, resulting in a total of 3 plants sampled per plot (to avoid edge effects), thus ensuring representative coverage of the entire plot. The plant samples were divided into roots, stems, leaves, bolls, seed cotton, and cotton hulls, then fixed at 105 °C for 30 min and dried to constant weight at 70 °C. Then, the total plant biomass per plant (BP: sum of all organs’ dry weights) was determined.

2.3.2. Yield Measurement

Yield determination was performed once at the late boll opening stage (>90% of cotton bolls dehisced) on 27 September 2023, and 22 September 2024, to ensure accuracy. In each plot, a 2.28 m2 subplot (1 m in length and 2.28 m in width) was selected, with the width covering 6 rows. The subplot was positioned 100 m away from the plot boundary to avoid edge effects. Within this subplot, the number of plants and bolls was counted, and 30 cotton bolls (10 from each of the upper, middle, and lower layers) were weighed to calculate the number of bolls per plant (BN-PP) and the single boll weight (BW). Seed cotton yield (SCY) was subsequently computed based on the counted number of bolls per unit area.

2.3.3. Soil Sample

Soil sampling for analysis of chemical properties was conducted on 10 September 2023, and 4 September 2024 (this period corresponds to the mid-boll opening stage of cotton). For each plot, sampling was initiated 40 m away from the plot boundary, using a soil drill with a 5 cm diameter to collect 5 subsamples along an “S”-shaped path. Each subsample was taken at a position 0–3 cm from cotton root systems and at a depth of 0–20 cm. These subsamples were pooled into one composite sample to avoid edge effects, ensuring representative coverage of the entire plot. Upon transport to the laboratory, visible roots and other debris were removed from the soil samples. The samples were then air-dried, ground, and sieved through 1 mm and 0.15 mm mesh sieves, after which they were stored for subsequent analysis. The determination of soil chemical parameters was conducted in accordance with the method established by Bao [59]. SOM was measured via potassium dichromate oxidation, pH using a pH meter (the soil-to-water ratio was 2.5:1), AN by alkaline diffusion, and AP via vanadium-molybdenum yellow colorimetric method, whilst AK was measured by flame photometry.
Rhizosphere soil sampling was conducted on 9 October 2024 (this period corresponds to the late boll opening stage of cotton, which is also the mechanical harvesting stage). Sampling was initiated 40 m away from the boundary of each plot, and 5 soil subsamples were collected along an “S”-shaped route in each plot and combined into one soil sample (to avoid edge effects). The specific sampling procedure was as follows: Cotton root systems were first excavated from the soil using a shovel; large soil clods and loose soil were removed by gentle shaking. Subsequently, soil adhering to the root surfaces was collected with a brush [37,48,60]. The collected rhizosphere soil was placed in an icebox, transported to the laboratory, and stored frozen at −80 °C for DNA extraction.

2.3.4. Rhizosphere Microbial DNA Extraction and Sequencing

We obtained soil DNA using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA). After DNA was extracted, its concentration and purity were measured, and 1% agarose gel electrophoresis was used to determine its integrity. After DNA was fragmented to 350 bp using a Covaris M220 (Gene Company Limited, Hong Kong, China), NEXTFLEX® Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA) was used to construct PE libraries. Sequencing was performed on the Illumina NovaSeq™ X Plus platform (Majorbio, Shanghai, China). Raw reads were quality-controlled via Fastp (version 0.20.0), assembled via MEGAHIT (version 1.1.2), and open reading frames (ORFs) were predicted via Prodigal (version 2.6.3). CD-HIT (version 4.6.1) was used to generate non-redundant gene sets. Species taxonomic annotation was performed based on gene alignment against the NR database.

2.4. Data Analysis

Data were processed in Excel 2021, and statistical analyses were performed in IBM SPSS Statistics 21. One-way analysis of variance (One-way ANOVA) was used to assess differences among different treatments, and the least significant difference (LSD) test was applied for post hoc multiple comparisons, with a significance level set at p < 0.05. Microbial-related data analysis was conducted on the Majorbio Cloud Platform (https://cloud.majorbio.com) [61]. Alpha diversity indices (ACE, Shannon, Pielou_e) were calculated using the algorithm of Mothur corresponding index analysis and R calculation. Redundancy analysis (RDA is mainly used to reflect the relationship between flora and environmental factors [62]) and principal coordinates analysis (PCoA is a non-binding data dimensionality reduction analysis method that can be used to study the similarities or differences in sample community composition [63]) were conducted. ANOSIM analysis based on the Bray–Curtis distance algorithm was used to test differences among groups. All analyses mentioned above were carried out in R (version 3.3.1) (Vegan). Network analysis was executed utilizing the Python (version 3.6.1) programming language (NetworkX) and subsequently visualized in Gephi (version0.9.2). R (version 3.3.1) (Pheatmap) and Origin 2021 were used to create the heatmaps.

3. Results

3.1. Cotton Yield and Composition Factors

BOF treatment significantly increased the biomass of all cotton plant organs and the final yield compared to CK (Figure 3). Specifically, in terms of vegetative organs, the dry matter weight of roots increased by 35.28–43.98%, stems by 69.45–85.73%, and leaves by 56.22–68.92%. For reproductive organs and harvestable parts, the dry matter weight of cotton boll hulls increased by 63.58–68.73%, and seed cotton (the economic harvest part) increased by 45.63–48.65%. Correspondingly, the total biomass per plant (BP)—sum of all organs’ dry weights—rose by 56.66–61.97%. In addition to biomass accumulation, the BOF treatment also improved key yield components and the final seed cotton yield: the boll number per plant (BN-PP) increased by 7.84–34.99%, single boll weight (BW) by 3.82–6.30%, and the final seed cotton yield (SCY) by 19.82–28.17%.

3.2. Chemical Characteristics of Soil

BOF treatment resulted in a statistically insignificant increase in soil pH. However, it produced a significant improvement in SOM and available nutrients (Table 1). SOM, AN (only in 2024; there was no significant difference between treatments in 2023), AP, and AK increased by 12.05–17.72%, 22.03%, 15.49–36.42%, and 5.27–12.59%, respectively. From 2023 to 2024, SOM increased by 9.95%–13.17%, AP and AK by 29.81–56.18% and 31.26–55.04%, respectively, while AN remained stable in the BOF treatment but decreased by 17.48% in the CK treatment. In addition, soil pH showed a slight downward trend, with a fluctuation range of 0.25–0.85%.

3.3. Correlation Analysis Between Cotton Yield and Its Component Factors and Soil Chemical Properties

The findings show that soil nutrient content, cotton yield, and its component factors were not significantly impacted by soil pH (Figure 4). A positive correlation was observed between SOM and the following: AP, AK, and BP. Furthermore, AP and AK demonstrated a positive influence on BP, BN-PP, and SCY. BP was positively correlated with BN-PP and SCY. At the same time, the increase in BN-PP had a positive effect on SCY.

3.4. Rhizosphere Microbial Communities

3.4.1. Microbial Diversity

BOF decreased the bacterial Shannon index (1.30%) and Pielou_e index (1.52%), and increased the fungal ACE index (11.26%) (Figure 5a,b). The results of the PCoA analysis demonstrated that BOFs have the capacity to regulate the structural composition of the microbial community within the rhizosphere soil (Figure 5c,d). For bacteria, the primary and secondary axes accounted for 81.54% and 13.66% of the overall variances, respectively, with bacterial community structures from the CK and BOF treatments being distinguished along the initial axis. For fungi, the initial and secondary principal coordinates accounted for 74.06% and 16.31% of the observed variation, respectively. There was partial overlap between the CK and BOF treatments.

3.4.2. Microbial Community Composition

The composition of phyla and genera for bacteria and fungi under different treatments showed no significant changes, but their relative abundances differed. Bacteria were dominated by Pseudomonadota (30.14–31.22%), Actinomycetota (16.99–21.88%), and Acidobacteriota (9.47–12.23%), with BOF significantly increasing Pseudomonadota (3.58%), Actinomycetota (28.78%), and Bacteroidota (38.05%) while decreasing Acidobacteriota (22.57%) and Chloroflexota (14.10%) (Figure 6a). In addition, BOF was found to have a positive effect on the relative abundance of beneficial bacterial genera, such as Arthrobacter (49.45%), Pseudarthrobacter (44.88%), Flavobacterium (60.78%), Lysobacter (50.53%), and others (Figure 6b). Fungal communities were dominated by Ascomycota (47.25–50.62%), Mucoromycota (22.95–25.09%), and Basidiomycota (15.68–16.60%) (Figure 6c). BOF increased Mucoromycota (9.32%), Basidiomycota (5.87%), Glomus (57.77%), Russula (55.00%), and Saitozyma (66.15%) and reduced Fusarium (36.08%) (Figure 6c,d). RDA showed SOM was strongly correlated with shifts in bacterial communities (R2 = 0.9088, p = 0.0222). In contrast, fungal communities displayed an absence of significant correlation with the measured soil factors (Figure 6e,f).

3.4.3. Microbial Ecological Networks

The rhizosphere soil bacterial ecological network treated with CK has 100 nodes and 1621 connections (Figure 7a), while the fungal ecological network has 99 nodes and 1545 connections (Figure 7c). The rhizosphere soil bacterial and fungal ecological networks treated with BOF have 100 nodes and 3250 connections (Figure 7b) and 100 nodes and 1625 connections (Figure 7d), respectively. Compared to the CK treatment, the BOF treatment significantly increased bacterial network connections (100.49%) and fungal connections (5.18%), with higher positive links in bacterial networks and increased negative links in fungal networks (Figure 7). The BOF-treated networks exhibited greater complexity and stability, with tighter microbial interactions.

3.5. Correlations Between Soil Properties, Yield, and Microbes

Soil pH and AN did not affect the rhizosphere bacteria (Figure 8a). Both SOM and AK contents were substantially positively connected with the relative abundance of Flavobacterium (R = 0.83; R = 0.93), Lysobacter (R = 0.89; R = 0.90), and Novosphingobium (R = 0.94; R = 0.81), while they were notably inversely associated with the relative abundance of Anaerolinea (R = −0.94; R = −0.81) and Nitrospira (R = −0.83; R = −0.84). Arthrobacter (R = 0.94), Mesorhizobium (R = 0.94), Nocardioides (R = 0.83), Pseudarthrobacter (R = 0.83), Sphingomonas (R = 0.89), and Terrimonas (R = 0.89) were found to be positively impacted by an elevated AP content, while Chloracidobacterium (R = −0.95), Luteitalea (R = −0.89), Pyrinomonas (R = −0.89), and Rubrivivax (R = −0.89) exhibited the opposite trend. Nocardioides’ relative abundance (R = 0.94) was shown to have a positive association with BP, while Sphingomonas’ relative abundance (R = 0.94) exhibited a similar trend with NB-PP. An increase in the proportion of Arthrobacter (R = 0.83; R = 0.89), Flavobacterium (R = 0.83; R = 0.77), Mesorhizobium (R = 0.83; R = 0.89), and Terrimonas (R = 0.94; R = 0.94), along with a decrease in the proportion of Chloracidobacterium (R = −0.83; R = −0.89), Gemmatimonas (R = −0.83; R = −0.43), Luteitalea (R = −0.94; R = −0.94), and Candidatus_Rokubacteria (R = −0.89; R = −0.89), had a positive effect on BW and SCY. SOM and AK had no discernible effect on fungi (Figure 8b), whereas the richness of Russula (R = −0.81) was found to be inversely correlated with soil pH. The richness of Penicillium (R = −0.83) was shown to be inversely proportional to AN content, while the abundance of Fusarium (R = 0.94) exhibited a positive association with AN content. A decrease in Fusarium (R = −0.66; R = −0.60; R = −0.71; R = −0.89) and Laccaria (R = −0.89; R = −0.83; R = −0.60; R = −0.89) had advantageous effects on BP, NB-PP, BW, and SCY.

4. Discussion

4.1. Impact of BOFs on Cotton Yield and Soil Nutrients

In line with earlier research findings [42,53], the utilization of BOFs under field conditions in this study enhanced the biomass of various organs, as well as the number and weight of bolls per plant (Figure 3). As demonstrated by previous studies, organic fertilizers can significantly improve the yield and biomass of plants [38,42]. This study revealed that while the utilization of BOFs resulted in an augmentation of soil pH, the disparities amongst the treatments did not attain statistical significance. This result differs from previous studies [64,65]. Short-term or low-dose inputs of organic fertilizers may be neutralized due to the soil’s large buffering capacity [66,67], which resulted in a limited regulatory effect of BOF utilization on soil pH. This finding highlights the importance of considering soil inherent properties (e.g., buffering capacity) when evaluating the effects of organic amendments, as soil type and historical management can significantly modulate the response to fertilization. Both CK and BOF treatments showed interannual accumulation effects on SOM, AP, and AK. Within a similar timeframe, the utilization of BOFs resulted in a substantial augmentation in SOM and available nutrient content, in comparison to conventional fertilization methods. This finding aligns with previous research on BOFs improving soil nutrients in ginseng [68], tobacco [69], and chili [6] cropping systems, likely because BOFs facilitate the alteration of SOM and the liberation of nutrients [70,71]. The crucial role that soil fertility plays in sustaining cotton productivity is highlighted by the positive association between SOM/AP/AK and yield (Figure 4). The above results verify Hypothesis (1) that BOFs can increase cotton yield by improving soil nutrients.

4.2. Impact of BOFs on Rhizosphere Microbial Communities

The basis for evaluating environmental health is the diversity of soil microbes [72]. Although existing studies have indicated that BOFs can enrich soil microbial diversity [5,35], this study found that the regulation of cotton field microorganisms by BOFs is driven by the rhizosphere microenvironment and microbial niche preferences. This process differs significantly from that in potted Chinese cabbage (BOF reduces α-diversity [38]) and continuous watermelon fields (BOF only increases bacterial diversity [5]). This finding reveals microbial response patterns unique to cotton fields. Luo et al. [73] found that BOFs alter the fungal community structure in cotton rhizosphere soil to alleviate the harm of pathogenic bacteria. Wang et al. [74] and Deng et al. [7], respectively, studied the effects of BOFs on the bacterial community structure and root rot of continuously cropped Angelica sinensis, and on the rhizosphere bacterial community and bacterial wilt of tomatoes. However, the interactions between microbial communities have not been thoroughly studied. Our research findings show that SOM and available nutrients directionally shape the community through “carbon source screening” (Table 1). At the bacterial level, eutrophic bacteria (Pseudomonadota, Bacteroidota) inhibit oligotrophic bacteria (Acidobacteriota, Chloroflexota), leading to decreased bacterial Shannon index and Pielou_e index (Figure 5a). Redundancy Analysis (RDA) further confirms that SOM is the primary factor driving changes in bacterial communities (Figure 6e), confirming the core role of carbon source regulation [51]. At the fungal level, saprophytic fungi (Mucoromycota, Basidiomycota) and arbuscular mycorrhizal fungi (Glomus) synergistically increase the fungal ACE index [75,76]. The core role of BOF is optimizing function rather than increasing diversity. For instance, it promotes the proliferation of Actinomycetota and Lysobacter to inhibit pathogenic bacteria [17,77,78], and Flavobacterium restricts Fusarium through resource competition [79]. Eventually, a rhizosphere network characterized by “beneficial bacteria enrichment—pathogenic bacteria inhibition” is formed [7]. The microbial community can maintain host functions and adapt to environmental changes through this ecological network [80]. Microbial communities’ mutualism, niche overlap or competition, and parasitism are indicated by positive or negative interactions [81,82]. Previous BOF studies on various crops have mostly focused on soil microbial diversity and relative abundance changes, with few on microbial network regulation [7,17,23,35,37,46]. Our study found that the application of BOF increased both the quantity and ratio of positive connections (Figure 7). This indicates that mutualism predominates in the bacterial community [83]. Following the implementation of BOFs, fungal networks exhibited a reduction in positive connections and a rise in negative connections, suggesting a higher prevalence of negative associations like parasitism and predation [84]. This regulatory pattern differs from the common result of “single enhancement of positive connections” in ordinary crop fields [85], and represents the specific adaptation of BOFs targeting continuous cropping obstacles in cotton fields. Furthermore, BOF significantly increased the number of connections and average degree of bacterial and fungal networks (Figure 7), enhancing community connectivity and network complexity. This brings stronger microbial activity, higher community stability [86], and promoted organic carbon decomposition and nutrient transformation [82]. More importantly, this complex network enables multiple beneficial microbes to synergistically inhibit pathogens, alleviating continuous cotton cropping biotic stress—an effect unachievable with conventional chemical fertilizers [87].
In summary, in continuous cotton cropping fields, BOFs regulate rhizosphere microorganisms through the synergistic effect of carbon source screening, functional bacteria-driven processes, and interspecific competition.. These conclusions reject Hypothesis (2) and verify Hypothesis (3).

4.3. The Link Between Soil-Microbe-Yield

Our study found that soil chemical properties, cotton yield, and its component factors are significantly correlated with microbial communities (Figure 8). Soil nutrient content was positively correlated with beneficial microbial groups (e.g., Arthrobacter, Lysobacter) and negatively correlated with potential pathogens (e.g., Fusarium). This is consistent with the previous conclusion that the presence of beneficial microorganisms is closely associated with nutrients [88]. Pathogen decline is intricately linked to the control of soil nutrients and microbial diversity [89,90]. The increase in beneficial microbial groups and the decrease in Fusarium contribute to the accumulation of cotton biomass and yield improvement (Figure 8), which is in accordance with other recent studies [46,91]. Furthermore, this study identified a positive correlation between Fusarium and AN in cotton fields (Figure 8). This indicates that excessive accumulation of AN in continuously cropped cotton fields promotes pathogenic bacteria proliferation. Meanwhile, Bacillus amyloliquefaciens (a BOF component) reduces soil organic nitrogen mineralization rate, thereby indirectly inhibiting their proliferation [92].

5. Conclusions

A two-year field experiment confirmed that in Xinjiang, the application of BOFs enhanced cotton productivity and improved soil degradation caused by continuous cropping. Soil nutrients were significantly increased in the BOF treatment, demonstrating the feasibility of BOFs for soil remediation. This was also reflected in soil microbial community changes: BOF application reduced the pathogen Fusarium (severe to cotton in continuous cropping) and increased biocontrol taxa (e.g., Flavobacterium). Network analysis revealed that BOF use resulted in a more complex soil network structure, promoting interactions among rhizosphere microorganisms. Against the challenges of continuous cotton cropping, our study provides farmers with an environmentally friendly and effective method for soil improvement and crop yield enhancement. It is recommended that farmers apply BOFs during continuous cotton cultivation to improve the soil environment and facilitate the sustainable development of cotton production in Xinjiang. Future research could further combine metatranscriptomic and metabolomic technologies to explore the functional expression of key microbial taxa and their metabolic interaction mechanisms with cotton roots.

Author Contributions

Conceptualization, M.Y. and J.L.; investigation, formal analysis and data curation, M.Y., H.H., L.C., S.L., T.W. and J.Q.; visualization, M.Y., H.H., and L.C.; writing—original draft, M.Y.; project administration and writing—review and editing, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Major Science and Technology Special Projects in Xinjiang Uygur Autonomous Region (2022A02007-4), the National Key Research and Development Project (2021YFD1900802), the Achievement Transformation and Technology Promotion Project of Shihezi University (CGZH202203), the High-level Talents Research Project of Shihezi University (RCZK202586), and the Tianchi Talent Program of Xinjiang Uygur Autonomous Region (2025, Hao He).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Egbuta, M.A.; McIntosh, S.; Waters, D.L.E.; Vancov, T.; Liu, L. Biological Importance of Cotton By-Products Relative to Chemical Constituents of the Cotton Plant. Molecules 2017, 22, 93. [Google Scholar] [CrossRef]
  2. Sheri, V.; Kumar, M.; Jaconis, S.; Zhang, B. Antioxidant Defense in Cotton under Environmental Stresses: Unraveling the Crucial Role of a Universal Defense Regulator for Enhanced Cotton Sustainability. Plant Physiol. Biochem. 2023, 204, 108141. [Google Scholar] [CrossRef]
  3. Wei, Z.; Yu, D. Rhizosphere Fungal Community Structure Succession of Xinjiang Continuously Cropped Cotton. Fungal Biol. 2019, 123, 42–50. [Google Scholar] [CrossRef]
  4. Zhao, F.; Xia, L.; Sun, S.; Li, H.; Rong, Y.; Wang, Y.; Wang, X.; Kong, X.; Zhu, L.; Zhang, X.; et al. A Synergistic Water-Saving Strategy Combining Stage-Specific Irrigation and Application of Exogenous Thiamine Enhances Drought Resistance of Xinjiang Cotton. In Plant Soil; Springer: Berlin/Heidelberg, Germany, 2025. [Google Scholar] [CrossRef]
  5. Zhang, H.; Yang, H.; Zhang, X.; Sun, J.; Dong, L.; Han, H.; Chen, Z. Bama Pig Manure Organic Fertilizer Regulates the Watermelon Rhizosphere Bacterial Community to Inhibit the Occurrence of Fusarium Wilt Under Continuous Cropping Conditions. Curr. Microbiol. 2022, 79, 364. [Google Scholar] [CrossRef] [PubMed]
  6. Zhang, C.; Zhang, L.; Cao, Y.; Zhang, S.; Hou, C.; Zhang, C. Effects of Microbial Organic Fertilizer, Microbial Inoculant, and Quicklime on Soil Microbial Community Composition in Pepper (Capsicum annuum L.) Continuous Cropping System. Horticulturae 2024, 10, 1142. [Google Scholar] [CrossRef]
  7. Deng, X.; Zhang, N.; Li, Y.; Zhu, C.; Qu, B.; Liu, H.; Li, R.; Bai, Y.; Shen, Q.; Falcao Salles, J. Bio-Organic Soil Amendment Promotes the Suppression of Ralstonia Solanacearum by Inducing Changes in the Functionality and Composition of Rhizosphere Bacterial Communities. New Phytol. 2022, 235, 1558–1574. [Google Scholar] [CrossRef]
  8. Zhang, W.; Long, X.; Huo, X.; Chen, Y.; Lou, K. 16S rRNA-Based PCR-DGGE Analysis of Actinomycete Communities in Fields with Continuous Cotton Cropping in Xinjiang, China. Microb. Ecol. 2013, 66, 385–393. [Google Scholar] [CrossRef] [PubMed]
  9. Chen, H.; Yang, L.; Mickan, B.S.; Li, Z.; Zhang, F. Long–Term (25 Years) Continuous Cotton Cropping Combined with Residue Incorporation Affects the Fungal Communities in Reclaimed Saline Soil. Pedobiologia 2024, 102, 150928. [Google Scholar] [CrossRef]
  10. Ma, Z.; Li, P.; Yang, C.; Feng, Z.; Feng, H.; Zhang, Y.; Zhao, L.; Zhou, J.; Zhu, H.; Wei, F. Soil Bacterial Community Response to Continuous Cropping of Cotton. Front. Microbiol. 2023, 14, 1125564. [Google Scholar] [CrossRef] [PubMed]
  11. Chen, W.; Tenga, Y.; Li, Z.; Liu, W.; Ren, W.; Luo, Y.; Christie, P. Mechanisms by Which Organic Fertilizer and Effective Microbes Mitigate Peanut Continuous Cropping Yield Constraints in a Red Soil of South China. Appl. Soil Ecol. 2018, 128, 23–34. [Google Scholar] [CrossRef]
  12. Zeeshan Ul Haq, M.; Yu, J.; Yao, G.; Yang, H.; Iqbal, H.A.; Tahir, H.; Cui, H.; Liu, Y.; Wu, Y. A Systematic Review on the Continuous Cropping Obstacles and Control Strategies in Medicinal Plants. Int. J. Mol. Sci. 2023, 24, 12470. [Google Scholar] [CrossRef]
  13. Chen, W.; Zhang, S.; Wang, B.; Zhang, M.; Ding, D.; He, J. The Combined Application of Biofertilizer Alleviates the Continuous Cropping Obstacles of Replanted Zanthoxylum Bungeanum: A Preliminary Study. Plants 2025, 14, 854. [Google Scholar] [CrossRef] [PubMed]
  14. Zheng, F.; Liu, X.; Ding, W.; Song, X.; Li, S.; Wu, X. Positive Effects of Crop Rotation on Soil Aggregation and Associated Organic Carbon Are Mainly Controlled by Climate and Initial Soil Carbon Content: A Meta-Analysis. Agric. Ecosyst. Environ. 2023, 355, 108600. [Google Scholar] [CrossRef]
  15. Ding, S.; Zhou, D.; Wei, H.; Wu, S.; Xie, B. Alleviating Soil Degradation Caused by Watermelon Continuous Cropping Obstacle: Application of Urban Waste Compost. Chemosphere 2021, 262, 128387. [Google Scholar] [CrossRef]
  16. Ma, X.; Zhang, Y.; Wei, F.; Zhao, L.; Zhou, J.; Qi, G.; Ma, Z.; Zhu, H.; Feng, H.; Feng, Z. Applications of Chaetomium globosum CEF-082 Improve Soil Health and Mitigate the Continuous Cropping Obstacles for Gossypium hirsutum. Ind. Crops Prod. 2023, 197, 116586. [Google Scholar] [CrossRef]
  17. Huang, B.; Chen, Y.; Pei, Z.; Jiang, L.; Zhang, Y.; Wang, J.; Wang, J. Application of Microbial Organic Fertilizers Promotes the Utilization of Nutrients and Restoration of Microbial Community Structure and Function in Rhizosphere Soils after Dazomet Fumigation. Front. Microbiol. 2023, 13, 1122611. [Google Scholar] [CrossRef]
  18. Huang, S.; Rui, W.; Peng, X.; Huang, Q.; Zhang, W. Organic Carbon Fractions Affected by Long-Term Fertilization in a Subtropical Paddy Soil. Nutr. Cycl. Agroecosyst. 2010, 86, 153–160. [Google Scholar] [CrossRef]
  19. Zhai, L.; Wang, Z.; Zhai, Y.; Zhang, L.; Zheng, M.; Yao, H.; Lv, L.; Shen, H.; Zhang, J.; Yao, Y.; et al. Partial Substitution of Chemical Fertilizer by Organic Fertilizer Benefits Grain Yield, Water Use Efficiency, and Economic Return of Summer Maize. Soil Tillage Res. 2022, 217, 105287. [Google Scholar] [CrossRef]
  20. Hlisnikovský, L.; Kunzová, E. The Content of Topsoil Nutrients, Ph and Organic Carbon as Affected by Long-Term Application of Mineral and Organic Fertilisers. Agriculture 2014, 60, 142–148. [Google Scholar] [CrossRef]
  21. Liao, Y.; Xu, D.; Cao, Y.; Zhu, Y.-G. Advancing Sustainable Agriculture: Enhancing Crop Nutrition with next-Generation Nanotech-Based Fertilizers. Nano Res. 2023, 16, 13205–13225. [Google Scholar] [CrossRef]
  22. Chen, D.; Wang, X.; Carrión, V.J.; Yin, S.; Yue, Z.; Liao, Y.; Dong, Y.; Li, X. Acidic Amelioration of Soil Amendments Improves Soil Health by Impacting Rhizosphere Microbial Assemblies. Soil Biol. Biochem. 2022, 167, 108599. [Google Scholar] [CrossRef]
  23. Li, R.; Tao, R.; Ling, N.; Chu, G. Chemical, Organic and Bio-Fertilizer Management Practices Effect on Soil Physicochemical Property and Antagonistic Bacteria Abundance of a Cotton Field: Implications for Soil Biological Quality. Soil Tillage Res. 2017, 167, 30–38. [Google Scholar] [CrossRef]
  24. Kong, W.; Liu, F.; Zhang, C.; Zhang, J.; Feng, H. Non-Destructive Determination of Malondialdehyde (MDA) Distribution in Oilseed Rape Leaves by Laboratory Scale NIR Hyperspectral Imaging. Sci. Rep. 2016, 6, 35393. [Google Scholar] [CrossRef]
  25. Cho, U.-H.; Sohn, J.-Y. Cadmium-Induced Changes in Antioxidative Systems, Hydrogen Peroxide Content, and Lipid Peroxidation in Arabidopsis thaliana. J. Plant Biol. 2004, 47, 262–269. [Google Scholar] [CrossRef]
  26. Feng, X.; Lai, Z.; Lin, Y.; Lai, G.; Lian, C. Genome-Wide Identification and Characterization of the Superoxide Dismutase Gene Family in Musa acuminata Cv. Tianbaojiao (AAA Group). BMC Genom. 2015, 16, 823. [Google Scholar] [CrossRef]
  27. González-Gordo, S.; López-Jaramillo, J.; Rodríguez-Ruiz, M.; Taboada, J.; Palma, J.M.; Corpas, F.J. Pepper Catalase: A Broad Analysis of Its Modulation during Fruit Ripening and by Nitric Oxide. Biochem. J. 2024, 481, 883–901. [Google Scholar] [CrossRef] [PubMed]
  28. Riseh, R.S.; Fathi, F.; Vatankhah, M.; Kennedy, J.F. Catalase-Associated Immune Responses in Plant-Microbe Interactions: A Review. Int. J. Biol. Macromol. 2024, 280, 135859. [Google Scholar] [CrossRef] [PubMed]
  29. Irfan, M.; Bhat, M.A.; Rashid, U.; Almanzalawi, E.A.; Alqahtani, T.M.; Mansoor, S.; Boo, K.-H. Enzymatic Activity Assays and Functional Component Profiling Reveals Coordinated Defense Response of Mungbean against Cercospora canescens during Leaf Spot Disease. Plant Biotechnol. Rep. 2024, 18, 563–577. [Google Scholar] [CrossRef]
  30. Zhang, G.; Liu, Q.; Zhang, Z.; Ci, D.; Zhang, J.; Xu, Y.; Guo, Q.; Xu, M.; He, K. Effect of Reducing Nitrogen Fertilization and Adding Organic Fertilizer on Net Photosynthetic Rate, Root Nodules and Yield in Peanut. Plants 2023, 12, 2902. [Google Scholar] [CrossRef] [PubMed]
  31. Roni, N.G.K.; Lindawati, S.A. Productivity of Gamal (Gliricidia sepium) and Indigofera (Indigofera zollingeriana) Forage on Different Dosage of Bioorganic fertilizer. Pastura 2019, 8, 105–109. [Google Scholar] [CrossRef]
  32. Ahmad, R.; Arshad, M.; Khalid, A.; Zahir, Z.A. Effectiveness of Organic-/Bio-Fertilizer Supplemented with Chemical Fertilizers for Improving Soil Water Retention, Aggregate Stability, Growth and Nutrient Uptake of Maize (Zea mays L.). J. Sustain. Agric. 2008, 31, 57–77. [Google Scholar] [CrossRef]
  33. Wang, X.-W.; Cai, H.; Liu, Y.-L.; Li, C.-L.; Wan, Y.-S.; Song, F.-P.; Chen, W.-F. Addition of Organic Fertilizer Affects Soil Nitrogen Availability in a Salinized Fluvo-Aquic Soil. Environ. Pollut. Bioavailab. 2019, 31, 331–338. [Google Scholar] [CrossRef]
  34. Gao, C.; El-Sawah, A.M.; Ali, D.F.I.; Alhaj Hamoud, Y.; Shaghaleh, H.; Sheteiwy, M.S. The Integration of Bio and Organic Fertilizers Improve Plant Growth, Grain Yield, Quality and Metabolism of Hybrid Maize (Zea mays L.). Agronomy 2020, 10, 319. [Google Scholar] [CrossRef]
  35. Ali, A.; Liu, X.; Yang, W.; Li, W.; Chen, J.; Qiao, Y.; Gao, Z.; Yang, Z. Impact of Bio-Organic Fertilizer Incorporation on Soil Nutrients, Enzymatic Activity, and Microbial Community in Wheat–Maize Rotation System. Agronomy 2024, 14, 1942. [Google Scholar] [CrossRef]
  36. Yan, B.; Zhang, Y.; Wang, Y.; Rong, X.; Peng, J.; Fei, J.; Luo, G. Biochar Amendments Combined with Organic Fertilizer Improve Maize Productivity and Mitigate Nutrient Loss by Regulating the C–N–P Stoichiometry of Soil, Microbiome, and Enzymes. Chemosphere 2023, 324, 138293. [Google Scholar] [CrossRef]
  37. Zheng, X.; Zhu, Y.; Wang, Z.; Zhang, H.; Chen, M.; Chen, Y.; Wang, J.; Liu, B. Effects of a Novel Bio-Organic Fertilizer on the Composition of Rhizobacterial Communities and Bacterial Wilt Outbreak in a Continuously Mono-Cropped Tomato Field. Appl. Soil Ecol. 2020, 156, 103717. [Google Scholar] [CrossRef]
  38. Wang, T.; Cheng, K.; Huo, X.; Meng, P.; Cai, Z.; Wang, Z.; Zhou, J. Bioorganic Fertilizer Promotes Pakchoi Growth and Shapes the Soil Microbial Structure. Front. Plant Sci. 2022, 13, 1040437. [Google Scholar] [CrossRef] [PubMed]
  39. Řezáčová, V. Organic Fertilization Improves Soil Aggregation through Increases in Abundance of Eubacteria and Products of Arbuscular Mycorrhizal Fungi. Sci. Rep. 2021, 11, 12548. [Google Scholar] [CrossRef]
  40. Ren, L.; Yang, H.; Li, J.; Li, X.; Han, Y.; Zou, H.; Zhang, Y. Soil Aggregates and Organic Carbon Affected by Bio-Fertilizer in Greenhouse Soil. Commun. Soil Sci. Plant Anal. 2025, 56, 784–799. [Google Scholar] [CrossRef]
  41. Saikia, P.; Bhattacharya, S.S.; Baruah, K.K. Organic Substitution in Fertilizer Schedule: Impacts on Soil Health, Photosynthetic Efficiency, Yield and Assimilation in Wheat Grown in Alluvial Soil. Agric. Ecosyst. Environ. 2015, 203, 102–109. [Google Scholar] [CrossRef]
  42. Duan, C.; Li, J.; Zhang, B.; Wu, S.; Fan, J.; Feng, H.; He, J.; Siddique, K.H.M. Effect of Bio-Organic Fertilizer Derived from Agricultural Waste Resources on Soil Properties and Winter Wheat (Triticum aestivum L.) Yield in Semi-Humid Drought-Prone Regions. Agric. Water Manag. 2023, 289, 108539. [Google Scholar] [CrossRef]
  43. Xia, H.; Liu, H.; Gong, P.; Li, P.; Xu, Q.; Zhang, Q.; Yang, C.; Meng, Q. Applying Bio-Organic Fertilizer Improved Saline Alkaline Soil Properties and Cotton Yield in Xinjiang. Sci. Rep. 2025, 15, 13235. [Google Scholar] [CrossRef]
  44. Yang, Y.; Wang, P.; Zeng, Z. Dynamics of Bacterial Communities in a 30-Year Fertilized Paddy Field under Different Organic–Inorganic Fertilization Strategies. Agronomy 2019, 9, 14. [Google Scholar] [CrossRef]
  45. Gao, J.; Pei, H.; Xie, H. Synergistic Effects of Organic Fertilizer and Corn Straw on Microorganisms of Pepper Continuous Cropping Soil in China. Bioengineered 2020, 11, 1258–1268. [Google Scholar] [CrossRef] [PubMed]
  46. Tao, C.; Li, R.; Xiong, W.; Shen, Z.; Liu, S.; Wang, B.; Ruan, Y.; Geisen, S.; Shen, Q.; Kowalchuk, G.A. Bio-Organic Fertilizers Stimulate Indigenous Soil Pseudomonas Populations to Enhance Plant Disease Suppression. Microbiome 2020, 8, 137. [Google Scholar] [CrossRef] [PubMed]
  47. Tan, X.; Hu, X.; Liu, X.; Zhang, P.; Yang, S.; Xia, F. Bioorganic Fertilizer Can Improve Potato Yield by Replacing Fertilizer with Isonitrogenous Content to Improve Microbial Community Composition. Agronomy 2024, 14, 2881. [Google Scholar] [CrossRef]
  48. Lang, J.; Hu, J.; Ran, W.; Xu, Y.; Shen, Q. Control of Cotton Verticillium Wilt and Fungal Diversity of Rhizosphere Soils by Bio-Organic Fertilizer. Biol. Fertil. Soils 2012, 48, 191–203. [Google Scholar] [CrossRef]
  49. Wei, Z.; Yang, X.; Yin, S.; Shen, Q.; Ran, W.; Xu, Y. Efficacy of Bacillus-Fortified Organic Fertiliser in Controlling Bacterial Wilt of Tomato in the Field. Appl. Soil Ecol. 2011, 48, 152–159. [Google Scholar] [CrossRef]
  50. Yuan, S.; Wang, L.; Wu, K.; Shi, J.; Wang, M.; Yang, X.; Shen, Q.; Shen, B. Evaluation of Bacillus-Fortified Organic Fertilizer for Controlling Tobacco Bacterial Wilt in Greenhouse and Field Experiments. Appl. Soil Ecol. 2014, 75, 86–94. [Google Scholar] [CrossRef]
  51. Wang, L.; Yang, F.; E, Y.; Yuan, J.; Raza, W.; Huang, Q.; Shen, Q. Long-Term Application of Bioorganic Fertilizers Improved Soil Biochemical Properties and Microbial Communities of an Apple Orchard Soil. Front. Microbiol. 2016, 7, 1893. [Google Scholar] [CrossRef] [PubMed]
  52. Wu, K.; Fang, Z.; Wang, L.; Yuan, S.; Guo, R.; Shen, B.; Shen, Q. Biological Potential of Bioorganic Fertilizer Fortified with Bacterial Antagonist for the Control of Tomato Bacterial Wilt and the Promotion of Crop Yields. J. Microbiol. Biotechnol. 2016, 26, 1755–1764. [Google Scholar] [CrossRef]
  53. Huang, B.; Chen, Y.; Cao, Y.; Liu, D.; Fang, H.; Zhou, C.; Wang, D.; Wang, J. The Structure and Function of Rhizosphere Bacterial Communities: Impact of Chemical vs. Bio-Organic Fertilizers on Root Disease, Quality, and Yield of Codonopsis pilosula. Front. Microbiol. 2024, 15, 1484727. [Google Scholar] [CrossRef]
  54. Qi, Y.; Wu, Z.; Wang, Y.; Zhou, R.; Liu, L.; Wang, Y.; Zhao, J.; Jiang, F. Bacillus Bio-Organic Fertilizer Altered Soil Microorganisms and Improved Yield and Quality of Radish (Raphanus sativus L.). Plants 2025, 14, 1389. [Google Scholar] [CrossRef]
  55. He, H. Organic Substitution Contrasting Direct Fertilizer Reduction Increases Wheat Productivity, Soil Quality, Microbial Diversity and Network Complexity. Environ. Technol. 2024, 36, 103784. [Google Scholar] [CrossRef]
  56. Fan, P.; Li, J.; Chen, P.; Wei, D.; Zhang, Q.; Jia, Z.; He, C.; Ullah, J.; Wang, Q.; Ruan, Y. Mitigating Soil Degradation in Continuous Cropping Banana Fields through Long-Term Organic Fertilization: Insights from Soil Acidification, Ammonia Oxidation, and Microbial Communities. Ind. Crops Prod. 2024, 213, 118385. [Google Scholar] [CrossRef]
  57. Lee, M.H.; Lee, S.-W. Bioprospecting Potential of the Soil Metagenome: Novel Enzymes and Bioactivities. Genom. Inform 2013, 11, 114–120. [Google Scholar] [CrossRef]
  58. Myrold, D.D.; Zeglin, L.H.; Jansson, J.K. The Potential of Metagenomic Approaches for Understanding Soil Microbial Processes. Soil Sci. Soc. Am. J. 2014, 78, 3–10. [Google Scholar] [CrossRef]
  59. Bao, S. Soil and Agricultural Chemistry Analysis; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
  60. Wang, X.; Wang, J.; Wang, Y.; Zhu, X.; Cheng, J.; Wang, W. Changes in Microbial Diversity, Soil Function, and Plant Biomass of Cotton Rhizosphere Soil Under the Influence of Chlorpyrifos. Curr. Microbiol. 2022, 79, 323. [Google Scholar] [CrossRef]
  61. Han, C.; Shi, C.; Liu, L.; Han, J.; Yang, Q.; Wang, Y.; Li, X.; Fu, W.; Gao, H.; Huang, H.; et al. Majorbio Cloud 2024: Update Single-Cell and Multiomics Workflows. Imeta 2024, 3, e217. [Google Scholar] [CrossRef]
  62. Yang, W.; Jiang, Y.; Zhang, J.; Wang, W.; Liu, X.; Jin, Y.; Li, S.; Qu, J.; Zhu, Y. Organic Materials Promote Soil Phosphorus Cycling: Metagenomic Analysis. Agronomy 2025, 15, 1693. [Google Scholar] [CrossRef]
  63. Yu, C.; Zhou, L.; Huang, X.; You, X.; Lin, J.; Han, H.; Huang, X. Effects of Salt Field Waste-Generated Bio-Organic Fertilizer Application on Bacterial Community Structure in Tea Plantations Rhizosphere Soil. Agronomy 2024, 15, 87. [Google Scholar] [CrossRef]
  64. Pan, X.; Yu, H.-J.; Zhang, B.; Guan, Y.-Q.; Zhang, N.; Du, H.-L.; Liu, F.-M.; Yu, J.; Wang, Q.-J.; Liu, J. Effects of Organic Fertilizer Replacement on the Microbial Community Structure in the Rhizosphere Soil of Soybeans in Albic Soil. Sci. Rep. 2025, 15, 12271. [Google Scholar] [CrossRef]
  65. Tran, T.T.; Nguyen, L.T.; Vu, H.T.T.; Phan, N.T.H.; Suralta, R.R. Inorganic-Microbial Organic Fertilizer Synergy: Improving Quinoa Yield and Seed Quality through Soil and Nutrient Modulation. Soil Sci. Plant Nutr. 2025, 71, 353–361. [Google Scholar] [CrossRef]
  66. Wang, C.; Kuzyakov, Y. Soil Organic Matter Priming: The pH Effects. Glob. Change Biol. 2024, 30, e17349. [Google Scholar] [CrossRef]
  67. Wang, J.; Huang, Q.; Li, Y.; Tu, X.; Chen, Z.; Elrys, A.S.; Cheng, Y.; Ma, L. A Shift from Nitrification to Denitrification-Dominated N2O Emission in an Acidic Soil Following Organic Amendment. Biol. Fertil. Soils 2023, 59, 117–122. [Google Scholar] [CrossRef]
  68. Pu, R.; Wang, P.; Guo, L.; Li, M.; Cui, X.; Wang, C.; Liu, Y.; Yang, Y. The Remediation Effects of Microbial Organic Fertilizer on Soil Microorganisms after Chloropicrin Fumigation. Ecotoxicol. Environ. Saf. 2022, 231, 113188. [Google Scholar] [CrossRef]
  69. Chen, D.; Wang, M.; Wang, G.; Zhou, Y.; Yang, X.; Li, J.; Zhang, C.; Dai, K. Functional Organic Fertilizers Can Alleviate Tobacco (Nicotiana tabacum L.) Continuous Cropping Obstacle via Ameliorating Soil Physicochemical Properties and Bacterial Community Structure. Front. Bioeng. Biotechnol. 2022, 10, 1023693. [Google Scholar] [CrossRef] [PubMed]
  70. Yang, R.; Su, Y.-Z.; Wang, T.; Yang, Q. Effect of Chemical and Organic Fertilization on Soil Carbon and Nitrogen Accumulation in a Newly Cultivated Farmland. J. Integr. Agric. 2016, 15, 658–666. [Google Scholar] [CrossRef]
  71. Hu, X.; Gu, H.; Liu, J.; Wei, D.; Zhu, P.; Cui, X.; Zhou, B.; Chen, X.; Jin, J.; Liu, X.; et al. Metagenomic Strategies Uncover the Soil Bioavailable Phosphorus Improved by Organic Fertilization in Mollisols. Agric. Ecosyst. Environ. 2023, 349, 108462. [Google Scholar] [CrossRef]
  72. Xing, Y.; Xie, Y.; Wang, X. Enhancing Soil Health through Balanced Fertilization: A Pathway to Sustainable Agriculture and Food Security. Front. Microbiol. 2025, 16, 1536524. [Google Scholar] [CrossRef]
  73. Luo, J.; Ran, W.; Hu, J.; Yang, X.; Xu, Y.; Shen, Q. Application of Bio-Organic Fertilizer Significantly Affected Fungal Diversity of Soils. Soil Sci. Soc. Am. J. 2010, 74, 2039–2048. [Google Scholar] [CrossRef]
  74. Wang, W.L.; Li, J.; Zhao, X. Effects of biological organic fertilizer on rhisosphere soil bacteria community and root rot diseases of continuous cropping Angelica sinensis. Ying Yong Sheng Tai Xue Bao 2019, 30, 2813–2821. [Google Scholar] [CrossRef]
  75. Semenov, A.V.; Pereira e Silva, M.C.; Szturc-Koestsier, A.E.; Schmitt, H.; Falcão Salles, J.; van Elsas, J.D. Impact of Incorporated Fresh 13C Potato Tissues on the Bacterial and Fungal Community Composition of Soil. Soil Biol. Biochem. 2012, 49, 88–95. [Google Scholar] [CrossRef]
  76. Hodge, A.; Campbell, C.D.; Fitter, A.H. An Arbuscular Mycorrhizal Fungus Accelerates Decomposition and Acquires Nitrogen Directly from Organic Material. Nature 2001, 413, 297–299. [Google Scholar] [CrossRef] [PubMed]
  77. Marimuthu, S.; Karthic, C.; Mostafa, A.A.; Mohammed Al-Enazi, N.; Abdel-Raouf, N.; Nageh Sholkamy, E. Antifungal Activity of Streptomyces sp. SLR03 against Tea Fungal Plant Pathogen Pestalotiopsis theae. J. King Saud Univ.-Sci. 2020, 32, 3258–3264. [Google Scholar] [CrossRef]
  78. Yang, M.; Tan, Y.; Ma, J.; Zhao, Y.; Yan, X.; Wang, N.; Wang, P.; Tan, J.; Ai, S.; Liang, X.; et al. A Unique Role of the Pyrimidine de Novo Synthesis Enzyme ODCase in Lysobacter enzymogenes. J. Integr. Agric. 2024, 23, 3066–3077. [Google Scholar] [CrossRef]
  79. Khan, S.; Subhan, F.; Haleem, K.; Khattak, M.; Khan, I.; Sultan, T.; Tauseef, I. Impact of Plant Growth-Promoting Rhizobacteria on Yield and Disease Control of Nicotiana Tabacum. Arch. Biol. Sci. 2018, 70, 717–725. [Google Scholar] [CrossRef]
  80. Hassani, M.A.; Durán, P.; Hacquard, S. Microbial Interactions within the Plant Holobiont. Microbiome 2018, 6, 58. [Google Scholar] [CrossRef]
  81. de Vries, F.T.; Griffiths, R.I.; Bailey, M.; Craig, H.; Girlanda, M.; Gweon, H.S.; Hallin, S.; Kaisermann, A.; Keith, A.M.; Kretzschmar, M.; et al. Soil Bacterial Networks Are Less Stable under Drought than Fungal Networks. Nat. Commun. 2018, 9, 3033. [Google Scholar] [CrossRef]
  82. Lang, D.; Zhou, R.; Hao, F.; Zhu, Z.; Zhang, P. Effect of Wheat Straw Addition on Organic Carbon Mineralisation and Bacterial Community in Orchard Soil. J. Soil Sci. Plant Nutr. 2023, 23, 4328–4341. [Google Scholar] [CrossRef]
  83. Siles, J.A.; García-Sánchez, M.; Gómez-Brandón, M. Studying Microbial Communities through Co-Occurrence Network Analyses during Processes of Waste Treatment and in Organically Amended Soils: A Review. Microorganisms 2021, 9, 1165. [Google Scholar] [CrossRef]
  84. Faust, K.; Raes, J. Microbial Interactions: From Networks to Models. Nat. Rev. Microbiol. 2012, 10, 538–550. [Google Scholar] [CrossRef]
  85. Yang, W.; Yang, Z.; Guan, Y.; Zhai, C.; Shi, D.; Chen, J.; Wang, T.; Gu, S. Dose-Dependent Effect of Compost Amendment on Soil Bacterial Community Composition and Co-Occurrence Network Patterns in Soybean Agroecosystem. Arch. Agron. Soil Sci. 2020, 66, 1027–1041. [Google Scholar] [CrossRef]
  86. Karimi, B.; Maron, P.A.; Chemidlin-Prevost Boure, N.; Bernard, N.; Gilbert, D.; Ranjard, L. Microbial Diversity and Ecological Networks as Indicators of Environmental Quality. Environ. Chem. Lett. 2017, 15, 265–281. [Google Scholar] [CrossRef]
  87. Kong, Y.; Kuzyakov, Y.; Ruan, Y.; Zhang, J.; Wang, T.; Wang, M.; Guo, S.; Shen, Q.; Ling, N. DNA Stable-Isotope Probing Delineates Carbon Flows from Rice Residues into Soil Microbial Communities Depending on Fertilization. Appl. Environ. Microbiol. 2020, 86, e02151-19. [Google Scholar] [CrossRef] [PubMed]
  88. Li, P.; Tedersoo, L.; Crowther, T.W.; Dumbrell, A.J.; Dini-Andreote, F.; Bahram, M.; Kuang, L.; Li, T.; Wu, M.; Jiang, Y.; et al. Fossil-Fuel-Dependent Scenarios Could Lead to a Significant Decline of Global Plant-Beneficial Bacteria Abundance in Soils by 2100. Nat. Food 2023, 4, 996–1006. [Google Scholar] [CrossRef]
  89. Wang, G.; Burrill, H.M.; Podzikowski, L.Y.; Eppinga, M.B.; Zhang, F.; Zhang, J.; Schultz, P.A.; Bever, J.D. Dilution of Specialist Pathogens Drives Productivity Benefits from Diversity in Plant Mixtures. Nat. Commun. 2023, 14, 8417. [Google Scholar] [CrossRef]
  90. Shoala, T.; El-Garhy, H.A.S.; Messiha, N.A.S.; El-Abeid, S.E. The Efficacy of Nanoparticles on Soil Microbial Biodiversity and the Prevention of Fusarium Wilt Disease (Fusarium oxysporum f.Sp. lycopersici). BMC Microbiol. 2025, 25, 305. [Google Scholar] [CrossRef]
  91. Trong, N.D.; Trang, T.T.T.; Quang, L.T.; Oanh, T.O.; Nguyen, P.C.; Nhan, T.C.; Xuan, L.N.T.; Xuan, D.T.; Khuong, N.Q. Effects of Rhodopseudomonas palustris and Rhodopseudomonas pentothenatexigens on Reducing Chemical NPK Fertilizer Used for Rice in Acid Sulfate Soil under Field Conditions. Paddy Water Environ. 2025, 23, 491–510. [Google Scholar] [CrossRef]
  92. Luo, L.; Li, L.; Raza, A.; Zhao, C.; Pang, X.; Zhang, J.; Müller, C.; Yin, C. Organic Fertilizer and Bacillus Amyloliquefaciens Promote Soil N Availability via Changing Different Mineralization–Immobilization Turnover Rates in Acidic Soils. Agric. Ecosyst. Environ. 2024, 366, 108950. [Google Scholar] [CrossRef]
Agronomy 15 02238 g001
Figure 1. Monthly average temperature and rainfall during the cotton growing season from 2023 to 2024.
Figure 1. Monthly average temperature and rainfall during the cotton growing season from 2023 to 2024.
Agronomy 15 02238 g001
Agronomy 15 02238 g002
Figure 2. Schematic diagram of experimental design layout and planting pattern.
Figure 2. Schematic diagram of experimental design layout and planting pattern.
Agronomy 15 02238 g002
Agronomy 15 02238 g003
Figure 3. Effects of different treatments on cotton yield and yield components during the boll opening stage of cotton from 2023 to 2024. CK, conventional chemical fertilizer (N−P−K: 300−180−150 kg·ha−1); BOF, conventional chemical fertilizer combined with BOFs (N−P−K: 300−180−150 kg·ha−1, bio-organic fertilizers: 4159 kg·ha−1 in 2023 and 4545 kg·ha−1 in 2024). (a): Different colors represent cotton’s organs; same-color bars with different lowercase letters indicate significant differences in the same organ among treatments at the boll opening stage, while different uppercase letters mean significant differences in total plant biomass (BP: sum of all organs’ dry weights) among treatments (p < 0.05). (b–d): Bars with different lowercase letters mean significant differences in boll number per plant (BN-PP), single boll weight (BW), and seed cotton yield (SCY), respectively, among treatments at the boll opening stage (p < 0.05). All data in the figures are presented as “mean ± standard deviation” (n = 3).
Figure 3. Effects of different treatments on cotton yield and yield components during the boll opening stage of cotton from 2023 to 2024. CK, conventional chemical fertilizer (N−P−K: 300−180−150 kg·ha−1); BOF, conventional chemical fertilizer combined with BOFs (N−P−K: 300−180−150 kg·ha−1, bio-organic fertilizers: 4159 kg·ha−1 in 2023 and 4545 kg·ha−1 in 2024). (a): Different colors represent cotton’s organs; same-color bars with different lowercase letters indicate significant differences in the same organ among treatments at the boll opening stage, while different uppercase letters mean significant differences in total plant biomass (BP: sum of all organs’ dry weights) among treatments (p < 0.05). (b–d): Bars with different lowercase letters mean significant differences in boll number per plant (BN-PP), single boll weight (BW), and seed cotton yield (SCY), respectively, among treatments at the boll opening stage (p < 0.05). All data in the figures are presented as “mean ± standard deviation” (n = 3).
Agronomy 15 02238 g003
Agronomy 15 02238 g004
Figure 4. Correlation analysis between cotton yield and its component factors and soil chemical properties. A—pH; B—SOM; C—AN; D—AP; E—AK; F—BP; G—BN-PP; H—BW; I—SCY. * p < 0.05, ** p < 0.01.
Figure 4. Correlation analysis between cotton yield and its component factors and soil chemical properties. A—pH; B—SOM; C—AN; D—AP; E—AK; F—BP; G—BN-PP; H—BW; I—SCY. * p < 0.05, ** p < 0.01.
Agronomy 15 02238 g004
Agronomy 15 02238 g005
Figure 5. Diversity of rhizosphere microbial communities. (a,b)—Alpha diversity index (genus level) of bacterial and fungal communities: ACE, Shannon, and Pelou_e, different lowercase letters indicated significant differences among treatments (p < 0.05); (c,d)—Beta diversity analysis (phylum level) of bacterial (R = 0.8148, p = 0.098) and fungal (R = −0.1481, p = 0.811) using PCoA. The dashed lines are the horizontal and vertical lines passing through the origin of the coordinates.
Figure 5. Diversity of rhizosphere microbial communities. (a,b)—Alpha diversity index (genus level) of bacterial and fungal communities: ACE, Shannon, and Pelou_e, different lowercase letters indicated significant differences among treatments (p < 0.05); (c,d)—Beta diversity analysis (phylum level) of bacterial (R = 0.8148, p = 0.098) and fungal (R = −0.1481, p = 0.811) using PCoA. The dashed lines are the horizontal and vertical lines passing through the origin of the coordinates.
Agronomy 15 02238 g005
Agronomy 15 02238 g006
Figure 6. The structure and makeup of the microbial communities in the rhizosphere. (a,b)—Bacterial phylum and genus level community composition; (c,d)—Fungal phylum and genus level community composition; (e,f)—Bacterial and fungal genus level RDA. The dashed lines are the horizontal and vertical lines passing through the origin of the coordinates.
Figure 6. The structure and makeup of the microbial communities in the rhizosphere. (a,b)—Bacterial phylum and genus level community composition; (c,d)—Fungal phylum and genus level community composition; (e,f)—Bacterial and fungal genus level RDA. The dashed lines are the horizontal and vertical lines passing through the origin of the coordinates.
Agronomy 15 02238 g006
Agronomy 15 02238 g007
Figure 7. The rhizosphere soil’s bacterial and fungal network architecture. (a,b)—Bacterial network structures; (c,d)—Fungal network structures. Different colors in nodes represent different phylum, and the size of the nodes indicates the genus abundance.
Figure 7. The rhizosphere soil’s bacterial and fungal network architecture. (a,b)—Bacterial network structures; (c,d)—Fungal network structures. Different colors in nodes represent different phylum, and the size of the nodes indicates the genus abundance.
Agronomy 15 02238 g007
Agronomy 15 02238 g008
Figure 8. Spearman correlation heatmap of the top 20 bacteria (a) and fungal (b) with soil chemical properties, cotton yield, and their component factors in terms of relative abundance at the genus level. * p < 0.05, ** p < 0.01.
Figure 8. Spearman correlation heatmap of the top 20 bacteria (a) and fungal (b) with soil chemical properties, cotton yield, and their component factors in terms of relative abundance at the genus level. * p < 0.05, ** p < 0.01.
Agronomy 15 02238 g008
Table 1. Effects of different treatments on the chemical properties of topsoil (0–20 cm) during the boll opening stage from 2023 to 2024.
Table 1. Effects of different treatments on the chemical properties of topsoil (0–20 cm) during the boll opening stage from 2023 to 2024.
YearTreatmentpHSOMANAPAK
g·kg−1mg·kg−1
2023CK8.10 ± 0.01 a9.99 ± 1.27 a49.55 ± 2.61 a14.33 ± 2.51 a267.00 ± 18.29 a
BOF8.22 ± 0.11 a11.76 ± 1.32 a49.18 ± 5.78 a16.55 ± 0.53 a300.60 ± 20.78 a
2024CK8.08 ± 0.04 a11.54 ± 0.27 b40.89 ± 0.45 b18.81 ± 2.16 b381.78 ± 2.96 b
BOF8.15 ± 0.04 a12.93 ± 0.38 a49.90 ± 0.85 a25.66 ± 0.51 a401.91 ± 9.96 a
Note: SOM—soil organic matter; AN—alkali-hydrolyzable nitrogen; AP—available phosphorus; AK—available potassium. Lowercase letters following the numbers in the same column indicate significant differences among different treatments in the same year (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yu, M.; He, H.; Cheng, L.; Li, S.; Wan, T.; Qin, J.; Li, J. Bio-Organic Fertilizers Enhance Yield in Continuous Cotton Cropping Systems Through Rhizosphere Microbiota Modulation and Soil Nutrient Improvement. Agronomy 2025, 15, 2238. https://doi.org/10.3390/agronomy15092238

AMA Style

Yu M, He H, Cheng L, Li S, Wan T, Qin J, Li J. Bio-Organic Fertilizers Enhance Yield in Continuous Cotton Cropping Systems Through Rhizosphere Microbiota Modulation and Soil Nutrient Improvement. Agronomy. 2025; 15(9):2238. https://doi.org/10.3390/agronomy15092238

Chicago/Turabian Style

Yu, Mengmeng, Hao He, Liyang Cheng, Shuai Li, Tingting Wan, Jie Qin, and Junhua Li. 2025. "Bio-Organic Fertilizers Enhance Yield in Continuous Cotton Cropping Systems Through Rhizosphere Microbiota Modulation and Soil Nutrient Improvement" Agronomy 15, no. 9: 2238. https://doi.org/10.3390/agronomy15092238

APA Style

Yu, M., He, H., Cheng, L., Li, S., Wan, T., Qin, J., & Li, J. (2025). Bio-Organic Fertilizers Enhance Yield in Continuous Cotton Cropping Systems Through Rhizosphere Microbiota Modulation and Soil Nutrient Improvement. Agronomy, 15(9), 2238. https://doi.org/10.3390/agronomy15092238

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop