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Article

Planting Diversification Enhances Phosphorus Availability and Reshapes Fungal Community Structure in the Maize Rhizosphere

by
Yannan Li
1,
Yuming Zhang
2,
Xiaoxin Li
2,*,
Hongjun Li
2,
Wenxu Dong
2,
Shuping Qin
2,
Xiuping Liu
2,
Lijuan Zhang
1,*,
Chunsheng Hu
2,
Hongbo He
3,
Pushan Zheng
4 and
Jingyun Zhao
5
1
College of Resources and Environmental Sciences, Hebei Agricultural University, No. 289 Lingyusi Street, Baoding 071001, China
2
The Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, No. 86 Huaizhong Road, Shijiazhuang 050021, China
3
Institute of Applied Ecology, Chinese Academy of Sciences, 72 Wenhua Road, Shenyang 110016, China
4
College of Resources and Environmental Sciences, Shanxi Agricultural University, No. 81 Longcheng Street, Taiyuan 030031, China
5
Institute of Economic Crops, Shanxi Agricultural University, No. 81 Longcheng Street, Taiyuan 030031, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1993; https://doi.org/10.3390/agronomy15081993
Submission received: 28 June 2025 / Revised: 11 August 2025 / Accepted: 15 August 2025 / Published: 19 August 2025
(This article belongs to the Special Issue Plant Nutrition Eco-Physiology and Nutrient Management)
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Abstract

Intercropping with green manures is an effective practice for increasing agricultural production and reducing environmental issues. However, the effects of green manure type and intercropping patten on soil nutrient availability and microbial communities remains underexplored. In the present study, the impacts of three green manure–maize intercropping patterns on maize yield, rhizosphere nutrient availability, and soil fungal community were evaluated. Four treatments (three replicate plots for each) were involved, including a monoculture treatment (MC) as a control and three intercropping patterns as follows: maize–ryegrass (Lolium perenne L.) (IntL), maize–forage soybean (Fen Dou mulv 2, a hybrid soybean cultivar) (IntF), and maize–ryegrass–forage soybean (IntLF) intercropping. The results showed that all three intercropping patterns significantly increased maize yield and rhizosphere available phosphorus (AP) compared with MC. Intercropping shifted the dominant assembly process of the maize rhizosphere fungal community from stochastic to deterministic processes, shaping a community rich in arbuscular mycorrhizal fungi (AMF) and limited in plant pathogens, primarily Exserohilum turcicum. AP showed significant correlations with fungal community and AMF, while maize yield was negatively correlated with plant pathogens. In addition, the dual-species green manure intercropping pattern (IntLF) had the strongest positive effects on maize yield, AP content, and fungal community compared with single-species patterns (IntL and IntF). These results illustrate the advantages of planting diversification in boosting crop production by improving nutrient availability and soil health in the rhizosphere and suggest that the maize–ryegrass–forage soybean intercropping system is a potential strategy for improving soil fertility and health.

1. Introduction

Agriculture plays a pivotal role in the development of human society. Modern intensive agriculture has been instrumental in meeting global food demands for a burgeoning population, yet its long-term environmental costs are now untenable. Unconscionable soil management practices have concurrently triggered soil degradation [1], acidification [2], environmental contamination [3], and biodiversity loss [4], directly threatening future food security. With the continuous evolution of sustainable development concepts, new requirements, such as ecological agriculture and sustainable agriculture, are being proposed for agricultural production [5]. Major entities have implemented policies to advance the development of sustainable agriculture. The European Union’s “Farm to Fork” strategy now mandates a 50% reduction in chemical inputs by 2030 [6], while China’s “Dual Carbon” policy framework prioritizes low-carbon agriculture [7]. Such policy shifts underscore an urgent scientific mandate: to develop ecologically intensified systems that reconcile productivity with environmental resilience.
Cropping patterns are a critical factor influencing farmland ecosystem functions, nutrient utilization, and environmental effects. The development of appropriate cropping patterns will significantly contribute to achieving sustainable agricultural development [8]. Intercropping, the practice of cultivating two or more crop species simultaneously on the same field, has gained increasing attention as a sustainable agricultural strategy. Recent studies highlight its multifaceted impacts on soil health, crop productivity, disease suppression, and ecosystem resilience, offering solutions to challenges posed by intensive monoculture systems [9]. Well-designed intercropping systems increase land equivalent ratios (LER), achieving higher total yields than monocultures [10]. Temporal and spatial niche differentiation allows crops to utilize resources (light, water, nutrients) more efficiently. For example, maize–legume intercropping optimizes light interception and increases nitrogen utilization efficiency [11]. Additionally, complementary root architectures reduce soil erosion and improve resource use through spatial niche differentiation. Deep-rooted crops enhance soil C stabilization in deep soil [12], and the intercropping of deep-rooted and shallow-rooted crops together allows for a greater exploitation of a larger soil volume and improved access to relatively immobile nutrients, thus increasing the utilization of water and nutrients [13]. Such systems are also beneficial for reducing fertilizer input, thus mitigating environmental issues in agricultural ecosystems, such as nitrate leaching and greenhouse gas emission [14,15]. Intercropping also helps to reduce plant diseases. Intercropping disrupts pathogen transmission through physical barriers and biochemical interactions. For example, wheat–faba bean intercropping reduced faba bean Fusarium wilt incidence by 10.98–27.88% primarily due to the reduction of plant pathogens and promoting the abundance of beneficial microorganisms in the faba bean rhizosphere [16].
Soil microorganisms are one of the most active members in agricultural systems. They are the primary engine for soil nutrient cycling and closely associated with plant growth and health [17,18,19,20,21]. Fungi, one of the primary types of soil microorganisms, are indispensable components of agricultural ecosystems, driving critical processes that sustain soil fertility [22], plant health [23], and ecosystem resilience [24]. Due to the close interaction relationships between plants and soil fungi, the impacts of cropping patterns on agricultural systems are greatly associated with soil microorganisms. Intercropping profoundly reshapes soil fungal community structure and function, modifying both biodiversity and ecosystem services. Studies indicate that diversified cropping systems increase fungal richness compared to monocultures, primarily through niche differentiation and resource complementarity [25]. The changes in microbial α diversity is coupled with microbial community assembly. It has been found that stochastic processes are dominant in high-diversity communities, while deterministic processes are dominant in low-diversity communities [26]. Thus, the increased microbial α diversity by intercropping indicates the increased contribution of deterministic processes in microbial community assembly, which has been observed in types of cropping system [27]. In addition, intercropping-induced shifts in soil nutrient profiles reshape microbial competition for resources, thereby modulating the dominance of microbial taxa with distinct ecological strategies within the community [28]. For example, legume-based intercropping (e.g., maize–clover systems) enriches symbiotic fungi like arbuscular mycorrhizal fungi (AMF), and the plant–AMF symbiont exhibits competitive superiority in nutrient acquisition like N and P, which concurrently enhances crop nutrient uptake efficiency to boost yields, while suppressing competing microorganisms such as soil-borne pathogens through niche dominance [29]. The advantages of intercropping in improving microbial community and functions have been widely explored; however, the effects are largely dependent on the type of intercropped plants, but critical knowledge gaps persist regarding how different plant configurations in intercropping systems differentially drive soil microbiome assembly and nutrient cycling.
Green manure, primarily composed of cover crops (e.g., legumes, clover, and vetch), plays a multifaceted role in enhancing agricultural sustainability. In general, green manure is rotated with crops. When incorporated into soil, green manure improves soil fertility by increasing organic matter content and other nutrients like N and stimulating microbial activity, which enhances nutrient availability [30]. The intercropping of green manures and crops is also considered an ecological practice for improving the sustainability of agricultural production [31]. Existing research establishes that crop–green manure intercropping contributes positively to soil quality improvements and crop yield increasement. For example, maize–green manure intercropping enhanced maize water productivity through reducing total evapotranspiration [32] and improved soil nutrient levels through increasing soil enzyme activities [33]. In addition, maize–green manure intercropping also remodels rhizosphere microbiomes to stimulate beneficial taxa, such as P-dissolving bacteria and plant growth-promoting rhizobacteria, leading to enhanced nutrient availability and pathogen suppression in the rhizosphere [34]. However, current research predominantly concentrates on crops intercropped with a single green manure, whereas the efficacy of systems incorporating multiple green manure species are poorly understood.
In the present study, based on a field experiment with four treatments, including maize under monoculture and intercropped with different green manures (Lolium perenne L., forage soybean, and both Lolium perenne L. and forage soybean), the effects of different intercropping patterns on crop yield and rhizosphere nutrient dynamics, as well as rhizosphere fungal community assemblage, were compared. We hypothesized that the different intercropping patterns would shape a distinct fungal community in the maize rhizosphere and that multi-species green manure configurations would generate more synergistic benefits than single green manure intercropping patterns. The results of this study will improve our understanding about the mechanisms of crop diversification in influencing crop production and provide guidance for cropping management.

2. Materials and Methods

2.1. Field Experiment

The field experiment was located at Changtu County, Liaoning Province, China (42°43′1″ N,123°51′21″ E, altitude 100.5 m, Figure 1a) and was initiated in 2022. This region features a warm-temperate continental monsoon climate with humid to semi-humid characteristics, exhibiting four distinct seasons and synchronous occurrence of precipitation and thermal peaks. Mean annual temperatures range from 7 to 8 °C, peaking at 24 °C in July (hottest month) and dropping to −13 °C in January (coldest month). The annual frost-free period is 100–160 days, and the annual mean precipitation is 700 mm. The soil type in the experimental field is alfisol. The soil exhibits the following characteristics: pH 5.40, organic matter 21.50 g kg−1, total nitrogen (TN) 1.26 g kg−1, nitrate nitrogen (NO3−-N) 2.58 mg kg−1, ammonium nitrogen (NH4+-N) 1.01 mg kg−1, available phosphorus (AP) 17.90 mg kg−1, and available potassium (AK) 153.2 mg kg−1.
Four treatments were incorporated in the present study, including monoculture maize (MC), maize intercropped with Lolium perenne L. (IntL), forage soybean (Fen Dou mulv 2), and both Lolium perenne L. and forage soybean (IntFL). The details of the experimental design are shown in Figure 1b. Each treatment comprised three replicated plots, with individual plot dimensions of 153 m2 (9 m × 17 m). The maize cultivar used in this study was Xianyu 1483. It is a high-yielding dent maize hybrid adapted to temperate regions, characterized by a 115-day growth cycle, plant height of ~280 cm, moderate drought tolerance, and robust performance in medium-fertility soils. Fen Dou mulv 2 is a hybrid soybean cultivar between a wild soybean germplasm accession, semi-wild Fen 2, and a cultivated soybean cultivar, Jindou 21. It was released by Shanxi Academy of Agricultural Sciences and delivers rapid biomass accumulation (8.2 t ha−1 fresh weight in 60 days) and exceptional nitrogen fixation capacity (85–110 kg N ha−1 season−1) via enhanced nodulation with Bradyrhizobium strains. Ryegrass (Lolium perenne L.) serves as a versatile green manure and forage crop, valued for its rapid establishment (full ground cover in 21 days) and deep root system extending up to 1.5 m, which effectively reduces soil compaction while enhancing water infiltration. Maize was drill-seeded at 67,500 plants ha−1. Fen Dou mulv 2 and perennial ryegrass were sown in rows at 37.5 kg ha−1 and 75 kg ha−1, respectively. The sowing method employed was no-till seeding. Maize was sown during the optimal window (late April–early May) and harvested at physiological maturity in late September, with fields remaining fallow through winter to facilitate soil restoration. In 2022, maize and green manures were sown on May 7. After maize harvest, both maize and green manure straws were removed from the field. Fertilizer application involved a single basal dose applied during maize sowing, using a compound fertilizer (NPK 25-10-10) at a rate of 825 kg ha−1. Three days after maize sowing, a 1:1 (v:v) tank-mix solution of acetochlor and thifensulfuron-methyl was applied for pre-emergence weed control (1400 g ha−1).

2.2. Soil Samples Collection and Biochemical Measurement

Samples were collected on 23 July 2022 (78 days post-sowing), the flare opening stage of maize—a critical phase coinciding with maize yield formation and peak green manure growth. The whole maize plant was carefully removed from soil. The soil that lightly adhered to the roots was carefully removed; then, the soil sticking close to the roots was thoroughly washed with a sterile brush and collected as the rhizosphere soil [35]. Five plants in each replicate plot were collected, and the rhizosphere soil from each plant was mixed thoroughly as a single one.
Soil total nitrogen (TN), available nitrogen (AN), ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), soil organic matter (SOM), and available phosphorus (AP) were measured according to the methods described in previous studies [1,36,37]. Briefly, the TN content was measured by an elemental analyzer (Vario MAX, Elementar, Langenselbold, Germany). AN was measured using the Illinois Soil Nitrogen Test Diffusion Method. NH4+-N and nitrate nitrogen (NO3−-N) were extracted with 2 M KCl solution and measured using a continuous flow analytical system (San++ System, Skalar, Holland, Breda, The Netherlands). SOM was measured using the dichromate oxidation method. AP was extracted by 0.5 M NaHCO3 solution and determined using the molybdenum blue method.

2.3. DNA Extraction and Fungal Community Measurement

Soil total DNA was extracted using a Soil DNA Extraction Kit (MP Biomedicals, Santa Ana, CA, USA). Soil fungal communities were determined using high-throughput sequencing [23,34]. Primer sets ITS1f/ITS2 (5′-CTTGGTCATTTAGAGGAAGTAA-3′; 5′-GCTGCGTTCTTCATCGATGC-3′) were used to amplify the ITS (internal transcribed spacer) region of fungal rRNA. The sequencing of the PCR products was conducted using the Illumina HiSeq2000 platform (Illumina, San Diego, CA, USA).

2.4. Bioinformatic Analysis of High-Throughput Sequencing Data

The primer and adaptor sequences in the raw data were cut using Cutadapt [38]. Then, the bioinformatic analysis was performed using th VSEARCH package following the protocol described in previous studies [39,40,41]. The paired-end data were first merged according to the overlap within the forward and reverse reads. Then, the low-quality reads were filtered. The UNOISE algorithm (version 3) was used to denoise the reads and generate zOTUs (zero-radius operational taxonomic units). The taxonomy of each zOTU was determined using SINTAX based on the UNITE database (version 8.3). After removing the zOTUs not assigned as fungi, the zOTU table was subsampled to 72,000 reads per sample (72,394–99,712 high-quality reads per sample) for statistical analysis. The ecological guilds of the zOTUs were predicted using FUNGUild [42].

2.5. Statistical Analysis

Statistical analysis was performed using R as described in previous studies [43,44]. The significance of the difference of the variables was checked by the Kruskal–Wallis rank sum test using the “dplyr” library, and the variables that were not normally distributed were determined by the Shapiro–Wilk test. Non-metric multidimensional scaling (NMDS) based on the Bray–Curtis distance was performed using the “vegan” library to explore the variation of fungal communities under different treatments. The abundance-based beta-null deviation (based on the Bray–Curtis distance and 999 null patches) was calculated to infer the changes in niche and neutral processes in the fungal community assembly [45,46]. The taxonomic normalized stochasticity ratio (tNST) was calculated to quantitate the ratio of stochastic processes in the fungal community assembly (dist.method = “bray”, abundance.weighted = TRUE, rand = 999, null.model = “PF”) [47]. The correlations between the fungal community and soil properties were determined by the Mantel test (permutations = 999) using “vegan” library.

3. Results

3.1. Variations in Soil Properties and Crop Yields Under Different Planting Patterns

Different plant patterns showed different effects on maize rhizosphere soil properties (Table 1). All four planting patterns showed no significant impacts on TN, AN, or SOM in maize rhizosphere soil. Rhizosphere soil NH4+-N under IntF treatments was significantly higher than that under MC treatment. Compared with MC, IntF and IntLF significantly decreased rhizosphere soil NO3−-N but significantly increased AP, while IntL had no significant impact on rhizosphere soil NO3−-N and AP.
Maize grain yields differed significantly across treatments (Table 1). Compared to MC, all intercropping systems significantly increased maize yields. However, in 2022, the maize yield under IntF was significantly lower than that under IntL and IntLF, while the maize yield was not significantly different between IntL and IntLF. IntLF achieved the highest yield—a 26.9% increase over MC (p < 0.01). In 2023, IntLF treatment also obtained the highest maize yield (p < 0.05), while maize yields were not significantly different between IntL and IntF.

3.2. Different Cropping Patterns Shaped Distinct Fungal Communities in the Maize Rhizosphere

The results of high-throughput sequencing showed that the fungal community in the maize rhizosphere soil was dominated by Basidiomycota, Mortierellomycota, and Ascomycota, which accounted for 70.84% of the total reads (Figure 2a). Cropping pattern changed the dominance of these taxa. The three intercropping models decreased the dominance of Mortierellomycota but increased that of Ascomycota. IntL showed no significant impact on the relative abundance of Basidiomycota, while IntF and IntLF resulted in significant dilution of Basidiomycota.
The NMDS plot directly showed the variation in the rhizosphere fungal community under different cropping patterns (Figure 2b). The samples from intercropping treatments were clearly separated from that of MC treatment. In addition, the samples from the three intercropping treatments were also separated from each other. In the NMDS plot, samples from IntLF were most far from the samples from MC, followed by IntF and IntL (Figure 2b), showing that IntLF produced the strongest impact on rhizosphere fungal community, followed by IntF and IntL.
Shannon index was calculated to infer the changes in fungal diversity in the maize rhizosphere under different cropping patterns. The Shannon index increased in the order of MC, IntL, IntF, and IntLF, even though the difference between MC and IntL was not significant (Figure 2c), showing a positive effect of intercropping on rhizosphere fungal diversity. IntLF showed greater enhancement on fungal diversity than IntL and IntF.

3.3. Planting Diversification Modified the Functional Profiles of the Rhizosphere Fungal Community

Ecological guilds of fungal communities were predicted using FUNGUild. It is worth noting that cropping patterns greatly impact the dominance of plant pathogens and AMF (Figure 3). The predominant potential pathogen was identified as Exserohilum turcicum, accounting for 90.33% of all pathogenic fungi detected on average. Glomeraceae was the dominant AMF, which accounted for 83.77% of the AMF community on average. Compared with MC, IntL and IntLF significantly diluted plant pathogen in rhizosphere fungal community (Figure 3a). In contrast, all three intercropping treatments largely increased the relative abundance of AMF (Figure 3b), and the positive effect increased in the order of IntL, IntF, and IntLF.

3.4. Planting Diversification Increased the Contribution of Deterministic Processes to Rhizosphere Fungal Community Assembly

Abundance-based beta-null deviation was calculated to compare the contribution of stochastic and deterministic processes to fungal community assemblage. The beta-null deviation values of the fungal community in all four treatments were positive, indicating that the communities were dissimilar with that constructed by the null model. However, compared with MC, intercropping significantly increased the beta-null deviation value (Figure 4), showing that intercropping increased the contribution of deterministic processes to fungal community assembly. To further quantificationally differentiate the contribution of stochastic and deterministic processes to fungal community assembly, the proportion of stochasticity in shaping community assemblage was determined. The results of tNST showed that the contribution of stochastic processes to fungal microbial community assembly was 68.1%, while it decreased to 22.5%, 20.7%, and 29.2% in IntL, IntF, and IntLF, showing the conversion of deterministic processes-dominated fungal communities to stochastic processes-dominated fungal communities. The results showed that the fungal community assembly in MC treatment was dominated by stochastic processes, while intercropping largely decreased the contribution of stochastic processes to fungal community assembly and shaped a deterministic processes-dominated fungal community.
Venn diagram showed that most of the zOTUs were detected in all treatments (Figure 5a). However, each treatment also contained distinct zOTUs. In MC treatment, 17.60% zOTUs were unique. This ratio was higher in the intercropping treatments, ranging from 21.37% to 23.46% (Figure 5b). However, the fungal communities were dominated by the shared zOTUs, which accounted for 98.52% to 99.45% of the total fungal community (Figure 5c). These results showed filter effects of intercropping on rare species and the high contribution of abundant species to community variation.

3.5. Correlation Analysis of Fungal Community Against Soil Properties and Crop Yields

The rhizosphere fungal community significantly correlated with one single soil property, AP (p < 0.05). In contrast, the potential plant pathogens had no significant correlation with soil properties. However, AMF was negatively correlated with AN, NO3−-N, and SOM (p < 0.05), while positively correlated with AP (p < 0.05). Crop yield had no significant correlation with fungal community and the relative abundance of AMF but was significantly negative correlated with the relative abundance of plant pathogens.

4. Discussion

Optimizing cropping patterns is an effective way to enhance the sustainable use of soil. Planting diversification has been confirmed to be a sustainable strategy for enhancing resilience and reducing the risks of monoculture [48]. Intercropping, a typical planting diversification model, has been widely used in agricultural practice. However, due to the complex interactions between plants, the type of intercropped plants greatly impact the effects of intercropping on agricultural systems. Thus, seeking optimal interaction patterns is vital for intercropping systems. The present study revealed that coupled green manure intercropping showed greater facilitation than single ones on rhizosphere nutrient availability and the recruitment of beneficial microorganisms. These results showed the greater advantages of higher cropping diversification on plant nutrient utilization and rhizosphere microbial ecology.
P, a critical macronutrient for crop growth, is often fixed by metal ions in soil, limiting its bioavailability [49]. The enhanced P availability in the maize rhizosphere under intercropping systems can be attributed to synergistic mechanisms involving root exudate dynamics and AMF. Studies have found that intercropping, particularly with legumes or deep-rooted species, stimulate root secretion of organic acids (e.g., citrate, malate) and acid phosphatase to solubilize inorganic P and mineralize organic P [13], thereby releasing fixed and organic P into plant-available forms.
Concurrently, the increase of P in the intercropped maize rhizosphere may be partly contributed by the enrichment of AMF (Figure 2b), as observed in other studies [29]. AMF enhances plants’ P acquisition through dual pathways: direct solubilization via extraradical hyphae that access P beyond root depletion zones and modulation of host root exudation patterns. Furthermore, AMF hyphae physically disrupt soil aggregates, exposing occluded P pools, while their carbon demand drives microbial turnover of organic P [50]. Thus, AMF is considered as a potential biofertilizer to optimize P-use efficiency and reduce dependence on mineral P fertilizers in sustainable agriculture [34], and there are several successful cases have demonstrated that incubation of AMF can significantly enhance crop P nutrition and boost yield gains [51,52]. However, the application of AMF still faces many challenges including the low colonization rate of AMF and systematic lack of high-quality products [53]. This study demonstrated that intercropping effectively enriched AMF populations, indicating that optimized cropping systems represent a viable agricultural strategy for leveraging AMF-mediated benefits for sustainable farming.
While intercropping enhanced P availability in the crop rhizosphere, it concurrently triggered a significant decline in N—particularly nitrate N (Table 1). And nitrate levels exhibited a strong negative correlation with AMF (Table 2). This pattern implies intensified AMF competitiveness for N resources within the intercropping system. As N is an important resource for AMF, more AMF indicate a higher need for N resources. This may be beneficial for mitigating N losses, such as nitrate leaching [54], while potentially exerting adverse impacts on plant N uptake—a trade-off deserving careful consideration. The negative effects of AMF on maize N acquisition has been observed in other study [55]. In addition, AMF can also obtain N resources from organic materials [56], which may enhance the degradation of SOM. Thus, a negative correlation between SOM and AMF was observed in the present study (Table 2). These findings indicate that the promotion of AMF by intercropping may unintentionally compromise crop N acquisition, underscoring the necessity for nutrient-specific management strategies when implementing intercropping systems in agricultural production.
The benefits of intercropping to crop production are also contributed by the decrease in plant pathogens [9]. In the present study, the fungal plant pathogens were greatly diluted in the maize rhizosphere by intercropping (Figure 3a) and were negatively correlated with maize yield (Table 2). The reduced relative abundance of fungal pathogens in intercropping systems likely stems from multifaceted ecological and biochemical mechanisms inherent to diversified cropping practices. First, intercropping disrupts pathogen monoculture-associated “host-specificity” by diluting the density of susceptible host roots, thereby impeding pathogen establishment and transmission [57]. For instance, non-host crops in maize–legume systems physically block the hyphal growth of maize-specific pathogens like Fusarium graminearum. Second, rhizosphere microbial communities in intercropped soils exhibit enhanced antagonistic capacities. Beneficial microbes such as Pseudomonas spp. and Trichoderma spp., which are enriched by diverse root exudates (e.g., flavonoids, benzoxazinoids), produce antifungal metabolites (e.g., phenazines, chitinases) that directly inhibit pathogenic fungi. Moreover, intercropping often elevates AMF colonization, which competitively excludes pathogens through niche preemption [56]. AMF hyphae occupy root cortical spaces, reducing infection sites, while concurrently inducing systemic resistance in host plants via jasmonic acid signaling pathways [58]. These mechanisms collectively reshape the soil microbiome equilibrium, favoring commensal and mutualistic fungi over pathogenic taxa.
The distinct rhizosphere fungal community in the maize rhizosphere under different cropping treatments indicated that intercropping changed the assembly of the fungal community. The dominance of deterministic processes in rhizosphere fungal community assembly under intercropping systems reflects an enhanced host-mediated regulatory capacity of maize over microbial recruitment. Deterministic assembly, driven by niche-based selection, implies that plant-induced environmental filtering and interspecific interactions outweigh stochastic factors (e.g., random dispersal or demographic drift). This shift indicates the enhancement of plant roots in shaping rhizosphere microbial community assembly under diversification planting, this may increase the robustness of the rhizosphere microbial community. The associations between plant diversity and soil microbial community have been widely explored in natural systems [59]. High plant diversity increases rhizosphere carbon inputs into the microbial community [59], which greatly modifies the nutrient composition of rhizosphere environments and results in the targeted assembly of the rhizosphere microbial community. The results from the present study showed that increased plant diversity is a potential way to manipulate rhizosphere microbial community assembly.
The benefits of intercropping to crop production have been widely confirmed. The present study further revealed the superior performance of maize intercropped with two green manure species compared to single green manure systems, highlighting the ecological advantages of diversified planting in enhancing nutrient acquisition and crop growth. This phenomenon may be partly attributed to synergistic soil–microbe interactions inherent to multi-species systems. In natural systems, soil microbial diversity is positively associated with plant diversity [60], and the functional profiles of soil microbial communities are largely dependent on plant diversity [61,62,63]. This fact was also confirmed by the present study, which showed that multi-species green manure systems fostered richer microbial diversity (Figure 2c). Different plant species occupy distinct ecological niches and input different substrates to soil through residues and root exudates, shaping diverse microbial consortia and enhancing soil microbial functionality [62]. In addition, higher plant diversity enhances the exudation of root-derived compounds into the rhizosphere, resulting in increased microbial activity [59,64], which enhances nutrient cycling and availability in rhizosphere. Our research revealed that the dual-species green manure intercropping pattern significantly enhanced P availability in the maize rhizosphere (Table 1) and enriched P-associated microorganisms (AMF) (Figure 2b). These results showed that planting diversification increased crop yield by improving rhizosphere nutrient supply and modifying microbial functions. Future research should optimize species combinations and planting configurations to maximize these benefits across varying agroecological contexts.

5. Conclusions

Based on a field experiment, the present study found that intercropping with green manure increased the AP content and shaped a fungal community enriched with AMF and decreased pathogen presence in the maize rhizosphere, increasing maize yield. However, these effects varied with the green manure species. In the studied field, maize–forage soybean intercropping performed better than maize–Lolium perenne L. intercropping in increasing maize yield. Nevertheless, the dual-species green manure intercropping system significantly outperformed single-species systems in boosting crop yields, with this synergistic effect mechanistically linked to enhanced nutrient profiles and more healthy fungal communities in the rhizosphere. These findings underscore intercropping as a powerful tool for engineering beneficial fungal communities and elucidating the role of diversified cropping systems in enhancing agricultural productivity from a rhizosphere microbiome perspective and also provides critical references for optimizing intercropping systems, selecting context-appropriate green manure cultivars adapted to local soil and climate conditions, and prioritizing multi-species intercropping systems over single species systems where feasible. Our findings demonstrate that the maize–ryegrass–Fen Dou mulv intercropping system outperforms single green manure intercropping in boosting maize yields, and thus we recommend its adoption within our experimental region. However, the findings are constrained by single-year/single-location field data. While revealing mechanistic linkages between intercropping patterns and rhizosphere processes, broader applicability requires validation across diverse seasons and soil types. Moreover, developing practicable mechanization solutions for multi-species intercropping systems represents a pivotal challenge for field deployment, as current equipment rarely accommodates the spatial heterogeneity and species-specific handling requirements inherent to such diversified planting regimes.

Author Contributions

Conceptualization: Y.Z., X.L. (Xiaoxin Li) and L.Z.; Data curation: W.D.; Formal analysis: Y.L., H.L. and H.H.; Investigation: Y.L.; Methodology: X.L. (Xiaoxin Li), S.Q., X.L. (Xiuping Liu), P.Z. and J.Z.; Supervision: Y.Z., X.L. (Xiaoxin Li) and C.H.; Visualization: Y.L.; Writing—original draft: Y.L. and Y.Z.; Writing—review & editing: X.L. (Xiaoxin Li), H.L., W.D., S.Q., X.L. (Xiuping Liu), L.Z., C.H., H.H., P.Z. and J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA28010301), the National Key Research and Development Program of China (Grant No. 2022YFD1500302, 2022YFD1901303 and 2022YFD1900302-04-03), and the Key Research and Development Program of Hebei Province (Grand No. 22326410D).

Data Availability Statement

The data presented in this study are available upon request from the corresponding authors.

Acknowledgments

The authors thank their colleagues at the Luancheng Agroecosystem Experimental Station for technical assistance and precious efforts in maintaining and measuring the long-term experiment.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sun, R.B.; Zhang, X.X.; Guo, X.S.; Wang, D.Z.; Chu, H.Y. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw. Soil Biol. Biochem. 2015, 88, 9–18. [Google Scholar] [CrossRef]
  2. Sun, R.B.; Li, W.Y.; Hu, C.S.; Liu, B.B. Long-term urea fertilization alters the composition and increases the abundance of soil ureolytic bacterial communities in an upland soil. FEMS Microbiol. Ecol. 2019, 95, fiz044. [Google Scholar] [CrossRef] [PubMed]
  3. Liu, M.; Zhang, W.; Wang, X.; Wang, F.; Dong, W.; Hu, C.; Liu, B.; Sun, R. Nitrogen Leaching Greatly Impacts Bacterial Community and Denitrifiers Abundance in Subsoil under Long-Term Fertilization. Agric. Ecosyst. Environ. 2020, 294, 106885. [Google Scholar] [CrossRef]
  4. Bijay, S.; Craswell, E. Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Appl. Sci. 2021, 3, 518. [Google Scholar] [CrossRef]
  5. Wu, W.; Zhai, F.; Liu, Z.; Liu, C.; Gu, Y.; Li, P. The Spatial and Seasonal Variability of Nutrient Status in the Seaward Rivers of China Shaped by the Human Activities. Ecol. Indic. 2023, 157, 111223. [Google Scholar] [CrossRef]
  6. Kiran, B.R.; Prasad, M.N.V.; Mohan, S.V. Chapter 2—Farm to Fork: Sustainable Agrifood Systems. In Sustainable and Circular Management of Resources and Waste Towards a Green Deal; Vara Prasad, M.N., Smol, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2023; pp. 25–38. ISBN 978-0-323-95278-1. [Google Scholar]
  7. Wang, Z.; Tang, K.; Struik, P.C.; Ashraf, M.N.; Zhang, T.; Zhao, Y.; Wu, R.; Jin, K.; Li, Y. Alteration of Microbial Carbon and Nitrogen Metabolism within the Soil Metagenome with Grazing Intensity at Semiarid Steppe. J. Environ. Manag. 2023, 347, 119078. [Google Scholar] [CrossRef]
  8. Mahlayeye, M.; Darvishzadeh, R.; Nelson, A. Cropping Patterns of Annual Crops: A Remote Sensing Review. Remote Sens-Basel 2022, 14, 2404. [Google Scholar] [CrossRef]
  9. Toker, P.; Canci, H.; Turhan, I.; Isci, A.; Scherzinger, M.; Kordrostami, M.; Yol, E. The advantages of intercropping to improve productivity in food and forage production—A review. Plant Prod. Sci. 2024, 27, 155–169. [Google Scholar] [CrossRef]
  10. Brooker, R.W.; Bennett, A.E.; Cong, W.-F.; Daniell, T.J.; George, T.S.; Hallett, P.D.; Hawes, C.; Iannetta, P.P.M.; Jones, H.G.; Karley, A.J.; et al. Improving intercropping: A synthesis of research in agronomy, plant physiology and ecology. New Phytol. 2015, 206, 107–117. [Google Scholar] [CrossRef]
  11. Fu, Z.; Chen, P.; Zhang, X.; Du, Q.; Zheng, B.; Yang, H.; Luo, K.; Lin, P.; Li, Y.; Pu, T.; et al. Maize-legume intercropping achieves yield advantages by improving leaf functions and dry matter partition. BMC Plant Biol. 2023, 23, 438. [Google Scholar] [CrossRef]
  12. Peixoto, L.; Olesen, J.E.; Elsgaard, L.; Enggrob, K.L.; Banfield, C.C.; Dippold, M.A.; Nicolaisen, M.H.; Bak, F.; Zang, H.; Dresbøll, D.B.; et al. Deep-rooted perennial crops differ in capacity to stabilize C inputs in deep soil layers. Sci. Rep. 2022, 12, 5952. [Google Scholar] [CrossRef]
  13. Homulle, Z.; George, T.S.; Karley, A.J. Root traits with team benefits: Understanding belowground interactions in intercropping systems. Plant Soil 2022, 471, 1–26. [Google Scholar] [CrossRef]
  14. Raseduzzaman, M.; Dong, W.; Gaudel, G.; Aluoch, S.O.; Timilsina, A.; Li, X.; Hu, C. Maize-soybean intercropping reduces greenhouse gas emissions from the fertilized soil in the North China Plain. J. Soils Sediments 2024, 24, 3115–3131. [Google Scholar] [CrossRef]
  15. Long, G.; Li, L.; Wang, D.; Zhao, P.; Tang, L.; Zhou, Y.; Yin, X. Nitrogen levels regulate intercropping-related mitigation of potential nitrate leaching. Agric. Ecosyst. Environ. 2021, 319, 107540. [Google Scholar] [CrossRef]
  16. Zheng, Y.; Zhang, J.; Wang, D.; Yang, S.; Cen, Z.; Dong, Y. Faba bean-wheat intercropping controls the occurrence of faba bean Fusarium wilt by improving the microecological environment of rhizosphere soil. Eur. J. Soil Biol. 2024, 123, 103685. [Google Scholar] [CrossRef]
  17. Sun, R.B.; Guo, X.S.; Wang, D.Z.; Chu, H.Y. Effects of long-term application of chemical and organic fertilizers on the abundance of microbial communities involved in the nitrogen cycle. Appl. Soil Ecol. 2015, 95, 171–178. [Google Scholar] [CrossRef]
  18. Li, W.Y.; Xiao, Q.; Hu, C.S.; Liu, B.B.; Sun, R.B. A comparison of the efficiency of different urease inhibitors and their effects on soil prokaryotic community in a short-term incubation experiment. Geoderma 2019, 354, 113877. [Google Scholar] [CrossRef]
  19. Sun, R.B.; Wang, F.H.; Hu, C.S.; Liu, B.B. Metagenomics reveals taxon-specific responses of the nitrogen-cycling microbial community to long-term nitrogen fertilization. Soil Biol. Biochem. 2021, 156, 108214. [Google Scholar] [CrossRef]
  20. Wang, X.L.; Chi, Y.K.; Song, S.Z. Important soil microbiota’s effects on plants and soils: A comprehensive 30-year systematic literature review. Front. Microbiol. 2024, 15, 1347745. [Google Scholar] [CrossRef] [PubMed]
  21. Sun, R.; Ding, J.; Li, H.; Wang, X.; Li, W.; Li, K.; Ye, X.; Sun, S. Mitigating nitrate leaching in cropland by enhancing microbial nitrate transformation through the addition of liquid biogas slurry. Agric. Ecosyst. Environ. 2023, 345, 108324. [Google Scholar] [CrossRef]
  22. Sun, R.; Dsouza, M.; Gilbert, J.A.; Guo, X.; Wang, D.; Guo, Z.; Ni, Y.; Chu, H. Fungal community composition in soils subjected to long-term chemical fertilization is most influenced by the type of organic matter. Environ. Microbiol. 2016, 18, 5137–5150. [Google Scholar] [CrossRef]
  23. Sun, R.B.; Chen, Y.; Han, W.; Dong, W.; Zhang, Y.; Hu, C.; Liu, B.; Wang, F. Different contribution of species sorting and exogenous species immigration from manure to soil fungal diversity and community assemblage under long-term fertilization. Soil Biol. Biochem. 2020, 151, 108049. [Google Scholar] [CrossRef]
  24. Frac, M.; Hannula, S.E.; Belka, M.; Jedryczka, M. Fungal Biodiversity and Their Role in Soil Health. Front. Microbiol. 2018, 9, 707. [Google Scholar] [CrossRef]
  25. Liu, P.F.; Zhao, Y.K.; Ma, J.N.; Cao, Y.; Zhang, M.X.; Yu, J.; Guan, H.B.; Xing, Y.S.; Wang, X.Q.; Jia, X. Impact of various intercropping modes on soil quality, microbial communities, yield and quality of Platycodon grandiflorum (Jacq.) A. DC. BMC Plant Biol. 2025, 25, 503. [Google Scholar] [CrossRef]
  26. Xun, W.; Li, W.; Xiong, W.; Ren, Y.; Liu, Y.; Miao, Y.; Xu, Z.; Zhang, N.; Shen, Q.; Zhang, R. Diversity-Triggered Deterministic Bacterial Assembly Constrains Community Functions. Nat. Commun. 2019, 10, 3833. [Google Scholar] [CrossRef]
  27. Guo, X.; Hou, Z.; Wu, X.; Liu, W.; Cai, J.; An, S. Long-Term Intercropping Shaped Soil Bacterial Microbiome Composition and Structure of Maize Fields in a Semiarid Region. Soil Tillage Res. 2025, 247, 106383. [Google Scholar] [CrossRef]
  28. Lee, H.; Bloxham, B.; Gore, J. Resource Competition Can Explain Simplicity in Microbial Community Assembly. Proc. Natl. Acad. Sci. USA 2023, 120, e2212113120. [Google Scholar] [CrossRef]
  29. Ge, S.; Chen, Y.; Wang, Z.; Li, Z.; Shen, C.; Zhang, T.; Wang, J. Deep insights into the diversified cropping and their impact on arbuscular mycorrhizal fungi: A global meta-analysis. Agric. Ecosyst. Environ. 2025, 383, 109537. [Google Scholar] [CrossRef]
  30. Zhou, G.; Li, G.; Liang, H.; Liu, R.; Ma, Z.; Gao, S.; Chang, D.; Liu, J.; Chadwick, D.R.; Jones, D.L.; et al. Green Manure Coupled With Straw Returning Increases Soil Organic Carbon via Decreased Priming Effect and Enhanced Microbial Carbon Pump. Glob. Change Biol. 2025, 31, e70232. [Google Scholar] [CrossRef]
  31. Li, H.; Fan, Z.; Wang, Q.; Wang, G.; Yin, W.; Zhao, C.; Yu, A.; Cao, W.; Chai, Q.; Hu, F. Green manure and maize intercropping with reduced chemical N enhances productivity and carbon mitigation of farmland in arid areas. Eur. J. Agron. 2023, 145, 126788. [Google Scholar] [CrossRef]
  32. Zhang, T.; Zuo, Q.; Shi, J.; Fan, Y.; Zhang, S.; Wu, X.; Ben-Gal, A. Evaluation and Improvement of Crop Root-Water-Uptake and Transpiration in AquaCrop. Agric. Water Manag. 2025, 318, 109686. [Google Scholar] [CrossRef]
  33. Zhao, X.; Liang, H.; Chang, D.; Zhang, J.; Bao, X.; Cui, H.; Cao, W. Maize-Green Manure Intercropping Improves Maize Yield and P Uptake by Shaping the Responses of Roots and Soil. J. Integr. Agric. 2025, in press. [Google Scholar] [CrossRef]
  34. Jiang, P.; Wang, Y.; Zhang, Y.; Fei, J.; Rong, X.; Peng, J.; Yin, L.; Luo, G. Intercropping Enhances Maize Growth and Nutrient Uptake by Driving the Link between Rhizosphere Metabolites and Microbiomes. New Phytol. 2024, 243, 1506–1521. [Google Scholar] [CrossRef]
  35. Sun, R.; Zhang, W.; Liu, Y.; Yun, W.; Luo, B.; Chai, R.; Zhang, C.; Xiang, X.; Su, X. Changes in phosphorus mobilization and community assembly of bacterial and fungal communities in rice rhizosphere under phosphate deficiency. Front. Microbiol. 2022, 13, 953340. [Google Scholar] [CrossRef]
  36. Sun, R.; Li, W.; Dong, W.; Tian, Y.; Hu, C.; Liu, B. Tillage Changes Vertical Distribution of Soil Bacterial and Fungal Communities. Front. Microbiol. 2018, 9, 699. [Google Scholar] [CrossRef]
  37. Zhang, L.; Niu, J.; Lu, X.; Zhao, Z.; Li, K.; Wang, F.; Zhang, C.; Sun, R. Dosage effects of organic manure on bacterial community assemblage and phosphorus transformation profiles in greenhouse soil. Front. Microbiol. 2023, 14, 1188167. [Google Scholar] [CrossRef] [PubMed]
  38. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet. J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  39. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahe, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 2016, 4, e2584. [Google Scholar] [CrossRef]
  40. Sun, R.; Wang, X.; Hamoud, Y.A.; Lu, M.; Shaghaleh, H.; Zhang, W.; Zhang, C.; Ma, C. Dynamic variation of bacterial community assemblage and functional profiles during rice straw degradation. Front. Microbiol. 2023, 14, 1173442. [Google Scholar] [CrossRef] [PubMed]
  41. Chen, Y.; Sun, R.; Sun, T.; Chen, P.; Yu, Z.; Ding, L.; Jiang, Y.; Wang, X.; Dai, C.; Sun, B. Evidence for involvement of keystone fungal taxa in organic phosphorus mineralization in subtropical soil and the impact of labile carbon. Soil Biol. Biochem. 2020, 148, 107900. [Google Scholar] [CrossRef]
  42. Nguyen, N.H.; Song, Z.; Bates, S.T.; Branco, S.; Tedersoo, L.; Menke, J.; Schilling, J.S.; Kennedy, P.G. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016, 20, 241–248. [Google Scholar] [CrossRef]
  43. Xiang, X.; Wu, Y.; Zhang, F.; Kuang, Y.; Li, C.; Sun, R.; Hui, C. Evidence for cross transmission of pathogens between wild hooded cranes and domestic geese. J. Avian Biol. 2023, 2023, e03083. [Google Scholar] [CrossRef]
  44. Sun, R.; Wang, D.; Guo, Z.; Hua, K.; Guo, X.; Chen, Y.; Liu, B.; Chu, H. Combined application of organic manure and chemical fertilizers stabilizes soil N-cycling microflora. Soil Ecol. Lett. 2023, 5, 220165. [Google Scholar] [CrossRef]
  45. Tucker, C.M.; Shoemaker, L.G.; Davies, K.F.; Nemergut, D.R.; Melbourne, B.A. Differentiating between niche and neutral assembly in metacommunities using null models of beta-diversity. Oikos 2016, 125, 778–789. [Google Scholar] [CrossRef]
  46. Sun, R.B.; Wang, X.; Tian, Y.; Guo, K.; Feng, X.; Sun, H.; Liu, X.; Liu, B. Long-Term Amelioration Practices Reshape the Soil Microbiome in a Coastal Saline Soil and Alter the Richness and Vertical Distribution Differently Among Bacterial, Archaeal, and Fungal Communities. Front. Microbiol. 2022, 12, 768203. [Google Scholar] [CrossRef] [PubMed]
  47. Ning, D.; Deng, Y.; Tiedje, J.M.; Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl. Acad. Sci. USA 2019, 116, 16892–16898. [Google Scholar] [CrossRef]
  48. Mihrete, T.B.; Mihretu, F.B. Crop Diversification for Ensuring Sustainable Agriculture, Risk Management and Food Security. Glob. Chall. 2025, 9, 2400267. [Google Scholar] [CrossRef]
  49. Lambers, H. Phosphorus Acquisition and Utilization in Plants. Annu. Rev. Plant Biol. 2022, 73, 17–42. [Google Scholar] [CrossRef]
  50. Li, M.; Hu, J.; Lin, X. The roles and performance of arbuscular mycorrhizal fungi in intercropping systems. Soil Ecol. Lett. 2022, 4, 319–327. [Google Scholar] [CrossRef]
  51. Weber, S.E.; Bascompte, J.; Kahmen, A.; Niklaus, P.A. AMF Diversity Promotes Plant Community Phosphorus Acquisition and Reduces Carbon Costs per Unit of Phosphorus. New Phytol. 2025, nph.70161. [Google Scholar] [CrossRef] [PubMed]
  52. Wu, Y.; Chen, C.; Wang, G. Inoculation with Arbuscular Mycorrhizal Fungi Improves Plant Biomass and Nitrogen and Phosphorus Nutrients: A Meta-Analysis. BMC Plant Biol. 2024, 24, 960. [Google Scholar] [CrossRef]
  53. Chaudhary, A.; Poudyal, S.; Kaundal, A. Role of Arbuscular Mycorrhizal Fungi in Maintaining Sustainable Agroecosystems. Appl. Microbiol. 2025, 5, 6. [Google Scholar] [CrossRef]
  54. Kwon, W.; Kim, D.; Kim, H.-Y.; Jeong, S.W.; Lee, S.-G.; Kim, H.-C.; Lee, Y.-J.; Kwon, M.K.; Hwang, J.-S.; Han, J.E.; et al. Microglial Phagocytosis of Polystyrene Microplastics Results in Immune Alteration and Apoptosis in Vitro and in Vivo. Sci. Total. Environ. 2022, 807, 150817. [Google Scholar] [CrossRef] [PubMed]
  55. Wang, X.-X.; Wang, X.; Sun, Y.; Cheng, Y.; Liu, S.; Chen, X.; Feng, G.; Kuyper, T.W. Arbuscular Mycorrhizal Fungi Negatively Affect Nitrogen Acquisition and Grain Yield of Maize in a N Deficient Soil. Front. Microbiol. 2018, 9, 418. [Google Scholar] [CrossRef]
  56. Hodge, A.; Storer, K. Arbuscular Mycorrhiza and Nitrogen: Implications for Individual Plants through to Ecosystems. Plant Soil 2015, 386, 1–19. [Google Scholar] [CrossRef]
  57. Zhu, S.; Morel, J.-B. Molecular Mechanisms Underlying Microbial Disease Control in Intercropping. Mol. Plant-Microbe Interact. 2019, 32, 20–24. [Google Scholar] [CrossRef]
  58. Yu, Y.; Gui, Y.; Li, Z.; Jiang, C.; Guo, J.; Niu, D. Induced Systemic Resistance for Improving Plant Immunity by Beneficial Microbes. Plants 2022, 11, 386. [Google Scholar] [CrossRef]
  59. Lange, M.; Eisenhauer, N.; Chen, H.; Gleixner, G. Increased Soil Carbon Storage through Plant Diversity Strengthens with Time and Extends into the Subsoil. Glob. Change Biol. 2023, 29, 2627–2639. [Google Scholar] [CrossRef]
  60. Liu, L.; Zhu, K.; Wurzburger, N.; Zhang, J. Relationships between plant diversity and soil microbial diversity vary across taxonomic groups and spatial scales. Ecosphere 2022, 11, e02999. [Google Scholar] [CrossRef]
  61. Chen, C.; Chen, H.Y.H.; Chen, X.; Huang, Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat. Commun. 2019, 10, 1332. [Google Scholar] [CrossRef]
  62. Zhou, T.; Liang, G.; Reich, P.B.; Delgado-Baquerizo, M.; Wang, C.; Zhou, Z. Promoting effect of plant diversity on soil microbial functionality is amplified over time. One Earth 2024, 7, 2139–2148. [Google Scholar] [CrossRef]
  63. Jayaramaiah, R.H.; Martins, C.S.; Egidi, E.; Macdonald, C.A.; Wang, J.-T.; Liu, H.; Reich, P.B.; Delgado-Baquerizo, M.; Singh, B.K. Soil function-microbial diversity relationship is impacted by plant functional groups under climate change. Soil Biol. Biochem. 2025, 200, 109623. [Google Scholar] [CrossRef]
  64. Qiao, M.; Sun, R.; Wang, Z.; Dumack, K.; Xie, X.; Dai, C.; Wang, E.; Zhou, J.; Sun, B.; Peng, X.; et al. Legume Rhizodeposition Promotes Nitrogen Fixation by Soil Microbiota under Crop Diversification. Nat. Commun. 2024, 15, 2924. [Google Scholar] [CrossRef] [PubMed]
Agronomy 15 01993 g001
Figure 1. The location (a) and design (b) of the experiment. MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
Figure 1. The location (a) and design (b) of the experiment. MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
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Agronomy 15 01993 g002
Figure 2. The dominant phyla of the fungal community in the maize rhizosphere under different treatments (a). NMDS plot showing the changes in the fungal community in the maize rhizosphere under different treatments (b). Changes in fungal α diversity (evaluated by Shannon index) in th maize rhizosphere under different treatments (c). Different letters above the bars indicate significant differences as checked by the Kruskal–Wallis rank sum test (p < 0.05). MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
Figure 2. The dominant phyla of the fungal community in the maize rhizosphere under different treatments (a). NMDS plot showing the changes in the fungal community in the maize rhizosphere under different treatments (b). Changes in fungal α diversity (evaluated by Shannon index) in th maize rhizosphere under different treatments (c). Different letters above the bars indicate significant differences as checked by the Kruskal–Wallis rank sum test (p < 0.05). MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
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Figure 3. (a) The changes in the relative abundance of plant pathogens under different treatments. (b) The changes in the relative abundance of AMF under different treatments. Different letters above the bars indicate significant differences as checked by the Kruskal–Wallis rank sum test (p < 0.05). MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
Figure 3. (a) The changes in the relative abundance of plant pathogens under different treatments. (b) The changes in the relative abundance of AMF under different treatments. Different letters above the bars indicate significant differences as checked by the Kruskal–Wallis rank sum test (p < 0.05). MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
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Figure 4. Abundance-based β-null deviation for fungal communities based on Bray–Curtis distance (bar plot) and the contribution of stochastic processed to fungal community assemblage determined by tNST. Different letters above the bars indicate significant differences as checked by the Kruskal–Wallis rank sum test (p < 0.05). MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
Figure 4. Abundance-based β-null deviation for fungal communities based on Bray–Curtis distance (bar plot) and the contribution of stochastic processed to fungal community assemblage determined by tNST. Different letters above the bars indicate significant differences as checked by the Kruskal–Wallis rank sum test (p < 0.05). MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
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Figure 5. (a) Venn chart shows the distribution of zOTUs in different treatment. (b) The contribution of shared and unique zOTUs to fungal OTU richness. (c) The relative abundance of shared zOTUs within the fungal community. MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
Figure 5. (a) Venn chart shows the distribution of zOTUs in different treatment. (b) The contribution of shared and unique zOTUs to fungal OTU richness. (c) The relative abundance of shared zOTUs within the fungal community. MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean.
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Table 1. Properties of rhizosphere soil and maize yield under different cropping patterns.
Table 1. Properties of rhizosphere soil and maize yield under different cropping patterns.
TreatmentTN
(g·kg−1)		AN
(mg·kg−1)		NH4+-N
(mg·kg−1)		NO3−-N
(mg·kg−1)		SOM
(g·kg−1)		AP
(mg·kg−1)		Maize Yield
(kg·hm−2)
20222023
MC1.42a146.32a3.30b16.64a25.77a19.31c5975c6665c
IntL1.44a147.86a3.40b17.53a26.44a20.10bc7186a8624b
IntF1.35a136.31a4.72a9.40b24.07a23.15b6617b8954b
IntLF1.44a140.16a3.75b9.02b25.34a42.32a7584a9202a
MC: monoculture; IntL: intercropped with Lolium perenne L.; IntF: intercropped with forage soybean; IntLF: intercropped with both Lolium perenne L. and forage soybean. TN: total nitrogen; AN: available nitrogen; NH4+-N: ammonia nitrogen; NO3−-N: nitrate nitrogen; SOM: soil organic matter; AP: available phosphorus. Different letters indicate significant differences between treatments.
Table 2. Correlations of total and specific fungal communities to soil properties and crop yield.
Table 2. Correlations of total and specific fungal communities to soil properties and crop yield.
FactorsFungal CommunityPlant PathogensAMF
Rp ValueRp ValueRp Value
TN0.0470.349−0.3900.210−0.2840.372
AN0.2770.067−0.4890.106−0.7730.003
NH4+-N−0.0730.6580.3080.3310.5100.094
NO3−-N−0.0180.513−0.3080.331−0.6850.017
SOM0.1360.225−0.4470.145−0.7940.002
AP0.4300.0050.1770.5810.7450.005
Crop yield0.3080.213−0.5030.0020.4930.103
TN: total nitrogen; AN: available nitrogen; NH4+-N: ammonia nitrogen; NO3−-N: nitrate nitrogen; SOM: soil organic matter; AP: available phosphorus. Bold values indicate significant correlations (p < 0.05).
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Li, Y.; Zhang, Y.; Li, X.; Li, H.; Dong, W.; Qin, S.; Liu, X.; Zhang, L.; Hu, C.; He, H.; et al. Planting Diversification Enhances Phosphorus Availability and Reshapes Fungal Community Structure in the Maize Rhizosphere. Agronomy 2025, 15, 1993. https://doi.org/10.3390/agronomy15081993

AMA Style

Li Y, Zhang Y, Li X, Li H, Dong W, Qin S, Liu X, Zhang L, Hu C, He H, et al. Planting Diversification Enhances Phosphorus Availability and Reshapes Fungal Community Structure in the Maize Rhizosphere. Agronomy. 2025; 15(8):1993. https://doi.org/10.3390/agronomy15081993

Chicago/Turabian Style

Li, Yannan, Yuming Zhang, Xiaoxin Li, Hongjun Li, Wenxu Dong, Shuping Qin, Xiuping Liu, Lijuan Zhang, Chunsheng Hu, Hongbo He, and et al. 2025. "Planting Diversification Enhances Phosphorus Availability and Reshapes Fungal Community Structure in the Maize Rhizosphere" Agronomy 15, no. 8: 1993. https://doi.org/10.3390/agronomy15081993

APA Style

Li, Y., Zhang, Y., Li, X., Li, H., Dong, W., Qin, S., Liu, X., Zhang, L., Hu, C., He, H., Zheng, P., & Zhao, J. (2025). Planting Diversification Enhances Phosphorus Availability and Reshapes Fungal Community Structure in the Maize Rhizosphere. Agronomy, 15(8), 1993. https://doi.org/10.3390/agronomy15081993

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