Next Article in Journal
Overexpression of Maize Expansin Gene ZmEXPA6 Improves Salt Tolerance of Arabidopsis thaliana
Previous Article in Journal
Bio-Organic Fertilizers Enhance Yield in Continuous Cotton Cropping Systems Through Rhizosphere Microbiota Modulation and Soil Nutrient Improvement
 
 
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/agronomy15092236
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

Faba Bean–Oat Mixtures Modify Rhizosphere Microbiota and Nutrient–Biomass Regulation on the Qinghai–Tibetan Plateau

by
Huilin Yan
1,†,
Xin Jin
2,†,
Panda Ye
2,
Changcai Teng
3 and
Yujiao Liu
3,*
1
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
2
College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
3
Academy of Agriculture and Forestry Science of Qinghai University, Xining 810016, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(9), 2236; https://doi.org/10.3390/agronomy15092236
Submission received: 13 August 2025 / Revised: 12 September 2025 / Accepted: 17 September 2025 / Published: 22 September 2025
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
Versions Notes

Abstract

Grass–legume mixtures are increasingly recognized for their potential to enhance soil health and forage productivity through belowground biotic interactions. In this study, we evaluated the effects of Vicia faba L. (faba bean 4060)–Avena sativa L. (oat ‘Baylor II’) mixtures on biomass, soil properties, and bacterial community dynamics. Results showed that mixtures significantly reduced the fresh weight of faba bean (6.2 kg/m2) compared to monoculture (8.8 kg/m2, p < 0.001), while oat biomass increased under mixtures (3.2 kg m−2 vs. 2.8 kg m−2, p < 0.01). Available phosphorus (AP) and available potassium (AK) significantly decreased in the rhizosphere of both mixtures, whereas alkali-hydrolyzable nitrogen (AN) significantly increased, particularly in oat. Mixtures significantly enhanced bacterial richness, evenness, and Shannon diversity in faba bean (p < 0.01) but had no significant effect on oat diversity metrics. NMDS indicated distinct shifts in bacterial community structures under mixtures. Acidobacteriota and Vicinamibacteraceae were enriched in faba bean mixtures, whereas Actinobacteriota decreased in both forages under mixtures. Source Tracker analysis suggested substantial microbial exchange between species, with over 40% of the bacterial community in mixed roots originating from the partner monoculture. Although microbial community stability tended to decline under mixtures, differences were not significant. Niche breadth was significantly expanded in faba bean mixtures. Community assembly processes remained predominantly stochastic; however, mixtures slightly shifted the balance toward deterministic processes. Structural equation model revealed that soil physicochemical properties had a significant negative effect on diversity (β = −0.371, p = 0.007), and diversity had a significant negative effect on freshweight (β = −0.770, p < 0.001), suggesting that bacterial diversity may play a mediating role in the relationship between soil properties and plant fresh weight (β = 0.285, p = 0.011). These findings demonstrate that mixture-induced changes in soil nutrient status and microbial community characteristics collaboratively mediate plant performance through altered community assembly and diversity–function relationships.

1. Introduction

In the context of sustainable agricultural development, traditional monoculture systems face numerous challenges, including low resource-use efficiency, declining soil fertility, and increased vulnerability to pests and diseases [1,2]. As an eco-friendly alternative, grass–legume mixture systems have garnered significant attention in recent years for their potential to enhance forage yield, improve soil health, and strengthen plant stress resistance [3]. In resource-limited regions such as the Qinghai–Tibet Plateau, the complementary interactions between legumes and grasses are particularly advantageous [4]. Vicia faba L. (faba bean 4060), a nitrogen-fixing legume, can enhance soil nitrogen availability [5], while Avena sativa L. (oat ‘Baylor II’), a grass species with a well-developed root system and vigorous early growth, excels at nutrient uptake [6]. Their combined cultivation is expected to increase the overall efficiency of resource utilization within the agroecosystem. Importantly, the rhizosphere microbial community—an integral component of the plant–soil interface—plays a pivotal role in nutrient cycling, plant growth promotion, and the stabilization of community dynamics [7]. As such, it represents a key entry point for elucidating the ecological mechanisms underpinning the benefits of mixed sowing systems.
In recent years, increasing attention has been paid to the effects of forage mixtures on rhizosphere microbial communities, particularly regarding shifts in community composition and diversity [8]. However, studies investigating the deeper ecological attributes of microbial communities under legume–grass mixture systems remain limited. While community diversity offers a snapshot of ecological status, it does not fully capture the stability or the underlying assembly mechanisms of microbial communities. Community stability, which reflects the ability of a system to resist external disturbances, is closely tied to the maintenance of ecosystem functions [9]. Niche breadth, on the other hand, reveals the range of resources utilized by microbial taxa and reflects their ecological strategies [10]. Moreover, deciphering community assembly processes provides critical insights into the dynamics governing microbial community formation. In legume–grass mixtures, changes in plant species composition may influence root exudation patterns and soil physicochemical properties, thereby driving shifts in microbial community structure [11]. Yet, systematic comparisons and mechanistic investigations in this context are still lacking. Therefore, it is essential to explore the ecological characteristics and functional implications of rhizosphere microbial communities across different planting patterns from multiple perspectives.
Based on this, the present study focused on a faba bean–oat mixtures in the alpine region of the Qinghai–Tibet Plateau. Three planting treatments were established: faba bean monoculture, oat monoculture, and their mixture. The objective was to systematically evaluate forage biomass performance and characterize the associated rhizosphere microbial communities under different planting patterns. Specifically, the study aimed to: (1) compare plant fresh weight across treatments to determine the effects of mixtures on forage productivity; (2) analyze key ecological attributes of the rhizosphere microbiome—including community composition, diversity indices, stability, niche breadth, and assembly processes—using high-throughput sequencing techniques; and (3) apply structural equation modeling (SEM) to integrate microbial traits and soil nutrient parameters, in order to elucidate the direct and indirect pathways influencing biomass accumulation. This research seeks to uncover the synergistic interactions among forage, rhizosphere microorganisms, and environmental factors under grass–legume mixtures, thereby providing a theoretical foundation and practical guidance for improving forage production and advancing ecological agriculture in high-altitude regions.

2. Materials and Methods

2.1. Study Sites and Sampling

The field experiment was conducted in May 2024 at Mangqu Town (100°70′ E, 35°58′ N), Guinan County, Hainan Tibetan Autonomous Prefecture, Qinghai Province, China, at an elevation of 3206.3 m. The plant materials used in this study included oat (Avena sativa L.) and faba bean (Vicia faba L.). The faba bean cultivar ‘4060’ is a forage-type line independently developed by our research group at Qinghai University. The oat cultivar ‘Baylor II’ is a forage-type variety widely cultivated in high-altitude regions of China and was obtained from the Qinghai Forage and Grain Research Institute (Xining, China) [12]. It is well adapted to the alpine continental climate and exhibits strong cold tolerance and high biomass yield. This cultivar was specifically bred for improved adaptability, biomass accumulation, and suitability for mixtures in alpine pastoral zones. A randomized complete block design was used, with three treatments—faba bean monoculture (Vicia faba, F), oat monoculture (Avena sativa, O), and faba bean–oat mixtures (1:1 row ratio, MF and MO)—each replicated six times, for a total of 18 plots. Each plot measured 3 m × 5 m (15 m2), and all treatments were maintained at the same planting density. In the mixture plots, faba bean and oat were sown in alternating rows at a 1:1 ratio to ensure interspecific interaction. The average weight of 100 faba bean seeds of variety ‘4060’ is 25 g. In a 15 square meter plot, the amount of ‘4060’ soybean seeds is 0.225 kg, and the amount of oats is 0.313 kg. The intercropping experiment followed a replacement (de Wit) design, where each species in the mixture was sown at 50% of its monoculture density, keeping the total plant density constant across treatments. Based on a 100-seed weight of 25 g for faba bean variety ‘4060’, the seeding rate corresponded to approximately 60 plants/m2 in monoculture. For oats, assuming an average seed weight of 35 mg, the seeding density was estimated at approximately 596 plants/m2 in monoculture. In the mixture plots, both faba bean and oat were sown at half of their respective monoculture rates, resulting in densities of 30 plants/m2 for faba bean and 298 plants/m2 for oat. All other field management practices (irrigation, fertilization, pest and disease control) followed local agronomic guidelines and were applied uniformly across treatments.
At forage maturity in October 2024, ten representative plants per replicate—uniform in growth—were sampled to assess biomass. Fresh weight (kg) was determined from a 1 m × 1 m quadrat in each plot. Although yield data were not collected in this study, future experiments will include productivity assessments to enable calculation of metrics such as Land Equivalent Ratio (LER) and competition indices. Due to inconsistencies observed during the drying process (e.g., potential incomplete drying or sample degradation), dry biomass data were excluded from the analysis. Fresh biomass was used as the primary indicator of aboveground productivity, as it was measured consistently across treatments under field conditions. For rhizosphere-soil collection, the roots of both faba bean and oat were carefully excavated in the field and placed in sterile 50 mL centrifuge tubes. Thirty milliliters of pre-sterilized phosphate-buffered saline (PBS; 0.1% Tween 80, pH 7.0) were added, and the tubes were gently inverted to suspend the soil adhering to the roots. This washing procedure was repeated three times, with each suspension then centrifuged at 6000 rpm for 5 min to pellet the soil [13]. The rhizosphere soil were freeze-dried at −40 °C for at least 12 h and stored at −80 °C until DNA extraction.
Soil for physico-chemical analysis was collected diagonally across each plot at five randomly selected points. At each point, soil was obtained with a 5 cm-diameter auger, combining three cores into a single composite sample per point. Samples were sealed in polyethylene bags and transported to the lab for determination of standard soil chemical and physical properties.

2.2. Soil Physicochemical Analyses

Soil pH was measured using a glass electrode method with a soil-to-water ratio of 1:2.5. Organic matter (OM) content was determined using the potassium dichromate external heating method. Total nitrogen (TN) content was measured by the Kjeldahl digestion method, while alkali-hydrolyzable nitrogen (AN) was determined using the alkali hydrolysis diffusion method. Available phosphorus (AP) was quantified by NaHCO3 extraction followed by the molybdenum-antimony colorimetric method, and available potassium (AK) was determined using ammonium acetate (NH4OAc) extraction followed by flame photometry. Total phosphorus (TP) and total potassium (TK) contents were measured by spectrophotometry and flame photometry, respectively, following high-temperature digestion [14].

2.3. DNA Extraction, Amplification, Sequencing, and Sequence Analysis

Total DNA was extracted from rhizosphere soil using the FastDNA® Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s protocol. DNA quality and concentration were evaluated with a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and by 1% agarose gel electrophoresis. For bacterial community profiling, the V3–V4 region of the 16S rRNA gene was amplified using primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [4]. Libraries were prepared with the KAPA HyperPrep Kit (KAPA Biosystems, Wilmington, MA, USA), and paired-end sequencing (2 × 300 bp) was carried out on an Illumina MiSeq platform at LingEn Biotech (Shanghai, China).

2.4. Statistical Analysis

Sequences were clustered into operational taxonomic units (OTUs) at a 97% similarity threshold using UNOISE [15]. A total of 18,597,463 reads were detected in all the samples. To normalize sampling effort, all libraries were rarefied to 60,042 reads—the minimum sequencing depth observed among the 16S datasets—and the resulting OTU table was used for downstream analyses. Alpha-diversity indices (Richness, Pielou’s evenness, and Shannon diversity) and phylogenetic diversity were compared between faba bean–oat mixtures and monoculture by Student’s t-test. We assessed differences among treatments using non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances, implemented in the vegan package [16]. SourceTracker analysis [17] was performed based on the OTU table after filtering out low-abundance OTUs (<1% relative abundance per sample) to reduce noise and potential sequencing artifacts. Rhizosphere bacterial communities from monocultured faba bean and oat were used as source environments, and the bacterial communities of faba bean and oat in mixture plots were treated as sinks. The relative contributions from each source were estimated using SourceTracker v1.0, and the reported values represent the average proportion assigned to each source across samples. No contamination was detected in our DNA extraction and PCR processes, as assessed by blank negative controls (included during both extraction and amplification), which yielded no detectable amplification. Although low-abundance OTUs were excluded from SourceTracker input, sensitivity analysis using unfiltered data showed consistent source attribution trends, indicating that filtering did not substantially bias the results. Community stability was quantified as the reciprocal of average variation degree (RAVD), where higher RAVD indicates lower temporal variability and thus greater stability. Although our study is based on a single time point, this approach provides a proxy for community compositional consistency among replicates, and has been used in cross-sectional studies as an indicator of structural robustness under similar environmental conditions [18]. Niche breadth for each rhizosphere taxon was calculated as Levin’s B index using the niche. Width function in the spaa package [19], and the mean B value across all taxa (Bkom) represented the community-level niche breadth. While niche breadth is traditionally interpreted in spatiotemporal contexts, in single time-point designs it can reflect the generalist or specialist tendencies of taxa across treatment types, providing insight into community functional potential and adaptability [20]. To quantify the relative contributions of stochastic and deterministic processes in community assembly, we employed the phylogenetic normalized stochasticity ratio (pNST). The pNST is based on phylogenetic beta diversity (βMNTD) and estimates the proportion of stochasticity by comparing observed phylogenetic turnover to that expected under an ecologically constrained null model. Specifically, phylogenetic β-distances between samples were first calculated using a provided phylogenetic tree. Then, a null distribution was generated through 999 randomizations, which preserved total community abundance/species richness and species occurrence frequency or abundance distribution. Finally, the observed value was normalized against the null distribution to yield a pNST value ranging from 0 to 1. A pNST < 0.5 indicates that deterministic processes dominate, whereas a pNST > 0.5 suggests that stochastic processes are predominant [21].
Key predictors of biomass among soil and microbial variables were identified via random forest with permutation testing, using the rfPermute function in the rfPermute package to rank variable importance and assess significance [22]. The overall significance of the full model mixtures selected predictors was evaluated with the A3 package [23]. After z-scoring, predictors were reduced by PCA: the first PCs of seven soil variables (TN, TP, TK, AP, AK, AN, OM) and four α-diversity indices (richness, Shannon, Pielou, PD_whole_tree) were taken as Soil PC1 and Diversity PC1, with signs oriented to be positively correlated with mean z-scores [24]. Soil PC1, Diversity PC1, Bcom, and freshweight (all z-scored) were modeled in lavaan SEM (ML; FIML for missing; meanstructure = TRUE; bootstrap SE/95% CI with 2000 resamples, percentile) [25], reporting χ2, the Comparative Fit Index (CFI) and Tucker–Lewis Index (TLI) are commonly used indicators to assess model fit, both ranging from 0 to 1, with higher values indicating better fit. The Root Mean Square Error of Approximation (RMSEA) evaluates model misfit, with values closer to 0 suggesting a better fit. The Standardized Root Mean Square Residual (SRMR) reflects the discrepancy between observed and predicted values; an SRMR below 0.08 is generally considered indicative of good model fit, CFI, TLI, RMSEA, SRMR, standardized path coefficients with bootstrap 95% CIs and R2, and effect decomposition of Soil→freshweight (direct, via Diversity and/or Bcom, total) with standardized estimates and bootstrap Cis [26].

3. Results

3.1. Biomass Reduction and Soil Changes

Our results showed that mixtures significantly decreased the fresh weight of faba bean (MF, 6.2 kg/m2) compared with its monoculture (M, 8.8 kg/m2) (p < 0.001). In contrast, oat fresh weight was significantly higher in mixtures (MO, 3.2 kg/m2) compared to monoculture (O, 2.8 kg/m2) (p < 0.01) (Figure 1). Regarding soil nutrients, total TP under faba bean mixtures (MF) significantly decreased by 7.53%, whereas it increased by 2.67% under oat mixtures (MO) compared with their respective monocultures (p < 0.01). AP was significantly reduced by 12.65% in the MF treatment but significantly increased by 9.39% in MO (p < 0.001). Additionally, AK significantly decreased by 12.42% in MF and 20.64% in MO compared to their monocultures (p < 0.001). AN significantly increased by 0.47% in MF and 13.89% in MO (p < 0.05) (Table 1).

3.2. Bacterial Communities Under Monoculture and Grass–Legume Mixtures

The results indicated that, compared with monoculture treatments, mixtures significantly increased the richness index of faba bean (MF) and oat (MO) (p < 0.001 and p < 0.05, respectively). Shannon and Pielou indices of faba bean mixtures (MF) were also significantly higher than those of monoculture (p < 0.01 and p < 0.05, respectively), whereas no significant differences were observed in oat mixtures (MO). Furthermore, there were no significant differences in phylogenetic diversity indices between monoculture and mixture treatments (Figure 2a). NMDS and ANOSIM analyses based on Bray–Curtis distances showed significant differences in rhizosphere soil microbial community structures between mixtures and monoculture treatments for both faba bean and oat (Figure 2b).
At the phylum level, compared with monoculture faba bean (F), mixture faba bean (MF) significantly increased the relative abundance of Acidobacteriota from 13.68% to 15.56% (an increase of 1.88%) and Gemmatimonadota from 4.77% to 5.46% (an increase of 0.69%). Conversely, the relative abundance of Actinobacteriota significantly decreased from 19.75% to 16.87% (a decrease of 2.88%). Compared with monoculture oat (O), the relative abundance of Actinobacteriota in mixture oat (MO) was also significantly reduced from 17.02% to 14.55% (a decrease of 2.47%), and the relative abundance of Chloroflexi significantly decreased from 6.94% to 6.65% (a decrease of 0.29%) (Figure 3a). At the genus level, compared with monoculture faba bean (F), mixture faba bean (MF) significantly increased the relative abundance of Vicinamibacteraceae from 4.11% to 7.85% (an increase of 3.74%) and significantly decreased the relative abundance of Gaiellales from 3.11% to 1.90% (a decrease of 1.21%). In oat, the relative abundance of Vicinamibacteraceae slightly increased from 4.99% to 5.03% (an increase of 0.04%), which was not significant, while Gemmatimonadaceae significantly decreased from 3.84% to 3.52% (a decrease of 0.32%) in mixture oat (MO) compared with monoculture oat (O) (Figure 3b).

3.3. Source Tracker Analysis of Microbial Origin in Mixtures

Due to the significant changes observed in the rhizosphere bacterial communities of faba bean and oat under monoculture and mixture conditions, SourceTracker was used to identify their origins (Figure 4). Specifically, monoculture faba bean (F) and monoculture oat (O) were defined as sources, while faba bean (MF) and oat (MO) mixtures were set as sinks. The results indicated that the bacterial community of faba bean mixtures (MF) predominantly originated from monoculture faba bean (F, 44.99 ± 0.06%) and monoculture oat (O, 41.99 ± 0.06%). Similarly, the bacterial community of oat mixtures (MO) mainly originated from monoculture oat (O, 48.59 ± 0.07%) and monoculture faba bean (F, 38.42 ± 0.07%).

3.4. Analysis of Bacterial Community Stability, Niche, and Assembly Processes Under Monoculture and Mixtures

To investigate the effect of mixtures on the stability of rhizosphere bacterial communities, we evaluated community stability using the average variation degree (RAVD), where a higher RAVD value indicates greater stability. The results showed that the rhizosphere bacterial communities of monocultured faba bean (F) and oat (O) exhibited slightly higher stability than their mixture counterparts; however, the differences were not statistically significant (Figure 5a).
The Habitat Niche Breadth (Bcom) of soil microbial communities reflects their capacity to utilize diverse resources and adapt to varying environmental conditions, providing insights into their ecological roles and adaptability. For the rhizosphere bacterial communities, the niche breadth of faba bean mixtures (MF: 27.88) was significantly greater than that of monocultured faba bean (F: 27.26) (p < 0.05), while the niche breadth of oat mixtures (MO: 28.07) was also higher than that of monocultured oat (O: 27.95), though the difference was not significant (Figure 5b). These findings suggest that mixtures enhance resource use diversity within bacterial communities, leading to broader ecological niches and greater interspecific differentiation in resource competition and adaptive strategies.
The Phylogenetic Normalized Stochasticity Ratio (pNST) was used to assess the relative importance of deterministic (e.g., environmental selection) versus stochastic (e.g., dispersal limitation) processes in community assembly. pNST analysis indicated that stochastic processes predominated in the assembly of all rhizosphere bacterial communities, and no significant differences were observed between monoculture and mixture treatments. However, the NST values of faba bean (MF: 68.99%) and oat (MO: 69.16%) mixtures were slightly lower than those of monocultured faba bean (F: 73.20%) and oat (O: 73.74%), respectively (Figure 5c). These results suggest that although the overall dynamics of bacterial community assembly were not significantly altered by mixtures (p > 0.05), there was a trend toward increased deterministic influence, potentially due to enhanced niche specificity or improved resource use efficiency in the grass–legume mixtures.

3.5. Structural Equation Modeling Analysis of the Relationships Between Biomass, Soil Physicochemical Properties, and Bacterial Communities

Key factors influencing the biomass of faba bean and oat were identified using random forest analysis. Among the soil physicochemical properties, available phosphorus (AP), total phosphorus (TP), total potassium (TK), available potassium (AK), alkali-hydrolyzable nitrogen (AN), organic matter (OM), and total nitrogen (TN) were found to contribute significantly to the biomass of both forages. In terms of rhizosphere microbial indicators, phylogenetic diversity (PD_whole_tree), richness, Shannon diversity, Pielou’s evenness, and bacterial community composition (Bcom) were identified as important contributors (Figure 6).
To further explore the relative importance of soil and microbial factors in shaping forage biomass, a structural equation model (SEM) was constructed based on the random forest-selected variables. Standardized path coefficients indicated that soil physicochemical properties had a significant negative effect on bacterial diversity (β = −0.371, p = 0.007, 95% CI: −0.611 to −0.112), and diversity had a strong negative effect on fresh weight (β = −0.770, p < 0.001, 95% CI: −1.014 to −0.533). This resulted in a significant positive **indirect** effect of soil on fresh weight via diversity (β = 0.285, p = 0.011, 95% CI: 0.083 to 0.481), while the direct effect of soil on fresh weight was non-significant. The SEM explained 59.2% of the variance in fresh weight, 13.8% in diversity, and 1.4% in Bcom (Figure 7; Table 2).

4. Discussion

Faba bean with oat mixtures significantly reduced the fresh weight of faba bean, while having no notable effect on the biomass of oat. This discrepancy is likely attributed to the redistribution of nutrient resources and intensified interspecific competition under the mixtures [27]. In the mixtures, total potassium (TK) and available potassium (AK) concentrations in the soil were reduced by 0.39–0.79 g/kg and 1.44–2.44 mg/kg, respectively, compared to the faba bean monoculture. In addition, available phosphorus (AP) decreased by 1.99 mg/kg, indicating that mixtures significantly reduced the accessibility of soil potassium and phosphorus. Potassium plays a vital role in osmotic regulation, enzyme activation, and carbohydrate transport in plant cells [28]. Deficiency in potassium directly inhibits plant growth and biomass accumulation [29]. As a nitrogen-fixing legume, faba bean requires larger amounts of potassium and phosphorus during its later growth stages [30]. However, the intensified competition for nutrients in mixtures may result in preferential uptake of these elements by oat or rhizosphere microorganisms, thereby limiting the nutrient availability for faba bean [31]. Interestingly, available potassium in the oat monoculture was 11.36 mg/kg higher than that in the faba bean monoculture. This may indicate that oat not only has a more efficient root system for K uptake, but also possesses the ability to enhance K mobilization in the rhizosphere, possibly through root exudates or rhizosphere microbial interactions, leading to increased available K despite active uptake. Additionally, root exudates from oat might facilitate the mobilization of fixed forms of potassium in the soil, thereby enhancing its own nutrient acquisition [32]. Furthermore, the decline in nutrient availability not only directly affects plant photosynthesis and physiological metabolism but also indirectly impacts crop growth by altering the composition and function of rhizosphere microbial communities [33]. The observed decrease in available P and K may reflect preferential uptake by oats and/or enhanced plant–microbe competition in the rhizosphere. Previous studies have shown that under phosphorus and potassium limitation, soil microbial communities tend to undergo functional restructuring, increasing their capacity for nutrient immobilization and cycling [34]. However, this also intensifies biological competition between microbes and plants [35]. Mixtures can also stimulate microbial activity and alter root exudation patterns, potentially leading to increased mineralization of organic nitrogen (priming effect), which could explain the rise in available N. The observed shifts in bacterial taxa associated with N cycling, such as increased abundance of Nitrosospira, further support this hypothesis.
Faba bean and oat mixtures significantly enhanced the richness, Shannon diversity, and Pielou evenness of rhizosphere microbial communities for both species, while phylogenetic diversity (PD) showed no significant differences among treatments. These findings suggest that mixtures improve the α-diversity of rhizosphere bacterial communities, with a particularly pronounced effect on the legume species. Previous studies have demonstrated that different plant species release a broader spectrum of root exudates, thereby expanding ecological niches and supporting more diverse microbial communities [36]. In legumes, the formation of root nodules and symbiotic nitrogen fixation often lead to greater carbon and nutrient input into the rhizosphere, shaping distinct microbial community structures [37].
The composition of rhizosphere bacterial communities was also reshaped under the mixtures. Shifts in microbial community structure are often driven by plant-induced changes in the rhizosphere microenvironment, such as pH, organic matter, and dissolved organic carbon availability [38]. At the phylum level, mixtures increased the relative abundance of Acidobacteriota and Gemmationadota in the rhizosphere of faba bean, while decreasing that of Actinobacteriota. Acidobacteriota are commonly found in acidic, nutrient-poor soils with high organic matter, and their increase may reflect the elevated carbon inputs associated with legume rhizospheres [39]. Similarly, Gemmationadota, a group of heterotrophic soil bacteria, may benefit from the higher concentrations of dissolved organic carbon released by faba bean roots [40]. In contrast, Actinobacteriota, which tend to prefer neutral to alkaline and nutrient-rich soils, may have declined due to shifts in pH or nitrogen forms under the mixtures [41].
At the genus level, mixtures significantly promoted the proliferation of norank_f__Vicinamibacteraceae, while suppressing norank_o__Gaiellales. Members of Vicinamibacteraceae (within Acidobacteriota) are known to thrive in soils with high organic carbon and low pH, suggesting their enrichment may be a direct response to the altered rhizosphere environment under mixtures [42]. In contrast, Gaiellales are often found in dry, oligotrophic environments and tend to be anaerobic or microaerophilic [43]; thus, increased soil nitrogen and carbon availability in the mixture plots may have constrained their ecological niche and reduced their abundance. Compared to faba bean, the shifts in bacterial community composition in oat under mixtures were relatively minor. This difference may be due to the limited diversity of root exudates and a weaker ability to alter the rhizosphere nutrient profile in grass species like oat [36].
Source Tracker analysis revealed that the rhizosphere bacterial communities of both faba bean (MF) and oat (MO) under mixtures received substantial inputs from each other’s monoculture microbial communities. This finding suggests a significant degree of rhizosphere microbial “complementarity” or “sharing” between the two crop species in the mixtures [44]. In other words, the interaction between plant roots promotes the bidirectional migration and assembly of microbial communities, resulting in a mixed-type community structure. While the current study focused on community-level source contributions, future work could incorporate indicator species analysis or taxonomic tracking to identify specific microbial lineages involved in cross-origin exchange between monoculture and mixture rhizospheres.
It is well established that the composition of rhizosphere microbial communities is shaped not only by the host plant itself but also by the influence of neighboring plant root exudates [7]. In mixture systems, faba bean, as a legume forage, releases a diverse array of signaling compounds such as amino acids, flavonoids, and organic acids [45]. These compounds can attract a variety of beneficial microorganisms and may even induce shifts in the rhizosphere microbial communities of adjacent non-leguminous plants [46]. Conversely, oat, a grass species with a highly developed fibrous root system, may exhibit a strong capacity to retain and recruit rhizosphere microbes [47]. Under mixtures, this may facilitate the uptake or attraction of faba bean-associated microbes into the oat rhizosphere. These results highlight the dynamic and reciprocal nature of root–microbe interactions in mixture systems, wherein the exchange of microbial taxa between species contributes to community reassembly and potentially enhances the functional capacity of the rhizosphere microbiome.
This study demonstrates that faba bean and oat mixtures do not significantly reduce the stability of rhizosphere microbial communities. This may be attributed to the increased complexity of the rhizosphere environment under mixtures, where mixed root systems can lead to greater heterogeneity and competition for resources, thereby influencing microbial community fluctuations [48]. Notably, analysis of niche breadth revealed a significant increase in the ecological niche width of faba bean-associated communities under mixtures, with a similar increasing trend observed in oat. This suggests that the mixture environment offers a wider range of resources and ecological niches, allowing microbes to exploit more diverse substrates and expand their functional potential [49].
Regarding community assembly mechanisms, both monoculture and mixtures were predominantly governed by stochastic processes, consistent with previous findings that microbial community assembly in soils at small spatial scales and under limited environmental gradients is often driven by randomness [50].
The structural equation modeling (SEM) results reveal a complex network of interactions among soil nutrients, microbial diversity, and forage biomass, highlighting the indirect pathways through which soil factors regulate plant growth via microbial community characteristics. The evidence indicates that biomass is biologically mediated rather than driven by a direct physicochemical pathway [51]. Soil physicochemical status was associated with a reduction in rhizosphere α-diversity (β = −0.371), and higher α-diversity was, in turn, linked to lower fresh weight (β = −0.770). These two negative associations yield a positive indirect effect of soil on fresh weight via diversity (β = 0.285). While positive biodiversity–productivity relationships are common in plant systems, negative associations have been reported in microbial–plant interactions under certain conditions. One plausible explanation is that increased microbial diversity may include a higher proportion of taxa that are neutral or even antagonistic to plant growth, thereby diluting the relative abundance of plant-beneficial groups (e.g., nitrogen-fixing or growth-promoting rhizobacteria) [52]. Another possibility is that high microbial diversity intensifies belowground competition for limited resources such as root exudates, phosphorus, or potassium, especially under constrained soil nutrient conditions. This competition may reduce the net availability of nutrients for plant uptake, thereby suppressing biomass production. These interpretations are consistent with previous findings in nutrient-limited agroecosystems [53]. Mechanistically, nutrient enrichment and improved edaphic conditions can compress niche heterogeneity and select for a limited set of symbiotic, plant-compatible (often copiotrophic) lineages [54]. This selection lowers overall diversity but enhances the efficiency of resource acquisition and transfer to the host, thereby increasing biomass [55]. Conversely, greater α-diversity may dilute growth-promoting guilds, intensify competition for root exudates, and increase the prevalence of neutral or antagonistic taxa, collectively constraining yield [56]. The non-significant direct path from soil to fresh weight further underscores that, within the observed gradients, microbial community composition is the proximal driver of biomass, rather than soil conditions per se.
From a management perspective, our results suggest that the faba bean–oat mixture may not consistently increase total biomass production compared to oat monoculture under the current 1:1 row ratio. However, intercropping still offers potential ecological benefits such as biological nitrogen input from legumes and improved soil microbial diversity. Optimizing row configuration (e.g., 2:1 or 3:1 oat-to-faba bean ratios) could potentially reduce competition for belowground resources while maintaining complementary interactions. Future studies should evaluate whether such spatial adjustments can enhance productivity and reduce trade-offs between species. Given the observed decline in available phosphorus and potassium under mixture conditions in our alkaline, high-elevation soils, we recommend that intercropping systems in similar environments adopt targeted nutrient management. This may include early-season K supplementation and the use of more soluble P fertilizers or microbial inoculants (e.g., phosphate-solubilizing bacteria) to enhance nutrient availability and mitigate resource competition in the rhizosphere.

5. Conclusions

This study demonstrated that faba bean–oat mixtures significantly altered forage biomass production, soil nutrient dynamics, and rhizosphere bacterial communities. Although mixtures reduced the yield of faba bean, they improved oat productivity and reshaped microbial diversity, community composition, and niche breadth. Changes in microbial assembly processes and taxonomic exchange between species under mixtures indicated enhanced microbial interactions across plant rhizospheres. Structural equation modeling further revealed that soil nutrient status regulates forage biomass through its influence on microbial diversity and community composition, thereby elucidating the interaction pathways among soil, microbes, and plants. These findings underscore the pivotal role of nutrient management—particularly the balanced input of phosphorus and potassium—in shaping rhizosphere microbial ecology and improving forage performance. Avoiding nutrient excess or antagonism is essential to maintaining microecological stability. Overall, this study provides new insights into the belowground mechanisms driving forage productivity under faba bean–oat mixtures in the Qinghai–Tibetan Plateau.

Author Contributions

Conceptualization, H.Y. and Y.L.; methodology, H.Y.; software, H.Y. and X.J.; validation, H.Y. and Y.L.; formal analysis, H.Y. and P.Y., investigation, P.Y.; resources, H.Y. and C.T.; data curation, P.Y. and C.T.; writing—original draft preparation, H.Y.; writing—review and editing, X.J.; visualization, H.Y.; supervision, Y.L.; project administration, H.Y.; funding acquisition, H.Y. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Qinghai Provincial Science and Technology Program—Basic Research Youth Project (2025-ZJ-916Q); Qinghai University 2024 Youth Scientific Research Fund (2024-QNY-2); 2024 Qinghai Province Agricultural and Animal Husbandry Science and Technology Innovation Project—Faba Bean Industry Platform Construction (ck24121902).

Data Availability Statement

All libraries from lllumina amplicon sequencing were submitted to the ScienceDB (Available online: https://www.scidb.cn/s/umemAb (accessed on 29 July 2025); Available online: https://www.scidb.cn/anonymous/dW1lbUFi (accessed on 29 July 2025)).

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

  1. Bacheva, V.; Madison, I.; Baldwin, M.; Baker, J.; Beilstein, M.; Call, D.F.; Deaver, J.A.; Efimenko, K.; Genzer, J.; Grieger, K.; et al. Transdisciplinary Collaborations for Advancing Sustainable and Resilient Agricultural Systems. Glob. Change Biol. 2025, 31, e70142. [Google Scholar] [CrossRef]
  2. Makate, C.; Wang, R.; Makate, M.; Mango, N. Crop diversification and livelihoods of smallholder farmers in Zimbabwe: Adaptive management for environmental change. SpringerPlus 2016, 5, 1135. [Google Scholar] [CrossRef]
  3. Fujiwara, R.; Nashida, H.; Fukushima, M.; Suzuki, N.; Sato, H.; Sanada, Y.; Akiyama, Y. Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards. Front. Plant Sci. 2022, 12, 763479. [Google Scholar] [CrossRef]
  4. Yan, H.; Gu, S.; Li, S.; Shen, W.; Zhou, X.; Yu, H.; Ma, K.; Zhao, Y.; Wang, Y.; Zheng, H.; et al. Grass-legume mixtures enhance forage production via the bacterial community. Agric. Ecosyst. Environ. 2022, 338, 108087. [Google Scholar] [CrossRef]
  5. Mousa, K.M.; Metwaly, M.M.; Alshehri, M.A.; Sayed, S.M.; Rakha, O.M. Soil pH alters the biological parameters of cowpea aphid Aphis craccivora Koch (Hemiptera: Aphididae) on its host plant Vicia faba. Saudi J. Biol. Sci. 2022, 29, 2926–2932. [Google Scholar] [CrossRef]
  6. Alemayehu, G.F.; Forsido, S.F.; Tola, Y.B.; Amare, E. Nutritional and Phytochemical Composition and Associated Health Benefits of Oat (Avena sativa) Grains and Oat-Based Fermented Food Products. Sci. World J. 2023, 2013, 2730175. [Google Scholar] [CrossRef] [PubMed]
  7. Pantigoso, H.A.; Newberger, D.; Vivanco, J.M. The rhizosphere microbiome: Plant-microbial interactions for resource acquisition. J. Appl. Microbiol. 2022, 133, 2864–2876. [Google Scholar] [CrossRef] [PubMed]
  8. Jiang, P.; Wang, Y.; Zhang, Y.; Fei, J.; Rong, X.; Peng, J.; Yin, L.; Zhou, X.; Luo, G. Enhanced productivity of maize through intercropping is associated with community composition, core species, and network complexity of abundant microbiota in rhizosphere soil. Geoderma 2024, 442, 116786. [Google Scholar] [CrossRef]
  9. Philippot, L.; Griffiths, B.S.; Langenheder, S. Microbial Community Resilience across Ecosystems and Multiple Disturbances. Microbiol. Mol. Biol. Rev. 2021, 85, e00026-20. [Google Scholar] [CrossRef] [PubMed]
  10. Muller, E.E. Determining Microbial Niche Breadth in the Environment for Better Ecosystem Fate Predictions. mSystems 2019, 4, e00080-19. [Google Scholar] [CrossRef] [PubMed]
  11. Sui, X.; Bao, X.; Xie, H.; Ba, X.; Yu, Y.; Yang, Y.; He, H.; Liang, C.; Zhang, X. Contrasting seasonal effects of legume and grass cover crops as living mulch on the soil microbial community and nutrient metabolic limitations. Agric. Ecosyst. Environ. 2025, 380, 109374. [Google Scholar] [CrossRef]
  12. Zhang, G.; Chai, J.; Zhao, G.; Zeng, L. Study on the Adaptability of 15 Oat Varieties in Different Ecological Regions. Agronomy 2025, 15, 391. [Google Scholar] [CrossRef]
  13. Yan, H.; Zhou, X.; Zheng, K.; Gu, S.; Yu, H.; Ma, K.; Zhao, Y.; Wang, Y.; Zheng, H.; Liu, H.; et al. Response of Organic Fertilizer Application to Soil Microorganisms and Forage Biomass in Grass–Legume Mixtures. Agronomy 2023, 13, 481. [Google Scholar] [CrossRef]
  14. Liu, T.; Wu, X.; Li, H.; Alharbi, H.; Wang, J.; Dang, P.; Chen, X.; Kuzyakov, Y.; Yan, W. Soil organic matter, nitrogen and pH driven change in bacterial community following forest conversion. For. Ecol. Manag. 2020, 477, 118473. [Google Scholar] [CrossRef]
  15. Li, X.; Huo, S.; Xi, B. Updating the resolution for 16S rRNA OTUs clustering reveals the cryptic cyanobacterial genus and species. Ecol. Indic. 2020, 117, 106695. [Google Scholar] [CrossRef]
  16. Yan, H.; Jin, X.; Zhou, X.; Gu, S.; Wu, X.; Li, P.; Shi, D.; Liu, H.; Lu, G.; Deng, Y. Long-term cultivation of grass–legume mixtures changed the assembly process of the microbial community and increased microbial community stability. ISME Commun. 2024, 5, ycae157. [Google Scholar] [CrossRef] [PubMed]
  17. Knights, D.; Kuczynski, J.; Charlson, E.S.; Zaneveld, J.; Mozer, M.C.; Collman, R.G.; Bushman, F.D.; Knight, R.; Kelley, S.T. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 2011, 8, 761–763. [Google Scholar] [CrossRef]
  18. Pei, L.; Wu, Z.; Qian, Y.; Li, X.; Zhang, J.; Sun, J.; Wang, Y. Plant community stability, indicator species and their driving factors at a gradient of grazing intensity in an alpine meadow. Ecol. Indic. 2024, 162, 112012. [Google Scholar] [CrossRef]
  19. Ahmad, M.; Sharma, P.; Rathee, S.; Singh, H.P.; Batish, D.R.; Lone, G.R.; Kaur, S.; Jaryan, V.; Kohli, R.K. Niche width analyses facilitate identification of high-risk endemic species at high altitudes in western Himalayas. Ecol. Indic. 2021, 126, 107653. [Google Scholar] [CrossRef]
  20. Finn, D.R.; Yu, J.; Ilhan, Z.E.; Fernandes, V.M.C.; Penton, C.R.; Krajmalnik-Brown, R.; Garcia-Pichel, F.; Vogel, T.M. MicroNiche: An R package for assessing microbial niche breadth and overlap from amplicon sequencing data. FEMS Microbiol. Ecol. 2020, 96, fiaa131. [Google Scholar] [CrossRef]
  21. 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]
  22. Bastida, F.; Eldridge, D.J.; García, C.; Kenny Png, G.; Bardgett, R.D.; Delgado-Baquerizo, M. Soil microbial diversity-biomass relationships are driven by soil carbon content across global biomes. ISME J. 2021, 15, 2081–2091. [Google Scholar] [CrossRef] [PubMed]
  23. Jiao, S.; Chen, W.; Wang, J.; Du, N.; Li, Q.; Wei, G. Soil microbiomes with distinct assemblies through vertical soil profiles drive the cycling of multiple nutrients in reforested ecosystems. Microbiome 2018, 6, 146. [Google Scholar] [CrossRef]
  24. Browne, M.W.; Cudeck, R. Alternative Ways of Assessing Model Fit. Sociol. Methods 1992, 21, 230–258. [Google Scholar] [CrossRef]
  25. Enders, C.K.; Bandalos, D.L. The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models. Struct. Equ. Model. Multidiscip. J. 2001, 8, 430–457. [Google Scholar] [CrossRef]
  26. Rosseel, Y. lavaan: An R Package for Structural Equation Modeling. J. Stat. Softw. 2012, 48, 1–36. [Google Scholar] [CrossRef]
  27. Zhang, Z.; Liu, Y.; Hardrath, A.; Jin, H.; Van Kleunen, M. Increases in multiple resources promote competitive ability of naturalized non-native plants. Commun. Biol. 2022, 5, 1150. [Google Scholar] [CrossRef]
  28. Johnson, R.; Vishwakarma, K.; Hossen, M.S.; Kumar, V.; Shackira, A.M.; Puthur, J.T.; Abdi, G.; Sarraf, M.; Hasanuzzaman, M. Potassium in plants: Growth regulation, signaling, and environmental stress tolerance. Plant Physiol. Biochem. 2022, 172, 56–69. [Google Scholar] [CrossRef]
  29. Wang, M.; Zheng, Q.; Shen, Q.; Guo, S. The critical role of potassium in plant stress response. Int. J. Mol. Sci. 2013, 14, 7370–7390. [Google Scholar] [CrossRef] [PubMed]
  30. Mendoza-Suárez, M.; Akyol, T.Y.; Nadzieja, M.; Andersen, S.U. Increased diversity of beneficial rhizobia enhances faba bean growth. Nat. Commun. 2024, 15, 10673. [Google Scholar] [CrossRef] [PubMed]
  31. Baker, N.R.; Zhalnina, K.; Yuan, M.; Herman, D.; Ceja-Navarro, J.A.; Sasse, J.; Jordan, J.S.; Bowen, B.P.; Wu, L.; Fossum, C.; et al. Nutrient and moisture limitations reveal keystone metabolites linking rhizosphere metabolomes and microbiomes. Proc. Natl. Acad. Sci. USA 2024, 121, e2303439121. [Google Scholar] [CrossRef]
  32. Wiesenbauer, J.; Gorka, S.; Jenab, K.; Schuster, R.; Kumar, N.; Rottensteiner, C.; König, A.; Kraemer, S.; Inselsbacher, E.; Kaiser, C. Preferential use of organic acids over sugars by soil microbes in simulated root exudation. Soil Biol. Biochem. 2025, 203, 109738. [Google Scholar] [CrossRef]
  33. Lu, Z.; Ren, T.; Li, Y.; Cakmak, I.; Lu, J. Nutrient limitations on photosynthesis: From individual to combinational stresses. Trends Plant Science 2025, 30, 872–885. [Google Scholar] [CrossRef]
  34. Dai, Z.; Liu, G.; Chen, H.; Chen, C.; Wang, J.; Ai, S.; Wei, D.; Li, D.; Ma, B.; Tang, C.; et al. Long-term nutrient inputs shift soil microbial functional profiles of phosphorus cycling in diverse agroecosystems. ISME J. 2020, 14, 757–770. [Google Scholar] [CrossRef]
  35. Kaye, J.P.; Hart, S.C. Competition for nitrogen between plants and soil microorganisms. Trends Ecol. Evol. 1997, 12, 139–143. [Google Scholar] [CrossRef]
  36. Dhungana, I.; Kantar, M.B.; Nguyen, N.H. Root exudate composition from different plant species influences the growth of rhizosphere bacteria. Rhizosphere 2023, 25, 100645. [Google Scholar] [CrossRef]
  37. Schwember, A.R.; Schulze, J.; Del Pozo, A.; Cabeza, R.A. Regulation of Symbiotic Nitrogen Fixation in Legume Root Nodules. Plants 2019, 8, 333. [Google Scholar] [CrossRef]
  38. Zhang, Y.; Huang, X.; Zhao, B.; Yan, C.; Zhao, H.; Zhang, H.; Halamka, T.A.; Peel, R.H.; Vreeken, M.; Gallego-Sala, A.V.; et al. Microbial responses to changing plant community protect peatland carbon stores during Holocene drying. Nat. Commun. 2025, 16, 6912. [Google Scholar] [CrossRef] [PubMed]
  39. Kim, H.S.; Lee, S.H.; Jo, H.Y.; Finneran, K.T.; Kwon, M.J. Diversity and composition of soil Acidobacteria and Proteobacteria communities as a bacterial indicator of past land-use change from forest to farmland. Sci. Total Environ. 2021, 797, 148944. [Google Scholar] [CrossRef]
  40. Mujakić, I.; Piwosz, K.; Koblížek, M. Phylum Gemmatimonadota and Its Role in the Environment. Microorganisms 2022, 10, 151. [Google Scholar] [CrossRef]
  41. Mitra, D.; Mondal, R.; Khoshru, B.; Senapati, A.; Radha, T.K.; Mahakur, B.; Uniyal, N.; Myo, E.M.; Boutaj, H.; Sierra, B.E.G.; et al. Actinobacteria-enhanced plant growth, nutrient acquisition, and crop protection: Advances in soil, plant, and microbial multifactorial interactions. Pedosphere 2022, 32, 149–170. [Google Scholar] [CrossRef]
  42. Hu, L.; Robert, C.A.; Cadot, S.; Zhang, X.I.; Ye, M.; Li, B.; Manzo, D.; Chervet, N.; Steinger, T.; Van Der Heijden, M.G.; et al. Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 2018, 9, 2738. [Google Scholar] [CrossRef]
  43. Chen, Y.J.; Leung, P.M.; Wood, J.L.; Bay, S.K.; Hugenholtz, P.; Kessler, A.J.; Shelley, G.; Waite, D.W.; Franks, A.E.; Cook, P.L.; et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021, 15, 2986–3004. [Google Scholar] [CrossRef] [PubMed]
  44. Zhu, S.G.; Cheng, Z.G.; Batool, A.; Wang, Y.B.; Wang, J.; Zhou, R.; Khan, A.; Zhu, S.Y.; Yang, Y.M.; Wang, W.; et al. Plant facilitation shifts along with soil moisture and phosphorus gradients via rhizosphere interaction in the maize-grass pea intercropping system. Ecol. Indic. 2022, 139, 108901. [Google Scholar] [CrossRef]
  45. Wahbi, S.; Prin, Y.; Thioulouse, J.; Sanguin, H.; Baudoin, E.; Maghraoui, T.; Oufdou, K.; Le Roux, C.; Galiana, A.; Hafidi, M.; et al. Impact of Wheat/Faba Bean Mixed Cropping or Rotation Systems on Soil Microbial Functionalities. Front. Plant Sci. 2016, 7, 1364. [Google Scholar] [CrossRef] [PubMed]
  46. Zhalnina, K.; Louie, K.B.; Hao, Z.; Mansoori, N.; Da Rocha, U.N.; Shi, S.; Cho, H.; Karaoz, U.; Loqué, D.; Bowen, B.P.; et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 2018, 3, 470–480. [Google Scholar] [CrossRef]
  47. Rekosz-Burlaga, H.; Garbolinska, M. Characterization of selected groups of microorganisms occurring in soil rhizosphere and phyllosphere of oats. Pol. J. Microbiol. 2006, 55, 227–235. [Google Scholar]
  48. 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]
  49. Che, Y.; Jin, G. Plant–soil microbial diversity and structural attributes jointly dominate the multifunctionality of the temperate forest. Ecol. Indic. 2024, 166, 112282. [Google Scholar] [CrossRef]
  50. Chen, Y.; Tao, S.; Ma, J.; Qu, Y.; Sun, Y.; Wang, M.; Cai, Y. New insights into assembly processes and driving factors of urban soil microbial community under environmental stress in Beijing. Sci. Total Environ. 2024, 947, 174551. [Google Scholar] [CrossRef] [PubMed]
  51. Spohn, M.; Bagchi, S.; Bakker, J.D.; Borer, E.T.; Carbutt, C.; Catford, J.A.; Dickman, C.R.; Eisenhauer, N.; Eskelinen, A.; Hagenah, N.; et al. Interactive and unimodal relationships between plant biomass, abiotic factors, and plant diversity in global grasslands. Commun. Biol. 2025, 8, 97. [Google Scholar] [CrossRef]
  52. Chen, C.; Xiao, W.; Chen, H.Y. Meta-analysis reveals global variations in plant diversity effects on productivity. Nature 2025, 638, 435–440. [Google Scholar] [CrossRef]
  53. Ma, W.; Tang, S.; Dengzeng, Z.; Zhang, D.; Zhang, T.; Ma, X. Root exudates contribute to belowground ecosystem hotspots: A review. Front. Microbiol. 2022, 13, 937940. [Google Scholar] [CrossRef] [PubMed]
  54. Isbell, F.; Reich, P.B.; Tilman, D.; Hobbie, S.E.; Polasky, S.; Binder, S. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc. Natl. Acad. Sci. USA 2013, 110, 11911–11916. [Google Scholar] [CrossRef] [PubMed]
  55. Stomph, T.; Dordas, C.; Baranger, A.; de Rijk, J.; Dong, B.; Evers, J.; Gu, C.; Li, L.; Simon, J.; Jensen, E.S.; et al. Chapter One-Designing intercrops for high yield, yield stability and efficient use of resources: Are there principles? Adv. Agron. 2020, 160, 1–50. [Google Scholar]
  56. Huber, K.J.; Overmann, J. Vicinamibacteraceae fam. nov., the first described family within the subdivision 6 Acidobacteria. Int. J. Syst. Evol. Microbiol. 2018, 68, 2331–2334. [Google Scholar] [CrossRef]
Agronomy 15 02236 g001
Figure 1. Comparison of biomass between monoculture and mixtures of faba bean and oat. *** p < 0.001, ** p < 0.01.
Figure 1. Comparison of biomass between monoculture and mixtures of faba bean and oat. *** p < 0.001, ** p < 0.01.
Agronomy 15 02236 g001
Agronomy 15 02236 g002
Figure 2. Rhizosphere bacteria α-diversity and β-diversity under monoculture and mixtures of faba bean and oat. The ovals mean confidence intervals. (a) α-diversity (b) β-diversity *** p < 0.001, ** p < 0.01, * p < 0.05, NS: Not significant.
Figure 2. Rhizosphere bacteria α-diversity and β-diversity under monoculture and mixtures of faba bean and oat. The ovals mean confidence intervals. (a) α-diversity (b) β-diversity *** p < 0.001, ** p < 0.01, * p < 0.05, NS: Not significant.
Agronomy 15 02236 g002
Agronomy 15 02236 g003
Figure 3. Rhizosphere bacteria community composition under monoculture and mixtures of faba bean and oat. (a) Phylum (b) Genus.
Figure 3. Rhizosphere bacteria community composition under monoculture and mixtures of faba bean and oat. (a) Phylum (b) Genus.
Agronomy 15 02236 g003
Agronomy 15 02236 g004
Figure 4. Source Tracker analysis of rhizosphere bacteria after mixtures.
Figure 4. Source Tracker analysis of rhizosphere bacteria after mixtures.
Agronomy 15 02236 g004
Agronomy 15 02236 g005
Figure 5. Rhizosphere bacterial stability, habitat niche breadth, and pNST analysis under monoculture and mixtures of faba bean and oat. (a) RAVD (b) habitat niche breadth (c) pNST * p < 0.05, NS: Not significant.
Figure 5. Rhizosphere bacterial stability, habitat niche breadth, and pNST analysis under monoculture and mixtures of faba bean and oat. (a) RAVD (b) habitat niche breadth (c) pNST * p < 0.05, NS: Not significant.
Agronomy 15 02236 g005
Agronomy 15 02236 g006
Figure 6. Key factors driving biomass variation. pH, Soil pH, Organic matter (OM), Total nitrogen (TN), alkali-hydrolyzable nitrogen (AN), Available phosphorus (AP), available potassium (AK), Total phosphorus (TP) and total potassium (TK), Betadiversity, α-diversity (PD_whole_tree, Richness, Shannon, Pielou) Bcom. * p < 0.05, ** p < 0.01, NS: Not significant.
Figure 6. Key factors driving biomass variation. pH, Soil pH, Organic matter (OM), Total nitrogen (TN), alkali-hydrolyzable nitrogen (AN), Available phosphorus (AP), available potassium (AK), Total phosphorus (TP) and total potassium (TK), Betadiversity, α-diversity (PD_whole_tree, Richness, Shannon, Pielou) Bcom. * p < 0.05, ** p < 0.01, NS: Not significant.
Agronomy 15 02236 g006
Agronomy 15 02236 g007
Figure 7. Model and Estimation Results of the relationships Among bacterial diversity, ecological niche, soil physicochemical properties, and forage biomass. Arrows represent hypothesized directional relationships based on established literature and the conceptual framework of this study. The model does not confirm causality. (Red solid arrows indicate significant causal paths (p < 0.05). The numbers beside the arrows are standardized path coefficients (β); a minus sign denotes a negative effect. Blue solid arrows indicate non-significant causal paths (p ≥ 0.05); ns denotes not significant. Red dashed arrows represent correlational/covariance relationships (not causal directions). The numbers are correlation coefficients (r) with their significance. (* p < 0.05; ** p < 0.01; *** p < 0.001; NS = Not significant).
Figure 7. Model and Estimation Results of the relationships Among bacterial diversity, ecological niche, soil physicochemical properties, and forage biomass. Arrows represent hypothesized directional relationships based on established literature and the conceptual framework of this study. The model does not confirm causality. (Red solid arrows indicate significant causal paths (p < 0.05). The numbers beside the arrows are standardized path coefficients (β); a minus sign denotes a negative effect. Blue solid arrows indicate non-significant causal paths (p ≥ 0.05); ns denotes not significant. Red dashed arrows represent correlational/covariance relationships (not causal directions). The numbers are correlation coefficients (r) with their significance. (* p < 0.05; ** p < 0.01; *** p < 0.001; NS = Not significant).
Agronomy 15 02236 g007
Table 1. Soil physicochemical variance analysis in different planting patterns. ** p < 0.01; * p < 0.05.
Table 1. Soil physicochemical variance analysis in different planting patterns. ** p < 0.01; * p < 0.05.
Planting PatternMonocultureGrass–Legume Mixtures
soil physical propertiesFOMF and MO
pH8.53 ± 0.088.51 ± 0.598.50 ± 0.08
TN (g/kg)1.98 ± 0.212.21 ± 0.162.12 ± 0.21
TP (g/kg)0.57 ± 0.03 *0.58 ± 0.03 *0.61 ± 0.03
TK (g/kg)10.79 ± 0.4311.31 ± 0.4710.51 ± 0.32
AP (mg/kg)14.38 ± 0.8717.72 ± 1.63 **15.73 ± 0.43
AK (mg/kg)71.30 ± 8.0466.44 ± 7.2459.11 ± 6.39
AN (mg/kg)102.27 ± 14.91 *115.94 ± 11.03116.48 ± 15.62
OM (g/kg)32.68 ± 1.6332.92 ± 1.9131.47 ± 2.02
Table 2. Effects decomposition (Standardized + Bootstrap CI).
Table 2. Effects decomposition (Standardized + Bootstrap CI).
Effect TypeFrom→ToEstimateCI LowerCI Upperp
Direct (std)Soil→Freshweight----
Direct (std)Diversity→Freshweight−0.770−0.899−0.640<0.001
Direct (std)Bcom→Freshweight----
Indirect (std)Bcom→Freshweight
(via Diversity)		0.2850.0650.5060.011
Indirect (std)Soil→Freshweight
(via Bcom)		0.0000.0000.000-
Total (std)Soil→freshweight0.2850.0650.5060.011
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

Yan, H.; Jin, X.; Ye, P.; Teng, C.; Liu, Y. Faba Bean–Oat Mixtures Modify Rhizosphere Microbiota and Nutrient–Biomass Regulation on the Qinghai–Tibetan Plateau. Agronomy 2025, 15, 2236. https://doi.org/10.3390/agronomy15092236

AMA Style

Yan H, Jin X, Ye P, Teng C, Liu Y. Faba Bean–Oat Mixtures Modify Rhizosphere Microbiota and Nutrient–Biomass Regulation on the Qinghai–Tibetan Plateau. Agronomy. 2025; 15(9):2236. https://doi.org/10.3390/agronomy15092236

Chicago/Turabian Style

Yan, Huilin, Xin Jin, Panda Ye, Changcai Teng, and Yujiao Liu. 2025. "Faba Bean–Oat Mixtures Modify Rhizosphere Microbiota and Nutrient–Biomass Regulation on the Qinghai–Tibetan Plateau" Agronomy 15, no. 9: 2236. https://doi.org/10.3390/agronomy15092236

APA Style

Yan, H., Jin, X., Ye, P., Teng, C., & Liu, Y. (2025). Faba Bean–Oat Mixtures Modify Rhizosphere Microbiota and Nutrient–Biomass Regulation on the Qinghai–Tibetan Plateau. Agronomy, 15(9), 2236. https://doi.org/10.3390/agronomy15092236

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