Next Article in Journal
Perspectives on Innovative Approaches in Agriculture to Managing Water Scarcity in the Middle Rio Grande Basin
Previous Article in Journal
The Influence of Substrate Composition on Nutritional Content and Biological Activity of Some Pleurotus Mushrooms Extracts
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 15
Issue 7
10.3390/agriculture15070794
agriculture-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

Impact of Split Nitrogen Topdressing on Rhizobacteria Community of Winter Wheat

by
Yu An
1,2,3,4,5,†,
Yang Wang
1,2,3,4,5,†,
Shuangshuang Liu
1,2,3,4,
Wei Wu
1,2,3,4,5,
Weiming Wang
1,2,3,4,
Mengmeng Liu
6,
Hui Xiao
6,
Jing Dong
6,
Hongjie Ren
7,
Huasen Xu
1,2,3,4,5,* and
Cheng Xue
1,2,3,4,5
1
College of Resources and Environment Science, Hebei Agricultural University, Baoding 071001, China
2
College of Land Resources, Hebei Agricultural University, Baoding 071001, China
3
Key Laboratory for Farmland Eco-Environment of Hebei, Hebei Agricultural University, Baoding 071001, China
4
State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China
5
Green Intelligent Fertilizer Tripartite Integrated Base of Mindefu, Baoding 072450, China
6
Cultivated Land Quality Monitoring and Protection Center of Hebei Province, Shijiazhuang 050000, China
7
Poverty Relapse Prevention Monitoring Center of Hebei Province, Shijiazhuang 050000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(7), 794; https://doi.org/10.3390/agriculture15070794
Submission received: 12 March 2025 / Revised: 4 April 2025 / Accepted: 5 April 2025 / Published: 7 April 2025
(This article belongs to the Section Crop Production)
Browse Figures
Versions Notes

Abstract

:
Previous research on soil bacteria focused on refining the nitrogen (N) rates during the wheat (Triticum aestivum L.) growth cycle. Studies concerning how additional and split N topdressing applications can affect wheat rhizobacteria are limited. To address this, a two-year field experiment took the cultivar ‘Gaoyou 2018’ of winter wheat as the experimental material from October 2020 to June 2022. Six nitrogen application regimes were established, including no nitrogen application (T1), single topdressing applications of 120 kg ha−1 (T2) and 80 kg ha−1 (T3) at the jointing stage, and split topdressing applications combining 80 kg ha−1 at jointing with 40 kg ha−1 at the booting stage (T4), the flowering stage (T5), and 10th day post-anthesis (T6). The delayed impacts of the split topdressing time on the rhizobacteria diversity were observed in the second year, with T4 exhibiting a 10.5% higher Chao1 index and 2% greater Shannon diversity than T6. Results from both years indicated that the dominant bacterial phylum compositions in the winter wheat rhizosphere were similar across the nitrogen treatments. The additional N treatments fostered 22.9–27.9% Bacteroidita abundance but diminished 24.0–35.9% Planctomycetota, compared to the thenon-fertilized control (T1). T6 increased the α-Proteobacteria abundance by 15.7–22.0% versus T4, while the N topdressing redistribution to the booting stage increased the MND1 genus abundance in Proteobacteria by 31.3–62.5% compared to T2. Redundancy analysis identified that the rhizosphere pH and soil moisture content were the predominant environmental drivers shaping the winter wheat rhizobacteria. Preliminary findings revealed that split nitrogen application during the jointing and booting stages of winter wheat improved the edaphic micro-environment and modulated the proliferation of beneficial rhizobacteria. However, this change was not transmitted to the yield variation. These results suggest that short-term N management strategies may enhance ecological benefits by intensifying soil–plant–microbe interactions, yet they lack direct agronomic yield advantages. Long-term trials are required to establish causality between rhizosphere microbial community dynamics and crop productivity under split N management regimes.

1. Introduction

As a primary global cereal crop, wheat plays a critical role in sustaining global food security. The strategic importance of securing the wheat yield and quality in China, a leading producer and consumer, directly correlates with socioeconomic stability [1]. Nitrogen critically regulates wheat development, serving as a key determinant of improvements in the productivity and grain quality [2,3,4]. Optimized nitrogen (N) application enhances the nitrogen use efficiency in wheat while mitigating environmental nitrogen losses [5,6]. Furthermore, the topdressing regime also profoundly modulates the grain yield and quality traits of wheat [7,8,9]. Split nitrogen application during the late growth stages aligns with the dynamic nitrogen demand of wheat, enhancing the yield, protein content, and nitrogen use efficiency [10].
As a hotspot for plant–soil–microbe interactions, the rhizosphere harbors highly diverse microbial consortia characterized by remarkable compositional and structural complexity [11]. Rhizosphere microorganisms augment the plant genetic potential and metabolic capacity, thereby promoting critical physiological processes, including nutrient acquisition, immune modulation, and stress adaptation [12]. Extensive research has linked the rhizosphere microbiota composition and temporal population dynamics to plant growth–productivity relationships [13,14,15,16,17]. As the dominant component of the soil microbiome, bacterial communities undergo structural reconfiguration in response to plant phenological stages and environmental fluctuations, thereby influencing the agronomic performance [18]. Research reveals that N fertilization profoundly restructures rhizosphere bacterial communities, yet these effects exhibit context-dependent variations across crops and edaphic conditions. N fertilization supplies nutrient substrates for rhizosphere bacteria, enhancing their diversity [19], and can also stimulate root exudate production, modulating the rhizosphere bacterial composition [20]. An elevated N rate beyond the critical thresholds amplifies the abundance and diversity of nitrogen-fixing and ammonifying bacteria, thereby accelerating N transformation pathways via the reinforcement of functional redundancy [21]. In contrast, sorghum rhizosphere microbiomes demonstrate N insensitivity, maintaining a stable community structure despite divergent fertilization regimes [22]. Wang et al. [23] found that nitrogen fertilization promotes the secretion of organic acids in plant roots, synthesized via root exudate degradation, thereby altering the soil pH balance and directly modulating the composition, structure, and diversity of microbial communities. Distinct soil microbial taxa exhibit differential responsiveness to nitrogen-induced shifts in soil physicochemical properties [24]. Subsequent shifts in soil fertility and environmental conditions drive divergent modifications in microbial community architectures and population dynamics [25]. Long-term fertilization experiments by Wang et al. [26] established that environmental factors predominantly govern bacterial community assembly, in which the soil pH, organic matter, and available phosphorus emerged as the primary determinants regulating the maize rhizosphere bacterial community size and composition. Differential nutrient availability governs taxon-specific microbial recruitment, thereby restructuring the structural architecture and functional potential of rhizosphere communities. Rhizosphere microbial activity exerts regulatory feedback on plant growth by modulating the nutrient bioavailability and enzymatic hydrolysis, which reciprocally shape the developmental trajectories. Diverse rhizosphere microbiomes bolster pedoecosystem resilience and improve plant growth and stress tolerance [27]. Current research predominantly examines N-induced rhizobacterial community shifts across crops, yet scant studies have dissected wheat rhizobacterial responses to split nitrogen fertilization regimes.
This study utilized high-throughput sequencing to characterize the rhizosphere bacterial diversity and community composition in winter wheat under divergent N management regimes, establishing quantitative linkages between the microbial community restructuring and edaphic parameters. These findings establish a predictive microbe–environment interaction framework to optimize precision nitrogen management strategies in wheat cultivation systems.

2. Experimental Design and Methods

2.1. Site Description

The field experiment was conducted at the Experimental Station of Hebei Agricultural University (37°47’ N, 115°17’ E) in Xinji, Hebei Province, China, from October 2020 to June 2022. The region exhibits a temperate continental climate with the following mean annual values: temperature: 12.5 °C; sunshine duration: 2738 h; frost-free period: 190 d; and precipitation: 458.6 mm. The monthly precipitation and average temperature during the wheat-growing seasons (2020–2022) are presented in Figure 1. The experimental field was classified as fluvo-aquic soil. Before the study, the site had been managed under a predominant winter wheat (Triticum aestivum L.)—summer maize (Zea mays L.) double-cropping system. Prior to wheat sowing in 2020–2021, the pH of the surface soil was 8.68, the organic matter was 22.3 g kg−1, the total nitrogen was 1.6 g kg−1, the available phosphorus was 19.9 mg kg−1, and the available potassium was 164.5 mg kg−1.

2.2. Experimental Design

This study employed ‘Gaoyou 2018’, a high-quality strong-gluten wheat variety, as the experimental material. Six nitrogen application regimes were established, including no nitrogen application (T1), single topdressing applications of 120 kg ha−1 (T2) and 80 kg ha−1 (T3) at the jointing stage, and split topdressing applications combining 80 kg ha−1 at jointing with 40 kg ha−1 at the booting stage (T4), flowering stage (T5), and 10th day post-anthesis (T6). All treatments applied 90 kg N ha−1, 100 kg P2O5 ha−1, and 60 kg K2O ha−1 as the base fertilizer (Table 1). T1 served as the control treatment, while T2 represented conventional fertilization practice, with both the application timing and dosage matching local farmers’ standard protocols. The basal fertilizer was a compound fertilizer (18-20-5), with urea (N content ≥46%) employed for the topdressing. At the jointing stage, soil fertilization was implemented; foliar fertilization was performed during the booting, flowering, and post-flowering (10 days after) stages, with late-stage foliar application utilizing a 1.5% urea solution. Four replicates per treatment were implemented following a randomized block design protocol. Each experimental plot covered an area of 60.5 m2. The two consecutive growing seasons (October 2020–June 2021 and October 2021–June 2022) were investigated in this field experiment. Both annual trials were carried out on the identical experimental field. The sowing rate for wheat in both growing seasons (2020–2022) was maintained at 255 kg ha−1 with 0.15 m row spacing in both growing seasons. Standard agronomic practices for irrigation, pest, disease, and weed management were implemented according to regional farming practices.

2.3. Sample Collection

Rhizosphere soil was sampled on day 15 post-anthesis across two consecutive growing seasons (2020–2021 and 2021–2022). In each plot, a representative number of plants were uprooted using a spade, and non-rhizosphere soil loosely adhering to roots was shaken off. Approximately 50 g of rhizosphere soil was gently brushed off using a sterilized brush, homogenized manually, and placed in a centrifuge tube as a composite sample for analysis. The samples were immediately stored at −80 °C for the subsequent high-throughput sequencing of the rhizosphere microbiome and soil physicochemical property analysis.

2.4. Measurement Parameters and Methods

2.4.1. Rhizosphere Soil Physicochemical Properties

Soil moisture content (SMC) was determined by oven-drying at 105 °C until constant weight. Soil pH was measured using the potentiometric method. Soil total nitrogen (STN) was quantified using the Kjeldahl digestion method with concentrated sulfuric acid. Soil organic matter (SOM) was analyzed via potassium dichromate oxidation. The activities of ammonia monooxygenase (AMO), hydroxylamine oxidoreductase (HAO), and nitrite oxidoreductase (NXR) were assessed using a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) [28].

2.4.2. Rhizobacteria Community Profiling

Rhizobacterial community profiling was conducted by Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China). The paired-end sequencing was performed on extracted microbial DNA using the Illumina platform. Detailed methods are available in Liu’s research [29].

2.5. Statistical Analysis

The dataset was processed and organized with Microsoft Excel 2021. One-way ANOVA was used to assess differences in the rhizobacteria diversity (Chao1, Shannon, Pielou_e, Observed_species, Simpson, Faith-pd) and compositions among different treatments, using the least significant difference (LSD) test at a significance level of p < 0.05 (SPSS 25.0, IBM, Armonk, NY, USA). Two-way ANOVA was used to assess yield differences among N management regimes in two growing seasons of winter wheat. The figures were generated with OriginPro 2022 (OriginLab Corporation, Northampton, MA, USA). The α-diversity indices of the rhizosphere bacterial communities in winter wheat were visualized with boxplots. The relative abundance profiles of winter wheat rhizosphere bacteria across taxonomic ranks were visualized through percentage-based stacked column graphs. The hierarchical relative abundance profile of winter wheat rhizosphere microbiota was visualized through phylogenetic cladograms. Redundancy analysis of rhizobacteria communities and soil parameters was performed via the Bioincloud platform (https://www.bioincloud.tech [accessed on 19 December 2024]).

3. Results

3.1. Rhizobacterial Diversity

The alpha diversity indices revealed that T2, T5, and T6 showed significantly higher Chao1 indices than that of T1 (nitrogen-free control) (p < 0.05), indicating enhanced operational taxonomic unit (OTU) richness, during the 2020–2021 wheat seasons (Figure 2). Furthermore, T5 and T6 exhibited significantly higher Observed_species indices than that of T1 (p < 0.05), confirming that late split N topdressing enhanced the bacterial species richness. However, the Shannon and Simpson indices showed no inter-treatment differentiation (p > 0.05), suggesting that the additional nitrogen regimes primarily affected the richness metrics rather than the overall diversity equilibrium. A comparative analysis of the topdressing schedules (T3-T6 vs. conventional T2) revealed marginal effects on all the alpha diversity parameters. The split N timing exerted measurable but nonsignificant influences on the rhizosphere bacteria diversity in the winter wheat (p > 0.05).
The alpha diversity profiling of the wheat rhizobacteria in the 2021–2022 season demonstrated that the additional split application of 40 kg N ha−1 at the booting stage (T4) exhibited a significantly higher Chao1 index than that at the 10th day post-anthesis (T6) (p < 0.05), indicating that the bacterial richness in T4 was significantly greater than that in T6 (Figure 3). Significant increases in the Observed_species index were recorded for T1 (zero-N control) and T4 compared to T6 (p < 0.05), which revealed the treatment-specific enhancement of the absolute species-level bacterial richness. The elevated Shannon index in T4 quantitatively demonstrated the positive effect of the split N topdressing at the booting stage on the bacterial species richness and community evenness (p < 0.05). The bacterial community diversity (Simpson index) in T1 and T4 was significantly higher than that in T6 (p < 0.05). The nonsignificant Pielou_e index variation between the N treatments revealed conserved abundance distributions among the core microbiota despite nitrogen-induced richness fluctuations. The Faith_pd index detected a significant expansion in the evolutionary diversity in T1, T4, and T5 compared to that in T3, and in T1 and T4 compared to that in T2 (p < 0.05). Notably, the rhizobacterial diversity and richness indicated by the Chao1, Observed_species, Shannon, and Simpson indices in T4 were higher than those in T6 (p < 0.05).

3.2. Dominant Rhizobacteria

The dominant bacterial phyla (relative abundance > 5%) in the wheat rhizosphere across all treatments were Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, and Chloroflexi during the 2020–2021 growing season (Figure 4). However, an analysis of the rhizosphere soil samples from the various treatments identified Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, Gemmatimonadetes, and Chloroflexi as the dominant bacterial phyla (relative abundance > 5%) during the 2021–2022 growing season (Figure 5).

3.3. Differences in Rhizobacterial Communities

Proteobacteria constituted the predominant phylum, with the relative abundances exceeding 30% in all the treatments during the 2020–2021 growing season of winter wheat (Figure 6). These phyla of T4 and T5 showed significantly higher abundances than those of T1 (p < 0.05). The Alphaproteobacteria abundance in T4 was significantly depressed compared to in the other treatments, but the relative abundance of Gammaproteobacteria was higher in T4 than that in T1, T2, T3, and T6. The Rhizobiales abundance was enriched in T3 compared to in T4, whereas that of Xanthomonadales in T4 exceeded that in T1. The Cellvibrionales showed higher abundance in T4 and T5 compared to in the N-free control (T1) and single N topdressing treatments (T2 and T3). The Betaproteobacteriales was more abundant in T4 than in T1 and T2, with no significant differences among the treatments with different nitrogen topdressing timings. The Geminicoccaceae family was more abundant in T1 and T6 compared to in T2, T3, T4, and T5, while T5 demonstrated greater abundance than that in T4. The family Xanthomonadaceae and genus Lysobacter exhibited significantly higher abundances in T4 than in T1. Cellvibrionaceae showed no significant difference between T4 and T5, but higher abundance than the other groups. The Burkholderiaceae family exhibited a higher abundance in T3, T4, and T5 compared to in T1. The MND1 genus abundance was higher in T4 than in T2, indicating that nitrogen relocation from the jointing stage to the booting stage enhanced its enrichment. The family Devosiaceae and genus Devosia were more abundant in T5 compared to in T1. The Phyllobacterium_ifriqiyense species exhibited a higher abundance in T3 compared to in T4.
The Actinobacteria phylum exhibited significantly higher relative abundance in T1, T2, and T6 compared to in T4 during the 2020–2021 growing season of winter wheat (p < 0.05) (Figure 6). The split N topdressing at the booting stage (T4) elevated the abundance of the Actinobacteria class relative to the single N topdressing at the jointing stage (T2). However, the Thermoleophilia class in T4 was lower than that in the N-free treatment (T1). The booting (T4) and flowering (T5) late topdressing inhibited the growth of the Acidimicrobiia class compared to T1, particularly in T4, whereas the post-anthesis application (T6) reversed this inhibition. Delayed split N application to the booting stage (T4) inhibited Micrococcales, whereas delayed post-anthesis split N application rescued the suppression. At 210 kg N ha−1, the relative abundances of the Propionibacteriales, Microtrichales, and Nocardioidaceae family decreased significantly in T1 and T2 compared to in T4. Gaiellales displayed the highest abundance in T1 among all the treatments. Delayed split N topdressing at the booting stage reduced the Micrococcaceae abundance, and the post-anthesis split N application restored it, but it remained below that of T2. The Agromyces and Mycobacterium_hodleri genus showed significantly higher abundance in T1 compared to that in T4 (p < 0.05). The no N application increased the Arthrobacter agilis species compared to T2, T3, T4, and T6. Lower N application decreased the abundance of Actinocorallia longicatena species more than the split N topdressing treatments, but there was no significant difference compared to the normal N application (T2). And this species in the booting split N topdressing still surpassed T2.
The Chloroflexi phylum exhibited significantly higher abundance in T4 and T5 compared to in T1 during the 2020–2021 growing season of winter wheat (Figure 6). The Chloroflexia class abundance in the no N application exceeded that in T5. The Gitt-GS-136 class (Chloroflexi) showed elevated abundance in T1 and T6 relative to in T4 and T5. Among the different split N topdressing timings, the SBR1031 order in T4 surpassed those in T5 and T6.
The Bacteroidetes phylum, Cytophagales, and the Microscillaceae family exhibited significantly higher abundances in T4 compared to those in T1 during the 2020–2021 growing season of winter wheat (Figure 6). Split N topdressing at the booting stage preferentially promoted the Bacteroidia class, Flavobacteriales and Chitinophagales, the Chitinophagaceae and Flavobacteriaceae family, and the Flavobacterium genus compared to those in T1, T2, T3, and T6. The Sphingobacteriales and Flavobacterium_psychrolimnae species in T4 showed significantly higher abundances than those in the no and low-N treatments and were also higher than those in the split N topdressing at the jointing and 10th-day post-anthesis stages with equal N application.
The relative abundance of Proteobacteria showed no statistically significant variations among the different treatments (Figure 7). The abundance of the Acidimicrobiia class in the wheat rhizosphere under the N application treatments was markedly lower than that in the N-free control (T1). The α-proteobacteria abundance in T4 (split N topdressing at the booting stage) demonstrated a notable decrease relative to that in T6 (split N topdressing at post-anthesis). At the order level, the Sphingomonadales abundance in T2 exceeded that in T4, indicating that postponing partial N application to the booting stage reduced the Sphingomonadales abundance. However, T4 demonstrated Burkholderiales enrichment compared to the N-free control and single-dose jointing stage treatments (T2, T3); this order declined when the nitrogen splitting extended to 10 days post-anthesis. T3 displayed minimal Microtrichales abundance compared to that in the other treatments.
The split nitrogen regimes (T4, T5, T6) exhibited decreased Cellvibrionaceae abundance compared to that of the single N topdressing applications (T2, T3), though the split timing showed no differential effect on this family (Figure 7). T4 demonstrated a decrease in the Sphingomonadaceae family but increased Nitrosomonadaceae colonization compared to T2. Counterintuitively, the reduced N application resulted in lower Devosiaceae relative abundance compared to the N-free control. The Pseudoxanthomonas and Devosia genera showed comparable abundances between the T1 and split N topdressing applications (T4-T6), yet T1 exhibited the unexpected enrichment of these genera compared to the low-N treatment (T3). The 10th-day post-anthesis split N topdressing application (T6) demonstrated significantly higher Sphingomonas genus abundance than that of the booting stage split N topdressing treatment. Compared to the single-dose applications (T2-T3), the booting stage split N topdressing significantly enhanced the MND1 and Subgroup_17 abundance while reducing the Altererythrobacter colonization. The single-dose regime (T2) demonstrated a higher abundance of Lysobacter dokdonensis and increased Lysobacter antibioticus colonization compared to the N-free control and split N topdressing treatments (T4-T6) (p < 0.05). Among the split N topdressing regimes, the flowering stage split N topdressing (T5) showed greater relative abundances of Arenimonas daejeonensis and Aquabacterium citratiphilum than those of the booting stage split N topdressing (T4). The Devosia sp. abundance in T3 exceeded that in T1, while the uncultured beta proteobacterium SC-I-84_beta_proteobacterium showed higher colonization in T1 and T5 than that in T6.
The relative abundance of Acidobacteria showed no significant differences across the treatments. However, delayed N allocation to the booting stage enhanced the Subgroup_17 genus proliferation (Figure 7). No significant variations were observed in the Actinobacteria abundance among the treatments (Figure 7). The Acidimicrobiia class exhibited higher abundance in T1 than in T3. The Microtrichales showed elevated abundance in T1, T2, and T5 relative to in T3. The Bacteroidetes phylum and Bacteroidia class abundances were higher in T2 and T3 than those in T1 (Figure 7). Cytophagales was more abundant in T3 than in T1 and T6. The Chitinophagales abundance in T3 surpassed that in T1, T4, T5, and T6. The Microscillaceae family showed greater abundance in T3 compared to in T6, whereas the Flavobacteriaceae abundance was elevated in T2 relative to in T1. The relative abundance of Gemmatimonadetes remained consistent across all the treatments (Figure 7). T2 and T3 exhibited increased Gemmatimonadetes class abundance compared to T1, but Gemmatimonadales demonstrated an inverse pattern. The Chloroflexi phylum abundance in T1 exceeded that in T2 and T3 (Figure 7). The KD4-96 group (class, order, family, genus) of Chloroflexi exhibited higher abundance in T1 than in T3. The relative abundance of the Chloroflexia class was significantly greater in T1 than in T2, T3, and T6.

3.4. Relationship Between Rhizobacteria and Environment Factors

The redundancy analysis showed that RDA1 and RDA2 explained 16.5% and 15.87% of the bacterial community variation, respectively (Figure 8a). The soil moisture content (SMC), soil organic matter (SOM), hydroxylamine oxidase (HAO), and nitrite oxidoreductase (NXR) emerged as the key factors significantly correlated with the rhizobacterial community structure of the winter wheat. The redundancy analysis indicated that RDA1 explained 15.96% of the variation, while RDA2 accounted for 15.41%, during the 2021–2022 growing season (Figure 8b). The SMC, pH, HAO, and ammonia monooxygenase (AMO) collectively shaped the rhizobacterial assemblages of the winter wheat with significant correlations. The two-year integrated RDA model revealed that RDA1 explained 20.42% of the variance and RDA2 accounted for 16.01% (Figure 8c). The SMC, SOM, soil total nitrogen (STN), pH, AMO, and NXR were identified as the primary edaphic factors governing the rhizosphere microbiome composition.

3.5. Yield Response

The N application significantly enhanced the winter wheat yield, and the N application treatments maintained consistent grain yields. During the 2020–2021 season, the T2 yields were significantly higher than those of T3 (the low-N regime), demonstrating that elevated N inputs enhanced the productivity. In the 2021–2022 season, however, the split N applications across the growth stages had no significant impact on the yield. Across both seasons, the split N treatments exhibited comparable yields (Figure 9), indicating that the application timing did not alter the productivity under the tested regimes.

4. Discussion

4.1. Effect of Nitrogen Topdressing Regime on Rhizobacteria Diversity

N application exerts substantial effects on the microbial diversity indices, community configuration, and functional potential in agricultural soils [30,31,32]. Soil bacteria constitute the predominant biological mediators of nutrient cycling and energy transfer, with their community profiles providing direct insights into the soil fertility status [33,34,35]. N application increased the rhizobacterial richness metrics (Chao1 and Observed_species indices) during the 2020–2021 growing season. Studies by Liu et al. demonstrated that prolonged nitrogen fertilization enhanced the rhizosphere bacterial diversity over time [36]. While nitrogen fertilization boosted the rhizosphere bacterial diversity in the 2020–2021 season, its impact on the bacterial richness was nonsignificant in 2021–2022, contrasting with Liu et al.’s results. Potential explanations include the multifactorial regulation of the rhizosphere bacterial diversity by nitrogen, climate, and soil conditions. Moreover, shifts in the nitrogen application timing showed no significant impact on the rhizosphere bacterial diversity across the two-year study. An interannual comparison showed lower richness indices in 2021–2022, potentially attributable to higher October–November precipitation (2021 vs. 2020), as wet–dry cycles reduce bacterial biomass [37]. The results indicate that the bacterial richness is strongly affected by climatic and edaphic factors. Interannual variation in the precipitation patterns during the critical topdressing stage (the boot stage to flowering) likely modulated the nitrogen–microbe interactions, with adequate moisture potentially buffering against ammonia toxicity [38]. The rhizobacteria of the wheat in the second year demonstrated a higher Chao1 index of 10.5% and a higher Shannon index of 2% with the booting split N topdressing compared to the 10th-day post-anthesis split N, indicating that phenology-dependent nitrogen delivery optimizes microbial diversity. Contrasting with Fu et al.’s null findings for soil-applied nitrogen timing [39], our foliar-based approach showed significant phenophase-dependent effects. The divergent results likely originate from the application methods: soil vs. foliar delivery. Foliar nutrition enhances the phyllosphere function and shoot–root signaling [40], potentially amplifying rhizosphere microbial diversity through improved carbon allocation.

4.2. Effect of Nitrogen Topdressing Regime on Rhizobacteria Community

The rhizobacterial profiles revealed that the dominant phyla (Proteobacteria, Actinobacteria, Chloroflexi, Bacteroidetes, Acidobacteria) constituted 88.12–89.63% of the communities in the 2020–2021 growth season, and Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, Gemmatimonadetes, and Chloroflexi were the dominant patterns in the 2021–2022 growth season. This phenomenon confirmed that additional and split nitrogen topdressing preserves core phyla despite application timing variations, aligning with Fu’s conclusions [39]. The results further demonstrate that the application method (soil dressing vs. foliar feeding) does not modify the predominant bacterial phyla in wheat rhizospheric soil. Nitrogen application significantly increased the Chloroflexi and Bacteroidetes abundances. The increase in the Chloroflexi abundance is attributed to the enhanced nitrogen availability from additional N, as these chemoautotrophs participated in nitrification through NO2- and CO2 utilization and formatted metabolism under nutrient-replete conditions [41]. Chloroflexi participate in carbon cycling through processes including CO2 fixation, CO oxidation, CH4 oxidation, and the degradation of macromolecules such as cellulose [41], which benefits plant growth promotion. Bacteroidetes proliferation may result from the nitrogen-mediated stimulation of copiotrophic taxa [42]. Bacteroidetes, being nutrient-demanding microorganisms, serve vital functions in promoting soil carbon circulation [43]. MND1, a Betaproteobacteria member of Nitrosomonadaceae, encodes an amoA gene (ammonia monooxygenase) essential for ammonia-oxidizing functional guilds critical to nitrogen cycling [44]. Genomic evidence confirms Nitrosomonadaceae’s capacity for comammox (complete ammonia oxidation) and thiosulfate oxidation, mediating nitrogen–sulfur co-metabolism [45]. The booting split N topdressing elevated the MND1 populations through AMO enzyme induction, accelerating the ammonium-to-nitrate conversion and rhizosphere soil nitrogen bioavailability.
Previous studies conducted during the same period as that of our trial demonstrated that the booting stage treatment resulted in lower nitrate-N and ammonium-N contents in the surface soil at harvest compared to other split N topdressing treatments [46], while significantly improving the wheat nitrogen use efficiency under this treatment [29]. However, those rhizobacteria communities reshaped by the additional and split N application in this short-term trial failed to translate into winter wheat yield improvements. Chen et al. [47] also found that N application recruits the plant growth-promoting rhizobacteria to change the physiological status of wheat; however, we do not know the extent to which the N treatments were effective at supporting wheat production. So, a long-term experiment would enhance the reliability of the results and demonstrate the prolonged effects of split nitrogen topdressing. The growth status of the plants and N fertilizer drive the changes in the rhizobacteria community structure of wheat [47]. In addition, the rhizobacterial community structure is strongly affected by the plant growth stage [48], and the dynamics of the plant–soil microbiome interaction impact the nutrient cycle and plant growth and productivity [49]. Future studies should adopt dynamic sampling strategies to systematically analyze the succession patterns of rhizobacterial communities and the interaction mechanisms with wheat growth.

4.3. Rhizobacteria Responses to Rhizosphere Soil Environment

Soil environmental parameters (pH, moisture content, nutrients) act as ecological filters, selecting for taxa with specific survival–reproduction–metabolism adaptations, thereby inducing structural and functional restructuring [50,51,52]. Complete ammonia oxidizers (comammox) demonstrated full nitrification through enzymatic cascades (AMO, HAO, NXR) [53,54], with the AOB and NOB populations exhibiting complementary functional partitioning in nitrogen transformation networks [55]. These enzymatic systems constitute the biochemical foundation of nitrogen cycle regulation. Interannual and intra-annual climatic variations during the study period drove periodic soil wet–dry fluctuations, ultimately leading to significant alterations in the soil water availability. This study confirms that the soil moisture content is the most important factor affecting wheat rhizosphere bacterial communities. Hydration gradients were shown to mediate diversity shifts in high-gluten wheat rhizospheres through osmoregulatory stress responses [39]. This phenomenon may even surpass the impact of nitrogen management-induced changes in the rhizosphere STN and SOM on bacterial communities, as the nitrogen application and split fertilization in our study did not alter the core phyla. Notably, nitrogen management still alters the rhizosphere soil environment (including the organic matter, total nitrogen, pH, AMO, and NXR) in each year, consequently reshaping the rhizosphere bacterial community. Chen et al. identified organic carbon as the primary environmental filter shaping bacterial assemblages under wheat–nitrogen co-regulation [47]. The enrichment of organic matter in rhizospheric soil facilitates greater microbial diversity associated with carbon and nitrogen cycling, which benefits both plant development and yield enhancement [56]. Under climatic variability scenarios, slow-release N fertilization induced STN-mediated bacterial succession [57]. The interactive effects of the pH gradients and C/N ratio explained the taxonomic turnover in wheat fields receiving controlled-release nitrogen with straw incorporation [58]. Fertilization significantly restructured the AOB and NOB communities [59,60], with AMO and NXR enzymatic activities potentially mediating cross-kingdom interactions. Environment change and soil enzymes profoundly affect the diversity and assembly of soil microbiomes [61,62]. The intertwinement between plants and soil microbiomes promotes their adaptability to changing conditions [63], influencing the soil nutrient cycle and the nutrition absorption and growth of plants.

5. Conclusions

The temporal partitioning of split N topdressing altered the rhizosphere bacterial diversity and richness profiles in winter wheat, though these modifications were temporally constrained, manifesting exclusively during the second growing season. During the 2021–2022 growing season, split N topdressing at the jointing and booting stages significantly elevated the α-diversity metrics (Shannon, Simpson, Chao1, and Observed_species) compared to the split N topdressing at the 10th day post-anthesis. Although it preserved the overall diversity patterns, it induced phylogenetically structured community reorganization, characterized by enriched Bacteroidetes abundance coupled with depleted Planctomycetes representation within the rhizosphere assemblages. Over the biennial study, split N topdressing at the booting stage significantly increased the MND1 (Nitrosomonadaceae family) genus abundance while reducing the Microtrichales populations compared to single-timing N topdressing with an equivalent dosage. Split N topdressing at the booting stage enhanced the Alphaproteobacteria (class) abundance compared to the 10th-day post-anthesis application, with interannually sustained legacy effects on Fibrobacteres colonization emerging specifically in the second growing season. The rhizobacterial community reconfiguration exhibited significant correlation with the soil pH and moisture, elucidating a mechanistic linkage between split N topdressing and edaphic parameters; yet, this regulatory axis did not translate to yield enhancement in the winter wheat. These findings provide critical insights into rhizobacterial assemblages as bioindicators of the split N topdressing efficacy in intensive wheat systems.

Author Contributions

Conceptualization, H.X. (Huasen Xu), C.X. and Y.W.; investigation, Y.A., Y.W., S.L., W.W. (Wei Wu), W.W. (Weiming Wang), M.L., J.D., H.X. (Hui Xiao) and H.R.; software, Y.A., W.W. (Weiming Wang), Y.W. and S.L.; methodology, Y.A., Y.W., S.L. and M.L.; formal analysis, Y.A., Y.W., S.L. and J.D.; visualization, Y.A., S.L., H.X. (Hui Xiao) and H.R.; writing—original draft preparation, Y.A. and Y.W.; writing—review and editing, Y.A., Y.W. and H.X. (Huasen Xu); English polishing, C.X. and Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2021YFD1901004), the Transformation Fund Program for Agricultural Science and Technology Achievements of Hebei Province (2025JNZ-F02), the Science and Technology Project of Hebei Education Department (QN2023065), and the Research Development and Application Program of Multi-Microbial Fulvic Acid Bio-Organic Fertilizer Produced via Bio-Nano Rapid Composting Technology.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
NNitrogen
T1No nitrogen application
T2Single topdressing applications of 120 kg ha−1 at the jointing stage
T3Single topdressing applications of 80 kg ha−1 at the jointing stage
T4Split topdressing applications combining 80 kg ha−1 at jointing with 40 kg ha−1 at the booting stage
T5Split topdressing applications combining 80 kg ha−1 at jointing with 40 kg ha−1 at the flowering stage
T6Split topdressing applications combining 80 kg ha−1 at jointing with 40 kg ha−1 at 10th day post-anthesis
SMCSoil moisture content
STNSoil total nitrogen
SOMSoil organic matter
AMOAmmonia monooxygenase
HAOHydroxylamine oxidoreductase
NXRNitrite oxidoreductase

References

  1. Lv, T.; Peng, S.; Liu, B.; Liu, Y.; Ding, Y. Planting suitability of China’s main grain crops under future climate change. Field Crops Res. 2023, 302, 109112. [Google Scholar] [CrossRef]
  2. Zörb, C.; Ludewig, U.; Hawkesford, M.J. Perspective on wheat yield and quality with reduced nitrogen supply. Trends Plant Sci. 2018, 23, 1029–1037. [Google Scholar] [CrossRef] [PubMed]
  3. Lv, M.; Chen, S.; Xin, S.; Tong, B.; Wang, S.; Ma, W.; Wei, J. Effects of nitrogen application rate on yield, quality and soil nitrogen balance of winter wheat. J. Hebei Agric. Univ. 2019, 42, 9–15. [Google Scholar] [CrossRef]
  4. Wang, L.; Wu, W.; Li, R.; Hu, J.; Yan, S.; Shao, Q.; Xu, F.; Zhang, C.; Zhou, Y.; Li, W. Effects of nitrogen rate on grain quality and nitrogen utilization of weak gluten wheat. Acta Agric. Zhejiang 2021, 33, 777–784. Available online: http://www.zjnyxb.cn/CN/Y2021/V33/I5/777 (accessed on 20 December 2024).
  5. Jia, K.; Liu, J.L.; Shen, B. Yield effect change of fertilizers in the past 14 years and optimized fertilization of winter wheat in north of China. Plant Nutr. Fertil. Sci. 2020, 26, 2032–2042. Available online: https://www.plantnutrifert.org/article/doi/10.11674/zwyf.20381 (accessed on 20 December 2024).
  6. Wang, T.; Cheng, H. Research progress of nitrogen fertilizer application and its environmental effects in agricultural production. J. Heilongjiang Environ. 2020, 33, 58–59. [Google Scholar]
  7. Yang, M.; Wu, W.; Zhang, J.; Fu, B.; Peng, Z.; Xue, C. Effects of nitrogen topdressing timing on grain yield, quality and nitrogen utilization of strong gluten wheat. J. Hebei Agric. Univ. 2020, 43, 11–18. [Google Scholar] [CrossRef]
  8. Yang, L.; Jiang, Z.; Sheng, K.; Li, X.; Zhao, Z. Effect of nitrogen dressing application in different period on the yield and grain quality of strong gluten wheat. Chin. Agric. Sci. Bull. 2015, 31, 26–30. [Google Scholar] [CrossRef]
  9. Liu, Z.; Guo, D.; Chang, H.; Wang, D.; Wang, Y.; Yang, Y.; Liu, X.; Wang, Y.; Shi, S.; Zhao, G. Response of nitrogen accumulation and translocation after anthesis in strong gluten wheat to nitrogen topdressing period and proportion. Crops 2023, 6, 114–120. [Google Scholar] [CrossRef]
  10. Pan, Y. Effects of Elevated Atmospheric CO2 Concentration and Split N application on Yield and Quality of Wheat. Master’s Thesis, Hebei Agricultural University, Baoding, China, 2023. [Google Scholar]
  11. Sahu, P.K.; Singh, D.P.; Prabha, R.; Meena, K.K.; Abhilash, P. Connecting microbial capabilities with the soil and plant health: Options for agricultural sustainability. Ecoll Indic. 2019, 105, 601–612. [Google Scholar] [CrossRef]
  12. Chen, J.; Sharifi, R.; Khan, M.S.S.; Islam, F.; Bhat, J.A.; Kui, L.; Majeed, A. Wheat microbiome: Structure, dynamics, and role in improvingperformance under stress environments. Front. Microbiol. 2022, 12, 821546. [Google Scholar]
  13. Samain, E.; Van Tuinen, D.; Jeandet, P.; Aussenac, T.; Selim, S. Biological control of septoria leaf blotch and growth promotion in wheat by Paenibacillus sp. strain B2 and Curtobacterium plantarum strain EDS. Biol. Control 2017, 114, 87–96. [Google Scholar] [CrossRef]
  14. Lu, T.; Ke, M.; Lavoie, M.; Jin, Y.; Fan, X.; Zhang, Z.; Fu, Z.; Sun, L.; Gillings, M.; Peñuelas, J.; et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 2018, 6, 231. [Google Scholar] [CrossRef]
  15. Wang, X.; Wang, Q.; Jin, L.; Sun, L.; Wang, Q.; Zhang, L.; Chen, Y. Arbuscular mycorrhizal fungi in the rhizosphere soil of poisonous plants depressed the growth of pasture grasses in the Tibetan Plateau Alpine meadow. Ecosyst. Health Sustain. 2019, 5, 226–236. [Google Scholar] [CrossRef]
  16. Zhang, F.; Xu, N.; Zhang, Z.; Zhang, Q.; Yang, Y.; Yu, Z.; Sun, L.; Lu, T.; Qian, H. Shaping effects of rice, wheat, maize, and soybean seedlings on their rhizosphere microbial community. Environ. Sci. Pollut. Res. 2023, 30, 35972–35984. [Google Scholar] [CrossRef]
  17. Bhat, M.A.; Mishra, A.K.; Jan, S.; Bhat, M.A.; Kamal, M.A.; Rahman, S.; Shah, A.A.; Jan, A.T. Plant growth promoting rhizobacteria in plant health: A perspective study of the underground interaction. Plants 2023, 12, 629. [Google Scholar] [CrossRef]
  18. Chen, X.; Wang, P.N.; Liu, B.B.; Dai, W.C.; Cai, H.M.; Zheng, B.Q.; Li, J.C. Effects of late spring coldness during the anther differentiation period on bacterial community structure in wheat rhizosphere. Chin. J. Agrometeorol. 2024, 45, 756. Available online: https://zgnyqx.ieda.org.cn/CN/Y2024/V45/I7/756 (accessed on 22 December 2024).
  19. Li, J.; Huang, S.; Wei, C.; Nong, Y.; Chen, Y.; Liao, C.; Qin, X. Effects of different nitrogen levels on tea rhizosphere microbial community and its role in adjusting soil nutrients. Acta Agric. Boreali-Sin. 2019, 34, 281–288. Available online: http://www.hbnxb.net/CN/Y2019/V34/IS1/281 (accessed on 22 December 2024).
  20. Qu, Q.; Zhang, Z.; Peijnenburg, W.; Liu, W.; Lu, T.; Hu, B.; Chen, J.; Chen, J.; Lin, Z.; Qian, H. Rhizosphere microbiome assembly and its impact on plant growth. J. Agric. Food. Chem. 2020, 68, 5024–5038. Available online: https://pubmed.ncbi.nlm.nih.gov/32255613/ (accessed on 22 December 2024).
  21. Xu, R.Y.; Zuo, M.X.; Yuan, Y.L.; Sun, J.; Gu, W.J.; Lu, Y.S.; Xie, K.Z.; Xu, P.Z. Effects of nitrogen fertilizer dosage optimization on nitrogen uptake content and utilization efficiency and microbial function genes of nitrogen cycle in sweet corn. J. Southern Agric. 2020, 51, 2919–2926. [Google Scholar] [CrossRef]
  22. Cui, P.; Wu, A.; Wang, J.; Dong, E.; Nan, J.; Bai, W.; Jiao, X. Effect of different fertilization treatments on soil microbial function diversity in rhizosphere of sorghum. Acta Agric. Boreali-Sin. 2018, 33, 195–202. Available online: http://www.hbnxb.net/CN/Y2018/V33/I5/195 (accessed on 22 December 2024).
  23. Wang, C.; Liu, D.; Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Boil. Biochem. 2018, 120, 126–133. [Google Scholar] [CrossRef]
  24. Yang, Q. Effects of Nitrogen Fertilizer Application on Root Exudatesand Microbial Diversity of Tomato. Master’s Thesis, Chinese Academy of Agricultural Sciences, Beijing, China, 2022. [Google Scholar]
  25. Li, M.S. Changes of Microbial Population Structure in Corn Rhizosphere Underdifferent Nitrogen Application Levels. Master’s Thesis, Jilin Agricultural University, Changchun, China, 2018. [Google Scholar]
  26. Wang, Q.; Jiang, X.; Guan, D.; Wei, D.; Zhao, B.; Ma, M.; Chen, S.; Li, L.; Cao, F.; Li, J. Long-term fertilization changes bacterial diversity and bacterial communities in the maize rhizosphere of Chinese Mollisols. Appl. Soil Ecol. 2018, 125, 88–96. [Google Scholar] [CrossRef]
  27. Nwachukwu, B.C.; Babalola, O.O. Perspectives for sustainable agriculture from the microbiome in plant rhizosphere. Plant Biotechnol. Rep. 2021, 15, 259–278. [Google Scholar] [CrossRef]
  28. Bao, S.D. Agrochemical Analysis of Soils, 3rd ed.; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
  29. Liu, S.S. The Plastic Responses of Winter Wheat Rhizosphere Microbial Community to Nitrogen Fertilizer Management and Their Subsequent Effects on Yield and Quality. Master’s Thesis, Hebei Agricultural University, Baoding, China, 2023. [Google Scholar]
  30. Wang, D.; Ren, H. Microbial community in buckwheat rhizosphere with different nitrogen application rates. PeerJ 2023, 11, e15514. [Google Scholar] [CrossRef]
  31. Xing, M.; Zhang, Y.; Guan, C.; Guan, M. Effects of nitrogen application rate on rhizosphere microbial diversity in oilseed Rape (Brassica napus L.). Agronomy 2021, 11, 1539. [Google Scholar] [CrossRef]
  32. Liang, M.; Meng, W.; Chen, Z.; Shen, Y.; Liu, Y.; Shen, Y.; Liu, Z.; Nan, Z.; Xu, J.; Zhang, Z. Effects of nitrogen application levels on microbial community structure and diversity in peanut rhizosphere soil. Shandong Agric. Sci. 2023, 55, 78–83. [Google Scholar] [CrossRef]
  33. Wang, S.Z. The Research on the Effects of Different Nitrogen Fertilizer Amount and Model on Soil Microorganism in Winter Wheat Season. Master’s Thesis, Henan University, Zhengzhou, China, 2019. [Google Scholar]
  34. Constancias, F.; Prévost-Bouré, N.C.; Terrat, S.; Aussems, S.; Nowak, V.; Guillemin, J.P.; Bonnotte, A.; Biju-Duval, L.; Navel, A.; Martins, J.M.; et al. Microscale evidence for a high decrease of soil bacterial density and diversity by cropping. Agron. Sustain. Dev. 2014, 34, 831–840. [Google Scholar] [CrossRef]
  35. Li, X.; Jiao, Y.; Dai, G.; Yang, M.; Wen, H. Soil bacterial community diversity under different degrees of saline-alkaline in the Hetao Area of Inner Mongolia. China Environ. Sci. 2016, 36, 249–260. [Google Scholar] [CrossRef]
  36. Liu, W.; Ling, N.; Guo, J.; Ruan, Y.; Zhu, C.; Shen, Q.; Guo, S. Legacy effects of 8-year nitrogen inputs on bacterial assemblage in wheat rhizosphere. Biol. Fert. Soil. 2020, 56, 583–596. [Google Scholar] [CrossRef]
  37. Bapiri, A.; Bååth, E.; Rousk, J. Drying–rewetting cycles affect fungal and bacterial growth differently in an arable soil. Microb. Ecol 2010, 60, 419–428. [Google Scholar] [CrossRef] [PubMed]
  38. Ma, G. Effects of irrigation and nitrogen treatments on nitrogen utilization and soil microbial community in winter wheat. Ph.D. Thesis, Henan Agricultural University, Zhengzhou, China, 2019. [Google Scholar]
  39. Fu, B.y.; Zhang, J.h.; Yang, M.x.; Xu, H.s.; Peng, Z.p.; Xue, C. Effects of nitrogen topdressing timing on microbial community sreucture in strong gluten wheat rhizosphere soil. J. Hebei Agric. Univ. 2022, 45, 1–8. [Google Scholar] [CrossRef]
  40. Li, X.M.; Long, J.J.; Zhou, Y.; Wang, Y.; Wang, H.Q. Application and research progress offoliarfertilizer. J. Anhui Agric. Sci. 2017, 45, 127–130. [Google Scholar] [CrossRef]
  41. Xian, W.; Zhang, X.; Li, W. Research status and prospect on bacterial phylum Chloroflexi. Acta Microbiol. Sin. 2020, 60, 1801–1820. [Google Scholar] [CrossRef]
  42. Gu, Y.; Wang, J.; Cai, W.; Li, G.; Mei, Y.; Yang, S. Different amounts of nitrogen fertilizer applications alter the bacterial diversity and community structure in the rhizosphere soil of sugarcane. Front. Microbiol. 2021, 12, 721441. [Google Scholar] [CrossRef]
  43. Jiang, W.; Xue, G.; Bai, H.; Du, J.; Zhu, C.; Li, J.; Song, Q.; Ji, S.; Wang, L. Effects of different treatments on the bacterial community composition and diversity in cucumber continuous cropping soil. J. North. Agric. 2022, 50, 28–37. [Google Scholar]
  44. Yu, M.; Su, W.Q.; Huang, L.; Parikh, S.J.; Tang, C.; Dahlgren, R.A.; Xu, J. Bacterial community structure and putative nitrogen-cycling functional traits along a charosphere gradient under waterlogged conditions. Soil Boil. Biochem. 2021, 162, 108420. [Google Scholar] [CrossRef]
  45. Liu, H.; Huang, X.; Tan, W.; Di, H.; Xu, J.; Li, Y. High manure load reduces bacterial diversity and network complexity in a paddy soil under crop rotations. Soil Ecol. Lett. 2020, 2, 104–119. [Google Scholar] [CrossRef]
  46. Zhang, J.H. Effects of Late Foliar N Application on Yield, Quality and N Uptake and Utilization of Wheat. Master’s Thesis, Hebei Agricultural University, Baoding, China, 2022. [Google Scholar]
  47. Chen, S.; Waghmode, T.R.; Sun, R.; Kuramae, E.E.; Hu, C.; Liu, B. Root-associated microbiomes of wheat under the combined effect of plant development and nitrogen fertilization. Microbiome. 2019, 7, 136. [Google Scholar] [CrossRef]
  48. Schlemper, T.R.; Leite, M.F.A.; Lucheta, A.R.; Shimels, M.; Bouwmeester, H.J.; van Veen, J.A.; Kuramae, E.E. Rhizobacterial community structure differences among sorghum cultivars in different growth stages and soils. FEMS Microbiol. Ecol. 2017, 93, 1–11. [Google Scholar] [CrossRef]
  49. Muhammad, M.; Wahab, A.; Waheed, A.; Hakeem, K.R.; Mohamed, H.I.; Basit, A.; Toor, M.D.; Liu, Y.H.; Li, L.; Li, W.J. Navigating climate change: Exploring the dynamics between plant–soil microbiomes and their impact on plant growth and productivity. Glob. Change Biol. 2025, 31, e70057. [Google Scholar] [CrossRef] [PubMed]
  50. Zu, M.; Yuan, Y.; Zuo, J.; Sun, L.; Tao, J. Microbiota associated with the rhizosphere of Paeonia lactiflora Pall.(ornamental cultivar). Appl. Soil Ecol. 2022, 169, 104214. [Google Scholar] [CrossRef]
  51. Bogino, P.; Abod, A.; Nievas, F.; Giordano, W. Water-limiting conditions alter the structure and biofilm-forming ability of bacterial multispecies communities in the alfalfa rhizosphere. PLoS ONE 2013, 8, e79614. [Google Scholar] [CrossRef] [PubMed]
  52. Feng, H.L.; Xu, C.S.; He, H.H.; Zeng, Q.; Chen, N.; Li, X.L.; Ren, T.-B.; Ji, X.M.; Liu, G.S. Effect of biochar on soil enzyme activity & the bacterial community and its mechanism. Environ. Sci. 2021, 42, 422–432. [Google Scholar] [CrossRef]
  53. Zhu, G.; Wang, X.; Wang, S.; Yu, L.; Armanbek, G.; Yu, J.; Jiang, L.; Yuan, D.; Guo, Z.; Zhang, H.; et al. Towards a more labor-saving way in microbial ammonium oxidation: A review on complete ammonia oxidization (comammox). Sci. Total Environ. 2022, 829, 154590. [Google Scholar] [CrossRef] [PubMed]
  54. Koch, H.; van Kessel, M.A.; Lücker, S. Complete nitrification: Insights into the ecophysiology of comammox Nitrospira. Appl. Microbiol. Biotechnol. 2019, 103, 177–189. [Google Scholar] [CrossRef]
  55. Xu, J.Y.; Mao, Y.P. From canonical nitrite oxidizing bacteria to complete ammonia oxidizer: Discovery and advances. Microbiol. China 2019, 46, 879–890. [Google Scholar] [CrossRef]
  56. Guo, N.; Li, R.; Bai, W.; Ma, J.; Li, A.; Qiao, H.; Liu, D.; Guo, Y.; Li, F. Responses of flax growth and rhizosphere bacterial communities to different dosage of organic fertilizer. Acta Agric. Boreali-Sin. 2025, 40, 146–156. [Google Scholar] [CrossRef]
  57. Ren, H.F.; Wang, L.; Hao, X.Y.; Zhang, D.S.; Zong, Z.Y.; Li, P. Response of soil bacteria and fungi communities in wheat fields to climate change under slow release fertilizer treatment. Soils Fertil. Sci. China 2022, 10, 50–63. [Google Scholar] [CrossRef]
  58. Li, R.; Gao, Y.; Chen, Q.; Li, Z.; Gao, F.; Meng, Q.; Li, T.; Liu, A.; Wang, Q.; Wu, L.; et al. Blended controlled-release nitrogen fertilizer with straw returning improved soil nitrogen availability, soil microbial community, and root morphology of wheat. Soil Till. Res. 2021, 212, 105045. [Google Scholar] [CrossRef]
  59. He, J.X. Community Structure of Ammonia-Oxidizing Bacteria During Long-Term Fertilizer Application in the Loess Plateau. Master’s Thesis, Lanzhou University, Lanzhou, China, 2011. [Google Scholar]
  60. Han, S.; Zeng, L.; Luo, X.; Xiong, X.; Wen, S.; Wang, B.; Chen, W.; Huang, Q. Shifts in Nitrobacter-and Nitrospira-like nitrite-oxidizing bacterial communities under long-term fertilization practices. Soil Boil. Biochem. 2018, 124, 118–125. [Google Scholar] [CrossRef]
  61. Bardgett, R.D.; Caruso, T. Soil microbial community responses to climate extremes: Resistance, resilience and transitions to alternative states. Philos. Trans. R. Soc. B 2020, 375, 20190112. [Google Scholar] [CrossRef]
  62. Ku, Y.S.; Liao, Y.J.; Chiou, S.P.; Lam, H.M.; Chan, C. From trade-off to synergy: Microbial insights into enhancing plant growth and immunity. Plant Biotechnol. J. 2024, 22, 2461–2471. [Google Scholar] [CrossRef] [PubMed]
  63. Martiny, J.B.; Martiny, A.C.; Brodie, E.; Chase, A.B.; Rodríguez-Verdugo, A.; Treseder, K.K.; Allison, S.D. Investigating the eco-evolutionary response of microbiomes to environmental change. Ecol. Lett. 2023, 26, S81–S90. [Google Scholar] [CrossRef]
Agriculture 15 00794 g001
Figure 1. Monthly precipitation and average temperature during the wheat-growing seasons (2020–2022).
Figure 1. Monthly precipitation and average temperature during the wheat-growing seasons (2020–2022).
Agriculture 15 00794 g001
Agriculture 15 00794 g002
Figure 2. Alpha diversity indices of rhizosphere soil bacterial community in winter wheat from 2020 to 2021. (Note: The boxplot components are defined as follows: upper/lower bounds: 25th (Q1) and 75th (Q3) percentiles; box length: interquartile range (IQR); central line: median; whiskers: 1.5 × IQR data range. Distinct lowercase superscripts denote statistically significant differences between N application treatments (p < 0.05).).
Figure 2. Alpha diversity indices of rhizosphere soil bacterial community in winter wheat from 2020 to 2021. (Note: The boxplot components are defined as follows: upper/lower bounds: 25th (Q1) and 75th (Q3) percentiles; box length: interquartile range (IQR); central line: median; whiskers: 1.5 × IQR data range. Distinct lowercase superscripts denote statistically significant differences between N application treatments (p < 0.05).).
Agriculture 15 00794 g002
Agriculture 15 00794 g003
Figure 3. Alpha diversity indices of rhizosphere soil bacterial community in winter wheat from 2021 to 2022. (Note: The boxplot components are defined as follows: upper/lower bounds: 25th (Q1) and 75th (Q3) percentiles; box length: interquartile range (IQR); central line: median; whiskers: 1.5 × IQR data range. Distinct lowercase superscripts denote statistically significant differences between N application treatments (p < 0.05).).
Figure 3. Alpha diversity indices of rhizosphere soil bacterial community in winter wheat from 2021 to 2022. (Note: The boxplot components are defined as follows: upper/lower bounds: 25th (Q1) and 75th (Q3) percentiles; box length: interquartile range (IQR); central line: median; whiskers: 1.5 × IQR data range. Distinct lowercase superscripts denote statistically significant differences between N application treatments (p < 0.05).).
Agriculture 15 00794 g003
Agriculture 15 00794 g004
Figure 4. Rhizosphere soil bacterial composition of each classification level in winter wheat during the 2020–2021 growing season. (Note: The bacterial taxonomic units were systematically arranged from the phylum to species level (phylum > class > order > family > genus > species) following internationally recognized microbial classification frameworks. (A) phylum level; (B) class level; (C) order level; (D) family level; (E) genus level; (F) species level).
Figure 4. Rhizosphere soil bacterial composition of each classification level in winter wheat during the 2020–2021 growing season. (Note: The bacterial taxonomic units were systematically arranged from the phylum to species level (phylum > class > order > family > genus > species) following internationally recognized microbial classification frameworks. (A) phylum level; (B) class level; (C) order level; (D) family level; (E) genus level; (F) species level).
Agriculture 15 00794 g004
Agriculture 15 00794 g005
Figure 5. Rhizosphere soil bacterial composition of each classification level in winter wheat during the 2021–2022 growing season. (Note: The bacterial taxonomic units were systematically arranged from the phylum to species level (phylum > class > order > family > genus > species) following internationally recognized microbial classification frameworks. (A) phylum level; (B) class level; (C) order level; (D) family level; (E) genus level; (F) species level).
Figure 5. Rhizosphere soil bacterial composition of each classification level in winter wheat during the 2021–2022 growing season. (Note: The bacterial taxonomic units were systematically arranged from the phylum to species level (phylum > class > order > family > genus > species) following internationally recognized microbial classification frameworks. (A) phylum level; (B) class level; (C) order level; (D) family level; (E) genus level; (F) species level).
Agriculture 15 00794 g005
Agriculture 15 00794 g006
Figure 6. Relative abundances of bacterial species compositions at different classification levels of rhizosphere soil bacteria during the 2020–2021 growing season. (Note: Black font denotes bacterial taxa showing no significant variations in relative abundances, while red font identifies taxa with significant differences validated by statistical testing. Annotations below bacterial names reflect significance testing results, where unique lowercase letters signify significant divergence across nitrogen treatments (p < 0.05).).
Figure 6. Relative abundances of bacterial species compositions at different classification levels of rhizosphere soil bacteria during the 2020–2021 growing season. (Note: Black font denotes bacterial taxa showing no significant variations in relative abundances, while red font identifies taxa with significant differences validated by statistical testing. Annotations below bacterial names reflect significance testing results, where unique lowercase letters signify significant divergence across nitrogen treatments (p < 0.05).).
Agriculture 15 00794 g006
Agriculture 15 00794 g007
Figure 7. Relative abundances of bacterial species compositions at different classification levels of rhizosphere soil bacteria during the 2021–2022 growing season. (Note: Black font denotes bacterial taxa showing no significant variations in relative abundances, while red font identifies taxa with significant differences validated by statistical testing. Annotations below bacterial names reflect significance testing results, where unique lowercase letters signify significant divergence across nitrogen treatments (p < 0.05)).
Figure 7. Relative abundances of bacterial species compositions at different classification levels of rhizosphere soil bacteria during the 2021–2022 growing season. (Note: Black font denotes bacterial taxa showing no significant variations in relative abundances, while red font identifies taxa with significant differences validated by statistical testing. Annotations below bacterial names reflect significance testing results, where unique lowercase letters signify significant divergence across nitrogen treatments (p < 0.05)).
Agriculture 15 00794 g007
Agriculture 15 00794 g008
Figure 8. Redundancy analysis of bacteria and environmental factors in the winter wheat rhizosphere. ((a) 2020–2021 growing season of winter wheat, (b) 2021–2022 growing season of winter wheat, and (c) 2020–2022 growing season of winter wheat. Red arrows represent environmental vectors: SMC (soil moisture content); SOM (soil organic matter); STN (soil total nitrogen); pH; AMO (ammonia monooxygenase); HAO (hydroxylamine oxidoreductase); NXR (nitrite oxidoreductase). Colored points indicate treatment-specific microbiome configurations.).
Figure 8. Redundancy analysis of bacteria and environmental factors in the winter wheat rhizosphere. ((a) 2020–2021 growing season of winter wheat, (b) 2021–2022 growing season of winter wheat, and (c) 2020–2022 growing season of winter wheat. Red arrows represent environmental vectors: SMC (soil moisture content); SOM (soil organic matter); STN (soil total nitrogen); pH; AMO (ammonia monooxygenase); HAO (hydroxylamine oxidoreductase); NXR (nitrite oxidoreductase). Colored points indicate treatment-specific microbiome configurations.).
Agriculture 15 00794 g008
Agriculture 15 00794 g009
Figure 9. Effect of nitrogen application regime on winter wheat yield. (Error bar represents SE. Y represents year, T represents treatment, and Y × T denotes the interaction between the year and treatment in each graph, and significant treatment effects are indicated in each graph (ns p > 0.05, * p < 0.05, ** p < 0.01). Different lowercase letters indicate significant differences (p < 0.05) among different nitrogen treatments within the same year.).
Figure 9. Effect of nitrogen application regime on winter wheat yield. (Error bar represents SE. Y represents year, T represents treatment, and Y × T denotes the interaction between the year and treatment in each graph, and significant treatment effects are indicated in each graph (ns p > 0.05, * p < 0.05, ** p < 0.01). Different lowercase letters indicate significant differences (p < 0.05) among different nitrogen treatments within the same year.).
Agriculture 15 00794 g009
Table 1. Experimental treatments of N application regimes (kg N ha−1).
Table 1. Experimental treatments of N application regimes (kg N ha−1).
TreatmentBasalJointing StageBooting StageFlowering Stage10th Day Post-Anthesis
T100
T290120
T39080
T4908040
T59080 40
T69080 40
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

An, Y.; Wang, Y.; Liu, S.; Wu, W.; Wang, W.; Liu, M.; Xiao, H.; Dong, J.; Ren, H.; Xu, H.; et al. Impact of Split Nitrogen Topdressing on Rhizobacteria Community of Winter Wheat. Agriculture 2025, 15, 794. https://doi.org/10.3390/agriculture15070794

AMA Style

An Y, Wang Y, Liu S, Wu W, Wang W, Liu M, Xiao H, Dong J, Ren H, Xu H, et al. Impact of Split Nitrogen Topdressing on Rhizobacteria Community of Winter Wheat. Agriculture. 2025; 15(7):794. https://doi.org/10.3390/agriculture15070794

Chicago/Turabian Style

An, Yu, Yang Wang, Shuangshuang Liu, Wei Wu, Weiming Wang, Mengmeng Liu, Hui Xiao, Jing Dong, Hongjie Ren, Huasen Xu, and et al. 2025. "Impact of Split Nitrogen Topdressing on Rhizobacteria Community of Winter Wheat" Agriculture 15, no. 7: 794. https://doi.org/10.3390/agriculture15070794

APA Style

An, Y., Wang, Y., Liu, S., Wu, W., Wang, W., Liu, M., Xiao, H., Dong, J., Ren, H., Xu, H., & Xue, C. (2025). Impact of Split Nitrogen Topdressing on Rhizobacteria Community of Winter Wheat. Agriculture, 15(7), 794. https://doi.org/10.3390/agriculture15070794

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