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Article

Legacy Effects of Long-Term Brackish Groundwater Irrigation on Bacterial Communities in Wheat Rhizosphere and Yield Performance

by
Husen Qiu
1,*,
Guangli Tian
2,
Jieyun Liu
1,
Shuai He
3 and
Dongwei Li
4
1
School of Environment and Surveying Engineering, Suzhou University, Suzhou 234000, China
2
Department of Agronomy and Horticulture, Jiangsu Vocational College of Agriculture and Forestry, Zhenjiang 212400, China
3
Research Institute of Farmland Water Conservancy and Soil-Fertilizer, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
4
Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453003, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(12), 2732; https://doi.org/10.3390/agronomy15122732
Submission received: 20 October 2025 / Revised: 18 November 2025 / Accepted: 26 November 2025 / Published: 27 November 2025
(This article belongs to the Section Water Use and Irrigation)
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Versions Notes

Abstract

This study aimed to investigate the legacy effects of prolonged brackish irrigation on rhizobacterial communities and agricultural productivity in wheat. Here, we conducted pot experiments to investigate the mechanisms through which different irrigation regimes (irrigation using brackish groundwater and normal water) regulate wheat production. We applied four irrigation treatments across different stages of wheat growth (early stages, seedling-to-jointing, and late stages, jointing-to-maturity). This included irrigation exclusively using normal water during both stages (RR), using normal water followed by brackish groundwater (RW), exclusively using brackish groundwater (WW), and using brackish groundwater followed by normal water (WR). Under the premise of retaining 10 seedlings per pot, the average number of effective spikes per 10 plants in the RR, RW, and WR treatments was approximately 1.3, 1.1, and 1.1 times that of WW (19 ± 1), respectively. The spike weight per 10 plants in the RR, RW, and WR treatments was approximately 1.8, 1.5, and 1.3 times that of WW (12.75 ± 1.74 g), respectively. Compared with brackish groundwater irrigation, the use of normal water during the early stages significantly reduced the relative abundance of Pseudomonadota and increased that of Chloroflexota (p < 0.05). The number of effective spikes was positively correlated with the relative abundances of Actinobacteriota, Acidobacteriota, Chloroflexota, and Bacteroidota, but negatively correlated with the abundance of Pseudomonadota (p < 0.05). Irrigation regimes altered the rhizobacterial community structure. However, the legacy effect of long-term irrigation using brackish groundwater resulted in the dominance of stochastic processes in bacterial community assembly and stability of the Shannon diversity across all treatments. The complexity of the rhizobacterial co-occurrence network was lower in the RW treatments than that in the WW treatments (p < 0.05). Structural equation modeling revealed that irrigation using normal water during early stages boosted the number of effective spikes in wheat. This enhancement was achieved by increasing rhizobacterial diversity, reducing rhizosphere sodium, and simplifying the microbial network. This study challenges the “legacy effect” of brackish water irrigation by demonstrating that optimal irrigation timing is key to enhancing crop yield.

1. Introduction

The yield stability of wheat, one of the most important global food crops, is heavily dependent on water management, besides fertilization [1,2]. The scarcity of normal water resources poses a significant threat to wheat growth and yield [3]. The use of brackish groundwater for irrigation is a critical strategy to mitigate water scarcity. However, its long-term irrational use can accelerate soil degradation and threaten crop productivity. Therefore, exploring how to mitigate its legacy effects—such as soil salinization and disrupted microbial communities—is vital. This involves combining brackish water with normal water based on crop growth stages. Such an approach is highly valuable for achieving sustainable agricultural production [4].
Brackish groundwater is an unconventional mineralized water source containing dissolved salts. It can serve as an alternative source for agricultural water supply in regions facing freshwater scarcity [5,6]. However, the high salinity of brackish groundwater may adversely affect crop growth and physiological metabolism, ultimately reducing yield [3]. Studies have indicated that irrigation using brackish water elevates soil salinity, triggering osmotic stress and ion toxicity in plant roots. These adverse conditions reduce root hair density and length, ultimately impairing the efficiency of water and nutrient uptake [7,8]. Of course, different crop growth stages exhibit varying sensitivities to soil salinity [9,10,11]. For example, the seedling stage demonstrates greater sensitivity to salinity than other growth stages. Additionally, the performance of the seedling stage is a key determinant of the ultimate crop yield. Irrigation with brackish water, which introduces elevated salt concentrations, can inhibit seedling growth in wheat, resulting in reduced plant height, smaller leaf dimensions (length and width), a reduced photosynthesis process, suppressed root elongation, and a lower root-to-shoot ratio [12]. Additionally, it impairs root development and biomass accumulation during both the grain-filling and maturity stages, thereby compromising the yield [13]. In regions where brackish water irrigation is unavoidable, application during later crop growth stages is preferable because of the higher sensitivity of young plants to salinity than the plants in later growth stages [14,15]. Desalination of irrigation water is cost-prohibitive and critical knowledge gaps hinder the development of dynamic salinity management systems. These gaps include insufficient understanding of how to (1) precisely align management practices with crop salt-tolerant phenophases, and (2) optimize irrigation regimes to maintain sustainable wheat yields.
Evidence indicates that brackish water irrigation significantly alters soil microbial properties compared with normal water irrigation [6,16]. Elevated soil salinity reduces the microbial biomass because of osmotic stress-induced cellular dehydration and lysis. Notably, the rhizosphere, a critical hotspot for plant–soil–microbe interactions, exhibits microbial communities that are highly sensitive to salt stress [17]. Several microbial taxa such as Actinobacteria, Bacteroidota, and Pseudomonas species have developed specialized adaptations to survive in high-salinity environments [18,19]. These microbial shifts subsequently enhance crop stress resilience through osmotic regulation, hormone signaling such as indole-3-acetic acid production, and improved nutrient acquisition [20,21,22]. Moreover, cooperative interactions among root-associated bacteria (e.g., Proteobacteria, Actinobacteria, and Bacteroidetes) further promote plant growth via metabolic cross-feeding and niche partitioning [23,24]. Long-term brackish irrigation can lead to reduced beta diversity and fewer clustered co-occurrence networks and microbial interactions than in freshwater-irrigated soils [25]. However, it remains unclear whether altering irrigation regimes (such as the use of normal water vs. brackish groundwater) across different growth stages in wheat modifies the legacy effects of long-term brackish groundwater irrigation on rhizosphere bacterial community structure diversity and assembly processes. Moreover, the relationship between the bacterial community characteristics and wheat yield components (spike number and grain weight) under irrigation regimes require further investigation.
An optimized irrigation regime integrating both water sources may not only mitigate soil salt accumulation but also reduce the negative impacts of brackish water on wheat growth, thereby preventing yield reduction [26]. In the present study, we aimed to test the hypothesis that blending normal water with brackish groundwater in a growth-stage-specific irrigation regime would accomplish the following: (1) modify rhizobacterial assembly processes, (2) attenuate salinity legacy effects on soil microbiomes, and (3) improve yield components through enhanced soil–microbe–plant feedback mechanisms. Our findings can provide novel insights into microbiome-based strategies for overcoming productivity limitations in wheat grown in brackish water irrigation systems.

2. Materials and Methods

2.1. Experiment Design and Soil Sampling

A pot experiment was performed at the Key Irrigation Experiment Station of Northern Henan (35°12′47″ N, 113°50′46″ E). The research station is located in Xinxiang City, Henan Province, China. The mean temperature of the study area is 14 °C and the mean annual precipitation is approximately 573.4 mm.
Prior to winter wheat seeding, fluvio-aquic soil subjected to long-term irrigation with brackish groundwater was used in a pot experiment. The soil pH, organic carbon, and nitrogen content were 8.50, 12.3 g kg−1, and 0.89 g kg−1, respectively. Soil from a cultivated horizon (0–20 cm) was collected, air-dried, sieved (≤10 mm), and mixed. Each pot (30 cm in height, 35.0 cm in diameter at the top, and 25 cm in diameter at the base) was filled with 20 kg of air-dried soil mixed with base fertilizer. An equal number of wheat seeds were sown in each pot, and soil moisture was maintained using normal water from the Yellow River. After the emergence of seedlings, ten vigorous seedlings were retained per pot. All pots were arranged randomly under field conditions. Additionally, each treatment was set up with 5 pots. The pot experiment was conducted from October 2019 to June 2020. Four distinct irrigation regimes were implemented based on water source switching timing: (i) normal water was exclusively applied during both early (seedling–jointing) and late (jointing–maturity) stages (RR regime); (ii) normal water was applied in the early growth phase, followed by brackish groundwater in the late phase (RW); (iii) brackish ground water was exclusively applied during both growth phases (WW); and (iv) brackish groundwater was applied in the early phase followed by normal water in the late phase. The electrical conductivities (ECs) of the normal water and brackish ground water were about 760 μS cm−1 and 4020 μS cm−1, respectively. Fertilizers were applied at twice the conventional local rate. At the wheat maturity stage, rhizosphere soil samples were collected from each treatment and homogenized by passing through a 2 mm sieve. One portion was air-dried for physicochemical analysis, whereas the other portion was stored at −80 °C for bacterial diversity assessment. Concurrently, all wheat spikes from the corresponding pots were harvested, and the number of spikes and total spike weight were recorded for each replicate.

2.2. Analysis of Soil Physicochemical Properties

The soil organic carbon (SOC) was determined using the Walkley–Black dichromate oxidation method [27]. The total nitrogen (TN) content was measured via the Kjeldahl method and analyzed using an FIA Star 5000 automatic flow injection analyzer (Foss Tecator, Höganäs, Sweden) [28]. Available phosphorus (Olsen-P) was measured using Olsen’s method [29]. The soil-available potassium (AK) was extracted using ammonium acetate coupled with flame photometry [30]. The soil pH and EC were measured at a soil-to-water ratio of 1:2.5 and 1:5 (w/v) using a pH meter and EC Meter, respectively. The soil moisture was determined by oven-drying soil samples in aluminum boxes at 105 °C until a constant weight was attained. The concentrations of Cl−, SO42−, Ca2+, Mg2+, and Na+ were based on [31]. The results of the EC, soluble salt ion content, soil moisture, and pH are shown in Supplementary Table S1.

2.3. Soil DNA Extraction and Sequence Processing

Genomic DNA was extracted from 0.5 g soil samples using the PowerSoil® DNA Isolation Kit (Omega Bio-tek, Norcross, GA, USA). The V3–V4 region of the bacterial 16S rRNA genes was amplified using the primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The total of 20 μL reactions contained 5×FastPfu Buffer (4 μL), 2.5 mM dNTPs (2 μL), 5 μM primers (each for 0.8 μL), FastPfu polymerase (0.4 μL), BSA, and template DNA (10 ng). The PCR was conducted with thermal cycling conditions of 95 °C for 3 min, followed by 27 cycles of denaturation (95 °C, 30 s), annealing (55 °C, 30 s), and extension (72 °C, 45 s), with a final extension at 72 °C for 10 min [32]. The PCR products were verified using 2% agarose gel electrophoresis, purified using an AxyPrep DNA gel extraction kit (Axygen Biosciences, Union City, CA, USA), and quantified using a QuantiFluor-ST Fluorometer (Promega, Madison, WI, USA). Sequencing was performed on the Illumina MiSeq platform, followed by bioinformatics processing using QIIME (v 2.0), which included quality filtering (removing reads with Q < 20 over 50 bp windows), removal of chimeras using UCHIME, and clustering of operational taxonomic units at 97% similarity using UPARSE [33,34]. The number of sequences ranged from 41,621 to 63,656, and the good coverage for the observed OTUs was 96.85% ± 0.13. Taxonomic annotation of the 16S rRNA gene sequences was conducted employing the RDP Classifier algorithm with the Silva (SSU123) 16S rRNA database, applying a confidence threshold of 70% [35]. To normalize for variations in sequencing depth, a process of rarefaction was performed by randomly sub-sampling all samples to a uniform depth of 29,084 reads per treatment, which was determined by the sample with the lowest sequence count. The representative sequence, defined as the most abundant one from each OTU, was subsequently extracted and used for a BLAST search of the GenBank nucleotide database.

2.4. Statistical Analyses

Based on SPSS 18.0, one-way analysis of variance was performed to assess the variations in the effective spikes and spike weight of wheat plants, bacterial copy number, Shannon diversity, species relative abundance, and average variation degree (AVD) across irrigation regimes. Pearson correlation analysis was conducted to quantify the associations between the relative abundance of bacterial phyla, edaphic variables, and critical wheat yield parameters. The changes in bacterial communities across the irrigation regimes were analyzed using canonical principal component analysis. The Mantel test was used to identify the relationships between microbial Bray–Curtis dissimilarity and soil physicochemical properties with the vegan package in R 4.3.0. All observed microbial phyla with a value of Spearman’s correlation coefficient (r) > 0.6 and p-value of <0.05 were included in the network analysis with the psych package in R 4.3.0, and a network diagram was created using Gephi version 9.2 [36]. Phylogenetic bin-based null model analysis (iCAMP) was used to quantify the relative importance of the basic community assembly processes with the iCAMP package in R 4.3.0 [37]. Structural equation modeling (SEM) was performed using the Amos software (v21.0; Small Waters Corp., Chicago, IL, USA) to quantify the direct and indirect effects of abiotic and biotic factors on wheat spike traits [38].

3. Results

3.1. Effect of Irrigation Regimes on the Number and Weight of Wheat Spikes

The average number of effective wheat spikes per 10 plants in the WW treatment was 19 ± 1, whereas those in the RR, RW, and WR treatments were approximately 1.3, 1.1, and 1.1 times that of WW, respectively (Figure 1a). The RR treatment resulted in a significantly higher number of spikes per 10 plants than the other three treatments (p < 0.05). Under the WW treatment, the average spike weight per 10 plants was 12.75 ± 1.74 g, whereas under the RR, RW, and WR treatments, the average weights were approximately 1.8, 1.5, and 1.3 times that of WW, respectively. Wheat spike weight exhibits a significant linear regression relationship with the effective spike number (Figure 1b, p < 0.05).

3.2. Effect of Irrigation Regimes on Bacterial Numbers, Diversity, and Community Structure

The bacterial copy number in RW was significantly lower than that in the RR and WR treatments (Figure 2a, p < 0.05). However, the WW treatment resulted in numerically lower values than the RR and WR treatments. Irrigation regimes exhibited no significant effect on bacterial Shannon diversity (Figure 2a), but had significantly different bacterial community structures (Figure 2b, p < 0.05). Under all treatments, the following phyla were predominant: Actinobacteriota, Pseudomonadota, Acidobacteriota, Chloroflexota, Bacteroidota, Gemmatimonadota, Firmicutes, and Myxococcota (Figure 2c). Compared to irrigation with normal water during the wheat early growth stages, the treatment using brackish groundwater significantly increases the relative abundance of Pseudomonadota, but decreases the Chloroflexota (p < 0.05).
iCAMP analysis revealed that irrigation regimes did not alter the dominant role of stochastic assembly in bacterial communities (Figure 2d). However, compared to the WW and WR treatments, the RR and RW treatments increased the proportion of homogeneous dispersal and reduced the contributions of dispersal limitation and drift. Additionally, irrigation with groundwater during the late phases increased the proportions of heterogeneous and homogeneous selection in bacterial communities compared with irrigation with normal water at late growth stages. The Mantel test results indicated that the soil moisture positively influenced the bacterial community structure (Figure 2e).

3.3. Effect of Irrigation Regimes on Bacterial Composition Associations

Network analysis revealed that the total number of positive and negative bacterial interactions at the phylum level followed the following order of irrigation regimes: WR > WW > RW > RR (Figure 3a). The RR and WW treatments exhibited a balanced ratio of positive to negative correlations among the bacterial phyla. The RW treatment showed 46.15% positive and 53.85% negative correlations, whereas the WR treatment displayed 52.57% positive and 47.43% negative correlations. Additionally, AVD values were higher in the WW and WR treatments compared to RR and RW, with WW being significantly higher than RW (Figure 3b, p < 0.05).

3.4. The Contribution of Biotic and Abiotic Factors to Spike Number

SEM analysis revealed that the selected biotic and abiotic factors collectively explained 93% of the variation in wheat spike numbers (Figure 4). The increase in soil moisture and Na+ content and decrease in bacterial community stability negatively affected wheat spike numbers. In contrast, an enhanced bacterial Shannon diversity index promoted spike formation. In general, Na+ and bacterial community stability were the most influential abiotic and biotic contributors to the spike number, respectively.
Positive correlations were observed between spikes and the relative abundances of Candidatus_Kapabacteria, Chloroflexota, Bacteroidota, Fibrocateriota, and Acidobacteriota (Figure 5, p < 0.05). The spike number was negatively correlated with the relative abundance of phyla such as Pseudomonadota, Dependentiae, NB1_j, RCP2-54, and Thermodesulfobacteriota (p < 0.05).

4. Discussion

4.1. Effect of Irrigation Regimes on Wheat Yield Traits

Under salt stress, the average root diameter, root volume density, and root surface area density of wheat are reduced, thereby impairing water and nutrient uptake and inhibiting seedling growth [8]. Therefore, during the early growth stages of wheat, brackish groundwater irrigation reduced the effective spike number and average spike weight of wheat plants compared to normal water irrigation (Figure 1). This yield penalty persisted even after switching to normal water post-jointing, indicating that the early growth stage of wheat plants is highly sensitive to water quality. Additionally, Na+ competitively inhibits the absorption of K+, disrupts the turgor pressure of tiller bud cells, and simultaneously reduces the synthesis of cytokinins, which suppresses spike differentiation [39]. Consequently, the formation of effective tillers and productive spikes diminishes [40,41]. Also, our results indicated that an elevated Na+ concentration due to brackish groundwater irrigation negatively affected effective spike formation.
Under resource-limited conditions, wheat exhibited an inverse relationship between spike density and single-spike weight [42]. An elevated spike density induces increased competition for photo assimilates, thereby reducing the grain number and weight per spike. In contrast, a lower spike density promotes better spike development via improved resource allocation [26,43]. Interestingly, our study found a significant positive correlation between spike number and spike weight in pot-grown wheat under a consistent seedling density (Figure 1, p < 0.01). Under conditions of mitigated salt stress, wheat may maintain a relatively stable source–sink relationship through enhanced photosynthetic capacity, osmotic regulation, and antioxidant defense, resulting in a positive correlation between spike number and weight [12,13]. Combined with the inverse relationship observed between soil Na+ content and spike number, we inferred that under conditions of uniform seedling emergence per unit area, brackish groundwater irrigation prior to the jointing stage reduced wheat yield compared to normal water irrigation.

4.2. Legacy Effect of Long-Term Brackish Groundwater Irrigation on Bacterial Community

Irrigation with brackish groundwater during the wheat later growth stages reduced the bacterial biomass, which may be attributed to salt-mediated microbial growth inhibition [6]. Additionally, the accumulation of soluble salts introduced into the rhizosphere by brackish water irrigation may interfere with fundamental cellular physiological processes, particularly DNA replication and protein biosynthesis, which require precise ion regulation [44]. Moreover, salinity-induced accumulation of excessive reactive oxygen species causes oxidative damage to rhizobacteria, thereby decreasing microbial biomass [45]. Therefore, although no significant correlation was observed between bacterial biomass and soluble salts in the present study, the accumulation of soluble salts introduced by irrigation with brackish groundwater in the root zone likely inhibited bacterial growth through the abovementioned mechanisms.
Short-term irrigation regimes significantly modified community structures, indicating that the legacy effects of long-term irrigation with brackish groundwater play a dominate role in maintaining diversity, whereas short-term adjustments in irrigation regimes can drive species turnover. Ahmed et al. (2018) [46] and Li et al. (2021) [20] observed that soil salinity accumulation under prolonged irrigation using brackish water selects stress-tolerant microbial taxa (e.g., Pseudomonas, Bacteroidetes, and Firmicutes), which makes the community less responsive to short-term changes in irrigation regimes, particularly regarding species richness and evenness. This is likely why the irrigation regimes did not alter the persistent stochastic assembly of rhizobacterial communities shaped by long-term use of brackish water for irrigation.
Notably, water adjustment during the later growth stages of wheat significantly influenced rhizobacterial community structure. The jointing stage represents a critical transition from vegetative to reproductive growth, during which the root-zone environment exhibits substantial changes in root exudation patterns, oxygen availability, and nutrient dynamics [47,48]. These modifications may ultimately drive the restructuring of the rhizobacterial community by altering the carbon supply, redox status, and nutrient fluxes [49]. Furthermore, irrigation using brackish groundwater during this period introduced additional soluble ions into the root zone, which elevated the osmotic potential of the soil solution and reduced water availability [50]. This likely created a physiological drought stress during the water-critical jointing stage, potentially triggering increased root exudation of osmolytes (such as organic acids and soluble sugars) to maintain cell turgor pressure and support normal growth. These exudate modifications likely contributed to the observed shifts in rhizobacterial community structure [21,51]. Moreover, changes in soil moisture significantly altered the rhizobacterial community structure, primarily because variations in water content modify the physicochemical environment of the rhizosphere, which in turn induces both quantitative and qualitative changes in root exudates [52,53]. Specifically, adjustments in irrigation water sources around the wheat jointing stage led to changes in the relative abundances of Pseudomonadota and Chloroflexota (p < 0.05), as well as their inverse relationship with soil moisture (p < 0.01). Chloroflexota frequently demonstrates enhanced resilience with soil moisture decreasing, often resulting in increased or maintained relative abundances. Conversely, soil with a low water content and good aeration is not conducive to the expansion of bacterial phyla like Pseudomonadota [54,55].

4.3. Potential Roles of Bacterial Communities in Wheat Yield Traits

Salinity stress has been reported to enhance rhizosphere microbial network complexity, largely driven by plant–microbe mutualism under stress [56,57]. In addition, compared with irrigation using normal water, the use of brackish groundwater from the seedling-to-jointing period resulted in a more complex phylum-level network and a greater proportion of positive microbial correlations (Figure 3). Under saline stress conditions, plants modulate the composition and abundance of root exudates, including organic acids, amino acids, and flavonoids, to disseminate specific chemical signals to the rhizosphere [21]. This exudate-mediated recruitment selectively enriches beneficial microorganisms that facilitate salt stress adaptation, thereby inducing shifts in the microbial community structure, enhancing interspecific interactions, and increasing the complexity of microbial networks [58,59]. In response, the microbial community initiates a “functional compensation” mechanism, whereby strengthened species’ interactions within the network contribute to the preservation of ecosystem stability and functionality under stress conditions [59]. Xun et al. (2021) [60] revealed that a higher complexity of microbial interspecies networks correlate with greater stability of the community structures reflected by AVD, and this is consistent with our results. Bacterial community stability negatively affected the effective wheat spike number (Figure 4), which implies that a higher complexity of rhizosphere microbial community relationships adversely affected wheat spike formation. Studies have shown that the structural simplicity of the wheat rhizosphere microbial network, compared to that of bulk soil, paradoxically strengthens disturbance resistance and resilience [61]. This enhanced stability, which is crucial for maintaining rhizosphere homeostasis and nutrient provision, is primarily attributed to the preservation of network integrity and function by keystone taxa [48,62].
Plants cultivated under conditions of high microbial diversity exhibit enhanced productivity [63], which supports our finding (Figure 4). Jia and Whalen (2020) [64] have demonstrated that enhanced microbial diversity strengthens functional redundancy and enables rapid compensation when environmental disturbances impair certain species. This safeguards critical functions, including nutrient cycling and pathogen suppression, and ensures a stable performance. Interspecific complementarity and synergy further boost community productivity and stability [65], fostering a more supportive rhizosphere environment for various crops, such as wheat. Moreover, the improved resistance and resilience derived from diversity allow ecosystems to maintain functionality and swiftly recover from stress, thereby supporting sustainable crop production [62].
Plant growth is shaped by and, in turn, shapes the microbial communities with it, with microbial abundance playing a critical role in promoting plant health and productivity [46,66]. Li et al. (2021) [20] concluded that many species belonging to the phylum Pseudomonadota can effectively help plants alleviate salinity stress by producing metabolites, such as exopolysaccharides, gibberellins, 1-aminocyclopropane-1-carboxylate deaminase, and indole acetic acid. Although most Pseudomonas species are beneficial, they compete with plants and other microorganisms for nutrients and spatial resources in the rhizosphere, which can adversely affect wheat growth [67]. Therefore, irrigation using brackish groundwater during the seedling-to-jointing period promoted the proliferation of Pseudomonadota, which in turn inhibited the number of effective spikes in wheat (Figure 5). The dominant phyla in the rhizosphere and microbial groups, such as Chloroflexota, Bacteroidota, Fibrocateriota, and Acidobacteriota, collectively facilitate the decomposition of soil organic matter, releasing inorganic nutrients including ammonium nitrogen (NH4+), phosphorus, and sulfur, which are subsequently absorbed and utilized by wheat [68,69], and therefore played a positive role in increasing the number of effective spikes in wheat. Members of the phylum Bacteroidetes can synthesize plant growth-promoting compounds, such as auxin (indole-3-acetic acid), which promotes root system development and enhances the capacity for water and nutrient uptake in wheat [70]. Therefore, reasonable irrigation with brackish groundwater can affect wheat production by regulating the relative abundance of certain bacterial phyla.

5. Conclusions

Our findings revealed that during the early growth stages, wheat should be irrigated with normal water instead of brackish groundwater to avoid the accumulation of Na+, which can reduce effective spike formation and weight. For wheat plants subjected to brackish groundwater irrigation during early-stage stress, remedial irrigation with normal water during later growth stages can effectively mitigate the yield reduction trend. Although irrigation changes during the growth period affected the rhizobacterial abundance and community structure, they could not eliminate the persistent stochastic assembly legacy caused by long-term irrigation with brackish groundwater. Importantly, the use of normal water during the early growth stages reduced the complexity of the rhizobacterial co-occurrence network but promoted effective spike formation. Meanwhile, using the normal water enhanced key organic matter-decomposing bacteria early, thereby supporting effective spike development in wheat.
The pot experiment provided a useful model for understanding how brackish groundwater application affects wheat growth and the underlying mechanisms. Nonetheless, the spatial limitations of pots might result in significant salt accumulation, creating a soil environment distinct from that of a field setting. Based on these findings, we propose implementing field trials to further verify the effects of irrigating with a balanced mixture of brackish and normal water on wheat yield.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15122732/s1, Table S1: Variations in rhizosphere soil electrical conductivity (EC) and base cations (sodium (Na⁺), chloride (Cl−), calcium (Ca2⁺), magnesium (Mg2⁺), and sulfate (SO42−) ions.) soil moisture and pH across irrigation treatments based on one-way ANOVAs (p < 0.05).

Author Contributions

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

Funding

This research was financially supported by the Scientific Research Foundation of Education Department of Anhui Province of China (2022AH030137); The National Natural Science Foundation of China (42007089), The Quality Engineering Project of Suzhou University (szxy2023jyxm17), The Anhui Provincial Quality Engineering Project for Higher Education Institutions (2024aijy384), and The Innovation Team for Reducing Pollution and Carbon Emissions in the Agricultural Ecological Environment of Northern Anhui (2024TD02).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Acknowledgments

The authors thank the anonymous reviewers and academic editors for their comments.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Liu, X.; Shao, L.; Sun, H.; Chen, S.; Zhang, X. Responses of yield and water use efficiency to irrigation amount decided by pan evaporation for winter wheat. Agric. Water Manag. 2013, 129, 173–180. [Google Scholar] [CrossRef]
  2. Wang, X.; Yang, J.; Liu, G.; Yao, R.; Yu, S. Impact of irrigation volume and water salinity on winter wheat productivity and soil salinity distribution. Agric. Water Manag. 2015, 149, 44–54. [Google Scholar] [CrossRef]
  3. Wang, P.; Cao, H.; Kang, S.; Du, T.; Tong, L.; Kang, J.; Gao, J.; Ding, R. Agronomic measures improve crop yield and water and nitrogen use efficiency under brackish water irrigation: A global meta-analysis. Agric. Syst. 2025, 226, 104304. [Google Scholar] [CrossRef]
  4. Wang, Q.; Xu, J.; Li, D.; Zhou, Y.; Chen, M.; Zhang, X.; Zhang, J.; Zhao, B. Brackish water irrigation-induced recruitment of specific consortium determines microbial resistance that facilitates crop yield in a field experiment. Pedosphere 2024, 35, 820–833. [Google Scholar] [CrossRef]
  5. Cucci, G.; Lacolla, G.; Boari, F.; Mastro, M.A.; Cantore, V. Effect of water salinity and irrigation regime on maize (Zea mays L.) cultivated on clay loam soil and irrigated by furrow in Southern Italy. Agric. Water Manag. 2019, 222, 118–124. [Google Scholar] [CrossRef]
  6. Arshad, M.; Awais, M.; Bashir, R.; Ahmad, S.R.; Anwar-ul-Haq, M.; Senousy, H.H.; Iftikhar, M.; Anjum, M.U.; Ramzan, S.; Alharbi, S.A.; et al. Assessment of wheat productivity responses and soil health dynamics under brackish ground water. Saudi J. Biol. Sci. 2022, 29, 793–803. [Google Scholar] [CrossRef]
  7. Al Farsi, S.; Nawaz, A.; Rehman, A.; Nadaf, S.; Al Sadi, A.; Siddique, K.; Farooq, M. Effects, tolerance mechanisms and management of salt stress in lucerne (Medicago sativa). Crop Pasture Sci. 2020, 71, 411–428. [Google Scholar] [CrossRef]
  8. Zou, Y.; Zhang, Y.; Testerink, C. Root dynamic growth strategies in response to salinity. Plant Cell Environ. 2021, 45, 695–704. [Google Scholar] [CrossRef]
  9. Isla, R.; Aragues, R. Yield and plant ion concentrations in maize (Zea mays L.) subject to diurnal and nocturnal saline sprinkler irrigations. Field Crops Res. 2010, 116, 175–183. [Google Scholar] [CrossRef]
  10. Zhai, Y.; Huang, M.; Zhu, C.; Xu, H.; Zhang, Z. Evaluation and application of the AquaCrop model in simulating soil salinity and winter wheat yield under saline water irrigation. Agronomy 2022, 12, 2313. [Google Scholar] [CrossRef]
  11. Liu, Z.; Gao, C.; Yan, Z.; Shao, L.; Chen, S.; Niu, J.; Zhang, X. Effects of long-term saline water irrigation on soil salinity and crop production of winter wheat-maize cropping system in the North China Plain: A case study. Agric. Water Manag. 2024, 303, 109060. [Google Scholar] [CrossRef]
  12. Gottlieb, L.; Ochman, D.; Huang, C.W.; Domec, J.C.; Schwartz, N.; Hartzell, S. Translating soil salinity to agricultural salt stress: Key salt-tolerance mechanisms for agrohydrologic models. iScience 2025, 28, 113139. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, X.; Zhang, K.; Gao, X.; Ai, X.; Chen, G.; Guo, X.; Wu, C.; Zhang, L. Effect of irrigation with magnetized and ionized water on yield, nutrient uptake and water-use efficiency of winter wheat in Xinjiang, China. Agric. Water Manag. 2025, 308, 10. [Google Scholar] [CrossRef]
  14. Yuan, C.; Feng, S.; Huo, Z.; Ji, Q. Effects of deficit irrigation with saline water on soil water-salt distribution and water use efficiency of maize for seed production in arid Northwest China. Agric. Water Manag. 2019, 212, 424–432. [Google Scholar] [CrossRef]
  15. Islam, M.Z.; Park, B.J.; Lee, Y.T. Effect of salinity stress on bioactive compounds and antioxidant activity of wheat microgreen extract under organic cultivation conditions. Int. J. Biol. Macromol. 2019, 140, 631–636. [Google Scholar] [CrossRef]
  16. Zhang, K.; Shi, Y.; Cui, X.; Yue, P.; Li, K.; Liu, X.; Tripathi, B.M.; Chu, H. Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems 2019, 4, e00225-18. [Google Scholar] [CrossRef]
  17. Zhang, X.C.; Zhai, H.L.; Xu, H.Y.; Song, W.Z.; Liu, M.; Dong, X.F.; Sun, W.; Ma, J.Y. Different salt stress types regulated rhizosphere rare bacterial communities through root exudates and soil physicochemical properties. Plant Soil 2025. ahead of print. [Google Scholar] [CrossRef]
  18. Shrivastava, P.; Kumar, R. Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J. Biol. Sci. 2015, 22, 123–131. [Google Scholar] [CrossRef]
  19. Li, W.; Liu, P.; Jin, Y.; Chen, L.; Zhao, M.; Wu, L.; Lan, S. Microbial community composition and interactions in saline biocrusts: Insights into cyanobacterial inoculation for soil restoration. Appl. Soil Ecol. 2025, 212, 106225. [Google Scholar] [CrossRef]
  20. Li, H.; La, S.; Zhang, X.; Gao, L.; Tian, Y. Salt-induced recruitment of specific root-associated bacterial consortium capable of enhancing plant adaptability to salt stress. ISME J. 2021, 15, 2865–2882. [Google Scholar] [CrossRef]
  21. Thakar, M.; Rajguru, B.; Thakkar, J.; Bhatt, V.; Shri, M.; Warghane, A. Impact of salt-induced chemo-signal on rhizomicrobiome-plant interaction. J. Crop. Health 2025, 77, 131. [Google Scholar] [CrossRef]
  22. Sharma, S.; Joshi, M. Microbial strategies for enhancing wheat and rice resilience to drought, salinity, and heat stress. Rhizosphere 2025, 34, 101108. [Google Scholar] [CrossRef]
  23. Hardoim, P.R.; Van Overbeek, L.S.; Berg, G.; Pirttilä, A.M.; Compant, S.; Campisano, A.; Döring, M.; Sessitsch, A. The hidden world within plants: Ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 2015, 79, 293–320. [Google Scholar] [CrossRef] [PubMed]
  24. Fierer, N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 2017, 15, 579–590. [Google Scholar] [CrossRef] [PubMed]
  25. Hu, Y.; Li, X.; Jin, M.; Wang, R.; Chen, J.; Guo, S. Reduced co-occurrence and ion-specific preferences of soil microbial hub species after ten years of irrigation with brackish water. Soil Tillage Res. 2020, 199, 104599. [Google Scholar] [CrossRef]
  26. Wang, H.; Zheng, C.; Ning, S.; Cao, C.; Li, K.; Dang, H.; Wu, Y.; Zhang, J. Impacts of long-term saline water irrigation on soil properties and crop yields under maize-wheat crop rotation. Agric. Water Manag. 2023, 286, 108383. [Google Scholar] [CrossRef]
  27. Nelson, D.W.; Sommers, L.E. Total carbon, organic carbon and organic matter. In Methods of Soil Analysis—Part 2, Chemical and Microbiological Properties, 2nd ed.; Page, A.L., Miller, R.H., Keeney, D.R., Eds.; American Society of Agronomy, Inc.: Madison, WI, USA, 1982; pp. 539–579. [Google Scholar]
  28. Bremner, J.M. Nitrogen-total. In Methods of Soil Analysis: Chemical Methods—Part 3; Sparks, D.L., Ed.; Soil Science Society of America, Inc.: Madison, WI, USA, 1996; p. 1085. [Google Scholar] [CrossRef]
  29. Sun, Q.; Qiu, H.; Hu, Y.; Wei, X.; Chen, X.; Ge, T.; Wu, J.; Su, Y. Cellulose and lignin regulate partitioning of soil phosphorus fractions and alkaline phosphomonoesterase encoding bacterial community in phosphorus-deficient soils. Biol. Fertil. Soils 2019, 55, 31–42. [Google Scholar] [CrossRef]
  30. Prasad, P.N.S.; Kavitha, P.; Chari, M.S.; Reddy, M.S. Comparison of extraction methods to assess potassium availability for rice growing soils of canal ayacut of Kurnool district. Asian J. Soil Sci. 2016, 11, 67–73. [Google Scholar] [CrossRef]
  31. Wang, Z.; Jin, M.; Šimůnek, J.; van Genuchte, M.T. Evaluation of mulched drip irrigation for cotton in arid Northwest China. Irrig. Sci. 2014, 32, 15–27. [Google Scholar] [CrossRef]
  32. Qiu, H.; Liu, J.; Boorboori, M.R.; Li, D.; Chen, S.; Ma, X.; Cheng, P.; Zhang, H. Effect of biochar application rate on changes in soil labile organic carbon fractions and the association between bacterial community assembly and carbon metabolism with time. Sci. Total Environ. 2023, 855, 158876. [Google Scholar] [CrossRef]
  33. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Pena, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef]
  34. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [Google Scholar] [CrossRef]
  35. Zhong, L.; Lai, Y.; Shi, L.D.; Wang, K.D.; Dai, Y.J.; Liu, Y.W.; Ma, F.; Rittmann, B.E.; Zheng, P.; Zhao, H.P. Nitrate effects on chromate reduction in a methane-based biofilm. Water Res. 2017, 115, 130–137. [Google Scholar] [CrossRef] [PubMed]
  36. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An open source software for exploring and manipulating networks. In Proceedings of the 3rd International Conference on Weblogs and Social Media, San Jose, CA, USA, 17–20 May 2009; Volume 3, pp. 361–362. [Google Scholar] [CrossRef]
  37. Ning, D.; Yuan, M.; Wu, L.; Zhang, Y.; Guo, X.; Zhou, X.; Yang, Y.; Arkin, A.P.; Firestone, M.K.; Zhou, J. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat. Commun. 2020, 11, 4717. [Google Scholar] [CrossRef] [PubMed]
  38. Hooper, D.; Coughlan, J.; Mullen, M. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 2008, 6, 53–60. [Google Scholar] [CrossRef]
  39. Munns, R.; Gilliham, M. Salinity tolerance of crops—What is the cost? New Phytol. 2015, 208, 668–673. [Google Scholar] [CrossRef]
  40. Zafar, S.; Hasnain, Z.; Danish, S.; Battaglia, M.L.; Fahad, S.; Ansari, M.J.; Alharbi, S.A. Modulations of wheat growth by selenium nanoparticles under salinity stress. BMC Plant Biol. 2024, 24, 35. [Google Scholar] [CrossRef]
  41. El-Hendawy, S.; Tahir, M.U.; Hu, Y.; Al-Suhaibani, N. Evaluating the Necessity of a Control Treatment for Assessing Salt Tolerance in Wheat Genotypes Based on Agro-Physiological Traits in Real-Field Conditions. Plants 2025, 14, 2488. [Google Scholar] [CrossRef]
  42. Feng, S.; Shi, C.; Wang, P.; Chang, S.; Liu, C.; Shen, C.; Li, S.; Hu, T.; Ru, Z. Optimizing Wheat Planting Density by Adjusting Population Structure and Stabilizing Stem Strength to Achieve High and Stable Yields. Agronomy 2024, 14, 1853. [Google Scholar] [CrossRef]
  43. Liu, Y.; Liao, Y.; Liu, W. High nitrogen application rate and planting density reduce wheat grain yield by reducing filling rate of inferior grain in middle spikelets. Crop J. 2020, 9, 412–426. [Google Scholar] [CrossRef]
  44. Xu, L.; Xin, C.; Haider, F.U.; Li, H.; Li, S.; Zhang, H.; Zhang, P.; Li, X. Rhizobacteria mitigate salinity stress in maize by modulating photosynthesis, antioxidant defense, and rhizosphere microbial diversity. Plant Stress 2025, 15, 100781. [Google Scholar] [CrossRef]
  45. Seixas, A.F.; Quendera, A.P.; Sousa, J.P.; Silva, A.F.; Arraiano, C.M.; Andrade, J.M. Bacterial response to oxidative stress and RNA oxidation. Front. Genet. 2022, 12, 821535. [Google Scholar] [CrossRef]
  46. Ahmed, V.; Verma, M.K.; Gupta, S.; Mandhan, V.; Chauhan, N.S. Metagenomic profiling of soil microbes to mine salt stress tolerance genes. Front. Microbiol. 2018, 9, 159. [Google Scholar] [CrossRef]
  47. Houlden, A.; Timms-Wilson, T.M.; Day, M.J.; Bailey, M.J. Influence of plant developmental stage on microbial community structure and activity in the rhizosphere of three field crops. FEMS Microbiol. Ecol. 2008, 65, 193–201. [Google Scholar] [CrossRef]
  48. 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]
  49. 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, fix096. [Google Scholar] [CrossRef]
  50. Dinneny, J.R. Developmental responses to water and salinity in root systems. Annu. Rev. Cell Dev. Biol. 2019, 35, 239–257. [Google Scholar] [CrossRef]
  51. Meng, X.; Zhou, J.; Sui, N. Mechanisms of salt tolerance in halophytes: Current understanding and recent advances. Open Life Sci. 2018, 13, 149–154. [Google Scholar] [CrossRef]
  52. Aulakh, M.S.; Wassmann, R.; Bueno, C.; Heinz Rennenberg, H. Impact of root exudates of different cultivars and plant development stages of rice (Oryza sativa L.) on methane production in a paddy soil. Plant Soil 2001, 230, 77–86. [Google Scholar] [CrossRef]
  53. Naylor, D.; Coleman-Derr, D. Drought stress and root-associated bacterial communities. Front. Plant Sci. 2018, 8, 2223. [Google Scholar] [CrossRef] [PubMed]
  54. Achtenhagen, J.; Goebel, M.O.; Miltner, A.; Woche, S.K.; Kästner, M. Bacterial impact on the wetting properties of soil minerals. Biogeochemistry 2015, 122, 269–280. [Google Scholar] [CrossRef]
  55. Chialva, M.; Ghignone, S.; Cozzi, P.; Lazzari, B.; Bonfante, P.; Abbruscato, P.; Lumini, E. Water management and phenology influence the root-associated rice field microbiota. FEMS Microbiol. Ecol. 2020, 96, fiaa146. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, X.; Sun, R.; Tian, Y.; Guo, K.; Sun, H.; Liu, X.; Chu, H.; Liu, B. Long term phytoremediation of coastal saline soil reveals plant species specific patterns of microbial community recruitment. mSystems 2020, 5, e00741-19. [Google Scholar] [CrossRef] [PubMed]
  57. Liu, Z.; Gong, H.; Li, J.; Hou, R.; Sun, Z.; Ouyang, Z. Microbes with high–abundance attributes dominate the prokaryote communities of saline–alkali soil and construct more complex networks in the plant rhizosphere. Geoderma 2023, 439, 116684. [Google Scholar] [CrossRef]
  58. Xiong, C.; Zhu, Y.G.; Wang, J.T.; Singh, B.; Han, L.L.; Shen, J.P.; Li, P.P.; Wang, G.B.; Wu, C.F.; Ge, A.H.; et al. Host selection shapes crop microbiome assembly and network complexity. New Phytol. 2021, 229, 1091–1104. [Google Scholar] [CrossRef]
  59. Du, Y.; Zhang, L.; Yang, Y.; Cheng, K.; Li, K.; Zhou, Y.; Li, L.; Jin, Y.; He, X. Assembly, network and functional compensation of specialists and generalists in poplar rhizosphere under salt stress. NPJ Biofilms Microbiomes 2025, 11, 28. [Google Scholar] [CrossRef]
  60. Xun, W.; Liu, Y.; Li, W.; Ren, Y.; Xiong, W.; Xu, Z.; Zhang, N.; Miao, Y.; Shen, Q.; Zhang, R. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome 2021, 9, 35. [Google Scholar] [CrossRef]
  61. Mendes, L.W.; Kuramae, E.E.; Navarrete, A.A.; Van Veen, J.A.; Tsai, S.M. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J. 2014, 8, 1577. [Google Scholar] [CrossRef]
  62. Fan, K.; Weisenhorn, P.; Gilbert, J.A.; Chu, H. Wheat rhizosphere harbors a less complex and more stable microbial co-occurrence pattern than bulk soil. Soil Biol. Biochem. 2018, 125, 251–260. [Google Scholar] [CrossRef]
  63. Prudent, M.; Dequiedt, S.; Sorin, C.; Girodet, S.; Nowak, V.; Duc, G.; Salon, C.; Maron, P.A. The diversity of soil microbial communities matters when legumes face drought. Plant Cell Environ. 2020, 43, 1023–1035. [Google Scholar] [CrossRef]
  64. Jia, Y.; Whalen, J.K. A new perspective on functional redundancy and phylogenetic niche conservatism in soil microbial communities. Pedosphere 2020, 30, 18–24. [Google Scholar] [CrossRef]
  65. Yang, P.; Loreau, M.; Huang, M.; Niu, M.; Schöb, C.; Zhou, R.; Lin, J.; Li, Z.; Sun, H.; Chen, J. Increased diversity by cushion plants enhances alpine community productivity and stability. New Phytol. 2025, 248, 125–139. [Google Scholar] [CrossRef]
  66. Fu, B.; Li, Z.; Gao, X.; Wu, L.; Lan, J.; Peng, W. Effects of subsurface drip irrigation on alfalfa (Medicago sativa L.) growth and soil microbial community structures in arid and semi-arid areas of northern China. Appl. Soil Ecol. 2021, 159, 103859. [Google Scholar] [CrossRef]
  67. Ahmadi, Z.; Mehrabadi, M.; Fazli, M.; Khalesro, S.; Abedi, R.; Mokhtassi-Bidgoli, A. Enhancing tolerance of wheat cultivars to metribuzin stress through bioremediation with Pseudomonas spp. BMC Plant Biol. 2025, 25, 857. [Google Scholar] [CrossRef] [PubMed]
  68. Eichorst, S.A.; Kuske, C.R.; Schmidt, T.M. Influence of plant polymers on the distribution and cultivation of bacteria in the phylum Acidobacteria. Appl. Environ. Microbiol. 2011, 77, 586–596. [Google Scholar] [CrossRef] [PubMed]
  69. Rezgui, C.; Trinsoutrot-Gattin, I.; Benoit, M.; Laval, K.; Riah-Anglet, W. Linking changes in the soil microbial community to C and N dynamics during crop residue decomposition. J. Integr. Agric. 2021, 20, 3039–3059. [Google Scholar] [CrossRef]
  70. Pan, X.; Raaijmakers, J.M.; Carrión, V.J. Importance of Bacteroidetes in host–microbe interactions and ecosystem functioning. Trends Microbiol. 2023, 31, 959–971. [Google Scholar] [CrossRef]
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Figure 1. (a) Effects of different irrigation regimes on the average number of effective spikes and spike weight per 10 plants and (b) the correlation between these two indicators (green color means 95% confidence interval). Different lowercase letters indicate significant differences (p < 0.05) in effective spike number or spike weight among treatments.
Figure 1. (a) Effects of different irrigation regimes on the average number of effective spikes and spike weight per 10 plants and (b) the correlation between these two indicators (green color means 95% confidence interval). Different lowercase letters indicate significant differences (p < 0.05) in effective spike number or spike weight among treatments.
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Figure 2. Effects of different irrigation regimes on the (a) number of bacteria and bacterial Shannon diversity, (b) bacterial community structure, (c) relative abundance of the dominant phyla, and (d) bacterial assembly processes, and (e) the relationship between soil physicochemical properties and bacterial community structure. Lowercase letters and red asterisks indicate significant differences (p < 0.05) in effective spike number or spike weight among treatments. *** p < 0.001, ** p < 0.01, * p < 0.05.
Figure 2. Effects of different irrigation regimes on the (a) number of bacteria and bacterial Shannon diversity, (b) bacterial community structure, (c) relative abundance of the dominant phyla, and (d) bacterial assembly processes, and (e) the relationship between soil physicochemical properties and bacterial community structure. Lowercase letters and red asterisks indicate significant differences (p < 0.05) in effective spike number or spike weight among treatments. *** p < 0.001, ** p < 0.01, * p < 0.05.
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Figure 3. Co-occurrence networks of bacterial phyla across irrigation regimes and the number of links in the network (a); and (b) effects of different irrigation regimes on the average variation degree (AVD). Different colors were used to distinguish modules in the network. Red lines represent significant positive linear relationships and blue lines represent negative linear relationships. Lowercase letters mean significant differences (p < 0.05) in AVD among treatments.
Figure 3. Co-occurrence networks of bacterial phyla across irrigation regimes and the number of links in the network (a); and (b) effects of different irrigation regimes on the average variation degree (AVD). Different colors were used to distinguish modules in the network. Red lines represent significant positive linear relationships and blue lines represent negative linear relationships. Lowercase letters mean significant differences (p < 0.05) in AVD among treatments.
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Figure 4. Structural equation model of the hypothesized causal relationships between abiotic and biotic variables and spike number. Arrow widths indicate the strength of standardized path coefficients. Solid blue lines indicate positive path coefficients, and dashed red lines indicate negative path coefficients. The numbers associated with the arrows indicate the path coefficients calculated from the correlation coefficients.
Figure 4. Structural equation model of the hypothesized causal relationships between abiotic and biotic variables and spike number. Arrow widths indicate the strength of standardized path coefficients. Solid blue lines indicate positive path coefficients, and dashed red lines indicate negative path coefficients. The numbers associated with the arrows indicate the path coefficients calculated from the correlation coefficients.
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Figure 5. Correlations between soil properties, effective spike number, spike weight, and bacterial phylum. *** p < 0.001, ** p < 0.01, * p < 0.05.
Figure 5. Correlations between soil properties, effective spike number, spike weight, and bacterial phylum. *** p < 0.001, ** p < 0.01, * p < 0.05.
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Qiu, H.; Tian, G.; Liu, J.; He, S.; Li, D. Legacy Effects of Long-Term Brackish Groundwater Irrigation on Bacterial Communities in Wheat Rhizosphere and Yield Performance. Agronomy 2025, 15, 2732. https://doi.org/10.3390/agronomy15122732

AMA Style

Qiu H, Tian G, Liu J, He S, Li D. Legacy Effects of Long-Term Brackish Groundwater Irrigation on Bacterial Communities in Wheat Rhizosphere and Yield Performance. Agronomy. 2025; 15(12):2732. https://doi.org/10.3390/agronomy15122732

Chicago/Turabian Style

Qiu, Husen, Guangli Tian, Jieyun Liu, Shuai He, and Dongwei Li. 2025. "Legacy Effects of Long-Term Brackish Groundwater Irrigation on Bacterial Communities in Wheat Rhizosphere and Yield Performance" Agronomy 15, no. 12: 2732. https://doi.org/10.3390/agronomy15122732

APA Style

Qiu, H., Tian, G., Liu, J., He, S., & Li, D. (2025). Legacy Effects of Long-Term Brackish Groundwater Irrigation on Bacterial Communities in Wheat Rhizosphere and Yield Performance. Agronomy, 15(12), 2732. https://doi.org/10.3390/agronomy15122732

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