Microbial Bioactives

1. Introduction

Modern agriculture stands at a defining crossroads. On one hand, it must supply sufficient, nutritious food for a global population projected to surpass nine billion within the next few decades. On the other, it must do so while confronting shrinking arable land, soil degradation, salinization, climate variability, and the ecological costs of decades-long dependence on synthetic agrochemicals. This dual pressure has exposed the limitations of conventional, input-intensive farming systems and intensified the search for biologically driven solutions that sustain productivity without further eroding environmental integrity (Jiménez-Mejía et al., 2022; Shahid et al., 2018).

Soil degradation remains one of the most pressing threats to global food security. Salinity alone affects millions of hectares worldwide, reducing nutrient availability, disrupting water uptake, and severely constraining plant growth and yield (Shahid et al., 2018). Compounding this problem, excessive use of chemical fertilizers has led to declining soil microbial diversity, nutrient imbalances, and increased greenhouse gas emissions, undermining long-term soil fertility and resilience (Adesemoye et al., 2009; Vessey, 2003). These challenges have prompted a paradigm shift toward sustainable agriculture, where biological processes are leveraged to restore soil health, improve nutrient-use efficiency, and enhance crop tolerance to abiotic stress.

Within this emerging framework, Plant Growth-Promoting Bacteria (PGPB) have gained recognition as powerful biological allies. These bacteria colonize the rhizosphere and establish intimate associations with plant roots, forming functional partnerships that directly influence plant physiology and soil processes (Hayat et al., 2010; Santoyo et al., 2021). Rather than acting as passive inhabitants of the soil, PGPB actively facilitate nutrient acquisition through nitrogen fixation, phosphate solubilization, and siderophore-mediated iron mobilization (Egamberdiyeva, 2007; Shaharoona et al., 2008). Through these mechanisms, they reduce the plant’s dependence on synthetic fertilizers while maintaining or even enhancing yield.

Beyond nutrient provision, PGPB play a central role in helping plants cope with abiotic stresses, particularly salinity. Stress conditions often trigger the accumulation of ethylene, a hormone that inhibits root elongation and limits plant growth. Many PGPB counteract this effect by producing 1-aminocyclopropane-1-carboxylate (ACC) deaminase, an enzyme that lowers ethylene levels and allows normal root development to continue under stress (Penrose & Glick, 2003; Orozco-Mosqueda et al., 2020). In parallel, these bacteria stimulate the accumulation of osmoprotectants such as proline and soluble sugars, helping plants maintain cellular homeostasis in saline or drought-affected soils (Afridi et al., 2019; Numan et al., 2018).

While PGPB–plant interactions have been widely studied, growing attention is being directed toward more complex microbial partnerships, particularly mutualistic interactions between microalgae and bacteria. Mutualism, defined as a reciprocal relationship that enhances the fitness of both partners, is a fundamental ecological strategy that enables organisms to survive in resource-limited or stressful environments (Bronstein, 1994; Doebeli & Knowlton, 1998). In aquatic and terrestrial systems alike, microalgae–bacteria mutualisms are structured around the exchange of metabolites within a microscale zone known as the phycosphere (Seymour et al., 2017).

Within this microenvironment, microalgae release photosynthetically fixed organic carbon that fuels bacterial metabolism. In return, bacteria supply essential nutrients and growth factors, including fixed nitrogen, iron-chelating siderophores, and vitamins such as cobalamin (vitamin B12), which many microalgae cannot synthesize independently (Amin et al., 2009; Croft et al., 2005). Approximately half of all microalgal species are auxotrophic for vitamin B12, making bacterial partners indispensable for their growth and metabolic efficiency (Croft et al., 2005; Helliwell et al., 2018). These finely balanced exchanges exemplify how mutualistic trade-offs can stabilize microbial communities and enhance overall productivity.

The relevance of these interactions extends beyond ecological theory into applied agricultural and biotechnological systems. Engineered microalgae–bacteria consortia have been shown to enhance biomass accumulation, nutrient recycling, and metabolite production, including lipids, proteins, and carbohydrates (Choix et al., 2012; Subashchandrabose et al., 2011). Such consortia integrate complementary metabolic pathways, allowing one organism to compensate for the physiological limitations of the other. This functional integration mirrors natural mutualisms while offering opportunities to design microbial systems with tailored agronomic benefits (Fabris et al., 2020).

From a sustainable agriculture perspective, these microbial consortia align closely with the principles of a circular bioeconomy. Microalgae–bacteria systems can be coupled with wastewater treatment, capturing excess nitrogen and phosphorus while simultaneously generating valuable biomass (González-González & de-Bashan, 2021). The residual biomass, enriched with organic matter and beneficial microorganisms, can then be returned to agricultural soils as biofertilizers or soil amendments, closing nutrient loops and restoring degraded lands (Sheirdil et al., 2019; Vuppaladadiyam et al., 2018).

Despite mounting experimental evidence supporting the benefits of PGPB and microalgae–bacteria interactions, results across individual studies often vary due to differences in microbial strains, crop species, soil conditions, and experimental design. This variability makes it difficult to draw generalized conclusions about the magnitude and consistency of microbial effects on plant growth and yield. Systematic reviews and meta-analyses offer a powerful approach to address this challenge by quantitatively synthesizing results across studies, identifying robust patterns, and reducing uncertainty associated with small-scale experiments.

By integrating data on shoot growth, grain yield, and stress mitigation, meta-analytical approaches allow for a more objective assessment of microbial inoculants as tools for sustainable agriculture. They also help distinguish between strain-specific effects and broader functional trends, such as the superiority of microbial consortia over single-strain inoculants or the potential to maintain yields with reduced fertilizer inputs. In doing so, systematic synthesis strengthens the evidence base needed to translate microbial technologies from experimental settings into field-scale applications.

In this context, the present systematic review and meta-analysis focus on evaluating the role of PGPB and microalgae–bacteria mutualisms in enhancing plant growth and yield under abiotic stress conditions, particularly salinity. By drawing on peer-reviewed studies published before 2024, this work aims to clarify how microbial partnerships contribute to sustainable crop production, identify the most effective functional traits, and highlight their potential to reduce chemical fertilizer dependence. Ultimately, understanding and harnessing these microbial mutualisms may represent one of the most promising pathways toward resilient, biologically driven agricultural systems capable of meeting future food security challenges.

2. Materials and Methods

2.1. Study Design and Reporting Framework

This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021) and designed to quantitatively synthesize evidence on the effects of plant growth–promoting bacteria (PGPB) on shoot length and grain yield. The study selection process followed PRISMA 2020 guidelines and is summarized in Figure 1. The methodological approach followed internationally accepted standards for evidence synthesis and reporting in biomedical and life-science literature, consistent with PubMed-indexed journal requirements. The review process was structured to ensure transparency, reproducibility, and methodological rigor, emphasizing predefined eligibility criteria, systematic data extraction, and statistical integration of outcomes.

A review protocol was established prior to data collection, defining objectives, outcomes of interest, analytical strategies, and bias assessment methods. The primary outcomes were shoot length (cm) as a measure of vegetative growth and grain yield (kg ha?¹) as a measure of agronomic performance. Both outcomes were selected due to their biological relevance and frequent reporting in controlled PGPB studies. The review focused on experimental studies that compared PGPB-inoculated treatments with uninoculated controls, including both single-strain inoculations and multi-strain microbial consortia, with or without reduced fertilizer inputs.

The meta-analysis emphasized quantitative synthesis rather than narrative interpretation, enabling estimation of pooled effect sizes and assessment of heterogeneity across studies. All stages of the review—from literature screening to statistical analysis—were conducted systematically to minimize selection bias and analytical subjectivity.

2.2. Literature Search Strategy and Eligibility Criteria

A comprehensive literature search was conducted across major scientific databases commonly indexed in PubMed, including multidisciplinary and life-science repositories. The search strategy combined controlled vocabulary terms and free-text keywords related to plant growth–promoting bacteria, biofertilizers, shoot length, grain yield,

Figure 1: PRISMA flow diagram illustrating study identification, screening, eligibility, and inclusion for the systematic review and meta-analysis. This figure illustrates the PRISMA-guided workflow used to identify, screen, assess eligibility, and include studies in the systematic review and meta-analysis. It documents exclusion criteria and ensures transparency and reproducibility in study selection.

rhizobacteria, and microbial inoculants. Boolean operators were used to refine search sensitivity and specificity.

Studies were included if they met the following criteria:

• Experimental evaluation of PGPB effects on plants under controlled or semi-controlled conditions.
• Presence of a clearly defined control group without bacterial inoculation.
• Quantitative reporting of shoot length and/or grain yield with sufficient statistical information (means and measures of variability).
• Use of identifiable bacterial strains or defined microbial consortia.

Exclusion criteria included review articles, modeling studies, studies lacking control treatments, and reports without extractable quantitative data. Studies focusing exclusively on non-agronomic endpoints or non-soil-based systems were also excluded.

All retrieved records were screened in two stages. First, titles and abstracts were assessed for relevance. Second, full-text articles were evaluated against eligibility criteria. Discrepancies during screening were resolved through consensus-based discussion. A flow diagram was generated to document the study selection process, including reasons for exclusion at each stage.

2.3. Data Extraction and Quality Assessment

Data extraction was conducted using a standardized form to ensure consistency and accuracy. Extracted variables included study characteristics (experimental design, crop species, bacterial strain or consortium), outcome measures (mean shoot length and grain yield), measures of dispersion (standard deviation or standard error), sample size, and treatment conditions such as fertilizer application rates.

When numerical data were presented graphically, values were estimated using validated digital extraction tools. If multiple measurements were reported for a single outcome, the final harvest or endpoint measurement was prioritized to maintain comparability across studies. For studies reporting multiple PGPB treatments, each treatment–control comparison was treated as an independent comparison, provided that control groups were appropriately matched.

Study quality was assessed based on predefined criteria, including clarity of experimental design, replication, reporting completeness, and outcome measurement consistency. Although formal risk-of-bias tools are less standardized for agronomic meta-analyses, methodological robustness was evaluated to contextualize heterogeneity and interpret pooled estimates. No studies were excluded solely on quality grounds; instead, quality considerations were integrated into the interpretation of findings.

2.4. Statistical Analysis and Data Synthesis

Meta-analytical computations were performed using standard statistical approaches for continuous outcomes. Mean difference (MD) was selected as the effect size metric because outcomes were measured using consistent units across studies. Effect sizes were calculated by subtracting control means from treatment means for each comparison. Variance estimates were derived from reported standard deviations and sample sizes.

A random-effects model was applied to account for expected biological and methodological heterogeneity among studies, including differences in bacterial strains, crop species, and experimental conditions. Each study was weighted by the inverse of its variance, ensuring that more precise estimates contributed proportionally to pooled results. Statistical heterogeneity was assessed qualitatively through forest plot inspection and quantitatively using heterogeneity indices. Forest plots were generated to visualize individual study effects and pooled estimates for shoot length and grain yield. Funnel plots were used to assess potential small-study effects and publication bias, recognizing the limited power of such analyses when the number of comparisons is small.

All statistical analyses were conducted using validated meta-analysis software. Results were reported with 95% confidence intervals, and statistical significance was inferred when confidence intervals did not cross zero. Findings were interpreted in the context of biological plausibility, experimental diversity, and sustainability implications rather than solely on statistical significance.

3. Results

The statistical analysis provides a quantitative foundation for interpreting the effects of plant growth–promoting bacteria (PGPB) on shoot length and grain yield,

Figure 2. Forest plot (Error bar) plot of shoot length differences across bacterial strains. This horizontal error bar plot displays the mean difference in shoot length (cm) for five plant growth–promoting bacterial strains compared to uninoculated controls. Each black dot represents the mean effect size, with horizontal lines indicating confidence intervals. All strains show positive mean differences, suggesting consistent vegetative growth enhancement. The dashed vertical line at 0 cm marks the null effect threshold, and none of the intervals cross it, reinforcing the statistical significance of the observed responses.

Figure 3. Forest plot of grain yield differences across bacterial treatments. This plot presents the mean difference in grain yield (kg/ha) for four individual bacterial strains compared to a baseline treatment of microbial consortium combined with half fertilizer input. Each black dot indicates the mean effect size, with horizontal error bars representing confidence intervals. All strains show positive yield differences, with varying degrees of precision, highlighting the comparative effectiveness of single-strain inoculations relative to the consortium benchmark.

integrating evidence across treatments and highlighting both consistency and variability in observed responses. The results presented in the forest plots and summary tables collectively demonstrate that PGPB inoculation exerts a measurable and positive influence on plant performance when compared with uninoculated controls. These outcomes are not isolated observations but emerge from weighted comparisons that account for variance and sample size, thereby strengthening the reliability of the conclusions drawn from the dataset.

Across all comparisons, the direction of effect sizes for shoot length is uniformly positive, indicating that PGPB treatments consistently enhanced vegetative growth relative to controls. Across all comparisons, shoot length responses were consistently positive, as illustrated in the forest plot (Figure 2). Individual bacterial strains differ in the magnitude of their effects, yet none show a negative mean difference. Shoot length outcomes following PGPB inoculation are summarized in Table 1. The pooled estimates reveal that treatments with narrower confidence intervals contributed greater statistical weight, reflecting higher precision in those estimates. Conversely, wider confidence intervals, as seen for some strains, indicate increased variability, likely arising from biological differences in plant–microbe compatibility or experimental conditions. Importantly, even in cases of broader intervals, the central tendency remains positive, suggesting that variability influences the strength rather than the direction of the response.The confidence intervals associated with most shoot length estimates do not cross the null value, providing statistical evidence that observed growth improvements are unlikely to be due to chance alone. This pattern underscores the robustness of the vegetative growth response to PGPB inoculation. The heterogeneity observed among strains, visualized by differing effect sizes and interval widths, aligns with the expectation that microbial traits such as phytohormone production, nutrient solubilization capacity, and rhizosphere colonization efficiency vary across taxa. Thus, the statistical dispersion captured in the forest plot reflects biological reality rather than analytical inconsistency.

Grain yield responses exhibit an even more pronounced statistical pattern. The forest plot for grain yield (Figure 3) shows substantially larger mean differences than those observed for shoot length, emphasizing that PGPB effects extend beyond early vegetative growth to influence final productivity. Grain yield responses to individual and consortium PGPB treatments are presented in Table 2. All treatments demonstrate statistically significant yield gains relative to controls, with confidence intervals that remain well above zero. The magnitude of these effects, indicates that PGPB-mediated yield enhancement is not marginal but agronomically meaningful.

Notably, the largest effect size is associated with the microbial consortium combined with reduced fertilizer input. This treatment not only exhibits the highest mean difference but also maintains a relatively narrow confidence interval, indicating both effectiveness and consistency. From a statistical perspective, this finding suggests a synergistic effect arising from multi-strain inoculation, where complementary functional traits collectively enhance nutrient acquisition and utilization. The weighting applied in the meta-analysis further reinforces this interpretation, as the consortium treatment contributes substantially to the pooled estimate due to its precision and effect magnitude.

The use of a random-effects model is particularly important for interpreting these results. By accounting for between-study and between-treatment variability, the model acknowledges that true effects may differ across experimental contexts. The persistence of statistically significant pooled effects under this conservative framework indicates that the positive influence of PGPB on both shoot length and grain yield is robust across heterogeneous conditions. Visual inspection of the forest plots (Figures 2 and 3) supports this conclusion, as the majority of individual estimates cluster on the positive side of the effect axis.

Potential publication bias for shoot length outcomes was evaluated using a funnel plot (Figure 4). Funnel plot analysis provides additional insight into the statistical reliability of the findings. The funnel plot for grain yield (Figure 5) displays a relatively symmetrical distribution of effect sizes around the pooled estimate, suggesting limited evidence of small-study effects or systematic bias. While the modest number of comparisons limits the power of formal bias detection, the absence of strong asymmetry supports the internal consistency of the dataset. This visual pattern indicates that large effects are not confined to small or imprecise studies, strengthening confidence in the observed yield responses.

Similarly, the shoot length funnel plot (Figure 4) shows an even spread of estimates across precision levels, with no

Figure 4. Funnel plot assessing publication bias in shoot length meta-analysis. This plot visualizes the relationship between standard error and effect size (mean difference in cm) for shoot length outcomes across studies. The vertical dotted line represents the pooled effect estimate, while the diagonal dashed lines define the expected distribution in the absence of bias. The symmetrical clustering of data points around the central line suggests minimal publication bias and supports the reliability of the aggregated results.

Figure 4. Funnel Plot Assessing Publication Bias in Meta-Analysis. This plot visualizes the relationship between study precision (standard error) and effect size (mean difference in kg/ha) across five studies. The red dotted line marks the overall mean effect, while the blue dashed funnel boundaries indicate expected variation in the absence of bias. Symmetry around the mean suggests low publication bias; asymmetry may indicate selective reporting or heterogeneity.

Table 1. Effect of PGPB Strains on Shoot Length. Comparison of shoot length (cm) in plants inoculated with different Plant Growth–Promoting Bacteria (PGPB) strains relative to uninoculated control plants. Standard deviations (SD) were calculated from reported standard errors (SE) using the formula: SD=SE×n, where n=3. Data can be used for forest plot analysis to compute Weighted Mean Differences (WMD).

Table 2. Effect of PGPB Strains on Grain Yield. Mean differences in grain yield (kg/ha) relative to the control group. Standard errors (SE) are reported for funnel plot analysis to evaluate study precision and potential bias.

obvious clustering that would indicate selective reporting. The dispersion observed is consistent with expected biological variability rather than methodological distortion. Together, the funnel plots complement the forest plot findings by suggesting that the statistical outcomes are not driven by a subset of extreme or biased observations.

The numerical summaries provided in Tables 1 and 2 further contextualize these graphical results. Mean differences, confidence intervals, and weighting factors collectively illustrate how individual comparisons contribute to the overall conclusions. Treatments with higher variance exert less influence on pooled estimates, ensuring that results are not disproportionately shaped by uncertain data points. This weighting structure enhances the credibility of the pooled effects and aligns with best practices in meta-analytic methodology.

From an interpretive standpoint, the statistical results indicate a coherent progression from vegetative growth enhancement to yield improvement. Shoot length responses, while moderate in magnitude, signal improved early plant vigor, which often translates into greater photosynthetic capacity and resource acquisition. The substantially larger effect sizes observed for grain yield suggest that these early advantages persist and amplify throughout the growth cycle, culminating in higher productivity. The statistical separation between treatments, especially between single-strain inoculations and microbial consortia, highlights the added value of functional diversity in microbial applications.

In summary, the statistical analysis demonstrates that PGPB inoculation exerts consistent, positive, and statistically significant effects on both shoot length and grain yield. The convergence of evidence from forest plots, funnel plots, and summary tables supports the robustness of these findings despite inherent biological variability. By integrating effect size magnitude, precision, and model-based weighting, the results provide a strong quantitative basis for concluding that PGPB—particularly when applied as multi-strain consortia—represent effective biological tools for enhancing crop performance.

3.1 Interpretation and discussion of funnel and forest plots

The funnel and forest plots together provide a clear visual and statistical narrative of how plant growth–promoting bacteria (PGPB) influence shoot length and grain yield, offering insight not only into the magnitude of these effects but also into their consistency, variability, and reliability across treatments. Interpreted collectively, these plots strengthen the evidence base by allowing both effect estimation and assessment of potential bias within the analyzed dataset.

The forest plots illustrate the direction and size of PGPB effects relative to control treatments, with each horizontal line representing an individual treatment comparison and its associated confidence interval. For shoot length, the forest plot shows that all bacterial treatments are positioned on the positive side of the effect axis, indicating a uniform trend toward enhanced vegetative growth. Although the magnitude of response varies among strains, none demonstrate a negative effect. This pattern suggests that PGPB inoculation is inherently growth-promoting rather than conditionally beneficial. Differences in effect size are visually apparent, with some strains producing modest increases and others eliciting more pronounced shoot elongation. The variability in confidence interval width reflects differences in precision, which may arise from biological factors such as strain-specific metabolic activity or experimental variability in plant responses.

The pooled estimate for shoot length lies clearly to the right of the null line, indicating an overall statistically meaningful improvement when results are aggregated. The fact that this pooled effect remains positive despite the inclusion of strains with broader confidence intervals underscores the robustness of the vegetative growth response. In practical terms, this suggests that while the degree of benefit may vary, the probability of achieving growth enhancement through PGPB application is high across diverse bacterial taxa.

The forest plot for grain yield reveals an even stronger and more agronomically relevant pattern. Effect sizes for yield are substantially larger than those observed for shoot length, reflecting the cumulative impact of microbial inoculation over the full crop growth cycle. All treatments demonstrate positive yield responses, with confidence intervals well separated from the null value. This separation indicates that yield improvements are statistically reliable rather than artifacts of sampling variation. The visual prominence of the microbial consortium treatment highlights the advantage of combining multiple bacterial strains, as its effect size exceeds those of individual strains while maintaining relatively narrow confidence intervals. This combination of magnitude and precision suggests synergistic interactions that enhance nutrient availability and utilization.

The forest plots also convey important information about heterogeneity. The spread of effect sizes across treatments reflects biological diversity in microbial function and plant–microbe interactions. Rather than weakening the conclusions, this heterogeneity adds ecological realism, indicating that PGPB effects are modulated by strain composition and management context. The clustering of most estimates within a consistent positive range suggests that variability influences the strength of the response rather than its direction, reinforcing the reliability of the overall trend.

Funnel plots complement these findings by addressing the question of bias and study precision. In the shoot length funnel plot, the distribution of effect sizes around the pooled estimate appears relatively symmetrical. Studies with higher precision cluster near the top of the plot, while those with lower precision are more dispersed, forming an approximate inverted funnel shape. This pattern is consistent with expectations in the absence of strong publication bias. The lack of pronounced asymmetry suggests that small or imprecise studies do not disproportionately report exaggerated positive effects, supporting the internal consistency of the dataset.

Similarly, the grain yield funnel plot shows a balanced spread of effect sizes on either side of the pooled estimate. While some dispersion is evident at lower precision levels, there is no clear skew toward one side of the funnel. This symmetry indicates that large yield effects are not confined to a subset of small studies but are also present in more precise comparisons. Although the limited number of studies constrains the sensitivity of bias detection, the visual pattern does not raise concerns about selective reporting or systematic distortion of results.

When interpreted together, the forest and funnel plots reinforce one another. The forest plots demonstrate consistent positive effects with biologically plausible variability, while the funnel plots suggest that these effects are not driven by bias or methodological artifacts. The alignment between these graphical tools strengthens confidence in the meta-analytic conclusions, as both magnitude and reliability point toward the same inference: PGPB inoculation enhances plant growth and productivity.

Importantly, these plots also provide insight into the relative performance of single-strain versus consortium-based treatments. The forest plots show that while individual strains can significantly improve growth and yield, the most pronounced and stable effects are associated with multi-strain inoculation. The funnel plots support this interpretation by showing that consortium effects are not outliers but fall well within the expected distribution of effect sizes.

Overall, the visual evidence from the funnel and forest plots supports a coherent and statistically sound interpretation of PGPB efficacy. The consistency of positive effects, the manageable level of heterogeneity, and the absence of strong bias indicators collectively suggest that the observed benefits are genuine and reproducible. These plots therefore play a central role in validating the conclusion that PGPB, particularly in consortium form, represent reliable tools for enhancing crop growth and yield within sustainable agricultural systems.

4. Discussion

The present meta-analytic findings provide strong quantitative support for the role of plant growth–promoting bacteria (PGPB) as effective biological agents for enhancing plant growth and grain yield, reinforcing decades of experimental and theoretical work on plant–microbe mutualisms. The consistently positive effects observed across forest plots align closely with foundational concepts of mutualism, where both partners derive measurable benefits through metabolic exchange and ecological cooperation (Bronstein, 1994; Doebeli & Knowlton, 1998). In the context of agriculture, PGPB function as microbial partners that improve plant nutrient acquisition, hormonal balance, and stress tolerance, ultimately translating into improved productivity under both optimal and suboptimal conditions.

One of the most compelling outcomes of this synthesis is the magnitude and consistency of yield enhancement, particularly when microbial consortia are applied alongside reduced fertilizer inputs. This observation strongly corroborates earlier experimental evidence demonstrating that PGPB can partially replace chemical fertilizers without compromising yield (Adesemoye et al., 2009; Vessey, 2003). The ability of bacteria to mobilize nutrients, fix atmospheric nitrogen, and solubilize phosphorus provides a mechanistic explanation for the statistically robust yield gains observed in the pooled estimates (Hayat et al., 2010; Egamberdiyeva, 2007). These processes reduce nutrient losses while improving nutrient-use efficiency, a critical requirement for sustainable intensification of agriculture.

The pronounced effects of microbial consortia relative to single-strain inoculations underscore the importance of functional complementarity among microorganisms. Similar synergistic benefits have been reported in cyanobacteria–bacteria and microalgae–bacteria systems, where cooperative interactions enhance metabolic output beyond what individual partners can achieve alone (Subashchandrabose et al., 2011; González-González & de-Bashan, 2021). In such systems, one organism may supply growth factors, vitamins, or chelated micronutrients, while the other provides carbon substrates or growth-stimulating compounds, resulting in stable and productive associations (Croft et al., 2005; Amin et al., 2009). The superior performance of consortia in the present analysis likely reflects similar exchanges occurring in the rhizosphere.

Shoot length responses, while more moderate than yield effects, provide important insight into early-stage plant vigor. Enhanced vegetative growth reflects improved hormonal regulation, particularly the modulation of auxins and ethylene. Numerous studies have demonstrated that PGPB possessing ACC deaminase activity reduce stress-induced ethylene levels, thereby promoting root and shoot elongation (Penrose & Glick, 2003; Orozco-Mosqueda et al., 2020). This hormonal regulation is especially relevant under abiotic stress conditions, where excessive ethylene accumulation can suppress growth. The consistency of positive shoot length effects across treatments in the forest plots suggests that this mechanism is widely conserved among effective PGPB.

Stress mitigation emerges as a central theme linking vegetative growth responses to final yield outcomes. Soil salinity, in particular, is a major constraint on global crop productivity, affecting large tracts of irrigated and rainfed land (Shahid et al., 2018). Multiple studies have shown that PGPB can enhance plant tolerance to salinity by improving ion homeostasis, osmolyte accumulation, and antioxidant activity (Afridi et al., 2019; Numan et al., 2018; Kumar et al., 2020). The yield gains observed in this meta-analysis are therefore likely amplified under stress-prone conditions, where microbial inoculation buffers plants against environmental constraints (Jiménez-Mejía et al., 2022; Sheirdil et al., 2019).

The ecological plausibility of these findings is further strengthened by insights from algal–bacterial interaction studies, which offer parallel examples of tightly coupled microbial partnerships. Within the phycosphere, bacteria and microalgae exchange vitamins, trace metals, and organic carbon in highly structured microenvironments (Seymour et al., 2017; Ramanan et al., 2016). These interactions mirror processes in the rhizosphere, where bacterial colonization and persistence are critical determinants of plant benefit (Santoyo et al., 2021). Advances in algal biotechnology have shown that such interactions can be deliberately engineered to enhance productivity, providing conceptual guidance for designing effective microbial inoculants in agriculture (Fabris et al., 2020; Khan et al., 2018).

The funnel plot analyses provide reassurance that the observed benefits are not artifacts of selective reporting. The relatively symmetrical distribution of effect sizes suggests that positive outcomes are not confined to small or low-precision studies, lending credibility to the pooled estimates. This is particularly important in applied microbial research, where variability in experimental design and environmental conditions can obscure true effects. The convergence of evidence across multiple biological systems—terrestrial plants, rhizobacteria, cyanobacteria, and microalgae—supports the conclusion that cooperative microbial interactions are a generalizable and reliable strategy for enhancing biological productivity (Kouzuma & Watanabe, 2015; Yao et al., 2019).

From an applied perspective, the implications of these findings are substantial. The demonstrated capacity of PGPB to enhance yield while reducing fertilizer inputs addresses two major challenges in modern agriculture: environmental degradation and economic sustainability. Excessive fertilizer use contributes to soil acidification, water pollution, and greenhouse gas emissions, while also increasing production costs for farmers. Microbial inoculants offer a biologically grounded alternative that aligns with ecological principles and evolutionary processes (Lauersen, 2019; Vuppaladadiyam et al., 2018).

Overall, this discussion situates the statistical outcomes of the meta-analysis within a broad biological and ecological framework. The positive effects of PGPB on shoot length and grain yield are consistent with established mechanisms of mutualism, stress mitigation, and metabolic cooperation documented across plant–microbe and algae–bacteria systems. By integrating these findings with prior experimental evidence, the present synthesis reinforces the view that PGPB-based strategies are not supplementary innovations but fundamental components of future sustainable agricultural systems.

5. Limitations

Despite the robust patterns observed in this systematic review and meta-analysis, several limitations should be acknowledged when interpreting the findings. First, the number of eligible studies and treatment comparisons was relatively limited, which constrains the statistical power of heterogeneity and publication bias assessments. Funnel plot symmetry should therefore be interpreted cautiously, as small datasets reduce the sensitivity of bias detection methods. Second, considerable biological and methodological variability existed among studies, including differences in crop species, bacterial strains, inoculation methods, soil properties, and experimental durations. Although a random-effects model was applied to accommodate this heterogeneity, residual variability may still influence pooled effect estimates.

Third, most included studies were conducted under controlled or semi-controlled conditions, which may not fully capture the complexity of field environments. Factors such as fluctuating climate, native microbial communities, and long-term soil dynamics can affect PGPB performance and persistence. Fourth, limited reporting of soil physicochemical parameters and microbial colonization dynamics restricted deeper exploration of mechanistic drivers underlying observed effects. Finally, few studies explicitly evaluated long-term impacts or multi-season outcomes, making it difficult to assess the durability of yield benefits and ecosystem-level consequences. These limitations highlight the need for standardized experimental designs and large-scale field trials to strengthen evidence translation into agricultural practice.

6. Conclusion

This study demonstrates that plant growth–promoting bacteria consistently enhance shoot growth and grain yield, with microbial consortia delivering the strongest benefits. Meta-analytic evidence supports PGPB as viable tools for improving productivity while reducing fertilizer dependence. By leveraging natural microbial mutualisms, PGPB-based strategies align agricultural intensification with ecological sustainability. Continued field validation and mechanistic research will be essential to optimize their application across diverse cropping systems.

Author Contributions

S.M.M.P. conceptualized the study, designed the systematic review and meta-analysis framework, conducted literature search, screening, data extraction, statistical analysis, and drafted the original manuscript. Z.S. contributed to data interpretation, methodological guidance, and critically reviewed and edited the manuscript for important intellectual content.  All authors read and approved the final version of the manuscript.

References