Enhancing drought tolerance in Pisum sativum and Vicia faba through interspecific interactions with a mixed inoculum of Rhizobium laguerreae and non-host beneficial rhizobacteria
Bibliographic record
Abstract
Introduction Harnessing plant growth-promoting rhizobia presents a sustainable and cost-effective method to enhance crop performance, particularly under drought stress. This study evaluates the variability of plant growth-promoting (PGP) traits among three strains of Rhizobium laguerreae LMR575, LMR571, and LMR655, and two native PGP strains Bacillus LMR698 and Enterobacter aerogenes LMR696. The primary objective was to assess the host range specificity of these strains and their effectiveness in improving drought tolerance in three legume species: Pisum sativum, Vicia faba, and Phaseolus vulgaris. Methods In-vitro experiments were conducted to assess the PGP traits of the selected strains, including phosphate solubilization, indole-3-acetic acid (IAA) production, and siderophore production. Greenhouse trials were also performed using a mixed inoculum of performing strains to evaluate their effects on plant physiological and biochemical traits under drought conditions. Results Significant variability in PGP traits was observed among the strains. R. laguerreae LMR655 exhibited the highest phosphate solubilization (113.85 mg mL-1 PO42-), while R. laguerreae LMR571 produced the highest IAA concentration (25.37 mg mL-1). E. aerogenes LMR696 demonstrated 82% siderophore production. Symbiotic interactions varied, with R. laguerreae LMR571 and LMR655 forming associations with P. sativum and V. faba, but none establishing compatibility with P. vulgaris. Greenhouse experiments showed that a mixed inoculum of R. laguerreae LMR571, LMR655, and E. aerogenes LMR696 significantly improved proline, total soluble sugars, proteins, and chlorophyll content under drought stress, with V. faba showing the strongest response. Discussion These findings highlight the importance of strain selection based on host specificity and PGP potential. The enhanced drought tolerance observed suggests that tailored microbial inoculants can improve legume resilience in water-limited environments. This study provides valuable insights for optimizing bioinoculant formulations to enhance crop performance under drought stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".