Biochar and plant growth-promoting rhizobacteria enhance physio-biochemical traits, secondary metabolites, oil, and grain yield of rapeseed under salinity stress
Bibliographic record
Abstract
Abstract Salinity stress poses a significant constraint on global productivity. This study was designed to investigate the combined effects of inoculation with Azotobacter chroococcum (Az) and soil amendment with biochar on the performance of rapeseed ( Brassica napus L.) under varying levels of NaCl-induced salinity. A factorial experiment evaluated key physio-biochemical responses, oxidative stress indicators, defense compound accumulation, and yield components. Salinity stress negatively impacted rapeseed growth, significantly reducing photosynthetic efficiency (Fv/Fm), chlorophyll content, relative water content (RWC), ATP levels, and ultimately grain and oil yields. Concurrently, salinity (120 mM and control treatments) increased indicators of oxidative damage — electrolyte leakage by 54%, malondialdehyde by 37% — and elevated levels of reactive oxygen species (O 2 •− at 97 µmol g −1 FW h −1 and H 2 O 2 at 86 µmol g −1 FW). Additionally, it induced defense responses such as increased antioxidant enzyme activity and osmolyte accumulation. However, the co-application of Az and biochar significantly mitigated these adverse effects of salinity. Notably, the combined treatment, particularly Az inoculation with an elevated biochar application rate (20%), demonstrated synergistic effects. This combination significantly enhanced photosynthetic efficiency, improved plant water status, and augmented antioxidant defense systems as well as osmotic adjustment mechanisms (including soluble sugars, proline, etc.) relative to untreated plants under saline conditions. Under these conditions, grain and oil yields increased substantially by 16.8% and 12.6%, respectively, compared to untreated saline-stressed plants. These findings highlight the potential of combining Az inoculation with biochar application as a promising and sustainable strategy to improve salinity tolerance and enhance the productivity of rapeseed in salt-affected agricultural environments.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".