Zero budget natural farming components Jeevamrit and Beejamrit augment Spinacia oleracea L. (spinach) growth by ameliorating the negative impacts of the salt and drought stress
Bibliographic record
Abstract
L.), can be significantly impeded by salinity and drought. However, pre-treating spinach plants with traditional biofertilizers like Jeevamrit and Beejamrit (JB) substantially reverses the salinity and drought-induced inhibitory effects. Hence, this study aims to elucidate the underlying mechanisms that govern the efficacy of traditional fertilizers. The present work employed comprehensive biochemical, physiological, and molecular approaches to investigate the processes by which JB alleviates abiotic stress. The JB treatment effectively boosts spinach growth by increasing nutrient uptake and antioxidant enzyme activity, which mitigates the detrimental effects of drought and salinity-induced stress. Under salt and drought stress conditions, the application of JB resulted in an impressive rise in germination percentages of 80 and 60%, respectively. In addition, the application of JB treatment resulted in a 50% decrease in electrolyte leakage and a 75% rise in the relative water content of the spinach plants. Furthermore, the significant reduction in proline and glycine betaine levels in plants treated with JB provides additional evidence of the treatment's ability to prevent cell death caused by environmental stressors. Following JB treatment, the spinach plants exhibited substantially higher total chlorophyll content was also observed. Additionally, using 16S rRNA sequencing, we discovered and characterized five plant-beneficial bacteria from the JB bio-inoculants. These bacterial isolates comprise a number of traits that contribute to growth augmentation in plants. These evidences suggest that the presence of the aforesaid microorganisms (along with additional ones) is accountable for the JB-mediated stimulation of plant growth and development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".