Pongamia pinnata L. Leaves Biochar Increased Growth and Pigments Syntheses in Pisum sativum L. Exposed to Nutritional Stress
Bibliographic record
Abstract
Pea (Pisum sativum L.) leaf chlorophyll and pigments syntheses are retarded under nutritional stress. Biochar has the potential to regulate soil nutrient supplies and optimize plant nutrient uptakes. We examine the role of Pongamia pinnata L. waste leaf biochar (PLB) in improving vegetative growth and leaf chlorophyll and accessory pigments of pea exposed to nutritional stress. Three PLB application rates (0, 1, and 2%) crossed with half (HF), and full NPK fertilizer (FF) recommended doses were applied to sandy soil field-pots (arranged in a completely randomized design). There were significant or maximum increases in plant vegetative or physiological traits, including the fresh or dry, above- and below-ground biomass weights, and photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll, carotenoids, and anthocyanin) in response to a 2%PLB + FF application (p = 0.002). Trait values also responded to 2%PLB + HF, which signified the nutrient regulatory character of PLB (p = 0.038). The PLB-driven reduction in nutritional stress resulted in diminished lycopene (antioxidant) content (p = 0.041). Therefore, we suggest that the soil application of 2%PLB + FF has the greatest impact on pea vegetative growth and leaf chlorophyll, carotenoids, anthocyanin, and lycopene contents in Pisum sativum L. Further research is recommended to investigate the relationship of PLB with soil nutrient availabilities and plant nutrient concentrations.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".