Foliar application of pyroligneous acid acts synergistically with fertilizer to improve the productivity and phytochemical properties of greenhouse-grown tomato
Bibliographic record
Abstract
Abstract Pyroligneous acid (PA) is rich in bioactive compounds and known to have the potential to improve crop productivity and phytochemical content. However, the synergistic effect of PA and fertilizer has not been thoroughly studied. In this study, we assessed the biostimulatory effect of different rates of foliar PA application (i.e., 0, 0.25, 0.5, 1, and 2% PA/ddH2O (v/v)) combined with full rate (i.e., 0.63, 0.28, 1.03 g) and half rate of nitrogen-phosphorus-potassium (NPK) fertilizer on the yield and nutritional quality of greenhouse-grown tomato (Solanum lycopersicum ‘Scotia’). Plants treated with 0.25% and 0.5% PA showed a significantly (p < 0.001) higher maximum quantum efficiency of PSII (Fv/Fm) and increased potential photosynthetic capacity (Fv/Fo), especially when combined with the full NPK rate. Leaf chlorophyll was significantly (p < 0.001) increased by ca. 0.60 and 0.49 folds in plants treated with 2% PA and full NPK rate compared to no spray and water, respectively. Total number of fruits was significantly (p < 0.001) increased by ca. 0.56 folds with the 2% PA irrespective of the NPK rate. The combined 2% PA and full NPK rate enhanced total fruit weight and the number of marketable fruits. Similarly, fruit protein, sugar and 2,2-diphenyl-1-picrylhydrazyl (DPPH) activity were significantly (p < 0.001) enhanced by the combined 2% PA and full NPK rate. In contrast, the 0.5% PA combined with half NPK rate increased fruit carotenoid and phenolic contents while the 2% PA plus half NPK rate enhanced fruit flavonoid content. Generally, the synergistic effect of PA and NPK fertilizer increased fruit elemental composition. These showed that foliar application of PA can be a novel and environmentally friendly strategy to increase the productivity and quality of tomato fruits.
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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) | 0.001 | 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".