Evaluating the nitrification inhibition potential of selected botanicals and their non-target effects
Bibliographic record
Abstract
Nitrification inhibitors (NI) are aimed at improving N-fertilizer use efficiency in cropping systems. This study aimed to assess the nitrification inhibition potential and non-target effects of dry leaf powders (botanicals) of 10 plant species (neem (Azadirachta indica), lantana (Lantana camara), karanda (Pongamia pinnata), Brachiaria (Brachiaria humidicola), cinnamon (Cinnamomum verum), clove (Syzygium aromaticum), wild-sunflower (Tithonia diversifolia), mee (Madhuca longifolia), nutmeg (Myristica fragrans), and pepper (Piper nigram)). The effect of botanicals on the growth of three ammonia oxidizing bacteria isolates (M4, M5, and M7) and NO3− formation in soil were tested in laboratory experiments. In a pot-experiment, botanicals were applied with urea to assess their effect on vegetative-growth of tomato and capsicum. The non-target effects of botanicals on soil bacteria and fungi, and seed germination were assessed separately. Dicyandiamide (DCD) was used as the positive control in all experiments. Only the growth of M7 was significantly suppressed by all botanicals except neem, Brachiaria, and pepper. The highest shoot-biomass of tomato was obtained when urea was applied with pepper. Nitrate leaching from pots was significantly reduced (P < 0.05) when urea was added with botanicals except for clove and wild-sunflower at basal-dressing. The abundance of culturable fungi and bacteria were not affected significantly by botanicals while seed germination was reduced significantly and consistently by clove only. From the 10 botanicals tested leaf powders of nutmeg, lantana, cinnamon, mee, and pepper were identified as materials with better potential to suppress nitrification with minimum non-target effects.
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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 | 0.000 |
| 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".