Impact of Organic NPK Nano Fertilizer on Growth and Physiological Parameters of Different Shallot Varieties
Bibliographic record
Abstract
The constant use of chemical fertilizers is associated with low nutrient-use efficiency and contributes to gas emissions, leading to global warming.Meanwhile, nano fertilizers represent an effort to increase nutrient-use efficiency.This study aimed to determine the effect of organic NPK nano fertilizers on growth performance and physiology of shallot plants.A completely randomized design was used in a factorial arrangement with 12 combinations and three replicates.The results showed that the best response was found in the treatment of Crok Kuning variety treated with 220 kg ha -1 Nitrogen, 160 kg ha -1 Phosphorus, and 120 kg ha -1 Potassium.This treatment increased plant height by 4.64%, leaf number by 8.89%, leaf area by 87%, plant fresh weight by 87%, stomatal aperture width by 33.20%, chlorophyll content by 23.7% and nitrate reductase activity by 9.6% compared to chemical fertilizers.The Tajuk variety treated with 160 kg/ha -1 Nitrogen, 100 kg ha -1 Phosphorus, and 60 kg/ha -1 Potassium fertilizers showed an increased in plant height by 4.80%, leaf number by 15.45%, leaf area by 60%, plant fresh weight by 60%, stomatal aperture width by 41.15%, chlorophyll content by 17.30% and nitrate reductase activity by 8.9% compared to chemical fertilizers.The application of organic nanofertilizers has the potential to improve nutrient efficiency and physiological performance of shallots, and to promote sustainable agriculture by reducing the environmental impact of chemical fertilizers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".