Effect of Plant Nutrition on Fruit Set
Bibliographic record
Abstract
Fruit set and retention determine the ultimate yield of mandarin; as adequate mineral elements play key role. The study investigated the effects of foliar applications of nitrogen, potassium, calcium including boron and zinc; alone and their combinations; on fruit set, growth and yield of mandarin; the experiments conducting in the Dhankuta district of Nepal during 2019 and 2020. The results revealed that severe flowers/fruitlets drop occurred to a range of 97.7% to 92.7% during bloom fruit set period as the highest set (7.3%) occurred at foliar spray of NK + micro-nutrients compared to the control treatment (2.3%). Accordingly, compared to the control treatments, spray application of NK+ micro-nutrients, and NPK soil + micro-nutrients increased the higher fruit set by 151.7 and 41.3% respectively during post-bloom since 28 th March until 1 st May. In regard to fruit drop, two treatments of foliar sprays: NPK + B + Zn + Ca; and NK + B + Zn + Ca had the lower fruit drops by 15.8 and 15.4 during June; and by 16.2 and 15.9% during preharvest period respectively, compared to the control treatment. The highest fruit diameter (58.9 mm) and weight (63.4 g) were found at the treatment NPK + B + Zn + Ca among the treatments; likewise, the corresponding the highest number of fruits (1790 nos) and total fruit yield (101.81 kg) per tree were also observed at the same treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".