Effect of Plant Nutrients on Vegetative Growth of Cavendish Banana
Bibliographic record
Abstract
A field experiment was conducted in Cavendish banana cv.'Grand Naine' to evaluate the effect of plant nutrients on vegetative growth.The nutrients comprised N, P, K, and Zn, Fe, Cu, and B supplied through soil application (NPK) and foliar spray (Zn, Fe, Cu, and B) at 3rd, 5th and 7th months after planting.Experiment was carried out under the alkaline soil condition of Directorate of Agricultural Research, Khajura, Banke, Nepal for two cropping seasons 2020-2021 (plant crop) and 2021-2022 (first ratoon crop).Trial was laid-out in randomized complete block design with seven treatments replicated three times.Planting of tissue culture banana was done on 4th July of 2020 as plant crop and suckers selected from the same plant at the same date was considered as the first ratoon crop.An experimental unit (16 m 2 ) consisted of four plants maintained 2×2 m spacing planted in 40 cm deep pit having the same diameter.FYM was supplied at four installments, basal, 3rd, 5th and 7th months after planting while chemical fertilizers (Urea, DAP, MoP) were applied at six installments, 30, 75, 110, 150, 180 days after planting and 100 g of MoP was applied during shooting.The pooled data of plant and first ratoon crop revealed that the greatest base circumference (68.92 cm), the tallest plant (222.00cm), the highest number of effective leaves (17.00), the greatest leaf area (16.07 m 2 ) and leaf area index (4.02), the highest plant spread (327.60 cm) and the highest number of cumulative leaves (44.00) were recorded at shooting in T3 (FYM 20 kg/plant + NPK 250:250:350 g per plant + ZnSO4 (0.5%) + FeSO4 (0.2%) + CuSO4 (0.2%) + Borax (0.1%).Therefore, from the study banana growers are highly recommended to use FYM, chemical fertilizers along with the foliar spray of micronutrients, at least for three times at vegetative growth phase of banana grown under alkaline soil condition for achieving the highest growth of plant.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".