Various Fertilization Managements Influence the Flowering Attributes, Yield Response, Biochemical Activity and SoilNutrient Status of Chrysanthemum (Chrysanthemum morifolium Ramat.)
Bibliographic record
Abstract
Optimal nutrient management is critical for optimizing flowering, yield, quality and improving soil health. A key approach for making chrysanthemum crop cultivation profitable is balanced fertigation at the right time. This is possible by fertigation through drip. The present study was designed in 2019–2021 at a model floriculture center, Pantnagar, to investigate the response of split application of NPK through drip fertigation on flowering attributes, yield, biochemical activity and soil nutrient status of chrysanthemum. Plants received application of NPK with five treatment combinations: T1-NPK @ 100:150:100 kg/ha/year, T2-NPK @ 100:150:100 kg/ha/year, T3-NPK @ 100:150:100 kg/ha/year, T4-NPK @ 75:112.5:75 kg/ha/year and T5-NPK @ 75:112.5:75 kg/ha/year at vegetative, bud and flowering stages. The results reveal that the plants treated with treatment T3 (NPK @ 100:150:100 kg/ha/year) exhibited maximum increases in floral bud diameter (31.45%), number of inflorescences per branch (24.44%), diameter of inflorescence (15.32–28.44%), weight of inflorescence (24.30%), stem diameter, inflorescence stem length, number of inflorescences per plant (6.16%), number of inflorescences per hectare (53.46%), chlorophyll a content, chlorophyll b content, total chlorophyll content (40.20%), carotene content of inflorescence (69.56%), organic carbon (1.22-fold), available nitrogen content (7.46%), available phosphorus and available potassium (1.14-fold) compared to the control. Conclusively, the results suggest that split application of NPK through drip fertigation may improve the inflorescence attributes, yield, biochemical activity and soil nutrient status of chrysanthemum.
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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".