Study on the Effects of Different Irrigation Strategies on the Yield and Quality of <i>Chrysanthemum morifolium</i>
Bibliographic record
Abstract
We studied the effects of different irrigation methods on the yield, quality, stress resistance and water use efficiency of Chrysanthemum morifolium . The results showed that water-saving irrigation and precision irrigation during the growth period could significantly improve the water use efficiency without affecting the yield. If sufficient irrigation is adopted, although the yield per mu is the highest, the water use efficiency is poor. We also noticed that the amount of effective components such as flavonoids and volatile oil would change with different irrigation methods. Among them, precision irrigation can make these medicinal ingredients accumulate more. Water saving irrigation also makes chrysanthemum more drought resistant and disease resistant, and powdery mildew and leaf spot disease occur less. The study also shows that efficient irrigation methods such as drip irrigation can further improve water use efficiency and provide a good help for planting in different regions. This study provides a theoretical basis and technical methods for the efficient planting of Chrysanthemum morifolium . By adjusting the irrigation strategy, it can not only save water resources, but also promote the development of chrysanthemum industry, and also help to promote the development direction of green agriculture.
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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".