Macronutrient application affects morphological, physiological, and seed yield attributes of Calendula officinalis L.
Bibliographic record
Abstract
Effects of N, P, and K applications alone or in various combinations and ratios were studied on the morphological, physiological, and seed yield attributes and on seed production of calendula. Treatment combinations were control (no N–P–K application), 150 kg ha−1 N, 80 kg ha−1 P, 150 kg ha−1 K, N–P, N–K, P–K, and N–P–K, while in a second experiment, different ratios of N–P–K were compared to determine the best combination and ratio of tested nutrients for optimal growth, quality, yield, and seed production. Plants supplied with N–P–K had vigorous growth, had higher total leaf chlorophyll content, and flowered earlier with greater flower fresh and dry weights, along with improved photosynthetic performance. Plant biomass and seed yield along with leaf N and K were also higher in plants fertilized with N–P–K. In the second experiment, the application of 200–100–100 kg ha−1 N–P–K resulted in maximum growth, flowering, and seed yield, along with higher photosynthetic activity. Increased leaf area and improved leaf nutrient status were observed at 150–150–150 kg ha−1 N–P–K, while 200–200–200 kg ha−1 N–P–K increased stomatal conductance, photosynthetic rate, leaf P, and flower weights. Results demonstrated that a higher level of N along with lower level of P and K are vital for quality calendula flower and seed production.
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 | 0.000 | 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".