Optimizing manganese and iron delivery for contrasting cultivars of subirrigated greenhouse-grown pot chrysanthemums
Bibliographic record
Abstract
Excessive fertilizer use in greenhouse floricultural operations results in low-nutrient use efficiency by plants and poses environmental risk. Here, we optimized the usage of fertilizer manganese (Mn) and iron (Fe) by modern cultivars of subirrigated pot chrysanthemum. Mn and Fe (approximately 100% to 6% of industry standards) were provided in an otherwise balanced nutrient solution during vegetative growth, and all nutrients were removed during reproductive growth. Two experiments were conducted for each nutrient in a naturally lit research greenhouse using a split-plot design with four blocks arranged randomly. Mn (5.00–0.3125 µmol L−1) or Fe (10.56–0.66 µmol L−1) was the main plot and cultivar (“Milton Dark Pink”, “Williamsburg Purple”, and “Olympia White”) was the subplot. The cultivars exhibited contrasting phenotypes. However, any treatment effects on plant yield and inflorescence development and quality were minor, so that Mn or Fe use efficiency increased approximately 16-fold with decreasing supply. Even though leaf Mn, zinc, and calcium levels were occasionally correlated inversely with decreasing Fe delivery, the leaf Mn (44.8–121.8 mg kg−1) and Fe (68.5–121.8 mg kg−1) levels were always considered acceptable. These findings contribute to the development of a low-input practice that would improve the sustainability of floricultural crop production.
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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 | 0.001 | 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 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".