Towards optimization of growth via nutrient supply phasing: nitrogen supply phasing increases broccoli (Brassica oleracea var. italica) growth and yield
Bibliographic record
Abstract
A greenhouse experiment on broccoli (Brassica oleracea var. italica, cvs Windsor and Arcadia) was carried out in order to demonstrate that supplying nitrogen (N) to meet the nitrogen demands of plant growth stages, through N phasing, improves plant growth and yield, as compared to fertilizing at the conventional, optimal, constant N rate. Two broccoli cultivars and two rates of starter nitrogen fertilizer (optimum, 250 mg l(-1) and sub-optimum, 150 mg l(-1)), were combined with three timings of fertigation change. Shifting N rate, at 60% and 75% of the market plant growth cycle significantly increased shoot dry weight and head fresh weight, compared to the constant-N rates treatments (controls). The highest yield and shoot dry weight were obtained when the N-rate was switched from the optimum level (250 mg l(-1)) to the sub-optimum level (150 mg l(-1)) at inflorescence initiation. The nitrogen-to-growth-stage-fitness effect was determined and partitioned into rate effect and phasing effect. The phasing effect was greatest, on both shoot dry weight and head fresh weight, at inflorescence initiation, and subsequently decreased until harvest time. None of the interactions was significant. The results demonstrated the superiority of nitrogen supply phasing over the conventional fixed-rate-supply method.
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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) | 0.001 | 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".