Effects of calcium and magnesium on growth, fruit yield and quality in a fall greenhouse tomato crop grown on rockwool
Bibliographic record
Abstract
Tomato (Lycopersicon esculentum Mill.) ‘Trust’ was grown on rockwool with two concentrations of calcium (150 and 300 mg L-1) in combination with four concentrations of magnesium (20, 50, 80 and 110 mg L-1) in fall, 1999, to investigate their effects on plant growth, leaf photosynthesis, and fruit yield and quality (fruit firmness, dry matter, soluble solids and russeting). High Ca (300 mg L-1) concentration increased fruit yield and reduced the incidence of blossom-end rot (BER) and fruit russeting, compared with the low Ca concentration (150 mg L-1). High Ca concentration reduced fruit firmness but did not affect fruit size and leaf photosynthesis. Plants grown at 20 mg L-1 Mg started to show leaf chlorosis on both the middle and bottom leaves 8 wk after planting. Leaves with moderate chlorosis lost about 50% of their photosynthetic capacity. Fruit yield in the late growth stage decreased at 20 mg L-1 Mg. Blossom-end rot incidence increased linearly with increasing Mg concentration in the early growth stage at low Ca, but BER incidence at high Ca was not affected by Mg concentration. Fruit firmness increased with increasing Mg concentration at low Ca. At high Ca, Mg concentration affected fruit firmness only late in the season; fruit firmness at 80 mg L-1 Mg was higher than at 50 mg L-1 Mg concentration. Fruit russeting in mid-season was affected by nutrient treatments, being the least at 300/50 mg L-1 Ca/Mg. Therefore, for a fall greenhouse tomato crop, the optimum Ca/Mg concentration for tomato production is estimated to be 300/50–80 mg L-1. The Mg concentration may be started at 50 mg L-1 and gradually increased to 80 mg L -1 towards the end of the season, to improve plant growth and fruit firmness. Key words: Greenhouse, tomato, Lycopersicon esculentum, yield, quality, photosynthesis, calcium, magnesium
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".