Effects of Water Stress at Different Growth Stages on Greenhouse Tomato Yield and Quality
Bibliographic record
Abstract
Tomatoes ( Lycopersicon esculentum Mill. cv. Sunstart) were grown in a greenhouse during Summer 1999 and again in Winter 2000. Two available soil water (ASW) deficit thresholds, 65% and 80%, at which plants were irrigated to field capacity were factorially combined with five irrigation timing patterns: 1) no water stress; 2) stress throughout the entire growing season; 3) stress during first cluster flowering and fruit set 4) stress during first cluster fruit growth; and 5) stress during first cluster fruit ripening. Crop yields, water use efficiency, as well as maximum and minimum equatorial fruit diameters and fruit height were measured. Quality parameters of soluble solids, pH, and fruit color were also measured. Water stress throughout the growing season significantly reduced yield and fruit size, but plants stressed only during flowering showed fewer but bigger fruit than completely non-stressed plants. Consequently, on a weight basis the stressed at flowering and nonstressed plants had similar yields. Nonstressed and flowering-stressed fruit showed lower soluble solids and a lighter color of red ripe fruit than the other stress treatments. No significant differences in yield or quality were found between the two stress levels (65% vs. 80% ASW depletion before irrigation). Water stress only during flowering resulted in better yields and quality than stress at other specific developmental stages or at all times, but equal or poorer yields and water use efficiency than nonstressed plants.
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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 | 0.001 |
| 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".