Irrigation Strategies for Greenhouse Tomato Production on Rockwool
Bibliographic record
Abstract
To address the concern that irrigation provides sufficient water to match the crop needs, while not impeding oxygen availability to the roots, we conducted an experiment to develop suitable irrigation schedule(s) for greenhouse tomato ( Lycopersicon esculentum Mill.) on rockwool. The experimental treatments incorporated the electrical conductivity (EC) of the nutrient solution in the rockwool slab (slab-EC) along with the water content (WC) in the rockwool slab (slab-WC) as the irrigation decision-making variables. They were: slab-WC ≤ 70% or slab-EC ≥ 1.4× normal or more (T1), slab-WC ≤ 70% or slab-EC ≥ 1.7× normal or more (T2), slab-WC ≤ 80% or slab-EC ≥ 1.4× normal or more (T3), slab-WC ≤ 80% or slab-EC ≥ 1.7× normal or more (T4), and the combined weight loss (WL) 700 g or more (T5) and WL 500 g or more (T6), in which “normal” means the feed solution EC as recommended in the seasonal fertigation schedule for a spring–summer tomato crop. The data on early-season marketable yield, total seasonal marketable yield, and fruit grades indicated the superiority of treatments T1, T2, and T6 over T3, T4, and T5. Better root growth was observed with T1, T2, and T6 and this was also associated with minimized nutrient solution leaching; furthermore, these plants had an abundance of coarse and fine roots, higher photosynthesis and transpiration, higher marketable yield, and a higher water use efficiency. Our results thus established that irrigation based on either a slab water content 70% or less or a 500-g weight loss is the best strategy for rockwool-grown greenhouse tomatoes in the spring–summer season. A variation in slab-EC between 1.4 and 1.7× normal, at a slab-WC of 70% or less, would have no significant effect on root growth, water use, marketable yield, or fruit grades.
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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 | 0.001 |
| 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".