Irrigation Strategies for Greenhouse Tomato Production on Rockwool
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 it