(308) Irrigation Strategies for Rockwool-grown Greenhouse Tomatoes
Bibliographic record
Abstract
In rockwool-grown greenhouse vegetables, unsatisfactory spatial root development, rapid root collapse, and increased incidence of root diseases are very common. Improved water management could alleviate these problems to some extent, because this could favorably modify water-air distribution in the slab, thereby improving greenhouse vegetable yield and quality. The present study tested six irrigation strategies on the productivity of rockwool-grown tomatoes (cv. Rapsodie) during Jan. o –Aug. 2004. The four treatments, based on electronic Grodan? water content meters (WCMs), received irrigations when the slab water content (SWC) was ≈60%, 70%, 80%, and 90% while the other two treatments, based on balances, applied irrigations after a 700- or 500-g loss in the daily-adjusted slab weight (LDASW). Initially, we noticed a heterogeneous EC build-up in the slabs across various treatments, which probably distorted the expression of treatment effects (if any) on plant growth, yield, and water use. To minimize this problem, an EC control strategy of applying extra irrigation was devised and adopted in two sequential phases: 1) application of a 30-minute-long extra irrigation twice a week (for 7 weeks); and 2) extra irrigation(s) using the irrigation control routine of an Argus computer when the slab EC was ≈3.5 mS/cm (for 5 weeks). Slab EC was well controlled in both these phases and we observed significant treatment effects on root growth and marketable yield. Analyzing the results, we concluded that irrigating at 70% to 80% SWC was best for maximum root growth as well as marketable yield. The two irrigation treatments based on the 700- and 500-g LDASW were well maintained and performed equally well, producing marketable yields comparable to those produced by irrigating at 70% and 80% SWC.
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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 | 0.000 | 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".