Growth and water-use characteristics of Romaine lettuce cultivated in Histosol as affected by irrigation management, compaction, and seeding type
Bibliographic record
Abstract
In Canada, most lettuce (Lactuca sativa L.) is produced on cultivated organic soils, which can be very productive but are also very sensitive to degradation and compaction. The objective of this work was to evaluate the effect of soil compaction, irrigation thresholds, and transplant type on the growth and water-use characteristics of Romaine lettuce that is grown in organic soil. The experiments were conducted in greenhouses at Laval University. Tensiometers and time-domain reflectometer probes were used to characterize the water-use characteristics of the Romaine lettuce. Most of the growth characteristics of the Romaine lettuce, with the exception of the dry weight, were significantly influenced by the available rooting depth (soil column height) and by the irrigation threshold used. Lettuce water uptake decreased significantly as the depth increased. In addition, in drier conditions, the deeper soil layers contributed more to the total water uptake than the surface soil layers. The water productivity was lower in the presence of a compacted layer combined with a direct seeding treatment, compared with all of the other treatments. First, it is concluded that the irrigation method should allow a certain degree of dryness by use of a lower irrigation threshold (ideally between −20 and −30 kPa) to stimulate deep rooting. Second, the use of small lettuce plant preseeded in small block of peat substrate instead of direct seeding in the field can compensate for a possible compaction effect.
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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) | 0.000 | 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 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".