Tensiometer‐based irrigation scheduling and water use efficiency of field‐grown strawberries
Bibliographic record
Abstract
Abstract In recent decades, moisture‐based subsurface drip irrigation management strategies lead to water savings while maintaining strawberry yields in open fields. Most soils in the Québec City area are characterized by a high proportion of schist fragments and high hydraulic conductivity, causing substantial water losses following irrigation. Until now, little was known about the efficiency of soil‐moisture‐based irrigation strategies used on moderately coarse‐textured soils and their potential to improve water use efficiency (WUE). The first objective of this study was to test two tensiometer‐based irrigation managements for strawberry plants grown in a conventional system. Since field fumigation is increasingly restricted in field‐grown strawberries, we designed a raised‐bed trough system for use in the field and tested its productivity potential. The two irrigation methods included two treatments using pulsed irrigation: one with a constant irrigation threshold of −15 kPa (Pl), and the other with a variable threshold (−15/−30 kPa) adjusted daily to the predicted crop evapotranspiration (PlETc), in addition to a soilless treatment (SL) using a peat substrate and an irrigation threshold of −5 kPa. Compared to conventional irrigation, the PlETc treatment increased the WUE by 8 to 44% while maintaining marketable yields and fruit quality. Thus, this pulsed irrigation strategy is recommended for strawberry plants grown in clay loam soils. Strawberry plants grown in the soilless system gave 73 to 98% higher early marketable yield than soil cultivation. These results may encourage North American farmers to consider the soilless raised‐bed trough system as a viable alternative to conventional strawberry production systems.
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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".