Determination of irrigation set points for cranberries from soil- and plant-based measurements
Bibliographic record
Abstract
Cranberry production requires accurate irrigation management to optimize crop yield and reduce water use. However, irrigation guidelines for that crop are scarce and empirical. The objective of this study was to identify appropriate soil matric potential (ψ) irrigation set points for cranberry production. A three-step process was used to evaluate the set points. Crop water requirements were first evaluated in the field and, second, combined to soil physical properties with a hydrological model to estimate irrigation set points. Third, experimental measurements were carried out in a growth cabinet and in the field to validate the set point estimates from independent observations. Irrigation set point estimates obtained from yield response curves, photosynthesis and transpiration measurements, and soil physical properties were all consistent and suggest that soil matric potential be maintained between −4.0 and −7.0 kPa to ensure an adequate water supply to the crop and optimal fruit yield. Yield responses suggest that cranberries are highly sensitive to small changes in soil matric potential, showing differences of about 20 000 kg ha−1 when outside of the −4.0 to −7.0 range, with a maximum yield between 35 000 and 40 000 kg ha−1, depending on the site.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".