Irrigation and drainage management strategies to enhance cranberry production and optimize water use in North America
Bibliographic record
Abstract
Recent research funding, as well as technological and management changes, has led to important scientific discoveries on irrigation and drainage of cranberry that could significantly impact on plant yield and water use. This paper integrates all this information into new proposed guidelines for irrigation and drainage management of cranberry. It explains the interaction of the different concepts, with the most recent ones published in this special issue. Cranberry yield is very sensitive to wet anaerobic conditions (soil matric potential >−4 kPa) or dry bed conditions (<−7 kPa) limiting capillary rise. It also appears that important water savings can be achieving by irrigating by a combination of overhead and subirrigation maintaining the top 15 cm of the bed within those soil matric potential limits and to meet an evapotranspiration demand up to 7.5 mm d−1, provide frost and heat protection, and avoid salt accumulation, as this crop also appears sensitive to salinity stress. Finally, following plantings, soil properties appear to evolve dynamically and should be followed through profile observations, and combination of soil water potential and ground penetrating radar data, to identify potential yield limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 teacher head, 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".