Optimization of Water Management of Cranberry Fields under Current and Future Climate Conditions
Bibliographic record
Abstract
Abstract.\nIn North America, cranberry production is on the rise. Since 2005, land area dedicated to cranberry doubled, principally in Canada. Recent studies have shown that sub-irrigation could lead to improvements in yield, water use efficiency and pumping energy requirements compared to conventional sprinkler irrigation. However, the experimental determination of the optimal water table level of each production site may be expensive and time-consuming. The primary objective of this study is to optimize the water table level as a function of typical soil properties, and climatic conditions observed in major production areas using a numerical modeling approach. The second objective is to evaluate the impacts of projected climatic conditions on water management of cranberry fields. To that end, cranberry-specific management operations such as harvest flooding, rapid drainage following heavy rainfall, or hydric stress management during dry weather conditions were simulated with the HYDRUS 2D software. Results have shown that maintaining the water table approximately at 60 cm provides optimal results for most of the studied soils. However, under certain extreme climatic conditions, the drainage system design may not allow maintaining optimal hydric conditions for cranberry growth. The long-term benefit of this study has potential to advance the design of drainage/sub-irrigation systems.
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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 | 0.000 | 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) | 0.002 | 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".