Effect of Soil Water Potential Threshold for Irrigation on Cranberry Yield and Water Productivity
Bibliographic record
Abstract
Abstract. As the cranberry industry implements irrigation automation, thresholding based on real-time monitoring of soil moisture to initiate irrigation is lacking. This study was conducted to determine the optimum soil water potential for starting sprinkler irrigation (SWPI) that would optimize water productivity (WP) without decreasing yield. During the 2011 and 2012 growing seasons, three sites in Québec and one site in Wisconsin were equipped with tensiometers, flowmeters, and weather stations for testing wet (-5.5 kPa), dry (-7.0 to -10.0 kPa), and control (-6.0 to -6.5 kPa) treatments. The experimental designs were developed to evaluate the impact of irrigation treatments on yield and WP. Dry treatments required 21% to 93% less irrigation water than the control treatments; wet treatments needed 54% to 186% more irrigation water than the control treatments. Irrigation treatments had no significant effect on yield when SWPI values ranged from -5.5 to -8.0 kPa; however, a significant yield reduction of 11% was observed for a SWPI value of -10.0 kPa. The WP values in dry treatments were always higher than those in control and wet treatments. Dry treatments, with SWPI ranging from -7.0 to -8.0 kPa, significantly improved the water productivity without decreasing yield.
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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 | 0.001 |
| 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".