Effect of Soil Water Potential Threshold for Irrigation on Cranberry Yield and Water Productivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<abstract> <bold><sc>Abstract.</sc></bold> 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 (SWP<sub>I</sub>) 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 SWP<sub>I</sub> values ranged from -5.5 to -8.0 kPa; however, a significant yield reduction of 11% was observed for a SWP<sub>I</sub> value of -10.0 kPa. The WP values in dry treatments were always higher than those in control and wet treatments. Dry treatments, with SWP<sub>I</sub> ranging from -7.0 to -8.0 kPa, significantly improved the water productivity without decreasing yield.
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.
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) | 0.000 | 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 it