Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island
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
Climate change induced uneven patterns of rainfall emphasize the use of supplemental irrigation in rainfed agriculture. The Penman–Monteith method was used to calculate supplemental irrigation for water budgeting of a potato crop in Prince Edward Island, Canada. Cumulative gaps between rainfall and crop evapotranspiration (ETc) during August and September of the study years were due to high crop coefficient factor, justifying the need for supplemental irrigation. Pressurized irrigation systems, including sprinklers, fertigation, and drip irrigation were installed, to evaluate the impact of scheduled supplemental irrigation in offsetting deficits in irrigation water requirements in comparison with conventional practice of rainfed cultivation (control). A two-way ANOVA examined the effect of irrigation methods and year on potato tuber yield, water productivity, tuber quality, and payout. Sprinkler and fertigation systems performed better than drip and control treatments. In terms of payout returns and potato tuber quality (percentage of marketable potatoes), the sprinkler treatment performed significantly better than the other treatments. However, for water productivity, fertigation treatment performed significantly better than control and sprinkler treatments during both years. The use of supplemental irrigation is recommended for profitable cultivation of potatoes in soil, agricultural, and environmental conditions resembling to those of Prince Edward Island.
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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 | 0.001 | 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 | 0.001 |
| 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 it