Crop Water Deficit and Supplemental Irrigation Requirements for Potato Production in a Temperate Humid Region (Prince Edward Island, Canada)
Bibliographic record
Abstract
The global increase in potato production and yield is expected to lead to increased irrigation needs and this has prompted concerns with respect to the sustainability of irrigation water sources, such as groundwater. The magnitude, and inter- and intra-annual variation, of the crop water requirements and irrigation needs for potato production together with their impact on aquifer storage in a temperate humid region (Prince Edward Island, Canada) were estimated by using long-term (i.e., 2010–2019) daily soil water content (SWC). The amount of supplemental irrigation required for the minimal irrigation scenario (SWC = 70% of field capacity; 0.7 FC) was relatively small (i.e., 17.0 mm); however, this increased significantly, to 85.2 and 189.6 mm, for the moderate (SWC = 0.8 FC) and extensive (SWC = 0.9 FC) irrigation scenarios, respectively. The water supply requirement for the growing season (GS) increased to 154.9 and 344.7 mm for a moderately efficient irrigation system (55% efficiency) for the SWC = 0.8 FC and SWC = 0.9 FC irrigation scenarios, respectively. Depending on the efficiency and the areal extent of the irrigation system, the irrigation water supply requirement can approach or exceed both the GS and annual groundwater recharge. The methodology developed in this research has been translated into a free online tool (SWIB—Soil Water Stress, Irrigation Requirement and Water Balance), which can be applied to other areas or crops where an estimation of soil water deficit and irrigation requirement is sought.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".