Modelling irrigation strategies to minimize deep drainage for two different climatic regions of Canada
Bibliographic record
Abstract
Irrigation is a vital part of agriculture in certain regions of Canada including the interior of British Columbia.In this study we examined the use of a soil water budget model for efficient irrigation management in two contrasting climatic regions of British Columbia: Abbotsford (AD) and Osoyoos (OS).The average annual precipitation at AD and OS are 1573 and 318 mm, respectively.The soil types (AD -silt loam and OS -sand) and major crops (AD -raspberry and OSapple) are also quite different between the two regions.We used the Simultaneous Heat and Water (SHAW) model to estimate the amount of deep drainage and soil water content under different irrigation management strategies.The SHAW model integrates detailed physics of vegetative cover, snow, residue and soil into one simultaneous solution.The model was run on a daily basis for 28 and 32 years for AD and OS regions, respectively.Different combinations of crop and irrigation conditions were run for each region.Based on this study, the "best" irrigation management strategy involves triggering every irrigation event when the soil water content (estimated by SHAW) in crop's rooting zone reaches a prescribed amount below field capacity.At that time, 40 mm of irrigation is added as rainfall.Other strategies involved adding more irrigation and a constant weekly irrigation regardless of rainfall and soil water content.In conclusion, while most of deep drainage in the dormant seasons (no irrigation) cannot be controlled, it can be well controlled to a minimum level in the growing seasons by "best" irrigation management practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".