Net irrigation water requirements for major irrigated crops with variation in evaporative demand and precipitation in southern Alberta
Bibliographic record
Abstract
Weather variability has a profound influence on crop and irrigation water requirements. Estimates of crop evapotranspiration (ETc) and net irrigation water requirements are needed for water allocation, risk management and irrigation system planning. Seasonal ETc and net irrigation water requirement estimates based on the standardized Penman-Monteith method were examined through frequency analysis of historical weather data. Seasonal ETc calculated using the Penman-Monteith equation is based on daily solar radiation, air temperature, relative humidity, and wind speed. Historical weather data from 1983 to 2012 at Lethbridge and Vauxhall were used to determine seasonal ETc, seasonal precipitation and net irrigation water requirements for 11 major (most prevalent) irrigated crops in southern Alberta. Seasonal ETc was consistently greater at Lethbridge than Vauxhall, whereas seasonal precipitation was generally less at Vauxhall than Lethbridge for all major crops. Mean seasonal ETc ranged from 355 mm for barley silage at Vauxhall to 728 mm for alfalfa hay at Lethbridge at a 10% chance of exceedance. Mean net irrigation water requirements ranged from 273 mm for barley silage at Vauxhall to 526 mm for alfalfa hay at Lethbridge at a 10% chance of exceedance. Area-weighted seasonal ETc demand within the irrigation districts is currently about 500 mm (2.8 billion m3) and the net irrigation water requirement within the irrigation districts is at least 380 mm (2.1 billion m3) at a 10% chance of exceedance. Annual gross diversion requirements for the irrigation districts could approach the licensed water allocation limit of 3.45 billion m3 at a 10% chance of exceedance when conveyance losses, irrigation system application efficiencies, and current irrigation management practices are considered. The frequency with which annual gross irrigation water requirements approach or exceed this licensed water allocation limit may increase in the future with climate change in southern Alberta.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".