Improved Technologies and Management Practices in Private Irrigation: Implications for Water Savings in Southern Alberta
Bibliographic record
Abstract
Increased water efficiencies on irrigation farms is viewed as a source of water savings especially in semi-arid regions like southern Alberta where 71 percent of consumptive water use is for irrigation purposes. The province's Water for Life strategy, the blueprint for long-term water planning, views increased water efficiency as essential to water management. This study examines the rate at which water efficiencies have been, and plan to be made, by employing improved technologies and management practices. Findings from a survey of private irrigators reveal that adopting improved technology has been limited in the past and will be even less so in the future. The research indicates that the main reason why irrigators adopt new technologies is to increase yield, but of almost equal importance is to save labor and energy costs. Most irrigators do not see saving water as the most important reason to improve water management, but over half indicate that they have concerns over water availability. Irrigators perceive financial constraints as one of the main impediments to invest in further improvements, therefore the study finds that the level of subsidies required to convince them to make such investments is considerable. Currently, a limited number of low cost management practices aimed at improving water use efficiency are being used. The adoption of such improved management practices therefore seem to present the greatest potential for future water savings. However, to ensure that such improvements result in water savings as sources of supply for new water users or the environment, new water allocation policies are needed since under current legislation it is up to the irrigators discretion how to use any water saved.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".