Policy Instruments and Agricultural Water Allocation in the Bow River Basin of Southern Alberta
Bibliographic record
Abstract
In Southern Alberta, agriculture is the largest water user. Thirteen irrigation districts plus numerous private irrigators hold licenses to withdraw more than 75% of the available surface water. Water use decisions made by farmers in irrigation districts have significant impacts on the productivity of water use and on environmental outcomes (instream flow needs) throughout the South Saskatchewan River Basin (SSRB), especially during periods of drought. The objective of this paper is to investigate current and alternative water allocation strategies and their effects on crop choices with a focus on the irrigation districts in the Bow River Sub-basin of the SSRB. A mathematical programming model is developed to optimize economic returns from crop production, subject to specified restrictions imposed by water supply, institutional and hydrological conditions, production technology and land characteristics. Positive Mathematical Programming is used for model calibration with data from 2002-2003 provided by Alberta Agriculture Food and Rural Development. This research provides an explicit framework for the design and comparison of water policy options in Southern Alberta. The findings provide information to address the twin objectives of increasing the productivity of agricultural water use and meeting the environmental flow requirements of the Bow River Basin.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".