Economic Footprint of Alberta's Irrigation Districts: An Economic Impact Analysis
Bibliographic record
Abstract
ABSTRACT Irrigation development can be an effective economic force for agricultural production, regional development and urban and rural community development. This study estimated the societal economic footprint of the Irrigation Districts on the economy in Alberta, Canada, via a variety of economic impact analysis models. The analysis indicates that producers, agricultural and non‐agricultural industries and communities benefit (either directly or indirectly) from irrigation development and related activities. These impacts result from the direct use of irrigation water for crop and livestock production, whereas other impacts are related to irrigation infrastructure (reservoirs and canals) that provides water for municipalities, food processing industries, recreation and wildlife habitat development. Irrigation Districts' direct annual contribution to Alberta's agri‐food gross domestic product (GDP), a traditional measure of economic growth, was about $1 billion. This contribution increased, through indirect and induced impacts, to $5.4 billion for the provincial GDP—about 5 times greater than the direct contribution. About 81% of the GDP generated by the Irrigation Districts accrued to the province, and about 19% to the irrigation producers. This study revealed that irrigation development is a beneficial economic strategy for the province, irrigation producers, food processing industries and sustainable community development. This study also demonstrated that economically successful irrigation projects should develop linkages between irrigation producers and regional food processing industries.
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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".