Farm economic impacts of water supply deficits for two irrigation expansion scenarios in Alberta
Bibliographic record
Abstract
A study was conducted to assess the farm financial impact and risk of two irrigation expansion scenarios based on the potential for water supply deficits in the irrigation districts of southern Alberta. The Irrigation Demand Model (IDM) was used to determine irrigation water demand based on annual crop water requirements, crop mix, irrigation system types and application efficiencies, irrigation district infrastructure, and the level of irrigation management within each irrigation block defined in the Water Resources Management Model (WRMM). Irrigation water supply values each year were then established for each irrigation block using the WRMM. The Farm Financial Impact and Risk Model (FFIRM), a farm financial simulation model that tracks farm finances (assets and liabilities) with time, subject to variability in crop water demand and crop prices, was used to determine the optimum allocation of water among fields within each farm operation during years of water supply deficits. Three scenarios were examined with the FFIRM – a baseline scenario and two irrigation expansion scenarios. Irrigation expansion was found to have negligible or very small adverse impacts on the financial well-being of typical farms in all six irrigation regions. Representative farms experienced essentially no change in net farm income (NFI) in the recent expansion (EXP1) scenario, and very small reductions in NFI in the future expansion (EXP2) scenario. Water conserved through irrigation efficiency gains in the next decade will likely offset the increased risk of negative impacts on NFI with irrigation expansion to the current limit.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".