Effect of Irrigation Development on Greenhouse Gas Emissions in Alberta and Saskatchewan
Bibliographic record
Abstract
Irrigation can be a major emitter of greenhouse gases, and can contribute to global climate change. Most studies dealing with the evaluation of irrigation projects worldwide have not taken this aspect of irrigation development into account. In this study, changes in greenhouse gas emissions from converting a given area from dryland into irrigated acreage in Alberta and Saskatchewan were estimated. A systems approach was used in which changes in crop production mix are considered in terms of induced livestock production, increased input demand, and other induced economic activities. A modified Canadian Economic and Emissions Model for Agriculture was employed to estimate the change in emissions. The net contribution of irrigation to emissions of three major greenhouse gases (GHG): carbon dioxide, methane, and nitrous oxide was estimated. Results suggest that each hectare of land converted into irrigation leads directly to an additional emission of major GHG of 1.68–2.61 t yr−1 (in carbon dioxide equivalents). When various indirect and induced sources of emissions were included with the direct emissions, irrigation’s net emission level is 3.35–3.65 t ha−1 yr−1 (in carbon dioxide equivalents). Additionally, storage reservoirs, developed for supplying water to these irrigation projects, could also be associated with emissions of GHGs. Considering all these sources, irrigation adds 3.7% and 6.5% of total (agriculture and agri-food sector level) GHG emissions in Saskatchewan and Alberta, respectively. This has implications for the assessment of future irrigation projects from an environmental perspective.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".