Comparative Greenhouse Gas Emission Intensities from Irrigated and Dryland Agricultural Activities in Canada
Bibliographic record
Abstract
Irrigation development may present trade-offs between economic and environmental quality. To assess this, a comparative analysis is undertaken of contributions of irrigation versus dryland production to greenhouse gas (GHG) emissions in two Canadian regions for the year 2000. This regional analysis was undertaken using three criteria—area, physical production, and economic value of production. Results indicate that irrigated agricultural crop production is responsible for 1.65 Mt of carbon dioxide equivalent (CO2E) GHG emissions in Canada. This is about six percent of the Canadian total GHG emissions from crop production. On a per hectare basis, irrigated crop production emits almost 6.5 times the GHGs of dryland crop production in western Canada, and four times the GHGs in eastern Canada. When factoring in physical productivity, western Canadian irrigated crop production, relative to dryland production, emitted an almost equal amount of GHGs. However, when the value of all crops and livestock enterprises are considered, irrigated production generates smaller GHG emissions than dryland farms in both western (2.15 kg per dollar of production for irrigation and 3.23 kg for dryland) and eastern (1.59 kg per dollar of production for irrigation versus 2.65 kg for dryland) Canada. Under the current crop mix and technology, Canadian irrigation can be considered both economically and environmentally efficient, if value of production is taken into account.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".