Potential of implementing irrigation in rainfed agriculture in Quebec: A review of climate change‐induced challenges and adaptation strategies
Bibliographic record
Abstract
Abstract Leading to growing‐season water stress, eastern Canada's evolving climate has disrupted the region's rainfed farming systems. Accordingly, this review was conducted to assess the status of irrigation, climate‐induced challenges and opportunities and the impact of adaptation strategies on economic returns and the environment in Quebec's agricultural regions. While irrigation is limited mainly to high‐value crops, controlled drainage with sub‐irrigation (CDSI) has been implemented at a limited number of field sites. Given the greater rainfall variability and rising number of growing‐season heatwave events anticipated, previous studies have mainly focused on developing climate change adaptation practices. The present study identified two research gaps: (i) a lack of analyses of drought frequency and its effect on root zone soil moisture, crop ET, crop phenology and agricultural production at a regional scale under historical and future climates and (ii) a lack of regional‐scale studies addressing climate change adaptation options, including supplementary irrigation, and their potential effects on economic return and water resources under a changing climate. Additional studies must address these gaps for the development of climate change adaptation practices to secure food demand and water sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".