Opportunities and trade-offs for expanding agriculture in Canada’s North: an ecosystem service perspective
Bibliographic record
Abstract
Climate change will create warmer temperatures, greater precipitation, and longer growing seasons in northern latitudes making agriculture increasingly possible in boreal regions. To assess the potential of any such expansion, this paper provides a first-order approximation of how much land could become suitable for four staple crops (corn, potato, soy, and wheat) in Canada by 2080. In addition, we estimate how the environmental trade-offs of northern agricultural expansion will impact critical ecosystem services. Primarily, we evaluate how the regulatory ecosystem services of carbon storage and sequestration and the habitat services supporting biodiversity would be traded for the provisioning services of food production. Here we show that under climate change projected by Canadian Earth System Model (CanESM2) Representative Concentration Pathway 4.5, ∼1.85 million km 2 of land may become suitable for farming in Canada’s North, which, if utilized, would lead to the release of ∼15 gigatonnes of carbon if all forests and wetlands are cleared and plowed. These land-use changes would also have profound implications for Indigenous sovereignty and the governance of protected and conserved areas in Canada. These results highlight that research is urgently needed so that stakeholders can become aware of the scope of potential economic opportunities, cultural issues, and environmental trade-offs required for agricultural sustainability in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".