Farmers’ Perspective on Agriculture and Environmental Change in the Circumpolar North of Europe and America
Bibliographic record
Abstract
Climate change may increase the importance of agriculture in the global Circumpolar North with potentially critical implications for pristine northern ecosystems and global biogeochemical cycles. With this in mind, a global online survey was conducted to understand northern agriculture and farmers’ perspective on environmental change north of 60° N. In the obtained dataset with 67 valid answers, Alaska and the Canadian territories were dominated by small-scale vegetable, herbs, hay, and flower farms; the Atlantic Islands were dominated by sheep farms; and Fennoscandia was dominated by cereal farming. In Alaska and Canada, farmers had mostly immigrated with hardly any background in farming, while farmers in Fennoscandia and on the Atlantic Islands mostly continued family traditions. Accordingly, the average time since conversion from native land was 28 ± 28 and 25 ± 12 years in Alaska and Canada, respectively, but 301 ± 291 and 255 ± 155 years on the Atlantic Islands and in Fennoscandia, respectively, revealing that American northern agriculture is expanding. Climate change was observed by 84% of all farmers, of which 67% have already started adapting their farming practices, by introducing new varieties or altering timings. Fourteen farmers reported permafrost on their land, with 50% observing more shallow permafrost on uncultivated land than on cultivated land. Cultivation might thus accelerate permafrost thawing, potentially with associated consequences for biogeochemical cycles and greenhouse gas emissions. About 87% of the surveyed farmers produced for the local market, reducing emissions of food transport. The dynamics of northern land-use change and agriculture with associated environmental changes should be closely monitored. The dataset is available for further investigations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".