Climate change creates opportunities to expand agriculture in the Hindu Kush Himalaya but will cause considerable ecosystem trade-offs
Bibliographic record
Abstract
Abstract Theoretically, climate change will create warmer temperatures and greater precipitation in mountainous regions, making agriculture possible in areas that were once unsuitable for cropping. But the extent and the nature of these ‘agricultural frontiers’ is as yet unknown. Building upon recent research on Climate Change Driven Agricultural Frontiers [CCDAFs], this paper assesses the potential of agricultural expansion in the Hindukush Himalaya [HKH]. Using FAO crop suitability data, we estimated the extent of CCDAFs under three Representative Concentration Pathways for 13 crops as well as the potential impacts of developing these frontiers on ecosystem services. We show that under climate change projected by the IPSL- CM5A-LR climate model, 34,507 km2 of agricultural frontiers may emerge in the HKH by 2100 under RCP 6.0. Additionally, results suggest that there will be new opportunities for crop diversification as individual crops will gain frontier area. However, developing these CCDAFs will impact supportive and regulating ecosystem services including carbon storage and sequestration, soil quality, biodiversity, and hydrological processes—with implications for regional water security. These impacts must be considered alongside the benefits of additional food production when evaluating the net benefits of developing CCDAFS.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".