Himalayan ecosystem services and climate change driven agricultural frontiers: a scoping review
Bibliographic record
Abstract
Climate change, rising temperatures, snow melts and more frequent droughts and floods are disproportionately affecting food and water security, habitat health, and agricultural productivity in the Himalayan region. These climatic changes are negatively impacting productivity of staple crops including wheat, maize, and rice at lower altitudes, but may provide opportunities to utilize Climate Change Driven Agricultural Frontiers [CCDAFs] at higher altitudes. Agricultural expansion into CCDAFs paired with behavioural shifts such as replacing traditional crop systems with commercial crops will predominantly affect forests, water resources, and soil health, which are already negatively affected by climate change unless adaptation options are directed to just and sustainable agroecological transitions. By trading regulating, supporting, and cultural services for food and water provisioning services, as are evident in land sparing strategy, the utilization of CCDAFs will have long-term implications for the sustainability of mountain farming systems. Climate change is affecting Himalayan agriculture, food security, and ecosystem services, and scientific literature predominantly focus on one of these topics in isolation, occasionally connecting results to another topic. By classifying literature as predominantly agriculture, food security, or ecosystem service themed, this scoping review identifies sources with multiple dominant themes and explores how the relationships between these topics are represented in literature to provide research based evidence to promote the future expansion of agriculture that is low-carbon, just and sustainable. Gaps in the literature reveal that research is needed on the extent of CCDAFs in the Himalayas and the potential trade-offs on utilizing the frontier areas. Supplementary Information: The online version contains supplementary material available at 10.1007/s43621-022-00103-9.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".