Managing uphill cultivation under climate change – An assessment of adaptation decisions among tribal farmers in Nagaland state of India
Bibliographic record
Abstract
Tribal farmers in the Himalayas are vulnerable to climatic changes, as their rain-fed cultivation systems, practiced on steep, sloping terrain, are susceptible to changes in rainfall while at the same time being the primary means of livelihood. Soil and water conservation practices (SWCP) can improve the resilience of these cultivation systems to adverse climatic conditions. However, little is known about adaptation within these tribal farming communities. This is the first empirical study on the adaptation decisions of tribal farmers in the Himalayan uplands of Northeast India. Starting from the analysis of future climate risks, we surveyed 372 tribal farmers in Nagaland state to analyze perceived climate and environmental changes in relation to socio-demographic factors. We estimate current adoption rates of SWCP together with farmers' goals and values and employ a binary logit model (BLM) to quantify the influence of diverse factors on adaptation decisions. Our results show that increases in temperatures and crop diseases were the most perceived changes by tribal farmers. Climate projections indicate that precipitation amount and intensity, along with temperatures, will increase towards the end of the century, underlining the importance of SWCP. However, all considered SWCP were employed by less than half of the tribal farmers. Adoption probabilities for all practices were significantly increased when farmers participated in agricultural training. After that, participation in a civil society organization, livestock ownership, high-altitude locations, and perceived increases in droughts were found to increase adoption probabilities significantly, while socio-demographic factors were of only minor importance. If the most effective factor was employed to all farmers, average adoption rates of SWCP could at least double. Adoption decisions were mainly motivated by improving livelihoods, sustaining natural resources, reducing workload, and preserving cultural aspects of cultivation. This research contributes to understanding adaptation decisions of tribal farmers and quantifies the untapped potential for climate change adaptation of marginalized and climate-vulnerable farming communities in mountain regions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".