What affects farmers in choosing better agroforestry practice as a strategy of climate change adaptation? An experience from the mid-hills of Nepal
Bibliographic record
Abstract
Determinants for choosing climate change adaptation strategies and selecting improved agroforestry practices have rarely been explored, while numerous studies have been conducted on climate change and agroforestry. This paper discusses; local understanding of climate change, climatic impacts, and factors that affect farmers' choices of adaptation strategies, and agroforestry practices. We focused on three districts located in the mid-hills of Nepal, where farmers were adopting agroforestry practices in two forms; traditional and improved practices. We followed three techniques of social survey; household survey (n = 420), focus group discussions (n = 6), and key informant interviews (n = 24). Almost all farmers of the study areas were experiencing climatic challenges, but only 59.29% of them accepted that the challenges are induced by climate change and, likewise, 55.24% have adopted climate change adaptation measures. Diversifying crop production, shifting farming practices, changing occupation, and emigration were local adaptation strategies. Livelihood improvement, income generation, and food production were the primary motives for adopting agroforestry practices in the study area. Agroforestry as an adaptation measure to climate change was considered secondary by most farmers. Statistical analysis using a logit model revealed that age, education, and habit of growing commercial species significantly influenced farmers adopting climate change adaptation strategies. Likewise, age, education, gender, habit of growing commercial species, and income from tree products significantly influenced the choice of improved agroforestry practices as a better option. Though agroforestry was widely considered a strategy to combat climate change, only some farmers accepted it due to their awareness level. Therefore, education programs such as training, farmer field schools, door-to-door visits, etc., should be intensified to sensitize farmers about climate change and encourage them to adopt improved agroforestry practices. The findings of the study could reinforce local, national, and international allied agencies to design operative actions in the days to come.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".