Suitability Analysis and Projected Climate Change Impact on Banana and Coffee Production Zones in Nepal
Bibliographic record
Abstract
The Government of Nepal has identified opportunities in agricultural commercialization, responding to a growing internal demand and expansion of export markets to reduce the immense trade deficit. Several cash crops, including coffee and bananas, have been identified in the recently approved Agriculture Development Strategy. Both of these crops have encouraged smallholder farmers to convert their subsistence farming practices to more commercial cultivation. Identification of suitable agro-ecological zones and understanding climate-related issues are important for improved production and livelihoods of smallholder farmers. Here, the suitability of coffee and banana crops is analyzed for different agro-ecological zones represented by Global Environmental Stratification (GEnS). Future shifts in these suitability zones are also predicted. Plantation sites in Nepal were geo-referenced and used as input in species distribution modelling. The multi-model ensemble model suggests that climate change will reduce the suitable growing area for coffee by about 72% across the selected emission scenarios from now to 2050. Impacts are low for banana growing, with a reduction in suitability by about 16% by 2050. Bananas show a lot of potential for playing an important role in Nepal as a sustainable crop in the context of climate change, as this study indicates that the amount of area suited to banana growing will grow by 40% by 2050. Based on our analysis we recommend possible new locations for coffee plantations and one method for mitigating climate change-related problems on existing plantations. These findings are expected to support planning and policy dialogue for mitigation and support better informed and scientifically based decision-making relating to these two crops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".