Exploring Pathways for Promoting and Scaling Up Climate-Smart Agriculture in Myanmar
Bibliographic record
Abstract
This paper explores potential pathways for promoting and scaling up the uptake of climate-smart agriculture (CSA) in Myanmar, using qualitative methods. Key informant interviews with stakeholders from government, research institutes, international and local development agencies, and the private sector identified technology development as an important investment and action area. A desk review of policy documents revealed that considerations on climate change adaptation in agriculture are embedded in Myanmar’s international commitments and national plans, including policies on making the agriculture sector resilient. Moreover, climate change resilience has been framed as a key component of the country’s sustainable development plans. This means the basic framework for advocating and promoting CSA is already in place. However, policies on land, water, environment, seed, and fertilizer and pesticide management are poorly enforced. In addition, the extension system has an inadequate coverage and reach of the remote communities. In the current political context of Myanmar, the process of policymaking has changed. Thus, the impetus for shaping an enabling environment for scaling up CSA will likely shift toward more active citizen engagement via local nongovernment organizations (NGOs), the private sector, and independent academic institutions. There are opportunities for policy integration to effectively scale up CSA, but much remains to be done. Donors of Myanmar have a special opportunity to support the integration of CSA into their respective country program strategies. Likewise, local and international NGOs may take this opportunity to mainstream CSA into various conventional development programs, such as livelihood development, women’s empowerment, and food security and nutrition. On the other hand, academic institutions can pursue research opportunities to support the development of CSA technologies and approaches and to generate evidence for input to capacity development, advocacy, and policymaking.
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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 | 0.001 | 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 | 0.001 | 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".