Myanmar local food systems in a changing climate: Insights from multiple stakeholders
Bibliographic record
Abstract
Understanding the impacts of climate on food systems is vital to identifying the most effective food system interventions to support climate-smart agriculture. The study examines how climate change is affecting food systems and what can be done to mitigate its effects. Two methodological approaches were combined in the study. The first was an Asia-wide regional consultation and forum to explore a range of initiatives that transform food systems among stakeholders working in Myanmar. The second method was an in-depth food systems study employing qualitative methods in Htee Pu Village in the Myanmar Central Dry Zone, a research site of IIRR since 2017. Key informant interviews (KII) and focus group discussions (FGD) were conducted to capture insights and data. Food systems consist of components, drivers, actors, and elements that interact with one another and other systems such as social, health, and transportation. The Myanmar food system is complex. Making it sustainable and transformative requires a mix of different approaches implemented at various scales from local to national. It also requires actions that engage various actors in the system from producers to consumers. The study of the local food system of Htee Pu Village indicates that the village has a rural and traditional food system and that climate change is one of its key food system drivers. Climate change negatively impacted farming and agricultural practices and disrupted the input supply of the local food systems. The role of intermediaries such as traders and consolidators is critical in the supply and distribution of food in the Central Dry Zone. Improved and more connected roads are essential for the supply and distribution of food for the village. The informal market outlets serve as the primary food source or sale points for households. Household diets are inadequate in quantity as the population remains highly dependent on their crops for their diets due to relatively low income. Climate adaptation must be embedded in the local level management to mitigate the effect of climate change in food production in the longer term.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".