Improving Rice Production Efficiency in Myanmar by Controlling for Environmental Production Factors
Bibliographic record
Abstract
Rice is the dominant crop in Myanmar and central to the agricultural economy. To increase rice productivity, farmers’ production performance is vital. This requires adjusting the availability of physical production inputs in response to environmental conditions. Very few studies have focused on the effects of relevant environmental conditions in Myanmar, including the impact of weather shocks during the rice production. This study aimed to the improve rice production based on the present performance of rice farmers, while controlling the impact of adverse environmental conditions. Information on rice production was extracted randomly from in-depth interviews with rice farmers in the Ayeyarwady Delta region. The Cobb-Douglas stochastic production frontier function was applied to examine the effects of the underestimated environmental factors. Erratic rainfall and excessive temperature during early growth stage have a significant negative impact on monsoon rice productivity. During the 2018-2019 monsoon cropping season, different levels of yield loss due to weather shock negatively affected rice farmers’ production efficiency. Controlling the environmental conditions improved technical efficiency from 88% to 93%. Based on these findings, policy makers and stakeholders should invest in climate services development, thus enhancing farmers’ understanding of weather variability and upscaling the use of local climate adaptation strategies in accordance with the Myanmar Climate Smart Agriculture Strategy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".