Profit Efficiency, Weather Risk and Climate Adaptation Practices of Rice Farmers in Myanmar
Bibliographic record
Abstract
In recent years most developing countries, including Myanmar, have been seriously affected by the negative effect of climate variability—such as erratic rainfall, increased temperatures, longer dry spells, flooding and saltwater intrusion—during the crop growing season. Furthermore, most farmers lack knowledge of climate variability and how to cope with the negative effect of climate change. This study aimed to evaluate the profitability and profit efficiency of rice farmers in the Ayeyarwady Delta, Myanmar, during 2019 monsoon growing season, taking into consideration the effect of weather shock and farmers’ agricultural adaptation practices to climate variability. The Cobb-Douglas functional form was applied, with maximum likelihood techniques, to estimate rice growing productivity and the influencing factors of profit inefficiency among individual rice farmers. The average profit efficiency level of the yield loss group was approximately 0.39, while that of the no yield loss group was 0.66, indicating a relatively large gap between the two groups (27% wider distribution). Observation of the climate adaptation performances of rice farmers indicated that rice production incorporating climate adaptation practices (CAP) led to a significantly better average profit efficiency score (66%) than rice production omitting CAP. This study clearly revealed that the effect of weather variability on individual rice farmers leads to large variations in net profit and profit efficiency for monsoon rice production in the study area. Climate-smart agricultural practices should be developed through agricultural extension services, and using farmer-to-farmer extension services, to share information and technologies among smallholder rice farmers.
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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 | 0.002 | 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 | 0.000 | 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".