Profit Efficiency of Rice Farmers in Cambodia The Differences between Organic and Conventional Farming
Bibliographic record
Abstract
This article highlights some important issues regarding the relative profit efficiency of organic and conventional farming in selected study areas of Cambodia, by estimating pool and separate profit frontiers of the two groups and accounting for the self-selection problem. We identify the relationship between the efficiency score from each frontier with farmers’ characteristics. The results indicate that farmers cannot manage their rice farming effectively in larger fields and fail to optimize their labor input and costs owing to limited skills and knowledge in rice production. Organic fertilizers can help to increase farmers’ rice income, while chemical fertilizers are less effective in doing so. Interestingly, being an organic farmer had no effect on farmers’ income elasticity when we conducted pool frontier estimation. However, these results were rejected by an LR test that was favorable to the estimation of a separate frontier, which suggested a better efficiency score if farmers adopted organic farming. We found some significant factors influencing the efficiency score, including <em>education, own-tractor,</em> and <em>credit use</em> (negative correlation) and <em>selling, other farming,</em> and <em>number of poultry</em> (positive correlation). <em>Off farm</em> was negatively correlated with the efficiency score in organic farming, but positively correlated in matched conventional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".