Stochastic Meta Frontier Analysis of Smallholder Rice Farmers’ Technical Efficiency
Bibliographic record
Abstract
The aim of this study is to compare the technical efficiency of System of Rice Intensification (SRI) and Conventional Rice Production System (CRPS) farmers in Mali. Using cross-sectional data for 208 randomly selected rice farmers, the Stochastic Meta Frontier model is applied. The results indicate that the mean technical efficiency is 0.96 and 0.79 for SRI and CRPS respectively. This implies that SRI farmers were more technically efficiency than their counterpart. Similarly, the mean technology gap ratio was 0.98 and 0.91 for SRI and CRPS farmers, respectively. We also find that rice paddy production (SRI) was positively influenced by labor and negatively by organic manure while rice paddy production (CRPS) was positively linked with inorganic fertilizer and land. Further investigation reveals that family labor and flooding level increased the technical inefficiency for SRI adopters whereas education had a negative impact. For the CRSP farmers, the current factors were unable to account for technical inefficiency except age of farm household head. Our study finds strong cause to encourage SRI adoption as it could be the highly searched for solution for farmers to increase their yields and eventually enhance their food security status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".