Effect of Seed and Fertilizer Subsidies on the Technical Efficiency of Rice Farmers in Senegal
Bibliographic record
Abstract
The aim of this study was to analyze the effect of seed and fertilizer subsidies (NPK and urea) on the technical efficiency of rice farmers in Senegal. Using data from the Annual Agricultural Survey (EAA) of the Directorate of Analysis, Forecasting and Agricultural Statistics of Senegal (DAPSA), the results of Stochastic Frontier Analysis (SFA) revealed that rice farmers in Senegal have on average a technical efficiency level of 0.545. This suggests that they could increase their current production by 45.5% while using the same level of inputs. Estimation of an SFA model for technical inefficiency revealed that seed and urea subsidies have a significant effect on reducing technical inefficiency. A farmer using subsidized seed saw a reduction in technical inefficiency level by 10.5% and using urea was associated with a 5.1% decrease in inefficiency. In contrast, the model showed no association between the use of subsidized NPK or the use of herbicides and technical inefficiency. And use of organic fertilizer was estimated to worsen technical inefficiency by 4.4% (perhaps reflecting greater reliance on lower-cost inputs among less productive farm households in Senegal). With regard to socio-demographic factors, the results further revealed older respondents experienced more severe technical inefficiency, and that women on average were 9.6% more inefficient than men. These barriers to improved efficiency among older farmers and women suggest targeted supports may be necessary alongside general subsidy programming.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".