Effect of Agricultural Credit Access on Rice Productivity: Evidence from the Irrigated Area of Anambe Basin, Senegal
Bibliographic record
Abstract
Rice is an important staple food in many developing countries, especially in Senegal. However, rice production in Senegal only meet 20% of the domestic demand largely due to the poor performance of rice farmers and low productivity. Access to agricultural credit has strong impacts on the technical efficiency of farmers and would promote inputs and new technology adoption. But that is not clear enough in previous studies. This study investigates the impact of agricultural credit access on rice productivity and technical efficiency with 260 random sampled rice farmers from Anambe basin in Senegal. The Stochastic Frontier Analysis (SFA) was adopted to estimate the technical efficiency. The results indicate that the inputs of rice production, including labor, pesticide, herbicides and fertilizer, have significant impacts on rice productivity. Furthermore, the results present that the average efficiency is of 0.813 and the inefficiency estimation model reveals that the influences of agricultural credit access, gender, education, ethnicity, use of improved seed and land tenure system on technical inefficiency of rice production are significant. Particularly, for the access to agricultural credit, rice farmers without agricultural credit would get 3.8% higher production inefficiency. The farmers with access to credit yield 37.32% higher rice production than their counterparts. Therefore, our study provides strong empirical evidence to promote agricultural credit in rice production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".