Efficiency Profiles of Vegetable Producers in Akwa Ibom State, Southern Nigeria
Bibliographic record
Abstract
Vegetables are among the major staple foods in Southern Nigeria. This study assessed the efficiency profiles of vegetable producers in Akwa Ibom State, Southern Nigeria with specific focus on farm level technical efficiency. One hundred and twenty (120) vegetable producers were randomly selected from three agricultural zones in the State. The Maximum Likelihood Estimates (MLEs) indicate positive relationships between input variables used by farmers and farm outputs. The Generalized Likelihood Ratio test confirms that vegetable producers in the area are relatively technically inefficient. The technical efficiency of the farmers ranged from 48 to 99 percent with a mean of 70 percent. The implication is that there is allowance to improve efficiency with available resource.There is, therefore, the need for policies to promote the availability of affordable farm inputs and technology to help improve farmers’ efficiency and increase vegetable production in the area. Extension services would also help provide useful information to the farmers on farm practices that would enhance output and ensure environmental sustainability of the production process by maintaining the quality of some critical environmental factors especially soil quality.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".