Analysis of Determinants of Cassava Production and Profitability in Akpabuyo Local Government Area of Cross River State, Nigeria
Bibliographic record
Abstract
The study examined the determinants and profitability of cassava production in Akpabuyo, Cross River State. A multistage sample procedure was used to select eighty (80) farmers and data were collected with structured questionnaire. Data were analyzed using descriptive statistics, gross margin and ordinary least square (OLS) criterion. Analysis shows that cassava production was dominated by females (67.5 percent) in the study area. The mean age (47.85) shows that farmers were in the active labour force with mostly small farm sizes (0.98ha). The profitability analysis also shows per hectare gross margin of N9,520.66 and the cost N7,001.94, implying that cassava production is profitable. The results further reveals that farm size, value of land, gender, age, educational level and farming experience influenced output positively, while value of cassava cuttings, labour and family size had negative influence on cassava output. However, the test of significance shows that cassava cuttings, labour, education and experience exerted greater influence on cassava output, implying that a change in any one of these variables resulted to a significant change in output. One of the most serious problems encountered by cassava farmers in the study area was high cost of inputs, while lack of implements constituted the least problem. Therefore, it is recommended that concerted effort should be made towards the implementation of policies that will enhance farmers output.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".