Cost of Energy Input in the Production of Cassava (Manihot Esculenta)
Bibliographic record
Abstract
The economic analysis of input energy in cassava production was considered in this study. Farms were surveyed to collect data on fuel, natural gas, fertilizer, pesticides and chemicals used on the farm for cassava production. The areas of study were Oyo, Ogun, Osun and Kwara States of Nigeria. The data for cost input resources in all the selected farms during cassava production from land preparation to transportation to market or house was obtained using structured questionnaire and oral interviews. Mathematical expressions were developed to evaluate cost analysis for each of the defined unit operations and the cost incurred were then determined. The total cost of production of one hectare of cassava was N82,055 and cost analysis revealed that profit of production of one hectare of cassava was N123,745. Benefit cost ratio was 2.50, which was greater than 1.0, indicating that cassava production is feasible from the economic stand point.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".