Optimum poultry enterprise combinations among small holder farmers in Osun State, Nigeria
Bibliographic record
Abstract
Poultry farmers are confronted with choice for efficient allocationof farm resources between the different enterprises so as to optimize production objectives. The study therefore, was focused on optimum poultry enterprise combinations among small holder farmers in Osun State, Nigeria. Primary data were collected using questionnaires and were analyzed using descriptive statistics, budgetary technique and linear programming model. Of the seven poultry enterprises identified, the most profitable enterprise combination was that of layers/broilers with a benefit cost ratio of 1.92 while the enterprise that yielded the least net farm income was the sole cockerel which had a benefit cost ratio of 1.57.The profitability of sole and combined poultry enterprises was limited by high cost of production in which the feed cost constitutesmore than three-quarter of the total cost. Although, the optimal poultry enterprise combination was layers/broilers, the farmers in the study area attested to the fact that poultry business was still highly profitable.It is therefore recommended that both farmers and government must partner to find a means of reducing feed cost by financing poultry research. Also, poultry farmers should concentrate and intensify their poultrycombination practices especially that of layers/broilers, which may be the appropriateoptimal combination enterprise.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".