Determinants of profitability of black soldier fly farming enterprise in Kenya
Bibliographic record
Abstract
Black soldier fly (BSF) farming is emerging as a new farm enterprise in Kenya poised to provide high-quality and affordable alternative protein sources for animal feed production. Consequently, commercialisation and adoption require farmers to understand if the enterprise is economically viable. This study sought to assess the determinants of profitability of the BSF farm enterprise. A census survey was conducted whereby 34 well-established smallholder BSF farmers were interviewed. A double log regression analysis on the determinants of profitability of the BSF farm enterprise was done. The results revealed that 93.6% of the variation in enterprise gross margin was explained by the independent variables. Feed and household size contributed positively and significantly to the enterprise gross margin. Labour was significantly and negatively correlated to the enterprise gross margin. Farm size, gender, level of education, and age of the farmer did not significantly influence the gross margin of the enterprise. Furthermore, the survey showed that a 1% increase in man-hours spent in the BSF farming enterprise would result in a 0.34% reduction in the gross margin while a 1% increase in the usage of the rearing substrate would lead to a 1.38% increase in the gross margin. There is a need for farmers to reduce the man-hours spent in the BSF farms but at the same time increase significantly the utilisation of more rearing substrate to improve their profitability. However, a long-term socio-economic impact assessment on the BSF farming enterprise would be valuable to attract investors and interest in the insect production sector for animal feed.
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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 | 0.000 | 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".