Smallholder farmers’ willingness to pay for commercial insect-based chicken feed in Kenya
Bibliographic record
Abstract
The cost of chicken production in developing countries is 300% higher than in developed nations. Overreliance on the key protein feed ingredients especially soybean and fishmeal (SFM) that are characterized by rising food-feed competition and supply chain impediments exacerbate the situation. The use of insect protein as a sustainable alternative protein source has attracted global attention recently. However, there is a dearth of empirical insights on farmers’ preferences for commercial insect-based feed for chicken production in Sub-Saharan Africa. This study evaluated farmers’ willingness to pay for attributes of insect-based commercial chicken feed in Kenya using a choice experiment based on a survey of 314 predominantly chicken farmers. Results show that the farmers are willing to pay premium prices ranging between US$ 0.35 and US$ 3.45 for insect-based feed in the form of either pellets or mash, feed explicitly labelled as containing insects, insect protein feed mixed with SFM and dark-colored feed. These findings provide evidence for multi-stakeholder collaborations to facilitate the creation of an inclusive insect-based feed regulatory framework for sustainable feed and chicken production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.003 | 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".