Smallholder farmers’ knowledge and willingness to pay for insect-based feeds in Kenya
Bibliographic record
Abstract
Edible insects are increasingly being considered as sustainable alternatives to fish and soybean meals in animal feed because of their high nutritional quality and environmental benefits. However, successful introduction of a new product to the market depends on the target user's acceptance. Thus, evaluating the potential demand of insect-based feeds would provide relevant information for policy development. The present study assessed farmers' knowledge on edible insects as feed, their acceptance of integrating insect meals in animal feeds and willingness to pay (WTP) for insect-based feed (IBF) using a contingent valuation method. A household survey was conducted among 957 randomly selected farmers including: 409 poultry, 241 fish and 307 pig farmers in four counties in Kenya. Results of the study reveal that over 70 and 80% of poultry and fish farmers, respectively, are aware that insects can be used as a feed ingredient. In addition, over 60 and 75% of poultry and fish farmers, respectively, consider insects as a good component of feed. Poultry, pig and fish farmers interviewed accepted and showed willingness to pay for IBF. Regression analysis indicated that age, gender, education, marital status, distance to feed trader, awareness of insects as feed, attitude towards insects, acceptance of insect species, availability of agricultural inputs, use of commercial feeds, availability of training and market information had a significant influence on the WTP for IBF. Therefore, increased extension services to educate famers on the nutritional benefits of insect meals in animal feeds and existing market opportunities are expected to improve farmers' attitude towards utilization and consequently enhance WTP for IBF, which in return would significantly reduce the existing pressure on conventional fishmeal feed resources. Our findings provide the first insights into the market opportunities of including insect meals in the animal feed value chain in Kenya.
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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 | 0.002 |
| 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 | 0.001 | 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".