Market Feasibility of Faecal Sludge and Municipal Solid Waste-Based Compost as Measured by Farmers’ Willingness-to-Pay for Product Attributes: Evidence from Kampala, Uganda
Bibliographic record
Abstract
There is a great potential to close the nutrient recycling loop, support a ‘circular economy’ and improve cost recovery within the waste sector and to create viable businesses via the conversion of waste to organic fertilizers. Successful commercialization of waste-based organic fertilizer businesses however largely depends on a sound market. We used a choice experiment to estimate farmers’ willingness-to-pay (WTP) for faecal sludge and municipal solid waste-based (FSM) compost in Kampala, Uganda and considered three attributes—fortification, pelletization and certification. Our results reveal that farmers are willing to pay for FSM compost and place a higher value on a ‘certified’ compost product. They are willing to pay US $0.4 per kg above the current market price for a similar certified product, which is 67 times higher than the cost of providing the attribute. Farmers are willing to pay US $0.127 per kg for ‘pelletized’ FSM compost, which is lower (0.57 times) than the cost of providing the attribute. On the other hand, farmers require US $0.089 per kg as a compensation to use ‘fortified’ FSM compost. We suggest that future FSM compost businesses focus on a ‘certified and pelletized’ FSM product as this product type has the highest production cost–WTP differential and for which future businesses can capture the highest percentage of the consumer surplus. The demand for FSM compost indicates the benefits that can accrue to farmers, businesses and the environment from the recycling of organic waste for agriculture.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".