An Assessment of the Challenges affecting Smallholder Farmers in Adopting Biogas Technology in Zambia
Bibliographic record
Abstract
The objective of this study was to assess the challenges of adopting biogas technology among rural households in Zambia. Three hundred and twenty households with and without biodigesters were selected randomly from five provinces in the country for the study. A household survey and qualitative methods such as focus group discussions and key interviews were used to collect information. The results show that firewood and charcoal are the main sources of cooking energy in the study areas despite the enormous potential for the utilization of biogas. The use of biogas technology is in its infancy and few households have adopted the technology. The study found that several challenges or factors were responsible for the low adoption status of biogas technology in the study areas. The main challenges were the high cost of installation of biodigesters; lack or limited access to credit to help meet the costs of construction of biodigesters; inadequate numbers of skilled biodigester technicians; and lack of awareness or limited information on biogas technology. Increasing access to affordable credit, as well as awareness raising on biogas technology among rural households are suggested as some of the ways that will assist to promote the adoption of biogas technology as a sustainable renewable energy source for rural populations in Zambia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".