Impact of Peste des petits ruminants for sub‐Saharan African farmers: A bioeconomic household production model
Bibliographic record
Abstract
Peste des petits ruminants (PPR), a contagious viral disease affecting small ruminants, has been targeted by the global community for eradication within the next 10 years. However, eradication will require substantial money, human resources, coordination among actors, and individual commitments. The objective was to estimate the cost of PPR at a household level, thereby providing information on the potential economic benefits of PPR eradication and the incentives for small ruminant keepers to actively participate in the PPR Global Eradication Programme. This study focused on four sub-Saharan countries: Ethiopia, Malawi, Mali and Rwanda. Publicly available household level data assembled by FAO were used. A bioeconomic model was built to estimate impacts of PPR for a standardized theoretical area, where each household raised an average small ruminant herd or flock. Model outputs were used to estimate, at a household level, income loss due to a PPR outbreak. We constructed various income scenarios to account for the variability of small ruminant income as a proportion of total household annual income. Household income losses ranged from 2% to 40% of total annual income; percentages varied depending on the income scenario and on the gross annual economic impact of PPR on small ruminant production, which ranged from 20% to 80%, based on results of the bioeconomic model. As expected, the more small ruminant production contributed to household income the greater the impact. Estimates provided herein warn decision makers that, given the heterogeneous impact of PPR on household income, the willingness of small ruminant decision makers to participate in the PPR Global Eradication Programme may vary widely and tailored approaches should be devised and implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".