DETERMINANTS OF THE ADOPTION OF SMALL RUMINANT RELATED TECHNOLOGIES IN THE HIGHLANDS OF ETHIOPIA
Bibliographic record
Abstract
This paper takes up the case of two market-sheds in the southern Ethiopian highlands (namely Adilo and Kofele) to examine the factors affecting the adoption of small ruminant related technologies in mixed-farming systems. A survey was conducted using semi-structured questionnaires with 155 randomly selected small ruminant keepers between May and June 2006. Farmers in each site initiated new practices like small ruminant fattening and managing a household ‘veterinary kit’. Logistic regression analysis revealed that size of land and livestock holdings significantly affected the adoption of small ruminant technologies in both study sites. Farmer variables such as gender, literacy, age and family size appeared to influence adoption only in one location. In the densely populated area, Adilo, the adoption of more intensive feeding technology of commercial concentrates decreased with increasing farm size only up to a point. Younger farmers, female farmers and literate household heads were more likely to adopt the utilization of commercial concentrates. In relatively resource rich Kofele, treating small ruminants via the household veterinary kit increased with number of livestock, however with farm size only up to the point at which it reached a maximum. The present study showed that location or production system remarkably affects the options of interventions and determines their adoption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".