Determinants of Participation in Contract Farming Among Smallholder Dairy Farmers: The Case of North Shewa Zone of Oromia National Regional State, Ethiopia
Bibliographic record
Abstract
The study analyzed the determinants of participation in dairy contract farming using data collected from 424 (192 participants and 232 non-participants) randomly selected milk-producing farmers from three districts of the North Shewa Zone of Oromia Regional State, Ethiopia. The study combines both quantitative and qualitative data obtained from household interview using semi structured questionnaire, key informant interview, focus group discussion, and direct personal observation. Descriptive statistics and econometric models were used to analyze data. The binary logistic regression model was employed to identify factors affecting participation in dairy contract farming. Results show that age, sex, perception of price uncertainty, frequency of extension contact and access to training significantly and positively affect participation in dairy contract farming while time taken to milk collection centers affected it significantly and negatively. Results suggest that the need to encourage young farmers, female-headed households, increasing frequency of extension contact, creating access for training, decentralization of milk collection centers and contract farming reduces perceived price uncertainty faced by smallholder farmers from the spot market through creating guaranteed milk price in the study area. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".