Drivers and barriers to the choice of production systems among smallholder pig farmers: Evidence from Northern Uganda
Bibliographic record
Abstract
Pork consumption has risen significantly in many emerging nations, prompting diverse pig production systems. This study explored the drivers and barriers to the choices of pig production systems and the challenges confronting pig farmers in Northern Uganda. Data were collected from 240 pig farmers using a structured questionnaire and analyzed using multinomial logit regression. Results revealed that 38.8 % of the pig farmers practiced the farrow-to-weaner (breeding) production system. Further, years of farming experience, access to extension service, number of initial stocks, and gender significantly influenced the choice of the farrow to finish production system. Significant predictors for the weaner-to-slaughter (fattening) system were market proximity, years of farming experience, household size, number of initial stocks, and access to extension service. It is recommended that extension services be enhanced and tailored to specific production systems, with a focus on breeding management, feeding practices, and marketing strategies to better support pig farmers. Further, investments should be made in transportation infrastructure to facilitate direct farm-to-market linkages for pig farmers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".