Factors Influencing Adherence to Food Safety Measures Among Smallholder Dairy Farmers in Central Uganda
Bibliographic record
Abstract
Promoting compliance with food safety measures among smallholder dairy farmers is critical for public health, market access, and the overall profitability and sustainability of the dairy sector. However, smallholder dairy farmers face challenges related to the implementation of food safety measures (FSMs) on their farms. To devise interventions to overcome these challenges, it is necessary to identify and understand the drivers of compliance with food safety measures among smallholder dairy farmers in Central Uganda. Therefore, this study investigated the factors influencing food safety compliance among smallholder dairy farmers in Central Uganda. Data were collected from 757 randomly selected smallholder dairy farmers using face-to-face interviews. The farmers were classified as low, middle, and high adopters of FSMs. The study employed an ordered Probit model to identify the determinants of compliance with FSMs. The results indicate that significant variables positively influencing the probability of being in the higher adopter class include level of formal education, type of livestock breed, number of cows milked, familiarity with FSMs, compliance perception, awareness of HACCP, standard operating procedures (SOPs), milk contamination, and the government’s role in FSMs. Conversely, factors negatively influencing the probability of being in the higher adopter class were farming experience, land size, herd size, and the cost of compliance with food safety. These issues call for policy interventions to enhance FSM knowledge through capacity-building programs for safer milk production. This will improve farmers’ perceptions and awareness of FSMs at the farm level. Considering the cost implications of FSM compliance, financial incentives are needed, such as higher premium prices for a quality-based milk payment system. Finally, the government should take a leading role in supporting smallholder farmers to adhere to FSMs through legislation and policies tailored to the farmers’ needs.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".