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Record W4412948633 · doi:10.5539/jas.v17n9p68

Factors Influencing Adherence to Food Safety Measures Among Smallholder Dairy Farmers in Central Uganda

2025· article· en· W4412948633 on OpenAlexvenueno aff
Andrew Kizito Seruma, George Owuor, Dickson Okello

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFood safetyEnvironmental healthAgricultural scienceAgricultural economicsMedicineFood scienceEconomicsEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.237
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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