Assessment of Good Agricultural Practices on Cocoa and Coffee Farms in Northern Haiti
Bibliographic record
Abstract
Haiti is the poorest country in the Western Hemisphere and presents a unique scenario for the food and agriculture industry, because there is no food safety legislation. The application of Good Agricultural Practices (GAPs) leads to improvements of quality, safety and sustainability of agricultural products. The purpose of the study was to assess the status of Good Agricultural Practices (GAPs) in cocoa and coffee farms in Northern Haiti. A general survey captured information about the farmer and the farm, and an audit checklist was used to assess compliance to GAPs. A total of 11 farms (n = 11) were audited, of which 7 were cocoa farms (64%) and 4 were coffee farms (34%) in the regions of Dondon, Limonade and Milot. Average overall audit scores for coffee farms (73%) were higher than for cocoa farms (55%). Farms affiliated with a cooperative scored higher (78%) than those that were not part of a cooperative (55%). The sections of the survey on “Practices related to premises and production site”, and the “use of agricultural inputs and chemicals” received the lowest scores but were confined to the cocoa farms. “Record keeping” plus “distribution, transportation, and traceability” were cause for concern with both the cocoa and coffee farms. Critical non-conformances included the access of livestock animals and domestic pets to processing and storage areas, the lack of control in the application of agricultural chemicals, a lack of safeguards on equipment and elevated surfaces, and washing of fresh cocoa beans to remove the mucilage with water that had not been treated or tested for potability. The root cause of the non-conformances, regardless of the commodity, was either related to poor physical and organizational infrastructures, or to a lack of technical training.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".