Food traceability system awareness and agricultural operation: A Study of tea farms in Fujian, China
Bibliographic record
Abstract
China is establishing a Food Traceability System (FTS), but the policy implementation is behind most developed countries.The lack of FTS awareness may be a factor contributing to farming practices that are not consistent with FTS policies.Furthermore, the structure of an agri-food supply chain is a factor influencing farms' compliance with FTS.The present study focuses on pesticide residue control and traceability issues in one of the largest tea production areas in China.It aims to examine the effect of FTS awareness and related policies on tea farms' operations as well as the influences of supply chain structure on the effects of policy awareness.In this study the data were collected from Fujian province, which is a traditional, major tea-growing region in China with 18% of national production.Farms were recruited through a Stratified Sampling procedure that included 428 participating farms from the four largest tea-producing counties in Fujian.The participating farms answered questions regarding their awareness of FTS and related policies as well as the supply chain structure.The participants also reported their agricultural record-keeping practices related to pesticide residue control, including pesticide use, pesticide residue test, and sales record.The results reveal that farm owners' or operators' FTS awareness has a positive effect on pesticide use and sales record-keeping practice, and the supply chain structure importantly moderates the effects of policy awareness on operations related to pesticide residue control.Compared to independent growers, tea farms within an integrated supply chain were more likely to take pesticide residue tests or keep sales records.The results suggest that increasing FTS awareness among tea growers would be crucial to establish a safe and traceable system.Furthermore, governments need to take the supply chain structure into account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".