The Impact of Marketing Channels on On-Farm Food Loss for Producers of Fruit and Vegetables in Quebec and Ontario
Bibliographic record
Abstract
Food loss and waste is an issue in Canada. The over production of agricultural products can lead to declining land fertility, deteriorating sustainable environment, and wastage of energy. In comparison with other categories of agricultural products, the loss rate of fruit and vegetables is much higher because they cannot be stored for a long time and easily. Like other developed countries, most food is lost in Canada at the retailing and consuming level, which researchers tend to pay more attention to than other stages of the supply chain. In developing countries, food loss and waste occur primarily at the post-harvest stage, where the local food system is dominant. Recent studies suggest that on-farm food loss could be more severe than estimated before. Producers in some studies suggested that high cosmetic standards set by retailers and distributors lead to higher on-farm food loss rates than local marketing channels. Therefore, it is necessary to understand if it is true that producers selling through local marketing systems have lower food loss rates than producers selling through other marketing channels. We conducted a survey of fruit and vegetable producers in Québec and Ontario about their food loss rates at farmgate, the composition of marketing channels, and other factors that potentially impact the on-farm food loss rate. We found out that, compared to local channels, selling more directly to retailers statistically significantly reduces the on-farm food loss rate, but the absolute value is quite small. Small-scale farms have lower on-farm food loss rates than larger farms. Processing on farm also significantly reduces food loss on the farm. This study suggests that local marketing channels may not necessarily mean a lower on-farm food loss rate and that selling more directly to retailers could reduce on-farm food loss. However, producers need to consider their own situation to take measures to reduce on-farm food loss without compromising their income and welfare
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".