Supply-disposition storage of fresh fruits and vegetables and food loss in the Canadian supply chain
Bibliographic record
Abstract
• There is an increasing trend of total food loss in Canada from 2000 to 2022. • There are 25.9% more vegetable waste than fruit waste at the storage stage. • Supply, imports, and domestic disappearance correlate strongly to food loss. • Imports and exports influence fruit and vegetable wastes in both prediction models. Analyzing transportation and storage inefficiencies at the initial stages of the food supply chain is crucial for minimizing early-stage losses and enhancing food lifecycle efficiency. However, most food system studies,focused on retail and consumer stages. This study delves into the intricate dynamics of fresh fruit and vegetable waste generation at the supply-disposition storage stage, aiming to identify distinct waste generation patterns and predict food loss in Canada using regression analysis. Total food waste generation for 64 fresh fruits and vegetables exhibited a notable increase over a 22-year study period from 2000 to 2022, and fresh vegetables consistently surpassed fresh fruits in average waste generation by 25.9 %. Despite a higher per capita waste generation for fresh vegetables (1.26 kg∙cap -1 ∙year −1 ), the steeper growth rate in fruit waste emphasizes the need for nuanced strategies for each category at the supply-disposition storage. The waste generation showed a positive linear relationship with supply, imports, and domestic disappearance in the food supply chain (R 2 = 0.80 to 0.99, p < 0.0001), denoting a significant potential impact of supply-disposition parameters on individual waste generation. Two distinct regression models were developed to forecast fresh fruits and vegetables waste generation, and both demonstrated high predictability (0.924 ≤ R 2 ≤ 0.975) and low RMSE values (1.571 ≤ RMSE ≤ 3.318). Imports and exports appear crucial to minimize food loss at supply and disposition storage. The proposed analytical approach can be beneficial elsewhere to enhance fresh food supply inventory management and minimize food loss at a global level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".