Sustainability potential of app-based food loss measurement: Farmers' perspectives in southwestern British Columbia, Canada
Bibliographic record
Abstract
Food loss is a systemic problem contributing to negative environmental, social, and economic impacts. However, despite food loss quantification being identified as critical for food loss prevention and reduction, there is a dearth of studies exploring opportunities to digitize or simplify food loss measurement. Moreover, post-harvest food loss estimates can be difficult to obtain as farmers grow different types of crops and have diverse technical skills as well as resources. Digital agriculture technologies such as farm management apps that can help farmers accurately record their yield and sales may provide a useful method for quantifying food loss. Accurate food loss quantification may also help provide better baseline measurement for policymakers. To assess the potential role of digital agricultural tools for food loss quantification, this exploratory study recruited seven farmers in southwest British Columbia to test an open access farm management app called LiteFarm for 2 months and digitally recorded their harvest logs. Drawing upon semi-structured key informant interviews, this study found that time scarcity and crop diversity were barriers to using the app. An unexpected benefit to the app is that it can better inform land use decisions when utilized for pre-harvest planning and therefore may help with loss prevention. Findings from this study highlight farmers' struggles to focus on sustainability and reducing food loss, especially when balancing their economic interests. Inclusive digital technologies and deeper engagement with farmers are needed to develop food loss quantification methods that fit diverse farming contexts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".