Optimizing Silage Strategies for Sustainable Livestock Feed: Preserving Retail Food Waste
Bibliographic record
Abstract
In Canada, approximately 11.2 million metric tons of avoidable food waste (FW) is produced per year. Preservation of a greater proportion of this FW for use as livestock feed would have significant environmental and socioeconomic benefits. Therefore, this study blended discarded fruits, vegetables, and bakery products from grocery stores into silage to assess the ability to preserve their nutritional value and contribute to the feed supply. Two treatments for reducing the water content of FW were evaluated, sun-dried (SD) and passive-dried (PD), and compared to control (C) using laboratory mini-silos over 60 days of ensiling. Although dry matter (DM) was increased by 1–5% for PD and SD, respectively, up to 41.9% of bread products were required to produce a targeted silage DM of 38%. All mature silages were high in crude protein (15.2 to 15.7%), crude fat (6.0 to 6.3%), sodium (0.48 to 0.52%), and sugars (0.95 to 1.53%) and were low in neutral detergent fiber (6.2 to 7.6%) as compared to traditional silages used as livestock feed. Mold and other signs of spoilage were visible on FW, but mycophenolic acid was the only mycotoxin above the limit of detection in material prior to ensiling. Plate counts of molds and yeasts declined (p < 0.001) by 5–7 log colony-forming units (CFU) over 60 days of fermentation and were not detected in mature silage. All silages were aerobically stable over 20 days. This study indicates that FW can produce good-quality silage but approaches other than SD and PD are required for increasing silage DM as insufficient bread products may be available for this purpose in all batches of FW.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".