PSXI-5 Transforming retailer’s food waste into silage for sustainable animal nutrition
Bibliographic record
Abstract
Abstract Food waste (FW) represents a major environmental and socio-economic problem. In Canada, about 11.2 million metric tons of avoidable food waste is produced yearly. An alternative to reduce this impact would be preserving a greater proportion of this, for livestock feed. Therefore, the objective of this study was to use discarded fruits, vegetables, and bakery products from retailers (FW), ensile them, and assess their nutritional value and contribution to a sustainable livestock feed supply. For this, two treatments for reducing the moisture content of FW were evaluated: Sun-dried (SD), and Passive-dried (PD), were compared with Control (C), using laboratory mini-silos over 60 d of ensiling. Although dry matter (DM) 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%. Silage aerobic stability was assessed during 20 d exposure to air. Chemical analyses (pH, volatile fatty acids, lactic acid, ammonia) were assessed in pre-fermentation (d 0), and post-fermentation (d 60). Nutritional quality was evaluated according to AOAC standard methods. Statistical analyses employed mixed models for nutritional and fermentation data, with drying treatments (C, PD, SD) as fixed effects. All mature silages were greater in crude protein (15.2 to 15.7%), crude fat (6.0 to 6.3%), sodium (0.48 to 0.52 %), sugars (WSC; 0.95 to 1.53 %), and less in neutral detergent fiber (6.2 to 7.6%) as compared with traditional silages used as livestock feed. Fermentation parameters showed that lactic acid content was greater than acetic acid, and pH decreased from a maximum of 5.26 at d 0 to a minimum of 3.85 at d 60. Mycophenolic acid was the only mycotoxin above the limit of detection in d 0 silage, with all mycotoxins below reference concentrations at d 60. Plate counts of molds and yeasts declined (P < 0.001) by 5 to 7 log colony-forming units (CFU) over 60 d of fermentation and were not detected in mature silage. Silage showed aerobic stability after 20 d of aerobic exposure (pH 3.86 ± 0.027). In conclusion, ensiling FW could be a viable way of converting low-value waste products into good-quality feed. However, 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. Further study of palatability along with digestibility testing will be needed for a scaling-up process.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".