Farmed cricket performance remains stable over five generations of rearing on a waste-based diet
Bibliographic record
Abstract
Farmed insects like crickets offer a sustainable protein source to feed the growing global population. A benefit of cricket farming is the potential to use waste diets instead of unsustainable, expensive feeds. Brewer's spent grain is a nutritionally valuable organic waste product that has been used to rear crickets in single-generation studies. However, long-term effects of spent grain-based feeds are unclear, which makes incorporation into commercial feed risky for producers. We reared a farmed cricket Gryllodes sigillatus (Orthoptera: Gryllidae) for 5 generations on a high inclusion (75%) spent grain diet. Crickets reared on spent grain were 19% smaller at adulthood than crickets reared on farm feed, resulting in decreased yield (mass of crickets harvested), but were able to reproduce and had high survival rates. Cricket performance remained stable over 5 generations, indicating that spent grain contains adequate nutrition to support long-term cricket production. We also reared crickets on a gradual inclusion spent grain diet that increased from 15% to 75% spent grain over 5 generations. While this "weaning" approach did not improve cricket performance on high-inclusion spent grain diets, crickets on low inclusion diets (15%) displayed about a 40% increase in survival and yield compared to the control. Therefore, inclusion of low amounts of spent grain in cricket feed may not only be beneficial from an environmental and feed cost perspective, but also from a production yield perspective. Our findings are the first to show that spent grain could act as a feed ingredient for long-term rearing of farmed crickets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".