Dose–response effects of dietary inclusion of agro‐industrial by‐products on <i>in vitro</i> ruminal fermentation and methane production
Bibliographic record
Abstract
Abstract BACKGROUND As the agro‐industry produces considerable amounts of by‐products globally, it is acknowledged that there is a need to address the environmental issues related to their disposal and the resource competition between food for humans and feed for animals. The aim of this study was to explore, in vitro, the effects of various by‐products from the agro‐industry on rumen fermentation and methane emission. Samples were collected from various food processing industries, including red and green apple pomace (RAP, GAP), hempseed cake (HC), coffee hulls (CH), coffee grounds (CG), spent mushroom compost (SMC) and distiller's dried grains with solubles (DDGS). In doses of 100, 200 and 300 g kg−1, the tested by‐products were incubated in rumen fluid, where the by‐products replaced equal amounts of substrates. RESULTS Gas production (GP) and dry matter digestibility (DMD) decreased linearly for most of the tested by‐products with the growth of doses (P < 0.001), while NH3‐N concentration increased linearly. Linear decreases were observed in CH4 production with increasing doses of all by‐products (P < 0.05). The reduction of CH4 production ranged from 21.4% to 33.6% at doses of 100–300 g kg−1, but reductions were only observed at a dose of 100 g kg−1 when CH4 productions were corrected by digested dry matter (P < 0.05). RAP, GAP and HC were higher than CH, CG and SMC for the comparison of key parameters including DMD, GP and volatile fatty acids. Better methane‐mitigating effects were observed for RAP, GAP and HC than for the control group and CH, CG and SMC. CONCLUSION Most of the by‐products tested were found to be a potential option for replacing conventional feed ingredients but should not exceed a dose at 200 g kg−1. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".