Evaluation of dietary strategies to reduce methane production in ruminants: A modelling approach
Bibliographic record
Abstract
The objective of this study was to use the modelling approach to assess the effectiveness of different existing nutritional strategies to reduce methane production from ruminants. For this purpose, a modified version of a mechanistic and dynamic model of rumen digestion was used. Simulated strategies included: dry matter intake (DMI), forage to concentrate ratio, nature of concentrate (fibrous vs. starchy concentrate), type of starch (slowly vs. rapidly degraded), forage species (legume vs. grass), forage maturity, forage preservation method (dried vs. ensiled), forage processing, and upgrading and supplementation of poor quality forages (straw). This study showed that mathematical modelling is a valuable tool to evaluate the impact of a given dietary manipulation not only on methanogenesis but also on the metabolism of the whole rumen system. Depending on the nature of the intervention, methane production can be reduced by 10 to 40%. Increasing DMI and the proportion of concentrate in the diet reduced methane production (–7 and –40%). Methane production was also decreased with the replacement of fibrous concentrate with starchy concentrate (–22%) and with the utilization of less ruminally degradable starch (–17%). The use of more digestible forage (less mature and processed forage) resulted in a reduction of methane production (–15 and –21%). Methane production was lower with legume than with grass forage (–28%), and with silage compared to hay (–20%). Supplementation or ammoniation of straw did not reduce methane losses, but had a positive impact on the efficiency of rumen metabolism. The modelling approach demonstrated that reduction of methane production from ruminants is a complex challenge. Implementation of any strategy must take into account the possible consequences on the efficiency of the entire rumen system. Key words: Ruminants, methane reduction, modelling approach
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".