Seaweed as a feed additive to mitigate enteric methane emissions in ruminants: Opportunities and challenges
Bibliographic record
Abstract
• Asparagopsis taxiformis has demonstrated significant potential in reducing enteric methane emissions in ruminants, achieving reductions of up to 99% in vitro and in vivo , primarily due to its bromoform content. • The application of seaweed as a feed additive faces challenges, including potential heavy metal contamination, environmental risks associated with bromoform, and the need for a sustainable cultivation and processing supply chain. • Further research is needed to identify low-bromoform seaweed species and investigate additional bioactive compounds to optimize methane mitigation strategies. Cutting farming-related methane emissions from ruminants is critical in the battle against climate change. Since scientists initially investigated the potential of marine macroalgae to reduce methane emissions, using seaweeds as an anti-methanogenic feed additive has become prevailing in recent years. Asparagopsis taxiformis is the preferred species because it contains a relatively higher concentration of bromoform. As a type of halogenated methane analogue, bromoform contained in A . taxiformis can specifically inhibit the activity of coenzyme M methyltransferase, thereby blocking the ruminal methanogenesis. However, bromoform is a potential toxin and ozone-depleting substance. In response, current research focuses on the effects of bromoform-enriched seaweed supplementation on ruminant productivity and safety, as well as the impact of large-scale cultivation of seaweeds on the atmospheric environment. The current research on seaweed still needs to be improved, especially in developing more species with low bromoform content, such as Bonnemaisonia hamifera , Dictyota bartayresii , and Cystoseira trinodis . Otherwise, seaweed is rich in bioactive substances and exhibits antibacterial, anti-inflammatory, and other physiological properties, but research on the role of these bioactive compounds in methane emissions is lacking. It is worthy of deeper investigation to identify more potential bioactive compounds. As a new focus of attention, seaweed has attracted the interest of many scientists. Nevertheless, seaweed still faces some challenges as a feed additive to ruminants, such as the residues of heavy metals (iodine and bromine) and bromoform in milk or meat, as well as the establishment of a supply chain for seaweed cultivation, preservation, and processing. We have concluded that the methane-reducing efficacy of seaweed is indisputable. However, its application as a commercial feed additive is still influenced by factors such as safety, costs, policy incentives, and regulations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".