Review: Effect of breeding strategies, feeding and manure management, to mitigate methane emissions in dairy cattle farming: an overview and the road ahead
Bibliographic record
Abstract
• Dairy production significantly contributes to CH 4 emissions. • Selective breeding for lower emissions is a promising strategy. • Storage and temperature are pivotal for reducing manures’ CH 4 emissions. • Some feed additives show promising results for reducing CH 4 emissions. • Combined approaches have the potential to reduce CH 4 emissions in dairy farming. Ruminant production systems, in particular those involving cattle, play a substantial role in greenhouse gas emissions, particularly because of the amount of methane ( CH 4 ) that they eruct. Here, we describe and incorporate the most relevant interdisciplinary approaches to mitigating CH 4 emissions in dairy cattle farming. We examine genetic selection for reduced daily CH 4 production, including key methods (direct measurement and mid-infrared spectroscopy predictions) now being integrated into breeding goals in some countries (e.g., Canada). We also evaluate feeding interventions, such as forage digestibility improvements and the use of additives (tannins, algae, and specialised compounds like 3-nitrooxypropanol), which may reduce CH 4 production in the rumen. Finally, we discuss manure management strategies (anaerobic digestion, reduced storage time, lower temperature) that can mitigate CH 4 release. By combining these approaches, producers can potentially reduce CH 4 emissions per unit of milk while maintaining productivity. However, important challenges persist — such as scaling up breeding practices without simply shifting emissions to beef systems, and verifying long-term impacts of dietary additives. We conclude that integrated breeding, feeding, and manure management strategies, supported by robust research and policy incentives, are essential for curbing dairy CH 4 emissions while ensuring sustainable milk production worldwide.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".