Could natural phytochemicals be used to reduce nitrogen excretion and excreta-derived N2O emissions from ruminants?
Bibliographic record
Abstract
Abstract Ruminants play a critical role in our food system by converting plant biomass that humans cannot or choose not to consume into edible high-quality food. However, ruminant excreta is a significant source of nitrous oxide (N2O), a potent greenhouse gas with a long-term global warming potential 298 times that of carbon dioxide. Natural phytochemicals or forages containing phytochemicals have shown the potential to improve the efficiency of nitrogen (N) utilization and decrease N2O emissions from the excreta of ruminants. Dietary inclusion of tannins can shift more of the excreted N to the feces, alter the urinary N composition and consequently reduce N2O emissions from excreta. Essential oils or saponins could inhibit rumen ammonia production and decrease urinary N excretion. In grazed pastures, large amounts of glucosinolates or aucubin can be introduced into pasture soils when animals consume plants rich in these compounds and then excrete them or their metabolites in the urine or feces. If inhibitory compounds are excreted in the urine, they would be directly applied to the urine patch to reduce nitrification and subsequent N2O emissions. The phytochemicals' role in sustainable ruminant production is undeniable, but much uncertainty remains. Inconsistency, transient effects, and adverse effects limit the effectiveness of these phytochemicals for reducing N losses. In this review, we will identify some current phytochemicals found in feed that have the potential to manipulate ruminant N excretion or mitigate N2O production and deliberate the challenges and opportunities associated with using phytochemicals or forages rich in phytochemicals as dietary strategies for reducing N excretion and excreta-derived N2O emissions.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".