A review of feeding supplementary nitrate to ruminant animals: nitrate toxicity, methane emissions, and production performance
Bibliographic record
Abstract
Lee, C. and Beauchemin, K. A. 2014. A review of feeding supplementary nitrate to ruminant animals: Nitrate toxicity, methane emissions, and production performance. Can. J. Anim. Sci. 94: 557–570. The purpose of this review is to discuss the risks and benefits of using supplementary nitrate to reduce enteric methane emissions in ruminants based on the results of a meta-analysis. The meta-analysis confirmed possible nitrate poisoning triggered by higher blood methemoglobin levels with increasing nitrate consumption of ruminants: methemoglobin (%)=41.3×nitrate [g kg −1 body weight (BW) d −1 ]+1.2; R 2 =0.76, P<0.001. However, acclimatizing animals to nitrate reduced the toxicity of nitrate: methemoglobin (%)=4.2×nitrate (g kg −1 BW d −1 )+0.4, R 2 =0.76, P=0.002. Animals fed nitrate reduced enteric methane emissions in a dose-response manner: methane [g kg −1 dry matter intake (DMI)]=−8.3×nitrate (g kg −1 BW d −1 )+15.2, R 2 =0.80, P<0.001. The reduction of enteric methane emissions due to supplementary nitrate was effective and consistent in both in vitro and in vivo studies and also persistent in several long-term studies. Dry matter intake and live weight gain (LWG) of cattle were not affected by nitrate: DMI change, R 2 =0.007, P=0.65; LWG change, R 2 =0.03, P=0.31. It is anticipated that supplementary nitrate as a substitute for urea may change urinary nitrogen composition in a manner that increases ammonia and nitrous oxide emissions from manure. Furthermore, supplementary nitrate may have various physiological roles in nitric oxide metabolism in ruminants. In conclusion, supplementary nitrate is a viable means of mitigating enteric methane emissions due to its consistent and persistent efficacy. Risk of toxicity can be lowered by gradual acclimation of animals to nitrate. However, lowered methane production may not re-direct additional metabolizable energy towards animal production.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".