MétaCan
Menu
← Back to cohort
Record W4417465918 · doi:10.3168/jds.2025-27514

A meta-analysis of the effects of nitrate supplementation on enteric methane emission, production performance, and blood methemoglobin in dairy cattle

2025· article· en· W4417465918 on OpenAlexaff
L. Dicks, Y. Roman-Garcia, Sanne van Gastelen, Morten Maigaard, P. L. Ingram, G.F. Schroeder

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsCargill (Canada)
FundersCargill
KeywordsNitrateMethemoglobinMilk productionSignificant differenceDairy cattleYield (engineering)Nitrite

Abstract

fetched live from OpenAlex

This meta-analysis evaluated the effect of nitrate supplementation on enteric CH 4 emissions and performance in lactating dairy cows.A literature search identified 9 publications including 10 studies.Nitrate dose ranged from 5.3 to 21.1 g/kg DM and DMI was 19.7 ± 2.47 kg/d, milk yield (MY) was 28.1 ± 6.50 kg/d, and CH 4 production was 348 ± 50.5 g/d (mean ± SD).The mean difference between control and nitrate supplementation was analyzed using the metafor package in R, applying 3 random effects models.Model 1 estimated the overall mean difference across all studies regardless of dose (mean dose: 13.8 g nitrate/kg DM).Model 2 included factor effects of nitrate dose centered around the 2 most tested doses: mode dose of 10.2 ± 1.94 g nitrate/kg DM (n = 15) and high dose of 19.8 ± 2.51 g nitrate/kg DM (n = 10).Model 3 was a linear model evaluating the nitrate dose response.Model 1 showed that nitrate reduced CH 4 production by 20.3%, CH 4 yield (g/kg/DMI) by 16.4% and CH 4 intensity (g/kg ECM) by 20.1% compared with control.Model 2 showed a reduction of 21.5% and 18.7% in CH 4 production, 15.6% and 16.9% in CH 4 yield and 20.2% and 18.9% in CH 4 intensity compared with control for the mode and high dose, respectively.No difference in CH 4 mitigation was observed between the mode and high nitrate dose (Model 2) nor was a linear effect of nitrate dose detected (Model 3).Nitrate supplementation reduced DMI by 0.825 kg/d in Model 1, whereas Model 2 showed that reduction of DMI only occurred at the mode dose (-1.04 kg/d).According to Model 1, nitrate supplementation reduced milk protein yield and content (-0.033 kg/d and -0.087% -units, respectively) and tended to reduce ECM yield (-0.55 kg/d).According to Model 2, nitrate supplementation reduced milk protein content at both mode and high dose (-0.059 and -0.133%-units, respectively), milk protein yield at the mode dose (-0.035 kg/d), and tended to decrease milk protein yield at the high dose (-0.028 kg/d).Furthermore, feed efficiency increased by 3.21% in Model 1 and by 3.59% in Model 2 for the mode dose only.Blood methemoglobin increased upon nitrate supplementation according to Model 1 (+0.695%-units) and Model 2 (high dose only; +1.32% -units) but did not reach levels that posed risks for animal health.In conclusion, the most commonly tested dose of 10 g nitrate/kg DM reduced CH 4 production by 21.5%, CH 4 yield by 15.6% and CH 4 intensity by 20.2%.Dose response analysis indicated no additional reductions when increasing the nitrate dose to a level higher than 10 g/kg DM.Despite the decrease in milk protein content and yield, which was observed at the mode dose of 10 g/kg DM, supplementing nitrate is an effective strategy to reduce CH 4 emission.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0100.032
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.277
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Dairy Science→Same topicRuminant Nutrition and Digestive Physiology→French-language works237,207→