Linseed oil supplementation to dairy cows fed diets based on red clover silage or corn silage: Effects on methane production, rumen fermentation, nutrient digestibility, N balance, and milk production
Bibliographic record
Abstract
The objective of this study was to examine the effect of linseed oil (LO) supplementation to red clover silage (RCS)- or corn silage (CS)-based diets on enteric CH4 emissions, ruminal fermentation characteristics, nutrient digestibility, N balance, and milk production. Twelve rumen-cannulated lactating cows were used in a replicated 4×4 Latin square design (35-d periods) with a 2×2 factorial arrangement of treatments. Cows were fed (ad libitum) RCS- or CS-based diets [forage:concentrate ratio 60:40; dry matter (DM) basis] without or with LO (4% of DM). Supplementation of LO to the RCS-based diet reduced enteric CH4 production (-9%) and CH4 energy losses (-11%) with no adverse effects on DM intake, digestion, ruminal fermentation characteristics, protozoa numbers, or milk production. The addition of LO to the CS-based diet caused a greater decrease in CH4 production (-26%) and CH4 energy losses (-23%) but was associated with a reduction in DM intake, total-tract fiber digestibility, protozoa numbers, acetate:propionate ratio, and energy-corrected milk yield. Urinary N excretion (g/d) decreased with LO supplementation to RCS- and CS-based diets, suggesting reduced potential of N2O emissions. Results from this study show that the depressive effect of LO supplementation on enteric CH4 production is more pronounced with the CS- than with the RCS-based diet. However, because of reduced digestibility with the CS-based diet, the reduction in enteric CH4 production may be offset by higher CH4 emissions from manure storage. Thus, the type of forage of the basal diet should be taken into consideration when using fat supplementation as a dietary strategy to reduce enteric CH4 production from dairy cows.
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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 | 0.000 |
| 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) | 0.001 | 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".