Evaluation of the red seaweed Mazzaella japonica as a feed additive for beef cattle
Bibliographic record
Abstract
Supplementing ruminant diets with macroalgae is gaining interest globally because bromoform-containing seaweeds (e.g., A s paragopsis spp.) have been shown to be highly effective enteric methane (CH 4 ) inhibitors. Some alternative seaweeds decrease in vitro CH 4 production, but few have been evaluated in animals. This study examined the effects of including the red seaweed Mazzaella japonica in the diet of beef cattle on dry matter intake (DMI), rumen fermentation, digestibility, nitrogen (N) utilization, and enteric CH 4 production. Six ruminally cannulated, mature beef heifers (824 ± 47.1 kg) were used in a double 3 × 3 Latin square with 35-d periods. The basal diet consisted of 52% barley silage, 44% barley straw, and 4% vitamin and mineral supplement [dry matter (DM) basis]. The treatments were (DM basis): 0% (control), 1%, and 2% M. japonica. The DMI increased quadratically ( P = 0.025) with the inclusion of M. japonica , such that the DMI of heifers consuming 1% was greater ( P < 0.05) than that of control heifers. The apparent total-tract digestibility of DM decreased linearly ( P = 0.002) with the inclusion of M. japonica , but there were no treatment differences in the digestibility of organic matter, crude protein (CP), neutral detergent fiber, or starch. The level of M. japonica linearly ( P < 0.001) increased the N intake of the heifers. Fecal N excretion linearly increased ( P = 0.020) with M. japonica , but there were no differences in total urinary N excretion, N fractions (allantoin, uric acid), total purine derivatives, microbial purine derivatives absorbed, microbial N flow, or retained N. There were no treatment effects on rumen pH or total volatile fatty acids (VFAs); however, adding M. japonica to the diet quadratically ( P = 0.023) decreased the proportion of acetate, whereas 1% inclusion decreased the acetate proportion. Methane production (g/day) decreased quadratically ( P = 0.037), such that the heifers receiving 2% M. japonica produced 9.2% less CH 4 than control animals; however, CH 4 yield (g/kg DMI) did not differ among treatments. We conclude that supplementing a forage-based diet with up to 2% M. japonica failed to lower the enteric CH 4 yield of beef heifers. M. japonica can be used in diets to help meet the CP requirements of cattle, but inclusion rates may be limited by high inorganic matter proportions. When comprising up to 2% of the diet, M. japonica cannot be recommended as a CH 4 inhibitor for beef cattle fed on high-forage diets.
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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) | 0.000 | 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) | 0.001 | 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".