Urea plus nitrate pretreatment of rice and wheat straws enhances degradation and reduces methane production in <i>in vitro</i> ruminal culture
Bibliographic record
Abstract
Abstract BACKGROUND Urea pretreatment of straw damages fiber structure, while nitrate supplementation of ruminal diets inhibits enteric methane production. The study examined the combined effects of these treatments on ruminal substrate biodegradation and methane production using an in vitro incubation system. Rice and wheat straws were pretreated with urea (40 g kg −1 straw dry matter, DM) and urea + ammonium nitrate (34 + 6 g kg −1 dry matter (DM), respectively), and each straw (control, urea, urea+nitrate) was used in batch culture incubations in three replications (runs). RESULTS Urea pretreatment increased ( P < 0.05) neutral‐detergent solubles (NDS) content (+17%) and in vitro DM degradation of rice straw, in comparison with control. Urea+nitrate pretreatment of rice and wheat straws had higher ( P < 0.05) NDS content, in vitro DM degradation and propionate molar proportion, and lower ( P < 0.05) acetate:propionate ratio and lower methane production with a decline of methanogens, in comparison to control. CONCLUSIONS Urea+nitrate pretreatment combines positive effects of urea pretreatment and nitrate supplementation, and can be a potential strategy to improve ruminal biodegradation, facilitate propionate production and reduce methane production from lignified straws. © 2018 Society of Chemical Industry
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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) | 0.001 | 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".