Effect of diet on odorant emissions from cattle manure
Bibliographic record
Abstract
Ammonia loss from livestock manure is of special concern because it contributes to odour, can impact non-targeted environments through atmospheric deposition, and represents a potential loss of available nitrogen in manure used as fertilizer for crop growth. Our study investigated the effect of three barley-based diets on manure composition and the emissions of ammonia and volatile fatty acids (VFA) from beef feedlot manure. The results of our study suggest that the metabolizable protein requirements of heavyweight feedlot cattle (400 to 550 kg) were met when finished on a barley grain and barley silage diet [12.9% crude protein (CP)]. Therefore, the ability to reduce total N content of manure or manipulate the route of N excretion is limited, unless lower protein ingredients, such as corn silage or cereal straw, were incorporated into the diet to lower the basal diet CP concentration. There was no relationship between protein level and animal weight gain in our study. There was a positive relationship (P < 0.05) between level of intake protein and ammonium-N content of the surface-sampled pen manure. However, chamber data suggested that the higher ammonium-N content of manure did not translate to any significant difference in ammonia emissions, although the lowest emission rate for surface manure coincided with the lowest protein level. There was also no significant difference in VFA emission related to diet treatments. Key words: Ammonia, volatile fatty acid, odour intensity, cattle, manure, diet
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".