Impact of dietary manipulation on nutrient flows and greenhouse gas emissions in cattle
Bibliographic record
Abstract
The growing global demand for meat and particularly in countries such as Brazil is expected to increase intensive animal production. Consequently the main pollutants of interest are nitrogen (N), phosphorus (P) and greenhouse gases (GHG). Nitrogen can be a problem through nitrate leaching to water bodies, ammonia, and nitrous oxide emissions to air. Phosphorus loading in soil from manure application can be the main issue due to the tendency of P to accumulate in soil and leach to groundwater and rivers. The sources of agricultural GHG emissions include methane from enteric fermentation, manure storage and spreading, and nitrous oxide mainly from application of manure on land. Dietary manipulation has proven to be an effective tool to reduce nutrient/mineral pollution and GHG emissions. Several studies have shown that decreasing crude protein in the diet could reduce N excretion and ammonia volatilization substantially without compromising productivity. Similarly, reducing P intake in dairy cattle has been shown to reduce P excretion by up to 10%. Changing the type of N and P consumed and energy level of diet has also been reported to affect the amount and type of N and P excreted. Dietary manipulation also has an impact on the amount of GHG emissions, particularly, from enteric fermentation. Feeding cattle with a high starch and low fiber diet, for example, reduces acetate production in the rumen, and leads to lower methane production. Emissions from stored manure from high fiber fed animals tend to be higher. Evidence is also available that diet affects emissions from manure applied soil. As level of production is increased to meet global demand for ruminant meat and milk products, dietary manipulation will be useful in addressing environmental concerns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".