Effect of high-sugar grasses on methane emissions simulated using a dynamic model
Bibliographic record
Abstract
High-sugar grass varieties have received considerable attention for their potential ability to decrease N excretion in cattle. However, feeding high-sugar grasses alters the pattern of rumen fermentation, and no in vivo studies to date have examined this strategy with respect to another environmental pollutant: methane (CH(4)). Modeling allows us to examine potential outcomes of feeding strategies under controlled conditions, and can provide a useful framework for the development of future experiments. The purpose of the present study was to use a modeling approach to evaluate the effect of high-sugar grasses on simulated CH(4) emissions in dairy cattle. An extant dynamic, mechanistic model of enteric fermentation and intestinal digestion was used for this evaluation. A simulation database was constructed and analysis of model behavior was undertaken to simulate the effect of (1) level of water-soluble carbohydrate (WSC) increase in dietary dry matter, (2) change in crude protein (CP) and neutral detergent fiber (NDF) content of the plant with an increased WSC content, (3) level of N fertilization, and (4) presence or absence of grain feeding. Simulated CH(4) emissions tended to increase with increased WSC content when CH(4) was expressed as megajoules per day or percent of gross energy intake, but when CH(4) was expressed in terms of grams per kilogram of milk, results were much more variable due to the potential increase in milk yield. As a result, under certain conditions, CH(4) (g/kg of milk) decreased. The largest increases in CH(4) emissions (MJ/d or % gross energy intake) were generally seen when WSC increased at the expense of CP in the diet and this can largely be explained by the representation in the model of the type of volatile fatty acid produced. Effects were lower when WSC increased at the expense of NDF, and intermediary when WSC increased at the expense of a mixture of CP and NDF. When WSC increased at the expense of NDF, simulated milk yield increased and, therefore, CH(4) (g/kg of milk) tended to decrease. Diminished increases of CH(4) (% gross energy intake or g/kg of milk) were simulated when DMI was increased with elevated WSC content. Simulation results suggest that high WSC grass, as a strategy to mitigate N emission, may increase CH(4) emissions, but that results depend on the grass composition, DMI, and the units chosen to express CH(4). Overall, this project demonstrates the usefulness of modeling for hypothesis testing in the absence of observed experimental results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".