The effect of high-sugar grass on predicted nitrogen excretion and milk yield simulated using a dynamic model
Bibliographic record
Abstract
High-sugar grass varieties have received considerable attention for their potential to reduce nitrogen (N) excretion and increase milk yield in cattle. However, considerable variation exists in the magnitude of response in published results. The purpose of this study is to explain the variation in response using a dynamic mechanistic model to predict observed N and milk yield results from the literature, and from simulated data. Examined effects were (1) water-soluble carbohydrate [WSC; g/kg of dry matter (DM)] increase; (2) change in crude protein (CP) and neutral detergent fiber (NDF) content of the plant with WSC increase; and (3) the level of N fertilization. The database for evaluation of model N and milk yield predictions consisted of 4 published studies with 28 treatment means for which high-sugar grasses were being evaluated. Water-soluble carbohydrate content of the diets ranged from 95 to 248 g/kg of DM, CP content ranged from 115 to 263 g/kg of DM, and the NDF content ranged from 400 to 568 g/kg of DM. Urine N, milk N, and total N excretion were predicted well by the model and followed the directional pattern of observed values within each study. Simulation results showed that the N utilization ratio increased as the WSC content of the diet increased, but to varying degrees depending on the grass scenario examined. The greatest benefit in terms of N utilization ratio and urine N levels were seen when the WSC content of grass increased at the expense of CP, followed by a 50:50 CP and NDF mix, followed by a trade for NDF. Simulated milk yield decreased slightly when WSC increased at the expense of CP, increased slightly when it increased at the expense of a CP and NDF mix, and increased most when WSC increased at the expense of NDF. Results were amplified slightly under conditions of low-N fertilization and in the absence of grain feeding. Overall, modeling is useful as an explanatory tool. The variation from results in the literature with high-WSC grass feeding may be, at least in part, the result of the level of WSC (g/kg of DM) increase, concurrent changes occurring within the CP and NDF fractions of the plant, and the plane of nutrition of the diet (grain feeding and N fertilization levels).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".