Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle
Bibliographic record
Abstract
Feed represents a substantial proportion of production costs in the dairy industry and is a useful target for improving overall system efficiency and sustainability. The objective of this study was to develop methodology to estimate the economic value for a feed efficiency trait and the associated methane production relevant to Canada. The approach quantifies the level of economic savings achieved by selecting animals that convert consumed feed into product while minimizing the feed energy used for inefficient metabolism, maintenance and digestion. We define a selection criterion trait called Feed Performance (FP) as a 1 kg increase in more efficiently used feed in a first parity lactating cow. The impact of a change in this trait on the total lifetime value of more efficiently used feed via correlated selection responses in other life stages is then quantified. The resulting improved conversion of feed was also applied to determine the resulting reduction in output of emissions (and their relative value based on a national emissions value) under an assumption of constant methane yield, where methane yield is defined as kg methane/kg dry matter intake (DMI). Overall, increasing the FP estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow's first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane with the economic values of CAD $0.82 and CAD $0.07, respectively. Therefore, the estimated total economic value for FP is CAD $0.89/unit. The proposed model is robust and could also be applied to determine the economic value for feed efficiency traits within a selection index in other production systems and countries.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".