Options for incorporating feed intake into national selection indexes
Bibliographic record
Abstract
Feed costs are a significant proportion of total costs in most dairy production systems and there is strong evidence for substantial genetic variation in total feed intake. However, a large component of this variation is unfavorably correlated with important maintenance and production functions of the animal. Ideally, selection indexes for improved feed efficiency would consider total feed intake that explicitly accounts for the feed required for valuable energy sinks such as milk production, high fertility and adequate body reserves. Residual feed intake (RFI), which is defined at the phenotypic level as the difference between actual feed intake and predicted feed intake, is a potential selection criterion to improve efficiency of feed utilization. However, there are other potential approaches that might have desirable attributes when considered in the context of well-established genetic evaluation systems with breeding objective definitions that are accepted by industry. In this study, we used simulations to unravel the complex inter-relationships among traits such as milk production, live weight and total feed intake. Feed intake phenotypes were simulated as a composite of simulated component phenotypes, so that the underlying genetic relationships between total feed intake and other traits of interest in dairy production systems could be specified precisely. Genetic variance components were then estimated on animals simulated from a simple pedigree structure and estimated breeding values (EBVs) were populated into several selection indexes with and without feed intake components included. The performance of each index was measured by comparing the index predictions against the true observed merit of simulated sires. Additionally, we examined how feed intake-based selection indexes would perform when only limited feed records are available because feed intake is not routinely recorded in dairy systems. Our results show that selection indexes that explicitly account for feed intake were more strongly correlated with the true observed merit than a selection criterion that is only parameterized with EBVs for milk production. All indexes that included feed intake parameters were more accurate than our base index (i.e. without feed intake) even under poor data conditions with limited feed intake recording (e.g. when only 10% of daughters were phenotyped for feed intake). Including wasted feed by adjusting total feed intake EBVs for other traits that represent known energy sinks while accounting for differences in EBV reliability would be very simple to deploy and we found that such an index was almost as efficient as our selection indexes for feed intake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".