Comparing the use of dry matter intake and residual feed intake to improve feed efficiency in Holstein cattle.
Bibliographic record
Abstract
The inclusion of feed efficiency into breeding objectives for dairy cattle has been a topic of discussion for many years. As feed costs rise and the environmental impacts of agriculture are increasingly scrutinized, improving the efficiency at which dairy cows convert feed to milk is becoming more important. There are many ways to define feed efficiency, with much discussion surrounding optimal traits and strategies. The objective of this research was to compare the effects of holding dry matter intake constant while selecting to increase production versus selecting on residual feed intake, both of which can be considered potential mechanisms for improving feed efficiency in dairy cattle. A subset of traits genetically evaluated in Canada were chosen to represent various aspects of the current breeding program. These traits included first parity measures for: 305-day fat yield, 305-day protein yield, body condition score, stature, age at first service (heifer), days from first service to conception, clinical ketosis, and displaced abomasum. Different breeding goals were considered using a deterministic modeling program. The inclusion of either dry matter intake or residual feed intake in the index was analyzed considering two methods. One scenario of the current breeding goal, where no selection pressure was applied on either dry matter intake or residual feed intake, and selection based on the indirect response was evaluated. The other method applied selection pressure to either hold dry matter intake constant or reduce residual feed intake, and the direct response to selection was evaluated. Annual genetic gain and monetary genetic gain were assessed for both scenarios. When no selection pressure was applied, both traits had an unfavourable response to selection, whereas with direct selection pressure, the response was favourable for both traits. Selecting to hold dry matter intake constant while selecting to increase production had a similar response to selection for improving feed efficiency compared to selecting on residual feed intake. This could indicate that both dry matter intake and residual feed intake would be effective at improving the efficiency at which cows utilize their feed for milk production.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".