Evaluation methods and technologies for improving feed efficiency in the turkey (Meleagris gallopavo)
Bibliographic record
Abstract
Feed efficiency is the most important breeding objective in the commercial turkey breeding programs as feed represents up to 70% of the total production costs at the commercial level. In this thesis focus was placed on evaluating different traits used in the assessment of feed efficient, alongside technologies which may aid in easier or more accurate measurements of feed efficiency traits. Feed efficiency has been historically measured via the feed conversion ratio (FCR), which is the feed intake over a given period of time divided by the body weight gain over that same period. Selecting for feed efficiency using FCR can however lead to unexpected results due to direct selection on a ratio trait. This has led to the investigation of different feed efficiency traits in the animal breeding industry. Once such trait is residual feed intake (RFI), the amount of feed consumed not accounted for by body weight maintenance or body weight gain. Selecting solely on RFI can allow slow growing animals that eat a small amount of feed be giving an excellent score. As such, a linear combination of RFI and residual body weight gain (RG) was examined in order to compensate for drawbacks in both residual traits. This combination, called residual intake and body weight gain (RIG) was found to be more efficient when used as the only selection criterion. A typical multiple trait evaluation system which focuses on both production and reproductive traits is generally used in the turkey breeding industry. The genetic trends concerning the use of such an index in both a sire and dam line show that genetic progress continues to be made over time and that the line breeding programs have been successful despite some antagonistic genetic relationships between traits. One important correlated trait with feed efficiency is the pendulous crop (PC). This defect leads to culling the individual from the population due to an engorged crop. The genetic correlation with feed efficiency traits was determined to be slightly negative in the large white male line of turkeys. This lead to the conclusion that PC must be considered alongside feed efficiency when being used in a multiple trait selection index. In order to calculate feed efficiency traits, certain component traits are required, namely feed intake and body weight gain both measured at the individual level. Traditionally, this has been accomplished through isolating individuals in a pen and measuring the amount of feed consumed by weighing a feeder and the amount of body weight gain by weighing the animal. New technologies have allowed for individual feed intake measurements to be monitored in a group-housed situation. As such, the feed efficiency traits are being measured in an environment that much more closely resembles the commercial production environment. This gives the turkey breeding industry a more powerful option when evaluating animals for use in subsequent generations.
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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 | 0.000 |
| 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 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".