Estimating the Value of Including Resilience in a Multi‐Trait Selection Index Designed for Australian Merino Sheep
Bibliographic record
Abstract
General resilience in livestock can be estimated from the variability in longitudinal data and may support balanced breeding objectives by helping animals better cope with environmental stress. However, its economic value and inclusion within multi-trait selection indexes remain largely unexplored. Current genetic improvement programs for sheep primarily focus on wool growth, reproduction and lean meat production. This study developed a bioeconomic model to estimate the economic value of general resilience, using the natural logarithm of variance of wool fibre diameter as a resilience indicator trait in sheep. A commercial Merino sheep enterprise was considered, and the economic value of resilience was derived from its association with health, mortality, and energy allocation for production. The breeding objective included key traits related to meat production, reproduction, and wool quality and quantity, to compare the economic impact of resilience with these traits. Among 13 traits, resilience contributed 2.02% to the total value of the index and ranked 11th in economic importance. After 10 years of simulated selection, resilience was expected to improved by 0.07 genetic standard deviations, translating to a modest economic gain of $0.08 per ewe. A sensitivity analysis was conducted to assess the impacts of increasing the relative contribution of resilience within the index on selection responses under four scenarios: (i) base genetic correlations, (ii) stronger correlations, (iii) weaker correlations, and (iv) adding genomic information. Resilience responded 50%-75% more effectively to selection when it had stronger genetic correlations with production traits or when genomic information was incorporated. However, placing greater emphasis on resilience (> 10% of the index) reduced progress in key production traits. Future research should explore these trade-offs in industry selection indexes to integrate resilience effectively without compromising productivity.
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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 | 0.000 | 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".