Genetic evaluation of profitability measures in Holstein dairy cows
Bibliographic record
Abstract
This study aimed to assess and estimate the genetic parameters for profit indices and identify the most reliable index for selection purposes. A total of 4 637 629 test-day records were collected from 255 804 cows across 124 herds over 15 years (2009–2024). However, the economic data were collected only from five dairy herds. Pedigree data included 575 707 animals (185 608 with records), traced using DMU Trace and structured with CFC software. Genetic parameters were estimated using single- and multiple-trait animal models with fixed effects of herd-year-season, lactation number, age at first calving, and random additive genetic effects. A DMU program was used for genetic evaluations and the best linear unbiased prediction of breeding values for the studied traits. The profitability indices include profit, income over feed cost (IOFC), income equal to feed cost (IEFC), money-corrected milk, and milk-to-feed price ratio. The results of this study revealed that profit has a moderate heritability (0.25), indicating a potential for genetic improvement. The IOFC per 100 kg of milk (IOFC milk ) had the highest genetic ( r = 0.97) and phenotypic ( r = 0.92) correlation with profit, making it the most reliable profit alternative. Furthermore, IEFC per 100 kg of milk (IEFC milk ) was genetically negatively correlated with profit (−0.80), indicating that cows with high IEFC were less economically efficient. While milk yield had improved, profitability had shown a negative genetic trend, which means that an exclusive focus on higher milk production is detrimental to long-term economic efficiency. Generally, indices such as IOFC milk and IEFC milk should be considered in breeding programs to increase profitability, efficiency, and sustainability within industrial dairy farms. This study emphasizes the importance of integrating profitability indicators into genetic evaluations. Providing novel selection criteria contributes to more efficient and sustainable breeding strategies for Holstein dairy cows.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".