Formation of milk productivity in cows depending on the selection indices of their parents from different breeding countries
Bibliographic record
Abstract
Recently, breeding indices have been used to evaluate and select animals, offering several advantages over traditional selection methods based on independent productivity levels, allowing for a comprehensive assessment of animals across a set of breeding traits. In modern breeding programs, alongside milk production traits, factors such as the animal’s conformation, limb strength, udder quality and health (somatic cell count in milk), fertility, productive lifespan, etc., are necessarily taken into account. However, the importance of these traits varies across European countries, which is explained by their economic significance and the breeding goals and objectives set for members of dairy cattle breeding associations at specific stages of breed selection work. Additionally, breeding indices in different countries differ in the relative weight assigned to dairy productivity traits. Because of the above, our research aimed to study the variability of cow milk production traits depending on the selection indices of bulls from different breeding countries. The research was conducted in the Volovikov State Farm LLC herd in the Goshcha district of Rivne region. The variability of cow milk production traits was studied for the first and higher lactations, depending on the selection indices of their sires from various countries: Ukraine, Germany, Canada, the USA, and MACE (Interbull). A total of 4,200 cows were included in the sample. The breeding value of the bulls was determined using the SC “Intesel Orsek” dairy cattle management system and bull sire catalogs. It was found that, depending on the selection indices of bulls from different breeding countries, there was intergroup differentiation in the milk production traits of their daughters. The milk yield of cows for the first lactation ranged from 5792.4 to 6838.5 kg, milk fat content from 3.50 to 3.58 %, and milk fat from 207.2 to 239.6 kg. For higher lactation, these traits were in the range of 7041.6–7522.3 kg, 3.49–3.55 %, and 250.0–265.5 kg, respectively. Among the daughters of Ukrainian bulls, the lowest milk yields and milk fat content for both first and higher lactation were observed in cows with the breeding value of their sires at -2635 to -1900 kg, while the highest yields were found in cows with a breeding value of +304 to +1042 kg. In cows descended from German bulls, these values corresponded to breeding indices of -226 to -69 and +403 to +557, and for Canadian and American bulls, +1469 to +1726 and +446 to +702, respectively. It is worth noting that among the daughters of both Ukrainian and German bulls, milk yield increased with higher breeding values of their sires, whereas in the daughters of Canadian and American bulls, milk yield tended to decrease with increasing breeding values.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".