Variability of cows milk productivity traits depending on origin of father and country of selection
Bibliographic record
Abstract
A number of specialized dairy breeds of intensive type have been created in Ukraine, among which the Ukrainian black-and-white dairy farm occupies a prominent place. Currently, the improvement of this breed is carried out by using various breeding techniques with a focus on achieving maximum milk productivity of cows, improving milk quality, body type, health, stress resistance and prolonging productive longevity. The aim of the research was to investigate the formation of signs of milk productivity of cows depending on the origin of the father and the country of his selection. The researches have been conducted on bred heifers and mature cows (third lactation) of Ukrainian Black-and-White dairy breed in the State Enterprise "Alexandrovske" in Vinnytsia region. The traits of milk productivity (yields, fat content in milk and quantity of milk fat ) were studied for the last 10 years by retrospective analysis of bred heifers and mature cows (3-rd lactation), theoretically substantiated and proven feasibility of the study variability of these traits and their intergroup differentiation depending on the father’s origin and the countries of its selection, the consideration and application of which in the selection process will support creating of highly productive competitive herds of dairy cattle. The research showed the great influence of the origin of the father and the country of its selection on the variability of milk yield, fat content in milk and cows’ milk fat yield. It was found that the cows of the controlled herd were characterized by quite high indicators of milk productivity: the yields of bred heifers – 6115, mature cows - 6899 kg. The descendants of the bull Jorin 114414759 were the most productive during the first lactation (milk yield - 6936 kg, milk fat - 248.9 kg), and for the third - the daughters of the breeder Detective 349159846 (8148 and 295.2 kg respectively). The descendants of breeding bulls of German selection gave the most milk and milk fat quantity for the studied lactations (6269- 7014 and 224.8-250.3 kg, respectively). At the same time, the daughters of Canadian breeders gave the most fat- milk yield for the first lactation (3.66%), and for the third lactation – the daughters of the Dutch bulls (3.59%). Breeding bulls had a more significant influence on the traits of milk productivity (depending on lactation 13.1-31.8%), the country of their selection – much smaller (0.9-11.9%). In this regard, these factors had the greatest impact on milk yields, and the least - on the fat content in milk.
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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 | 0.000 |
| 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".