Estimation of genetic parameters for milking temperament in Holstein-Gyr cows
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Bibliographic record
Abstract
Introduction Dairy cattle with poor temperament can cause several inconveniences during milking, leading to labor difficulties, increasing the risk of accidents with animals and workers, and compromising milk yield and quality. This study aimed to estimate variance components and genetic parameters for milking temperament and its genetic correlations with milk yield in crossbred Holstein-Gyr cattle. Methods Data were collected at three commercial farms, resulting in 5,904 records from 1,212 primiparous and multiparous lactating cows. Milking temperament (MT), measured as the milking temperament of each cow, was assessed during pre-milking udder preparation (RP) and when fitting the milking cluster (RF) by ascribing scores from 1 (cow stands quietly) to 8 (the cow is very agitated, with vigorous movements and frequent kicking). The number of steps and kicks were also recorded during pre-milking udder preparation (S RP and K RP , respectively) and when fitting the milking cluster (S RF and K RF , respectively). Milk yield (MY) was obtained from each farm database. In two of them, MY was recorded during the monthly milk control (that could or could not coincide with the date when the milking temperament assessments were carried out) and in the remaining farm, MY was recorded on the same day that the milking temperament assessments were made. Genetic parameters were estimated using the THRGibbs1f90 program applying a threshold model, which included 89 contemporary groups as fixed effects, animal age at the assessment day and the number of days in milking as covariates, and direct additive genetic and residual effects as random effects. Results and discussions The heritability estimates were MT= 0.14 ± 0.03 (for both, M RP and M RF ), MY= 0.11 ± 0.08, S RP = 0.05 ± 0.03, K RP = 0.14 ± 0.05, S RF = 0.10 ± 0.05, and K RF = 0.32 ± 0.16. The repeatability estimates were 0.38 ± 0.05, 0.42 ± 0.02, and 0.84 ± 0.006 for MT RP , MT RF , and MY, respectively; and 0.38 ± 0.02, 0.30 ± 0.07, 0.52 ± 0.02, and 0.46 ± 0.15 for S RP , K RP , S RF , and K RF , respectively. The estimates of most genetic correlation coefficients between MT RP -MT RF were all strong and positive (MT RR -MT RF = 0.63 ± 0.10, MT RP -S RP = 0.65 ± 0.12, MT RP -K RP = 0.56 ± 0.16, MT RF -S RF = 0.77 ± 0.06, and MT RF -K RF = 0.56 ± 0.34) except for MY (MT RP -MY= 0.26 ± 0.26 and MT RF -MY= 0.21 ± 0.23). Despite the low magnitude of MT heritability, it can be included as a selection trait in the breeding program of Holsteins-Gyr cattle, although its genetic progress will be seen only in the long term. Due to the low accuracy of the genetic correlation estimates between MT and MY and the high range of the 95% posterior density interval, it cannot be affirmed by this study that the selection of a milking temperament trait will infer on milk yield. More data is therefore needed per cow and more cows need to be observed and measured to increase the reliability of the estimation of these correlations to be able to accurately interpret the results.
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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 | 0.000 |
| 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 it