Analysis of foot and claw diseases/disorders in Czech Holstein cows
Bibliographic record
Abstract
Received: 2018-05-07 | Accepted: 2018-05-14 | Available online: 2018-11-26 https://doi.org/10.15414/afz.2018.21.04.194-196 Foot and claw diseases/disorders from 24 545 lactations of 10 340 Holstein cows were recorded on 7 farms in the Czech Republic from 1999 to 2018. There were defined a three groups of foot and claw disorders/diseases: diseases of skin (SD), which cover digital, interdigital dermatitis and interdigital phlegmon; then disorders of the claw horn (CH) including ulcers, white line disease, horn fissures, together with double sole and overall claw diseases (OCD) ingluding all the recorded disorders. The OCD ratio observed during 1 th and 305 th days of lactation was 52.56% of all evaluated lactations. The observed ratio of SD and CH were 28.61% and 27.15%, respectively. A foot and claw disorders were defined as 0/1 occurrence per lactation, for the purposes of analyses,. Genetic parameters were estimated using by linear animal models for evaluated traits. Models included the random additive genetic effect of animal (A), the permanent environmental effect of cow (PE), fixed effects of parity, farm, year and season of calving, and age at calving as discreet variables in classes. The estimated heritability were 13.84%, 12.64% and 9.83%, 8.73% and 9.97%, for OCD, CH, SD, ulcers (U) and for dermatitis digitalis and interdigitalis (DD), respectively. Genetic correlation was 17.66% between SD and CH, whereas traits SD and DD equal high genetic similarity (98.4%). Also correlation between CH and U traits was high (92.62%). The presented results indicate to possibility of selection against foot and claw disorders/diseases for Czech Holstein population. The work was supported by the project QJ1510144 and the institutional support MZE-RO0718 of the Ministry of Agriculture of the Czech Republic. Keywords : cattle, foot and claw disorders, genetic parameters, health traits, udde References Buch, L. H., A. C. Sørensen, J. Lassen, P. Berg, J. A. Eriksson, J. H. Jakobsen, M. K. Sørensen (2011) Hygiene-related and feed-related hoof diseases show different patterns of genetic correlations to clinical mastitis and female fertility. J. Dairy Sci., 94, 1540-1551. Chapinal, N., A. Koeck, A. Sewalem, D. F. Kelton, S. Mason, G. Cramer,F. Miglior (2013) Genetic parameters for hoof lesions and their relationship with feet and leg traits in Canadian Holstein cows. J. Dairy Sci., 96, 2596-2604. Egger-Danner, C., O.K. Hansen, K. Stock, J.E. Pryce, J. Cole, N. Gengler, B. Heringstad (2013) Challenges and benefits of health data recording in the context of food chain quality, management and breeding. ICAR Technical Series. Groeneveld, E., M. Kovač, and N. Mielenz (2008) VCE User’s Guide and Reference Manual, Version 6.0. Krpálková, L., M. Štípková & M. Krejčová, 2016. Vliv zdraví paznehtů a úrovně reprodukce na výkonnost a zisk stáda dojnic. Náš chov, 76 (9), 58-63. Krupová, Z., Krupa, E., Michaličková, M., Wolfová, M., Kasarda, R. (2016) Economic values for health and feed efficiency traits of dual-purpose cattle in marginal areas. Journal of Dairy Science, ,. 99, s. 644-656. Madsen, P. , J. Jensen. 2010. DMU – a package for analysing multivariate mixed models. Version 6, release 5.0., Aarhus University, Foulum, Denmark. Pérez-Cabal, M. A. ,N. Charfeddine, N. (2015) Models for genetic evaluations of claw health traits in Spanish dairy cattle. J. Dairy Sci., 98 (11), 8186-8194. Sogstad, A. M., T. Fjeldaas, O. Østerås, K. P. Forshell. (2005) Prevalence of claw lesions in Norwegian dairy cattle housed in tie stalls and free stalls. Prev. Vet. Med., 70, 191-209. van der Spek, D., J.A. van Arendonk, A.A. Vallée, H. Bovenhuis (2013) Genetic parameters for claw disorders and the effect of preselecting cows for trimming. J Dairy Sci., 96 (9), 6070-6078. van der Waaij, E. H., M. Holzhauer, E. Ellen, C. Kamphuis, G.de Jong. (2005) Genetic parameters for claw disorders in Dutch dairy cattle and correlations with conformation traits. J. Dairy Sci., 88, 3672-3678.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".