Bibliographic record
Abstract
Submitted 2020-07-17 | Accepted 2020-08-18 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.250-257 This study aimed to evaluate the score for subclinical ketosis risk, which is routinely monitored in Czech Holstein cows. The score is based on milk recording indicator traits which include fat-to-protein ratio, fat-to-lactose ratio, citric acid, β-hydroxybutyrate, and acetone concentrations. The score was significantly (P <0.001) affected by the age of cow at calving, days in milk (DIM) and season of test-day recording. Variance components were estimated with a univariate linear animal model for the score on the first test-day and with a multivariate linear animal model for the score in 3 successive test-days (6-40, 30-70, 60-100 DIM). The heritability estimate was lower at the beginning of lactation (0.08) and increased gradually to 0.11 at the end of the recorded period. Genetic correlations between the score at the first and the other two test-days were lower than 1 indicating that they are genetically different traits. Estimated breeding values were normally distributed with mean 0.20 and reliabilities up to 0.66 in females and 0.98 in males. Breeding values were negatively correlated with most of other routinely evaluated traits, with the strongest correlations with milk fat percentage (0.39), body condition score (-0.26) and fertility of cows (-0.25). The score for subclinical ketosis risk showed sufficient genetic variability and had the potential to be used in genetic improvement of resistance to (sub)clinical ketosis of Czech Holstein cows. Keywords: metabolic status, indicator trait, ketone body References Bastin, C. et al. (2016) On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs. Journal of Dairy Science, 99(5), 4080–4094. https://doi.org/10.3168/jds.2015-10087 Belay, T.K. et al. (2017) Genetic parameters of blood β-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian Red cows. Journal of Dairy Science, 100(8), 6298–6311. https://doi.org/10.3168/jds.2016-12458 Costa, A. et al. (2019) Genetic association of lactose and its ratios to other milk solids with health traits in Austrian Fleckvieh cows. Journal of Dairy Science, 102(5), 4238–4248. https://doi.org/10.3168/jds.2018-15883 Gebreyesus, G. et al. (2020) Predictive ability of host genetics and rumen microbiome for subclinical ketosis. Journal of Dairy Science, 103(5), 4557–4569. https://doi.org/10.3168/jds.2019-17824 Hanuš, O. et al. (2017) Analyse of relationships between some milk indicators of cow energy metabolism and ketosis state. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(4), 1135–1147. Hanuš O. et al. (2013) Identification of subclinical ketosis in early lactation of cows according to the results of milk yield and individual milk samples in milk recording scheme and interpretation of results. Certified methodology. Prague: Institute of Dairy Science. In Czech. Jamrozik, J. et al. (2016) Multiple-trait estimates of genetic parameters for metabolic disease traits, fertility disorders, and their predictors in Canadian Holsteins. Journal of Dairy Science, 99(3), 1990–1998. https://doi.org/10.3168/jds.2015-10505 Kašná, E. et al. (2020) Fat-to-protein ratio in milk of Holstein cows. Náš chov, 80(2), 32–36. In Czech. Kašná, E. et al. (2019) Genetic evaluation of reproductive and metabolic disorders and displaced abomasum in Czech Holstein cows. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(4), 939–946. Koeck, A. et al. (2014) Genetic analysis of milk β-hydroxybutyrate and its association with fat-to-protein ratio, body composition score, clinical ketosis, and displaced abomasum in early first lactation of Canadian Holstein. Journal of Dairy Science, 97(11), 7286–7292. https://doi.org/10.3168/jds.2014-8405 Madsen, P. & Jensen, J. (2013) A User’s Guide to DMU. Retrieved June 25, 2020 from https://dmu.ghpc.au.dk/DMU/Doc/Current/dmuv6_guide.5.2.pdf Martens, H. (2020) Transition period of the dairy cow revisited: I. Homeorhesis and its changes by selection and management. Journal of Agricultural Science, 12(3), 1–24. https://doi.org/10.5539/jas.v12n3pl Pryce, J. E. et al. (2016) Invited review: Opportunities for genetic improvement of metabolic diseases. Journal of Dairy Science, 99(9), 6855–6873. https://doi.org/10.3168/jds.2016-10854 SAS Institute Inc. (2017) Base SAS® 9.4 Procedures Guide, Seventh Edition. SAS Institute Inc., Cary, NC, USA. Šlosárková, S. et al. (2016) Monitoring of dairy cattle diseases in the Czech Republic. Veterinářství, 66(11), 859–866. In Czech. Van der Drift, S. G. A. et al. (2012) Genetic and non-genetic variation in plasma and milk β-hydroxybutyrate and milk acetone concentrations of early-lactation dairy cows. Journal of Dairy Science, 95(11), 6781–6787. https://doi.org/10.3168/jds.2012-5640 Vosman, J. J. et al. (2015) Genetic evaluation for ketosis in the Netherlands based on FTIR measurements. Interbull Bulletin No. 49: Proceedings of the 2015 Interbull Meeting, Orlando, Florida, 1–5.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".