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Short communication: Prediction of hyperketonemia in dairy cows in early lactation using on-farm cow data and net energy intake by partial least square discriminant analysis
- Source :
- Journal of Dairy Science, 103(7), 6576-6582, Journal of Dairy Science 103 (2020) 7
- Publication Year :
- 2019
-
Abstract
- The objectives of this study were (1) to evaluate if hyperketonemia in dairy cows (defined as plasma β-hydroxybutyrate ≥1.0 mmol/L) can be predicted using on-farm cow data either in current or previous lactation week, and (2) to study if adding individual net energy intake (NEI) can improve the predictive ability of the model. Plasma β-hydroxybutyrate concentration, on-farm cow data (milk yield, percentage of fat, protein and lactose, fat- and protein-corrected milk yield, body weight, body weight change, dry period length, parity, and somatic cell count), and NEI of 424 individual cows were available weekly through lactation wk 1 to 5 postpartum. To predict hyperketonemia in dairy cows, models were first trained by partial least square discriminant analysis, using on-farm cow data in the same or previous lactation week. Second, NEI was included in models to evaluate the improvement of the predictability of the models. Through leave-one trial-out cross-validation, models were evaluated by accuracy (the ratio of the sum of true positive and true negative), sensitivity (68.2% to 84.9%), specificity (61.5% to 98.7%), positive predictive value (57.7% to 98.7%), and negative predictive value (66.2% to 86.1%) to predict hyperketonemia of dairy cows. Through lactation wk 1 to 5, the accuracy to predict hyperketonemia using data in the same week was 64.4% to 85.5% (on-farm cow data only), 66.1% to 87.0% (model including NEI), and using data in the previous week was 58.5% to 82.0% (on-farm cow data only), 59.7% to 85.1% (model including NEI). An improvement of the accuracy of the model due to including NEI ranged among lactation weeks from 1.0% to 4.4% when using data in the same lactation week and 0.2% to 6.6% when using data in the previous lactation week. In conclusion, trained models via partial least square discriminant analysis have potential to predict hyperketonemia in dairy cows not only using data in the current lactation week, but also using data in the previous lactation week. Net energy intake can improve the accuracy of the model, but only to a limited extent. Besides NEI, body weight, body weight change, milk fat, and protein content were important variables to predict hyperketonemia, but their rank of importance differed across lactation weeks.
- Subjects :
- Farms
Net energy
Biochemie
Cattle Diseases
Lactose
Biology
Biochemistry
Sensitivity and Specificity
Protein content
chemistry.chemical_compound
Animal science
Milk yield
Pregnancy
Lactation
Genetics
medicine
Animals
Systems and Synthetic Biology
Adaptatiefysiologie
Least-Squares Analysis
subclinical ketosis
VLAG
Systeem en Synthetische Biologie
3-Hydroxybutyric Acid
Body Weight
Postpartum Period
Discriminant Analysis
Ketosis
Linear discriminant analysis
Milk Proteins
metabolic status
Parity
True negative
medicine.anatomical_structure
Milk
chemistry
partial least square discriminant analysis
WIAS
Adaptation Physiology
Animal Science and Zoology
Cattle
Female
Energy Intake
Somatic cell count
Food Science
Subjects
Details
- ISSN :
- 15253198 and 00220302
- Volume :
- 103
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- Journal of dairy science
- Accession number :
- edsair.doi.dedup.....696ae654fc91e820c3cdfbf737b9d0e6