ArticleJournal of advanced veterinary and animal research2020
Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models.
Article in Journal of advanced veterinary and animal research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- AI and Data Analytics in the Dairy Farms: A Scoping Review.Animals : an open access journal from MDPI · 2025Review
- Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations.BMC veterinary research · 2024Article
- Historical Evolution of Cattle Management and Herd Health of Dairy Farms in OECD Countries.Veterinary sciences · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objective of this study was to assess the veracities of most admired strategy discriminant analysis (DA), in comparison to the artificial neural network (ANN) for the anticipation and classification of milk production level in Holstein Friesian cattle using their performances. MATERIALS AND
methodsA total of 3,460 performance records of imported and locally born Holstein Friesian cows were gathered during the period from 2000 to 2016 to compare two alternative techniques for predicting the level of production based on performance traits in dairy cattle with the use of statistical software (Statistical Package for the Social Sciences, version 20.0).
resultsThe findings of the comparison indicated that ANN was more impressive in the expectancy of milk production level than did an imitator statistical method based on DA. The accuracy of the ANN model was high for the winter season (79.5%), whereas it was 47.3% for DA. The current findings were assured via the areas under receiver operating characteristic curves (AUROC) for DA and ANN. AUROC curves were smaller in the condition of the DA model across different calving seasons compared with the ANN model. The inaccuracies of variations were significant at a 5% significance level utilizing paired sample
conclusionANN model can be used efficiently to predict the level of production across the different calving seasons compared to the DA model.
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Registered trials
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