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Comparison of methods for predicting cow composite somatic cell counts

Anglart, Dorota and Hallen-Sandgren, Charlotte and Emanuelson, Ulf and Rönnegård, Lars (2020). Comparison of methods for predicting cow composite somatic cell counts. Journal of Dairy Science. 103 , 8433-8442
[Research article]

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One of the most common and reliable ways of monitoring udder health and milk quality in dairy herds is by monthly cow composite somatic cell counts (CMSCC). However, such sampling can be time consuming, and more automated sampling tools entail extra costs. Machine learning methods for prediction have been widely investigated in mastitis detection research, and CMSCC is normally used as a predictor or gold standard in such models. Predicted CMSCC between samplings could supply important information and be used as an input for udder health decision-support tools. To our knowledge, methods to predict CMSCC are lacking. Our aim was to find a method to predict CMSCC by using regularly recorded quarter milk data such as milk flow or conductivity. The milk data were collected at the quarter level for 8 wk when milking 372 Holstein-Friesian cows, resulting in a data set of 30,734 records with information on 87 variables. The cows were milked in an automatic milking rotary and sampled once weekly to obtain CMSCC values. The machine learning methods chosen for evaluation were the generalized additive model (GAM), random forest, and multilayer perceptron (MLP). For each method, 4 models with different predictor variable setups were evaluated: models based on 7-d lagged or 3-d lagged records before the CMSCC sampling and additionally for each setup but removing cow number as a predictor variable (which captures indirect information regarding cows' overall level of CMSCC based on previous samplings). The methods were evaluated by a 5-fold cross validation and predictions on future data using models with the 4 different variable setups. The results indicated that GAM was the superior model, although MLP was equally good when fewer data were used. Information regarding the cows' level of previous CMSCC was shown to be important for prediction, lowering prediction error in both GAM and MLP. We conclude that the use of GAM or MLP for CMSCC prediction is promising.

Authors/Creators:Anglart, Dorota and Hallen-Sandgren, Charlotte and Emanuelson, Ulf and Rönnegård, Lars
Title:Comparison of methods for predicting cow composite somatic cell counts
Series Name/Journal:Journal of Dairy Science
Year of publishing :2020
Page range:8433-8442
Number of Pages:10
Publisher:Elsevier, Fass Inc.
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 402 Animal and Dairy Science > Animal and Dairy Science.
Keywords:generalized additive model, multilayer perceptron, random forest, udder health
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Additional ID:
Type of IDID
Web of Science (WoS)000563078600020
ID Code:17554
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Clinical Sciences
(VH) > Dept. of Animal Breeding and Genetics
Deposited By: SLUpub Connector
Deposited On:06 Oct 2020 07:46
Metadata Last Modified:15 Jan 2021 19:16

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