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The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

Authors :
Hong Tian
Xiaojing Zhou
Hao Wang
Chuang Xu
Zixuan Zhao
Wei Xu
Zhaoju Deng
Source :
Animals, Vol 14, Iss 3, p 427 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In commercial dairy farms, mastitis is associated with increased antimicrobial use and associated resistance, which may affect milk production. This study aimed to develop sensor-based prediction models for naturally occurring clinical bovine mastitis using nine machine learning algorithms with data from 447 mastitic and 2146 healthy cows obtained from five commercial farms in Northeast China. The variables were related to daily activity, rumination time, and daily milk yield of cows, as well as milk electrical conductivity. Both Z-standardized and non-standardized datasets pertaining to four specific stages of lactation were used to train and test prediction models. For all four subgroups, the Z-standardized dataset yielded better results than those of the non-standardized one, with the multilayer artificial neural net algorithm showing the best performance. Variables of importance had a similar rank in this algorithm, indicating the consistency of these variables as predictors for bovine mastitis in commercial farms with similar automatic systems. Moreover, the peak milk yield (PMY) of mastitic cows was significantly higher than that of healthy cows (p < 0.005), indicating that high-yielding cattle are more prone to mastitis. Our results show that machine learning algorithms are effective tools for predicting mastitis in dairy cows for immediate intervention and management in commercial farms.

Details

Language :
English
ISSN :
20762615
Volume :
14
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Animals
Publication Type :
Academic Journal
Accession number :
edsdoj.63a63542e66b4b2f8668883108c83c0e
Document Type :
article
Full Text :
https://doi.org/10.3390/ani14030427