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Article

Plasma and Milk Variables Classify Diet, Dry Period Length, and Lactation Week of Dairy Cows Using a Machine Learning Approach

1
Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6708 WE Wageningen, The Netherlands
2
Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE Wageningen, The Netherlands
3
Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
4
Veterinary Physiology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
*
Authors to whom correspondence should be addressed.
Metabolites 2025, 15(11), 698; https://doi.org/10.3390/metabo15110698
Submission received: 3 June 2025 / Revised: 17 October 2025 / Accepted: 18 October 2025 / Published: 28 October 2025
(This article belongs to the Special Issue NMR-Based Metabolomics in Biomedicine and Food Science)

Abstract

Background/Objectives: The aim of this study was to classify cows with respect to different diets, dry period (DP) lengths, and lactation weeks based on body weight, milk variables, and plasma metabolites measured in early lactation. Methods: Holstein–Friesian cows (n = 95) were randomly assigned to three DP lengths (0, 30, or 60 d; n = 31, 34, and 30) and two early-lactation diets (lipogenic: n = 47; glucogenic: n = 48) in a 3 × 2 factorial design. From 10 d pre-calving to 8 weeks postpartum, cows received experimental diets. An XGBoost model was trained for classification using weekly body weight, milk variables, and plasma metabolites, validated via 1000 repeated hold-out partitions with stratified sampling. Results: Classification performance for lactation week, relative to week 1 in lactation, was good, with an area under the curve (AUC) > 0.9, independent of diet or DP length. The classification for 0 d vs. 60 d DP length was better than that for 0 d vs. 30 d or 30 d vs. 60 d DP length, showing an AUC > 0.8, independent of diet or lactation week. The top features to classify diet were plasma urea and milk fat content. Milk yield and protein content were the important features for classifying lactation weeks regardless of diet, while milk fat content was a critical predictor specific to the glucogenic diet. Conclusions: Our findings demonstrate that milk and plasma features can retrospectively classify management groups in early lactation using machine learning approaches.
Keywords: cattle; algorithm; transition period; cow management; metabolism cattle; algorithm; transition period; cow management; metabolism

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MDPI and ACS Style

Wang, X.; Jahagirdar, S.; Kemp, B.; Gross, J.J.; Bruckmaier, R.M.; Saccenti, E.; van Knegsel, A. Plasma and Milk Variables Classify Diet, Dry Period Length, and Lactation Week of Dairy Cows Using a Machine Learning Approach. Metabolites 2025, 15, 698. https://doi.org/10.3390/metabo15110698

AMA Style

Wang X, Jahagirdar S, Kemp B, Gross JJ, Bruckmaier RM, Saccenti E, van Knegsel A. Plasma and Milk Variables Classify Diet, Dry Period Length, and Lactation Week of Dairy Cows Using a Machine Learning Approach. Metabolites. 2025; 15(11):698. https://doi.org/10.3390/metabo15110698

Chicago/Turabian Style

Wang, Xiaodan, Sanjeevan Jahagirdar, Bas Kemp, Josef J. Gross, Rupert M. Bruckmaier, Edoardo Saccenti, and Ariette van Knegsel. 2025. "Plasma and Milk Variables Classify Diet, Dry Period Length, and Lactation Week of Dairy Cows Using a Machine Learning Approach" Metabolites 15, no. 11: 698. https://doi.org/10.3390/metabo15110698

APA Style

Wang, X., Jahagirdar, S., Kemp, B., Gross, J. J., Bruckmaier, R. M., Saccenti, E., & van Knegsel, A. (2025). Plasma and Milk Variables Classify Diet, Dry Period Length, and Lactation Week of Dairy Cows Using a Machine Learning Approach. Metabolites, 15(11), 698. https://doi.org/10.3390/metabo15110698

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