Modeling the Adaptation of Dairy Cows to Automatic Milking Systems Using Statistical Methods and Machine Learning: Development of the Robotic Adaptability Index.

Dariusz Piwczyński, Wilhelm Grzesiak, Daniel Zaborski, Kamil Siatka

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Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Dariusz PiwczyńskiDepartment of Animal Biotechnology and Genetics, Bydgoszcz University of Science and Technology, 85-084 Bydgoszcz, Poland.ORCID 0000-0001-8298-2316

Wilhelm GrzesiakDepartment of Biostatistics, Bioinformatics and Animal Research Methods, West Pomeranian University of Technology in Szczecin, 71-270 Szczecin, Poland.ORCID 0000-0003-2899-2243

Daniel ZaborskiDepartment of Biostatistics, Bioinformatics and Animal Research Methods, West Pomeranian University of Technology in Szczecin, 71-270 Szczecin, Poland.ORCID 0000-0003-3590-3229

Kamil SiatkaDepartment of Animal Breeding and Nutrition, Bydgoszcz University of Science and Technology, 85-084 Bydgoszcz, Poland.ORCID 0000-0003-2107-268X

Funding

Bydgoszcz University of Science and Technology Study funded under statutory activity, topic BN-WHiBZ-9/2026

6 · The paper itself

Abstract

The objectives of this study were: (1) to identify factors influencing the performance of dairy cows in an automated milking system (AMS); (2) to construct a synthetic robotic adaptability index (RAI) of cows' adaptation to the AMS; (3) to evaluate the predictive capabilities of traits describing the milking process and RAI; and (4) to compare the predictive power of different modeling approaches. The data on 796 primiparous Polish Holstein-Friesian cows (40,233 milkings) were obtained from the milking robot management system. Milking efficiency (ME), and the average number (AA) and time (AT) of the teat cup attachments and RAI served as predicted variables. Days in milk, and four AMS milking-related and 18 linear conformation traits were used as predictors. The highest predictive ability for ME was achieved with multilayer perceptron (R

Indexed as

adaptationartificial neural networksautomatic milking systembooted treesdairy cowslogistic regressionmultiple linear regressionrandom forest

Identifiers

PMID42278135

DOI10.3390/ani16111703

PMCPMC13255849

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.