Multi-algorithm clustering analysis for characterizing cow productivity on automatic milking systems over lactation periods

•A novel multi-algorithmic clustering methodology and a merging index were proposed.•Multi-algorithmic clustering analysis was applied to long-term milk production.•Dairy cows were classified into Low and High Productivity Groups.•Protein, fat, and lactose content were smaller in the High Productivi...

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Bibliographic Details
Published in:Computers and electronics in agriculture Vol. 211; p. 108002
Main Authors: Rebuli, Karina Brotto, Ozella, Laura, Vanneschi, Leonardo, Giacobini, Mario
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2023
Subjects:
agriculture
Automatic Milking System
automation
cattle productivity
Cluster algorithms merging index
data collection
electronics
farms
Future lactation period
herds
Holstein
insemination
lactation
lactose
milk
Milk production
milk quality
Multi-algorithm clustering
udders
Future lactation period
Multi-algorithm clustering
Cluster algorithms merging index
Automatic Milking System
Milk production
ISSN:0168-1699, 1872-7107
Online Access:Get full text
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Summary:•A novel multi-algorithmic clustering methodology and a merging index were proposed.•Multi-algorithmic clustering analysis was applied to long-term milk production.•Dairy cows were classified into Low and High Productivity Groups.•Protein, fat, and lactose content were smaller in the High Productivity Group.•Productivity Groups were found to be weakly stable over lactation periods. The introduction of Automated Milking Systems (AMSs), or milking robots, represented a significant advancement in dairy farming techniques. AMSs enable real-time monitoring of udder health and milk quality during each milking episode, which provides a wealth of data that can be utilized to optimize herd management practices. ML algorithms are well-suited for handling large and multi-dimensional datasets, making them a valuable tool for analyzing the vast amount of data generated by AMSs. This study introduces a novel approach to characterize the milk productivity of Holstein Friesians cows milked by AMSs during individual lactation periods and evaluate their stability over time. Four unsupervised ML clustering algorithms were employed to cluster the cows within each lactation period, and a merging index was proposed to combine the clustering results. The dairy cows were grouped into clusters based on their productivity, and the stability of these Productivity Groups (PGs) over time was analyzed. The PGs were found to be weakly stable over time, indicating that selecting cows for insemination based solely on their present or past lactation productivity may not be the most effective strategy. In addition, the results revealed that the High Productivity Group exhibited lower levels of protein, fat, and lactose content in the milk. The proposed methodology was demonstrated using data from one farm with dairy cows that exclusively uses the AMS, however, it can be applied to any context and dataset in which a multi-algorithm clustering analysis is suitable, including data from conventional milking parlors. Understanding milk productivity and its factors in future lactation periods is essential for effective herd management. A comprehensive long-term analysis is of significant importance for the zootechnical sector as it could assists farmers in selecting cows for insemination and making decisions on which ones to retain for future lactation periods.
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ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2023.108002