Next Article in Journal
Effects of Iron Deficiency on Serum Metabolome, Hepatic Histology, and Function in Neonatal Piglets
Previous Article in Journal
Effects of 5-Aminolevulinic Acid as a Supplement on Animal Performance, Iron Status, and Immune Response in Farm Animals: A Review
Article

Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm

by 1,†, 2,†, 2,* and 1,*
1
Division of Animal and Dairy Sciences, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
2
Department of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work as first author.
Animals 2020, 10(8), 1348; https://doi.org/10.3390/ani10081348
Received: 13 July 2020 / Revised: 30 July 2020 / Accepted: 30 July 2020 / Published: 4 August 2020
A lactation curve (LC) provides valuable insights in planning appropriate management strategies related to health, nutrition, and breeding in dairy cows. A clustering based approach on LC patterns analysis is presented. The k-medoids algorithm is adopted for the clustering. This approach generates several clusters which have similar milking characteristics of total milk yield, peak milk yield, and days in milk at peak yield. The LCs of some groups represent characteristics of atypical milking patterns which are not considered much in previous approaches, whereas LCs of the other groups show the typical LC patterns similar to the results of previous methods. This approach could be used as a tool to manage an abnormal herd of cows.
The aim of the study was to group the lactation curve (LC) of Holstein cows in several clusters based on their milking characteristics and to investigate physiological differences among the clusters. Milking data of 330 lactations which have a milk yield per day during entire lactation period were used. The data were obtained by refinement from 1332 lactations from 724 cows collected from commercial farms. Based on the similarity measures, clustering was performed using the k-medoids algorithm; the number of clusters was determined to be six, following the elbow method. Significant differences on parity, peak milk yield, DIM at peak milk yield, and average and total milk yield (p < 0.01) were observed among the clusters. Four clusters, which include 82% of data, show typical LC patterns. The other two clusters represent atypical patterns. Comparing to the LCs generated from the previous models, Wood, Wilmink and Dijsktra, it is observed that the prediction errors in the atypical patterns of the two clusters are much larger than those of the other four cases of typical patterns. The presented model can be used as a tool to refine characterization on the typical LC patterns, excluding atypical patterns as exceptional cases. View Full-Text
Keywords: dairy cow; lactation curve; k-medoids clustering; milking characteristics; model fitting dairy cow; lactation curve; k-medoids clustering; milking characteristics; model fitting
Show Figures

Figure 1

MDPI and ACS Style

Lee, M.; Lee, S.; Park, J.; Seo, S. Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm. Animals 2020, 10, 1348. https://doi.org/10.3390/ani10081348

AMA Style

Lee M, Lee S, Park J, Seo S. Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm. Animals. 2020; 10(8):1348. https://doi.org/10.3390/ani10081348

Chicago/Turabian Style

Lee, Mingyung, Seonghun Lee, Jaehwa Park, and Seongwon Seo. 2020. "Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm" Animals 10, no. 8: 1348. https://doi.org/10.3390/ani10081348

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop