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

Abstract

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.

Modelling Extended Lactations in Polish Holstein–Friesian Cows

“…Additionally, Cole et al (2009), while estimating lactation curves for first and later parities in 6 breeds of dairy cattle, found that parameters describing the shapes of the curves varied considerably [ 28 ]. Studies of many authors, cited by Bouallegue et al (2014) and Lee et al (2020), proved that the shapes of individual lactation curves were affected by numerous factors such as genetic background, calving year, calving season, calving age, parity, service period, calving to first test-day interval, feeding, health status, environmental conditions and herd [ 44 , 45 ]. Græsbøll et al (2016) reported large differences in the shape of Wood [ 10 ] lactation curves among more than 600 Danish Holstein herds randomly selected from the approximately 3000 herds covered by the regular milk recordings.…”

Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey

“…Therefore, it is a parameter to choose by experience or by some other methods, such as the elbow method. The very first application of the elbow method can be traced back to an article in Psychometrika [28], and it has been used as one of the methods for determining the number of clusters in a data set in different domains, such as electrical engineering [29,30], computer science [31], education [32], statistics [33,34], and communications [35,36].…”

Data Mining dalam Pengelompokan Penyakit Pasien dengan Metode K-Medoids

“…Penelitian Irwansyah dkk (2020), penelitian ini mengangkat topik tentang pengelompokan pasien penyakit cardiovascular, penelitian ini dilakukan menggunakan metode K-Medoids [3]. Penelitian Lee dkk (2020), penelitian ini mengangkat topik tentang pengelompokan laktasi kurva sapi perah menggunakan metode K-Medoids [4] Penelitian Samudi dkk (2020), penelitian ini mengangkat topik tentang pengelompokan aplikasi pembelajaran pada masa pandemi Covid-19 dengan menggunakan metode K-Medoids [5]. Penelitian Rifa dkk (2020), topik yang di bahas pada penelitian ini adalah tentang pengelolaan resiko gempa di indonesia menggunakan K-Medoids Clustering [6].…”