Evaluation of the Physical Properties of Bedding Materials for Dairy Cattle Using Fuzzy Clustering Analysis

SIMPLE SUMMARY: The bedding material used in dairy cow housing systems plays a key role in animal welfare and performance, since it influences the time that the animals remain lying down. The primary aim of this paper was to evaluate the physical properties of different bedding materials for dairy cattle and, further, to employ different fuzzy clustering algorithms to effectively cluster these alternative materials based on their physical properties. To perform nine physical analyses, 51 different bedding materials from various places in Europe were used. These data were analysed by principal components analysis (PCA) and then by fuzzy clustering analysis. Three clustering algorithms were tested for different numbers of clusters (2–8). They were compared by five validation indexes to choose the best clustering algorithm and the number of clusters. By these analyses it was possible to conclude that alternative materials can be classified based on their physical properties. The Gustafson–Kessel (GK) clustering algorithms, with eight clusters, fit better regarding the division of materials according to their properties. Posidonia oceanica showed potential to be used as an alternative bedding material due to its favourable physical properties. ABSTRACT: The bedding materials used in dairy cow housing systems are extremely important for animal welfare and performance. A wide range of materials can be used as bedding for dairy cattle, but their physical properties must be analysed to evaluate their potential. In the present study, the physical properties of various bedding materials for dairy cattle were investigated, and different fuzzy clustering algorithms were employed to cluster these materials based on their physical properties. A total of 51 different bedding materials from various places in Europe were collected and tested. Physical analyses were carried out for the following parameters: bulk density (BD), water holding capacity (WHC), air-filled porosity (AFP), global density (GD), container capacity (CC), total effective porosity (TEP), saturated humidity (SH), humidity (H), and average particle size (APS). These data were analysed by principal components analysis (PCA) to reduce the amount of data and, subsequently, by fuzzy clustering analysis. Three clustering algorithms were tested: k-means (KM), fuzzy c-means (FCM) and Gustafson–Kessel (GK) algorithms. Furthermore, different numbers of clusters (2−8) were evaluated and subsequently compared using five validation indexes. The GK clustering algorithm with eight clusters fit better regarding the division of materials according to their properties. From this clustering analysis, it was possible to understand how the physical properties of the bedding materials may influence their behaviour. Among the materials that fit better as bedding materials for dairy cows, Posidonia oceanica (Cluster 6) can be considered an alternative material.