Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/42544
metadata.artigo.dc.title: Evaluation of the physical properties of bedding materials for dairy cattle using fuzzy clustering analysis
metadata.artigo.dc.creator: Ferraz, Patrícia Ferreira Ponciano
Ferraz, Gabriel Araújo e Silva
Leso, Lorenzo
Klopcic, Marija
Rossi, Giuseppe
Barbari, Matteo
metadata.artigo.dc.subject: Alternative bedding material
Cattle housing systems
Clustering algorithm
Water holding capacity
Sistemas de alojamento de gado
Algoritmo de clustering
Capacidade de armazenamento de água
Animal welfare
Bem estar animal
metadata.artigo.dc.publisher: MDPI
metadata.artigo.dc.date.issued: 2020
metadata.artigo.dc.identifier.citation: FERRAZ, P. F. P. et al. Evaluation of the physical properties of bedding materials for dairy cattle using fuzzy clustering analysis. Animals, [S. l.], v. 10, n. 2, p. 1-14, 2020. DOI: https://doi.org/10.3390/ani10020351.
metadata.artigo.dc.description.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.
metadata.artigo.dc.identifier.uri: http://repositorio.ufla.br/jspui/handle/1/42544
metadata.artigo.dc.language: en_US
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