Evaluation of the physical properties of bedding materials for dairy cattle using fuzzy clustering analysis

Autor: Patrícia Ferreira Ponciano Ferraz, Lorenzo Leso, Gabriel Araújo e Silva Ferraz, Giuseppe Rossi, Matteo Barbari, Marija Klopčič
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Fuzzy clustering
Bedding
krave molznice
govedo
02 engineering and technology
Fuzzy logic
Article
Kessel
alternative bedding material
lcsh:Zoology
Statistics
0202 electrical engineering
electronic engineering
information engineering
dairy cows
nastil
lcsh:QL1-991
Cluster analysis
gustafson–kessel
Dairy cattle
Mathematics
udc:636.2
lcsh:Veterinary medicine
General Veterinary
water holding capacity
0402 animal and dairy science
Gustafson
Alternative bedding material
Cattle housing systems
Clustering algorithm
Dairy cows
Water holding capacity
04 agricultural and veterinary sciences
040201 dairy & animal science
Bulk density
Effective porosity
stelja
cattle housing systems
clustering algorithm
Gustafson–Kessel
Principal component analysis
lcsh:SF600-1100
020201 artificial intelligence & image processing
Animal Science and Zoology
Zdroj: Animals, vol. 10, no. 2, 351, 2020.
Animals, Vol 10, Iss 2, p 351 (2020)
Animals
Volume 10
Issue 2
Animals : an Open Access Journal from MDPI
ISSN: 2076-2615
Popis: 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&ndash
Kessel (GK) algorithms. Furthermore, different numbers of clusters (2&minus
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.
Databáze: OpenAIRE
Externí odkaz: