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Mapping the Spatial Distribution of the Rumen Fluke Calicophoron daubneyi in a Mediterranean Area

Rumen flukes (Calicophoron daubneyi) represent a growing threat to the animal health, productivity and welfare of ruminants. The present study aimed to assess the spatial distribution of C. daubneyi infections in ruminants and to develop a predictive model of the environmental suitability for rumen...

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Detalles Bibliográficos
Autores principales: Bosco, Antonio, Nocerino, Martina, Santaniello, Mirella, Maurelli, Maria Paola, Cringoli, Giuseppe, Rinaldi, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469533/
https://www.ncbi.nlm.nih.gov/pubmed/34578154
http://dx.doi.org/10.3390/pathogens10091122
Descripción
Sumario:Rumen flukes (Calicophoron daubneyi) represent a growing threat to the animal health, productivity and welfare of ruminants. The present study aimed to assess the spatial distribution of C. daubneyi infections in ruminants and to develop a predictive model of the environmental suitability for rumen flukes in a Mediterranean area. A cross-sectional coprological survey was conducted in 682 sheep, 73 goat and 307 cattle farms located in the Basilicata region (southern Italy). Faecal samples collected were analysed using the FLOTAC technique. Geographical Information Systems (GIS) and statistical models were developed to determine environmental risk factors and to delimitate the areas at highest risk of infections in small ruminants. The results showed 7.9% (95% CI 6.05–10.27) of sheep farms, 2.7% (95% CI 0.48–10.44) of goat farms and 55.0% (95% CI 49.62–60.99) of cattle farms were infected by C. daubneyi. The areas with high predicted risk were situated in the western part of the region. The soil texture, land use and the presence of streams and brooks were the variables statistically significant (p < 0.05) in explaining the C. daubneyi distribution in the study area. The study confirms the importance of geospatial technology in supporting parasite control strategies in livestock and demonstrates that a combined use of different geostatistical techniques can improve the prediction of the C. daubneyi infection risk in ruminants.