Resource selection by Sarda cattle in a Mediterranean silvopastoral system

Authors: ACCIARO, MARCO - Agris Sardegna; PITTARELLO, MARCO - University Of Torino; DECANDIA, MAURO - Agris Sardegna; SITZIA, MARIA - Agris Sardegna; GIOVANETTI, VALERIA - Agris Sardegna; ROGGERO, PIER PAOLO - University Of Sassari; LOMBARDI, GIAMPIERO - University Of Torino; Clark, Pat

Submitted to: Frontiers in Veterinary Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/12/2024
Publication Date: 3/7/2024
Citation: Acciaro, M., Pittarello, M., Decandia, M., Sitzia, M., Giovanetti, V., Roggero, P., Lombardi, G., Clark, P. 2024. Resource selection by Sarda cattle in a Mediterranean silvopastoral system. Frontiers in Veterinary Science. 11. Article 1348736. https://doi.org/10.3389/fvets.2024.1348736.
DOI: https://doi.org/10.3389/fvets.2024.1348736

Interpretive Summary: Knowledge of how grazing cattle utilize these heterogeneous landscapes in Mediterranean silvopastural areas and the factors involved is scarce. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables based on GPS tracking data from cattle in silvopastural pastures of Agricultural Research Agency of Sardinia experimental farm (AGRIS Sardegna, Oristano, Italy). Prediction performance ranged between 0.7 and 0.94 among seasons for a final model which emphasized that elevation, distance to fences and distance to water were important factors affecting cattle resource-selection patterns. Although caution should be exercised in generalizing to other silvopastural areas, the satisfactory Spearman correlations scores obtained for the final RSF model applied to different seasons, indicate this resource selection function is a powerfully predictive model.

Technical Abstract: The environmental preservation of silvopastoral systems and their ecosystem services goes through a sustainability of animal production relying on pasture. Knowledge of how grazing cattle utilize these heterogeneous landscapes in Mediterranean silvopastural areas and the factors involved is scarce. Global positioning system (GPS) to track animals, together with geographic information systems (GISs) made it possible to relate animal distribution to landscape features. Over 2 years, free-roaming Sarda cows, grazing a silvopastoral area, were fitted with Global Positioning System (GPS) collars, in order to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to 5 of environmental predictor variables, were fitted to data from all collared animals of all sampling periods pooled together, to develop a more general RSF model. To identify a model providing a robust prediction of cattle resource selection pattern across the different seasons (final model), the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the fitted models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that elevation, distance to fences and distance to water were important factors affecting cattle resource-selection patterns for all experimental periods. The prediction performances (as Spearman rank correlation scores) of final model, when fitted to each season, ranged between 0.7 and 0.94. Although caution should be exercised in generalizing to other silvopastural areas, the satisfactory Spearman correlations scores from final RSF model applied to different season, indicate resource selection function is a powerfully predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model's ability to interpret changes in animal behavior at different times of the year.