Virtual fencing and accelerometers trials: experimental design for Tuscany pilot farms
6 Feb 2026
During the latest SUREPASTOR Technical Meeting, held in December at the IAV Hassan II facilities in Morocco, the Italian partners from the University of Florence presented the results of a field trial focused on the use of Virtual Fencing in sheep farming.
The trial aimed to validate behavioural data collected through tri-axial accelerometers by comparing sensor outputs with direct visual observations. Five sheep (three Comisana and two Massese) were involved in this validation phase, each equipped with Virtual Fencing collars and HOBO accelerometers.
The field trial was structured into two main experiments:
Experiment 1: Learning Study
The learning study consisted of a 12-day training period involving four groups of 15 sheep, all equipped with Virtual Fencing collars. During this phase, virtual pasture boundaries were modified every four days, while the grazing area remained the same for all groups.
Following the training period, animals were equipped with both Virtual Fencing collars and HOBO pendant tri-axial accelerometers. Sheep behaviour—grazing, rumination, and resting—was then observed over two days by five operators, enabling a detailed comparison between visually recorded behaviours and accelerometer data.
Experiment 2: Grazing Study
The grazing study focused on assessing the effectiveness of Virtual Fencing in real pasture management conditions. Conducted over 30 to 40 days, this phase compared traditional electric fencing with Virtual Fencing. Four groups of sheep grazed equivalent pasture areas, allowing researchers to evaluate differences in grazing patterns, animal behaviour, and production performance between the two management systems.
Overall, these experiments contribute to demonstrating the potential of Precision Livestock Farming technologies to enhance grazing management, improve animal welfare, and support the sustainability of pastoral systems, while also addressing practical challenges such as costs, battery life, and data reliability.
