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First test of Sensowave, within the AFarCloud project, in cattle about the extensive grazing systems

On April 26, the first tests were carried out within the AFarCloud project. Attendees could see how the new tools that are being worked on within the project, will change the way cattle are managed in extensive grazing systems.

In EU-28 there are more than 180M of ruminants. On average a ruminant has a need of grazing surface of 0.66ha, so approximately there is 120Mha devoted to grazing. Grazed herbage is the cheapest livestock feed available, and it has been associated with lower levels of GHG emissions. Society also perceives grazing as being safe, natural and welfare-friendly and grazing systems can promote animal welfare by facilitating the expression of normal behaviour. Grazing improves production, environment and biodiversity. Grazing management systems can maximize livestock production. Through grazing, livestock encourages plant growth, consequently increasing forage production. Grazing also helps to promote the growth of native plants and grasses. Furthermore, the animal’s urine and feces, recycle nitrogen, phosphorus, potassium and other plant nutrients, that return to the soil, to became prosperous and capable for production and stimulating the growth of plant varieties. Additionally, management in many parks makes use of grazing to help lower fire hazards by reducing the amount of potential fuel, such as large build-ups of forage. Grazing management has two overall goals: maintain the sustainability of the pasturage and protecting animals’ welfare.

However, management of grazing is challenging for farmers, due to they lack the tools to help them measure available herbage mass and grass intake, but also lack approaches to help them control grazing. There are several difficulties in order to use grazing such as: difficult to control rations and optimize grassland utilization, unstable weather conditions, labour efficiency, etc. On the other hand, grazing currently has a main disadvantage, animal losses during the grazing period. Not having located or monitored the animal for months, makes impossible taking preventive action that mitigates the effects of possible diseases, attacks or lack of adequate nutrition. This led to the intensification of some ruminant production systems, which utilised housing and mechanically harvested feed to better control production. These intensive production systems are now being challenged as being unsustainable, in terms of production efficiency, environmental impact and animal welfare.

To date, the only major livestock farming technology on extensive ruminant production is electronic identification. However, managing animals on rangeland requires automatic recording of grazing behaviour and other animal parameters. Rangeland livestock farming solutions have potential impact improving animal welfare by ensuring freedom from hunger and thirst, freedom from pain, injury or disease, freedom from discomfort and freedom from fear and distress and freedom to express normal behaviour.

IoT and machine learning can offer a breakthrough solution especially to the extensive livestock farmers. Wearables for animals including sensors combined with GNSS technology can provide tracking information related to animal behaviour. Monitoring behaviour is relevant for detection of cow fertility or illness. It is also important for providing information on pasture use density and to manage fields accordingly to the information recorded previously.

SensoWave developed a smart collar with GNNS, motion and temperature sensors that allows animal monitoring in livestock farms. This first set of devices was validated in the livestock farm owned by the family Blanco, managed by the father with the help of his sons in Ávila, Spain. 

During the following months, the new SensoWave developments within the AfarCloud project will be validated in that farm.

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