The key on-farm sensor technology that will be deployed by SILF is
1. detection of lameness because it severely reduces dairy production and is indicative of adverse welfare conditions (the cow is suffering, medicine use prevents milk utilisation, and cow longevity is decreased), and
2. ambient environment because these can be used to optimize ventilation, reduce ammonia emissions and reduce energy consumption.
The technologies that are being considered are
1. The Gaitwise sensor which consists of an interface between hoof and floor that measures the positions and/or force of the hoof with time. It consists of an enclosed measurement zone 1m x 6m) that each cow is driven through individually. The separation fence is equipped with an antenna to read each cows’ RFID tag. Each measurement from the Gaitwise system analyses a set of 30 gait parameters to indicator lameness from a range of scoring systems.
2. Stepmetrix is a commercially available lameness sensor developed by Boumatic. This sensor identifies the cow and analyses the force and duration of her steps to calculate a numerical score for each hind leg using similar indicator scores to Gaitwise.
3. Accelerometers attached to the head or neck have been shown to generate data that can be used to predict heat (Heatime®), rumination (Ruminact®) grazing time (Icetag®) and lameness, but the latter needs verification, which is a key component of the SILF project.
4. Thermal sensor arrays to registration of indoor climate and calculate air and energy flows. The study will focus on demonstration farms in Greece (cooling), Ireland and Denmark (humidity and ammonia control).
Stakeholder analysis
is important for the development of smart farming systems. Many ideas have been developed as a result of technological advances and not as a result of bottom-up user requirements. Studies have shown that user-centric design leads to effective systems that are more likely to be adopted.
In disseminating its visions, research efforts and results, SILF will address the need for external networks involving a wide range of stakeholders, enabling them to become proactive in the development of smart farming innovations.
The system analysis will consider a range of stakeholders including farmers, technology developers and data owners, in order to identifying costs and benefits associated with these systems together with the factors that determine the cost / benefits outcomes from deployment.
The analysis will consider both economic and environmental costs, thus the specific farm centric data will be used for ‘footprint’ assessments to express environmental impacts. LCA studies of European milk production will provide the theoretical basis for model development, which will be linked to appropriate dairy system models and real-time data from the dairy farm (in the form of links to national animal and land use databases and sensing systems on farm) in order to calculate costs from real-time data from farms.
The results of the stakeholder analysis will be incorporated in business models, together with appropriate modelling methods. Cost-benefit analysis is often being used for evaluating economic and in extension also sustainability effects of management decisions. In management sciences, also mathematical programming and production-theory-based frontier analysis are used for measuring performances and identifying performance benchmarks. These methods measure technical, economic and/or environmental performances through positioning farms against a best practice frontier, which is assessed using production-theoretical principles.
The effect of management decisions (e.g. smart lameness detection) can then be evaluated against these benchmarks. This helps to evaluate whether or not it is useful for a particular farm to adopt the measure.
