The aim of DigitalCow is to use real-time longitudinal data from automatic and conventional milking systems, combined with sensory data of cow behaviour, farm and climate, management interventions, and genomic breeding values to predict future performance of each cow and the probability of health events with or without interventions by the farmer. DigitalCow will be built with modules for genetic information, behaviour, health, resilience and performance to get a summary of the current state of a cow and another module to predict future performance, health and behaviour and its distribution using Monte Carlo simulation. Data from the experimental dairy farm Dairy Campus in Leeuwarden will be used to build and validate DigitalCow.
DigitalCow is envisioned to yield a wealth of information on how sensor and other cow data can be used to understand genetic and non-genetic differences between cows in health, well-being and resilience. Furthermore, DigitalCow enables farmers, veterinarians and farm advisers to predict the effects of various management interventions to maximize profit of the farm and minimize poor health and well-being of individual cows and the whole herd. Management interventions can be both at the level of the cow (insemination, culling and treatment decisions) as well as at the herd level (change in breeding goal, ration, frequency of milking, footbaths, housing). DigitalCow can support farmers in decision-making to improve health, well-being and performance of cows and farm profit.
|Effective start/end date||1/01/19 → 31/12/19|