What should the Digital Future Farm look like?

Do you have ideas or suggestions of interventions possible, models available, processes to be described, data sources required that should be part of the Digital Future Farm? Please bring your own lunch and take part in the dialogue about the Digital Future Farm.

Right now the WUR investment theme Digital Twins (KB-41) supports several groups to write project proposals for three flagship projects. A flagship project is expected to deliver a demonstrable prototype Digital Twin in one of the Wageningen domains. We aim to develop the flagship: the Digital Future Farm. With colleagues from all over WUR we came up with ideas how the Digital Future Farm should look like. We would like to go in dialogue with you about our ideas.

The idea!

To ensure farming to continue in the Netherlands, farms of the future need to be economically healthy and sustainable. Sound, evidence-based decisions using up-to-date information are required. The latter aspect is enabled by the increasing adoption of sensor technologies. However, there are strong interactions between the different physical aspects of farms. For example, crop fertilization may affect the quality of feed for dairy cows and opportunities for biodiversity. Farmers face the challenge of taking these interactions into account. To aid farmers in meeting this challenge, it is essential to model, explore, study business processes and propose actions using a full-inclusive digital representation of a farm. We believe a Digital Future Farm (DFF) could be such a model. We envisage our DFF to represent the current state of an arable or dairy farm, fuelled with real-time (sensor) information. Our DFF, consisting of interacting modules, will be visualized on a digital map of the farm using an interface that facilitates communication of information and interaction with its constituting modules. Each module will represent a certain aspect of the physical world that constitutes a farm (e.g., field, soil, crop, animal, herd, or barn) that can be used to interact, explore, tweak, and simulate scenarios to predict the impact of actions taken, and to find optimum solutions. Real-time data will be translated into real-time alerts on issues that require immediate action, e.g., nutrients, crops, or perturbations like pests and diseases.