A Digital Twin for Arable Crops and for Grass

Evert, Frits van


In precision agriculture, farmers need precise, real-time information about the status of crops, soils and livestock, as well as information about the likely outcome of management decisions. In this study we investigate the use of digital twin (DT) technology as a tool to provide this information. The Digital Future Farm (DFF) is a digital twin for arable crops and for grass. The DFF consists of dynamic models of arable crops and grass, as well as a method to use real-time data from sensors to keep the models synchronized with the real-world object that is simulated. In this way the DFF provides a comprehensive real-time view of the system and can thus be used to monitor crops and soils. The DFF can also be used to predict the future state of the system (by using forecast weather). Finally, the DFF can be used to investigate the expected outcome of alternative management scenarios, for example with respect to fertilization and irrigation. The utility of the DFF depends on how well it is able to monitor and predict the state of crop and soil. We explore how the utility is affected by the information content of the observations used to synchronize the model. We explore how fertilization and irrigation decisions are impacted when the DFF is used, and what is the effect on performance indicators such as yield and use efficiencies of nitrogen and water. The use of DTs in agriculture is new therefore we outline a research agenda.