Operational yield forecasting and crop management with a digital twin

Summary

Crop growth models can provide real-time forecasts of upcoming drought or nutrient deficiencies and can thus in principle be used to support decisions about irrigation and fertiliser application. To be useful for supporting in-season crop management, forecasts need to be (1) frequently updated and (2) sufficiently accurate. This second point is problematic in practice, because crop models tend to deviate from reality, due to insufficient calibration, and because not all relevant processes are included. A digital twin (van Evert et al. 2021, Knibbe et al. 2022) combines crop growth modelling with updating model state variables based on in-season observations. A digital twin provides daily updated estimates of growth forecasts and also shows how these forecasts are corrected with observations. Ideally, such a digital twin results in greater forecasting accuracy, for the end-user more confidence in model predictions and ultimately more efficient resource management. We developed a fully automated operational
digital twin for a strip cropping experiment in the Netherlands