SWIM²: A data-driven digital twin for field-scale soil moisture predictions, irrigation advice, and quantification of water use efficiency in vegetable production in Flanders

The rise of affordable, autonomous in-situ sensors and IoT technology has enabled real-time monitoring of soil moisture, opening new opportunities for sustainable irrigation management. We present SWIM² (Sensor Wielded Inverse Modelling of a Soil Water Irrigation Model), a data-driven digital twin designed for field-scale soil moisture predictions, irrigation advice, and quantification of crop water stress, crop water use efficiency, and irrigation efficiency in vegetable production. SWIM² integrates real-time soil moisture sensor data with a soil water balance model using the DREAM(zs) Bayesian inverse modeling approach to estimate 12 key parameters, including soil and crop growth characteristics, along with their uncertainty distributions. This probabilistic framework with integration of weather forecasts allows for dynamic soil moisture predictions with uncertainty estimates, enabling farmers to make informed decisions about irrigation scheduling. By providing an estimate of the water required to mitigate drought risk, SWIM² supports efficient water use while maintaining crop health.

We validated and implemented SWIM² in commercial fields and irrigation trials across Flanders to evaluate its performance and demonstrate its utility in predicting soil moisture, scheduling irrigation, and quantifying water use efficiency under diverse conditions. In 2022, SWIM² was used in real-time to guide irrigation decisions during a celery trial, and in 2023, it was applied to celery, chicory, leek, and sweet potato trials. During these irrigation trials, the model was calibrated two times a week, after which soil moisture was predicted, and irrigation scheduling was based on the predicted probability of water stress over the following four days. Retrospectively, model calibration based on the 100% irrigation treatment enabled a comparison of various irrigation treatments, providing insights into crop water stress and irrigation surpluses.

Our results show that SWIM² accurately predicts soil moisture and improves irrigation scheduling, while also providing insights into resource optimization, contributing to sustainable agricultural practices. Due to the probabilistic nature of the framework, the irrigation strategy can be tailored to suit a conservative or risk-tolerant approach, depending on the farmer's preferences and water availability. By bridging advanced modeling with practical applications, SWIM² empowers farmers to make data-driven decisions for resilient and efficient crop management.