HYDRAS: Imaging the Hidden Half with Electrical Resistivity Tomography (ERT): HYDRAS: Below-ground phenotyping with ERT

Changes in precipitation distribution and frequency across Europe related to climate change are a challenge for agriculture. With more intense rainfall and more extended drought periods becoming more likely, agroecosystems need to be made more resilient. Breeding drought-resistant genotypes or adapting agricultural management practices are two aspects to improve the resilience of agriculture against drought. In this context, soil moisture is a key variable that will help crops bridge periods of drought. Hence, understanding what is happening in the hidden half of the crop, i.e., how soil moisture is being used, is essential to select resilient genotypes and develop suitable management practices. Practical breeding applications and developing soil moisture preserving management practices require field-scale studies to fully assess the crop phenotype in real soil conditions comparable to production fields. Soil moisture point sensors are useful to monitor soil moisture in the field, but their small volume of observation makes it difficult to obtain information at a high spatial resolution. In this research, we showcase the use of electrical resistivity tomography (ERT) as a means to monitor soil moisture in the root zone and this in experimental fields of 300 m² over the entire growth period and, hence, scale up, information from soil moisture point sensors. As such HYDRAS, the HYdrology, Drones and RAinout Shelter open access international research infrastructure provides both High-Throughput Field Phenotyping (HTFP) both above and below-ground. ERT uses surface electrodes to send current into the soil and measure the resulting voltage. In this way we obtain the resistivity of the subsurface, what can be related to soil moisture. The large soil volume investigated by the current paths in the soil enables ERT to have a large support volume, allowing the collection of spatial information at a similar scale to that obtained using drone imaging above-ground. By coupling above-ground and below-ground observations it is then possible to obtain a complete representation of the whole crop phenotype throughout the growing season. Automated ERT systems enable data collection at an hourly frequency, a time scale relevant to studying crop processes in the root zone. While the use of ERT to follow soil moisture content is not new, the implementation of ERT within a field phenotyping facility is (Blanchy et al., 2025). In this work, we present our latest findings and current challenges on the use of ERT to phenotype the hidden half of the crop within the HYDRAS field phenotyping facility. Two field experiments will demonstrate how ERT was able to differentiate between soybean varieties and different mulch applications in celery. Derivation of traits useful for crop breeding and to investigate the effects of management will be detailed. Finally, the combination of below-ground and above-ground information in a coupled modelling framework will be discussed.