Wheat is one of the most widely grown crops and it plays an important role in food production. Currently there is considerable interest in identifying traits that contribute to high yields. Trait selection has mainly focused on the above-ground part of the plant neglecting below-ground processes. Climate change and the greater uncertainty in weather conditions challenge our current food system and create the need to select more varieties with grater resilience against the effects of climate variation. The root system of the plant plays a key part in this resilience, but it is difficult to study at the field-scale which is essential for the effective selection of breeding lines. Geophysical tools such as electromagnetic induction (EMI) and electrical resistivity tomography (ERT) offer the possibility to study the below-ground phenotype of the plant in a non-destructive and high-throughput manner. In this study, changes in soil moisture induced by root water uptake are monitored using time-lapse ERT/EMI surveys. These methods were applied at two scales: (a) at a high-spatial resolution where hundreds of wheat varieties were monitored monthly using EMI in a wheat breeding field trial and (b) at high-temporal resolution where hourly ERT measurements were collected along with above-ground phenotyping traits on a few plots with a field facility (Field Scanalyzer). Coupling these geophysically-based below-ground data with above-ground canopy measurements can increase our understanding of the crop response to its environment. Good correlation was found between leaf area index (LAI) and soil drying inferred from EMI measurements for the high-spatial experiment (a). The ERT monitoring experiment (b) accurately showed the dynamics of two different nitrogen treatments, their interactions with weather conditions and their correlation with above-ground crop growth. Coupling geophysically-based below-ground measurements with above-ground data allows the increased understanding of the whole plant phenotype. This might help to identify useful traits to select for increased crop yield and resilience.

Seed germination is regulated by multiple environmental cues and understanding their relationships is critical to planning seed drilling and subsequent seedling management. We develop a new framework by viewing the metabolic reactions associated with seed germination as a moving event in a physiological dimension to simulate seed germination. Fluctuations in environmental cues and genetic heterogeneity of seed lot make the metabolic reactions in each seed uncertain, and we use an average germination rate to describe the average metabolic reactions and a dispersion coefficient to describe the genetic heterogeneity. We apply the model to winter wheat seeds drilled at different dates in plots under different soil water contents and prove that the model accurately reproduces the time course of germination in all treatments. We found the average germination rate increases nonlinearly with temperature in the base-suboptimal temperature range, and there is an optimal soil water content where the germination rate peaks due to soil anaerobicity. Our model can be fitted to field data using temperature and soil water content to describe the trade-off impact of soil water on soil anaerobicity and imbibition, whereas the difficulty of obtaining accurate water potential and oxygen measurements makes this difficult with the hydrothermal time models.