Geophysical methods, such as electromagnetic induction (EMI), can be effective for monitoring changes in soil moisture at the field scale, particularly in agricultural applications. The electrical conductivity (σ) inferred from EMI needs to be converted to soil moisture content (θ) using an appropriate relationship. Typically, a single global relationship is applied to an entire agricultural field; however, soil heterogeneity at the field scale may limit the effectiveness of such an approach. One application area that may suffer from such an effect is crop phenotyping. Selecting crop varieties based on their root traits is important for crop breeding and maximizing yield. Hence, highthroughput tools for phenotyping the root system architecture and activity at the field scale are needed. Water uptake is a major root activity and, under appropriate conditions, can be approximated by measuring changes in soil moisture from time-lapse geophysical surveys. We examine here the effect of heterogeneity in the θ–σ relationship using a crop phenotyping study for illustration. In this study, the θ–σ relationship was found to vary substantially across a field site. To account for this, we propose a range of local (plot specific) θ–σ models. We show that the large number of parameters required for these models can be estimated from baseline σ and θ measurements. Finally, we compare the use of global (field scale) and local (plot scale) models with respect to ranking varieties based on the estimated soil moisture content change.