Imaging plant responses to water deficit using electrical resistivity tomography

Abstract

Background and aims Monitoring root water uptake dynamics under water deficit (WD) conditions in fields are crucial to assess plant drought tolerance. In this study, we investigate the ability of Electrical Resistivity Tomography (ERT) to capture specific soil water depletion induced by root water uptake. Methods A combination of surface and depth electrodes with a high spatial resolution (10 cm) was used to map 2-D changes of bulk soil electrical conductivity (EC) in an agronomic trial with different herbaceous species. A synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots. The impact of root segments was incorporated in the forward electrical model using the power-law mixing model. Results The time-lapse analysis of the synthetic ERT experiment shows that different root water uptake patterns can be delineated for measurements collected under WD conditions but not under wet conditions. Three indices were found (depletion amount, maximum depth, and spread), which allow capturing plant-specific water signatures based moisture profile changes derived from EC profiles. When root electrical properties were incorporated in the synthetic experiments, it led to the wrong estimation of the amount of water depletion, but a correct ranking of plants depletion depth. When applied to the filed data, our indices showed that Cocksfoot and Ryegrass had shallower soil water depletion zones than white clover and white clover combined with Ryegrass. However, in terms of water depletion amount, Cocksfoot consumed the largest amount of water, followed by White Clover, Ryegrass+White Clover mixture, and Ryegrass. Conclusion ERT is a well-suited method for phenotyping root water uptake ability in field trials under WD conditions.

Sensing the electrical properties of roots: A review

“…Overall, ERT may lack direct sensitivity to root properties due to the variations in the contrast between bulk soil and root electrical resistivity and the relatively low volume fraction of roots. Using resistivity as a predictor for root biomass without taking these factors into account is therefore highly uncertain (Rao, Lesparre, Orozco, Wagner, & Javaux, 2020).…”

Hydrodynamic characterization of soil compaction using integrated electrical resistivity and X‐ray computed tomography

“…It is however worth mentioning that this pedophysical model is only valid for bare soil. Therefore, if future studies consider soil with roots, additional uncertainty arises and needs to be addressed (Rao et al., 2020; Werban, Al Hagrey, & Rabbel, 2008). In addition, one must consider that an ERT model can show misleading variability due to factors contributing to its uncertainty, such as regularization, error model, and inversion related artifacts (different electrode contact impedance, finite electrode size).…”

“…where d is the data vector, m is the model given by the parameters of the inversion m j = log(ρ j ) (Ω m), f(m), is the forward model for parameters m, m 0 is a homogeneous starting model vector, W ε is a smoothing operator, λ is the regularization parameter that determines the amount of smoothing imposed on m, and W s is an error weighting matrix. We acknowledge the importance of choosing appropriate regularization parameters for accurate inversion results (Rao, Lesparre, Orozco, Wagner, & Javaux, 2020). After a series of synthetic model simulations, we concluded that 100 is an appropriate value for λ.…”

“…It produces an image of M voxels of electrical resistivities (ρ i with i = 1,2, …, M ) given a set of N four electrode resistances ( R j [Ω] with j = 1,2, …, N ) by minimizing the objective function Ψ: where d is the data vector, m is the model given by the parameters of the inversion m j = log(ρ j ) (Ω m), f ( m ), is the forward model for parameters m , m 0 is a homogeneous starting model vector, W ε is a smoothing operator, λ is the regularization parameter that determines the amount of smoothing imposed on m , and W s is an error weighting matrix. We acknowledge the importance of choosing appropriate regularization parameters for accurate inversion results (Rao, Lesparre, Orozco, Wagner, & Javaux, 2020). After a series of synthetic model simulations, we concluded that 100 is an appropriate value for λ.…”

Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects

“…Great progress has been made over the past decade to combine geophysical observation data with hydrological models (Camporese et al, 2010(Camporese et al, , 2011Tso et al, 2020). Relatively few hydro-geophysical applications, though, have been focused on root system parametrization (Manoli et al, 2015;Cassiani et al, 2016;Mary et al, 2020;Rao et al, 2020). In a series of studies, (Kuhl et al, 2018), Kuhl et al (2021) propose a novel coupled hydrogeophysical approach to estimate evapotranspiration (ET) and root depth from ER, and soil water content combining both soil water content from Time Domain Reflectometry (TDR) and ER measurements with a hydrological and plant model.…”