Root water uptake of biofuel crops revealed by coupled electrical resistivity and soil water content measurements

Abstract

Biofuel crops, including annuals such as maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and canola (Brassica napus L.), as well as high-biomass perennial grasses such as miscanthus (Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large-scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009-2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots being notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2-34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011.

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

“…Hence, Gaussian priors are not suitable for characterizing discontinuous (non-smooth) fields and we would prefer edge-preserving priors (Arridge et al, 2019). Note that prior information is also useful to constrain the deterministic inversion by setting the lower and upper bounds for each parameter (e.g., Kuhl et al, 2021). Prior knowledge can be estimated from onsite calibration measurement or from literature values.…”

“…In order to decide which of the model and observation parameters need to be fixed, a global sensitivity analysis is often useful. To this end, Kuhl et al (2021) quantified the sensitivity of each tested value by computing the root mean square error (RMSE) between the reference synthetic ER data and the perturbed ER data modeled. More advanced approaches can be used such as the Morris' method (1991) employed to determine the few most influential parameters among a large number of parameters (Hu et al, 2017).…”

“…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. The data integration using a coupled inversion showed the advantages of ER tomography for extrapolation beyond the local scale of soil water content sensors.…”