An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize ( Zea mays ) and Common Bean ( Phaseolus vulgaris )

Abstract

A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.

Root system architecture and environmental flux analysis in mature crops using 3D root mesocosms

“…One idea is to generate in silico soil cores or minirhizotron images from the point clouds. This method could provide a sensitivity analysis for empirical sampling strategies using actual, rather than virtual ( Burridge et al., 2020 ; Morandage et al., 2019 ), groundtruths. Such information could also be used as a valuable resource for improving root structure-function simulations ( Kalogiros et al., 2016 ; Postma et al., 2017 ; Schnepf et al., 2018 ), or for the development of artificial intelligence approaches to complement missing data ( Falk et al., 2020 ; Ruiz-Munoz et al., 2020 ; Gaggion et al., 2021 ).…”

LEADER (Leaf Element Accumulation from DEep Roots): A nondestructive phenotyping platform to estimate rooting depth in the field

“…We note that rooting depth is spatiotemporally dynamic and subject to plant genetics as well as multiple soil factors, especially soil mechanical impedance, which is sensitive to soil water content and is therefore influenced by weather (Bengough et al, 2011;Colombi et al, 2018). We also note that soil coring is a noisy and imperfect sampling of rooting depth (Burridge et al, 2020). Additionally, in order to assess LEADER in a range of soils and genotypes, we could not intensively sample the same set of genotypes or locations over multiple years without massively inflating an already large original dataset.…”

Bioenergy Underground: Challenges and opportunities for phenotyping roots and the microbiome for sustainable bioenergy crop production

“…Destructive methods are the standard for root measurements to allow direct access to entire roots. These methods usually require at least destroying a portion of the root system, such as with root ingrowth cores (Andreasson et al., 2016) and soil coring (Burridge et al., 2020) but can also involve the destructive harvest of the entire root system, such as from pot studies (Griffiths, Wang, et al., 2021). In all cases, roots are washed free from debris and scanned on flatbed scanners that produce high contrast images for further analyses described below.…”