Dr Dongxue Zhao1, Prof Daniel Rodriguez1
1The University of Queensland,
Biography:
Dr Dongxue Zhao is a Research Fellow within the QAAFI, UQ. Her research aims to contribute to sustainable gains in crop productivity by improving our understanding of how crop-soil interactions determine crop and root growth, water and nutrient uptake, and final yield. In her research, she combines innovative proximal and remote sensing techniques of crops, soils and roots, with predictive modelling and artificial intelligence tools. These include integrating electromagnetic induction (EMI) techniques, and drone and satellite imagery to monitor crop root growth and water use dynamics over time; 3D mapping of soil properties and sub-soil constraints to map resource constraints.
Abstract:
Crop rooting system determines the capacity of crop to take up water for growth and yield, underpinning agricultural productivity. Here we test a new approach for high-throughput phenotyping root growth and activity in the field based on electromagnetic induction (EMI) instrument coupled with a quasi-2D inversion algorithm, and crop canopy sensing technologies. A 3D root activity factor (R) was calculated as a function of crop water use, soil water availability, and an indicator of crop demand. A maximum rooting depth – RD, and a root architecture index– RA was quantified based on the distribution of R with soil depth, in a set of commercial sorghum hybrids (G) grown in contrasting field environments (E), and plant population densities (M). The derived root trait indices from the approach were validated for the presence of roots using field root coring. The phenotypic plasticity of the root traits, grain yield and yield components were calculated using a reaction norm method. Results show that R is closely related to the measured root length density across a wide range of environmental conditions. The phenotyping approach can capture ExM effects on root traits. The plasticity of RD and RA are related to differences between hybrids in the plasticity of yield and yield components. We conclude that the proposed root phenotyping approach can provide a rapid and efficient option to phenotype crop rooting system in the field and can be a powerful tool to phenotype both mean traits and plasticity of the rooting system.