Search for: All records

Improving root traits to improve efficiency of nutrient uptake in plants is an opportunity to increase crop production in response to climate change induced edaphic stresses. Maize (Zea mays L.) studies showed a large variation of root architecture traits in response to such stresses. Quantifying this response uses highthroughput, image-based phenotyping to characterize root architecture variation across edaphic stresses. Our objective is to test if commonly used root traits discriminate stress environments and if a single mathematical description of the complete root architecture reveals a phenotypic spectrum of root architectures in the B73 maize line using manual, DIRT/2D (Digital Imaging of Root Traits) and DIRT/3D measurements. Maize B73 inbred lines were grown in three field conditions: nonlimiting conditions, high nitrogen (N), and low N. A proprietary 3D scanner captured 2D and 3D images of harvested maize roots to compute root descriptors that distinguish shapes of root architecture. The results showed that the normalized mean value of computational root traits from DIRT/2D and DIRT/3D indicated significant discrimination among B73 across environments. We found a strong correlation (R2> 0.8) between the traits measured in 3D point clouds and manually measured traits. Ear weight and shoot biomass in low N significantly decreased by 45% and 21%, respectively. Low N reduced the maximum root system diameter by 13%, root system diameter by 10%, and root system length by 9%. The 2D and 3D whole root descriptors distinguished three different root architectural shapes of B73 in the same field. Our study assists plant breeders to improve crop productivity and stress tolerance in maize.