Assessment of Genotypic Variation in Root Architectural Traits of Maize (Zea mays L.) Using an Agar-based Phenotyping Platform

Abstract

Maize (Zea mays L.) is one of the world's most important cereal crops, and its productivity is strongly influenced by root system architecture (RSA), which determines the spatial exploration of soil for water and nutrients. However, phenotyping root traits remains challenging because most root development occurs belowground, limiting efficient characterisation of genetic variation under conventional conditions. The present study was undertaken to assess the genetic variability of selected RSA traits among 102 maize genotypes using a controlled agar-based phenotyping platform. Five seedling-stage root traits, namely number of basal roots (NBR), root angle (RA), total root length (TRL), root biomass (RBM), and number of lateral roots (NLR), were evaluated under standardised agar-based conditions. Analysis of variance revealed significant genotypic variation (p < 0.05) for all five traits, confirming substantial genetic diversity within the evaluated germplasm. NBR ranged from 0.97 to 14.25, RA from 19.42° to 93.96°, TRL from 2.85 to 20.17 cm, RBM from 0.02 to 0.89 g, and NLR from 0.99 to 23.78. The highest coefficients of variation were observed for NLR (71.88%) and RBM (68.88%), followed by NBR (45.21%), TRL (44.53%) and RA (29.96%), indicating particularly pronounced variation in lateral root production and root biomass among the genotypes. Distribution analysis further revealed contrasting patterns of root architectural expression, including bimodal distributions for root angle and number of lateral roots. The results demonstrate that the agar-based platform provides a simple, controlled and efficient approach for rapid screening and characterisation of genetic variation in early-stage maize RSA traits. The substantial diversity identified for root architectural traits provides valuable preliminary material for maize root improvement; however, validation under soil and field conditions is required to establish their relationships with mature-plant performance, drought adaptation and nutrient-use efficiency.