High-throughput root phenotyping in the field using electromagnetic induction sensors: Implications for breeding and agronomy

Even though availability of water and nutrients are the main limitations for grain production globally, little is known about the rooting system, the critical plant organ involved in accessing soil water and nutrients. We know that the crop’s genetic background (G), crop management (M), and the environment (E) interact to alter the architecture of the rooting system. However, root traits are hard to measure, and the lack of quick, cheap, accurate, and functional root phenotyping approaches in the field has limited the capacity of breeding, agronomy, and precision agriculture to develop traits and services for farmers. Recent advances in high-resolution root-zone soil moisture monitoring show potential to reveal genotypic and management differences in crop root systems across contrasting environments. This paper describes novel approaches for the high-throughput phenotyping of functional root traits of value for yield and yield stability. First, we introduce the phenotyping approach for in-situ 3D characterisation of sorghum water use and the root system in soil profiles. Second, we demonstrate its application to characterise two functional root traits, i.e., maximum rooting depth (MxRD), and an index of root activity (RAindex), and their phenotypic plasticities. The experiment results show that the proposed root phenotyping method could capture G´E´M effects at different crop growing stages. The plasticity of functional root traits was associated with the stability of grain yield traits. Hybrids with high root plasticity tend to have more stable grain numbers and grain weights. There is valuable genetic diversity in the mean value and plasticity of root traits that could be used to match root phenotypes to target production environments. The root phenotyping approach can be a valuable tool for understanding the dynamic interactions between root function, root architecture and yield traits in the field under variable environments.