Phenotyping Root Architecture of Soil-Grown Rice: A Robust Protocol Combining Manual Practices with Image-based Analyses.

Abstract

BackgroundBreeding towards resilient rice varieties is often constrained by the limited data on root system architecture obtained from relevant agricultural environments. Knowledge on the genotypic differences and responses of root architecture to environmental factors is limited due the difficulty of analysing soil-grown rice roots. An improved method using imaging is thus needed, but the existing methods were never proven successful for rice. Here, we aimed to evaluate and improve a higher throughput method of image-based root phenotyping for rice grown under field conditions. Rice root systems from seven experiments were phenotyped based on the "shovelomics" method of root system excavation followed by manual root phenotyping and digital root analysis after root imaging. Analyzed traits were compared between manual and image-based root phenotyping systems using Spearman rank correlations to evaluate whether both methods similarly rank the phenotypes. For each trait, the relative phenotypic variation was calculated. A principal component analysis was then conducted to assess patterns in root architectural variation. ResultsSeveral manually collected and image-based root traits were identified as having a high potential of differentiating among contrasting phenotypes, while other traits are found to be inaccurate and thus unreliable for rice. The image-based traits projected area, root tip thickness, stem diameter, and root system depth successfully replace the manual determination of root characteristics, however attention should be paid to the lower accuracy of the image-based methodology, especially when working with older and larger root systems. ConclusionsThe challenges and opportunities of rice root phenotyping in field conditions are discussed for both methods. We therefore propose an integrated protocol adjusted to the complexity of the rice root structure combining image analysis in a water bath and the manual scoring of three traits (i.e. lateral density, secondary branching degree, and nodal root thickness at the root base). The proposed methodology ensures higher throughput and enhanced accuracy during root phenotyping of soil grown rice in fields or pots compared to manual scoring only, it is cheap to develop and operate, it is valid in remote environments, and it enables fast data extraction.