Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean.

Area of Science:

• Plant Biology
• Agronomy
• Microbial Symbiosis

Background:

• Soybean (Glycine max) roots form symbiotic nodules with Bradyrhizobium japonicum, fixing atmospheric nitrogen (N2) into ammonia (NH3).
• Nodules are crucial for plant growth, and the Soybean Nodule Acquisition Pipeline (SNAP) facilitates their quantification and genetic analysis.
• Understanding nodule development across diverse soybean root architectures is key to optimizing nitrogen fixation.

Purpose of the Study:

• To explore genotypic differences in soybean nodule development in taproot versus non-taproot zones.
• To investigate the relationship between nodule characteristics and total seed nitrogen at maturity.
• To propose a refined definition of nodulation based on nodule count and area.

Main Methods:

• Studied six diverse soybean genotypes across three field years and three early vegetative stages.
• Utilized the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification.
• Analyzed nodule count, size, and total area in taproot and non-taproot zones, and correlated with seed nitrogen content.

Main Results:

• Identified unique nodule growth patterns in taproots, with significant genotypic differences in nodule count, size, and total area compared to non-taproot nodules.
• Proposed defining nodulation as total nodule area (nodule count × individual nodule area).
• Found a strong positive correlation between taproot nodule characteristics and final seed nitrogen content at maturity.

Conclusions:

• Taproot nodulation is a critical factor influencing soybean yield and nitrogen content.
• The findings support a refined understanding of the plant-Bradyrhizobium symbiosis.
• This research can inform strategies for enhancing nitrogen use efficiency and carbon to nitrogen production efficiency in soybean breeding programs.