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

Crop Science

article

Clayton N. Carley¹  Melinda J. Zubrod¹  Somak Dutta²  Asheesh K. Singh¹ 

[1] Dep. of Agronomy;Dep. of Statistics

DOI  :  10.1002/csc2.20861

学科分类：农业科学（综合）

来源: Crop Science

【 摘 要 】

The symbiotic relationship between soybean [Glycine max L. (Merr.)] roots andbacteria (Bradyrhizobium japonicum) lead to the development of nodules, importantlegume root structures where atmospheric nitrogen (N2) is fixed into bio-availableammonia (NH3) for plant growth and development. With the recent development ofthe Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybeanroot system architectures. We explored six diverse soybean genotypes across threefield year combinations in three early vegetative stages of development and reportthe unique relationships between soybean nodules in the taproot and non-taprootgrowth zones of diverse root system architectures of these genotypes. We foundunique growth patterns in the nodules of taproots showing genotypic differences inhow nodules grew in count, size, and total nodule area per genotype compared tonon-taproot nodules. We propose that nodulation should be defined as a function ofboth nodule count and individual nodule area resulting in a total nodule area per rootor growth regions of the root. We also report on the relationships between the nodulesand total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findingscould lead to an enhanced understanding of the plant-Bradyrhizobium relationshipand exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybeangermplasm.

【 授权许可】

CC BY