Canopy Coverage Phenotyping and Field Spatial Variability Adjustment as an Efficient Selection Tool in Soybean Breeding

Abstract

Accurate estimation of grain yield potential in soybean [Glycine max (L.) Merr.] progeny rows (PR) by measuring yield itself is challenging due to the small number of seeds available. To obtain more precise phenotypes and control non‐genetic sources of variability, soybean breeders in the US use secondary traits, visual assessments, adjustments for field spatial variation, pedigree information, and unmanned aerial systems (UAS)‐based plant phenotypes; however, there are limited comparisons among the different PR testing procedures. We conducted a selection experiment in 2018 PR populations developed for yield and diversity from four soybean breeding programs. Then we compared the performance of the lines selected using thirteen selection categories in 2019 preliminary yield trials (PYT). The sources of information used across categories included spatially adjusted traits (SP), pedigree information (PED), the canopy of the plant measured by aerial and ground digital images (CC), reproductive length (RL), and yield. Spatially adjusted trait covariates and canopy coverage data were the information sources most highly associated with lower yield ranks and higher yield performance of PYT. The most effective secondary trait was average canopy coverage (ACC) measured by high‐throughput phenotyping (HTP) platforms. Our selection experiment shows that ACC used as a secondary trait in combination with spatial variation adjustment effectively selects high‐yielding lines from non‐replicated experiments. Based on the scenarios considered in this study, it may be possible to increase the gain from selection by phenotyping secondary traits using HTP and implementing spatial variation adjustments in PR trials, which could help enhance crop productivity.

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