Improving the efficiency of soybean breeding with high-throughput canopy phenotyping

Area of Science:

• Plant breeding and genetics
• Agricultural engineering
• Agronomy

Background:

• High-quality phenotypes are crucial for genetic gain in early plant breeding stages.
• Field-based high-throughput phenotyping (HTP) platforms offer rapid, high-resolution data collection.
• Secondary traits, like average canopy coverage (ACC), correlate with yield and can improve selection efficiency.

Purpose of the Study:

• To compare direct selection for yield versus indirect selection using ACC.
• To evaluate ACC as a covariate in yield prediction models (Yield|ACC) for early soybean breeding.
• To assess the effectiveness of these methods in identifying high-yielding soybean lines.

Main Methods:

• Utilized unmanned aerial systems (UAS) with RGB cameras for ACC measurements.
• Collected yield and days to maturity (R8) data from progeny rows (PR).
• Selected soybean lines based on Yield, ACC, and Yield|ACC for advancement to preliminary yield trials (PYT).

Main Results:

• In 2016 PYT, no significant difference was found between ACC and Yield selection after adjusting for R8.
• In 2017 PYT, direct yield selection resulted in the highest mean yield, potentially due to environmental factors affecting canopy growth.
• Selection using Yield|ACC consistently identified top-ranking lines in advanced yield trials.

Conclusions:

• Aerial HTP platforms are valuable tools for early plant breeding.
• While ACC selection alone had variable performance, it plays a role in effective high-yield soybean line selection.
• Integrating ACC into yield prediction models (Yield|ACC) enhances the identification of superior soybean genotypes.