Optimization of sparse phenotyping strategy in multi-environmental trials in maize

Area of Science:

• Plant breeding
• Genomics
• Agricultural science

Background:

• Optimizing phenotyping is critical for cost-effective selection in plant breeding.
• Selection decisions heavily rely on multi-environmental trials, necessitating precise phenotyping.
• Genomic prediction offers a powerful tool for enhancing breeding efficiency.

Purpose of the Study:

• To optimize sparse phenotyping strategies in plant breeding.
• To investigate the use of relationship measurements for efficient line allocation.
• To reduce phenotyping costs while maintaining genetic gain.

Main Methods:

• Utilized genomic data and relationship measurements between training and testing sets.
• Simulated various sparse phenotyping designs (e.g., percentage of lines, number of environments).
• Applied eight different relationship measurements to assess genotype relatedness.

Main Results:

• Balanced allocation designs with 50% of lines in the full set showed higher accuracy than 30% allocation.
• Reducing untested environments per sparse set improved measurement accuracy.
• Relationship measurements showed a low but significant positive correlation (0.20-0.31) with accuracy.

Conclusions:

• Relationship measurements and genomic prediction effectively optimize line allocation in sparse phenotyping.
• Cost reduction is achievable without compromising genetic gain through optimized phenotyping.
• Balanced allocation and maximizing accuracy are key to successful sparse phenotyping designs.