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

## TL;DR

This paper explores how to efficiently allocate maize genotypes in multi-environment trials to reduce costs while maintaining genetic gain through sparse phenotyping.

## Contribution

The study introduces a novel approach using genomic data and relationship measurements to optimize sparse phenotyping in plant breeding.

## Key findings

- Balanced designs with 50% of lines in the full set show higher accuracy in sparse phenotyping.
- Reducing untested environments improves measurement accuracy in sparse phenotyping.
- Relationship measurements show low but significant correlations (0.20-0.31) with accuracy.

## Abstract

The relatedness between the genotypes of the training and the testing set using sparse phenotyping experiments helps optimize the line allocation by utilizing the relationship measurements to reduce cost without compromising the genetic gain.

The phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing sets (sparse testing genotypes) to optimize the allocation of genotypes to subsets in sparse testing. Different sparse phenotyping designs were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, and balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate the relatedness between full and sparse set genotypes. The results demonstrate that balanced and allocating 50% of lines to the full set designs have shown a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, results enhance the accuracy of measurements. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs.

The online version contains supplementary material available at 10.1007/s00122-025-04825-y.