A regression approach to identify discriminating locations

Abstract

Selection of planting locations is an important issue in plant breeding and may significantly impact the probability of correctly identifying the desired genotype based on noisy data. This is especially true for early‐stage experimentation where a set of experimental genotypes is observed in very few locations. This study aims to identify locations that are most helpful for discriminating the main effects of two or more genotypes. A regression‐based model is proposed that characterizes the sensitivity of location to the genotype main effect, and locations with significant positive sensitivity are termed discriminative. Comparing or ranking genotypes using these locations results in exaggerated expected differences between genotype main effects, allowing for more accurate comparisons in the presence of noise. Based on the proposed model, an expression for the probability of making correct genotype selection is derived given a set of locations with known location sensitivities. The probability of correct selection is then used to characterize an optimal set of location that maximizes the probability of selecting the genotype with better main effect. These locations are independent of the genotypes being compared and only depend on the number of locations being considered and the sensitivities of these locations to genotype effects. The theoretical implications of the model are validated using data obtained from a commercial soybean breeding program. The results show that by using discriminating locations correct selection is made 76% of the time, versus 67% for all locations and only 63% of the time for non‐discriminating locations.