Pareto Optimization for Selecting Discriminating Test Locations in Plant Breeding - Mohammadreza Kiaghadi; Sigurdur Olafsson (Agronomy)
Developing improved food crop cultivars requires time-consuming field trials, and the selection of experimental planting locations plays a key role. In early experiments, a plant breeder may prefer locations that have the best ability to discriminate the main genotype effects, that is, to screen the potential high performers from the average-to-poor cultivars. Traditionally, this discriminative ability of locations has focused on precision, but recent work suggests that some locations may be more sensitive to changes in the main genotype effect, having a higher relative discriminative value. We show that while both valuable, these are competing measures, meaning that no location maximizes both precision and the relative discriminative value, and there is a tradeoff that should be considered by breeders. We address this tradeoff by constructing a set of discriminating locations using Pareto optimization. We first identify a set of locations that cannot be improved along one measure without deteriorating the other and then expand this set by adding locations that are not significantly worse. Visualization facilitates evaluating the tradeoff and provides decision support when making choices about locations based on breeder preferences and status of the field experiments. The method is illustrated using publicly available barley data.