High throughput can produce better decisions than high accuracy when phenotyping plant populations

Abstract

Studies assessing phenotypes of plant populations traditionally place their primary focus on increasing measurement precision and improving accuracy. Phenotyping methods that use imaging, remote sensing, and spectroscopy, continue to increase throughput capacity, but information has been unavailable to assess the tradeoffs between increased throughput and any potential decreases in measurement accuracy. In this simulation study, we compare four levels of measurement accuracy across varying levels of throughput, and discuss how an increased error rate can be compensated for via increased throughput, if experimental resources are allocated appropriately. Under the tested scenarios of increased throughput, the correct values of genotypes were best estimated by increasing the number of environments. Genetic mapping studies should increase population size as well to see improvements over more accurate measurement methods. This simplistic simulation mimics many empirical findings and will be of interest to any researcher interested in assessing how high-throughput phenotyping methods affect decision-making in crop research programs.