In-Season Predictions Using Chlorophyll a Fluorescence for Selecting Agronomic Traits in Maize

Traditional maize (<i>Zea mays</i> L.) breeding approaches use directly measured phenotypic performance to make decisions for the next generation of crosses. Indirect assessment of cultivar performance can be utilized using various methods such as genomic predictions and remote sensing. However, some secondary traits might expand the breeder's ability to make informed decisions within a single season, facilitating an increase in breeding speed. We hypothesized that assessment of photosynthetic performance with chlorophyll <i>a</i> fluorescence (ChlF) might be efficient for in-season predictions of yield and grain moisture. The experiment was set with 16 maize hybrids over three consecutive years (2017-2019). ChlF was measured on dark-adapted leaves in the morning during anthesis. Partial least squares models were fitted and the efficiency of indirect selection was assessed. The results showed variability in the traits used in this study. Genetic correlations among all traits were mainly very weak and negative. Heritability estimates for all traits were moderately high to high. The model with 10 latent variables showed a higher predictive ability for grain yield (GY) than other models. The efficiency of the indirect selection for GY using biophysical parameters was lower than direct selection efficiency, while the indirect selection efficiency for grain moisture using biophysical parameters was relatively high. The results of this study highlight the significance and applicability of the ChlF transients in maize breeding programs.