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

Area of Science:

• Plant breeding
• Crop physiology
• Maize genetics

Background:

• Traditional maize breeding relies on direct phenotypic performance for selecting next-generation crosses.
• Indirect assessment methods like genomic prediction and remote sensing can supplement traditional approaches.
• Secondary traits offer opportunities for faster, in-season breeding decisions.

Purpose of the Study:

• To investigate the efficiency of chlorophyll a fluorescence (ChlF) for in-season predictions of maize yield and grain moisture.
• To assess the potential of ChlF as an indirect selection tool in maize breeding programs.

Main Methods:

• Experiments conducted with 16 maize hybrids over three consecutive years (2017-2019).
• Chlorophyll fluorescence (ChlF) measured on dark-adapted leaves during anthesis.
• Partial least squares models used to assess indirect selection efficiency.

Main Results:

• Genetic correlations among traits were weak and negative, but heritability estimates were moderately high to high.
• A partial least squares model with 10 latent variables demonstrated higher predictive ability for grain yield.
• Indirect selection for grain moisture using ChlF was relatively efficient, while for grain yield it was less efficient than direct selection.

Conclusions:

• Chlorophyll fluorescence transients hold significance and applicability for maize breeding programs.
• ChlF shows promise as a tool for improving breeding speed, particularly for grain moisture prediction.
• Further research may optimize the use of ChlF for enhancing grain yield prediction in maize.