Evaluation of digital imaging stay-green as a method of indirect selection for grain yield in maize

Supplemental Files
Date
2020-01-01
Authors
Beltran, Juan
Major Professor
Dr. Anthony Assibi Mahama
Dr. Thomas Lubberstedt
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Agronomy
Organizational Unit
Journal Issue
Series
Department
Agronomy
Abstract

Stay-green has been used in the past as a secondary trait for indirect selection in grain yield for maize. Stay-green is usually successfully used in stressed environments, low nitrogen and drought, but tends to have poor heritability in the absence of stress compared to grain yield, in contrast to grain yield which tends to drop under stressed environments. This leads to stay-green being overshadowed by other popular secondary traits such as anthesis silking interval. Some of the variability in stay-green is produced by subjectivity. With the use of unmanned aerial devices or drones becoming more popular, imaging was taken for a set of ERA Pioneer hybrids grown under well-watered and drought stress treatments to eliminate statistical noise and improve heritability and correlations. Images for these plots produced RGB values which were then converted to a green leaf index to substitute for conventional stay-green.

Genetic correlation and heritability values were computed for full water and drought stress conditions, with drought stress as the control. Heritability was high for both grain yield and stay-green. Indirect selection efficiency was found to still be more efficient under a drought stress condition. However, UAD stay-green was able to provide significance values for the full water condition, something rarely found in previous research with conventional stay-green for non-stressed environments.

Comments
Description
Keywords
Citation
DOI
Source