<div>Towards Increased Genetic Gain: Utilizing Spectral Data in a Large Scale
Wheat Breeding Program under a Drought Year</div><div>Andrew Herr, Arron Carter</div><div>Department of Crop and Soil Sciences, Washington State University,
Pullman, WA 99164</div><div>Multispectral imaging with unmanned aircraft systems (UAS) is a
promising high-throughput phenotyping technology that has been shown to
help understand the causal mechanisms associated with crop productivity.
This imaging technology can accurately predict complex agronomic traits
like grain yield within a given generation, creating the potential to
fast-track selections in plant breeding and increase genetic gains. The
objective of this study was to determine the effectiveness and
efficiency of prediction on grain yield in an abnormal drought year
across locations within a breeding program. Eleven spectral reflectance
indices (SRI) including NDRE, NWI, NDVI, and percent canopy cover were
used to evaluate Washington State University winter wheat breeding lines
between 2018 and 2021. Data was collected using a DJI Inspire 2 drone,
equipped with a Sentera Quad Multispectral Sensor, and collected at the
heading date. Lines were observed from single location, single
replication preliminary yield trials to multi-location, replicated
advanced yield trials. Lines advanced in the breeding program were
evaluated across 13 different location-year trials. The calculated SRIs
and canopy cover were used individually and in combination as fixed
effects in mixed model prediction for grain yield under drought
conditions. Models were independently validated with 2021 data. Across
locations, SRIs are shown to improve the prediction performance for
grain yield under abnormal drought conditions by as much as 40% in the
case of NDRE. This research is vital for plant breeders to understand
the utility of UAS imaging in variety improvement when dealing with
abnormal growing seasons.</div>