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ARS Home » Pacific West Area » Pullman, Washington » WHGQ » Research » Publications at this Location » Publication #413544
Research Project: Wheat and Barley Adaptation to a Changing Climate - Discovery of Genetic and Physiological Processes for Improved Crop Productivity and Quality

Location: Wheat Health, Genetics, and Quality Research

Title: Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials

Author
item HERR, ANDREW - Washington State University
item Garland Campbell, Kimberly
item Li, Xianran
item CARTER, ANDREW - Washington State University

Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2024
Publication Date: 11/7/2024
Citation: Herr, A., Garland Campbell, K.A., Li, X., Carter, A. 2024. Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials. The Plant Phenome Journal. 7(1). Article e70007. https://doi.org/10.1002/ppj2.70007.
DOI: https://doi.org/10.1002/ppj2.70007

Interpretive Summary: In order to increase the food supply, plant breeders need to select for increased grain yield. In a breeding program, possible new plant breeding lines are evaluated in small plots in multi-environment field trials. Data from these trials can be biased due to small scale variation across the field. In this work, we show that use of unmanned aerial vehicle (drone) images, combined with methods of spatial statistical analyses can improve the reliability of the data collected from these trials. Use of these results can increase the accuracy and efficiency of plant breeding trials and lead to the development of better cultivars.

Technical Abstract: An important aspect of reliable cultivar development is good field trial evaluations. In more extensive field experiments, trial design and modeling of spatial variability are critical to control field variability and minimize error in genotype evaluations. Unoccupied aircraft systems (UAS) are a popular high-throughput phenotyping tool that has been used to successfully evaluate plant stress and other canopy characteristics. In precision agriculture applications, UAS imagery has been used to identify spatial variability in field settings. Here we use UAS spectral imagery to improve field trial spatial analysis, better control spatial variability and reduce error for more reliable selections. UAS imagery data was collected across 47 breeding trials planted in augmented complete block design (ACBD) or alpha-lattice replicated designs from 2020 through 2023. Trials were evaluated using three spatial analysis strategies: linear models incorporating block effect, row-column effect, or 2D splines. UAS-derived spectral reflectance indices (SRI) were combined with each model as covariates. Modeling strategies were used across all trials and evaluated for autocorrelation, model fitness, and coefficient of variation (CV). Akaike information criterion (AIC) was used to assess model fitness. For spatial analysis trials, SRIs improved model AIC by an average of 38.4 for alpha-lattice trials and 69.1 for ACBD trials. CV scores were also improved when SRIs were utilized, with average CV values being 2.6 lower for alpha-lattice and 2.1 for ACBD trials. This study highlights the potential for SRIs to improve the analyses of field breeding trials, despite extreme environmental variables and climate conditions. Further research needs to be done to determine the impact of SRIs on genotype trial rank and overall breeder selection.