Genetic Architecture of Phenomic-Enabled Canopy Coverage inGlycine max

Abstract

Digital imagery can help to quantify seasonal changes in desirable crop phenotypes that can be treated as quantitative traits. Because limitations in precise and functional phenotyping restrain genetic improvement in the postgenomic era, imagery-based phenomics could become the next breakthrough to accelerate genetic gains in field crops. Whereas many phenomic studies focus on exploratory analysis of spectral data without obvious interpretative value, we used field images to directly measure soybean canopy development from phenological stage V2 to R5. Over 3 years, we collected imagery using ground and aerial platforms of a large and diverse nested association panel comprising 5555 lines. Genome-wide association analysis of canopy coverage across sampling dates detected a large quantitative trait locus (QTL) on soybean ( Glycine max , L. Merr.) chromosome 19. This QTL provided an increase in yield of 47.3 kg ha -1 Variance component analysis indicated that a parameter, described as average canopy coverage, is a highly heritable trait ( h 2 = 0.77) with a promising genetic correlation with grain yield (0.87), enabling indirect selection of yield via canopy development parameters. Our findings indicate that fast canopy coverage is an early season trait that is inexpensive to measure and has great potential for application in breeding programs focused on yield improvement. We recommend using the average canopy coverage in multiple trait schemes, especially for the early stages of the breeding pipeline (including progeny rows and preliminary yield trials), in which the large number of field plots makes collection of grain yield data challenging.

Plant phenotyping relevance

圃場画像を用いてダイズの季節的なキャノピー被覆率を直接測定し、地上・空撮プラットフォームによる再利用可能な表現型取得を大規模に適用しているため、画像ベース表現型解析が研究の中心的要素です。

Code and data availability

The paper makes two paper-specific assets publicly available: (1) visual canopy field images of the SoyNAM parents hosted on SoyBase, and (2) the study's phenotype and genotypic data (the 'met' dataset) distributed within the authors' R package NAM on CRAN, which also implements the gwas2 analysis function used in the