Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping

Article

Thoday-Kennedy, Emily, Banerjee, Bikram, Panozzo, Joe, Maharjan, Pankaj, Hudson, David, Spangenberg, German, Hayden, Matthew and Kant, Surya. 2023. "Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping." Agriculture. 13 (3). https://doi.org/10.3390/agriculture13030620
Article Title

Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping

ERA Journal ID210069
Article CategoryArticle
AuthorsThoday-Kennedy, Emily, Banerjee, Bikram, Panozzo, Joe, Maharjan, Pankaj, Hudson, David, Spangenberg, German, Hayden, Matthew and Kant, Surya
Journal TitleAgriculture
Journal Citation13 (3)
Article Number620
Number of Pages18
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2077-0472
Digital Object Identifier (DOI)https://doi.org/10.3390/agriculture13030620
Web Address (URL)https://www.mdpi.com/2077-0472/13/3/620
Abstract

Safflower (Carthamus tinctorius L.) is a highly adaptable but underutilized oilseed crop capable of growing in marginal environments, with crucial agronomical, commercial, and industrial uses. Considerable research is still needed to develop commercially relevant varieties, requiring effective, high-throughput digital phenotyping to identify key selection traits. In this study, field trials comprising a globally diverse collection of 350 safflower genotypes were conducted during 2017–2019. Crop traits assessed included phenology, grain yield, and oil quality, as well as unmanned aerial vehicle (UAV) multispectral data for estimating vegetation indices. Phenotypic traits and crop performance were highly dependent on environmental conditions, especially rainfall. High-performing genotypes had intermediate growth and phenology, with spineless genotypes performing similarly to spiked genotypes. Phenology parameters were significantly correlated to height, with significantly weak interaction with yield traits. The genotypes produced total oil content values ranging from 20.6–41.07%, oleic acid values ranging 7.57–74.5%, and linoleic acid values ranging from 17.0–83.1%. Multispectral data were used to model crop height, NDVI and EVI changes, and crop yield. NDVI data identified the start of flowering and dissected genotypes according to flowering class, growth pattern, and yield estimation. Overall, UAV-multispectral derived data are applicable to phenotyping key agronomical traits in large collections suitable for safflower breeding programs.

KeywordsEVI; flowering; high-throughput phenotyping; NDVI; oil profile; safflower
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020310806. Plant physiology
401304. Photogrammetry and remote sensing
309999. Other agricultural, veterinary and food sciences not elsewhere classified
Byline AffiliationsAgriculture Victoria
La Trobe University
University of Melbourne
GO Resources, Australia
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