Dissecting Physiological and Agronomic Diversity in Safflower Populations Using Proximal Phenotyping

Emily Thoday-Kennedy, Bikram Banerjee, Joe Panozzo, Pankaj Maharjan, David Hudson, German Spangenberg, Matthew Hayden and Surya Kant ()
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Emily Thoday-Kennedy: Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd., Horsham, VIC 3400, Australia
Bikram Banerjee: Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd., Horsham, VIC 3400, Australia
Joe Panozzo: Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd., Horsham, VIC 3400, Australia
Pankaj Maharjan: Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd., Horsham, VIC 3400, Australia
David Hudson: GO Resources Pty Ltd., 15 Sutherland Street, Brunswick, VIC 3056, Australia
German Spangenberg: School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
Matthew Hayden: School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
Surya Kant: Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd., Horsham, VIC 3400, Australia

Agriculture, 2023, vol. 13, issue 3, 1-18

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.

Keywords: EVI; flowering; high-throughput phenotyping; NDVI; oil profile; safflower (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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