Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status

Journal of Experimental Botany
Kim S ElyAlistair Rogers

Abstract

Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.

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Citations

Dec 7, 2019·The Plant Journal : for Cell and Molecular Biology·Omar Vergara-DiazJosé Luis Araus
Nov 13, 2020·Frontiers in Plant Science·Lea Antonia FreyRoland Kölliker
Nov 9, 2020·Food Chemistry·Naiyue HuZhimin Wang
Jan 12, 2021·Ecological Processes·Susan L Ustin, Elizabeth M Middleton
Mar 26, 2021·Plant, Cell & Environment·Angela C BurnettAlistair Rogers
Mar 19, 2021·Journal of Experimental Botany·Thomas SextonAsaph B Cousins
Jul 9, 2021·Journal of Experimental Botany·Angela C BurnettAlistair Rogers

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