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Title: Spectroscopy can predict key leaf traits associated with source–sink balance and carbon–nitrogen status

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 (R 2 > 0.85), good predictive capability for starch, sucrose, glucose and free amino acids (R 2 = 0.58–0.80) and some predictive capability for nitrate (R 2 = 0.51) and fructose (R 2 = 0.44). As a result, 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.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1494042
Report Number(s):
BNL-211248-2019-JAAM
Journal ID: ISSN 0022-0957
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Experimental Botany
Additional Journal Information:
Journal Volume: 70; Journal Issue: 6; Journal ID: ISSN 0022-0957
Publisher:
Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; amino acids; carbohydrates; carbon; leaf traits; metabolites; nitrogen; PLSR; remote sensing; source–sink; spectroscopy

Citation Formats

Ely, Kim S., Burnett, Angela C., Lieberman-Cribbin, Wil, Serbin, Shawn, and Rogers, Alistair. Spectroscopy can predict key leaf traits associated with source–sink balance and carbon–nitrogen status. United States: N. p., 2019. Web. doi:10.1093/jxb/erz061.
Ely, Kim S., Burnett, Angela C., Lieberman-Cribbin, Wil, Serbin, Shawn, & Rogers, Alistair. Spectroscopy can predict key leaf traits associated with source–sink balance and carbon–nitrogen status. United States. doi:10.1093/jxb/erz061.
Ely, Kim S., Burnett, Angela C., Lieberman-Cribbin, Wil, Serbin, Shawn, and Rogers, Alistair. Tue . "Spectroscopy can predict key leaf traits associated with source–sink balance and carbon–nitrogen status". United States. doi:10.1093/jxb/erz061.
@article{osti_1494042,
title = {Spectroscopy can predict key leaf traits associated with source–sink balance and carbon–nitrogen status},
author = {Ely, Kim S. and Burnett, Angela C. and Lieberman-Cribbin, Wil and Serbin, Shawn and Rogers, Alistair},
abstractNote = {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). As a result, 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.},
doi = {10.1093/jxb/erz061},
journal = {Journal of Experimental Botany},
number = 6,
volume = 70,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
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This content will become publicly available on February 19, 2020
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