Journal
JOURNAL OF EXPERIMENTAL BOTANY
Volume 69, Issue 3, Pages 483-496Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jxb/erx421
Keywords
Electron transport rate; hyperspectral reflectance; leaf dry mass per area; leaf nitrogen; partial least squares; photosynthesis; Rubisco; Triticum aestivum; velocity of carboxylation
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Funding
- Sustainable Modernization of Traditional Agriculture (MasAgro) initiative from the Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA)
- CIMMYT
- CSIRO
- Australian National University
- CONACYT, Mexico [207607]
- Australian Research Council Centre of Excellence for Translational Photosynthesis [CE140100015]
- Grains Research & Development Corporation funding [CSP00168]
- United States Department of Energy [DE-SC0012704]
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Improving photosynthesis to raise wheat yield potential has emerged as a major target for wheat physiologists. Photosynthesis-related traits, such as nitrogen per unit leaf area (N-area) and leaf dry mass per area (LMA), require laborious, destructive, laboratory-based methods, while physiological traits underpinning photosynthetic capacity, such as maximum Rubisco activity normalized to 25 degrees C (V-cmax25) and electron transport rate (J), require time-consuming gas exchange measurements. The aim of this study was to assess whether hyperspectral reflectance (350-2500 nm) can be used to rapidly estimate these traits on intact wheat leaves. Predictive models were constructed using gas exchange and hyperspectral reflectance data from 76 genotypes grown in glasshouses with different nitrogen levels and/or in the field under yield potential conditions. Models were developed using half of the observed data with the remainder used for validation, yielding correlation coefficients (R-2 values) of 0.62 for V-cmax25, 0.7 for J, 0.81 for SPAD, 0.89 for LMA, and 0.93 for N-area, with bias <0.7%. The models were tested on elite lines and landraces that had not been used to create the models. The bias varied between -2.3% and -5.5% while relative error of prediction was similar for SPAD but slightly greater for LMA and N-area.
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