Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Silva-Perez, Viridiana; Molero, Gemma; Serbin, Shawn P.; Condon, Anthony G.; Reynolds, Matthew P.; Furbank, Robert T.; Evans, John R.

doi:10.1093/jxb/erx421

Title: Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Journal Article · 22 December 2017 · Journal of Experimental Botany

DOI: https://doi.org/10.1093/jxb/erx421 · OSTI ID:1439796

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CSIRO Agriculture, Canberra, ACT (Australia); Australian National Univ., Canberra, ACT (Australia). ARC Centre of Excellence for Translational Photosynthesis. Research School of Biology
International Maize and Wheat Improvement Centre (CIMMYT), Mexico City (Mexico)
Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental and Climate Sciences Dept.
Australian National Univ., Canberra, ACT (Australia). ARC Centre of Excellence for Translational Photosynthesis. Research School of Biology

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 °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 (R² 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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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States); International Maize and Wheat Improvement Centre (CIMMYT), Mexico City (Mexico); CSIRO Agriculture, Canberra, ACT (Australia); Australian National Univ., Canberra, ACT (Australia)

Sponsoring Organization:: USDOE; Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA) (Mexico); National Council of Science and Technology (CONACYT) (Mexico); Australian Research Council (ARC); Grains Research & Development Corporation (Australia)

Grant/Contract Number:: SC0012704; 207607; CE140100015; CSP00168

OSTI ID:: 1439796

Report Number(s):: BNL-114530-2017-JAAM

Journal Information:: Journal of Experimental Botany, Vol. 69, Issue 3; ISSN 0022-0957

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 147 works

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Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat Borrill, Philippa; Harrington, Sophie A.; Uauy, Cristobal The Plant Journal https://doi.org/10.1111/tpj.14150	journal	November 2018
High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel Ge, Yufeng; Atefi, Abbas; Zhang, Huichun Plant Methods, Vol. 15, Issue 1 https://doi.org/10.1186/s13007-019-0450-8	journal	June 2019
Fast photosynthesis measurements for phenotyping photosynthetic capacity of rice Du, Tingting; Meng, Ping; Huang, Jianliang Plant Methods, Vol. 16, Issue 1 https://doi.org/10.1186/s13007-020-0553-2	journal	January 2020
Spectral characterization of wheat functional trait responses to Hessian fly: Mechanisms for trait-based resistance Campos-Medina, Veronica A.; Cotrozzi, Lorenzo; Stuart, Jeffrey J. PLOS ONE, Vol. 14, Issue 8 https://doi.org/10.1371/journal.pone.0219431	journal	August 2019
Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms Fu, Peng; Meacham-Hensold, Katherine; Guan, Kaiyu Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.00730	journal	June 2019
Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes El-Hendawy, Salah E.; Alotaibi, Majed; Al-Suhaibani, Nasser Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.01537	journal	November 2019
Advancing Bromegrass Breeding Through Imaging Phenotyping and Genomic Selection: A Review Biswas, Dilip K.; Coulman, Bruce; Biligetu, Bill Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.01673	journal	January 2020

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