Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

Greater availability of leaf dark respiration (Rdark) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of Rdark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non‐destruct...

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Bibliographic Details
Published in:Plant, cell and environment cell and environment, 2019-07, Vol.42 (7), p.2133-2150
Main Authors: Coast, Onoriode, Shah, Shahen, Ivakov, Alexander, Gaju, Oorbessy, Wilson, Philippa B., Posch, Bradley C., Bryant, Callum J., Negrini, Anna Clarissa A., Evans, John R., Condon, Anthony G., Silva‐Pérez, Viridiana, Reynolds, Matthew P., Pogson, Barry J., Millar, A. Harvey, Furbank, Robert T., Atkin, Owen K.
Format: Article
Language:English
Subjects:
Australia
Breeding
Carbon cycle
Carbon Dioxide - metabolism
Cell Respiration - physiology
Cell Respiration - radiation effects
Crop yield
Destructive testing
Genotypes
Growth conditions
High-Throughput Screening Assays
high‐throughput phenotyping
Leaf area
leaf reflectance
Leaves
Light
machine learning
mitochondrial respiration
Nitrogen
Oxygen consumption
Phenotype
Photosynthesis
Plant breeding
Plant Leaves - growth & development
Plant Leaves - metabolism
Plant Leaves - radiation effects
Predictions
proximal remote sensing
Reflectance
Respiration
Triticum - growth & development
Triticum - metabolism
Wheat
wheat (Triticum aestivum L.)
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Summary:Greater availability of leaf dark respiration (Rdark) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of Rdark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non‐destructive and high‐throughput method of estimating Rdark from leaf hyperspectral reflectance data that was derived from leaf Rdark measured by a destructive high‐throughput oxygen consumption technique. We generated a large dataset of leaf Rdark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for Rdark. Leaf Rdark (per unit leaf area, fresh mass, dry mass or nitrogen, N) varied 7‐ to 15‐fold among individual plants, whereas traits known to scale with Rdark, leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf Rdark, N, and LMA with r2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for Rdark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf Rdark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of Rdark are discussed. Measuring leaf dark respiration is either slow and cumbersome or rapid and destructive. We used light reflected from wheat leaf surfaces to rapidly and non‐destructively estimate wheat respiration. Predictions were largely independent of the relationships between leaf dark respiration and leaf nitrogen or leaf mass per unit area. This finding highlights the potential for rapid non‐invasive monitoring of various aspects of leaf energy metabolism in wheat.
ISSN:0140-7791
1365-3040
DOI:10.1111/pce.13544