Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

Onoriode Coast, Shahen Shah, Alexander Ivakov, Oorbessy Gaju, Philippa B. Wilson, Bradley C. Posch, Callum J. Bryant, Anna Clarissa A. Negrini, John R. Evans, Anthony G. Condon, Viridiana Silva-Pérez, Matthew P. Reynolds, Barry J. Pogson, A. Harvey Millar, Robert T. Furbank, Owen K. Atkin

Research output: Contribution to journalArticle

Abstract

Greater availability of leaf dark respiration (R dark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of R dark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating R dark from leaf hyperspectral reflectance data that was derived from leaf R dark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf R dark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for R dark . Leaf R dark (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 R dark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf R dark , N, and LMA with r 2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for R dark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf R dark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of R dark are discussed.

Original languageEnglish
Pages (from-to)2133-2150
Number of pages18
JournalPlant, Cell & Environment
DOIs
Publication statusE-pub ahead of print - 28 Mar 2019

Fingerprint

Respiratory Rate
respiratory rate
Triticum
reflectance
Carbon Cycle
wheat
Growth
Oxygen Consumption
Breeding
leaves
Respiration
Nitrogen
Genotype
cell respiration
Datasets
oxygen consumption
crop yield
leaf area
developmental stages

Cite this

Coast, O., Shah, S., Ivakov, A., Gaju, O., Wilson, P. B., Posch, B. C., ... Atkin, O. K. (2019). Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Plant, Cell & Environment, 2133-2150. https://doi.org/10.1111/pce.13544
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. / Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. In: Plant, Cell & Environment. 2019 ; pp. 2133-2150.
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abstract = "Greater availability of leaf dark respiration (R dark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of R dark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating R dark from leaf hyperspectral reflectance data that was derived from leaf R dark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf R dark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for R dark . Leaf R dark (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 R dark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf R dark , N, and LMA with r 2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18{\%} for R dark and 7–12{\%} for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf R dark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of R dark are discussed.",
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author = "Onoriode Coast and Shahen Shah and Alexander Ivakov and Oorbessy Gaju and Wilson, {Philippa B.} and Posch, {Bradley C.} and Bryant, {Callum J.} and Negrini, {Anna Clarissa A.} and Evans, {John R.} and Condon, {Anthony G.} and Viridiana Silva-P{\'e}rez and Reynolds, {Matthew P.} and Pogson, {Barry J.} and Millar, {A. Harvey} and Furbank, {Robert T.} and Atkin, {Owen K.}",
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Coast, O, Shah, S, Ivakov, A, Gaju, O, Wilson, PB, Posch, BC, Bryant, CJ, Negrini, ACA, Evans, JR, Condon, AG, Silva-Pérez, V, Reynolds, MP, Pogson, BJ, Millar, AH, Furbank, RT & Atkin, OK 2019, 'Predicting dark respiration rates of wheat leaves from hyperspectral reflectance' Plant, Cell & Environment, pp. 2133-2150. https://doi.org/10.1111/pce.13544

Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. / 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.

In: Plant, Cell & Environment, 28.03.2019, p. 2133-2150.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

AU - Coast, Onoriode

AU - Shah, Shahen

AU - Ivakov, Alexander

AU - Gaju, Oorbessy

AU - Wilson, Philippa B.

AU - Posch, Bradley C.

AU - Bryant, Callum J.

AU - Negrini, Anna Clarissa A.

AU - Evans, John R.

AU - Condon, Anthony G.

AU - Silva-Pérez, Viridiana

AU - Reynolds, Matthew P.

AU - Pogson, Barry J.

AU - Millar, A. Harvey

AU - Furbank, Robert T.

AU - Atkin, Owen K.

PY - 2019/3/28

Y1 - 2019/3/28

N2 - Greater availability of leaf dark respiration (R dark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of R dark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating R dark from leaf hyperspectral reflectance data that was derived from leaf R dark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf R dark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for R dark . Leaf R dark (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 R dark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf R dark , N, and LMA with r 2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for R dark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf R dark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of R dark are discussed.

AB - Greater availability of leaf dark respiration (R dark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of R dark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating R dark from leaf hyperspectral reflectance data that was derived from leaf R dark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf R dark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for R dark . Leaf R dark (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 R dark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf R dark , N, and LMA with r 2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for R dark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf R dark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of R dark are discussed.

KW - high-throughput phenotyping

KW - leaf reflectance

KW - machine learning

KW - mitochondrial respiration

KW - proximal remote sensing

KW - wheat (Triticum aestivum L.)

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U2 - 10.1111/pce.13544

DO - 10.1111/pce.13544

M3 - Article

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JO - Plant, Cell and Environment.

JF - Plant, Cell and Environment.

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