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 language | English |
|---|---|
| Pages (from-to) | 2133-2150 |
| Number of pages | 18 |
| Journal | Plant, Cell & Environment |
| DOIs | |
| Publication status | E-pub ahead of print - 28 Mar 2019 |
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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 journal › Article
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.)
UR - http://www.scopus.com/inward/record.url?scp=85063583746&partnerID=8YFLogxK
U2 - 10.1111/pce.13544
DO - 10.1111/pce.13544
M3 - Article
SP - 2133
EP - 2150
JO - Plant, Cell and Environment.
JF - Plant, Cell and Environment.
SN - 0140-7791
ER -