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 journalArticlepeer-review

51 Citations (Scopus)
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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
Issue number7
Publication statusPublished - Jul 2019


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