Projects per year
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 |
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Pages (from-to) | 2133-2150 |
Number of pages | 18 |
Journal | Plant, Cell & Environment |
Volume | 42 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 2019 |
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Dive into the research topics of 'Predicting dark respiration rates of wheat leaves from hyperspectral reflectance'. Together they form a unique fingerprint.Projects
- 1 Finished
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ARC Centre of Excellence in Plant Energy Biology 2014 (CPEB2)
Millar, H., Pogson, B., Tyerman, S., Small, I., Whelan, J., Borevitz, J., Lister, R., Atkin, O. & Munns, R.
1/01/14 → 31/05/21
Project: Research