Coast, Onoriode ORCID: https://orcid.org/0000-0002-5013-4715, Shah, Shahen, Ivakov, Alexander, Gaju, Oorbessy, Wilson, Philippa B. ORCID: https://orcid.org/0000-0001-9548-691X, Posch, Bradley C., Bryant, Callum J., Negrini, Anna Clarissa A., Evans, John R. ORCID: https://orcid.org/0000-0003-1379-3532, Condon, Anthony G., Silva‐Pérez, Viridiana, Reynolds, Matthew P., Pogson, Barry J., Millar, A. Harvey ORCID: https://orcid.org/0000-0001-9679-1473, Furbank, Robert T. and Atkin, Owen K. ORCID: https://orcid.org/0000-0003-1041-5202 (2019) Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Plant, Cell & Environment, 42 (7). pp. 2133-2150. ISSN 0140-7791 (Print), 1365-3040 (Online) (doi:10.1111/pce.13544)

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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.

Item Type:	Article
Uncontrolled Keywords:	high‐throughput phenotyping; leaf reflectance; machine learning; mitochondrial respiration; proximal remote sensing; wheat (Triticum aestivum L.)
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences S Agriculture > S Agriculture (General) S Agriculture > SB Plant culture
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified:	10 Aug 2020 12:07
URI:	http://gala.gre.ac.uk/id/eprint/29161

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