Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. (28th March 2019)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Plant, cell and environment. Volume 42:Number 7(2019)
• Page Start: 2133
• Page End: 2150
• Publication Date: 2019-03-28
• Subjects: high‐throughput phenotyping -- leaf reflectance -- machine learning -- mitochondrial respiration -- proximal remote sensing -- wheat (Triticum aestivum L.); Plant physiology -- Periodicals; Plant cells and tissues -- Periodicals; Plant communities -- Periodicals; 581.105
• DOI: 10.1111/pce.13544
• Languages: English
• ISSNs: 0140-7791

Abstract

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. Abstract : Measuring leaf dark respiration is either slow and cumbersome or rapid and destructive. We used light reflected from wheat leaf surfaces to rapidly and non‐destructively estimate wheat respiration. Predictions were largely independent of the relationships between leaf dark respiration and leaf nitrogen or leaf mass per unit area. This finding highlights the potential for rapid non‐invasive monitoring of various aspects of leaf energy metabolism in wheat.