Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

Abstract

<p>Greater availability of leaf dark respiration (<i>R</i>_dark) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of <i>R</i>_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 <i>R</i>_dark from leaf hyperspectral reflectance data that was derived from leaf <i>R</i>_dark measured by a destructive high‐throughput oxygen consumption technique. We generated a large dataset of leaf <i>R</i>_dark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for <i>R</i>_dark. Leaf <i>R</i>_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 <i>R</i>_dark, leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf <i>R</i>_dark, N, and LMA with <i>r</i>² values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for <i>R</i>_dark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf <i>R</i>_dark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of <i>R</i>_dark are discussed.</p>