Abstract

Greater availability of leaf dark respiration (Rdark) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of Rdark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non‐destructive and high‐throughput method of estimating Rdark from leaf hyperspectral reflectance data that was derived from leaf Rdark measured by a destructive high‐throughput oxygen consumption technique. We generated a large dataset of leaf Rdark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for Rdark. Leaf Rdark (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 Rdark, leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf Rdark, N, and LMA with r2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for Rdark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf Rdark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of Rdark are discussed.