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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.