Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. uri icon

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.

publication date

  • 2019
  • 2019