Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

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 r² 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.

Keywords: high-throughput phenotyping; leaf reflectance; machine learning; mitochondrial respiration; proximal remote sensing; wheat (Triticum aestivum L.).