ARC Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Australian Capital Territory, 2601, Australia.
The University of Agriculture Peshawar, Peshawar, 25130, Pakistan.
Plant Cell Environ. 2019 Jul;42(7):2133-2150. doi: 10.1111/pce.13544. Epub 2019 Mar 28.
Greater availability of leaf dark respiration (R ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of R 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 from leaf hyperspectral reflectance data that was derived from leaf R measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf R for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for R . Leaf R (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 , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf R , N, and LMA with r values of 0.50-0.63, 0.91, and 0.75, respectively, and relative bias of 17-18% for R and 7-12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf R is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of R are discussed.
更多叶暗呼吸（R）数据的可用性可以促进提高作物产量和改善全球碳循环模型的育种工作。然而，由于测量繁琐、耗时或具有破坏性，R 数据的可用性有限。我们报告了一种从叶片高光谱反射率数据中估算 R 的非破坏性、高通量方法，该方法源自通过破坏性高通量耗氧技术测量的叶片 R。我们为小麦生成了一个包含 90 个基因型、多个生长阶段和生长条件的 R 大数据集，以生成 R 的模型。单个植株之间的 R（单位叶面积、鲜重、干重或氮，N）差异为 7 到 15 倍，而与 R、叶片 N 和比叶面积（LMA）成比例的性状仅相差 2 到 5 倍。我们的模型分别以 0.50-0.63、0.91 和 0.75 的 r 值预测叶片 R、N 和 LMA，以及 R 的相对偏差为 17-18%，N 和 LMA 的相对偏差为 7-12%。我们的结果表明，高光谱模型对小麦叶片 R 的预测在很大程度上独立于叶片 N 和 LMA。讨论了 R 高光谱特征的潜在驱动因素。
