Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA.
J Exp Bot. 2019 Mar 27;70(6):1789-1799. doi: 10.1093/jxb/erz061.
Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.
高通量、非破坏性测量植物性状的方法对于通过生理育种提高作物产量的计划至关重要。然而，许多关键性状仍然需要使用缓慢、劳动密集型和破坏性的方法进行测量。我们研究了从叶片光学特性中获取与叶片源库平衡和碳氮状况相关的关键性状的潜力。使用偏最小二乘回归，对八种作物的结构和生化特性以及叶片反射率（500-2400nm）进行了测量，并用于开发预测“光谱-性状”模型。独立验证数据表明，该模型对 C、N、C:N 比、叶面积质量、含水量和蛋白质含量具有很高的预测能力（R2>0.85），对淀粉、蔗糖、葡萄糖和游离氨基酸具有良好的预测能力（R2=0.58-0.80），对硝酸盐（R2=0.51）和果糖（R2=0.44）具有一定的预测能力。我们的光谱-性状模型是为涵盖与粮食或生物燃料作物相关的性状空间而开发的，因此可以广泛应用于各种表型研究。
