Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK.
International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP.
J Exp Bot. 2021 May 4;72(10):3756-3773. doi: 10.1093/jxb/erab115.
Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.
在许多地区，小麦的产量都在停滞不前或下降，这就需要努力提高光转化效率，即所谓的辐射利用效率（RUE）。RUE 是植物生理学中的一个关键特征，因为它将光能捕获和初级代谢与生物量积累和产量联系起来，但它的测量既耗时又费力，这限制了它在基础研究和大规模生理育种中的应用。在这项研究中，在具有 RUE 和光合特性变化的田间生长的小麦群体中使用高通量植物表型（HTPP）方法，建立 RUE、生物量和截获的光合有效辐射（IPAR）的预测模型。使用了三种方法：传感器的最佳组合；冠层植被指数；和偏最小二乘回归。与地面真实数据相比，遥感模型对 RUE 的预测准确率高达 70%。水指数和冠层绿色度指数[归一化差异植被指数（NDVI），增强型植被指数（EVI）]是预测 RUE、生物量和 IPAR 的更好选择，而与气体交换有关的指数[光化学反射指数（PRI）]和衰老[结构不敏感色素指数（SIPI）]则分别是预测这些在营养生长和灌浆阶段特征的更好指标。这些模型将有助于解释冠层过程，改进作物生长和产量模型，并有可能用于预测不同作物或生态系统中的 RUE。
