Field based remote sensing models predict radiation use efficiency in wheat.

Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, i.e. 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 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 square regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared to ground truth data. Water indices and NDVI are the better option to predict RUE, biomass and IPAR, and indices related to NPQ (PRI) and senescence (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, yield modelling, and potentially be used to predict RUE in different crops or ecosystems.