Leaf hyperspectral reflectance spectra as a tool to measure photosynthetic characters in wheat Viridiana Silva‑Pérez 1,2 , John R. Evans 1 , Gemma Molero 3 , Tony Condon 2 , Robert Furbank 2 , Matthew Reynolds 3 1 RSB ANU, Australia; 2 CSIRO and HRPPC, Australia; 3 CIMMYT Mexico Abstract Leaf hyperspectral reflectance spectra may help to predict photosynthetic properties such as Rubisco (V cmax ) and electron transport (J) capacity, chlorophyll or nitrogen content. We tested predictions using Partial Least Square Regression in five sets of wheat genotypes. The best prediction was for the chlorophyll content surrogate SPAD. As predictions for leaf mass area (LMA) and J showed consistent biases, the technique needs further refinement. Wide genetic variation in electron transport rate (J) independent of leaf mass per area or chlorophyll content was found, which could be useful when selecting for improved photosynthetic performance in wheat. Introduction One way to raise yield is to increase crop biomass by improving photosynthesis (Parry et al., 2011). The photosynthetic performance of a leaf can be measured with a portable gas exchange system (e.g. Li-Cor LI-6400XT) and analysed with a model (von Caemmerer and Farquhar, 1981) to extract biochemical parameters such as Rubisco activity (V cmax ) and electron transport rate (J). However, the time taken by this method limits its use in high- throughput screening. By contrast, high resolution reflectance spectra can be measured on a leaf in about 30 seconds. It has been shown that these spectra can be used to predict V cmax , J, nitrogen content and leaf mass area in different species (Doughty et al., 2011, Serbin et al., 2012, Ecarnot et al., 2013). Reflectance spectra can be obtained non-destructively which has advantages over traditional methods for measuring nitrogen content and leaf mass per area. Such traits are important determinants of photosynthetic capacity and efficiency (Poorter et al., 2009, Badger, 2013). Leaf spectra also have the potential to predict grain yield, grain water content, protein, starch and gluten concentration (Overgaard et al., 2010) and other leaf constituents such as chlorophyll and carotenoids (Asner et al., 2009). The spectra from 350 to 2500 nm provide the basis for comparison with remotely sensed vegetation indices (Feret et al., 2011). Remote sensing of canopies has been extensively used over the previous 40 years and still has a huge potential in crop predictions. This technique is complex as it involves the analysis of solar radiation and the interaction with different surfaces, vegetation, soil, water, atmosphere, etc. (Jones and Vaughan, 2010). The advantage of measuring at the leaf level is that most of these problems are eliminated. However, other kinds of issues still need to be solved. This research seeks to develop and validate new tools to screen for superior photosynthetic performance among a broad range of wheat germplasm. Here we tested the use of hyperspectral reflectance to predict the main photosynthetic parameters in five sets of wheat genotypes and was compared with the data obtained with a portable gas exchange system (Li-Cor LI-6400XT). Materials and methods Five set of wheat genotypes were measured: two grown in glasshouses in Australia: 1) Early Vigour (EV), n=94; 2) Best and UNrealized YIeld Potential (BUNYIP), n=120; three grown in the field in Mexico: 3) CIMCOG Subset II at anthesis (CIMAV), n=90; 4) Candidates for CIMCOG (Elite population), n=48 and 5) Landraces (L), n=52. More details of these experiments are presented in (Silva-Pérez et al., 2014).